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--- title: In Silico Examination of Single Nucleotide Missense Mutations in NHLH2, a Gene Linked to Infertility and Obesity authors: - Allison T. Madsen - Deborah J. Good journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC9968165 doi: 10.3390/ijms24043193 license: CC BY 4.0 --- # In Silico Examination of Single Nucleotide Missense Mutations in NHLH2, a Gene Linked to Infertility and Obesity ## Abstract Continual advances in our understanding of the human genome have led to exponential increases in known single nucleotide variants. The characterization of each of the variants lags behind. For researchers needing to study a single gene, or multiple genes in a pathway, there must be ways to narrow down pathogenic variants from those that are silent or pose less pathogenicity. In this study, we use the NHLH2 gene which encodes the nescient helix-loop-helix 2 (Nhlh2) transcription factor in a systematic analysis of all missense mutations to date in the gene. The NHLH2 gene was first described in 1992. Knockout mice created in 1997 indicated a role for this protein in body weight control, puberty, and fertility, as well as the motivation for sex and exercise. Only recently have human carriers of NHLH2 missense variants been characterized. Over 300 missense variants for the NHLH2 gene are listed in the NCBI single nucleotide polymorphism database (dbSNP). Using in silico tools, predicted pathogenicity of the variants narrowed the missense variants to 37 which were predicted to affect NHLH2 function. These 37 variants cluster around the basic-helix-loop-helix and DNA binding domains of the transcription factor, and further analysis using in silico tools provided 21 SNV resulting in 22 amino acid changes for future wet lab analysis. The tools used, findings, and predictions for the variants are discussed considering the known function of the NHLH2 transcription factor. Overall use of these in silico tools and analysis of these data contribute to our knowledge of a protein which is both involved in the human genetic syndrome, Prader–Willi syndrome, and in controlling genes involved in body weight control, fertility, puberty, and behavior in the general population, and may provide a systematic methodology for others to characterize variants for their gene of interest. ## 1. Introduction Multiple in silico tools exist for the analysis of single nucleotide variants, whose numbers have exponentially increased from ~52 million in 2017, to over 715 million in the latest release of Ensembl 2022 [1]. However, even with these tools, there are few pipelines to systematically analyze variants in a gene of interest. Different protein types (i.e., transcription factors versus cellular signaling proteins versus extracellular matrix proteins) may need to be analyzed through different pipelines for in silico characterization of their individual functions, yet current pipelines of tools are not yet sophisticated enough for these endeavors. In this analysis, we use freely-available online in silico tools to analyze missense variants in the neuronal basic helix-loop-helix transcription factor, with the goal of providing a template for developing a pipeline for pathogenicity analysis of other transcription factor proteins. It has been 30 years since the original cloning and identification of the nescient helix-loop-helix 2 transcription factor, NHLH2 [2]. Much has been learned about the role of the gene and protein, especially with regards to its role in maintaining fertility and normal body mass, using mouse models, humans, and phylogeny. In a review published nearly 10 years ago, the authors pose five outstanding questions on NHLH2, including “Do human SNPs in NHLH2 contribute to any human obesity, physical activity, or fertility phenotypes?” [ 3]. We now have that answer for several NHLH2 SNPs. For example, a nonsynonymous single nucleotide polymorphism/variant (SNV) in NHLH2 changing an alanine to a proline at position 83 (A83P, NC_000001.10:g.115838126 C>G, CRCh38.p12, chr 1) structurally changes NHLH2 protein as predicted by in silico analyses, and as shown using Western blotting [4]. This SNV was originally discovered in two individuals with obesity who had no other detectable variants [5,6]. The SNV has never been added to the NCBI SNP database, likely due to low frequency but is within the same codon as rs1368574494, which results in the same alanine changed to a threonine. We have recently added four more clinical variants in NHLH2 to the ClinVar database [7]. These variants are linked to hypogonadotropic hypogonadism in humans [8]. In particular, a R79C variant (Variation ID 1326288) found in a consanguineous family completely inactivates the ability of NHLH2 transcription factor to bind to the MC4R promoter (as shown previously [9]), and transactivates the KISS1 promoter (also as shown previously [10]). The 19-year-old Turkish man who was homozygous for this variant displayed not only hypogonadotropic hypogonadism, but early adolescent obesity (OMIM 162361, [8]). His parents and sister, who were heterozygous for the variant, did not demonstrate these phenotypes; indicating recessive inheritance. In this article, we use variants in the NHLH2 gene to provide a pathway for discovery of variants that have a high pathogenicity prediction, and which should be further analyzed in wet labs. To carry this out, more than 300 missense variants currently listed in the NCBI database for NHLH2 were characterized using several different SNP prediction programs. Those with the strongest predicted pathogenicity were further analyzed with respect to whether the individual variant might affect tertiary structure, post-translational modification, nuclear/nucleolar localization, and phylogenetic conservation. The use of these in silico techniques on missense variants allows for sorting based on the predicted ability to inactivate NHLH2 function in DNA binding, and regulation through post-translational modifications. The 21 identified missense variants can then be further analyzed using lab-based techniques, so that genotype–phenotype predictions can be made for individual carriers of these variants. ## 2.1. Chromosomal Location, mRNA Transcripts, Protein Structure, and Identification of Variants The human NHLH2 gene is located on chromosome 1p13.1 with the longest transcript (X1 variant) at 9782 bp long (Figure 1A). The gene is located in the complement orientation, according to the latest assembly of the human genome (annotation release 110, 2022; Assembly GRCh38.p14). Transcript variant 1 has three exons, and a 2512 bp linear RNA, while transcript variant 2 has two exons and is 2492 bp in length. All three transcripts contain the protein coding region of 408 nucleotides, which is contained within one exon (exon 3 for transcript variant 1 and exon 2 for transcript variant 2) (Figure 1B). The NHLH2 protein is 135 amino acids long, and shares C-terminus homology with basic helix-loop-helix transcription factors. Multiple gene regulatory targets of mouse Nhlh2 have been characterized, and many are key players in the body weight, and hypothalamic–pituitary–gonadal axis (for a review, see [3]). There are currently 318 missense SNVs listed for the NHLH2 protein coding region in the NCBI dbSNP database [11]. Variants that only affected NHLH2 transcript variant X1 were removed from the list, as this transcript makes both the recognized protein, and another transcript that may code for protein, but is currently without biological confirmation. The resulting 109 missense mutations are found only in the coding region that is consistent between the three splice variants shown in Figure 1B. These variants were analyzed using the PROVEAN pathogenic variant analysis prediction program prior to decommission of the online site [12]. Seventy-three variants had PROVEAN scores higher than −2.5 and were eliminated on that criterion for the purpose of this study. The PROVEAN scores for these 73 variants ranged from 0.0003 to −2.113 and were predicted to be either tolerated, or with lower potential for pathogenicity. The remaining 37 variants (one with a single number, but two associated changes) had PROVEAN scores ranging from −2.648 to −8.367 and were considered pathogenic by this criterion. As the online version of PROVEAN was recently retired, the 37 variants were further analyzed using three additional tools: Mutation Assessor, release 3 [13], SNAP [14], and CRAVAT [15]. Using Mutation Assessor, the Functional Impact (FI) scores show that only 9 out of the 37 predicted pathogenic variants were considered “high” impact, and 16 out of 27 had medium impact (Supplemental Table S1). Using the two other tools, eight of the nine variants identified by Mutation Assessor were again considered deleterious. In addition, five of the variants identified by Mutation Assessor as low–medium impact were identified as higher impact by both SNAP and CRAVAT. Two variants that were given low impact scores by Mutation Assessor were high for only SNAP, and not CRAVAT, while three additional variants had medium scores for Mutation Assessor, and high scores for SNAP only. None of the SNVs were predicted to be neutral by SNAP2 (Supplemental Figure S1). According to one comparative analysis of prediction tools, Mutation Assessor had one of highest accuracies ($81\%$), combined with high specificity ($86\%$) [16]. Both SNAP and CRAVAT (CHASM) tools were analyzed in this article as well, but PROVEAN was not included in the comparison. SNAP had relatively lower accuracy ($68\%$), but similar specificity at ($81\%$), while CRAVAT was highest at $89\%$ accuracy and $99\%$ specificity. A combined approach as carried out here provides additional insight into variants with the most deleterious effects on NHLH2 protein function. As shown, and consistent with many missense variants, the frequency for each of these variants is generally low, ranging from 0 identified individuals in a dataset, to a high of $\frac{3}{238512}$ ($0.001\%$). The new release of the All of Us Research Program data through their genome variants database represents sequences from ~98,500 whole genome sequencing results, and ~165,000 genotyping arrays in the aggregated data from 168,080 participants (Accessed on 12 December 2022) [17]. These are likely to increase as more genome data from the 831,000 individuals registered for the program become available. Of the 37 variants in Supplemental Table S1, only 5 were found in the All of Us genome variants database, and these are also listed in the frequency columns. Currently, one is not able to obtain additional information on the carriers of these alleles, but it is expected that genotype and clinical, lifestyle, and other data being collected by the program may eventually be linked and available for mining by researchers. The location of the 37 variants on the NHLH2 protein is shown in Figure 2. For this analysis, none of the variants with PROVEAN scores below −2.5 were found in the N-terminal domain of the protein. This is in contrast with our previous work which identified human carriers of A9L and V31M variants, which were shown to be defective in gene transactivation in HEK293 cells [8]. Variants in the current analysis were clustered in the basic region (9 variants affecting 6 amino acids), helix 1 (8 variants affecting 8 amino acids), loop (7 variants affecting 6 amino acids), and helix 2 (12 variants affecting 9 amino acids). In comparison with the data from Supplemental Table S1, many of the variants with high predicted pathogenic scores are found within the loop and second helix of the protein. The NHLH2 protein shows strong homology within the basic helix loop helix domain, as assessed by phylogenetic animals of a distinct set of vertebrates (Figure 3A). Orthologs of the NHLH2 protein are found in both vertebrate and invertebrate species. To date, 270 orthologs have been sequenced from jawed vertebrates (Gnathostomata), with 179 of these from mammals, 55 from birds, and 58 from turtles, alligators, and lizards/snakes combined. These are 6 amphibian orthologs of NHLH2, and 1 each in the lungfishes (P. annectens), and cartilaginous fishes (A. radiata), as listed in the NCBI orthologs database. Boney fishes are not included in the NCBI orthologs page, but a search reveals that both the common carp (C. carpio) and zebrafish (D. rerio) sequences are available through the HomoloGene database on NCBI. The zebrafish sequence for Nhlh2 is smaller at 122 amino acids, sharing only $79.2\%$ homology with humans, mainly in the basic helix-loop-helix domain (position 66–122 in the zebrafish sequence), with only one conserved amino acid change at position 82 in the zebrafish sequence which occurs within the first helix of the protein. The N-terminal end of the zebrafish Nhlh2 protein has deletions and alternative amino acids in 50 of 65 amino acids [18]. Several invertebrate versions of the NHLH2 gene exist as well, including D. melanogaster (fruit fly), L. salmonis (salmon louse), and R. varioornatus (waterbear tardigrade), which are actually all more homologous to the paralogous gene NHLH1, and exist as a single gene, rather than paralogues in the organisms. Nuclear and nucleolar localization sequences are present in all protein sequences analyzed for NHLH2, spanning from amino acid 66–80 (nuclear localization sequences) and 61–82 (nucleolar localization sequence) (Figure 3B,C). These data predict that the NHLH2 protein would normally be present in the nucleus or nucleolus. While nuclear localization is consistent with NHLH2’s function in transcriptional regulation, NHLH2 has not been localized in the nucleolus. While initially the nucleolus was thought to be only a site for ribosome biogenesis, recent data have demonstrated a role for the nucleolus in sequestering proteins during certain stress responses, including nutrient deprivation, and cold/warm stress conditions [19]. Previous lab-based studies from our laboratory have shown that hypothalamic NHLH2 mRNA levels are reduced with food deprivation and cold exposure and increased with food return or rewarming [20,21,22], and the identification of a nucleolar localization signal in the NHLH2 protein suggests that we should examine protein localization during these and other stress responses. ## 2.2. Predicted Effects of Variants on Protein Post-Translational Modifications The MuSiteDeep PTM prediction software predicts alterations in several different types of PTM including phosphorylation, glycosylation, ubiquitination, palmitoylation, addition of hydroxyproline or hydroxylysine, SUMOlaytion, and methylation [23,24,25]. There are predicted phosphorylation sites: each of the 37 variants, as well as the normal sequence for NHLH2 listed in Table 1 underwent analysis using MuSiteDeep. MuSiteDeep checks for glycosylation, ubiquitination, SUMOlaytion, acetylation, methylation, palmitoylation, pyrrolidone carboxylic acid, and hydroxylation in a FASTA entered sequence. NHLH2 reference protein analysis yielded only phosphorylation, ubiquitination, pyrrolidone carboxylic acid modification, and glycosylation. As shown in Figure 4A, the NHLH2 protein has 10 predicted serine and one threonine phosphorylation site within the N-terminus of the protein, and none exist past the threonine site at amino acid 75. Variants marked with yellow triangles result in loss of phosphorylation at position 75. Two variants, rs75107396 and ra1194455186, lead to additional phosphorylation, at positions 108 (threonine) and position 65 (serine), respectively, as indicated by the light-yellow triangles. Both variants change the amino acids at those positions into amino acids with the potential for phosphorylation. A putative glycosylation site is lost at position 106 with a proline to serine change at position 105. The remaining predicted changes due to missense mutations result in the addition of post-translational modifications with an addition of hydroxylated residues at amino acids 124 and 125, due to the tyrosine to cysteine change from rs1433737875. Deacetylation of NHLH2 protein by SIRT1 deacetylase at lysine 49 was previously described [26]. However, MuSiteDeep did not detect lysine acetylation of the WT protein. One variant, rs1650931348, led to a change from a glutamine to a lysine residue which MuSiteDeep predicted would be acetylated. In addition, we had previously used a different in silico tool, the GPS-PAILS program http://bdmpail.biocuckoo.org/, accessed on 15 July 2022) [27] which had predicted acetylation of seven lysine residues on the WT NHLH2 protein, including the position 49 acetylation. ## 2.3. Predicted Effect of Variants on Protein Tertiary Structure and Function The basic region along with the first part of helix 1 of the helix-loop-helix family of transcription factors is known to contribute to DNA binding to an E-box motif (for a review, see [28]). Each transcription factor dimerizes with a partner, and contacts half of the E-box motif, which in the case of NHLH2 is most commonly “CAG”, using the 5th, 6th, 8th, 9th, and 13th amino acids as primary contacts with the DNA [29]. However, the NCBI Conserved Domain Search [30] predicts amino acids shown in yellow circles (Figure 5A) as those for the putative DNA binding sites. The variants in NHLH2 for this region are shown in Figure 5A (red hexagons). The remainder of helix 1 along with helix 2 (Figure 5B,C) interact with the dimerization partner along the predicted dimer interface. NHLH2 has a very short non-helical region immediately following helix 2 with unknown function. Tertiary structural analysis using the IntFOLD server was used to study any gross deformations of structure called by each of the 37 variants analyzed, as well as to analyze if the DNA binding domain was intact in the variant sequences. As shown in Figure 5D, four variants resulted in loss of predicted DNA binding domain in its entirety, although these variants were with amino acids outside of the direct DNA binding domain (Figure 5A). Previous published work in our lab showed that the R79C variant found in a human with hypogonadotropic hypogonadism led to an alteration in the DNA binding amino acids from R79, H82, R85 to Y78, H82, E86 [8]. While we did not carry out a DNA-amino acid binding analysis with these 37 variants, Supplemental Table S2 shows that modeling the NHLH2 protein with DNA for an additional 11 variants results in what appears to be an altered DNA binding structure for the protein (Supplemental Table S2). For example, Y78C and Y78H variants both appear to alter the tertiary model of NHLH2 bound to DNA, although the protein is still clearly interacting with DNA. More analysis, including wet lab experiments would be needed to confirm these predictions. ## 2.4. List of Most Pathogenic Variants, Predicted by In Silico Analysis Using SNV pathogenicity tools, followed by specific analysis of possible alterations in protein post-translational modifications and since NHLH2 is a transcription factor, analysis of any changes in predicted DNA binding, a list of 21 variants (with 22 amino acid changes) for further lab-based studies were generated (Table 1). The in silico experiments initially examined 318 missense/nonsynonymous variants in the NHLH2 protein sequence. The 21 remaining variants for further investigation represent a $93\%$ enrichment (just $6.6\%$ of the variants are deemed significantly pathogenic for further analysis). In addition, laboratory experiments can be tailored to the predicted pathogenic consequence, such as DNA binding or changes in secondary modifications. The position of the variants along the 3D protein structure are shown in Figure 6. ## 3. Discussion Polymorphisms in NHLH2 have been implicated in human hypogonadotropic hypogonadism with the associated phenotypes of low exercise and increased body weight [8]. These variants were identified by deep sequencing, but 318 other missense variants in the NCBI SNP database have not been further characterized in humans or in vitro. PROVEAN analysis of variants was used to initially identify 37 missense variants that were predicted to have functional consequences. These 37 variants were further analyzed using in silico tools, yielding 4 variants that are predicted to result in no DNA binding activity, and 12 with changes in predicted post-translational modifications. Interestingly, none of the variants overlapped with respect to prediction of DNA binding or post-translational modification changes. In addition, the variant analysis programs often differed in severity predictions. Only two variants, Y125C (rs1433737875) and K115N (rs1354640857), were predicted to be deleterious by all four single nucleotide polymorphism analyses and have an additional deleterious prediction (Y125C for additional of hydroxylation post-translational modification; and K115N for loss of predicted DNA binding ability). A total of 16 SNVs were identified for further wet lab analysis. Most of these SNVs are very infrequent in the databases, with only zero to one individual identified. These SNVs are similar in frequency to the three SNVs that we added to dbSNP recently which had never previously been characterized [8]. Interestingly, K115N which is considered deleterious by multiple criteria has the highest frequency with the alternate allele at $0.04\%$ globally and in the European subgroup. This could suggest that carriers could number in the millions worldwide. Use of these in silico tools can help researchers to focus their wet lab research on variants with the highest predicted consequences. In addition, these tools can help identify amino acids that are key to protein function, and to then design experiments to directly test these effects. Variants in NHLH2 have been previously shown to affect DNA binding (R79C) [8], and gene transactivation (A9L, V31M, R79C, A83P) [4,8] in wet lab experiments. These types of experiments would be the next to be carried out for variants such as K115N and Y125C-containing proteins created by in vitro mutagenesis experiments. This set of studies did not use in silico protein interaction prediction tools as no bHLH proteins have been shown to experimentally interact directly with NHLH2. We and others have shown SP1 [26] and STAT3 [31] to form protein:protein interactions with NHLH2 in cell line-based studies, but these interactions were not replicated by the in silico tool used (PEPPI [32]). As new tools become available, and predictions are tested with wet laboratory analysis, the tools will become better at predicting dimer partners that may be in macromolecular complexes. In addition, variants in non-coding regions of the gene that could result in alternative splicing, mRNA stability, or translation were not analyzed in this study, although two of our previous studies have included non-coding variants from humans [4,33]. In summary, in silico-based analyses are tools that can inform future wet experiments, and also aid genetic therapists in determining if new variants have the potential to be disease causing. NHLH2 variants in humans are rare, but this does not diminish their importance for the individual carrier, and for the researcher who is dissecting the biological mechanisms of the protein. Future predictions in non-coding regulatory regions and in the NHLH2 promoter region are also necessary to fully characterize upstream and downstream consequences of SNVs in NHLH2. ## 4. Materials and Methods All of the work in this manuscript was performed using online databases and in silico web-based prediction programs. ## 4.1. Identification of NHLH2 Missense SNVs for Further Study Missense variants in the NHLH2 coding region were identified using the search function in the dbSNP database (National Center for Biotechnology Information, Bethedsa, MD, USA) [11], and the gene name, NHLH2, along with the search filter “missense”. Population frequency data for each SNV were recorded and used to narrow down the dataset to 109 missense mutations. Each variant was further analyzed using the pathogenic prediction program PROVEAN (J. Craig Venter Institute, La Jolla, CA, USA) [12], prior to its retirement this year. Variants with scores less than −2.5 were selected for further analysis, resulting in 37 missense mutations in this category. Because PROVEAN has become outdated, the potential impact of each SNV was reassessed using three different scoring techniques. SNVs were first run through the Mutation Assessor server (Memorial Sloan Kettering Cancer Center, New York, NY, USA), which yields an FI score for each mutation. FI scores are ranked neutral (<0.85, green), low (0.85–1.9, blue), medium (1.9–3.5, yellow), or high (>3.5, red). In total, 5 of the 37 original deleterious SNVs were marked as low impact (green highlight) by Mutation Assessor, and 7 more SNVs were considered neutral (blue highlight) by FI scoring. The remaining 25 deleterious SNVs were confirmed by Mutation Assessor to likely have significant physiological impact. After FI scoring was completed, the SNVs were run though the SNAP2 variant prediction software (Technische Universitat, Munich, Germany). And the CRAVAT analysis tool (Johns Hopkins University, Baltimore, MD, USA) The SNAP2 tool runs all possible single point mutations along a desired amino acid sequence and provides an impact score and accuracy percentage for each possible mutation [14,34,35]. The thousands of possible results from SNAP2 were filtered and the results for the 37 SNVs of interest were recorded. SNAP2 scoring yielded 16 SNVs with medium impact (score between −50 and 50, yellow highlight) and 21 SNVs with high impact scores (>50, red highlight). CRAVAT scoring was also performed on each of the 37 SNVs of interest. The CRAVAT server produces a VEST score with an associated p-value that is used to predict the physiological impact of single point mutations [36,37]. SNVs that received a VEST score over 0.745 (on a 0 to 1 scale) were predicted to have possible significant pathogenic impact. Only 14 of the original 37 deleterious SNVs had high VEST scores when analyzed via CRAVAT and were considered to have significant impact. ## 4.2. Illustrator for Biological Sequences The online program “Illustrator for Biological Sequences” (IBS) was used to draw NHLH2 protein and annotate variants on the protein (Figure 2) as well as the DNA binding domain and variants (Figure 5A). It can be found at http://ibs.biocuckoo.org/ (Cuchoo Group, Wuhan, China) [38]. ## 4.3. Clustal Omega Phylogenetic Alignment Analysis Phylogenetic alignment using the Clustal Omega Multiple Sequence Alignment program (European Molecular Biology Laboratory, Cambridge, UK) [39,40], and inputting the normal and variant human NHLH2 sequences, along with the Nhlh2 protein sequences from Pan troglodytes (chimpanzee), Macaca mulatta (Rhesus monkey), Mus musculus (mouse), Bos taurus (cattle), Gallus gallus (chicken), and Danio rerio (zebrafish), with the output as ClustalW with character counts, and all other settings were defaults for the server. ## 4.4. Nucleolar and Nuclear Localization Signal Prediction NP_005590.1 (human) and NP_848892.1 (mouse) amino acid sequences were used as inputs for the online sequence prediction programs. NLStradamaus was used for the nuclear localization signal sequence (University or Toronto, Toronto, Canada) [41], and NoD for nucleolar localization (University of Dundee, Dundee, Australia) [42]. ## 4.5. Post-Translational Modification Prediction Potential effects on PTM pattern were analyzed using MuSiteDeep post-translational modification software (University of Missouri, Columbia, MO, USA) [24]. Normal and variant NHLH2 sequences were compared in the output from the in silico analysis. Any predicted alteration in PTM pattern from the normal protein was recorded. ## 4.6. Protein Structure (2D and 3D) and DNA Binding Prediction The NCBI Conserved Domain Search tool (National Center for Biotecnology Information, Bethesda, MD, USA) [30] was used to identify the predicted residues that were needed for DNA binding and dimerization. The IntFOLD server (University of Reading, Reading, UK) [43] was used to generate 3D structural models of the NHLH2 WT and missense variant proteins. Within the IntFOLD6 server interface, the FunFold2 server was used to predict the DNA binding domains for both the WT and variant proteins [44], and models were visualized using JMol (developed at the Minnesota Supercomputer Center, University of Minnesota, Minneapolis, MN, USA) [45]. ## 4.7. PyMOL 3D Visualization of WT Structure A PDB file containing data to create 3D rendering of the wild type NHLH2 protein was obtained from AlphaFold Protein Structure Database (European Molecular Biology Laboratory, Cambridge, UK) [46,47]. The data contained in the PDB file was uploaded into pyMOL (Schrodinger, Inc, New Yori, NY, USA) [48], a molecular visualization platform, to view the location of the 22 SNVs of interest listed in Table 1. Amino acid positions highlighted in yellow denote positions of variants that allow for DNA interaction that appears altered by FunFold2. The amino acids highlighted in red are predicted to have no DNA binding activity by FunFold2. 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--- title: Whole Transcriptome Analysis of Hypothalamus in Mice during Short-Term Starvation authors: - Eun-Young Oh - Byong Seo Park - Hye Rim Yang - Ho Gyun Lee - Thai Hien Tu - Sunggu Yang - Mi-Ryung Han - Jae Geun Kim journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC9968171 doi: 10.3390/ijms24043204 license: CC BY 4.0 --- # Whole Transcriptome Analysis of Hypothalamus in Mice during Short-Term Starvation ## Abstract Molecular profiling of the hypothalamus in response to metabolic shifts is a critical cue to better understand the principle of the central control of whole-body energy metabolism. The transcriptional responses of the rodent hypothalamus to short-term calorie restriction have been documented. However, studies on the identification of hypothalamic secretory factors that potentially contribute to the control of appetite are lacking. In this study, we analyzed the differential expression of hypothalamic genes and compared the selected secretory factors from the fasted mice with those of fed control mice using bulk RNA-sequencing. We verified seven secretory genes that were significantly altered in the hypothalamus of fasted mice. In addition, we determined the response of secretory genes in cultured hypothalamic cells to treatment with ghrelin and leptin. The current study provides further insights into the neuronal response to food restriction at the molecular level and may be useful for understanding the hypothalamic control of appetite. ## 1. Introduction A great deal of attention has been paid over the last few decades to identifying the function of the hypothalamus in controlling a wide range of behaviors and physiological adaptions, including reproduction, the circadian rhythm, social behaviors, and multiple-body homeostasis [1,2,3,4,5,6,7,8]. The circuit activity of hypothalamic neurons, in particular, governs the whole-body energy homeostasis by driving the homeostatic behaviors, including energy intake and expenditure, and thus, the perturbation of normal hypothalamic function leads to the development of metabolic disorders, such as obesity and diabetes [9,10]. In line with these aspects, studies on obesity patients and rodent models have revealed that abnormalities in appetite regulation are primarily triggered by impairment of the hypothalamic neurocircuitry [10,11]. Based on these clinically significant findings, intensive studies have been conducted to verify the hypothalamic control of appetite by identifying the molecular mediators in hypothalamic cells and the biochemical components derived from metabolically active peripheral organs. Using tissue specimens from the entire hypothalamus [12] or arcuate nucleus (Arc) [13,14], gene expression profiling in response to short-term calorie restriction has previously been reported using microarray methods, a hybridization-based technology that is commonly used for gene expression profiling. RNA-sequencing (RNA-seq) has recently emerged as a viable alternative for gene expression profiling and has been used for the transcriptome profiling of living organisms. Notably, it has contributed to significant progress in disease diagnosis and obtaining genetic information that can predict disease onset. Although transcriptome profiling of the hypothalamus has been previously performed on some animal models [15,16,17,18], information regarding the hypothalamic mediators that control energy intake and expenditure is insufficient. Particularly, profiling of hypothalamic-specific gene expression associated with appetite regulation has not yet been performed using an RNA-seq technique. In the current study, we performed RNA-seq experiments using the total hypothalamus and hippocampus of young adult male mice after overnight food deprivation. In this study, we aimed to determine the hypothalamus-specific genetic alterations associated with appetite regulation by comparing overnight-fasted and fed control mice using the RNA-seq technique. We then explored the hypothalamus-specific secretory-related genes among the differentially expressed genes (DEGs) and validated their responses to metabolic hormones, including leptin and ghrelin, in a cultured hypothalamic cell line. ## 2.1. Differentially Expressed Genes In order to select the hypothalamus-specific genes that are potentially associated with whole-body energy metabolism, we obtained the profiling results from both the hypothalamus and hippocampus by using the RNA-seq technique. DEGs in the hypothalamus and hippocampus were compared using the edgeR R package. A total of 81 and 21 genes of the hypothalamus and hippocampus, respectively, were differentially expressed, with a false discovery rate (FDR) ≤ 0.05 and logarithm of fold change (log2FC) > 1.0 or <−1.0 in fasted mice compared with those of fed mice (Figure 1A, Tables S1–S3). Among these genes, 11 were detected in both the hypothalamus and hippocampus (Figure 1A, Table S3). Of the 70 hypothalamus-specific genes, 45 were significantly upregulated and 25 were significantly downregulated in fasted mice when compared with those of fed mice (FDR ≤ 0.05, abs(log2FC) >1.0, Table S1). Of the 10 hippocampus-specific genes, 7 significantly upregulated genes and 3 significantly downregulated genes were identified in fasted mice compared to those of fed mice (Table S2). With more stringent criteria (cut-off FDR ≤ 0.05 and abs(log2FC) > 1.5), we found 32 genes from the 70 hypothalamus-specific genes (Table 1). In the hypothalamus of fasted mice, secretory-related genes WAP, follistatin/kazal, immunoglobulin, kunitz, and netrin domain containing 2 (Wfikkn2) (log2FC = 2.73, FDR = 0.002), fibulin 5 (Fbln5) (log2FC = 1.51, FDR = 0.002); collagen type V alpha 3 (Col5a3) (log2FC = 1.14, FDR = 0.002); peptidoglycan recognition protein 1 (Pglyrp1) (log2FC = 1.08, FDR = 0.034); agouti related neuropeptide (Agrp) (log2FC = 1.24, FDR = 0.054); and retbindin (Rtbdn) (log2FC = 1.17, FDR = 0.038) were associated with upregulation and endothelial lipase (Lipg) (log2FC = −1.26, FDR = 0.026) was associated with downregulation in the mouse secretome database [19] when compared with those of fed mice (Figure 1B,C, Table 1). ## 2.2. Secretory-Related Genes Are Altered in the Hypothalamus of Fasted Mice Since secretory factors derived from hypothalamic cells, including neurons, astrocytes, tanycytes, and microglia, act as chemical messengers that regulate the hypothalamic circuit activity, we explored the mouse secretome database [19] and obtained seven secreted genes whose expressions in the hypothalamus of fasted mice were changed compared with those of fed control mice. Agrp, Col5a3, Pglyrp1, Wfikkn2, Fbln5, and Rtbdn were upregulated, and Lipg was downregulated (Figure 2A). To confirm the RNA profiling, we determined the mRNA expression levels of secretory-related genes in the hypothalamus of fed and fasted mice using quantitative polymerase chain reaction (qPCR). In accordance with previous reports, the elevated mRNA expression of Agrp, the gene encoding an orexigenic neuropeptide, was observed in the hypothalamus of fasted mice compared with that in fed mice (Figure 2B). Fasted mice indicated an increase in the expression of Col5a3, a low-abundance fibrillar collagen; Pglyrp1, an antibacterial and pro-inflammatory innate immunity protein; Wfikkn2, a protease inhibitor; Fbln5, a member of the fibulin protein family; and Rtbdn, an extracellular rod-expressed protein, in the hypothalamus compared with those in fed mice (Figure 2C–G). In addition, food deprivation resulted in a decrease in the mRNA level of Lipg, a member of the lipoprotein lipase family, in the hypothalamus of fasted mice compared with that of fed mice (Figure 2H). ## 2.3. Selected Secretory-Related Genes Respond to the Metabolic Hormones To verify whether the secretory-related genes selected from the hypothalamus of fasted mice participate in the hypothalamic control of energy metabolism, we evaluated the responsiveness of the seven selected genes to the metabolic hormones, such as leptin, an anorexigenic hormone derived from adipose tissue, and ghrelin, an orexigenic hormone secreted from the gut in the mouse hypothalamic mHypoA cell line. We first confirmed that mRNA levels of Agrp were significantly reduced by leptin treatment and drastically elevated by ghrelin treatment (Figure 3A). Further, qPCR data revealed a significant increase in the mRNA expression of Col5a3, Pglyrp1, Wfikkn2, and Fbln5 in ghrelin-treated mHypoA cells compared with that in the vehicle-treated group. However, no significant alterations in the mRNA levels of these genes were observed in leptin-treated mHypoA cells (Figure 3B–E). The Rtbdn mRNA levels were not altered by leptin or ghrelin treatment (Figure 3F). In addition, the mRNA expression of Lipg was increased by leptin treatment and decreased by ghrelin treatment in cultured mHypoA cells (Figure 3G). Collectively, these data suggest that selected secretory-related genes can be physiologically involved in hypothalamic functions that integrate metabolic signals. ## 2.4. Enriched Functional Annotation and Canonical Pathway Enriched functional annotations of 70 hypothalamus-specific genes (FDR ≤ 0.05, abs(log2FC) > 1.0) were obtained using ingenuity pathway analysis (IPA; Table 2). The top 20 enriched functional annotations from IPA are listed in Table 2. Ingestion by mice, hyperphagia, appetite, vascular endothelial cell function, and obesity caused by epididymal fat were among the top functional annotations indicated by this dataset (Table 2). In addition, the top-ranking functional annotation “ingestion by mice”, involving six genes (five upregulated and one downregulated), was associated with digestive system development and function [20,21,22,23,24]. Agrp and neuropeptide Y (Npy) were functionally annotated in ingestion by mice, hyperphagia, and appetite. These annotations are in accordance with the physiological processes that are known to be involved in food deprivation [25]. As for the canonical pathway analysis results, several pathways associated with a comparison between the hypothalamus of fasted and fed mice were identified: triacylglycerol degradation, gustation pathway, leptin signaling in obesity, ketogenesis, and mevalonate pathway I (Table 3). These pathways include core signaling genes (Lipg, Agtr1a, Col5a3, Agrp, Npy, and Hmgcs2), which are associated with pathways related to fasted mice compared with those of fed mice. ## 2.5. Gene Network Identification The top 70 hypothalamus-specific genes were used to identify gene networks. The two networks with the highest scores are depicted in Figure 4. The network with a score of 23, including genes angiopoietin like 2 (Angptl2), apelin receptor (Aplnr), and Lipg, was associated with lipid metabolism, molecular transport, and small-molecule biochemistry (Figure 4A). In support of this identified network, previous studies have indicated that the lipid metabolism in hypothalamic cells is associated with the physiological and pathological roles of the hypothalamus on whole-body energy metabolism [26,27]. The network with a score of 19 was associated with digestive system development and function, lipid metabolism, and molecular transport, including the Agrp and Npy genes (Figure 4B). Multiple hormones originating from the digestive system act as afferent inputs to the hypothalamic circuit, thereby controlling homeostatic behaviors [10,11]. Moreover, hypothalamic control of energy metabolism is closely linked to nutrient metabolism controlled by nutrient transporters, including glucose and monocarboxylate transporters [28]. Thus, the identified network depicted in Figure 4B was correlated with selected hypothalamus-specific genes that responded to calorie restriction. Collectively, our network results suggest that the top 70 genes were associated with various cellular processes connected to the metabolic functions of hypothalamic cells. ## 3. Discussion Energy homeostasis is a critical biological event for maintaining life at the cellular and physiological levels. Thus, it has long been studied to understand the underlying mechanisms of biological processes in a variety of organisms and to develop strategies that can be applied to treat human diseases. In a state of energy deficiency, multiple molecular and biochemical processes occur in various organs. Catabolic processes, in particular, are activated to maintain cellular energy homeostasis [29]. In line with these findings, studies have indicated that cellular and molecular responses coupled with altered energy status in metabolic organs are responsible for the control of whole-body energy metabolism [30]. The hypothalamus is a central unit that governs the systemic regulation of energy balance by integrating afferent inputs derived from peripheral organs and other brain areas that are metabolically involved [10]. Therefore, the responsiveness of hypothalamic cells drives homeostatic behaviors, including energy intake, energy expenditure, and physical activity [31]. In the present study, we performed RNA-seq experiments with the hypothalamus and hippocampus extracted from fed and fasted mice and proposed novel molecular mediators that potentially participate in appetite regulation, using the profiling data of hypothalamus-specific mRNA in a model of short-term calorie restriction. We confirmed the alteration of genes previously defined as appetite regulators, including those that encode hypothalamic neuropeptides, such as Agrp and Npy. The profiling results revealed that 81 genes were altered in the hypothalamus and 21 in the hippocampus under overnight fasting conditions, suggesting that the hypothalamus is more metabolically active than the hippocampus. The analyzed data also indicated that 11 genes were commonly altered in both the hypothalamus and hippocampus, and 70 genes were only altered in the hypothalamus in response to overnight fasting. Among the 45 upregulated genes, we identified the ones that encode metabolic enzymes, such as Hmgcs2, which is involved in the synthesis of ketone bodies. In accordance with these findings, our previous studies indicated that the hunger-promoting condition triggered by fasting led to an increase in ketone bodies and a decrease in lactate in the hypothalamus, while mice retaining anorexigenic responses to lipopolysaccharide treatment revealed a contradicting pattern in the circulating levels of ketone bodies and lactate [32,33]. Circulating factors derived from peripheral organs, such as fat, muscle, and liver, dynamically act as signal messengers that propagate information on the current status of energy availability to integrating centers, such as the hypothalamus. In support of this evidence, hypothalamic neurons strongly express receptors of metabolic hormones, and impairment of these receptors in hypothalamic neurons leads to metabolic abnormalities. Given that hypothalamic neurons also release a variety of signal messengers that trigger homeostatic behaviors, identifying novel factors secreted from hypothalamic cells to better understand the hypothalamic control of whole-body metabolism is significant. Among the 70 hypothalamic genes that responded to short-term calorie restriction, we selected seven secretory-related genes, including Fbln5, Col5a3, Pglyrp1, Wfikkn2, Rtbdn, Lipg, and Agrp. Notably, previous studies have identified the roles of Wfikkn2, a protease inhibitor, involved in lipid metabolism in the adipose tissue and brain [34]. In addition, Lipg, an enzyme involved in lipid accumulation, was found among the 25 downregulated genes. Since lipid metabolism in hypothalamic cells is regarded as an effective cellular event for maintaining whole-body energy homeostasis, hypothalamic Wfikkn and Lipg may play a role in the control of energy metabolism. Pglyrp1 is involved in anti- and pro-inflammatory homeostasis and glucose metabolism. Notably, long-term calorie restriction prevents inflammatory responses, which is a possible reason for the beneficial effects of calorie restriction [35]. Our profiling result indicating elevated Pglyrp1 expression in the hypothalamus under fasting conditions suggests that secreted hypothalamic Pglyrp1 could be associated with cellular homeostatic responses for adjusting the inflammatory tone during starvation. These previous findings and our profiling results suggest that the newly identified secretory-related factors induced by short-term calorie restriction impact the hypothalamic control of energy metabolism. The metabolic functions of the selected secretory-related genes have not yet been unveiled. Therefore, whether they participate in the regulation of energy metabolism governed by the hypothalamic circuit is worth investigating. To further verify the physiological involvement of the selected secretory-related genes in controlling homeostatic behaviors coupled with whole-body energy metabolism, we performed experiments that confirmed the expression patterns of the seven selected secretory-related factors in cultured hypothalamic cells in response to treatment with leptin, an anorexigenic hormone derived from adipose tissues, and ghrelin, an orexigenic hormone originating from the gut. Notably, four of the five selected secretory-related genes, which were upregulated under starvation conditions, positively responded to ghrelin treatment, but not to leptin treatment. In line with these observations, hypothalamic Lipg mRNA levels were strongly elevated by leptin treatment and marginally reduced by ghrelin treatment. These observations further suggest that hypothalamic secretory-related factors that are released in increased quantities in response to starvation are oriented toward orexigenic responses during energy-deficit conditions, and hypothalamic Lipg mediates the orexigenic and anorexigenic signals from the peripheral organs involved in metabolic regulations. However, further studies are needed to clarify whether the selected secretory factors are actively involved in the hypothalamic control of whole-body energy metabolism by utilizing the recombinant proteins or the transgenic animals, in which the selected secretory genes are specifically ablated in the hypothalamic cells. Collectively, the current findings provide useful information for regarding the whole-body energy metabolism governed by the hypothalamus and for establishing clinical strategies for patients with metabolic disorders. ## 4.1. Animals Eight-week-old male C57BL/6 mice (Dae Han Bio Link, Eumsung, Republic of Korea) were housed in a 12 h light–dark cycle at 25 °C and 55 ± $5\%$ humidity. The mice were allowed access to normal diet and tap water ad libitum. For food deprivation experiments, food was withdrawn for 18 h starting at 5:00 p.m. Mice were sacrificed by decapitation and their hypothalamus and hippocampus were quickly removed for RNA extraction. All animal care and experimental procedures were performed in accordance with a protocol approved by the Institutional Animal Care and Use Committee (IACUC) at the Incheon National University (permission number: INU-2016-001). ## 4.2. Culture and Treatment of the Cells Mouse hypothalamic mHypoA cells purchased from CELLutions Biosystems (CELLutions Biosystems Inc., Toronto, ON, Canada) were cultured in Dulbecco’s modified Eagle medium (DMEM) containing high glucose (Gibco BRL, Grand Island, NY, USA) and $10\%$ (v/v) fetal bovine serum (Gibco BRL) at 37 °C with $5\%$ CO2 condition. For the gene expression assay, mHypoA cells were seeded in 3 × 105 cells/well in 6-well plates and attached overnight. After serum starvation for 5 h, the attached cells were treated with leptin (200 ng/well, R&D Systems, Minneapolis, MN, USA) or ghrelin (400 nM/well, R&D Systems) for 1 h and then subjected to RNA extraction. ## 4.3. Quantitative Real-Time Reverse Transcription-Polymerase Chain Reaction Total RNA was extracted from the hypothalamus of mice and cultured mHypoA cells according to the Tri-Reagent (Invitrogen, Carlsbad, CA, USA) protocol. First-strand cDNA was synthesized with 2 μg total RNA using a high-capacity cDNA reverse transcription kit (Intron Biotechnology, Seoul, Republic of Korea). The mRNA expression levels were measured using a Bio-Rad CFX 96 Real-Time Detection System (Bio-Rad Laboratories, Hercules, CA, USA) with the SYBR Green Real-time PCR Master Mix Kit (TaKaRa Bio Inc., Foster, CA, USA). The results were analyzed using the CFX Manager software and normalized to the levels of β-actin, a housekeeping gene. The primers used were: Agrp, F-TGTGTAAGGCTGCACGAGTC and R-GGCAGTAGCAAAAGGCATTG; Col5a3, F-CGGGGAGGAGTCTTTTGAG and R-GCCTGAGGGTCTGGAATTAAC; Pglyrp1, F-GTGGTGATCTCACACACAGC and R-GTGTGGTCACCCTTGATGTT; Wfikkn2, F-GAGTCGACGCGCACACCGCCCTGCCGCGCC and R-GCGAAGCTTGGAGTGCGTTTATTCACCAGG; Fbln5, F-GTGTGTGGATGTGGACGAGT and R-TACCCTCCTTCCGTGTTGAT; Rtbdn, F-TACAGCCCACTAGGGCCTTAACTC and R-TACAGTACCGCGGAGATGGAGAT; Lipg, F-TGCAACAGCCAAAACCTTCT and R-TGTCCCACTTTCCTCGTGTT; and β-actin, F-TGGAATCCTGTGGCATCCATGAAAC and R-TAAAACGCAGCTCAGTAACAGTAACAGTCCG. ## 4.4. Whole Transcriptome Sequencing For the whole transcriptome sequencing of each hypothalamus extracted from the fed and fasted mice ($$n = 3$$, respectively), double-stranded cDNA libraries were prepared from a total of 1 μg of RNA molecules using the TruSeq Stranded mRNA Sample Prep Kit (Illumina, San Diego, CA, USA). The quality and quantity of the cDNA libraries were evaluated with the Agilent 2100 BioAnalyzer (Agilent, Santa Clara, CA, USA) and the KAPA library quantification kit (Kapa Biosystems, Wilmington, MA, USA). By using Illumina Novaseq6000 (Illumina), paired-end sequencing (2 × 100 base pairs) was carried out. For the whole transcriptome sequencing of each hippocampus extracted from the fed and fasted mice ($$n = 3$$, respectively), Quant-IT RiboGreen (Invitrogen) was used to assess the total RNA concentration and TapeStation RNA ScreenTape (Agilent) was run to estimate the integrity of the total RNA. A library with LIN greater than 7.0 was equipped using the Illumina TruSeq Stranded mRNA Sample Prep Kit (Illumina) in 1ug of total RNA for each sample. The quantity of the libraries was assessed using KAPA Library Quantification kits for Illumina Sequencing platforms according to the qPCR Quantification Protocol Guide (Kapa Biosystems), and the quality of the libraries was assessed using the TapeStation D1000 ScreenTape (Agilent). An Illumina NovaSeq (Illumina) was used for paired-end sequencing (2 × 100 bp). ## 4.5. Whole Transcriptome Analysis Sequence quality was assessed by FastQC v0.11.9. Trimming of low-quality and adapter sequences was performed by cutadapt v3.4 [36] and Trimmomatic v0.36 [37] for the hypothalamus and hippocampus, respectively. Sequences were mapped by STAR v.2.7.3a [38], using the 2-pass method–to–mouse reference genome from the Ensembl database (GRCm38, 101 released). Assigning sequence reads to genes was carried out by the featureCounts [39] algorithm, which is a suitable program for reading the summarization from RNA-sequencing experiments. ## 4.6. Differential Expression Genes Analysis, Secretory Genes Analysis, and Pathway Analysis The EdgeR v3.32.1 [40] R package was used to estimate DEGs from RNA-sequencing data based on the over-dispersed Poisson model and Empirical Bayes methods. The DEG analysis was conducted with the glmQLFit/glmQLFTest function of edgeR, which applied the quasi-likelihood F-test method in fasted mice compared to fed mice. The FDR and the log2FC were extracted. FDR was calculated using the Benjamini–Hochberg method. DEGs results were visualized with a volcano plot and heatmap using calibrate v1.7.7 and ggplot2 v3.3.5 in the R package. MetazSecKB [19], secretome and proteome knowledgebase of the human and animal, was used to get lists of “curated secreted” proteins and “high likely” secreted proteins. “ Curated secreted” proteins are the reviewed dataset to be ‘secreted’ or ‘extracellular’ from UniProtKB/Swiss-Prot, and “high likely” secreted proteins are a dataset called to be secreted or a secretory signal peptide predicted from four algorithms, SignalP4, Phobius, TargetP, and WoLF PSORT7. Two protein datasets were converted to the gene ID of the Ensembl using the biomaRt [41] v2.46.3 R package, the mapping tools for the incorporation of a genomic dataset. Only genes with FDR less than 0.05 and absolute log2FC greater than 1.0 were used. Raw counts of feature Counts’ output were transformed to TPM (Transcripts Per Million) to visualize the heatmap of the mean of expression level. The Seaborn [42] v0.11.1 python library was used for visualization. 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--- title: RNA-Seq Reveals the mRNAs, miRNAs, and lncRNAs Expression Profile of Knee Joint Synovial Tissue in Osteoarthritis Patients authors: - Linghui Qiao - Jun Gu - Yingjie Ni - Jianyue Wu - Dong Zhang - Yanglin Gu journal: Journal of Clinical Medicine year: 2023 pmcid: PMC9968173 doi: 10.3390/jcm12041449 license: CC BY 4.0 --- # RNA-Seq Reveals the mRNAs, miRNAs, and lncRNAs Expression Profile of Knee Joint Synovial Tissue in Osteoarthritis Patients ## Abstract Osteoarthritis (OA) is a chronic disease common in the elderly population and imposes significant health and economic burden. Total joint replacement is the only currently available treatment but does not prevent cartilage degeneration. The molecular mechanism of OA, especially the role of inflammation in disease progression, is incompletely understood. We collected knee joint synovial tissue samples of eight OA patients and two patients with popliteal cysts (controls), measured the expression levels of lncRNAs, miRNAs, and mRNAs in these tissues by RNA-seq, and identified differentially expressed genes (DEGs) and key pathways. In the OA group, 343 mRNAs, 270 lncRNAs, and 247 miRNAs were significantly upregulated, and 232 mRNAs, 109 lncRNAs, and 157 miRNAs were significantly downregulated. mRNAs potentially targeted by lncRNAs were predicted. Nineteen overlapped miRNAs were screened based on our sample data and GSE 143514 data. Pathway enrichment and functional annotation analyses showed that the inflammation-related transcripts CHST11, ALDH1A2, TREM1, IL-1β, IL-8, CCL5, LIF, miR-146a-5p, miR-335-5p, lncRNA GAS5, LINC02288, and LOC101928134 were differentially expressed. In this study, inflammation-related DEGs and non-coding RNAs were identified in synovial samples, suggesting that competing endogenous RNAs have a role in OA. TREM1, LIF, miR146-5a, and GAS5 were identified to be OA-related genes and potential regulatory pathways. This research helps elucidate the pathogenesis of OA and identify novel therapeutic targets for this disorder. ## 1. Introduction Osteoarthritis (OA) is characterized by articular cartilage degeneration, subchondral osteosclerosis, osteophyte formation, and synovitis. OA is the most common chronic joint disease in the elderly [1,2,3]. An estimated 250 million people have knee OA worldwide, and the incidence rate is increasing [4,5]. This disorder has significant health and economic burden, and the current treatment methods cannot effectively prevent disease progression. The pathogenesis of OA is complex and multifactorial. Recent studies have shown an association between synovial tissue and OA. OA can lead to low-grade synovitis. Increasing evidence shows that synovitis can increase OA symptoms and cartilage degeneration. In OA, the synovium is infiltrated by fibroblast-like synoviocytes and other immune cells [6]. Synovial tissue obtained by closed needle or arthroscopic biopsy can help diagnose OA [7]. Interestingly, the miRNA expression pattern is similar between synovial fluid and synovial tissue [8]. This study evaluated synovial tissue, which is most severely affected by OA. Epigenetic regulators, including long non-coding RNAs (lncRNAs) and miRNAs, mediate OA [9,10]. LncRNAs are non-coding RNAs (ncRNAs) with more than 200 nucleotides and are widely transcribed in the human genome. LncRNAs are abnormally expressed in human diseases and affect disease progression [11]. For instance, the dysregulated expression of HOTAIR and CIR in OA cartilage indicates that these lncRNAs can assist in OA diagnosis and prognosis and can be used as biomarkers of disease progression [12,13]. miRNAs are small (19–25 base pairs) highly conserved ncRNAs that act as post-transcriptional regulators of gene expression. These molecules bind to target mRNAs to form miRNA-mRNA complexes, leading to target transcript degradation and translation inhibition [14]. A single miRNA can target multiple transcripts, and individual mRNAs can be regulated by several miRNAs, characterizing a complex regulatory network [15,16]. Moreover, some lncRNAs have miRNA-binding sites and act as miRNA sponges in cells, preventing the inhibition of target mRNAs by miRNAs, increasing the expression of target genes, and forming a regulatory network of competing endogenous RNAs (ceRNAs). Many studies have shown that the expression of miR-140 is low in OA [17,18]. In this respect, the intra-articular injection of miR-140 can alleviate OA progression in rats by regulating extracellular matrix (ECM) homeostasis and can become a treatment option for OA [19]. Furthermore, a new method for treating OA is the use of exosomes to introduce miR-140 into chondrocytes [20]. CircSERPINE2 expression is relatively low in OA tissues, and the injection of CircSERPINE2 alleviates OA in a rabbit model. CircSERPINE2 regulates apoptosis and ECM metabolism in human chondrocytes (HCs) by targeting miR-1271-5p and ERG [21]. Similarly, circRNA.33186, acting as a sponge of miR-127-5p, is directly targeted by MMP-13 in an OA mouse model [22]. This study analyzes the patterns of expression of lncRNAs, miRNAs, and related mRNAs in the knee joint synovial tissue of 8 OA patients, and compared the expression profiles with two controls. Then we download the miRNAs expression data from the Gene Expression Omnibus (GEO) database (GSE143514) to identify the overlapped differentially expressed miRNAs (DEM). GO and KEGG functional enrichment analyses were performed to elucidate molecular functions and pathways in OA. In enriched pathways, inflammatory pathways were focused, and related differentially expressed genes (DEGs) were further analyzed based on the GSE114007 dataset. Severe inflammation was found in the synovial tissue and was associated with pain and structural decline [23]. Our study analyzes the patterns of expression of lncRNAs, miRNAs, and mRNAs in OA synovial tissues and predicts inflammation-related target genes. ## 2.1. Patients and Samples Synovial tissue samples were obtained during joint surgeries performed in the Wuxi No. 2 People’s Hospital in 2021 and were snap-frozen in liquid nitrogen. All patients provided written informed consent and were treated according to the ethical guidelines of the research ethics committee of our hospital. Transcriptome analysis was performed in eight OA patients and two patients with the popliteal cyst. The diagnosis was performed according to the 2010 ACR/EULAR criteria. All OA patients were end-stage lesions requiring surgical treatment, and there were no significant differences in the extent of the lesions. Eight biopsies from OA patients served as the intervention group, and two biopsies from patients with popliteal cysts patients served as controls. Since intact synovial tissue in healthy individuals is difficult to obtain in practice, patients with simple popliteal cysts were selected for this study. It was determined that none of these patients had any symptoms or history of OA or knee injury, and the possibility of having OA was completely excluded from the imaging examination. The relevant blood parameters also excluded OA, joint cavity infection, and rheumatoid arthritis in this group of patients. Therefore, the synovial tissues of these two patients with simple popliteal cysts excluded the possibility of various inflammatory changes and could be considered healthy synovial tissues. This was used as a control group to carry out the study. We had access to information that could identify individual participants during or after data collection. ## 2.2. RNA Library Construction and Sequencing Total RNA was isolated and purified using TRIzol (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. The amount and purity of RNA were determined using a NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA). RNA integrity was assessed using an Agilent 2100 Bioanalyzer, and samples with an RNA integrity number >7.0 were considered to be high-quality samples. Approximately 5 mg of total RNA was used to deplete ribosomal RNA according to the recommendations of the Ribo-Zero™ rRNA Removal Kit (Illumina, San Diego, CA, USA). rRNA-depleted RNA was fragmented using divalent cations under high temperatures. First-strand cDNA was synthesized using reverse transcriptase and random hexamers, and second-strand cDNA was synthesized in a buffer containing polymerase, followed by end-repair, poly(A)-tailing, and sequencing adaptor ligation. Paired-end adapters were ligated to the DNA fragments, and size selection was performed with AMPure XP beads. After UDG treatment of second-strand DNA, the ligated products were amplified by PCR using the following amplification conditions: initial denaturation at 95 °C for 3 min, eight cycles at 98 °C for 15 s, 60 °C for 15 s, and 72 °C for 30 s, and an extension step at 72 °C for 5 min. The amplification products were purified using AMPure XP beads, resuspended in the elution buffer, and heat-denatured. The average read size of the cDNA library was 300 ± 50 bp. Paired-end sequencing was performed on an Illumina HiSeq 4000 platform (LC Bio, Hangzhou, China) following the manufacturer’s recommendations. ## 2.3. Bioinformatics Analysis Raw reads were processed using proprietary software ACGT101-miR (LC Sciences, Houston, TX, USA) to remove adapter dimers, reads with low-quality bases, abundant RNA (rRNA, tRNA, snRNA, and snoRNA), and PCR duplicates. Unique sequences with a length of 18–26 nucleotides were mapped to human reference genome HG19 using BLAST and miRBase 22.0 (miRBase database, http://www.mirbase.org/, 26 January 2022). Unique sequences that mapped to mature miRNAs in hairpin arms were considered known miRNAs, and unique sequences that mapped to the other arm of known precursor hairpin opposite the annotated mature miRNA-containing arm were considered to be novel 5′ or 3′-derived miRNA sequences. The remaining sequences were mapped to other databases using BLAST and miRBase 22.0 and mapped pre-miRNAs were aligned against human databases to determine their genomic locations and were considered known miRNAs. Unmapped sequences were aligned against human genomes, and hairpin RNA structures were predicted from flanking 80 nt sequences using RNAfold software (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi, 19 February 2022). ## 2.4. Analysis of Differential Expressed miRNAs (DEmiRNAs) and Target Genes Prediction Differential expression of miRNAs based on normalized deep-sequencing counts was analyzed by selectively using Fisher exact test, Chi-squared 2X2 test, Chi-squared non-test, Student t-test, or ANOVA based on the design of the experiment. The significance threshold was set to be 0.01 and 0.05 in each test. In order to predict the most abundant miRNA target genes, we used two computational target gene prediction algorithms (Targets can, V5.0 and Miranda, v3.3a) to identify miRNA binding sites. The data predicted by the two algorithms were combined to calculate the overlaps. The GO terms and KEGG Pathway of these most abundant miRNAs, and miRNA targets were also annotated. ## 2.5. LncRNA Transcript Assembly Adaptor sequences and reads with low-quality bases were removed using Cutadapt. Sequence quality was verified using FastQC (http://www. Bioinformatics. Babraham.ac. Uk/projects/fastqc/, 16 March 2022). High-quality sequences were mapped to the genome of Homo sapiens using Bowtie2 and Hisat2. Mapped reads from each synovial sample were assembled into transcripts using StringTie, and transcripts from all samples were assembled de novo into a transcriptome using Perl scripts. Transcript expression levels were estimated using StringTie and edgeR. ## 2.6. LncRNA Identification, Analysis of DE mRNAs and lncRNAs, and Prediction of Target Genes The transcripts that overlapped with known mRNAs and transcripts shorter than 200 bp were discarded. Transcripts with coding potential were predicted using CPC and CNCI. Transcripts with CPC scores < −1 and CNCI scores < 0 were removed, and the remaining transcripts were considered lncRNAs. The relative expression of mRNAs and lncRNAs was measured as fragments per kilobase of exon per million fragments mapped using StringTie. DE mRNAs and lncRNAs (log2 fold change > 1 or <−1 at $p \leq 0.05$) were identified using edgeR. DE genes at 100 Kb upstream or downstream of DE lncRNAs were selected as potential cis-target genes using Python scripts. The potential functions of these target genes were determined using BLAST2GO. Statistical significance was established at $p \leq 0.05.$ ## 3.1. Clinical and Biochemical Features of the Included Individuals The patient’s details are shown in Table 1. After admission, all tests were completed to exclude the possibility of exogenous infection. ## 3.2. Differentially Expressed mRNA, miRNA and LncRNA Profile A total of 77821 mRNAs, 890 miRNAs, and 47522 lncRNAs were identified in 10 synovial tissues. in the OA group, we found 343 mRNAs significantly upregulated and 232 mRNAs significantly downregulated compared with the control group. For lncRNAs, 270 were significantly upregulated and 109 were significantly downregulated. For miRNAs, 247 were significantly upregulated and 157 were significantly downregulated ($p \leq 0.05$, fold change > 2). The most upregulated lncRNA was NEAT1 with a 210-fold change; the most downregulated lncRNA was FP671120 with a 0.047-fold change. The most upregulated mRNA was LEP with a 57-fold change; the most downregulated mRNA known was LAMP5 with a 0.079-fold change. Table 2 shows the top five up- and downregulated mRNAs, miRNAs, and LncRNAs. mRNA, miRNA, and LncRNA volcano and heat maps are shown in Figure 1. As shown in Figure 2, the detected lncRNAs were widely distributed on all classes of chromosomes. We used circus (ww.circos.ca, 3 May 2022) software to perform genomic mapping of the lncRNAs obtained from the screening. Mapping was performed by taking each chromosome as a basic unit per 25 MB, respectively, and the expression of lncRNAs in each segment was counted for mapping when visualizing the lncRNA genomes in different samples, and the genomes of different lncRNA types were mapped. The number of lncRNAs in each segment was counted during visualization. ## 3.3. GO and KEGG Enrichment Analysis of lncRNAs/miRNA Target mRNAs GO analysis was performed on all the target mRNAs expressing significantly different increases. As shown in Figure 3A, negative regulation of receptor-mediated endocytosis, ribosomal large subunit binding, and filopodium membrane signaling pathway is the main functions associated with dysregulated lncRNAs. We performed KEGG pathway analysis on the target mRNA of this dysregulated lncRNA. The main enrichment pathways include ABC transporters, EGFR tyrosine kinase inhibitor resistance, and allograft rejection (Figure 3C). We also predicted the possible target genes of differential miRNA. Go enrichment analysis showed that thromboxane-a synthase activity, B cell apoptotic process, and fibrin odium membrane were the main enriched GO terms (Figure 3B). Pathway in cancer was the main enriched KEGG signaling pathway (Figure 3D). ## 3.4. Prediction of the Interaction between Differentially Expressed lncRNA-mRNA To further study the regulation of lncRNAs on gene expression through a possible ceRNA network during OA, we predicted the interaction between the upstream and downstream of differentially expressed lncRNAs within 100 k bp. A total of 55 pairs of differential lncRNA-mRNA were identified. These differentially expressed lncRNAs may affect the OA process through cis-regulation of mRNAs, which were differentially expressed. The results are shown in Table 3 (TOP10). ## 3.5. Expression Validation of OA-Related Overlapped DEMs, lncRNAs, and mRNAs in Synovial Tissue We focused on the expression of OA-related genes and inflammatory markers, some of which were dysregulated in OA. OA-related genes CHST11 and ALDH1A2 and inflammatory genes TREM1, IL-1β, IL-8, CCL5, and LIF were dysregulated in OA. Furthermore, miR-146a-5p and miR-335-5p (targeting the IL-1β gene) and lncRNAs GAS5 (miR-34a-Bcl2 axis), LINC02288 (miR-374a-3p-RTN3 axis), and LOC101928134 (targeting the IFNA1 gene) were differentially expressed in the synovial tissue of OA patients vs. the control group based on our sample data and GSE143514. For upregulated genes, the expression of TREM1, LIF, miR146-5a, and GAS5 increased, which was consistent with our sample data (Figure 4). However, IL-1β, IL-8, CCL5, LINC02288, and LOC101928134 were not significantly different ($p \leq 0.05$). For downregulated genes, CHST11 and miR335-5p were incompatible with the previous results. Thus, TREM1, LIF, miR146-5a, and GAS5 were identified to be OA-related genes in our study. ## 4. Discussion OA affects the joints of the knees, hands, hips, and spine and is the main cause of limited mobility among the elderly [24,25]. Although genetic susceptibility, aging, obesity, and joint malalignment are risk factors for OA, the pathogenesis of OA is unclear [26,27]. Therefore, other than total joint replacement, interventions that delay disease progression and cartilage degeneration are currently unavailable [28]. In the past decade, genome-wide association studies have increased the amount of genomic data exponentially. In this study, RNA-seq was used to determine transcriptome changes in the synovial tissue of OA and RA patients. A total of 1358 RNA transcripts were DE in the OA group (860 upregulated and 498 downregulated), and some target genes were involved in inflammation. Carbohydrate (chondroitin 4) sulfotransferase 11 (CHST11) belongs to the thioltransferase 2 family and catalyzes the transfer of sulfate to position four of the N-acetylgalactosamine (GalNAc) residue of chondroitin. Chondroitin sulfate is the most abundant proteoglycan in cartilage and is widely found on the cell surface and ECM. The abnormal expression of CHST11 is associated with osteochondral dysplasia, short fingers/toes, and other dysfunctions. In 2017, a genome-wide association study found that CHST11 was associated with OA in North American Caucasians with knee OA [29]. In our study, CHST11 was downregulated in the OA group ($p \leq 0.05$). The enzyme aldehyde dehydrogenase 1A2 (ALDH1A2) catalyzes the synthesis of retinoic acid from the retinal. Retinoic acid is an active metabolite of vitamin A (retinol), which plays a role in tissue development. Genetic studies in mice have shown that this enzyme, with the help of cytochrome CYP26A1, maintains retinoic acid levels, promotes bone development, and prevents spina bifida [30]. The expression level of ALDH1A2 is closely related to the pathogenesis of hand OA in Icelandic patients [31]. In our study, the expression of ALDH1A2 was significantly downregulated in OA patients. Toll-like receptor 4 (TLR4) is activated during acute inflammation and increases the expression of TREM1, and the latter triggers the expression of proinflammatory cytokines, including TNF-a and IL-1β [32]. TREM1 is highly expressed in inflamed synovium [33]. Moreover, TREM1 is significantly upregulated in damaged cartilage [34], consistent with our results. The expression of TREM1 in OA patients was higher than that in the RA group, suggesting that the degree of inflammation is increased in OA cartilage. This result was corroborated by the high expression of downstream IL-1β. IL-1β is implicated in many pathological features of OA [35]. IL-1β signaling has been shown to regulate the expression of 909 of 3459 genes in primary human articular chondrocytes, including genes encoding chemokines and inflammatory mediators, such as IL-11 and CCL5 [36]. Other OA-related inflammatory factors include IL-8 and LIF [37]. DE ncRNAs have been identified in synovial samples. miR-146a-5p was upregulated in the articular cartilage and serum of OA patients [38]. miR-335-5p attenuates chondrocyte inflammation in OA by activating autophagy [39]. The overexpression of miR-335-5p downregulates the expression of inflammatory mediators IL-1β, IL-6, and TNF-α and increases the expression of autophagy marker beclin-1 and autophagy-related proteins 5 and 7. LncRNA GAS5 expression is high in OA cartilage and increases with disease progression. GAS5 acts as a negative regulator of miR-21 and thereby controls cell survival [40]. Similarly, LINC02288 expression was significantly upregulated in a rat model of OA. LINC02288 knockdown reduced IL-1β-induced apoptosis and proinflammatory cytokine production in OA chondrocytes [41]. The lncRNA LOC101928134 was highly expressed in the synovial tissue of OA rats, regulated IFNA1 expression, and inhibited the JAK/STAT signaling pathway. Moreover, the downregulation of LOC101928134 improved synovial inflammation and cartilage injury, enhanced synovial cell apoptosis, and inhibited chondrocyte apoptosis in OA rats [42]. NEAT1 was the most upregulated lncRNA in the synovial tissue in this study. Despite being significantly downregulated in chondrocytes, NEAT1 controls chondrocyte proliferation and apoptosis through the miR-543/PLA2G4A axis [43] and regulates cartilage matrix degradation through the miR-193a-3p/SOX5 axis [44]. However, NEAT1 is upregulated in synovial cells and is believed to control their proliferation by competitively binding to miR-181c [45]. The distinct expression of NEAT1 in chondrocytes and synovial cells may indicate different pathophysiological mechanisms in OA. The most upregulated gene in our study, LEP, a weighted gene, was associated with OA and can be a new biomarker and drug target [46]. Studies have shown that plasma LEP levels are positively correlated with the risk of OA [47]. In OA patients, obesity was significantly associated with an increase in LEP promoter methylation. Lysosomal-associated membrane protein LAMP5 is involved in gastric cancer and leukemia. However, no studies have assessed the role of LAMP5 in OA. While this study focuses on joint-related inflammatory RNAs, other studies have focused on exploring the pathogenesis of OA at the RNA level. Huang ZY et al., used paired scRNA-seq and bulk RNA-seq data to create and validate an “arthritis” specific feature matrix that significantly outperformed the default feature matrix for synovial cells. RNA-seq data for rheumatoid arthritis (RA) and OA were analyzed by estimating relative subsets of RNA transcripts for cell type identification using machine learning tools. The authors, [48] X Zhang et al., retrieved and downloaded mRNA expression data from a comprehensive gene expression database to identify differentially expressed genes (DEGs) in OA and normal individual synovial tissue [49]. Dysregulated genes including USP46, CPVL, FKBP5, FOSL2, GADD45B, PTGS1, ZNF423, ADAMTS1, and TFAM were found to be potentially involved in the pathology of OA. This overlaps with some of the mRNA loci identified in this study. Microarray and RNA-seq data from the comprehensive gene expression database were also obtained for the study by Le Kang et al., who collected joint fluid from patients who underwent inpatient arthroplasty for validation experiments [50]. Ultimately, it was found that there was a difference in gene expression between OA and RA and that the presence of ADAMDEC1 in the joint fluid was a good biomarker for RA. In this study, complete whole genome sequencing was performed and many differentially expressed RNAs were obtained, but in contrast to the above study, no cross-sectional comparisons were made using gene sets from online databases, and no other clinical specimens were collected for validation experiments. This part will be refined in a later study. The samples for this study were collected largely within three months and then sequenced uniformly on the Illumina platform. This avoids the errors associated with split experiments. If more samples are to be included it may take too long and the long storage time of the samples may lead to loss of RNA fragments. With new sequencing technologies such as long read length, RNA sequencing and advances in direct RNA sequencing (dRNA-seq), more complete data can be obtained to some extent. However, this also means increased costs and no great advantage in sequencing miRNA, mRNA, etc. Moreover, because of the strict entry criteria for this study, there were relatively few patients with simple popliteal cysts without arthritis, which led to a reduction in the number of controls. If conditions permit at a later stage, a multi-center collaboration to increase the sample size and update the technique can be considered for further study. 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--- title: Association of Types of Sleep Apnea and Nocturnal Hypoxemia with Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy authors: - Haobo Xu - Juan Wang - Shubin Qiao - Jiansong Yuan - Fenghuan Hu - Weixian Yang - Chao Guo - Xiaoliang Luo - Xin Duan - Shengwen Liu - Rong Liu - Jingang Cui journal: Journal of Clinical Medicine year: 2023 pmcid: PMC9968174 doi: 10.3390/jcm12041347 license: CC BY 4.0 --- # Association of Types of Sleep Apnea and Nocturnal Hypoxemia with Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy ## Abstract Background: Data regarding the association between sleep apnea (SA) and atrial fibrillation (AF) in hypertrophic cardiomyopathy (HCM) are still limited. We aim to investigate the association of both types of SA, obstructive sleep apnea (OSA) and central sleep apnea (CSA), and nocturnal hypoxemia with AF in HCM. Methods: A total of 606 patients with HCM who underwent sleep evaluations were included. Logistic regression was used to assess the association between sleep disorder and AF. Results: SA was presented in 363 ($59.9\%$) patients, of whom 337 ($55.6\%$) had OSA and 26 ($4.3\%$) had CSA. Patients with SA were older, more often male, had a higher body mass index, and more clinical comorbidities. Prevalence of AF was higher in patients with CSA than patients with OSA and without SA ($50.0\%$ versus $24.9\%$ and $12.8\%$, $p \leq 0.001$). After adjustment for age, sex, body mass index, hypertension, diabetes mellitus, cigarette use, New York Heart Association class and severity of mitral regurgitation, SA (OR, 1.79; $95\%$ CI, 1.09–2.94) and nocturnal hypoxemia (higher tertile of percentage of total sleep time with oxygen saturation < $90\%$ [OR, 1.81; $95\%$ CI, 1.05–3.12] compared with lower tertile) were significantly associated with AF. The association was much stronger in the CSA group (OR, 3.98; $95\%$ CI, 1.56–10.13) than in OSA group (OR, 1.66; $95\%$ CI, 1.01–2.76). Similar associations were observed when analyses were restricted to persistent/permanent AF. Conclusion: Both types of SA and nocturnal hypoxemia were independently associated with AF. Attention should be paid to the screening of both types of SA in the management of AF in HCM. ## 1. Introduction Hypertrophic cardiomyopathy (HCM) is the most common hereditary cardiomy-opathy characterized by left ventricular hypertrophy and a spectrum of clinical manifestations [1]. Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with significant morbidity and mortality in patients with HCM [2]. Nowadays, considerable evidence supports sleep apnea (SA) as a risk factor for AF [3]. Previous studies including our works showed a high prevalence of SA in HCM and the most common type of SA, obstructive sleep apnea (OSA), is independently associated with AF in HCM [4,5,6]. Unlike OSA, central sleep apnea (CSA) is characterized by a lack of drive to breathe during sleep and is less common [7]. Recently, an increasing number of investigations have linked CSA to AF [8,9]. Whether CSA also has a relationship with AF in HCM is still unknown. In addition, few studies have analyzed the respective relations of OSA and CSA to AF in HCM and there is also a paucity of evidence on the association between nocturnal hypoxemia, an essential pathophysiological feature in SA, and AF. To address the above limitations, the overall aim of the current study was designed to examine the association of both types of SA and nocturnal hypoxemia with AF in a large HCM cohort. ## 2.1. Study population This retrospective cross-sectional study included consecutive patients who were diagnosed with HCM and underwent overnight diagnostic sleep examination at the inpatient department of Fuwai Hospital between February 2010 and January 2019. The study cohort has been described in detail previously [10]. Diagnostic criteria of HCM were consistent with the 2020 American Heart Association/American College of Cardiology which mainly include unexplained septal hypertrophy with a thickness of at least 15 mm [11]. Patients with rest left ventricular outflow tract (LVOT) peak gradient ≥ 30 mmHg or rest LVOT peak gradient < 30 mmHg with provoked LVOT peak gradient ≥30 mmHg were considered as obstructive. Otherwise, patients were considered as nonobstructive. Patients were excluded if they had New York Heart Association (NYHA) class IV, incomplete sleep data, were younger than 18 years old, had septal reduction therapy before (septal myectomy or alcohol septal ablation), or had a history of heart transplantation. Patients were also excluded if they were receiving treatment with continuous positive airway pressure or oxygen therapy. According to the exclusion criteria, a total of 606 patients were finally enrolled. All patients provided informed consent before enrollment. The study was approved by the ethics committee of Fuwai Hospital (2020-ZX25). All studies were conducted in accordance with the ethical principles stated in the Declaration of Helsinki. ## 2.2. Definition of AF Prevalence of AF was documented. Data including medical histories, 12-lead elec-trocardiograms and 24-h Holter electrocardiography were collected to help with diagnosis during inpatient stays. Type of AF was defined according to the 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation [12]. Briefly, paroxysmal AF was defined as AF that terminated without intervention within 7 days of onset. Persistent AF was defined as continuous AF that is sustained over 7 days. Long-standing persistent AF that lasted at least 1 year when deciding to adopt a rhythm control strategy was classified into persistent AF in our study. Permanent AF was defined when AF was accepted by the patients and physicians and stop further attempts to restore or maintain sinus rhythm. ## 2.3. Diagnosis of Sleep Apnea Portable polysomnography monitoring was performed before the time of septal reduction therapy by using the system Embletta (Medcare Flaga, Reykjavik, Iceland) in all included patients. All patients underwent testing on room air. This device records nasal airflow by an airflow pressure transducer, finger pulse oximetry, thoracic and abdominal movement, body position, snoring, heart rate, and ECG, and has been validated against full polysomnography [13]. All polysomnograms were scored blindly. Apnea was defined when cessation of airflow or airflow reduction to ≤$10\%$ of the baseline value lasted for 10 s or more. An apnea was scored as obstructive if a respiratory effort was present during the event or central in the absence of effort during the event. Hypopnea was defined as a $50\%$ or discernible decrement in airflow lasting 10 s or longer associated with oxygen desaturation of $3\%$. Hypopneas were scored as obstructive if snoring, and/or flow limitation was noted on the nasal pressure signal or if paradoxical movement was noted on respiratory inductance plethysmography during the event. In the absence of snoring, flow limitation, and paradoxical movement, the hypopnea was scored as a central event. It should be noted that the precise scoring of obstructive or central hypopnea is difficult without the measurement of esophageal pressure. The apnea–hypopnea index (AHI) was the number of apneas and hypopneas per hour of total sleep time. Diagnosis of SA was made solely when the AHI was 5 events/h or more, irrespective of daytime symptoms, which allowed objective evaluation of the disease severity [14]. Patients with SA were grouped into CSA when at least $50\%$ of the disordered breathing events were central (apnea or hypopnea); whereas, if greater than $50\%$ of disordered breathing events were obstructive (apnea or hypopnea), patients were grouped into OSA. The oxygen desaturation index, mean oxygen saturation (SaO2), minimal SaO2, average pulse frequency and snoring proportion were also recorded. The severity of nocturnal hypoxemia was measured based on percentage of total sleep time (TST) spent with SaO2 < $90\%$. It was assessed as a categorical variable (tertiles with T1 [<$0.3\%$], T2 [≥0.3 to <$5.1\%$] and T3 [≥$5.2\%$]) or as a continuous variable in regression models. ## 2.4. Statistical Analysis The numeric variables were expressed as mean and standard deviation, and the categorical variables were expressed as number (percentage). Continuous variables were tested for normal distribution with the Kolmogorov–Smirnov test. Comparison of categorical variables was performed using the χ2 or Fisher’s exact test, as appropriate. Differences of continuous variables between groups were compared using the Student’s unpaired t-test or Mann–Whitney U test, as appropriate. Univariate and multivariate logistic regression analyses were used to determine the association between the presence of both types of SA or severity of nocturnal hypoxemia and prevalence of AF. The results are expressed as odds ratio (OR) and $95\%$ confidence interval (CI). Covariates including age, sex, body mass index (BMI), hypertension, diabetes mellitus, cigarette use, NYHA class, and severity of mitral regurgitation were adjusted. Tests of interaction were performed to assess whether the association of SA and nocturnal hypoxemia with AF was affected by obstruction of LVOT, sex, or obesity using the abovementioned multivariate model. All reported probability values were 2-tailed, and a p-value of <0.05 was considered statistically significant. SPSS version 24.0 (IBM Corp., Armonk, NY, USA) was used for calculations and illustrations. ## 3.1. Baseline Characteristics A total of 606 patients were enrolled. The study flowchart is shown in Figure 1. SA was diagnosed in 363 ($59.9\%$), of whom 337 ($55.6\%$) had OSA and 26 ($4.3\%$) had CSA. Patients with SA were older, more likely to be male and smokers and had more clinical comorbidities such as hypertension, hyperlipidemia, diabetes mellitus, and coronary heart disease (Table 1). Compared with patients without SA, the prevalence of AF was higher in patients with SA ($26.7\%$ versus $12.8\%$, $p \leq 0.001$) (Table 1) and the prevalence was even higher in the CSA group when compared with the OSA group (Table 1 and Figure 2A). In addition, patients with CSA had higher NYHA class and N-terminal brain natriuretic peptide (NT-pro BNP) level. Prevalence of AF increased with tertiles of percentage of TST spent with SaO2 < $90\%$ ($14.9\%$ in T1, $16.8\%$ in T2 and $31.7\%$ in T3, $p \leq 0.001$) (Figure 2B). ## 3.2. Echocardiographic Data Echocardiographic data are shown in Table 2. Obstruction of LVOT was more common in patients without SA, while the LVOT gradient in obstructive HCM was not different between groups. Patients with SA were associated with enlarged left atrial diameter (LAD), left ventricular end-diastolic diameter, and ascending aorta diameter compared with patients without SA. LAD was even larger in the CSA group than the OSA group. The mean LVEF was lower and the ratio of patients with LFEV < $50\%$ was higher in the CSA group than the OSA group. ## 3.3. Sleep Parameters Data from sleep study are summarized in Table 3. Patients with SA had a significantly higher value of AHI and oxygen desaturation index compared with patients without SA. The value was even higher in the CSA group than OSA group. The longest apnea/hypopnea time, percentage of TST spent with SaO2 < $90\%$, and snoring time ratio were higher, and the lowest SaO2 and mean SaO2 were lower in patients with SA than those without. ## 3.4. Association of Sleep Apnea and Nocturnal Hypoxemia with AF In the univariate analyses, SA as well as both types of SA, OSA and CSA, were significantly associated with AF (Table 4). OSA was associated with an OR of 3.24 ($95\%$ CI, 1.63–6.44), meanwhile, CSA was associated with a higher OR of 6.84 ($95\%$ CI, 2.91–16.10). Significant associations were also observed between measures of nocturnal hypoxemia and AF. After controlling for age, sex, BMI, hypertension, diabetes mellitus, cigarette use, NYHA class, and severity of mitral regurgitation, the association of SA (OR, 1.79; $95\%$ CI, 1.09–2.94) and nocturnal hypoxemia (higher tertile of percentage of TST spent with SaO2 < $90\%$ with OR, 1.81; $95\%$ CI, 1.05–3.12) with AF remained statistically significant. Adjusted risk of AF was also stronger in the CSA group (OR, 3.98; $95\%$ CI, 1.56–10.13) than the OSA group (OR, 1.66; $95\%$ CI, 1.01–2.76). Similar associations were observed when analyses were restricted to persistent/permanent AF. The interaction analysis is shown in Table 5. The association of SA or nocturnal hypoxemia with AF was stronger in obstructive HCM compared with non-obstructive HCM (p for interaction = 0.025 and 0.074, respectively). Additionally, associations between SA and AF were greater in obese (BMI ≥ 25 kg/m2) patients compared with non-obese (BMI < 25 kg/m2) patients (p for interaction = 0.024). No significant interaction was present between nocturnal hypoxemia and obesity for AF (p for interaction = 0.396). There was no significant interaction between SA or nocturnal hypoxemia and sex for AF (p for interaction = 0.534 and 0.793, respectively). ## 4. Discussion The current investigation demonstrated that SA was common and was independently associated with AF in patients with HCM. Even though CSA was less common compared with OSA, CSA was more strongly associated with AF than OSA after adjusting for confounders. Nocturnal hypoxemia, an important pathophysiological feature in SA, was also independently associated with AF. Previous studies have reported a prevalence of SA ranging from $40\%$ to $83\%$ in HCM [15]. Findings from the current study showed that nearly $60\%$ of patients with HCM had SA which was congruent with previous reports, demonstrating that SA was a much more common condition in HCM. The independent association of SA with AF in HCM had been demonstrated in studies with small sample sizes [5,6]. In our study, a significant association between SA and AF was found in a larger HCM cohort. Clinical comorbidities such as obesity, hypertension, and coronary heart disease as well as heart remodeling such as enlarged LAD, which were contributors to AF, were found more common in patients with SA. Several physiologic stressors involved in SA could enhance arrhythmogenicity in HCM including intermittent hypoxemia, hypercapnia, autonomic nervous system fluctuations, and intrathoracic pressure swings. However, much of previous data are focused on analyzing the association between OSA and AF, while, the link between CSA and AF is not as well studied. In our study, we first found that both types of SA were independently associated with AF in HCM. Interestingly, CSA was more strongly associated with AF than OSA. These results were in alignment with previous investigations. Sin et al. found AF to be associated with CSA, but not OSA, in a sample of 450 individuals with heart failure [16]. Mehra et al. also documented a stronger cross-sectional relationship of CSA than OSA to AF in an unselected community cohort of 2911 men [17]. Additionally, Tung et al. showed a similar finding that CSA, but not OSA, was a predictor of incident AF in a community-based cohort [9]. CSA may be linked with increased risk for AF beyond OSA through the following mechanisms. Intermittent fluctuations in PaCO2 levels and periodic arousals, occurs to be greater in CSA than OSA, and may predispose to arrhythmia by enhancing sympathetic activation and then resulting in electrical and structural remodeling [18]. CSA is often concomitantly found in patients with systolic heart failure which usually occurred frequently with AF and would exacerbate each other [19]. In our study, the CSA group had higher NYHA class and NT-pro BNP level, enlarged LAD, and decreased LVEF com-pared with the OSA group. These results indicated that the above mechanisms contribute to the stronger association between CSA and AF. Interestingly, only a small proportion of patients with CSA had systolic heart failure. We propose that these patients are more likely to have diastolic dysfunction which is a prominent clinical feature in HCM [20]. Nocturnal hypoxemia is an important pathophysiological feature in SA. Previous works had proved the relationship between nocturnal hypoxemia and AF [21]. However, studies have failed to find out the association between nocturnal hypoxemia and AF in HCM, which was possibly due to a relatively small study population [5,6]. In our works, patients in highest tertiles of percentage of TST spent with SaO2 < $90\%$ were associated with AF prevalence. We proposed that patients with HCM were associated with AF only with more severe hypoxemia which were consistent with results of previous studies performed in the general population [22]. It is well established that obesity and male sex are risk factors for SA. Our interaction analysis showed that the association between SA and AF was more prominent in patients with obesity but not in the male sex. Meanwhile, the association between nocturnal hypoxemia and AF was irrespective of obesity and sex. Therefore, the association between SA and AF in HCM was not different between males and females. It is still unknown whether LVOT obstruction, a special hemodynamic feature in HCM, plays a role between SA and AF. Our interaction results showed that the association of SA and nocturnal hypoxemia with AF was stronger in HCM patients with LVOT obstruction than those without. These results indicated that obesity and LVOT obstruction might exert synergistic effects on AF together with SA. To date, SA remains underestimated in HCM. Therefore, a high degree of suspicion for SA is warranted and clinicians should have a low threshold to refer for diagnostic sleep evaluation. Importantly, treatment of OSA was shown to be associated with reduced AF burden as well as cardiovascular benefits in the general population [23,24]. We expect that identification and treatment of both types of SA may serve to further improve outcomes in HCM. Several limitations in the current study merit discussion. First, this study is a cross-sectional study. Although our results suggested an independent association be-tween SA and AF in HCM, the retrospective nature of this study limited our ability to determine a causal relationship. Second, the sample size of the present study was relatively low, especially in the CSA group, which should be increased in the future to confirm the findings. Third, prevalence of SA and AF in this HCM cohort may represent an overestimation of the prevalence in a general HCM population because of selection bias as patients presented to a tertiary medical center for their care and many were symptomatic. 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--- title: Prognostic Imaging Biomarkers in Diabetic Macular Edema Eyes Treated with Intravitreal Dexamethasone Implant authors: - Eliana Costanzo - Daniela Giannini - Daniele De Geronimo - Serena Fragiotta - Monica Varano - Mariacristina Parravano journal: Journal of Clinical Medicine year: 2023 pmcid: PMC9968175 doi: 10.3390/jcm12041303 license: CC BY 4.0 --- # Prognostic Imaging Biomarkers in Diabetic Macular Edema Eyes Treated with Intravitreal Dexamethasone Implant ## Abstract Background: The aim was to evaluate predictive value of baseline optical coherence tomography (OCT) and OCT angiography (OCTA) parameters in diabetic macular edema (DME) treated with dexamethasone implant (DEXi). Methods: OCT and OCTA parameters were collected: central macular thickness (CMT), vitreomacular abnormalities (VMIAs), intraretinal and subretinal fluid (mixed DME pattern), hyper-reflective foci (HRF), microaneurysms (MAs) reflectivity, ellipsoid zone disruption, suspended scattering particles in motion (SSPiM), perfusion density (PD), vessel length density, and foveal avascular zone. Responders’ (RES) and non-responders’ (n-RES) eyes were classified considering morphological (CMT reduction ≥ $10\%$) and functional (BCVA change ≥ 5 ETDRS letters) changes after DEXi. Binary logistic regression OCT, OCTA, and OCT/OCTA-based models were developed. Results: Thirty-four DME eyes were enrolled (18 treatment-naïve). OCT-based model combining DME mixed pattern + MAs + HRF and OCTA-based model combining SSPiM and PD showed the best performance to correctly classify the morphological RES eyes. In the treatment-naïve eyes, VMIAs were included with a perfect fit for n-RES eyes. Conclusion: The presence of DME mixed pattern, a high number of parafoveal HRF, hyper-reflective MAs, SSPiM in the outer nuclear layers, and high PD represent baseline predictive biomarkers for DEXi treatment responsiveness. The application of these models to treatment-naïve patients allowed a good identification of n-RES eyes. ## 1. Introduction Diabetic macular edema (DME) is an important complication of diabetic retinopathy, representing the main cause of visual impairment in diabetic patients [1]. The current/modern approach to center-involved DME consists of the intravitreal injection of anti-vascular endothelial growth factor (VEGF) agents and/or steroid drugs, in particular, dexamethasone implant (DEXi) [2]. The clinician’s choice between these different drugs is usually based on several factors that consider the patient’s compliance and motivation, age, and some general and ophthalmological comorbidities [2]. Furthermore, several imaging biomarkers have been identified as particularly useful to guide the therapeutical option choice [3], but a univocal consensus has not yet been reached on this topic. Optical coherence tomography (OCT) and OCT angiography (OCTA) allow non-invasive recognition of imaging features that define a “DME profile”, distinguishing an inflammatory DME pattern characterized by the presence of subretinal fluid (SRF) and a high number of hyper-reflective foci (HRF) [4,5]. Furthermore, numerous imaging (OCT and OCTA) biomarkers have been identified in DME eyes as predictive of treatment response or disease progression, such as the retinal thickening characteristics [3,6,7], the presence, position, and internal reflectivity of microaneurysms (MAS) [8,9], the vitreomacular interface abnormalities (VMIAs) [6], the ellipsoid zone/external limiting membrane (EZ/ELM) integrity/disruption [7], the presence of nonperfusion areas (NPAs) [10,11], the foveal avascular zone (FAZ) abnormalities in terms of circularity and dimension [12], the involvement of deep capillary complex (DCP) [10,13], and the suspended scattering particles in motion (SSPiM) [14,15]. This study aimed to explore the influence of baseline OCT and OCTA parameters, isolated or combined, on the DEXi response in diabetic macular edema eyes, integrating the information provided by these two different techniques through statistical models. ## 2.1. Study Participants In this study, consecutive type 2 diabetic patients affected by DME treated with DEXi were retrospectively collected and analyzed at the Department of Ophthalmology, IRCCS-Fondazione Bietti, Rome. This observational study was approved by the Institutional Review Board of the IRCCS-Fondazione Bietti and followed the tenets of the Declaration of Helsinki. Written informed consent was obtained from all participants. Each patient received a single DEXi (0.7 mg, Ozurdex; Allergan, Inc., Irvine, CA, USA) to treat DME, as per clinical practice. Inclusion criteria were age ≥ 18 years, type 2 diabetes mellitus, naïve or previously treated DME eyes (at least 3 months after the last anti-VEGF injections or 6 months after the last DEXi or other steroid drug injection), and a minimum follow-up (FU) period of 4 months after treatment, with full imaging protocol. Exclusion criteria were macular edema secondary to other causes (e.g., retinal vein occlusion), significant lens opacity, graded above NO3 or NC3 [16], refractive error equal to or lower than ± 2 diopters, measured as spherical equivalent, absence of moderate to dense corneal opacities, refractive surgery, glaucoma or ocular hypertension, medical history of intraocular inflammation, poor-quality images with a signal strength index (SSI) lower than 7 for the PlexElite OCTA, or the presence of significant motion artifacts (seen as large dark or grey lines on the enface angiograms) that impaired the slabs’ quality. Patients received a complete ophthalmological examination, which included the measurement of best corrected visual acuity (BCVA) using Early Treatment of Diabetic Retinopathy Study (ETDRS) visual charts, intraocular pressure (IOP), and dilated fundus examination. All patients were imaged by spectral domain (SD) OCT using Spectralis (Heidelberg Engineering, Heidelberg, Germany) and by swept source (SS) OCTA using PlexElite 9000 (Carl Zeiss Meditec Inc., Dublin, CA, USA) device. Patients’ charts, BCVA, SD-OCT, and SS-OCTA parameters (see below) at baseline were reviewed, and their influence on treatment response 4 months after DEXi was analyzed. ## 2.2. Imaging Protocol SD-OCT images were acquired at baseline, monthly, and at the end of follow-up (4 months after DEXi). The scans were obtained using Spectralis (Heidelberg Spectralis version 1.10.2.0, Heidelberg Engineering, Heidelberg, Germany) with a raster scan using an acquisition protocol of a minimum 20 × 15 degree pattern constituting 19 consecutive B-scans and a macular map centered on the fovea. All raster B-scan images were checked for errors in automatic segmentation, and a manual correction was made in case of segmentation errors. SS-OCTA images were acquired using the PlexElite 9000 device (software version 1.7.027959; Carl Zeiss Meditec, Inc., Dublin, CA, USA), which uses a swept laser source with a central wavelength of 1050 nm and a bandwidth of 100 nm [17]. This instrument has an axial resolution of approximately 5 μm and a lateral resolution estimated at approximately 14 μm. OCTA images were acquired using a 6 × 6 mm volume captured with FastTrac eye motion correction software. The built-in segmentation software automatically segmented the whole retina slab, the superficial vascular complex (SVC) slab, and the deep capillary plexus (DCP); the whole retina vasculature slab includes automatic segmentation from the inner limiting membrane (ILM) up to 70 mm above the retinal pigment epithelium (RPE) [18]; the SVC was segmented between the ILM and the inner plexiform layer (IPL); for the DCP, the upper limit was the IPL and the lower was defined by the outer plexiform layer (OPL). The correctness of retinal boundaries was checked by two single experienced examiners (EC and MP); in case of misplacing, the segmentation was manually adjusted. ## 2.3. SD-OCT Parameters The quantitative OCT parameters were the central macular thickness (CMT), automatically measured using instrument software, and the number of hyper-reflective foci (HRF). The number of HRF was manually counted in the parafoveal region, using the ETDRS grid integrated into the Spectralis, identifying the correct area. The qualitative parameters were the pattern of DME, the presence and the internal reflectivity of the microaneurysms (MAs) (i.e., hyporeflective, hyperreflective, or mixed), the presence of vitreomacular interface abnormalities (VMIAs), and the disruption of ellipsoid zone (EZ). The DME pattern was defined as “mixed” if intraretinal cysts (IRC) were detected in combination with subretinal fluid (SRF). The MAs and the VMIAs were evaluated by scrolling all raster B-scans. The EZ disruption was evaluated in a linear scan passing through the fovea. ## 2.4. OCTA Parameters and Analysis The quantitative OCTA parameters evaluated at baseline were the foveal avascular zone (FAZ) area, the perfusion density (PD), and the vessel length density (VLD) at the whole retina vasculature slab. The qualitative OCTA parameters included the presence of suspended scattering particles in motion (SSPiM), FAZ erosion, and nonperfusion areas (NPAs) in the whole retina vasculature slab, as well as the MAs visualization both in the SVC and DCP. The quantitative OCTA parameters were measured as follows: the OCTA whole retina vasculature slab was opened on FIJI (an expanded version of ImageJ: 2.0.0-rc-$\frac{69}{1.52}$p; National Institutes of Health) [19], the FAZ border was manually outlined, and the surface area, expressed in mm2, was measured as previously reported [20]. In addition, the slabs were binarized to generate a black and white image for measuring the PD, and then the images were skeletonized to calculate VLD [20,21]. The PD defines the ratio of the area occupied by the vessels divided by the total area, providing complete vasculature information in terms of size and length [21]; the VLD defines the total vessel length divided by the total number of pixels in the analyzed skeletonized image [22] and may be more sensitive to the microvasculature changes [21]. The OCT and OCTA qualitative parameters were independently evaluated by two expert readers (EC and MP), and the interclass correlation coefficient (ICC) was calculated. In case of disagreement, a third reader (DDG) assigned the final grade. All OCT and OCTA parameters included in our analysis were evaluated at baseline to explore their influence on the DEXi 4-months response. ## 2.5. Criteria for Groups’ Classification The study population was classified either morphologically or morpho-functionally. The morphological response was staged according to the Protocol T and Protocol I definitions: “improvement” if CMT decreased at least $10\%$ and “no improvement” for a CMT variation <$10\%$ since baseline visit [5,23]. Using the CMT value, the change in macular thickness (Δ CMT) between baseline and 4 months after treatment was calculated. Based on this definition, we subdivided our sample into two groups: responder (RES) if a CMT reduction of at least $10\%$ was recorded and non-responder (n-RES) in all other cases. Additionally, as a secondary outcome of our study, we evaluated the changes in BCVA after treatment ≥5 ETDRS letters [24] in order to perform a morpho-functional classification considering CMT + BCVA changes from baseline. A sub-analysis of treatment-naïve eyes was also conducted. ## 2.6. Statistics Statistical evaluation was performed using SPSS (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, version 25.0. Armonk, NY, USA: IBM Corp.). Continuous variables, including age, BCVA ETDRS letters score, and instrument parameters were expressed as mean ± standard deviations (SD), while categorical variables were expressed as frequencies. The ICC was calculated to estimate the absolute agreement between the two expert readers’ (EC and MP) grading on OCT DME mixed pattern, EZ disruption, VMIAs, MAs internal reflectivity, and OCTA FAZ erosion, presence of NPAs, SSPiM, and MAs visualization (ICC < 0.5, poor reliability; 0.5 < ICC < 0.75, moderate reliability; 0.75 < ICC < 0.9, good reliability; ICC > 0.9, excellent reliability) [25]. The normal data distribution was tested using the one-sample Kolmogorov–Smirnov test. The independent sample 𝑡-test and the Mann–Whitney test were used to compare the parameter values between the two groups. A chi-square test or a Fisher exact test two sides, as appropriate, was performed to investigate the relationship between the groups and the clinical categorical variables. A binary logistic regression model was applied using the OCT and OCTA variables as independent explanatory variables (i.e., predictors) to classify responder (RES) and non-responder (n-RES) eyes. A threshold of 0.5 was chosen. The variance inflation factor (VIF), which assesses how much the variance of an estimated regression coefficient increases if the predictors are correlated, was used to assess multicollinearity; only variables with a VIF > 1 and VIF < 10 were considered as covariates. To evaluate and select the final developed models, we measured the complexity by the Akaike Information Criterion (AIC) and the Bayesian Information Criteria (BIC), while the accuracy of estimated probability was measured by the Brier’s score [26,27]. Lower values of AIC, BIC, and Brier’s score indicate better goodness of fit, while higher area under the curve (AUC) values indicate better discriminative ability. The model performance to distinguish between RES and n-RES eyes was measured by the receiver operating characteristic (ROC) analysis with the AUC. Statistically significant differences were set at p-value < 0.05 for all the tests performed. ## 3. Results A total of 34 eyes (18 treatment-naïve) of 30 DME patients (10 females and 20 males) were enrolled; for four DME patients (one female and three males), we included both eyes. The mean ± SD patient age was 67.4 ± 9.3 years (range 46–81 years). BCVA at baseline was 61.3 ± 14.5 ETDRS letters ($\frac{20}{50}$ Snellen equivalent, ranging from $\frac{20}{20}$ to $\frac{20}{400}$). The mean ± SD at baseline CMT was 544.3 ± 137 µm, as determined on the macular map. All included eyes showed moderate non-proliferative diabetic retinopathy and no cases of proliferative diabetic retinopathy had been enrolled. By following the morphological response classification into two subgroups (RES and n-RES), 20 out of 34 eyes were classified as the RES group ($58.8\%$) and 14 out of 34 eyes were classified as the n-RES group ($41.2\%$). The RES eyes group (20 eyes) included 18 DME patients (7 females and 11 males) and the n-RES eyes group (14 eyes) included 14 DME patients (3 females and 11 males). Of note, for two DME patients (one female and one male), we included both eyes in the RES group; for the other two DME patients (both males), one eye was RES and the fellow eye n-RES to dexamethasone implant. Demographic, OCT, and OCTA characteristics of RES vs. n-RES eyes at baseline are detailed in Table 1. No statistically significant differences were found between groups at baseline (all p-values > 0.05). As secondary outcomes, the morpho-functional (MF) response classification identified 24 out of 34 eyes as MF-RES eyes and 10 out of 34 eyes as MF-nRES eyes. An ICC agreement of 0.985 was found between two readers (EC and MP) on the parameter qualitative evaluations. ## Model Results We used a binary logistic regression model including OCT and OCTA (isolated and combined) baseline parameters as predictive variables for a 4-month DEXi response. Different combinations of OCT, OCTA, and OCT/OCTA parameters were tested, exploring all variables collected for each patient. AIC, BIC, and Brier’s scores were calculated to choose the best statistical models. Nine statistical models were derived, distinguishing between models for morphological classification (models 1, 2, and 3) and morpho-functional classification (models 4, 5, and 6) of the overall population, while three additional models were derived for treatment-naïve eyes (models 7, 8, and 9). The OCT-based models for the overall population (morphological and morpho-functional classification) (models 1 and 4) included DME mixed pattern + MAs internal reflectivity + number of parafoveal HRF. The OCT-based model for the treatment-naïve eyes (model 7) also included the VMIAs as an additional variable. The OCTA-based models for all eyes (morphological overall and treatment-naïve and morpho-functional classification) (models 2, 5, and 8) included PD of the whole retina vasculature slab and SSPiM. The OCT/OCTA-based models combined the OCT and OCTA variables considered in the isolated models for all groups. The variables included in each model and the values of AIC, BIC, and Brier’s score are reported in Table 2. Table 3 reports the models’ performance analysis, where the sensitivity was related to RES detection and the specificity to the n-RES. Model 1 correctly classified $80\%$ of RES and $64.29\%$ of n-RES eyes and the overall accuracy was $72.1\%$ ($$p \leq 0.006$$). In model 2, the overall AUC was $55.7\%$ and the performance of this model was not statistically significant, $$p \leq 0.390.$$ Model 3 correctly classified $70\%$ of RES and $64.3\%$ of n-RES eyes, with an overall accuracy of $67.1\%$ ($$p \leq 0.043$$). The performance for models 4, 5, and 6 (based on the sample of morpho-functional classification) was not statistically significant. Models from 7 to 9, considering the treatment-naïve eyes, were all statistically significant ($p \leq 0.001$ for models 7 and 9, $$p \leq 0.005$$ for model 8), with a perfect fit detection of RES and n-RES eyes by the combination of OCT and OCTA parameters. By the analysis of the combination of these models, the RES eyes were more likely to show a DME mixed pattern (IRC + SRF) and hyper-reflective MAs associated with a higher number of HRF in the parafoveal region at baseline compared with n-RES correctly classified by the model. Furthermore, RES eyes showed SSPiM in the ONL ($72.2\%$ of eyes), DCP ($44.4\%$), and SVC ($22.2\%$) but, also, a higher PD compared with n-RES (44.34 ± 1.29 vs. 40.39 ± 0.9, respectively). The combination of OCT and OCTA parameters (model 3) showed a good performance but lower than model 1 alone. The subgroup analysis of treatment-naïve eyes allowed a good identification of n-RES eyes through OCT- and OCTA-based models, reaching a perfect fit of RES and n-RES detection in the combined model (model 9). The n-RES eyes at baseline were characterized by the presence of VMIAs, mixed/hyporeflective MAs, lower number of parafoveal HRF, and absence of SRF at baseline compared with RES eyes correctly classified by model 7. Regarding OCTA parameters (model 8), n-RES eyes likely presented SSPiM in the ONL ($50\%$ of eyes) and in the SVC and DCP ($16.67\%$ of eyes for both) and showed a lower PD compared to RES eyes (43 ± 2.37 vs. 45 ± 1.04). ## 4. Discussion The present study explored the baseline OCT and OCTA variables that may predict the DEXi response in DME eyes. All the parameters analyzed are known to be differently associated with treatment response, but we aimed to understand whether some variables could be more predictive than others by using binary logistic regression models. The OCT-based model took into account different parameters that can be usually found in DME eyes, such as the presence of a mixed fluid pattern (IRC + SRF), parafoveal HRF, the internal reflectivity of MAs, the VMIAs, and the EZ disruption, discovering that not all are equally important within predictive models. We found that DME eyes responsive to DEXi showed most likely the presence of both intraretinal and subretinal fluid on OCT B-scans at baseline. This finding is in line with several previous studies in which the foveal neuroretinal detachment was defined as an inflammatory biomarker predictive of a better response to steroid agents [4,5,28]. Vujosevic et al. [ 4] defined an inflammatory pattern of DME characterized by the presence of SRF, a high number of HRF, and areas of increased fundus autofluorescence. Among these factors, eyes with SRF at baseline exhibited a greater decrease in CMT (morphological response) compared with those without SRF at baseline. This pattern seemed to be most responsive to DEXi compared to anti-VEGF agents, suggesting a targeted therapy considering these parameters [4]. Another interesting real-world study [7] reported that eyes with DME mixed pattern had a DME recurrence at ≥ 6 months from the first DEXi compared with eyes with only IRC that showed the earliest DME recurrence, confirming the best response to DEXi when SRF exists. Zur et al. [ 29] also demonstrated the presence of SRF as a predictive biomarker for a better morpho-functional response to DEXi. The authors found a more remarkable BCVA improvement after DEXi in the case of DME with SRF. Moreover [29], the authors also reported that eyes without HRF showed a better DEXi response. Contrariwise, in our study, RES eyes showed a higher number of parafoveal HRF than n-RES eyes correctly classified by the models for both morphological and morpho-functional classification. Our results agree with other studies [4,30,31] in which a better morphological response to intravitreal anti-VEGF and steroids was associated with the presence of HRF. In addition, a greater HRF reduction after DEXi compared with anti-VEGF treatment has been documented [28,32,33], confirming the HRF as a biomarker of inflammatory DME pattern [4,5,34]. The role of HRF in the prediction of treatment response is still unclear. A recent review [35] analyzing 36 studies (11 prospective and 25 retrospective) with low or moderate risk of bias concluded that it was unclear whether HRF predicts the treatment outcome in patients with DME. In our model, a high number of parafoveal HRF, in association with the presence of SRF and hyper-reflective MAs, represented significant predictors for the DEXi response. The third significant parameter in our OCT-based models was the internal reflectivity of MAs. Parravano et al. [ 9] have described differences in the internal reflectivity of MAs, suggesting different patterns of blood flow dynamic distinguishing between hyporeflective and hyper-reflective MAs. The hyper-reflective pattern at baseline was found to be strongly associated with extracellular fluid accumulation in a 1-year follow-up study [8,9], hypothesizing that the hyper-reflective MAs could have a higher inflammatory factor component, responsible for blood–retinal barrier (BRB) changes [8]. Furthermore, the presence of hyper-reflective MAs could lead to a good response to anti-VEGF or steroids due to their high vascular flow within [8]. In our series, RES eyes showed more often hyper-reflective MAs than n-RES in both the overall population and treatment-naïve subgroup, further corroborating their influence on a better treatment response. The subgroup analysis of treatment-naïve eyes allowed the identification of VMIAs as an additional parameter in the OCT-based model. The abnormalities in the vitreomacular interface have already been reported as a negative predictive factor of treatment response [36,37]. Despite this, our analysis represented a step forward in the characterization of an n-RES phenotype characterized by the absence of SRF, VMIAs, mixed/hyporeflective MAs, and a lower number of parafoveal HRF at baseline. These results were consistent with those obtained from the OCT-based models on the overall population, confirming the importance and the weight of the variables explored and derived from our models. Our predictive models also investigated several OCTA parameters (SSPiM, PD, VLD, FAZ area, FAZ erosion, and MAs’ visualization in SVC and DCP), of which the most representative were SSPiM and PD. Our results showed that RES eyes were more likely to have SSPiM in the outer retinal layers than in the inner retina ($72.2\%$ in ONL, $44.4\%$ in DCP, and $22.2\%$ in SVC) and had a higher PD compared with n-RES correctly classified. In a recent paper by our group [14], the presence of SSPiM and its pyramidal stratification from outer to inner retinal layers indicates a severe breakdown of BRB. In the present analysis, the SSPiM, considered alone, did not reach the power to predict the DEXi response but, when associated with other OCT parameters and PD on OCTA, it was able to characterize only the RES eyes in statistically significant models. The RES eyes showed the highest prevalence of SSPiM in the outer retina, with a partial sparing of inner retina indicating that, despite the damage of BRB, typical of DME eyes, a good response to steroid agents could be, however, expected. Given the pyramidal stratification of SSPiM, it may be conceivable that the BRB damage is more severe when the inner retinal layers are involved. Therefore, eyes with SSPiM confined within the outer retinal layers can still be more responsive to the treatment. A relatively small percentage of SSPiM within the SVC in RES eyes may further reinforce this hypothesis ($22\%$ vs. $78\%$ in the ONL). The result of this combined model agrees with a theory for the SSPiM being closely related to the number of HRF and being considered the product of inner BRB breakdown, and, in fact, the hyper-reflective cystoid spaces often colocalize with HRF [38]. We have also found that the RES eyes showed a higher PD measured in the whole retina vasculature slab compared to n-RES eyes. A previous study reported that a high baseline PD value in the DCP was significantly correlated with a better baseline VA [39]. Another study reported a lower vessel density (VD) in the DCP in the poor-response group, considering both morphological and functional parameters [40]. All these previous data are consistent with the results of our OCTA-based models, suggesting that the retinal microvascular parameters could predict the treatment response in DME and help optimize clinical outcomes [40]. An interesting consideration concerns the weight of the OCT parameters, which seemed to be greater than the OCTA parameters in our models. The performance of combined models, particularly model 3, was lower than the models including only the OCT variables, indicating that the predictive value of the OCT-based model did not significantly improve by adding the OCTA factors. In fact, the OCTA-based models alone were not significant in the overall population but only in the treatment-naïve eyes. This finding could reasonably be explained with potential confounders that may alter the OCTA metrics in previously treated eyes. Interestingly, the morpho-functional classification of our population did not lead to significant predictive models. We hypothesize that the complexity of the functional evaluation of diabetic eyes cannot be based only on the measurement of the BCVA, as different degrees of DME may result in a high variability of visual acuity [41]. Further studies including systemic parameters or more sophisticated functional techniques should be considered for defining more accurate predictive models. The main limitation of our study was represented by the small sample size for the explorative nature of our study, focusing on the predictive value of different OCT and OCTA biomarkers at baseline. Another limitation was the lack of consideration of the metabolic parameters within the variables that can influence the treatment response. However, we aimed to analyze real-world data considering objective variables that can guide the clinician in predicting DEXi response. In our opinion, the promotion of glycemic control still represents one of the most important factors influencing the treatment response and disease progression of diabetic patients with ocular involvement. Further analysis with a higher number of patients is needed to validate our results, as the statistical models explored in our study could be potentially useful for artificial intelligence in the construction of a strong predictive model for DME eyes to treat with DEXi. In conclusion, this study demonstrated the predictive value of baseline OCT and OCTA parameters in DME eyes treated with DEXi using statistical logistic regression models. By analyzing the models, the presence of DME mixed pattern, a high number of parafoveal HRF, hyper-reflective MAs, SSPiM in the outer nuclear layers, and high PD represent baseline predictive biomarkers for DEXi treatment responsiveness. ## References 1. Panozzo G., Cicinelli M.V., Augustin A.J., Battaglia Parodi M., Cunha-Vaz J., Guarnaccia G., Kodjikian L., Jampol L.M., Jünemann A., Lanzetta P.. 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--- title: Optimisation of the Extraction Process of Naringin and Its Effect on Reducing Blood Lipid Levels In Vitro authors: - Xiao-Lei Yu - Xin Meng - Yi-Di Yan - Jin-Cheng Han - Jia-Shan Li - Hui Wang - Lei Zhang journal: Molecules year: 2023 pmcid: PMC9968178 doi: 10.3390/molecules28041788 license: CC BY 4.0 --- # Optimisation of the Extraction Process of Naringin and Its Effect on Reducing Blood Lipid Levels In Vitro ## Abstract The naringin extraction process was optimised using response surface methodology (RSM). A central component design was adopted, which included four parameters: extraction temperature (X1), material–liquid ratio (X2), extraction time (X3), and ultrasonic frequency (X4) of 74.79 °C, 1.58 h, 1:56.51 g/mL, and 28.05 KHz, respectively. Based on these optimal extraction conditions, naringin was tested to verify the model’s accuracy. Naringin yield was 36.2502 mg/g, which was equivalent to the predicted yield of 36.0124 mg/g. DM101 macroporous adsorption resin was used to purify naringin. The effects of loading concentration, loading flow rate, and sample pH on the adsorption rate of naringin and the effect of ethanol concentration on the desorption rate of naringin were investigated. The optimum conditions for naringin purification using macroporous resins were determined. The optimal loading concentration, sample solution pH, and loading flow rate were 0.075 mg/mL, 3.5, and 1.5 mL/min, respectively. Three parallel tests were conducted under these conditions, and the average naringin yield was $77.5643\%$. Naringin’s structure was identified using infrared spectroscopy and nuclear magnetic resonance. In vitro determination of the lipid-lowering activity of naringin was also conducted. These results showed that naringin has potential applications as a functional food for lowering blood lipid levels. ## 1. Introduction Pomelo (Citrus grandis L.) is a plant belonging to the Rutaceae family and the Citrus genus [1]. It has been grown in China for over 3000 years. Due to the limitations of traditional processing technology, productivity, and technical conditions, most people only eat the flesh of pomelo and discard the pomelo peel as waste, which not only pollutes the environment but also wastes resources. Research shows that pomelo peel is rich in cellulose, pectin, other nutrients, and natural active ingredients. It has a variety of biological activities and physiological effects, such as anti-cancer, anti-oxidation, anti-bacterial, cardiovascular, and cerebrovascular protective effects, and it can be widely used in the fields of food science and medicine [2,3,4]. Therefore, extracting the main active ingredients in pomelo peel, such as naringin, is essential in order to make rational and effective use of pomelo peel resources. Hyperlipidaemia is a lipid metabolism disorder and a major risk factor for coronary heart disease, myocardial infarction, sudden cardiac death, and other diseases [5,6]. It accelerates systemic atherosclerosis by causing hidden, progressive, systemic, and organic damage to the body. Every year, approximately 12 million people worldwide die of cardiovascular diseases caused by hyperlipidaemia. Therefore, it is important to identify ways to prevent and control hyperlipidaemia [7,8]. Currently, the identification of natural blood lipid-lowering functional components in plants has become a research hotspot [9,10]. This study focused on naringin, and its ability to combine with sodium glycine cholate and sodium bovine cholate was evaluated in vitro by simulating the gastrointestinal environment to evaluate its blood lipid-lowering activity, in order to provide a theoretical basis for the development of naringin functional food. ## 2.1.1. Naringin Standard Curve Figure 1 shows a standard curve for naringin. The abscissa is the concentration of naringin (mg/mL), the ordinate is the absorbance value, and the standard curve equation obtained by the least squares method was $y = 33.713$X − 0.016, R2 = 0.9992, and the linear range was 0–0.0288 mg/mL. ## 2.1.2. Single Factor Experimental Results of Ultrasonic-Assisted Extraction The effect of the extraction temperature is shown in Figure 2A. With increasing extraction temperature, the naringin extraction rate gradually increased and reached a maximum at 75 °C, which was identified as the optimum extraction temperature, as shown in Figure 2B. As the material–liquid ratio increased gradually, the extraction rate of naringin also increased. When the material–liquid ratio reached 1:55 g/mL, the naringin extraction rate did not increase. Therefore, the optimum material–liquid ratio was 1:55 g/mL. The extraction times are shown in Figure 2C. As the extraction time gradually increased, the extraction rate of naringin also increased; however, after 1.5 h, the extraction rate did not increase significantly. Considering the rate efficiency, 1.5 h was determined as the optimal extraction time, and the ultrasonic frequency is shown in Figure 2D. With an increase in ultrasonic frequency, the extraction rate of naringin also increased. When the ultrasonic frequency reached 40 KHz, the naringin extraction rate did not increase significantly. Therefore, the optimal ultrasonic frequency was determined to be 40 KHz. ## 2.1.3. Response Surface Optimisation Test Results and Analysis of Variance The processing and results of the response surface optimisation tests are listed in Table 1, and the response surface model variance analysis is presented in Table 2. Using Design-Expert 8.0.6 software, model fitting analysis was performed on the test results in Table 1, taking the extraction temperature (X1), material ratio (X2), extraction time (X3), and ultrasonic frequency (X4) as independent variables, and the extraction rate (Y) as the response value to obtain the regression equation:$Y = 36.84$ + 2.74X1 + 0.81X2 + 1.19X3 + 1.21X4 − 0.29X1X2 − 0.069X1X3 − 0.85X1X4 + 1.39X2X3 − 0.21X2X4 − 1.07X3X4 − 3.89X12 − 2.86X2 − 2.23X32 − 1.92X42[1] According to the evaluation of the fitting model through the analysis of variance in Table 1, the primary, secondary, and interactive terms X1X4, X2X3, and X3X4, respectively, reached a significant level. The mismatch error ($$p \leq 0.1408$$ > 0.05) showed that the data fitting effect was good, and the model design was reasonable. This shows that the response surface model accurately reflects the relationship between the yield of naringin and the extraction conditions. ## 2.1.4. Response Surface Graphic Analysis The response surface and contour lines of naringin were drawn according to the test results, as shown in Figure 3A–L. The influence of each extraction condition on the naringin yield and the interactions between the factors can be evaluated through the graph. If the contour line is circular, the interaction is not significant; if the contour line is saddled or oval, the interaction is significant. If the response surface curve is steeper, the impact is more significant, and if the curve is smoother, the impact is less. The shape of the response surface and contour map indicated that there was a significant interaction between factors other than the extraction time, extraction temperature, and material ratio. The changes in temperature, material ratio, and ultrasonic waves had a significant effect on the extraction of naringin, as indicated by their steep curves; whereas, the relatively smooth curve of the extraction time graph shows that it significantly affected the yield. ## 2.1.5. Determination of Optimum Extraction Conditions The quadratic polynomial regression equation was solved using Design-Expert 8.0.6 software. The optimal extraction conditions were 74.79 °C, an extraction time of 1.58 h, liquid–material ratio of 1:56.51 g/mL, and ultrasonic frequency of 28.05 KHz. The predicted naringin yield was 36.0124 mg/g. Naringin was used, according to the optimal extraction conditions, to verify the accuracy of the model. Five parallel experiments were conducted, and the average naringin yield was 36.2502 mg/g. The accuracy of the extraction method and the results was high, which is consistent with the predicted values, indicating that the method is feasible. ## 2.2.1. Single Factor Experiments of Adsorption Rate of DM101 Macroporous Resin for Naringin It can be seen from Figure 4A that within the concentration range of 0.025–0.125 (mg/mL) sample solution, the adsorption rate of naringin on the macroporous resin increased and then decreased with an increase in concentration. When the concentration of naringin was 0.05 mg/mL, the highest adsorption rate reached $78.26\%$. However, when the concentration exceeded 0.05 mg/mL, there was a sharp decline trend, which may be due to the small contact area between the resin surface and naringin molecules preventing the resin from reaching saturated adsorption. When the concentration further increases, the carbonyl and hydroxyl groups contained in the naringin molecules polymerise to form macromolecules through hydrogen bonds, making it more difficult to be adsorbed by the resin. It can be seen from Figure 4B that with the increase in pH, naringin adsorption increases first and then decreases when the pH is 2 ~ 6. At pH 3, the adsorption rate reaches the maximum. The reason may be that when the pH of the sample solution is too low, the strong acidity of the solution will lead to the precipitation of naringin compounds, resulting in a low adsorption rate; when the pH is too high, the phenolic hydroxyl group in naringin readily loses H+, which weakens the interactions with water molecules in the solution, making it more difficult to be adsorbed by the resin. As shown in Figure 4C, with an increase in the flow rate of the sample water, naringin adsorption first increased and then decreased gradually when the loading flow rate was 0.5–2.5 mL/min. ## 2.2.2. Response Surface Optimisation The establishment and results of the response surface model, combined with the results of the single factor test, consider naringin adsorption rate (%) (Y) as the response value of the test design, and the concentration of the sample solution (mg/mL) (A), sample solution pH (B), and flow rate of the sample water mL/min (C) as independent variables. Additionally, the Box–Behnken centre combination from the Design Expert 8.0.6 software is used to design the response surface test. See Table 3 for the results. By multiple regression fitting of the test data in Table 3, the quadratic multiple regression equation of naringin adsorption rate under current conditions is determined as follows:$Y = 80.70628$ + 0.680675A + 1.3890125B − 1.6582375C + 0.778425AB + 0.916875AC + 0.27005BC − 2.73839A2 − 5.430915B2 − 2.230815C2 ## 2.2.3. Response Surface Optimisation Variance Analysis Results The results of ANOVA are shown in Table 4. It can be seen from Table 4 that the model $p \leq 0.0001$ is the extremely significant level, indicating that the lower the probability of extreme results of the test model, the more reliable the test method is. The p value of the mismatched item is 0.0877 (>0.05), and the difference is not significant. This suggests that the equation is less mismatched with the test, and reflects the authenticity of the test; therefore, it can be used for analysis and calculation. From the F value, it can be seen that the order of influence of single factors on naringin yield is C > B > A. ## 2.2.4. Response Surface Graphic Analysis The response surface analysis of the interactions between various factors is shown in Figure 5A–F. A steep surface of the response surface shows a significant interaction between two factors; when the contour map is elliptical, the interaction between the two factors is also significant. According to the analysis chart, and in combination with the p value, the order of the variables in decreasing interaction significance is AC ($$p \leq 0.0172$$) > AB ($$p \leq 0.0337$$) > BC ($$p \leq 0.3911$$), which means that the loading concentration and flow rate, loading concentration and pH of the sample solution, and the pH and flow rate of the sample solution have significant effects on the model. ## 2.2.5. Prediction of Optimal Conditions and Verification As determined from a regression model, the optimal process conditions for the adsorption of naringin by macroporous resin were as follows: loading concentration of 0.075 mg/mL, sample solution pH of 3.67, and loading flow rate of 1.5 mL/min, and the predicted yield was $78.1846\%$. The final conditions chosen were 0.075 mg/mL of loading concentration, sample solution pH of 3.5, and 1.5 mL/min loading flow rate. Three parallel tests were conducted under these conditions, and the average yield of naringin was $77.5643\%$. ## 2.3. IR spectrum Analysis The IR results of our refined naringin products are shown in Figure 6. Figure 6 shows a large absorption peak at 3180–3680 cm−1 in the IR spectrum, which is attributed to the alcohol hydroxyl group and multiple phenolic hydroxyl groups in naringin. The absorption peak at 3000–3300 cm−1 is attributed to the C-H stretching vibration of the benzene ring. The absorption peak at 2850–2950 cm−1 is attributed to the C-H bond stretching vibration of the saturated carbon in the structure. Generally, the carbonyl stretching vibration absorption peak of flavonoids is approximately 1650 cm−1, and the carbonyl stretching vibration absorption peak of dihydroflavonoids is approximately 1695 cm−1. However, the hydroxyl group at position 5 in naringin forms an intramolecular hydrogen bond with the carbonyl group, which reduces the frequency of the absorption peak. The carbonyl stretching vibration at position 4 in the final naringin appeared at 1647 cm−1, 1581 cm−1, 1518 cm−1, and 1447 cm−1, which is attributed to the C=C stretching vibration of the aromatic ring in the structure. The 1369 cm−1 and 1296 cm−1 peaks are attributed to the in-plane bending vibration of methylene in the structure. The multiple absorption peaks at 1208–1035 cm−1 are attributed to the C-O stretching vibration of aromatic ether and fatty ether bonds. The glycosidic bond absorption peak at 885 cm−1 indicates that the glycosidic bond is β-D-pyranoside, which is consistent with the structure of naringin. The absorption peak at 818 cm−1 is attributed to the C-H out-of-plane bending vibration of the B-ring aromatic ring para-substituted structure. ## 2.4. NMR Spectrum Analysis The NMR analysis of our refined naringin products is shown in Figure 7A–D. As shown in Figure 7A–D, the 1H NMR and 13C NMR spectra of our refined naringin products is highly consistent with those of the standard. 1H NMR (400 MHz, Acetone-d6): δ 12.09 (s, 1H), 8.59 (s, 1H), 7.40–7.42 (dd, $J = 2.72$, 8.3 Hz, 2H), 6.90–6.91 (d, 8.11, 2H), 6.16–6.17 (d, $J = 9.27$, 2H), 5.48–5.52 (t, 1H), 5.35 (s, 1H), 5.17–5.19 (dd, $J = 7.09$, 10.43 Hz, 1H), 4.66 (s, 1H), 4.44–4.45 (d, $J = 4.44$, 1H), 3.83–3.95 (m, 4H), 3.69–3.77 (m, 4H), 3.56–3.61 (m, 2H), 3.48–3.49 (m, 1H), 3.41–3.42 (m, 1H), 3.22–3.29 (dd, 1H), 2.86 (s, 2H), 2.76–2.79 (dt, 1H), 1.27–1.28 (d, $J = 5.89$, 3H). 13C NMR (400 MHz, Acetone-d6): δ 205.75, 197.13, 197.05, 196.97, 165.39, 165.26, 163.81, 163.53, 163.29, 163.22, 157.88, 129.61, 129.51, 128.21, 128.15, 115.39, 115.31, 103.69, 100.70, 98.29, 98.19, 96.62, 95.52, 95.48, 79.26, 79.17, 77.79, 76.77, 72.81, 72.69, 71.29, 70.92,70.81, 70.29, 68.36, 61.47, 61.35,42.70,42.57,17.42. Of these results, δ = 12.09 and 8.59 belong to the hydrogen of the phenolic hydroxyl groups at positions 5 and 4, respectively. Since the oxygen atom and benzene ring form a conjugated large π bond, the hydrogen nucleus of the phenolic hydroxyl group is affected by the electron absorption of the oxygen atom and the conjugated π bond; the electron cloud density is further reduced, and the chemical shift appears at a higher level. Due to the effect of electron absorption of the carbonyl group at position 4, the de-shielding effect of the hydroxyl proton at position 5 is stronger than that at position 4; thus, the chemical shift is higher. δ = The DD peak of 7.40–7.42 belongs to the hydrogen on 2’ and 6’ of the B ring, whilst δ = The D peak of 6.90–6.91 belongs to the hydrogen on 3’ and 5’ of the B ring. As the electron cloud density of the ortho atoms (3’ and 5’) in the hydroxyl group is higher than that of the meta-atoms (2’ and 6’), the hydrogen shielding effect at 3’ and 5’ is strong and appears at relatively low chemical shifts. δ = The D peak of 6.16–6.17 belongs to the hydrogen atom at positions 6 and 8 of the α ring. Due to the electron donor conjugation effect of the three oxygen atoms on the benzene ring, the electron cloud density at positions 6 and 8 is increased; hence, their shielding effect is stronger than that of other hydrogen nuclei on the benzene ring, and their chemical shifts are lower. δ = 5.5 and 5.35 belongs to the hydrogens at the end of “a” position and “g” position, respectively. The coupling constants at the “a” site are all approximately 8, indicating that the configuration of the terminal carbon atom here is a β-Glycoside bond, which is consistent with the conclusions of infrared spectrum analysis and the structure of naringin. The type of the glycosidic bond at the G position cannot be determined by the coupling constant because the glycosyl structure here is rhamnose, and the hydrogen atom at the H position is always on the transverse bond. The peak at δ = 5.18 belongs to the hydrogen atom at position 2 on the C ring. Two hydrogen atoms belonging to the methylene group at position ’f’ show peaks at 3.70–3.52. At $d = 3.94$ and $d = 2.76$–2.79, the two hydrogens belonging to the methylene group at the 3 position of the C ring have different chemical environments; hence, their chemical shifts are quite different, which is also consistent with reports in the literature (Study on the chemical composition and activity of Huyou peel). δ = From 1.27–1.28, three hydrogens on methyl group are in the rhamnose fraction. The peaks at other positions, belonging to the hydrogen atom on the methylene group of the sugar group and the hydrogen on the hydroxyl group of the alcohol, are not repeated. ## 2.5. Cholate Standard Curve It can be seen from Figure 8A that the regression equation of the standard curve of sodium glycocholic acid is $y = 4.1771$x + 0.0172, R2 = 0.9991, with a strong linear relationship. Additionally, it can be seen from Figure 8B that the regression equation of the standard curve of sodium taurocholate is $y = 2.6006$x − 0.0062, R2 = 0.9991, with a strong linear relationship. ## 2.6. The Binding Capacity of Naringin to Bile Salts Cholate is an amphoteric macromolecular substance with a steroid nucleus structure that is derived from cholesterol. It is mainly concentrated in the bile of humans and animals. It plays an important role in the metabolism and absorption of lipids, cholesterol, fat-soluble vitamins, and other substances in humans and animals. Flavonoids can reduce the reabsorption of bile acids by combining with bile salts, thus improving the reduction of cholesterol and lipids in the body. Bile acids are obtained by cholesterol oxidation. Therefore, the higher the binding rate between the sample and these two bile salts, the stronger the binding capacity, the lower the cholesterol content, and the stronger the blood lipid-lowering function. As seen in Figure 9, with an increase in naringin concentration, the binding rate of sodium glycocholic acid and sodium taurocholate increase significantly ($p \leq 0.05$). When the concentration of naringin was 0.5 mg/mL, the binding rate of naringin and sodium glycocholic acid was $61.01\%$, and that of naringin and sodium taurocholate was $41.31\%$, indicating that the binding capacity of naringin and sodium glycocholic acid was stronger than that of sodium taurocholate. This may be related to the spatial structure of naringin molecules, and the specific reasons need to be further confirmed. Therefore, the higher the binding rate of naringin with sodium glycocholic acid and sodium taurocholate, the higher the binding capacity of sodium cholate, the stronger the blood lipid-lowering function, and the more significant the blood lipid-lowering effect. ## 3.1. Materials and Reagents Pomelo peel was obtained from Rongxian County, Guangxi Province, China and served as a source of naringin. Naringin (purity > $98\%$) was purchased from Hefei Bomei Biotechnology Co. Ltd. (Hefei, China). Sodium glycolic acid (purity > $98\%$) was purchased from Hefei Qiansheng Biotechnology Co. Ltd. (Hefei, China). DM101 macroporous resin, sodium taurocholate, Trypsin, Pepsin were acquired from Solebo Biotechnology Co., Ltd. (Beijing, China). Chromatography-grade methanol was purchased from Tianjin Guangfu Reagent Co., Ltd. software (Tianjin, China). Spectral grade deuteroacetone was purchased from Shanghai Yien Chemical Technology Co. Ltd. (Shanghai, China). Spectral grade potassium bromide was purchased from Tianjin Hengchuang Lida Technology Development Co. Ltd. software (Tianjin, China). Spectral grade tetramethylsilane was purchased from Hangzhou Sloan Material Technology Co. Ltd. (Hangzhou, China). Ethanol, sodium hydroxide, sulfuric acid, hydrochloric acid, and other chemicals and solvents were of analytical grade and were purchased from Tianjin Tianli Chemical Reagent Co. Ltd. software (Tianjin, China). ## 3.2.1. Pomelo Peel Pre-Treatment First, the yellow exocarp of the fresh and mature pomelo peels was removed, the white spongy mesocarp was then cleaned, and dried at constant temperature and humidity in an oven at 60 °C. After drying, it was crushed through a 60-mesh sieve to produce peel powder. Thereafter, it was sealed in a packaging bag and placed in a dryer maintained at 4 °C until further use [11]. ## 3.2.2. Establishment of a Naringin Standard Curve A sample of 40 mg naringin standard was weighed accurately and diluted with absolute ethanol in a 250 mL volumetric flask to obtain a 0.16 mg/mL naringin standard solution. Different volumes of the solution were distributed into 10 mL colorimetric tubes (0, 0.3, 0.6, 0.9, 1.2, 1.5, and 1.8 mL). Then, 5 mL of absolute ethanol and 0.1 mL of 4 M NaOH solution were successively added to each tube [12]. Distilled water was added to the scale and the samples were shaken well and placed in a water bath maintained at 40 °C for 10 min. After rapid cooling, the absorbance was measured at 420 nm. The average values of the three replicates for each concentration were obtained. ## 3.2.3. Calculation of Extraction Rate of Naringin Pomelo peel powder (0.500 g) was accurately weighed for extraction (W), filtered, and placed in a 50 mL volumetric flask. Thereafter, 2 mL (V) of the filtrate was placed in a 10 mL cuvette, 5 mL of absolute ethanol, and 0.1 mL of 4 M NaOH solutions were successively added, and distilled water was added to the scale mark. The samples were then shaken well and placed in a water bath at a constant temperature of 40 °C for 10 min. Absorbance was measured at 420 nm after rapid cooling. According to the naringin standard curve, the concentration (C) of the diluent and the extraction rate were calculated according to the following formula: Naringin extraction rate (mg/g) = 500 C/VW where C—concentration of naringin in diluent (mg/mL);V—volume of extract (mL);W—mass of pomelo peel powder (g). Three parallel experiments were performed in each group and the average value was obtained [13]. ## 3.2.4. Single Factor Experimental Design of Ultrasonic-Assisted Extraction Half a gram of citrus pomace powder from the pomelo peel was accurately weighed. The effects of the solid–liquid ratio, extraction time, extraction temperature, and ultrasonic frequency on the extraction rate of naringin were investigated under experimental conditions, and the scope of an orthogonal design test of various factors was determined. The basic conditions of a single-factor screening test were $75\%$ ethanol volume fraction, 1:30 g/mL solid–liquid ratio, 60 min extraction time, 60 °C extraction temperature, and 24 KHz extraction power. When a single factor was studied, the other conditions remained unchanged [14]. ## 3.2.5. Response Surface Experimental Design for Ultrasonic-Assisted Extraction Based on a single factor experiment and according to the Box–Behnken central combination design principle, four factors, including extraction temperature(X1), material liquid ratio(X2), extraction time(X3), and ultrasonic frequency(X4), were selected as independent variables [15,16,17,18]. The naringin extraction rate was used as the response value. We used Design-Expert 8.0.6 software for data analysis to obtain the optimisation model of four factors and three levels for a total of twenty-nine groups of experiments. The factors and level designs of the response surface tests are listed in Table 5. ## 3.3.1. DM101 Macroporous Resin Pre-Treatment The DM101 macroporous resin was soaked in $95\%$ ethanol for 24 h, packed into the column using the wet method, rinsed several times with distilled water until there was no alcohol smell, and rinsed with $2\%$ NaOH solution at a flow rate of 2 mL/min for 20 min. Then, the mixture was rinsed with $5\%$ HCl solution at a flow rate of 2 mL/min until the effluent was neutral and dried at room temperature for standby [19]. ## 3.3.2. Optimisation of Purification Conditions of Naringin with DM101 Macroporous Resin After selecting the most suitable macroporous resin for the purification of naringin, the effects of loading concentration, loading flow rate, and sample PH on the adsorption rate of naringin and the effect of ethanol concentration on the desorption rate of naringin were investigated. The optimum conditions for the purification of naringin with macroporous resins have been previously determined [20,21]. ## 3.3.3. Response Surface Method Optimisation Experiment On the basis of a single factor test, a response surface test was conducted using the design principle of the Box–Behnken central composite test in the Design Expert 8.0.6 software [22]. Three factors were selected as independent variables: concentration of sample solution (A), sample solution pH (B), and flow rate of the sample water (C). The low, medium, and high levels were selected, and a factor level table of three factors and three levels was designed. The response surface factor level design is shown in Table 6. ## 3.3.4. Purification and Analysis of Samples After purification under the above optimal conditions, the eluent was collected, concentrated in a vacuum to approximately $\frac{1}{4}$ of the volume of the original solution, cooled, and maintained at 4 °C for 12 h to separate the yellowish crystals. The sample was then dissolved in a water bath at 50 °C with a small amount of distilled water, filtered while hot, and freeze-dried to obtain naringin. The purified naringin was then vacuum dried until constant weight after multiple recrystallization experiments to obtain the naringin refined product [23,24]. ## 3.4. Structural Identification of Naringin A Nicolet iS5 Fourier infrared spectrometer (MA, USA) was used to detect the samples using infrared spectroscopy (FT-IR). The potassium bromide tableting method was used by mixing the sample in potassium bromide evenly and grinding the tableting in an agate mortar with the following scanning range: 500–4000 cm−1. The refined product was analysed via nuclear magnetic resonance (NMR; deuterated acetone solvent; tetramethylsilane internal standard; AVANCE NEO 600M; Bruker Corp., Billerica, MA, USA) and compared with literature values to confirm it was pure naringin [25,26]. ## 3.5.1. Drawing the Standard Curve of Cholic Acid Salt Standard solutions of sodium glycocholic acid and sodium taurocholate were prepared at concentrations of 0.05, 0.10, 0.15, 0.20, and 0. Standard solutions (2 mL) of different concentrations were placed in different test tubes. Then, 6 mL of a $60\%$ H2SO4 solution was added, and the mixture was heated in a 70 °C water bath for 20 min, then placed in an ice bath for 5 min. Its absorbance was measured at 387 nm. The calculation of the standard curve of the acid salt was repeated thrice [27,28,29]. ## 3.5.3. Determination of Conjugation Rate of Cholic Acid Salts The ability of naringin to reduce blood lipids was determined by the binding rate of the two sodium cholates with naringin. Sodium glycocholic acid binding rate (%) = (amount of sodium glycocholic acid before adsorption − amount of sodium glycocholic acid after adsorption)/sample mass × $100\%$ Sodium taurocholate binding rate (%) = (amount of sodium taurocholate before adsorption − amount of sodium taurocholate after adsorption)/sample mass × $100\%$ ## 3.6. Data Processing The test data were analysed using Design-Expert 8.0.6 (Stat-Ease Inc., Minneapolis, MN, USA) and Origin 2021 software (OriginLab Inc., Massachusetts, MA, USA). ## 4. Conclusions The optimum extraction temperature was 74.79 °C; the extraction time was 1.58 h; the ratio of material to liquid was 1:56.51 g/mL; and the ultrasonic frequency was 28.05 KHz. 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--- title: Alterations in Inflammatory Markers and Cognitive Ability after Treatment of Pediatric Obstructive Sleep Apnea authors: - Mohamed Shams Eldin - Mohamed Alahmer - Ebrahim Alkashlan - Mahmoud Zahran - Mohamed Eltonsy - Amr Zewail - Abdelfattah Kasem - Khaled Abdelaal - Mahrous Seddeek - Zakaria Ahmed journal: Medicina year: 2023 pmcid: PMC9968190 doi: 10.3390/medicina59020204 license: CC BY 4.0 --- # Alterations in Inflammatory Markers and Cognitive Ability after Treatment of Pediatric Obstructive Sleep Apnea ## Abstract Background and Objectives: Determination of the impact of obstructive sleep apnea (OSA) on the cognitive function (CF), and serum tumor necrosis factor-α (TNF-α), interleukin (IL)-6 and 1β levels and the effect of OSA management on these variables in children. Materials and Methods: A total of 224 patients were evaluated using the Pediatric Sleep Questionnaire, the NEPSY score for CF, and polysomnography (PSG) to grade OSA severity according to the apnea/hypopnea index (AHI). Adentonsillectomy (AT) was performed for patients with adenotonsillar hypertrophy grade > 2. Patients with overweight or obesity with mild or moderate OSAS underwent a 6-month protocol of lifestyle intervention (LSI). Blood samples were obtained for an enzyme-linked immunosorbent assay (ELISA) estimation of cytokine levels. All variables were re-evaluated at the end of the 6-month follow-up period. Results: A total of 181 patients had surgical interference and 43 patients underwent a LSI trial; 15 patients failed to respond and underwent surgery. At the end of the follow-up, 33 patients had residual OSAS with a significantly higher incidence among patients with severe OSAS, the mean score of the pediatric sleep questionnaire was significantly decreased in all patients, 181 patients showed an improved NESPY score, and cytokine levels were decreased. The baseline NESPY score, AHI index and sleep questionnaire score were negatively correlated. The percentage of change in the NESPY score and serum cytokine levels showed a positive correlation. Conclusions: OSAS is associated with cognitive dysfunction that significantly improves after adenotonsillectomy. LSI as a therapeutic line is satisfactory for children with mild OSAS and minimal cognitive dysfunction and is of value preoperatively to improve the surgical outcomes of AT. ## 1. Introduction Sleep-disordered breathing is associated with sleep fragmentation and reduced blood oxygenation due to apnea and hypopnea episodes [1]. There is a significant incidence of obstructive sleep apnea (OSA) in children, which may have a variety of unfavorable effects on their health and behavior. Several studies have linked obstructive sleep apnea to serious medical problems in children, including epilepsy, hypertension, nocturnal enuresis, and failure to thrive. Multiple individual variables and systemic inflammation may influence the link between OSA and cognitive function (CF), which is currently being studied [2]. Multiple studies indicate that OSA can have a negative impact on CF, primarily the executive functions, attention, and episodic memory [1]. Childhood obesity is going to be a pandemic with a progressively increasing prevalence, reaching up to $5.6\%$ in girls and $7.8\%$ in boys as a worldwide prevalence, with much evidence supporting the assumption that obesity is a major preventable risk factor for some respiratory conditions, especially asthma and OSA [3]. The risk of pediatric OSA has been attributed mainly to the interaction of two considerations: BMI and tonsil/adenoid size. As a result, it was proposed that there are two forms of pediatric OSA, one associated with significant lymphoid hypertrophy in the absence of obesity (type I) and the other with obesity but only mild lymphoid hypertrophy (type II), with substantial overlap between these different entities. The global obesity epidemic has resulted in the development of a phenotypic variant of pediatric OSA in children that closely matches that of adults, along with a unique method for classifying pediatric OSA. [ 4] It was discovered that losing weight is an effective therapy for relieving OSAS; however, there was a large degree of variability in the improvement brought about by this treatment. Obesity exacerbates the effects of adenotonsillar hypertrophy (ADH), a common childhood condition that is a risk factor for obstructive sleep apnea. A change in BMI z-score is significant in pediatric OSA following AT, which suggests the valuable effect of this line of treatment [5]. The mechanisms that mediate the neurocognitive consequences of pediatric OSA includes intermittent hypoxia, arousal and sleep fragmentation besides the evolving role of a molecular basis responsible for end-organ damage including many recently studied biomarkers as plasma insulin growth factor-1 (IGF-1) plasma IL-6 and high-sensitivity C-reactive protein urinary neurotransmitters urinary catecholamines, taurine and GABA [6]. Interleukin-6 (IL-6), interleukin-1, tumor necrosis factor (TNF), and C-reactive protein (CRP) are examples of inflammatory biomarkers that have been found to be marginally related to a wide range of cognitive function indicators in overweight or obese children. These indicators include academic performance, executive function, behavioral functioning, and emotional functioning [7]. This study aimed to determine the impact of OSA on cognitive function and inflammatory mediators in children and to evaluate the effect of varied therapeutic modalities for OSA on these variables. ## 2. Materials and Methods This prospective interventional clinical study was conducted in Ansari Specialized hospital and National Yanbu hospital KSA in the period from May 2020 to June 2022. All children aged 5–12 years who attended the outpatient clinics of otorhinolaryngology and/or pediatrics with complaints suggestive of OSAS were eligible for evaluation for exclusion and inclusion criteria according to the conditions of the Ethical Committee, which approved the study protocol. Children with mild to severe OSAS, free of exclusion criteria, were enrolled in the study after informed, written consent from their parents. Exclusion criteria included the presence of craniofacial anomalies, neurological disorders, very severe OSAS, OSAS complicated by complex comorbidities, residual OSAS following adenotonsillectomy, hypothyroidism, or refusal of the suggested therapeutic plans. Additionally, children who failed or were unable to undergo the cognitive function evaluation and those whose parents refused to undergo polysomnography were excluded from the study. The following evaluation tools were used for all participants in the study:Evaluation of body mass index (BMI): BMI was calculated as weight (kg) divided by the square of height (m2) and was interpreted according to the International Obesity Task Force (IOTF) BMI cut-offs according to the percentile of BMI adjusted for age and gender [8].Pediatric Sleep Questionnaire (PSQ) using the sleep-related breathing disorders scale, which consists of 3 domains including 22 items with three responses to each item: yes (=1), no (=0), and do not know = missed answer [9].Neurocognitive Assessments using the NEPSY II score which is designed to assess six domains, where each domain was expressed as a scaled score, with lower scores indicating cognitive dysfunction [10].Otorhinolaryngologic assessment variables: a. Assessment of the volume of the palatine tonsils and adenoid size by the Brodsky grading scale [11] and X-ray soft tissue nasopharynx lateral view. Clinical grading of the examined upper airway was performed according to the modified Mallampati method [12]. b. Polysomnography (PSG) was performed according to the guidelines of the American Academy of Sleep Medicine (AASM) for the scoring of sleep and associated events in the Sleep Lab Unit of the Al-Ansari Specialized Hospital (Xltek® Brain Monitor with Natus® SleepWorks™ Middleton, USA PSG software) Overnight recording and scoring were carried out in accordance with the American Academy of Sleep Medicine’s 2020 guidelines [13]. Based on the apnea–hypopnea index values, patients were graded as mild if the AHI was 1–4.9, moderate if the AHI was 5–9.9, or severe if the AHI > 10; and if the AHI was >30, OSAS was very severe. v.Laboratory investigation: venous blood samples (5 mL) were collected for ELISA estimation of serum levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-6 and 1β. Therapeutic plans were designed according to BMI, OSAS severity, and clinical evaluation. Patients with adenoid, tonsillar, or adenotonsillar hypertrophy of grade > 2 with clinically evident obstructive airways underwent adenoidectomy, tonsillectomy, or adenotonsillectomy. Overweight or obese patients with mild or moderate OSAS were randomly assigned to a 6-month trial of lifestyle intervention (LSI) and CPAP if indicated; responders spent more time in follow-up visits if surgical intervention was not indicated. For lifestyle intervention (LSI), a structured 6-month LSI program was costumed consisting of dietary modification and exercise. Intensive dietary counseling was provided weekly for the first 4 weeks of the intervention and monthly thereafter until 6 months. A target caloric deficit of 250.500 cal/d was recommended throughout dietary counseling. Dietary regimens consisted of diets composed of nutrients contributing to total energy as $55\%$ carbohydrates, $15\%$ protein, and $30\%$ fat. Other lifestyle changes included calorie restriction based on snack consumption frequency, consumption of low-calorie and low-fat snacks, limitation of sugar-based carbonated drinks, and limitation of television viewing or mobile gaming duration. Exercise sessions consisted of both aerobic and strength training three times weekly. All patients were followed up for 6 months, either as part of watchful management and LSI or as a postoperative follow-up. At the end of the follow-up, all patients underwent evaluation for residual OSAS with PSG, a sleep questionnaire, CF using a NESPY score, and an estimation of serum levels of the studied cytokines. Obtained data were presented as the mean, standard deviation, numbers, percentages, median, and interquartile range. Results were analyzed using one-way ANOVA for the analysis of variance between groups, paired t-tests for the analysis of inter-group variance, and Mann–Whitney and Chi-square tests (X2 test) for the analysis of non-numeric data. Spearman’s correlation analysis was applied to evaluate correlations between baseline variables and between the percentages of changes in variable values at the end of the 6-month follow-up in relation to baseline values. The percentage of change was calculated as the difference between the baseline and end of the follow-up values, divided by the baseline value, and multiplied by 100. Statistical analysis was conducted using IBM® SPSS® Statistics (Version 25.0; IBM Corp., Armonk, NY, USA) for the Windows statistical package. A p value < 0.05 was considered statistically significant. ## 3. Results During the study duration, 279 patients were eligible for evaluation, 11 patients were excluded for not fulfilling the inclusion criteria, 17 patients refused to participate in the study, and 27 patients were missed during follow-up; thus, 224 patients were enrolled in the study and completed the study protocol and follow-up. As shown in Figure 1, patients were classified as mild ($$n = 120$$; $53.6\%$), moderate ($$n = 63$$; $28.1\%$), or severe ($$n = 41$$; $18.3\%$) based on their AHI score at the time of enrollment. There was a non-significant difference between patients in the three groups with regard to age and gender distribution. However, the calculated BMI defined only 24 patients ($10.7\%$) with an average BMI, while 75 patients ($33.5\%$) were overweight, and 125 patients ($55.8\%$) were obese. The BMI of patients with mild OSAS was significantly lower than the BMI of patients with moderate (p1 = 0.0001) and severe (p1 = 0.0001) OSAS, with patients with moderate OSAS having a non-significantly (p2 = 0.902) lower BMI than those with severe OSAS. Otorhinolaryngological examination defined 62 patients ($27.7\%$) with grade 4 tonsillar hypertrophy, 99 patients ($44.2\%$) with grade 3, 61 patients ($27.2\%$) with grade 2, and only two patients ($0.9\%$) with grade 1 tonsillar hypertrophy. The difference between the three groups was non-significant. As regards adenoid volume grading, 12 patients ($5.4\%$) had no adenoid hypertrophy, 13 patients ($5.7\%$) had adenoid hypertrophy of grade 1, 92 patients ($41.1\%$) had grade 2, 81 patients ($36.2\%$) had adenoid hypertrophy of grade 3, and 26 patients ($11.5\%$) had adenoid hypertrophy of grade 4, with a non-significant difference between the three groups. Modified Mallampati score class IV was detected in 9 patients ($4\%$), class III in 31 patients ($13.8\%$), class II in 71 patients ($31.7\%$), and class I in 113 patients ($50.4\%$), with a lower frequency of patients having high Mallampati grades among patients with mild OSAS, and the difference was non-significant (p1 = 0.074), but was significantly (p1 = 0.0175) higher in comparison to the frequencies detected in patients with moderate and severe OSAS. High Mallampati grades showed a non-significantly lower frequency among patients with moderate OSAS than those having severe OSAS (Table 1). One hundred and eighty-one patients with adenotonsillar hypertrophy of grades 3 or 4 underwent an adenoidectomy ($$n = 19$$), tonsillectomy ($$n = 76$$), or adenotonsillectomy ($$n = 86$$), without trial of LSI or CPAP. Forty-three patients underwent the LSI trial for 6 months: 25 patients with mild, 14 patients with moderate, and 4 patients with severe OSAS, and 15 of these patients’ received CPAP in addition to the LSI protocol. Unfortunately, 15 patients failed to respond; 7 had mild, 5 had moderate, and 3 had severe OSAS and underwent surgery, while the remaining 28 patients succeeded in responding. With conservative treatment, the surgery was avoided, yielding a success rate of $65.1\%$. A total of 196 patients were surgically treated. The frequency of patients requiring a tonsillectomy or adenotonsillectomy was significantly higher among patients with severe OSAS but was non-significantly higher among patients with moderate OSAS in comparison to patients with mild OSAS, with a non-significantly lower frequency among patients with moderate OSAS, than among those with severe OSAS (Figure 2). At the end of the follow-up, 33 patients had residual OSAS, with a significantly higher incidence of residual OSAS in patients with severe OSAS in comparison to its incidence among patients with mild OSAS (p2 = 0.001). Additionally, there was a significant difference between patients with mild and moderate OSA (p1 = 0.04), with a non-significantly (p3 = 0.092) higher incidence of residual OSAS among patients with severe OSAS than in patients with moderate OSAS. The pediatric sleep questionnaire baseline score was significantly higher in patients with severe OSAS compared to patients with mild (p2 = 0.002) and moderate OSA (p3 = 0.005), with a non-significantly (p1 = 0.692) higher score in patients with moderate OSAS compared to patients with mild OSAS. At the end of the follow-up, the mean score was significantly (p4 < 0.001) decreased in all patients in comparison to their baseline score. However, the score determined at the end of the follow-up was still significantly higher in patients who had severe OSAS in comparison to those who had mild (p1 = 0.002) and moderate (p2 = 0.047) OSAS, with a non-significantly (p1 = 0.192) higher score in patients with moderate OSAS in comparison to patients with mild OSAS (Table 2). Regarding cognitive function, the baseline NESPY score was significantly higher in patients with mild OSAS in comparison to patients with moderate (p1 = 0.004) and severe OSAS (p1 = 0.001), with a non-significantly (p2 = 0.096) higher NESPY score in patients with severe OSAS in comparison to patients with moderate OSAS. At the end of the follow-up, all patients showed a significantly (p3 < 0.001) higher NESPY score in comparison to their baseline score. At the end of the follow-up, the NESPY score of patients with severe OSAS was significantly (p1 = 0.030) higher in patients with mild OSAS in comparison to patients with severe OSAS but was non-significantly (p1 = 0.137) higher than the NESPY score of patients with moderate OSAS, while patients with moderate OSAS showed a non-significant (p2 = 0.798) higher NESPY score in comparison to patients with severe OSAS. The NESPY score at the end of the follow-up was significantly higher in all patients in comparison to their baseline score (p4 < 0.001). Differentially, at the end of the follow-up, 43 patients ($19.2\%$) showed no change in NESPY score, while 181 patients ($80.8\%$) showed an improved NESPY score, with a non-significantly higher frequency of patients showing an improved NESPY score among patients with severe OSAS in comparison to patients with mild or moderate OSAS. However, the percentage of improvement of the NESPY score was significantly higher in patients with severe OSAS in comparison to those with mild (p2 < 0.001) or moderate OSAS (p3 = 0.002), with a significantly (p1 = 0.002) higher percentage of improvement in patients with moderate OSAS compared to patients with mild OSAS (Table 2). Baseline serum levels of inflammatory cytokines were significantly higher in patients with severe OSAS in comparison to patients with mild and moderate OSAS, with significantly lower levels in the serum of patients with mild OSAS than those with moderate OSAS. All patients, irrespective of OSAS severity, showed significantly lower serum cytokine levels at the end of the follow-up in comparison to their baseline levels. At the end of the follow-up, serum levels of TNF-α showed a non-significant difference between studied patients, irrespective of their baseline severity of disease. However, the percentage of decreased serum levels of TNF-α was significantly higher in patients with severe OSAS in comparison to patients with mild (p1 < 0.001) and moderate (p2 = 0.002) OSAS, with non-significant differences between patients with mild or moderate OSAS. As regards the percentage of change in serum IL-6 levels estimated at the end of the follow-up, it was significantly lower in patients with mild OSAS in comparison to that detected in levels estimated in the sera of patients with moderate or severe OSAS (p1 = 0.005 and 0.002, respectively), but was a non-significantly lower percentage of change in patients with moderate OSAS than in patients with severe OSAS. On the contrary, the percentage of change in serum levels of IL-1β in patients with moderate OSAS was non-significantly lower than that in patients with mild OSAS (p1 = 0.146) but was non-significantly higher than that in patients with moderate (p2 = 0.207) OSAS, with a significantly (p1 = 0.002) lower percentage of change in serum IL-1β in patients with severe OSAS than in patients with mild OSAS (Table 3). TNF-α and IL-1β serum levels correlated positively and significantly with baseline BMI, whereas IL-6 serum levels correlated insignificantly. Additionally, the baseline AHI index showed a positive and significant correlation with baseline BMI and serum levels of the studied cytokines. Similarly, the baseline sleep questionnaire score showed a non-significant correlation with baseline BMI and serum levels of TNF-α and IL-6, but not with serum levels of IL-1β. The baseline NESPY score had a negative significant correlation with baseline BMI and serum levels of IL-6, but the correlation was non-significant with serum levels of TNF-α and IL-1β (Table 4). The baseline NESPY score was negatively correlated with the baseline AHI index (Rho = −0.428, $$p \leq 0.001$$) and sleep questionnaire score (Rho = −0.196, $$p \leq 0.003$$). The percentage of change in NESPY score at the end of the study correlated positively and significantly with the percentage of change in serum levels of TNF (Rho = −0.248, $$p \leq 0.001$$), IL-6 (Rho = −0.196, $$p \leq 0.001$$), and IL-1 (Rho = −0.149, $$p \leq 0.026$$) (Figure 3, Figure 4 and Figure 5). ## 4. Discussion The current study detected an evident impact of OSAS on cognitive function (CF), as evidenced by the improvement of CF assessment after management of the underlying cause of OSAS, and such improvement was found to inversely correlate with the severity of OSAS. Similarly, multiple studies detected such an effect of OSAS on neurocognitive functions, where mild to moderate childhood OSAS was found to adversely affect CF, particularly in children younger than 6 years of age [14]. Furthermore, other studies documented that OSAS is often complicated by cognitive dysfunction manifested in executive function, attention, memory and learning, especially in children [15]. On the other hand, it was found that school-aged children included in a wide community-based research study with a higher AHI showed significantly lower cognitive performance but with subtle statistically significant differences in office-based cognitive tests, and attention was specifically affected in habitual snorers with normal polysomnographic indices [16]. However, the effect of AT on the neurocognitive functions was a matter of discrepancy, where no differences found between AT and watchful management for OSAS on CF in children and AT may have limited benefits in reversing any cognitive effects of OSAS [17]. Thereafter, surgical treatment in school-age children did not lead to improvements in objective attention measures parallel to improvements in polysomnography (PSG) and parent-reported symptoms [18]. On the contrary, the results of the current study concerning the improved CF after AT support a meta-analysis reporting that OSAS children perform worse than healthy children in all cognitive domains, but after 6–12 months following AT, significant improvement in attention–executive function and verbal ability were reported in comparison to their baseline level, with restoration of attention–executive function and memory in comparison to healthy children [19]. In another review of the literature, 11 studies investigated changes in behavior and cognitive outcomes after AT, and almost all of them reported a significant improvement of the scores after AT [20]. Recently, improved anxiety and decreased auditory/visual sustained attention abnormalities in children with OSAS after AT were documented [21]. The cause–effect relationship between adenotonsillar hypertrophy and OSAS and the impact of AT on OSAS manifestation are evident in the current study by the significantly lower scores in the pediatric sleep questionnaire (PSQ) after management of the underlying pathology for OSAS in comparison to the baseline score. Thereafter, improvements in sleep and behavior following AT have been detected by PSG monitoring and parental questionnaires [22]. Interestingly, the current study detected significantly higher baseline serum levels of inflammatory markers in OSAS patients in comparison to age- and BMI-matched non-OSAS children and reported a significant decrease in these levels after AT with or without LSI in comparison to baseline levels. Similarly, significantly higher levels of inflammatory markers were documented in patients with OSAS than in OSAS-free people, with significant associations with increased BMI [23]. Serum levels of IL-6, IL-8, IL-17, IL-18, CRP, and TNF-α were also reported to be significantly higher in patients with OSAS [24]. Moreover, higher CRP was prospectively associated with increased OSA risk, and these abnormally high CRP levels detected in OSAS children were significantly reduced after AT [25]. The obtained data and review of the literature point to a possible causal relation between high serum inflammatory markers and development and severity of OSAS. In line with these data and suggestions, the levels of TNF-α and IL-6 and their gene polymorphisms have been found to be significantly related to the susceptibility to OSAS [26]. Furthermore, the current study detected a positive and significant correlation between baseline serum levels of inflammatory cytokines and AIH as a measure for OSAS severity. In support of these findings, recently, high serum levels of IL-6 and CRP were detected in OSAS patients, and these elevated levels were found to be parallel to the OSAS severity, confirming that OSA and inflammation are interconnected [27]. Additionally, it was found that elevated CRP levels are connected with a higher risk of neurocognitive impairments in pediatric OSAS. In addition, the inflammatory response, as demonstrated by TNF-α, may indicate the existence or absence of OSAS-related excessive daytime sleepiness. Changes in the urine concentrations of a variety of neurotransmitters have been also found to be associated with cognitive impairments in OSAS in children [28]. Another form of support for this interrelation is the persistent and significantly higher serum inflammatory markers in patients with residual OSAS (AHI > 1 event/h) after AT, and this could be attributed to still-present obesity, despite the trial of LSI. The relation between improved OSA and obesity treatment has been documented, and a significant reduction in AHI after 6 months of BMI z-score reduction has been detected [29]. Improved cognitive functions after AT with or without LSI could be attributed to the reported decreased serum levels of inflammatory cytokines, as evidenced by the inverse relation between the percentage of improvement in NSPY score and the percentage of decrease in serum levels of inflammatory cytokines, and this defines a vicious circle consisting of OSA with subsequent sleep disorders, elevation of serum inflammatory cytokine levels and cognitive dysfunction. ## 5. Conclusions OSAS is associated with cognitive dysfunction that may be secondary to altered levels of inflammatory cytokines or as an impact of apnea/hypoxia episodes during sleep. AT significantly improved OSAS manifestations, cognitive function, and reduced serum cytokine levels. LSI is a satisfactory therapeutic line for children with mild OSAS and minimal cognitive dysfunction, and it is required preoperatively to improve AT outcomes. ## References 1. 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--- title: Obesity-Associated Hepatic Steatosis, Somatotropic Axis Impairment, and Ferritin Levels Are Strong Predictors of COVID-19 Severity authors: - Davide Masi - Elena Gangitano - Anna Criniti - Laura Ballesio - Antonella Anzuini - Luca Marino - Lucio Gnessi - Antonio Angeloni - Orietta Gandini - Carla Lubrano journal: Viruses year: 2023 pmcid: PMC9968194 doi: 10.3390/v15020488 license: CC BY 4.0 --- # Obesity-Associated Hepatic Steatosis, Somatotropic Axis Impairment, and Ferritin Levels Are Strong Predictors of COVID-19 Severity ## Abstract The full spectrum of SARS-CoV-2-infected patients has not yet been defined. This study aimed to evaluate which parameters derived from CT, inflammatory, and hormonal markers could explain the clinical variability of COVID-19. We performed a retrospective study including SARS-CoV-2–infected patients hospitalized from March 2020 to May 2021 at the Umberto I Polyclinic of Rome. Patients were divided into four groups according to the degree of respiratory failure. Routine laboratory examinations, BMI, liver steatosis indices, liver CT attenuation, ferritin, and IGF-1 serum levels were assessed and correlated with severity. Analysis of variance between groups showed that patients with worse prognoses had higher BMI and ferritin levels, but lower liver density, albumin, GH, and IGF-1. ROC analysis confirmed the prognostic accuracy of IGF-1 in discriminating between patients who experienced death/severe respiratory failure and those who did not (AUC 0.688, CI: 0.587 to 0.789, $p \leq 0.001$). A multivariate analysis considering the degrees of severity of the disease as the dependent variable and ferritin, liver density, and the standard deviation score of IGF-1 as regressors showed that all three parameters were significant predictors. Ferritin, IGF-1, and liver steatosis account for the increased risk of poor prognosis in COVID-19 patients with obesity. ## 1. Introduction Coronavirus disease (COVID-19) is an infectious disease that still poses a global pandemic challenge [1], and the prognostic factors responsible for its severity have yet to be thoroughly investigated [2,3,4]. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily affects the respiratory tract, but it can also cause multi-organ dysfunction due to the widespread presence of the angiotensin-converting enzyme–2 (ACE–2) receptor, which is the entry site for the virus [5]. Possible effects of SARS-CoV-2 infections on the endocrine system, including changes in the function of the thyroid [6], pancreas, adrenal glands, and gonads have been increasingly reported [7,8]. However, only a few clinical studies have explored the potential link between SARS-CoV-2 infections and the growth hormone (GH)/insulin-like growth factor–1 (IGF-1) axis [9,10] and between liver steatosis and COVID-19 severity [11,12,13]. It is well known that GH deficiency states are associated with severe non-alcoholic steato-hepatitis (NASH) [14,15] and that obesity is generally associated with low GH levels and hepatic steatosis [16,17]. SARS-CoV-2 seems to reduce the insulin/IGF signaling in lung and metabolic tissues (e.g., liver, adipose tissue) [18]. Recently, it has also been reported that the farnesoid X receptor (FXR) may modulate ACE2 transcription in multiple human tissues [19]. Furthermore, FXR plays a pivotal role in regulating iron homeostasis [20] and interestingly transcriptomic profiles of liver biopsies revealed a strong relationship between IGF-1 expression and FXR signaling [21]. Interestingly, there is a similar pattern between ACE–2 receptor expression and age-related changes in growth hormone (GH) secretion [22]. Recently, speculations have been made about reduced GH action in patients with COVID-19 and obesity [23,24]. Individuals with obesity, particularly those who are older and have visceral adipose tissue (VAT) accumulation, are at higher risk of more severe COVID-19 complications [25], and reduced GH secretion is a hallmark of this population subgroup [16,26]. A preclinical study conducted in hamsters infected with SARS-CoV-2 confirmed that diet-induced obesity and NASH impair disease recovery [27]. In addition, a recent review further highlighted the close association of COVID-19 with hepatic metabolic dysfunction [28]. Obesity may also act as an effect modifier of smog-induced lung injury, and the concomitant presence of these two factors could better explain the higher virulence, faster spread, and more significant mortality in polluted areas [29]. Furthermore, GH has been identified as a candidate disease-modifying target in non-alcoholic fatty liver disease (NAFLD) because of its lipolytic and anti-inflammatory properties [17,30]. First, unequivocal evidence suggests that immune dysregulation is a core element in determining the severity of SARS-CoV-2 and the association with GH deficiency (GHD) in adulthood [23]. In addition, as GH is physiologically involved in developing and maintaining the immune system, its pharmacological replacement in GHD patients appears to positively influence their inflammatory status [31]. Furthermore, impairment of fibrinolysis associated with GHD may represent a further link between the impairment of the GH–IGF-1 axis and the severity of COVID-19, as it has been associated with both conditions [32,33]. Preventive measures based on an understanding of the pathophysiology of the disease may be of utmost importance to mitigate its spread. Several pieces of evidence supported a possible relationship between GHD and COVID-19 severity and have also shed light on the potential beneficial effects of treatment with recombinant GH on COVID-19 patients [31]. In this regard, recently published articles showed that among the hormonal changes in long COVID patients are reduced GH levels and possibly reduced insulin/IGF-1 signaling [34,35]. Considering all the above, a close relationship between ferritin metabolism, obesity, hepatic steatosis, and the GH/IGF-1 axis with COVID-19 severity is conceivable. Therefore, the objective of the study was to evaluate if weight-related alterations of the GH/IGF-1 axis, liver attenuation on CT, and iron metabolism were present in COVID-19 patients upon their first admission to the emergency department and possibly related to COVID-19 severity, comparing individuals who progressed to a more severe form of the disease with individuals with a stable infection that did not require oxygen-supported intervention, intubation, or admission to intensive care units. ## 2.1. Study Design This is an observational, single-center, retrospective study conducted according to the guidelines of the Declaration of Helsinki. ## 2.2. Study Population A total of 143 COVID-19 patients admitted to the Emergency Medicine Department of the Polyclinic Umberto I in Rome between March 2020 and May 2021 were included in the study and provided verbal consent to participate. Serum samples were collected upon admission before starting any treatment and tested by the Laboratory Department. Inclusion criteria were as follows: [1] SARS-CoV-2 infection defined as the presence of at least two positive reverse transcriptase polymerase chain reaction results from nasopharyngeal swabs; [2] chest CT imaging suggestive of COVID-19 pneumonia; and [3] age >18 years. According to the WHO guidelines, patients were divided into four different groups according to the severity of pulmonary impairment in CT and respiratory failure [36]: patients with no CT alterations (Group 0–mild); patients with changes in CT scan requiring no oxygen support (Group 1–moderate); patients with CT scan plus oxygen supplementation (Group 2–severe) and patients with CT abnormalities plus intensive care unit (ICU) admission (Group 3–critical). The lung involvement, reported as the percentage of parenchyma affected by the disease, was established through the analysis of the chest CT by expert radiologists following a standardized procedure [37,38]. After the initial evaluation and management, patients were discharged in home isolation or were hospitalized in low, medium, or sub-intensive/intensive care units according to their medical needs. All patients were followed up to 60 days after emergency department admission. Patients were further grouped based on whether they experienced death/ARDS (acute respiratory distress syndrome) within two months of admission or not. ## 2.3. Measurements Demographic characteristics, including race (which was self-reported), clinical history, and clinical presentation, were collected from the electronic medical records at admission and during hospitalization. Biochemical variables (such as white blood cell count, serum electrolytes, creatinine, ferritin, C-reactive protein (CRP), fibrinogen, D–dimer test, glucose, and liver parameters), arterial blood gas analysis with the corresponding PaO2/FiO2 ratio (P/F ratio), and the need for oxygen supplementation were also evaluated. Apart from the routine laboratory workup for COVID-19, we collected serum samples from patients with diverse presentations (from asymptomatic cases to the most severe forms), clinical trends, and outcomes. Samples were then transferred to the local laboratory and handled according to the local standards of practice. In doing so, we measured GH and IGF-1 levels within 3 h of admission to the emergency ward. Specifically, IGF-1 was assayed by an immunoradiometric assay after ethanol extraction (Diagnostic System Laboratories Inc., Webster, TX, USA). As serum IGF-1 levels highly depend on age and sex, we also normalized IGF-1 values for these two parameters. We expressed them as standard deviation scores (zSDSs), as previously described by Chanson et al. [ 39]. Hepatic steatosis index (HSI) was calculated as follows: 8 X(ALT/AST ratio)+BMI (+2, if female; +2, if diabetes mellitus) [40] and Fibrosis–4 index for liver fibrosis (FIB-4) was calculated using the following formula: age(years) X AST [U/L]/(platelets [109/L] X (ALT [U/L])$\frac{1}{2}$) [41]. A threshold value of <1.45 has a negative predictive value for the exclusion of extended fibrosis of $90\%$. A threshold value of >3.25 has a positive predictive value for the diagnosis of extensive fibrosis of $65\%$. ## 2.4. CT Imaging of the Liver All examinations were performed using two multidetector CT scanners (Somatom Sensation 16 and Somatom Sensation 64; Siemens Healthineers, Erlangen, Germany). Scan parameters corresponded to the manufacturer’s recommended standard presets for a chest routine. Chest CT images were retrospectively analyzed for hepatic measurement at hospital admission. Two expert radiologists independently interpreted the images. First, each reader chose the CT scan that allowed the best visualization of the liver. Liver attenuation was defined as the mean attenuation of three regions of interest (ROIs) expressed in Hounsfield units (HUs). Two ROIs were placed in the anterior and posterior segments of right liver lobe and one ROI in the left liver lobe. Liver attenuation <48 HU was used as a cut-off for the diagnosis of hepatic steatosis [42]. ## 2.5. Statistical Analysis Statistical analysis has been performed using MedCalc® Statistical Software version 20.111 (MedCalc Software Ltd., version 20.2, Ostend, Belgium; https://www.medcalc.org (accessed on 1 September 2022); and StatSoft, Inc. STATISTICA® version 12, Helsinki, Finland; (Data analysis software system; www.statsoft.com (accessed on 1 September 2022). Distribution of continuous variables was tested with the Shapiro–Wilk test; linearity was established by visual inspection of a scatterplot. Data points greater or less than two standard deviations from the mean have been considered statistical outliers and excluded from all analyses. Descriptive statistics (n, mean, SD) were calculated for continuous variables, whereas other variables were expressed as percentages and frequencies, as appropriate. Relationships between study variables were calculated using univariate regression analysis with Pearson or Spearman (Rho) coefficients for skewed data, with a two-tailed $p \leq 0.05$ indicating statistical significance. Multivariate stepwise linear regression was used to evaluate the independent predictors of COVID-19 severity. The receiver operating characteristic (ROC) curve was assessed. ## 3. Results A total of 143 patients were included in the study. Clinical parameters, biochemical tests, respiratory status, and hormonal evaluation recorded on admission are presented in Table 1. The mean age of participants was 60.63 ± 17.04 years and 75 patients ($52.45\%$) were male. At the time of admission to the Emergency Department, 51 patients ($35.66\%$) exhibited a P/F ratio <300, and 61 patients ($42.66\%$) displayed severe lung involvement. GH and IGF-1 levels were 0.92 ± 1.06 ng/mL and 97.2 ± 63.4 ng/mL, respectively. After normalization for age and sex, we found that the zSDS–IGF-1 was −2.62 ± 1.64. ## Predicors of COVID-19 Severity The mean liver density was 50.7 ± 9.5 HU. Based on assessments of CT scans, hepatic steatosis was present in 49 patients ($34\%$, HUliver: 40.04 ± 8.19) and was absent in 94 patients ($66\%$, HUliver: 55.65 ± 4.57). According to pulmonary involvement, patients were divided as follows: 17 ($11.89\%$) in Group 0 without CT scan alterations or hypoxia; 26 ($18.18\%$) in Group 1 with CT scan signs suggestive of pneumonia but without oxygen supplementation; 37 ($25.87\%$) in Group 2 with CT scan signs suggestive of pneumonia and oxygen supplementation; and 63 ($44.06\%$) in Group 3 with CT scan changes suggestive of ARDS requiring intensive care unit (ICU) admission [36]. One-way analysis of variance between groups, depicted in Table 2, showed that higher BMI is associated with more severe disease, confirming obesity as an adverse prognostic factor. In addition, the group of patients with worse prognosis had significantly higher levels of ferritin, fibrinogen, lactate dehydrogenase, and CRP but, at the same time, a lower hepatic attenuation, P/F ratio and significantly lower concentrations of GH, IGF-1, and zSDS–IGF-1. The main findings are presented in Figure 1. A Student’s t-test for independent samples, shown in Table 3, was performed to assess the differences between patients with ARDS/death and all other patients, which constituted the control group. The test confirmed that higher values of indices estimating the amount of scarring and steatosis in the liver, as well as low levels of liver attenuation, IGF-1, and zSDS–IGF-1 values, are associated with the need for ventilation or occurrence of death in COVID-19 patients. A multivariate regression analysis to assess independent predictors of COVID-19 severity (groups 0–3) is presented in Table 4 and reveals that ferritin was positively correlated with the severity of the disease. In contrast, both hepatic attenuation and zSDS-IGF-1 were negatively correlated. In addition, we built the receiver operating characteristic (ROC) curve with the Youden index to test the predictive value of serum IGF-1 as a continuous variable against 60-day outcomes (Figure 1). For this purpose, the optimal cut-off of serum IGF-1 < 64.91 ng/dl could discriminate patients with critical clinical conditions with a sensitivity and specificity of $64.1\%$ and $69.7\%$, respectively (AUC 0.688, CI: 0.587 to 0.789, $p \leq 0.001$). ## 4. Discussion Obesity is an established risk factor for severe COVID-19 outcomes, but the reasons for this association are not fully understood. Strands of research support the study of systemic inflammatory pathways in obesity-associated severe COVID-19 since significantly higher CRP, ferritin [44], and ESR values have been found [45]. Furthermore, central obesity, hypertension, and smoking habits seem to be associated with lower Ab titers following COVID-19 vaccination [46]. Moreover, available clinical data suggest a more aggressive course of SARS-CoV-2 infections in the elderly, males, and patients with obesity; therefore, in a previous article, we suggested the possibility that GH insufficiency could be the missing link between all these factors and COVID-19 severity [23]. Several reasons support this assumption: GH is physiologically involved in developing and maintaining the immune system [40] and different lymphocytes express GH receptors [47]. Furthermore, liver steatosis appears to contribute to the worse prognosis of COVID-19 patients [11]. It is tempting to speculate that the clinical severity of SARS-CoV-2 infections may be related to the interplay among ferritin metabolism, obesity, hepatic steatosis, and the GH/IGF-1 axis. To confirm our hypothesis, we conducted a retrospective analysis of the GH/IGF-1 axis in COVID-19 patients and evaluated several baseline factors to see which were associated with worse outcomes. We found that IGF-1 can be a simple and accurate tool to predict mortality or the need for ventilation in patients with COVID-19, as seen by other authors [48,49,50]. Our results are partially discordant with those of Sandeep Dhindsa et al. [ 10] but these authors did not consider the interference of sex and age on the IGF-1 values and did not normalize IGF-1 values for these two parameters nor expressed them as zSDSs. However, we are aware that in view of the small sample size and the complex clinical picture of SARS-CoV-2 infection, the single IGF-1 value is not sufficient to discriminate all patients hospitalized for COVID-19. Nevertheless, it can still provide a clue for the clinician to better classify the patient, especially when evaluated together with other parameters such as ferritin or steatosis indices. Understanding the link between obesity and severe COVID-19 outcomes requires further research and other studies concerning the discovery of new prognostic factors should be encouraged. IGF-1 is a key predictive factor for metabolic alterations in obesity as it represents a mitogenic hormone involved in processes like growth, angiogenesis, and differentiation [51]. In individuals with obesity, lower IGF-1 serum levels and a blunted response to GH-stimulating dynamic tests are associated with more significant metabolic impairment and even morpho-functional cardiological alterations [52]. Higher serum IGF-1 in obese patients correlates with lower inflammatory patterns and better skeletal health [53]. Moreover, in a preliminary analysis with a machine learning approach, our group found that IGF-1 plays a crucial role in the pathogenesis of the metabolic derangement observed in many patients with obesity [54]. Furthermore, our work confirms that ferritin levels and liver damage (in the form of hepatic steatosis) worsens the prognosis of COVID-19, as already shown by Bucci and colleagues [55] and by our group [3,4]. A further step was to develop a model that could predict the risk of lung impairment and the need for oxygen therapy in COVID-19 patients by integrating clinical characteristics and laboratory parameters for each patient. Intriguingly, with a multivariate regression analysis, we demonstrated that ferritin, hepatic attenuation, and zSDS-IGF-1 are all independent predictors of COVID-19 severity. The findings herein are consistent with data from other clinical trials [4,5,56]. A possible mechanism linking somatotropic axis defect and COVID-19 prognosis may be related to enhanced FXR signaling, with alterations of ACE2 expression and iron metabolism [19,20]. COVID-19 places a considerable burden on healthcare economics and thus requires significant adaptation of hospital facilities. In light of this, the results of our study indicate that, in addition to clinical assessment, the use of the rapid ferritin test and the IGF-1 assay can be a helpful tool to reliably determine whether admission to an intensive care unit is necessary for a specific patient. Faster identification of the most critical patients could save time in their clinical management and avoid overcrowding in the emergency department. Therefore, these results could have a positive impact on both the patient and the national economy. Our study has several limitations. First, this is a single-center study, with a relatively small cohort of patients included; therefore, data should be confirmed in a larger trial. Second, the study’s retrospective design does not allow any cause-and-effect relationship to be established. Moreover, liver damage was assessed only at the time of admission, and it has not been possible to determine whether it persisted after the acute phase of COVID-19. Despite its limitations, the present study, performed in a population that underwent chest CT scan, blood gas analysis, and accurate biochemical evaluation, is the first one that investigates the somatotropic axis in relation to hepatic steatosis in SARS-CoV-2-infected patients and could provide a fascinating insight into the link between GH/IGF-1 axis impairment and lung disease severity in COVID-19 patients. ## 5. Conclusions This retrospective study evaluated the GH/IGF-1 axis status at the time of hospital admission in a cohort of patients hospitalized for COVID-19. Our data show that the severe forms of COVID-19 are associated with an impairment of the GH–IGF-1 axis, along with higher BMI, higher ferritin levels, and reduced liver attenuation on non-contrast CT, all of which are associated with an increased likelihood of ventilation or death. In conclusion, our results may shed light on the possible role of IGF-1 as a new metabolic health parameter capable of effectively predicting the development of more severe forms of COVID-19. Thus, the finding of low serum IGF-1 levels and zSDS–IGF-1 in COVID-19 patients at admission should predict more severe disease and could lead to more rapid and necessary therapeutic measures, modulating FXR expression [56]. In addition, studying the immune responses underlying the different clinical presentations of COVID-19 in relation to the GH/IGF-1 axis could unveil new targets for more effective treatments [57]. 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--- title: Occasions, Locations, and Reasons for Consuming Sugar-Sweetened Beverages among U.S. Adults authors: - Seung Hee Lee - Sohyun Park - Thomas C. Lehman - Rebecca Ledsky - Heidi M. Blanck journal: Nutrients year: 2023 pmcid: PMC9968197 doi: 10.3390/nu15040920 license: CC BY 4.0 --- # Occasions, Locations, and Reasons for Consuming Sugar-Sweetened Beverages among U.S. Adults ## Abstract Frequent intake of sugar-sweetened beverages (SSBs) is associated with adverse health outcomes such as obesity, type 2 diabetes, and cardiovascular disease. Little is known about when, where, and why U.S. adults consume SSBs. This study, using data from an online survey distributed in 2021, examined the occasions, locations, and reasons for consuming SSBs and the characteristics of the adults who consume them. Nearly 7 of 10 adults reported consuming a SSB (1–6 times) in the past 7 days, and more than a third ($38\%$) reported doing so once or more per day (on average). For comparative purposes, the sample was limited to adults who reported consuming SSBs within the last 7 days. Mealtimes were reported as the most frequent occasion for the intake of SSBs ($43\%$) and SSBs were most often consumed at home ($70\%$). Over half of respondents ($56\%$) reported they consume SSBs because they enjoy the taste. Younger adults (18–34 years old) were more likely to consume SSBs in social settings than older adults (≥50 years old). Hispanic adults were less likely to consume SSBs at the beginning of the day compared to non-Hispanic White adults. Younger (18–34 years old) and middle-aged (35–49 years old) adults were more likely to consume SSBs in restaurants, at work, and in cars than older adults (≥50 years old). Women were less likely to consume SSBs at work than men. Hispanic adults were less likely to consume SSBs in cars than non-Hispanic White adults, while those earning USD 50,000–<USD 100,000 were more likely to consume SSBs in cars than those earning ≥USD 100,000. Younger and middle-aged adults were more likely to consume SSBs due to cravings and enjoyment of the carbonation compared to older adults. These findings provide insights on specific populations for whom to tailor messaging and adapt interventions to help reduce SSB intake. ## 1. Introduction Sugar-sweetened beverages (SSBs) are the largest sources of added sugars in the U.S. adult diet and include carbonated and non-carbonated soft drinks, fruit drinks, sports drinks, energy drinks, sweetened water, and sweetened coffee/tea drinks that contain added sugars [1]. Excess intake of SSBs is associated with adverse health outcomes such as weight gain and the risk of type 2 diabetes, cardiovascular diseases, and related risk factors [2,3,4,5]. While the consumption of SSBs has decreased over the past decades, SSB intake among U.S. adults remains high [6]. A study reported that $63\%$ of U.S. adults drank SSBs at least once per day in 2010 and 2015 [7]. National Health and Nutrition Examination Survey (NHANES) data between 2011 and 2014 showed that U.S. adults consumed an average of 145 kcal/day of SSBs, corresponding to $6.5\%$ of the total calories with higher intake levels reported among younger age groups and among non-Hispanic Black and Hispanic men and women [8]. There is an abundant body of evidence regarding the high prevalence of SSB intake and sociodemographic characteristics associated with SSB intake among U.S. adults [6,9,10]. However, little is known about when, where, and why individuals consume SSBs with reference to a national data set. While several studies examined eating occasions, locations, and reasons for consuming SSBs, they only included limited response options, were based on older data, and/or had small sample sizes [11,12,13,14,15]. Understanding defined eating occasions, locations, and reasons that influence SSB intake could aid the design of future communication campaigns to decrease SSB intake among U.S. adults. Therefore, the purposes of our study were to describe the eating occasions, locations, and reasons for consuming SSBs among U.S. adults and to explore associations between the outcome variables (i.e., eating occasions, locations, and reasons for consuming SSBs) and sociodemographic and other characteristics among SSB consumers. ## 2.1. Sample and Survey Administration A cross-sectional study was conducted using the Ipsos G&A Omnibus Survey 2021, a nationally representative sample of U.S. adults, via KnowledgePanel®, an online research panel [16]. KnowledgePanel’s recruitment process employs an address-based sampling methodology employing the latest Delivery Sequence Files of the USPS, which is a database with full coverage of all delivery points in the US. Households invited to join the panel were randomly selected from all available households in the US, and persons in the sampled households were invited to join and participate in the panel. A tablet and internet connection were provided (if needed). Participants received unique password-protected log-in information used to complete online surveys. In 2021, the Omnibus survey was sent to 1750 adults on the panel and 1013 adults completed the survey, yielding a response rate of $58\%$. For subset analyses that explored the occasions, locations, and reasons for consuming SSBs, we limited samples to adults who reported consuming any SSBs during the past 7 days ($$n = 658$$). All sampled adults received an invitational message from Ipsos with a link to an IRB-approved study information sheet. Those who consented to participate could then proceed to the online survey. ## 2.2. Measures Outcome variables defined in this study were occasions, locations, and reasons for consuming SSBs on a typical day. Regarding the occasions of SSB intake, we asked the following question: “When do you drink sugary drinks? Check all that apply.” There were 10 responses: [1] at the beginning of the day, [2] at mealtime, [3] between meals/when snacking, [4] at the end of the day, [5] during or after exercising or being physically active, [6] when commuting, [7] for a special event or celebration, [8] for a special meal with family or friends, [9] in social settings/when others are drinking these drinks, and [10] none of the above. Regarding the locations of SSB intake, we asked the following question: “Where do you drink sugary drinks? Check all that apply.” There were 6 responses: [1] at home; [2] at restaurants/bars; [3] in the car; [4] at work; [5] in parks, gyms, or other recreation areas; and [6] none of the above. With regard to the reasons for SSB intake, we asked the following question: “Which of the following are reasons why you drink sugary drinks? Check all that apply.” There were 14 responses preceded by the prompt, “I drink sugary drinks because…”: [1] I enjoy the taste, [2] they make me feel happy, [3] they give me energy, [4] they make my meal better, [5] they are an alternative to a food snack, [6] they satisfy my thirst, [7] they are a habit/part of my routine, [8] they are easy to carry/transport, [9] they are convenient to drink, [10] they are easy to find/buy, [11] I like the carbonation/fizz/bubbles, [12] they are inexpensive/affordable, [13] they satisfy my cravings for something sweet, and [14] none of the above. SSB intake was determined by the following five questions: [1] “During the past 7 days, how many times did you drink REGULAR SODA or POP? These are drinks that contain added sugars, such as Coke, Pepsi, or Sprite.”; [ 2] “During the past 7 days, how many times did you drink SPORTS or ENERGY DRINKS? These are drinks that contain added sugars such as Gatorade, Red Bull, Monster, and Vitamin Water.”; [ 3] “During the past 7 days, how many times did you drink SWEETENED FRUIT DRINKS? These are drinks that contain added sugars such as Kool-Aid, fruit punch, cranberry, and lemonade. Include fruit drinks you made at home and added sugars to such as homemade lemonade.”; [ 4] “During the past 7 days, how many times did you drink SWEETENED COFFEE or TEA? These include drinks that are served hot or cold and drinks that are purchased in cups, cans, or bottles. They include presweetened tea and coffee drinks such as Arizona Iced Tea and Starbucks Frappuccino. These drinks also include coffee and tea drinks you sweetened yourself by adding sugar or honey.”; and [5] “During the past 7 days, how many times did you have a DRINK WITH A SWEETENED MIXER? These include drinks sweetened with mixers such as regular soda or pop, energy drinks, tonic water, simple syrup, or sweetened fruit drinks like cranberry juice cocktail.” The response options for each question were none, 1–3 times/week, 4–6 times/week, 1 time/day, 2 times/day, 3 times/day, and ≥4 times/day. To estimate weekly intake, 1–3 times/week was converted to 2 times/week, 4–6 times/week was converted to 5 times/week, and ≥4 times/day was converted to 4 times/week. To calculate the total frequency of daily SSB intake, we added the responses from five SSB questions. Sociodemographic variables assessed included age (18–34 years old, 35–49 years old, and ≥50 years old), gender, race/ethnicity (non-Hispanic (NH) Black, Hispanic, NH other, and NH White), education level (≤high school, some college, and college graduate), annual household income (<USD 25,000, USD 35,000–USD 74,999, USD 75,000–USD 99,999, or ≥USD 100,000), and marital status (married/domestic partnership and not married). Not married comprised widowed, divorced, separated, or never married. Other covariates included weight status, census region (Northeast, Midwest, South, or West) [17], having children aged < 18 years in the household (yes or no), and self-identified urbanicity (urban, rural, or suburb). BMI was calculated using self-reported weight and height data, and weight status was grouped into underweight/healthy weight (BMI < 25 kg/m2), overweight (BMI 25–<30 kg/m2), or obesity (BMI ≥ 30 kg/m2) [18]. ## 2.3. Statistical Analyses For unadjusted bivariate analyses, descriptive statistics were used to examine sociodemographic characteristics associated with SSB intake as well as occasions, locations, and reasons for consuming SSBs using chi-square tests. In this study, p value < 0.05 is considered as statistically significant. For adjusted analyses, we used a total of 12 multivariate logistic regression models to calculate adjusted odds ratios and $95\%$ confidence intervals (Cis) for sociodemographic characteristics associated with four most frequent responses according to occasions, locations, and reasons. Each model included all sociodemographic characteristics. We used survey procedures to account for sampling weights and sampling design using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). ## 3. Results Nearly 7 of 10 of the surveyed adults reported consuming any SSB (1–6 times) in the past 7 days, and $38\%$ reported doing so at least 7 times during the past 7 days (on average 1 or more times per day). The prevalence of SSB intake more than seven times per week was the highest among adults aged 35–49 years old, NH Black and Hispanic adults, those with ≤high school education, those with the lowest household income (<USD 25,000/year), and with children (<18 years old) in the household ($p \leq 0.05$ based on χ2 tests) (Table 1). Overall, occasions for which SSBs were consumed on a typical day were highest at mealtime ($43\%$), followed by between meals/when snacking ($29\%$), in social settings/when others are drinking SSBs ($26\%$), at the beginning of the day ($20\%$), at special events/celebrations ($17\%$), at special meals with family or friends ($17\%$), at the end of the day ($16\%$), and when commuting ($12\%$). The locations where SSBs were consumed most were at home ($70\%$), followed by restaurants/bars ($40\%$), work ($24\%$), and in the car ($23\%$). The most common reasons for drinking SSBs were enjoying the taste ($56\%$), satisfying cravings for something sweet ($28\%$), liking the carbonation ($21\%$), and satisfying thirst ($20\%$) (Figure 1). Of those who reported consuming SSBs during the past 7 days ($$n = 658$$), the prevalence of the top four occasions, locations, and reasons for SSB intake significantly varied by certain characteristics. In unadjusted bivariate analysis of the top four occasions for drinking SSBs (i.e., at mealtime, between meals/snacking, in social settings, and at the beginning of the day), significant differences were observed according to age, marital status, and weight status regarding the consumption of SSBs in a social setting and according to race/ethnicity concerning consumption at the beginning of the day (Table 2). Based on multivariable logistic regression analysis, younger adults had higher odds of consuming SSBs in social settings (18–34 years old, AOR:2.1, and $95\%$ CI: 1.2–3.6) than older adults (≥50 years old); Hispanic adults had lower odds of consuming SSBs between meals/snacking (AOR:0.4; $95\%$ CI: 0.2–0.9) or at the beginning of the day (AOR:0.3; $95\%$ CI: 0.1–0.6) than NH White adults (Table 2). In an unadjusted bivariate analysis of the top four locations in which SSBs were consumed (i.e., home, restaurant, work, and car), significant differences were present according to age concerning the consumption of SSBs in restaurants; according to age, gender, and having children in the household with respect to the consumption of SSBs at work; and according to age, race/ethnicity, annual household income, and having children in the household with regard to the consumption of SSBs in a car (Table 3). Based on multivariate logistic regression analysis, younger (18–34 years old) and middle-aged adults (35–49 years old) had higher odds of consuming SSBs in restaurants (AOR:1.9, $95\%$ CI: 1.2–3.2; AOR:2.3, and $95\%$ CI: 1.4–3.7, respectively), at work (AOR: 2.9 and $95\%$ CI: 1.6–5.2; AOR:2.7 and $95\%$ CI: 1.5–4.9), and in cars (AOR:2.0 and $95\%$ CI: 1.1–3.7; AOR:3.1 and $95\%$ CI: 1.7–5.6) than older adults (≥50 years old). Women were less likely to consume SSBs at work (AOR:0.6 and $95\%$ CI: 0.4–0.9) than men. Hispanic adults were less likely to consume SSBs in cars (AOR:0.3 and $95\%$ CI: 0.1–0.6) than NH White adults, while those earning USD 50,000–<USD 100,000 were more likely to consume SSBs in cars (AOR:2.3 and $95\%$ CI: 1.4–4.1) than those earning ≥USD 100,000 (Table 3). In an unadjusted bivariate analysis, of the top four reasons (i.e., taste, craving, fizz, and thirst) for consuming SSBs, significant differences existed according to annual household income with respect to the consumption of SSBs because of taste, and according to age with respect to the consumption of SSBs because of craving or fizz (Table 4). Based on multivariate logistic regression analysis, younger adults and middle-aged adults had almost three times higher odds of consuming SSBs due to cravings (AOR:2.8, $95\%$ CI: 1.7–4.8; AOR:2.8, $95\%$ CI: 1.7–4.9) or because they like the fizz/bubble (AOR:2.4, $95\%$ CI: 1.4–4.1; AOR:2.1, $95\%$ CI: 1.2–3.7) than older adults (Table 4). ## 4. Discussion Overall, nearly 7 of 10 of the surveyed U.S. adults consumed any SSB in the past week, and $38\%$ reported doing so at least 7 times during the past week (or on average once a day) in 2021. The daily intake of SSBs in our study was lower than previous findings in which it was shown that $63\%$ of U.S. adults in 2010 and 2015 consumed SSBs at least once a day [7], with significant differences in sociodemographic characteristics. While most adults reported drinking SSBs at least one time in the previous seven days, the groups with the highest levels of consumption include adults aged 35–49 years old, NH Black and Hispanic adults, those with ≤high school education, those with low household income (<USD 25,000/year), and those who have children (<18 years old) in their household. In our study, the four most common occasions at which SSBs were consumed among U.S. adults were [1] at mealtime, [2] between meals/when snacking, [3] in social settings/when others were drinking SSBs, and [4] at the beginning of the day. Similar to our findings, a previous study showed that adults consumed 85 kcal and 66 kcal from SSBs during mealtime and for snacks, respectively [11]. We also found that the top four leading locations in which SSBs were consumed were [1] at home, [2] in restaurants/bars, [3] at work, and [4] in cars. In accordance with our findings, previous studies reported that more than half of the calories obtained from SSBs were consumed at home among U.S. adults [12,19]. Based on 2011–2012 NHANES data, SSB consumers purchased $52\%$ of their SSB-derived calories from supermarkets/grocery stores, $16\%$ from fast-food restaurants, $11\%$ from convenience stores, $8\%$ from full-service restaurants, and $4\%$ from vending machines [20]. It is possible that U.S. adults purchase most of their SSBs from supermarkets/grocery stores and consume SSBs at home. In our study, the top four leading reasons for drinking SSBs among U.S. adults were [1] enjoying the taste, [2] satisfying cravings for something sweet, [3] liking the carbonation, and [4] satisfying thirst. Another study also reported that the taste is one of the most important driving factors of the consumption of SSBs [13]. While communication campaigns have been demonstrated to reduce SSB sales and consumption among adults [21,22,23]. the findings from this research provide additional insights into opportunities for identifying an audience, messaging, and SSB counter-marketing. SSB counter-marketing messages can be placed in locations where SSBs are likely to be purchased (i.e., grocery stores) and consumed (i.e., home). Geofencing or location-based marketing, wherein virtual boundaries are established around a location and a digital notification or advertisement is prompted when a mobile device crosses these boundaries, can be used in grocery stores and select neighborhoods with high SSB consumption [24]. In addition, since SSB consumption is highest at mealtime, SSB counter-marketing messages can be sent through channels likely to be viewed in the home (e.g., internet, streaming media, radio, and television) [25]. Such placements can be concentrated at times when adults are most likely to be making decisions regarding beverage choices associated with meals, and through channels they are most likely to be using at these times (e.g., mobile and tablet recipe applications, YouTube food channels, etc.). Among the adults who drank any SSBs during the past 7 days, most said they did so because they enjoy the taste. Future research could explore the taste appeal of healthy alternatives to SSBs and the effectiveness of messaging (e.g., counter-marketing) that prioritizes the benefits of not drinking SSBs over the appeal of taste. ## 5. Conclusions This study is one of the first studies examining when, where, and why American adults consume SSBs using nationally representative data. However, this study is subject to at least two limitations. First, it is a cross-sectional study and, therefore, causal relationships cannot be determined. Second, the outcome of SSB intake was measured by frequency; thus, the volume of intake was not measured. Third, the questionnaire used in the study was not validated. Lastly, it is possible that the COVID-19 pandemic might have had an impact on the responses to the occasions, locations, and reasons for consuming SSBs. In conclusion, however, the findings from this study can help public health programs, educators, dieticians, and others that engage with individuals and families to consider producing messaging information that may help focused populations reduce SSB intake to support their health. ## References 1. 1. 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Adolesc. Health (2013.0) 52 702-706. DOI: 10.1016/j.jadohealth.2012.11.017 14. McNaughton S.A., Pendergast F.J., Worsley A., Leech R.M.. Eating occasion situational factors and sugar-sweetened beverage consumption in young adults. Int. J. Behav. Nutr. Phys. Act. (2020.0) 17 71. DOI: 10.1186/s12966-020-00975-y 15. Liu J.L., Han B., Cohen D.A.. Associations between eating occasions and places of consumption among adults. Appetite (2015.0) 87 199-204. DOI: 10.1016/j.appet.2014.12.217 16. KnowledgePanel® Methodological Overview 17. Census Regions and Division of the United States 18. 18. National Heart, Lung, and Blood Institute National Institute of Diabetes and Digestive and Kidney Diseases (US) Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence ReportNational Heart, Lung, and Blood Institute in Cooperation with the National Institute of Diabetes and Digestive and Kidney DiseasesBethesda, MD, USA1998. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report (1998.0) 19. Ervin R.B., Ogden C.L.. Consumption of Added Sugars among U.S. Adults, 2005–2010 (2013.0) 1-8 20. An R., Maurer G.. Consumption of sugar-sweetened beverages and discretionary foods among US adults by purchase location. Eur. J. Clin. Nutr. (2016.0) 70 1396-1400. DOI: 10.1038/ejcn.2016.136 21. Schwartz M.B., Schneider G.E., Choi Y.-Y., Li X., Harris J., Andreyeva T., Hyary M., Vernick N.H., Appel L.J.. Association of a Community Campaign for Better Beverage Choices with Beverage Purchases from Supermarkets. JAMA Intern. Med. (2017.0) 177 666-674. DOI: 10.1001/jamainternmed.2016.9650 22. Farley T.A., Halper H.S., Carlin A.M., Emmerson K.M., Foster K.N., Fertig A.R.. Mass Media Campaign to Reduce Consumption of Sugar-Sweetened Beverages in a Rural Area of the United States. Am. J. Public Health (2017.0) 107 989-995. DOI: 10.2105/AJPH.2017.303750 23. 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--- title: Formation and Investigation of Physicochemical and Microbiological Properties of Biocomposite Films Containing Turmeric Extract Nano/Microcapsules authors: - Natalia Stanisławska - Gohar Khachatryan - Karen Khachatryan - Magdalena Krystyjan - Małgorzata Makarewicz - Marcel Krzan journal: Polymers year: 2023 pmcid: PMC9968218 doi: 10.3390/polym15040919 license: CC BY 4.0 --- # Formation and Investigation of Physicochemical and Microbiological Properties of Biocomposite Films Containing Turmeric Extract Nano/Microcapsules ## Abstract In the era of growing plastic consumption, food waste by consumers and overproduction caused by economic reasons, the global goal is to decrease these phenomena. Biocomposite films investigated in the past years are creating a promising future toward ecological, intelligent and active packaging. Due to their unique properties, they can be used in many areas of our life and reduce the constantly increasing pollution of our planet. The aim of our study was to obtain innovative and flexible biopolymer films based on sodium alginate and chitosan, as well as to develop methods for generating nanocapsules with turmeric extract in them. Bionanocomposites were analyzed using UV-VIS, FTIR, photoluminescence spectroscopy and SEM microscopy, while contact angles, surface free energy, particle size (DLS) and zeta potential were determined. The mechanical and colorimetric properties of the produced films were investigated, and the water content, solubility and water absorption were determined. Microbiological tests were carried out to analyze the influence of the produced films on the development of microorganisms. The results of the performed analyses allowed us to confirm the presence of curcumin nano- and microcapsules in the alginate–chitosan composite. Moreover, studies have shown that the structure of polysaccharides does not change during capsule manufacturing. The film with the highest concentration of the capsules showed better parameters in tests of solubility, water content, degree of swelling and mechanical properties. The obtained properties of the developed films allow them to be used as active and intelligent packaging materials, or as their parts. ## 1. Introduction A key element in ensuring food safety is packaging. This is designed to protect food from harmful environmental influences (moisture, temperature), biological hazards (microorganisms, pests) and mechanical damage (impact, pressure). For most food products, it is essential both to ensure the right quality and safety during storage and transport, with the aim of extending the shelf life. Packaging also provides product information, such as composition, origin, nutritional value and minimum durability date. With their good protective properties, plastics dominate the packaging market, being cheap and so far irreplaceable, although they have negative environmental impacts. This is why alternatives are increasingly being sought to produce packaging using substances of natural origin that will reduce harmful effects on the ecosystem [1,2,3,4]. All these factors are driving the development of innovative packaging to maintain and monitor food safety, extend the shelf life and reduce the production of non-biodegradable waste [5]. These are the characteristics of smart packaging, i.e., active and intelligent packaging. Polymer nanocomposites, obtained by dispersing modifiers with dimensions of a few to several hundred nanometres in a polymer matrix, show great potential in this field. Even a small amount of nanoadditives is able to significantly improve the mechanical, optical, electrical, thermal and bacteriostatic properties of a composite material [6,7,8,9]. Selecting the right ingredients for nano/microcomposites and improving their manufacturing methods are important parts of enabling technological development [3]. When it comes to the production of innovative packaging, biopolymer films, which are a mixture of polysaccharides, are gaining popularity. Due to their easy availability and low production costs, they have great potential for applications in many industrial sectors. Furthermore, the diversity of functional groups makes them particularly amenable to modification [9,10,11]. Among the polysaccharides used for manufacturing composites, chitosan and alginate are attracting much attention. In addition to a number of benefits characteristic of all sugar polymers, such as biodegradability, bioavailability and membrane-forming ability, they exhibit a number of unique physicochemical and functional properties [12,13,14,15,16,17,18,19]. They form a good barrier, preventing the loss of volatile flavor compounds and providing protection against microorganisms and viruses. The problem is that their tensile strength, structural strength and physical properties, such as water-vapor permeability after film manufacture, do not fully meet the requirements for food packaging. Promising effects are achieved by the formation of nanocomposites and their combination with biological extracts in the form of nano/microcapsules. Curcumin (1,7-bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione) is the main polyphenol in the rhizome of Curcuma longa, known as turmeric. Turmeric is known worldwide, primarily for its use as a spice included in curry mixes and as a yellow coloring agent. It has also equally valuable, if less popular, preservative, antimicrobial, antioxidant and even medicinal properties. The medicinal effects of turmeric have not yet been confirmed by clinical studies, but the properties of its main active ingredient have been of interest to scientists for many years [20,21,22]. Current research, conducted by many scientists, seeks to determine the scope of curcumin’s action in the prevention and treatment of diseases, with a view to developing effective and non-toxic drugs that could replace modern pharmaceuticals and minimize unwanted side effects of therapy. Curcumin has been proven to have antioxidant, antiviral and anti-inflammatory effects [23,24]. Considering the use of curcumin as an ingredient in active films, its antimicrobial properties have attracted the most attention. Tosati et al. have developed edible films containing curcumin extract, showing that their use in sausages effectively prevents the growth of Listeria innocua [25]. The antibacterial and preservative properties of curcumin offer a whole range of possibilities for its use as an ingredient in active food packaging [26]. Significantly, since it changes color under the influence of pH, it may also become an indicator of product freshness. Its acidic pH gives it a light yellow color, while its color in an alkaline environment is strong red. This property makes it a good indicator of spoilage in, for example, shrimps, which produce volatile amines during decomposition, so that the pH changes to alkaline and the color of the film becomes red [27]. The aim of the present study was to obtain films based on chitosan and sodium alginate, containing curcumin nanocapsules, and to investigate their physicochemical and antimicrobial properties in order to assess their suitability for application in active packaging. The study investigated the antimicrobial and antifungal activity with respect to selected strains of microorganisms and the biodegradation rate of the composites produced in soil. ## 2.1. Materials The following chemical reagents were used to produce the nanocomposites: chitosan (high molecular weight: 310,000–375,000 Da, degree of deacetylation >$75\%$) from shrimp shells, (Sigma-Aldrich, Saint Louis, MI, USA), sodium alginate (Sigma-Aldrich), acetic acid (Chempur, $99.5\%$), glycerine ($99.5\%$, Chempur), deionized water, extra virgin olive oil, turmeric, ethanol $96\%$ p.a. grade. The following microbial strains were used for microbiological testing: Escherichia coli (DSMZ 1116), Aspergillus fumigatus (LOCK 042600), Penicillium expansum (LOCK 0535). The growth mediums used in the experiment: Mueller Hinton Lab-Agar (BIOMAXIMA), DRBC Lab-Agar (BIOMAXIMA). ## 2.2.1. Method for Obtaining Turmeric Extract The ground turmeric rhizome was extracted with ethanol using a Soxhlet extractor. The extraction lasted approximately 5 h. A total of 1 g of the extract contains 4 mg of curcumin. ## 2.2.2. Preparation of Emulsion with Curcumin Nanocapsules A total of 32 g of emulsion containing curcumin nanocapsules was prepared by placing 8 g of demineralized water, 8 g of curcumin extract and 16 g of olive oil in a conical flask (50 mL). This flask was then placed in an ultrasonic bath cooled in an ice bath (at a temperature of 2 °C). The mixture was exposed to ultrasound, at 40 kHz, for 25 min. ## 2.2.3. Preparation of the Chitosan–Alginate Matrix A suspension of 56 g of sodium alginate in 2716 g of water was prepared. The resulting suspension was stirred on a magnetic stirrer at 70 °C, 700 rpm, until a homogeneous gel was obtained. Then 28 g of glycerine was added as a plasticizer. A $2\%$ solution of sodium alginate was thus obtained. Similarly, 1400 g of $1.5\%$ chitosan solution was prepared by weighing out 21 g of chitosan and dissolving it in 1368.5 g of $2\%$ acetic acid. The mixture was placed on a magnetic stirrer (700 rpm, temperature: 70 °C), and then 10.5 g of glycerine was added as a plasticiser. The polymers gels were combined at a 2:1 weight ratio of alginate: chitosan, using a homogenizer (Polytron PT 2500 E, Kinematica AG, Malters, Switzerland). The gel was treated as a polymer matrix in subsequent steps. In order to select the best concentration of the active ingredient, three samples with different concentrations of curcumin were prepared and further analyzed. ## 2.2.4. Preparation of the Films The previously prepared matrix was mixed with demineralized water and emulsion containing curcumin nanocapsules (Table 1), with a homogenizer. The obtained gel was poured into 240 × 350 mm rectangular stainless-steel trays, 200 g each. All the films were dried for two days at room temperature (24 °C) and a humidity of approximately $45\%$. After drying, the films were detached from the trays and placed in tightly sealed string bags until analysis. ## 2.3.1. FTIR Spectroscopy The FTIR spectra of the fabricated composites were analyzed in a wavelength range of 4000–700 cm−1 using a MATTSON 3000 FTIR spectrophotometer (Madison, WI, USA), equipped with a 30SPEC 30 Degree Reflectance accessory (MIRacle ATR, PIKE Technologies Inc., Madison, WI, USA). ## 2.3.2. UV-VIS Spectroscopy The UV-Vis absorption spectra of all the prepared films were analyzed using a Shimadzu 2101 scanning spectrophotometer (Shimadzu, Kyoto, Japan) in the wavelength range 200–700 nm. ## 2.3.3. Photoluminescence Spectroscopy Photoluminescence measurements for the films were carried out at room temperature using a HITACHI F7000 spectrophotometer (Hitachi Co. Ltd., Tokyo, Japan). The emission spectra of the film were measured using an excitation wavelength of 360 nm. ## 2.3.4. Determination of Water Content, Solubility and Degree of Swelling Squares of 2 × 2 cm were cut from the control, Cur-1, Cur-2 and Cur-3 samples and weighed on an analytical balance (m1). The samples were then dried in an oven at 70 °C for 24 h and weighed again (m2). The water content was calculated using the following formulae:[1]Water content [%]=(m1−m2)m1 ·$100\%$ Then, the squares were placed in beakers containing 30 mL of deionized water, covered and stored for 24 h at room temperature (22 ± 2 °C). The remaining water was removed and the samples were dried on the surface with filter paper and then weighed (m3). The remaining samples were dried in an oven at 70 °C for 24 h and then weighed (m4). Three measurements were taken for each sample and the average value of the parameter was determined. These solubility and degree of swelling were calculated using the following equation [28]:[2]Solubility [%]=(m2−m4)m2 ·$100\%$ [3]Degree of swelling [%]=(m3−m4)m3 ·$100\%$ ## 2.3.5. Mechanical Tests The analysis was performed in accordance with ISO Standards [ISO 527-1:2019]. The films were cut into 35 × 6 mm strips and placed in grips. The initial distance between the grips was 20 mm and the peel rate was 2 mm/min. Tensile strength (TS) was calculated by dividing the maximum force at break of the film by the cross-sectional area of the film. The percent elongation at the break (EAB) was calculated by dividing the elongation at the break point by the initial measurement length and multiplying by 100. The results reported were the averages of ten repetitions. ## 2.3.6. Film Color Measurement The color of the film surface was measured with a Konica MINOLTA CM-3500d (Konica Minolta Inc., Tokyo, Japan), using the standard illuminant D$\frac{65}{10}$° observer with a 3 mm diameter window. The results were expressed using the CIELab system. The following parameters were determined: L* (L* = 0 black, L* = 100 white), a—the proportion of green (a < 0) or red (a* > 0), b—the proportion of blue (b < 0) or yellow (b* > 0) [10]. The measurements were taken on a white standard background. The experiment was repeated 5 times. ## 2.3.7. Opacity of Films The degree of UV impermeability of the films was measured by exposing the film sample to absorption of 600 nm light from a Helios-Gamma 100–240 UV/V spectrophotometer [29]. Rectangular film samples were placed directly into the test cell of the spectrophotometer. The empty test cell served as a reference. The opacity (O) of the films was calculated according to the equation:O = A600/x,[4] where A600 is the absorbance at 600 nm and x is the film thickness [mm]. A higher O value indicated a higher degree of opacity/opacification of the sample. The analyses were performed in five replicates. Based on the above analyses, the film with the best properties (Cur-2) was selected and further tests were performed on the control film and the film with the selected concentration. ## 2.3.8. Scanning Electron Microscopy The size and morphology of the prepared nanoparticles were analyzed using a JEOL JSM-7500F high-resolution scanning electron microscope (SEM) (Akishima, Tokyo, Japan). ## 2.3.9. Determination of the Wetting Angles The wetting angles were determined using a Kruss-DSA100M device (Kruss GmbH, Hamburg, Germany). The contact angles of distilled water and pure diiodomethane on the tested surfaces of the polysaccharide films were determined using the stalagmometric method. All the measurements were taken in an environmental chamber, under constant temperature (22 ± 0.3 °C) and constant humidity conditions (40 ± $5\%$). The wetting angles were measured using a device that allowed photographs of the deposited droplet to be taken to a suitable approximation, and then the angles formed by the flat surface of the film and the plane tangent to the surface of the liquid bordering it were determined using DSA4 computer software (Kruss GmbH, Hamburg, Germany). ## 2.3.10. Surface Free Energy (SFE) Surface free energy was investigated using the Owens–Wendt method [30]. Two liquids, bipolar water and polar diiodomethane, were used to characterize the surface free energy of the examined hydrogels. The method assumes that the interactions between the molecules of two substances present in their surface layer are equal to the geometric average of the intermolecular interactions within each substance. A detailed introduction to this method was presented by Rudawska et al. [ 31]. ## 2.3.11. Particle/Aggregate Size (DLS) and Zeta Potential The zeta potential and particle/aggregate sizes were measured using a Malvern Zetasizer Nano ZS with disposable measurement cuvettes (DTS 1065, Malvern). The zeta potential was calculated from the electrophoretic mobility of the particles using the Smoluchowski model. The results were expressed as the average of measurements in 20 consecutive series. All the measurements were taken in aqueous mixtures obtained after dissolving the unwrapped packaging film in 1 wt. % acetic acid (this method was used to completely dissolve the chitosan). Film samples of 0.1 g (± 0.01 g) were dissolved in 5 mL of $1\%$ wt. aqueous acetic acid solution. A magnetic stirrer stirred the mixtures for 1 h until the films were completely dissolved. ## 2.3.12. Antimicrobial Activity of the Films (Disc Diffusion Test) The antimicrobial activity of the alginate–chitosan film with curcumin was tested against the bacteria Escherichia coli (DSMZ 1116) and the fungi Penicillium expansum (LOCK 0535) and Aspergillus fumigatus (LOCK 04260), which are common causes of the microbial contamination of food products. For the determination of antimicrobial activity, the spread plate technique was employed for pure bacterial cultures (OD 8.3 × 108 jtk/cm3) or plate cultures of filamentous fungi with a sterile swab onto Mueller Hinton Lab-Agar medium (BIOMAXIMA)—E. coli or DRBC Lab-Agar medium (BIOMAXIMA)—molds. Next, discs cut from the test films with a diameter of 1 cm were placed on the surface of the inoculated culture medium (Figure 1). The negative control was the film without curcumin, while the positive control was commercial tissue paper discs soaked in Penicillin G 10U (BIOMAXIMA) (bacteria) or Nystatin 100U (BIOMAXIMA) (molds). After the incubation time (24–48 h, 37 °C (bacteria), 72–96 h, 25 °C (fungi)), the diameter of the zones of inhibition (mm) was measured. All analyses were performed in triplicate. ## 2.3.13. Evaluation of the Susceptibility of the Tested Films to Soil Microorganisms Degradation Preliminary tests aimed at determining the susceptibility of the tested films to degradation with the participation of soil microorganisms were performed by cutting 1 × 4 cm strips of the control film and the film with the active substance, and then placing them in glass dishes filled with garden soil so that 1 cm of the film in question protruded above the soil surface. Prepared in this way, the samples were placed in a dark plastic bag together with a beaker of distilled water to preserve the appropriate humidity during the process. The tightly closed bag was stored at room temperature. All analyses were performed in triplicate. The degree of structure damage of the film was assessed after 30 and 60 days of storing the samples. Macroscopic changes (etchings, color changes, etc.) were determined using a MSZ 200 stereo microscope. ## 2.3.14. Statistical Analysis Experimental data were analyzed in terms of variance, with a confidence level of $$p \leq 0.05$$, using a Statistica v. 8.0 (Statsoft, Inc., Tulsa, OK, USA). The Fisher test was used to determine statistically the significant differences. ## 3. Results and Discussion Measurements of the UV-Vis, ATR-FTIR and emission spectra were performed to compare the degree of encapsulation. In order to determine possible chemical changes and interactions between the components in the obtained films, ATR-FTIR spectra were performed, as shown in Figure 2. The control film consists mainly of chitosan and sodium alginate. The other composites contained the addition of a nanoemulsion containing curcumin and olive oil. The ATR-FTIR spectra of the resulting composites contained characteristic bands for chitosan: bands at 3000 to 3500 cm−1 represent the stretching vibrations of the free hydroxyl groups and the symmetric and asymmetric N-H stretching bonds in the amine group; bands at 1078 and 1024 cm−1 are associated with the stretching vibrations of the -OH, 3′-OH and 5′-OH groups; extended bands at 1600 cm−1 (from 1550 to 1640 cm−1) are related to the stretching vibrations of the C=O group and deformation vibrations of the -NH group2; bands at 1406 cm−1 correspond to the symmetric deformation vibrations of the CH2 group; bands at 1022 cm−1 and 1300 cm−1 correspond to the C-O bonds and the amide group [32]. The following bands characteristic of sodium alginate were also observed in the spectra: 3230 cm−1 of the OH group adjacent to the amide group; and 1600 cm−1 and 1406 cm−1 asymmetric and symmetric vibrations of the COO- group, respectively. Characteristic saccharide bands were also present: at wavelengths of 1300, 1092 and 820 cm−1, they correspond to the C-O stretching bond vibrations, and the peak at 1032 cm−1 shows the CO-C bonds [33]. The characteristic peaks at about 3400 cm−1 (phenolic O-H stretching vibrations) were observed in composites containing curcumin, 2930 and 2850 cm−1 (methyl (-CH3) and methylene (-CH2) symmetric and asymmetric vibration), 1628 cm−1 (C=C stretching vibration in aromatic molecules), 1597 cm−1 (benzene ring stretching vibrations), 1740 cm−1 (C=O vibrations), 1510 cm−1 (C=C vibrations in the aromatic system), 1428 cm−1 (C-H deformation vibrations), 1278 cm−1 (aromatic C-O stretching vibrations), 1024 cm−1 (C-O-C stretching vibrations) [34,35,36,37]. The observed changes in the spectra of the obtained nanocomposites are due to the superposition of oil-derived bands. Strong absorption bands in the 3000–2800 cm−1 range are caused by the corresponding oil-derived C-H stretching vibrations. Stretching vibrations of the methylene (–CH2–) and methyl (–CH3) groups can be observed at the frequencies of 2930 and 2850 cm−1, respectively. The large peak around 1740 cm−1 is due to the stretching vibration of the C=O double bond of the oil, which overlaps with the peak from curcumin [38,39,40]. The FTIR spectra of the films with added curcumin nanocapsules do not differ in shape from those of the control film. This suggests that, as expected, the biopolymers are a carrier for the curcumin nanocapsules. Figure 3 shows the absorption spectra in the ultraviolet and visible range of the resulting films. With the addition of curcumin, absorbance increases over the entire range tested, but particularly in the range from 260 to 380 nm. The Cur-2 film has the highest absorbance. Pure curcumin shows characteristic absorption bands at 250 nm and 427 nm due to π-π* and n-π* transitions. By contrast, encapsulation causes a strong absorption band to appear at about 275 nm. These results are consistent with those obtained by Omrani et al. [ 41], who encapsulated curcumin in cyclodextrins. They suggest that the Cur-2 film has the highest concentration of capsules, so the ratio of nanoemulsion to polysaccharides is the most optimal in this composite. Figure 4 shows the emission spectra of the resulting films when excited with a wavelength of 360 nm. One can see a characteristic emission band for the turmeric nanocapsules at 435 nm. Numerous studies have shown that encapsulating turmeric increases the emission intensity. Bechnak et al. encapsulated turmeric in polyethylene [42], showing that the relative fluorescence yield increased by a factor of 6 in the nanocapsules. Rahimzadeh et al. [ 43] synthesized curcumin nanocapsules and also found an increase in emission intensity. The relationship between emission intensity and concentration is linear only at very low concentrations, while with particle aggregation excitation radiation is unable to excite all the molecules present. The photoluminescence results obtained correlate with the UV-VIS results, indicating that the optimum concentration for curcumin encapsulation occurs in the Cur-2 composite, which has the highest emission intensity. Table 2 shows the results of the measurements of the water content, solubility and degree of swelling of the control film and the film with curcumin added at different concentrations. The control film has the highest water content, while the Cur-2 has the lowest. The water content in the control film reaches $17\%$, decreasing slightly for the Cur-1 and Cur-3 film ($15\%$ and $12\%$). For the Cur-2 film, it is less than $10\%$. The solubility of the films for all samples with the addition of curcumin is not statistically different and is around $13\%$. This parameter is significantly higher for the control film ($24.04\%$). This means that the addition of the active substance has a positive effect on reducing the solubility of the film. The control film had the highest value for the degree of swelling, around $31\%$. The samples with the addition of curcumin nanocapsules had a much lower degree of swelling: $18.63\%$ for the Cur-1 film, while the Cur-3 and Cur-2 films were not statistically different at $12.57\%$ and $11.22\%$, respectively. The best parameters for use as food packaging were the lowest possible water content, solubility and degree of swelling. Among the films tested, the Cur-2 film has the most promising properties. Table 3 shows the mechanical properties of the film at 25 °C and $25\%$ humidity for the control film and the film with curcumin added at different concentrations. The parameters studied were thickness, tensile strength and percentage elongation at the break point. The results illustrate that the thickness of the film increases as the concentration of the added active substance increases. For the control film, it is 0.09 mm, and for the Cur-3 film it is almost twice as much. The film thickness may enhance the opacity of samples [44], due to the increase in the solid part in the sample [10]. Tearing strength (TS) is the force required to break a section of film of specified dimensions, given in Pascals. The higher the tearing strength, the better the packaging material of the tested sample. In the case of the composites tested, the addition of curcumin adversely affects this parameter, the result for the Cur-1 and Cur-3 films being significantly lower than for the control film. Concentration 2 (Cur-2 film), however, does not cause such a large decrease; the result achieved is statistically equal to the strength of the control film. The percent elongation at the break (EAB) determines the extensibility of a given material, indicating by what percentage it can be stretched before it breaks. The elasticity of a given material is not a key property for determining whether it will perform well as packaging; still, in combination with a specific tearing strength, it provides a good basis for characterizing a given composite. For the samples tested, the elongation at break for the control film is $34.5\%$. The addition of curcumin at concentrations of 1 and 2 (Cur-1 and Cur-2 films) causes this value to drop to around $29\%$. However, it can be observed that an increase in this parameter is observed for the Cur-3 film compared to the control film (approximately $40\%$). From the perspective of the application of the material in packaging, the most important of the parameters studied is its high tearing strength combined with low thickness. The control and Cur-2 films have such parameters. Table 4 shows the results of the color measurement of the individual films in the Lab* color space and the transparency measurement. The color of a material is defined by three components. The L* component describes the brightness (luminance) of the color from 0 to 100, where 100 is the brightest color. This component for the materials tested varies between 94.14 and 97.16. This means that all the films tested are relatively bright. The component a* represents the proportion of green or red in the analyzed color, with green shades having a negative value and red shades having a positive value. The component a* represents the proportion of blue or yellow in the analyzed color, with blue shades having a negative value and yellow shades having a positive value. The scales of the a* and b* parameters range between -150 and +100, and -100 and +150. The greatest differences can be seen in the values of the b* component. Curcumin is a yellow pigment, so the positive value of this parameter increases for films with added nanocapsules. Comparing the images of the films (Figure 5), we can observe that the last two samples (Cur-2 and Cur-3) with a higher amount of encapsulated turmeric extract were more yellow. These samples also had different values for the a* component. Statistically significant differences were observed between the films. Transparency defines the degree of light transmission through the material without scattering. The addition of curcumin in all concentrations reduces the transparency of the film. The sample with the highest opacity was Cur-1, which is also confirmed by the film images (Figure 5). For food packaging, light-sensitive foodstuffs require less transparency, so this phenomenon may be desirable. However, it is also important to make the product visible through the packaging, so the evaluation of the film’s properties depends on its intended use [10]. The increase in the opacity of films with the addition of emulsions containing nanoparticles can be caused by the increase in film thickness, which is due to the increase in the carrier concentration [10,45,46,47]. The best parameters (degree of encapsulation, mechanical and optical properties) were displayed by the Cur-2 film, which was selected for further study. Figure 6 displays the SEM images of the finished Cur-2 film’s surface (Figure 6a) and cross-section (Figure 6b). A generally uniform surface with multiple undulations and no discernible fissures is observed. Cross-sectional imagery of the interior structure (Figure 6b) reveals a spongy one. Figure 7 shows the microscopic images of the Cur-2 film taken at magnifications of ×1700 (Figure 7a) and ×5000 (Figure 7b). The images show the presence of capsules evenly distributed in the polysaccharide matrix and the presence of micropores in the internal structure. Another photograph (Figure 8) shows a microscopic image of the film in another area at the magnifications of ×3000 (Figure 8a) and 25,000× (Figure 8b). At a higher magnification (Figure 8b), the polysaccharide layer was broken and a single spherical capsule of 521 nm was visible. The surface free energy was determined by the Owens–Wendt method [28,31]. The surface free energy of solids is an important thermodynamic parameter, making it possible to infer the adsorption of different substances on the surface of a solid and at the solid–liquid interface. The surface free energy of a solid also determines the level of adhesion of a liquid to its surface. The results of the measurements are shown in Table 5. The wetting angles for a polar liquid, such as water, indicate the hydrophilicity or hydrophobicity of the surface. The results show that the addition of the nanoemulsion with curcumin increases the hydrophilicity of the film. The wetting angle for the control sample is close to 80°, while for the sample with the active substance it decreases to 68°. The measurement for a dispersion liquid such as diiodomethane (DIM) indicated a much smaller wetting angle, 53° and 41°, respectively. The surface free energy determined based on these angles is the sum of two components: polar and dispersion. The addition of nanocapsules with curcumin results in an increase in both components of the surface free energy. It can be therefore assumed that the Cur-2 film will be better suited for bonding or printing on it, a factor that can significantly facilitate its use in packaging. The zeta potential (ζ), or electrokinetic potential, is measured to determine the stability of the resulting nanoparticles. With an increase in the absolute value of the zeta potential, generally colloidal particles show good dispersion properties, at the same time as the electrostatic repulsion forces increase. When the zeta potential is close to zero, they become so unstable that they are prone to form aggregates. It is assumed that a result of more than +/−30 mV means that the test sample can be considered stable. The results of the measurements are shown in Table 6. The zeta potential values for the tested materials are −41.3 mV for the control film and −37.8 mV for the Cur-2 film, respectively. For both samples, the zeta potential is high enough to conclude that they are not prone to form aggregates. The addition of curcumin nanocapsules causes a slight decrease in this value. The table also includes the result of the particle size measurement using the DLS method. The particle size determined by scanning electron microscopy is about 500 nm, while the average particle size determined by DLS is much larger. It should be notated that the DLS result provides the size of the combination of nanostructures in the polysaccharide environment and, in general, the measurement results are much larger than the corresponding results obtained from electron microscopy methods. Based on the measurements of the size of the zones of inhibition (Table 7), only the film containing curcumin nanocapsules (Cur-2) showed antibacterial properties. The zones of inhibition obtained for E. coli were even greater than for the positive control (penicillin G disc) (Table 7). This confirms the antimicrobial properties of curcuminoids. Curcumin nanocapsules, slowly released from the polymer-lipid envelope, could easily penetrate the thin murein barrier in the E.coli cell wall and effectively inhibit its growth [48]. Varying activity results of the tested films were obtained for the tested filamentous fungi (P. expansum, A. fumigatus). Effective growth inhibition was obtained solely for Aspergillus fumigatus, the measured zone being comparable to the one induced by the antibiotic (positive control). The effect was significantly weaker in relation to Penicillium expansum (Table 7). The antimicrobial properties of curcumin are related to its ability to disrupt the cell wall integrity of the microorganisms, synthesize reactive oxygen species and induce cellular apoptosis [49,50]. Figure 9 shows the biodegradation effects of the tested films. After cleaning the film surface from the soil, the morphological changes in the form of cavities and intense surface discoloration were observed initially (after 30 days) on both the control film (Figure 9b) and the Cur-2 film (Figure 9c). After 60 days of storage in soil, the Cur-2 film had decomposed practically completely (Figure 9d). In most cases, attempts to develop new biocomposites do not include degradation tests being carried out. It is difficult to perform reproducible tests of polymer biodegradation under laboratory conditions since the natural environment usually contains complex mixtures of enzymes and microorganisms that are not necessarily conducive to the degradation of a particular polymer tested, and the composition of the soil depends not only on its place of origin but also on the current weather conditions or season. All these factors vary according to the environment. Further, more detailed studies, taking into account different environments, are needed for a more precise determination of the time required for the decomposition of the prepared bionanocomposites. ## 4. Conclusions Bionanocomposites with a smooth surface and porous structure consisting of a chitosan–alginate matrix and nanocapsules containing curcumin of approximately 500 nm were successfully produced. The analysis of FTIR, absorption and emission spectra indicate that the chemical structure of polysaccharides does not change during the production of capsules, there are no strong interactions between the curcumin nanoemulsion and the polysaccharide matrix, and the concentration of the nanoemulsion in the Cur-2 film is the most optimal. The aforementioned sample also showed better parameters in tests of solubility, water content, degree of swelling and tearing strength combined with low thickness. The film produced has an inhibitory effect on the bacteria Escherichia coli, on the mold Aspergillus fumigatus and, to a lesser extent, on Penicillium expansum. These microorganisms are often present in food, so the film can serve well as active packaging. In the soil, the tested films entirely disintegrated after 60 days. The research carried out confirms that the presence of curcumin nanocapsules improves the properties of the chitosan–alginate film, allowing its use in packaging. 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--- title: 'SARS-CoV2 Infection and Comorbidity in Inmates: A Study of Central Italy' authors: - Emma Altobelli - Francesca Galassi - Marianna Mastrodomenico - Fausto Frabotta - Francesca Marzi - Anna Maria Angelone - Ciro Marziliano journal: International Journal of Environmental Research and Public Health year: 2023 pmcid: PMC9968227 doi: 10.3390/ijerph20043079 license: CC BY 4.0 --- # SARS-CoV2 Infection and Comorbidity in Inmates: A Study of Central Italy ## Abstract Background and Objective: The presence of multiple chronic diseases is associated with an increase in mortality when related to COVID-19 infection. The aims of our study were: (i) to evaluate the association between the severity of the COVID-19 disease, defined as symptomatic hospitalized in prison or symptomatic hospitalized out of prison, and the presence of one or more comorbidities in two prisons in central Italy: L’Aquila and Sulmona; (ii) to describe the profiles of inmates using multiple correspondence analysis (MCA). Methods: A database was created including age, gender and clinical variables. The database containing anonymized data was password-protected. The Kruskal–Wallis test was used to evaluate a possible association between diseases and the severity of COVID-19 stratified by age groups. We used MCA to describe a possible characteristic profile of inmates. Results: Our results show that in the 25–50-year-old age group (COVID-19-negative) in the L’Aquila prison, $\frac{19}{62}$ ($30.65\%$) were without comorbidity, $\frac{17}{62}$ ($27.42\%$) had 1–2 comorbidities and only $3.23\%$ had >2 diseases. It is interesting to note that in the elderly group, the frequency of 1–2 or >2 pathologies was higher than in the younger group, and only $\frac{3}{51}$ ($5.88\%$) inmates did not have comorbidities and were COVID-19 negative ($$p \leq 0.008$$). The MCA identified the following profiles: the prison of L’Aquila showed a group of women over 60 with diabetes, cardiovascular and orthopedic problems, and hospitalized for COVID-19; the Sulmona prison presented a group of males over 60 with diabetes, cardiovascular, respiratory, urological, gastrointestinal and orthopedic problems, and hospitalized or symptomatic due to COVID-19. Conclusions: our study has demonstrated and confirmed that advanced age and the presence of concomitant pathologies have played a significant role in the severity of the disease: symptomatic hospitalized in the prison; symptomatic hospitalized out of the prison. ## 1. Introduction It is known that in prisons, the risk to the health of prisoners is greater if there is overcrowding and if they present comorbidities [1,2,3]. Italy was among the first countries in Europe to be affected by the COVID-19 pandemic [4,5,6,7]. This required the definition and implementation, in the shortest possible time, of management and prevention protocols, both for the Italian and prison populations. By definition, prisons represent closed, highly complex community structures, whose health services in Italy are managed by the Ministry of Health [8]. Italy is in third place in Europe in terms of prison population density, with an occupancy rate of $115\%$ [9] and inadequate infrastructure in general. As of 22 November 2022, there are 55,734 inmates in 189 institutes, compared with a regulatory capacity of 50,942 places [10]. It is known that the prison population does not, on average, enjoy the same state of health as the rest of the free population [11]. Several studies have shown that prisons, also due to socio-economic and health determinants [12,13], are characterized by a higher prevalence of chronic diseases and an increased risk of contagion from infectious diseases, including COVID-19, compared to non-confined contexts [14]. The presence of a pathological condition, even if not serious, was found in $67.5\%$ of the total number of prisoners present in 57 prisons in Italy, and an average of 2.2 diseases was reported for each prisoner [15,16]. Furthermore, a high number and the presence of specific comorbidities affect the immune response in patients who test positive for COVID-19 infection [17], presenting an increased risk of in-hospital mortality following infection. The presence of multiple pre-existing chronic diseases is significantly associated with an increase in the probability of mortality compared to the absence of comorbidities [18]. Age is also closely related to progression and a poor prognosis in patients with COVID-19 infection [19,20]. The aims of our study were: (i) to evaluate the association between the severity of the COVID-19 disease defined as symptomatic hospitalized in prison or symptomatic hospitalized out of prison, and the presence of one or more comorbidities in two prisons in central Italy: L’Aquila and Sulmona; (ii) to describe the profiles of inmates using multiple correspondence analysis. ## 2. Materials and Methods A database was created including data from medical records of both prisons relating to the period under study: November 2020–August 2022. The authorization to process the data was obtained on 3 August 2021 (protocol number $\frac{0169071}{21}$). Furthermore, the study protocol was approved by the Internal Review Board of the University of L’Aquila (protocol number 41795) on 5 April 2022. In using the data, the privacy and confidentiality of the inmates were guaranteed. For this purpose, technical and organizational measures were put in place in compliance with the principle of data minimization and anonymization. No ethnic or other identification information was codified. The database containing anonymized data was password-protected. L’Aquila houses prisoners according to the law of 10 October 1986, n. 663, art. 41 bis, which identifies the so-called “hard prison” in Italy. It is a form of restrictive detention intended for individuals who commit offenses related to organized crime. The 41 bis has 2 main characteristics: the limitation of the outdoor activity of the detainees to a maximum of 2 h and a maximum number of 3 inmates with whom an inmate can socialize. The 41 bis is applied for very long periods, even $\frac{20}{30}$ years, a real form of isolation. Sulmona houses prisoners of the “high security circuit” known as 1 and 3. Furthermore, it also houses collaborators of justice (art. 21 of the Penitentiary Regulations). Prison circuit 1 is dedicated to prisoners who have committed Mafia crimes; circuit 3 houses inmates who have committed crimes related to drug dealing. The variables available regarding inmates were: age at the time of enrollment in the cross-sectional study, gender, COVID-19-positive and -negative cases, the severity of the disease: symptomatic hospitalized in the prison or symptomatic hospitalized out of the prison and the presence or absence of comorbidities classified according to the number of diseases (absence, 1–2 and >2 comorbidities). The groups of comorbidities analyzed were: respiratory, cardiovascular, urological, orthopedic, gastroenteric diseases, type 2 diabetes, renal chronic insufficient, hematologic diseases, cancers, immunodeficiency and HIV. Age was divided into 3 classes: 25–50, 51–60 and >60 years old. The following tests were used to diagnose SARS-CoV2 infection: Hologic, COPAN Italia S.p. A. (molecular test) and a SARS-CoV2 Antigen Rapid test kit. For the L’Aquila prison alone, stratification by sex was carried out to evaluate the distribution of frequency, because Sulmona contains only males. Subsequently, we used multiple correspondence analysis (MCA) to describe a possible characteristic profile of inmates. We have included the variables described above in the MCA statistical models. The database was managed using Microsoft Excel software. The mean age was calculated as mean ± standard deviation (SD). The Kruskal–Wallis test was used to evaluate a possible association between the number of diseases and the severity of COVID-19, stratified by the age groups considered. Multiple correspondence analyses were applied using the Facto Mine R library of statistical software R (Version R 4.2.2, R Foundation for Statistical Computing, Vienna, Austria). ## 3. Results The distribution of the prison population in the two penitentiary institutes of the Abruzzo region is shown below. The population of the Sulmona prison is made up of 424 males, with mean age and SD of 52.7 ± 11.18. The L’Aquila prison is made up of a population of 150 males ($92.02\%$) and 13 females ($7.98\%$), with a mean age and SD of 51.81 ± 11.07 for males and 63.31 ± 8.66 for females. Moreover, L’Aquila showed a mean age for males of 53.19 ± 9.07 vs. 50.61 ± 11.07, respectively, positive vs. negative ($$p \leq 0.15$$), and for females, 61.80 ± 9.05 vs. 63.31 ± 8.66 ($$p \leq 0.27$$), respectively, positive vs. negative; Sulmona showed the mean age was for males 56.80 ± 11.29 vs. 51.11 ± 10.75, respectively, positive vs. negative ($$p \leq 0.001$$). In L’Aquila, $49.1\%$ of the prisoners tested were positive for COVID-19, of which $38.7\%$ belonged to the age group between 25 and 50 years old, $66.0\%$ to the group between 51 and 60 years and $45.1\%$ belonged to the highest group of >60 years. In Sulmona, the percentage of positives was $28.3\%$, $17.6\%$ of them were in the 25–50-year-old age group, $31.7\%$ in the 51–60 age group and $43.5\%$ in those over 60 years of age. Table 1 shows the frequencies of the variables analyzed relating to the inmates of both prisons of L’Aquila and Sulmona. Our results show that the most common comorbidities in inmates of L’Aquila are cardiovascular and orthopedic diseases, whereas for Sulmona, they are orthopedic and vascular diseases. Table 2 shows the following stratified frequency distributions for the prisons of L’Aquila and Sulmona and for age groups according to the number of diseases and their severity due to COVID-19. Subsequently, the association between the severity of COVID-19 disease and the presence of comorbidities, stratified by age group, was evaluated for the inmates of both prisons (Table 3). Regarding the 25–50-year-old age group (COVID-19-negative) in L’Aquila, $\frac{19}{62}$ ($30.65\%$) were without comorbidity, $\frac{17}{62}$ ($27.42\%$) had 1–2 comorbidities and only $3.23\%$ had >2 diseases. In this age group, only $8\%$ were hospitalized out of the prison despite the presence of >2 pathologies ($$p \leq 0.0001$$). On the other hand, in the 51–60y age group, there was no statistically significant association between disease severity and the number of comorbidities ($$p \leq 0.17$$). Instead, it is interesting to note that in the elderly group, the frequency of 1–2 or >2 pathologies was higher than in the younger group, and only $\frac{3}{51}$ ($5.88\%$) of inmates did not have comorbidities and were COVID-19 negative ($$p \leq 0.008$$). It is important to underline that only $\frac{1}{50}$ ($2.0\%$) of the 51–60y group with >2 diseases needed an external hospitalization. In total, for both prisons, external hospitalizations were 11. Analyzing the results relating to the prisoners in Sulmona, no statistically significant differences were observed between the severity of the COVID-19 disease and the presence of comorbidities in any age group, respectively, $$p \leq 0.73$$, $$p \leq 0.33$$ and $$p \leq 0.29$$/163 ($11.04\%$) (Table 3). Therefore, both in L’Aquila and in Sulmona, in the group of restricted subjects with >2 concomitant pathologies, there was a rising trend of infection due to an increase in age. None of the inmates of L’Aquila and Sulmona were admitted to intensive care. Finally, we conducted the analysis of multiple correspondences to identify groups of prisoners who developed the most serious forms of COVID-19: the prison of L’Aquila showed a group made up of women, aged over 60, with diabetes, with cardiovascular and orthopedic problems and hospitalized for COVID-19; the Sulmona prison presented a group of inmates aged over 60 with diabetes, with cardiovascular, respiratory, urological, gastrointestinal and orthopedic problems and hospitalized for COVID-19 (Figure 1 and Figure 2). ## 4. Discussion The SARS-CoV-2 health emergency that affected the whole world inevitably had repercussions on the prison situation. The perception that penitentiary facilities are isolated and separated environments from the rest of society—and, therefore, more protected from the risk of contagion—has proved to be illusory and wrong, colliding with reality and everyday prison life [21]. The restricted population is considered “fragile”, not only due to some determinants such as the condition of the deprivation of personal freedom, overcrowding and low sanitation standards but also because it has a greater burden of chronic, infectious diseases at an advanced age which places it at a higher risk of infection [22,23,24]. Our study has demonstrated and confirmed that advanced age and the presence of concomitant pathologies have played a significant role in the severity of the disease: symptomatic hospitalized in prison and symptomatic hospitalized out of prison. We therefore identified characteristic profiles of inmates, determined through the multiple correspondence analysis, confirming in both institutions at an age greater than 60 the presence of cardiovascular diseases, diabetes mellitus and orthopedic disorders. In Sulmona, the profile is also characterized by pathologies affecting the respiratory and urological systems. The conditions of multimorbidity, commonly present among prisoners, appear more often with an early onset and with greater severity than in the general population [25] since the stress inmates are subjected to can favor the onset of pathologies [20]. In prisoners, the stress due to the rehabilitation imposed by the prison environment leads to an acceleration of aging, to the point that some 50-year-olds present psycho-physical conditions more typical of subjects over 65 [26]. The odds of a 50-year-old prisoner becoming ill would be the same as for a 60-year-old outside of the prison [27]. In Italy, the health of the prison population was the subject of a multi-center study financed by the Ministry of Health, which involved over 15,000 prisoners present in 57 structures [15]. This study showed that $67.5\%$ of people detained in Italy are affected by at least one disease, and it is important to underline that our results are in line with the national data, $70.4\%$. Furthermore, it is important to highlight that in the prison environment, all the main risk factors for cardiovascular diseases are found: cigarette smoking, a diet rich in fats, a sedentary lifestyle, overweight and obesity [28]. A more recent study [20], carried out on the Italian prison population, demonstrated that heart disease had the highest percentage in the sample of elderly prisoners. S.A. Kinner et al. state that in prisoners, as in the general population, the number of comorbidities increases with age [22,29]. Regarding the SARS-CoV2 infection, the international literature has shown a more severe form among elderly people and in those with comorbidities [6,26]. These results are largely confirmed in our study. In fact, it has emerged that there is a statistically significant association between the number of comorbidities and the severity of the COVID-19 disease in reference to different age groups. In the 25–50-year-old age group, $30.65\%$ were negative and without comorbidities. On the contrary, only $5.88\%$ of older prisoners did not have comorbidities and were COVID-19-negative. In particular, in L’Aquila and Sulmona, the inmates who required hospitalization in or out of the same institution were over 60 years of age, with one or more comorbidities. However, the infections did not cause intensive care admissions or deaths. A limitation of our study is that it was not possible to compare our comorbidity data with those of the Italian population, with those of the 12 Italian prisons (41 bis) or with those of maximum-security institutions because they are not available in open source. Another limitation regards the problem of overcrowding which was not possible to evaluate because those two correctional settings were very different with respect to the rest of Italy for these types of criminal provisions. In fact, L’Aquila is a form of detention with a 2 h outdoor maximum limitation for the detainees and a maximum number of three inmates that an inmate can socialize with. Sulmona is a “high security circuit” that consists of limitations aimed at reducing the frequency of contact with the outside world. It is therefore a preventive tool that aims to “isolate” the person from the rest of the criminal organization. Therefore, it is not possible to hypothesize which measures to adopt for the health protection of inmates. ## 5. Conclusions Our study has demonstrated and confirmed that advanced age and the presence of concomitant pathologies have played a significant role in the severity of the disease: symptomatic hospitalized in prison and symptomatic hospitalized out of prison. 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--- title: Association of Sonographic Sarcopenia and Falls in Older Adults Presenting to the Emergency Department authors: - Thiti Wongtangman - Phraewa Thatphet - Hamid Shokoohi - Kathleen McFadden - Irene Ma - Ahad Al Saud - Rachel Vivian - Ryan Hines - Jamie Gullikson - Christina Morone - Jason Parente - Stany Perkisas - Shan W. Liu journal: Journal of Clinical Medicine year: 2023 pmcid: PMC9968231 doi: 10.3390/jcm12041251 license: CC BY 4.0 --- # Association of Sonographic Sarcopenia and Falls in Older Adults Presenting to the Emergency Department ## Abstract Background and Objective: To determine the association between point-of-care-ultrasonography (POCUS)-measured sarcopenia and grip strength, as well as the history of prior-year falls among older adults admitted to the emergency department observation unit (EDOU). Materials and Methods: This cross-sectional observational study was conducted over 8 months at a large urban teaching hospital. A consecutive sample of patients who were 65 years or older and admitted to the EDOU were enrolled in the study. Using standardized techniques, trained research assistants and co-investigators measured patients’ biceps brachii and thigh quadriceps muscles via a linear transducer. Grip strength was measured using a Jamar Hydraulic Hand Dynamometer. Participants were surveyed regarding their history of falls in the prior year. Logistic regression analyses assessed the relationship of sarcopenia and grip strength to a history of falls (the primary outcome). Results: Among 199 participants ($55\%$ female), $46\%$ reported falling in the prior year. The median biceps thickness was 2.22 cm with an Interquartile range [IQR] of 1.87–2.74, and the median thigh muscle thickness was 2.91 cm with an IQR of 2.40–3.49. A univariate logistic regression analysis demonstrated a correlation between higher thigh muscle thickness, normal grip strength, and history of prior-year falling, with an odds ratio [OR] of 0.67 ($95\%$ conference interval [$95\%$CI] 0.47–0.95) and an OR of 0.51 ($95\%$CI 0.29–0.91), respectively. In multivariate logistic regression, only higher thigh muscle thickness was correlated with a history of prior-year falls, with an OR of 0.59 ($95\%$ CI 0.38–0.91). Conclusions: POCUS-measured thigh muscle thickness has the potential to identify patients who have fallen and thus are at high risk for future falls. ## 1. Introduction Falls are common and frequently result in serious traumatic injuries among older adults [1], leading to high morbidity, mortality, and hospital costs [2,3]. Emergency department (ED) visits for fall-related injuries among older adults are increasing [4]. Recurrent falls are associated with worse outcomes, such as higher health care costs, increased fracture risk, and long-term nursing home admissions [5]. Identifying patients at high risk for falls and applying individualized interventions to prevent falls are key factors in reducing fall-related mortality and hospital costs [3,6]. Sarcopenia is the age-related progressive and generalized loss of skeletal muscle mass associated with falls [7]. Recent studies show that exercise and nutritional interventions can prevent sarcopenia-related falls [8,9,10,11]. New methods, such as grip strength [12,13] and ultrasounds [14], have been identified as novel methods to identify sarcopenia. Such novel methods could be useful for diagnosing sarcopenia in the ED. US is emerging as a promising alternative tool to assess sarcopenia through direct measurement of muscle mass. Multiple studies have shown US measurements of muscles correlate with sarcopenia [14,15,16,17,18]. An ultrasound would be a very logical tool to use in the ED to assess sarcopenia and, hence, a novel tool to predict future falls. Ultrasounds are widely used in the ED, easy to use, and would be an inexpensive, rapid surrogate method of measuring sarcopenia that potentially could be scaled up for use not only in the overcrowded, chaotic, urban ED environment, but also in rural EDs, resource-limited settings, and potentially during home hospital visits. Shah conducted a prospective study of geriatric ED trauma patients and demonstrated a correlation between sarcopenia, measured by the limbs and abdominal wall thickness via US, and the Clinical Frailty Score (CFS) against the reference Simplified Frail Scale [19]. We propose examining a novel, objective, and feasible approach to identifying patients who are at high risk of falls among older adults in the ED with the POCUS measurement of muscle mass. Given muscle strength is also a part of the European consensus guidelines on sarcopenia [20], and the SDOC recently found that low grip strength (GS), defined as <35.5 kg (kg) in men and <20 kg in women, predicts falls, we also will measure GS [20,21,22]. Our study is novel in several ways. Other than our study, no study has evaluated the use of POCUS to assess muscle mass among ED patients to predict falls. Recent research revealed a statistically significant link between muscle strength and the thickness of the biceps brachii, rectus femoris, and vastus intermedius measured using POCUS [23,24]. A previous study showed that ultrasounds could help shorten the time to diagnosing sarcopenia [14]. Several studies have reported that ultrasounds have good intra- and inter-observer accuracy for assessing muscle quantity in older adults [15,25,26,27]. Point-of-Care Ultrasound (POCUS) may be useful for screening sarcopenia in geriatric ED patients by measuring muscle thickness [23]. Previous research has suggested a correlation between muscle thickness and muscle quality. Measuring muscle thickness can be a quick and cost-effective way to assess muscle quality in the ED [23,26,28,29]. Studies have shown that the psoas, a less dependent muscle, cannot accurately reflect sarcopenia throughout the body due to the chronic illness [30,31]. Some studies have shown that ultrasound-measured muscle thickness can predict previous and future falls [32]. However, this study recruited subjects from the community. In a pilot study [33], we showed that older ED patients with subsequent falls had smaller quadriceps muscles. However, no study has measured the association between POCUS-measured muscle mass and previous falls among ED patients. Therefore, we aimed to determine the association between the (POCUS)-measured sarcopenia and grip strength and history of prior-year falls among older adults admitted to the emergency department observation unit (EDOU). We hypothesized that POCUS-measured muscle thickness would predict previous falls among geriatric ED patients. ## 2. Method We conducted a cross-sectional observational study at an urban teaching hospital with approximately 117,000 visits annually and Level-1 ED. The study was approved by our Institutional Review Board. We consecutively enrolled patients aged 65 years and older, of any race, who were admitted to the Emergency Department Observation Unit (EDOU) over an 8-month study period from 6 October 2020–25 May 2021. The in-charge nurse provided lists of appropriate and stable patients who were not in critical condition and not likely to be neutropenic (to avoid possible infection). Given that the period of study occurred during the beginning of the SARS-CoV-2 (COVID-19) pandemic, we also excluded the patients who tested positive or were at risk of the SARS-CoV-2 infection to minimize the risk of infection and contamination (e.g., patients who had to have two negative COVID tests to be eligible). Research Assistants (RAs) screened all eligible patients’ cognitive function with the 6-Item Cognitive Impairment Test (6-CIT) [34]. Patients with a 6-CIT score of less than 8 were deemed to have the capacity to consent and were asked to provide verbal consent to participate in the study. For patients with a score of 8 or more, their caregiver or proxy was asked to provide consent. The Charlson Comorbidity index (CCI) score has been used to predict sarcopenia or physical performance in previous studies [35,36]. Although the term of polypharmacy does not have a clear-cut definition [37] following multiple studies, we defined polypharmacy as using 5 or more medication [38,39]. The Principal Investigator (PI), co-investigators, and RAs were trained with a 2 h lecture and a 4 h hands-on training session given by a POCUS-certified instructor from the Division of Emergency Ultrasound. Post-training, the intraclass correlation (ICC) [25,26] of sonographic measurements of the biceps and thigh muscle between the research team was 0.92. The research team included two emergency physicians, three EM Advanced practice practitioners with ultrasound experience, three EM ultrasound fellows, and two geriatric research fellows who are emergency physicians (Figure 1 and Figure 2). RAs asked participants about their history of falls and fall-risk factors and then measured the thickness of upper and lower limb muscles using POCUS as well as measured grip strength, and they asked the patients’ cooperation to do the Timed up and go (TUG) test. We used the standard POCUS ultrasound machines (Mindray, TE7, 2019) and linear transducer with a frequency range of 2–8 MHz available in the ED to measure the muscle thickness of each patient’s upper and lower extremities on their dominant side. Following Perkisas, the biceps landmark was the midpoint between the acromion process and the elbow crease at the anatomical position [29,40]. The thigh muscle (rectus femoris/vastus intermedius) landmark was the midpoint between the anterior superior iliac spine and the proximal patella at the anatomical position [41,42]. Participants were instructed to lie down on the couch with their hips and knees extended against the couch. A copious amount of water-soluble gel was applied to the skin to avoid pressure on the muscle. The RA measured the muscle by including the muscle belly and fascia and excluding subcutaneous adipose tissue or skin and stored the images in the protected hospital cloud data storage system. We used the Jamar Hydraulic Hand Dynamometer to measure participants’ grip strength by asking them to squeeze the Hand Dynamometer with their dominant hand 3 times. The results were recorded in kilograms (kg), and the association with the previous fall was determined using both the average of three numbers and the maximum number. We used the cut-point references according to Dodds et al. ’s study [13,43]; measurements were categorized as normal or low using cut-offs at ≥27 kg (males) and ≥16 kg (females). We surveyed patients on the occurrences of falls during the past 12 months. Falling was defined as an unintentional change in position from a higher to a lower level [44], including falling while sitting or standing. We defined the “previous fall group” as participants who had at least one fall in the past 12 months. When clinicians deemed patients to be safe, we conducted the TUG test. We measured the time it took for patients to stand up from a chair/stretcher, walk 10 feet, and then sit back down in the chair or on their stretcher. [ 32]. We defined greater than 12 s as an abnormal TUG test, according to Bischoff et al. [ 45]. The Kolmogorov-Smirnov test was used to assess the Gaussian fit of the data, and we analyzed data using standard parametric and nonparametric techniques. Group differences for continuous variables were compared using Student’s t-tests and Wilcoxon’s rank sum tests, whenever appropriate. Categorical variables were compared using chi-square tests and Fisher’s exact tests. The association between biceps/thigh muscle thickness, grip strength, and a previous history of falls was assessed using univariate and multivariate logistic regression analyses. Variables included in the multivariate analyses were those felt to be clinically important confounders: CCI, polypharmacy, age, and sex/gender. We reviewed patients’ medical records to calculate CCI scores of study participants. Although the term of polypharmacy does not have clear-cut definition [37] following multiple studies, we defined polypharmacy as using 5 or more medications [38,39]. We reported analyzed data as odd ratios (OR) and $95\%$ conference intervals (CI). Based on a previous study, assuming Group 1 (non-fallers) had a mean muscle mass (right leg) of 2.87 cm and standard deviation (SD) = 0.71, and Group 2 (fallers) had a mean muscle mass of 2.54 cm and SD = 0.59, with sample ratio = 1:1, we calculated that a sample size of 126 = 63 + 63 would be needed to provide $80\%$ power to detect the difference between the two groups using two-sided Student’s t-test with alpha = 0.05. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). ## 3. Results Our study enrolled 200 individuals. However, one participant was excluded because of machine error (images were not stored), resulting in a total of 199 participants in our study. Among the 199 participants ($55\%$ female), $46\%$ reported falling in the prior year. The median biceps thickness was 2.22 cm with an Interquartile range [IQR] of 1.87–2.74, and the median thigh muscle thickness was 2.91 cm with an IQR of 2.40–3.49. Table 1 shows the demographic information for the study cohort. Participants were an average of 77.3 years old (SD = 8.3). There were no statistically significant differences in age, sex, BMI, CCI, or polypharmacy between people who had fallen and those who had not. The fall rates of different age groups are presented in Table 1. We found that the fall rate increased as people aged. In the Table 2, the highest of three grip strength measurements revealed statistically significant differences, with OR = 0.52 ($95\%$ CI 0.29–0.93). In univariate logistic regression analyses (Table 3), only thigh muscle thickness (OR = 0.67, $95\%$ CI = 0.47–0.95) and the grip strength tests (OR = 0.52, $95\%$ CI = 0.29–0.93) were significantly associated with a history of falling in the past year. We analyzed the variables by using multiple logistic regression to find independent variables that have statistically significant association with the previous fall history. In multiple logistic regression analysis (Table 4), after adjusting for age, sex, CCI, and the presence of polypharmacy, only thigh muscle thickness was independently associated with a history of previous falls (OR = 0.59, $95\%$ CI 0.38–0.91). There was no association between grip strength test results and a history of falls (OR = 0.59, $95\%$ CI = 0.31–1.14). Furthermore, we found no evidence of collinearity among the variates when we estimated multicollinearity. Age, sex, CCI, and the presence of polypharmacy were the confounding factors. A correlation matrix is provided in Supplementary Materials. ## 4. Discussion In this observational study, we found that POCUS-measured thigh muscle thickness was associated with a previous fall among geriatric ED patients. We discovered a strong link between thigh muscle thickness and a previous 12-month fall incident, which is an important predictor of future fall risk [46,47,48]. In univariate analysis, maximum grip strength was also associated with a previous fall, but this association did not remain in multi-variate analysis. A growing number of studies indicate a link between thigh muscle thickness and fall risk or fall injuries [49,50]. Gadelha et al. reported a significant association between body mass, BMI, thigh-muscle thickness, TUG, and previous falls [32] in 167 older women. However, these patients were recruited from the community. In our research, we studied 199 men and women who visited the ED and were admitted to EDOU. Only thigh-muscle thickness was associated with previous falls. BMI and TUG were not significantly associated with previous falls. However, we only had 41 patients complete the TUG test. Our patients are likely sicker than those recruited in Gadelha’s study. Another minor difference concerns the thigh muscle landmark. Ours was located midway between the ASIS and the patella, which is easier to identify. The landmark used in the Gadelha study was two-thirds of the distance between the great trochanter and the lateral epicondyle. However, a big challenge in the field of POCUS measurement of sarcopenia is a lack of standardized measurements. Perkisas et al. conducted a recent systematic review and concluded that there needs to be a standard approach to POCUS muscle measurements for sarcopenia [29]. Studies have shown grip strength to be a good predictor of sarcopenia [12,20]. In our study, while we did find a statistically significant link between the highest of three grip strength tests and having a fall in the previous 12 months, in multivariate logistic regression, this link was not significant. The majority of grip strength studies have been conducted in older women from community-dwelling populations; this may differ from our population who were admitted to EDOU and were, therefore, likely acutely ill at the time the grip strength tests were conducted. As a result, some patients may not have been acutely able to exert normal grip function. Furthermore, Li et al. found a link between biceps muscle thickness and sarcopenia [51] among participants aged 60+ from a health management center, signifying community-dwelling seniors. Participants with hemiplegia or unilateral neuromuscular dysfunction were excluded. Even though many studies suggest a link between sarcopenia and the risk of falling, the biceps muscle was not associated with a history of falls in our study. Again, our findings may differ because our patients were admitted to an EDOU and likely sicker compared to those recruited from a health center. Given ED patients are more likely to experience functional decline compared to those who do not visit the ED [52], it is important to prevent falls in this particularly vulnerable population [53,54]. Sarcopenia would be important to identify in the ED if it can objectively identify geriatric patients who are highly likely to have future falls. It could also enhance traditional fall tools if it could replace functional testing in those who cannot perform such tests. Recognizing sarcopenia could identify a reversible risk for falls among ED patients. However, it would be very time-consuming and impractical for emergency physicians to perform, for instance, a CT of various muscles [19]. US is appealing as it is portable, inexpensive, non-invasive, does not use radiation, is widely available in the ED, and can be repeated [40]. In conclusion, we found that POCUS-measured thigh muscle thickness predicted a history of prior fall(s) in the previous year in our cross-sectional observational analysis of EDOU patients aged 65 and older. As a result, emergency physicians may consider using POCUS-measured thigh muscle as a fall risk screening tool to identify high-risk patients and refer them to a fall prevention program that targets muscle strength. Future studies might use POCUS to assess older persons who present to the ED to predict future falls and fractures. The study has several limitations. First, it was conducted in a single academic hospital, so the results may not represent all EDs. Second, many patients who would normally have come to the ED may have stayed home during the first year of the COVID pandemic, and thus the participants in this study may have been sicker than the general population or ED patients before the pandemic. Third, the study was only able to include patients with two negative COVID screening tests, which may have led to a patient population that is sicker than the normal ED population as they were admitted to the observation unit for various reasons. Fourth, the study could not identify changes in muscle strength over time, as some people may have improved their diet and exercised their muscles after falling. Fifth, balance deficits caused by orthostatic hypotension or other diseases, or medications, can lead to falls, which this study did not consider [55,56,57]. Sixth, the study did not identify central obesity, which increases the risk of falling and includes abdominal obesity and a greater waist-hip ratio [58,59,60]. Also, we considered quantifying the number of previous falls as an outcome. However, in general, even a history of one fall more than doubles the odds of a future fall, while multiple falls more than triple the risk of a future fall [61]. Hence, since one of the strongest predictors of a future fall was a history of falling, we chose to focus on the binary outcome. 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--- title: 'Renin-Angiotensin-Aldosterone System Inhibitors and Development of Gynecologic Cancers: A 23 Million Individual Population-Based Study' authors: - Nhi Thi Hong Nguyen - Phung-Anh Nguyen - Chih-Wei Huang - Ching-Huan Wang - Ming-Chin Lin - Min-Huei Hsu - Hoang Bui Bao - Shuo-Chen Chien - Hsuan-Chia Yang journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC9968233 doi: 10.3390/ijms24043814 license: CC BY 4.0 --- # Renin-Angiotensin-Aldosterone System Inhibitors and Development of Gynecologic Cancers: A 23 Million Individual Population-Based Study ## Abstract The chronic receipt of renin-angiotensin-aldosterone system (RAAS) inhibitors including angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been assumed to be associated with a significant decrease in overall gynecologic cancer risks. This study aimed to investigate the associations of long-term RAAS inhibitors use with gynecologic cancer risks. A large population-based case-control study was conducted from claim databases of Taiwan’s Health and Welfare Data Science Center (2000–2016) and linked with Taiwan Cancer Registry (1979–2016). Each eligible case was matched with four controls using propensity matching score method for age, sex, month, and year of diagnosis. We applied conditional logistic regression with $95\%$ confidence intervals to identify the associations of RAAS inhibitors use with gynecologic cancer risks. The statistical significance threshold was $p \leq 0.05.$ A total of 97,736 gynecologic cancer cases were identified and matched with 390,944 controls. The adjusted odds ratio for RAAS inhibitors use and overall gynecologic cancer was 0.87 ($95\%$ CI: 0.85–0.89). Cervical cancer risk was found to be significantly decreased in the groups aged 20–39 years (aOR: 0.70, $95\%$ CI: 0.58–0.85), 40–64 years (aOR: 0.77, $95\%$ CI: 0.74–0.81), ≥65 years (aOR: 0.87, $95\%$ CI: 0.83–0.91), and overall (aOR: 0.81, $95\%$ CI: 0.79–0.84). Ovarian cancer risk was significantly lower in the groups aged 40–64 years (aOR: 0.76, $95\%$ CI: 0.69–0.82), ≥65 years (aOR: 0.83, $95\%$ CI: 0.75–092), and overall (aOR: 0.79, $95\%$ CI: 0.74–0.84). However, a significantly increased endometrial cancer risk was observed in users aged 20–39 years (aOR: 2.54, $95\%$ CI: 1.79–3.61), 40–64 years (aOR: 1.08, $95\%$ CI: 1.02–1.14), and overall (aOR: 1.06, $95\%$ CI: 1.01–1.11). There were significantly reduced risks of gynecologic cancers with ACEIs users in the groups aged 40–64 years (aOR: 0.88, $95\%$ CI: 0.84–0.91), ≥65 years (aOR: 0.87, $95\%$ CI: 0.83–0.90), and overall (aOR: 0.88, $95\%$ CI: 0.85–0.80), and ARBs users aged 40-64 years (aOR: 0.91, $95\%$ CI: 0.86–0.95). Our case-control study demonstrated that RAAS inhibitors use was associated with a significant decrease in overall gynecologic cancer risks. RAAS inhibitors exposure had lower associations with cervical and ovarian cancer risks, and increased endometrial cancer risk. ACEIs/ARBs use was found to have a preventive effect against gynecologic cancers. Future clinical research is needed to establish causality. ## 1. Introduction Cervical, endometrial, and ovarian carcinomas make up the majority of tumors in gynecologic cancers [1]. Cervical cancer was reported as the most common in all gynecologic cancers, with more than 604,120 new cases and 341,830 new deaths diagnosed in 2020 [2,3]. The evidences indicated that ovarian cancer accounted for the highest fatality rate among gynecological malignancies due to silent progression and advanced stage at diagnosis [4,5,6]. There were nearly 320,000 new cases and 207,000 new deaths recorded in ovarian cancer [2]. Endometrial cancer ranked sixth among female cancers, with over 417,000 new cases [7]. The most common female gynecologic malignancies in Taiwan were uterine body, ovary, and other adnexa, and cervix cancers [8]. While the incidence rate of cervix uterine cancers increased until 80 years, those uterine body and ovarian cancers reached a peak at 50 and 60 years, respectively. The circulating renin–angiotensin-aldosterone system (RAAS) is primarily known for its pivotal role in regulating aldosterone secretion, blood pressure, cardiovascular homeostasis, fluid volume, and electrolyte balance [9,10,11]. Both angiotensin-converting-enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are commonly used and regarded as safe therapies with few side effects [10]. However, there is an increasing evidence that long-term drugs affecting the RAAS may have impacts on the risk of cancers [12], including gynecological cancers [13,14,15]. Numerous observational studies on the associations of ARBs and ACEIs with gynecological cancers have produced contradictory findings. Some studies indicated a higher overall incidence of cancer among ARB users [13], whereas others found a lowered risk of disease progression and lower recurrence in ovarian cancer [16]. In addition, previous studies demonstrated that women who used ACEIs had decreased rates of gynecologic tract cancer [15], while others highlighted that individuals with ovarian cancer had higher serum ACEI levels. Circulating ACEIs may be linked to ongoing pathobiologic processes in the development of ovarian cancer [17] and endometrial cancer [14]. Some evidence has indicated that RAAS inhibitors may affect angiogenesis, tumor cell proliferation, follicle maturation, cell proliferation, and vascularization in gynecological human tissues both in vitro and in vivo [1,18,19,20,21]. Therefore, long-term intake of RAAS inhibitors has increased apprehensions [20]. To our knowledge, a few studies have been conducted on gynecologic cancer risks in RAAS inhibitors users and stratified by age. This study aimed to investigate the associations of long-term RAAS inhibitors use with gynecological cancer risks in particular age groups. ## 2.1. Descriptive Analysis A total of 97,736 gynecologic cancer cases, including 64,382 cases of cervical cancer, 19,580 cases of endometrial cancer, and 13,774 cases of ovarian cancer, were identified between 2002 and 2016. After each case was matched with four controls, there were 390,944 patients without any cancer diagnosis as control group. The number of control individuals with cervical, endometrial, and ovarian cancers was 257,528, 78,320, and 55,096, respectively (Figure 1). The average age of gynecologic cancer cases and controls was 50.81 years (Table 1). The individuals aged 40–64 years was dominant in gynecologic cancers, consisting of $59.41\%$. The case group had higher rates of diabetes ($14.19\%$) and peptic ulcer disease ($12.66\%$) than the control group, which were higher by $2.3\%$ and $2.18\%$, respectively. The case group used metformin, aspirin, and statins more frequently than the control group by $1.58\%$, $1.29\%$, and $2.45\%$, respectively (Table 1). ## 2.2. Association of RAAS Use with Overall Gynecologic Cancer Figure 2 indicates the associations of RAAS inhibitors intake and gynecologic cancers by age groups. RAAS medication use was associated with a decreased risk of gynecologic cancers (adjusted odds ratio (aOR): 0.87, $95\%$ CI: 0.85–0.89). The degree of gynecologic cancer risk was observed to have significant associations with RAAS users aged 40–64 years (aOR: 0.86, $95\%$ CI: 0.83–0.89) and ≥65 years (aOR: 0.87, $95\%$ CI: 0.85–0.89). A significantly decreased risk of cervical cancer was found in RASS users in the groups aged 20–39 years, 40–64 years, ≥65 years, and overall, with an aOR of 0.70, 0.77, 0.87, and 0.81, respectively (Figure 3). Meanwhile, RAAS inhibitors were more likely to develop endometrial cancer in the users aged 20–39 years, 40–64 years, and overall, with an aOR of 2.54, 1.08, and 1.06, respectively. The risk of ovarian cancer was significantly decreased in RAAS drug users in the groups aged 40–64 years, ≥65 years, and overall, with an aOR of 0.76, 0.83, and 0.79, respectively. Figure 4 presents gynecologic cancer risk among ARBs and ACEIs users by age groups. There was a significantly lowered risk of gynecologic cancers in ACEIs users aged 40–64 years, ≥65 years, and overall, with an aOR of 0.88, 0.87, and 0.88, respectively. In addition, ARBs use demonstrated a decreased risk of gynecologic cancers in those 40–64 years, with an aOR of 0.91. ## 3.1. Main Findings This large population-based case-control study highlighted that RAAS inhibitors intake was significantly associated with a decrease in overall gynecologic cancer risks. When stratified by age groups, gynecologic cancer risks were observed to have significant associations with groups aged 40–64 years and ≥65 years. RAAS inhibitors were associated with a lowered cervical cancer risk in 20–64-year-old and ≥65-year-old users, and a reduced ovarian cancer risk in those aged 40–64 years, ≥65 years, overall age group. In contrast, endometrial cancer was shown to be increased risk in users aged 20–64 years, and overall. When stratified by drug groups, ACEIs users were found to have a preventive effect against gynecologic cancers in the groups aged 40–64 years, ≥65 years, and overall age group, whereas ARBs demonstrated a decreased risk of gynecologic cancers in 40–64-year-old users. ## 3.2.1. Postulated Mechanisms of RAAS Inhibitors against Gynecologic Cancers Mechanisms have been proposed to elucidate the RAAS’s antineoplastic effects against gynecological cancers. First, RAAS inhibitors encourage the potential invasion and release vascular endothelial growth factor (VEGF), which is a potent angiogenic agent in many different types of malignancies [1]. The increase in VEGF production was found in cervical cancer in Siha cell line [1,22,23], endometrial cancer with HEC-1A cell line, [1], and ovarian cancer with SKOV3 cell lines [24]. Second, RAAS affects processes such as proliferation, apoptosis, autography, migration, inflammation, oxidative stress, or angiogenesis [25]. In cervical, ovarian [26], and endometrial carcinomas, altered expression of the system’s peptides and receptors was seen [27,28]. This mechanism was demonstrated in in vitro studies [27,28,29,30,31]. Third, mRNA of RAAS receptors were highly expressed in endometrioid carcinomas and their adjacent endometrium, suggesting that these receptors may play a role in development of endometrial cancer [19]. Some previous studies indicated that body mass index (BMI) and are most significantly linked to endometrial cancer incidence and mortality [32,33,34]. The association between obesity and endometrial cancer can be explained by mechanistic pathways. Visceral fat is a complex endocrine organ that contains adipocytes and preadipocytes as well as stromal, neuron, stem, and macrophage infiltration. Together, they release a variety of adipokines that have both localized and systemic effects, promoting carcinogenesis and enhancing endometrial proliferation [35,36,37]. In addition, adipose tissue is also a source of mesenchymal stem cells, which can be used to promote the development and growth of tumors [38,39]. Four, the overexpression of mRNA and KDR (kinase domain-containing receptor) protein itself has been proposed for the mechanism related to RAAS and gynecological cancer risk. The concentration of mRNA and KDR has been shown in ovarian cancer [40,41,42,43]. In this study, our findings indicated a lowered overall risk of gynecologic cancers in RAAS inhibitors users. Lee SH et al. [ 2022] conducted a population-based cohort study in Korea and indicated that RAAS inhibitors use was not associated with gynecologic cancers [44]. A meta-analysis of observational studies found no preventive effect of RAAS against gynecologic cancers [45]. Inconsistences between our finding and other studies may be due to the differences in study design, and adjusted confounders. ## 3.2.2. Postulated Mechanisms of ARBs/ACEIs against Gynecologic Cancers When stratified by drug groups, ACEIs use was found to have a preventive effect against gynecologic cancers in the groups aged 40–64 years, ≥65 years, and overall age group, whereas ARBs demonstrated a decreased risk of gynecologic cancers in 40–64 year-old users. These results can be supported by some possible mechanisms. In general, ARBs and ACEIs, being potent angiogenic agents in several types of malignancies [1], often encourage invasive potential and VEGF production, which in turn boost angiogenesis and pro-tumorigenic transcription factors [22,23]. These medications also promote inflammation and participate in metastasis, invasion, and migration processes [13,31]. While in vitro and in vivo studies presented that up-regulation of ACEIs was beneficial for establishing local tumor angiogenesis, ARBs may be able to affect angiogenic pathways via restraining cancer cell proliferation and enhancing medication delivery [46,47]. A previous study reported that losartan (ARB) played a vital role in enhancing drug delivery and efficacy via decreasing solid stress, tumor hypoxia, extracellular matrix and augmenting vascular perfusion [48]. This finding contributed to clarifying the physiological mechanism in our study. Another study showed that increasing the ACEI activity remained unexplicit, it might be linked to aging [49]. However, some researchers had suggested that the level of ACEI serum could be used to detect disseminated germinoma tumors and track the effectiveness of treatment [50]. A retrospective cohort study conducted by Cho MA et al. [ 2020] among Korean patients with ovarian cancer revealed that those who used ARBs were associated with $35\%$ decreased risk of disease progression and recurrence in ovarian cancer [13]. Likewise, women taking ACEIs was found to be associated with the lowest risk of gynecologic tract cancer [15]. A network meta-analyses and trial sequential analyses of 324,168 participants from randomized trials, nevertheless, showed ACEIs/ARBs use were not associated with risk of all cancers [51]. In addition, a population-based cohort study in Denmark demonstrated that no risk reductions were observed for ACEIs and female reproductive tract [52]. These differences could be because of the study population, sample size, and adjusted confounders. Further investigations are encouraged to clarify the significance of ARBs and ACEIs use and gynecological cancers by stratification of age. This present study has several strengths: First, patients’ information was gathered from a reliable registry that included diagnoses, prescriptions, and definitions of cancer. Secondly, the database contained a large population, therefore, we were able to categorize individuals into age groups. Finally, we considered potential confounding variables that may be associated with gynecologic cancer risks. However, our study has several limitations. First, this study found associations between RAAS inhibitors and gynecologic cancer risks rather than causality. The findings gave prospective medication-cancer signals that clinicians or researchers can utilize to identify the mechanisms or their causality in the future. Second, information such as patient lifestyles, medication adherence, laboratory data, etc., were not accessible for our analysis. Third, this study could not include some risk factors, including hormone replacement treatment, oral contraception, HPV infection or immunization, hypertension, hyperinsulinemia, number of pregnancies/infertility, BMI, obesity etc. ## 4.1. Data Sources Data were provided by Health and Welfare Data Science Center (HWDC), which is established by Taiwan’s Ministry of Health and Welfare (MOHW). HWDC contain de-identified claims data of the National Health Insurance (NHI) beneficiaries [53], which covers $99.9\%$ of the Taiwanese population [54]. Now, it provides more than 100 different databases for research, such as Ambulatory Care Expenditures by Visits, Inpatient Expenditures by Admissions, Details of Ambulatory Care Orders, Details of Inpatient Orders, Cause of Death Data, Taiwan Cancer Registry, and so on. In this study, medication and diagnosis data (2000–2016) were retrieved from HWDC, and cancer is confirmed by Taiwan Cancer Registry (TCR) (1979–2016) (Figure 1). The cancer diagnoses in this study were identified from validated International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) codes and linked to the pathological data. The study was approved by the Joint Institutional Review Board of Taipei Medical University (TMU-JIRB), Taipei, Taiwan (approval number: N202003609). ## 4.2. Definition of Case and Control This study includes all newly diagnosed female patients with gynecologic cancers from 1 January 2002 to 31 December 2016. Gynecologic cancers were defined based on the International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) (e.g., ICD-9-CM codes 180 for cervical cancer, 182 for endometrial cancer, and 183 for ovarian cancer). The initial date of diagnosis with gynecologic cancers was determined as the index date. Controls were defined as those without any cancer diagnosis between 2000 and 2016. Each eligible case would match with four controls using the propensity score from age, sex, and year of diagnosis. Controls assigned the same index date with their matched cases [55]. We excluded patients under 20 years or with inconsistent data. ## 4.3. RAAS Users Medications were extracted from the details of ambulatory care orders in the HWDC database. Medication information, including NHI drug codes, drug names, drug dosage, frequency, dispensing date, the total daily dose, and so on. ARBs (C09A), and ACEIs (C09C) were classified using Anatomical Therapeutic Chemical (ATC) codes (see Appendix A). The analyses of ARBs, and ACEIs exposure were conducted only before the cancer diagnosis (e.g., index date). We took into account the patients’ prior exposure to ARBs and ACEIs or not. Therefore, individuals who had received prescriptions for ARBs and ACEIs for at least 60 days within the two years before the index date were categorized as ARB and ACEI users. We defined non-users who had never been prescribed any RAAS drug (ARBs or ACEIs) or prescribed less than 60 days. ## 4.4. Confounding Factors Comorbid conditions, Charlson Comorbidity Index, and other drugs, such as metformin (ATC: A10BA02) [56,57,58], aspirin (ATC: B01AC06) [58,59,60], and statin (ATC: C10AA) [61] were regarded as potential confounders in our analysis (Table 1). Patients who had been prescribed aspirin, metformin, and statin for at least two months (e.g., 60 days) in the two years before to the index date were considered to have been exposed to those medications. ## 4.5. Statistical Analysis We applied the McNamara test and paired t-test to test the difference between the case and control groups [62]. Conditional logistic regression with $95\%$ confidence intervals (CIs) was utilized to identify the associations of RAAS inhibitors, ARBs, and ACEIs use with gynecologic cancer risks [63]. The models were categorized into different age groups, such as aged ≥20 years, 20–39 years, 40–64 years, and ≥65 years. We utilized SAS v.9.4 software (SAS Institute Inc., Cary, NC, USA) for statistical analysis. A p-value ≤ 0.05 was regarded as statistically significant. ## 5. Conclusions Our finding highlighted that RAAS inhibitors use was significantly associated with decreased risks in overall gynecologic cancers. When RAAS inhibitors users were stratified by age, gynecologic cancer risks were associated with groups aged 40–64 years and ≥65 years. Users of RAAS inhibitors were shown to have a significantly lower risk of cervical cancer in the 20–64 and ≥65-year-old age groups, and a lower risk of ovarian cancer in the 40–64, ≥65-year-old age groups, and overall age group. However, endometrial cancer was observed to be increased risk in the groups aged 20–39 years, 40-64 years, and overall. 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--- title: 'Serum Copper-to-Zinc Ratio and Risk of Chronic Obstructive Pulmonary Disease: A Cohort Study' authors: - Setor K. Kunutsor - Ari Voutilainen - Jari A. Laukkanen journal: Lung year: 2022 pmcid: PMC9968252 doi: 10.1007/s00408-022-00591-6 license: CC BY 4.0 --- # Serum Copper-to-Zinc Ratio and Risk of Chronic Obstructive Pulmonary Disease: A Cohort Study ## Abstract ### Purpose Serum copper (Cu), zinc (Zn), and Cu/Zn-ratio have emerged as ageing-related biomarkers. We sought to assess the association between Cu/Zn-ratio and chronic obstructive pulmonary disease (COPD) risk. ### Methods Serum Cu and Zn were measured using atomic absorption spectrometry in 2,503 men aged 42–61 years. ### Results During a median follow-up of 27.1 years, 210 COPD cases occurred. Serum Cu/Zn-ratio and Cu concentrations were linearly associated with COPD risk, whereas the relationship was curvilinear for Zn and COPD risk. A unit increase in Cu/Zn-ratio was associated with an increased COPD risk in multivariable analysis (hazard ratio, HR 1.81; $95\%$ CI 1.08–3.05). The corresponding adjusted HR ($95\%$ CI) was 3.17 (1.40–7.15) for Cu. Compared to the bottom tertile of Zn, the HRs ($95\%$ CIs) were 0.68 (0.48–0.97) and 1.01 (0.73–1.41) for the middle and top tertiles of Zn, respectively. ### Conclusions Increased serum Cu/Zn-ratio and Cu concentrations were linearly associated with an increased COPD risk in men. ## Introduction Chronic obstructive pulmonary disease (COPD) is an inflammatory respiratory disease that is associated with significant morbidity, mortality, and healthcare costs [1]. In 2019, there were 212.3 million global cases of COPD and it accounted for 3.3 million deaths, representing the third leading cause of death globally [1]. Major contributors to COPD include active smoking, comorbidities, genetics, occupational exposures, indoor and outdoor air pollution, and infections [2]. Though COPD is largely incurable once diagnosed, it is a preventable disease. Ageing also constitutes a major risk factor among the wide range of comorbidities and risk factors associated with COPD. This is due to the physiological changes associated with ageing such as weakening of the immune system [3], increased inflammation, and the high prevalence of comorbidities in older people. With global population ageing, there is increasing research focussed on identifying ageing-related biomarkers [4], which could be clinically useful for the prevention of ageing-related diseases such as COPD. Copper (Cu) and zinc (Zn) are essential micronutrients involved in several cellular processes [5, 6], and they have been identified as ageing-related biomarkers, given their close relationships with inflammatory parameters rather than the nutritional ones [7]. Insufficiency, deficiency, or toxic levels of these nutrients can lead to an increased incidence of age-related degenerative conditions such as vascular disease, cancer, and infections [8, 9]. Serum concentrations of Cu and Zn are biologically interrelated and strictly regulated by compensatory mechanisms that act to stabilize them within certain ranges of nutritional intake [10]. During pathological states such as systemic inflammation, serum Cu concentrations increase and that of Zn decreases [11]. Age-related chronic diseases are typically characterised by an increase in the concentrations of Cu-to-Zn ratio (Cu/Zn-ratio) [10]. There is documented observational cohort evidence on the relationships between elevated serum Cu/Zn-ratio and an increased risk of cardiovascular diseases [12], heart failure[13], cancer [12], all-cause mortality[7] as well as infectious diseases such as pneumonia [14]. Given the overall evidence, we hypothesized that serum Cu/Zn-ratio is linked to the risk of COPD. Thus, our aim was to assess the association between serum Cu/Zn-ratio and COPD risk, using a population-based prospective cohort of 2,503 middle-aged and older Finnish men. In a subsidiary analysis, we assessed the individual associations of serum Cu and Zn with COPD risk. ## Methods Participants included in this study were part of the Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD), a population-based prospective cohort study that comprised a representative sample of men aged 42–61 years recruited from Kuopio, eastern Finland. Baseline examinations were performed between March 1984 and December 1989. The study protocol was approved by the Research Ethics Committee of the University of Kuopio, and written informed consent was provided by each study participant. The study design, recruitment methods and assessment of risk markers have been described in detail in previous reports [13, 14]. Serum Cu and Zn concentrations were measured from frozen serum samples stored at − 20 ℃ for 1–5 years, using the PerkinElmer 306 atomic absorption spectrophotometer (Norwalk, Connecticut, USA). We included all incident cases of COPD that occurred from study enrolment through 2018. All KIHD study participants are under continuous annual surveillance for outcomes including COPD events using personal identification codes and no losses to follow-up have been recorded. Incident COPD cases were collected by data linkage to the National Hospital Discharge Register and a comprehensive review of hospital records. The diagnoses of COPD were made by qualified physicians based on clinical history, symptoms and spirometry findings [15]. Multivariable adjusted hazard ratios (HRs) with $95\%$ confidence intervals (CIs) for incident COPD were estimated using Cox proportional hazard models. Stata version MP 17 (Stata Corp, College Station, Texas) was used to conduct all statistical analyses. ## Results The overall mean (standard deviation, SD) age of study participants at recruitment was 53 [5] years. The means (SDs) of serum Cu/Zn-ratio, Cu, and Zn were 1.21 (0.27), 1.11 (0.18) and 0.94 (0.12), respectively (Table 1). During a median (interquartile range) follow-up of 27.1 (17.3–31.1) years, 210 COPD cases occurred. Multivariable restricted cubic spline curves suggested positive and linear relationships of serum Cu/Zn-ratio and Cu concentrations with COPD risk, whereas the relationship was inverse and curvilinear between serum Zn and COPD risk (Fig. 1). The HR ($95\%$ CI) for COPD per unit increase in serum Cu/Zn-ratio was 2.54 (1.60–4.04) in analysis adjusted for age, body mass index, smoking, history of type 2 diabetes, prevalent coronary heart disease, history of asthma, chronic bronchitis or tuberculosis, alcohol consumption, socioeconomic status, leisure-time physical activity, total energy intake, intake of fruits, berries and vegetables, and intake of processed and unprocessed red meat (model 2), which was attenuated to 1.81 (1.08–3.05) after further adjustment for high-sensitivity C-reactive protein (hsCRP), a potential mediator (Table 2). The corresponding adjusted HRs ($95\%$ CIs) were 1.79 (1.24–2.57) and 1.47 (1.00–2.16) comparing the top versus bottom tertiles of serum Cu/Zn-ratio. Higher concentrations of serum Cu were also associated with increased COPD risk but the corresponding adjusted HRs were more extreme than that those for serum Cu/Zn-ratio and COPD risk (Table 2). Compared to the bottom tertile of Zn, the HRs ($95\%$ CIs) for COPD were 0.66 (0.47–0.94) and 0.96 (0.69–1.34) for the middle and top tertiles of Zn, respectively, in analysis that adjusted for model 2 covariates (Table 2). The respective HRs ($95\%$ CIs) were 0.68 (0.48–0.97) and 1.01 (0.73–1.41) in further analysis adjusted for hsCRP.Table 1Baseline characteristics of study participantsCharacteristicsMean (SD) or median (IQR)Serum copper-to-zinc ratio1.21 (0.27)Serum copper, mg/l1.11 (0.18)Serum zinc, mg/l0.94 (0.12)Questionnaire/prevalent conditions Age (years)53 [5] Alcohol consumption, g/week31.8 (6.2–91.0) History of type 2 diabetes, %99 (4.0) Current smoking, %791 (31.6) History of CHD, %617 (24.7) History of asthma, %91 (3.6) History of chronic bronchitis, %189 (7.6) History of tuberculosis, %97 (3.9)Physical measurements BMI, kg/m226.9 (3.6) SBP, mmHg134 [17] DBP, mmHg89 [11] Physical activity, KJ/day1204 (630–1999) Socio-economic status8.48 (4.23)Blood-based markers Total cholesterol, mmol/l5.90 (1.08) HDL-C, mmol/l1.29 (0.30) Fasting plasma glucose, mmol/l5.35 (1.28) High-sensitivity C-reactive protein, mg/l1.29 (0.71–2.48)Dietary intakes Total energy intake, kJ/day9855 [2595] Processed and unprocessed red meat, g/day145 [77] Fruits, berries and vegetables, g/day251 [156]BMI body mass index; CHD coronary heart disease; DBP diastolic blood pressure; GFR glomerular filtration rate; HDL-C high-density lipoprotein cholesterol; SD standard deviation; SBP systolic blood pressureFig. 1Restricted cubic splines of the hazard ratios of chronic obstructive pulmonary disease with serum Cu/Zn-ratio, Cu and Zn. A Serum Cu/Zn-ratio and COPD; B Serum Cu and COPD; C Serum Zn and COPD. Dashed lines represent the $95\%$ confidence intervals for the spline model (solid line). Models were adjusted for age, body mass index, smoking status, history of type 2 diabetes, prevalent coronary heart disease, history of asthma, history of chronic bronchitis, history of tuberculosis, alcohol consumption, socioeconomic status, leisure-time physical activity, total energy intake, intake of fruits, berries and vegetables, and intake of processed and unprocessed red meat. COPD chronic obstructive pulmonary disease; Cu copper; Zn zincTable 2Associations of serum copper, zinc and copper-to-zinc ratio with risk of chronic obstructive pulmonary diseaseExposureEvents/ TotalModel 1Model 2Model 3HR ($95\%$ CI)P valueHR ($95\%$ CI)P valueHR ($95\%$ CI)P valueSerum copper-to-zinc ratio Per unit increase210 / 2,5034.51 (2.95–6.88) <.0012.54 (1.60–4.04) <.0011.81 (1.08–3.05).025 T1 (0.48–1.07)46 / 835refrefref T2 (1.08–1.27)65 / 8371.46 (1.00–2.13).0511.20 (0.82–1.76).341.11 (0.76–1.64).59 T3 (1.28–3.12)99 / 8312.59 (1.82–3.68) <.0011.79 (1.24–2.57).0021.47 (1.00–2.16).052Serum copper, mg/l Per unit increase210 / 2,50310.33 (5.28–20.20) <.0015.11 (2.49–10.47) <.0013.17 (1.40–7.15).005 T1 (0.46–1.02)42 / 875refrefref T2 (1.03–1.17)73 / 8261.94 (1.33–2.83).0011.79 (1.22–2.63).0031.65 (1.12–2.43).012 T3 (1.18–2.32)95 / 8023.10 (2.16–4.46) <.0012.17 (1.49–3.15) <.0011.79 (1.20–2.66).004Serum zinc, mg/l T1 (0.50–0.89)94 / 911refrefref T2 (0.90–0.98)52 / 8020.57 (0.41–0.80).0010.66 (0.47–0.94).0210.68 (0.48–0.97).033 T3 (0.99–1.62)64 / 7900.78 (0.56–1.07).120.96 (0.69–1.34).811.01 (0.73–1.41).93Model 1: Adjusted for age. Model 2: Model 1 plus body mass index, smoking status, history of type 2 diabetes, prevalent coronary heart disease, history of asthma, history of chronic bronchitis, history of tuberculosis, alcohol consumption, socioeconomic status, leisure-time physical activity, total energy intake, intake of fruits, berries and vegetables, and intake of processed and unprocessed red meat. Model 3: Model 2 plus high-sensitivity C-reactive proteinCI confidence interval; CRF cardiorespiratory fitness; HR hazard ratio; ref reference; T tertile ## Discussion In a cohort of middle-aged and older Finnish men, higher serum Cu/Zn-ratio was associated with an increased risk of COPD in a linear dose–response manner. In separate evaluations of serum Cu and Zn, increased serum Cu was associated with increased COPD risk in a graded manner, whereas serum Zn was inversely associated with COPD risk in a curvilinear manner. On adjustment for inflammation (as measured by hsCRP), the associations were markedly attenuated but remained significant. These findings reflect the fact that inflammatory pathways are involved in the development of COPD. High serum Cu concentrations increase COPD risk via increased inflammation, given its close link with ceruloplasmin, which is an acute phase response protein and markedly increased during inflammation [16]. Given that the associations persisted following further adjustment for inflammation, other mechanistic pathways may underline the observed associations of serum Cu/Zn-ratio and Cu and Zn concentrations with the risk of COPD. Despite its beneficial role in numerous biological processes, serum Cu can exhibit toxic effects in high concentrations. Increased serum Cu concentrations may also increase COPD risk via oxidative stress and activation of lung fibroblasts, which lead to pulmonary fibrosis [17]. Zinc has antagonistic effects on the toxicity of Cu [18] and may play a protective role in the development of COPD via its anti-inflammatory, antioxidant, immune, and metabolic modulatory properties [19]. The overall findings suggest that serum Cu/Zn-ratio, Cu and Zn could be potential risk markers for incident COPD. Therefore, measurement of serum Cu and Zn concentrations as well as Cu/Zn-ratio could be used to identify individuals at high risk of COPD. It is well known that Zn deficiency in old age is usually due to insufficient dietary Zn intake, reduced intestinal absorption or increased losses [20], hence, micronutrient-based therapies that can boost Zn levels could also provide optimal concentrations of serum Cu/Zn-ratio and reduce the risk of COPD. Zinc supplementation has been observed to have favourable effects on oxidant–antioxidant balance in patients with COPD [21]. This is the first prospective evaluation of the associations of serum Cu/Zn-ratio, Cu and Zn with the risk of COPD. Other strengths include the use of a representative sample of Finnish middle-aged to older men, use of a relatively large prospective cohort with long-term follow-up, the zero loss to follow-up, availability of a comprehensive panel of essential covariates which enabled adequate control of confounding factors, and assessment of the dose–response relationships. The limitations include the possibility of selection bias, the generalisability of the findings to only middle-aged and older men and biases inherent in observational cohort designs such as reverse causation and residual confounding due to errors in measured confounders and/or relevant unmeasured confounders such as environmental factors and comorbidities that affect values of the exposures. Furthermore, we did not have data on changes in nutritional status and biomarkers over the long-term follow-up. 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--- title: 'Reference Intervals for Bone Impact Microindentation in Healthy Adults: A Multi-Centre International Study' authors: - Pamela Rufus-Membere - Kara L. Holloway-Kew - Adolfo Diez-Perez - Natasha M. Appelman-Dijkstra - Mary L. Bouxsein - Erik F. Eriksen - Joshua N. Farr - Sundeep Khosla - Mark A. Kotowicz - Xavier Nogues - Mishaela Rubin - Julie A. Pasco journal: Calcified Tissue International year: 2023 pmcid: PMC9968254 doi: 10.1007/s00223-022-01047-y license: CC BY 4.0 --- # Reference Intervals for Bone Impact Microindentation in Healthy Adults: A Multi-Centre International Study ## Abstract Impact microindentation (IMI) is a novel technique for assessing bone material strength index (BMSi) in vivo, by measuring the depth of a micron-sized, spherical tip into cortical bone that is then indexed to the depth of the tip into a reference material. The aim of this study was to define the reference intervals for men and women by evaluating healthy adults from the United States of America, Europe and Australia. Participants included community-based volunteers and participants drawn from clinical and population-based studies. BMSi was measured on the tibial diaphysis using an OsteoProbe in 479 healthy adults (197 male and 282 female, ages 25 to 98 years) across seven research centres, between 2011 and 2018. Associations between BMSi, age, sex and areal bone mineral density (BMD) were examined following an a posteriori method. Unitless BMSi values ranged from 48 to 101. The mean (± standard deviation) BMSi for men was 84.4 ± 6.9 and for women, 79.0 ± 9.1. Healthy reference intervals for BMSi were identified as 71.0 to 97.9 for men and 59.8 to 95.2 for women. This study provides healthy reference data that can be used to calculate T- and Z-scores for BMSi and assist in determining the utility of BMSi in fracture prediction. These data will be useful for positioning individuals within the population and for identifying those with BMSi at the extremes of the population. ## Introduction Fracture resistance of bone is a function of bone mass, geometry, microarchitecture and material properties. Various diseases impact bone strength through alterations in these bone characteristics. Osteoporosis is the most common skeletal disorder, with osteoporosis-related fractures set to escalate as the global population ages. The many individuals who sustain fractures experience additional complications including ill health, disability, a reduced quality of life, and possibly even death [1–3]. The burden of bone disease is enormous, with global estimates of 158 million people aged 50 years and older at high risk for osteoporotic fracture in 2010, an estimate which is set to double by 2040 [4]. Hip fractures are by far the most devastating type of fracture, accounting for about 300,000 hospitalisations each year in the United States of America (USA) alone [5]. Data from a global systematic review conducted in 2017, estimated that health and social care costs for each fragility hip fracture in the year following fracture was USD 43,669, exceeding the estimates for acute coronary syndrome and ischaemic stroke [6]. Targeting of effective interventions depends on the ability to discriminate fracture risk. Currently, fracture risk estimates are based on assessment of bone mineral density (BMD) using radiographic imaging (dual energy X-ray absorptiometry, DXA; or quantitative computed tomography, QCT), finite element analysis based on QCT [7, 8] and magnetic resonance imaging (MRI) scans [9], and use of absolute fracture risk calculations that combine such measures with other clinical risk factors [10]. These tools are predicated on the assumption that BMD (i.e. bone quantity) and age are the dominant factors in determining bone health. The utility of other technologies such as advanced imaging (e.g. peripheral quantitative computed tomography (pQCT)/high resolution pQCT (HR-pQCT, magnetic resonance imaging (MRI)), for prediction of fracture risk remains under investigation. Bone quantity and bone quality are considered the primary contributors to bone strength [11]. Bone quantity, synonymously referred to as bone mass, can be clinically evaluated using DXA with an output of areal bone mineral density (aBMD). Measurement of aBMD has remained the medical community’s front-line surrogate of bone strength for decades, due to the observation that fracture risk increases as aBMD decreases. However, the largest absolute number of fractures in patients do not occur in those with osteoporosis on bone density criteria [12]. Data from Australia indicate that $26.9\%$ of women with low trauma fractures have aBMD in the normal range (T-score > − 1) [12], indicating that skeletal fragility may arise from structural or material properties of bone that are not detected by densitometry. Recently, clinical research as well as governing bodies, including the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) have indicated the need for new, different, tools to clinically assess bone health [13]. A promising new measurement method, bone impact microindentation (IMI), utilises a novel handheld device, the OsteoProbe, to assess fracture resistance of cortical bone in vivo in a minimally invasive way [14]. The potential clinical significance of the new IMI technology has been previously reported [15–21]. During IMI, bone’s resistance to a microindentation is quantified as the inverse of the indentation depth. The device quantifies the microindentation distance in bone relative to a microindentation distance into a controlled reference material and expresses the resulting ratio as the (unitless) Bone Material Strength index (BMSi). It is believed that greater indentation depth reflects less resistance to propagation of microcracks. In non-clinical testing on traditional plastic materials, BMSi was significantly correlated to both Rockwell and Vickers Hardness [22]. Previous studies have evaluated BMSi in relation to fragility fractures [23–26], chronic kidney disease [20], type 2 diabetes mellitus (T2DM) [15–17, 21], hyperparathyroidism [27], acromegaly [28], Paget’s disease [29], therapy with bisphosphonates [30] and glucocorticoid induced osteoporosis [31]. In most of these studies, BMSi was lower in the presence of disease. However, most have compared individuals with diseases that predispose to fracture, or those who have suffered a fracture, with controls who have been selected on the basis of being free of exposures that affect bone and calcium metabolism. Furthermore, since bone fragility is typically diagnosed after a fracture and most fractures occur in patients with BMD in the osteopenic range [12, 32], it is likely that the definition of healthy used in such studies may include individuals whose bone fragility remains undetected [33]. To our knowledge, no studies to date have reported reference data for BMSi in a large, heterogeneous broad-based population. The primary aim of this study was to develop reference data for BMSi in a healthy sample of men and women in the USA, Europe and Australia. ## Study Participants Participants for this study were healthy men and women drawn from study groups in the USA, Europe and Australia. The term “healthy” as used in this manuscript, refers to a population without comorbidities that are suspected to affect bone and are fracture-free at the time of assessment. Table 1 shows the number of participants included from each of these study centres. For all centres, participants with active disease or illness that affects bone material quality, any history of fragility fracture, or allergy to lidocaine were excluded. Each patient that was measured had their medical records reviewed by physician or trained research personnel to verify their disease state and history of fragility fracture. Each qualified clinical site received the protocol questionnaire and reviewed their measurement population according to the specific inclusion and exclusion criteria for the healthy reference interval analysis. Only eligible participants’ data were included in the analysis. Table 1Detailed list of recruitment inclusion, center details, recruitment period, number, and race/ethnicity of participantsSite ID (Region):Healthy recruitment inclusionCentre(s):Recruitment periodNRace/ethnicityMenWomenMAYO (USA)Female adult (18 +), agreed to the measurement via informed consent, visiting the Clinical Research Unit (CRU) at the Mayo Clinic (Rochester, MN, USA)Random sample of Olmsted County, MN, USA, residents, augmented by newspaper and website advertisements. Clinical Research Unit (CRU) at the Mayo Clinic (Rochester, MN, USA)2010–201830–Caucasian, Non-Hispanic ($100\%$)LUMC(The Netherlands)Male or Female adult (18 +), agreed to the measurement via informed consent, visiting the outpatient clinic of the Center for Bone Quality or the regional Fracture Liason Service (FLS) of the Leiden University Medical CenterThe outpatient clinic of the Center for Bone Quality or the regional Fracture Liaison Service (FLS) of the Leiden University Medical Center. This center serves all patients of all ages that are referred or come in on their own2013–201851Caucasian ($100\%$)Caucasian ($100\%$)COLUMBIA (USA)Female adult (18 +) visiting the Columbia University Medical Center that, is able to lie on bed, signs informed consent, has no allergy to lidocaine, and is able to be measured on the left or right tibiaColumbia University Medical Center recruited postmenopausal women through advertisement flyers. This center admits patients of all ages and disease states that are referred to them2014–201822–Caucasian ($100\%$), Hispanic ($30\%$)OSLO (Norway)Healthy male and female adult (18 years +) controls are recruited from the Department of Endocrinology, Oslo University Hospital, having been admitted and found to have a normal skeleton. To participate as healthy controls, they must have no active illness or disease that effects bone, have no history of fragility fracture, and consent to the measurementDepartment of Endocrinology, Oslo University Hospital by advertisement, self-referral, or physician referral2012–201871Caucasian ($100\%$)Caucasian ($100\%$)HDM (Spain)Healthy male and female adult (18 years +) controls are recruited from the outpatients clinic at the Hospital del Mar in Barcelona, Spain and agree to the measurement via informed consentOutpatient clinic Hospital del Mar Barcelona, Spain2010–201881–Caucasian ($100\%$)MGH (USA)Healthy male and female adult (18 years +) volunteers are recruited from the greater Boston, MA community and agree to the measurement via informed consentMassachusetts General Hospital (MGH) in Boston, MA2013–$2018812\%$ Asian $7\%$ Black $91\%$ White$2\%$ Asian $7\%$ Black $91\%$ WhiteGEELONG (Australia)Healthy male (20 + years) participants were selected from within the Geelong Osteoporosis Study between 2016 and 2018 and agreed to the measurement via informed consent. An age-stratified sampling method was utilised for the broader Geelong Osteoporosis Study, involving 12 strata for each sex. Individuals were selected at random from the electoral rollDeakin University-Barwon Health (University Hospital Geelong)2016–2018143Caucasian ($98\%$)– Studies included adult (18 + years) females from the USA (Minnesota) [21], healthy adult men and women (18 + years) recruited from the greater Boston community (Massachusetts) [34]; female adults (18 + years) from USA (Columbia, New York) [15]; male and female adults (18 + years) visiting outpatient clinic from Europe (Leiden University Medical Center (LUMC) [28, 35]; healthy male and female adult controls recruited from the Department of Endocrinology, Europe (Oslo) [24, 36]; healthy males and females (adult 18 + years) recruited from the outpatients clinic at the Hospital del Mar in Barcelona, Spain, and the Geelong Osteoporosis Study [37] (GOS), a population-based cohort study situated in a geographically well-defined region in Australia (Geelong). The inclusion criterion for this study were:Men or women aged 25 years and older as skeletal maturity is known to occur at 25 years of age [38].Ability to ambulate independentlyAbility to lie motionless in the supine position for 15 minMeasurements on the left or right tibia with OsteoProbe. The exclusion criterion were:A DXA-confirmed T-score ≤ − 2.5 at femoral neck or lumbar spinePrevious tibial stress fractureTibial lesion or tumourActive infection, significant oedema or obesity that puts a thick layer of soft tissue over the tibial surfacePregnancySecondary osteoporosis as indicated by markers for diseases:Fragility fracture(s)Any disorder associated with altered skeletal structure or function including the presence of chronic renal impairment (chronic kidney disease [CKD] stage IV or V), chronic liver disease, severe neuropathic disease, peripheral neuropathy, unstable cardiovascular disease, malignancy, chronic gastrointestinal disease, neoplasia, osteomalacia, hypoparathyroidism or hyperparathyroidism, acromegaly, Cushing’s syndrome, hypopituitarism, severe chronic obstruct pulmonary disease, alcoholism, or Type 1 or Type 2 diabetes, pathological fracture (e.g. due to Paget’s disease, myeloma, metastatic malignancy) or hereditary/genetic diseases that affect the skeletonUndergoing treatment for blood clots or coagulation defects, or treatment with any of the following drugs:Glucocorticoids (> 3 months at any time or > 10 days within the previous year)Anticonvulsant therapy within the previous yearSupraphysiological doses of thyroid hormone causing thyroid stimulating hormone to decline below normalAnabolic steroidsAromatase inhibitorsCalcitoninCalcium supplementation > 1500 mg/d within the preceding 3 monthsVitamin D supplementation > 2000 IU/D within in preceding 12 monthsBisphosphonates within previous 3 yearsEstrogen or selective estrogen receptor modulator within the past yearParathyroid hormoneSodium fluorideDenosumab, any use in last 12 monthsThiazolidinediones Thus, data for this cross-sectional analysis of healthy participants were generated for 197 men and 282 women (ages 25 to 98 years) measured between 2011 and 2018. ## Methods All participants were drawn from studies approved by the Ethics Committees at each institution. All participants provided written informed consent. Data were evaluated following an a posteriori method as defined by the US Clinical and Laboratory Standards Institute [39]. ## Bone Impact Microindentation (IMI) IMI was measured using the OsteoProbe (Active Life Scientific, Inc., Santa Barbara, CA, USA). Each clinical site performed measurements using a single OsteoProbe device. The indentation site on the anterior surface of the mid-tibia was determined by measuring the midpoint from the medial border of the tibial plateau to the distal edge of the medial malleolus. Following disinfection of the area and administration of local anesthetic, the OsteoProbe was inserted through the skin and periosteum until reaching the surface of the bone at the anterior face of the mid-tibia. A minimum of eight and a maximum of 18 indentations were performed for each participant. At each of the participating research centres, a trained operator performed the measurement. A person was considered a trained operator if they had been taught to use the OsteoProbe by an Active Life qualified personnel, had measured at least 20 volunteers, and had the results of the measurement assessed and verified by Active Life, the manufacturer of the device. The recommendations for the standard procedure for using the OsteoProbe has been published elsewhere [40]. The procedure is well tolerated. An Investigational Device Exemption (IDE) clinical trial that focussed on the safety of the procedure was completed in 2020, with only one reported adverse event (classified by an independent Clinical Events Committee as “mild”), a report of joint pain with a reported pain of 1 out of 10 on the Numeric Rating Scale pain scale [41]. Other studies to report adverse events include one case of minor skin infection that was resolved with oral antibiotics, reports of minor discomfort and minor bruising that required no medical interventions [40] and an adverse event related to the local anaesthesia [34]. A prospective study of men from Australia reported that participants tolerated the procedure well, demonstrating the high feasibility of performing IMI measures [42]. ## Bone Mineral Density (BMD) Areal BMD (g/cm2) was measured at the femoral neck, total hip and lumbar spine using DXA. The DXAs at each site were: Hologic at the Mayo, Hologic at Columbia, Hologic at MGH, Hologic at LUMC, Lunar (iDEXA) at OSLO, QDR 4500 SR; and Horizon Wi, Hologic, Inc., Bedford, MA, USA at Hospital del Mar, Barcelona and Lunar (Prodigy Pro) at Geelong. In order to compare BMD values measured using the different DXA scanners, standardised BMD (sBMD) was computed [43]. ## Statistical Analysis The original data were obtained from qualified clinical sites. A qualified clinical site is a clinical centre with one or multiple operators that had measured over 50 patients prior to collecting data for the study. BMSi data are stored locally on the medical device and is available for retrospective analysis at any time. Each qualified clinical site received the protocol and reviewed their measurement population according to the specific inclusion and exclusion criteria for the reference interval analysis for the period between and including January 1, 2010, and February 5, 2018. All data are stored in the same manner on each medical device. Each clinical site captured additional clinical and demographic variables that were stored in local institutional databases. Each clinical site was queried to request BMSi data from the device along with associated clinical and demographic information for each de-identified subject. Each centre also provided BMD data; however; two centers measured BMD as per the National Osteoporosis Foundation guidance, “Clinician’s Guide to Prevention and Treatment of Osteoporosis”, hence only BMD values for male participants over 70 years and female participants over 65 years were available from those centres. Data at each clinical site were further evaluated retrospectively to determine which individuals met inclusion/exclusion criteria as defined below. Table 1 lists centres who qualified to participate. The following questionnaire was used to query clinical sites for healthy reference interval participants. The following participant data were obtained from each study centre:Participant ID (de-identified)Sex (M/F)Age (years)BMD/T-Score (g/cm2/T-score) for femoral neck, total hip and lumbar spineWas informed consent received? ( Y/N)Institutional Review Board approved? ( Y/N) Approval ID Number (if available)Was the measurement made by a trained operator? ( Y/N)Were there any device-related adverse events? ( Y/N) If device-related adverse event observed, what was observed?Did the participant meet all of the inclusion criteria (Y/N)Did any of the exclusion criteria apply to the participant? ( Y/N) The original data included all individual indentations for each subject and the corresponding reference material measurements (i.e. raw data). All raw data were included in analysis (including data previously flagged by the operator as an ‘outlier’) and no raw data were excluded. All raw indentation values were re-run according to the latest version of the OsteoProbe software to recalculate the BMSi for each participant. The latest software deploys an automatic filter to identify and remove outlier indentations (the ‘filter’), thereby eliminating any potential operator inconsistencies in outlier selection. The filter used by the OsteoProbe software to identify outliers was thoroughly reviewed by Food and Drug Administration (FDA) as part of the FDA clearance process. The filter is based on the principal that IMI indentations on bone follow a normal distribution. Furthermore, the standard deviation used by the filter is based on the upper end of the range for the observed standard deviations of OsteoProbe indentations on bone across thousands of indentations spanning animal models and human cadavers and confirmed by clinical in vivo measurements. By using the upper end of the observed standard deviations, the filter only eliminates extreme BMSi values that are highly unlikely to be true bone indentations. Among all participants, including the sex and age-specific subgroups, BMSi values were normally distributed as indicated by Ryan-Joiner Test. BMSi scores were transformed ([z-mean]/SD) to normal score standard where z is the BMSi of a participant, and mean and SD are the average and standard deviation of the male or female cohort. Means and standard deviations (SDs) were calculated for each of the following age groups: < 35, 35–44, 45–54, 55–64, 65–74, and ≥ 75 years. Relationships between BMSi values and age and BMD were described using Pearson’s correlation (for continuous variables) and one-way ANOVA when age was grouped into categories. Differences in BMSi between men and women were assessed using a two-sample t-test. A one-way ANCOVA was further conducted to compare the difference while controlling for age. Multivariable linear regression models were developed to determine how BMSi was associated with age, sex and BMD; the residuals for the regression models were visualised for normality. Two-sided Healthy Reference Intervals for men and women were calculated whereby the lower and upper boundaries corresponding to the − 1.96 to + 1.96 SD from the mean are considered outside the $95\%$ confidence intervals. This standard statistical method describes the distribution of BMSi in this population of healthy men and women and does not imply that individuals whose BMSi lies outside the $95\%$ confidence interval are necessarily at risk of fracture or other adverse bone pathology. Statistical analyses were performed using SAS, IBM SPSS Statistics (v28.0.0.0) and Minitab (v16, USA). ## Results Descriptive characteristics for all participants are shown in Table 2.Table 2Participant characteristics (mean ± SD, [minimum, median, maximum])All ($$n = 479$$)Men ($$n = 197$$)Women ($$n = 282$$)Age (yr)56.4 ± 15.458.1 ± 15.055.3 ± 15.5[25.0, 59.1, 98.0][25.0, 61.0, 98.0][25.0, 58.0, 87.0]sBMD femoral neck (g/cm2)0.747 (± 0.306)1.005 (± 0.187)0.818(± 0.214)[0.268, 0.633, 1.500][0.484, 1.015, 1.400][0.484, 0.756, 1.500]sBMD total hip (g/cm2)0.975 (± 0.161)1.063 (± 0.129)0.914 (± 0.150)[0.032, 0.976, 1.389][0.760, 1.057, 1.389][0.032, 0.899, 1.281]BMSi81.3 (± 8.6)84.4 (± 6.9)79.0 (± 9.1)[48.1, 81.7, 101.4][62.3, 84.2, 101.4][48.1, 80.0, 101.1]sBMD standardised bone mineral density; BMSi bone material strength indexMissing data: BMD femoral neck $$n = 45$$, total hip $$n = 97$$, lumbar spine $$n = 57$$ In the linear regression models, no interactions between BMSi and age or sex were identified. The correlation between BMSi and age for the whole group was not significant (r = + 0.032, $$p \leq 0.479$$); the pattern was similar for each sex (men r = + 0.039, $$p \leq 0.583$$; and women r = − 0.12, $$p \leq 0.841$$). No difference between age-categories was detected for the whole group (ANOVA $$p \leq 0.969$$) or when stratified by sex (ANOVA; men $$p \leq 0.862$$, women $$p \leq 0.816$$). Table 3 and the boxplots in Fig. 1 show no discernable inter age-group differences in BMSi. Figure 2 shows the BMSi data for men and women. Table 3Bone material strength index (BMSi) by age (decade). Data are shown for all participants and by sexGroupAge (yr)NMeanSDMinimumLower quartileMedianUpper quartileMaximumALL < 356481.19.758.575.381.888.4101.435–444781.98.465.577.082.587.896.745–548580.78.948.176.581.488.096.755–6411481.18.457.076.380.587.3101.165–7412981.78.356.076.882.587.497.9 > 754081.28.853.876.281.188.394.7MEN < 352383.68.462.379.083.289.2101.435–441286.34.480.083.286.088.094.945–544183.96.472.179.582.789.496.755–644884.26.971.178.583.189.998.565–745385.07.0567.280.285.690.597.9 > 752083.77.173.078.981.191.094.7WOMEN < 354179.710.158.571.480.487.497.335–443580.48.965.571.980.087.896.745–544477.810.048.171.979.884.691.555–646678.88.757.074.079.384.0101.165–747679.38.456.074.580.684.696.2 > 752077.99.453.873.679.085.093.0Fig. 1Bone material strength index (BMSi) for men and women combined, by age (decade)Fig. 2Histograms showing BMSi data for men and women In the whole group, BMSi was positively associated with BMD at the femoral neck (r = + 0.223, $p \leq 0.001$) and at the total hip (r = + 0.107, $$p \leq 0.037$$). However, when stratified by sex, correlations between BMSi and BMD at both sites were not significant; men (femoral neck; r = + 0.035, $$p \leq 0.634$$, total hip; r = + 0.013, $$p \leq 0.870$$), women (femoral neck; r = − 0.072, $$p \leq 0.256$$, total hip; r = − 0.090, $$p \leq 0.177$$). Mean BMSi was greater in men than women (84.4 ± 6.9 vs 79.0 ± 9.1, $p \leq 0.001$). The absolute mean difference between men and women was 5.386 ($p \leq 0.001$)]. This significant difference persisted when adjusted for age [F [1, 476] = 49.086, $p \leq 0.001$]. Calculations of Healthy Reference Intervals indicate values ranging from 71.0 to 97.9 for men and 59.8 to 95.2 for women (Fig. 3).Fig. 3BMSi Schematic showing reference norms for men and women. * BMSi cutpoints are rounded to the nearest whole number ## Discussion Here we present BMSi data for healthy men and women drawn from the USA, Europe and Australia. Participant ages ranged from 25 to 98 years and BMSi values ranged from 48.1 to 101.4. No associations were detected between BMSi and age within this healthy population. However, mean BMSi for women was 5.4 lower than for men. We have provided mean and SD values for each age decade, which can be used to calculate T- and Z-scores for BMSi. T-scores indicate how much a person’s BMSi varies from the mean, and a Z-score compares a person’s BMSi to the average BMSi of people of the same age. Since BMSi did not vary with age in either sex, the Z-score and T-score of BMSi are the same. Importantly, men whose BMSi is below 71.0 and women whose BMSi is below 59.8 can be considered as having low BMSi. On the other hand, men with BMSi greater than 97.9 and women with BMSi greater than 95.2 can be considered as having high BMSi. Aligning with our observations, no association between BMSi and age was detected in two studies limited to older women designed to investigate fractures [44] and bisphosphonate treatment [26]. It is interesting to note that while population-based data involving 252 men from Australia indicated no correlation between age and BMSi [42], when the sample size was increased to 405, a small age-related decline in BMSi of approximately 0.8 units was detected for each decade increase in age [45]. In another study, a negative association between BMSi and age (r = − 0.539, $p \leq 0.001$) was also reported among 90 male and female patients with low bone mass [28]. Such a pattern may seem plausible, given that age-related factors such as loss of bone mass and structure [46, 47], accumulation of microcracks and deterioration of bone microarchitecture contribute to diminished bone strength [48, 49]. Notably, however, most studies reporting an association between age and BMSi have included individuals with underlying risk factors. It is plausible that an age-related decline in BMSi is more prominent in a population with comorbidities. It is also plausible that BMSi does not change with age in healthy individuals but may be altered by disease states and therefore remains a determinant of fracture risk. Further, it is likely that exclusion criteria for our study and the heterogeneity of the population might have limited the range of age-related factors that affect bone. Similarly, our exclusion criteria produced a sample where BMD at the proximal femur did not decrease with age (data not shown). There is growing evidence that IMI may have a future in clinical practice as a complementary tool to conventional bone testing methods for predicting fracture. It has been shown to capture unique properties of bone that are not captured by DXA, particularly in populations where BMD has limited ability to discriminate fracture risk, for example, in patients with type 2 diabetes [15–19, 21] or chronic kidney disease [20, 25]. It is known that fracture risk increases with age at any given BMD [50], suggesting this increased fracture risk is a function of, at least in part, other structural or material changes not captured by DXA. This age-related increase in fracture risk is likely to be related to the cumulative effect of comorbidities on bone structure and possibly material properties as well as increased propensity to falls. BMSi is lower in the presence of certain comorbidities [16, 17, 20, 21] and, as people age and accumulate comorbidities, altered bone material properties as detected by BMSi might contribute to the observed age-related increase in fracture risk [50]. However, its diagnostic utility for fracture has not been proven, and there is not sufficient evidence to support the introduction of IMI into clinical practice. This additional information provided by IMI will be useful for identifying people who would benefit from early intervention and for those at low risk, as treatment for low-risk people should be avoided. Further, the portability of the OsteoProbe device, and lack of radiation make it a practical alternative in rural settings, as access to radiation equipment and trained personnel is often inadequate. We acknowledge several strengths and limitations in this study. The major strength of this study is that data were obtained from a heterogeneous population of participants from the USA, Europe and Australia; thus, it will be relevant for a broad population in these regions. To the best of our knowledge this study is the first to include comparable data for varying geographical regions. We explored the associations between BMSi, age, and sex in the largest sample, and widest age range of men and women to date. Notwithstanding, the nature of this study was cross-sectional, the data were collected retrospectively, and prospective follow-up was not performed. Since it is impossible to pre-screen for fragile bone without the presence of a fracture, it is possible that some participants who met the inclusion/exclusion criteria to define healthy, and had very low BMSi, might go on to fracture. We acknowledge that it is likely that individuals with undetected bone issues may have been included in the healthy population, particularly in the female sample, where most were already visiting a hospital for some reason. Further, incomplete BMD data may have reduced the power to compare BMSi with BMD and given we did not observe an age-related decline in BMSi, the interpretation of T-scores is limited. The authors also acknowledge that the sample size is relatively small when compared to those used to develop the BMD reference data [51]. However, the sample size achieved adequate statistical power to allow meaningful conclusions. Further, IMI is a relatively new technology, currently only being used by a small number of researchers around the world, hence, there is a dearth of prospective studies, limiting their interpretation with respect to the ability of IMI to predict fractures. As more data emerge, our observations will serve as a reference study and lay the foundation for future work. Moreover, it is not yet clear what properties of bone IMI measures. There is evidence that BMSi correlates with tissue mineral density and degree of collagen cross linking [52], cortical density [53], cortical porosity and cortical volumetric BMD [54], as detected by peripheral quantitative computed tomography. Another study assessed bone material properties in iliac bone biopsies obtained concurrently with BMSi measurements in twelve participants, showing that BMSi correlates with subperiosteal bone properties [55]. It is also possible that geographical variation in populations has a role in BMSi. In the only published study evaluating geographical variation in BMSi, significant differences in BMSi were observed between countries, with BMSi higher in healthy Spanish women than in healthy Norwegian women [44]. This suggests that the observations from any one region may not be generalisable to other populations, as there may be differences in BMSi values between geographical areas. Similarly, as participants in this study were largely Caucasian, the results may not apply more broadly to individuals of other race or ethnic origins. In conclusion, we suggest that low BMSi corresponds to values below 71.0 for men and below 59.8 for women. This study also provides reference data that can be used to calculate T- and Z-scores for BMSi. These data will be useful for positioning individuals within the population and identify those with BMSi at the extremes of the population. 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--- title: 'Mental illness rates among employees with fixed-term versus permanent employment contracts: a Danish cohort study' authors: - Harald Hannerz - Hermann Burr - Martin Lindhardt Nielsen - Anne Helene Garde - Mari-Ann Flyvholm journal: International Archives of Occupational and Environmental Health year: 2022 pmcid: PMC9968265 doi: 10.1007/s00420-022-01936-7 license: CC BY 4.0 --- # Mental illness rates among employees with fixed-term versus permanent employment contracts: a Danish cohort study ## Abstract ### Purpose It has been hypothesized that employment in a fixed-term instead of permanent contract position is associated with an increased risk of development of mental health problems. The present study aimed at estimating rate ratios between fixed-term and permanent employees in the Danish labor force, for use of psychotropic drugs and psychiatric hospital treatment due to mood, anxiety or stress-related disorders, respectively. ### Methods Employment data were drawn from the Danish Labor Force Survey of 2001–2013, which is a part of the European Labor Force Survey. Full-time employed survey participants without mental illness at the baseline interview ($$n = 106$$,501) were followed in national health registers for up to 5 years. Poisson regressions were used to estimate rate ratios for redeemed prescriptions of psychotropic drugs and psychiatric hospital treatments due to mood, anxiety or stress-related disease. The analyses were controlled for age, gender, industrial sector, nighttime work, level of education, calendar year, disposable family income and social transfer payments within 1 year prior to the baseline interview. ### Results The rate ratio for hospital diagnosed mood, anxiety or stress-related disorders among employees with fixed-term vs. permanent employment contracts was estimated at 1.39 ($99.5\%$ CI 1.04–1.86), while the corresponding rate ratio for redeemed prescriptions of psychotropic drugs was estimated at 1.12 ($99.5\%$ CI 1.01–1.24). ### Conclusion The present study supports the hypothesis that employment in a fixed-term rather than permanent contract position is associated with an increased risk of developing mental health problems. ### International registered report identifier (IRRID) DERR2-$\frac{10.2196}{24392.}$ ### Supplementary Information The online version contains supplementary material available at 10.1007/s00420-022-01936-7. ## Introduction It has been hypothesized that fixed-term contract workers are at higher risk of developing mental health problems than permanently employed workers. A reason for this hypothesis is that the job insecurity associated with a non-permanent employment position may act as a stressor that may induce fears and worries about future unemployment, which in turn may increase a person’s vulnerability to mental ill health (Rönnblad et al. 2019). Another reason for believing in a prospective association between fixed-term contract positions and the risk of developing mental ill health is that the expiry date of a fixed-term employment contract may be followed by a spell of involuntary unemployment, which is a well-established risk factor of mental ill health (Paul and Moser 2009). The relationship between perceived job insecurity and subsequent mental ill health is well established. A recent review and meta-analysis of longitudinal studies (Rönnblad et al. 2019) estimated the odds ratio (OR) for adverse mental health among workers with self-reported job insecurity compared with workers without self-reported job insecurity at 1.52 [$95\%$ confidence interval (CI) 1.35–1.70]. The meta-analysis included 14 studies, with a total number of 43 568 participants. The OR was greater than one in all of the included studies. The same review article could, however, not establish a relationship between objective indicators of job insecurity, i.e., specific types of employments, and mental ill health. In particular, it could not establish that employment in a fixed-term instead of permanent contract position was associated with an increased risk of mental ill health; only a few such studies had sufficient quality, and their results were inconsistent (Rönnblad et al. 2019). Four longitudinal studies, one German ($$n = 2009$$), one Swedish ($$n = 660$$) and two Finnish ($$n = 65$$,208 and $$n = 107$$,828), have estimated longitudinal associations between fixed-term vs permanent employment and indicators of mental ill health (Hammarström et al. 2011; Virtanen et al. 2008; Ervasti et al. 2014; Demiral et al 2022). Only the German study was on a representative employee population, the two Finnish were on public sector employees and the Swedish study was on a follow-up of ninth-grade graduates. So we know little regarding the possible mental health effects of fixed-term contracts in representative employee populations. The effects of fixed-term contracts on health might be dependent on welfare state type. The Danish flexicurity welfare state type, characterized by low employment protection, high compensation for unemployed—even if the level of compensation has decreased somewhat—and a high turnover (Madsen 2006, 2013). This has led to a relatively low fraction of long-term unemployed among the unemployed and a low overall experience of job insecurity (Madsen 2006). One recent international comparative analysis shows that—in Denmark—the level of employment protection is generally low both among fixed-term contracts and open-end contracts—a combination which has been found to be beneficial for general health among those in fixed-term contracts (Voßemer et al. 2018). This comparative study, however, indicates that findings even from other North European countries, such as those mentioned above, cannot be transferred to a Danish context. Also, it should be noted that perceived job insecurity is a subjective construct that may be influenced by an individual’s personality traits. It has, for example, been shown that neuroticism is associated with perceived job insecurity (Blackmore and Kuntz 2011) as well as mental ill health (Lahey 2009). Hence, until it has been established that the prospective association between job insecurity and mental ill health also holds good for objective indicators of job insecurity, we cannot rule out the possibility that the positive association between self-reported job insecurity and development of mental ill health only appears among questionnaire respondents with, e.g., varying degrees of neuroticism. The present study aimed at estimating rate ratios between fixed-term and permanent employees in the Danish labor force, for use of psychotropic drugs and psychiatric hospital treatment due to mood, anxiety or stress-related disorders, respectively. The present study would thereby contribute to the international literature with information on relative rates of mental ill health between fixed-term and permanent employees in a nation with generous unemployment benefits and a legislation, which protects fixed-term contract workers against discrimination at the work place (The Council of the European Union, 1999). ## Methods The data material and statistical methods of the present study were completely specified, peer reviewed and published in a study protocol (Hannerz et al. 2021) before we looked for any relation between the exposure and the outcome data of the study. The protocol defines two separate studies. One of the studies would compare rates for use of psychotropic medicine and psychiatric hospital treatment among fixed-termed versus permanently employed people, while the other would do the same thing for fixed-termed employed versus unemployed people. The former of these studies is reported in the present paper, while the latter will be reported elsewhere. The study protocol contains the following copyright and license information: “©Harald Hannerz, Hermann Burr, Helle Soll-Johanning, Martin Lindhardt Nielsen, Anne Helene Garde, Mari-Ann Flyvholm. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 05.02.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited.” Relevant methodological details from the study protocol will be repeated or paraphrased in the method section of the present paper. ## Data material This study was based on baseline data on employment status from the Danish Labor Force Survey (DLFS) (Statistics Denmark 2019), which is the Danish part of the European Labor Force Survey (Eurostat 2021). Data from 2001 to 2013 were used for baseline, and follow-up data on health came from a series of registers, which cover the entire population of Denmark. The following registers were used: the Central Person Register (CPR) (Pedersen 2011), the Employment Classification Module (ECM) (Petersson et al. 2011), the Danish Education Registers (Jensen and Rasmussen 2011), the Danish Family Income Register (Statistics Denmark 2020), the Danish Register for Evaluation of Marginalization (DREAM) (The Danish Agency for Labour Market and Recruitment 2019), the Psychiatric Central Research Register (Mors et al. 2011), and the National Prescription Register (Kildemoes et al. 2011). Linkage on an individual level was based on participants’ personal identification numbers (Pedersen 2011). DLFS is based on quarterly random samples of 15- to 74-year-old residents of Denmark, with systematic oversampling of unemployed people. Each participant is invited to be interviewed four times over the course of a year-and-a-half. The purpose of the interviews is to collect person-based information on inter alia, labor market attachment, type of contract, and working hours (Statistics Denmark 2019; Eurostat 2021). Among those invited for the DFLS, the response rate decreased over time from $70\%$ in 2002 to $53\%$ in 2013 (Hannerz et al. 2018). The CPR contains, inter alia, information on gender and dates of birth, death, and migrations for every person who is or has been a resident of Denmark sometime between 1968 and the present time. The ECM contains annual, person-based information on, inter alia, the socioeconomic status, occupation, and industry of the residents of Denmark. The Danish Education Registers contain person-based information on, inter alia, a person’s highest educational attainment. The Danish Family Income Register contains information on household income. DREAM contains weekly, person-based information on social transfer payments (welfare benefits payments) such as maternity and paternity benefits, sickness absence benefits, unemployment benefits, social security cash benefits, and state educational grants. DREAM has existed since 1991 and covers all residents of Denmark. The weekly benefits data are recorded if the person has been on a benefit for 1 or more days of the week. However, as only one type of social transfer payment can be registered per week, types of benefits are prioritized in the case of data overlap. The above-mentioned social transfer payments are prioritized in the order listed, that is, maternity and paternity benefits have higher priority than sickness absence benefits, which in turn have higher priority than unemployment benefits, etc. The Psychiatric Central Research Register contains person-based information on inpatients, outpatients, and emergency ward visits in all psychiatric hospital departments in Denmark. The National Prescription Register contains person-based data on all redeemed prescriptions at pharmacies in Denmark. ## Clinical end points Rate ratios were examined for the following end points:Redeemed prescriptions for any type of psychotropic medicine, that is, drugs in the ATC-code category N05 (psycholeptica) or N06 (psychoanaleptica)Psychiatric hospital treatment with mood, anxiety, or stress-related disorder (ICD-10: F30–F41 or F43) as the principal diagnosis The following mental disorders are included in the above case definition:F30 Manic episodeF31 Bipolar affective disorderF32 Depressive episodeF33 Recurrent depressive disorderF34 Persistent mood (affective) disordersF38 Other mood (affective) disordersF39 Unspecified mood (affective) disorderF40 Phobic anxiety disordersF41 Other anxiety disordersF43 Reaction to severe stress and adjustment disorders ## Exposure The participants were categorized as “employed on a fixed-term contract position” or “employed on a permanent contract” in accordance with their responses to the question “Do you have a temporary or permanent employment contract?” ## Control variables The analyses were controlled for gender, age (10 year classes), calendar year of the interview (2001–2003, 2004–2006, 2007–2009, 2010–2013), disposable family income (tertiles), educational level (low, medium, high, unstated), industry (“agriculture, forestry, hunting, and fishing”, “manufacturing, mining, and quarrying”, “construction”, “wholesale, retail and repair of motor vehicles”, “transporting and storage”, “accommodation and food service activities”, “human health and social work activities”, “other industries”, “unstated”), nighttime work (regularly, occasionally, never) and reception of maternity or paternity benefits (yes, no), unemployment benefits (yes, no) and state educational grants (yes, no) sometime during the 1-year period preceding the baseline interview. The variables “gender”, and “age”, refer to the status at the time of the baseline interview. The variables “disposable family income”, “educational level” and “industry group”, refer to the status in the calendar year preceding the interview. The variable “nighttime work” refers to a 4-week period preceding the interview. Further details about the operationalization of the control variables are given in our study protocol (Hannerz et al. 2021). ## Follow-up The follow-up in the register data started on the date when 6 weeks had passed since the first DLFS interview and ended on the date when any of the following events occurred: the participant emigrated, the participant died, the participant met the clinical end point of the analysis, 5 years had passed since the date of the start of the follow-up, or the study period ended. The end of the study period was set at the end of the calendar year 2014 for redeemed prescriptions of psychotropic drugs and 2017 for psychiatric hospital treatments. Person-years at risk were calculated for each of the included participants. Participants who died or emigrated during the follow-up were censored at the time of the event. The present study had access to the data on Anatomical Therapeutic Chemical Classification System (ATC) codes from the National Prescription Register for the time period 2000–2014 and International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes from the Psychiatric Central Research Register for the time period 1995–2017. Thus, the follow-up periods regarding the two outcomes differed in length. ## Inclusion criteria The primary analyses were based on data from the participants’ first interview in the time period 2001–2013. Participants were eligible for inclusion if the following criteria were fulfilled:The participants were aged between 20 and 59 years at the time of the interview. They were employed, according to the interview. They usually worked ≥ 32 h a week, according to the interview. They did not receive any social transfer payments (other than holiday allowance, unemployment benefits, maternity/paternity benefits, or state educational grants) during the 1-year period preceding the interview. They did not receive any psychiatric hospital treatment with mental disorders (ICD-10: F00–F99) as the principal diagnosis during the 1-year period preceding the start of follow-up. They did not redeem any prescription for psychotropic drugs (ATC: N05–N06) during the 1-year period preceding the start of follow-up. Since the fulfillment of inclusion criteria 4–6 only could be ascertained for participants who lived in Denmark throughout the 1-year period preceding baseline, we excluded all participants who migrated within this period. We also excluded participants with missing values on the covariates of the analysis. The reason for excluding part-time workers is that some workers may have chosen to work part time due to ill health. Based on survey data (cf. Feveile et al., 2007), we estimated that part-time workers who also had a fixed-term contract constituted approximately $1.5\%$ of all employees in 2005. ## Primary statistical analysis Poisson regression was used to estimate RRs for psychiatric hospital treatment for mood, anxiety, or stress-related disorders and redeemed prescriptions for psychotropic drugs, as a function of employment status at baseline (full-time fixed-term contract versus full-time permanent contract). The analyses were adjusted for age, gender, disposable family income, educational level, calendar year of the interview, baseline industry group, nighttime work and reception of maternity or paternity benefits, unemployment benefits and state educational grants sometime during a 1-year period preceding baseline. The logarithm of person-years at risk was used as an offset. Likelihood ratio tests were used to test first for main effects and then for effects of interaction with gender, age, and education level. The main effects were tested both for psychiatric hospital treatments and redeemed prescriptions for psychotropic drugs. Due to power concerns, the interaction effects were only tested for redeemed prescriptions for psychotropic drugs. Each of the tests were conducted at the significance level 0.005. We controlled for industry, as a previous study has found significant industry-related inequalities in the rate of mood disorders among employees in the general working population of Denmark (Hannerz et al. 2009). We controlled for unemployment benefits and state educational grants in the 1-year period preceding the interview, as we believe that people’s attitudes toward fixed-term and permanent contracts may depend on their previous labor market attachment. We controlled for nighttime work because it has been shown that the prevalence of psychotropic drug usage in Denmark is greater among shift workers than among workers without shift work (Albertsen et al. 2020). We controlled for reception of maternity or paternity benefits, since the birth of a child may result in maternal (O'Hara and McCabe 2013) and paternal (Scarff 2019) postpartum depression. The remaining control variables were included, since the literature suggests that the risk of mental ill health depends on gender (Parker and Brotchie 2010; McLean et al. 2011), age (Wittchen and Hoyer 2001; Tjepkema 2005; Kessler et al. 2010), calendar year (Steinhausen and Bisgaard 2014), education level (Andrade et al. 2000), and income (Orpana et al. 2009; Schlax et al. 2019; Kosidou et al. 2011; Patel et al. 2018). We tested for interactions, as it has been suggested that the strength of adverse health effects of fixed-term contracts depends on gender (Pirani and Salvini 2015), age (Wanberg et al. 2016), and education level (Virtanen et al. 2008). ## Sensitivity analyses A series of pre-specified sensitivity analyses were conducted to: (i) estimate rate ratios in a subset of the study population where exposure is more stable over time, (ii) estimate rate ratios without control for industrial sector, nighttime work, calendar year, disposable family income and welfare benefits within the 1-year prior to baseline, (iii) estimate rate ratios by industrial sector, (iv) compare rate ratios obtained with and without exclusion of former cases of psychiatric treatment, (v) examine rates as a function of reason for having a fixed-term employment contract, and (vi) estimate relapse rate ratios. The motivations, methods and results of the sensitivity analyses are presented in the appendix. ## Results Between 1 January 2001 and 31 December 2013, 325 553 persons participated in the DLFS, whereof 106 501 were eligible for inclusion in the primary analysis of the present study. A flowchart for inclusions and exclusions of the primary analysis is given in Fig. 1.Fig. 1Flowchart for inclusions and exclusions of the primary analysis Among the included participants, we detected 11 616 cases of redeemed prescriptions for psychotropic drugs and 948 cases of mood, anxiety or stress-related psychiatric hospital treatment, in 430 733 and 519 162 person-years at risk, respectively. Among the cases of psychiatric hospital treatment, $0.6\%$ were manic episodes (ICD-10: F30); $2.0\%$ were bipolar affective disorders (F31); $27.5\%$ were depressive episodes (F32); $8.9\%$ were recurrent depressive episodes (F33); $0.7\%$ consisted of persistent (F34) other (F38) or unspecified affective mood disorders (F39); $2.5\%$ were phobic anxiety disorders (F40); $9.0\%$ were other anxiety diagnoses (F41); and $48.7\%$ consisted of adjustment disorders and reactions to severe stress and (F43). Among the cases of psychotropic drug use, $2.6\%$ of the prescribed drugs were antipsychotics (ATC-code: N05A); $21.9\%$ were anxiolytics (N05B); $35.8\%$ consisted of hypnotics and sedatives (N05C); $39.1\%$ were antidepressants (N06A); $0.57\%$ were psychostimulants (N06B); $0.00\%$ were antidepressants in combination with psycholeptics (N06C); and $0.05\%$ were antidementia drugs (N06D). The rate ratio for hospital diagnosed mood, anxiety or stress-related disorders among employees with fixed-term vs. permanent employment contracts was estimated at 1.39 ($99.5\%$ CI 1.04–1.86), while the corresponding rate ratio for redeemed prescriptions of psychotropic drugs was estimated at 1.12 ($99.5\%$ CI 1.01–1.24). The rate ratios for redeemed prescriptions of psychotropic drugs seemed to be statistically independent of age, gender and education level; the P values of the tests for interaction were estimated at 0.62, 0.23 and 0.22, respectively. The statistical power was too low to allow testing for interaction effects for psychiatric hospital treatment. The rate ratios, number of persons and person-years at risk in the analyses of redeemed prescriptions of psychotropic drugs are given in Table 1, with and without stratification by gender, age and education level. The results of the hospital treatment analysis are given in Table 2. Table 1Rate ratio (RR) with $99.5\%$ confidence interval (CI) for use of psychotropic drugs, as a function of type of employment contract among full-time employees in Denmark 2001–2013Type of populationType of employment contractPersonsPerson-yearsCasesRR$99.5\%$ CIAll employeesaFixed-term746029,2429031.121.01–1.24Permanent99,041401,49110,7131–Gender stratabMenFixed-term340013,1623231.181.00–1.40Permanent54,590223,73748431–WomenFixed-term406016,0795801.080.95–1.23Permanent44,451177,75458701–Age stratac20–29 yearsFixed-term352213,4902841.050.87–1.27Permanent13,22254,03910431–30–39 yearsFixed-term181471712461.180.97—1.43Permanent26,526109,24826151–40–49 yearsFixed-term105642771711.100.88–1.38Permanent30,547122,50534691–50–59 yearsFixed-term106843042021.160.94–1.44Permanent28,746115,69835861–Educational level stratadHighFixed-term227987102591.100.92–1.33Permanent30,425120,01930901–MediumFixed-term320812,8554041.150.99–1.34Permanent51,442210,73253741–LowFixed-term190874982281.060.86–1.29Permanent16,39367,85021541–UnstatedFixed-term65179122.050.87–4.86Permanent7812890951–aAdjusted for age, gender, industrial sector, nighttime work, education, calendar year, disposable family income and state educational grants, unemployment benefits and maternity/paternity benefits within 1 year prior to baselinebAdjusted for all of the above control variables except gendercAdjusted for all control variables except agedAdjusted for all control variables except education levelTable 2Rate ratio (RR) with $99.5\%$ confidence interval (CI) for psychiatric hospital treatment due to mood, anxiety or stress-related disorders, as a function of type of employment contract among full-time employees in Denmark 2001–2013Type of employment contractPersonsPerson-yearsCasesRRa$99.5\%$ CIFixed-term746035,6351321.391.04–1.86Permanent99,041483,5278161–aAdjusted for age, gender, industrial sector, nighttime work, education, calendar year, disposable family income and state educational grants, unemployment benefits and maternity/paternity benefits within 1 year prior to baseline None of the results obtained in the sensitivity analyses were drastic enough to invalidate the findings of the primary analyses [cf. Appendix: Tables S1–S6]. The sensitivity analysis, which stratified rate ratios for use of psychotropic drugs by industrial sector, suggested, however, that the effect of having a fixed-term versus permanent employment contract may be especially high in the transport and storage industry, where the rate ratio was estimated at 1.87 ($99.5\%$ CI 1.14–3.07) [cf. Appendix: Table S6]. Apart from the pre-specified sensitivity analyses, we conducted two post hoc sensitivity analyses. In one of the analyses, we excluded phobic anxiety disorders from the case definition. All other details of the analysis were the same as in the primary analysis of the psychiatric hospital treatments. In this post hoc sensitivity analysis, the concerned rate ratio was estimated at 1.42 ($99.5\%$ CI 1.06–1.91). In the other post hoc analyses, we extended the required period of “no psychiatric hospital treatment” from 1 to 5 years prior to the baseline interview. All other details of the analysis were the same as in the primary analysis of the psychiatric hospital treatments. In this post hoc sensitivity analysis, the concerned rate ratio was estimated at 1.34 ($99.5\%$ CI 0.98–1.82). ## Main findings We found that the rate ratios for use of psychotropic drugs and psychiatric hospital treatment due to mood, anxiety or stress-related disease, in the Danish labor force, were statistically significantly higher among employees with fixed-term vs. permanent employment contracts. The tests for interactions with age, gender and education level were not statistically significant. ## Results in relation to previous research We found four relevant studies that estimated longitudinal associations between fixed-term vs permanent employment and indicators of mental ill health, one from Germany (Demiral et al. 2022), one from Sweden (Hammarström et al. 2011) and two from Finland (Virtanen et al. 2008; Ervasti et al. 2014). The German study dealt with employees in employments subject to social security payments (Demiral et al. 2022) aged 31–60 years—representing $80\%$ of all people working in that age range ($$n = 2009$$). Odds ratios for depressive symptoms as a function of fixed-term employment contract (yes vs. no) were 2.20 ($95\%$ CI 0.80–6.06) among men and 1.42 (0.61–3.32) among women. The analyses were adjusted for baseline [2012] age, partnership status and socioeconomic position. The study population of the Swedish study (Hammarström et al. 2011) consisted of all ninth-grade graduates of the calendar year 1981, in Luleå, who held temporary and/or permanent employment contracts between the age of 30 and 42 years ($$n = 660$$). Questionnaire data were collected at the age of 30 and 42 years. Odds ratios at the age of 42, for the contrast “temporary employment for a total time of more than 10 months” versus “permanent employment during the whole 12-year period” were estimated at 1.90 ($95\%$ CI 1.33–2.71) for psychological distress and 1.79 ($95\%$ CI 1.04–3.08) for depressive symptoms. The analyses were controlled for gender, self-rated health, psychological distress and depressive symptoms at age 30. One of the Finnish studies (Virtanen et al. 2008) examined associations between temporary employment and redeemed prescriptions for antidepressant medication (1998–2002) among 17,071 men and 48,137 women employed municipalities in Finland. After adjustment for age, socioeconomic status (SES), and calendar year, the odds ratio for the contrast “fixed-term > 6 months vs. permanent employment” was estimated at 1.18 ($95\%$ CI 1.03–1.37) for antidepressant use in men and 0.99 ($95\%$ CI 0.93–1.06) in women. The corresponding odds ratios for the contrast “fixed-term < = 6 months vs. permanent employment” were estimated at 1.43 ($95\%$ CI 1.19–1.73) in men and 1.18 ($95\%$ CI 1.09–1.28) in women, and for the contrast “subsidized temporary work vs. permanent employment” they were estimated at 1.57 ($95\%$ CI 1.23–2.02) in men and 1.38 ($95\%$ CI 1.20–1.59) in women. The association between type of employment contract and use of antidepressants was statistically significantly weaker among women than it was among men ($$p \leq 0.007$$). The association was, moreover, weaker among men with high SES than it was among men with low SES ($$p \leq 0.033$$). The other Finnish study (Ervasti et al. 2014) examined associations (2005–2011) between temporary vs. permanent employment and sickness absence due to medically certified depressive disorders (ICD-10 codes F32–F34) among 107,828 Finnish public sector employees. The concerned rate ratio was estimated at 1.02 ($95\%$ CI 0.97–1.08). The analysis was adjusted for age, gender, level of education, chronic somatic disease and history of work disability due to mental or behavioral disorder (ICD-10 codes F00–F99). No significant interaction with gender, age, or education was observed ($p \leq 0.25$). The associations between fixed-term contracts and mental ill health that were observed in the present study aligns well with the findings of the Swedish study (Hammarström et al. 2011) and the first of Finnish studies (Virtanen et al. 2008). The German study’s relatively small population size combined with low prevalence of fixed-term contracts might explain its insignificant findings (Demiral et al. 2022). A possible explanation for the null-finding observed in the second of the Finnish studies (Ervasti et al. 2014) is that it did not estimate rate ratios for depressive disorders but for sickness absence due to depressive disorders. Some workers with depressive disorders may call in sick while others may continue to work, and it has been shown that temporary employees, due to job insecurity, tend to have higher rates of sickness presence than permanent employees do (Virtanen et al. 2003; Reuter et al. 2019). It might also be that different levels of employment protection in fixed-term contract and in permanent contracts across countries could lead to country dependent associations between contract type and health (Voßemer et al. 2018). ## Methodological considerations Our study has several strengths. The study was quite large and the statistical power was high enough to investigate main effects of having a fixed-term versus permanent employment contract. Bias from missing follow-up data was substantially reduced, since the endpoints of the study were ascertained through national registers that cover all inhabitants of the target population. Within-study selection bias was eliminated, since all hypotheses, significance criteria, endpoints and statistical methods were completely defined and published before we looked at any relation between the exposure and outcome data of the study (Hannerz et al. 2021). The major drawback of the study is that it is observational and thereby has a weaker design than a randomized controlled trial, which is the golden standard in determining causality. Another weakness is the low response rate, which means that we cannot rule out the possibility of non-response bias. We believe, however, that any such bias has been mitigated by the many control variables that were included in the analyses. Individual participant data were available on a large variety of socioeconomic and occupational factors, which enabled us to control the analyses for a series of possible confounders and health selection effects such as age, gender, education, industry, nighttime work, unemployment benefits and income. Control for unemployment is relevant in order to take selection into part-time work into account. Control for income is important, as it has been found that effects of insecurity in employment can be alleviated by increased wage levels (Böckerman et al. 2011). It has been shown that the risk of developing depression is associated with smoking habits (Pasco et al. 2008; Korhonen et al. 2007) and body mass index (Luppino et al. 2010). In the present study, we did not have any individual participant data on smoking habits and body mass index, and could therefore not include these factors as control variables in the analyses. We had, however, access to collateral data, which we have used to estimate age, gender and education standardized prevalence of smoking, overweight, and obesity among 20–59 year-old employees in Denmark, by type of employment contract (Hannerz et al. 2021). The estimated prevalence among people with fixed-term contracts were very similar to those among people with permanent contracts. It is therefore unlikely that the results of the present study have been influenced by differential prevalence of smoking, overweight and obesity. We have not conducted any validation study of self-reported information on employment contract. We believe, however, that most (if not all) employees know if they have a permanent or temporary employment contract and that the question that was used to obtain the information in the present study is very easy to understand and difficult to misinterpret. Moreover, the question is not sensitive and it is not subject to recall bias. It should, however, be noted that our analysis do not account for time-variant unobservable characteristics that may have an impact on the results. It is, for example, possible that a person with fixed-term employment at baseline will become permanently employed or unemployed during the 5 year follow-up period. It is also possible that a person with permanent employment at baseline will become unemployed or shift to fixed-term contract position. Such transitions are probably associated with a bias toward unity. In the present study, we used rate ratios of hospital treatment and redeemed prescriptions of drugs as proxy measures for underlying morbidity ratios. Hence, we need to consider the possibility of detection, prescription, and referral bias. In Denmark, all citizens are covered by a tax-funded health insurance, which enable them to consult a general practitioner and to receive psychiatric treatment free of charge, whenever it is needed. Since fixed-term and permanent employees have equal access to general practitioners as well as psychiatric hospitals and specialists, we do not think that the present study is subject any detection, prescription or referral bias of practical importance. Psychiatric treatment is a rare event; hence, insufficient statistical power restricted the study of that outcome to a main effect only model. Psychotropic drugs include a few types that are used for disorders not expected to be associated with stressors like fixed-time contracts, e.g., psychostimulants and antidementia drugs. However, only a few promille of the cases were due to such drugs. The underlying research hypothesis of the present project was that objective job insecurity may act as a stressor that increases a person’s vulnerability to mental ill health, without further specification. From this viewpoint, it may seem natural to include all types of mental disorders in the case definition of psychiatric hospital treatment. We chose, however, to exclude the vast majority ($87\%$) of the diagnoses listed in the chapter on “mental and behavioral disorders” of the ICD-10 classification, and to focus solely on diagnoses that are labeled as mood, anxiety or stress-related disorders. We excluded F00–F09 “Organic mental disorders” because of their etiology in cerebral disease or brain injury, which make them quite irrelevant to the context of the present study; F60–F69 “Personality disorders”, F70–F79 “Mental retardation”, F80–F89 “Disorders of psychological development” and F90–F98 “Behavioral and emotional disorders with onset usually occurring in childhood and adolescence” because such disorders typically develop well before the entering of the labor market; somatoform disorders, firstly, because of an extraordinarily long expected duration between the onset of the complaints and the diagnosis (Herzog et al. 2018) and, secondly, because the labeling of such disorders as mental illnesses is controversial (Rief and Isaac 2007; Kroenke 2007); F10–F19 “Mental and behavioral disorders due to psychoactive substance use”, F42 “Obsessive–compulsive disorder” and F50–F59 “Behavioral syndromes associated with physiological disturbances and physical factors” because we wanted to keep our case definition simple and easy to communicate, which would not have been the case if we had included these diverse sets of behavioral disorders; and F20–F29 “Schizophrenia, schizotypal and delusional disorders” because they are associated with an extraordinarily high heritability (Hilker et al. 2018) and a low labor market attachment (Marwaha and Johnson 2004; Rinaldi et al. 2010), which make them quite irrelevant to the context of the present study. Here, it should be noted that the last mentioned category contains F25 “Schizoaffective disorders” and that manic, bipolar and depressive schizoaffective disorders thereby were excluded from our case definition. We chose to base our case definition on diagnostic standard groupings at the two- or three-character level rather than on an ad hoc collection of four-digit level sub-categories, for several reasons. Firstly, because we wanted to decrease the probability that relevant cases were missed. Secondly, because the probability of misclassifications, i.e., false positive and false negative diagnoses, are likely to be higher at the four-character level than they are at the two and three-character level (Jensen et al. 2010). Thirdly, because a wider diagnostic category is less sensitive to random variation than a narrower diagnostic category. Since (i) participants who received social security cash benefits, sickness absence benefits, psychotropic medicines or psychiatric hospital treatment within a 1-year period prior to baseline were excluded from the analysis and (ii) most of the mental disorders that are likely to depend on factors occurring before adulthood were excluded from the case definition, we do not believe that the study is subject to reverse causality bias of practical importance. The case definition included, however, phobic anxiety disorders (ICD-10: F40), which often manifest themselves already in childhood or adolescence (Kessler et al. 2007; Solmi et al. 2022). It is possible that some of the cases of phobic anxiety that were observed in the present study existed already at the start of the follow-up. It is also possible that that some people may be unable to obtain or hold a permanent employment position due to phobic anxiety disorders. Hence a possibility of reversed causation. To explore this possibility, we conducted a post hoc sensitivity analysis in which we excluded phobic anxiety disorders from the case definition. All other details of the analysis were the same as in the primary analysis of the psychiatric hospital treatments. In this post hoc sensitivity analysis, the concerned rate ratio was estimated at 1.42 ($99.5\%$ CI 1.06–1.91). To further explore the possibility of reversed causation in the analysis of the hospital treatment data, we conducted a post hoc sensitivity analysis in which we extended the required period of “no psychiatric hospital treatment” from one to five-year prior to the baseline interview. All other details of the analysis were the same as in the primary analysis of the psychiatric hospital treatments. In this post hoc sensitivity analysis, the concerned rate ratio was estimated at 1.34 ($99.5\%$ CI 0.98–1.82). In the analysis of psychotropic drugs, we aimed at estimating the association between our exposure variable and redeemed prescriptions for psychotropic medicine, and with such an aim, it made sense to include all types of psychotropic medicine in the case definition. Two types of health selection bias need to be considered in the interpretation of the results. The first one concerns the possibility of bias due to health selection into a fixed-term or permanent employment position. It is, for example, possible that some people are unable to obtain or hold a permanent employment position due to lingering mental health problems. The second type of bias concerns health selection into the analysis. In our primary analysis, we included only DLFS participants with no social security cash benefits, no sickness absence benefits, no redeemed prescriptions for psychotropic medicines and no psychiatric hospital treatment during a whole year prior to the baseline interview. It was, moreover, required that they were full-time employees at the time of the interview. The purpose of the rigorous inclusion criteria was to counter potential bias from health selection into a fixed-term employment position. The consequence of the rigorous inclusion criteria is that the subset of fixed-term contract employees that was included in the primary analysis was far from representative of the full set of DLFS participants with a fixed-term contract position at baseline. It goes without saying that those who were permanently employed at baseline are more likely to have been permanently employed also prior to baseline and vice versa. Hence, if there are any health risks associated with not having a permanent employment contract then the fixed-term employees who were included in the primary analysis are likely to be more privilege and less vulnerable to the consequences of not having a permanent employment than the ones who were excluded. Seen from this perspective, selecting away cases 1 year—and especially 5 years—prior to baseline can be regarded as a very conservative approach underestimating possible effects of fixed-term contracts on depressive symptoms, as effects can have occurred before the follow-up period. To shed some light on these health selection effects, we conducted two sensitivity analyses. In one of the analyses, we (i) removed the requirement of not receiving sickness benefits or social security cash benefits during a 1-year period prior to the baseline interview and (ii) removed all control variables except for gender, age, and education. We kept, however, the requirement of full-time employment at baseline and no redeemed prescriptions for psychotropic medicines and no psychiatric hospital treatment during a whole year prior to the baseline interview. The purpose of this analysis was to obtain an unbiased estimate of the rate ratio of psychotropic drug use between “a representative set of the DLFS participants with a full-time fixed-term contract position” and “a representative set of the DLFS participants with full-time permanent employment” after standardization for gender, age and education. The rate ratio in this analysis was estimated at 1.31 ($99.5\%$ CI 1.21–1.42). In another sensitivity analysis, we extended the required period of “no redeemed prescriptions for psychotropic medicines and no psychiatric hospital treatment” from 1 to 5 years prior to the baseline interview (on top of the rigorous inclusion criteria and potentially over-adjusted confounder control of the primary analysis). In this sensitivity analysis, the rate ratio of psychotropic drug use between employees with fixed-term vs. permanent employment contracts was estimated at 1.05 ($99.5\%$ CI 0.90—1.23). Further details about our pre-specified sensitivity analyses are given in the appendix. ## Generalizability The results of the present paper should be seen in the light of specific conditions at the Danish labor market, which have been labeled flexicurity, a certain combination of low employment contract protection and generous compensation levels regarding unemployment benefits (Bredgaard and Madsen 2018; Madsen 2006, 2013). Effects of fixed-term contracts on health might be dependent on the welfare states’ employment protection regarding fixed-term and permanent contracts (Voßemer et al. 2018). This means that experienced job insecurity in fixed-term and permanent contracts could vary considerably between welfare state regimes making inference of study results across countries difficult. ## Conclusions We know very little about the possible effects of contract type and mental health across countries. Increased cooperation between labor market and health researchers could contribute to shed more light into this question. The present study supports the hypothesis that employment in a fixed-term rather than permanent contract position is associated with an increased risk of developing mental health problems in Denmark. The results in themselves do not warrant specific interventions regarding fixed-term contracts as they can range from (a) restrictions in establishing fixed-term contracts over (b) improvements in working conditions for this group of workers to (c) specific health-related interventions. We can, however, conclude that the results of the study lend support to the necessity of the EU council directive $\frac{1999}{70}$/EC of 28 June 1999 concerning the framework agreement on fixed-term work (The Council of the European Union 1999). 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--- title: The potential utility of the SAGIT instrument in the clinical assessment of patients with acromegaly, a large single-centre study authors: - Nadia Sawicka-Gutaj - Paulina Ziółkowska - Aleksandra Biczysko - Abikasinee Erampamoorthy - Katarzyna Ziemnicka - Marek Ruchała journal: Scientific Reports year: 2023 pmcid: PMC9968276 doi: 10.1038/s41598-023-29957-3 license: CC BY 4.0 --- # The potential utility of the SAGIT instrument in the clinical assessment of patients with acromegaly, a large single-centre study ## Abstract SAGIT is an instrument created for the clinical assessment of acromegaly. Our objective was to test the usefulness of this tool in assessing disease activity of acromegalic patients in a single centre of Poznan, Poland using a retrospective study. Medical records of patients with acromegaly hospitalised at the Department of Endocrinology, Metabolism and Internal Medicine of Poznan University of Medical Sciences in Poland between January 2015 and December 2020 were analysed. SAGIT scores were assessed according to each patient's clinical and biochemical data. The results show that SAGIT scores were higher in treatment-naïve patients and the lowest in controlled patients. There were positive correlations between SAGIT scores and concentrations of calcium, phosphorus, HbA1C levels, and tumour invasiveness at the time of diagnosis. However, parameters such as age, vitamin D concentration, and time from diagnosis showed an inverse relationship with the SAGIT score. In ROC curve analysis, SAGIT scores of 5 or less discriminated controlled patients from uncontrolled ($p \leq 0.0001$, sensitivity $76.7\%$, specificity $78.5\%$, AUC 0.867). Also, SAGIT higher than 6 indicated for treatment start or escalation ($p \leq 0.0001$, sensitivity $80.88\%$, specificity $77.59\%$, AUC 0.866). Lack of signs and symptoms ($S = 0$) could not discriminate between controlled and uncontrolled disease, but predicted therapy maintenance ($p \leq 0.0004$, sensitivity $59.5\%$, specificity $58.2\%$, AUC 0.604). In conclusion, The SAGIT instrument is easy to use even when completed in the retrospective medical record review. It can be useful for distinguishing clinical stages of acromegaly and in decision-making. ## Introduction Acromegaly is a chronic disease caused by growth hormone (GH)-secreting pituitary adenomas1, leading to increased levels of insulin-like growth factor (IGF-1). Hence, some profound somatic changes are observed, such as enlarged hands and feet, thickened skin, mandible growth, joint tenderness, and enlarged facial features involving the facial bones, lips, nose, and tongue2,3. Other symptoms of acromegaly include headaches, excessive sweating, snoring or apnea, changes in voice timbre, weight gain, and sexual dysfunction2,3. These signs and symptoms significantly reduce the quality of life of patients suffering from acromegaly4,5. In addition, acromegaly leads to the development of many diseases, particularly affecting the cardiovascular system (hypertension, heart failure), and metabolism (diabetes), and increasing the risk of various cancers (thyroid cancer, colorectal cancer)6–10. These associated comorbidities in turn, also lower the quality of life and shorten life expectancy11,12. The goals of acromegaly therapy, such as neurosurgical treatment and somatostatin analogues are mainly to normalise GH and IGF-1 levels, lower symptoms, reduce the risk of developing comorbidities, and prolong survival13,14. Therefore, it is essential to balance the delay of the intensification of therapy with the overtreatment of the disease. It is also crucial to consider the patient's well-being when making therapeutic decisions along with monitoring the intensity of symptoms typical for acromegaly15. Therefore, the search for a tool began that would be of decisive importance and would also consider both clinical and biochemical parameters in the care of acromegalic patients. The SAGIT instrument was created to help endocrinologists care for patients with acromegaly in everyday clinical practice by providing crucial information about the disease activity and severity and assisting in the therapeutic decision-making process16,17. SAGIT is an acronym which reflects each part of the tool: signs and symptoms (S), associated comorbidities (A), GH levels (G), IGF-1 levels (I) and tumour profile (T)16,17. Recently, SAGIT was recognised as a sensitive tool, helpful in the management of acromegaly18. Nevertheless, studies have shown that acromegalic clinical features and complications differ between countries and cultures19. Therefore, investigating this tool in a Poland cohort can add more information in this field. In this study, we aimed to test the usefulness of SAGIT in assessing disease activity and establishing therapeutic decisions in acromegalic patients of an endocrine centre in Poznan, Poland. For this purpose, a retrospective single-centre study based on patients' medical history was conducted. ## Study design Medical charts of adult patients with acromegaly hospitalised at the Department of Endocrinology, Metabolism and Internal Medicine of Poznan University of Medical Sciences in Poland between January 2015 and December 2020 were retrospectively reviewed. Clinical and biochemical data were collected. SAGIT instrument was completed using patients' medical records. Patients were divided into three categories: stable/controlled; active/uncontrolled and treatment-naïve. Also, treatment decisions were recorded as continuing current therapy with no change/no treatment initiation; intensifying current therapy/initiating a treatment; or reducing the current treatment. Acromegaly was diagnosed according to the current guidelines20. ## Ethics approval The Bioethical Committee of Poznan University of Medical Sciences approved this study and waived the requirement for informed consent due to the retrospective nature of the study (Decision No $\frac{633}{22}$). All methods were performed in accordance with the relevant guidelines and regulations21. The Bioethical Committee of Poznan University of Medical Sciences approved this study and waived the requirement for informed consent due to the retrospective nature of the study (Decision No $\frac{633}{22}$)21. ## Clinical and laboratory assessment At admission, every patient had a medical interview and underwent a physical examination. Blood samples of all patients were taken after overnight fasting. IGF-1 and GH were measured. According to guidelines, nadir GH in the oral glucose tolerance test was measured in all non-diabetic patients. In patients with diabetes mellitus and in those, who had already been treated with somatostatin analogues, random GH was calculated as an arithmetic mean of five measurements of blood samples obtained every 30 min. MRI scan of the pituitary gland was also performed routinely in all patients with acromegaly unless there were no contraindications. Treatment naïve acromegaly was diagnosed when all of the following criteria were fulfilled:IGF-1 elevated above the age-adjusted upper norm limitLack of suppression of GH below 1 ng/mL in 75 g oral glucose tolerance test (patients without diabetes) or random GH levels above 2.5 ng/mL (patients with diabetes)A pituitary gland tumour visualised in magnetic resonance imaging (MRI) or computed tomography (CT) (in patients with contraindications for MRI). Previously treated patients who achieved age-normalisation of IGF-1 and normalisation of GH (GH < 1 ng/mL in 75 g OGTT or random GH < 2.5 ng/mL in diabetic patients) were classified as a stable/controlled group. Acromegaly was considered active/uncontrolled (non-remission group) when IGF-1 and GH were elevated in patients who had already been treated. Patients who could not be classified according to the above-mentioned criteria were excluded from the analysis. Tumour invasiveness was defined as the infiltration of surrounding tissues. ## SAGIT instrument The SAGIT instrument was created to assess the clinical disease activity of acromegaly and assist in making therapeutic decisions. SAGIT is an acronym reflecting key components of acromegaly: signs and symptoms (S), associated comorbidities (A), GH levels (G), IGF-1 levels (I) and tumour features (T)16,17. Each of the above units is scored by the clinician: S(0–4), A(0–6), G(0–4), I(0–3), and T(0–5). The higher the score in each category and the total sum of points, the greater the advancement of a given factor and overall disease activity16–18. Therefore, this tool assesses clinical and biochemical factors, ensuring a comprehensive estimation of the patient's condition. The SAGIT has been recently validated by performing an international multicentre, non-interventional validation study18. ## Statistical analyses MedCalc® Statistical Software version 20.015 (MedCalc Software Ltd, Ostend, Belgium; https://www.medcalc.org; 2021) was used to perform statistical analysis. Normality was assessed by the D'Agostino-Pearson test. Comparisons between two and three groups were completed with the Mann Whitney and Kruskal Wallis tests, respectively. The Spearman Rank Correlation test was used to find an association between analysed parameters. To determine SAGIT utility to reflect the clinical status of acromegaly, receiver operating characteristics (ROC) curves were calculated. A P-value less than 0.05 was considered statistically significant. ## Patients and patient-admissions Three patients were excluded as they did not fulfil all criteria of remission/non-remission groups. Finally, 316 hospitalisations of 175 patients (53 patients were hospitalised twice, 15 were hospitalised three times, ten patients were hospitalised four times, and seven patients were hospitalised five times) were included for analysis. Two hundred-two admissions were of female patients ($63.9\%$). The median age of patient admissions was 58 years (IQR 45–64 years), and the median time of disease duration was 48 months (IQR 9–132 months). Median BMI was 28.3 kg/m2 (IQR 25.4–32.5 kg/m2). There were 55 treatment-naïve patients. One hundred forty-five patient admissions of previously treated patients (transsphenoidal surgery/current pharmacotherapy/radiotherapy) had active acromegaly: 35 were treated with octreotide, 70 with lanreotide, and three with pasireotide. Other clinical and biochemical data are presented in Table 1.Table 1Clinical and biochemical data of three study groups. CharacteristicNaïveN = 55Non-remission groupN = 145 patient-admissionsStable/controlled groupN = 116 patient-admissionspGender (M male; F female)M 29F 26M 50F 95M 35F 810.0141Age (years) Me (IQR)($$n = 55$$)54# (39–62)($$n = 145$$)56(45–65.25)($$n = 116$$)59#(47–64.5)0.041124BMI (kg/m2) Me (IQR)($$n = 51$$)28 (24.7–30.7)($$n = 134$$)28.1 (25.5–32.6)($$n = 106$$)29 (25.7–33.6)0.5583Duration time from diagnosis (months) Me (IQR)NA($$n = 140$$)48 (21–99.5)($$n = 132$$)132 (48–187) < 0.0001Tumour size (mm)Me (IQR)($$n = 55$$)11 (9–14)($$n = 137$$)11 (0–17.25)#($$n = 113$$)0 (0–8.3)# < 0.000001SAGITMe (IQR)($$n = 55$$)11 (9–14)($$n = 145$$)8 (5–11)($$n = 116$$)3 (3–5)* < 0.000001IGF-1 [ng/mL]Me (IQR)($$n = 55$$)702 (544.25–921)($$n = 145$$)342 (219.5–568.5)($$n = 116$$)167 (116–214)* < 0.000001nadir GH in OGTT($$n = 35$$)6.91 (3.05–18.3)($$n = 77$$)2.35 (1.52–4.33)($$n = 84$$)0.62 (0.28–0.88)$p \leq 0.000001$GH random($$n = 20$$)9.71 (4.49–17.02)($$n = 68$$)4.24 (1.96–8.8)($$n = 32$$)0.724 (0.44–1.34)$p \leq 0.000001$Significant values are in [bold].Me, median; IQR, interquartile range; BMI, body mass index; OGTT, oral glucose tolerance test; IGF-1, insulin-like growth factor; GH, growth hormone. Note: Data marked with the same markers differ significantly. ## SAGIT The highest median SAGIT global score was in treatment-naïve patients, and the lowest was in controlled patients ($p \leq 0.000001$, Fig. 1). There was an inverse correlation between patients' age and SAGIT score ($$p \leq 0.0397$$; r = − 0.122). SAGIT global score correlated with tumour invasiveness at the diagnosis ($$p \leq 0.0009$$; $r = 0.294$).Figure 1SAGIT score comparison between three study groups. ## SAGIT components "S" component (signs and symptoms) was higher in de novo acromegalic patients, while there was no difference between active and non-active patients. " A" component (associated comorbidities) was higher in the non-remission group. Comparisons of all SAGIT components are presented in Table 2.Table 2Comparison between SAGIT components of three study groups. NaïveN = 55Non-remission groupN = 145 patient-admissionsStable/controlled groupN = 116 patient-admissionspS1 (0–2)#0 (0–1)0 (0–1)#0.003176A2 (1–2)2 (1–3)#2 (1–3)#0.01G4 (2–4)2 (1.75–3)0 (0–1)* < 0.000001I3 (2–3)1 (0–3)0 (0–0)* < 0.000001T2 (1–4)2 (0–4)0 (0–1)* < 0.000001Note: Data marked with the same markers differ significantly. ## Association between SAGIT and laboratory parameters Higher SAGIT was associated with HbA1C and fasting glucose levels ($p \leq 0.0001$; $r = 0.394$ and $p \leq 0.0001$; $r = 0.368$, respectively). Also, SAGIT positively correlated with calcium and phosphorus concentrations ($p \leq 0.0001$; $r = 0.335$ and $p \leq 0.0001$; $r = 0.476$, respectively), while there was an inverse association with vitamin D ($$p \leq 0.0156$$; r = − 0.192) and time from diagnosis ($p \leq 0.0001$; r = − 0.410). "S" component correlated with IGF-1 concentrations ($p \leq 0.0001$, $r = 0.263$), glucose levels ($$p \leq 0.005$$, $r = 0.158$), time from diagnosis ($$p \leq 0.0039$$, r = − 0.165), and tumor size ($$p \leq 0.0001$$, $r = 0.226$). ## SAGIT as a predictor of further therapy In ROC curve analysis, SAGIT scores of 5 or less discriminated controlled patients from uncontrolled ($p \leq 0.0001$, sensitivity $76.7\%$, specificity $78.5\%$, AUC 0.867). Also, SAGIT higher than 6 indicated for treatment start or escalation ($p \leq 0.0001$, sensitivity $80.88\%$, specificity $77.59\%$, AUC 0.866). Lack of signs and symptoms ($S = 0$) could not discriminate between controlled and uncontrolled disease, but predicted therapy maintenance ($p \leq 0.0004$, sensitivity $59.5\%$, specificity $58.2\%$, AUC 0.604). Also, S higher than 0 indicated for treatment initiation/intensification ($p \leq 0.0001$, sensitivity $63.24\%$, specificity $60.92\%$, AUC 0.644). ## Discussion In this study, we investigated the usefulness of the SAGIT instrument in assessing disease activity and making therapeutic decisions during the management of patients with acromegaly in a single centre in Poznan, Poland. We found that the SAGIT score differed between disease activity groups and was the highest in naïve treatment but lowest in the stable/controlled group. These findings agree with the results shown in the SAGIT validation study18. Therefore, this supports the usefulness and credibility of this tool in the comprehensive assessment of disease activity in everyday clinical practice. Furthermore, we observed that the SAGIT-S component does not differ between controlled and uncontrolled disease. This conclusion raises a crucial issue which suggests that the normalisation of the biochemical parameters of disease activity does not equal a clinical cure for the disease. Many morphological changes in the body during acromegaly occur irreversibly, implying that these symptoms change in a continuous manner of greater or lesser intensity. This should be borne in mind when making therapeutic decisions. SAGIT, which contains both clinical and biochemical components, will be very helpful in this potential issue18. It is essential to mention the correlation of the SAGIT global score with specific biochemical parameters. There was also a strong association between SAGIT scores and fasting glucose and HbA1C levels. The effect of acromegaly and GH on glucose regulation is extensively studied. Insulin resistance and the development of diabetes mellitus as a complication have been established in patients with acromegaly22,23. It has been recently reported, that $95\%$ of patients with acromegaly suffer from comorbidities24. Therefore, the presence of associated comorbidities (A) in SAGIT is an advantage in assessing patients with acromegaly. We also observed a positive correlation between the SAGIT global score and the serum phosphorus concentration. It is well known that hyperphosphatemia is observed in patients with acromegaly. However, research on this issue is still ongoing, including in the context of disease activity25,26. Xie et al., concluded in their study that the level of phosphorus reflects the disease status as a product of metabolism. Moreover, it can help monitor the disease with divergent GH and IGF1 values26. Another positive correlation that we noticed is between the global score of SAGIT and the level of calcium. Mild hypercalcemia in patients with acromegaly is common and primarily parathyroid hormone-dependent which occurs as a result of concurrent parathyroid hyperplasia in patients with MEN-1. However, overt hypercalcemia in patients with acromegaly is very rare and is associated with parathyroid hormone-independent hypercalcemia27. Few cases have been reported, with authors concluding that the mechanism behind this is related to an increased level of 1,25 dihydroxy vitamin D28,29. Shi et al., point out that activation of 1-alpha hydroxylase by increased levels of IGF-1 in acromegaly could potentially be the cause of this phenomenon. The combination of absorption of calcium from the gut and kidney with increased bone turnover contributes to this. They also pointed out that hypercalcemia in these patients is reversible as remission of acromegaly is achieved30. Conversely, our study showed an inverse relationship between vitamin D levels and SAGIT scores. Researchers have also explored the potential vitamin D deficiency in acromegalic patients31. Therefore, the relationship between acromegaly and vitamin D regulation remains a complex topic and needs further research. We also found higher SAGIT scores among younger patients. The impact of age on endocrine parameters of acromegaly was studied by Colao et al., Their results show that IGF-1 levels, GH levels, and nadir GH after glucose load are inversely related to age. In addition, older patients had smaller adenomas than younger patients at the time of diagnosis32. Also, a study by Park et al.33 showed that younger patients with acromegaly tend to have more aggressive adenomas and biochemically hyperactive disease. Recently, a study evaluating the gender and age differences among acromegalic patients demonstrated that hyperprolactinemia, hypogonadism and macroadenomas are more frequent in younger patients34. However, it is also important to note that this finding could be attributed to the age differences of groups, with the treatment-naive group being younger than the disease-controlled group. Although research on this correlation is limited, age appears to affect acromegaly's clinical and biochemical parameters. Thus, the SAGIT system again demonstrates its excellency in assessing various aspects of the disease. The strengths of our analysis are, among others, a large research group and the analysis of SAGIT correlation with biochemical parameters. The study's retrospective nature is both positive- it proves that SAGIT can be determined based on medical records, and negative, as retrospective design leads to an information bias. It further illustrates the additional benefit of the SAGIT tool in that it can be assessed with ease without the use of any other third-party tools. The single centre of the study is the most critical limiting factor. More research on SAGIT is undoubtedly needed (especially on large groups of subjects) to explore the positive aspects of this tool and also to learn about its limitations. Nevertheless, the SAGIT tool turns out to be valuable and reliable in the assessment of disease activity. It might be helpful in the therapeutic decision-making process in patients with acromegaly. ## Conclusions The SAGIT instrument is easy to use even when completed in the retrospective medical record review. Our study indicates its potential utility for distinguishing clinical stages of acromegaly in patients from Poland. Therefore, we recommend that SAGIT be included in the patient's medical records. ## References 1. 1.Melmed, S. Acromegaly pathogenesis and treatment. J. Clin. Invest. 119(11), 3189–3202 (2009). https://www.mp.pl/paim/issue/article/16232. 2. Lugo G, Pena L, Cordido F. Clinical manifestations and diagnosis of acromegaly. Int. J. Endocrinol. (2012.0) 2012 1-10. DOI: 10.1155/2012/540398 3. 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--- title: Interpersonal variability of the human gut virome confounds disease signal detection in IBD authors: - Stephen R. Stockdale - Andrey N. Shkoporov - Ekaterina V. Khokhlova - Karen M. Daly - Siobhan A. McDonnell - Orla O’ Regan - James A. Nolan - Thomas D. S. Sutton - Adam G. Clooney - Feargal J. Ryan - Donal Sheehan - Aonghus Lavelle - Lorraine A. Draper - Fergus Shanahan - R. Paul Ross - Colin Hill journal: Communications Biology year: 2023 pmcid: PMC9968284 doi: 10.1038/s42003-023-04592-w license: CC BY 4.0 --- # Interpersonal variability of the human gut virome confounds disease signal detection in IBD ## Abstract Viruses are increasingly recognised as important components of the human microbiome, fulfilling numerous ecological roles including bacterial predation, immune stimulation, genetic diversification, horizontal gene transfer, microbial interactions, and augmentation of metabolic functions. However, our current view of the human gut virome is tainted by previous sequencing requirements that necessitated the amplification of starting nucleic acids. In this study, we performed an original longitudinal analysis of 40 healthy control, 19 Crohn’s disease, and 20 ulcerative colitis viromes over three time points without an amplification bias, which revealed and highlighted the interpersonal individuality of the human gut virome. In contrast to a 16 S rRNA gene analysis of matched samples, we show that α- and β-diversity metrics of unamplified viromes are not as efficient at discerning controls from patients with inflammatory bowel disease. Additionally, we explored the intrinsic properties of unamplified gut viromes and show there is considerable interpersonal variability in viral taxa, infrequent longitudinal persistence of intrapersonal viruses, and vast fluctuations in the abundance of temporal viruses. Together, these properties of unamplified faecal viromes confound the ability to discern disease associations but significantly advance toward an unbiased and accurate representation of the human gut virome. A longitudinal analysis of unamplified fecal virome of healthy controls and IBD patients reveals interpersonal variability is a factor that limits our ability to identify associations between the virome composition and IBD. ## Introduction The microbiome of patients with inflammatory bowel disease (IBD) has been the subject of frequent investigations as it is deemed a central component of disease pathogenesis1–3. Assessing the intra- and interpersonal variations in bacterial composition through 16S rRNA gene amplicon sequencing has become a keystone of microbiome studies and it has been shown that bacterial diversity is often indicative of disease activity and severity, particularly in IBD4. In recent years, the viral constituents of the human microbiome have gained recognition for their potentially important role in the maintenance of health and wellness. Previous studies have observed alterations in the diversity, composition, and/or functionality of the gut virome associated with a range of conditions, including type 1 and type 2 diabetes, irritable bowel syndrome, and IBD5–10. As highlighted by Gregory et al.11 during their compilation of a human gut virome database, $96\%$ of the studies they analysed employed multiple displacement amplification (MDA). This was included in the vast majority of studies to provide sufficient DNA for metagenomic sequencing. However, when applied to viromes, MDA selectively amplifies small circular and single-stranded DNA viruses, is associated with chimeric sequence formation, and under amplifies GC-rich genomes with non-uniform amplification of linear genomes12–14. Recent improvements in sequencing technology mean difficult-to-achieve starting quantities of nucleic acids are no longer an issue, eliminating the MDA step previously required. Therefore, it is an opportune and appropriate time to re-analyse the IBD virome, particularly considering the conflicting conclusions previously reported. Many of the contradictions reported between IBD virome studies can be attributed to inconsistent methodological and computational approaches. For example, Norman et al.15 concluded that the richness of tailed phages was increased amongst patients with Crohn’s disease (CD) and ulcerative colitis (UC) relative to controls. This was later refuted by Clooney et al.16 who included “viral dark matter” sequences with no database representatives. Zuo et al.17 believed the mucosal virome of patients with UC contained a high viral load of low diversity phages including giant viruses. The presence of giant viruses was later argued by Sutton et al.18 to result from flawed taxonomic assignment approaches. Finally, Manrique et al.19 proposed there was a globally distributed healthy gut phageome of core and common viruses that was diminished in patients with IBD. However, this concept was thrown into doubt by the immense diversity and interpersonal variability of human-associated viruses observed in subsequent meta-analyses of Earth’s virome20–23. The study reported here performed an original longitudinal analysis of the human gut, analysing both the virome of healthy controls and patients with IBD, without an MDA step, in combination with matched 16S rRNA amplicon sequencing. Our analysis of unamplified faecal viromes confirms that modest but significant alterations are observed in the intra- and interpersonal diversity metrics of IBD gut viromes compared to controls. However, 16S rRNA amplicon sequencing of corresponding samples showed much more clearly the distinct intra- and inter-sample diversity features frequently observed in IBD. During our investigation of unamplified viromes as potential biomarkers of IBD, we concluded that the lack of a disease signal could be attributed to (i) the infrequent detection of interpersonal communal viruses, (ii) the prevalence of transiently detected intrapersonal viruses and (iii) the temporal fluctuations of intrapersonal persistent viruses, as compared to bacterial taxa. This analysis highlights the interpersonal variability of the human gut virome and the need for the viral research community to establish stringent and consistent criteria when discerning associations between viruses and biomarkers of health and disease. ## An overview of unamplified faecal viromes In this study, we analysed unamplified faecal viromes to perform one of the most unbiased assessments of the viral components of the human microbiome to date. A total of 40 healthy controls and 39 patients with IBD donated two ($$n = 4$$) or three ($$n = 75$$) faecal samples at approx. 100-day intervals (T2: mean 93.2, SD 28.8; T3: mean 192.3, SD 71.7; see Supplementary Data). Virome samples were sequenced across four runs, with the randomisation of control, CD, and UC samples per run resulting in no significant difference in the final number of reads obtained per condition (Supplementary Fig. 1a–c). A significant decrease in the number of viral-recruited reads (to the viral contig database created in this study) per sample was observed for patients with IBD, with a concomitant increase in bacterial and human-recruited reads (Supplementary Fig. 1d–h). Extensive metadata encompassing 88 variables were collected from patients with IBD, with Supplementary Fig. 2 providing a simplified overview of the cohorts’ lifestyle choices and medical history (for further information, see Supplementary Data). Similar to previous gut virome studies24, strict filtering criteria were applied to viral enriched metagenomes to remove potential contaminants during analyses (Supplementary Fig. 3). Out of 65,852 viral contigs, 18,149 represented previously reported viral species (with ≥$95\%$ nucleotide identity and ≥$85\%$ coverage cut-off)25 from recent comprehensive surveys of the human gut virome21,26. The rest of the viral contigs were unique to the present study. The majority of the taxonomically identifiable human faecal virus are tailed phages of the class Caudoviricetes ($94.3\%$), historically exemplified as families Siphoviridae, Myoviridae, and Podoviridae (Fig. 1a). Unsurprisingly, human-associated Crassvirales phages with characteristic ~100 kb circular genomes were prominent within faecal viromes (Fig. 1b). As expected, the unamplified gut virome is dominated by “unclassified viruses”. Specifically, only $26.6\%$ and $13.7\%$ of this study’s gut virome database could be assigned taxonomic or life cycle information, respectively (Fig. 1c, e). Even without MDA, this study identified 911 circular viral genomes (Fig. 1d) including 112 circular Microviridae genomes. Human-host and plant-infecting viruses constitute only a small fraction of the total faecal viruses detected (Fig. 1f). For instance, eleven genomes of plant RNA viruses (Alphaflexiviridae, Tombusviridae, Virgaviridae), apparently of dietary origin, were detected. In addition, a single enterovirus genome (Picornaviridae) and several human adenovirus genome fragments were detected. However, in contrast to previous studies employing MDA27,28, relatively few eukaryote-infecting viruses with small circular genomes (CRESS DNA viruses29,) were identified (e.g., Anelloviridae and Circoviridae).Fig. 1Overview of unamplified faecal viromes detected in control and IBD subjects.a The frequencies of the most abundant viruses assigned familial taxonomic ranks, showing b their contig length distributions. The number of viruses identified in this study that (c) could be assigned a taxonomic rank, d characterised as circular (True = circular; False = non-circular partial sequence/linear), or e encoding genes for phage lysogenic replication. f The contig length relative to sequence coverage for infrequently detected eukaryotic faecal viruses that could be assigned a taxonomic rank. ## Differences in unamplified virome and 16S diversity metrics in IBD While distinctly visible in the 16S β-diversity analysis, no clear ordination separation of controls versus patients with CD or UC is discernible using unamplified virome data (Fig. 2a, b). Indeed, a statistically smaller variance (R2) is attributable to the condition variable for the virome data, compared to 16 S (PERMANOVA p ≤ 0.001, $1.28\%$ and $4.15\%$, respectively). Like previously reported30,31, α-diversity analyses of 16 S data showed a clearly significant reduction in the diversity, evenness, and richness metrics associated with CD and UC samples compared to control samples (Supplementary Fig. 4b). However, differences in the α-diversity of unamplified viromes are less pronounced with only the richness of control samples versus CD samples showing a clear significant difference ($$p \leq 0.008$$; Supplementary Fig. 4a).Fig. 2Diversity analyses of unamplified faecal viromes. PCoA β-diversity ordinations, using Canberra distances, highlighting compositional variation within a unamplified viromes and b 16S rRNA gene compositions, grouped by condition. Again, β-diversity ordinations of c unamplified virome and d 16S data, but each individual sample is coloured by its Shannon index α-diversity value. PCoA centroids for control and IBD sample ordinations, and the top 10 most and least α-diverse samples, for the e unamplified virome and f 16S ordinations. Spearman’s correlation (rs) and p-values are shown above (e, f), calculating the association of sample α -diversity to condition. Spearman’s correlation coefficients (+/−): 0.0–0.19 = very weak; 0.40–0.59 = moderate. Statistics are based on $$n = 118$$ control samples, $$n = 56$$ CD samples and $$n = 59$$ UC samples. Superimposing the α-diversity of individual samples onto the β-diversity PCoA ordinations tentatively indicated that 16 S differences in α-diversity occur along the same β-diversity PCoA axis as disease separation (Fig. 2c, d). To highlight this result more clearly and determine if the same was true for the virome data, the β-diversity PCoA centroids for control and IBD patients were calculated alongside the centroids for the ten highest and lowest α-diversity samples. For the 16S data, there is a clear convergence of centroids from the highest α-diversity/non-IBD samples to the lowest α-diversity/IBD samples (Fig. 2f). Whereas for unamplified viromes, α-diversity and IBD status are juxtaposed (Fig. 2e). This observation was supported using multiple β-diversity distance metrics (Supplementary Fig. 5). Finally, while the Shannon index α-diversity of 16 S samples shows a moderate but significant correlation with condition, no significant correlation is observed for unamplified viromes (Fig. 2e, f). Furthermore, only “very weak” and “weak” insignificant correlations are observed between the α-diversities of unamplified virome and 16 S samples, even when stratified by condition (Supplementary Fig. 6). ## The individuality of faecal viromes is more extreme in IBD Despite the limited differences observed in the α-diversities of controls and patients with IBD, there were large disproportional changes in the number of viral taxa shared between cohort members. To avoid introducing a bias by sampling cohorts of differing sizes, first, the number of viruses or viral clusters (VCs) shared across individuals (henceforth termed “communal” viruses) was calculated. Subsequently, 10 individuals from each condition were randomly selected 20 times and the frequency at which the previously identified communal viruses occurred was calculated. Immediately it is evident that there is a significantly greater chance for controls to possess viruses or VCs that are shared by two or more individuals and therefore their viromes are not as individual-specific (Fig. 3a). The observation that different cohorts harbour communal viruses to an altered extent became insignificant between control and UC individuals when viruses or VCs present in more than eleven of the 79 cohort members were considered. However, there remained a significant difference in the detection of communal viruses between controls and CD patients across >$50\%$ of the total study’s population. Fig. 3Communal and compositional analyses of unamplified faecal viromes.a The number of communal viruses and viral clusters (VCs) shared by an increasing number of control, CD, and UC individuals. Bootstrapped subsampling of a single time point from 10 random individuals, for each condition, was performed 20 times. The relative abundance of faecal viruses, demonstrating b lysogenic phages shared between increasing percentages of control, CD, and UC individuals (Student’s t test was used to calculate statistical differences between cohorts), and c the prevalence of dominant viral taxa shared amongst increasing percentages of the total cohort individuals. The values at the top of each boxplot indicates the number of viruses or VCs averaged. Boxplots represent the standard Tukey representation, with boxes representing the 25th, 50th (median) and 75th interquartile range (IQR) percentiles, and the whiskers encompassing values within 1.5 times the IQR. Next, we focused our analysis on the fraction of human faecal viruses that were capable of a temperate lifestyle due to the presence of gene(s) associated with a lysogenic lifecycle. Previous reports found an increase in the presence of temperate phages in the faeces of patients with IBD, possibly the result of an inflamed gut environment inducing lysogenic phages16. We similarly observed an increase in the average relative abundance of phages harbouring lysogenic genes in patients with IBD relative to controls, particularly when viruses were stratified by their degree of sharing across the total cohort (Fig. 3b). However, as before, the number of viruses carrying lysogenic genes and VCs present in the unamplified faecal viromes of patients with CD and shared by >$40\%$ of the total cohort is fewer than amongst controls (199 versus 529, respectively). When unamplified faecal viromes are analysed with respect to assignable taxonomic information, there are both predictable and novel features associated with viruses and VCs shared across the study’s total cohort. As expected, crAssvirales are frequently observed in human faeces and constitute a large proportion of the average relative abundance of viromes (Fig. 3c)32–36. In contrast, the average relative abundance of Siphoviridae shared by the study population is more evenly distributed across all strata investigated. Unexpectedly, there are discrete relative abundance peaks associated with communal viruses shared across the total cohort for Myoviridae (30–$40\%$), Podoviridae (10–$20\%$) and Microviridae (10–$20\%$ and 20–$30\%$). Therefore, while not as universally present in viromes as the crAss-like phages, there are potentially discrete distributions of putative Myoviridae, Podoviridae and Microviridae viruses or VCs within human microbiome populations. Finally, we analysed the cumulative versus the average relative abundance of communal viruses shared by an increasing number of this study’s cohort. As the majority of unamplified faecal viruses or VCs are uniquely or rarely shared by cohort individuals (0–$10\%$) they have the greatest accumulative relative abundance, but viruses or VCs frequently encountered in viromes (>$40\%$) have the greatest average relative abundance (Supplementary Fig. 7a). Of note, communal viruses or VCs in controls vs patients with CD (0–$10\%$) reached statistical significance (Supplementary Fig. 7b). For the remaining unshared, less frequently encountered unamplified gut viruses with taxonomic information identified, most are only sporadically detected in cohort members (Supplementary Fig. 7c). An exception is dietary plant viruses of the Virgaviridae family that are frequently detected (30–$40\%$). ## Temporal variability is a feature of faecal viromes Variability in the human gut virome has clear implications when discerning potential associations of viruses or VCs that co-occur with microbial or physiological biomarkers. Using our unamplified faecal virome data, we set about characterising the persistence and fluctuations of viruses or VCs longitudinally. When the average number of viruses or VCs detected per faecal virome by condition is analysed without using rarefied data, the richness of control viromes is greater than that of patients with IBD (Fig. 4a). Statistically, fewer viruses or VCs persist across all three time points of UC viromes compared to the viromes of controls and patients with CD (Fig. 4b). However, both CD and UC viromes had statistically fewer viruses present in two of the three time points when compared to controls (Fig. 4c). Finally, patients with CD and UC had the greatest percentage of unique viruses or VCs present in only one of their three time points (Fig. 4d).Fig. 4Temporal stability of unamplified faecal viromes.a The total number of viruses and viral clusters (VCs) detected across control, CD, and UC faecal viromes where three time points could be analysed ($$n = 225$$ samples). The percentage of viruses and VCs detected in b all three time points ($$n = 225$$ samples), c two time points ($$n = 233$$ samples), and d unique to only one time point ($$n = 233$$ samples). Wilcoxon test p-values for specific group comparisons are shown. Boxplots represent the standard Tukey representation, with boxes representing the 25th, 50th (median) and 75th interquartile range (IQR) percentiles, and the whiskers encompassing values within 1.5 times the IQR. Red diamonds with values underneath display the mean. e Ternary plots showing the relative abundance, as a percentage, of viruses and VCs with taxonomic assignments across the three time points. Viruses and VCs with a relative abundance of $33\%$ (pink triangle) were equally present across all three time points. The shape aesthetics indicates putative viral familial assignments, while the background shading of the ternary plot triangles represents viral positional density. While 16S and virome sequencing data can provide different views of the microbiome, we can look at how bacterial and viral taxa are shared across healthy individuals or patients with IBD to determine if there are notable differences in these cohorts. The number of 16S communal taxa shared by cohort members gradually decreases for controls and patients with CD and UC (Supplementary Fig. 8a). However, there is a noticeable drop-off in shared 16S communal taxa by CD and subsequently UC microbiomes, as the criterion for sharing viruses, that is presence of viruses across individuals is increased (by approx. 20 and 25 individuals, respectively). For viromes, there is a rapid decrease in the sharing of communal viruses or VCs by controls and patients with IBD (Supplementary Fig. 8b). In all instances, Control viromes share the most communal viral taxa, followed by UC and finally CD viromes. Despite the clear starting difference in the number of 16S and viral taxa, the interpersonal variability of the virome in this study results in little sharing of taxa across cohort members. Fluctuations in the relative abundance of viruses or VCs present at all three time points were performed (Fig. 4e). There were 38 Control, 18 CD and 19 UC viromes where all three time points were available. Viruses located in the central pink triangle of each ternary plot represent viruses or VCs with an equal relative abundance, expressed as a percentage, across the three time points (i.e., $33\%$ on each axis). It is striking that (a) the greater number of viruses present in the Control ternary plot, and (b) the seemingly random fluctuation of viruses or VCs across the three time points. The latter is influenced by both the greater number of control samples available for analysis and the increased richness associated with controls, while the former is true for both viruses with and without taxonomic information (Fig. 4e and Supplementary Fig. 9, respectively). To discern if there is a statistical difference between intra- and interpersonal unamplified gut viromes, the dissimilarity of viromes was calculated using the Bray-Curtis index. Interestingly, while no intra-personal variability is discernible using the matched 16S sample data, there is a statistically significant difference in both the intra- and interpersonal virome (Supplementary Fig. 10). ## Discussion Recent gut microbiome studies are demonstrating bacteria and their phages co-exist in stable equilibrium, lasting for many months and even years24,37–39. Such equilibrium is achieved through convergence of different ecological and co-evolutionary mechanisms operating at the level of individual phage-host pairs (evolutionary arms race, fluctuating selection) and at the level of complex polymicrobial community as a whole (kill-the-winner dynamics, host jumps). This complex web of interactions may lead to a situation where both the phages and their hosts mutually benefit from each other’s presence in the system, termed “antagonistic co-evolutionary mutualism” in the recent review39. Phages may improve the fitness and resilience of their host bacteria populations as predators, driving diversifying selection40,41, or as lysogens altering phenotypic properties42. Understanding these complex interactions, particularly to the point of correctly discerning and/or modulating health or disease-associated properties, remains a significant challenge for those studying the human microbiome and its viral constituents. In this study, we compared unamplified faecal viromes alongside 16S rRNA gene analysis of matched samples, contrasting healthy controls and patients with CD and UC. Even when we generated viral clusters from assembled viral strains to resemble the taxonomic rank of 16S OTUs, unamplified viromes and 16S rRNA gene amplicons are different datatypes that represent different aspects of the microbiome. Therefore, direct comparisons need to be performed and interpreted carefully. Indeed, all virome and 16S rRNA gene comparisons that are described in this study were performed with the same diversity metrics and are presented, where possible, side-by-side. We believe the analysis presented, in contrast to prior landmark studies that employed MDA15,16,19,24,27,43–48, is a progressive step towards an unbiased understanding of human microbiomes and the most accurate assessment of healthy and IBD viromes to date. The inter-individual variability of virome is a known fact demonstrated in previous studies by our group16,24 becomes even more prominent when shotgun sequencing methods are used which avoid biases of MDA. A principal reason for that is that the virome is fundamentally analysed at strain level. High strain-level diversity of bacteriophages, rapid evolution of their genomes, lack of evolutionary-conserved genes, high levels of genome mosaicism, lack of correspondence between phage taxonomy and taxonomy of their bacterial hosts create staggering levels of phage diversity in the human gut, within and between individual human subjects, even twins44. With advances in database collation of viral data and taxonomic classification (higher-order phage taxa introduced in recent revision of ICTV taxonomy) and genome-based grouping of viruses (vConTACT2 and similar approaches) we hope to gather better insight into their biological function/correlation with disease phenotypes. The α-diversity of unamplified gut viromes, compared to 16 S rRNA genes, is poor at differentiating healthy controls from patients with IBD. Marginally significant results (assuming $p \leq 0.05$) are seen between specific groups with respect to their intra-sample diversity, evenness, and richness metrics. However, clear α-diversity differences are seen in the 16 S analysis of controls versus patients with IBD, which subsequently translates into interpersonal microbiome variations. The inconsistency between virome and 16 S rRNA gene α-diversity reflects the intrinsic characteristics of the human gut microbiome, whereby healthy viromes can consist of a few dominant viruses or a multitude37. Manrique et al. [ 2016] previously proposed a “healthy gut phageome” was composed of globally distributed core and common viruses19. With hindsight, their conclusions of a worldwide core virome is at odds with the vast diversity and interpersonal variability of human faecal viromes. However, given the advances in sequencing technologies and viral databases, we chose to further investigate the concept that fewer viruses are shared between patients with IBD. Initially, due to the high intra- and inter-individuality of gut viromes, we determined which viruses within our database were potentially gut-specific and communal, i.e., shared by two or more viromes. Our analysis of unamplified faecal viromes demonstrated that control viromes were significantly more likely to be composed of communal viruses compared to IBD viromes. Furthermore, communal viruses with an identifiable gene responsible for temperate replication were shared to a greater degree by patients with IBD, particularly CD. Therefore, within discrete geographic locations and disease states, there are likely gut-specific communal viruses shared by populations that differ in their genotypic and phenotypic characteristics, such as replication strategy. Building upon a previous hypothesis by Shkoporov et al. [ 2020] that gut microbiomes have a core persistent personal virome (PPV) while the majority are transiently detected24, we compared the longitudinal carriage of viruses by controls and patients with IBD. Indeed, the majority of gut viruses are present only in a single time point analysed, with IBD viromes containing more viruses unique to each time point. The core PPV of controls and patients with IBD, spanning approx. 200 days, constituted less than $10\%$ of their overall detected viromes. Therefore, studies investigating gut viromes over longer timeframes, or even over consecutive days, would help develop our understanding of the stability of viruses within PPVs. Finally, given that our analysis of gut viromes was performed without MDA, we wanted to conduct an assessment of the fluctuations of viruses comprising PPVs without an amplification bias. For both controls and patients with IBD, the relative abundance of longitudinally persistent viruses fluctuates seemingly randomly between time points. Additionally, the intra-personal dissimilarity of gut viromes was more pronounced for patients with IBD. However, recent virome sequencing studies have included a spike-in control for conducting absolute quantifications of viruses38. Therefore, future gut virome analyses utilising optimised and standardised procedures have the potential to resolve ambiguities of the gut virome that will help generate a more holistic model of the human gut microbiome in health and disease. This will be further enhanced by the curation and taxonomic classification of viruses in our publicly available databases. Previous gut virome studies employed MDA to obtain sufficient DNA for sequencing. Amusingly, however, the use of MDA in gut virome studies could be considered a double-edged sword. While MDA undoubtedly biases the true composition of viromes, it also reduces the interpersonal variability of gut viromes. Our analysis of unamplified gut viromes shows intrapersonal α-diversity metrics are limited in their discrimination of healthy control and IBD viromes. Furthermore, while endeavouring to detect viral biomarkers associated with IBD, we identified three major factors hampering disease signal detection. Firstly, there is a high compositional variability of viromes between individuals. Secondly, many gut viruses are only transient. And finally, the abundance of longitudinally persistent viruses fluctuates dramatically. Considering these complex properties of viromes, correctly identifying associations between viral taxa and disease biomarkers will be significantly more challenging than for 16 S rRNA gene analyses. However, a better understanding of all constituents of human microbiomes is nonetheless required before targeted interventions could become a possible treatment for complex gastrointestinal diseases. ## Faecal sample collection and nucleic acid sequencing Patients with IBD were recruited to donate faecal samples for microbiome analysis through a speciality IBD clinic, run by experienced physicians. Control subjects were enroled in study protocol APC055, which was approved by the Clinical Research Ethics Committee of the Cork Teaching Hospitals. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all adult donors with a written questionnaire completed to partake in the study. Relevant clinical data and characteristics were recorded for controls and recruited patients, including basic lifestyle information (Supplementary Table 1), disease activity for patients with IBD (Supplementary Table 2), and generic and IBD-specific medications (Supplementary Table 3). For a more complete overview of this study’s metadata resource, see Supplementary Data. Faecal samples were collected from volunteers without additives or preservatives, transported to the research facility at ambient temperature, and were stored at −80oC until processed. Virus-like particle (VLP) extraction was performed from, 0.5 g faeces resuspended in 10 mL of SM buffer, mixed by vigorous vortexing for 5 min. Samples were then cooled on ice for 5 min prior to centrifugation at 5000 rpm in a swing bucket rotor for 10 min at + 4 °C. Supernatants were decanted into new tubes, and centrifugation was repeated. The resulting supernatants were then filtered twice through a 0.45-μm pore PES syringe-mounted membrane filters. NaCl and PEG-8000 powders were then added to the filtrates to give a final concentration of 0.5 M and $10\%$ w/v, respectively. Following complete dissolving, samples were incubated overnight (16 h) at + 4 °C. On the following day, the samples were centrifuged at 5000 rpm for 20 min at + 4 °C to collect the precipitate. The supernatant was discarded, and tubes were inverted on paper towels for 5 min to remove any remaining liquid. Pellets were then resuspended in 400 μl of SM buffer and gentle shaken with an equal volume of chloroform. Emulsions were then centrifuged at 2500 g for 5 min using a desktop centrifuge. The aqueous phase (~ 360 μl) was pipetted into a clean Eppendorf tube and mixed with 40 μl of a solution of 10 mM CaCl2 and 50 mM MgCl2. After addition of 8 U of TURBO DNase (Ambion/ThermoFisher Scientific) and 20 U of RNase I (ThermoFisher Scientific) free DNA/RNA digestion was carried out at 37 °C for 1 h before inactivating enzymes at 70 °C for 10 min. Proteinase K (40 μg) and 20 μl of $10\%$ SDS were then added, and incubated for 20 min at 56 °C. Finally, viral particles were lysed using 100 μl of Phage Lysis Buffer (4.5 M guanidinium isothiocyanate, 44 mM sodium citrate pH 7.0, $0.88\%$ sarkosyl, $0.72\%$ 2-mercaptoethanol) with incubation at 65 °C for 10 min. Lysates were then extracted twice by gentle shaking with equal volume of Phenol/Chloroform/Isoamyl Alcohol 25:24:1 (Fisher Scientific) followed by centrifugation at 8000 g for 5 min at room temperature. The resulting aqueous phase was subjected to final round of purification using DNeasy Blood & Tissue Kit (Qiagen) according to manufacturer’s instruction with a final elution volume of 50 μl. The concentration of viral nucleic acids were assessed using the Qubit dsDNA HS kit (ThermoFisher Scientific). Extracted VLPs yielded an average DNA concentration of 3.99 ng/µl (see Supplementary data for individual sample concentrations). Subsequently reverse transcription of potential RNA viral genomes was performed, and 100 nanograms of each purified DNA sample was sheared with M220 Focused-Ultrasonicator (Covaris) applying the 350 bp DNA fragment length settings (peak power 50 W, duty factor $20\%$, 200 cycles per burst, total duration of 65 s). Sequencing libraries were subsequently created using the Accel-NGS 1 S Plus DNA library kit (Swift Biosciences). Ready-to-load libraries were sequenced using 2 × 150 nt paired-end sequencing runs on an Illumina HiSeq 2500 platform at GATC Biotech AG, Germany. Similar methodology was previously used by our group for virome studies49. ## Computational analysis The treatment of raw VLP and 16S rRNA gene sequencing data followed established pipelines24,38,49. Briefly, for 16S rRNA amplicon data processing, paired-end reads were merged and filtered using a < 0.5 expected error rate per nucleotide and total length. Reads were dereplicated and singletons removed, following the trimming of the forward and reverse primers (“-stripleft 17” and “-stripright 21”, respectively). OTUs were clustered at $97\%$ identity and reference-based chimera removal was performed using UCHIME. OTUs were assigned taxonomic information by aligning reads to the RDP Gold database using the RDP Classifier (v2.12)50. The VLP sequencing data was manipulated as follows. Read quality, adaptor removal, and quality trimming (SLIDINGWINDOW:4:20 MINLEN:60 HEADCROP:10) was performed using FastQC (v0.11.5), cutadapt (v1.9.1), and TrimmomaticPE (v0.36), respectively51–53. Levels of bacterial and human contamination in the VLP sequencing data were estimated using Bowtie2 alignments against a cpn60 gene database and through Kraken alignments against the reference human genome GRCh38, respectively54,55. Contigs were assembled using SPAdes (v3.11) in metagenomic mode56,57, with short (<1 kb) and redundant ($90\%$ identity over $90\%$ length) contigs discarded. Open reading frames were predicted using Prodigal (v2.6.3) in metagenomic mode with Shine-Dalgarno training disabled. The detection of putative viruses within the VLP sequencing data was performed as described previously58 in a manner that avoids inclusion of potential bacterial contaminants. Briefly, contig-encoded proteins were queried against the Prokaryotic Viral Orthologous Groups database (pVOGs) using HMMER version 3.1.b252. The following cut-offs were employed to detect sequences rich in viral proteins: contigs <5 kb needed ≥3 pVOG hits; ≥5 and <10 kb, 4 pVOGs; ≥10 and <20 kb, 5 pVOGs; ≥20 and <40 kb, 6 pVOGs; ≥40 and <60 kb, 7 pVOGs; and ≥60 kb, 8 pVOGs. Sequences identified through the different approaches were pooled together and made nonredundant, keeping the larger of two sequences when the BLAST identity and coverage between sequences exceeded $90\%$. Viruses were deemed truly present within a sample, and not a spurious detection, when ten or more reads with a SAMTools (v0.1.19)/BEDTools (v2.26.0) calculated breadth of coverage for Bowtie2 mapped reads spanned $50\%$ of contigs <5 kb, $30\%$ of contigs ≥5 kb and <20 kb, or $10\%$ of contigs ≥20 kb59,60. The final read counts of the virome and 16 S rRNA gene analyses, with accompanying metadata, were imported into R Studio (v3.6.1) for analysis61. Dataframes and matrices were manipulated, as necessary, using the reshape2 package62. Read counts were converted into relative abundances using the funrar package63. Images were generated using ggplot2 with the ggpubr extension64,65. Colour palettes were sourced from the RColorBrewer, pals, and viridis packages66–68. The taxonomic information for putative viruses, encompassing both historic and incumbent terms, were generated using Demovir (https://github.com/feargalr/Demovir). Intra- and inter-personal diversity metrics were calculated using vegan and phyloseq69,70. The α-diversity metrics presented are the Shannon index for diversity, Pielou’s J for evenness, and rarified richness for species richness. The β-diversity distances were calculated using Canberra distances, unless otherwise stated, with two-dimensional ordination of samples employing PcoA. The PCA analysis was generated using the base R stats package71. Ternary plots were created using the Ternary package72. ## Statistics and Reproducibility Analysis is based on 233 samples ($$n = 118$$ control samples, $$n = 56$$ CD samples and $$n = 59$$ UC samples) donated by volunteers at up to 3 time points. Patients with IBD and Control subjects were enroled in study protocol APC055, which was approved by the Clinical Research Ethics Committee of the Cork Teaching Hospitals. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all adult donors in the study. Graphical representation of analyses includes boxplots, which represent the standard Tukey representation, with boxes representing the 25th, 50th (median) and 75th interquartile range (IQR) percentiles, and the whiskers encompassing values within 1.5 times the IQR. Bar plots depict mean values with error bars representing the standard deviation. Student T-test or the Wilcoxon test were employed to determine statistical difference between two specific groups. Statistical significance was assumed as a p-value ≤ 0.05, with false discovery rate adjustments employing Bonferroni correction. Centroids were calculated as the mean location of data points with regard the relevant axes. Permutational multivariate analysis of variance (PERMANOVA) statistical tests were calculated using the adonis function of vegan. Associations between diversity values were calculated using Spearman’s correlation through the base R stats package. Individual images were organised into their final multi-panel display using Inkscape (v 1.1.2). ## Reporting Summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Supplementary information Supplementary Figures and Tables Description of Additional Supplementary Files Supplementary data NR reporting summary The online version contains supplementary material available at 10.1038/s42003-023-04592-w. ## Peer review information Communications Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Sridhar Mani and Zhijuan Qiu. ## References 1. Willing BP. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology (2010.0) 139 1844-1854. DOI: 10.1053/j.gastro.2010.08.049 2. Morgan XC. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. 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--- title: Ssu72 phosphatase is essential for thermogenic adaptation by regulating cytosolic translation authors: - Eun-Ji Park - Hyun-Soo Kim - Do-Hyoung Lee - Su-Min Kim - Joon-Sup Yoon - Ji-Min Lee - Se Jin Im - Ho Lee - Min-Woo Lee - Chang-Woo Lee journal: Nature Communications year: 2023 pmcid: PMC9968297 doi: 10.1038/s41467-023-36836-y license: CC BY 4.0 --- # Ssu72 phosphatase is essential for thermogenic adaptation by regulating cytosolic translation ## Abstract Brown adipose tissue (BAT) plays a pivotal role in maintaining body temperature and energy homeostasis. BAT dysfunction is associated with impaired metabolic health. Here, we show that Ssu72 phosphatase is essential for mRNA translation of genes required for thermogenesis in BAT. Ssu72 is found to be highly expressed in BAT among adipose tissue depots, and the expression level of Ssu72 is increased upon acute cold exposure. Mice lacking adipocyte Ssu72 exhibit cold intolerance during acute cold exposure. Mechanistically, Ssu72 deficiency alters cytosolic mRNA translation program through hyperphosphorylation of eIF2α and reduces translation of mitochondrial oxidative phosphorylation (OXPHOS) subunits, resulting in mitochondrial dysfunction and defective thermogenesis in BAT. In addition, metabolic dysfunction in Ssu72-deficient BAT returns to almost normal after restoring Ssu72 expression. In summary, our findings demonstrate that cold-responsive Ssu72 phosphatase is involved in cytosolic translation of key thermogenic effectors via dephosphorylation of eIF2α in brown adipocytes, providing insights into metabolic benefits of Ssu72. Brown adipose tissue (BAT) is a specialized thermogenic organ that undergoes high demands of protein synthesis during thermogenic adaptation. Here, the authors show that the cold responsive phosphatase Ssu72 is required for mRNA translation that affects thermogenic adaptation in BAT. ## Introduction Adipose tissue plays an important role in responding to both changes in nutrient supply and ambient temperature to preserve whole-body energy homeostasis1. Brown adipose tissue (BAT) is a specialized thermogenic organ that produces heat to maintain body temperature in an adaptive process. Under physiological conditions, cold exposure increases the thermogenic activity of BAT, and further recruits beige or brite adipocytes with thermogenic capacity to white adipose tissue (WAT) depots2,3. Emerging evidence suggests that activation of these thermogenic adipose tissues can improve systemic metabolism4–7. Reduced activity or dysfunction of adipose tissues is related to impaired metabolic health8,9. Therefore, it is of great clinical importance to identify molecular mechanisms regulating the function of thermogenic adipose tissues. In response to acute cold exposure, pre-existing brown adipocytes rather than newly recruited beige adipocytes are activated via the sympathetic nervous system to elevate body temperature in both humans and mice10–13. Brown adipocytes are rich in mitochondria, and they express higher levels of uncoupling protein 1 (UCP1) than white adipocytes14. The mitochondrial oxidative phosphorylation (OXPHOS) system, which is composed of electron transport chain (ETC), is a key functional unit in the mitochondria. It creates a proton gradient whose electromotive force drives ATP synthase15. When the proton conductance of UCP1 is increased in the inner mitochondrial membrane, UCP1 uncouples mitochondrial respiration from ATP synthesis and dissipates energy as heat12. Although UCP1-independent thermogenic pathway has been recently identified in thermogenic adipocytes16, uncoupling of ATP synthesis in mitochondria remains a major mechanism of BAT thermogenesis14,17. The thermogenic function of BAT is critical for maintaining homeostatic body temperature in mammals17; however, it remains unclear how multiple thermogenic mechanisms are coordinately regulated in BAT. The dramatic metabolic adaptation of BAT involves its action in responding to metabolic demand by coordinating cellular organelles (such as mitochondria and endoplasmic reticulum (ER)) and by controlling abundance of lipids and proteins18. Disruptions in ER homeostasis can trigger unfolded protein response of ER (UPRER) and regulate transcription and translation to alleviate the unfolded protein load and expand the folding capacity19,20. Because fine-tuned protein synthesis is crucial for sustaining cell viability and function, dysregulation of UPRER has been implicated in the pathogenesis of many diseases including metabolic diseases19,21. UPRER consists of three branches controlled by inositol requiring protein 1α (IRE1α), PKR-like ER-regulated kinase (PERK), and activating transcription factor 6 (ATF6). PERK phosphorylates the α subunit of eukaryotic initiation factor 2 (eIF2α) at serine 51 (Ser51)19. Importantly, the phosphorylation of eIF2α represses the initiation phase of translation, mediating global decrease of cytosolic translation22. Due to the evolution of dynamically modulating ER stress signaling with different tissue distributions and activation profiles, studies on eIF2α phosphatases have been expanded22,23. Dephosphorylation eIF2α by inducible expression of eIF2α phosphatase GADD34 in the liver can improve insulin sensitivity and reduce hepatosteatosis in mice fed a high-fat diet (HFD)24, and GADD34-deficient mice become obese with fatty liver by aging and HFD25. GADD34-deficient mice also exhibit resistance to diet-induced obesity with reduced food intake26, consistent with an effect of hypothalamic eIF2α phosphorylation in regulating feeding behaviors27. In addition, it has been reported that protein-tyrosine phosphatase 1B (PTP1B) is involved in ER stress signaling28 and PTP1B deficiency attenuates protein synthesis in brown adipocytes by increasing PERK phosphorylation29. However, little is known about how translational regulation through eIF2α phosphorylation directly affects metabolic adaptation of BAT. Ssu72 is a dual-specific protein phosphatase that is expressed in a tissue-specific manner30. Recent studies have demonstrated that Ssu72 dephosphorylates both Ser 5 and Ser 7 in the carboxyl-terminal domain (CTD) of RNA polymerase II (RNAPII), thus playing an important role in controlling CTD function during the transcription cycle31–33. However, Ssu72 also exerts RNAP II-independent phosphatase activity in a tissue-specific manner involving epithelial cells, immune cells, and hepatocytes, thereby affecting physiological function and pathogenesis30,34–36. We have previously found that the activity of Ssu72 profoundly affects the maintenance of hepatic chromosome integrity, and it can be used to monitor the development of liver diseases, including non-alcoholic fatty liver disease (NAFLD), fibrosis, and steatohepatitis-associated hepatocellular carcinoma (HCC)30,37. Although some evidence for the role of Ssu72 in the pathogenesis of metabolic diseases has been found, its functions in major metabolic tissues such as adipose tissue are still largely unknown. We initially found that Ssu72 was elevated in BAT and inguinal WAT (iWAT), and it was expressed much higher in BAT. Importantly, the level of Ssu72 was upregulated upon acute cold exposure, implying that Ssu72 might play a role in the thermogenic function of adipose tissues. We found that adipocyte-specific Ssu72 ablation resulted in mitochondrial dysfunction and cold intolerance in response to cold exposure. Furthermore, Ssu72 affected protein synthesis required for thermogenesis by directly regulating the function of eIF2α through hypophosphorylation. Taken together, these results revealed that Ssu72 phosphatase plays an important role in cytosolic translation affecting mitochondrial oxidative phosphorylation and thermogenesis in brown adipocytes. ## Ssu72 is highly expressed in BAT and induced by cold exposure To investigate the clinical significance of Ssu72 expression in adipose tissue, we initially compared Ssu72 levels in isolated adipocytes between lean and non-diabetic obese individuals from Gene Expression Omnibus (GEO) public genomics database (GSE2508). Intriguingly, we found significantly decreased expression of Ssu72 in the isolated adipocytes from obese individuals (Supplementary Fig. 1a, b). To explore the role of Ssu72 phosphatase in adipose tissue metabolism, we thus assessed the expression and distribution of selected representative phosphatases (serine/threonine phosphatase and dual specificity phosphatase) in various tissues including different fat depots. Surprisingly, Ssu72 phosphatase was highly enriched in brown adipose tissue (BAT) depot relative to white adipose tissue (WAT) depots, including inguinal WAT (iWAT) and epididymal WAT (eWAT) (Fig. 1a, b). Since the major function of BAT is thermogenesis, we examined endogenous difference in adipose Ssu72 expression in response to cold exposure. Notably, Ssu72 mRNA level was increased significantly in BAT within 4 h after mild cold (15 °C) exposure, in parallel to relative Ucp1 mRNA level (Fig. 1c and Supplementary Fig. 2a). In addition, the level of Ssu72 in iWAT was increased significantly at 12 h after mild cold (15 °C) exposure (Supplementary Fig. 2b). We further assessed whether Ssu72 protein expression was increased during severe cold exposure. Thermogenic adipose tissues, iWAT and BAT, were taken from wild-type (C57BL/6J) mice housed at room temperature (RT, 23 °C) or exposed to acute severe cold (4 °C) for 4 h. Of note, the expression of Ssu72 protein in BAT was significantly increased after acute cold exposure (Fig. 1d). This indicates that Ssu72 might play an essential role in BAT thermogenesis. Moreover, Ssu72 protein levels in BATs were greater than those in iWATs at 4 h after cold exposure (Supplementary Fig. 2c), similar to our findings after housing mice at RT. Given the increased Ssu72 mRNA level in iWAT at 12 h of mild cold exposure, we further examined the induction of Ssu72 protein in iWAT over a longer period of acute cold exposure. The expression of Ssu72 protein was increased in iWAT after cold exposure for 8–12 h (Supplementary Fig. 2d), implying that Ssu72 might also have a thermogenic function in iWAT during chronic cold exposure. Fig. 1Ssu72 phosphatase is potentially involved in physiological regulation of thermogenic adipose tissues, especially BAT.a Various mouse tissues including epididymal white adipose tissue (eWAT), inguinal white adipose tissue (iWAT), and brown adipose tissue (BAT) were extracted from 7–8 weeks old C57BL/6 (WT) male mice, and lysates from tissues were immunoblotted with antibodies against several representative phosphatases, which were classified into four phosphatase groups ($$n = 5$$, biologically independent samples). b Relative mRNA expression levels of Ssu72 in eWAT, iWAT, and BAT from 9-week-old WT mice ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using one-way ANOVA. c Relative mRNA expression levels of Ssu72 in BAT of 7 to 8-week-old WT mice after mild cold exposure (15 °C) ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using one-way ANOVA. d Western blots of lysates from BAT of WT mice housed at RT (23 °C) or exposed to acute cold (4 °C) for 4 h (left) ($$n = 3$$ mice per group), with quantification of Ssu72 and UCP1 protein levels (right). Graph shows quantification of Ssu72 protein levels normalized to β-actin protein level using ImageJ. Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed Student’s t test. e Animated images of Ssu72flox/flox (Ssu72 WT), Adiponectin-Cre, and Ssu72flox/flox; Adiponectin-Cre mice (Ssu72 aKO). f Representative images of BAT from 11-week-old Ssu72 WT and aKO male mice (scale bar, 5 mm; $$n = 10$$ per group, biologically independent samples from 8–11 weeks old mice). g Hematoxylin and eosin (H&E) staining of representative sections of BAT from 6- and 22-weeks-old Ssu72 WT and aKO male mice ($$n = 5$$ per group, biologically independent samples). For (f) and (g), both male and female mice were used, and no sex-specific phenotype was observed. Source data are provided as a Source Data file. ## Ssu72 is required for the maintenance of thermogenic adipose tissue To identify the repercussion of increased Ssu72 level in thermogenic adipose tissues, we determined whether Ssu72 was involved in the maintenance and function of adipose tissues. To this end, we generated Ssu72flox/flox; Adiponectin-Cre (hereafter Ssu72 aKO) mouse with Ssu72 deleted specifically in adipocytes by intercrossing Ssu72flox/flox (hereafter Ssu72 WT) mice with Adiponectin-Cre mice (Fig. 1e and Supplementary Fig. 3a–c). Resulting Ssu72 aKO mice were born in normal Mendelian ratio. They were apparently indistinguishable from their littermates of Ssu72 WT mice. Body weight gain was also similar between genotypes from 6 to 20 weeks of age (Supplementary Fig. 3d). Surprisingly, BAT from Ssu72 aKO mice appeared pale compared to BAT from Ssu72 WT mice, even when mice were fed a normal chow diet and housed at RT (23 °C) (Fig. 1f). Total BAT weights were similar between genotypes (Supplementary Fig. 3e). In addition, Ssu72-deficient BAT exhibited increased conversion of multilocular into unilocular adipocytes, which was highly correlated with whitening of brown fat (Fig. 1g). Both iWAT and eWAT between Ssu72 WT and aKO mice at 6 and 22 weeks of age were very similar in size (Supplementary Fig. 3f). However, hematoxylin-eosin (H&E) staining and size analyses showed that adipocyte size and lipid droplet of iWAT from Ssu72 aKO mice were increased compared to those from Ssu72 WT mice (Supplementary Fig. 3g). There was no significant difference in adipocyte size or lipid droplet of eWAT between Ssu72 WT and aKO mice (Supplementary Fig. 3h). In addition, tissue staining analysis using embryos at 15.5 and 18.5 days (E15.5 and E18.5, respectively) showed no significant difference in BAT morphology or Ucp1 expression in embryos between genotypes, indicating that Ssu72 might not be involved in BAT development (Supplementary Fig. 4a). Notably, there were no significant changes in expression levels of general adipogenic genes such as Fabp4 and Pparg1 in BATs, between WT and aKO mice housed at RT (Supplementary Fig. 4b). Taken together, these results imply that adipose Ssu72 is involved in the maintenance and thermogenic function of BAT. ## Ssu72 deficiency in BAT decreases thermogenic activation and fatty acid oxidation during acute cold exposure Given the abnormal histological appearance of Ssu72-deficient BAT, we assume that adipocyte Ssu72 may be required for response to acute cold exposure. To examine thermogenic activity in Ssu72-deficient BAT, 7-week-old Ssu72 WT and aKO mice were challenged with acute cold exposure (4 °C). No significant change was seen in resting body temperature between the genotypes (Fig. 2a, time = 0). Importantly, in response to cold exposure, Ssu72 WT mice were able to maintain their body temperature at around 36.5 °C after an initial drop of approximately 1.5 °C; however, Ssu72 aKO mice showed significant sensitivity to cold temperature. In Ssu72 aKO mice, the rectal temperature was significantly dropped within 4 h, dropped to below 30 °C in less than 6 h after cold exposure, and fatal hypothermia was found after 6 h of cold exposure (Fig. 2a). The BAT from Ssu72 aKO mice showed multiple interspersed cells, each containing a single lipid droplet in both RT and cold conditions (Fig. 2b), indicating that Ssu72 aKO mice exhibited defects in acquiring the active brown adipocyte phenotype. Fig. 2Adipose Ssu72 deficiency results in cold intolerance and thermogenic defect of BAT.a Rectal core body temperatures of 7-week-old Ssu72 WT and aKO male mice under acute cold conditions (4 °C) at indicated time points ($$n = 8$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using two-way ANOVA. b H&E staining of BAT from 7-week-old Ssu72 WT and aKO male mice housed at RT or exposed to acute cold (4 °C) for 4 h (scale bar, 100 µm; $$n = 5$$ per group, biologically independent samples). c Relative mRNA expression levels of thermogenic genes (Ucp1, Ppargc1α, Elovl3, and Cidea) in BAT from Ssu72 WT and aKO mice exposed to RT or acute cold (4 °C) ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using two-way ANOVA. d Western blots of Ucp1 and Ssu72 in BAT from Ssu72 WT and aKO mice exposed to acute cold (4 °C) ($$n = 4$$ mice per group). e Relative mRNA expression levels of fatty acid β-oxidation-related genes (Cpt1b and Ppara) in BATs of Ssu72 WT and aKO mice exposed to RT or acute cold ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using two-way ANOVA. f Whole-body oxygen consumption (VO2) of 7-week-old Ssu72 WT and aKO male mice treated with 10 mg/kg CL-316,243 ($$n = 5$$ mice per group). Data are presented as mean ± SD. Statistical comparisons of mean VO2 between WT and aKO mice under basal (before) and CL-316,243 stimulation conditions (after) were made using two-way ANOVA. Source data are provided as a Source Data file. The thermogenic function of BAT is known to be associated with the expression of several BAT thermogenic genes38. We thus compared mRNA expression levels of thermogenic genes (Ucp1, Ppargc1α, Elovl3, and Cidea) between BATs from Ssu72 WT and aKO mice maintained at RT or exposed to cold (4 °C) for 4 h. Under acute cold exposure, expression levels of genes related to BAT thermogenesis were significantly decreased in BAT of Ssu72 aKO mice than Ssu72 WT mice (Fig. 2c). Consistent with lower mRNA expression of thermogenic genes, we observed a decreased expression of UCP1 protein in Ssu72 aKO mice compared to Ssu72 WT mice (Fig. 2d). Notably, cold-induced stimulation of β3-adrenergic receptor (β3-AR) leads to activation of mitochondrial fatty acid oxidation (β-oxidation) and increases mitochondrial biogenesis in BAT39,40. To investigate whether Ssu72 was linked to the signaling pathway of fatty acid oxidation, we measured expression levels of fatty acid β-oxidation-related genes in both RT and acute cold conditions. While acute cold exposure induced increases of expression levels of genes involved in fatty acid β-oxidation (Cpt1b and Pparα) in Ssu72 WT BAT, cold-induced expression of these genes was significantly attenuated in Ssu72 aKO BAT (Fig. 2e). To determine the physiological contributions of Ssu72 in systemic whole-body energy expenditure, we housed Ssu72 WT and aKO mice individually in metabolic cages and measured metabolic parameters continuously during ad libitum feeding condition. Consistent with a requirement for thermogenesis during cold stimulation, an injection of CL-316,243 (a highly selective β3-adrenergic receptor agonist) rapidly increased oxygen consumption in Ssu72 WT mice. However, it was completely ineffective in altering oxygen consumption in Ssu72 aKO mice (Fig. 2f). Additionally, there were no significant changes of respiratory exchange ratio (RER) and locomotor activity in Ssu72 aKO mice compared to controls (Supplementary Fig. 5a, b). Unlike ACC2 lacking mice model where changes of fatty oxidation drive alterations in feeding behavior41, Ssu72 aKO mice had no change in food intake (Supplementary Fig. 5c), implying that reduction of energy expenditure in Ssu72 aKO mice might reflect physiological dysfunctions of adipose fatty acid oxidation, but not a compensatory requirement of energy expenditure from skeletal muscle. These results indicate that adipose Ssu72 contributes to fatty acid oxidation and whole-body energy expenditure to maintain core body temperature during cold stimulation. ## Loss of Ssu72 alters mitochondrial morphology in BAT Defects in the thermogenic function of BAT are often closely linked to functional changes of mitochondria42. As noted above, a white-like phenotype in Ssu72-deficient BAT and a rapid decrease in body temperature of Ssu72 aKO mice during acute cold exposure (4 °C) suggest that thermogenic activation of Ssu72-deficient BAT is already disrupted even at ambient temperature. In fact, housing mice at RT (23 °C) is considered a mild cold exposure because this temperature is below thermoneutrality (30 °C), thus affecting the basal metabolic rate of mice43. Therefore, we investigated the probability of mitochondrial dysfunction in BAT of Ssu72 aKO mice housed at RT (23 °C). Interestingly, transmission electron microscopy (TEM) analysis revealed aberrant mitochondrial structure in the BAT of Ssu72 aKO mice housed at RT (Fig. 3a). BAT of Ssu72 aKO mice showed enlarged mitochondria and disorganized cristae structures in comparison with Ssu72 WT mice, but there was no difference in the number of mitochondria between Ssu72 WT and aKO mice (Fig. 3b–d). Thus, Ssu72 appears to be related to maintaining mitochondrial cristae structure in BAT at ambient temperature. Previous studies have reported that disruption of mitochondrial dynamics affects the maintenance of mitochondrial DNA (mtDNA) copy number or integrity44. Despite aberrant changes in cristae structure, mtDNA copy number showed no significant difference between genotypes at room temperature (Fig. 3e), suggesting that disorganization of cristae caused by Ssu72 depletion did not directly affect mtDNA copy number. Fig. 3Ablation of Ssu72 disrupts mitochondrial structure in BAT.a TEM analysis of BAT from 8-week-old WT and aKO male mice housed at RT (scale bar, 500 nm). M and L refer to mitochondria and lipid droplets, respectively (WT, $$n = 5$$; aKO, $$n = 4$$). Both male and female mice were used for TEM analysis, and no sex-specific phenotype was observed. b Quantitative analyses of mitochondrial size distribution (upper panels) and average size of mitochondria in BAT from Ssu72 WT ($$n = 20$$) and Ssu72 aKO ($$n = 15$$) mice (bottom panels). Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed unpaired Student’s t test. c Quantitative morphometric measurements of the number of mitochondria per cell (WT, $$n = 9$$; aKO, $$n = 7$$). Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed unpaired Student’s t test ($$p \leq 0.3092$$). d The percentage of disrupted cristae based on TEM images of BAT over 10 electron micrograph sections (WT, $$n = 11$$; aKO, $$n = 10$$). Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed unpaired Student’s t test. e Quantitative PCR analysis of mitochondrial DNA (mtDNA) copy number normalized to nuclear DNA (NucDNA) from BAT of Ssu72 WT and aKO mice ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed unpaired Student’s t test ($$p \leq 0.078$$). Source data are provided as a Source Data file. ## Ssu72 is involved in regulation of mitochondrial homeostasis, fatty acid metabolism, and UPRER signaling pathways To understand the underlying mechanism of the defect in BAT conferred by Ssu72 depletion, we employed RNA-sequencing (RNA-seq) and performed signaling enrichment analysis. Total RNA of BAT from Ssu72 WT and Ssu72 aKO mice housed at RT were prepared for analysis. The FPKM value was used to quantify mRNA expression. We analyzed profiles of differentially expressed genes (DEGs) between genotypes. The analysis of DEGs (≥2-fold change (FC)) identified a total of 1919 DEGs in the Ssu72 aKO group compared to the Ssu72 WT group, including 1,006 upregulated genes and 913 downregulated genes. Based on false discovery rate (FDR)-adjusted 0.05 p-value threshold, 819 genes were downregulated and 889 genes were upregulated among 1,708 DEGs ($p \leq 0.05$, FDR < 0.1) in Ssu72-deficient BAT than in WT BAT (Supplementary Fig. 6a). Enrichment analysis of gene ontology (GO) cellular component and biological process of upregulated and downregulated genes in BAT of Ssu72 aKO mice relative to BAT of Ssu72 WT mice was then performed. As expected, GO cellular component analysis revealed that downregulated genes of Ssu72 aKO BAT were predominantly involved in mitochondria and respiratory ETC. Downregulated genes enriched in GO biological process were stratified into categories of oxidation-reduction process, mitochondrial translation, oxidative phosphorylation, and particularly the regulation of eIF2α phosphorylation (Supplementary Fig. 6b, upper panels). We next assessed genes upregulated in Ssu72-deficient BAT compared to those in WT BAT. The GO biological process analysis showed that fatty acid biosynthesis genes were upregulated in Ssu72 aKO BAT. Unexpectedly, ER unfolded protein response (UPRER) genes were also upregulated in Ssu72 aKO BAT compared to those in Ssu72 WT BAT, and cellular component analysis showed that the ER membrane and integral component of ER were upregulated in Ssu72-deficient BAT (Supplementary Fig. 6b, bottom panels). ## Ssu72 dephosphorylates eIF2α in BAT We next performed gene set enrichment analysis (GSEA) using RNA-seq results to examine whether Ssu72 was involved in UPRER signaling pathways. GSEA showed significant enrichment of UPRER genes within ranked gene expression of Ssu72 aKO BAT compared with Ssu72 WT BAT (Fig. 4a). To further examine the activation of three ER stress sensors of UPR, PERK, IRE1, and ATF6, we compared levels of phosphorylated PERK and IRE1 (P-PERKThr980 and P-IRE1Ser724) and cleaved ATF6 in BAT from Ssu72 WT and aKO mice. Although UPR-related genes were upregulated in Ssu72 aKO BAT compared to those in Ssu72 WT BAT, activation of three stress sensors of UPRER showed no significant differences (Supplementary Fig. 7a), suggesting that there might be activation of downstream molecules involved in UPR signaling pathway rather than upstream ER stress sensors. To explore changes in the expression of specific target genes in the UPRER signaling pathways by Ssu72 deficiency in BAT, we calculated the relative log2-fold normalized average count (RNA-seq) values of selected genes induced by activation of three branches (PERK-eIF2α, IRE1α-Xbp1 and ATF6 pathway) of UPRER. An interesting observation was that the majority of PERK-eIF2α target genes (Gdf15, Fgf21, Atf3, Ppp1r15a, and Ddit3) were significantly upregulated (log2-fold change ≥1, $p \leq 0.05$) in Ssu72 aKO BAT than in WT BAT (Fig. 4b). Consistent with RNA-seq results, we also found that mRNA levels of eIF2α target genes (Atf3, Ddit3, and Fgf21) were clearly upregulated in Ssu72 aKO BAT (Fig. 4c).Fig. 4Ssu72 phosphatase controls eIF2α signaling but not IRE1α and ATF6 signaling.a Gene set enrichment analysis (GSEA) of hallmark unfolded protein response (UPR) gene sets in 8-week-old BAT of WT and aKO male mice ($$n = 3$$ mice per group). Green curve shows the enrichment score, which reflects the degree to which each gene is enriched (black vertical lines). A GSEA algorithm was used to evaluate the statistical significance of gene expression and calculate normalized enrichment scores (NES). Nominal p value was calculated as two-sided t-test. FDR, false discovery rate. b Relative fold changes expressed as log2 of normalized average count (RNA-seq) of each gene in aKO BAT samples divided by that in WT BAT ($$n = 3$$ mice per group). Gray dashed lines indicate log2 fold changes of 1 or −1. c Relative mRNA expression levels of ATF4 target genes (Atf3, Ddit3, Fgf21) ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed unpaired Student’s t test. d Western blots in BAT from 10-week-old Ssu72 WT and aKO male mice housed at RT ($$n = 2$$ mice per group). e Immunofluorescent (IF) staining of P-eIF2αSer51 (red) and DAPI (blue) in BAT from 11-week-old WT and aKO female mice (Scale bar, 100 μm) ($$n = 5$$ per group, biologically independent samples). f Co-immunoprecipitation (Co-IP) assays from 293T cells transfected with expression vectors for Myc, Myc-tagged Ssu72 (Myc-Ssu72) and HA-tagged eIF2α (HA-eIF2α). Lysates were immunoprecipitated with anti-Myc antibody. Immunoprecipitated Ssu72 was tested for interaction with eIF2α ($$n = 4$$ independent biological replicates). Panels below present western blots of input proteins or β-actin loading control and quantitative scans of amounts of each protein detected in western analysis of input protein panels. g In vitro phosphatase assay was performed with full-length glutathione S-transferase (GST)-fused phosphorylated eIF2α and recombinant His-Ssu72. Total eIF2α and P-eIF2αSer51 were analyzed by immunoblotting ($$n = 3$$ independent biological replicates). IP immunoprecipitation, IB Immunoblotting. Source data are provided as a Source Data file. To assess whether Ssu72 could regulate eIF2α phosphorylation in BAT, we conducted immunoblotting analysis in BAT of Ssu72 WT and aKO mice housed at RT. Surprisingly, phosphorylation of eIF2α at serine 51 was sharply increased in the BAT of Ssu72 aKO mice compared to that in the BAT of Ssu72 WT mice (Fig. 4d). In addition, protein expression levels of both ATF4 and CHOP induced by eIF2α phosphorylation were also increased in Ssu72 aKO BAT (Fig. 4d). To determine whether the increase in eIF2α phosphorylation shown above occurred in the whole BAT tissue, immunofluorescence (IF) staining was performed using BAT frozen sections of Ssu72 WT and aKO mice. We observed a substantial increase in eIF2α phosphorylation at serine 51 in whole-tissue section of Ssu72 aKO BAT (Fig. 4e), indicating that Ssu72 was essential for eIF2α signaling pathway. In mice exposed to acute cold (4 °C), we also observed an increase of eIF2α phosphorylation in the BAT of Ssu72 aKO mice than in Ssu72 WT mice (Supplementary Fig. 7b). Previous reports have demonstrated that IRE1α-Xbp1 pathway of UPR is required for adipogenesis and that activation of IRE1α-Xbp1 in a PKA-dependent manner promotes the transcription of Ucp1 in brown adipocytes45. To assess whether Ssu72 depletion affected IRE1α-Xbp1 signaling pathway under cold exposure, we measured levels of spliced Xbp1 induced by activation of IRE1α in BAT. Indeed, there was no significant difference in IRE1α activation between Ssu72 WT and aKO mice under RT or cold condition (Supplementary Fig. 7c). We also assessed expression levels of other eIF2α phosphatases such as GADD34, CReP, and PP1 in BAT of Ssu72 WT and aKO mice housed at RT. We found that only Ppp1r15a (GADD34) mRNA expression level was upregulated in Ssu72 aKO mice, but there was no difference in mRNA expression of Ppp1r15b (CReP) and Ppp1ca (PP1) between the genotypes (Supplementary Fig. 8a). The increased expression level of Ppp1r15a in aKO mice was probably due to compensation for hyperphosphorylation of eIF2α. Considering the feedback control of other eIF2α phosphatases in normal cells, we next explored effects of eIF2α phosphatases in adipocytes. Remarkably, data analysis of differential correlation score between each phosphatase gene and cell-type-enriched transcripts showed that only Ssu72 was predominantly enriched in adipocytes compared to other eIF2α phosphatases (GADD34, CReP, PP1) (Supplementary Fig. 8b). This result suggests that the effect of Ssu72 phosphatase on eIF2α dephosphorylation in adipocytes might be greater than those of other eIF2α phosphatases. To determine the potential interaction between Ssu72 and eIF2α, co-immunoprecipitation (Co-IP) assay was performed using cell lysates of 293T cells transfected with Myc-tagged Ssu72 (Myc-Ssu72) and HA-tagged eIF2α (HA-eIF2α). Interestingly, a complex formation of Ssu72 with eIF2α was found (Fig. 4f). This result was confirmed by in vitro binding assays using glutathione-S-transferase (GST) or GST-fused eIF2α (GST-eIF2α) and purified His-tagged Ssu72 (His-Ssu72). After His-Ssu72 protein was individually mixed with glutathione beads-bound GST or GST-eIF2α, results showed that His-Ssu72 bound to GST-eIF2α specifically as compared to control GST alone (Supplementary Fig. 9a, b). We further examined whether Ssu72 phosphatase could control eIF2α phosphorylation depending on Ssu72 phosphatase activity. As a negative control, we generated His-tagged Ssu72 phosphatase-dead mutant (His-Ssu72 C12S) and assessed the phosphatase activity of His-Ssu72 WT and C12S through p-nitrophenyl phosphate (pNPP) assay (Supplementary Fig. 9c). To directly investigate whether eIF2α could be dephosphorylated by active Ssu72, purified GST-eIF2α was phosphorylated by recombinant PERK. Phosphorylated GST-eIF2α (P-eIF2αSer51) was then incubated with purified His-Ssu72 WT or His-Ssu72 C12S. Results clearly showed that only Ssu72 WT, but not phosphatase-dead Ssu72 C12S, dephosphorylated P-eIF2αSer51, indicating that Ssu72 could dephosphorylate P-eIF2αSer51 in vitro (Fig. 4g and Supplementary Fig. 9d). We next asked whether expressing ectopic Ssu72 was sufficient to reduce eIF2α phosphorylation in vivo. To this end, we generated conditional transgenic (cTg) mice with Rosa26loxP-STOP-loxP-HA-tagged Ssu72 gene (Rosa26 HA-Ssu72). These cTg mice were crossed with Ssu72f/f; Adiponectin-Cre (Ssu72 aKO) mice to generate Ssu72f/f; Adiponectin-Cre; Rosa26 HA-Ssu72 mice (hereafter aKO;cTg), which expresses a single copy of ectopic HA-tagged Ssu72 in a Cre-mediated adipocytes specific manner in the genetic background of Ssu72 aKO mice. In the absence of Cre recombinase, the generated cTg mouse line was unable to express ectopic HA-Ssu72 gene nor to delete the endogenous Ssu72 gene. Upon Cre recombination, ectopic HA-Ssu72 cassette flanked by loxP sites was removed, HA-Ssu72 became expressed, and endogenous Ssu72 gene was deleted (Fig. 5a, b). Immunoblotting analysis revealed that ectopic HA-Ssu72 protein was expressed higher in BAT and iWAT than in eWAT from cTg mice (Fig. 5c). To assess sizes of adipocytes in Ssu72 WT, aKO, and aKO;cTg mice housed at RT, we performed H&E staining of BAT sections from 14-week-old mice. As expected, Ssu72 aKO mice exhibited white-like phenotype of BAT compared with WT mice. However, this morphological alteration was restored by adipose HA-Ssu72 expression in aKO;cTg mice (Fig. 5d). To further examine whether ectopic Ssu72 expression could reduce eIF2α phosphorylation in Ssu72-deficient BAT, immunohistochemistry staining and immunoblotting analysis were performed. We found that an increased eIF2α phosphorylation at serine 51 in Ssu72 aKO BAT was reduced by ectopic HA-Ssu72 expression in Ssu72 aKO;cTg BAT (Fig. 5e, f). Collectively, these data suggest that Ssu72 can act as eIF2α phosphatase in BAT.Fig. 5Ectopic Ssu72 expression in adipose tissue restores morphological alteration of Ssu72-deficient BAT and reduces eIF2α phosphorylation in vivo.a Schematic strategy for generating Adiponectin-Cre; Ssu72flox/flox; Rosa26 HA-Ssu72 (Ssu72 aKO;cTg) conditional transgenic (cTg) mice. b PCR analysis with genomic DNA from mouse tail containing floxed (f), Adiponectin-Cre (Cre), and Rosa26 HA-Ssu72 loci ($$n = 4$$–5 per group, biologically independent samples). c BAT extracts of 6-week-old WT, aKO and aKO;cTg mice were immunoblotted with anti-Ssu72, anti-HA (ectopic HA-Ssu72), and anti-GAPDH antibodies ($$n = 4$$ per group, biologically independent samples). d H&E staining analysis of BATs from 14-week-old female WT, aKO, and aKO; cTg mice housed at RT ($$n = 4$$ per group, biologically independent samples). e Immunohistochemical analysis with α-P-eIF2αSer51 in BATs of WT, aKO, and aKO;cTg mice housed at RT (Scale bar, 50 μm) ($$n = 4$$ per group, biologically independent samples). f Western blots of BATs from WT, aKO, and aKO;cTg mice housed at RT. The asterisk marks the HA-Ssu72 ($$n = 2$$ mice per group). Source data are provided as a Source Data file. ## Ssu72-mediated translational control seems to be critical for BAT thermogenesis Since phosphorylation of eIF2α inhibits global protein synthesis19, we next assessed changes in translation between Ssu72 WT and Ssu72 aKO mice. To directly monitor mRNA translation in BAT, surface sensing of translation (SUnSET) assay, a nonradioactive method for detecting puromycin incorporated neosynthesized proteins46,47, was performed. Immunoblotting analysis revealed that puromycin incorporation was remarkably blocked in BAT of Ssu72 aKO mice than in Ssu72 WT mice (Fig. 6a), indicating that general protein synthesis was inhibited by Ssu72 deficiency in BAT. Given that deficiency of Ssu72 caused cold intolerance in Ssu72 aKO mice during acute cold exposure (4 °C), we further investigated changes in the translation of major thermogenic factors in BAT. Of note, protein expression levels of PGC-1α, AMPKα, and PKA were significantly lower in Ssu72 aKO BAT under basal condition (RT) and acute cold condition (4 °C) than in Ssu72 WT BAT (Fig. 6b). Moreover, in BAT of Ssu72 aKO mice, protein expression levels of UCP1 were significantly repressed during acute cold exposure, with a trend toward the decreased expression of these proteins at RT (Fig. 6b). To investigate whether protein synthesis was regulated by Ssu72 expression, we performed immunoblotting analyses for BATs of Ssu72 WT, aKO, and aKO;cTg mice housed at RT. Intriguingly, downregulated PGC-1α protein expression in BAT of Ssu72 aKO mice was completely restored by ectopic Ssu72 expression in BAT of aKO;cTg mice, along with dephosphorylated P-eIF2αSer51 (Fig. 6c). We also found that slightly reduced AMPKα protein expression in aKO mice was restored in aKO;cTg mice (Fig. 6c). We further assessed expression levels of thermogenic genes in BAT and found that mRNA levels of thermogenic genes (Ucp1, Cidea, Dio2, and Ppargc1a) in aKO;cTg mice were similar to those in WT mice (Supplementary Fig. 10a). The higher mRNA expression level of Ppargc1a in BAT of aKO mice than in WT mice (Supplementary Fig. 10a) was probably due to compensation for reduced protein expression of PGC-1α. Fig. 6Disturbances of translational control by Ssu72 depletion induces thermogenic defect in BAT.a Representative images of WB-SUnSET analysis of BAT from 11-week-old WT and aKO male mice treated with low concentration of puromycin. Changes in protein synthesis were compared against vehicle treated cells and equal loading was confirmed with HSP90 ($$n = 3$$ mice per group). b Western blots of thermogenic factors in BAT from 11-week-old Ssu72 WT and aKO male mice exposed to RT or acute cold (4 °C) for 4 h (upper panels) ($$n = 3$$ mice per group), with quantification of protein levels (bottom panels). Graph shows quantification of protein levels normalized to α-tubulin protein level using ImageJ. Data are presented as mean ± SEM. Statistical comparisons were made using two-way ANOVA. c Western blots in BAT from 12-week-old Ssu72 WT, aKO and aKO;cTg female mice housed at RT ($$n = 3$$ mice per group). d Representative images (upper panels; scale bar, 5 mm) and H&E staining analysis (bottom panels; scale bar, 50 µm) of BATs from 8-week-old WT, aKO, and aKO;cTg male mice exposed to acute cold (4 °C) for 4 h ($$n = 5$$ per group, biologically independent samples). e Immunohistochemical analysis with α-UCP1 in BATs of 11-week-old WT, aKO, and aKO;cTg female mice exposed to cold (4 °C) for 4 h (Scale bar, 60 μm) ($$n = 3$$ per group, biologically independent samples). Source data are provided as a Source Data file. To further examine whether expression of ectopic HA-Ssu72 in BAT could rescue the thermogenic defect of Ssu72 aKO mice, 7-week-old Ssu72 WT, aKO, and aKO;cTg mice were challenged with acute cold exposure (4 °C). When observing behaviors of mice in response to acute cold, we found that the sluggish and shivering behavior of Ssu72 aKO mice after 4 h of cold exposure seemed to be restored by ectopic Ssu72 expression in aKO;cTg mice (Supplementary Movie 1 and Supplementary Fig. 10b). Morphological alterations that occurred in BAT of Ssu72 aKO mice were almost completely restored in BAT of aKO;cTg mice under severe cold condition (Fig. 6d). Remarkably, the severe cold intolerance of Ssu72 aKO mice was lethal, whereas cold susceptibility of aKO;cTg mice was similar to that of WT mice (Supplementary Fig. 10c). We further assessed UCP1 protein expression in BAT of 11-week-old cold-exposed mice and found that reduced UCP1 expression in Ssu72 aKO mice was restored by ectopic Ssu72 expression in aKO;cTg mice during cold exposure (Fig. 6e). Taken together, these results indicate that Ssu72 can mediate translation of key thermogenic factors in BAT under RT condition, which may affect cold tolerance of mice during acute cold exposure. ## Prolonged eIF2α phosphorylation by Ssu72 depletion reduces mitochondrial OXPHOS translation in BAT Mitochondrial unfolded protein response (UPRmt) is activated by mitochondrial stress and multiple forms of mitochondrial defects, subsequently inducing nuclear transcriptional response to restore impaired mitochondrial proteome48,49. To address whether prolonged eIF2α phosphorylation by Ssu72 depletion was associated with mitochondrial dysfunction in BAT, we next measured levels of UPRmt transcripts as a marker of mitochondrial defects. We found that expression levels of UPRmt genes (Hspa9, Lonp1, Hspd1, and Yme1l1) in BAT of Ssu72 aKO mice were increased compared with those of Ssu72 WT mice (Supplementary Fig. 11a). A recent study has reported that prolonged UPRER suppresses mitochondrial protease ClpP expression through eIF2α pathway50. Consistent with the report, mitochondrial protease ClpP expression was downregulated in Ssu72 aKO mice at both transcriptional and translational levels (Fig. 7a and Supplementary Fig. 11b). Furthermore, it has been reported that downregulation of ClpP in mammalian cells can attenuate mitochondrial OXPHOS capacity50,51, and ClpP-deficient mice show decreased mitoribosomal assembly in heart mitochondria, thereby affecting mitochondrial translation52. OXPHOS complex subunits are encoded on both nuclear and mitochondrial genomes53. To investigate whether regulation of ClpP expression in Ssu72-deficient BAT could inhibit mitochondrial OXPHOS capacity, we examined expression levels of OXPHOS subunits encoded by nuclear DNA, including NDUFB8 (complex I, CI), SDHB (complex II, CII), UQCRC2 (complex III, CIII), COX4 (complex IV, CIV), and ATP5A (complex V, CV), in BAT of Ssu72 WT and aKO mice housed at RT. It was found that mRNA levels of these nuclear-encoded OXPHOS genes were not significantly different between genotypes (Fig. 7b). Surprisingly, protein expression levels of CI, CII, and CIV subunits were markedly reduced in BAT of Ssu72 KO mice than in Ssu72 WT mice (Fig. 7c), supporting the notion that downregulation of ClpP expression in Ssu72-deficient BAT could inhibit cytosolic translation of OXPHOS subunits. We further investigated expression levels of mitochondrial-encoded OXPHOS subunits. We found that there was no significant difference at transcriptional levels (Fig. 7d). Notably, protein expression levels of MT-ND1 and MT-CO1 were downregulated in Ssu72-deficient BAT (Fig. 7e). To validate the effect of Ssu72 on the translation of OXPHOS complex, we examined whether ectopic Ssu72 expression could restore defects in mitochondrial translation in Ssu72-deficient BAT. We found that reduced protein expression levels of OXPHOS subunits in BAT of Ssu72 aKO mice were restored by ectopic HA-Ssu72 expression in BAT of aKO;cTg mice (Fig. 7f, g). Overall, we found that prolonged eIF2α phosphorylation by Ssu72 depletion in BAT could affect the expression of key functional factors while attenuating global mRNA translation and decrease ClpP expression, thereby reducing mitochondrial OXPHOS capacity and thermogenesis (Supplementary Fig. 11c). These findings suggest that cytosolic translation regulated by Ssu72 is critical for mitochondrial function and BAT thermogenesis during cold exposure. Fig. 7Ssu72 depletion in BAT affects translation of OXPHOS subunits.a Western blots of ClpP and phosphorylated eIF2α (P-eIF2αSer51) in BAT from Ssu72 WT and aKO mice housed at RT ($$n = 3$$ mice per group). b Relative mRNA expression levels of OXPHOS complex subunits encoded by nuclear DNA in BAT from Ssu72 WT and aKO mice housed at RT ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed Student’s t test. c Western blots of mitochondrial OXPHOS complex subunits (CI-V) encoded by nuclear DNA in BAT from Ssu72 WT and aKO mice housed at RT ($$n = 3$$ mice per group). d Relative mRNA expression levels of OXPHOS complex subunits encoded by mitochondrial DNA (Complex I, IV, and V) in BATs from Ssu72 WT and aKO mice housed at RT ($$n = 5$$ mice per group). Data are presented as mean ± SEM. Statistical comparisons were made using two-tailed Student’s t test. e Western blots of MT-ND1 and MT-CO1 in BAT from Ssu72 WT and aKO mice housed at RT ($$n = 3$$ mice per group). f Western blots of OXPHOS complex subunits (CI-V) encoded by nuclear DNA in BAT from 12-week-old Ssu72 WT, aKO, and aKO;cTg female mice housed at RT ($$n = 3$$ mice per group). g Western blots of MT-ND1 and MT-CO1 in BAT from 12-week-old Ssu72 WT, aKO and aKO;cTg female mice housed at RT ($$n = 3$$ mice per group). For a–e, 11-week-old male Ssu72 WT and aKO mice were used. Source data are provided as a Source Data file. ## Discussion The prevalence of metabolic diseases is increasing, and defective adaptive thermogenesis is associated with the progression of metabolic diseases in humans54–56. Therefore, identifying molecular mechanisms that increase BAT activity will be interesting future avenues. In this study, we demonstrate that Ssu72 phosphatase plays a critical role in BAT thermogenesis. The findings of this study revealed that protein expression of Ssu72 was specifically high in BAT, and its expression was markedly increased upon acute cold challenge. We also found that cold tolerance was dramatically reduced in mice lacking adipocyte Ssu72 (Ssu72 aKO) upon acute cold (4 °C) exposure, and that defect could be restored by ectopic Ssu72 expression in adipose tissues. These findings suggest that Ssu72 expression in adipose tissue can enhance the thermogenic activity of BAT. During thermogenic adaptation, BAT undergoes complex catabolic pathways for respiration, while simultaneously challenging demands of synthetic anabolic processes such as increased protein synthesis and de novo lipogenesis18,57. Indeed, not only transcriptional regulation but also post-transcriptional regulation such as RNA processing and translation are highly induced during BAT activation by acute cold exposure58, suggesting that mRNA translation is important for thermogenic adaptation of BAT in response to acute cold. Phosphorylation of eIF2α is a well-known mechanism for the regulation of mRNA translation initiation, which inhibits global protein synthesis in eukaryotic cells22. In an obese state, eIF2α is phosphorylated to cope with chronic ER stress and to maintain protein homeostasis59. Although recent evidence has revealed that proteasomal protein quality control is essential for thermogenesis upon chronic exposure to cold or excess nutrients60, little is known about how BAT responds to the high demand for protein synthesis for thermogenic functions during acute cold exposure. Here, we found that Ssu72 could act as an eIF2α phosphatase and mediates protein synthesis of key thermogenic effectors in BAT. Loss of Ssu72 increased phosphorylation of eIF2α at Ser51 in BAT, whereas adipocyte-specific Ssu72 expression dramatically downregulated phosphorylation of eIF2α. Our findings provide evidence for a role of Ssu72 in protein synthesis upon mild (RT) and severe cold (4 °C) exposure, thereby affecting thermogenic activity of BAT. Unexpectedly, transcriptional control of thermogenic genes was also shown in Ssu72-deficient BAT during severe cold exposure. Several studies have shown that impaired mitochondrial respiratory capacity can result in decreased expression of thermogenic genes in BAT61–63, implying that BAT mitochondria can sense its respiratory capacity and communicates with the nucleus to regulate the transcription of genes64. Since severe cold stimulation requires high respiratory capacity in brown adipocytes and enhances the thermogenic gene program16,65, expression levels of thermogenic genes are increased within a few hours of severe cold exposure with normal respiratory capacity. However, thermogenic gene expression in Ssu72-deificent brown adipocytes might not be increased in response to severe cold due to a secondary effect of reduced mitochondrial respiratory capacity. Why and how does Ssu72 phosphatase predominantly control the function of BAT relative to WAT? BAT has a high density of mitochondria with a higher amount of Ucp1 than WAT66. It should be noted that Ssu72 plays a substantial role in mitochondria-rich adipocytes. Much evidence from our study clearly shows that Ssu72 deficiency causes severe mitochondrial dysfunction and affects thermogenic adaptation of BAT. Another interesting implication is that BAT whitening that occurs with sustained high-fat food intake is closely related to mitochondrial dysfunction67. Of note, in mice housed at room temperature and fed a normal chow diet, Ssu72 deficiency resulted in BAT whitening with large and unilocular lipid droplets. The white-like phenotype of Ssu72-deficient BAT was partially restored by the expression of ectopic Ssu72 in vivo. In fact, housing mice at room temperature (20 °C−24 °C) is considered mild cold exposure, as about $30\%$ of resting energy expenditure is used for thermoregulation at this temperature43. Upon adaptation to thermoneutrality (TN, 30 °C), BAT seems to adopt a white-like characteristics68. Hence, Ssu72 serves as a key regulator to prevent brown-to-white adipose tissue conversion, allowing BAT to maintain its thermogenic activity under cold condition. Inguinal WAT (iWAT) also undergoes adaptive and dynamic changes in response to cold. Chronic severe cold exposure or β3-adrenergic receptor agonists can induce the generation of mitochondrial-rich thermogenic beige adipocytes69. At room temperature, Ssu72 expression in iWAT was lower than that in BAT. However, Ssu72 expression in iWAT was increased after exposure to cold (4 °C) for 8–12 h. This finding may point to an additional role for Ssu72 in iWAT. Further studies are needed to investigate whether Ssu72 affects the browning or thermogenic function of iWAT during chronic exposure to severe cold. It has been reported that eIF2α kinase PERK can coordinate mitochondrial molecular quality control in response to ER stress70. Under ER and nutrient stress conditions, PERK-eIF2α pathway promotes respiratory supercomplex assembly through supercomplex assembly factor 1 (SCAF1); however, this pathway is insufficient to form mitochondrial cristae71. A recent study has shown that PERK is required for cristae formation though the PERK-GABPα pathway during brown adipocyte differentiation72. In addition, PERK promotes cristae formation by enhancing mitochondrial import of MIC19, a key organizer of cristae formation, and the induction of MIC19 is independent of the canonical PERK-eIF2α pathway73. Considering the direct effect of Ssu72 on eIF2α dephosphorylation, mitochondrial dysfunction in Ssu72 aKO mice is more affected by P-eIF2α-dependent translational control than by PERK-dependent mechanisms. An important question emerging from our study is how persistent translational control causes mitochondrial dysfunction. An earlier study using advanced ribosome profiling approach has revealed that inhibition of cytosolic translation affects mitochondrial translation and that synchronization of translation is controlled by unidirectional communication from cytosol to mitochondria74. In addition, a flux of nuclear-encoded molecules from cytosol to mitochondria triggers translation of mitochondrial-encoded OXPHOS subunits75. Indeed, in BAT of Ssu72 aKO mice, cytosolic translation was globally reduced and the translation of nuclear-encoded OXPHOS subunits was attenuated. Consistent with previous reports50,52, we also found that prolonged eIF2α phosphorylation by Ssu72 depletion downregulated ClpP expression, possibly affecting mitochondrial translation of OXPHOS subunits. In this regard, we believe that Ssu72 is also associated with mitochondrial translation. We found that the translation of mitochondrial-encoded OXPHOS subunits, MT-ND1 and MT-CO1, was attenuated in Ssu72-deficient BAT. Our results suggest that persistent control of translation through eIF2α phosphorylation by Ssu72 deficiency can reduce cytosolic translation and even mitochondrial translation of OXPHOS subunits. In summary, Ssu72 is a cold stress-responsive protein phosphatase that mediates cytosolic translation and mitochondrial oxidative phosphorylation, which is required for thermogenesis in brown adipocytes (Fig. 8). Our results demonstrate that Ssu72 phosphatase affects protein synthesis and thermogenic capacity of BAT upon cold exposure. Our results also demonstrate that Ssu72-mediated mRNA translation by regulating eIF2α phosphorylation can induce the expression of key functional proteins and increase mitochondrial OXPHOS capacity, fueling thermogenic activation in BAT. Beyond the major role of BAT in heat production, recent studies using murine models have shown that BAT transplantation improves systemic energy expenditure and protects mice from obesity, diabetes, and liver steatosis76–78. Furthermore, targeting and enhancing BAT activity could be a promising therapeutic tool to treat metabolic diseases in humans12,79,80. In this context, our study raises the strong possibility that Ssu72 is a potential therapeutic target for several metabolic diseases including obesity-related diseases and non-alcoholic fatty liver disease (NAFLD). It is of great interest to investigate metabolic benefits of increasing Ssu72 expression and/or activity in patients. Fig. 8A proposed model for the role of Ssu72 in translation required for thermogenic adaptation of BAT.Ssu72, whose expression is increased in response to acute cold, acts as an eIF2α phosphatase that dephosphorylates P-eIF2αSer51 and induces cytosolic translation of key thermogenic effectors such as OXPHOS. Through these mechanisms, mitochondria with normal oxidative capacity further activate adaptive thermogenesis in brown adipocytes. In Ssu72-deficient brown adipocytes, cytosolic translation is attenuated by hyperphosphorylation of eIF2α, resulting in mitochondrial dysfunction and thermogenic defect. ## Mice Male C57BL/6J (WT) mice were obtained from Laboratory Animal Research Center (LARC) of Sungkyunkwan University School of Medicine. Ssu72 flox/flox mice (Ssu72 WT) were bred with Adiponectin-Cre mice to generate mature adipocyte-specific Ssu72 knockout (Ssu72 flox/flox; Adiponectin-Cre) mice (Ssu72 aKO). We also generated conditional transgenic mice (cTg) with gene encoding ectopic HA-tagged Ssu72 in the Rosa26 locus (Rosa26loxp-STOP-loxP-HA-Ssu72). These transgenic mice (Rosa26 HA-Ssu72) were then crossed with Ssu72 aKO mice to generate aKO;cTg mice. All mice were maintained on a C57BL/6J background. Genotyping was performed by PCR analysis, and PCR primers for genotyping are as follows: Ssu72 flox (forward), 5′-TCAAAGCATGATTGAGAGCAGCAG-3′; Ssu72 flox (reverse), 5′-GTGATAGGCAAGCAGGTGTGAG-3′; Cre (forward), 5′-GTCGATGCAACGAGTGATGA-3′; Cre (reverse), 5′-TCATCAGCTACACCAGAGAC-3′; Rosa26 HA-Ssu72 (forward), 5′-AAAGTCGCTCTGAGTTGTTAT-3′; Rosa26 HA-Ssu72 (reverse), 5′- GGAGCGGGAGAAATGGATATG-3′. Mice were maintained under temperature- (23 °C) and humidity-controlled (40–$60\%$) conditions with free access to food (normal chow diet; LabDiet, #5053) and water, on a 12-h light/12-h dark cycle. All mice are age and sex matched for individual experiments. Both male and female mice were used in the experiments, and no sex-specific phenotype was observed in vivo. For acute cold challenge experiments, mice had ad libitum access to water, although food was removed for a short period of time when animals were placed at 4 °C. During cold stress, a temperature probe (JEUNG DO Bio & Plant, #JD-DT-08g) was implanted into the anus of each mouse every hour. All mice used in the experiments were euthanized with CO2 inhalation until breathing and heartbeat stopped. All animal experiments were conducted in accordance with guidelines of the Institutional Animal Care and Use Committee (IACUC) of Sungkyunkwan University School of Medicine (SUSM), which is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC International) and abides by the Institute of Laboratory Animal Resources (ILAR) guidelines. ## Gene expression Omnibus (GEO) dataSets analysis For comparison of Ssu72 mRNA expression in human adipocytes between lean and obese individuals, each gene expression datasets were obtained from GEO database. Relative Ssu72 expression value in human subcutaneous adipocytes from non-diabetic obese individuals ($$n = 19$$) was compared with non-diabetic lean individuals ($$n = 20$$) (GEO accession number: GSE2508). ## Cell culture 293T cells (ATCC, #CRL-3216) were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (GenDEPOT) supplemented with $10\%$ FBS (Gibco) and $1\%$ penicillin/streptomycin. These cells were maintained at 37 °C in a humidified atmosphere with $5\%$ CO2. ## RNA isolation from adipose tissues Total RNA was isolated from adipose tissues using QIAzol Lysis Reagent (QIAGEN, #79306). Adipose tissues (BAT, iWAT, eWAT) were placed in 1 ml QIAzol Lysis Reagent, homogenized with a Polytron Homogenizer PT 1300D (Kinematica AG, Switzerland), and centrifuged at 12,000×g for 10 min (4 °C) to remove and discard the fatty layer. Chloroform was then added to cleared homogenized samples. The samples were centrifuged at 12,000×g for 15 min (4 °C) and aqueous phases were obtained. Total RNA was precipitated with the addition of isopropanol and centrifugation at 12,000×g for 10 min (4 °C). The supernatant was removed, and RNA pellet was washed with ethanol. The pellet was resuspended with RNase-free water and the concentration was measured using a NanoDrop spectrophotometer. ## Quantitative real-time PCR (qRT-PCR) cDNA templates were synthesized with the cDNA synthesis kit (Applied Biological Materials Inc. (Abm), #G236) using random primers. The cDNA was then subjected to PCR amplification using 2X PCR Taq MasterMix (Abm) and gene-specific primers. Quantitative real-time PCR analysis was performed with PowerUP SYBR Green Master Mix (Applied Biosystems) reagent. Reactions were performed in triplicate. Relative gene expression was calculated using the comparative Ct (2−∆∆Ct) method81, where values were normalized to a 5S or 18S rRNA gene expression. The mRNA expression of candidate genes was determined by QuantStudio 6 Flex Real-Time PCR (Life technologies). All primer sequences for qRT-PCR are provided in Supplementary Table 1. Mitochondrial DNA (mtDNA) copy number was measured by qRT-PCR. Total genomic DNA was isolated from BAT tissues using a DNeasy 96 Blood & Tissue Kit (QIAGEN, #69504). Then, 100 ng total DNA was used for mtDNA quantification. The copy number was normalized to nuclear DNA. The primer sequences are provided in Supplementary Table 1. ## Western blotting and Coomassie brilliant blue staining Protein lysates were prepared from tissues or cells using RIPA buffer (20 mM pH 7.4 Tris-HCl, 150 mM NaCl, $1\%$ Triton X-100, $0.1\%$ SDS, 1 mM EGTA, 1 mM Phenylmethylsulfonyl fluoride (PMSF), 1 mM Na3VO4 and 1X Protease inhibitor cocktail (PIC)). For tissue lysate preparation, tissues were homogenized with a Polytron Homogenizer PT 1300D (Kinematica AG, Switzerland) in cold RIPA buffer. For cell lysis, cells were resuspended in cold RIPA buffer and homogenized by passing cells through a syringe tip. Equal amounts of protein lysates as quantified by Bradford assay were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes using a Mini Trans-Blot Electrophoretic Transfer Cell (Bio-Rad). Membranes were blocked with Tris-Buffered Saline with $0.1\%$ tween 20 (TBS-T) containing $5\%$ (w/v) skim milk for 1 h at room temperature (RT) and incubated with primary antibodies in $5\%$ skim milk or $5\%$ BSA for 3 h at RT or overnight at 4 °C. These membranes were washed for 30 min with TBS-T, subsequently incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies in $5\%$ skim milk for 2 h at RT, and then washed for one hour with TBS-T. Blotted proteins were detected using ECL solution (AbFrontier) and exposed on Medical X-ray film blue (AGFA). The following primary antibodies were used (at the indicated dilutions): anti-Ssu72 (Cell signaling Technology (CST), #12816, 1:2000), anti-PP1α (CST, #2582, 1:750), anti-CDC25B (Thermo Fisher Scientific, #PA5-17759, 1:1000), anti-PTEN (CST, #9188, 1:1000), anti-PP2A-B56-α (Santa Cruz Biotechnology (SCBT), #sc-271311, 1:1000), anti-β-actin (Sigma-Aldrich, #A2066, 1:3000), anti-UCP1 (SCBT, #sc-6529, 1:750), anti-GAPDH (CST, #2118, 1:3000), anti-PGC1α (SCBT, #sc-13067, 1:750), anti-HSP90 (SCBT, #sc-13119, 1:1000), anti-phospho-eIF2α (Ser51) (CST, #3398, 1:1000), anti-eIF2α (CST, #5324, 1:2000), anti-ATF4 (SCBT, #sc-390063, 1:1000), anti-CHOP (CST, #2895, 1:750), anti-phospho-PERK (Thr980) (Thermo Fisher Scientific, #MA5-15033, 1:1000), anti-phospho-IRE1α (Ser724) (Thermo Fisher Scientific, #PA1-16927, 1:1000), anti-ATF-6 (CST, #65880, 1:1000), anti-Myc-Tag (CST, #2276, 1:1500), anti-HA-Tag (SCBT, #sc-7392, 1:1000), anti-HA-Tag (CST, #3724, 1:2000), anti-β-actin (CST, #3700, 1:3000), anti-GST (SCBT, #sc-138, 1:2000), anti-puromycin (DSHB, #PMY-2A4, 1:200), anti-AMPKα (CST, #5831, 1:2000), anti-PKA C alpha (GeneTex, #GTX104934, 1:1000), anti-ClpP (SCBT, #sc-271284, 1:1000), anti-total OXPHOS Rodent WB Antibody Cocktail (Abcam, #ab110413, 1:500), anti-COX IV (CST, #4850, 1:1000), anti-MT-ND1 (Abcam, #ab181848, 1:1000). The following secondary antibodies were used (at the indicated dilutions): anti-Rabbit IgG(H + L)-HRP (GenDEPOT, #SA002-500, 1:7000), anti-Mouse IgG(H + L)-HRP (GenDEPOT, #SA001-500, 1:7000), anti-Goat IgG(H + L)-HRP (GenDEPOT, #SA007-500, 1:7000). A list of all antibodies used in this study is provided in Supplementary Table 2. For Coomassie brilliant blue staining, SDS-PAGE gels were stained for 1 h using Coomassie staining buffer ($0.25\%$ Coomassie Brilliant Blue R-250, $50\%$ methanol, $10\%$ acetic acid, $40\%$ distilled water) and destained with a destaining buffer ($50\%$ methanol, $10\%$ acetic acid, $40\%$ distilled water) overnight at RT. ## Metabolic mouse studies Metabolic studies were conducted at Soonchunhyang University under an approved SCH-IACUC protocol. Energy expenditure and associated experiments were measured using a Phenomaster (TSE systems) at Soonchunhyang Biomedical Research Core-facility of Korea Basic Science Institute (KBSI). Oxygen consumption (VO2) and CO2 release rates (VCO2) were measured every 24 min. Energy expenditure and Respiratory exchange rate (RER) were calculated by the ratio between VO2 and VCO2. Food intake was automatically monitored by Phenomaster food measurement module and locomotor activity was obtained by counting the number of the x-axis and y-aixs beam breaks. Ssu72 WT and aKO mice were injected intraperitoneally with a β3-adrenergic receptor-specific agonist CL-316,243 (Sigma-Aldrich, #5976) at a dose of 10 mg/kg to examine their responses to adrenergic stimulation, and VO2 was measured every 2 min. ## Transmission electron microscopy (TEM) analysis BAT was fixed with $2\%$ glutaraldehyde and $2\%$ paraformaldehyde in 0.1 M sodium cacodylate buffer (pH 7.4) for 48 h. After dehydration in a graded acetone series, tissues were embedded in Spurr resin. Sections were cut on a Leica UCT ultramicrotome and placed onto Cu girds. Sections were post-stained with uranyl acetate and lead citrate, and then examined with a Hitachi H600AB transmission electron microscope at 75 kV. Three mice were analyzed for each genotype. The size of mitochondria was quantified using ImageJ software v.1.52a (NIH, https://imagej.nih.gov/ij/). Mitochondria with disrupted cristae and total mitochondria were counted from each image and then expressed as % cristae disruption (mitochondria with disrupted cristae over total mitochondria). ## Histological analysis and Immunohistochemistry (IHC) staining Tissue samples (BAT, iWAT, and eWAT) were fixed in $10\%$ formalin solution (Sigma-Aldrich, #HT501320) for 24–36 h at 4 °C, embedded in paraffin, cut at 5 µm (BAT) and 6 µm (eWAT and iWAT) thick sections, and then stained with hematoxylin and eosin (H&E). For immunohistochemistry, deparaffinization and dehydration of tissue paraffin sections were conducted with xylene and ethanol. Heat-induced antigen retrieval was performed by boiling a section in 10 mM citric acid buffer (pH 6.0) for 15 min at 95–100 °C. Slides were incubated with $3\%$ hydrogen peroxidase to block the endogenous peroxidase activity. After washing with TBS-T buffer, sections were blocked with goat serum ($1.5\%$ blocking solution) for 1 h at room temperature followed by incubation with anti-eIF2αSer51 (Cell Signaling Technology, #3398) antibody diluted 1:150 or anti-UCP1 (Abcam, #ab10983) antibody diluted 1:500 at 4 °C overnight. These slides were incubated with biotinylated goat anti-rabbit IgG secondary antibody (Vector Laboratories, #PK-6101) diluted 1 drop (50 μl) to 10 ml blocking solution at room temperature for 1 h. Labeling was then visualized with 3,3’-diaminobenzidine (DAB). Nuclei were stained with hematoxylin. Slides were scanned with a MoticEasyScan Pro 6 (Motic) scanner and scanned images were viewed with Aperio ImageScope software v.12.4.3.5008 (Leica Biosystems). ## Immunofluorescence (IF) staining For the preparation of frozen sections, BAT tissues were fixed with $4\%$ paraformaldehyde (PFA) (Sigma-Aldrich, #P6148) at 4 °C for 24 h and washed with ice cold PBS. Fixed tissues were placed in $15\%$ sucrose (Sigma-Aldrich, #S9378) in PBS at 4 °C for 6 h and then incubated in $30\%$ sucrose in PBS at 4 °C overnight using a tube rotator. Tissues were embedded in OCT embedding matrix (Sakura Finetek, #4583) and stored at −70 °C. For immunofluorescence (IF) staining, frozen sections were incubated with $0.3\%$ Triton X-100 in PBS for 30 min, washed with PBS, and blocked with PBS containing $5\%$ goat serum and $0.1\%$ Triton X-100 at RT for 40 min. These sections were then incubated with anti-eIF2αSer51 (Cell Signaling Technology, #3398) antibody diluted 1:200 at 4 °C overnight followed by incubation with Alexa Fluor 568-conjugated anti-rabbit IgG secondary antibody (Thermo Fisher Scientific) diluted 1:300 at RT for 2 h. Nuclei were stained with DAPI (4′,6-diamidino-2-phenylindole) using VECTASHIELD Antifade Mounting Medium with DAPI (Vector Laboratories, #H-1200). Images were taken with an Axio Imager microscope (ZEISS). ## RNA sequencing (RNA-Seq) and data analysis RNA-Seq libraries were prepared with a TruSeq Stranded mRNA Library Prep kit (Illumina) using purified RNAs isolated from BAT of Ssu72 WT and aKO mice, including three independent samples. Transcriptomic sequencing was performed on a Novaseq platform using the standard sequencing protocol. In total, 50–65 million base pair (bp) reads were generated per sample. An initial sequence-level quality assessment was performed using FastQC (v0. 11. 9). RNA-Seq reads were then mapped to the mouse mm10 reference genome using Tophat (v2.0.13). Differential gene expression analysis was performed using cuffdiff 82 package (v2.2.0) (http://cole-trapnell-lab.github.io/cufflinks/releases/v2.2.0/). Raw read counts were used and modeled based on a negative binomial distribution. Genes were considered to be differentially expressed if they met the following criteria: [1] expression changes of 2-fold or greater between means of both Ssu72 WT and aKO samples, [2] p value was less than 0.05, and [3] FDR was less than 0.1. Data were obtained from three independent experiments and processed using DAVID Bioinformatics Resources 6.7 software for Gene ontology, BIOCARTA, KEGG pathway, and gene set enrichment analyses. List of differentially expressed genes (DEGs) in BAT between Ssu72 WT and aKO mice from RNA-seq data is provided in Supplementary Data 1. ## Gene set enrichment analysis (GSEA) Gene set enrichment analysis (GSEA) was performed with software version 4.1.0 (Broad Institute and University of California, https://www.gsea-msigdb.org/gsea/index.jsp). Prior to analysis, a ranked list was computationally calculated with each gene based on log2 fold change. Gene sets were defined using the molecular signature database (MSigDB) curated from BIOCARTA, Kyoto Encyclopedia of Genes and Genomes (KEGG), REACTOME, and Pathway Interaction Database (PID). Gene sets with an FDR < 0.25 and a normal p value <0.05 were considered statistically significant. ## Recombinant protein purification To generate His-Ssu72 phosphatase-dead mutant (His-Ssu72 C12S), PCR-based site-directed mutagenesis was performed using a Muta-DirectTM Site Directed Mutagenesis Kit (INtRON, #15071). His-tagged recombinant proteins (His-Ssu72 WT and His-Ssu72 C12S mutant) were expressed in Escherichia coli (E. coli) strain BL21 competent cell (Enzynomics, #CP110) by overnight induction at 18 °C with 0.2 mM isopropyl-β-D-1-thiogalactopyranoside (IPTG). Pelleted bacteria were resuspended in a lysis buffer (300 mM NaCl, 20 mM Sodium phosphate (pH 8.0), 20 mM Imidazole, $1\%$ Triton X-100, and $10\%$ glycerol) supplemented with 1 mM PMSF and 1 mM dithiothreitol (DTT)). After sonication, lysates were centrifuged at 48,384×g for 30 min at 4 °C. The supernatant of the lysate was incubated with $50\%$ slurry of Ni-NTA beads (QIAGEN) for 3 h at 4 °C. Immobilized His proteins-Ni-NTA were then loaded onto a column and washed twice with a wash buffer (300 mM NaCl, 20 mM Sodium phosphate (pH 8.0), and 20 mM Imidazole). His proteins were then eluted with an elution buffer containing a high concentration of imidazole (300 mM NaCl, 20 mM Sodium phosphate (pH 8.0), and 300 mM Imidazole). Purified His proteins were concentrated using centrifugal concentrators (Sartorius). For GST-tagged proteins purification, pGEX-KG (GST) and pGEX-KG-EIF2S1 (GST-eIF2α) were expressed in BL21 competent cell by overnight induction at 18 °C with 0.5 mM IPTG. Briefly, pelleted bacteria were resuspended with STE buffer (10 mM Tris-HCl (pH 8.0), 150 mM NaCl, and 1 mM EDTA) and lysed in STE buffer containing 100 μg/ml lysozyme, 0.5 mM PMSF, and 1X PIC. After sonication, lysates were centrifuged at 20,442×g for 30 min at 4 °C. The supernatant was incubated with $50\%$ slurry of Glutathione Sepharose 4B beads (Cytiva) overnight at 4 °C. These beads were then washed three times with cold 1X PBS. All purified proteins were separated by SDS-PAGE and then analyzed by Coomassie brilliant blue staining. ## In vitro binding assay and in vitro kinase/phosphatase assay For in vitro binding assay, bead bound GST or GST-eIF2α proteins (2 μg) were incubated with His-Ssu72 WT (2 μg) in binding buffer (20 mM Tris-HCl (pH 7.4), 100 mM NaCl, $0.5\%$ NP-40, 1 mM EDTA, and 1 mM DTT) with glutathione beads for 3 h at 4 °C. These beads were then washed three times with a wash buffer (20 mM Tris-HCl (pH 7.4), 150 mM NaCl, $1\%$ NP-40, 1 mM EDTA, and 1 mM DTT). Bound proteins were eluted with an SDS sample buffer and then separated by SDS-PAGE. For in vitro kinase/phosphatase assay, bead bound 2 μg GST or 2 μg GST-eIF2α protein was incubated with 0.5 μg recombinant PERK protein (Abcam, #ab101115) in kinase buffer (100 mM MOPS/NaOH (pH 7.2), 20 mM MgCl2, 1 mM DTT and 1 mM PMSF) containing 1 mM adenosine 5′-triphosphate (ATP) (Sigma-Aldrich) at 37 °C for 1 h. Phosphorylated GST-eIF2α proteins were washed three times with phosphatase buffer (50 mM Tris-HCl (pH 7.4), 20 mM MgCl2, 0.2 mM EDTA, and 0.2 mM EGTA), and incubated with purified His-Ssu72 WT or His-Ssu72 C12S mutant proteins at 37 °C for 90 min. The reaction was terminated by the addition of SDS sample buffer. Phosphorylation of eIF2α was then analyzed by western blotting. ## Co-immunoprecipitation (Co-IP) assay 293T cells were transfected with pCMV-Myc (Myc), pCMV-Myc-Ssu72 (Myc-Ssu72), and pCMV3-HA-EIF2S1 (HA-eIF2α; Sino Biological, #HG13107-NY). At 48 h after transfection, cells were washed twice with 1X PBS and harvested. Harvested cells were lysed using an IP lysis buffer (20 mM Tris-HCl (pH 7.4), 150 mM NaCl, $1\%$ Triton X-100, 1 mM EDTA, and 1 mM EGTA) supplemented with 1 mM PMSF, 1 mM β-glycerophosphate, and 1X protein inhibitor cocktail (PIC). Lysates were clarified by centrifugation at maximum speed. The protein concentration of supernatant was determined using Bradford assay. After 500 μg of proteins in IP lysis buffer were rotated overnight at 4 °C with α-Myc antibody, they were then rotated with 20 μl of $50\%$ slurry of protein A/G PLUS-Agarose (Santa Cruz Biotechnology) for 3 h at 4 °C. Immunoprecipitated proteins were collected by centrifugation and then washed three times with lysis buffer. These beads were then mixed with SDS sample buffer and prepared for SDS-PAGE. ## Western blot-SUnSET analysis Ssu72 WT and aKO mice were injected with puromycin dihydrochloride (Sigma-Aldrich, #P9620) at a dose of 40 nmol/g of body weight and then sacrificed at 30 min after administration47. BAT tissue lysates from mice were prepared for SDS-PAGE analysis. Western blotting was conducted as described above. The membrane was blocked with $5\%$ skim milk. To monitor mRNA translation, the membrane was incubated with primary antibody (DSHB, #PMY-2A4) diluted to 1:200 in $5\%$ BSA for detecting puromycin-labeled peptides. These membranes were washed with TBS-T and incubated with anti-mouse IgG-HRP secondary antibody (GenDEPOT, #SA001-500) diluted to 1:7000 in $5\%$ skim milk. ## Additional resource Analyzed cell type-enriched transcripts datasets with expression profiles83 were obtained from Human Protein Atlas website (https://www.proteinatlas.org/humanproteome/tissue+cell+type). Differential correlation score between each gene and the cell-type-enriched transcripts was analyzed. ## Statistical analysis Results were analyzed using two-tailed Student’s t test, one-way ANOVA, or two-way ANOVA where appropriate using GraphPad Prism software (version 7). A Bonferroni post hoc test was used to test for significant differences as determined by ANOVA. Significance was accepted at $p \leq 0.05.$ Data are presented as mean ± SD or SEM. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Supplementary information Supplementary Information Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Supplementary Movie 1 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-36836-y. ## Source data Source Data ## Peer review information Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. ## References 1. Bartelt A, Heeren J. Adipose tissue browning and metabolic health. Nat. Rev. Endocrinol. (2014.0) 10 24-36. DOI: 10.1038/nrendo.2013.204 2. Ikeda K, Maretich P, Kajimura S. The common and distinct features of brown and beige adipocytes. Trends Endocrinol. Metab. (2018.0) 29 191-200. 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--- title: Replication and mediation of the association between the metabolome and clinical markers of metabolic health in an adolescent cohort study authors: - Christian Brachem - Leonie Weinhold - Ute Alexy - Matthias Schmid - Kolade Oluwagbemigun - Ute Nöthlings journal: Scientific Reports year: 2023 pmcid: PMC9968318 doi: 10.1038/s41598-023-30231-9 license: CC BY 4.0 --- # Replication and mediation of the association between the metabolome and clinical markers of metabolic health in an adolescent cohort study ## Abstract Metabolomics-derived metabolites (henceforth metabolites) may mediate the relationship between modifiable risk factors and clinical biomarkers of metabolic health (henceforth clinical biomarkers). We set out to study the associations of metabolites with clinical biomarkers and a potential mediation effect in a population of young adults. First, we conducted a systematic literature review searching for metabolites associated with 11 clinical biomarkers (inflammation markers, glucose, blood pressure or blood lipids). Second, we replicated the identified associations in a study population of $$n = 218$$ (88 males and 130 females, average age of 18 years) participants of the DONALD Study. Sex-stratified linear regression models adjusted for age and BMI and corrected for multiple testing were calculated. Third, we investigated our previously reported metabolites associated with anthropometric and dietary factors mediators in sex-stratified causal mediation analysis. For all steps, both urine and blood metabolites were considered. We found 41 metabolites in the literature associated with clinical biomarkers meeting our inclusion criteria. We were able to replicate an inverse association of betaine with CRP in women, between body mass index and C-reactive protein (CRP) and between body fat and leptin. There was no evidence of mediation by lifestyle-related metabolites after correction for multiple testing. We were only able to partially replicate previous findings in our age group and did not find evidence of mediation. The complex interactions between lifestyle factors, the metabolome, and clinical biomarkers warrant further investigation. ## Introduction Chronic diseases such as cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), and cancers are among the largest public health burdens modern societies’ face1,2. Important for the prevention of these diseases are key modifiable risk factors such as body composition and dietary intake. Metabolomics is a rich resource in the process of elucidating the etiology of diseases3,4. To realize the potential of the metabolome it is important to validate putative biomarkers (henceforth called “metabolites”) and replicate their associations across studies and settings5. Well-established clinical biomarkers of metabolic health (henceforth called “clinical biomarkers”), for example cholesterol as a clinical biomarker for CVD6,7, HBa1C for T2DM8, or inflammation markers (e.g. CRP, IL-8)4 appear to be intricately linked with metabolites3,5,9,10. However, study findings are largely inconsistent, and might differ by sex and age groups11–16 calling for in depth confirmation and replication across sexes and age groups. Modifiable lifestyle factors including body composition and food intake are linked to a number of chronic diseases such as type 2 diabetes17–20, CVD7,21–23 or cancer types24–27 through alterations in the human metabolome. With respect to prevention, a life course approach elucidates preventive potential in younger age groups, e.g. early adulthood, which has been shown to be relevant28,29. In these age groups, clinical biomarkers are of importance to evaluate chronic disease risk. While the relationship of body composition and dietary intake with clinical biomarkers is well reported, less is known on potential mediation through the metabolome. We recently reported associations between body composition and the metabolome (19 metabolites for body mass index (BMI) and 20 for body fat (BF) in urine30, as well as between habitual food intake (in food groups) and the metabolome (6 metabolites) in urine and blood31). The association of body composition and dietary intake with clinical biomarkers may be linked via some of these metabolites. To investigate this complex relationship, the aims of the current study were first to identify associations of metabolites with clinical biomarkers based on a systematic literature review (SLR), second to replicate these associations in our study population, and third to evaluate whether our previously reported body composition- and habitual food intake-associated metabolites mediate the association of body composition and habitual food intake with clinical biomarkers. Of note, we focused on the age groups of adolescents and young adults as a particular time window of relevance for prevention. ## Systematic literature review We first conducted a SLR of studies indexed in PubMed, separate for each clinical biomarker, to identify relationships between metabolites and clinical biomarkers to be replicated in our study. A detailed description of the search terms and flow-charts can be found in Additional File S1. Briefly, we included studies that reported associations between Inflammation markers (C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-18 (IL-18), Adiponectin, and Leptin), glucose, blood pressure (BP) (systolic blood pressure, diastolic blood pressure, and Hypertension) and blood lipids (high-density lipoprotein (HDL), low-density lipoprotein (LDL), total triglycerides) and the human blood or urine metabolome. We developed a search term for each of these clinical biomarkers. The review was conducted by CB only. We included all studies where at least one association was reported. We identified additional studies through screening of citations and literature reviews. Information about associations of metabolites and clinical biomarkers was finally extracted from each included study. Of these, only associations reported in at least two independent studies were considered “consistent” and further used in the current study. We identified 41 metabolites associated with clinical biomarker variables in at least two studies. Interestingly, these were distributed only between two clinical biomarker variables: blood pressure and CRP. Most of the metabolites (36 of 41) were associated with blood pressure. The methods applied to investigate the relationships of the metabolome and the clinical biomarker variables were very heterogeneous. They ranged from correlation analysis (e.g.54), through regression (e.g.46) to advanced machine learning methods like random forests (e.g.55) or PLS (partial least squares) variants (e.g.56). Additionally, it would be very useful for future SLR to have an easier format to export all study results in an appendix. Because of the large number of associations usually present in metabolomics studies, this would greatly increase the possibility for future studies to build on. Another important observation in the SLR is that only four out of 50 studies43,52,57,58 stratified by sex, with two additional studies having cohorts restricted to either males59 or females60, though many additional studies adjusted for or matched by sex. Given how strong the influence11–13,15,16,30,31,52 of sex is on many different aspects of the metabolome, a better and ideally unified strategy to account for these influences in future studies is needed. Most studies included in the SLR were in exclusively adult study populations. Three studies studied children53,61,62 and two studies52,57 adolescents and young adults. Age is another influential factor in the composition of the metabolome that may need additional adjustment strategies in the long term63. ## Study design Both, the confirmation and mediation analyses were conducted in a subpopulation of the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study32,33. Briefly, the DONALD Study is an ongoing longitudinal open cohort study in Dortmund, Germany, with the goal of analyzing detailed data on diet, growth, development, and metabolism between infancy and adulthood32,33. Participants are first examined at the age of 3 months and return for three more visits in the first year of life, two in the second and annually thereafter until the age of 18, when examinations start following a five-year cycle. Examinations include 3-day weighed dietary records (3d-WDR), anthropometric measurements, collection of 24-h urine samples (starting at age 3–4), collection of blood samples (starting at age 18), and interviews on lifestyle and medical examinations. Further details on the study design have been published elsewhere32,33. ## Study participants We included all DONALD study participants that were singletons, full term births (37–42 weeks of gestation) and had a birth weight of at least 2500 g. For the current analysis participants had to have a measurement of both the urine and blood metabolome, as well as at least one measurement of each clinical biomarker. Overall, 218 participants were eligible for the current study. ## Assessment of clinical biomarkers Inflammation markers (C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-18 (IL-18), Adiponectin, and Leptin), glucose, and blood lipids (high-density lipoprotein (HDL), low-density lipoprotein (LDL), total triglycerides) were measured in non-fasted blood plasma. Measurements in blood were always at the same follow-up and from the same sample as metabolome measurement. Blood measurement was always at the same follow-up visit or later than urine metabolome measurement. Blood pressure (mmHg) was measured multiple times by experienced nursing staff. We used the mean of two repeated measurements for both systolic and diastolic blood pressure. We chose the blood pressure measurement closest after the corresponding metabolome measurement for analysis of the respective participant, which was always at the next study visit. ## Untargeted metabolomic profiling The metabolome measurement was already described elsewhere31. Briefly, Metabolon Inc. (Morrisville, NC, USA) performed an untargeted metabolomics assay with lipidomics on plasma and an untargeted metabolomics assay on urine samples. For both the plasma and urine untargeted assays, Metabolon used ultra-high performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) to identify metabolites in the samples. Peak identification was done in their propriety Laboratory Information Management System. Compounds were identified by comparison of their retention time/index (RI), mass to charge ratio (m/z) and chromatographic data (e.g. MS/MS spectral data) to library standards. Metabolon maintains a library of authenticated standards with over 3300 commercially available purified standard compounds. Structurally unnamed biochemicals were identified by occurrence. Peaks are quantified using area-under-the-curve and normalized with block correction correcting for inter-day instrument tuning differences. Further details on the metabolic profiling have been reported elsewhere34. Both blood and urine untargeted assays were performed in this fashion. Urine metabolite values were additionally normalized to urine osmolality to account for differences in metabolite levels due to differences in the amount of material present in each sample. Metabolon quantified 1042 (811 known and 231 unknown) and 1407 (940 known and 467 unknown) in blood and urine, respectively. A deeper explanation of the metabolomics methods can be found in Additional File S2. ## Complex lipid platform measurement Lipids were extracted from samples in methanol:dichloromethane in the presence of internal standards. The extracts were concentrated under nitrogen and reconstituted in 0.25 mL of 10 mM ammonium acetate dichloromethane:methanol (50:50). The extracts were transferred to inserts and placed in vials for infusion-MS analysis, performed on a Shimazdu LC with nano PEEK tubing and the Sciex SelexIon-5500 QTRAP. The samples were analyzed via both positive and negative mode electrospray. The 5500 QTRAP scan was performed in MRM mode with the total of more than 1100 MRMs. Individual lipid species were quantified by taking the peak area ratios of target compounds and their assigned internal standards, then multiplying by the concentration of internal standard added to the sample. Lipid class concentrations were calculated from the sum of all molecular species within a class, and fatty acid compositions were determined by calculating the proportion of each class comprised by individual fatty acids. We identified 966 lipid species in 14 classes as well as 265 fatty acids. A deeper explanation of the lipidomics methods can be found in Additional File S2. ## Body composition and habitual dietary intake Body weight and height were measured at every follow-up by experienced nursing staff. Body mass index (BMI) was calculated using height (m) and weight (kg) with the formula \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$BMI = \frac{{weight}}{{height^{2} }}$$\end{document}BMI=weightheight2. Body fat percent (BF) was calculated from four skin/fold thickness measurements (biceps, triceps, iliaca, and scapula), using age, puberty status, and sex/specific equations from Deurenberg et al.35. Previous associations with BMI used in the mediation analysis and further details on body composition assessment were reported in Brachem et al.30. We used multiple annually applied 3d-WDRs to assess habitual food intake on the food group level. Participants had to have at least two 3d-WDR in adolescence (according to the WHO definition, age 10–19). We defined habitual intake as the mean intake across all available records in adolescence. To account for differences in consumed calories, we standardized intake to grams per 1000 kcal. Previous associations with habitual food intake used in the mediation analysis and further details on dietary assessment were reported in Brachem et al.31. ## Statistical analysis Statistical analysis was performed using R software (Version 4.0.3)36. All analyses were stratified by sex. ## Metabolomics data pre-treatment Both urine and blood metabolite values were log transformed, centered to a mean of zero and scaled to a standard deviation of one prior to analysis. ## Replication We used ordinary least squares regression to replicate associations between the metabolites and clinical biomarkers in the DONALD study. The clinical biomarkers were used as the dependent variables and metabolites as the independent variables. We adjusted the models for BMI and age, both at sample collection. Data was split into training ($70\%$) and testing ($30\%$) data to evaluate overfitting. We trained the model on the training data and used these models to predict clinical biomarker values in the test data. Results from the test data were used only to evaluate the model quality. We additionally accounted for multiple testing by holding the false discovery rate at $5\%$37. We were able to replicate 10 out of the 41 metabolites testable in our study sample. We found more metabolites replicated in females compared to males (six and five, respectively) and only one metabolite, phenylalanine, associated with systolic blood pressure across sexes. In this replication analysis, only one association, the negative association between betaine measured in the blood of males and CRP remained significant after correction for multiple testing. Betaine is an essential osmolyte derived from either diet or by oxidation of choline64,65. Insufficiencies of betaine have been associated with many chronic diseases, such as metabolic syndrome, T2DM or vascular diseases65. Additionally, betaine is considered as an anti-oxidant64 and fulfills anti-inflammatory functions66. The inverse association between betaine in male blood and CRP we observed is therefore in line with the literature. Phenylalanine was not significantly associated with systolic blood pressure after correction for multiple testing but it is interesting. It is the only metabolite associated across sexes and indirectly across bio specimen. It’s association with higher blood pressure is in line with previous literature, that reported a strong association with infant pulmonary hypertension67 and more generally elevated cardiovascular risk68. Furthermore, it was elevated in metabolically unhealthy obese (compared to metabolically healthy obese)69. Because it is a precursor to catecholamines an increase in blood pressure even has a known physiological pathway already64. More studies are needed to discern the causal order and exact mechanism of phenylalanine on blood pressure. ## Mediation analysis We used causal mediation analysis to evaluate whether our previously reported body composition-30 and habitual food intake-related metabolites31 mediate the association of body composition and habitual food intake with clinical biomarkers. For the first, BMI and BF were the exposure and the clinical biomarker (BP, IL-6, IL-18, CRP, Adiponectin, leptin, total cholesterol, HDL, LDL, and triglyceride levels) were the outcomes. The 19 (5-dodecenoylcarnitine (C12:1), 7-hydroxyindole sulfate, decanoylcarnitine (C10), formiminoglutamate, glucuronide of C10H18O2 [12], guanidinosuccinate, isobutyrylglycine (C4), isovalerylglycine, nicotinamide N-oxide, proline, succinimide, thymine, tigloylglycine, X—12839, X—21441, X—21851, X—24469, X—24801, and X—25003) BMI-associated metabolites and 20 (3-methylcrotonylglycine, glucuronide of C10H18O2 [12], glutamine conjugate of C8H12O2 [1], glycine conjugate of C10H14O2 [1], guanidinosuccinate, isobutyrylglycine (C4), isovalerylglutamine, isovalerylglycine, nicotinamide N-oxide, succinimide, tigloylglycine, X—11261, X—15486, X—17676, X—21851, X—24345, X—24350, X—24469, X—24801, X—25442, and X—25464) BF-associated metabolites were considered as mediators. For the second, habitual food intake was the exposure, the aforementioned clinical biomarker markers were the outcomes, and the six (eggs: indole-3-acetamide, N6-methyladenosine; vegetables: hippurate, citraconate/glutaconate, X—12111; processed and other meat: vanillylmandelate (VMA)) food group-associated metabolites were considered as mediators. We used the ‘mediate()’-function in the R package ‘mediation’38 for the analysis. We used 1000 simulations (the recommended default) and quasi-Bayesian approximation to estimate the standard errors. We used the model-based approach38. The mediator model is the linear regression model that regresses the metabolites on BMI, BF, or habitual food intake adjusted for age at sample collection. The habitual food intake models were additionally adjusted for BMI at sample collection. The outcome model is a linear regression model that regresses clinical biomarker on BMI, BF, or habitual food intake, the mediator (metabolites), and adjustment variables. From these models the causal mediation analysis is performed as described by Imai et al.39. Briefly, the model estimates the average causal mediation effect (ACME), which is a numeric measure of how much influence the presence of the mediator has on the total effect of the exposure-outcome association, as well as the average direct effect, the average total effect, and the proportion mediated. We corrected for multiple testing by holding the false discovery rate at $5\%$. ## Missing values We excluded metabolites from the analysis when more than $70\%$ of data was missing. Based on this we excluded 91 and 67 metabolites in female blood and urine, respectively, and 87 and 74 metabolites in male blood and urine, respectively. For the mediation analysis and the regression models, we performed a single imputation with the “missRanger” package, using 10 trees with a maximum depth of six and three non-missing candidate values for predictive mean matching. We used random forest imputation, as it is recommended for imputation of missing metabolomics data40. ## Sensitivity analysis We performed sensitivity analysis on the choice of the missing data threshold in the imputation approach, repeating the complete study protocol excluding metabolites with more than $30\%$ missing data (instead of $70\%$ in the main analysis). In males we additionally excluded 103 metabolites and 106 metabolites in blood and urine, respectively, while in females we excluded 123 and 108 additional metabolites in blood and urine, respectively. We performed sensitivity analysis on the amount of missing data permitted in the metabolites prior to imputation. We excluded over 100 additional metabolites, but the results did not change in meaning. In the replication analysis, as was expected by reducing the number of metabolites and therefore statistical tests, the metabolite closest to significance in the main analysis was statistically significant in the sensitivity analysis. However, the point estimates of the metabolites remained the same. In the mediation analysis, one additional total effect remained significant after correcting for multiple testing but no mediating effects, the same as the main analysis. Therefore, interpretation of the results was not depended on the choice of missingness permitted in the metabolites prior to imputation. ## Ethics approval and consent to participate Informed written consent was obtained from parents and from participants themselves on reaching adolescence. The ethics committee of the University of Bonn, Germany (project identification: $\frac{098}{06}$) approved the study. We confirm that all methods were performed in accordance with relevant guidelines and in accordance with the Declaration of Helsinki. ## Results In the SLR, we found metabolites associated with blood pressure and CRP in at least two independent studies (Table 1). Six metabolites (4-hydroxyhippurate, Androsterone sulfate, Glutamine, Isoleucine, Phenylalanine, and Tryptophan) for blood pressure and four metabolites (Betaine, Glutamine, Isoleucine, and Tryptophan) for CRP were present in more than two studies. The full metabolite list we identified in at least one study with their corresponding references can be found in Additional File S3.Table 1Metabolites associated with conventional systemic markers of chronic disease risk in at least two independent observational studies. MetaboliteSourcesBlood pressure 4-HydroxyhippurateZheng et al.41,42 Androsterone sulfateZheng et al.41,42 GlutamineGoïta et al.43, Le Wang et al.44 IsoleucineLiu et al.45, Le Wang et al.44 PhenylalanineHao et al.46, Wawrzyniak et al.47, Goïta et al.43, Meyer et al.48, Øvrehus et al.49 TryptophanLiu et al.45, Le Wang et al.44CRP BetaineJutley et al.50, Pietzner et al.51 GlutamineJutley et al.50, Pietzner et al.51 IsoleucineJutley et al.50, Oluwagbemigun et al.52 TryptophanJutley et al.50, Kosek et al.53, Oluwagbemigun et al.52According to systematic search in PubMed. Metabolites without a match in our metabolites and those we did not replicated are available in Additional File S3. In Table 2, we present characteristics of the DONALD study population. Aside from the BMI at blood sampling, there were no differences for basic characteristics between the sexes. Except for diastolic blood pressure at urine collection, IL-6, IL-18, and total blood triglycerides, all clinical biomarkers were significantly different between the sexes though directions differed. Blood pressure (diastolic at blood draw and systolic at both blood draw and urine collection) and blood glucose were higher in males, while CRP, leptin, adiponectin, total cholesterol, HDL, LDL, and triglycerides were higher in females. Table 2Characteristics and markers of metabolic health of 218 DONALD participants. NOverall, $$n = 2181$$Male, $$n = 881$$Female, $$n = 1301$$p-value2BMI [kg/m2] at blood draw21822.30 [20.65, 24.91]23.25 [21.21, 26.16]21.89 [20.31, 24.09]0.005BMI [kg/m2] at urine collection21821.88 [19.96, 23.63]22.06 [20.38, 23.54]21.85 [19.88, 23.68]0.4Age [years] at blood draw21818.00 [18.00, 23.00]18.00 [18.00, 23.00]18.00 [18.00, 23.75]0.5Age [years] at urine collection21818.00 [17.00, 18.00]18.00 [16.00, 18.00]18.00 [17.00, 18.00]0.7Age difference [years] between last dietary record and blood draw2181.00 [0.00, 6.75]1.00 [0.00, 4.00]1.50 [0.00, 7.00]0.7Age difference [years] between last dietary record and urine collection2180.00 [0.00, 0.00]0.00 [0.00, 0.00]0.00 [0.00, 0.00]0.43-Methylcrotonylglycine2060.99 [0.71, 1.25]1.00 [0.83, 1.25]0.94 [0.61, 1.24]0.0755-Dodecenoylcarnitine (c12:1)2180.93 [0.58, 1.38]1.18 [0.73, 1.66]0.84 [0.50, 1.26]0.0017-Hydroxyindole sulfate2110.92 [0.54, 1.62]1.25 [0.81, 1.97]0.68 [0.46, 1.11]< 0.001Citraconate/glutaconate2181.00 [0.63, 1.80]1.09 [0.72, 2.00]0.88 [0.57, 1.68]0.042Decanoylcarnitine (c10)2170.95 [0.64, 1.44]0.92 [0.64, 1.58]1.00 [0.64, 1.39]0.7Formiminoglutamate2180.97 [0.64, 1.33]1.01 [0.76, 1.35]0.85 [0.54, 1.31]0.020Glucuronide of c10h18o2 [12]2171.00 [0.68, 1.58]1.03 [0.79, 1.44]0.98 [0.66, 1.65]0.4Glutamine conjugate of c8h12o2 [1]2180.98 [0.68, 1.54]0.98 [0.72, 1.45]0.99 [0.63, 1.58]0.7Glycine conjugate of c10h14o2 [1]2180.95 [0.59, 1.68]1.00 [0.68, 1.92]0.87 [0.59, 1.63]0.4Guanidinosuccinate2180.96 [0.70, 1.30]1.10 [0.81, 1.31]0.83 [0.61, 1.23]0.005Hippurate2180.99 [0.69, 1.44]0.92 [0.69, 1.35]1.07 [0.70, 1.50]0.3Indole-3-acetamide1980.93 [0.58, 1.87]1.06 [0.57, 2.03]0.88 [0.61, 1.78]0.5Isobutyrylglycine (c4)2180.98 [0.67, 1.31]1.08 [0.82, 1.33]0.87 [0.61, 1.28]0.011Isovalerylglutamine2180.99 [0.66, 1.36]1.15 [0.96, 1.44]0.83 [0.56, 1.16]< 0.001Isovalerylglycine2181.00 [0.69, 1.44]1.21 [0.82, 1.51]0.86 [0.62, 1.34]< 0.001N6-methyladenosine2150.99 [0.71, 1.42]1.06 [0.77, 1.44]0.96 [0.69, 1.41]0.14Nicotinamide n-oxide2141.01 [0.60, 1.59]1.00 [0.65, 1.50]1.05 [0.57, 1.78]0.7Proline2180.99 [0.69, 1.27]1.09 [0.78, 1.42]0.90 [0.66, 1.13]0.004Succinimide2170.98 [0.70, 1.35]1.14 [0.90, 1.60]0.86 [0.60, 1.22]< 0.001Thymine2171.00 [0.75, 1.33]1.09 [0.82, 1.42]0.90 [0.64, 1.23]0.004Tigloylglycine2180.93 [0.71, 1.28]1.11 [0.83, 1.41]0.82 [0.68, 1.23]0.004Vanillylmandelate (vma)2180.99 [0.79, 1.36]1.11 [0.86, 1.44]0.93 [0.76, 1.27]0.0071Median [IQR].2Wilcoxon rank sum test Males VS Females. Unknown metabolites are not displayed. We were able to test associations for 41 of the 46 metabolites associated with clinical biomarkers. We were able to replicate metabolite associations for 10 out of the 41 metabolites we found (Table 3), four out of five for CRP and six out of 36 for blood pressure. We discovered six significant associations in female participants, while there were five significant associations in male participants. Across bio specimens, there were more associations present in urine (six) compared to blood (five). One metabolite, phenylalanine, was associated across sexes with systolic blood pressure. Another metabolite, glutamine, was associated with both diastolic and systolic blood pressure. However, the association between glutamine and diastolic blood pressure for male participants was positive, while the association between glutamine and systolic blood pressure for females was inverse. Across sexes and bio specimen more metabolites (six) were associated negatively. When correcting for multiple testing only the association between betaine and CRP in females remained significant. The complete model results, including direct sex comparisons, can be found in Additional File S4. The test set model metrics can be found in Additional File S5.Table 3Replicated risk-markers-metabolites association for CRP, diastolic blood pressure, and systolic blood pressure. MetaboliteSexBio specimenSuper pathwaySub pathwayβ$95\%$ CIp-valuep-value (FDR)Clinical biomarker: CRP BetaineFemaleBloodAmino acidGlycine, serine and threonine metabolism− 0.40− 0.61 to − 0.190.00020.0220 GlutamineMaleUrineAmino acidGlutamate metabolism− 0.39− 0.63 to − 0.150.00220.1008 IsoleucineMaleUrineAmino acidLeucine, isoleucine and valine metabolism− 0.29− 0.53 to − 0.040.02180.2060 TryptophanMaleUrineAmino acidTryptophan metabolism− 0.38− 0.63 to − 0.130.00330.1137Clinical biomarker: diastolic blood pressure GlutamineMaleBloodAmino acidGlutamate metabolism0.250.02–0.480.03370.2682Clinical biomarker: systolic blood pressure 4-HydroxyhippurateFemaleUrineXenobioticsBenzoate metabolism0.280.08–0.480.00720.1374 Androsterone sulfateFemaleBloodLipidAndrogenic steroids− 0.17− 0.35 to − 0.000.04960.3076 GlutamineFemaleBloodAmino AcidGlutamate metabolism− 0.24− 0.41 to − 0.070.00640.1368 PhenylalanineFemaleUrineAmino AcidPhenylalanine metabolism0.190.00–0.380.04770.3076MaleBlood0.250.02–0.480.03280.2682 TryptophanFemaleUrineAmino AcidTryptophan metabolism0.190.00–0.380.04660.3076Estimates are generated from linear regression. Models were adjusted for age and BMI, both at sample collection. Metabolites were log-transformed prior to analysis. Estimates and $95\%$ CI are on the log scale. We controlled the false discovery rate (FDR) at $5\%$ to account for multiple testing. Metabolites significant after correction for multiple testing are marked in italics. We found no metabolite significantly mediating the relationship of either body composition or habitual food intake and clinical biomarkers after correcting for multiple testing (Table 4). However, we observed two significant total effects, both in male urine. One between CRP and BMI, CRP is estimated to increase by 0.5 standard deviations (SD) as BMI increases by one unit (p-Value (FDR) < 0.0001) and one between leptin and BF, leptin is estimated to increase by 0.62 standard deviations as BF increases by 1 unit (p-Value (FDR) = 0.040). The full model results are available in Additional File S6. The test set model metrics can be found in Additional File S5.Table 4Metabolites mediating the association of body composition and habitual food intake with clinical biomarkers. Bio specimenSexClinical biomarkerMediating metaboliteTotal effectACMEEstimate$95\%$ CIp-value1Estimate$95\%$ CIp-value1Exposure: BMI UrineMaleCRP5-Dodecenoylcarnitine (C12:1)0.510.254–0.7470.000− 0.03− 0.134 to 0.0350.983Exposure: body fat (%) UrineMaleLeptinGlucuronide of C10H18O2 [12]0.620.203–1.0400.040− 0.10− 0.285 to 0.0180.983Estimates and confidence intervals are in standard deviations. ACME average causal mediation effect, CRP C-reactive Protein.1p-values are corrected for multiple testing by holding the false discovery rate at $5\%$. In our sensitivity analysis on the amount of missing data we observed one additional significant association after correcting for multiple testing, between glutamine and CRP in male urine (p (FDR) = 0.046, ß = − 0.39, − 0.63 to − 0.15). Additionally, we observed one additional significant total effect in the mediation analysis, between BMI and leptin in male urine and no mediators. The full results tables for the sensitivity analysis can be found in Additional File S7. ## Discussion In the present study, we conducted an SLR identifying 41 metabolite- clinical biomarker associations (36 for BP, 5 for CRP) that were reported in at least two independent studies. Of these 41, we were able to replicate 10 associations, in our own study population one of which was significant after multiple testing correction. Additionally, we found no evidence of a metabolite mediating the association between body composition or habitual food intake and clinical biomarkers. ## Mediation We did not identify any metabolite as potential mediator of the relationship between either body composition or habitual food intake and clinical biomarkers. While we did not identify any mediators in our sample, we still believe there will be mediators identified in the future. Mediators are notoriously hard to identify, as their study requires many association tests (which in turn requires a correction for multiple testing), a large study population and large effect sizes. All three of which were limiting factors within our study. ## Future research Future research should take the sex differences we reported into consideration in their own study design, ideally by stratification, in order to further our understanding of the physiological differences in metabolism between males and females. A study evaluating the metabolites associated with metabolic health markers as mediators to lifestyle factors would be a great continuation of the present study, ideally in a larger cohort. Lastly, metabolomics would greatly benefit from both a more unified data analysis approach as well as a unified measurement approach to better facilitate meta-analysis and ease the burden of replicating results from different studies. ## Strengths and limitations The present study has some notable strengths. We used results from our own previous studies to investigate mediation and conducted a SLR to facilitate replication of previously reported associations in the literature. We were able to use global measurements of the urine and blood metabolome in the same participants for both analyses in a comparatively (for metabolomics) large study population. Though the number of statistical tests required for metabolomics in relation to the available data in our study is high, therefore sampling power may be a reason for few total associations found. We employed state of the art statistical analysis and machine learning to investigate both the mediation and the replication. However, we acknowledge several limitations to the study. Our participants are Caucasians (Germans), residing in a large city (Dortmund) and surrounding area and are mostly from a high socio-economic background. This may limit the generalizability of our findings. We used non-fasted plasma samples, which increases the variability of inter and intra participant variability of measurements introducing non-differential measurement error. We constructed habitual diet from multiple measurements in adolescence, which increases the time difference between diet measurement and metabolome assessment. This limits results to more long-term markers but increases the effect size needed to detect a signal. Additionally, we cannot rule out residual confounding by either unknown or unmeasured confounders or related factors such as genetics. In our mediation analysis, we had, compared to other mediation analysis, a relatively small sample size. Lastly, we only have one measurement of the metabolome available, so the temporal reproducibility of these findings is unknown. ## Conclusions In summary, we identified 41 metabolites associated in at least two independent studies with clinical biomarker and replicated ten associations in our own data, only one of which was significant after multiple testing correction. Additionally, there was no metabolite mediating the relationship between body composition or habitual diet and clinical biomarker. The intricate interplay between lifestyle factors, the metabolome, and metabolic health warrants further investigation. ## Supplementary Information Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8. 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--- title: A highly elastic absorbable monofilament suture fabricated from poly(3-hydroxybutyrate-co-4-hydroxybutyrate) authors: - Atsuhiko Murayama - Hidemasa Yoneda - Akira Maehara - Noriyuki Shiomi - Hitoshi Hirata journal: Scientific Reports year: 2023 pmcid: PMC9968320 doi: 10.1038/s41598-023-30292-w license: CC BY 4.0 --- # A highly elastic absorbable monofilament suture fabricated from poly(3-hydroxybutyrate-co-4-hydroxybutyrate) ## Abstract To address the growing demand for more elastic sutures free from unwanted knot loosening, we fabricated an absorbable monofilament suture from poly(3-hydroxybutyrate-co-4-hydroxybutyrate) and subjected it to physical property characterization and performance evaluation (in vitro and in vivo degradability tests and a porcine abdominal wall suture test). As this flexible, highly stretchable, and difficult-to-untie suture exhibited additional advantages of small knot size and medium to long-term bioabsorbability, it was concluded to be a safe alternative to existing monofilament sutures, with far-reaching potential applications. ## Introduction Many medical devices used for soft tissue reconstruction have constant lengths, meaning that they are incapable of axial stretching. Therefore, they cannot be used for soft tissue reconstruction that requires the dynamic function of human joint tissue. For example, complications from their removal due to discomfort caused by poor distensibility, fistula formation, and neuroma are observed when inflexible artificial nerve conduits are used near finger joints1–3. Additionally, artificial ligaments contain fibers made of non-absorbent materials woven in a sheet or cylinder shape exhibiting sufficient linear tensile strength4,5. However, their ecological functions (extension and shape restoration) are not correctly executed. Therefore, the biomechanical reconstruction of joints using artificial ligaments is currently impossible. Ligament reconstruction grafting is primarily performed using autograft tendons, owing to their excellent rigidity and elasticity. The elastic properties of sutures used in surgical procedures may prevent knot loosening and damage to tissue margins under shear6,7. Although conventional absorbable monofilament sutures possess smooth surfaces and are infection resistant, they tend to exhibit low knot security and their manipulation is sometimes challenging because they are more rigid and less flexible than multifilament sutures8. Many surgeons demand the development of absorbable monofilament sutures with easier handling and higher knot security. Polyhydroxyalkanoates (PHAs) are biodegradable polymers of microbial origin. They exhibit high biocompatibility and eco-friendliness and are attractive in the biomedical engineering field as components of elastic medical devices1. Among currently available PHAs, only 4-hydroxybutyrate (4HB) polymers have clinical applications, mainly in sutures and cardiovascular stents9. Poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P(3HB-co-4HB)) becomes more extensible with an increase in the 4HB fraction10; therefore, it is a promising material for extensible medical devices. However, the development of P(3HB-co-4HB) has been hindered by the following factors: (i) the inability to chemically synthesize high-molecular-weight P(3HB-co-4HB)11, (ii) limited facilities available for conducting microbial synthesis, (iii) technical difficulty of culturing and purifying stable high-molecular-weight copolymers while maintaining a constant copolymerization ratio, (iv) challenges of scale-up bioprocessing, and (v) challenges of spinning the purified copolymers. In this study, we solved the aforementioned problems and produced high-purity, high-molecular-weight copolymers. Furthermore, we developed elastic nonwoven fabrics and pouched structures that can be applied to fracture treatment12. Herein, the extensive characterization and suitability of highly elastic absorbable monofilament sutures fabricated from P(3HB-co-4HB) for practical applications are reported. ## Materials The chemical structures of the repeating units of the absorbable monofilament sutures used in this study, P(3HB-co-4HB), Monomax® (P(4HB)), Maxon® (Poly(glycolide-co-trimethylene carbonate), PGA-TMC copolymer), PDS® II (Poly-p-dioxanone) and LACLON® (Poly(L-lactide/ε-caprolactone), P(LA/CL)) are presented in Fig. 1.Figure 1Chemical structures of the absorbable monofilament sutures used in this study: (A) P(3HB-co-4HB), (B) P(4HB), (C) PGA-TMC copolymer, (D) Poly-p-dioxanone, and (E) P(LA/CL). High-molecular-weight P(3HB-co-16 mol% 4HB) (Mw ≈ 600,000 Da; Mitsubishi Gas Chemical Corporation) was obtained using a combination of fermentation (biosynthesis) and purification methods and then processed into fibers using partial melt-spinning35,36. This method produces polymer molded products and involves polymers that contain lamella crystals with different lamella thicknesses. During this method, a melt molding procedure takes place in a temperature range where some lamella crystals are melted and fluidized, whereas the rest of the lamella crystals remain unchanged36. Figure 14 shows a flow curve and a differential scanning calorimetry (DSC) curve of P(3HB-co-16 mol% 4HB) using the flow tester heating method36. The chemical compositions (mol% 4HB) of the copolymers used in this study were determined by the gas chromatography method described in the international patent WO $\frac{2019}{044836}$A137. The standard was calibrated using 3HB methyl and gamma-butyrolactone. Figure 14Flow curve (solid line) and DSC curve (dashed line) of P(3HB-co-16 mol% 4HB) using the flow tester heating method. The melting point (Tm) is ~ 60–175 °C. The glass-transition temperature (Tg) is below 0 °C and cannot be observed in the first heating of DSC. All animal experiments complied with the basic ethical guidelines of the Center for Animal Research and Education and were approved by the Institutional Animal Care and Use Committee of Nagoya University. Moreover, we certify compliance with the ARRIVE guidelines. ## Physical properties The appearance and scanning electron microscopy images of the P(3HB-co-4HB) monofilament suture [United States Pharmacopoeia (USP) 2.5-0, 4HB content = 16 mol%] are shown in Fig. 2. Compared to Monomax® (USP 2-0), P(3HB-co-4HB) exhibited a $50\%$ lower tensile strength (167 MPa), a ~ $50\%$ lower Young’s modulus (261 MPa), and a two-fold higher breaking elongation ($113\%$), meaning that it was supple and extensible (Table 1). Despite a substantial residual strain after $100\%$ extension deformation ($15\%$) and a plastic deformation exceeding its elastic limit, it did not break, unlike other sutures (Fig. 3). After $50\%$ extension deformation in the cyclic test, the residual strains of Monomax® and the newly developed suture were 20 and $7\%$, respectively (i.e., the respective recoveries were 80 and $93\%$) (Fig. 4).Figure 2Photographic (left), side-view SEM (center), and cross-sectional SEM (right) images of the P(3HB-co-16 mol% 4HB) suture. Table 1Physical properties of absorbable monofilament sutures. Tensile strength (MPa)Breaking elongation (%)Young’s Modulus (MPa)Residual strain after $50\%$ extension deformation (%)Residual strain after $100\%$ extension deformation (%)P(3HB-co-16 mol% 4HB)2.5-0167 ± 51113 ± 22261 ± 24715PDS® II3-0496 ± 2059 ± 91563 ± 230RuptureRuptureMonomax®2-0425 ± 13288 ± 31603 ± 5620RuptureAverage ± $95\%$ CI, $$n = 1$.$Figure 3Photographs of the P(3HB-co-16 mol% 4HB) sutures without tension (top) and under tension (bottom).Figure 4Tensile testing results of the P(3HB-co-16 mol% 4HB) (left) and Monomax® (right) monofilament sutures ($$n = 1$$). The tensile strength, breaking elongation, Young’s modulus, and residual strain after $100\%$ deformation of the P(3HB-co-16 mol% 4HB) monofilament sutures (USP 2.5-0), PDS® II (USP 3-0), and Monomax® (USP 2-0) were measured using a tensile testing machine (AGS-50 NX, Shimadzu Corporation, Kyoto, Japan). In the corresponding tests ($$n = 3$$), 120-mm-long fibers were fixed using a length of 10 mm above and below, and an assessment was performed at a 100-mm-distance between the chucks of the tensile testing machine using a tensile speed of 10 mm min−1. The residual strain (S100, %) was calculated from the tensile elongation recovery (R100, %) as S100 = $100\%$ − R100. After the suture was stretched to a $100\%$ strain (200 mm, which corresponds to two times the initial length or a 100 mm displacement length), it was contracted by moving the gripper at a constant speed until the pre-stretch length was reached. Assuming that the displacement length at the first time point of the second stretch (which is assumed to approximately equal the end point of the first stretch) is X100 mm, the tensile elongation recovery is given by R100 (%) = $100\%$ × [200 − (X100 + 100)]/100. In the cycle test ($$n = 1$$), both ends (10 mm) of the 120-mm-long suture were gripped, and the suture was stretched to a $50\%$ strain at an initial length (chuck-to-chuck distance) of 100 mm at room temperature (23 °C) using a tensile speed of 20 mm min−1. The gripper was then moved to the original length at the same speed to shrink the suture. This procedure was repeated five times. Error analysis was performed with a confidence interval of $95\%$ ($95\%$ CI). ## Knot size and security Using the P(3HB-co-4HB) monofilament suture (USP 2.5-0), a knot was formed and is shown in Fig. 5. Table 2 lists the related thread diameter and knot size. The corresponding data for PDS® II (USP 4-0) as a comparative example are presented in Fig. 5 and Table 3. Compared to PDS® II, the suture exhibits a shorter circumference (4.03 vs. 4.77 mm), a smaller knot area (0.843 vs. 1.20 mm2), and a smaller knot area/thread diameter ratio (3.28 vs. 7.32). However, it has a larger thread diameter (0.256 vs. 0.163 mm) and therefore a significantly smaller knot size ($$P \leq 0.0079$$).Figure 5Photographs of surgical knots made using the P(3HB-co-4HB) (USP 2.5-0) (left) and PDS® II (USP 4-0) (right) absorbable monofilament sutures. Table 2Knot sizes obtained for the P(3HB-co-16 mol% 4HB) suture. Sample no. Diameter (mm)Perimetera (mm)Areab (mm2)Area/Diameter ratioA-10.2684.991.063.96A-20.2614.061.003.82A-30.2503.770.7262.90A-40.2443.520.6942.84A-50.2583.790.7372.86Average0.2564.030.8433.28aPeripheral length of the knot outline as seen from above.bArea enclosed by the knot outline as seen from above. Table 3Knot size of PDS® II.Sample no. Diameter (mm)Perimetera (mm)Areab (mm2)Area/Diameter ratioB-10.1654.961.328.02B-20.1614.240.9175.69B-30.1655.301.358.16B-40.1635.101.408.58B-50.1624.240.9936.14Average0.1634.771.207.32aPeripheral length of the knot outline as seen from above.bArea enclosed by the knot outline as seen from above. Table 4 lists the knot security factors (KSFs) of existing and newly developed absorbable monofilament sutures, revealing a lower KSF [2] for P(3HB-co-4HB), which is superior to those of currently used sutures. Table 4KSFs of absorbable monofilament sutures. USPKSFP(3HB-co-16 mol% 4HB)2.5-02PDS® II2-033-034-03LACLON® (P(LA/CL))2-033-034-02 ## In vitro degradability A decrease in the initial linear tensile strength of the P(3HB-co-4HB) sutures from 161 to 104.6 MPa (i.e., by $35.0\%$) occurred after immersion in Dulbecco’s buffer for 12 weeks (Fig. 6). The fractional weight-average molecular weight (Mw; value before immersion = 320,000, Da = $100\%$) as a function of immersion time (Fig. 7) exhibits a $50\%$ decrease after 16 weeks. Furthermore, the P(3HB-co-4HB) suture maintained high elasticity and substantial elongation at break ($160\%$, cf. initial value of $240\%$) (Fig. 6).Figure 6Retention of tensile strength and breaking elongation in vitro. Figure 7Retention of weight-average molecular weight in vitro. ## In vivo degradability The results of the tensile tests performed on P(3HB-co-4HB) sutures subcutaneously implanted in rats (residual strength ≈ $77\%$ at week 4 and ≈ $63\%$ at week 16) were consistent with those of the in vitro tests. The initial linear tensile strength was maintained at $50\%$ ($50\%$ tensile strength holding period) for 26 weeks (Fig. 8). The fractional decrease in Mw with increasing implantation time (Fig. 9) shows a $50\%$ decrease, 16 weeks after implantation. Furthermore, the elongation at the break of the P(3HB-co-4HB) suture remained close to $200\%$, 26 weeks after implantation (Fig. 8).Figure 8Retention of tensile strength and breaking elongation in vivo. Figure 9Retention of weight-average molecular weight in vivo. ## Performance in pig abdominal wall suture test Seven weeks after integrating the abdominal wall suture, no substantial complications (signs of infection, wound dehiscence, abdominal wall incisional hernia, or adhesions) were macroscopically observed at any of the three suture sites (Fig. 10). The histological evaluation of the P(3HB-co-4HB) suture by an independent pathologist returned a score of unity for inflammation, necrosis, and fibrous thickening (Table 5). The histological images of the weak expansion of the hematoxylin and eosin (HE) staining of the surrounding tissue of the P(3HB-co-4HB), PGA-TMC copolymer, and P(4HB) sutures are shown in Figs. 11, 12, and 13, respectively. These results suggest that after seven weeks, the P(3HB-co-4HB) suture exhibited less inflammation than the other two absorbable monofilament sutures and was also non-inferior in terms of necrosis and fibrous thickening. Figure 10Abdominal (left) and abdominal wall (right) images of Microminipigs® seven weeks after surgery. Table 5Histological evaluation of the region around the porcine abdominal wall suture seven weeks after surgery. P(3HB-co-4HB)PGA-TMC copolymerP(4HB)Inflammation123Necrosis111Fibrous thickening112Figure 11Microscopic image of HE-stained P(3HB-co-4HB) perisuture tissue. The suture (white oval) is surrounded by purple-stained inflammatory cells. The accumulation of inflammatory cells around the new suture was less pronounced than that around the Maxon® and Monomax® sutures. Figure 12Microscopic image of HE-stained Maxon® perisuture tissue. Figure 13Microscopic image of HE-stained Monomax® perisuture tissue. ## Discussion Compared to its commercially available analogs, the P(3HB-co-4HB) monofilament suture had a lower Young's modulus, better extensibility (almost twice the breaking elongation), and a smaller knot size (i.e., a higher loosening resistance). Additionally, the newly developed suture did not break when extended to twice its original length, exhibiting an $85\%$ length restoration upon relaxation. As its $50\%$ tensile strength retention period determined by the in vivo test was close to 26 weeks, the suture was concluded to be of a gradually hydrolyzed medium to long-term absorbable type. Compared to existing absorbable sutures, it did not cause infection or incisional hernias in the porcine abdominal wall test and was histologically non-inferior in inducing inflammation. The Young’s moduli of existing absorbable monofilament sutures substantially exceeded those previously reported for visceral organs (stomach, large intestine, and bladder), subcutaneous and other tissues (≤ 10 MPa), and tendons (450 MPa)13–15. The use of these sutures results in tissue damage due to stiffness mismatch; mechanical failure and cheese wiring (tendon cut-through) were observed after repairing the rotator cuff tear16–18. Moreover, cheese wiring causes postoperative rerupture after repairing the rotator cuff and meniscus19,20. A long-term evaluation of open meniscus repair over 10 years showed that 21–$29\%$ of retears occurred postoperatively21–23, while Beamer et al. reported tissue failure due to suture stiffness24. Reducing the stiffness mismatch can therefore decrease the possibility of rupture after tissue repair, with the newly developed suture well-suited for this purpose. The P(3HB-co-4HB) suture gained more elasticity by increasing the 4HB fraction. Previously, the breaking elongation of a copolymer film containing 16 mol% 4HB was $444\%$10, while copolymer films containing ≥ 30 mol% 4HB exhibited rubber-like elastic behavior25. Given its advantageous physical properties, the P(3HB-co-16 mol% 4HB) monofilament suture developed herein could accommodate temporary tissue swelling and deformation due to body motion, and also extend without overtightening which prevents a decrease in blood flow to the tissue. Moreover, unlike conventional absorbable monofilament sutures, the newly developed suture did not suffer from looseness. Conventional suture knots loosen because of impliability and incompliance, while the number of ligations or the ligature force can be increased to prevent this. Sufficient knot security is achieved using four or more ligations26, although here, the large knot size and the negative influence on the surrounding tissue pose concerns. Van Rijssel et al. semiquantitatively evaluated the perisuture tissue response in rats. They reported that the knot size increased 4- to 6-fold with an increase of two suture sizes, which triggered an increased tissue response around the suture27. Herein, the small knot size of the suture was expected to reduce the reaction of the surrounding tissue. KSFs of 2–4 are considered excellent for monofilament sutures28. The KSF value of our suture [2] indicated higher knot security than that achieved by PDS® II and LACLON®. The high knot security and the decreased knot number of our developed suture may achieve shorter operative times, e.g., for endoscopic suturing in intraperitoneal surgery, which is a basic yet technically demanding procedure29. The $50\%$ tensile strength-duration of synthetic absorbable monofilament sutures in animals has been reported as ~ 6 weeks for PDS® II [3-0] and 12–16 weeks for Monomax® [3-0]28. The P(3HB-co-16 mol% 4HB) monofilament suture (2.5-0) reached this at ~ 26 weeks and was, therefore, more durable and more suitable for suturing slow-healing wounds than existing synthetic absorbable monofilament analogs. From the chemical structure of P(3HB-co-4HB) shown in Fig. 1, it is apparent that it is a relatively hydrophobic material compared with the aforementioned commercial monofilament sutures. Although foreign body reactions have been reported for many absorbable sutures30, in the porcine suture model the abdominal wall healed without rupture, seven weeks after surgery. For various P(3HB-co-4HB) materials, tests on cultured cells and implantation tests on experimental animals indicated no obvious toxicity31. Both 3HB and 4HB, which are the degradation products of P(3HB-co-4HB), existed in vivo32–34. As the degree of inflammation around the new suture was histologically small and the suture was slowly hydrolyzed in vivo, it was concluded that interference with tissue healing did not occur. This study has several limitations. In the porcine abdominal wall suture test, the histological evaluation could only be performed seven weeks after the operation. Therefore, it is unclear when maximum inflammation due to the foreign body reaction of the new suture occurred, and whether the biocompatibility of our suture is superior to that of existing absorbable sutures throughout the postoperative period. The results of in vivo and in vitro degradation studies do not allow an estimation of the time required for complete bioabsorption of P(3HB-co-16 mol% 4HB), while the influence of pH on this degradation is also unclear. Additionally, regarding knot size comparisons, a two-dimensional assessment was performed using images captured directly above the knots. The absorbable P(3HB-co-16 mol% 4HB) monofilament suture is characterized by high flexibility and elasticity, small knots that are difficult to untie, and high biocompatibility and safety. These features are useful when suturing fragile tissues or when performing laparoscopic internal ligation. Thus, the newly developed product complements the lack of extensibility observed for conventional absorbable monofilament sutures and may significantly change the applications of absorbable monofilament sutures. ## In vitro degradation test Each of the eight ethylene oxide gas (EOG)-sterilized P(3HB-co-4HB) sutures with a thread diameter of 0.200–0.249 mm and a length of 300 mm was immersed in Dulbecco’s phosphate buffered saline (pH 7.4, 37 °C) contained in a conical tube. After immersion for 1, 2, 3, 4, 6, 8, 12, and 16 weeks, the sutures were removed and vacuum-dried. The 300-mm-long suture was cut at 30-mm intervals. Both ends of the suture were grasped and tensile tests (tensile strength and breaking elongation evaluations) were performed at room temperature (23 °C), under an initial length (chuck-to-chuck distance) of 10 mm, and a tensile speed of 10 mm min−1 ($$n = 3$$–10), until the suture ruptured. Molecular weight measurements were performed on sutures at 2, 4, 6, 8, 12, and 16 weeks after immersion. Week 0 (initial value) was defined as no immersion in the buffer solution. The molecular weights were determined by gel permeation chromatography using an HPLC Prominence system (Shimadzu Corporation, Kyoto City, Japan). The tensile properties were determined using an AGS-50 NX machine. The error analysis was performed with a $95\%$ CI. ## In vivo degradation test Fifteen rats (Male F 344/NSLc, 20 weeks old) were prepared, the dorsal skin was incised 80 mm along the spinal column, and EOG-sterilized P(3HB-co-4HB) sutures (diameter = 0.200–0.249 mm, length = 100 mm) were implanted into the subcutaneous tissue. At 4, 8, 12, 16, and 26 weeks after implantation (three animals per time point), the sutures were removed and vacuum-dried for tensile testing and molecular weight determination ($$n = 9$$–12). The initial value (0 weeks) was defined as no implantation. Tensile tests and molecular weight measurements were performed using the same conditions and equipment as those employed in the in vitro tests. Similarly, the error analysis was performed with a $95\%$ CI. ## Evaluation of knot size Five P(3HB-co-4HB) sutures (USP 2.5-0) and five PDS® II sutures (USP 4-0) were prepared, and their diameters were measured using a dial thickness gauge (Techlock Co., Ltd., SM-1201 L Model, scale interval = 0.001 mm). Measurements were performed in three locations ($\frac{1}{4}$, $\frac{1}{2}$, and $\frac{3}{4}$ of the total length), and the average was defined as the thread diameter. Surgical knots were prepared on an artificial skin sheet made of a soft elastomer and tightened using a force of 5 N (Standard Model Digital Force Gauge, ZTS-100 N, IMADA Co., Ltd.). Each knot (five samples in total) was photographed from above using a camera (DP 26, Olympus Co., Ltd.) attached to a stereomicroscope (SZX7, Olympus Co., Ltd.). The knot size (knot perimeter and area) was determined using an image analysis software (cellSens, Olympus Co., Ltd.) (Fig. 4) and expressed as the knot area and the knot area/thread diameter ratio. The knot area/thread diameter ratio was processed using the statistical software EZR version 4.0.4 (Saitama Medical Center, Jichi Medical University, Saitama City, Japan). The lack of normality in both groups was tested using the Shapiro–Wilk test. The Mann–Whitney test was then performed between the two groups, and p values of < 0.05 indicated significant differences. ## Evaluation of knot security KSF was used as an index of knot unraveling difficulty28,38. P(3HB-co-16 mol% 4HB) (2.5-0), PDS® II [2-0, 3-0, 4-0], and LACLON® [2-0, 3-0, 4-0] sutures were wound around a 2.9-cm-diameter plastic tube, tightly tied with a surgical knot, and cut at the side opposite the knot to create a single thread. Both sides were attached to a tensile tester and pulled at a rate of 100 mm min−1. Multiple sets of 10 samples were prepared, and if one of the 10 samples had a knot untied, a single nodule was added above the surgical knot until no knot could be untied. KSF was defined as the number of single nodules added until all 10 knots could not be untied, i.e., denotes the number of knots required to maintain adequate ligation. No statistical processing was performed in this experiment. ## Pig abdominal wall suture test Pregnant 32-week-old female Microminipigs® (Fuji Micra Co., Ltd., Fujinomiya City, Japan) were used for abdominal wall suturing with P(3HB-co-4HB) sutures and two existing absorbable monofilament sutures. Macroscopic and microscopic assessment of any occurring complications and inflammatory reactions followed. After delivering the fetus by cesarean section, a 12-cm longitudinally incised abdominal wall was sutured. Three apical P(3HB-co-4HB) sutures, two central PGA-TMC copolymer sutures, and three caudal P(4HB) sutures were used. The presence or absence of complications (signs of infection, wound dehiscence, abdominal wall incisional hernia, and adhesions) at the suture site was macroscopically observed seven weeks after suturing. Additionally, the abdominal wall (including the suture part) was collected, various suture parts were cut, and after each paraffin fixation, HE staining was carried out by the standard procedure. The extent of inflammation was assessed using a light microscope. The pathologist blindly evaluated the tissues in terms of inflammation, necrosis, and fibrous thickening on three different, nonadjacent sections using a scale of 0–4 (0: No inflammatory response, no necrosis, no thickening; 1: Very small inflammatory response, very little necrosis, very little thickening; 2: Inflammatory response, necrosis, thickening; 3: Strong inflammatory response, strong necrosis, strong thickening). 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--- title: DAPL1 prevents epithelial–mesenchymal transition in the retinal pigment epithelium and experimental proliferative vitreoretinopathy authors: - Xiaoyin Ma - Shuxian Han - Youjia Liu - Yu Chen - Pingping Li - Xiaoyan Liu - Lifu Chang - Ying-ao Chen - Feng Chen - Qiang Hou - Ling Hou journal: Cell Death & Disease year: 2023 pmcid: PMC9968328 doi: 10.1038/s41419-023-05693-4 license: CC BY 4.0 --- # DAPL1 prevents epithelial–mesenchymal transition in the retinal pigment epithelium and experimental proliferative vitreoretinopathy ## Abstract Epithelial–mesenchymal transition (EMT) of the retinal pigment epithelium (RPE) is a hallmark of the pathogenesis of proliferative vitreoretinopathy (PVR) that can lead to severe vision loss. Nevertheless, the precise regulatory mechanisms underlying the pathogenesis of PVR remain largely unknown. Here, we show that the expression of death-associated protein-like 1 (DAPL1) is downregulated in PVR membranes and that DAPL1 deficiency promotes EMT in RPE cells in mice. In fact, adeno-associated virus (AAV)-mediated DAPL1 overexpression in RPE cells of Dapl1-deficient mice inhibited EMT in physiological and retinal-detachment states. In a rabbit model of PVR, ARPE-19 cells overexpressing DAPL1 showed reduced ability to induce experimental PVR, and AAV-mediated DAPL1 delivery attenuated the severity of experimental PVR. Furthermore, a mechanistic study revealed that DAPL1 promotes P21 phosphorylation and its stabilization partially through NFκB (RelA) in RPE cells, whereas the knockdown of P21 led to neutralizing effects on DAPL1-dependent EMT inhibition and enhanced the severity of experimental PVR. These results suggest that DAPL1 acts as a novel suppressor of RPE-EMT and has an important role in antagonizing the pathogenesis of experimental PVR. Hence, this finding has implications for understanding the mechanism of and potential therapeutic applications for PVR. ## Introduction Proliferative vitreoretinopathy (PVR) is a disease that arises as a complication of rhegmatogenous retinal detachment, is characterized by the formation of subretinal or epiretinal membranes, and can lead to vision loss or severe blindness [1]. PVR occurs in 5–$10\%$ of retinal detachment cases, and surgical treatment is the main current therapy, but recurrence occurs readily after the operation [2]. Retinal pigment epithelium (RPE) cells are the principal cell components within the PVR membranes, and epithelial–mesenchymal transition (EMT) in these cells is considered the hallmark of PVR pathogenesis [3, 4]. During PVR progression, the EMT of RPE cells can promote their reprogramming to fibrotic cells, leading to the formation of a contractile epiretinal or subretinal membrane [1, 5, 6]; however, the regulatory mechanisms underlying this progression remain largely unknown. It is therefore paramount to explore the molecular mechanisms driving this process during PVR in more detail to identify more specific therapeutic targets for future precision therapeutic interventions. The mature RPE is a monolayer of tightly connected pigmented cells abutting retinal photoreceptors that plays critical roles in the normal retinal structure and visual function as it absorbs scattered light, maintains the blood-retinal barrier, is involved in the material transfer, secretes trophic factors, and clears shed photoreceptor outer segment fragments via phagocytosis [7, 8]. Under the disease conditions of retinal detachment or retinal surgery, RPE cells will migrate into the vitreous, undergo EMT, re-enter the cell cycle, and eventually develop into contractile membranes on retinal surfaces to cause PVR and subsequent visual impairment [9, 10]. During the EMT process, the morphology of RPE cells is changed, the expression of epithelial molecular markers, such as the tight junction zonula occludens protein 1 (ZO-1), is decreased, and the expression of mesenchymal molecular markers, such as α-smooth muscle actin (α-SMA) and vimentin, is increased [11, 12]. The tight junction and barrier function of RPE cells will thus be damaged after EMT, and inflammatory factors and external substances can then enter the retina to cause inflammation. Previous investigations have shown that various growth factors and cytokines (such as TGF-β), intracellular signaling pathways (such as WNT), transcription factors (SNAIL, ZEB1), and other noncoding RNAs are involved in the regulation of EMT in RPE cells [4, 13, 14]. However, RPE-EMT is a complicated process, the precise underlying mechanisms remain largely unknown, and effective therapeutic or preventive strategies for clinical PVR have not yet become readily available. Therefore, there is an urgent need to further identify novel regulators involved in the EMT of RPE cells and elucidate their functional roles in PVR progression; these could be exploited to treat PVR by targeting RPE cells. Death-associated protein-like 1 (DAPL1, also known as early epithelial differentiation-associated protein, EEDA) is expressed in epithelial cells [15], and its genetic variant is associated with age-related macular degeneration (AMD) [16]. DAPL1 could be involved in the differentiation of renal mesenchymal progenitor cells and the morphogenesis and development of ovarian granulosa cells [17–19]. Nevertheless, although DAPL1 highly expresses in the RPE and inhibits the proliferation of RPE cells [20, 21], its functional role in the inhibition of EMT in RPE cells is still unknown. In the present study, we found that the expression level of DAPL1 is decreased in the PVR membrane compared with that in primary human RPE cells. In addition, we show that knockout of the Dapl1 gene in mice promotes the EMT of RPE cells both in physiological and retinal-detachment states in vivo, whereas AAV-p.RPE65-DAPL1 mediated gene transfer inhibits RPE-EMT in the Dapl1−/− mice. Furthermore, we reveal that the lentivirus-mediated overexpression of DAPL1 in ARPE-19 cells reduces their ability to induce experimental PVR in the Chinchilla rabbit and that AAV-CMV-DAPL1 gene delivery can antagonize the severity of experimental PVR progression. Mechanistically, DAPL1 was found to promote P21 phosphorylation and its stabilization by up-regulating NFκB (RelA) in RPE cells, whereas the knockdown of P21 in DAPL1-overexpressing ARPE-19 cells neutralized its functions in inhibiting EMT and promoted experimental PVR progression. Hence, it appears that the DAPL1-NFκB-P21 pathway in RPE cells is an important contributor to RPE cell homeostasis. ## Ethical approval The current research involving clinical samples was approved by the ethics committee of Wenzhou Medical University Eye hospital (2020–007-K-07). All animal experiments were approved by the Wenzhou Medical University Animal Care and Use Committee and performed in compliance with the Association for Research in Vision and Ophthalmology (ARVO) Statement on the Use of Animals in Ophthalmic and Vision Research (WYDW2022-0193). ## Animals used in this study CRISPR/Cas9-mediated Dapl1-knockout mice (Dapl1−/−) were generated in the Nanjing Biomedical Research Institute of Nanjing University, China, as described in our previous work [20, 22]. C57BL/6J mice were obtained from The Jackson Laboratory and were maintained in the specific pathogen-free facility of the Wenzhou Medical University, China. Pigmented rabbits (Chinchilla rabbits) were purchased from Danyang Changyi experimental animal breeding Co., Ltd, China, and cultured in the animal center of Wenzhou Medical University. ## Cell culture The ARPE-19 cells were purchased from ATCC and cultured in DMEM/F12 supplemented with $10\%$ FBS, 100 U/ml of penicillin, and 100 mg/ml of streptomycin. The DAPL1-overexpressing ARPE-19 cells (ARPE-19 + DAPL1) or EGFP-overexpressing EGFP cells (ARPE-19+EGFP) were produced as previously described [20]. P21-stable knockdown ARPE-19 + DAPL1 cells were established using lentivirus-mediated shP21 infection. ## Mouse retinal detachment and rabbit experimental PVR models After being anesthetized with $10\%$ pentobarbital sodium, 1 μl of $0.25\%$ sodium hyaluronate was injected into the subretinal space of 8-week-old WT or Dapl1−/− mice to cause retinal detachment, which was analyzed by H&E staining or immunostaining 10 days later. Rabbit experimental PVR was established as previously reported [23, 24]. Briefly, the adult Chinchilla rabbits were anesthetized and intravitreally injected with 20 μl of 5 × 105 ARPE-19 cells. The fundus photographs and OCT were used for the examination after 30 and 38 days. PVR was graded using Fastenberg’s scale as described previously [4] and as follows: stage 0, normal fundus; stage 1, the presence of epiretinal membrane (ERM); stage 2, ERM with focal retinal traction or an abnormal vessel appearance; stage 3, localized retinal detachment (RD); stage 4, extensive RD of at least two quadrants without complete RD; stage 5, complete RD. For the pathological analysis, the rabbits were euthanized via intravenous injection of sodium pentobarbital, and the eyes were enucleated. The eyes were fixed with $4\%$ paraformaldehyde and embedded in paraffin. Sections (10 μm) were prepared and subjected to H&E staining and immunostaining for α-SMA. ## Immunofluorescence and quantification of fluorescent intensity The immunofluorescence method was previously described [22]. For the quantification of the immunofluorescence intensity, a simple method for quantitating confocal fluorescent images was used [25]. Briefly, the relative fluorescence intensity of anti-α-SMA in each projected fluorescent image was performed using a Zeiss LSM880 confocal microscope with the fixed parameters between the experimental and control group using Image J. The images were color-split, the particle edges were smoothed, and each image intensity threshold was automatically adjusted and applied. ## AAV9 vector construction and virus injection The AAV9-p.RPE65-MCS-SV40-PolyA and AAV9-CMV-MCS-SV40-PolyA vectors were acquired from Shanghai Genechem Co., LTD, China. Human HA-DAPL1 (NM_001017920) was constructed as previously described [21]. For the experiments in mice, 0.5 μl of AAV9-p.RPE65-DAPL1 or the AAV9 vector virus supernatant (1012 genome copies/ml) was injected into the subretinal spaces of mice, using a pulled angled glass pipette under direct observation aided by a dissecting microscope under dim light [22]. For the experiments in rabbits, 2 μl of AAV9-CMV-DAPL1 or the AAV9 vector virus supernatant (1012 genome copies/ml) was injected into the vitreous of the rabbits. ## Gene overexpression and knockdown The lentivirus-mediated overexpression of DAPL1 in ARPE-19 cells was described in our previous work [20]. SiRNAs or shRNA were used to knockdown respective target genes. Lentivirus harboring shP21, with the sequence of aaGACCATGTGGACCTGTCAC (108 genome copies/ml), was purchased from Shanghai Genechem Co., LTD, China. Specific siRNAs for human P21 and NFκB were synthesized by Gene Pharma Co. (Shanghai, China) with the following sequences: si-P21-1, CAGGCGGUUAUGAAAUUCATT; si-P21-2, GAUGGAACUUCGACUUUGUTT; si-P21-3, CCUCUGGCAUUAGAAUUAU; si-RelA-1, GCACCAUCAACUAUGAUGATT; si-RelA-2, GGAGUACCCUGAGGCUAUATT; si-RelA-3, UCUUCCUACUGUGUGACAATT; si-NC, AUUUCUUUCAUGUUGUGGGTT. For transfections, 5 μl of siRNA and 5 μl of Lipofectamine™ 2000 were diluted with 90 μl of serum-free F12/DMEM medium and mixed after 5 min incubation. The mixture was then incubated for 20 min at room temperature and added drop-wise to each culture well containing 800 μl of serum-free F12/DMEM medium (final siRNA concentration, 100 nM). The medium was discarded 6 h thereafter and replaced with fresh complete culture medium with $10\%$ FBS. Experiments such as western blotting were performed after culturing for 48 h. ## Protein stability assay CHX (cycloheximide, 10 μg/ml) was added to the culture medium of ARPE-19 + EGFP or ARPE-19 + DAPL1 cells, and cells were collected at different time points; proteasome inhibitor MG132 (20 μg/ml) was added to the culture medium of ARPE-19 + EGFP or ARPE-19 + DAPL1 cells for 4 h, the total lysates were analyzed by immunoblotting using an anti-P21antibody. ## Antibodies and primer sequences Anti-DAPL1 (ab150969), anti-P-Cadherin (ab242060), anti-ZEB1 (ab87280) and anti-p-P21 (ab47300) were purchased from Abcam. Anti-ZO-1 (8193T), anti- E-Cadherin (3195S) anti-N-cadherin (13116T), anti-vimentin (5741T), anti-SNAIL (3879T), anti-αSMA (19245S), anti-P-smad$\frac{2}{3}$ (8828S), anti-P21 (2947S) and anti- NFκB (RelA) (4764s) were obtained from Cell Signaling Technology. The anti-GAPDH (KC-5G4) antibody was purchased from Aksomics. The anti-OTX2 (AF1979) antibody was purchased from R&D systems. The anti-p-P38 MAPK (AM063) antibody was purchased from Beyotime China. Primer sequences for real-time PCR were as follows: DAPL1-F, GAAAGCTGGAGGGATGCGAA; DAPL1-R, TGATGTCCGTGTGAACTGT; GAPDH-F, AGGTCGGTGTGAACGGATTTG; GAPDH-R, TGTAGACCATGTAGTTGAGGTCA; P21-F, AGTCAGTTCCTTGTGGAGCC; P21-R, CATTAGCGCATCACAGTCGC. ## Statistical analysis Each cell-based experiment was repeated three times, whereas mouse and rabbit experiments were repeated six times. All quantitative data were presented as the mean ± SEM. The statistical significance of differences between groups was obtained using one-way ANOVA with GraphPad (San Diego, CA). Differences were considered significant at $P \leq 0.05.$ ## Dapl1 deficiency promotes the EMT of RPE cells To address whether DAPL1 plays any role in the EMT of RPE cells and PVR progression, the expression level of DAPL1 in the PVR membrane was analyzed and compared with that in primary human RPE cells. As shown in Fig. 1A, the expression of DAPL1 in the PVR membrane was significantly lower than that in the primary human RPE cells, suggesting that DAPL1 might be involved in the regulation of an RPE-EMT state and PVR progression. To further address this question, Dapl1-deficient mice (hereafter named Dapl1−/−) were used to analyze EMT in RPE cells. Western blot results showed that RPE cells in Dapl1−/− mice did not express DAPL1 protein, whereas expression of the epithelial marker ZO-1 and E-cadherin were decreased and that of the mesenchymal biomarkers ZEB1, N-cadherin, vimentin, and SNAIL were significantly increased (Fig. 1B, C). To further confirm the functional roles of DAPL1 in RPE-EMT progression, ZO-1 immunostaining was performed on RPE flat mounts of 2-week- and 8-week-old wild-type (hereafter WT) and Dapl1−/− mice. As shown in Fig. 1D, RPE cells displayed a regular hexagonal structure in 2-week-old WT and Dapl1−/− mice (Fig. 1D). Interestingly, compared with the regular hexagonal structure of RPE cells in 8-week-old WT mice, the tight connections among RPE cells were lost and the cell morphology was partially changed to a mesenchymal phenotype in 8-week-old Dapl1−/− mice (Fig. 1E). These results suggest that DAPL1 deficiency promotes RPE-EMT under normal physiological conditions. Fig. 1DAPL1 deficiency promotes the EMT of RPE cells. A Real-time PCR showing the expression level of DAPL1 in proliferative vitreoretinopathy (PVR) membranes and primary human RPE cells. B Western blotting showing the protein expression levels of DAPL1, ZO-1, E-cadherin, ZEB1, N-cadherin, vimentin, and SNAIL in 8-week-old WT and Dapl1 − /− RPE cells. C The bar graph shows quantification of the bands based on the results in (B). D, E Immunostaining images of ZO-1 expression in RPE flat mounts of 2-week-old or 8-week-old WT and Dapl1−/− mice. Scale bar: 50 μm. F Histological images of H&E staining of the retinal structure showing that 8-week-old WT and Dapl1−/− mice were subretinally injected with sodium hyaluronate to induce retinal detachment for 10 days Scale bar: 100 μm. ( G, H) Immunohistochemistry images showing the expression of OTX2 (white arrows in G) and α-SMA (white arrows in H) on the surfaces of the detached retinas. Scale bar: 50 μm. $$n = 6$$, ** or * indicates $P \leq 0.01$ or $P \leq 0.05$, respectively. As retinal detachment is one of the causative factors of PVR, we next examined whether DAPL1 deficiency would promote experimental PVR progression. For this, we performed subretinal injection of sodium hyaluronate to induce retinal detachment in 8-week-old WT and Dapl1−/− mice and maintained them for 10 days. As shown in Fig. 1F, RPE cells remained in a single-layer state in the WT mice, but they migrated into the detached subretinal space, underwent mesenchymal transformation, and proliferated abnormally in the Dapl1−/− mice, as shown by hematoxylin and eosin (H&E) staining (Fig. 1F, black arrow). In addition, those subretinal pigmented cells were positive for OTX2, indicating they were RPE cells (Fig. 1G, white arrow). We further analyzed expression of the mesenchymal marker α-SMA in the detached retina, and immunohistochemical staining showed that RPE cells expressed low levels of this marker in WT mice, but its expression was dramatically increased in RPE cells of Dapl1 − /− mice in the retinal-detachment state (Fig. 1H, white arrows, Fig. S1A). These data indicate that DAPL1 deficiency promotes the EMT of RPE cells in both physiological and retinal-detachment conditions. ## Overexpression of DAPL1 in the RPE inhibits EMT in Dapl1−/− mice As the aforementioned results indicated a role for DAPL1 in the EMT of RPE cells, we asked whether DAPL1 would play a protective role in this process. Hence, we overexpressed DAPL1 through AAV (adeno-associated virus)-mediated gene transfer using the RPE cell-specific RPE65 (retinoid isomerohydrolase RPE65) promoter. As previously described, this promoter controls gene expression specifically in RPE cells [22], and we constructed the AAV9 expression vector AAV9-p.RPE65-DAPL1-HA for the expression of HA epitope-tagged DAPL1 and packaged it (hereafter called AAV9-DAPL1). To assess the functions of DAPL1 in RPE cells in vivo, AAV9-DAPL1 was injected into the subretinal space of the right eyes of 6-week-old Dapl1−/− mice, whereas AAV9-NC was injected into the left eyes as a control. Two weeks after virus injection, protein levels of DAPL1, HA, and EMT markers were analyzed by western blotting. As shown in Fig. 2A, B, AAV9-DAPL1 injection led to increased levels of HA-tagged DAPL1, promoted the expression of ZO-1, E-cadherin, and decreased the protein levels of ZEB1, N-cadherin, vimentin, and SNAIL in RPE cells of Dapl1−/− mice. Moreover, the overexpression of DAPL1 in RPE cells of Dapl1−/− mice restored the tight connections among and the hexagonal structure of RPE cells (Fig. 2C). Most importantly, mesenchymal transformed and abnormally proliferating RPE cells could be observed in the detached retina of the AAV9-NC injected eyes, but the area of the subretinal membrane was dramatically decreased in the AAV9-DAPL1-injected eyes (Fig. 2D, black arrow, 2E). OTX2 immunostaining further confirmed the multiple layers of RPE cells in the AAV9-injected eyes, whereas a single layer was observed in the AAV-DAPL1-injected eyes (Fig. 2F, white arrow). In addition, immunohistochemical staining showed that the expression of α-SMA was dramatically decreased in the detached retinas of the AAV-DAPL1-injected eyes (Fig. 2G, white arrow, Fig. S1B). These data suggest that the overexpression of DAPL1 in RPE cells can inhibit EMT in vivo. Fig. 2Overexpression of DAPL1 in the RPE of Dapl1−/− mice inhibits EMT in vivo. A At 2 weeks after infection with AAV9-NC or AAV9-DAPL1, western blotting showed the protein expression levels of HA tag, DAPL1, ZO-1, E-cadherin, ZEB1, N-cadherin, vimentin, and SNAIL in RPE cells of Dapl1−/− mice. B The bar graph shows quantification of the bands based on the results in (A). $$n = 3$.$ C At 2 weeks after infection with AAV9 or AAV9-DAPL1, immunostaining images of ZO-1 in RPE flat mounts of 6-week-old Dapl1−/− mice. D At 2 weeks after the injection of AAV9-NC or AAV9-DAPL1, with the subsequent subretinal injection of sodium hyaluronate to cause retinal detachment for 10 days, histological images of H&E staining show retinal structures from 6-week-old Dapl1−/− mice. Note that subretinal membranes were observed in the control AAV9-NC- but not in the AAV9-DAPL1-injected eyes (black arrows). E The bar graph shows quantification of the area of the subretinal membrane based on the results from (D). F Immunohistochemistry for OTX2 (white arrow indicated) on the surfaces of the detached retinas of Dapl1−/− mice after the injection of AAV9-NC or AAV9-DAPL1 for 2 weeks. G Immunohistochemistry images showing α-SMA expression (white arrow indicated) on the surfaces of the detached retinas of Dapl1−/− mice after the injection of AAV9 or AAV9-DAPL1 for 2 weeks. $$n = 6$$, * indicates $P \leq 0.01.$ Scale bar: 50 μm. ## Overexpression of DAPL1 inhibits the EMT of RPE cells in vitro To further address the functional roles of DAPL1 in RPE-EMT and experimental PVR progression, we utilized in vitro experiments based on the overexpression of DAPL1 in a human RPE cell line, ARPE-19. We first elevated the expression level of DAPL1 via lentivirus-mediated DAPL1 overexpression (Fig. 3A, B). Transwell assay showed that the migration capacity of ARPE-19 cells decreased after the overexpression of DAPL1 (Fig. 3C, D). Meanwhile, wound healing assay showed that 48 hours after the wound was made, EGFP infected ARPE-19 cells almost covered the wound area, but the DAPL1 infected ARPE-19 cells migrated slower, with about $40\%$ of the wounds are not healed (Fig. 3E, F). These results suggest that DAPL1 inhibits RPE-EMT. Next, we examined biomarkers of EMT in ARPE-19 cells by western blotting and immunostaining. As shown in Fig. 3G, H, the overexpression of DAPL1 in ARPE-19 cells increased the expression of ZO-1, E-cadherin, P-cadherin and decreased the expression of ZEB1, N-cadherin, vimentin and SNAIL, which was also confirmed by immunostaining (Fig. 3I). These results reveal that the overexpression of DAPL1 inhibits the EMT of ARPE-19 cells in vitro. Fig. 3Overexpression of DAPL1 inhibits the EMT of RPE cells in vitro. A, B Western blotting showing the protein expression level of DAPL1 in ARPE-19 cells and its quantification. C Transwell assays for analyses of the migration of ARPE-19+EGFP or ARPE-19 + DAPL1 cells. D Quantification of the number of migrated cells based on the results from C. E, F Wound healing assay at 0, 24 and 48 h respectively in ARPE-19 + EGFP and ARPE-19 + DAPL1 cells and the wound healing percentage. Note that the cell migration was strongly inhibited by DAPL1 overexpression. G, H Western blotting showing the protein expression levels of ZO-1, E-cadherin, P-cadherin, ZEB1, N-cadherin, Vimentin, and SNAIL in ARPE-19+EGFP or ARPE-19 + DAPL1 cells and quantification. I The immunostaining images of ZO-1, Vimentin, and SNAIL in ARPE-19+EGFP or ARPE-19 + DAPL1 cells. $$n = 3$$, ** or * indicates $P \leq 0.01$ or $P \leq 0.05$, respectively. Scale bar: 50 μm. ## DAPL1 inhibits the severity of experimental PVR progression Whereas the aforementioned results demonstrated that DAPL1 inhibits EMT in RPE cells, the relevant function of DAPL1 in PVR progression in vivo was still unclear. To determine whether DAPL1 exerts a suppressive effect on PVR, we used an experimental PVR rabbit model. To achieve this, 5 × 105 ARPE-19+EGFP or ARPE-19 + DAPL1 cells in a 20 μl volume were injected into the vitreous of the Chinchilla rabbit, and PVR severity was evaluated after 30 and 38 days. As shown in Fig. 4A, B, based on fundus photographs and optical coherence tomography (OCT) analyses, ARPE-19+EGFP-injected eyes displayed severe retinal detachment, whereas the ARPE-19 + DAPL1-injected eyes had a much more organized retinal structure. H&E staining results also confirmed the results of the retinal detachment state (Fig. 4C). Furthermore, immunohistochemical staining showed that DAPL1 overexpression in ARPE-19 cells inhibited the formation of the epiretinal and subretinal membranes (Fig. 4C, red stars) and suppressed α-SMA expression (Fig. 4D, Fig. S1C). Finally, upon evaluating the severity of PVR according to Fastenberg’s score, the ability of ARPE-19 cells overexpressing DAPL1 to induce PVR was reduced when compared with that of ARPE-19 + EGFP cells (Fig. 4E). These results clearly indicate that DAPL1 inhibits the severity of experimental PVR progression. Fig. 4DAPL1 inhibits PVR progression in a rabbit model. ARPE-19+EGFP or ARPE-19 + DAPL1 cells were injected into the vitreous body of pigmented rabbits to induce experimental PVR. A Representative images showing fundus photographs captured on day 38 in each group. Note that severe retinal detachment with retinal folds was observed in ARPE-19+EGFP-injected eyes but not in ARPE-19 + DAPL1-injected eyes. B Optical coherence tomography (OCT) scanning showing that severe retinal detachment could be observed in the ARPE-19+EGFP-injected eyes but not in the ARPE-19 + DAPL1-injected eyes after 38 days. C Representative histological images of H&E staining of the rabbit eye in each group were obtained and analyzed. Note that retinal folds (black triangles), retinal detachment, and the formation of the epiretinal and subretinal membranes (red stars) were observed in the ARPE-19+EGFP-injected eyes but not in the ARPE-19 + DAPL1-injected eyes. D Immunohistochemistry images showing the detection of α-SMA expression around the retinas in the rabbits; positive signals were observed in the epiretinal and subretinal membranes in ARPE-19+EGFP-injected eyes (yellow arrows). E The severity of PVR in each group was graded according to Fastenberg’s score at the indicated times. $$n = 6$$, Scale bar: 50 μm. ## DAPL1 regulates P21 to inhibit the EMT of RPE cells in vitro P21 is a cell proliferation inhibitor that plays an important regulatory role in the proliferation and differentiation of RPE cells [26]. Our previous work has demonstrated that DAPL1 upregulates the protein level of P21 in RPE cells, but the underlying mechanisms are unclear [20]. The overexpression of P21 in RPE cells can inhibit the progression of PVR in the rabbit model [27]. The P21 level increased in studies showing the inhibitory effects of crocetin or trichostatin A on the RPE-EMT [28, 29]. These facts prompted us to analyze whether DAPL1 regulates the EMT of RPE cells through P21. We showed that DAPL1-overexpressing ARPE19 cells expressed higher protein levels of P21 (Fig. 5A, B), and the AAV9-DAPL1-mediated overexpression of DAPL1 in Dapl1−/− mouse RPE cells dramatically elevated the protein level of P21 (Fig. 5C, D). Then, we addressed whether the knockdown of P21 in ARPE-19 + DAPL1 cells could neutralize its functional role in inhibiting EMT in RPE cells. As shown in Fig. 5E, siRNAs specifically targeting P21 were able to significantly knock down the expression of P21 in ARPE-19 + DAPL1 cells. The protein level of the epithelial cell markers ZO-1 and E-cadherin were decreased, whereas the EMT markers N-cadherin, vimentin and SNAIL were increased in the ARPE-19 + DAPL1 cells after P21 knockdown (Fig. 5F, G), which was also confirmed by the immunostaining (Fig. 5H). These results suggest that DAPL1 regulates the EMT of RPE cells at least in part through P21.Fig. 5DAPL1 inhibits the EMT of RPE cells by regulating P21.A, B Western blots showing the expression of DAPL1 and P21 in ARPE-19 + DAPL1 cells and quantification (B). C, D At 2 weeks after infection with AAV9 or AAV9-DAPL1, western blots show the expression of DAPL1 and P21 in Dapl1 − /− mouse RPE cells and quantification (D). E Real-time PCR showing the knockdown efficiency of P21 in ARPE-19 + DAPL1 cells. F, G Western blots showing the expression of P21, ZO-1, E-cadherin, N-cadherin, Vimentin, and SNAIL in ARPE-19 + DAPL1 cells after the knockdown of P21 and quantification. H Immunostaining images for ZO-1, vimentin, and SNAIL in ARPE-19 + DAPL1 cells after the knockdown of P21. $$n = 3$$, * or * indicates $P \leq 0.01$ or $P \leq 0.05$, respectively. ## DAPL1 increases P21 stability through NFκB The aforementioned data demonstrated that DAPL1 promotes an increase in the protein level of P21 and inhibits RPE-EMT; however, how DAPL1 regulates P21 in RPE cells was still unclear. Hence, to analyze the mechanism by which DAPL1 leads to the increase in P21 in RPE cells, we focused on whether DAPL1 regulates P21 expression. We first analyzed the mRNA expression of P21 by real-time PCR and showed there was no significant difference between ARPE-19+EGFP and ARPE-19 + DAPL1 cells (Fig. 6A), suggesting that DAPL1 does not affect P21 at the transcriptional level, further prompting us to investigate whether DAPL1 affects P21 protein levels through post-transcriptional regulation, such as by modulating the stability of P21 protein. Hence, ARPE-19+EGFP or ARPE-19 + DAPL1 cells were treated with a translation inhibitor, cycloheximide (CHX) for 1 to 4 h. As expected, P21 protein had a prolonged half-life and was relatively abundant in the ARPE-19 + DAPL1 cells (Fig. 6B, C). Intriguingly, ARPE-19+EGFP and ARPE-19 + DAPL1 cells express similar protein levels of P21 after the treatment with proteasome inhibitor MG132 for 4 h (Fig. 6D, E). These results suggest that DAPL1 promotes the stability of the P21 protein in RPE cells. The P21 phosphorylation regulates its stability by suppressing proteasomal degradation [30]. Consistently, our data showed that DAPL1 increased the phosphorylation level of P21 (p-P21) in RPE cells; but did not affect the p-P38 protein level (Fig. 6F, G). Based on the fact that DAPL1 was not found to affect AKT and ERK pathways [20], we hypothesized that DAPL1 regulates P21 phosphorylation and stabilization through other regulatory mechanisms. It has been shown that NFκB (RelA) can positively regulate the protein level of P21 and stabilize P27 protein [31, 32], although it is unclear if NFκB (RelA) could be involved in the regulation of P21 stabilization. As shown in Fig. 6F, G, the protein level of NFκB (RelA) was increased when DAPL1 was overexpressed in ARPE-19 cells, whereas the knockdown of NFκB (RelA) in ARPE-19 + DAPL1 cells led to a decrease in the protein level of P21 (Fig. 6H, I). Moreover, the knockdown of NFκB (RelA) in ARPE-19 + DAPL1 cells for 2 days with subsequent CHX treatment for 0.5–2 h led to the increased degradation of P21 relative to that in the si-NC transfected cells (Fig. 6J, K). Taken together, these results indicate that DAPL1 promotes P21 phosphorylation and its stability at least partially through NFκB (RelA).Fig. 6DAPL1 promotes P21 stability through NFκB.A Real-time PCR showing the mRNA expression level of P21 in ARPE-19 + DAPL1 cells. B Western blots showing the level of P21 protein in ARPE-19+EGFP or ARPE-19 + DAPL1 cells upon treatment with cycloheximide (CHX) for various times, as indicated. C Line chart showing the percentage of remaining P21 protein at different times based on the results from (B). ( D, E) Western blots showing the protein level of P21 in ARPE-19+EGFP or ARPE-19 + DAPL1 cells upon treatment with proteasome inhibitor MG132 for 4 h or not, as indicated. F, G Western blots showing the expression of DAPL1, NFκB, p-P21 and p-P38 in ARPE-19 + DAPL1 cells and quantification of the bands. H, I Western blots showing the expression of NFκB, P21 and p-P21 in ARPE-19 + DAPL1 cells, where NFκB was knocked down, and quantification results. J After the knockdown of NFκB in ARPE-19-DAPL1 cells for 48 h and subsequent treatment with CHX for various times, as indicated, the expression level of P21 protein was analyzed by western blotting. K The line chart shows the percentage of remaining P21 protein at different times based on (J). $$n = 3$$, * indicates $P \leq 0.01$ or $P \leq 0.05$, respectively. ## P21 knockdown suppresses DAPL1-dependent experimental PVR inhibition To determine whether P21 mediated the effect of DAPL1 in inhibiting PVR progression, we constructed an shRNA lentivirus targeting P21 (lv-sh-P21) and knocked down P21 in ARPE-19 + DAPL1 cells. As shown in Fig. 7A, B, the knockdown of P21 in ARPE-19 + DAPL1 cells led to a decrease in the expression of ZO-1 and an increase in levels of the EMT biomarkers vimentin and SNAIL. Then, we injected 5 × 105 ARPE-19 + DAPL1 + shNC or ARPE-19 + DAPL1 + shP21 cells in a 20 μl volume into the vitreous of the Chinchilla rabbit, and PVR severity was evaluated after 30 and 38 days. After the injection of ARPE-19 + DAPL1 + shP21 cells into the eye, fundus photographs (Fig. 7C) and OCT (Fig. 7D) analyses showed that the injected eyes displayed severe retinal detachment, whereas the eyes injected with ARPE-19 + DAPL1 + shNC cells showed a much more organized retinal structure. H&E staining also confirmed the results of retinal detachment (Fig. 7E). Moreover, the knockdown of P21 in ARPE-19 + DAPL1 cells promoted the formation of epiretinal and subretinal membranes and resulted in increased expression of α-SMA in the membranes, as measured by immunohistochemical staining (Fig. 7F, Fig. S1D). In contrast, α-SMA expression was less obvious in the epiretinal and subretinal membranes after the injection of ARPE-19 + DAPL1 + shNC cells. Finally, upon evaluating PVR severity according to Fastenberg’s score, the ability of DAPL1-overexpressing ARPE-19 cells to induce PVR was increased after the knockdown of P21 at days 30 and 38 (Fig. 7G). These results clearly indicate that the knockdown of P21 attenuates the inhibitory effect of DAPL1 on the progression of experimental PVR.Fig. 7Knockdown of P21 in ARPE-19-overexpressing DAPL1 cells promotes PVR progression in a rabbit model. A, B Western blots showing the expression of P21, ZO-1, vimentin, and SNAIL in ARPE-19 + DAPL1 cells after the knockdown of P21 via infection with sh-P21 lentivirus and related quantification. C Representative images showing fundus photographs captured at day 38. Note that severe retinal detachment with retinal folds was observed in ARPE-19 + DAPL1 + sh-P21-injected eyes. D Optical coherence tomography (OCT) scans were performed on day 38. Note that severe retinal detachment was observed in the ARPE-19 + DAPL1 + shP21-injected eyes. E Histological images of H&E staining of the rabbit eye in each indicated group showing severe retinal detachment (red stars) in the ARPE-19 + DAPL1 + sh-P21-injected eyes. F Images show that α-SMA expression was detected by immunohistochemistry around the retinas in the rabbits, and its positive signals could be observed in the epiretinal and subretinal membranes in ARPE-19 + DAPL1 + shP21-injected eyes. G PVR severity in each group was graded according to Fastenberg’s score at the indicated times. $$n = 6$$, * or * indicates $P \leq 0.01$ or $P \leq 0.05$, respectively. ## Gene transfer-mediated DAPL1 overexpression prevents the progression of PVR Finally, we addressed whether DAPL1 gene transfer could be used as a therapy for experimental PVR under these disease conditions. To this end, we injected 5 × 105 ARPE-19 cells in a 20 μl volume into the vitreous of the Chinchilla rabbit to induce experimental PVR, and AAV9-CMV or AAV9-CMV-DAPL1 virus was injected at day 3; PVR severity was evaluated at days 30 and 38 (Fig. 8A). Western blot analysis showed that the injection of AAV9-CMV-DAPL1 led to a dramatic increase in the DAPL1 protein level in the infected ARPE-19 cells (Fig. 8B, C). The fundus photographs (Fig. 8D) and OCT (Fig. 8E) analyses showed that AAV9-CMV-NC-injected eyes displayed severe retinal detachment, whereas the AAV9-CMV-DAPL1-injected eyes had a much more organized retinal structure. H&E staining also confirmed the results of retinal detachment (Fig. 8F). Upon evaluating PVR severity according to Fastenberg’s score, the AAV9-CMV-DAPL1-mediated overexpression of DAPL1 efficiently inhibited the progression of PVR (Fig. 8G). These results suggest that gene transfer-mediated DAPL1 overexpression prevents the severity of experimental PVR progression. Fig. 8AAV-mediated DAPL1 overexpression prevents the progression of experimental PVR.A Schematic diagram of AAV9-CMV-DAPL1. ARPE-19 cells were injected into the vitreous of the Chinchilla rabbits to induce experimental PVR. AAV9-CMV or AAV9-CMV-DAPL1 virus was injected at day 3, and the PVR severity was evaluated at days 30 and 38. B, C Western blots showing the expression of DAPL1 in ARPE-19 cells after infection with AAV9-CMV-DAPL1 for 2 days and related quantification. $$n = 3$.$ D Fundus photographs in each group were captured at day 38. Note that retinal detachment with retinal folds was less severe in the AAV9-CMV-DAPL1-injected eyes. E Optical coherence tomography (OCT) scans were performed at day 38, and severe retinal detachment could be observed in the AAV9-CMV-NC-injected eyes, but the AAV9-CMV-DAPL1-injected eyes showed normal retinal structures. F Histological images of H&E staining of the rabbit eye in each indicated group showing retinal folds and severe retinal detachment in the AAV9-CMV-NC-injected eyes but a normal retinal structure in the AAV9-CMV-DAPL1-injected eyes. G PVR severity in each group was graded according to Fastenberg’s score at the indicated times. $$n = 6$$, * indicates $P \leq 0.01.$ ## Discussion In this work our findings reveal that DAPL1 acts as a novel suppressor of RPE-EMT in mice, and that it can antagonize PVR progression in an experimental model. This conclusion is based on multiple observations. First, the expression level of DAPL1 is decreased in the PVR membranes of PVR patients. Second, DAPL1 deficiency promotes RPE-EMT in mice. Third, the overexpression of DAPL1 in RPE cells inhibits the EMT of RPE cells both in vitro and in vivo. Fourth, DAPL1 enhances P21 stabilization partially through NFκB (RelA), and the knockdown of P21 neutralizes the function of DAPL1 in inhibiting RPE-EMT and PVR progression. Fifth, in the experimental PVR state, AAV9-CMV-DAPL1 gene therapy could effectively reduce the severity of experimental PVR progression. Hence, the molecular process involved in this event seems to be arranged linearly in the following way: DAPL1➝NFκB➝ P21 stabilization —\| RPE-EMT. This is likely only one of many pathways involved in maintaining the homeostasis of RPE cells and inhibiting EMT in the retina. The link between DAPL1 and RPE-EMT is of particular interest because it is well established that RPE-EMT is one of the main causative factors in the pathogenesis of PVR [3, 4]. In addition, RPE-EMT also occurs in other retinopathies, including AMD and inherited retinal degeneration [6]. AMD is one of the leading causes of irreversible blinding diseases, and a synonymous single nucleotide polymorphism (rs17810398) in the DAPL1 gene was reported to be a female-specific susceptibility locus for AMD [16]. Our work demonstrated that Dapl1−/− animals display age-related retinal degeneration (unpublished results), but the precise underlying mechanisms are still being studied. EMT is important for the dysfunction of RPE cells, suggesting that DAPL1-mediated regulation of RPE cell EMT might not only be involved in PVR pathogenesis but could also participate in the regulation of other retinopathies, such as AMD. Our previous study indicated that DAPL1 inhibits RPE cell proliferation by up-regulating P21 protein levels, but its regulatory mechanism remains unknown [20]. Here, we further show that DAPL1 does not regulate the mRNA level of P21 in ARPE-19 cells, but regulates P21 protein phosphorylation and stabilization. P21 is a cell proliferation inhibitor, which plays critical roles in the regulation of RPE cell proliferation and differentiation [26]. The overexpression of P21 in RPE cells could inhibit PVR progression in a rabbit model [27]. Our current data proved that the knockdown of P21 in DAPL1-overexpressing ARPE-19 cells promotes the EMT of RPE cells, which was based on a decrease in ZO-1, E-cadherin and increase in N-cadherin, vimentin and SNAIL expression. Consistent with our findings, P21 has been reported to regulate the EMT of ovarian cancer and non-small-cell lung cancer cells [33, 34]. For the past several decades, increasing evidence indicates that P21 expression is tightly controlled by multiple mechanisms, including P53-dependent and independent transcriptional regulation and post-transcriptional regulation [35]. The stability of P21 protein is essential for proper cell fate decisions, and this can be regulated by MEK/ERK, AKT, JNK, and P38 pathways [36, 37]. At the post-transcriptional, the phosphorylation event has been reported to increase the stability of P21 [30, 38]. In fact, in the current work we showed that DAPL1 promoted the phosphorylation of P21 protein and increased its stability in RPE cells partially through NFκB (RelA), an important target of the PI3K/AKT pathway [39]. In addition, NFκB could interact with Wnt/β-catenin signaling pathways [40], while GSK-3β, the key mediator of Wnt/β-catenin, was reported to regulate the phosphorylation of P21 [41]. Moreover, NFκB can also directly regulate the transcription of P21 by binding to its promoter region [42], and its activity also affects heterogonous pathways, such as the P53 axis, an important regulator of P21 [43]. However, the precise mechanisms through which DAPL1 regulates NFκB and then promotes P21 stabilization have not been fully elucidated, and further investigation is needed to address the issue. Moreover, NFκB participates in the regulation of multiple biological processes, including immune responses, inflammatory reactions, and apoptosis [43], which suggests that DAPL1 might have other biological functions in RPE cells and other RPE-dysfunction related retinopathies. Together, our findings in this study have provided new evidence that in addition to the known functions of DAPL1, it also plays a role in the inhibition of EMT in RPE cells by up-regulating P21 protein stability. The overexpression of DAPL1 in RPE cells can prevent EMT and reduce the severity of experimental PVR progression. As the EMT of RPE cells is closely related to RPE dysfunction and relative retinopathies, we believe that these findings will provide novel insights into the regulatory mechanisms of not only RPE-EMT and PVR but also other forms of retinal diseases in humans, suggesting potential therapeutic interventions for the treatment of RPE dysfunction-related retinopathies in the future. ## Supplementary information supplement data Original Data File Reproducibility checklist The online version contains supplementary material available at 10.1038/s41419-023-05693-4. ## References 1. Pastor JC, Rojas J, Pastor-Idoate S, Di Lauro S, Gonzalez-Buendia L, Delgado-Tirado S. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical consequences. Prog Retin Eye Res (2016) 51 125-55. DOI: 10.1016/j.preteyeres.2015.07.005 2. Greene WA, Kaini RR, Wang HC. 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--- title: Targeted metabolomics detects a putatively diagnostic signature in plasma and dried blood spots from head and neck paraganglioma patients authors: - Simone De Fabritiis - Silvia Valentinuzzi - Gianluca Piras - Ilaria Cicalini - Damiana Pieragostino - Sara Pagotto - Silvia Perconti - Mirco Zucchelli - Alberto Schena - Elisa Taschin - Gloria Simona Berteşteanu - Diana Liberata Esposito - Antonio Stigliano - Vincenzo De Laurenzi - Francesca Schiavi - Mario Sanna - Piero Del Boccio - Fabio Verginelli - Renato Mariani-Costantini journal: Oncogenesis year: 2023 pmcid: PMC9968333 doi: 10.1038/s41389-023-00456-4 license: CC BY 4.0 --- # Targeted metabolomics detects a putatively diagnostic signature in plasma and dried blood spots from head and neck paraganglioma patients ## Abstract Head and neck paragangliomas (HNPGLs), rare chemoresistant tumors curable only with surgery, are strongly influenced by genetic predisposition, hence patients and relatives require lifetime follow-up with MRI and/or PET-CT because of de novo disease risk. This entails exposure to electromagnetic/ionizing radiation, costs, and organizational challenges, because patients and relatives are scattered far from reference centers. Simplified first-line screening strategies are needed. We employed flow injection analysis tandem mass spectrometry, as used in newborn metabolic screening, to compare the plasma metabolic profile of HNPGL patients (59 samples, 56 cases) and healthy controls (24 samples, 24 cases). Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) highlighted a distinctive HNPGL signature, likely reflecting the anaplerotic conversion of the TCA cycle to glutaminolysis and catabolism of branched amino acids, DNA damage and deoxyadenosine (dAdo) accumulation, impairment of fatty acid oxidation, switch towards the Warburg effect and proinflammatory lysophosphatidylcholines (LPCs) signaling. Statistical analysis of the metabolites that most impacted on PLS-DA was extended to 10 acoustic neuroma and 2 cholesteatoma patients, confirming significant differences relative to the HNPGL plasma metabolomic profile. The best confusion matrix from the ROC curve built on 2 metabolites, dAdo and C26:0-LPC, provided specificity of $94.29\%$ and sensitivity of $89.29\%$, with positive and negative predictive values of $96.2\%$ and $84.6\%$, respectively. Analysis of dAdo and C26:0-LPC levels in dried venous and capillary blood confirmed that dAdo, likely deriving from 2′-deoxy-ATP accumulated in HNPGL cells following endogenous genotoxic damage, efficiently discriminated HNPGL patients from healthy controls and acoustic neuroma/cholesteatoma patients on easily manageable dried blood spots. ## Introduction Paragangliomas (PGLs) are rare, highly vascularized neural crest neoplasms originating from the paraganglia, organelles with neuroendocrine and/or chemoreceptor function(s) associated with the autonomic nervous system across the head/neck and trunk. Based on anatomic location, clinicopathological presentation and mainly parasympathetic (non-chromaffin) versus mainly sympathetic (chromaffin) lineage, PGLs are divided in at least two clinically distinct subsets, i.e., head and neck PGLs (HNPGLs), mostly arising from the non-chromaffin paraganglia along the lower cranial nerves or, much less frequently, the sympathetic paravertebral ganglia of the neck, and thoraco-abdominal PGLs (TAPGLs), originating from the chromaffin paraganglia of the thoraco-abdominal region, including the adrenal medulla. Additionally, but rarely, nonchromaffin PGLs arise from the pelvic parasympathetic paraganglia [1, 2]. Germline mutations in one of at least 18 nuclear genes, especially those encoding the 4 subunits of the SDH mitochondrial (mt) enzyme (complex II), i.e., SDHA/B/C/D and its assembly factor SDHAF2 (SDHx genes), are associated with at least 35-$40\%$ of all PGLs. These mutations may predict higher risk of aggressive disease, of synchronous/metachronous HNPGLs/TAPGLs and of other syndromic tumors that may relate to hereditary PGL [3–6]. Knowledge about PGL genetics impacts on surveillance and follow-up programs for patients and for first/second-degree relatives [6, 7]. Here, we focus on the plasma metabolomics of patients affected with HNPGLs, tumors that preferentially originate from the carotid body or from the parasympathetic paraganglia of the tympanic region and along the jugular vein and the vagal nerve. HNPGLs aggressively infiltrate the adjacent anatomic structures, with severe complications that impair the quality of life and may even be lethal [2, 8–10]. As PGLs in general, HNPGLs are chemo-resistant and radical surgery, difficult in advanced cases, remains the only effective therapy, however targeted peptide receptor radionuclide therapies can control inoperable or progressive disease [11–15]. Metastases are rare but can never be excluded [16, 17]. Anyhow, lifetime follow-up of all patients is granted by the strong genetic basis of PGL, which implies risk of multiple synchronous/metachronous HNPGLs and/or TAPGLs [1]. First/second-degree relatives that result carriers of predisposing mutations identified in index patients should also be regularly controlled [18]. This mandatory follow-up is challenging, because HNPGLs are mostly non-chromaffin and negative screening for plasma or urinary metanephrines is diagnostically irrelevant [8]. Only about one-third of the HNPGLs produce dopamine, determinable by plasma methoxytyramine level and not associated with signs of catecholamine excess [19, 20]. Thus, subjects at high HNPGL risk must be screened with diagnostic scans that expose to ionizing radiation (high-resolution PET/TC), or strong magnetic fields (MRI) [21]. Recently, plasma metabolomics by mass spectrometry (MS) proved useful for the quantification of predetermined tumor markers and for the identification of cancer-associated metabolomic profiles [22–24]. In patients affected with chromaffin PGLs a targeted metabolomic approach demonstrated changes in the concentration of specific amino acids (ornithine, sarcosine, tyrosine, creatinine, histidine, threonine) and lysophosphatidylcholine, that correlated with the results of the urine catecholamines and plasma free metanephrines tests [25, 26]. The present study aimed at characterizing the plasma metabolic profile of HNPGL patients relative to that of healthy controls (HCs) and of patients affected with acoustic neuroma (AN, also known as vestibular schwannoma) or cholesteatoma (CH), common expansive lesions unrelated to HNPGL that originate within the temporal bone. Our approach was based on rapid targeted flow injection analysis (FIA) coupled with tandem MS (MS/MS); a method widely used in newborn screening for metabolic disorders. FIA-MS/MS allows to assess the concentrations of clinically important compounds, including amino acids, organic acids, and fatty acids, in plasma or dried blood spotted on filter paper, the latter adaptable to sampling at home or in general practitioner’s offices and easily deliverable to centralized screening labs by regular mail [27]. Our results outline a distinctive HNPGL metabolomic signature detectable in both plasma and dried blood spots, potentially useful for HNPGL diagnosis and surveillance. ## Targeted metabolomics distinguish HNPGL patients from healthy controls FIA-MS/MS analysis on plasma quantified 14 amino acids, succinylacetone, 2 nucleosides, free carnitine, 35 acylcarnitines and 4 lysophosphatidylcholines (Table S1). These metabolites were used to conduct multivariate analysis on the HNPGL patients and HCs. Principal components analysis (PCA) and partial least square discriminant analysis (PLS-DA) showed a fair separation between the HNPGL and HC groups. The PCA plot (Fig. 1A) defined the tolerance ellipse based on the Hotelling’s T-squared distribution, which identified 4 outliers (HC24, PTJ146, PTJ150, PV158, Table S2), excluded from subsequent analyses. No significant subclusters based on SDHx status, age, sex, Fisch/Shamblin classification, tumor site and embolization status were observed within the HNPGL cluster (Tables S3-S4).Fig. 1Multivariate analysis of the head and neck paraganglioma patients (HNPGLs) and healthy controls (HCs) based on the metabolites detected by FIA-MS/MS.Panel A shows the principal component analysis (PCA) plot with the spatial distribution of the HNPGL patients and the HCs based on similarity. Panel B shows the partial least square discriminant analysis (PLS-DA) plot. Only few outliers situate outside the tolerance ellipses. Blue dots: HCs; green dots: HNPGLs. The PLS-DA plot (Fig. 1B) confirmed the spatial distribution outlined by PCA and, considering the first two components, yielded a value of 0.82 for R2Y, which measures goodness of model-to-data fit, and 0.77 for Q2, which assesses consistency between original and predicted data. These values were close to 1, providing robust proof that the PLS-DA model fits the training set and predicts class membership (Y variable). All the R2 and Q2 values to the left of the permutation test plot were lower than the original points to the right of the Q2 regression line, whose vertical intercept was below zero (R2 = 0.0, 0.184; Q2 = 0.0, -0.239), supporting model reliability. The closeness of the R2 and Q2 values obtained by permutation test also suggests that the model explains and predicts the variation of X (metabolite levels) and Y (HNPGL versus HC classification). Specifically, the intercept values were 0.0, 0.184 for R2 and 0.0, -0.239 for Q2 (Fig. S1). ## Metabolites affecting the separation of HNPGL patients from healthy controls The variable importance in the projection (VIP) plot, which lists in descending order the most significant metabolites, highlights the loadings relevant for the modeling of Y, i.e., sample class (HNPGL versus HC), identified by VIP values ≥ 1 (Fig. S2). Specifically, excluding the loadings with VIP < 1, the metabolites taken into account were: Glu, C10:2, C26:0-LPC, d-Ado, C16:1, C24:0-LPC, C18:1, Gln/Lys, C18:2OH, C18:2, C18, C10:1, C5DC/C6OH, C16, C10, SA, Arg, C14, C2, C16:1OH/C17, Val, Leu/Ile/Pro-OH, C18:1OH, Orn, C26 and C8. Glutamate (Glu) was the amino acid with highest VIP, indicating a major impact on clustering. This was confirmed by statistical analysis, as Glu plasma levels were significantly higher in the HNPGL patients than in the HCs ($p \leq 0.0001$, Fig. 2A). Correspondingly, the plasma concentrations of glutamine (Gln) plus lysine (Lys), isobar species indistinguishable through FIA-MS/MS, of ornithine (Orn), metabolically related to Gln, and of succinylacetone (SA) were significantly higher in the HNPGL patients ($p \leq 0.0001$ in all cases, Fig. 2A). The amino acids with VIP value < 1 and plasma levels significantly higher in the HNPGL patients included alanine (Ala) ($$p \leq 0.0353$$), glycine (Gly) ($$p \leq 0.0454$$), methionine (Met) ($$p \leq 0.0158$$), phenylalanine (Phe) ($$p \leq 0.0006$$), proline (Pro) ($$p \leq 0.0306$$) and tyrosine (Tyr) ($$p \leq 0.0494$$). The ratios of Leu/Pro ($$p \leq 0.004$$) and Phe/Tyr ($$p \leq 0.0116$$) were also significantly higher in the HNPGL group (Fig. S3). Intriguingly, arginine (Arg) presented significantly lower levels in the HNPGL group ($p \leq 0.0001$), while valine (Val) and the pooled isobars leucine (Leu), isoleucine (Ile) and hydroxyproline (Pro-OH) resulted lower in the HC group ($p \leq 0.0001$, Fig. 2A). Regarding nucleosides, 2-deoxyadenosine (dAdo) emerged as fourth impacting variable because of its high VIP value, consistent with the significantly higher dAdo levels in the HNPGL patients ($p \leq 0.0001$, Fig. 2B).Fig. 2Bar charts for the metabolites with VIP values > 1 in the head and neck paraganglioma patients (HNPGLs) versus the healthy controls (HCs).Data are presented for amino acids (A), adenosines (B), acyl-carnitines and lysophosphatidylcholines (C). Significance was established by t-test (*$p \leq 0.001$; *$p \leq 0.0001$). Among the short-chain acyl-carnitines (SCACs), the plasma levels of C2 and of the combined isobars C5DC/C6OH resulted significantly higher in the HNPGL patients ($p \leq 0.0001$ in all cases, Fig. 2C). Several medium/long-chain acyl-carnitines (MCACs/LCACs), including the MCACs C8, C10, C10:1, C10:2 and the LCACs C14, C16, C16:1, C18, C18:1, C18:2, C18:1OH, C18:2OH, C16:1OH, C17, showed an inverse behavior, with significantly higher plasma levels in the HCs ($p \leq 0.0001$ in all cases, except C8, $$p \leq 0.0005$$, Fig. 2C). Notably, C10:2 was associated with the second highest VIP value in the multivariate analysis. Instead, the very long-chain acyl-carnitine (VLCAC) C26 and the lysophosphatidylcholines (LPCs) C24:0-LPC and C26:0-LPC, the latter clearly originating from C26, exhibited higher concentrations in the plasma samples of the HNPGL patients ($p \leq 0.0001$ in all cases, Fig. 2C). Bar charts of the metabolites whose plasma concentrations significantly differed in the HNPGL patients versus the HCs (VIP values < 1) are displayed in Fig. S3. Among the acyl-carnitines, C22 presented lower levels in the HNPGL group ($$p \leq 0.0098$$), while C5 ($$p \leq 0.0058$$), the pooled isobars C3DC and C4OH ($p \leq 0.0001$), C6 ($p \leq 0.0001$), C6DC ($p \leq 0.0001$), C14:2 ($$p \leq 0.0337$$), C18OH ($$p \leq 0.0029$$) and C24 ($$p \leq 0.0186$$) resulted higher in the HNPGL group. Among the LPCs, only C22:0-LPC showed higher levels in the HNPGLs ($$p \leq 0.0003$$). ## HNPGL versus acoustic neuroma and cholesteatoma Statistical analysis of the plasma concentrations of the metabolites that most impacted on the PLS-DA model (VIP values > 1) was expanded to include 10 AN and 2 CH patients (Table S3). Significant differences were confirmed for Glu ($p \leq 0.0001$), Gln/Lys ($p \leq 0.0001$), Leu/Ile/Pro-OH ($$p \leq 0.0169$$), SA ($p \leq 0.0001$), Val ($$p \leq 0.0002$$), dAdo ($p \leq 0.0001$), C5DC/C6OH ($p \leq 0.0001$), C26 ($$p \leq 0.0017$$), C24:0-LPC ($p \leq 0.0001$) and C26:0-LPC ($p \leq 0.0001$) (Fig. 3), suggesting that these metabolites may define an HNPGL-specific metabolic signature. The other metabolites with high VIP values in the original PLS-DA model did not show statistically significant differences in the comparison with the AN/CH patients, suggesting that they participate in a metabolic signature associated with a range of proliferative/inflammatory skull base lesions. Fig. 3Bar charts for the metabolites that most impacted on the partial least square discriminant analysis (PLS-DA) model. Bar charts show the plasma concentrations of the metabolites with VIP values > 1 in head and neck paraganglioma patients (HNPGLs), healthy controls (HCs), and patients affected with acoustic neuroma or cholesteatoma (AC/CH). Only the metabolites that presented significant differences by t-test are shown ($p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; **$p \leq 0.0001$). ## Plasma metabolite levels versus HNPGL site, stage and SDHx status We investigated whether the plasma levels of the above-mentioned HNPGL-associated metabolites could be influenced by tumor site, stage and SDHx status. We first compared the plasma levels of the relevant metabolites (i.e.: Gln/Lys, Glu, Leu/Ile/Pro-OH, SA, Val, dAdo, C5DC/C6OH, C26, C24:0-LPC, C26:0-LPC) in the various subtypes of HNPGL investigated in this study (tympanic, tympanojugular, vagal and carotid body). No significant differences were observed, except for C5DC/C6OH that showed lower levels in vagal compared to carotid body ($$p \leq 0.0192$$) or tympanic HNPGLs ($$p \leq 0.0271$$) (not shown). To investigate possible relationships with stage, we considered only tympanic and tympanojugular HNPGL patients, accounting for the largest subset of our case series and uniformly staged accordingly to the Sanna’s modified Fisch classification (Table S4). We compared the concentrations of the HNPGL-associated metabolites in patients with low (A1 to B3) versus high (C3-C4) Sanna’s modified Fisch stage. Statistical analysis did not demonstrate significant differences. Considering mutational status, only Gln/Lys plasma* levels differed significantly between SDHx carriers and SDHx noncarriers ($$p \leq 0.0041$$) (Fig. S4). ## Putative HNPGL biomarkers A ROC curve-based multivariate analysis was conducted for the variables with VIP values > 1 that significantly discriminated among the three test groups (HNPGL patients, HCs, AN/CH patients). The analysis considered HNPGL versus non-HNPGL cases (inclusive of HCs and AN/CH patients). ROC analyses obtained with different numbers of metabolites [2, 3, 4, 5, 6, 7, 8, 9,10] always yielded a large area under the curve (AUC), indicating ability to distinguish between diagnostic groups (Fig. 4). The best confusion matrix obtained from the ROC curve based on 2 metabolites, dAdo and C26:0-LPC, provided a measure of classification accuracy, detecting correct and incorrect predictions for class after cross-validation. An error of $14.3\%$ was observed for prediction of HNPGL class (labeled 1), as 8 patients were misclassified, whereas an error of $5.7\%$ occurred for the definition of non-HNPGL class (labeled 0), as 2 AC/CH patients were misclassified (Fig. 4). These misclassifications cannot be explained based on the information available in the current study (age, sex, tumor site, stage, SDHx mutational status).Fig. 4ROC curve-based multivariate analysis for the variables with VIP values > 1 that were statistically significant for the distinction of head and neck paraganglioma (HNPGL) patients from healthy controls (HCs) and acoustic neuroma/cholesteatoma (AN/CH) patients. To the left, cumulative curves based on 2, 3, 4, 5, 6, 7, 8, 9 and 10 metabolites respectively, with area under the curve (AUC) and confidence interval (CI) values. To the right, confusion matrix after cross-validation of the cumulative ROC curve obtained with dAdo and C26:0-LPC. The HNPGL and non-HNPGLs samples, inclusive of HCs and AN/CH, are displayed as black and white dots, respectively. The 2 metabolites selected by multivariate ROC curve analysis were manually combined to create biomarker models by logistic regression algorithm. Having established that dAdo and C26:0-LPC were noncollinear based on the values of their variance inflation factor (VIF = 2.8) and coefficient of determination (R2 = 0.64), the logistic regression model provided the following equation:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\mathrm{logit}}}}\left({{{\mathrm{P}}}} \right) = \log \left({{{{\mathrm{P}}}}/\left({1 - {{{\mathrm{P}}}}} \right)} \right) = - 4.296 + 20.04\;{{{\mathrm{dAdo}}}} + 1.532\;{{{\mathrm{C}}}}26\!:\!0 {\hbox{-}} {{{\mathrm{LPC}}}}$$\end{document}logitP=logP/1−P=−4.296+20.04dAdo+1.532C26:0-LPCwhere P is Pr($y = 1$ \| x). The classification cutoff for the predicted P is 0.5. The specificity and sensitivity values of the logistic regression model based on dAdo and C26:0-LPC were $94.3\%$ and $89.3\%$, with positive and negative predictive values of $96.2\%$ and $84.6\%$, respectively. The estimated AUC was 0.9704 (0.9399 ~ 1). The performance of the logistic regression model is shown in Table S5. AUC, sensitivity, and specificity values for dAdo and C26:0-LPC used separately and in combination are shown in Table S6. ## dAdo and C26:0-LPC in plasma versus dried venous and capillary blood spotted on paper The analysis of dAdo and C26:0-LPC in plasma versus paired dried venous blood spotted on paper (DVB, $$n = 21$$ samples) revealed a moderate correlation between dAdo plasma and DVB levels, supported by Pearson r (0.49) and p value (0.0259), while no correlation emerged for C26:0-LPC. Finally, dAdo levels were assessed in DVBs from HNPGL patients and HCs versus dried capillary blood (DCB) samples, also spotted on filter paper, from paired HCs. This confirmed that the levels in the DVB and paired DCB samples were comparable, while there were statistically significant differences between the DVBs from the HNPGL patients and the DVBs ($$p \leq 0.034$$) and the DCBs ($$p \leq 0.017$$) from the HCs (Fig. 5).Fig. 5Comparison of dAdo levels in dried venous blood (DVB) versus dried capillary blood (DCB) from head and neck paraganglioma (HNPGL) patients and healthy controls (HCs).Statistically significant differences were observed between the DVBs from the HNPGL patients and the DVBs from the HCs. The levels of dAdo in the DVBs and paired DCBs from the HCs were comparable. Significance was established by ANOVA (HNPGLs: 21 cases; DVBs from HCs: 9 cases; DCBs from HCs: 16 cases) ($p \leq 0.05$). ## Discussion To our knowledge, this is the first metabolomic study of HNPGL patients. We exploited FIA-MS/MS, a validated method widely used in newborn screening for inborn errors of metabolism [27]. Using this approach, applicable to both plasma and whole dried blood spotted on filter paper, easy to prepare, preserve and ship, we highlight a putative metabolomic signature of HNPGLs, elegantly supported by clustering in the validated PLS-DA model, that reflects both the biological uniqueness of the samples and their substantial diversity relative to the control group. Based on their high VIP values, 26 different metabolites were identified as variables affecting the model, including dAdo, 7 amino acids, all involved in the urea and reverse TCA cycles, 16 acylcarnitines and 2 LPCs. Quantitative data analysis of these metabolites hints at possible metabolic mechanisms deregulated in HNPGLs. Low arginine and high ornithine concentrations suggest urea cycle blockage at ornithine transcarbamylase (OTC), consistently with the observed high glutamate levels, likely deriving from transamination reactions involving oxaloacetate from the TCA cycle [28]. OTC downregulation was reported in hepatocellular carcinoma [29], which shares HIF signaling activation with PGL [30]. High glutamate is coherent with the observed increase in branched-chain amino acids (Val and combined Leu, Ile and Pro-OH), whose catabolism contributes to the biosynthesis of nucleotides and non-essential amino acids through the mTOR/glutamate/glutamine axis, and supplies succinyl- and acetyl-CoA to feed the TCA cycle, lipogenesis, and histone acetylation [31–33]. In this respect, HIF2A, the HIFA isomer preferentially activated in HNPGL [34], promotes glutaminolysis [35], a feature of the PGL metabolic phenotype [36, 37]. FIA-MS/MS does not allow direct glutamine quantification, but we hypothesize that the high Gln/Lys values reflect glutamate/glutamine conversion by glutamine synthetase and/or lysine catabolism, consistent with the high levels of glutaryl carnitine (C5DC). Glutamine is the major substrate of the reductive anaplerotic TCA cycle [29, 38] typical of SDH-deficient PGL cells [30], which incorporate glutamine to support anabolism and buffer the leakage of reactive oxygen species (ROS) via glutamate and glutathione (GSH) production [39]. High succinyl-acetone fits into this picture, being a metabolic feature of HIF-dependent cancers, such as HCC [40]. Acyl-carnitines serve as carriers of long-chain fatty acids to the inner mt membrane for β-oxidation, as donors of acyl groups and as source of carnitine involved in the biogenesis of the mt membranes [41]. These functions are strongly deregulated in PGLs, where there is a sharp increase in mt mass accompanied by lipid droplets accumulation [42, 43]. Deregulation of acyl-carnitines has been implicated in various cancers, again including HCC, where it presents a specific pattern, characterized by decrease in SCACs/MCACs, and increase in LCACs [44]. High LCAC levels, as observed in the HNPGL patients, suggest impairment of carnitine palmitoyl-transferase 2 activity, implicated in STAT3-dependent tumorigenesis [44]. High acyl-carnitines were reported in pro-inflammatory conditions, both non-neoplastic, including diabetes, heart failure, and sepsis, and neoplastic, particularly breast cancer, where they were connected to the Warburg effect and to upregulated oxidation of fatty acids and branched amino acids [45]. A pro-inflammatory metabolomic signature is also consistent with the upregulation of phospholipids linked to the expression of proinflammatory cytokines such as C24:0 and C26:0-LPCs [46]. Univariate statistical analysis of the metabolites that most impacted on the PLS-DA model was also carried out on some patients affected with acoustic neuroma and cholesteatoma, common expansive lesions of the skull base that are unrelated to HNPGL. This confirmed the above-described trends for Glu, Gln/Lys, Leu/Ile/Pro-OH, SA, Val, dAdo, C5DC/C6OH, C26, C24:0-LPC and C26:0-LPC, highlighting a potentially specific HNPGL metabolic signature, reasonably associated with: 1) anaplerotic conversion of the TCA cycle to reductive glutamine metabolism and catabolism of branched amino acids; 2) DNA damage and toxic dAdo accumulation; 3) impairment of mt fatty acid oxidation (FAO) with the switch towards the Warburg effect; 4) proinflammatory LPC-mediated signaling. These putatively-specific metabolites were combined in multivariate ROC curves to identify leading biomarkers suitable to HNPGL screening and diagnosis. The best classification tool in the model was based on C26:0-LPC and dAdo, the two metabolites that most impacted on clustering. C26:0-LPC, implicated in microglial dysfunction and brain diseases, is typically elevated in X-linked adrenoleukodystrophy, a hereditary peroxisomal disorder caused by mutations of the ABCD1 gene, required for the import of very long chain fatty acids into peroxisomes for β-oxidation [47, 48]. Thus, our data point to a major role of peroxisomal dysfunction in HNPGL. dAdo is a purine deoxyribonucleoside that typically accumulates in subjects with adenosine deaminase (ADA) deficiency, where high dAdo levels support sustained biosynthesis of cytotoxic dATP. This, particularly in B- and T-cells, inhibits ribonucleotide reductase (RNR), required for DNA synthesis, causing severe combined immunodeficiency accompanied, in highly penetrant cases, by multi-organ disease [49]. The distinctively high dAdo blood/plasma levels found in HNPGL patients, who are putatively not ADA-deficient, could reflect ROS-induced genotoxic damage and S phase replication arrest occurring in proliferating HNPGL cells under constitutive metabolic stress. This could result in the accumulation of 2′-deoxy-ATP unspent in DNA replication, whose extra/intra-cellular catabolism would lead to dAdo accumulation [50]. dAdo inhibits pyrimidine synthesis, but its release within HNPGL tissue could be advantageous in terms of immune response inhibition, vascular vasodilation, and metabolic modulation. Accumulation of dAdo may also block transmethylation reactions through the inactivation of S-adenosylhomocysteine hydrolase (SAHH), thus promoting the buildup of the methyltransferase inhibitor S-adenosyl-L-homocysteine (SAH), possibly implicated in the hypermethylated HNPGL phenotype [19]. Furthermore, high 2′-deoxy-ATP/ATP ratios and dAdo availability could facilitate the synthesis of adenosine 2′-diphosphoribose, an endogenous superagonist of the transient receptor potential (TRP) melastatin 2 (TRPM2) plasma membrane cation channel that, under ROS-induced stress, controls Ca2+ fluxes and hence Ca2+ signaling, mitochondrial respiratory uncoupling and immune function [51–53]. Sustained Ca2+ influx and oxidative stress rapidly reach a cytotoxic and apoptotic threshold in normal cells. Conversely, in HNPGL cells, where alternative metabolism implies reduced dependence on the mitochondrial electron transport chain, ROS exposure activates key transcription factors, most notably HIF2A, whose downstream targets support cell survival and proliferation [1–3, 42, 43, 54, 55]. Importantly, by applying a linear equation to the C26:0-LPC and dAdo concentrations, logistic regression correctly identified the HNPGL patients. Considering the observed positive correlation between levels in plasma and in dried venous or capillary blood levels, dAdo quantification alone discriminated the HNPGL patients from the healthy controls and the acoustic neuroma and cholesteatoma patients. The FIA-MS/MS method is already used worldwide for newborn screening and is fully validated on both plasma and dried blood spotted on filter paper, as we confirm here for HNPGL patients and controls. Dried venous or capillary blood samples give several advantages in terms of sample size, autonomy in sampling, minimal invasiveness, easy handling, and affordable storage. We suggest that this validated method, meeting the requirements of speed (1 min analysis), simplicity, and costs, could be repositioned for high-throughput cancer screening relying on the existing networks of newborn metabolic screening labs. Such repositioning could be particularly important for the HNPGL patients and their relatives at genetic risk, who require lifelong surveillance, presently based on CT and/or MRI scans [8, 54]. The present study has limitations related to sample size which could affect statistical power, particularly in the assessment of possible relationships between metabolite plasma levels and specific HNPGL features such as site, size, stage, and mutational status. Larger confirmatory studies including TAPGLs, which may co-occur with HNPGLs, and other more common head and neck tumors, especially adenocarcinomas and squamous cell carcinomas, are needed to assess the repositioning of newborn metabolic screening by FIA-MS/MS to the screening and follow of HNPGL and possibly TAPGL patients. ## Cases, controls, and sample collection The study was part of research project IG 2020 ID 24501, supported by the Italian Association for Cancer Research (AIRC) and approved by the Ethical Committee of the Regional Health District “Area Vasta Emilia Nord” (AVEN, http://www.ausl.pc.it/comitato_etico/), protocol #$\frac{2021}{0081925.}$ Fasting (12 h) blood specimens were collected before surgery in lavender-top blood tubes (K2-EDTA) at a quaternary skull base and neurotologic center (Gruppo Otologico Clinic, Piacenza, Italy), from 56 patients affected with HNPGL (59 samples, as 3 patients were sampled at two metachronous surgical stages), 10 patients with acoustic neuroma (AN), and 2 with cholesteatoma (CH). Fasting blood samples from 24 healthy age-matched controls (HCs) were donated by consenting in-house staff declaring no specific diseases and regularly controlled by institutional health check (Table S2). Informed consent was obtained from all recruited subjects. All the HNPGL plasma samples, except 4 from tympanic HNPGLs, derived from patients who had been embolized approximately 72 h before blood sampling. Blood samples were maintained at ambient temperature, plasma was prepared within 24 h by centrifugation (10 min at 1000 x g speed at room temperature), aliquoted in sterile 1.5 ml Eppendorf tubes and stored at -80 °C until testing. 21 HNPGL patients were tested on both plasma and whole dried venous blood (DVB) samples. The latter were obtained by spotting 50 μL (~1 drop) of fresh blood onto untouched Whatman 903 (W-903) filter paper discs. Spotted filters were dried for 2–3 h under hood at room temperature and saved in sterile envelopes at room temperature until testing. Twenty-four healthy volunteers were selected as controls. Nine [9] individual DVB specimens were obtained as described above from healthy controls. In parallel, samples of dried capillary blood (DCB) obtained by spotting few drops of capillary blood onto W-903 filter paper were donated from 16 healthy controls. Age and sex distribution of the healthy controls (HCs) and of the HNPGL and acoustic neuroma/cholesteatoma (AN/CH) patients are reported in Table S3; essential clinicopathologic and genetic characteristics of the HNPGL patients, including tumor localization, Shamblin class (used here for both carotid body and vagal PGLs according to Sanna M. et al.) [ 9, 56, 57], Sanna’s modified Fisch class (for tympanic/tympanojugular PGLs) [10, 58] and SDHx germline mutation status, are listed in Table S4. ## Genetic characterization Point mutations and large deletions/rearrangements in SDHA, SDHB, SDHC, SDHD, SDHAF2 and TMEM127 were assessed by bidirectional Sanger sequencing of the coding regions and splice sites and multiplex ligation-dependent probe amplification (MLPA) [59]. SDHx/TMEM127 status was assessed in 47 out of the 56 HNPGL cases studied (Tables S2 and S4). A putatively pathogenic germline mutation in one of the 6 susceptibility genes was detected in $\frac{19}{47}$ cases ($40\%$), including 3 mutated in SDHA, 7 in SDHB, 3 in SDHC, 5 in SDHD and 1 in SDHAF2. The remaining 28 cases resulted SDHx/TMEM127 noncarriers. Detected variants were classified into pathogenicity classes according to the guidelines of the American College of Medical Genetics and Genomics (ACMG) [60] and the Cancer Variant Interpretation Group UK [61]. ## Metabolite extraction from plasma and targeted FIA-MS/MS analysis The neonatal screening center of G. d’Annunzio University participates in an accredited national network of laboratories that adopts a uniform screening panel covering 57 clinically-relevant metabolites, including 14 amino acids, 2 nucleosides, free carnitine, 35 acyl-carnitines, 4 lysophosphatidylcholines and succinylacetone. Details of the metabolites evaluated and their reference values, deriving from the NeoBase™ 2 Non-derivatized MSMS kit (PerkinElmer Life and Analytical Sciences, Turku, Finland) are reported in Table S1. Metabolites were extracted from plasma specimens using a modified protocol based on the manufacturer’s workflow designed for newborn screening. Briefly, 10 µL of plasma were subjected to protein precipitation by incubation with 125 µL of internal standards (IS) solution (PerkinElmer) for 30 min at 45 °C, 700 rpm (Eppendorf ThermoMixer® C). Proteins were removed after centrifugation (at max speed in an Eppendorf 5424) and clear supernatants (125 µL) were transferred into new vials. An additional 1 h incubation step was required to derivatize succinylacetone (SA). Finally, 100 µL of centrifuged samples were transferred to 96-well plates for injection of 10 µL into the ion source. Acquisition, 1.2 min long injection-to-injection, was carried out on a FIA platform RenataDX Screening System, including a 3777 C IVD Sample Manager and an ACQUITY™ UPLC™ I-Class IVD Binary Solvent Manager, coupled to a Xevo™ TQD IVD tandem quadrupole mass spectrometer (both from Waters Corporation, Milford, MA, USA). The flow gradient for the mobile phase provided by the kit was set as follows: 0.15 mL/min from 0 to 0.170 min; 0.01 mL/min from 0.170 to 0.980 min; 0.7 mL/min from 0.980 to 1.180 min; 0.15 mL/min from 1.180 min to the end. Data were processed by MassLynx™ (IVD) Software V4.2 with NeoLynx™ Application Manager (Waters Corp). Mass spectrometry (MS) parameters and a complete list of the metabolites and their internal standards (ISs) are provided in Table S3. Metabolite extraction from dried venous or capillary blood spotted on filter paper was performed according to PerkinElmer’s protocol using the NeoBase™ 2 non-derivatized MSMS kit. As already described [62–65], samples were punched out into 3.2 mm disks for the extraction of amino acids, nucleosides, free carnitine, acyl-carnitines, lysophosphatidylcholines and succinylacetone. Finally, 10 µL of supernatant were analyzed on the same FIA-MS/MS platform described above, using the same parameters for injection, MS acquisition and raw data processing. The micromolar (µM) concentrations of all tested metabolites are presented in Table S7. ## Statistical analysis Multivariate analysis was performed on SIMCA® 14.1 (Sartorius Stedim Data Analytics AB). Principal component analysis (PCA) was used to evaluate the clustering pattern and identify outliers. Partial least square discriminant analysis (PLS-DA) was performed to highlight relationships between predictors (X) and dependent (Y) variables, and to discover the loadings most relevant for the modeling of Y – Variable Importance in the Projection (VIP) by focusing on VIPs > 1. The R2Y and Q2 parameters, which respectively measure goodness of fit of the model to the original data and consistency between original and predicted data by cross-validation were used to assess the reliability of the PLS-DA model. R2Y and Q2 values close to 1 indicate reliability and reproducibility of the obtained model outcomes and power to explain and eventually predict the phenomenon under investigation. The PLS-DA model was validated by 100-fold permutation approach. Univariate statistical differences were evaluated by t-test and ANOVA on GraphPad Prism 9.0 using Tukey’s test for multiple comparisons. The Mann-Whitney test was applied to evaluate differences in continuous or ordinal variables, the Chi-square test was employed for categorical variables. Values of $p \leq 0.05$ were considered significant. Exploratory analysis based on multivariate Receiver Operating Characteristic (ROC) curve (Explorer) was performed on the MetaboAnalyst 5.0 platform (www.metaboanalyst.ca) to detect relevant features and evaluate the diagnostic performance, using the Support-Vector Machine (SVM) algorithm as classification method and the SVM built-in as feature ranking method. According to the subsampling approach for model training, a prediction of class probabilities was computed by cross-validation using the best classifier, identified by the area under the curve (AUC) value. Based on the selection of specific features, a multiple logistic regression model was generated on GraphPad Prism 9.0, evaluating first the correlation matrix to exclude multicollinearity and whether the null hypothesis was rejected by Log-likelihood ratio (G squared). Correlation analysis was performed on GraphPad Prism 9.0. ## Supplementary information Supplemental Figures and Tables Supplementary Table S7 The online version contains supplementary material available at 10.1038/s41389-023-00456-4. ## References 1. Neumann HPH, Young WF, Eng C. Pheochromocytoma and paraganglioma. N. Engl J Med* (2019.0) 381 552-65. DOI: 10.1056/NEJMra1806651 2. Mete O, Asa SL, Gill AJ, Kimura N, de Krijger RR, Tischler A. 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--- title: Sex-specific differences in resting-state functional brain activity in pediatric concussion authors: - Bhanu Sharma - Cameron Nowikow - Carol DeMatteo - Michael D. Noseworthy - Brian W. Timmons journal: Scientific Reports year: 2023 pmcid: PMC9968337 doi: 10.1038/s41598-023-30195-w license: CC BY 4.0 --- # Sex-specific differences in resting-state functional brain activity in pediatric concussion ## Abstract Pediatric concussion has a rising incidence and can lead to long-term symptoms in nearly $30\%$ of children. Resting state functional magnetic resonance imaging (rs-fMRI) disturbances are a common pathological feature of pediatric concussion, though no studies have explicitly examined sex-differences with respect to this outcome, precluding a sex-specific understanding of the functional neuropathology of pediatric concussion. Therefore, we performed a secondary data analysis of rs-fMRI data collected on children with concussion ($$n = 29$$) recruited from in a pediatric hospital setting, with greater than 12:1 matched control data accessed from the open-source ABIDE-II database. Seed-based and region of interest (ROI) analyses were used to examine sex-based rs-fMRI differences; threshold-free cluster enhancement (TFCE) and a family-wise error (FWE) corrected p-values were used to identify significantly different clusters. In comparing females with concussion to healthy females, groupwise differences were observed irrespective of seed selected. Notably, we observed (in order of largest effect) hypo-connectivity between the anterior cingulate cortex of the salience network and the thalamus and precuneus (TFCE = 1473.5, p-FWE < 0.001) and the cingulate gyrus (TFCE = 769.3, p-FWE = 0.009), and the seed (posterior cingulate cortex (PCC)) of the default mode network and the paracingulate gyrus (TFCE = 1275.7, p-FWE < 0.001), occipital pole right (TFCE = 1045.0, p-FWE = 0.001), and sub-callosal cortex (TFCE = 844.9, p-FWE = 0.005). Hyper-connectivity was observed between the salience network seed and the cerebellum (TFCE = 1719.3, p-FWE < 0.001) and the PCC and the thalamus (TFCE = 1198.3, p-FWE < 0.001), cuneal cortex (1070.9, p-FWE = 0.001), and lateral occipital cortex left (TFCE = 832.8, p-FWE = 0.006). ROI analyses showed 10 and 5 significant clusters of hypo- and hyper-connectivity in females, respectively. Only one cluster of difference was found between males with concussion and healthy males on seed-based analyses, and 3 clusters on ROI analyses. There are alterations in rs-fMRI in females with concussion at one-month post-injury that are minimally present in males, which provides further evidence that recovery timelines in pediatric concussion may differ by sex. ## Introduction Concussion is a mild form of traumatic brain injury (TBI) that results in altered neurological function after biomechanical impact1. In pediatric populations, concussion is of particular concern given that it is one of the most common injuries among children and adolescents2–4 and has a rapidly rising incidence in those aged 10–195,6. While the injury is transient for the majority of pediatric patients, between 14 and $29\%$ experience persistent concussion symptoms7–9 (PCS; also referred to as post-concussion syndrome, marked by symptoms which last in excess of four weeks1). PCS can include somatic, cognitive, emotional, and sleep-related features that negatively impact academic outcomes10 and health-related quality of life11,12. Brain function in pediatric concussion has been studied to understand the nature and extent of its impairment post-injury, as well as its potential etiological role with respect to concussion symptoms. Studies have measured brain function using resting-state functional magnetic resonance imaging (rs-fMRI), which maps regions of brain activity (by proxy of the blood-oxygen-level-dependent, or BOLD, response) and the relative associations between them in a task-independent manner. Pediatric rs-fMRI studies have shown increased functional connectivity in comparison to healthy controls in widely-studied brain networks within the first-week of injury13–15. At one-month post-injury (the expected time of recovery1), results of rs-fMRI studies are mixed13,14,16–18. With respect to studies of children diagnosed with PCS, one study found that within-network functional connectivity across seven validated brain networks did not differ between children with PCS ($$n = 110$$) vs. healthy peers ($$n = 20$$), although select PCS symptoms, sleep impairment, and poorer cognition were associated with connectivity in the concussed cohort19,20. A notable limitation common to many pediatric concussion rs-fMRI studies are the imbalanced samples with respect to sex. Some studies involved male only cohorts14,17,21, whereas others had less than $25\%$ female representation in their samples13,18; in some cases, data on sex were not reported16,22. Only a few studies had samples that approached balance (40–$45\%$ female) with respect to sex distribution15,19,20,23, though these studies did not stratify their results by sex, instead providing group-level data comparing mixed-sex cohorts of children with concussion to their healthy peers. The most direct data on sex-specific rs-fMRI differences come from a recent study involving adults with PCS24. More specifically, in this study, three commonly studied networks were examined through seed-based analyses, namely the default mode network (DMN), salience network, and fronto-parietal network; the authors reported reduced connectivity between the fronto-parietal network and nodes of the salience network in females with PCS. The lack of a sex-specific understanding of rs-fMRI differences in pediatric concussion is a considerable knowledge gap, given that sex, as a biological variable, has been recognized as an understudied yet important consideration in neuroscience25–27. Further, a growing body of research demonstrates that concussion presents differently in boys vs. girls28,29. For example, a recent cohort study ($$n = 986$$) found that female adolescents with concussion endorse more symptoms on the 22-item and widely used SCAT530 than concussed males, and are more likely to have a higher total symptom score31. Two large-scale, multi-center cohort studies have shown that females have a protracted recovery in comparison to males28,29, which align with other clinical data on disparate sex effects in concussion summarized in two recent systematic reviews32,33. Furthermore, differences in mechanism of injury between males and females, as well as factors that influence neurodevelopment (such as pubertal and hormonal status and genetics) can increase vulnerability to brain injury-induced pathologies in a sex specific manner (which are reviewed in detail by Arambula et al.)64. Therefore, we studied sex-specific rs-fMRI differences in pediatric concussion to address an important knowledge gap, and advance our understanding of how the functional neuropathology of concussion differs between males and females. ## Design The present study is a secondary analysis of data collected as part of two cohort studies (sharing recruitment methods, inclusion/exclusion criteria, and imaging parameters, as detailed below) on pediatric concussion. Control data were obtained from an open-source pediatric neuroimaging database (detailed below). This study was approved by the Hamilton Integrated Research Ethics Board (https://hireb.ca). ## Participants Children (aged 9–17) experiencing concussion symptoms were recruited by the clinical study team from sites at or affiliated with McMaster University, including the McMaster Children’s Hospital and associated rehabilitation and sports medicine clinics, as well as through direct referral from community physicians. Children diagnosed with a concussion, and their families, were recruited for an intake assessment. Neuroimaging data were then collected as soon after recruitment as scheduling permitted. For the present study, exclusion criteria included: (i) more severe forms of head injury that required surgery, resuscitation, or admission to the critical care unit, (ii) complex injuries involving multiple organ systems, and (iii) diagnosed neurological disorder or developmental delay. Imaging data on healthy children were acquired from the multi-site, internationally compiled, open-source Autism Brain Imaging Data Exchange II (ABIDE-II) database34. The ABIDE-II database is comprised of over one-thousand anonymized brains (including 557 healthy controls across age in our current study) collected from 19 sites, primarily in North America and continental Europe, yielding nearly 75 publications to date. Both anatomical and functional scans from the ABIDE-II database were pulled to serve as ~ 12:1 age- and sex-matched typically developing controls for our participants with concussion. Specific matching criteria were not applied when selecting controls. Rather, all children within the age range of interest (9–17) were retrieved as potential controls. While the hardware (with respect to make of the scanners, and the type of head coil used, although all scanners were 3.0 T) varied slightly between sites that participated in the ABIDE initiative, quality control data are indicative of homogeneity in data (with respect to signal-to-noise ratio, data smoothness, number of outlier scans) across sites34. Imaging parameters and scanner make and models can be found in Supplemental Table 1. ## MRI procedures data acquisition All children with concussion were scanned at a single site (Imaging Research Centre [IRC] at St. Joseph’s Healthcare, Hamilton) using a 3-Tesla GE Discovery MR750 MRI scanner and a 32-channel phased array head receiver coil. Upon entering the IRC, participants (as well as their parents and/or guardians if aged 16 years or younger) were led through an intake questionnaire by the MRI technologist, who then situated the participant in the MRI, using foam cushioning to minimize discomfort and motion during the scan. The MRI technologist remained in verbal contact with the participant via intercom throughout the scan. With respect to MRI data collection, first, a 3-plane localizer sequences was acquired. Anatomical images were then collected using a 3D inversion recovery-prepped fast SPGR T1-weighted sequence (TR/TE = $\frac{11.36}{4.25}$ ms, TI = 450mms, flip angle = 12°, 512 × 256 matrix interpolated to 512 × 512, 22 cm axial FOV, 1 mm thick). Resting state fMRI (rs-fMRI) involved BOLD imaging (gradient echo EPI, TR/TE = $\frac{2000}{35}$ ms, flip angle = 90°, 64 × 64 matrix, 180 time points, 3 mm thick, 22 cm FOV), wherein participants were asked to remain awake, keep their eyes open, and not to think of anything in particular. A B0 map was acquired for resting state scans, using the same geometric prescription as the rs-fMRI scan. A Bo mapping tool available on the GE scanner provided a parametric map of field homogeneity in Hz. In regards to the scanning sequence, the rs-fMRI data were acquired within 10-min of entering the MRI, as to avoid motion onset by restlessness later in scans as we have observed in this population. Additional data were collected (including DTI35 and task-based fMRI data36) as part of the imaging battery, but are not relevant to the present study. With respect to control data from the ABIDE-II database, only scans with a minimum of 180 time-points were used as age- and sex-matched controls. B0 data were not available for healthy controls. Approximately $10\%$ of the ABIDE-II data did not meet our quality control processes (as implemented in CONN 21a, as below) and were ultimately discarded from the analysis. ## MRI pre-processing and analyses Pre-processing of imaging data was performed in CONN 21a37 (which draws on some the functionality of SPM1238), run on MATLAB R2021b. For concussion data only, given that B0 maps were not available for controls, unwarping of functional data was performed outside of CONN using the epiunwarp script39. Fieldmaps were not available for all controls, and were thus only applied to the concussion group (to ensure our data were as rigorously pre-processed prior to comparison to the larger control group). While it would be ideal to apply fieldmap corrections to all participants, we were unable to do so given the lack of such data on controls. However, spatial smoothing (per the prescription below) is likely to make the effects of fieldmap correction minimal. Per our quality assurance, the timeseries of the fieldmap corrected and uncorrected data (for the concussion group) were similar (Supplemental Fig. 1), and statistical tests revealed no differences between the corrected and uncorrected data. Unwarped images were inputted into the pre-processing pipeline which involved the following steps: [1] Functional realignment with co-registration to the first acquired image40; [2] Slice-timing correction to the mid-point of each TR41; [3] Functional data outlier detection using SPM’s Artifact Detection Tool (ART)38; [4] Direct segmentation and normalization/registration of functional data to MNI space (1 mm and 2 mm isotropic voxels for anatomical and functional data, respectively), based on posterior tissue probability maps; and [5] spatial smoothing of functional data with a Gaussian kernel of full-width at half-maximum (FWHM) of 6 mm. All data were inspected visually after pre-processing, as well as by running CONN’s quality assurance assessment tool. Next, de-noising procedures were performed in CONN. First, CONN’s anatomical component-based noise correction procedure (aCompCorr) was used to project out noise components (associated with cerebral white matter and cerebrospinal regions42, outlier scans43, and subject motion44). Subsequently, temporal filtering was performed, filtering out frequencies below 0.008 Hz and above 0.1 Hz. Data were again inspected visually and per the quality assurance metrics offered by CONN. Seed-based connectivity and ROI-to-ROI based connectivity measures were then computed for each individual subject. Seed regions from four, large-scale, validated and clinically-salient (in pediatric concussion and otherwise) resting-state brain networks were used45–48. These included the DMN (seeded at the posterior cingulate cortex [1, − 61, 38]), salience network (SN, seeded at the anterior cingulate cortex [0, 22, 35]), fronto-parietal network (FPN, seeded at the lateral pre-frontal cortices), and sensorimotor network (SMN, seeded superiorly at the pre-central gyrus [0, − 31, 67]). Given that this study is the first to look at sex-differences in rs-fMRI in pediatric concussion, seed-based analyses were employed to explore the relation between these seeds and all other voxels of the brain; an accompanying ROI-to-ROI analysis was also performed (which examines the associations between 164 regions defined by the Harvard–Oxford atlas). Cluster-level inferences were made per Threshold Free Cluster Enhancement (TFCE)49, which avoids the use of an a priori cluster-forming height threshold. For each groupwise contrast (as specified above) permutation tests (involving 1000 permutations) were used to derive a null distribution that the observed effects were then compared to, and a TFCE score associated with family-wise error (FWE) corrected p-value for each cluster was obtained. Through these permutations, the expected distribution of TFCE scores under the null hypothesis is estimated. Then, at each voxel, the observed TFCE score is compared to the permuted null distribution, and each voxel/cluster is given a TFCE score. Only those observed voxels/clusters that survive comparison to the permuted null distribution are reported. This method is conservative and reduces family-wise error, which also speaks to the robustness of our findings/analysis. Further, TFCE has the advantage of being associated with a lower false-positive rate than traditional cluster-size tests based on random field theory50. Between-group contrasts were set up in CONN 21a, controlling for the effects of age. More specifically, we performed the following between-group analyses: All Concussion vs. All Controls, FemaleConcussion vs. FemaleControl, MaleConcussion vs. MaleControl, and a 2 × 2 (Group × Sex) ANCOVA. For each contrast and for each seed-region, significantly different clusters were identified using TFCE. The effect sizes associated with each significant cluster were computed, along with a t-score to statistically compare effect size differences at the cluster level between groups. ## Results Demographic and injury data of the 29 children with concussion and 361 controls are summarized in Table 1. Age and sex distribution did significantly differ between cohorts; however, we controlled for age in all analyses and performed both mixed-sex cohort analyses as well as single-sex (i.e., healthy female vs. female with concussion) analyses. Within the concussion group, males and females had similar PCSS scores (47.8 vs. 41.6, $$p \leq 0.511$$) at time of imaging per an independent samples t-test. Patients with concussion were, on average, approximately one-month post-injury (28.8 ± 14.5 days) at time of imaging, and had no history of anxiety, depression, sleep disorder, or psychiatric diagnosis. Table 1Demographic and injury-related variables. Concussion ($$n = 29$$)Healthy ($$n = 361$$)SignificanceAge14.2 (2.5)11.0 (2.2)$p \leq 0.05$ Male13.8 (2.7)10.9 (2.2)$p \leq 0.05$ Female14.8 (2.3)11.4 (2.4)$p \leq 0.05$% female$55.2\%$$33.2\%$$p \leq 0.05$Time-post injury28.5 (16.5)–– Male24.2 (14.2) Female34.1 (21.9)Previous concussion0, $$n = 17$$––1, $$n = 82$$, $$n = 2$$ Male0, $$n = 91$$, $$n = 32$$, $$n = 1$$ Female0, $$n = 101$$, $$n = 52$$, $$n = 1$$Mechanism of injury$70.3\%$ sport––$22.2\%$ non-sport related falls$7.4\%$ motor vehicle collision The following figures depict the five clusters of greatest change (as per TFCE scores and associated p-values), per network and per analysis. A full list of significant clusters can be found in Supplemental Table 2. ## Concussion vs. control group comparison In the concussion cohort, there was significantly reduced functional connectivity (all p-FWE < 0.05, with corresponding TFCE scores in Fig. 1) between the seed-region of the: (i) DMN and the hippocampus, amygdala, and caudate (right), precuneous cortex, paracingulate gyrus (right), occipital cortex, and precuneous cortex; (ii) SMN and the cerebellum (left), frontal pole (left), temporal fusiform cortex (left), lateral occipital cortex (left); (iii) SA and the precuneous cortex, lateral occipital cortex (left), cingulate gyrus (left), cerebellum, and precentral gyrus (left); FPN-L and the temporal pole (left), and middle temporal gyrus (left). Further, there was increased functional connectivity between the seed-region of the: (i) DMN and cerebellum, lateral occipital cortex (right); SA and the cerebellum; FPN R and the cerebellum, lateral occipital cortex (right), precentral gyrus (right). These data are depicted in Fig. 1.Figure 1rs-fMRI differences between children with concussion and their healthy peers (mixed-sex cohorts). Clusters (x, y, z) denote standard MNI coordinates at the center of cluster mass, and size represents number of voxels. Up to five clusters with the largest TFCE scores that survived a p-FWE < 0.05 per TFCE are displayed; see Supplemental Table 2 for all clusters that survived analysis. At the ROI-to-ROI level, there was a broad pattern of decreased functional connectivity between multiple brain regions bilaterally, and fewer instances of increased connectivity between pairs of ROIs. The clusters of ROIs with significantly reduced connectivity included: regions within the default mode network itself; the default mode network and the hippocampi; the cerebellum and the amygdala, putamen, and thalamus (see Fig. 2).Figure 2Significantly increased (warm colours) and decreased (cool colours) ROI-to-ROI connectivity in children with concussion in comparison to controls (mixed-sex cohort). ## Healthy males vs. males with concussion Per seed-based analyses, hypoconnectivity was observed between the seed of the salience network and a small voxel of clusters in the cingulate gyrus (Fig. 3). ROI-to-ROI analyses showed 3 clusters of hypoconnectivity between frontal and medial brain structures, as well as within the occipital lobe (Fig. 4).Figure 3rs-fMRI differences between males with concussion and healthy males. Clusters (x, y, z) denote standard MNI coordinates at the center of cluster mass, and size represents number of voxels. Only one cluster survived analysis. Figure 4Significantly decreased (cool colours) ROI-to-ROI connectivity in males with concussion in comparison to healthy male controls. ## Healthy females vs. females with concussion In females, with respect to the DMN, there was increased connectivity between the DMN seed and parts of the cuneal cortex (right), caudate, and thalamus, and reduced connectivity between said seed and primarily the paracingulate gyrus (right), occipital pole (right), hippocampus (right), and precentral gyrus (right). Hypoconnectivity was observed between the SMN seed and the cerebellum, parahippocampal gyrus (left), and vermis. The seed region of the SA had reduced connectivity with regions including the thalamus, cingulate gyrus, and cerebellum. Further, the FPN R was associated with increased functional connectivity with the precentral gyrus, and the FPN L was associated with reduced connectivity in the temporal pole (left), paracingulate gyrus (bilaterally) and superior frontal gyrus (bilaterally); see Fig. 5.Figure 5rs-fMRI differences between females with concussion and healthy females. Clusters (x, y, z) denote standard MNI coordinates at the center of cluster mass, and size represents number of voxels. Only one cluster survived analysis for the frontoparietal right; with multiple clusters identified for all other seeded networks. In females, groupwise ROI-to-ROI analyses showed that there was increased connectivity between ROIs in the cuneal cortex and cerebellum, as well as between the cuneal cortex and default mode network. There was also reduced connectivity between temporal brain structures and those in the occipital region in females with concussion compared to healthy females (Fig. 6).Figure 6Significantly increased (warm colours) and decreased (cool colours) ROI-to-ROI connectivity in females with concussion in comparison to healthy females. Per the ANCOVA, seed-based analyses identified one cluster of hyperconnectivity in the paracingulate gyrus relative to the FPN L seed (TFCE = 863.4, p-FWE = 0.002). No differences were observed per the ANCOVA analyses on ROI-to-ROI metrics. ## Discussion Our study is the first to report that in pediatric concussion, there are rs-fMRI disturbances observed in females that are not present in the males. To date, the majority of studies on rs-fMRI in pediatric concussion have either not studied females or had a small female representation (< $25\%$) in their clinical samples. Our findings, therefore, provide the first insight into functional disturbances in pediatric concussion by sex. In pediatric concussion, studies have attempted to link rs-fMRI disturbance to symptoms but have not found a clear relationship14,16,23. This may in part be attributable to symptom and rs-fMRI data not being disaggregated by sex in prior studies that have studied both of these measures. Our results show that following concussion, at approximately one-month post-injury, there are alterations in rs-fMRI activity in females (in comparison to their healthy age- and sex-matched peers) that are not observed in males. Symptom studies align with this, reporting sex-differences with respect to symptoms in pediatric concussion28,29,31–33. With a large evidence-base suggesting that symptom presentation differs in pediatric concussion by sex, and with the current study demonstrating sex-based rs-fMRI differences in children with concussion, there is reason to hypothesize that this variable symptom presentation has an underlying functional sex-specific neuropathology. Past studies that did not find a clear relationship between concussion symptoms and functional brain pathology may not have observed such an effect because their analyses were not stratified by sex, which (as shown in the present study) provides insights that mixed-sex analyses do not. Future studies should collect data on symptoms and rs-fMRI and stratify analyses by sex to better understand the relationship between these variables. Past studies that have identified differences in rs-fMRI activity between controls and those with a brain injury have been conducted in primarily male samples13,14,17,18,21. Therefore, these studies reported rs-fMRI differences that, with respect to percent representation within the sample, were largely driven by males. These findings do not reconcile with ours, given that we found minimal differences between healthy males and males with concussion with respect to their resting state fMRI activity. Further, in our study, we found differences when comparing females with concussion to healthy females that were otherwise masked when performing a 2 × 2 (Group × Sex) ANCOVA. Our findings, however, do converge with data on adults with PCS, where there were marked differences between healthy females and females with concussion24. With respect to pre-clinical research, animal studies are also discrepant with respect to sex effects of brain injury on, for example, brain morphology and functional outcomes in adolescent rodents64. Collectively, these data suggest that additional research is needed to understand how the neuropathology of concussion varies by sex. Data on the risk of secondary concussion by sex are limited, with the majority of studies to date focusing on predominantly male samples51. Our study, however, shows that in females, concussion can impact regions of the brain including the insular cortex, cuneal cortex, and thalamus, which are involved in processing of sensory and/or visual information as well as motor control52. While data on whether brain function remains impaired at medical clearance to return to activity are mixed53,54, our findings (wherein imaging was performed, on average, at the time when clinical recovery from concussion is expected to occur) suggest that functional brain impairments persist in females but not males. This suggests that resolution of functional pathologies in sensory and motor areas of the brain may be sex-dependent, and that return-to-sport guidelines stand to be informed by sex-specific data. A recent review on sex differences in concussion (pediatric and adult) identified that injury may lead to alterations in the hypothalamic-pituitary-ovarian axis, and subsequent hormonal fluctuations that may be responsible for the more severe symptoms in females55. rs-fMRI studies in other endocrinological populations demonstrate that alterations in functional brain activity are associated with abnormal hormonal responses56–59. With our study pointing to rs-fMRI disturbances in females with concussion that are not present in males, these functional brain changes may mediate or be related to a variable hormonal response that has an ultimate impact on the female concussion symptomology. Research that directly examines the relationship between brain activity, hormonal fluctuations, and symptomatology is required to build on this possibility. Large-scale studies (such as the Philadelphia Neurodevelopmental Cohort [PNC], which included nearly 1600 imaging assessments on those aged 8 to 21 years60,61) have shown that rs-fMRI patterns vary by sex throughout neurodevelopment. Other studies have also demonstrated sex differences with respect to functional brain activity (and brain morphology, more broadly), and that these differences relate to variable neurodevelopment of networks such as the DMN62. In our analyses, we compared concussed males and females to their respective age- and sex-matched control groups, thereby avoiding the potential confounding neurodevelopmental effects that may arise when comparing males to females directly. ## Limitations and future directions Future studies should include longitudinal assessments to determine if sex-differences in rs-fMRI activity in pediatric concussion vary from acute to the chronic stages of injury. This line of research, combined with existing evidence on pediatric symptom trajectories post-concussion, would help in understanding whether there is a functional neuropathology driving the symptom response longitudinally in children who have delayed recoveries. These studies should also perform rs-fMRI assessments in children who are asymptomatic, given that the broader literature has shown that neurophysiological disturbances can outlast symptoms63; understanding whether functional neuropathology outlasts clinical recovery can improve our understanding of the vulnerability of the brain to secondary injuries in a sex-specific manner. Moreover, additional experimental control in prospective research may help us better understand rs-fMRI changes in pediatric concussion. Specifically, pubertal status has a known impact on neurodevelopment, and we were unable to control for these effects in this secondary data analysis. Future research should consider measuring pubertal status (using the Tanner Stages, for example) and understanding its effects on resting state brain activity in pediatric concussion. Studying interactions between sex and pubertal status may also offer additional insight into rs-fMRI following pediatric concussion. ## Conclusions This is the first study to explicitly study and report on sex-specific rs-fMRI differences in pediatric concussion. At one-month post-injury, we report on differences in females with concussion (in comparison to their healthy peers) that are not apparent in males. This research further speaks to the need for more sex-specific analyses in concussion research. ## Supplementary Information Supplementary Table 1.Supplementary Table 2.Supplementary Legends. Supplementary Figure 1. The online version contains supplementary material available at 10.1038/s41598-023-30195-w. ## References 1. McCrory P. 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--- title: Intra-pituitary follicle-stimulating hormone signaling regulates hepatic lipid metabolism in mice authors: - Sen Qiao - Samer Alasmi - Amanda Wyatt - Philipp Wartenberg - Hongmei Wang - Michael Candlish - Debajyoti Das - Mari Aoki - Ramona Grünewald - Ziyue Zhou - Qinghai Tian - Qiang Yu - Viktoria Götz - Anouar Belkacemi - Ahsan Raza - Fabien Ectors - Kathrin Kattler - Gilles Gasparoni - Jörn Walter - Peter Lipp - Patrice Mollard - Daniel J. Bernard - Ersin Karatayli - Senem Ceren Karatayli - Frank Lammert - Ulrich Boehm journal: Nature Communications year: 2023 pmcid: PMC9968338 doi: 10.1038/s41467-023-36681-z license: CC BY 4.0 --- # Intra-pituitary follicle-stimulating hormone signaling regulates hepatic lipid metabolism in mice ## Abstract Inter-organ communication is a major hallmark of health and is often orchestrated by hormones released by the anterior pituitary gland. Pituitary gonadotropes secrete follicle-stimulating hormone (FSH) and luteinizing hormone (LH) to regulate gonadal function and control fertility. Whether FSH and LH also act on organs other than the gonads is debated. Here, we find that gonadotrope depletion in adult female mice triggers profound hypogonadism, obesity, glucose intolerance, fatty liver, and bone loss. The absence of sex steroids precipitates these phenotypes, with the notable exception of fatty liver, which results from ovary-independent actions of FSH. We uncover paracrine FSH action on pituitary corticotropes as a mechanism to restrain the production of corticosterone and prevent hepatic steatosis. Our data demonstrate that functional communication of two distinct hormone-secreting cell populations in the pituitary regulates hepatic lipid metabolism. Gonadotropes in the pituitary secrete follicle-stimulating hormone and luteinizing hormone to control gonadal function and fertility, but whether they exert actions on extra-gonadal organs is not fully understood. Here the authors report that gonadotropes regulate liver steatosis independent of the ovaries in mice. ## Introduction The anterior pituitary gland is a major regulator of mammalian body homeostasis and orchestrates communication between different organs via the coordinated release of distinct hormones1. Different pituitary cell types produce growth hormone, prolactin, thyroid-stimulating hormone, adrenocorticotropic hormone, or gonadotropins, which act on distinct target organs2. Previous studies suggest that most, if not all pituitary cells form structural and functional homotypic cell networks which are highly plastic in response to demands3. These cell networks are topologically intermingled and their wiring within the gland is spatially organized both between each other and between each cell network and the rich vasculature which provides the delivery of circulating inputs and hormone clearance into the bloodstream4. While communication between distinct intermingled endocrine cell types is of high functional relevance in other endocrine tissues including the pancreas5, whether this is also the case in the pituitary is not known. Developmental studies have provided evidence for the interaction of different pituitary cell networks during fetal life6,7 and shortly after birth8. While it has been shown that the corticotrope network plays an early developmental role in the establishment and organization of the gonadotrope network just before birth7, the physiological relevance of inter-network communication in animals of reproductive age has, however, remained unclear. While corticotropes regulate the secretion of stress hormones from the adrenal cortex, gonadotropes are essential for fertility and provide a functional link between the brain and the gonads by integrating neuroendocrine and steroid hormone signals9. Gonadotropes respond to gonadotropin-releasing hormone (GnRH) pulses delivered by neurons in the hypothalamus into the hypophyseal portal blood to secrete gonadotropins, luteinizing hormone (LH), and follicle-stimulating hormone (FSH)10. Subsequently, LH and FSH regulate gamete and sex-hormone production by the gonads. Gonadal sex steroids in turn provide direct and indirect feedback to gonadotropes, regulating reproductive cycle control in females. Recent studies have indicated that in addition to the gonads, other organs including bone, liver, and fat may also be directly regulated by gonadotropins, in particular by FSH11–13. Several clinical studies have reported an association of FSH with bone resorption as well as with metabolic disorders including obesity and hepatic steatosis, which occur independently of other hormones14–16. Since gonadotropins tightly regulate gonadal function, which itself exerts multiple effects on body homeostasis throughout the whole lifespan of the organism, it has been difficult to experimentally disentangle direct gonadotropin effects on extra-gonadal tissues from indirect effects. Previous studies using either gonadotropin knockout or overexpression mouse strains carrying global genetic modifications reported partially contradicting data in particular regarding FSH actions on extra-gonadal tissues11,17. Here, we combined complementary genetic approaches in mice to demonstrate that acute gonadotrope depletion in adults triggers distinct metabolic disorders including hepatic steatosis and bone loss. We show that while most of these phenotypes are indirect effects due to the hypogonadism caused by gonadotrope ablation, FSH plays a protective role in preventing hepatic steatosis that is independent of the ovary. We uncover paracrine FSH action on corticotropes as a mechanism mediating communication of two distinct cell populations in the pituitary that is essential to restrain the development of hepatic steatosis. ## Adult female mice develop metabolic disorders upon acute gonadotrope ablation To systemically dissect extra-gonadal gonadotropin actions and uncover potential communication between distinct endocrine cell networks within the pituitary, we acutely ablated gonadotropes in adult mice. To do this, we expressed the diphtheria toxin receptor (iDTR) specifically in these cells by generating a mouse strain bearing a gonadotrope Cre recombinase (GRIC)18 and Cre-dependent inducible diphtheria toxin receptor (iDTR)19 as well as GFP reporter (eR26-τGFP)20 alleles (GRIC/R26-iDTR/eR26-τGFP mice; Fig. 1a). Importantly, the iDTR requires the presence of Cre to be expressed but remains inactive in the absence of diphtheria toxin (DT), permitting normal sexual maturation and therefore bypassing developmental effects on the gonads observed in global gonadotropin knockout mice21,22. Two months post-DT injection, sexually mature Cre allele-positive (Cre+) adult males and females exhibited a near total ablation of gonadotropes (Fig. 1b) and profound hypogonadism (Fig. 1c), confirming the efficacy of this experimental approach. The numbers of the other hormone-secreting cell types in the anterior pituitary were not affected by gonadotrope ablation (Supplementary Figs. 1 and 2), demonstrating the specificity of our experimental approach. Acute gonadotrope ablation in adults also triggered a dramatic increase in body weight (Fig. 1d), impaired glucose tolerance (Fig. 1e), and decreased insulin sensitivity (Fig. 1f) exclusively in females (Fig. 1d–i), demonstrating sexually dimorphic effects on metabolism upon the severing of functional connectivity between the brain and the gonads. Strikingly, we also observed hepatic steatosis as early as 10 days after gonadotrope ablation in both sexes, which became progressively more severe over time (Fig. 1j, k). Plasma triglycerides were significantly increased in ablated compared with control females, while males showed a possible trend toward an increase but this was not significant (Supplementary Fig. 3). Plasma cholesterol levels were unchanged across all conditions. Next, we analyzed bone phenotypes in gonadotrope-ablated mice, as the bone was previously suggested to be regulated by gonadotropins11. We found that bone volume, mineral density, and trabecular number were all significantly reduced in femurs from gonadotrope-ablated mice irrespective of sex (Fig. 1l–p). In addition, cortical bone thickness and bone strength were decreased in female femurs after gonadotrope ablation (Supplementary Figs. 4 and 5). Taken together, these data demonstrate that acute gonadotrope loss in adults recapitulates major constituents of metabolic syndrome in female and, to a lesser extent, male mice. Fig. 1Acute gonadotrope ablation induces metabolic disorders in female mice.a Illustration depicting the strategy to acutely ablate gonadotropes via the injection of diphtheria toxin (DT) in mice selectively expressing the diphtheria toxin receptor in gonadotropes. b Number of GFP-positive cells within the pituitaries of DT- or saline-injected GRIC/R26-iDTR/eR26-τGFP mice and a number of follicle-stimulating hormone (FSH) or luteinizing hormone (LH) positive cells within the pituitaries of DT-injected Cre+ and Cre− (control) GRIC/R26-iDTR/eR26-τGFP mice. c Representative images of testes and female reproductive tracts from Cre+ and Cre− (control) GRIC/R26-iDTR/eR26-τGFP mice 2 months after DT injection. Body weight (d, g), glucose tolerance (e, h), insulin tolerance (f, i), and liver oil red O staining quantification (k) from gonadotrope-ablated (Cre+) and control (Cre−) female and male mice. j Representative images of liver oil red O staining from Cre+ GRIC/R26-iDTR/eR26-τGFP female mice 10 days or 2 months after DT injection (scale bars = 200 μm. Insets highlight the distribution of lipid droplets (filled arrowheads)). l Representative micro-computed tomography (μCT) images from gonadotrope-ablated (Cre+) and control (Cre−) female and male mice (filled arrowheads indicate lack of trabecular bone). m Anatomic sites for μCT bone measurements are indicated as blue (cortical bone) and red (trabecular bone) rectangles. Fractional bone volume (n), bone mineral density (o), and trabecular number (p) of trabecular bone from gonadotrope-ablated (Cre+) and control (Cre−) female and male mice. Error bars represent standard error mean. * = $P \leq 0.05$, = $P \leq 0.01$ and * = $P \leq 0.001.$ For statistical details, including individual p-values, see Supplementary Data 1. Source data are provided as a Source Data file. ## Hepatic steatosis in gonadotrope-ablated females is independent of the gonads To dissect the underlying mechanisms, we first asked whether any of the observed phenotypes were mediated by disrupted gonadal function. To address this question, we performed gonadectomy one week prior to diphtheria toxin injection in Cre+ and Cre− mice (Fig. 2a). Ovariectomy followed by DT injection led to a massive reduction of both LH and FSH plasma levels in Cre+ females (Supplementary Fig. 6) and triggered weight gain (Fig. 2a) and decreased insulin sensitivity (Fig. 2b) to a similar extent in Cre+ and Cre− females. Conversely, glucose tolerance was more significantly impaired in ovariectomized Cre+ than Cre- mice (Fig. 2c). Plasma triglycerides and cholesterol levels showed no significant differences between any group (Supplementary Fig. 7). Most notably, in the absence of gonadotrope ablation (Cre−), hepatic steatosis was virtually absent post-ovariectomy. These data demonstrate that the maintenance of systemic glucose homeostasis and hepatic lipid metabolism following the loss of ovarian function depends on intact gonadotropes. In males, no significant differences were found in body weight, insulin sensitivity, or glucose tolerance between castrated Cre+ and Cre− mice after DT injection (Fig. 2d–f); however, both groups developed hepatic steatosis (Fig. 2g, h), indicating that, in contrast to females, fatty liver in males is triggered by the loss of testicular function and revealing an additional sexually dimorphic effect of gonadotrope ablation on metabolism. Gonadectomy alone was sufficient to trigger a bone loss in both sexes, regardless of gonadotrope status (Fig. 2i–l and Supplementary Fig. 8), demonstrating that bone loss is mediated by gonadal dysfunction. Fig. 2Gonadal loss in females mimics metabolic syndrome but does not trigger hepatic steatosis.a, d Body weight, glucose tolerance (b, e), insulin tolerance (c, f), and liver oil red O staining quantification (g) and representative images (h scale bars = 50 μm, filled arrowheads indicate lipid droplets.) in gonadotrope-ablated (Cre+) and control (Cre−) ovariectomized (OVX) female and gonadectomized (GDX) male mice. Representative μCT images (i), fractional bone volume (j), bone mineral density (k), and trabecular number (l) of trabecular bone from gonadotrope-ablated (Cre+) and control (Cre−) ovariectomized (OVX) female and gonadectomized (GDX) male mice. Error bars represent the standard error of the mean. = $P \leq 0.01$ and * = $P \leq 0.001.$ For statistical details, including individual p-values, see Supplementary Data 1. Source data are provided as a Source Data file. ## Sex-steroid replacement rescues the majority of metabolic disorders but not hepatic steatosis in females To determine whether the phenotypes observed in gonadectomized mice were due to the loss of sex steroids or perhaps other substances released by the gonads, we performed gonadectomy together with hormone replacement followed by gonadotrope ablation (Fig. 3a). Treatment with either estradiol (females) or testosterone (males) abolished differences in glucose tolerance, insulin sensitivity, weight gain, and bone density in gonadectomized mice regardless of sex or whether gonadotropes were ablated (Fig. 3a–f, Fig. 3i–l and Supplementary Fig. 9). Consistent with the bone phenotype we observed, FSH receptor (Fshr) and LH receptor (Lhcgr) expression were low to undetectable in a murine monocyte cell line differentiated into osteoclasts or in primary murine osteoclasts (Supplementary Fig. 10a–d), suggesting that gonadotropins do not seem to act on bone via osteoclasts. Moreover, FSH did not significantly affect the RANKL-stimulated expression of osteoclast-specific genes (Supplementary Fig. 10e–l) in these cells. Sex steroid replacement also prevented hepatic steatosis in males but had no effect in females (Fig. 3g, h). These data demonstrate that the majority of cardinal features of metabolic syndrome can be precipitated by the absence of gonadal sex steroids; however, uniquely in females, liver steatosis appears to depend on the loss of gonadotropes. Fig. 3Sex-steroid replacement abolishes metabolic disorders but does not improve hepatic steatosis in gonadotrope-ablated female mice.a, d Body weight (a, d), glucose tolerance (b, e), insulin tolerance (c, f), and liver oil red O staining quantification (g) and representative images (h; scale bars = 50 μm, filled arrowheads indicate lipid droplets.) in gonadotrope-ablated (Cre+) and control (Cre−) ovariectomized (OVX) female and gonadectomized (GDX) male mice with estradiol (E) or testosterone (T) replacement, respectively. Representative μCT images (i), fractional bone volume (j), bone mineral density (k), and trabecular number (l) of trabecular bone from gonadotrope-ablated (Cre+) and control (Cre−) ovariectomized (OVX) female and gonadectomized (GDX) male mice with estradiol (E) or testosterone (T) replacement, respectively. Error bars represent standard error mean. * = $P \leq 0.05.$ For statistical details, including individual p-values, see Supplementary Data 1. Source data are provided as a Source Data file. ## Gonadotrope activation or FSH administration is sufficient to improve metabolic disorders including hepatic steatosis in female mice To corroborate these results, we employed a reverse complementary experimental approach and asked whether chronic chemogenetic activation of gonadotropes would relieve the symptoms of metabolic syndrome. To do this, we generated a mouse strain expressing a Gq-coupled DREADD specifically in gonadotropes (GRIC/eR26-DREADD/eR26-τGFP mice; Supplementary Fig. 11a). In this model, clozapine-N-oxide (CNO) triggers robust activation of Gq signaling in gonadotropes, as demonstrated by c-FOS expression (Fig. 4a). We next gonadectomized mice, followed one week later by concomitant administration of CNO in the drinking water and a high-fat diet (HFD; $22\%$ carbohydrates, $24\%$ protein, $54\%$ fat); a well-established model of metabolic syndrome23) (Fig. 4b). Remarkably, weight gain was significantly attenuated in female mice with activated gonadotropes relative to controls (Fig. 4b). Likewise, we found that glucose tolerance (Fig. 4c) and hepatic steatosis (Fig. 4d, e) were significantly improved by chronic gonadotrope activation, raising the question as to which gonadotropin might mediate these effects. We, therefore, performed multiplexed hormone analysis on OVX/HFD female mice with chronically administered CNO and found that FSH—but not LH—was significantly increased in response to chemogenetic gonadotrope activation (Fig. 4f and Supplementary Fig. 11b). To determine whether FSH indeed mediates these effects, we performed daily injections of FSH in ovariectomized wild-type mice on HFD. These injections were sufficient to attenuate weight gain (Fig. 4g), improve glucose tolerance, and improve hepatic steatosis (Fig. 4h, i), demonstrating the potential for FSH treatment to improve the symptoms of metabolic syndrome. Plasma triglyceride levels were significantly reduced in the FSH-treated group when compared to controls while cholesterol levels in these animals seemed somewhat lower, however, this was not significant (Supplementary Fig. 12). To gain mechanistic insight into how FSH treatment impinges on the development of liver steatosis, we performed transcriptome analyzes on livers from FSH-treated OVX/HFD females and compared them to control samples. We found differentially expressed genes to be enriched in pathways including cholesterol biosynthesis, FGFR2 ligand binding and activation, and GPCR ligand binding (Supplementary Fig. 13). To gain a detailed insight into transcriptomic changes on lipogenesis, fatty acid oxidation, and lipolysis, we specifically investigated the differentially expressed genes which are involved in lipid metabolism (GO0006629) (Supplementary Fig. 14a). We found that key genes in the control of lipid biosynthesis such as Elovl3, Egr1, Slc45a324–26 were significantly downregulated in the FSH treated group when compared with controls, meanwhile, genes which control lipid catabolism such as Cyp7a127 were significantly upregulated in the FSH treated group when compared with controls, consistent with the fatty livers seen in the FSH treatment group. Interestingly, we also found that genes previously reported to be upregulated by glucocorticoids (dexamethasone) in the liver28 were downregulated upon FSH treatment (Supplementary Fig. 14b). These results provide molecular insight into the mechanism underlying liver steatosis in these animals and raise the possibility that FSH may regulate liver steatosis by affecting glucocorticoid signaling in the liver. Fig. 4Gonadotrope activation or FSH administration is sufficient to improve metabolic disorders including hepatic steatosis in female mice.a Representative images of pituitaries from PBS (left) and clozapine-N-oxide (right) i.p. injected GRIC/eR26-DREADD mice. The expression of cFos (shown in green) was used as a marker for cellular activation. Gonadotropes are shown in red, corresponding to the expression of LH and FSH. DAPI is shown in blue (scale bars = 50 μm in full-size images, 20 μm in insets, and filled arrowheads indicate activated gonadotropes.). b Body weight, c glucose tolerance, d liver oil red O staining quantification, and e representative images (scale bars = 100 μm, filled arrowheads indicate lipid droplets) in clozapine-N-oxide (CNO) treated Cre+ (chemogenetically-activated) and Cre− (control) gonadectomized mice on a high-fat diet (HFD). f Plasma FSH levels from CNO-injected Cre+ (chemogenetically activated) and Cre− (control) OVX mice on HFD. g Body weight, h glucose tolerance, and i liver oil red O staining quantification in FSH-treated and saline-treated OVX mice on HFD. Error bars represent standard error mean. * = $P \leq 0.05$, = $P \leq 0.01$ and * = $P \leq 0.001.$ For statistical details, including individual P-values, see Supplementary Data 1. Source data are provided as a Source Data file. ## Disrupting FSHR signaling in the pituitary increases corticosterone levels and induces hepatic steatosis To uncover the site of action for FSH in mediating these effects, we performed RT-PCR for the Fshr on major tissues prepared from female mice. We detected Fshr in only two tissues, the ovary and the pituitary (Supplementary Fig. 15), which we further verified via RNA-scope (Fig. 5a) and RT-qPCR (Fig. 5b), but not in the liver. In addition, a re-analysis of bulk RNA-seq data confirmed that FSHR is also not expressed in the human liver (Supplementary Fig. 16)29. To understand the role of FSH signaling within the pituitary, we established an in vitro whole pituitary assay in which we blocked the action of FSH by incubating pituitaries taken from diestrus (post-ovulation) females with a monoclonal antibody targeting FSH prior to stimulation with GnRH to release the gonadotropins (Fig. 5c). Among all six of the hormones produced by the anterior pituitary, we found that adrenocorticotropic hormone (ACTH) exclusively was significantly elevated as a result of FSH sequestration by the anti-FSH antibody (Fig. 5d and Supplementary Fig. 17). Vice versa, we also found that ACTH was significantly reduced after chronic gonadotrope activation in vivo (Supplementary Fig. 11b). These findings were consistent with our RNA-scope experiments in which we observed Fshr expression in corticotropes (Fig. 5e). Furthermore, re-analysis of pituitary single-cell RNA-seq data30 confirmed that Fshr is also expressed in human corticotrope cells (Supplementary Fig. 18).Fig. 5Paracrine FSH action in pituitary corticotropes in female mice.a Expression levels of Fshr in the pituitary, ovary and liver from adult female mice were measured using RNA scope (scale bars = 10 μm, filled arrowheads indicate positive signals) and b RT-qPCR. c Schematic representation of in vitro pituitary assay. d ACTH levels quantified from in vitro GnRH-stimulated pituitaries incubated with an anti-FSH antibody or IgG (control). e Colocalization of Fshr mRNA (RNAscope, filled arrowhead) and ACTH (IF) (scale bar = 100 μm). f Plasma corticosterone levels from control and experimental female mice. Error bars represent the standard error of the mean. * = $P \leq 0.05$, = $P \leq 0.01$ and * = $P \leq 0.001.$ For statistical details, including individual P-values, see Supplementary Data 1. Source data are provided as a Source Data file. Corticotropes release ACTH to control corticosterone release by the adrenal gland and corticosterone was previously implicated in the regulation of steatosis28. We, therefore, hypothesized that FSH reduces corticosterone release via paracrine signaling in the pituitary leading to decreased ACTH release and thus reduced steatosis. Consistent with this, we found significantly elevated ACTH levels in gonadotrope-ablated intact females (Supplementary Fig. 19). ACTH levels after OVX were more variable and also increased, some trend towards further elevated ACTH levels after gonadotrope ablation was, however, not statistically significant. Importantly, gonadotrope ablation either in intact or ovariectomized females resulted in significantly elevated plasma corticosterone concentrations, whereas FSH injections were sufficient to significantly reduce plasma corticosterone levels in ovariectomized mice on HFD (Fig. 5f). To functionally analyze paracrine FSH action, we used a CRISPR–Cas9 system to specifically disrupt FSH receptor expression in the pituitary. We stereotaxically injected an adeno-associated virus (AAV) to deliver Cas9 in combination with a guide RNA targeting Fshr into the pituitary (Fig. 6a). Strikingly, even partial knockout of Fshr (Fig. 6b, c) within the pituitary was sufficient to trigger hepatic steatosis in AAV-injected female mice, when compared to controls (Fig. 6d, e). In contrast, Fshr knockout within the female pituitary after adrenalectomy did not result in a fatty liver phenotype (Fig. 6f). Consistent with this, we found corticosterone levels to be elevated in the adrenal-intact pituitary-specific Fshr knockout females (Supplementary Fig. 20). Taken together, these data demonstrate that paracrine FSH action on corticotropes in the pituitary regulates hepatic lipid metabolism by reducing ACTH and subsequently corticosterone secretion. Fig. 6FSH receptor knock-out in the pituitary induces hepatic steatosis in females.a Illustration depicting the strategy to specifically disrupt FSH receptor expression in the pituitary via stereotaxic delivery of an adeno-associated virus (AAV) encoding Cas9 and a guide RNA targeting Fshr into the pituitary. b Percentage of unmodified and modified alleles and c distribution of identified alleles around the predicted cleavage site determined by CRISPResso2 of the pituitary DNA from an AAV5-saCas9-Fshr injected mouse. d Representative images (scale bars = 50 μm) and e quantification of liver oil red O staining from AAV-Cas9 (with ($$n = 3$$ mice) or without ($$n = 5$$ mice) guide RNA against follicle-stimulating hormone receptor; Fshr) pituitary-injected mice. f Liver oil red O staining quantification in AAV-Cas9 (with guide RNA against follicle-stimulating hormone receptor; Fshr) pituitary-injected adrenalectomized or sham-operated female mice. g Model of gonadotrope actions on the gonads and the pituitary (filled blue arrow illustrates gonadotrope actions on the gonads; dashed blue arrows indicate indirect gonadotrope effects on pancreas and bone via sex steroids released by the gonads; filled red arrow indicates corticotrope action on the adrenal cortex (via paracrine FSH intra-pituitary action); dashed red arrow indicates the effect of elevated corticosterone levels on the liver). Error bars represent the standard error of the mean. ** = $P \leq 0.01.$ For statistical details, including individual P-values, see Supplementary Data 1. Source data are provided as a Source Data file. ## Discussion To analyze extragonadal gonadotropin action(s), we combined complementary genetic strategies to manipulate gonadotrope cells in mice. These experiments yielded several important results. First, adult female but not male mice develop metabolic disorders upon acute gonadotrope ablation. Second, hepatic steatosis in gonadotrope-ablated females is independent of the gonads. Third, sex-steroid replacement rescues the majority of metabolic disorders but not hepatic steatosis in females. Fourth, gonadotrope activation or FSH administration is sufficient to improve metabolic disorders including hepatic steatosis in female mice. Finally, disrupting FSHR signaling in the pituitary increases corticosterone levels and induces hepatic steatosis. Taken together, our data uncover paracrine FSH action within the pituitary gland as a mechanism to restrain the development of hepatic steatosis. While we show here that gonadotropes act via paracrine FSH action on corticotropes in adults, previous experiments had suggested that the establishment of the corticotrope network controls the anatomical organization of the gonadotrope network during fetal development7, raising the possibility that corticotropes communicate with gonadotropes in utero. Functional communication between these two endocrine cell populations may also provide an explanation for why gonadotropes and corticotropes are intermingled and invariantly positioned in close proximity to each other in the gland7. FSH plays a protective role in the liver restraining the development of hepatic steatosis through a previously unanticipated paracrine mechanism in the pituitary. These data provide clear evidence in the pituitary in vivo for an emerging endocrine paradigm; the structural and functional organization of one endocrine cell type can impinge on the functioning of other endocrine cells within the same gland. Other important examples include pancreatic islets in which alpha glucagon cells act on beta cells5. Functional communication between gonadotropes and corticotropes might represent the first of several paracrine systems in the pituitary. Consistent with this, re-analysis of human pituitary single-cell RNA-seq data30 confirmed the expression of heterotypic hormone receptors in the distinct endocrine pituitary cell types (Supplementary Fig. 21). Our data now set the stage to look at communication between the cell networks producing growth hormone, prolactin or thyroid-stimulating hormone in the anterior pituitary gland. Future experiments will also need to address the question of whether FSH also acts on endocrine pituitary cell networks other than corticotropes. Progress analyzing inter-network communication should also be facilitated by the technical advances presented here including stereotactic injections of Cre-dependent AAVs carrying specific guide RNAs into the pituitary to achieve gene conditional knockouts in this tissue representing a major temporal advantage over classical gene targeting techniques31. Furthermore, an adaptation of this technique to include several guide RNAs should allow the knockout of multiple genes simultaneously, providing an additional temporal and spatial advantage and also allowing a reduction of the number of animals needed for multiple gene knockouts. Future studies will address the mechanism(s) underlying the sexually dimorphic effects on metabolism (increase in body weight, impaired glucose tolerance, and decreased insulin sensitivity in females, but not in males) upon acute gonadotrope ablation in adults. Why does FSH not regulate hepatic steatosis in male mice? To address this question, we analyzed Fshr expression in males. Strikingly, and in contrast to females, we did not detect Fshr expression in the pituitary in males (Supplementary Fig. 22), suggesting that FSH does not exert paracrine actions in males and providing a molecular mechanism explaining why FSH does not regulate hepatic steatosis in male mice. Our data do not support the previous model of FSH action in bone. Instead, by using complementary approaches in mice, we demonstrate unequivocally that gonadotropins influence bone metabolism indirectly via sex steroid production and not by direct action on bone. Loss of sex hormones was previously described to contribute to bone loss; however, direct FSH effects on bone were debated32,33. While osteoporosis after gonadectomy had been attributed to decreased sex hormone levels34, several studies challenged this view by reporting that FSH directly regulated bone metabolism. Global FSHβ or FSHR knock-out in female mice resulted in hypogonadism without bone loss, consistent with a protective role for FSH on bone11. Combined with in vitro studies, the authors speculated that this effect of FSH on bone metabolism is direct and independent of estrogen. One caveat we need to consider is that FSHβ or FSHR were removed in the germline in these animals. Since FSH signaling is one key regulator of sexual maturation, a global knock-out of FSHβ or FSHR could have developmental effects including compensation in these models. Therefore, the reported bone phenotype could for example result from elevated LH and/or testosterone levels as reported35,36. Reproductive axis dysfunction had previously been implicated in the development of metabolic disorders. For example, in women with polycystic ovary syndrome (PCOS), aberrantly elevated LH secretion results in anovulation and hyperandrogenism, as a result of elevated testosterone production by ovarian theca cells37. Nearly half of all women with PCOS are affected by metabolic disorders38 including hyperinsulinemia and insulin resistance and nonalcoholic fatty liver disease (NAFLD), however, whether PCOS is the result of metabolic disorders or vice versa PCOS triggers metabolic disorders is still not well understood. The acute ablation of gonadotropes in adults, which shuts down the HPG axis, clearly induced metabolic disorders in this experimental setup. This may provide insights to understand the association between reproductive disorders, including PCOS, and metabolic disorders. Furthermore, men with congenital testosterone deficiency as a result of Klinefelter syndrome (47, XXY) are four times more likely to develop metabolic syndrome39. Suppressing the reproductive axis in men using GnRH analogs, as for the treatment of prostate cancer, triggers weight gain, bone loss, and insulin resistance40,41. Our findings that FSH injection reduces weight gain and improved glucose tolerance in the absence of either ovarian or supplemented estradiol highlight the potential clinical benefit of FSH receptor agonists for the treatment of metabolic syndrome and also open up the possibility of drug repurposing for FSH. ## Generation of the Rosa26-NLSiRFP720-2A-Gq (eR26-DREADD) knock-in mice DREADD mice were generated by homologous recombination in mouse embryonic stem (ES) cells using a targeting construct designed to insert a CAGS promoter (CMV enhancer plus chicken ß-actin promoter)-driven NLSiRFP720-2A-Gq receptor (DREADD receptor) within the first intron of the Rosa26 gene locus. This encodes both an infrared fluorescent protein, which is directed to the cell nucleus, and a Gq-coupled receptor, which can be specifically activated by CNO administration. To ensure that this expression is Cre-dependent, floxed strong transcriptional stop signals (three SV40 polyA signals) are present in such a way that the CAGS promoter can only drive expression following Cre-dependent removal of the stop signals. Correct insertion of the NLSiRFP720-2A-Gq receptor construct was verified using Southern blot analysis as follows. DNA was extracted from tail tip biopsies using lysis buffer containing 0.1 mg/mL proteinase K (1 mg/mL was used for extraction from ES cells). Following extraction, genomic DNA was digested overnight with EcoRI and run on a $0.7\%$ agarose gel, then transferred to a nylon membrane by capillary transfer and screened by hybridization of a 491 bp 32P-labeled probe complementary to sequences located 5′ to the 5′ homology arm of the targeting construct. Probe hybridization produces a 15.6-kb band from the wild-type allele, whereas the correctly targeted allele generates a 5.8-kb band. Correctly targeted ES cells were injected into C57BL/6J blastocysts to generate male chimeras that were backcrossed to C57BL/6J females to produce heterozygous Rosa26-NLSiRFP720-2A-Gq mice. Mice were then further crossed to produce a homozygous colony. Homozygous Rosa26-NLSiRFP720-2A-Gq mice were crossed with appropriate Cre-expressing lines to generate offspring in which specific cell populations can be activated by CNO. Mice were maintained on a mixed genetic background of 129S × C57BL/6J. The genotypes of the Rosa26-NLSiRFP720-2A-Gq mice were confirmed by PCR using the primer sequences: 5-GGAAGCACTTGCTCTCCCAAAG-3′ (common forward primer); 5′-GGGCGTACTTGGCATATGATACAC-3′ (DREADD allele reverse primer) and 5′-CTTTAAGCCTGCCCAGAAGACTC-3′ (wildtype allele reverse primer). Wild-type offspring were confirmed by the presence of a single band of 256 bp. For the Rosa26-NLSiRFP720-2A-Gq allele, heterozygous offspring gave two products of 256 and 495 bp, whereas homozygous offspring were identified by the presence of one band at 495 bp. ## Mice All mice were kept under SPF housing with food (V1534-300, Ssniff) and water ad libitum. Animal care and experimental procedures were conducted under the approval of the animal welfare committees of Saarland University and Xi’an Jiatong University. Mice were monitored on a daily basis by trained personnel. To label and ablate or pharmacologically activate Gnrhr-expressing cells, we used the GnRHR-IRES-Cre (GRIC) knock-in mouse strain18 crossed either with eROSA26-τGFP (eR26-τGFP)20 and ROSA26-DTR (R26-DTR)19 animals, or with eROSA26-τGFP (eR26-τGFP) and eROSA26-DREADD (eR26-DREADD) animals. In the resulting GRIC/eR26-τGFP/R26-DTR or GRIC/eR26-tGFP/eR26-DREADD mice, Cre recombinase is expressed under the control of the Gnrhr promoter. Cre-mediated recombination results in the removal of a transcriptional stop cassette from the ROSA26 locus and subsequent constitutive reporter expression in GnRHR cells. All mice were kept on the same mixed 129 × C57BL/6J genetic background. ## Gonadectomy and sex hormone replacement Adult mice (8–12 week-old female mice or 14-16 week-old male mice) were bilaterally ovariectomized or castrated. For sex hormone replacement, a 2 cm length of silastic tubing (inner diameter: 1.58 mm; outer diameter: 3.18 mm) containing 36 μg 17β-estradiol/mL (E2758, Sigma)42 in sesame oil or testosterone powder43 (T1500, Sigma) were inserted on the dorsal aspect of the animal’s neck immediately after gonadectomy. Mice were allowed to recover for 1 week before further treatment. For ablation experiments, 8- to 10-week-old female mice or 14- to 16-week-old male mice were intraperitoneally injected with 20 ng/g bodyweight diphtheria toxin (DT; 322326, EMD Millipore) twice with a 3-day interval between injections. For activation experiments, 2.5 mg of clozapine-N-oxide (CNO; Tocris Bioscience) in 200 ml of drinking water was administered to the mice with a high-fat diet ($22\%$ carb, $24\%$ protein, $54\%$ fat, E15742-347, Ssniff). For FSH administration, mice with a high-fat diet received daily 30 IU/kg i.p FSH (GONAL-f, Merck) injections. Mice were weighed and monitored daily before being euthanized as experimental mice. Mice were humanely euthanized if postsurgical complications progressed to a pre-defined humane endpoint at which the mice started to suffer. ## Glucose and insulin tolerance test An intraperitoneal glucose tolerance test (ipGTT) was performed in mice fasted for 16 h. Briefly, the blood was collected from the tip of the tail and blood glucose was measured before (0 min) and after (30, 60, 90, and 120 min) glucose administration (2 g/kg body weight, i.p.) using a digital glucometer (Accu-Check Performa). Intraperitoneal insulin tolerance test (ipITT) was performed in mice fasted for six hours and the blood glucose was measured before (0 min) and after (30, 60, and 90 min) insulin administration (Pharma Gerke Arzneimittelvertrieb) 0.5 U/kg body weight, i.p. ## Transcardial perfusion and immunostaining Mice were perfused, and tissues were sectioned as previously described except cardiac puncture was performed to harvest blood for plasma preparation prior to PBS perfusion44. ## Immunohistochemistry Immunostaining was performed as previously described44. In brief, pituitaries were sectioned with 14 μm thickness, and every 10th section was stained. Antisera used were as follows: rabbit anti-LH (1:5000, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)), guinea pig anti-FSH (1:5000, NIDDK), guinea pig anti-ACTH (1:1000, NIDDK), guinea pig anti-GH (1:1000, NIDDK), guinea pig anti-TSH (1:1000, NIDDK), rabbit anti-prolactin (1:1000, NIDDK), rabbit anti-c-Fos (1:500, #2250, Cell Signaling Technology), chicken anti-GFP (1:1000, #A10262 ThermoFisher), goat anti-chicken Alexa 488 (1:500, Invitrogen, #A11039), goat anti-rabbit Cy3 (1:500, Jackson ImmunoResearch, #711-165-152) and goat anti- guinea pig Cy5 (1:500, Jackson ImmunoResearch, #706-175-148). Images were captured using a ZEISS Axio Scan Z1. The number of positive cells was multiplied by 10 to estimate the cell number in the whole pituitary. ## RNA-seq Transcriptomic analyses were performed as previously described45. In brief, total RNA from liver tissue was purified by using the RNeasy Plus Mini kit (QIAGEN) according to the manufacturer’s instructions. RNA samples with an RNA integrity number greater than eight were used to build RNA-seq libraries. One microgram of total RNA for each sample was used to build libraries employing the NEB Next Ultra RNA Library preparation kit (Ipswich). The library was sequenced in the Illumina Hiseq2500 platform with 2× 100-bp paired-end reads. After alignment, transcripts with an absolute value of log2 (fold change) larger than 1 and a q value below 0.05 were considered to be differentially expressed. Gene Set Enrichment Analysis (GSEA) was performed to functionally study the transcriptomic changes. ## RNAscope in situ hybridization Fshr mRNA expression was assessed in the pituitary, liver, and ovary from adult female mice using RNAscope 2.5 High-definition Assay-RED (Advanced Cell Diagnostics, Hayward, CA), according to the manufacturer’s instruction as previously described46. The Fshr probe used is designed to target transcript NM_013523.3 with a target sequence spanning nucleotides 554–1487. Probes targeting the DapB gene from Bacillus subtilis were used as a negative control. In brief, endogenous peroxidase activity was blocked with an RNAscope hydrogen peroxide solution. Tissues were permeabilized with RNAscope protease plus. Sections were then hybridized with either the Fshr probe or the negative control probe. This was followed by a series of amplification incubation steps. Finally, the hybridization signals were detected by detection reagents. Nuclei were stained with bisbenzimide. Images were captured using a ZEISS Axio Scan Z1. Positive hybridization was determined using Advanced Cell Diagnostic’s RNAscope scoring guidelines. To determine the colocalization of Fshr mRNA and ACTH, immunolabelling of ACTH was performed on the same pituitary sections after RNAscope in situ hybridization. ## μCT analysis Femora were dissected from mice, stored at −80 °C, and then scanned by high-resolution μCT (Skyscan 1176, Bruker MicroCT, Kontisch, Belgium). The scanner was set at a voltage of 50 kV, a current of 200 μA, and an isotropic resolution of 9 μm by using a 0.5 mm aluminum filter. We used image reconstruction software (NRecon (version 1.6.10.6)), orientation software (DataViewer (1.5.1.2)), data analysis software (CTAn (version)1.16.4.1+), and three-dimensional model visualization software (CTVox (version 3.2.0r1294)) in order to analyze the parameters of the distal femoral metaphyseal trabecular bone. Trabecular bone was analyzed over 200 slices, starting with 50 slices distal from the growth plate. The trabecular bone volume fraction (BV/TV) and trabecular number (Tb. N) were analyzed for trabecular bone. The cortical bone thickness was analyzed for cortical bone. Bone mineral density was estimated using calcium hydroxyapaptite (CaHA) phantoms of known densities. ## Cloning and guideRNA selection Potential guideRNA (gRNA) sequences were identified using Chopchop47 for the Fshr gene. From the identified gRNAs, an optimal sequence was selected to have the lowest possibility of off-target events whilst having a high predicted efficiency and frameshift likelihood at the targeting site after analysis with Cas-OFFinder48 and CRISPOR49. The following gRNA was selected for targeting; Fshr: 5′-GAGATTTGTGCTCACCAAGCT. This gRNA was generated as a primer dimer and then ligated into pX601-AAV-CMV (a gift from Feng Zhang50; Addgene plasmid #61591) using the BsaI sites present within this plasmid. This inserted the gRNA sequence subsequent to a U6 promoter and immediately upstream of the gRNA scaffold sequence required for correct interaction with the Cas9 protein. The saCas9 sequence was also present within this plasmid under the control of the strong CMV enhancer and promoter. Following cloning, all elements were verified by sequencing of the entire region contained between the two ITR sites. For the production of a control virus, the unmodified pX601-AAV-CMV plasmid without gRNA was used. ## AAV vector production Both the gRNA and control viruses were produced using the triple transfection helper-free method. This involved transfecting HEK293T cells in culture with 3 plasmids in a 1:1:1 ratio, the first containing essential viral genes such as E2 and E4 (pAdDeltaF6 was a gift from James M. Wilson; Addgene plasmid # 112867), the second which facilitates the generation of serotype 5 AAV vectors (pAAV$\frac{2}{5}$ was a gift from Melina Fan; Addgene plasmid # 104964) and the third, which dictates the packaged contents of the virus particles. Transfection was undertaken when the cells reached 60-$70\%$ confluency using a 4:1 (v:w) ratio of Polyethylenimine (PEI) to plasmid DNA. Sixty to seventy-two hours after transfection, cells were pelleted and processed to recover the virus. Virus samples were then subjected to purification through an iodixanol gradient before desalting and concentration using a centrifugal filter (MWCO 100). Viral titer was measured by qPCR analysis with primers specific to the ITR region of the packaging plasmid (fwd ITR primer: 5′-GGAACCCCTAGTGATGGAGTT, rev ITR primer: 5′-CGGCCTCAGTGAGCGA). ## Stereotaxic injection Stereotaxic injections were performed as previously described44. Briefly, six injections with 0.5μl per injection of AAV virus (AAV5-saCas9-Fshr or AAV5-saCas9) were injected into the pituitary. The coordinates were −2.55 mm, −2.7 mm, and −2.95 mm antero-posterior, ±0.6 mm lateral to the midline, 300 µm above the sella turcica. Mice were humanely euthanized if postsurgical complications progressed to a pre-defined humane endpoint at which the mice started to suffer. ## Adrenalectomy Bilateral adrenalectomy was performed in wild-type adult female mice. The adrenalectomized animals were supplied with $0.9\%$ saline to maintain salt levels. One week after surgery, the AAV virus (AAV5-saCas9-Fshr) was stereotaxically injected into the pituitary to knock down the expression of Fshr. Mice were humanely euthanized if postsurgical complications progressed to a pre-defined humane endpoint at which the mice started to suffer. ## Oil red O (ORO) staining Liver tissue sections at −80 °C were first allowed to reach room temperature over 30 min. Then the slides were incubated with $60\%$ (v/v) isopropanol for five minutes, followed by 10 min incubation with fresh ORO (Oil Red O, O0625, Sigma) working solution (7:5 dilution of ORO stock solution with water; ORO stock solution: 300 mg ORO in 100 ml $100\%$ isopropanol). Then two washing steps for three minutes in $60\%$ isopropanol with 100 rpm shaking were performed. After three washing steps of one minute each in water, slides were mounted with Fluormount G onto Superfrost Plus microscopy slides. For visualization, slides were imaged using a ZEISS Axio Scan Z1 with ZenBlue software (supported by the DFG INST $\frac{256}{434}$-1 FUGG) with 20× magnification. We noticed ORO signals were not evenly distributed in sections. Therefore at least three complete sections per mouse liver were stained and the ORO units (ORO area/tissue area * 100) were calculated by Image J as previously described51. ## In vitro pituitary assay Intact pituitaries were removed from adult females at diestrus, then incubated with 1 ml DMEM in a 12-well plate at 37 °C, $5\%$ CO2, with constant shaking for 1 h. After resting, pituitaries were incubated with 1 ml of fresh DMEM with either mouse monoclonal FSH antibody (10 μg/ml; MIF2709, Invitrogen) or mouse IgG (10 μg/ml; MAB002, R&D Systems) for 1 h. 50 μl of culture medium was taken as a 0-time point. Then pituitaries were stimulated with 100 nM GnRH (L7134, Sigma-Aldrich). Fifty microlitres of culture medium were removed at each time point. ## Hormone measurements Pituitary hormone measurements were performed with a Milliplex MAP mouse pituitary magnetic bead panel (RPTMAG-86K; Millipore, Billerica, MA) on a Luminex Magpix (Austin, TX) with Milliplex Analyst software according to the manufacturer’s protocol. ## Corticosterone measurements Circulating corticosterone was measured via colorimetric ELISAs according to the manufacturer’s protocol (ab10882, Abcam). The assays were done in duplicate with an inter and intra-assay variability of $10.6\%$ and $6.3\%$, respectively. ## CRISPR efficiency verification Eight weeks after the AAV injection, pituitaries and livers were removed from the mice. DNA was extracted from pituitaries and then targeted amplification was performed via PCR with the primers against Fshr (primers used in PCR are described in Supplementary Table 1). Amplicons were generated using region-specific primers with the Illumina universal adapter sequences. PCR products were purified with Agencourt AmpureBeads and indexed in a second PCR using Illumina TruSeq adapters. After the final AmpureBead Purification amplicons were pooled in an equimolar ratio and sequenced on a MiSeq (Illumina) using the MiSeq Reagent Kit v2 (500-cycles) in paired-end mode, aiming at 10,000 reads per amplicon. The sequence was then analyzed by CRISPResso252. ## Osteoclast differentiation in RAW 264.7 cells and primary murine monocytes RAW 264.7 cells (ATCC TIB-71) were seeded in 96-well plates at a density of 1000 cells/well in Dulbecco’s Modified Eagle Medium (DMEM, Wisent Inc, 319-005-CL), containing $10\%$ fetal bovine serum (FBS), and $1\%$ Antibiotic-Antimycotic solution (ThermoFisher Scientific, 15240062) at 37 °C/$5\%$ CO2 in a humidified water-jacketed incubator. After overnight incubation, cells were treated with 0, 35, 70, or 140 IU/L human FSH (hFSH, 17.5 IU/μg, R&D Systems, 5925-FS-010) in the presence of 50 ng/mL receptor activator of nuclear factor κ B ligand (RANKL, Peprotech, 315-11) for seven days. The medium was changed every other day. Femora and tibiae from Fshrfx/fx female mice53 were collected for bone marrow cell extraction closely following a standard protocol54 adapted for 48-well plates. Briefly, the animals were euthanized and one animal was used per experimental replicate. Femora and tibiae were collected and kept in 1× phosphate-buffered saline (1X PBS, Wisent Inc., Canada) on ice. Small cuts (approximately 1–2 mm) were made at both the proximal and distal ends of the bones before placing each in a 200 μL tip with 2 cm removed from both ends within 1.5 mL tubes. Tubes were centrifuged at 10,000×g for 15 s at room temperature. Bone marrow was pulled to the bottom of the tubes before being transferred to a 50 mL tube with 3 mL filtered RBC lysis buffer (Geneaid, Taiwan) and incubated at room temperature for 5 min. The reaction was stopped by adding 27 mL 1× PBS and tubes were centrifuged at 500×g for 5 min at room temperature. Supernatants were aspirated and cells were resuspended in 10 mL Alpha Modified Eagle’s Medium (AMEM; Wisent Inc., Canada) containing $10\%$ FBS, $1\%$ Antibiotic–Antimycotic solution, and 25 ng/mL M-CSF (Peprotech, USA) prior to culturing in 10 cm dishes. After overnight incubation, media with non-adherent cells (enriched in the myeloid lineage) were collected and seeded in 48-well plates at a density of 2 × 105 cells/well with 25 ng/mL M-CSF in the absence or presence of 50 ng/mL RANKL and 0, 70, or 140 IU/L hFSH for 4 days. Differentiation media were refreshed on day 3. ## Reverse transcription and real-time PCR To analyze Fshr expression in mouse tissues, total RNA was purified from adult female tissues using the RNeasy Plus Micro kit (Qiagen) according to the manufacturer’s instructions. For reverse transcription PCR, genomic DNA removal and cDNA synthesis were performed using a Maxima H Minus First Strand cDNA-Synthesis kit (Thermo Fisher). PCR was performed using a MyTaq Red Mix kit (Bioline). Real-time PCR was performed using a SensiFAST SYBR No-ROX one-step kit (Bioline) with a CFX-96 real-time PCR detection system (Bio-Rad Laboratories) as described previously45. To examine gene expression in cultured cells, total RNA from cells was extracted in TRIzol (Life Technologies, USA; Catalog No. 15596026) following the manufacturer’s instructions. 200 ng of RNA per sample was used for reverse transcription. Quantitative PCR was performed on a Corbett Rotorgene 600 instrument (Corbett Life Science) using BrightGreen 2× qPCR MasterMix (ABM, Mastermix-S). Primers used in reverse transcription and real-time PCR are described in Supplementary Table 1. ## Statistical analysis GraphPad Prism was used to perform the statistical analysis. Data are presented as mean ± standard error of the mean. For body weight, GTT, and ITT experiments, a two-tailed Student’s t test followed by Wilcoxon signed-rank test was performed for individual time points. For two group comparisons, a two-tailed Student’s t test was used. Statistical details are described in Supplementary Data 1. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Supplementary information Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-36681-z. ## Source data Source Data ## Peer review information Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. ## References 1. Friesen H, Astwood EB. Hormones of the anterior pituitary body. N. Engl. J. Med. (1965.0) 272 1328-1335. DOI: 10.1056/NEJM196506242722506 2. Dingman JF. Pituitary function. N. Engl. J. Med. (1971.0) 285 617-619. DOI: 10.1056/NEJM197109092851107 3. Hodson DJ, Molino F, Fontanaud P, Bonnefont X, Mollard P. Investigating and modelling pituitary endocrine network function. J. 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--- title: On the potential of transauricular electrical stimulation to reduce visually induced motion sickness authors: - Emmanuel Molefi - Ian McLoughlin - Ramaswamy Palaniappan journal: Scientific Reports year: 2023 pmcid: PMC9968344 doi: 10.1038/s41598-023-29765-9 license: CC BY 4.0 --- # On the potential of transauricular electrical stimulation to reduce visually induced motion sickness ## Abstract Perturbations in the autonomic nervous system occur in individuals experiencing increasing levels of motion sickness. Here, we investigated the effects of transauricular electrical stimulation (tES) on autonomic function during visually induced motion sickness, through the analysis of spectral and time-frequency heart rate variability. To determine the efficacy of tES, we compared sham and tES conditions in a randomized, within-subjects, cross-over design in 14 healthy participants. We found that tES reduced motion sickness symptoms by significantly increasing normalized high-frequency (HF) power and decreasing both normalized low-frequency (LF) power and the power ratio of LF and HF components (LF/HF ratio). Furthermore, behavioral data recorded using the motion sickness assessment questionnaire (MSAQ) showed significant differences in decreased symptoms during tES compared to sham condition for the total MSAQ scores and, central and sopite categories of the MSAQ. Our preliminary findings suggest that by administering tES, parasympathetic modulation is increased, and autonomic imbalance induced by motion sickness is restored. This study provides first evidence that tES may have potential as a non-pharmacological neuromodulation tool to keep motion sickness at bay. Thus, these findings may have implications towards protecting people from becoming motion sick and possible accelerated recovery from the malady. ## Introduction Motion sickness is an age-old physiological malady associated with epigastric discomfort, nausea and, in its severity, emesis. More than 2000 years ago, the Greek physician Hippocrates observed this physiological phenomenon1. Alas, motion sickness still poses as an aversive experience in modern transportation. Although being a technological advancement for humanity, the advent of autonomous and semiautonomous vehicles may well heighten the incidence and risk of motion sickness2,3. Moreover, digital devices and displays also pose an emerging hazard to many prone to the ailment4. 3D displays have also been found to evoke symptoms of motion sickness in contrast to their 2D counterparts5. Explanation for the aetiology of motion sickness is not well understood. Hence many theories aiming to elucidate its mechanisms have been proposed3. Currently, a widely influential theory is one proposed by Reason and Brand1, defined as the sensory conflict theory. This theory posits that because of ambiguous sensory information from the eyes and the vestibular system, motion sickness is onset. Reason6 further developed the neural mismatch theory as a corollary to the sensory conflict theory. We now know from neuroimaging studies that this ambiguity in sensory information, particularly arising from visual stimulation, triggers prominent areas of the brain, for example the limbic system (known for regulating autonomic and endocrine function)3,7 and the insula (also involved in autonomic regulation)8. In addition, a motion video was shown to elicit autonomic changes leading to increased sympathetic and reduced parasympathetic activities of the autonomic nervous system (ANS)9. The symptomatology of motion sickness is characterised by a vast array of features which include, for instance, sweating, dizziness, drowsiness, headache, eyestrain, nausea and vomiting (a number of which are autonomically mediated). Motion sickness changes ANS response; the ANS is a branch of the peripheral nervous system comprising the sympathetic and parasympathetic divisions. These physiological systems have been hypothesised to reflect low frequency power (LF) and high frequency power (HF) of the heart rate variability (HRV) signal; derived from electrocardiogram (ECG) measurements. Often the LF and HF metrics are expressed as a ratio (i.e., LF/HF), however, there is lack of agreement on the anatomical basis of the LF/HF ratio10. Reported findings on decreased HF power (HF; 0.15–0.40 Hz) of the HRV spectra in reponse to motion sickness have been consistent11–13. Furthermore, multiple studies have shown that the power ratio between low frequency (LF; 0.04–0.15 Hz) and HF band powers (LF/HF) increases with symptom development of motion sickness12,14,15. Given that the aforementioned metrics are features of altered physiological arousal by motion sickness, which has no cure, studying these changes in ANS response could enable researchers to manipulate them for non-pharmacologic interventions, in an effort to mitigate motion sickness. Here we applied transauricular electrical stimulation (tES) non-invasively by sending electrical impulses from the tragus of the auricle to study its influence on motion sickness. Electrical stimulation of the auricular region has been found to have therapeutic effects in early research16,17. Additionally, by targeting this region there is a possibility of activating the vagus nerve18, which could serve as a conduit for balanced autonomic modulation. The vagus nerve (cranial nerve X) originates from the brainstem in an area called the medulla oblongata. From there it extends bilaterally receiving afferent signaling from the auricular branch, before travelling down the neck carrying parasympathetic innervation via the cervical branch, targeting major organs in the thorax, and making its way down to the gastrointestinal tract. It forms a complex neural network comprising afferent and efferent neural pathways. Conversely, it is possible that tES is not effectively able to activate the vagus nerve; hence, the current study does not address whether tES achieves its objective through vagus nerve stimulation or not. Recently, Sawada et al.19 showed that visually induced motion sickness from driving simulators can be reduced by, for example, coupling presentation of sound and vibration. Based on HRV analysis, Zhao et al.20 investigated the protective effects of transcutaneous electrical acustimulation (TEA) on motion sickness induced by a rotary chair. Here, we propose a non-invasive tES neuromodulation application to help in the management of motion sickness. Our primary hypothesis is that by administering tES simultaneously with increasing levels of motion sickness, the metrics for LF and LF/HF ratio would decrease while the HF metric would increase. Furthermore, we hypothesized that the aforementioned objective measurements would be complemented by decreases in the scores of behavioral measurements obtained using an established and validated tool, the motion sickness assessment questionnaire (MSAQ). This is capable of measuring the severity of motion sickness in four dimensions (gastrointestinal, central, peripheral, sopite). Our rationale on the potential therapeutic/interventional effects of tES being that we may trigger a restoration in the autonomic imbalance observed during motion sickness. ## Results We first sought to establish that the nauseogenic stimulation was inducing motion sickness by evaluating the HRV spectral parameters. Thus, baseline measurements were compared with “Nausea” measurements in the sham condition with the expectation that the parasympathetic tone would be decreased from baseline. Additionally, we expected the power ratio between LF and HF as measured by LF/HF ratio to increase from baseline. As expected, we did find a significant decrease in parasympathetic activity measured by HF n.u. ( t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{[13]}$$\end{document}[13] = 3.82, $$p \leq 0.0011$$) and increase in LF/HF ratio (t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{[13]}= -3.71$$\end{document}[13]=-3.71, $$p \leq 0.0013$$) as shown in (Fig. 1a). This is in line with previous studies that found decreases and increases in normalized HF and LF/HF ratio respectively with increasing symptoms of motion sickness. Further, a significant increase from baseline during nausea was found for LF n.u. ( t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{[13]}= -3.82$$\end{document}[13]=-3.82, $$p \leq 0.0011$$). This result indicates that the nauseogenic stimulus was efficacious in eliciting robust physiological arousal. The delta change of LF n.u. power and LF/HF ratio during nausea section and baseline was compared between sham and tES conditions (Fig. 1b). A significant decrease was found in LF n.u. ( t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{[13]}$$\end{document}[13] = 2.24, $$p \leq 0.0217$$) and LF/HF ratio (t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{[13]}$$\end{document}[13] = 2.20, $$p \leq 0.0234$$) suggesting that tES was able to pull the level of autonomic arousal down. There was a significant increase in HF n.u. ( t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{[13]} = -2.24$$\end{document}[13]=-2.24, $$p \leq 0.0217$$) between sham and tES (Fig. 1b), indicating that tES was triggering an enhancement in parasympathetic modulation. The HF n.u. power for differences between the participants receiving sham before tES and those receiving tES before sham (i.e., sham \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document}→ tES and tES \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document}→ sham) did not appear to arise from fixed effect of order (F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{[1]}$$\end{document}[1] = 0.54, $$p \leq 0.4782$$).Figure 1Spectral heart rate variability (HRV) measurements between participants at baseline and “Nausea” section, and between sham and transauricular electrical stimulation (tES) condition. ( a) Average spectral power of low frequency (LF) and high frequency (HF), and LF/HF power ratio in response to nauseogenic stimuli. ( b) Average change in LF, HF and LF/HF ratio in participants during sham and tES condition. Error bars represent standard error of the mean (SEM). The box plot central marks, box edges and whiskers represent medians, 25th to 75th percentiles and data range, respectively. The box plot also shows an outlier (red plus) in the sham condition for LF/HF ratio. The time-frequency representations of HRV were computed by performing smoothed pseudo Wigner-Ville distribution (SPWVD) for the sham and tES condition, and the difference between the two conditions (Figs. 2 and 3). SPWVD was used due to its ability to represent a signal in a robust manner in both time and frequency planes, circumventing trade-offs between time and frequency resolution. It also reveals dynamics of autonomic function during symptomatology development that may be associated with this complex syndrome. In Fig. 2, we show the time-frequency representation for an example participant. The blue colour in the time-frequency power map in Fig. 2a,b indicates that Baseline had higher power in those particular regions whereas it had lower power in the bright yellow regions. Note that (Sham vs Baseline) is interpreted as performing the operation (Sham–Baseline), similarly to other time-frequency maps shown in (Figs. 2 and 3). We observe that in Fig. 2a; Sham vs Baseline, the participant was showing attenuated HF power and increases in LF power for particular timepoints. While during (tES vs Baseline) the participant had a pronounced HF power at particular timepoints. The power difference between (tES vs Baseline) and (Sham vs Baseline) is shown in (Fig. 2c). After performing a pixel-based permutation test, a temporal cluster around 270–290 s was found to be statistically significant (p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$< 0.05$$\end{document}<0.05). We performed cluster-based permutation tests at the sample level. The power differences at the sample level are shown in Fig. 3a; tES vs Sham. The resulting z-statistic map of (tES vs Sham) is shown in Fig. 3b with significant clusters outlined with a black contour. During tES stimulation, activity of HF HRV was increased (Fig. 3a); a cluster-based permutation test found two interpretable temporal clusters (cluster around 5–20 s, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${z} = -2.17$$\end{document}z=-2.17, $$p \leq 0.0150$$) and (cluster around 10–15 s, $z = 2.12$, $$p \leq 0.0170$$).Figure 2Time-varying power representations of heart rate variability (HRV) using the smoothed pseudo Wigner–Ville distribution (SPWVD) for an example participant. ( a) Time-frequency power during sham (baseline subtracted) condition (b) and tES (baseline subtracted) condition. ( c) Shows the time-frequency power difference between (Sham vs Baseline) and (tES vs Baseline). A statistical pixel-based permutation test using a pixel-level significance threshold of p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$< 0.05$$\end{document}<0.05 revealed 1 cluster around 270–290 s (two-tailed non-parametric permutation tests). The significant cluster (region) is outlined by a black contour in (c).Figure 3Time-varying power representations of heart rate variability (HRV) using the smoothed pseudo Wigner–Ville distribution (SPWVD) at the sample level. ( a) Time-frequency power representation showing the power differences in tES and sham condition (after baseline subtraction within each condition). ( b) Statistical z-map of the time-frequency power representation at the sample level, based on a cluster-level significance threshold p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$< 0.05$$\end{document}<0.05 (two-tailed non-parametric permutation tests). Significant clusters (regions) are indicated by black contours on the statistical z-map. The MSAQ scores were computed as delta change from baseline then compared between sham and tES conditions (Fig. 4). In addition to a Shapiro-Wilk normality test, histogram and normal probability visualisations were used as further tools to assess normality. Subsequently, statistical differences in the MSAQ data between sham and tES conditions were evaluated by a non-parametric Wilcoxon signed rank test. The MSAQ total score was found to be significantly lower in the tES condition compared to the sham condition ($$p \leq 0.0166$$) (Fig. 4a). A significant decrease was found in the Central ($$p \leq 0.0049$$) and Sopite ($$p \leq 0.0342$$) category subscores during tES compared to sham. The decrease in sickness symptoms that is observed in (Fig. 4b) for the Gastrointestinal and Peripheral category subscores did not reach statistical significance between sham and tES conditions (Gastrointestinal: $$p \leq 0.3125$$; Peripheral: $$p \leq 0.2188$$). Linear regression showed a link between MSAQ scores during sham condition and the difference between tES and sham condition (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2 = 0.3905, $$p \leq 0.0169$$) (Fig. 5a). A positive relationship was observed between the latency to a maximum nausea rating and HF power during tES condition (Spearman’s rank correlation; $r = 0.6405$, $$p \leq 0.0136$$) (Fig. 5b). As presented in (Fig. 5c,d), we did not find any statistical differences in the relationships between LF/HF ratio and HF n.u. power after performing a Pearson’s correlation analyses. Although response bias and underreporting of malaise symptoms could be a plausible explanation for the lack of relationship, our small sample size may be an equally valid factor. Figure 4Summary of motion sickness assessment questionnaire (MSAQ) total and subscale scores across participants. ( a) Average change in MSAQ total scores between sham and tES condition. ( b) Average change in MSAQ scores within each category (gastrointestinal, central, peripheral, sopite) between sham and tES condition. Data are shown as mean ± SEM.Figure 5Scatterplots of linear regression, Spearman, and Pearson correlation analysis. ( a) A linear regression between MSAQ during sham and the difference between tES and sham condition. ( b) A Spearman correlation between HF power during tES condition and the latency to maximum nausea rating. ( c) A Pearson correlation between the MSAQ total scores and log-transformed LF/HF ratio at Baseline and during tES as well as the difference between during and before tES intervention. ( d) And MSAQ total scores and normalized HF at Baseline and during tES as well as the difference between during and before tES intervention. r, Spearman’s or Pearson’s correlation coefficient (p-values based on two-tailed statistical test). ## Discussion This pilot study provides initial insights into the therapeutic potential of tES for reducing visually induced motion sickness. We demonstrate that the spectra and time-varying features of HRV differ significantly when comparing sham and tES conditions. Whereby tES increases parasympathetic cardiac modulation and reduces the activity of the sympathetic nerves, supporting our hypothesis. Moreover, we show that the total MSAQ scores and MSAQ categorical subscores for the central and sopite dimensions were significantly lower in the tES condition when compared to the sham condition. Because motion sickness has been implicated with decreased parasympathetic activity, and in turn contributes to perturbed autonomic function, we asked if tES could trigger a decrease in LF power while increasing HF power in order to reduce symptoms. While mechanistic underpinnings of electrical stimulation applied here are not fully parsed, evidence from studies done both in animals21 and humans22,23 indicate that the seesaw of autonomic function can be altered by electrical stimulation. The rationale therefore is that by targeting cutaneously accessible sensory (or somatic) receptors at the tragus of the auricle via electrical stimulation, we may alter the signal processing of the peripheral nerves to lower the stress on the nervous system caused by the build up of factors inducing motion sickness. Parasympathetic neural activity was significantly greater in tES conditions compared to sham as measured by HF power (Fig. 1b). This suggests that participants were symptomatic in the sham condition and asymptomatic in the tES condition on the basis that decreased parasympathetic modulation has been found during increasing levels of motion sickness. Importantly, it indicates that tES enhanced relaxation in the physiology of the participants. Previous evidence showed that ANS response to nauseogenic stimuli changes autonomic function as if the nauseogenic stimulus is a threat; thereby triggering sympatho-excitatory circuits9. Moreover, Napadow et al.7 showed activation of the limbic system during presentation of a nauseogenic stimulus. We have long known that the threat and threat detection response is processed on components of the limbic system, which regulates e.g., autonomic function. Here, by introducing electrical sensory stimulation, sensory integration in the brain may be altered in a manner that suggests the nauseogenic stimuli seems innocuous. That is, the brain may be labeling sensory input from the nauseogenic stimuli as not ‘fearful’; therefore, leading to a balanced autonomic activity. Previous work showed that electrical stimulation can reduce sympathetic nerve activity23–25, and therefore, yield parasympathetic predominance. These observations align with the present study, and here we show that individuals exposed to nauseogenic stimuli causing an aversive experience, have reduced sympathetic nerve outflow during tES intervention. This suggests tES may be eliciting effects of less agitation and as such, positively influencing autonomic response toward a desirable state of well-being. Parasympathetic and sympathetic systems can be considered foils of each other. Therefore, the explanation for parasympathetic activation from above, would mean inhibition of efferent sympathetic activity may be the reason we found reduced LF power, and consequentially, reduced LF/HF ratio (Fig. 1b). In addition to HRV static spectral analysis, we computed the time-frequency features of HRV using SPWVD. Despite this being the first time, to the authors knowledge, that SPWVD is being applied in the context of motion sickness-associated ANS response; it may well allow us to discern the complexities of malaise progression. The time-varying power maps suggest motion sickness influences normal regulation of autonomic function by attenuating the activity of the HF component (Fig. 2a; Sham vs Baseline panel). Of note, this HF power attenuation appears to be portrayed by phasic alterations over time, perhaps simulating autonomic flushes that are implicated with cardiovagal bursts precursory nausea rating14. In Fig. 2b (tES vs Baseline panel), we observe the time-varying evolution of autonomic activity when tES is administered simultaneously with sickness-inducing stimuli. Interestingly, we observe that tES (Fig. 2c; difference panel) is able to fairly restore autonomic imbalance that we saw in the untreated (sham) condition (Fig. 2a; Sham vs Baseline panel). In particular, it is now easy to see the frequency component around 0.25 Hz that is driven by respiratory modulation. This respiratory modulation tends to be disrupted in the sham condition suggesting that motion sickness enhances lower frequency components while concurrently reducing parasympathetic modulation; indicating an increased level of autonomic arousal. Taken together, the time-frequency maps presented in (Figs. 2 and 3) may be useful to distinguish malaise severity between sham and tES. Our findings from the MSAQ data showed significant decreases in the total scores and in all but two categorical subscores (gastrointestinal and peripheral) between sham and tES condition. Although there were no significant differences found for the gastrointestinal and peripheral category subscores, decreases were present as can be observed in (Fig. 4b). One possible explanation for this result could be an underreporting of sickness symptoms in the sham condition due to a negative emotion experienced from the nauseogenic stimulus. This could be driven by the markedly decreased MSAQ scores in the sopite category where we can find individual symptoms of ‘I felt annoyed/irritated’, ‘I felt drowsy’, ‘I felt tired/fatigued’ and ‘I felt uneasy’26. This dominance of sopite-like symptoms may have clouded participant judgement in reliably reporting how they felt after the experiment. Previous research has also suggested male participants may have a motion sickness response bias27. In addition, participants may have been cognitively constrained to reliably report their symptoms post-experiment as motion sickness is implicated with decreased cognitive performance28. Conceivably, the physiology of some participants may have been more responsive to the aversive experience of motion sickness and thus overshadowing the effects of tES. We have long known that susceptibility to the malaise of motion sickness is a highly individual experience29. Moreover, electrical stimulation has individual variability in and of itself30. The change in sham MSAQ total scores was a significant predictor of symptom outcome after tES intervention. That is, participants with more symptoms after sham, experienced a greater response to tES. This could be useful in individually targeted therapeutic intervention settings in order to help individuals with predispositions to the ailment. We also found a positive relationship between maximum nausea rating and HF power during tES. This suggests that during tES, participants tended to take longer to report a maximum nausea rating and thus finding it easier to cope with the nauseogenic visual stimulus. To further understand tES potential to reduce motion sickness, we sought associations between autonomic and subjective changes using Pearson’s linear correlation coefficient. The lack of correlation between MSAQ total scores and observed HRV changes was somewhat surprising. While there is always a possibility that the relationship is complex and non-linear, we suggest caution when interpreting our findings. A more likely explanation is that the vagus nerve was not involved at all, and that the effects we found can be caused by any kind of electrical stimulation above sensory threshold. Future studies should take this into account. Our study is not without limitations. First, the sample size in our study was very small, thus reducing the power for our statistical analysis. A second limitation is that sham sessions had no active stimulation; hence indicating that some participants could tell apart tES and sham conditions. We acknowledge that participants being aware of verum and sham stimulation could potentially bias the motion sickness-related changes in this study. Future studies should replicate these findings with different sham conditions that minimise potentially confounding effects of expectations. We note, however, that participants, were naïve to tES and sham stimulation electrode placement. A further limitation is that this study did not include a matching or sufficient number of male participants. The participant cohort was naïve to the nauseogenic stimuli and tES stimulation. Stimulation parameters utilised in this study were well-tolerated by all participants and no unexpected adverse effects were reported. While some participants prematurely stopped the presentation of nauseogenic stimulus due to a high severe feeling of nausea, no one vomited at the end of stimulus presentation. Future research is warranted where the findings here are augmented by the assessment of physiological correlates of motion sickness (i.e., neuroendocrine hormones such as arginine vasopressin and norepinephrine31). Moreover, the key questions to look forward to include; could we identify optimal stimulation parameters to improve effects of tES toward motion sickness reduction, and could increased stimulation duration improve the efficacy of tES to ameliorate motion sickness? In our future research, our investigations will extend to these questions and also administer an individually adjusted electrical current intensity. In conclusion, insights from data in our pilot study showed that transauricular electrical stimulation may have potential on reducing the symptoms of visually induced motion sickness. With the caveats hitherto taken into consideration, we may be inching a step closer into preventing or slowing the onset of motion sickness, an incurable malady. Together, our findings suggest auricular electrical stimulation may hold promise for managing motion sickness, however we wish to emphasise that the autonomic data in this preliminary study were obtained from a very small number of participants. Future at-scale studies are necessary to confirm our results. ## Participants Sixteen healthy participants were recruited for the study. Of the 16 participants, 14 were retained (mean age ± S.D. = 26.7 ± 4.0 years, age range = 21–34 years, 12 females) for further analysis after two were excluded due to unsaved data and loss of follow-up respectively. Participant cohort had normal or corrected-to-normal vision. Inclusion criteria were no medical history of stroke, epilepsy or any neurological disorders. Additionally, participants were not using cardiac pacemakers, had no metal plates and were not on any medication. Participants received financial compensation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pounds 30$$\end{document}£30) in the form of an Amazon gift voucher for their participation. The methods were approved by the Central Research Ethics Advisory Group (ref: CREAG015-12-2021) of the University of Kent. Prior to participation, written informed consent was obtained from all participants. All study methods conformed to the principles set by the Declaration of Helsinki. ## Experimental protocol The experimental paradigm was designed as a within-participants cross-over study where each participant visited the lab for sham and active tES sessions. To allow for a washout period, sessions for sham and active tES were on separate days, with a minimum of 1 week in between. The order in which sham and active tES were administered was randomly assigned. Participants completed a pre- and post-MSAQ during both sessions. During both sham and tES sessions, participants observed a black crosshair projected at the centre of a screen for 5 min (baseline), followed by the Nauseogenic stimulus for a maximum of 20 min, and finally, a further black crosshair for 5 min (recovery) projected similar to baseline (Fig. 6c). Simultaneous to the presentation of the nauseogenic stimulus, the electrode of the tES stimulator was clipped to the earlobe of the left ear for sham sessions (Fig. 6b) and for tES sessions, to the tragus of the left ear (Fig. 6a). Participants provided subjective ratings to report their level of nausea by pressing on a keypad where (0 = “no nausea”), (1 = “mild”), (2 = “moderate”) and (3 = “strong”). We found that participants generally reached “mild” nausea at around 6 minutes relative to nauseogenic stimulus onset (Table 1). At the subjective level of experiencing a severe sensation of nausea, that is, when the participant had exceeded a rating of 3 (considered “strong”) and was on the verge of vomiting, the participant would stop the presentation of nauseogenic stimulus by pressing a button on a keypad. The computerised nauseogenic stimulus would then skip to the recovery section. The mean durations of the nauseogenic stimulus that participants received before pressing the button to quit the presentation were 13.2 (S.D. = 1.1 min, range = 12.4–14.4 min). Regardless of whether the participant stopped the stimulus prematurely or the 20 min elapsed, the recovery section always followed. From the beginning of the baseline section through to the end of the recovery section, ECG measurements were being recorded continuously. The researcher was with the participant in the lab during data acquisition proceedings, both to ensure the safety of the participant and to monitor smooth running of the experiment. Of note, the researcher was not in the view of the participant during the stimulus presentation. Participants were instructed to focus on the stimulus throughout, minimise conversation and to avoid body movements during the data acquisition process. Figure 6Experimental overview illustrations. ( a) The electrode was clipped at the tragus site of the left ear during tES condition. ( b) And clipped to the earlobe of the left ear during sham condition. ( c) Participants underwent a baseline, nauseogenic visual stimulation and recovery section, respectively in both week 1 (first visit) and week 2 (follow-up visit), separated by 1 week. Electrocardiogram (ECG) signals were recorded continuously from beginning of baseline to end of recovery. Participants were randomly assigned to receive tES or sham in week 1 (first visit) and to receive opposite treatment on follow-up. Table 1Mean ± S.D. subjective nausea intensity ratings in minutes. Subjective nausea ratingsConditionMild nauseaModerate nauseaStrong nauseaSham6.6 (4.6)8.6 (5.9)10.8 (6.1)tES6.3 (5.1)10.5 (6.7)12.3 (5.4)The durations are given relative to stimulus onset. ## Nauseogenic stimulus We created a nauseogenic visual stimulus to induce nausea. MATLAB (The MathWorks, Inc., Natick, MA, USA) software was used to develop source code for the nauseogenic visual stimulus using the Psychophysics Toolbox Version 3 (https://github.com/Psychtoolbox-3/Psychtoolbox-3)32–34. The stimulus was a horizontal translation of black and white vertical stripes with a circular shift of 62.5 °/s (Fig. 6c). This computerised stimulus simulates the visual input provided by the classic rotating optokinetic drum utilised to induce motion sickness/nausea35–37. It is well-known in the literature that this translation of alternating black and white stripes elicits illusory self-motion (vection) and nausea38,39. An fMRI compatible variant of the stimulus has been used effectively for inducing nausea in cardiac modulation and neuroimaging studies investigating motion sickness7,8,13,14,40,41. We presented the stimulus on a 47-inch LG LCD Widescreen (47LW450U, LG Electronics UK, UK) at a distance filling the participant’s full visual field. The display refresh rate was 60 Hz. The complete and contiguous study stimulus was a crosshair fixation section at baseline, nauseogenic stimulus section and a crosshair fixation section at recovery. ## Electrical stimulation Non-invasive tES was applied using the EM6300A TENS (Med-Fit UK Ltd, Stockport, UK); a battery driven medical stimulation device with electrodes that can be clipped onto the ear (tragus or earlobe for example). Active tES was administered at the tragus site of the left ear (Fig. 6a); and sham (no actual stimulation) administered to the left earlobe (Fig. 6b). Stimulation current was delivered as asymmetric biphasic square-wave pulses with a pulse width of 200 µs, pulse frequency of 20 Hz and current intensity of 1.0 mA. Stimulation parameters were chosen based on literature assessing autonomic modulation. The parameters were tested prior to beginning each experimental session. The tES stimulation device was triggered manually by the researcher and had a countdown timer set for 20 min, in synchrony with the maximum duration of the nauseogenic stimulus. Moreover, the researcher turned off the device when the 20 min elapsed. In addition, the researcher immediately turned off the device for scenarios involving participants stopping the nauseogenic stimulus prematurely (see Experimental protocol for details on how premature stoppage of the nauseogenic stimulus was implemented). Perception of the above-mentioned stimulation parameters was reported by all participants without painful sensation. Our rationale for targeting the left ear was based on the fact that most previous studies had investigated with it42. ## ECG data acquisition, processing and analysis Continuous ECG data acquisition was performed using a BioSemi ActiveTwo system (BioSemi B, V., Amsterdam, Netherlands). During the experiment, 64-channel electroencephalography (EEG) activity was also recorded. EEG data will be presented in a separate publication, allowing scope for discussion focused on ANS response for current analysis. The ECG signal was digitized at a sampling rate of 256 Hz using LabVIEW (National Instruments, USA) software. Electrode signal transduction was optimised by applying SIGNAGEL conductive gel. Electrode offset was within ± 10 µV. The recorded data was persisted as.bdf files for offline processing. ECG signal processing and analysis was performed using custom code developed in MATLAB software in accordance with recommended standards for HRV signals43. First, raw ECG data was visually inspected for any disturbances or distortions. Epochs of 5 minutes were extracted from the continuous ECG data for ‘Baseline’ and ‘Nausea’ sections. The logic for extracting epochs of the ‘Nausea’ section was as follows: if the participant completed the whole 20-min section without a subjective rating of at least 2 (moderate nausea), then the 5-minute epoch was obtained from right before the recovery section started. If the participant had a subjective rating of at least 2, then the 5-min epoch was obtained from right before the maximum rating was triggered. For scenarios where the participant prematurely stopped the nauseogenic stimulation due to a subjective severe feeling of nausea rating, then the 5-min epoch was obtained from right before the recovery section followed (note that the recovery section followed regardless of natural or subjective stoppage of the nauseogenic stimulation) - see Experimental protocol for technical implementation of nauseogenic stimulus premature stoppage. In essence, our data epoch selection was based on intertwining two methods that have been previously reported in the literature (i.e., percept- and condition-based analysis)8,14,41. Using the obtained 5-minute ECG epochs, we performed R-peak detection using the Pan-Tompkins algorithm44 and subsequently, the RR time-series (RR intervals) were generated. The filtfilt MATLAB function was used for implementation of ECG waveform filters in order to perform zero-phase digital filtering. Visual inspection was performed to ensure the quality of the RR series retained. ## Frequency domain analysis To perform spectral analysis of the HRV, we used the Lomb-Scargle periodogram to compute power spectral density (PSD) estimation on the obtained RR series. Subsequently, we computed the total power of HRV spectra (Total power; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.40 Hz), and the power of very low frequency (VLF; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.04 Hz), low frequency (LF; 0.04–0.15 Hz), high frequency (HF; 0.15–0.40 Hz) and the power ratio of LF to HF (LF/HF). Note that VLF power was only calculated to compute LF and HF in normalized units (n.u.) using formulae below43, and we took the natural logarithm (ln) of the LF/HF ratio.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} L{F}_{norm}=\frac{LF \; power}{Total \; power-VLF\; power}\times 100\ \end{aligned}$$\end{document}LFnorm=LFpowerTotalpower-VLFpower×1002\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} H{F}_{norm}=\frac{HF \; power}{Total \; power-VLF\; power}\times 100\ \end{aligned}$$\end{document}HFnorm=HFpowerTotalpower-VLFpower×1003\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} lnLF/H{F}_{ratio}={\rm{log}}\bigg (\frac{LF \;power}{HF\; power}\bigg) \end{aligned}$$\end{document}lnLF/HFratio=log(LFpowerHFpower) ## Time-frequency representation analysis We sought to perform time-frequency analysis knowing that this has the added advantage of observing how the frequency components of a signal varies over time. Thus, being able to observe the progression of symptoms as the participant suffers bouts of sickness may help elucidate how motion sickness evolves, in addition to how tES may be involved in the sickness evolution process. First, we applied a 4 Hz cubic spline interpolation for the resampling of the RR intervals into a uniformly sampled time series14,45. The interp1 MATLAB function was used for implementation of cubic spline interpolation with the interpolation method set to “spline”. Resampling in HRV literature is mostly performed before frequency/time-frequency decomposition when methods such as those based on Fourier transforms, or wavelet-based PSD estimates are applied (i.e., these approaches assume uniform sampling)41,43. It should be noted that other resampling methods such as linear interpolation are possible. The Hilbert transform was applied to obtain the analytic signal association to the HRV signal. Then we performed smoothed pseudo Wigner-Ville distribution (SPWVD) to compute time-frequency power. The SPWVD is a well-known method for its good time-frequency resolution and robustness towards cardiovascular signal analysis46. Previous studies have utilised the SPWVD method to detect obstructive sleep apnea (OSA) in ECG recordings (e.g.,47) in addition to drowsiness detection (e.g.,45). The SPWVD is a member of the Cohen’s class family of time-frequency distributions48, and can be defined as follows:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} SPWVD_{x}^{g,H} \left({t,f}\right) =\int _{-\infty }^{\infty }g\left({t}\right) H\left({f}\right) x\left({t+\frac{\tau }{2}}\right) x^{}\left({t-\frac{\tau }{2}}\right) e^{-j2\pi ft}d\tau \end{aligned}$$\end{document}SPWVDxg,Ht,f=∫-∞∞gtHfxt+τ2x∗t-τ2e-j2πftdτThe term g(t) represents performing a convolution (smoothing) in time. Whereas, the term H(f) represents spectral smoothing of g(t) using a nonparametric fast Fourier transformation (FFT). Of utmost importance, by looking at the local features of the signal x in time t, SPWVD enables independent smoothing in time and in frequency49. ## Motion sickness questionnaire In order to assess symptoms of motion sickness, participants completed the motion sickness assessment questionnaire (MSAQ)26 before and after the experiment. The MSAQ is a well-known validated tool consisting of 16 symptoms categorised into 4 dimensions of motion sickness defined as gastrointestinal, central, peripheral and sopite-related. The individual symptoms are rated on a nine-point scale where (1 = “not at all”) and (9 = “severely”). The MSAQ total score is computed as a percentage of summed points from all symptoms. While scores for the four categories are percentages of summed points from within each category. ## Statistical analysis All statistical analyses were performed using MATLAB software. We reported variables of the HRV spectra and the MSAQ as means with standard error (SEM). Changes of HRV spectral data were analysed using a paired one-tailed t-test to assess the effects of [1] “Baseline” and “Nausea” measurements in the sham condition, and [2] comparisons between sham and tES condition. We computed Pearson’s correlation coefficient between the MSAQ subjective total scores and the log-transformed LF/HF ratio and normalized HF power (two-tailed). Relationship between the latency to a maximum nausea rating and HF power was tested using Spearman’s rank correlation coefficient (two-tailed). Linear regression was performed on MSAQ data using the fitlm MATLAB function. Time-frequency representations (TFR) of SPWVD were examined using non-parametric permutation testing to analyse differences between sham and tES condition. We note that the idea of permutation-based statistics is heavily used in neuroscience, however, the methods are amenable to statistical analysis of time-frequency matrices. Early, neuroscience focused research comprehensively detailed the theory and justifications of permutation statistics50,51. All our permutation-based statistical tests were two-tailed. At the participant level, we examined TFR power using pixel-based permutation statistics (i.e., to correct for multiple comparisons). At each of the 1000 random permutations, we extracted the two most extreme values (i.e., minimum and maximum) from the TFR difference matrix to generate a null distribution. Then our threshold was determined by taking the 2.5th and 97.5th percentiles from our distribution, applying $$p \leq 0.05.$$ Clusters found using this statistical method can be observed in (Fig. 2c). We used non-parametric cluster-based permutation tests52 to examine the differences in the time-frequency representations of SPWVD at the sample level between sham and tES condition. Herein, it is important to note the necessity of cluster-based permutation tests on time-frequency data. From the basis of a significance threshold at ($$p \leq 0.05$$), we computed a z-value, which, in turn, was used to threshold the z-transformed time-frequency power difference between tES and sham condition. To* generate a null distribution, we performed 1000 random permutations, where at each permutation extracting the maximum cluster mean of the z-values. Our analysis is based on discussing the temporal clusters found as shown in (Fig. 3b). However, it is worthy of caution that clusters identified using this method may represent effects detected though maybe not supported by the permutation test53. A difference worthy of note between cluster and extreme-pixel correction is that, cluster correction tends to support bigger clusters. Whereas, pixel-based correction is more stringent though it can detect smaller clusters or single pixels. Hence, our rationale for using cluster correction at the sample level. 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--- title: Physicochemical and nanomedicine applications of phyto-reduced erbium oxide (Er2O3) nanoparticles authors: - Hamza Elsayed Ahmad Mohamed - Ali Talha Khalil - Khaoula Hkiri - Muhammad Ayaz - Jamil Anwar Abbasi - Abdul Sadiq - Farhat Ullah - Asif Nawaz - Ikram Ullah - Malik Maaza journal: AMB Express year: 2023 pmcid: PMC9968365 doi: 10.1186/s13568-023-01527-w license: CC BY 4.0 --- # Physicochemical and nanomedicine applications of phyto-reduced erbium oxide (Er2O3) nanoparticles ## Abstract Hyphaene thebaica fruits were used for the fabrication of spherical erbium oxide nanoparticles (HT-Er2O3 NPS) using a one-step simple bioreduction process. XRD pattern revealed a highly crystalline and pure phase with crystallite size of ~ 7.5 nm, whereas, the W–H plot revealed crystallite size of 11 nm. FTIR spectra revealed characteristic Er-O atomic vibrations in the fingerprint region. Bandgap was obtained as 5.25 eV using K-M function. The physicochemical and morphological nature was established using Raman spectroscopy, reflectance spectroscopy, SAED and HR-TEM. HT-Er2O3 NPS were further evaluated for antidiabetic potential in mice using in-vivo and in-vitro bioassays. The synthesized HT-Er2O3 NPS were screened for in vitro anti-diabetic potentials against α-glucosidase enzyme and α-amylase enzyme and their antioxidant potential was evaluated using DPPH free radical assay. A dose dependent inhibition was obtained against α-glucosidase (IC50 12 μg/mL) and α-amylase (IC50 78 μg/mL) while good DPPH free radical scavenging potential (IC50 78 μg mL−1) is reported. At 1000 μg/mL, the HT-Er2O3 NPS revealed $90.30\%$ and $92.30\%$ inhibition of α-amylase and α-glucosidase enzymes. HT-Er2O3 NPs treated groups were observed to have better glycemic control in diabetic animals (503.66 ± 5.92* on day 0 and 185.66 ± 2.60* on day 21) when compared with positive control glibenclamide treated group. Further, HT-Er2O3 NPS therapy for 21 days caused a considerable effect on serum total lipids, cholesterol, triglycerides, HDL and LDL as compared to untreated diabetic group. In conclusion, our preliminary findings on HT-Er2O3 NPS revealed considerable antidiabetic potential and thus can be an effective candidate for controlling the post-prandial hyperglycemia. However, further studies are encouraged especially taking into consideration the toxicity aspects of the nanomaterial. ## Introduction Advances in nanobiotechnology over the past few years have yielded exciting applications across different fields, especially in medicine and drug delivery (Chittaranjan Patra 2021; Khalil et al. 2021a). It has been well established that materials on the nanometer scale possess a unique surface area to volume ratio that provide peculiar characteristics like enhanced reactivity and higher efficacy (Mohamed et al. 2020a). Among the metallic nanoparticles; silver, gold, zinc, iron etc. are well studied for their biomedical and nanomedicine applications, however, the lanthanides rare earth oxides are not well-explored (Ovais et al. 2019, 2020). The lanthanides rare earth oxides are known for their distinctive features and therefore used in number of applications such as energy, catalysis, photonics, environment, solid state optoelectronics, telecommunications, solar cells etc. Nano rare earth oxides are anticipated as exciting materials attributed to the quantum confinement and shape specific attributes (Rahimi-Nasrabadi et al. 2017). Mostly research on rare earth oxides is usually centered on the evaluation of physical, electrical, optical properties (Diallo et al. 2016; Rajaji et al. 2019). Among them, trivalent erbium oxide nanoparticles (Er2O3 NPs) are considered as one of the fascinating materials for applications in physical and chemical sciences. It usually has a pinkish color and cubic structure. Er2O3 is known for its mechanical properties, stiffness, thermal and chemical durability, inertness and corrosion resistance. Mechanical strength of the Er2O3 is comparable to alumina and magnesia. Er2O3 is a wide bandgap material i.e., ~ 5.4 eV (Acikgoz et al. 2021; Azad and Maqsood 2014; Rajaji et al. 2019). Er2O3 nanomaterials have revealed excellent potential in applications related to the gas sensors, optical communication, phosphorus display monitors, imaging and photoelectrochemical water splitting materials (Mohammadi and Fray 2009; Radziuk et al. 2011). Previously, antibacterial activity of Er2O3 is also reported against different pathogens like P. aeruginosa, E. faecalis, S. aureus and E. coli (Dědková et al. 2017). Various chemical and physical processes can be utilized for preparation of the metal oxide nanoparticles such as hydrolysis, hydrothermal, sol–gel, precipitation, pyrolysis, thermal decomposition, ball milling etc. Previously, Er2O3 NPs were synthesized using chemical precursor like erbium nitrate (Azad and Maqsood 2014). Other methods like chemical bath deposition, microemulsion, sonochemical synthesis, low-pressure metallorganic chemical vapor deposition (MOCVD), thermal decomposition, solvo-hydrothermal, sol–gel, radio frequency (RF) sputtering etc. have been published for the preparation of Er2O3 NPs (Losurdo et al. 2007; Nguyen et al. 2010; Pacio et al. 2021; Que et al. 2001; Rajaji et al. 2019; Tabanli et al. 2017). Hitherto, being effective, these processes have disadvantages such as the wet chemistry-based approaches can generate toxic waste while, the physical methods are energy intensive. Furthermore, nanoparticles synthesized using chemical approaches can have low biocompatibility that limits their applications in medicine (Ovais et al. 2021; Sani et al. 2021). Contrary to physico-chemical methods, biological synthesis provides a suitable, one-step and economical alternative to other conventional means. Biological materials like the extracts of medicinal plants can be used as reducing, stabilizing and capping agents for the preparation of metal-based NPs (Khalil et al. 2021b; Nasar et al. 2022). Diabetes mellitus (DM), disorder of the glucose metabolism and associated with hyperglycemia as well as other complications like neuropathy, retinopathy, nephropathy, and micro and macrovascular complications (Arky 1982). Defects in the production, secretion and action of insulin are responsible for hyperglycemia (Booth et al. 2016). DM affects about five percent population worldwide and its occurrence is increasing at an alarming rate, and is also associated with number of other diseases (Rahim et al. 2019). There are about 450 million peoples around the world which are effected by DM which is estimated to effect 690 million people till 2044 (Cho et al. 2018). Broadly the disease has two types including Type-1 diabetes (T1DM) and Type-2 diabetes (T2DM) (Del Prete et al. 1977; Himsworth and Kerr 1939). Inhibitors of two vital enzymes including alpha-glucosidase and alpha-amylase implicated in gastrointestinal glucose absorption are considered as vital drug targets. These enzymes breakdown polysaccharides (starch) to glucose and the inhibition of these enzymes play an important role to reduce glucose absorption in the intestine (Gin and Rigalleau 2000). Beside these targets, scavenging excessive number of free radicals is important. These free radicals generated during metabolic processes are responsible for a series of human illnesses including atherosclerosis, immune system destruction, cancer, neurological disorders, heart disorders, cerebro-vascular diseases and metabolic disorders (Saeed et al. 2021). In DM, free radicals cause lipids peroxidation, glucose oxidation and glycation of proteins (non-enzymatic) leading to DM and its complications (Mir et al. 2019; Sadiq et al. 2020). Number of antioxidants which may be natural or synthetic are used and can be helpful in metabolic disorders management (Mahnashi et al. 2022b). Herein, we have reported extracts modulated biosynthesis of HT-Er2O3 NPs for the 1st time by using plant extracts as bioreducing agents. Aqueous extracts of the edible fruit of Hyphaene thebaica (Egyptian Doum; gingerbread in English) was used as bioreducing agents. H. thebaica has well-established uses in the folkloric medicines and have been reported for dyslipidemia, hypertension, haematuria, bleeding, diuretic, diaphoretic, lowering blood pressure etc. ( Abdulazeez et al. 2019; Khalil et al. 2018). After characterization different bioassays were performed (in-vitro & in-vivo) for determination of their antioxidant and antidiabetic potential & their safety was evaluated. Fig. 1 indicate the general study design for the present research. Fig. 1Graphical scheme of study ## H. thebaica processing The fruit material of H. thebaica was purchased in Aswan (Egypt), and was verified from a plant taxonomist. Fruit material was rinsed with double distilled water to clean any potential particulate material and kept for shade drying. Later, the fruit material grounded to fine powder and stored in zipper bags. Aqueous extraction was performed by heating 10 g powdered material in double distilled water (100 mL) for 1 h at 80 °C. Obtained mixture was cooled to room temperature and filtered three times for removing residual waste. The visually clear and transparent solution was used for synthesis. ## Biosynthesis of HT-Er2O3 NPs For biosynthesis, the precursor salt i.e. erbium nitrate (Er(NO3)3) (4 g) was introduced to 100 mL extract solution and allowed to stand room temperature for few minutes. Later, the solution was centrifuged at 8000 RPM for 20 min, and the pelleted precipitates were washed thrice in distil water. The washed precipitates were dried for 2 h (at 100 °C). Resultant powdered material (assumed as HT-Er2O3 NPs) was placed in the ceramic boat and calcinated for 2 h (500 °C) in a tube furnace. ## Characterization The physicochemical characteristics of the as synthesized NPs were analyzed via various techniques. XRD pattern was obtained to study the crystalline properties of HT-Er2O3 NPs. The x-ray diffractometer is equipped with Cu Kα (1.54 Å) radiation and the system is operating in Bragg–Brentano geometry The diffraction pattern was compared with the standard crystallography database and the crystallographic reflections were utilized to measure the average grain or crystallite size using Debye Scherrer approximation (Eq. 1) and Williamson-Hall equation (Eq. 2), as follows;1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\langle D\rangle_{{{\text{SIZE}}}} = {\raise0.7ex\hbox{${K\lambda }$} \!\mathord{\left/ {\vphantom {{K\lambda } {\Delta \theta_{{{1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-0pt} 2}}} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\Delta \theta_{{{1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-0pt} 2}}} }$}}$$\end{document}⟨D⟩SIZE=Kλ/Δθ$\frac{1}{22}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta \cos \theta = \left({{\raise0.7ex\hbox{${K\lambda }$} \!\mathord{\left/ {\vphantom {{K\lambda } D}}\right.\kern-0pt} \!\lower0.7ex\hbox{$D$}}} \right) + 4\varepsilon \sin \theta.$$\end{document}βcosθ=Kλ/D+4εsinθ.here “λ” = 1.54 Å, and “K” = 0.9 The values of β cosθ were plotted as a function of 4 sinθ on the x-axis, followed by the linear fit for calculating the y-intercept and strain (ε) for obtaining the grain or crystallite size (D) using Williamson-Hall plot method (Zak et al. 2011). Furthermore, the dislocation density was determined using the formula (Bindu and Thomas 2014);3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta = {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 {D^{2} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${D^{2} }$}}$$\end{document}δ=1/D2Where as; “δ” indicate dislocation density and “D” indicate grain or crystallite size of the nanoparticles. The vibrational properties of the nanoparticles were studied by FTIR in the spectral range (4000 cm−1 to 400 cm−1) and Raman spectra at room temperature with laser line 473 nm and average excitation power of 2.48 mW. Reflectance spectra was obtained in the range of 200 nm to 800 nm, and was used to obtain the optical bandgap using Kubelka–Munk function (Guler et al. 2015). High Resolution Transmission Electron Microscopy (HR-TEM) was performed to study the nanoparticles shape and morphology. Particle size distribution was obtained after processing the images using Image J. SAED pattern (Selected Area Electron Diffraction Pattern) was also obtained. The room temperature physico-chemical attributes of the HT-Er2O3 NPs were studied using XRD, FTIR spectroscopy, Reflectance spectroscopy as indicated in the inset Fig. 2(A–D). The X-ray diffraction pattern revealed sharp peaks for 2θ position at 29°, 33°, 48° and 57.9° which can be assigned to the crystallographic reflections of [222], [400], [440] and [622]. These results are perfectly in accord with the JCPDS card no. 00-043-1007, that corresponds to the Er2O3 with body centered cubic crystal lattice. The average crystallite or grain size of HT-Er2O3 NPs calculated according to the Debye Scherrer approximation (Eq. 1) was found to be ~ 7.56 nm. Obtained calculations are shown in Table 1. The crystallite or grain size calculated from the W–H plot method was found to be 11 nm using (Eq. 2). The results are indicated in Fig. 2B. The dislocation density was calculated as 0.Fig. 2Room temperature physical properties of the HT-Er2O3 NPs; (A) X-ray diffraction pattern; (B) Williamson Hall Plot analysis of the HT-Er2O3 NPs (From the linear fit, data is extracted to calculate the strain from the slop and crystallite size from the y-intercept); (C) FTIR spectra of HT-Er2O3 NPs; (D) Reflectance spectra of HT-Er2O3 NPsTable 1Grain or crystallite size of the HT-Er2O3 NPs2θhklFWHMGrain or crystallite size (nm)2222221.163357.374004001.068468.134404401.16697.796226221.354036.98Average grain or crystallite sizeD = 7.56 nmDislocation densityδ = 1/D2 = 0.0174 nm−2Grain or crystallite size using Williamson-Hall method11 nmDislocation densityδ = 1/D2 = 0.008 nm−2Strain (ε)0.0378 Figure 2C indicates the FTIR spectra of the HT-Er2O3 NPs, for evaluation of their vibrational properties to understand their structural and functional nature. The FTIR spectra reveals typical metal oxide vibrations in the fingerprint region. Peaks positioned at ~ 438 cm−1 and 582 cm−1 can be assigned to the Er-O and Er-O-Er, that confirms the synthesis of erbium oxide nanoparticles. The vibrations between 800 cm−1 and 3000 cm−1 can be ascribed to the specific surface activities, attached functional groups, surface adsorbed moisture or other phenolic compounds. The IR bands can be assigned to different functional groups like ~ 801 cm−1 (amine group), 891 cm−1 (C–H vibration from aromatic compounds), 965 cm−1 (O–H vibration), 1227 cm−1 (C–O vibration), 1472 cm−1 (C–H bending mode of alkanes), 1566 cm−1 (C–C stretching of aromatic compounds), 2925 cm−1 (C–H stretching of alkanes). Figure 2D indicates reflectance spectra which was used to derive the optical bandgap (5.25 eV) using Kubelka–Munk function (K–M), i.e., by plotting (F(R)hv)2 on y-axis against hv on x-axis, as indicated in Fig. 3A. The K-M function can be determined by using the following formula;4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$FR = {\raise0.7ex\hbox{${\left({1 - R} \right)^{2} }$} \!\mathord{\left/ {\vphantom {{\left({1 - R} \right)^{2} } {2R}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${2R}$}}$$\end{document}FR=1-R$\frac{2}{2}$Rhere, “R” is the reflectance, “FR” is the Kubelka–Munk function. Fig. 3Bandgap calculation and Raman Spectra (A) Plots of (F(R)hν)2 versus the photon energy (hν) after applying K–M function for calculated the optical bandgap; (B) Raman spectra of HT-Er2O3 NPs Raman spectrum revealed (100 cm−1–1400 cm−1) revealed seven major peaks centered at ~ 261 cm−1, 562 cm−1, 647 cm−1, 741 cm−1, 832 cm−1, 978 cm−1 and 1071 cm−1 as indicated in Fig. 3B. Between 240 and 300 cm−1 there are an isolated group of three lines (2 Ag + Bg) is observed to which no Raman line corresponds in A -type spectra. These bands are assigned to the (2 Ag + Bg) modes deriving from (A2u + Eu) infrared active modes and involving Er-O bonds. Between 350 and 600 cm−1, the last group of Raman lines would correspond to the splitting of the A1g + Eg stretching vibrations the frequencies of which are very close in the cubic phase. Peaks between 800 and 1200 cm−1 are assigned to different functional groups coming from the H. thebaica extract which confirmed as well with FTIR. HR-TEM images at different magnifications were used to identify shape and obtain the size distribution as indicated in the inset Fig. 4A–C. The nanoparticles appeared to be cuboidal and quasi-spherical (Figure A–C), whereas, the size distribution results histogram revealed that most of the nanoparticles were around the range of 6–8 nm (Fig. 4E). The selected area electron diffraction pattern as indicated in Fig. 4D, revealed spotty rings which reaffirmed that the HT-Er2O3 NPs are of highly crystalline nature. Fig. 4High resolution transmission electron microscopy of HT-Er2O3 NPs; (A–C): HR-TEM images at different magnifications; (D): Selected Area Electron Diffraction pattern of HT-Er2O3 NPs; (E) Particle size distribution of the HT-Er2O3 NPs ## Inhibition of alpha-glucosidase enzyme The inhibitory potentials of Er2O3 NPs against alpha-glucosidase enzyme was evaluated according to protocol described previously with minor modification (Hussain et al. 2019). Commercially available enzymes were used in the study. Alpha-glucosidase (CAS 9001-42-7) used in the study was purchased from (sigma- Aldrich, USA). Glucosidase solution was prepared by dissolving α-glucosidase enzyme (0.5 µ/ml) in 120 µL of phosphate buffer with pH adjusted at 6.9. Buffer solution (0.1 M having pH 6.9) was used to prepare solution the enzyme substrate. Various serial samples dilutions were prepared ranging from 62.5 to 1000 μg mL−1 concentration were prepared. Initially, solution of enzyme was added to sample solution with subsequent incubation at 37 °C for 20 min. To this mixture, 20 μL of substrate solution was added and incubated again at the same temperature for fifteen minutes. To abort the reaction, 80 μL of Na2O3 solution was added to the mixture and the absorbance at 405 nm was recorded via spectrophotometer. Positive control was acarbose whereas, the blank one contained the same mixture without inhibitor agents. Finally, % α-glucosidase inhibitory results were calculated using our previously described procedure (Mahnashi et al. 2022b). ## α-amylase inhibition Α-amylase inhibitory studies of the selected nanoparticles were performed as per the previously established procedure (Nair et al. 2013). Alpha-amylase from human saliva, Type XIII-A, lyophilized powder, 300–1500 units/mg protein (CAS 9000-90-2, EC 232-565-6) was obtained from Merck KGaA, Darmstadt, Germany. Briefly, α-amylase enzyme (20 μL) and 0.02 M sodium phosphate buffer (200 μL) was added to the test samples of different concentrations (31.25–1000 μg mL−1), followed by 10 min incubation at room temperature and subsequent addition of starch (200 μl). 400 μL DNS (dinitrosalicylic acid) was added to stop the reaction, followed by boiling in water bath for 5 min and then allowed to cool. Finally, 15 mL double distilled water was introduced to the reaction mix and readings were recorded at 540 nm. No test sample was added the controls. Acarbose was utilized as standard α-amylase inhibitor. ## DPPH-radicals scavenging assay Anti-oxidant potential of HT-Er2O3 NPs (test compound) was determined by using 1,1-Diphenyl, 2-Picrylhydrazal (DPPH) (Dhanasekaran et al. 2015; Kamal et al. 2015). For making DPPH solution, 100 mL methanol was taken, to which 24 mg of DPPH and dissolved. Different dilutions of the test compound, ranging from 62.50 to 1000 μg mL−1 were prepared. One mL of DPPH solution was added to 1 mL of test sample dilution and subsequently incubated in dark place for 30 min at 23 °C. Subsequent to incubation, absorbance values were recorded at 517 nm via UV–visible spectrophotometer. The positive control was ascorbic acid, whereas the negative control was DPPH solution without test sample. Percent scavenging of test compounds was determined via formula;\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\user2{\% FRSA} = \left({\frac{{{\varvec{Ab}}_{{\varvec{C}}} - \user2{ Ab}_{{\varvec{S}}} }}{{{\varvec{Ab}}_{{\varvec{C}}} }}} \right) \times 100$$\end{document}%FRSA=AbC-AbSAbC×100 ## Animals and approval of ethical committee BALB/c albino mice were utilized for the anti-diabetic study. Animals were acquired from NIH Pakistan and looked after in the animal house of the University of Malakand. Animals were maintained under normal day/night cycle (12 h light and 12 h dark) and provided enough food and ad libitum. The procedure and project were approved by research ethics committee at the Department of Pharmacy, University of Malakand. All animals procedure and handling were performed following the Commission on life sciences, National research council 1996 (Council) (NRC 1996). ## Acute toxicity test To analyze the toxicological effects of our test sample, albino mice were classified into different groups ($$n = 5$$) and were administered i/p dose of 200–2000 mg kg−1 of our test compound. Animals were closed observed for one week for the appearance of any symptoms of toxicity including lethality or any aberrant behavior (Ayaz et al. 2017; Mir et al. 2019). In acute toxicity study, no animals’ lethality was observed for one week. Further, no cyanosis, spontaneous activity, tail pinch, aggressiveness, convulsions or bizarre behavior was observed during the course of study. So, our sample was observed to be safe at the tested concentrations, though its chronic effects on individual organs need further detailed studies. ## Induction of diabetes For diabetes induction, mice were kept in fasting for 8–12 h and ten percent alloxan-monohydrate (150 mg kg−1) was administered intraperitoneally (I/P). Normal saline (NS) was given to control group. Following inducing of DM, blood glucose level was calibrated with glucose meter and only diabetic animals for further proceedings were proceeded (Hussain et al. 2019). ## Experiment design Mice were divided randomly into 4 groups and each group constituted of 5 mice. The concentration of glucose in blood was observed at 0th, 1st, 4th, 7th, 11th, 14th, and 21st days of treatment. Group I: Diabetic group was given alloxan and Tween 80 (I/P).Group II: Control nondiabetic group was given normal saline only (I/P).Group III: Treatment group was given glibenclamide (dose μg kg−1) PO.Group IV: Treatment group received test compound (dose μg kg−1) (I/P). ## Oral glucose tolerance test (OGTT) To evaluate the animal’s glucose tolerance capacity after oral glucose administration, animals of both groups including normal control and disease control were kept on fasting overnight. Subsequently, 2 mg/kg oral glucose was administered to each animal. Blood glucose was monitored at different times from (0 to120 min) after administration of glucose (Mahnashi et al. 2022a). ## Drugs administration and assessment of blood glucose level Animals were distributed in four groups. Group 1 was administered Alloxan at 150 mg kg−1 dose and tween 80 and acted as disease control group. This group was untreated and was kept for comparison with the treated groups. Group 2 was placebo group which was maintained on normal diet for comparison, Group 3 was disease group administered with standard drug glibenclamide at 5 mg/kg dose daily for 21 days. Further, group 4 was disease group which was maintained on 30 mg/kg dose of our test compound. Blood glucose levels were monitored three times a day using glucometer (Viva check Inox Laboratories Wilmington USA) and were observed for 21 days (Shaheen et al. 2016). ## Blood biochemistry analysis Animals were euthanized at the end of experiments, via halothane anesthesia following standard procedure and samples of blood from lateral tail were collected for further analysis. Blood tests including cholesterol level, triglycerides, LDL, HDL, and total lipids were analyzed via techno 786 semi-automated bio-chemistry analyzer. Colorimetric enzymatic test using glycerol-3-phosphate-oxidase (gpo) and Chod-Pap enzymatic photometric tests were employed for the analysis (Pundir and Aggarwal 2017). ## Statistical analysis Experimental procedures were performed three times and the results are reported as Mean ± SEM. One way ANOVA and multiple comparison DUNNETT’s test was used for the statistical comparison of the treatment and control groups using Graph Pad Prism version 5. ## Estimation of median inhibitory concentrations The dose–response curve was used to calculate the Median inhibitory concentrations (IC50) using using Graph Pad Prism version 5. ## α-glucosidase inhibition The synthetic HT-Er2O3 NPs exhibited concentration-dependent inhibitory potentials against alpha-glucosidase enzyme. Results of the standard drug acarbose were comparable as shown in Fig. 5. The percent inhibition against α-glucosidase by HT-Er2O3 NPs at the different test concentrations in the range of (1000 μg mL−1 to 62.5 μg mL−1) were 92.30, 83.60, 81.15, 74.43 and 66.13 percent respectively with IC50 value of 12 μg mL−1. The positive control, acarbose displayed the IC50 values of 9.5 μg mL−1.Fig. 5Results of Er2O3 NPS inhibitory potentials against α-glucosidase enzyme. Results were presented as mean ± SEM of three independent experimental observations. * represents p value < 0.001 when compared with placebo group. Acarbose was used as positive control ## HT-Er2O3 NPs inhibit α-amylase enzyme Again HT-Er2O3 NPs exhibited considerable inhibition against α-amylase enzyme in comparison to control drug as shown in Fig. 6. The observed percent inhibitions by HT-Er2O3 NPs were 90.30, 79.63, 71.20, 58.67, and 45.00 at concentrations of (1000 μg mL−1 to 62.5 μg mL−1), respectively with IC50 value of 78 μg mL−1. The IC50 value of positive control, acarbose was 9.5 μg mL−1.Fig. 6Results of Er2O3 NPS inhibitory potentials against α-amylase enzyme. Results were presented as mean ± SEM of three independent experimental observations. * represents p value < 0.001 when compared with placebo group ## HT-Er2O3 NPs scavenge DPPH radicals In this assay, the synthesized HT-Er2O3 NPs exhibited high percent activity against DPPH free radicals with concentration dependent manner (Fig. 7). The synthesized HT-Er2O3 NPs showed 66.25, 53.63, 47.23, 39.82 and 28.31 percent scavenging at the concentrations of (1000 μg mL−1 to 62.5 μg mL−1) respectively with IC50 of 78 μg mL−1. The % DPPH scavenging of HT-Er2O3 NPs was compared with Ascorbic acid (positive control), whose IC50 value was 39 μg mL−1.Fig. 7Results of Er2O3 NPS DPPH radical scavenging potential. * represents p value < 0.001 when compared with positive control group ## OGTT results After induction of diabetes and administration of oral dose of glucose the blood glucose was 370.66 μg dL−1 at 0 time, 411.00 μg dL−1 after 30 min, 449.33 μg dL−1 at 60 min and 552.66 μg dL−1 after 120 min. Results of the assay is indicated in Fig. 8.Fig. 8Results of the oral glucose tolerance test (OGTT) ## HT-Er2O3 NPs exhibited considerable anti-hyperglycemic effects Results of various treatments on blood glucose level of animals is summarized in Table 2. Briefly, in disease group (group 1) the blood glucose level was observed to be elevated persistently 567.00 ± 3.46 mg dL−1 on day 0, till 389.33 ± 4.50 mg dL−1 on day 21. Blood glucose level of saline treated group (group 2) was within the range throughout the study. Though it was little elevated 115.33 ± 7.86* mg dL−1 l at the start of experiment but then stabilized and on day 21 it was 93.50 ± 1.60* mg dL−1. Positive control, group (group 3) maintained the blood glucose level of diabetic animals in an average range of (376.66 ± 1.20* mg dL−1 on day 0, 351.33 ± 5.20* mg dL−1on day 1, 341.00 ± 3.66* mg dL−1 on day 4, 301.38 ± 2.33* mg dL−1 on day 11, 279.88 ± 3.80* mg dL−1 on day 14 and 235.33 ± 2.33* mg dL−1 on day 21. Overall glibenclamide was unable to control hyperglycemia in diabetic animals. Our tested compound HT-Er2O3 NPs have exhibited better control over blood glucose level of the diabetic animals (503.66 ± 5.92* on day 0 and 185.66 ± 2.60* on day 21).Table 2Effect of various treatments on blood glucose levels of diabetic animalS. No. GroupsDose mg kg−1Blood glucose level (GL) (mg dL−1)Day 0Day 1Day 4Day 7Day 11Day 14Day 21Group1After Alloxan + Tween 80150567.00 ± 3.46507.00 ± 5.77522.66 ± 3.75500.66 ± 4.33469.66 ± 2.60454.33 ± 2.96389.33 ± 4.50Group2Normal control saline0.35115.33 ± 7.86121.00 ± 3.51116.50 ± 2.66108.33 ± 2.89103.00 ± 4.5097.00 ± 1.5093.50 ± 1.60Group3Glibenclamide05376.66 ± 1.20351.33 ± 5.20341.00 ± 3.66361.50 ± 2.50301.38 ± 2.33279.88 ± 3.80235.33 ± 2.33Group4HT-Er2O3 NPs30503.66 ± 5.92458.66 ± 2.02375.00 ± 3.09310.66 ± 2.33298.00 ± 4.04205.33 ± 2.60185.66 ± 2.60***$p \leq 0.001$ at $95\%$ confidence interval ## Biochemical analysis of blood Results of the biochemical analysis of blood is summarized in the inset Fig. 9A–D. In disease group, average total lipids were 682.33 μg dL−1, cholesterol level was 180.66 μg dL−1, triglycerides were 315.00 μg dL−1, HDL was 51.33 and LDL was 105 mg/dl respectively (Fig. 9A). Treatment with HT-Er2O3 NPs have normal blood parameters i.e. total lipids, cholesterol, triglycerides, HDL and LDL after 21 days as observed in Fig. 9C. The blood glucose level was at comparatively low level in the positive control group indicating reduction in the hyperglycemia mediated changes in the blood total lipids, triglycerides, cholesterol, HDL and LDL levels (Figure C).Fig. 9Effect of Er2O3 NPs therapy on serum lipids including total lipids, cholesterol, triglycerides, HDL and LDL. ( A): Disease group; (B): Positive control group, (C): Er2O3 NPs treated group; (D): Normal group ## Discussion Biological synthesis of the metal NPs provides an eco-friendly and one-step process for the synthesis of novel metal nanoparticles. Previously, we have demonstrated synthesis of Fe2O3 (Mohamed et al. 2020b), CeO2 (Mohamed et al. 2020a), Cr2O3 (Mohamed et al. 2020), BiVO4 (Mohamed et al. 2019b), ZnO (Mohamed et al. 2020c), CuO (Mohamed et al. 2021) and Ag nanoparticles (Mohamed et al. 2019a) using the fruit material of Hyphaene thebaica* fruit material. To date, the exact mechanism of bioreduction leading to the synthesis of metal nanoparticles is not well explored because of the heterogeneity of the plant extracts (Dauthal and Mukhopadhyay 2016). Plant phytochemicals possess different chemical and physical properties including metal ion reduction and stabilization (Abomuti et al. 2021). The generally proposed mechanism includes a simple bioreduction and stabilization process which is mediated by the phytochemical constituents in the plant extracts. Phytochemicals plays a dual role, thereby, at first reduces the metal salt in to ions, in this case reducing the erbium nitrate, followed by stabilization through the capping or coating agents found in the fruit extracts of the Hyphaene thebaica. The intricate nature and phytochemistry of the plant extracts makes it difficult to specify a particular class of specific biomolecules in metal reduction, stabilization and morphological control of NPs (Huo et al. 2022). Here, the green synthesis of nano-erbia is reported for the first time in the literature. Medicinal plants like H. thebaica have a rich phytochemistry and includes compounds like vanillic acid, cinnamic acid, sinapic acid, caffeic acid, chlorogenic acid, epicatechin, hesperetin, naringin, glycosides, quercetin, rutin, tannins, saponins etc. Such phenolic and flavonoid compounds have the ability to execute redox reactions and subsequently stabilize the nanoparticles (El-Beltagi et al. 2018; Islam et al. 2022; Jadoun et al. 2021; Jeevanandam et al. 2022; Karatoprak et al. 2017). Previous studies have indicated that the phenolic compounds possess strong reducing ability and hence most likely agents for catalyzing the bioreduction as compared to other macromolecules in plant extracts like carbohydrates and proteins (Huo et al. 2022). X-rays diffraction pattern was found to be consistent with the previously published literature (Munawar et al. 2020; Wang et al. 2018b). Similarly, the FTIR peak at ~ 438 (Er-O) cm−1 and ~ 582 cm−1 (Er-O-Er) are in agreement with reported literature (Azad and Maqsood 2014). The FTIR signature indicated that different phytochemicals available in the pristine aqueous fruit extracts were responsible for the reduction and capping process and eventually led to the synthesis of stable nanoparticles (Khan et al. 2013). The optical bandgap of the HT-Er2O3 NPs was calculated 5.25 eV by applying the K–M function which is correlates with values reported in the literature (Kamineni et al. 2012; Kao et al. 2012). The Raman vibrations corelates with the previous studies (Joya et al. 2019). Diabetes mellitus (DM) is a disorder of metabolism of glucose that encompasses raised levels of blood glucose and other complications like neuropathy, retinopathy, nephropathy, and micro and macrovascular complications (Arky 1982). Defects in the production, secretion and action of insulin are responsible for hyperglycemia (Booth et al. 2016). DM affects about five percent population worldwide and its occurrence is increasing at an alarming rate, and is also associated with number of other diseases (Rahim et al. 2019). There are about 450 million peoples around the world which are effected by DM and the numbers are expected to grow up to 690 million by 2044 (Cho et al. 2018). Type 1 (T1DM) and Type 2 (T2DM) are the important kinds of DM. ( Del Prete et al. 1977; Himsworth and Kerr 1939). Type 1 diabetes is an immune-associated, destruction of insulin-producing pancreatic β cells (Atkinson et al. 2011). Type 1 diabetes treatment includes the use of insulin and its analogues. T2DM can be managed by controlling diet, obesity and with anti-diabetic drugs (Ohlson et al. 1985). In the development of antidiabetic therapeutic agents, α–amylase and α–glucosidase inhibitors are the vital targets. These enzymes breakdown polysaccharides (starch) to glucose and the inhibition of these enzymes play an important role to reduce glucose absorption in the intestine (Gin and Rigalleau 2000). There are several therapeutic approaches used for the treatment of diabetes, like lowering postprandial hyperglycemia by hydrolyzing α-amylase and α-glucosidase enzyme inhibition (Wang et al. 2018a). Anti-diabetic drugs as enzyme inhibitors like acarbose, voglibose, and miglitol were utilized for years to treat diabetes (Chaudhury et al. 2017). Plants contains numerous secondary metabolites exhibiting different properties including the metal ion reduction and stabilization. The interaction of plants with metals as a natural process and have got the attention of many scientists to utilize the secondary metabolites of plants as a reducing and stabilizing agent (Javed et al. 2021). The biosynthesis of metal NPs utilizing medicinal plants serve as alternative to utilizing hazardous chemical and physical synthetic techniques (Anand et al. 2017). We for the first-time synthesized HT-Er2O3 NPs by using plant extracts as reducing and stabilizing agents. The prepared nanoparticles were extensively characterized in their in-vitro and in-vivo assays were performed for determination of its antioxidant and antidiabetic potential. The synthetic HT-Er2O3 NPs exhibited concentration dependent inhibition against α-glucosidase and the percent inhibition at the concentrations of 1000, 500, 250, 125 and 62.5 μg mL−1 was 92.30, 83.60, 81.15, 74.43 and 66.13 percent respectively with IC50 value of 12 μg mL−1. In α-amylase inhibition assay, HT-Er2O3 NPs showed 90.30, 79.63, 71.20, 58.67, and 45.00 percent inhibition at concentrations of 1000, 500, 250, 125, and 62.50 μg mL−1, respectively with IC50 value of 78 μg mL−1. In DPPH free radical scavenging assay, the synthesized HT-Er2O3 NPs showed 66.25, 53.63, 47.23, 39.82 and 28.31 percent inhibition at the concentrations of 1000, 500, 250, 125 and 62.5 μg mL−1 respectively with IC50 of 78 μg mL−1 (Fig. 1). Animal studies revealed that our tested sample are safe at higher doses and was effective in lowering post-prandial hyperglycemia. As summarized in Table 2, our tested compound HT-Er2O3 NPs have exhibited better control over blood glucose level of the diabetic animals (503.66 ± 5.92* on day 0 and 185.66 ± 2.60* on day 21 as compared to standard drug glibenclamide. Er2O3 NPs therapy for 21 day has normalized hyperlipidemia. Thus in-vitro enzyme inhibition coupled with anti-radical properties and in-vivo anti-hyperglycemic and anti-hyperlipidemic potentials make it a potential candidate for further detailed studies. 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--- title: A novel shark single-domain antibody targeting OGT as a tool for detection and intracellular localization authors: - Xiaozhi Xi - Guokai Xiao - Guiqi An - Lin Liu - Xiaochun Liu - Peiyu Hao - Jennifer Yiyang Wang - Dandan Song - Wengong Yu - Yuchao Gu journal: Frontiers in Immunology year: 2023 pmcid: PMC9968394 doi: 10.3389/fimmu.2023.1062656 license: CC BY 4.0 --- # A novel shark single-domain antibody targeting OGT as a tool for detection and intracellular localization ## Abstract ### Introduction O-GlcNAcylation is a type of reversible post-translational modification on Ser/Thr residues of intracellular proteins in eukaryotic cells, which is generated by the sole O-GlcNAc transferase (OGT) and removed by O-GlcNAcase (OGA). Thousands of proteins, that are involved in various physiological and pathological processes, have been found to be O-GlcNAcylated. However, due to the lack of favorable tools, studies of the O-GlcNAcylation and OGT were impeded. Immunoglobulin new antigen receptor (IgNAR) derived from shark is attractive to research tools, diagnosis and therapeutics. The variable domain of IgNARs (VNARs) have several advantages, such as small size, good stability, low-cost manufacture, and peculiar paratope structure. ### Methods We obtained shark single domain antibodies targeting OGT by shark immunization, phage display library construction and panning. ELISA and BIACORE were used to assess the affinity of the antibodies to the antigen and three shark single-domain antibodies with high affinity were successfully screened. The three antibodies were assessed for intracellular function by flow cytometry and immunofluorescence co-localization. ### Results In this study, three anti-OGT VNARs (2D9, 3F7 and 4G2) were obtained by phage display panning. The affinity values were measured using surface plasmon resonance (SPR) that 2D9, 3F7 and 4G2 bound to OGT with KD values of 35.5 nM, 53.4 nM and 89.7 nM, respectively. Then, the VNARs were biotinylated and used for the detection and localization of OGT by ELISA, flow cytometry and immunofluorescence. 2D9, 3F7 and 4G2 were exhibited the EC50 values of 102.1 nM, 40.75 nM and 120.7 nM respectively. VNAR 3F7 was predicted to bind the amino acid residues of Ser375, Phe377, Cys379 and Tyr 380 on OGT. ### Discussion Our results show that shark single-domain antibodies targeting OGT can be used for in vitro detection and intracellular co-localization of OGT, providing a powerful tool for the study of OGT and O-GlcNAcylation. ## Introduction O-GlcNAcylation, an abundant protein post-translational modification, modifies thousands of nucleocytoplasmic proteins (1–4). These O-GlcNAcylated proteins are involved in many important biological processes, including the regulation of metabolism, proteasomal degradation, DNA replication and signal transduction [5]. Aberrant O-GlcNAcylation is closely associated with the development of various diseases such as immune system disorders [6], cancer [7] cardiovascular disease [8] and diabetes [9]. OGT attaches GlcNAc from glycosyl donors to serine/threonine residues of proteins. It is an important enzyme required for O-GlcNAcylation to occur. The regulation of OGT function is a hot topic of research in the fields of biology, biochemistry, medicine and pharmacology [10, 11]. However, one of the main obstacles for the study of O-GlcNAcylation and OGT is the lack of favorable research tools. Antibodies with only heavy chains (HCAbs) were first reported by researchers from camelids in the early 1990s [12]. Two years later, it was discovered that sharks also possessed a type of antibody with only two heavy chains, termed immunoglobulin neoantigen receptors (IgNARs) [13]. Variable structural domains (VNARs) of IgNARs identify naturally occurring independent heavy chain-only binding structural domains with a molecular weight of ~12 kDa [14]. VNARs differ from classical and single-domain camelid antibodies due to the lack of complementarity determining region 2 (CDR2) [15, 16]. In addition, VNARs have two highly variable loops (HV2 and HV4) [17, 18]. VNARs have a longer CDR3 segment than classical antibodies and form an additional intercellular disulfide bond, allowing for more stable structure from VNARs while recognizing invisible epitopes on target antigens [19, 20]. Furthermore, VNARs can be produced at low cost by non-mammalian expression systems [21] and exhibit good stability under a variety of conditions [22]. In addition to these unique features, due to the small size of VNARs, they have strong potential for applications in super-resolution imaging [23]. Here, two whitespotted bambooshark were immunized with recombinant OGT protein. Then, an anti-OGT VNAR phage display library was constructed using mRNA from the immunized shark peripheral blood lymphocytes (PBLs). After three panning cycles, three VNARs targeting OGT were isolated. The three VNARs of 2D9, 3F7 and 4G2 were expressed in E. coli. The affinities of the three VNARs were examined. The intact molecular weight of recombinant VNARs was assayed by LC-MS/MS. The VNARs can be used for ELISA and immunofluorescence assays. VNAR 3F7 was predicted to bind with OGT via amino acid sites of Ser375, Phe377, Cys379 and Tyr 380. This study provides a new tool for the research of OGT and O-GlcNAcylation. ## Expression and purification of OGT recombinant protein The recombinant plasmid pET-28a-OGT was preserved by our laboratory. The expression and purification experiments were carried out as descripted previously [24]. Briefly, the plasmid DNA was transformed into E. coli BL21 (DE3) cells and induced with isopropyl-β-D-thiogalactoside (IPTG). The induced bacterial was collected by centrifugation and fragmented. Ni-NTA column (GE Healthcare 17-5268-02, USA) was used to purify the recombinant OGT protein. OGT expression and purification were assessed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). ## Shark immunization Two male Chiloscyllium plagiosum sharks were first immunized with recombinant ncOGT 100 µg emulsified in complete Freund’s adjuvant (Sigma-Aldrich, USA). Two weeks later, the sharks were immunized by emulsifying 12 µg of ncOGT using incomplete Freund’s adjuvant. Immunization was carried out every fortnight. The final booster was administered with 2 µg ncOGT dissolved in phosphate-buffered saline (PBS) through intravenous injection. Blood samples were collected at week 0 (before immunobleeding), weeks 6, 8 and 10. PBLs were isolated and total RNA was prepared as described by Vincke et al. [ 25]. ## Detection of OGT specific IgNAR in shark serum The titers of shark serum IgNAR were measured using ELISA. Immunized shark plasma was diluted in a $4\%$ (w/v) milk PBS (MPBS) gradient (1:10; 1:100; 1:1000; 1:10,000; 1:100,000) and added to ELISA plates. Detection was carried out using a laboratory-prepared rabbit monoclonal antibody against shark [26]. Then, a 1:5000 dilution of anti-rabbit IgG-HRP antibody was added to the plates and incubated at 37°C for 1 h. Finally, TMB colour solution was added to each well and incubated at 37°C for 15 minutes. The reaction was stopped with 1 M H2SO4 and the optical density (OD) 450 was measured. ## Phage display library construction Total RNA was extracted from the shark’s PBLs and used as templates to synthesizing first-strand cDNA using oligo(dT) primers. The library encoding sequences were amplified by PCR from cDNA, the framework specific primers Bam VF1 CGCGGCCCAGCCGGCCATGGCCGCCSMACGGSTTGAACAAACACC and Bam VF2 CGCGGCCCAGCCGGCCATGGCCGCCGCACGGGTTGAACAAACACCG. DNA fragments were cleaved with restriction enzymes Nco I and Not I (NEB) for use in subsequent experiments. An anti-OGT phage display library of about 108 independent transformants was obtained following the detailed protocol as Ubah et al. [ 27]. ## Selection from libraries by phage display Library amplification was carried out overnight at 30°C in 2 x TY medium, ampicillin and kanamycin. The phage was precipitated and pannning. For the first round of panning, immunotubes were washed 10 times with PBS containing $0.1\%$ Tween 20 (PBST). For the second and third rounds, immunotubes were washed 20 times with PBST at the time of panning. After the third round of panning, the screened phages were detected by polyclonal and monoclonal phage ELISA and the results were used to evaluate the effectiveness of the panning. the ELISA method was similar to that in 2.4, with the difference that the HRP-conjugated anti-M13 antibody (1:1000) was used as a secondary antibody and OD 450 was measured using a microplate reader. ## Shark VNAR protein expression and purification The VNAR gene was amplified by PCR and cloned into pET-28a by Nco I and Not I restriction sites. The recombinant plasmid was identified by sequencing. The expression and purification method was similar to the OGT preparation method in 2.1. ## VNAR affinity determination Biomolecular interaction analysis (BIA) was used to analyse the affinity of shark single-domain antibodies [28].. Anti-OGT VNARs were diluted to a concentration of 100 nM. Recombinant ncOGT protein was diluted to a series of different concentrations with PBS: 50, 100, 200, 400 and 800 nM; Calibration solution (PBST solution containing $1\%$ glycerol) and OGT solution with gradient dilution were introduced successively. The binding time was 120 s. PBS buffer was introduced, and the dissociation time was 200 s. After dissociation, phosphoric acid (1:100 dilution) regeneration solution was introduced, and the regeneration time was 200 s. The response data were normalized using Langmuir combined with model fit analysis (Biacore evaluation software). ## Molecular weight analysis Antibody samples are processed by adding 200 μL ddH2O to a 10 kDa ultrafiltration tube, 11000 g, centrifuged for 1 min, and removing a small amount of glycerol from the ultrafiltration tube. Add 200 μg of antibody to the ultrafiltration tube, 11000 g for 3 min. Add 200 μL of ddH2O to displace the sample once for desalination. UPLC separation was performed using an ACQUITY UPLC Protein BEH C4 Column, 300 Å, 1.7 µm, 2.1 mm x 50 mm. The column temperature was set at 80°C and UPLC separation was performed using solvent A ($0.1\%$ v/v FA in water) and solvent B ($0.1\%$ FA in acetonitrile). The flow rate of the UPLC was 0.3 ml/min and the mobile phase B was eluted from $10\%$ to $95\%$ in 8 min using a gradient elution. LC-MS TIC (total ion count) data were collected in the resolution mode in the m/z range 500-3,500. The mass spectrometry raw data were processed using BiopharmaLynx software (v 1.2) in complete protein mode at a resolution of 10,000, mass matching tolerance was set at 20 ppm. ## Biotin labeling of single-domain antibody and establishment of the indirect ELISA Anti-OGT VNARs were dissolved 1 mg/mL with PBS. 10 mM solution of biotin reagent was immediately prepared in an organic solvent dimethyl sulfoxide (DMSO). The protein solution was spiked with the appropriate amount of 10 mM NHS-LC-Biotin reagent (Cat. No.: C100212/C100215, angon Biotech) and the reaction was incubated on ice for 2 h. At this point the labelling of the protein had been completed. Although, there was still excess un-reacted and un-hydrolyzed biotin reagent in the solution, the labelled protein could usually be tested initially by means of a biotin quantification kit [29]. OGT proteins (1, 2, 4 and 10 μg/mL) were directly immobilized on 96-well plates at 4°C and the optimal coating antigen concentration (2667-0.2667 nM) of biotinylated anti-OGT VNARs were determined by indirect ELISA. ELISA plates were closed with $3\%$ BSA in PBST. Follow-up was similar to project 2.4, with the difference that binding of anti-OGT VNAR was shown with HRP-conjugated streptavidin (1:5000) secondary antibody. At the end of the reaction, absorbance was measured at 450 nm. ## Western blot NCI-H1299 cells were lysed in SDS lysis buffer ($1\%$ SDS, 50 mM Tris-HCl pH 7.5, 100 mM NaCl, and Complete™ Protease Inhibitor). Lysates were resolved on 4-$12\%$ SDS polyacrylamide gels (SDS PAGE), transferred to Immobilon-FL PVDF membranes (#IPVH00010, MerckMillipore), and immunoblotted with the indicated antibodies. Blots are identified with the antibody, dilution and clone/catalogue number in parentheses. The antibodies used were anti-OGT (1:1000, ab96718, Abcam), Goat Anti-Rabbit IgG H&L (HRP) (1:2000, ab6721, Abcam), and HRP-Streptavidin (1:5000, RABHRP3, Sigma-Aldrich). ## Flow cytometry Wild-type NCI-H1299 cells and OGT-silenced NCI-H1299 (lab constructs for preservation) [30] were collected, washed with PBS, and then fixed and permeabilized with $4\%$ paraformaldehyde and $0.2\%$ Triton-100. Cells were incubated with FACS buffer (1 x PBS with $0.5\%$ BSA) containing 10 µg/ml of commercial OGT antibody (ab96718, Abcam), 2D9 and 3F7 for 30 min at 4°C. Subsequently, cells were washed twice with FACS buffer and stained with goat anti-rabbit (ab150077, Abcam) coupled to A488 and anti-His fluorescent antibody (EPR20547, Abcam). Cells were washed twice with PBS. Samples were analyzed by flow cytometry. ## Transfections NCI-H1299 cells were seeded on glass coverslips of six-well plates at 1 x 105 cells per well. After cell apposition, the p3×Flag-OGT plasmid was transfected into the cells using Attractene Transfection reagents (QIAGEN), according to the manufacturer’s instructions. ## Immunofluorescence and co-localization NCI-H1299 cells were seeded at 5 x 104 cells per well on glass slides and incubated for 16 h. Fixation and permeabilization were performed with $4\%$ paraformaldehyde and $0.1\%$ Triton X-100. Cells were incubated overnight at 4°C with anti-OGT/O-linked N-acetylglucosaminyl transferase antibody (ab96718) and biotinylated anti-OGT VNAR (1 μg/ml). Detection of antigen-bound VNARs was achieved by the addition of anti-6-His A488 MAbs (CST, #14930). Nuclear staining was then performed with 4’,6-diamidino-2-phenylindole (DAPI). Images were obtained using fluorescence microscopy. NCI-H1299 cells transfected with p3×Flag-OGT plasmid were spread on glass coverslips in 24-well plates grown overnight and then subjected to immunofluorescence co-localization. An Alexa Fluor® 488 fluorescent Anti-DDDK tag (ab205606) was used to identify the OGT within the transfected cells. Antibody detection was similar to the immunofluorescence operation. ## Computational modeling of OGT proteins and VNAR The 3D structures of single-domain antibodies and ncOGT were predicted by SWISS MODEL [31], and the top ranking was selected as their 3D structure. VNARs (PDB ID: 7FBK [32]) and OGT (PDB ID: 7NTF [32] were predicted to be suitable 3D structures. ZDock [33] and PDBePISA [34] were used to obtain binding patterns between VNARs and OGT. First, ZDock was used to initially explore the location of VNAR and OGT, and 10 predictive composite models with good fit were screened. PDBePISA was used to identify the binding chains and binding sites of OGT and 3F7. The binding chains for these two protein interactions can be obtained from the analysis. ## Statistical analysis The data were analyzed using Graph Pad Prism (version 5.0), three times in parallel for each set of experiments. All raw MS data were analyzed using the MNIFI software (Waters Company, U.K.). Data were presented as the mean ± standard deviation. ## Recombinant purification of OGT and animal immunization The OGT was expressed and purified by the E. coli expression system. SDS-PAGE analysis was performed and no major impurities from E. coli were seen (Figure 1A). A good quality antigen was produced and used to immunize shark. To promote effective immunity in sharks, we optimized immunization parameters, including route of administration and injection site (Figures 1B, C). Shark antigen-driven immune responses were determined by measuring IgNAR titers in serum (pre- and post-bleeds, respectively). An increase in OGT-specific IgNAR titers were observed after successive immunization boost up. Figure 1: Immunization of bamboo sharks with OGT. (A) Results of ncOGT protein isolation and purification. MW, protein molecular markers; Lane 1: bacterial lysate supernatant by pET28a-ncOGT; Lane 2: penetrating liquid; Lane 3: 20 mM imidazole eluent; Lane 5-13: the purified OGT protein. SDS-PAGE gel was stained using Coomassie Brilliant Blue. (B) The injection and bleeding sites. (C) The immunization schedules. ## The Construction of shark VNAR phage display immune library The VNAR sequences were amplified from isolated PBLs by PCR and cloned into a phage vector containing the M13 phage truncated coat protein PIII gene in frame. The display library size was estimated to be 2.65 x 108 transformants (Figures 2A, B). Ten single clones of the library formed by the construct were randomly selected for sequencing to determine the quality of the library. The results showed that $90\%$ of the libraries incorporated VNAR sequences and $80\%$ encoded functional inserts with a unique amino acid sequence in CDR3 (Figure 2C). Figure 2: Construction and quality evaluation of phage library. (A) Determination of the titer of the single domain antibody phage library. 1-4: The dilution of original bacterial solution for 10-2, 10-3, 10-4 and 10-5 times respectively. (B) Detection of positive rate of single domain antibody library phage by PCR; (C) Sequences of VNAR domains from the randomly selected ten colonies of the phage library. FR is framework region; CDR is complementarity-determining region; HV is hypervariable region. Canonical Cys residues are enclosed in red. ## Isolation of Anti-OGT VNARs by phage display OGT-specific shark VNARs were screened out by three rounds of phage panning with decreasing OGT concentration. After three rounds of panning, the specific OGT phage was enriched (Figure 3A). Fourteen clones from the third panning of the phage library were selected by monoclonal phage ELISA (Figure 3B). Three VNARs, named as D9, 3F7 and 4G2, were identified by sequencing, and they were efficiently expressed in E. coli and purified (Figure 3C). By analyzing the sequences of the three VNARs, we found that the VNARs belong to type II VNAR. In type II VNAR, a cysteine residue was found in FR1, FER3, CDR1 and CDR3, respectively, a result consistent with those reported in the literature (Figure 3D) [19]. Figure 3: Selection of anti-OGT VNARs. (A) Detecting the titer of phage single domain antibody library in each round of panning; (B) Detection of specific bacteriophage by monoclonal ELISA; (C) Characterization and analysis of molecular weight of 2D9,3F7 and 4G2; (D) Amino acid sequence alignment of the three anti-OGT VNARs. FR is framework region; CDR is complementarity-determining region; HV is hypervariable region. Canonical Cys residues are enclosed in red. ## Affinity kinetics of Anti-OGT VNARs Biacore was used to detect the affinity of the three anti-OGT VNARs. The detected affinity determination results and kinetic parameters are shown in Figure 4 and Table 1. The equilibrium dissociation constants (KD) of three single domain antibodies combined with ncOGT are 3.55, 5.34 and 8.97×10-8 M respectively. The results showed that the three VNARs, especially 2D9 and 3F7, bind OGT with good affinity (Figures 4A, B, C). Figure 4: Sensorgram of biomolecular interaction analysis of VNARs binding to OGT. The binding activity of anti-OGT VNARs was analyzed at concentrations between 50 nM and 800 nM. (A) 2D9; (B) 3F7; (C) 4G2. TABLE_PLACEHOLDER:Table 1 ## Molecular weight analysis of Anti-OGT VNARs In order to efficiently characterize and qualify the VNARs, the complete molecular weight of 2D9 and 3F7 were determined by LC-MS/MS. LC-MS/MS data were analyzed using BioFinder 3.0 software. The total ion chromatogram was shown in Figures 5A, C, and deconvolution plots were shown in Figures 5B, D. The molecular masses of VNARs were indicated in Table 2. The relative molecular mass of the primary peak of 3F7 and 2D9 were 12544.9961 and 12531.4451 respectively, which deviated from the theoretical relative molecular mass by 1.18 and 0.674, which was within the error range. Figure 5: Molecular weight determination results of the intact anti-OGT VNARs. (A) Original mass spectrum of the intact 2D9. (B) Deconvolution diagram of the intact 2D9. (C) Original mass spectrum of the intact 3F7. (D) Deconvolution diagram of the intact 3F7. TABLE_PLACEHOLDER:Table 2 ## Generation and determination of biotinylated Anti-OGT VNARs To facilitate the application, they were labelled with biotin. Biotinylated VNARs were prepared and used to detect OGT protein by ELISA. The results show that optimal concentration of antigen for OGT protein was 1 μg/mL (Figure 6A). As shown in Figure 6B, the VNARs were exhibited high reactivity, and the EC50 values of the biotinylated 2D9, 3F7 and 4G2 were identified as 102.1, 40.75 and 120.7 nM by indirect ELISA. Combined with the above results, 3F7 was shown to be the best one (Figure 6B). To investigate whether the anti-OGT-VNARs can be used for Western blot (WB) analysis, NCI-H1299 cell lysates were used for the detection. The commercial anti-OGT antibody (as a positive control) can detect the OGT protein expressed in NCI-H1299 cells (Figure 6C). However, there was no band when NCI-H1299 cell lysates were examined by WB using 2D9 and 3F7 (Figures 6D, E). The reasons might be that the antigen used in immunizing the sharks and panning is native recombinant OGT protein, which produces antibodies that may only bind conformational epitopes, whereas WB experiments need antibodies that can bind linear epitopes of the antigen. Therefore, anti-OGT VNARs prepared in this study cannot be used in WB analysis. Figure 6: Evaluation of specificity and reactivity of the biotinylated anti-OGT VNAR. (A) Determination of the optimal conditions of antigen. (B) The reactivity of VNAR against OGT was detected by indirect ELISA. C-E. NCI-H1299 whole-cell extracts were analyzed by WB using the commercial anti-OGT antibody (C), 2D9 (D) and 3F7 (E), respectively. ## Binding analysis of Anti-OGT VNARs to cells We examined the ability of the antibody to bind to the cells using flow cytometry. In our experiments, we used NCI-H1299 cells previously constructed in the laboratory with OGT-silenced NCI-H1299 and wild type NCI-H1299 cells. The results showed that our prepared shark nanobodies 2D9 and 3F7 could bind to NCI-H1299 cells highly expressing OGT and the binding effect was comparable to that of commercial OGT (Figure 7A). At the same time, the binding of anti-OGT antibodies decreased in the OGT-silenced NCI-H1299 (Figure 7B), demonstrating that the antibodies have specific recognition of intracellular OGT. Figure 7: Flow cytometric evaluation of the binding ability of shark nanobodies to cells. (A) and (B) Binding of commercial anti-OGT monoclonal antibodies, shark single domain antibodies 2D9 and 3F7, to wild type H1299 cells (A) and OGT-silenced NCI-H1299 (B). ## Immunofluorescence analysis of intracellular OGT by VNARs To assess the function of OGT VNARs, 2D9 and 3F7 were used to detect the localization of OGT in cells by immunofluorescence microscopy. It had been reported in some literature that OGT proteins were localized in the nucleus along cytoplasmic [35]. We localized OGT using commercial antibody (Figure 8A), 2D9 (Figure 8B) and 3F7 (Figure 8C) by immunofluorescence, the results showed that most of OGT (Red) localized in nucleus (Blue) in NCI-H1299 cells as previously reported [36]. To further confirm the specific binding of VNARs to OGT, we transfected Flag-tagged OGT in NCI-H1299 cells for immunofluorescence analysis, as shown in Figures 8D–F. Anti-Flag antibody (Green) and VNARs (Red) had strong co-localization in the nucleus and cytoplasm of NCI-H1299 cells, as can be seen in the Merge section. These results suggest that anti-OGT VNARs could be suitable candidate tools for intracellular imaging. Figure 8: Cytosolic and cytoplasmic localization of OGT. (A) Binding of anti-OGT/O-linked N-acetylglucosaminyltransferase (ab96718); (B) 2D9; (C) 3F7 in wild-type H1299 cells, respectively. Immunofluorescence images representing OGT (Red) and DAPI (Blue) were overlaid (Merge) to show localization within the nucleus. (D–F). Commercial anti-OGT antibody (D), 2D9 (E), and 3F7 (F) were each co-localized with p3×flag-ncOGT transfected H1299 cells. Immunofluorescence images of OGT represented in green, the three antibodies represented in Red and DAPI Blue were overlaid (Merge) to show co-localization. ## Models of VNARs-OGT complexes To predict the binding site of OGT and VNAR 3F7, the 3D model for 3F7-OGT complex was generated by merging the homology models developed for 3F7 and OGT. Complex models of 3F7 and OGT were constructed using ZDOCK server based on VNAR 3F7 (PDB ID 7BFK) and OGT crystal structures (PDB ID 7NTF) (Figure 9A), respectively. PDBePISA was applied for identification of possible binding sites. The residue ARG96/GLY99/TYR100/GLU102/TYR104 in VNAR 3F7 QA74: Table/Figure XXX has not been mentioned in the article. Please add a citation within the text, noting that Figures and Tables must appear in sequence.establishing hydrogen bond with a conserved SER375/PHE377/CYS379/TYR380 in the OGT receptors was formed in the generated model signifying its validity (Figure 9B). Figure 9: The 3D model for 3F7-OGT. (A) Surface representation of the VNAR 3F7 and OGT. OGT (blue) and 3F7 (green). (B) Cartoon representation of VNAR 3F7-OGT model. VNAR 3F7 hydrogen bond sites (Blue stick) and OGT (Yellow sticks). The residue ARG96/GLY99/TYR100/GLU102/TYR104 in VNAR 3F7 establishing hydrogen bond with a conserved SER375/PHE377/CYS379/TYR380 in the OGT receptors. The TPR repeat consists of 34 highly conserved amino acid residues, which are mainly involved in the binding of OGT to substrate proteins [37]. The grooves in the supercoiled structure on the right contain amino acid residues involved in binding the target proteins of OGT [38], and the protein-binding ability of OGT is affected by the number of TPRs [39]. The predicted four amino acid binding sites of OGT were located on TPR13, which is shared by all the three types of OGT isoforms ncOGT, mOGT and sOGT, indicating that VNAR 3F7 can facilitate the in-depth research on the functional of OGT. ## Discussion O-GlcNAc is a universal protein modification with a variety of important physiological functions. The addition of GlcNAc to protein is catalyzed by a unique OGT. The study of protein O-GlcNAc function has been limited by the lack of suitable research tools and methods [40, 41]. Therefore, in order to further explore the physiological and pathological role of OGT, high specificity antibodies against OGT remain to be prepared. The structure in VNARs and the characteristics of the composition of the individual Loop loops allow them to recognize more hidden epitopes, and therefore VNARs may play an unexpected role in development studies of OGT and O-GlcNAcylation. Conventional monoclonal antibodies are limited in their in-depth application by their large size, complex structure and sensitivity to extreme environmental temperatures [42]. Shark VNAR is better able to penetrate tissues and dense structures and penetrate deeper into the target protein. In this study, three shark VNARs (2D9, 3F7 and 4G2) against OGT were screened by phage display assay. The three single-domain antibodies were purified to obtain high purity and high affinity antibodies. Their KD values could reach above 10-8 M, and it is well documented that KD values below 10-8 M indicate a high affinity between the antigen and the antibody [14, 16, 21]. Shark VNARs were successfully prepared against OGT and these were then labelled with biotinylated markers for subsequent antibody application. Chemical biotinylation is the binding of biotin to a non-specific covalent bond in the target molecule thereby linking the two [43]. In our experiment, chemical biotinylating was used. Polyclonal or monoclonal antibodies were often used in traditional ELISA methods, but the quality of polyclonal antibodies could be inconsistent from batch to batch and the process of industrial mass production of monoclonal antibodies can be complex. VNARs could be easily produced in recombinant protein expression systems with hosts including bacteria and yeast. This advantage allowed VNARs to be produced on a large scale and ensures batch-to-batch consistency. Additionally, single domain antibody can be intracellular expressed, which can be used for live-cell imaging. Then, in order to confirm the application scope of the antibody. The nucleus localization of OGT was verified by immunofluorescence, suggesting that anti-OGT VNARs might be used as a powerful tool for super-resolution imaging of OGT. The amino acid binding site of 3F7 to OGT was analyzed using computer simulations. The protein-binding site of OGT is a discontinuous epitopes located on TPR13 domain, which is consistent with the results that 3F7 can’t be used for WB assay. ## Conclusion In this study, three VNARs against OGT proteins were isolated from an immune phage display VNAR library. VNAR 3F7 was shown to be more reactive, sensitive and reproducible. Immunofluorescence and ELISA have shown that OGT can be accurately detected by this VNAR with results comparable to those of commercial antibodies. Therefore, this small single domain antibody would contribute to the research of OGT and O-GlcNAcylation in vitro. This VNAR would like to have further applications for live-cell imaging of OGT by VNAR intracellular expression and super-resolution fluorescence imaging. ## Data availability statement The GenBank Submissions Staff and have received confirmation of the email address. Data already available on NCBI.2D9: https://www.ncbi.nlm.nih.gov/search/all/?term=OQ065564. 3F7: https://www.ncbi.nlm.nih.gov/search/all/?term=+OQ065565. 4G2: https://www.ncbi.nlm.nih.gov/search/all/?term=+OQ065566. ## Ethics statement The animal study was reviewed and approved by Institutional Animal Care and Use Committee of Ocean University of China. ## Author contributions YG proposed the concept and design of the study. XX and GX wrote the article. XXi, GX and DS conducted the research and validation of the experiment. XXand GX constructed the graphs and tables. LL, XL, GA, JW and PH collected and analyzed the data. YG, WY and XX were responsible for the accuracy of the study and the review of the article. 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--- title: Evidence of SARS-CoV-2 infection in postmortem lung, kidney, and liver samples, revealing cellular targets involved in COVID-19 pathogenesis authors: - Viviana Falcón-Cama - Teresita Montero-González - Emilio F. Acosta-Medina - Gerardo Guillen-Nieto - Jorge Berlanga-Acosta - Celia Fernández-Ortega - Anabel Alfonso-Falcón - Nathalie Gilva-Rodríguez - Lilianne López-Nocedo - Daina Cremata-García - Mariuska Matos-Terrero - Giselle Pentón-Rol - Iris Valdés - Leonardo Oramas-Díaz - Anamarys Suarez-Batista - Enrique Noa-Romero - Otto Cruz-Sui - Daisy Sánchez - Amanda I. Borrego-Díaz - Juan E. Valdés-Carreras - Ananayla Vizcaino - José Suárez-Alba - Rodolfo Valdés-Véliz - Gretchen Bergado - Miguel A. González - Tays Hernandez - Rydell Alvarez-Arzola - Anna C. Ramírez-Suárez - Dionne Casillas-Casanova - Gilda Lemos-Pérez - Omar R. Blanco-Águila - Angelina Díaz - Yorexis González - Mónica Bequet-Romero - Javier Marín-Prida - Julio C. Hernández-Perera - Leticia del Rosario-Cruz - Alina P. Marin-Díaz - Maritza González-Bravo - Israel Borrajero - Nelson Acosta-Rivero journal: Archives of Virology year: 2023 pmcid: PMC9968404 doi: 10.1007/s00705-023-05711-y license: CC BY 4.0 --- # Evidence of SARS-CoV-2 infection in postmortem lung, kidney, and liver samples, revealing cellular targets involved in COVID-19 pathogenesis ## Abstract There is an urgent need to understand severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-host interactions involved in virus spread and pathogenesis, which might contribute to the identification of new therapeutic targets. In this study, we investigated the presence of SARS-CoV-2 in postmortem lung, kidney, and liver samples of patients who died with coronavirus disease (COVID-19) and its relationship with host factors involved in virus spread and pathogenesis, using microscopy-based methods. The cases analyzed showed advanced stages of diffuse acute alveolar damage and fibrosis. We identified the SARS-CoV-2 nucleocapsid (NC) in a variety of cells, colocalizing with mitochondrial proteins, lipid droplets (LDs), and key host proteins that have been implicated in inflammation, tissue repair, and the SARS-CoV-2 life cycle (vimentin, NLRP3, fibronectin, LC3B, DDX3X, and PPARγ), pointing to vimentin and LDs as platforms involved not only in the viral life cycle but also in inflammation and pathogenesis. SARS-CoV-2 isolated from a patient´s nasal swab was grown in cell culture and used to infect hamsters. Target cells identified in human tissue samples included lung epithelial and endothelial cells; lipogenic fibroblast-like cells (FLCs) showing features of lipofibroblasts such as activated PPARγ signaling and LDs; lung FLCs expressing fibronectin and vimentin and macrophages, both with evidence of NLRP3- and IL1β-induced responses; regulatory cells expressing immune-checkpoint proteins involved in lung repair responses and contributing to inflammatory responses in the lung; CD34+ liver endothelial cells and hepatocytes expressing vimentin; renal interstitial cells; and the juxtaglomerular apparatus. This suggests that SARS-CoV-2 may directly interfere with critical lung, renal, and liver functions involved in COVID-19-pathogenesis. ### Supplementary Information The online version contains supplementary material available at 10.1007/s00705-023-05711-y. ## Introduction Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leading to coronavirus disease 19 (COVID-19) has been posing a great threat to global public health since 2020 [111, 121]. There is an urgent need to understand virus-host interactions involved in the mechanisms of SARS-CoV-2 infection and pathogenesis that may contribute to the identification of new therapeutic targets. About $20\%$ of COVID-19 patients develop serious manifestations such as severe pneumonia, acute respiratory distress syndrome (ARDS), sepsis, and death [104]. SARS-CoV-2 is an enveloped positive-sense single-stranded RNA virus with a genome size of approximately 30 kb [100]. Two overlapping open reading frames (ORFs) are translated from the 5’ region: ORF1a and ORF1b. The latter is translated from a -1 frameshift that allows a large polyprotein to be produced beyond the stop codon of ORF1a. The two polyproteins are proteolytically processed by viral proteases to yield the non-structural proteins NSP1-NSP16. Additional smaller ORFs encode the structural proteins: spike (S), envelope (E), membrane (M), nucleocapsid (NC), and other polypeptides [30]. Angiotensin-converting enzyme (ACE) 2 (ACE2) has been identified as the main functional receptor of SARS-CoV-2, interacting with the viral S protein. Importantly, the primary physiological role of ACE2 is the regulation of vasoconstriction and blood pressure [23]. Detection of SARS-CoV-2 in different organs and various COVID-19 manifestations such as cardiovascular and nervous system complications, kidney injury, and gastrointestinal tract symptoms suggest that extrapulmonary sites of infection contribute to disease pathogenesis [8, 11, 13, 25, 68, 70, 73, 90]. In particular, the kidney has been shown to be involved in COVID-19 pathogenesis, and renal injury is associated with morbidity and mortality [55]. Postmortem analysis and the possible impact of SARS-CoV-2 on different organs are valuable for understanding virus spread and the pathophysiological mechanisms of infection. Investigating the cell tropism of the virus and its role in virus-induced pathogenesis is especially important for understanding the mechanisms of SARS-CoV-2 infection and identifying new therapeutic targets. In this work, we investigated the presence of SARS-CoV-2 in various tissues of patients who died with COVID-19 and its relationship with host factors involved virus-induced pathogenesis. We identified potential cellular and molecular targets that may be involved in and affected by SARS-CoV-2 infection, with implications for virus-induced pathogenesis and therapeutics. ## Patients Five patients (R, J, D, B, and T) with a nasopharyngeal swab that was positive for SARS-CoV-2 by real-time RT-PCR (qRT-PCR) [67] and who died with COVID-19 from April to September 2020, were studied in this work. Lung samples were also obtained from a person who died from a cause that was unrelated to COVID-19 (Table 1). These cases were part of a larger cohort whose main pathological findings have been summarized previously [15].Table 1Features of patients and clinical presentationPatientAge/sexSymptomsCoexisting conditionsChest radiograph; treatmentAltered blood laboratory findingsTime of death from onset of symptomsPostmortem tissuesR77/F-Shortness of breath-Dyspnea-Cough-Fatigue-Fever-Ischemic cardiomyopathy-Hypertension-Type II diabetes mellitus-NASH1-Dementia-NHC/B2-BGGO3-Kaletra4, chloroquine, CeftriaxoneIncreased creatinine levels, proteinemia, albuminemia18 daysTracheaLung, Kidney, LiverJ70/M-Dyspnea-Fever-Ischemic cardiomyopathy-Hypertension-Coronary artery disease-Dementia-NHC/B-BGGO-Kaletra, interferon alfa-2b, AzithromycinIncreased creatinine levels15 daysLung, KidneyD85/M-Dyspnea-Fever-Coronary artery disease-Hypertension-Dementia-NHC/B-BGGO--Kaletra, interferon alfa-2b, AzithromycinIncreased creatinine levels15 daysLung, KidneyB68/F-Shortness of breath-Dyspnea-Cough-Coronary artery disease-Asthma-Obesity-NASH- Hypertension-NHC/B-BGGO-Kaletra, interferon alfa-2b, AzithromycinIncreased creatinine levels, proteinemia, albuminemia, Increased liver damage markers13 daysLung, Kidney, LiverT80/F-Dyspnea-Cough-Fatigue-Coronary artery disease-Hypertension-BGGO-Kaletra, interferon alfa-2b, Azithromycin15 daysLungC (No COVID)75/M-Chronic obstructive pulmonary disease-Hypertension-Coronary artery disease--Lung1Nonalcoholic steatohepatitis2No history of HCV/HBV infection3Bilateral ground glass opacity4Lopinavir, ritonavir ## Procurement of specimens With permission from the patient’s family, a limited autopsy to collect postmortem specimens [6] was performed in all cases by the autopsy service of the pathology department from the Hospital “Luis Díaz Soto” of Havana. This study received approval from the Ethics Committees of Hospital “Luis Díaz Soto” and the Center for Genetic Engineering and Biotechnology (CIGB). Postmortem tissue samples were obtained from visceral organs, including the lungs, liver, and kidneys within 3 hours after death. Autopsy was performed following recommendations and guidance on postmortem examinations of COVID-19 cases [38] and procedures established by the Cuban Health Ministry. Tissues were fixed with $4\%$ paraformaldehyde in PBS for 1 hour and then routinely processed under standard biosafety conditions. To prepare frozen sections, tissues were washed with $20\%$ sucrose in PBS overnight and then embedded in Tissue-Tek OCT compound (Sakura FineTek, Cat #4583, Tokyo, Japan). Ten-µm frozen sections were used. ## Masson’s trichrome and Picro Mallory staining Staining of frozen lung and kidney sections with Masson’s trichrome and Picro Mallory stain was performed as described elsewhere [57, 122]. Staining was quantified using the open-source image processing package Fiji (National Institute of Health). ## Immunofluorescence staining and confocal microscopy Immunofluorescent staining and confocal microscopy analysis of frozen lung, kidney, and liver sections was done as described previously with minor modifications [25, 26]. Tissue sections were washed with PBS, permeabilized with $0.5\%$ Tween 20 (T20) in PBS (PBS+T20 $0.5\%$), and saturated with $2\%$ BSA in PBS+T20 $0.1\%$, for 30 min. To detect the SARS-CoV-2 NC, sections were incubated with either mouse monoclonal IgG antibody (SINO Biologicals, catalog no. 40143-MM05) or SARS-CoV-2 NC peptide-specific (PKKDKKKKADETQALPQRQKK) [41] rabbit polyclonal antibodies or mouse monoclonal IgG antibody (CIGB Sancti Spíritus, Cuba; catalog no. CBSSNCov.2) for 2 hours at 27 °C (1:200 dilution in PBS+T20 $0.1\%$). In addition, the following primary antibodies were used (1:100-200 dilutions in PBS+T20 $0.1\%$): rabbit polyclonal anti-fibronectin (Dako Omnis, Agilent; catalog no. A0245), mouse monoclonal anti-microtubule-associated protein 2 (MAP2; Sigma; catalog no. M2320), rabbit polyclonal anti-ACE2 (a kind gift from the Center of Molecular Immunology, Havana, Cuba), rabbit polyclonal anti-DDX3X (a kind gift from A.H. Patel, MRC-University of Glasgow Centre for Virus Research, Glasgow, UK), rabbit polyclonal anti-NG2 (chondroitin sulphate proteoglycan 4 [CSPG4]; Abcam; catalog no. Ab81104), rabbit polyclonal anti-phospho S112 peroxisome proliferator activated-receptor γ (PPARγ; Abcam; catalog no. Ab60953), rabbit polyclonal anti-prohibitin (PHB; Abcam; catalog no. Ab 75766), rabbit polyclonal anti-PGC1 (Abcam; catalog no. Ab 72230), mouse monoclonal anti-keratin 10 (K10) (Thermo Scientific, catalog no. MA1-35857), mouse monoclonal anti-CD34-FITC conjugate (Dako, catalog no. F7081), mouse monoclonal IgG anti-CD68 (Dako Omnis, Agilent; catalog no. M0814), mouse monoclonal anti-CD163-FITC conjugate (Pharmigen, catalog no. 563697), mouse monoclonal anti-CD163-APC conjugate (Invitrogen, catalog no. 17-1639-42), mouse monoclonal anti-IL1β-FITC conjugate (Invitrogen, catalog no. 11-7018-42), mouse monoclonal anti-PD1-APC conjugate (Invitrogen, catalog no. 17-2799-42), mouse monoclonal anti-CD47-FITC conjugate (Biolegend, catalog no. 323106), mouse monoclonal anti-PDL1-APC conjugate (Invitrogen, catalog no. 17-5983-42), mouse monoclonal anti-IL6-PE conjugate (Invitrogen, catalog no. 12-7069-82), and mouse monoclonal anti-vimentin (VMT) (Sigma Aldrich, catalog no. V6389). After washing with PBS+T20 $0.1\%$, slides were incubated for 1 hour at 27 °C with one of the following secondary antibodies: fluorescein-conjugated goat anti-rabbit IgG (KPL; catalog no. 172-1506) or goat anti-mouse IgG (KPL; catalog no. 02-18-18), Alexa Fluor 647–conjugated either anti-mouse IgG (Cell Signalling; catalog no. 4410S) or anti-rabbit IgG (Cell Signalling; catalog no. 4414S), Alexa Fluor 594–conjugated anti-rabbit IgG (Cell Signalling; catalog no. 8889) (1:250-500 dilution in PBS+T20 $0.1\%$, depending on the combination of primary and secondary antibodies used). When required, lipid droplets (LDs) were stained using Oil Red O for 30 min and washed with water. Some negative controls included sections that were incubated directly with Alexa 647– or fluorescein-conjugated goat anti-mouse/rabbit IgGs. Subsequently, samples were washed with PBS+T20 $0.1\%$, nuclei were counterstained with 4′,6-diamidino,2-phenylindole (DAPI) (1 mg/mL) (KPL; catalog no. 1-03-01, Gaithersburg, USA), and the preparation was coverslipped in Vectashield mounting medium (Vector Laboratories, catalog no. H-1000, Burlingame, CA., USA). Samples were analyzed using an Olympus FV1000 IX81 laser scanning fluorescence microscope (Olympus Corporation, Japan) and the imaging software FlowView Viewer v3.1. Images were also taken with differential interference contrast (DIC) microscopy. Channels were recorded sequentially, and images were acquired as z-stack series. Images with a field of view of 512×512 pixels, 621,000.0 µm/pixel, were acquired with a sampling speed of 20000.0 µs/pixel. All images were taken with a bit depth of 12 bits. Colocalization between the different channels (described in Supplementary methods) was analyzed from image stacks using the open-source image processing package Fiji with Just Another Colocalization Plugin (JACoP) [10]. ## Analysis of postmortem lung samples Both clinical and pathology evidence indicated the development of ARDS in all patients [27]. The pulmonary tissue showed evidence of a distinctive diffuse acute alveolar damage (DAD) pattern with predominant advanced phases (fibro-proliferative and fibrotic phases) (Supplementary Fig. S1, representative results illustrated for patient R). Lung remodeling with typical interstitial fibrosis showing features of fibrosis by accretion was the most important pathological mechanism identified in all patients. Alveolar damage with destruction of the alveolar wall lining with desquamation of alveolar type I pneumocytes (AT1) and type 2-like pneumocytes (AT2) proliferating along the surface of fibrous alveolar septa were frequently observed (Supplementary Figs. S1 and S2A). On the other hand, SARS-CoV-2 isolated from nasal swaps from patient R was grown in cell culture (Vero E6 cells), where numerous virus-like particles (VLPs) (ranging from 80 nm to 125 nm in diameter) could be seen at low magnification (Supplementary Fig. S3). In addition, aged hamsters could be infected by cell-culture-adapted SARS-CoV-2 (Supplementary Fig. S4) pointing to the infectious nature of these viral isolates. Interestingly, infected hamsters developed features of pulmonary fibrosis (Supplementary Fig. S2B). Next, SARS-CoV-2 was detected in samples using confocal microscopy and antibodies specific for the NC protein of SARS-CoV-2 as described previously [25, 41, 61]. First, primary anti-NC antibodies were detected in lung samples from SARS-CoV-2-infected hamsters. As shown in Supplementary Fig. S4A-C, NC was detected (by all of the primary anti-NC antibodies used) in the lungs of SARS-CoV-2-infected hamsters, but not in those of mock-infected animals. The presence of SARS-CoV-2 was then analyzed in postmortem lung samples. While SARS-CoV-2 NC could not be detected in lung samples from patient T or from a non-COVID-19 patient who died after suffering from chronic obstructive pulmonary disease (COPD) (Fig. 1A, Supplementary Fig. 4D, 5A), it was identified in lung sections from patients R, J, D, and V (Fig. 1, Supplementary Figs. S4E and F and S5). These results indicated the presence of SARS-CoV-2 in the lungs of patients R, J, D, and B, but not of patient T, at advanced stages of DAD.Fig. 1Representative confocal microscopy images of lung sections from a person who died from a cause unrelated to COVID-19 and from patient R, incubated with various combinations of rabbit and mouse (CIGB, Sancti Spíritus) antibodies against NC and host proteins, followed by Alexa 647 (A647)- and fluorescein/(FITC)-conjugated anti-mouse/rabbit IgGs in different combinations, or other host-protein-specific (IL1β, CD163) primary mouse monoclonal antibodies conjugated to either FITC or APC. DAPI was used to stain the nucleus (blue channel). Colocalization was quantified using calculated intensity correlation quotients (ICQ) and Pearson’s (PC) and Manders’ (M1, M2) coefficients (see Supplementary Fig. S6). Bars: 50 µm. ( A) Lung section of a person who died from a cause unrelated to COVID-19, showing no staining for NC (A647). As a reference, a mouse monoclonal antibody against LC3B was used (FITC; arrows) (20X magnification). ( B–E) Illustrative regions of interest (ROIs) of lung sections from patient R showing colocalization between NC (A647) and LC3B (FITC) (B) (20X magnification) localization of NC (FITC or A647) with CD34+ (FITC) (C) or K10+ (D) cells and, concomitant with CD163+ (APC) and IL1β (FITC). Arrows indicate positive co-staining (40X magnification) Interestingly, a striking colocalization of NC and LC3B was observed, indicating the possible involvement of autophagic functions during SARS-CoV-2 infection (Fig. 1B). NC localized to alveolar epithelial cells, endothelial-like cells (ECLs), and macrophage-like cells (MLCs) (Figs. 1, 2, and 4; Supplementary Figs S5-S9). Markers of endothelium and endothelial progenitor cells (CD34) [88, 114] (Fig. 1C), bronchial and alveolar epithelial cells (K10) [87] (Fig. 1D), and monocyte/macrophages (CD163) [14] (Fig. 1E; Supplementary Figs. S5-S9)/(CD68, [25]) showed that NC was present in these cell types. NC was detected in both alveolar and interstitial CD163+ MLCs [14] (Supplementary Figs. S5C and S8B). In agreement with our previous study showing activation of NLRP3 in lung macrophages containing SARS-CoV-2 [25], NC was detected in CD163+ MLCs showing expression of IL1β (Fig. 1E; Supplementary Fig. S6D).Fig. 2Representative confocal microscopy images of lung sections from patient R incubated with various combinations of rabbit or mouse (CIGB, Sancti Spíritus) antibodies against NC and anti-fibronectin, anti-VMT, anti-DDX3X, or anti-phospho S112 PPARγ (PPARγ-P) antibodies, followed by fluorescein- (FITC) or Alexa $\frac{594}{647}$ (A$\frac{594}{647}$)-conjugated anti-rabbit/mouse IgGs) or stained with Oil Red O (ORO, TxRed channel). DAPI was used to stain the nucleus (blue channel). Colocalization was quantified using calculated intensity correlation quotients (ICQ) and Pearson’s (PC) and Manders’ (M1, M2) coefficients (see Supplementary Fig. S7). Bars: 50 µm. ( A–D) Illustrative ROIs of lung sections showing NC (FITC) detected in fibronectin+ cells (A647) (A) and VMT+ (VIM) cells (A647) (B) (40X magnification); NC (A647) detected in cells showing concomitant LDs (ORO) and PPARγ-P (FITC) (C) (20X magnification) colocalization of NC (FITC) and DDX3X+(A647) (D) (40X magnification). Arrows indicate positive co-staining In addition, NC staining was found on ACE2+ cells (Supplementary Figs. S5B andS8A), and interestingly, NC also displayed a staining pattern indicating its presence in the interface of the capillary endothelium and alveolar epithelial cells, along the inside of the alveolar septum, and surrounding the blood vessels representing the connective tissue, including fibroblast-like cells (FLCs) (Supplementary Fig. S5D). In addition, analysis of tracheal sections from patient R showed the presence of SARS-CoV-2 NC in the connective tissue and FLCs (Supplementary Fig. S5D). To investigate possible interactions of SARS-CoV-2 with extracellular matrix (ECM) components and FLCs, we performed double immunofluorescence staining of NC with key molecular targets involved in the wound healing response and lung pathogenesis. First, co-staining of NC and fibronectin was studied. As shown in Fig. 2A, NC was co-detected with fibronectin in the alveolar septa, suggesting its presence in fibronectin-expressing cells, including FLCs, and also in the ECM (Fig. 2A; Supplementary Fig. S7A). Then, VMT (major type III intermediate filament cytoskeletal protein of mesenchymal cell origin, including FLCs), which is also expressed in alveolar epithelial cells undergoing epithelial-to-mesenchymal transition (EMT) during injury repair [47], was studied. Notably, NC was strongly co-detected with VMT (Fig. 2B; Supplementary Fig. S7B), suggesting the presence of SARS-CoV-2 in the connective tissue and associated cells. Subsequently, the presence of SARS-CoV-2 in lipofibroblast-like cells (LPFs), a critical cell type for lung homeostasis and injury repair, was investigated. LPFs are the main LD-producing cells in the alveolar interstitium [71, 72], and therefore, the presence of LDs and expression of PPARγ are key features of LPFs [71, 72]. Notably, NC was detected in interstitial cells showing the simultaneous presence of LDs and activated (phosphoS112) PPARγ, suggesting the presence of SARS-CoV-2 in LPFs displaying PPARγ signaling (Fig. 2C; Supplementary Fig. S7D). On the other hand, DDX3X, a host protein involved in the life cycle of various viruses that has been shown to be recruited to LDs during HCV infection [4], was co-detected with NC (Fig. 2D; Supplementary Fig. S7C), and DDX3X also colocalized with LDs (not shown). Interestingly, NLRP3 staining was observed not only in CD68+ and CD163+ cells but also in CD68- and CD163- interstitial cells (Fig. 3A and B, Supplementary Fig. S8C and D). Given that FLCs are involved in the inflammasome-mediated response [77] and that VMT has been shown to play a key role in biogenesis of LDs and activation of NLRP3 [24, 40], we investigated the colocalization of these proteins. Interestingly, NLRP3 partially colocalized to alveolar interstitial cells showing concomitant expression of vimentin and ORO staining (Fig. 3C, Supplementary Fig. S8F), suggesting that LD-containing cells possibly representing LPFs may be involved in the inflammasome response. IL1β staining was also observed in fibronectin-expressing cells (Fig. 3D, Supplementary Fig. S8E). We noted frequent mitochondrial damage in the samples analyzed by electron microscopy (Supplementary Fig. S2, S11, and S14). As mitochondrial damage is related to NLRP3 activation through mitochondrial reactive oxygen species (mtROS) and oxidized mitochondrial DNA (mtDNA), which are increased in airway macrophages in cases of pulmonary fibrosis [99], we searched for colocalization of NC with proteins that are commonly recruited or present in mitochondria, including prohibitin (PHB) and PGC1α [5, 89]. As illustrated in Fig. 3E, NC colocalized with PHB in some cells (Fig. 3, Supplementary Fig. S8F), raising the possibility of direct virus-mediated mitochondrial impairment. Fig. 3Representative confocal microscopy images of lung sections from patient R incubated with various combinations of rabbit and mouse antibodies against NC, fibronectin, VMT, CD68, NLRP3, and PHB, followed by fluorescein- (FITC) or Alexa $\frac{594}{647}$ (A$\frac{594}{647}$)-conjugated anti-rabbit/mouse IgG or host-protein-specific (IL1β, CD163) primary mouse monoclonal antibodies conjugated to either FITC or APC, respectively; or stained with Oil Red O (ORO, TxRed channel). DAPI was used to stain the nucleus (blue channel). Colocalization was quantified using calculated intensity correlation quotients (ICQ) and Pearson’s (PC) and Manders’ (M1, M2) coefficients (see Supplementary Fig. S8C-G). Bars: 50 µm. ( A–D) Illustrative ROIs of lung sections showing NLRP3 (FITC) localized to either CD68+ (arrows) or CD68- cells (arrowheads) (A647) (A) (20X magnification); NLRP3 (A594) localized to either CD163+ (arrows) or CD163- cells (arrowheads) (APC) (B); concomitant localization of VMT (VIM)(FITC) with ORO (TxRed) and NLRP3 (A647) (C); IL1B localized to Fib-expressing cells (D) (40X magnification). Arrows indicate positive co-staining. Previous studies have shown that populations of lung-fibrotic fibroblasts (expressing JUN and IL6 with upregulation of the immune-checkpoint proteins CD47 and PDL1) and immunosuppressive PD1+ macrophages (expressing IL1β) are involved in impaired alveolar regeneration and a weakened adaptive T cell immune response during pulmonary fibrosis in humans and mice, as well as during SARS-CoV-2 infection [18, 19, 21, 59]. Given our evidence for impaired epithelial regeneration, induction of IL1β, and the presence of SARS-CoV-2 NC in FLCs and MLCs, we next studied the occurrence of NC in CD47+ PDL1+ IL6+ and CD163+ PD1+ cells. As shown in Fig. 4A, NC was detected concomitantly with CD163 and PD1, indicating its presence in regulatory CD163+ PD1+ macrophages (Supplementary Fig. S9A). Interestingly, NC was also co-detected with PD1 and IL1β, suggesting that regulatory PD1+ macrophages containing NC were able to produce IL1β (Fig. 4B, Supplementary Fig. S9B). In addition, CD47 and PDL1 were identified simultaneously with fibronectin (Fig. 4C, Supplementary Fig. S9C) as well as with IL6 (Fig. 4D, Supplementary Fig. S9D), indicating the presence of CD47+ PDL1+ FLCs able to produce IL6. Notably, NC was also found together with CD47 and IL6 (Fig. 4E, Supplementary Fig. S9E), suggesting the presence of SARS-CoV-2 in lung-fibrotic FLCs. Fig. 4Representative confocal microscopy images of lung sections from patient R incubated with various combinations of rabbit polyclonal antibodies against NC or fibronectin, followed by Alexa $\frac{594}{647}$ (A$\frac{594}{647}$)-conjugated anti-rabbit IgG and host protein-specific (IL1β, CD163, PD1, CD47, IL6, PDL1) primary mouse monoclonal antibodies conjugated to FITC, PE, or APC. DAPI was used to stain the nucleus (blue channel). Colocalization was quantified using calculated intensity correlation quotients (ICQ) and Pearson’s (PC) and Manders’ (M1, M2) coefficients (see Supplementary Fig. S9). Bars: 50 µm. ( A–E) Illustrative ROIs of lung sections showing NC (A594) detected in PD1+ cells (APC) concomitantly with either CD163 (A) or IL1β (B) (FITC) (arrows) (20X and 40X magnification, respectively); Fib (A594) (C) or IL6 (PE) (D) detected concomitantly with CD47 (FITC) and PDL1 (APC) (arrows); NC (A647) detected in CD47+ cells (FITC) concomitantly with IL6 (PE) (E) (arrows) (40X magnification) ## Analysis of postmortem kidney samples Pathological features observed in kidney samples included acute tubule injury and interstitial fibrosis (Supplementary Fig. S10A and S11) [15]. Some glomeruli were shrunken with widened Bowman space. There was also some occlusion of the microvascular lumen in peritubular and glomerular capillary loops. Damaged mitochondria were commonly observed. Some VLPs ranging from 60 nm to 84 nm in diameter were found in a proximal tubule cell (Supplementary Fig. S11). However, as these are low-magnification electron microscopy images, these VLPs lacked sufficient ultrastructural detail to be identified as SARS-CoV-2-related particles. Immunofluorescence analysis of SARS-CoV-2 showed patchy granular cytoplasmic staining of NC in tubular epithelial cells (Fig. 5A). Importantly, NC staining in the juxtaglomerular apparatus was also observed (Supplementary Fig. S10B1, B2 and C). In addition, NC could be detected in podocytes, mesangial cells, and endothelial cells in some glomeruli (Supplementary Fig. S10B3). Moreover, NC was found in the medullar region, in CD34+ endothelium of vessels, and in interstitial cells (Fig. 5B, Supplementary Fig. S12A). Interestingly, NC localized to peritubular fibronectin+ interstitial cells and also to some VMT+ cells (Fig. 5A, C, and D; Supplementary Fig. S12B, and C). In addition, NC staining colocalized with PGC1α (Fig. 5E, Supplementary Fig. S12D) and PHB (not shown), providing further support for the presence of NC in or near mitochondria. Fig. 5Representative confocal microscopy images of kidney sections from patient R incubated with various combinations of rabbit or mouse (CIGB, Sancti Spíritus) antibodies against NC and host proteins, followed by Alexa 647 (A647)- and fluorescein/FITC-conjugated anti-mouse/rabbit IgGs, either alone or in different combinations. DAPI was used to stain the nucleus (blue channel). Bars: 50 µm. ( A) Renal cortex section showing detection of NC (A647) in tubule epithelial cells (arrows) and peritubular interstitial cells (arrowhead) (20X magnification). ( B) Renal medullary section showing that NC (A647) localized to the endothelium of CD34+ vessels and interstitial cells (arrowheads) (40X magnification). C–E) Renal cortex sections showing localization of NC (FITC) to Fib+ peritubular interstitial cells (A647) (arrows) (40X magnification). Note the negative control of a section incubated only with secondary fluorescent-probe-conjugated antibodies without primary antibodies (Merge, No Fib, No NC) (20X magnification). ( C) NC (A647 or FITC) localized to VMT+ (VIM) cells (FITC) (arrows) (D) and colocalized with PGC1α (arrows) (E) (40X magnification) A key feature of interstitial cells is the accumulation of LDs (Supplementary Fig. S11C) as well as the expression of NG2, especially in the medullar region [52]. Notably, NC was found in NG2+ cells displaying strong LD staining, further suggesting the presence of SARS-CoV-2 in renal interstitial cells (Fig. 6A, Supplementary Fig. S13A). NC localized to both renal tubular and interstitial ACE2+ cells (Supplementary Figs. S10D and S13B). Moreover, similar to what was observed in lung samples, NC colocalized with LC3B and DDX3X (Fig. 6B and C and Supplementary Fig. S13C and S13D).Fig. 6Representative confocal microscopy images of kidney sections from patient R incubated with various combinations of rabbit and mouse (CIGB, Sancti Spíritus) antibodies against NC and host proteins, followed by Alexa 647 (A647)- and fluorescein/FITC-conjugated anti-mouse/rabbit IgG, either alone or in different combinations, or stained with Oil Red O (ORO, Tx Red channel). DAPI was used to stain the nucleus (blue channel). Bars: 50 µm. ( A) Renal medullary section showing that NC (A647) localized to NG2+ cells displaying LDs (ORO) (arrows). ( B) Renal cortex section showing colocalization between NC (FITC) and LC3B (A647). G, glomerulus. ( C) Colocalization between DDX3X (FITC) and NC (A647) in a renal medullary section (40X magnification) ## Analysis of postmortem liver samples Steatosis was the major pathological finding observed in liver samples [15]. In addition, clusters of small LDs in lipolysosome-like structures similar to those reported in hepatocytes [82] and in non-alcoholic fatty liver disease (NAFLD) patients [16], as well as damaged mitochondria, were frequently observed (Supplementary Fig. S14). Immunostaining of NC was detected in portal tracks including the connective tissue (Supplementary Fig. S15) and hepatocytes (Fig. 7, Supplementary Fig. S15). NC was also found in liver endothelial sinusoidal cells (LESCs) and adjacent hepatocytes, some of which showed ACE2 staining (Supplementary Figs. S15 and S16F). Interestingly, NC could be detected despite scarce ACE2 staining in liver samples from patient B (Supplementary Fig. S15C). Moreover, NC was detected in CD34+ cells, indicating the presence of SARS-CoV-2 in LSECs (Fig. 7B, Supplementary Fig. S16B). Furthermore, NC showed strong co-staining with LC3B, LDs, DDX3X, and VMT (Fig. 7; Supplementary Fig. S16). DDX3X (not shown) and LC3B (Fig. 8) also colocalized with LDs. This granulated staining pattern may indicate cellular redistribution of viral and host proteins to LDs and/or possibly viral replication-morphogenesis sites. Moreover, NC colocalized with PGC1α (Fig. 8A; Supplementary Fig. S16G) and PHB (not shown). These staining patterns prompted us to investigate the relationship of VMT with LDs, autophagy, and inflammasome markers in liver samples [9, 24, 40]. As shown in Fig. 8B and C (Supplementary Fig. S17), VMT localized together with LDs, LC3B, and NLRP3, suggesting its involvement in lipid metabolism, autophagy, and inflammasome functions. Fig. 7Representative confocal microscopy images of liver sections from patient R incubated with various combinations of rabbit and mouse (CIGB, Sancti Spíritus) antibodies against NC and host proteins, followed by Alexa 647 (A647)- and fluorescein/FITC-conjugated anti-mouse/rabbit IgG, either alone or in different combinations. DAPI was used to stain the nucleus (blue channel). Bars: 50 µm. ( A–E) Illustrative ROIs of liver sections from patient R showing and colocalization between NC (FITC) and LC3B (A647) (A), LDs (ORO, TxRed) (C), VMT (A647) (arrows) (E); localization of NC (A647) in CD34+ cells (FITC) (arrows) (B); colocalization between NC (A647) and DDX3X (FITC) (arrows) (D) (40X magnification)Fig. 8Representative images from confocal microscopy analysis of liver sections of patient R incubated with various combinations of rabbit and mouse antibodies against NC, PHB, VMT (VIM), LC3B, or NLRP3, followed by fluorescein- (FITC) and Alexa 647 (A647)-conjugated anti-rabbit/mouse IgG or stained with Oil Red O (ORO, Tx Red channel). DAPI was used to stain the nucleus (blue channel). Colocalization was quantified using calculated intensity correlation quotients (ICQ) and Pearson’s (PC) and Manders’ (M1, M2) coefficients (see Supplementary Fig. S13). Bars: 50 µm. ( A–C) Illustrative ROIs of lung sections from patient R showing NC colocalized with PHB (arrows) (A), LC3B (A647) detected in VMT+ (FITC) cells showing LDs (ORO) (arrows) (B); and NLRP3 (A647) detected in VMT+ (FITC) cells showing LDs (ORO) (arrows) (C) (40X magnification) ## Findings in postmortem lungs DAD is the pathological hallmark of ARDS [7]. SARS-CoV-2-mediated direct lung injury has been shown previously to be particularly relevant at early stages of infection, while later stages of DAD development have mostly been associated with host cellular responses [11, 79]. Advanced DAD with fibro-proliferation and fibrosis was seen in all of the samples. The patients included in this study had features that might have influenced the development of interstitial lung diseases and pulmonary fibrosis, such as age and certain comorbidities (Table 1) [64, 83]. However, none of these patients showed evidence of a previous pulmonary fibrosis disorder, and no fibrotic-like radiographic abnormalities were found when they were first diagnosed. These observations point to SARS-CoV-2 infection as a driver of the observed pathological changes, which were possibly enhanced by age and comorbidities. The ability of SARS-CoV-2 to induce fibrosis was also shown in infected aged hamsters. Interestingly, although there was evidence of proliferating AT2, the abundant loss of alveolar epithelial cells suggests that the epithelial cell regenerative response failed to restore the damaged alveolar epithelium. This is consistent with other studies describing impaired AT2 regeneration in postmortem lungs from COVID-19 cases [59, 74]. Detection of NC in lung samples from patients R, J, D, and B indicated the presence of SARS-CoV-2 at advanced stages of DAD. Thus, the approach used in this work to detect NC in human tissues, based on indirect immunofluorescence (IF) followed by confocal microscopy analysis, was found to be effective for monitoring the presence of SARS-CoV-2 in a variety of samples, in agreement with our previous report [25]. In addition, postmortem tissue samples were collected soon after the patient’s death, thus limiting tissue damage and increasing the chances of detecting both viral and host antigens under our experimental conditions. Although a small number of cases were studied in this work, our IF approach performed similarly to the highly sensitive approaches used by others, such as RT-qPCR and RNA sequencing, for detection of SARS-CoV-2 at advanced stages of DAD [21, 68, 75, 110]. On the other hand, IF has been described to be more sensitive and specific than immunohistochemistry (IHC) [45, 109] and has been used successfully by others to detect SARS-CoV-2 in different tissue samples, including kidney and liver [68, 108]. Accordingly, some reports have illustrated that IHC could be less specific than in situ hybridization (ISH) to detect SARS-CoV-2 in formalin-fixed paraffin-embedded (FFPE) tissue samples [58, 110]. The presence of NC in the interface of the capillary endothelium and alveolar epithelial cells as well as adjacent connective tissue suggests that SARS-CoV-2 may contribute directly to sustained damage and interference with the alveolar air-blood interface, deregulation of the wound-healing response (WHR) and immune responses leading to impaired viral clearance, reduced epithelium regeneration, tissue remodeling, and pathology. In addition, detection of NC in ELCs and their associated damage, not only in lung samples but also in kidney and liver, strongly suggests viral infection of endothelial cells. This is in agreement with other studies describing the presence of SARS-CoV-2 components in lung capillary endothelium and increased ACE2 expression in activated vascular endothelium [21, 42, 49, 56, 78, 102, 113, 118]. Macrophages have been shown to be key players during SARS-CoV-2 infection and its associated pathogenesis [25, 34, 59, 74, 118]. Interestingly, NC was identified in CD163+ and CD68+ cells corresponding to alveolar and interstitial MLCs, in accordance with other reports [14, 21, 25, 74, 105]. Importantly, co-detection of NC with NLRP3 and IL1β in MLCs and possibly in LPFs (see below) might indicate direct viral induction of inflammatory responses, which has been associated with COVID-19 pathogenesis [39]. We observed the common occurrence of mitochondrial damage in different cells in the tissues analyzed by electron microscopy. Colocalization of NC with PHB and PGC1α suggests that NC could be recruited to or close to mitochondria, suggesting a possible direct virus-mediated mitochondrial dysfunction, which may be associated with the generation of mtROS and mtDNA, contributing to NLRP3 activation and production of IL1β. Evidence supporting this view includes the observations that SARS-CoV-2 infection affects mitochondria structure and function [17], NSP2 interacts with mitochondrial PHB [20], and viral double-stranded RNA (dsRNA) is localized in mitochondria, leading to mitochondrial dysfunction in infected cultured cells [84]. Thus, mitochondrial dysfunction mediated by both viral and inflammatory responses may be connected to the ability of SARS-CoV-2 infection to stimulate the NLRP3 inflammasome and IL1β production [25, 95]. Lung fibrosis have been suggested to contribute to the progression of COVID-19 disease and post-COVID-19 sequelae [74, 103]. Notably, NC was detected in connective tissue and FLCs. LPFs are adipocyte-like cells that play a key role in mesenchymal-epithelial communication, providing triglyceride substrate to AT2 for surfactant synthesis [72]. Of note, impairment of homeostatic communications between AT2 and LPFs in the alveolar wall, leading to surfactant insufficiency, has been implicated in chronic lung diseases. These communications play an essential role in the repair response to lung injury, supporting AT2 growth and differentiation [72]. A key feature of this process is the activation of PPARγ signaling in LPFs induced by AT2-produced parathyroid hormone-related protein. Results from this work suggested the presence of SARS-CoV-2 in LPFs. The lipogenic nature of these cells was suggested by the concomitant detection of LDs and activated PPARγ. Thus, SARS-CoV-2 may impact LPFs, disrupting normal mesenchymal-epithelial homeostatic communications and surfactant production and contribute to lung pathogenesis. This may be particularly relevant, as reduced pulmonary surfactant levels are a hallmark of COVID-19 ARDS [81]. Impaired regulatory functions of LPFs may promote transdifferentiation to myofibroblasts and increased fibrosis. This is also important given that transdifferentiated LPFs are unable to support AT2 growth and differentiation during injury/repair responses [97]. Therefore, together with the direct influence of SARS-CoV-2 on AT2, this study raises the interesting possibility that SARS-CoV-2 may disrupt the regulatory functions of LPFs to promote fibrosis and disturb epithelial regeneration. Interestingly, PPARγ agonists have been used to promote repair responses in the lung by restoring epithelial–mesenchymal interactions and alveolar homeostasis in various models of lung injury [72]. Collectively, these observations point to PPARγ as a potential therapeutic target for COVID-19. Additional findings from this work indicating the occurrence of SARS-CoV-2 in fibronectin+ FLCs support the above-mentioned hypothesis. Fibronectin is a key component of the ECM involved in the pathogenesis of lung diseases. Although collagens are the predominant ECM proteins identified in fibrotic lesions, highly increased levels of fibronectins have been described to localize in pulmonary areas of active fibrogenesis [51]. Consequently, increased fibronectin deposition and fibronectin expression in fibroblasts have been described in various pathological conditions of the lung, including idiopathic pulmonary fibrosis (IPF), COPD, and cancer [37, 50, 62, 120]. Fibronectin has also been described to induce EMT of alveolar epithelial cells during lung injury, a cellular process that is involved in the opening of epithelial barriers and cell migration [47]. Thus, co-detection of NC with fibronectin in the connective tissue and FLCs suggest that SARS-CoV-2 may modulate fibronectin production and related functions [98]. Inhibition of fibronectin assembly has been proposed as a therapeutic opportunity for fibrosis [2, 98] and may also be considered as a potential target against SARS-CoV-2-related lung pathology. VMT, on the other hand, has been implicated in IPF and the invasive properties of fibroblasts in IPF, EMT during pulmonary fibrosis, non-alcoholic steatohepatitis, and hepatocellular carcinoma [47, 53, 76, 94, 107, 117]. The results from this study are in accordance with a recent report describing colocalization of VMT and the SARS-CoV-2 M protein in fibrotic lungs from COVID 19 patients [101]. However, although the role of VMT in lung WHR has not been completely elucidated, it has been shown to be required for remodeling of the alveolar epithelium and increased wound repair [47, 76]. Thus, usurping key VMT functions by SARS-CoV-2 in alveolar epithelial cells may contribute to impaired epithelial regeneration and WHR. Conversely, as VMT is involved in the life cycle of several viruses, including HIV, SARS-CoV, and SARS-CoV-2, it is considered to be an important antiviral target [28, 69, 115]. VMT has been shown to be required for SARS-CoV-2 replication and entry [3, 17, 93]. Accordingly, an interesting possibility is that EMT and increased VMT expression may render cells more susceptible to SARS-CoV-2 infection, particularly by facilitating viral entry and replication. Notably, a therapeutic peptide that modifies the supramolecular structure of VMT intermediate filaments has been shown to inhibit infection with betacoronaviruses, including SARS-CoV-2, in cell culture [28, 29]. Additionally, VMT is involved in inflammatory and fibrosis responses in the lung through activation of the NLRP3 inflammasome and induction of IL1β [24]. In this work, we found evidence of the possible involvement of VMT-expressing and LDs-containing cells in NLRP3 responses. Thus, in addition to MLCs, other cell types (including FLCs and epithelial cells with features of EMT) might contribute to inflammasome-mediated inflammatory responses. Taking into account that IL1β has been shown to play a role in lung injury and pulmonary fibrosis [44, 48], detection of NC in VMT-positive cells (including FLCs) indicates the involvement of VMT not only in the viral life cycle but also the pathogenesis induced by SARS-CoV-2 infection. Previous studies have shown that chronic inflammation driven by IL6 and macrophage-derived IL1β is associated with impaired alveolar regeneration through induction of damage-associated transient progenitors (DATPs) from AT2 cells that are unable to make a full transition to AT1 cells during pulmonary fibrosis in both humans and mice [18, 19]. Notably, lung fibrotic fibroblasts and immunosuppressive PD1+ macrophages have been linked to pulmonary fibrosis and an impaired adaptive T-cell immune response in both humans and mice [18, 36]. Similarly, increased macrophages expressing IL1β and lung fibrotic fibroblasts have been associated with DATPs in impaired alveolar regeneration during SARS-CoV-2 infection [21, 59]. Interestingly, increased proximity and interactions between MLCs and FLCs have been observed in late COVID-19 disease associated with expansion of mesenchymal cells and fibroblasts [74]. By upregulating CD47 and PDL-1, fibrotic fibroblasts enhance their survival, avoiding phagocytosis by PD-1+ macrophages while contributing, with IL6, to inflammation. Notably, combined immunotherapy with CD47- and IL-6-blocking agents has been shown to reverse fibrotic conditions in mice, suggesting new therapeutic alternatives for treating pulmonary fibrosis [18, 54]. Our work provides additional evidence that SARS-CoV-2 may directly influence this immunoregulatory route, thus contributing to the development of fibrosis and failure of the compensatory alveolar epithelial regeneration response. The localization of NC to LDs indicates a link between SARS-CoV-2 and lipid metabolism and LD biogenesis. An association between SARS-CoV-2 infection and lipid metabolism and LDs has been demonstrated in cell culture and animal models as well as in virus-infected patients [22, 35, 65]. NC has been shown to induce expression of diacylglycerol acyltransferase (DGAT) and LD formation [22, 116]. Further association of NC with adipocyte differentiation-related protein (ADRP) on the surface of LDs promotes the viral replication cycle. Interestingly, interfering with LD synthesis inhibits SARS-CoV-2 replication and associated cell and pulmonary inflammation in cell culture and animal models of viral infection, implicating LDs not only in the viral life cycle but also in lung pathogenesis [22, 116]. Another interesting finding of the current work was the simultaneous localization of NC with LC3B and DDX3X, which were also detected on LDs. This suggests the possible involvement of these host factors in the viral life cycle, pointing to LDs as a platform involved not only in inflammation, viral replication, and morphogenesis but also in the regulation of cellular functions and processes associated with these proteins (see Supplementary Discussion). ## Findings in postmortem kidney and liver The results of this work pointed to the ability of SARS-CoV-2 to infect various cell types from kidney and liver. This supports previous reports of SARS-CoV-2 in several organs, including kidney and liver [11, 68, 108]. Lung-kidney interactions during SARS-CoV-2 infection are common and associated with significant morbidity and mortality [63]. Importantly, the presence of SARS-CoV-2 in the kidney has been associated with older age, an increased number of coexisting conditions, acute kidney injury, and increased risk of premature death within the first 3 weeks of disease [12]. These features were present in the cases examined in this work. We recognize that pathological features observed in the studied cases, particularly interstitial fibrosis and frequent occurrence of LDs, could be related to the combination of age and co-morbidities in these patients (Table 1) [43, 60, 96]. However, SARS-CoV-2 infection and direct viral injury could also contribute to renal tissue damage. SARS-CoV-2 infection of tubular epithelial cells has been shown previously to be associated with acute tubular renal injury [1]. The detection of SARS-CoV-2 in a variety of renal cell types such as epithelial tubular cells, endothelial cells, glomerular podocytes, and mesangial cells is consistent with the previously reported wide cellular tropism of SARS-CoV-2 in kidney [68]. Another interesting finding was the presence of NC in both cortical peritubular and medullary fibronectin+ interstitial cells. In accordance with the findings in lung and liver (see below), NC was co-detected with LDs that also co-stained with VMT, LC3B, and DDX3X. FLCs form the major mass of interstitial cells and perform a variety of endocrine functions in different intrarenal zones [52]. It is interesting to note that localization of NC in interstitial cells was associated with detection of collagen-like fibers in the renal interstitium. Interstitial cells from the peritubular capillary bed of the renal cortex have been involved in sensing the arterial oxygen content, which is related to alveolar oxygen tension and alveolar gas exchange [31, 46]. This process regulates the production of erythropoietin (EPO) and erythropoiesis by renal interstitial cells [52]. Consequently, hypoxemia due to SARS-CoV-2-infection-associated lung disease is a key trigger of EPO production. Thus, the presence of SARS-CoV-2 in peritubular cells of the renal cortex may also contribute to disturbance of the normal regulation of oxygen homeostasis, thus contributing to the pathogenesis of COVID-19. Notably, in some glomerular regions, NC localized predominantly to the juxtaglomerular apparatus, including epithelial cells of the macula densa, juxtaglomerular/perivascular interstitial cells, and extraglomerular mesangial cells. This finding raises the possibility that SARS-CoV-2 may affect and deregulate critical functions of these cells such as regulation of the renin-angiotensin-aldosterone system (RAAS), which is involved in blood pressure regulation and electrolyte homeostasis [80]. Importantly, the tissue balance between ACE and ACE2 activity regulates the effector functions of RAAS, including inflammatory and fibrotic responses [33]. It has been proposed that SARS-CoV-2 infection may diminish the effects of ACE2, favoring ACE-related functions [86]. We would like to propose that SARS-CoV-2 interactions with FLCs and the juxtaglomerular apparatus directly promote pro-inflammatory and pro-fibrotic responses in the lungs and kidneys, thus contributing to RAAS imbalance. We propose that this scenario is particularly relevant in individuals who have various concomitant co-morbidities and, consequently, are at increased risk of infection of the kidney by SARS-CoV-2, contributing to premature death [12]. Further studies are needed to understand the functional implications of SARS-CoV-2 infection of these cells for regulation of RAAS and oxygen homeostasis and viral pathogenesis. COVID-19 severity has been associated with acute liver injury and elevated liver enzymes [85, 108], and various mechanisms have been proposed [32]. On the other hand, SARS-CoV-2 has been detected in postmortem liver samples [8, 68, 70, 108]. In this work, we identified NC in hepatocytes and CD34+ cells, possibly representing SLECs. CD34 may be expressed at low levels in SLECs in the normal liver, depending on zonation, which increases under pathological conditions [66, 91]. Accordingly, CD34 has been associated with capillarization of LSECs in a mouse model of cirrhosis [91]. Therefore, the presence of SARS-CoV-2 in liver CD34+ cells may be associated with viral pathogenesis and/or pre-existing conditions in the patient, such as hepato-steatosis (which was the main pathological finding in liver samples). Interestingly, NC co-localized with VMT showing a characteristic granulated pattern, suggesting altered VMT localization and its involvement in viral life cycle. The abundance of LDs in the liver samples made it easier to study the recruitment of NC, VMT, DDX3X, LC3B, and NLRP3 to or near LDs. VMT has been shown to play a critical role in LD biogenesis [40], regulation of autophagy [9, 106], and NLRP3 inflammasome activation [24]. Concomitant detection of SARS-CoV-2, VMT, LDs, LC3B, and NLRP3 supports the role of VMT and LDs in the viral life cycle and pathogenesis involving autophagy and inflammasome functions. This is also particularly relevant because VMT has been observed in injured hepatocytes and may be associated with the pathogenesis of liver diseases [53, 112, 117] and de-regulated inflammatory responses [24]. In particular, it has been shown that an EMT-like phenotype and expression of EMT markers such as VMT are induced under various pathological conditions in the liver, including steatohepatitis and fibrosis in humans and mice [53, 92, 119]. It is therefore possible that previous pathological conditions and/or injury of the liver caused by viral infection may promote VMT expression and infection of hepatocytes by SARS-CoV-2. ## Conclusions We have identified potential cellular and molecular targets that may be related to and affected by SARS-CoV-2 infection, with implications for virus-induced pathogenesis and therapeutics. This study provides evidence for the presence of SARS-CoV-2 in lung epithelium, MLCs, FLCs, and LPFs at advanced stages of DAD development, suggesting sustained viral injury and deregulation of tissue repair functions; NC colocalization with mitochondrial proteins and frequent mitochondrial damage in analyzed samples, pointing to mitochondrial involvement in the viral life cycle and pathogenesis; SARS-CoV-2-associated NLRP3 and IL1β responses related to VMT and LDs, not only in MLCs but also in FLCs, possibly associated with mitochondrial dysfunction; the presence of NC in regulatory cells expressing immune-checkpoint proteins involved in tissue repair responses and contributing to inflammatory responses in the lung; key host proteins localizing with NC and/or LDs that have been implicated in WHR and/or the SARS-CoV-2 life cycle (VMT, NLRP3, LC3B, DDX3X, fibronectin, and PPARγ); the presence of SARS-CoV-2 in endothelial cells from lungs, kidney, and liver, which is probably involved in endothelial damage and tissue injury; the presence of SARS-CoV-2 in hepatocytes expressing vimentin, renal interstitial cells, and the juxtaglomerular apparatus, suggesting possible virus-mediated deregulation of critical hepatic and renal functions involved in RAAS, oxygen tension regulation, and COVID-19 pathogenesis. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 54 KB)Supplementary file2 (TIF 20487 KB)Supplementary file3 (TIF 8936 KB)Supplementary file4 (TIF 7876 KB)Supplementary file5 (TIF 8229 KB)Supplementary file6 (TIF 6929 KB)Supplementary file7 (TIF 7671 KB)Supplementary file8 (TIF 1263 KB)Supplementary file9 (TIF 1165 KB)Supplementary file10 (TIF 1223 KB)Supplementary file11 (TIF 644 KB)Supplementary file12 (TIF 1235 KB)Supplementary file13 (TIF 1254 KB)Supplementary file14 (TIF 642 KB)Supplementary file15 (JPG 6770 KB)Supplementary file16 (TIF 6423 KB)Supplementary file17 (TIF 1083 KB)Supplementary file18 (TIF 1226 KB)Supplementary file19 (TIF 3955 KB)Supplementary file20 (TIF 7538 KB)Supplementary file21 (TIF 639 KB)Supplementary file22 (TIF 1114 KB)Supplementary file23 (TIF 1338 KB) ## References 1. 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--- title: 'Pharmacists’ involvements and barriers in the provision of health promotion services towards noncommunicable diseases: Community-based cross-sectional study in Northwest Ethiopia' authors: - Ashenafi Kibret Sendekie - Abera Dessie Dagnaw - Ephrem Mebratu Dagnew journal: Archives of Public Health year: 2023 pmcid: PMC9968412 doi: 10.1186/s13690-023-01038-x license: CC BY 4.0 --- # Pharmacists’ involvements and barriers in the provision of health promotion services towards noncommunicable diseases: Community-based cross-sectional study in Northwest Ethiopia ## Abstract ### Background Community drug retail outlets (CDROs) are among the initial healthcare facilities where pharmacists play a crucial role in preventing and managing noncommunicable diseases (NCDs). Therefore, this study assessed pharmacists’ level of involvement and barriers in the provision of health promotion for noncommunicable diseases at CDROs in Northwest Ethiopia. ### Methods A community-based multicenter cross-sectional study was conducted among community pharmacists in Northwest Ethiopia from April to June 2022. Data was collected using a self-administered structured questionnaire, and analyzed using the Statistical Package for Social Science (SPSS) version 26. The level of involvement mean score difference among pharmacists was investigated using an independent samples t-test and a one-way ANOVA. Logistic regression analysis was used to examine the association between pharmacists’ level of involvement and other variables. A p-value < 0.05 at a $95\%$ confidence interval (CI) was considered statistically significant. ### Results A total of 285 ($94.4\%$) participants participated in the study out of 302 approached samples. Overall, more than half ($58.9\%$) of the participants showed a high level of involvement in health promotion. Pharmacists who had a degree and/or above (AOR = 0.03, $95\%$ CI: 0.01–0.63; $p \leq 0.001$) and served a lower number of clients per day (AOR = 0.19, $95\%$ CI: 0.04–94; $$p \leq 0.042$$) were less likely to have low involvement in health promotion services. Pharmacists who worked fewer hours per day (AOR = 3.65, $95\%$ CI: 1.79–7.48; $$p \leq 0.005$$) were more likely to have low involvement. Lack of an appropriate area in the CDROs ($52.1\%$) and lack of coordination with other healthcare providers ($43.6\%$) were the most reported barriers to the provision of health promotion. ### Conclusion Most pharmacists were found to have a high level of involvement in health promotion activities. A lack of an appropriate area in the CDROs and a lack of coordination with other healthcare providers were among the most reported barriers. Pharmacists might benefit from training to increase their educational backgrounds, and barriers could be addressed to enhance the pharmacist involvement. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13690-023-01038-x. ## Introduction Because of social changes and unhealthy physical environments, along with unhealthy lifestyle practices, the prevalence of noncommunicable diseases (NCDs) is increasing across the globe [1]. NCDs are the major cause of global death and have been faced by both developed and underdeveloped countries. For instance, the major NCDs such as cardiovascular disease, cancer, chronic lung diseases, and diabetes account for three in five global deaths [2]. Further, according to the World Health Organization (WHO) report, it is the cause of death for 41 million people each year and accounts for $71\%$ of all deaths globally [3]. In 2019, only cardiovascular disease-associated deaths accounted for about one-third of all deaths, and these deaths occurred prematurely in the population under 70 years old [4]. Its burden has increased, particularly in low- and middle-income countries, which account for $80\%$ of all deaths [5, 6]. In Ethiopia, evidence from the national NCD STEPS survey reported a significantly high prevalence of raised blood pressure, hyperglycemia, dyslipidemia, and metabolic syndrome [7]. An analysis of the evidence from the Global Burden of Disease (GBD) 2016 study also reported that chronic disease contributed to $39.3\%$ of the total death rate and $53\%$ of the age-standardized death rate (ASDR) [8]. The occurrence of NCDs has been linked to various reasons, which are strongly related to sedentary lifestyles like smoking, obesity, physical inactivity, and an unhealthy diet, which result in NCDs such as dyslipidemia, diabetes, cardiovascular disease, renal diseases, retinopathy, and cerebrovascular diseases [9, 10]. In Ethiopia, age, alcohol intake, and abdominal obesity are also reported as significant risk factors for developing the NCDs [11, 12]. Thus, changing sedentary lifestyle behavior and appropriate management of modifiable risk factors can play a role in avoiding these risks [13]. Further, appropriate management of the modifiable risk factors has been established in both undiagnosed and known NCDs to reduce mortality and morbidity risks and improve healthcare systems [14]. Health promotion enables people to improve their health conditions and focuses on keeping people healthy [15]. Individuals and/or communities can be involved and allowed to appreciate healthy behavior and make changes focusing on the root causes and risks of developing chronic diseases [16]. The promotion may be implemented through educational and social communication activities that promote healthy conditions, lifestyles, behavior, and environments. Finally, health promotion is targeted to create and promote health literacy, good governance for health, and healthy cities. Though the practice of health promotion commonly involves the population as a whole [17], it has been implemented and is necessary for specific groups of the population, particularly in the prevention and management of chronic diseases. As a result, the National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP) has promoted chronic disease prevention efforts using health promotion systems [18]. Worldwide, pharmacy practice has shifted from dispensing only to healthcare approaches, and health promotion is now considered part of current pharmacy services [19–22]. Subsequently, pharmacists are among the most frequently visited and first points of contact for the public, and they play a crucial role in preventing and managing NCDs by providing more direct interventions in medication education and disease management, resulting in improved medication adherence, achieving desired therapeutic outcomes, and improving safe medication use practices [23, 24]. Community drug retail outlets (CDROs) are the initial healthcare sites where patients could receive their medications and interventions at the community level. Community pharmacists can play a significant role in the prevention and management of chronic illnesses, cardiovascular disorders, diabetes, and metabolic syndromes, contribute to better patient care [24–29], and are considered the main contributors to the establishment of health promotion activities for individuals and communities [30]. Because patients are having difficulty accessing primary care physicians and healthcare prices are rapidly increasing, more community-based care models have been promoted. However, pharmacists, in particular community pharmacists, have faced many challenges in providing effective health promotion activities in the public’s priorities to individuals and communities [31, 32]. Among the common barriers that affect community pharmacists in providing public healthcare are lack of knowledge and skills, confidence, and adequate training and policies; poor recognition of the healthcare system; low patient demand; and inadequate pharmacy staff [19, 29]. Lack of appropriate areas in the CDROs, increased workloads, a lack of educational material and training, and insufficient management support were the most commonly reported barriers to counseling and involvement in public healthcare activities by Ethiopian community pharmacists [21, 25]. A study on pharmacy students also showed that clients’ lack of time and interest, as well as the absence of guidelines for health promotion services, were the main barriers perceived to hinder health promotion services [33]. Evidence showed that community pharmacists play an important role in different health promotion services [19–21, 28, 34, 35]. CDROs are among the initial healthcare settings where pharmacists could play an important role in health promotion. Although some literature has investigated the role of community pharmacists in health promotion services [33], to the best of the authors’ knowledge and search, there is still limited evidence investigated to assess community pharmacists’ involvement in health promotion activities and barriers, in particular in the prevention and management of NCDs in the study areas. Therefore, this study assessed the level of involvement and barriers of community pharmacists in the health promotion activities towards the prevention and management of NCDs at CDROs in selected cities in Northwest Ethiopia. ## Study design, settings and samples A community-based multicenter cross-sectional study was conducted among the pharmacists working in CDROs in selected cities from April to June 2022. The participants were recruited from three cities in Northwest Ethiopia: Gondar, Bahira Dar, and Debre Tabor. The cities were selected by lottery from other cities in the Amhara regional states. The Amhara regional state was purposefully selected from other regions and administrative cities in the country. All three selected cities were considered to have comparable community pharmacy practices and practitioners. A local report revealed that there were around 195 licensed CDROs in these three cities in total as of December 2021. Initially, considering the number of available samples in the selected cities, we calculated and found the sample size. The sample size needed to conduct the survey was calculated using the simple proportion formula: n = p (1-p) * (Z)2/d2; assuming a $5\%$ margin error or degree of accuracy ($d = 0.05$), reliability coefficient for $95\%$ confidence level ($Z = 1.96$), and $$p \leq 0.5$$ ($50\%$) response distribution. Considering 195 active CDROs in the selected cities, we used the correction formula and added $10\%$ contingency. The final sample size was 143 CDROs. As a result, considering the limited number of available CDROs in the selected cities, we approached all licensed community pharmacy professionals who had been working in the CDROs (more than 1 pharmacist per CDRO) during the data collection period. The survey was conducted among pharmacists who had worked at the CDROs in any selected city as qualified and licensed pharmacy professionals for at least three months. Those who were not available at the CDROs during the data collection and those who refused to participate in the study were excluded. Consequently, we initially approached 302 pharmacists, and 285 of them participated in this study. ## Community pharmacists In this study, this term refers to pharmacy professionals who worked at CDROs in the selected cities, regardless of their educational background. ## Health promotion Indicates the participation and engagement of community pharmacists in the promotion, counseling, education, and provision of services in the prevention and management of NCDs, focusing on the root causes and risks of chronic diseases. ## The level of health promotion involvement The degree (level) of pharmacist involvement in health promotion services. The mean health promotion involvement score was determined for each participant, with a score ranging from 1 to 5. The overall mean score of pharmacists' health promotion service involvement in NCD prevention and management was then computed out of five. The overall score was dichotomized using the mean as a cut-off point because of the lack of earlier evidence. Respondents who scored less than the overall mean score (3.82) and those who scored greater than or equal to the overall mean score had low and high levels of involvement in health promotion services, respectively. ## Data collection instruments and procedures, and data quality control methods The data was collected using a structured questionnaire. The questionnaire was first prepared in English, then translated to Amharic (the local language), and then back to English to maintain its consistency. The questionnaire was developed after reviewing the previous literature [19–21, 25, 28, 34, 36]. The data collection instrument consisted of four parts (Supplementary file): [1] the first part consisted of socio-demographic characteristics; [2] the second part contained statements used to assess the willingness of the pharmacists to participate in the health promotion activities; [3] the third part was used to assess the level of involvement of pharmacists in promotion and counseling services in the prevention and management of NCDs; and [4] the fourth part contained different sources of barriers for community pharmacists involved in the health promotion activities. Before the actual data collection period, the questionnaire was validated for its content by two senior clinical pharmacists and then pretested in ten CDROs (approximately $5\%$ of the total CDROs in the selected cities). In the pretest, we administered the prepared questionnaire to the pharmacists in the ten selected CDROs and checked the questionnaire for easy understandability, clarity, and consistency. Then, using the pretest feedback, we made slight modifications to the questionnaire in terms of avoiding redundancy, making clear some ambiguous phrases, and avoiding long statements before distrusting the questionnaire. Then, the data was collected by three graduating clinical pharmacists after they received a half-day training on data collection procedures and ethical aspects. Initially, the study participants were briefed about the objectives of the study and then asked for their consent to participate in it. A self-administered questionnaire was provided to eligible participants who volunteered to participate in this study. While on data collection, the supervisor explicitly followed the data collection procedures. The data was checked for its completeness, cleanliness, and clarity every day during the data collection period. Health promotion service items used to assess pharmacists’ health promotion involvement in prevention and management of NCDs had 14 statements with a five-point Likert scale (not at all involved, little involved, uncertain, involved, very involved) with a value of 1 up to 5 points, respectively. A reliability test for the items used to assess the health promotion involvement was performed, yielding a Cronbach’s alpha (α) value of 0.86. Factor analysis of the instrument was also performed with three factors. Factor 1 was comprised of 5 items reported on a 5-point Likert scale that explained $61.7\%$ of the variance, with factor loadings from 0.701 to 0.821. Factor 2 also contained seven items that explained $73.4\%$ of the variance, with factor loadings ranging from 0.591 to 0.825. The third factor also consisted of 2 items reported on a 5-point Likert scale that explained $79.3\%$ of the variance with factor loadings from 0.744 to 0.834. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was also found to be adequate and had a significance level for the Bartlett's test (KMO = 0.825; $p \leq 0.001$). ## Data entry and statistical analysis After the data was collected and checked for completeness and cleanliness, it was entered into EPI-info version 8. Then, it was transformed into Statistical Package for Social Science (SPSS) version 26, and after it was checked for its completeness, clarity, and consistency, it was analyzed. A histogram and Q-Q plot were used to test the normal distribution of variables. The results were presented by means and standard divisions (SD) for continuous variables and by frequencies and percentages for categorical variables. Factor analysis of the instrument was performed using the principal components method of extraction and varimax rotation. An independent samples t-test and a one-way ANOVA were employed to compare pharmacists’ mean score differences regarding their health promotion involvement in the prevention and management of NCDs. A logistic regression analysis was used to identify predictor variables in the pharmacists' involvement in health promotion. A p-value of < 0.05 at the $95\%$ confidence interval (CI) was considered statistically significant. ## Sociodemographic characteristics of the study participants Out of 302 samples approached, 285 completed the study, resulting in a $94.4\%$ response rate. More than half ($52.6\%$) were males, with a mean (± SD) age of 32.0 ± 8.3 years. Most of the participants ($56.5\%$) had a lower educational background (diploma level), and a higher proportion of them ($48.4\%$) had work experience of 1–5 years. Furthermore, most of the participants were employees ($79.6\%$). Around two-thirds ($66.3\%$) could not receive training related to health promotion activities (Table 1).Table 1Sociodemographic characteristics of the community pharmacists, Northwest Ethiopia ($$n = 285$$)VariablesFrequency (%)Mean (± SD)Sex:Male150 (52.6)Female135(47.4)Average age in years:--32.0(± 8.3)Cities where the participants worked:Bahira Dar116 (40.7)Gondar104 (36.5)Debera Tabor65 (22.6)Pharmacists’ educational level:Druggist (diploma)161 (56.5)Bachelor's degree and above124 (43.5)Work experience in years: < 1 year53 (18.6)1–5 Years138(48.4)4.6(± 1.7) > 5 years94 (33.0)Employment status:Employee227(79.6)Owner58(20.4)Monthly income (ETB):1500–299986 (30.2)3000–4999126 (44.2)4518.6(± 674.3) ≥ 500073 (25.6)CDRO types where participants worked:Drug store128 (44.9)Pharmacy157 (55.1)Number of clients served/day: < 50159 (55.8)43.5(± 17.4)50–100109 (38.2) > 10017 (6.0)Working hours/day: ≤ 8120(42.1)8.6(± 2.3) > 8165 (57.9)Training on health promotion practice:Yes96 (33.7)No189 (66.3) ## Community pharmacists’ involvement in health promotion services Most of the participants responded that health promotion is part of pharmacists’ responsibility ($93.3\%$), and they were willing to provide health promotion and education for patients with NCDs (> $83\%$). Additionally, around three-fourths ($72.3\%$) also responded that the pharmacy curriculum was adequate for providing health promotion (Fig. 1).Fig. 1Community pharmacists’ willingness to health promotion practices The overall health promotion involvement score of the pharmacists in the prevention and management of the NCDs was 3.82(± 0.60) out of 5. Most of the community pharmacists responded that they were involved or very involved in most of the items assessing the health promotion involvement of pharmacists in the prevention and management of NCDs. More than three-fourths ($75.8\%$) of pharmacists were involved or very involved in promoting service of advising patients on a healthy weight reduction by a non-weight-bearing diet, with an involvement score of 3.85, which is higher than the overall average score. Additionally, most of the community pharmacists were involved or very involved in advising patients on promoting physical activities, alcohol consumption restrictions, smoking cessation, and salt restriction, with an involvement score of greater than four of five points. In contrast, the proportion of pharmacists who were involved in promoting the consumption of cholesterol-lowering diets, giving advice on increased consumption of soluble fibers, screening and measuring blood pressure, weight, and glucose levels, promoting cautions about over-the-counter drugs or herbal products, and monitoring the patients’ response to the treatment was limited, with a mean involvement score that was far below the average mean score (Table 2).Table 2Community pharmacists’ involvement in health promotion practices in the prevention and management of chronic diseasesItems of health promotion activitiesLevel of pharmacists’ response on health promotion activities (n, %)Mean score (± SD), out of 5 pointsNot involvedLittle involvedUncertainInvolvedVery involvedPromotion of weight reduction by low calorie and non-weight bearing diets4 (1.4)40 (14.0)25 (8.8)141 (49.5)75 (26.3)3.85(± 1.01)Promotion on physical activity4 (1.4)32 (11.2)17 (6.0)134 (47.0)98 (34.4)4.02(± 0.99)Promotion on alcohol consumption restriction5 (1.8)23 (8.1)22 (7.7)123 (43.2)112 (39.3)4.10(± 0.97)Promotion on smoking cessation7 (2.5)19 (6.7)20 (7.0)125 (43.9)114 (40.0)4.12(± 0.97)Promotion salt restriction4 (1.4)21 (7.4)20 (7.0)117 (41.1)123 (43.2)4.17(± 0.95)Promotion on consumption of cholesterol-lowering diets4 (1.4)38 (13.3)38 (13.3)159 (55.8)46 (16.1)3.72(± 0.94)Promotion on consumption of vegetables4 (1.4)46 (16.1)36 (12.6)131 (46.0)68 (23.9)3.75(± 1.04)Advice on increasing the consumption of soluble fiber6 (2.1)46 (16.1)53 (18.6)139 (48.8)41 (14.4)3.57(± 0.99)Counsel on cautions of over-the-counter drugs or herbal products9 (3.2)70 (24.6)36 (12.6)125 (43.9)45 (15.8)3.45(± 1.12)Advice on routine weight, blood pressure and blood glucose monitoring and maintaining the target goals3 (1.1)44 (15.4)15 (5.3)130 (45.6)93 (32.6)3.93(± 1.04)Involving in screening and measurement of blood pressure, weight and glucose level9 (3.2)67 (23.5)27 (9.5)110 (38.6)72 (25.3)3.59(± 1.19)Advice on prescription treatment of chronic diseases1 (0.4)43 (15.1)27 (9.5)151 (53.0)63 (22.1)3.81(± 0.96)Encourage patients’ adherence with treatment1 (0.4)27 (9.5)25 (8.8)149 (52.3)83 (29.1)4.00(± 0.89)Involving in monitor patients’ treatment response6 (2.1)66 (23.2)47 (16.5)127 (44.6)39 (13.73.45(± 1.06)Community pharmacists’ Overall health promotion involvement score3.82(± 0.60)Not involved = 1; little involved = 2; uncertain = 3; involved = 4; very involved = 5 ## Pharmacists’ involvement difference in the health promotion activities An independent sample t-test and one-way ANOVA were employed to compare the mean score difference among participants regarding the overall involvement in health promotion services towards the prevention and management of NCDs. Pharmacists who had a graduating degree and above (Mn = 4.18) were found to have significantly ($p \leq 0.001$) higher involvement mean scores than druggists (Mn = 3.55). Regarding working hours per day, pharmacists who worked for more than eight hours per day had a higher involvement score (Mn = 3.90) than those who worked less than or equal to eight hours (Mn = 3.72); $$p \leq 0.012.$$ A one-way ANOVA test also showed that there was a significant difference in the mean involvement score of pharmacists regarding the number of clients served per day ($$p \leq 0.037$$). The post hoc test using Tukey’s test disclosed that there was a mean difference between the two pairs: pharmacists who served more than 100 clients/day had a lower involvement score (Mn = 3.50) than those who served 50–100 clients/day (Mn = 3.86); $$p \leq 0.021$$) (Table 3).Table 3Independent samples t-test and one-Way ANOVA analysis to mean score difference among the participants in the provision of health promotion towards NCDsVariablesCategoryHealth promotion involvement mean scoresMean (± SD)t/FP-valueSexMale3.80(± 0.48)0.59a0.556Female3.85(± 0.62)Cities where participants involvedGondar city3.88(± 0.61)0.48b0.543Bahir Dar3.80(± 0.59)Debre Tabor3.78(0 ± 0.60)CDRO types where participants workedDrug store3.80(± 0.60)-0.67a0.505Pharmacy3.85(± 0.60)Working hours/day ≤ 83.72(± 0.62)-2.52a0.012 > 83.90(± 0.58)Employment statusEmployee3.81(± 0.61)-0.34a0.532Owner3.85(± 0.56)Pharmacists’ educational levelDruggist3.55(± 0.57)-10.51a0.000Degree and above4.18(± 0.46)Training on health promotion practice:Yes3.85(± 0.61)0.55a0.584No3.81(± 0.60)Work experience in years < 13.73(± 0.67)1.70b0.1841–53.89(± 0.55) > 53.78(± 0.62)Monthly income (ETB)1500–29993.77(± 0.67)0.86b0.4233000–49993.82(± 0.60) ≥ 50003.89(± 0.53)Number of clients served/day < 503.83(± 0.59)2.73b0.03750–1003.86(± 0.62) > 1003.50(± 0.52)aDenotes independent sample t-test; bindicates one-way ANOVA; bold letter at p-value indicates $P \leq 0.05$ ## Pharmacists’ level of involvement in the health promotion activities and its determinants Overall, most pharmacists ($58.9\%$) had a high level of health promotion involvement in the prevention and management of NCDs. This study assessed the potential predictor variables linked to the health promotion activities of community pharmacists in the prevention and management of NCDs. The finding showed that pharmacists who had a bachelor’s degree and above in pharmacy were less likely to have low involvement compared with those who were druggists [AOR = 0.027, $95\%$ CI (0.011–0.63); $p \leq 0.001$]. Similarly, community pharmacists who served fewer than 50 clients per day were found to be less likely to have low levels of health promotion activities compared with those who served more than 100 clients per day [AOR = 0.194, $95\%$ CI (0.040–944); $$p \leq 0.042$$]. In contrast, pharmacists who worked for less than 8 h per day in CDROs were found to be more likely to have low involvement in health promotion services than pharmacists who worked for more than 8 h per day [AOR = 3.645, $95\%$ CI (1.775–7.487); $$p \leq 0.005$$] (Table 4).Table 4Association of pharmacists’ level of involvement towards health promotion activities and predictor variablesVariablesLevel of involvement$95\%$ CIP-valueLowHighCORAORSexMale70801.638(1.016–2.642)1.103(0.552–2.207)0.781Female478811Educational levelDegree and above81160.033(0.015–0.072)0.027(0.011–0.63)0.000Druggist1095211Employment statusEmployee951321.178(0.651–2.129)1.140(0.208–6.241)0.880Owner223611Work experience in years < 127261.342(0.683–2.638)0.887(0.332–2.368)0.6001–549890.712(0.416–1.217)0.685(0.317–1.481) > 5415311Monthly salary (ETB)1500–299937491.365(0.719–2.593)2.447(0.452–13.244)0.2513000–499954721.356(0.748–2.458)1.283(0.241–6.847) ≥ 5000264711CDRO types where pharmacists workedDrug store50780.861(0.535–1.385)0.611(0.296–1.262)0.183Pharmacy679011Number of clients served/day < 5062970.349(0.123–0.991)0.194(0.040–0.944)0.04250–10044650.369(0.127–1.072)0.236(0.049–1.147) > 10011611Working hours/day ≤ 863572.272(1.400–3.686)3.645(1.775–7.487)0.005* > 85411111Training on health promotion practiceYes43531.261(0.767–2.073)1.201(0.569–2.539)0.063No7411711CI Confidence interval, COR Crude odds ratio, AOR Adjusted odds ratioindicates $P \leq 0.05$ ## Barriers to the involvement of pharmacists in health promotion activities Participants responded that the lack of appropriate areas in the CDROs ($52.1\%$), followed by the lack of coordination with other healthcare providers ($43.6\%$), the increase in workload/lack of time ($35.4\%$), and insufficient resources (trainings, guidelines) ($27.8\%$), were the most common reported barriers of community pharmacists in preventing and managing NCDs (Fig. 2).Fig. 2Barriers of community pharmacists in the health promotion practice ## Discussion Although assessing the extent of community pharmacists’ involvement in health promotion activities in the prevention and management of NCDs is critical in tackling the increasing prevalence of NCDs and its associated burdens, comprehensive evidence is lacking in Ethiopia. Because community pharmacists have varying public health roles from their primary dispensing service to a more comprehensive involvement in healthcare issues and are recognized throughout the world [37, 38], assessing their level of involvement and barriers in health promotion activities for NCDs would be vital for taking measures. Thus, this community-based multicenter study explored the willingness, involvement, and barriers of community pharmacists in health promotion activities for the prevention and management of NCDs at CDROs in selected cities in Northwest Ethiopia. Consequently, this study showed that more than half of the community pharmacists had a high level of involvement in health promotion activities. The findings also revealed that differences in educational status and the number of clients per day were associated with the level of involvement. In this study, the lack of appropriate working areas in the CDROs, increased workload, lack of time, insufficient guidance resources and training, and low management support were the most commonly reported barriers. In this study, more than half of the community pharmacists had a high level of involvement in health promotion for the prevention and management of NCDs. The results highlighted that a most community pharmacists were willing to provide health education and health promotion, and a majority of the participants also agreed that health promotion activities are responsibilities for the pharmacy profession. This indicates that community pharmacists are among the healthcare professionals involved in promoting public health priorities and issues. Currently, community pharmacists provide wide public health services from drug dispensing to medication therapy management, child immunization, health education, screening of diabetes, advice on health risks such as smoking cessation, weight management, blood glucose and blood pressure monitoring, osteoporosis, substance abuse, response to symptoms, and general medication and health information [21, 25, 28, 36]. Consistent with other studies [21, 26, 28, 39–43], in this study, community pharmacists were involved in health promotion activities for the prevention and management of NCDs. Because community pharmacists believe that NCDs are largely associated with obesity, a sedentary lifestyle, and an unhealthy diet, they agreed to give more attention to healthy lifestyle modifications, and they are also involved in lifestyle modification promotion activities such as healthy weight reductions with low-calorie non-weight-bearing diets, promotion of alcohol consumption restrictions, healthy physical activity promotions, and promotion of cigarette smoking cessation, which could decrease the potential risks of NCDs. The result highlighted the fact that most pharmacists have good involvement in NCD health promotion activities for the prevention and management of these disorders. This may implicate those pharmacists have enormously increased their awareness and attitudes about the public’s healthcare priorities and the risks of NCDs. As a result, they are willing to get involved in health promotion activities aimed at preventing and managing NCDs. This study implies that the majority of community pharmacists were involved with various degrees of involvement, from being involved to being very involved, in most of the important health promotion activities in the prevention and management of NCDs. The reason might be that those pharmacists have been involved in patient education and counseling approaches following the new development of the country’s pharmacy education curriculum, which has undergone a paradigm shift from traditional dispensing-only practices to patient-oriented approaches. Herewith, pharmacists working in CDROs have been graduated after being prepared with various clinical cases and scenarios, along with patients’ approaches and communication skills. In this study, most of the participants also agreed that the curriculum is adequate for providing health care, and they also agreed that health promotion activities are the responsibility of pharmacists. Thereby, this study showed that most of the community pharmacists had a better level of involvement in most of the health promotion activities in the prevention and management of NCDs. Conversely, fewer pharmacists were trained regarding health promotion activities related to the prevention and management strategies of NCDs, which need to be improved to maximize the effectiveness of pharmacists in these services. Therefore, community health authorities, national NCD STEPS authorities, and other stakeholders could have been involved in taking initiatives and endorsements on training and regularly promoting the educational background of the pharmacists. Collaborations between CDROs, health authorities, and relevant educational training centers could be encouraged with the goal of involving community pharmacists in initiatives and workshops promoting health priorities such as NCDs. In contrast to previous studies [44, 45], this study showed that pharmacists’ involvement in promoting alcohol consumption and smoking cessation was much higher. These findings may indicate that the pharmacists in this study may have observed that an increase in the sedentary lifestyles of the community became their concern and motivated them to become involved in promoting the risks of unhealthy alcohol consumption and smoking habits. Furthermore, in contrast with earlier studies [44, 46], pharmacists’ involvement was low in some health promotion activities, such as promoting the consumption of cholesterol-free and lowering diets, increasing the consumption of soluble fiber, participating in screening and measuring weight, blood pressure, and glucose levels, and monitoring patients’ treatment responses. Lower involvement levels would imply that community pharmacists were not inclusively involved in all basic points concerning the prevention and management of NCDs. This might be because of a gap in skills in some selected areas or the poor commitment of the pharmacists in these areas. But this was correlated with previous studies conducted on the counseling involvement of pharmacists in cardiovascular disorders [25] and metabolic syndrome [26]. The current study also assessed the potential predictor variables linked to the health promotion activities of community pharmacists in the prevention and management of NCDs. Consequently, the finding showed that those pharmacists graduating with a bachelor’s degree and above were more likely to have a high level of involvement in the provision of health promotion services compared with those who were druggists. An independent samples t-test revealed that pharmacists with a bachelor's degree or higher had higher mean health promotion involvement scores than pharmacists with a lower educational level. This study correlated with an earlier study regarding pharmacists’ involvement in preventing cardiovascular diseases [25]. The reason for these disparities could be that pharmacists with a higher level of education may have more up-to-date knowledge and the ability to participate in health promotion activities than pharmacists with a lower level of education. They could also easily access the updated resources and understand how they could be implemented and changed in practice, which helped them become highly involved in health promotion activities for the prevention and management of NCDS. In addition, an earlier survey of bachelor’s degree pharmacy students regarding their health promotion activities while on attachment also revealed that professional training, knowledge, and standard guidelines for the services are important [33]. But in this study, most of the pharmacists had a lower educational background. Therefore, pharmacists would be recommended to upgrade their educational backgrounds to provide better healthcare services in the area of public health priorities such as health promotion aiming to prevent and manage NCDs. Besides their academic education, they must also increase their knowledge, skills, and confidence through training in health promotion services. Moreover, this study revealed that community pharmacists who served lower numbers of clients per day were found to be more likely to have high involvement in health promotion activities in the prevention and management of NCDs. This could be justified by the fact that those who serve more clients per working day might be busy with other duties and dispensing services. Their counseling services may also go through traditional medicine dispensing rather than adequately engaging in health promotion activities and counseling. The finding may also imply that community pharmacists would serve an optimal number of clients in their working day to provide better counseling, health promotion activities, and healthcare services to patients. Furthermore, pharmacists who worked shorter working hours (8 h/day) in CDROs were found to be less likely to participate in health promotion activities than pharmacists who worked longer working hours (> 8 h/day). This might be because pharmacists working shorter hours may not have better access to clients, and they may use their longer working hours for other duties that are not related to healthcare activities. This finding may indicate those pharmacists could be attached to the optimal number of clients for sufficient hours per working day to increase their healthcare activities in public health priorities like NCDs, which require pharmacists’ involvement. Although most of the participants had high levels of health promotion involvement in most important health services for the prevention and management of NCDs, many barriers were reported. Community pharmacists reported different types of barriers to being involved in health promotion activities, which need to be addressed to optimize the effectiveness of pharmacists in health promotion activities for the prevention and management of NCDs. Consistent with previous studies [19, 21, 25, 29, 44, 47], the lack of an appropriate working area in the CDROs, increased workload/lack of time, insufficient guidance resources and training, and low management support were the most commonly reported barriers. Additionally, a lack of coordination with other healthcare providers is also an important reported barrier that limits the active involvement of pharmacists in public healthcare priorities like promoting NCDs [19]. Moreover, a study conducted on pharmacy students who were in community pharmacy practice revealed that clients’ lack of time and interest, the absence of a guideline for health promotion services, a lack of training and/or knowledge, and a lack of confidence by pharmacists were the main barriers perceived to hinder the provision of health promotion services. These findings indicate that the barriers are common and similar across different study settings. The findings also suggest that these barriers are multifactorial and related to pharmacy professionals’ knowledge, attitudes, and skills; the structural systems of healthcare; and the clients themselves. Most of the reported barriers are also preventable and modifiable. Therefore, a system could be designed to minimize the effects of barriers and boost the effectiveness of pharmacists in health promotion services. In addition to formal education they received, pharmacists must improve their knowledge, skills, and confidence through formal and informal life-long training in health promotion services. It can reduce the barriers related to pharmacists’ knowledge and skills, and the educational gaps. In particular, the provision of health promotion services for NCDs is crucial because their burden has been increasing in low- and middle-income countries, including Ethiopia. As a result, minimizing the effect of barriers to health promotion practices is among a multifactorial intervention that could be important to tackle the significant burdens associated with NCDs. Generally, this study has highlighted the levels and extent of involvement and barriers of community pharmacists in health promotion activities for the prevention and management of NCDs. These roles range from promoting lifestyle modification by maintaining a healthy weight with low-calorie diets, promoting physical activities, alcohol consumption restrictions, salt restrictions, and cigarette smoking cessation, to screening and monitoring of weight, blood pressure, glucose levels, and treatment responses, and promoting medication adherence. Therefore, this study may indicate that the rapid rise in the prevalence and burden of NCDs in developing countries like Ethiopia is* an urgent call for multisectoral and multidirectional prompt prevention to minimize associated burdens. In fact, promoting healthy behavior among the public is a key population strategy for reducing the burden of NCDs, and this may also be the driving point for community pharmacists to deliver NCD prevention and management. ## Study limitations and strengths The current study has some limitations. Initially, the findings from this study may not be generalized to all community pharmacists in the country, particularly in rural settings. Data collection may be influenced by participants' honesty and faith in the outcome, resulting in an overestimation or underestimation of current practices and community pharmacists' involvement in NCD health promotion activities. Therefore, the findings of this study should be interpreted with caution. Despite this limitation, this study assessed the extent and level of involvement of community pharmacists in health promotion services in the prevention and management of NCDs at CDROs in selected cities of Northwest Ethiopia, where there is a need for evidence in the area. We hope this study may add a body of knowledge to the existing literature gap in the area, and we believe it will inform policymakers to integrate CDROs with health promotion practices and nationwide efforts to tackle the increasing prevalence of NCDs and their associated burdens in the country. Finally, we recommend that future research investigate the attitudes and beliefs of pharmacists regarding their involvement in the provision of health promotion for noncommunicable diseases in the study settings, which according to existing research, has proven to be very significant. ## Conclusion Community pharmacists provided NCD health promotion services, and most of them had a high level of involvement in prevention and management strategies. The level of involvement of pharmacists was associated with their level of educational background, the number of clients they provided service to, and their working hours per day. Lack of appropriate counseling areas in the CDROs and lack of coordination with other healthcare providers are the most reported barriers to health promotion involvement that need to be addressed. Pharmacists would be recommended to take training and promote their educational background to a higher level, and coordination with other healthcare providers would be recommended. ## Supplementary Information Additional file 1: Supplementary file. Data collection instrument. ## References 1. 1.World Health O. Home/Publications/Overview/Noncommunicable Diseases Progress Monitor 2020. Noncommunicable Diseases Progress Monitor 2020. Available: https://www.who.int/publications/i/item/9789240000490). Accessed 25 Apr 2022 2. Wang H, Naghavi M, Allen C, Barber RM, Bhutta ZA, Carter A. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. 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--- title: Outcomes of Hospitalized Patients With Fecal Occult Positive Stool Prior to Cardiac Catheterization in Acute Coronary Syndrome (ACS) journal: Cureus year: 2023 pmcid: PMC9968416 doi: 10.7759/cureus.34263 license: CC BY 3.0 --- # Outcomes of Hospitalized Patients With Fecal Occult Positive Stool Prior to Cardiac Catheterization in Acute Coronary Syndrome (ACS) ## Abstract Introduction Cardiac catheterization is an essential component of patient care in Acute Coronary Syndrome (ACS). Fecal occult blood testing (FOBT) has been used in the inpatient setting to evaluate the risk of bleeding with dual anti-platelet therapy prior to cardiac catheterization although no guidelines exist for this indication and FOBT testing in the inpatient setting is not recommended for evaluation of GI blood loss. We sought to assess the outcomes of patients with fecal occult positive stool prior to cardiac catheterization compared to those that did not undergo FOBT during admission for non-ST-elevation myocardial infarction (NSTEMI). Methods We identified patients between 18 and 90 years old with admission for NSTEMI in the Trinetx Research Network from January 1, 2019 to December 31, 2020. Patients were then divided into those who had an FOBT prior to cardiac catheterization and those that did not have an FOBT. We compared all-cause mortality, bleeding, troponin levels, and length of stay between propensity-matched (PSM) pairs of patients. Results We identified 46,349 that met inclusion criteria, of which 1,728 had an FOBT ($3.7\%$) and 44,621 ($96.3\%$) had no FOBT prior to cardiac catheterization. Patients in the FOBT group were older and had a higher prevalence of hypertension, coronary artery disease, heart failure, diabetes, chronic obstructive pulmonary disease, and higher BMI. Two well-matched groups of $$n = 1$$,$\frac{728}{1}$,728 were used for comparing outcomes. The FOBT group had similar 30-day mortality ($4.45\%$ vs 4.01, $$P \leq 0.56$$) as well as similar bleeding events ($0.98\%$ vs $0.69\%$, $$P \leq 0.35$$). Troponin levels in the FOBT group were on average lower (0.41 vs 0.95, $$P \leq 0.04$$). The FOBT groups also had a similar average length of stay of (14.1 days vs 14.2 days, $$P \leq 0.42$$). 233 patients who received FOBT underwent endoscopic evaluation with either upper endoscopy or colonoscopy ($13.5\%$), and there was no significant difference in 30-day mortality ($6.86\%$ vs $4.7\%$, $$P \leq 0.321$$). Among patients who underwent endoscopy, 72 had some form of endoscopic intervention ($30.9\%$). There was no difference in 30-day mortality between patients undergoing endoscopy with intervention and without intervention ($14.49\%$/$14.49\%$) $$P \leq 1.00.$$ Readmission was similar between patients undergoing endoscopy with and without intervention. Conclusions In a large multi-center national database, we observed similar outcomes in patients who were admitted with NSTEMI and had FOBT and those not receiving FOBT in terms of all-cause mortality and bleeding events. In patients with positive FOBT, endoscopy with and without intervention we observed no significant difference in 30-day mortality. We conclude that there is no compelling evidence for FOBT testing in patients with NSTEMI. ## Introduction Cardiac catheterization is an essential component in the treatment of acute coronary syndrome (ACS). Stent placement is a significant risk factor for new gastrointestinal bleeding, with a risk between $1.3\%$ and $2.4\%$ for GI bleeding within 30 days of ACS in patients on dual antiplatelet therapy (DAPT) [1,2]. Gastrointestinal bleeding following ACS is associated with increased morbidity and mortality [3-8]. Fecal occult blood testing (FOBT) has been used in studies in an attempt to predict the need for DAPT discontinuation and assess the bleeding risk of DAPT [9,10]. In these studies, up to $25\%$ of patients had at least one positive FOBT, and >$80\%$ of patients with positive FOBT successfully remained on DAPT. FOBT is recommended as a non-invasive colon cancer screening test, however, is commonly ordered in the inpatient setting for anemia and suspected gastrointestinal bleeding [11,12]. Inpatient FOBT has been highlighted in The Society of Hospital Medicine’s choosing wisely campaign and is problematic secondary to high type 1 error (around $50\%$) [13]. The timing and safety of endoscopy in patients with ACS and overt gastrointestinal bleeding have been studied. Urgent endoscopy has been shown to be beneficial prior to cardiac catheterization in patients with upper GI blood loss, hemodynamic instability, or hematemesis. In patients with less severe clinical features, endoscopy was safely delayed until after cardiac catheterization [3,14]. Little data exists regarding performing endoscopy prior to cardiac catheterization in patients with non-ST-elevation myocardial infarction (NSTEMI) on the basis of positive FOBT. The aim of this study is to assess the utility of FOBT in patients hospitalized with NSTEMI and to assess the utility of endoscopy on the basis of positive FOBT. ## Materials and methods We identified adult patients aged 18-90 years that had an inpatient admission for NSTEMI between January 1, 2019 to December 31, 2020 using the TriNetX research network database, which comprises 57 healthcare organizations. We identified ($$n = 46$$,349) patients meeting inclusion criteria, and divided patients into cohorts with FOBT during admission ($$n = 1$$,728) compared to those who did not undergo FOBT ($$n = 44$$,621). In order to understand potential differences in the groups, we constructed a 1:1 propensity match model to control for the literature-driven covariates which included age, white, male, female, black or African American, Hypertension, atherosclerotic heart disease of the native coronary artery, chronic heart failure, diabetes mellitus, chronic obstructive pulmonary disease, BMI< 30 (Tables 1, 2). Data source The Trinetx Inc. (Cambridge, MA) database is a global federal research network that combines real-time data from electronic medical records into a user-friendly platform for easy user access. Study sample We queried the Trinetx (Research Network) which is a collection of 57 healthcare organizations from January 1, 2019 to December 31, 2020. We identified ($$n = 46$$,349) aged 18-90. Trinetx, LLC is compliant with (HIPPA) and US federal law which protects the privacy and security of health care data. Statistical analyses The TriNetX platform uses descriptive statistics and creates several frequencies with differing percentages that are transferred into categorical variables using standard mean ± deviation for continuous measures. In order to understand baseline characteristics, Pearson’s chi-squared test is created for categorical variables. To account for potential differences in the cohorts a 1:1 propensity match using logistic regression to create two well-matched cohorts for analysis. The propensity analysis uses logistic regression for scores for differing propensity metrics for differing selected covariates. The propensity score match uses the Python libraries (NumPy and Sklearn). The final results compare the results to R to compare and verify the results. A final step in the verification process uses the nearest neighbor function set to a tolerance level of 0.01 and a deference of value >0.1. To address the endpoint of mortality a measure of difference of association was used and well as a Kaplan Meier as a verification test. Sensitivity analysis In order to understand any potential external variables that could be affecting the design of the study. a falsification endpoint of bleeding was created. ## Results We identified 46,349 patients meeting the inclusion criteria. Of those 1,728 had an FOBT administered ($3.7\%$) and 44,621 ($96.3\%$) had no FOBT administered prior to cardiac catheterization. Patients in the FOBT group were older (67.8 ± 11.5 vs 64.2 ± 12.6, $P \leq 0.001$). The FOBT group also had a higher prevalence of hypertension ($92.5\%$ vs $66.1\%$, $P \leq 0.01$), coronary artery disease ($84.4\%$ vs $49.0\%$, $P \leq 0.01$), heart failure ($63.5\%$ vs $30.0\%$, $P \leq 0.01$), diabetes ($58.0\%$ vs $35.2\%$, $P \leq 0.01$), chronic obstructive pulmonary disease ($32.2\%$ vs $16.0\%$, $P \leq 0.01$) and higher BMI (28.8 ± 6.87 vs 29.7 ± 6.87, $P \leq 0.001$). We were able to create two well-matched groups of $$n = 1$$,$\frac{728}{1}$,728. The FOBT group had similar 30-day mortality ($4.45\%$ vs 4.01, $$P \leq 0.56$$) confirmed with a Kaplan Meier curve in Figure 1 with a log-rank of ($$P \leq 0.75$$) as well as similar bleeding events ($0.98\%$ vs $0.69\%$, $$P \leq 0.35$$) confirmed with a Kaplan Meier curve in Figure 2 with a log-rank of ($$P \leq 0.41$$). Compared to the non-FOBT group, hemoglobin levels in the FOBT group were lower (10.7±2.56 vs 13.1±2.33, $P \leq 0.01$) and troponin levels were on average lower (0.41 vs 0.95, $$P \leq 0.04$$). The FOBT and non-FOBT groups had a similar average length of stay (14.1 days vs 14.2 days, $$P \leq 0.42$$). Two hundred and thirty-three patients who received FOBT underwent endoscopic evaluation with either upper endoscopy or colonoscopy during index admission ($13.5\%$). Of patients receiving an endoscopic evaluation, there was no significant difference in mortality ($6.86\%$ vs $4.7\%$, $$P \leq 0.321$$). Among patients who underwent endoscopy, 72 had some form of endoscopic intervention ($30.9\%$). There was no difference in 30-day mortality between patients undergoing endoscopy with the intervention compared to those without intervention ($14.49\%$/$14.49\%$) $$P \leq 1.00$$ confirmed with log-rank test ($92.60\%$ vs $92.37\%$) $$P \leq 0.91.$$ Readmission was similar between patients undergoing endoscopy with and without intervention. Figure 1: Freedom from all-cause mortality in patients receiving FOBT and not receiving FOBT over time Figure 2: Freedom from bleeding events in patients receiving FOBT and not receiving FOBT over time ## Discussion Performing endoscopy on patients with ACS is an area of concern for gastroenterologists. Sedation associated with endoscopy is a known stressor on the heart, however, has been shown to be safe following acute myocardial infarction in several studies [3,15-22]. In the absence of upper GI bleeding, hematemesis, or hemodynamic instability, urgent endoscopy can safely be delayed [23]. ACS with stent placement is a significant risk factor for new gastrointestinal bleeding. Patients undergoing standard medical therapy for ACS with DAPT, and low molecular weight heparin are at risk of clinically significant GI bleeding, with $1.3\%$-$2.4\%$ risk of GIB within 30 days of ACS [15,16]. At one year following PCI, GIB is the most common source of bleeding [24]. Gastrointestinal bleeding following an acute cardiovascular event is associated with increased morbidity, and patients undergoing endoscopy who were admitted with ACS are more critically ill in general than those admitted with ACS not undergoing endoscopy. These patients have higher mortality [3-8] increased length of hospital stay [7,8], increased risk of an in-hospital major adverse cardiac event [17], increased blood/platelet infusions [18] as well as increased resource utilization and cost of care than patients without GIB following PCI [25]. FOBT is frequently utilized in inpatient settings for inappropriate reasons [11,26-29]. FOBT (either guaiac-based testing or immunohistochemical) is recommended as a non-invasive screening tool for colorectal cancer, however, is frequently used in the inpatient setting for alternative indications. Anemia and suspected gastroenterology bleeding have been identified as leading factors for an FOBT order [11,29]. A single-center study showed $74\%$ of FOBT tests ordered for anemia were negative, and hospitalized patients with a positive FOBT are more likely to undergo endoscopic procedures during their stay and were more likely to receive a gastroenterology consult [11,29]. The Society of Hospital Medicine has identified inpatient FOBT testing as a quality issue in their Choosing Wisely campaign. Inpatient FOBT testing is problematic due to the high type 1 error (around $50\%$), and other factors attributing to positive test results. Bleeding from any source, inflammation such as gastritis, certain foods, and medications can lead to false positive results. Although only clinically indicated as a cancer screening tool and not indicated for inpatient use [28,29]. FOBT testing has also been attempted to predict DAPT discontinuation and bleeding risk post-PCI. Data on using FOBT prior to cardiac catheterization is limited and has been studied regarding the safety profile for DAPT in patients prior to PCI, avoiding premature discontinuation of DAPT in patients receiving coronary stenting, and as an indicator for DAPT discontinuation following PCI. In a study from Japan [9], FOBT was used as a screening tool prior to PCI, and endoscopy was performed on patients with two positive FOBT results. Twenty-five percent of 647 patients screened had one positive FOBT, and $11\%$ tested positive twice. In an additional single-center study [10], patients were screened with endoscopy if FOBT testing was positive prior to cardiac catheterization. FOBT was positive in about $6\%$ of patients and was associated with increased DAPT discontinuation; however, >$80\%$ of patients with positive FOBT results successfully remained on DAPT following PCI. Although little data exists regarding endoscopy or FOBT prior to cardiac catheterization in patients with NSTEMI, it is frequently performed in the inpatient setting. Our results show no benefit in performing FOBT prior to cardiac catheterization in patients hospitalized with NSTEMI. Utilizing a national database allowed for a large sample size including 46,349 patients across multiple health systems. The increased comorbidity noted in the population receiving FOBT is unsurprising, as many of these comorbidities also have an increased risk of gastrointestinal bleeding, especially age. Hemoglobin levels in the group receiving FOBT were significantly lower, suggesting FOBT is ordered as a means to assess for GI blood loss. Lower troponin levels in those receiving FOBT suggest that patients receiving FOBT could have less urgent cardiac risk. Overall, the percentage of patients presenting with NSTEMI and undergoing cardiac catheterization who received FOBT remains low at $3.7\%$. Our results show no statistically significant difference in 30-day mortality and bleeding events in patients undergoing FOBT. Of patients receiving FOBT, endoscopy was performed in $13.5\%$, with a slight increase in mortality noted for those patients undergoing endoscopy ($6.86\%$ vs $4.7\%$); however, statistical significance was not noted. $30.9\%$ of patients who underwent endoscopy did have an intervention, however, intervention during endoscopy did not affect mortality in these patients. Skewing of mortality and bleeding events in the population receiving FOBT may correlate with increased overall comorbid conditions noted in patients receiving this testing. Of note, performing FOBT did not increase the hospital's length of stay. Without a difference in mortality or clinically significant GI bleeding, there is no compelling evidence to suggest FOBT prior cardiac catheterization in patients hospitalized with NSTEMI to be beneficial. Our study was not without limitations. Utilizing a large national database with de-identified data, we were unable to assess if overt bleeding was present on admission. Our study is a retrospective chart review, having the inherited limitations of selection bias and inability to assess incidence. In the future, further assessment is needed to evaluate if endoscopy had a significant impact on the length of stay. Assessment is also needed to evaluate if patients receiving FOBT or undergoing endoscopy had a delay in time to cardiac catheterization compared to patients not receiving FOBT. As this study is a retrospective review utilizing a database, there may have been additional factors such as the need for blood transfusion, development of shock, socioeconomic status, and another clinical acumen that could not be taken into consideration for ordering FOBT. Our study did not differentiate between guaiac-based FOBT (gFOBT) and immunohistochemical FOBT (iFOBT). Hospitalization cost is an important factor that can be assessed in the future, to see if a difference in total hospitalization cost exists in patients receiving FOBT or endoscopy inpatient as a pre-PCI screening. ## Conclusions We present a large data registry that illustrates no significant difference in all-cause mortality, bleeding events, or length of stay in the cohort of patients receiving FOBT testing. 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--- title: 'Association between coronary artery disease and incident cancer risk: a systematic review and meta-analysis of cohort studies' authors: - Hsin-Hao Chen - Yi-Chi Lo - Wei-Sheng Pan - Shu-Jung Liu - Tzu-Lin Yeh - Lawrence Yu-Min Liu journal: PeerJ year: 2023 pmcid: PMC9968460 doi: 10.7717/peerj.14922 license: CC BY 4.0 --- # Association between coronary artery disease and incident cancer risk: a systematic review and meta-analysis of cohort studies ## Abstract ### Objective Coronary artery disease (CAD) and cancer are the two leading causes of death worldwide. Evidence suggests the existence of shared mechanisms for these two diseases. We aimed to conduct a systematic review and meta-analysis to investigateassociation between CAD and incident cancer risk. ### Methods We searched Cochrane, PubMed, and Embase from inception until October 20, 2021, without language restrictions. Observational cohort studies were used to investigate the association between CAD and incident cancer risk. Using random-effects models, the odds ratio (OR) and $95\%$ confidence interval (CI) were calculated. We utilized subgroup and sensitivity analyses to determine the potential sources of heterogeneity and explore the association between CAD and specific cancers. This study was conducted under a pre-established, registered protocol on PROSPERO (CRD42022302507). ### Results We initially examined 8,533 articles, and included 14 cohort studies in our review, 11 of which were eligible for meta-analysis. Patients with CAD had significantly higher odds of cancer risk than those without CAD (OR = 1.15, $95\%$ CI = [1.08–1.22], I2 = $66\%$). Subgroup analysis revealed that the incident cancer risk was significantly higher in both sexes and patients with CAD with or without myocardial infarction. Sensitivity analysis revealed that the risk remained higher in patients with CAD even after >1 year of follow-up (OR = 1.23, $95\%$ CI = [1.08–1.39], I2 = $76\%$). Regarding the specific outcome, the incident risk for colorectal and lung cancers was significantly higher (OR = 1.06, $95\%$ CI = [1.03–1.10], I2 = $10\%$, and OR = 1.36, $95\%$ CI = [1.15–1.60], I2 = $90\%$, respectively) and that for breast cancer was lower (OR = 0.86, $95\%$ CI = [0.77–0.97], I2 = $57\%$) in patients with CAD than in those without CAD. ### Conclusion CAD may be associated with incident cancer risk, particularly for lung and colorectal cancers, in men and women as well as patients with or without myocardial infarction. Early detection of new-onset cancer and detailed cancer surveillance programs should be implemented in patients with CAD to reduce cancer-related morbidity and mortality. ## Background Cancers and coronary artery disease (CAD) are the two leading causes of death worldwide. They are closely associated with shared risk factors, which may indicate common biological characteristics, such as common pathways that result in smoking-related CAD and lung cancer (Das, Asher & Ghosh, 2019). Some studies have also suggested that cardiovascular diseases, such as myocardial infarction and cancer share similarities in terms of obesity, oxidative stress, and inflammation (Koene et al., 2016; Pischon & Nimptsch, 2016). People with mild CAD before cancer diagnosis may experience disease progression due to the cancer-induced proinflammatory and hypercoagulable states. Furthermore, CAD may cause a delay in the initiation of cancer treatment due to a decline in the patient’s heart condition or increased risk of surgery (Das, Asher & Ghosh, 2019). Thus, early detection of neoplasm in patients with CAD through appropriate strategies is critical for reducing future morbidity. Some studies have reported increased incidence of CAD and stroke after cancer diagnosis. Various radio- and chemotherapeutic agents may affect the development and progression of cardiovascular disease (Arthurs et al., 2016; Navi et al., 2015; Zöller et al., 2012; Zhang et al., 2021). Further, several studies have indicated a high prevalence of occult cancer in patients with cardiovascular disease and reported that it is important to identify cancer risk factors as it may aid in developing new and effective preventive strategies (Corraini et al., 2018; Tybjerg, Skyhøj Olsen & Andersen, 2020; Wang et al., 2020). In contrast, several recent clinical and epidemiological studies have revealed a link between myocardial infarction and new-onset cancer; (Dreyer & Olsen, 1998; Pehrsson, Linnersjö & Hammar, 2005) however, the findings were inconsistent and contradictory (Malmborg et al., 2018; Rinde et al., 2017). According to a systematic review, increased cancer risk after myocardial infarction was only significant in women and patients with certain cancers such as lung cancer. However, some of the review’s analytic findings were based on only two or three studies and it only included patients with myocardial infarction, not all patients with CAD (Li et al., 2019). Recently, a large cohort study demonstrated that atherosclerotic cardiovascular disease itself increased cancer incidence after a median follow-up of 1,020 days (Suzuki et al., 2017). Thus, the potential of CAD as a causal factor in cancer remains unknown. Furthermore, it has not yet been elucidated whether occult cancer occurs before the emergence of CAD. Therefore, this study aimed to conduct a comprehensive systematic review and meta-analysis to determine the association between CAD and incident cancer risk. ## Data sources and study selection This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Table S1) (Page et al., 2021). This protocol was registered into the PROSPERO International Prospective Register of Systematic Reviews (CRD42022302507). The first author (Hsin-Hao Chen, HHC) and a medical librarian (Shu-Jung Liu, SJL) independently conducted an unrestricted search of electronic databases (Cochrane, PubMed, Embase (excluding Medline), and Taiwan Airiti Library) from inception until October 20, 2021. The following search terms were used: coronary artery disease, atherosclerosis, ischemic heart disease, myocardial infarction, neoplasms, cancer, and malignancy. The disagreements between the authors were resolved by a third reviewer (Tzu-Lin Yeh, TLY). We also examined potentially relevant studies in the references of relevant articles. Table S1 presents a complete description of the search strategies. To identify eligible studies, we first removed duplicates. Two authors (Yi-Chi Lo, YCL and Wei-Sheng Pan, WSP) independently screened the titles and abstracts of each article, followed by a review of the full texts. If there was a disagreement, the third author (HHC) was consulted to reach consensus. Studies were included if they met the following criteria: [1] retrospective or prospective cohort studies; [2] studies investigating the association between fatal or nonfatal CAD and cancer risk; [3] studies wherein cancer occurred after CAD diagnosis; and [4] studies reporting adjusted cancer relative risk (RR), odds ratio (OR), and hazard ratio (HR) with $95\%$ confidence interval (CI). Further, the exclusion criteria were as follows: [1] animal studies; [2] cross-sectional and case–control studies wherein cancer may have occurred before or concurrently with CAD; [3] nonobservational article types; [4] studies that did not report the relevant data for extraction; or [5] literature reviews, republished data, case reports, dissertations, editorial, letter, or conference abstracts. We initiated the formal screening of search results while registering the protocol into PRSOPERO because we were afraid that the COVID-19 pandemic would affect the writing and review process at that time. ## Data extraction and quality assessment Two authors (YCL and WSP) independently extracted the following data from each included article: first author, publication year, publication country, study design, CAD type, number of enrolled participants, age, follow-up duration, adjusted factors, cancer type, and main results (Table 1). Any disagreements were resolved through discussion with the third author (HHC). If any information was missing from the study results, the authors of original studies were contacted via email. The Newcastle Ottawa Scale (NOS) (Wells et al., 2022) was used by two authors (HHC and YCL) to independently assess the quality of the included studies. In cohort studies, the quality assessment tool (NOS) was used to rate each study in three domains—selection, comparability, and outcome—using a star system, with scores ranging from 0 to 9 stars (Zeng et al., 2015). The selection domain indicates representativeness of the exposed cohort, selection of the nonexposed cohort, and determination of exposure and outcome of interest that were absent at the beginning of the study. The comparability domain indicates whether exposed and nonexposed cohorts matched in the study design and/or whether confounders were adjusted for in the analysis. The outcome domain indicates whether the data were assessed accurately and whether the follow-up was adequate. If there was disagreement between two authors, the corresponding author (Tzu-Lin Yeh) made the final decision. A cohort study was considered to be of high quality if it received at least six stars. Table 1 \| Study \| Country \| CAD typea and number of participants(men %) \| Age (years) \| Follow up(mean or median, years) \| Adjusted factors \| Cancer type \| Main results(CAD vs non-CAD or CAC = 0, presented as OR, HR, or RR with 95% CI) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Dreyer & Olsen (1998) \| Denmark \| MI296,891 (67.97) \| M: 63F: 69 \| 5.9 (1–17) \| \| All \| Total: 1.05 [1.03–1.07]M: 1.03 [1.01–1.06]F: 1.08 [1.04–1.12] \| \| Pehrsson, Linnersjö & Hammar (2005) \| Sweden \| MI2N/A (65.20) \| <80 \| 9.3 (0–28) \| Age \| All \| M: 1.08 [1.04–1.11]F: 1.15 [1.09–1.21] \| \| Thomas et al. (2012) \| USA \| CAD1547 (100) \| 66 (62–70) \| 4 \| Age, race, FH of prostate cancer, PSA, BMI, TRUS, HTN,DM, HL, aspirin, statin, alcohol, smoke, geographic area, DRE \| Prostate \| 1.35 [1.08–1.67] \| \| Erichsen et al. (2013) \| Denmark \| MI2297,523 (63.8) \| 69.4 \| 3.1 (0–33) \| Sex, age, duration \| CRC \| 1.08 [1.05–1.11] \| \| Handy et al. (2016) \| USA \| CAD36,814 (47.1) \| 62.15 ± 10.2 \| 10.2 (IQ: 9.7–10.7) \| Age, sex, race, insurance, SES, BMI, PA, diet, smoke, drug,SBP, DBP,HTN drugs, TG, HDL, LL drugs, DM, aspirin \| All \| CAC >400 vs CAC = 0:1.53 [1.18–1.99];CAC = 0 vs CAC >0:0.76 [0.63–0.92] \| \| Vinter et al. (2017) \| Denmark \| CAD328,549 (45.8) \| 49–66.5 \| M: 2.8(IQ: 1.5–4.2);F: 2.9(IQ: 1.7–4.3) \| Age, BMI, DM, smoke, LL drugs, HTN drugs, Cr, HF. \| All \| M:CAC = 1–99:1.07 [0.83–1.39]= 100–399:1.24 [0.94–1.63]= 400–999:0.88 [0.62–1.25]≥1,000:0.96 [0.66–1.41]F:CAC = 1–99:0.96 [0.77–1.19]= 100–399:0.99 [0.75–1.31]= 400–999:1.11 [0.76–1.62]≥1,000:1.16 [0.73–1.83] \| \| Rinde et al. (2017) \| Norway \| MI21,747 (62) \| 62 \| 15.7 \| Age, sex, BMI, SBP, DM, HDL, smoke, PA, Edu. \| All \| All: 1.46 [1.21–1.77]M: 1.29 [1.02–1.62]F: 1.65 [1.19–2.29] \| \| Suzuki et al. (2017) \| Japan \| CAD132095 (59) \| 65 ± 16 \| 2.8 (IQ: 1.8–3.7) \| Age, sex, lifestyle-related disease, smoke, f/u periods \| All \| 1.42 [1.02–1.96] \| \| Berton et al. (2018) \| Italy \| CAD1589 (70) \| 67 (58–74) \| 17 \| \| All \| Incidence: 17.8 per 1,000 person-years \| \| Malmborg et al. (2018) \| Denmark \| MI2122,275 (61.2) \| M: 59.2(49.5–69.5)F: 68.5(58.1–76.0) \| 0.5–17 \| Age, sex, calendar year, HTN, HL, DM, COPD, SES \| All \| Total: 0.97 [0.92–1.01]M: 0.97 [0.91–1.03]F: 0.99 [0.92–1.06](exclude first 6 months) \| \| Kwak et al. (2020) \| Korea \| CAD2753,678 (72.1) \| 63.5 \| 4.56 (IQ:3.06–6.13) \| Age, sex, income, DM, BMI, smoke, alcohol, PA. \| All \| 1.06 [1.04–1.09];exclude first year:1.02 [0.99–1.05] \| \| Mirbolouk et al. (2020) \| USA \| CAD3Nonsmoker:48,331 (65.5) \| 54.6 ± 10.6y \| 11.9 (IQ:10.2–13.3) \| Age, sex, HL, FH of CAD, HTN, DM. \| All \| CAC = 1–99: 1.05 [0.84–1.30]= 100–399:1.19 [0.93–1.51]>400: 1.19 [0.92–1.55] \| \| Mirbolouk et al. (2020) \| USA \| Smoker5,147 (67.6) \| 52.8 ± 9.9 \| 11.9 (IQ: 10.2–13.3) \| Age, sex, HL, FH of CAD, HTN, DM. \| All \| CAC = 1–99: 0.83 [0.48–1.43]= 100–399:1.06 [0.60–1.89]>400:1.85 [1.07–3.22] \| \| Peng et al. (2020) \| USA \| CAD366,636 (67%) \| 54.4 ± 9.6 \| 12.3 ± 3.9 \| Age, sex, HTN, HL, smoke, DM, FH of CAD. \| All \| CAC = 1–399:1.10 [0.95–1.28]= 400–999:1.18 [0.94–1.47]>1,000:1.51 [1.19–1.91] \| \| Dzaye et al. (2021) \| USA \| CAD36,271 (47.3%) \| 61.7 ± 10.2 \| 12.9 ± 3.1 \| Age, sex, ethnicity, BMI, PA, SES, Edu, insurance, smoke, diet \| Lung/rectal \| CAC = 1–99: 1.2 [0.81–1.78]= 100–399:1.87 [1.20–2.92]>400:2.01 [1.20–3.35] \| \| Dzaye et al. (2021) \| USA \| CAD36,271 (47.3%) \| 61.7 ± 10.2 \| 12.9 ± 3.1 \| Age, sex, ethnicity, BMI, PA, SES, Edu, insurance, smoke, diet \| Prostate \| CAC = 1–99:1.52 [1.00–2.30]= 100–399:1.07 [0.62–1.85]≥400:1.13 [0.65–1.95] \| \| Dzaye et al. (2021) \| USA \| CAD36,271 (47.3%) \| 61.7 ± 10.2 \| 12.9 ± 3.1 \| Age, sex, ethnicity, BMI, PA, SES, Edu, insurance, smoke, diet \| Breast/uterine/ovary \| CAC = 1–99 = 0.76 [0.44–1.30]= 100–399 = 0.54 [0.24–1.19]≥400 = 1.13 [0.51–2.51] \| ## Statistical analysis and data synthesis We calculated pooled ORs with $95\%$ CIs to estimate incident cancer risk in patients with CAD and compared it with that in patients without CAD. For our meta-analysis, we used statistical computing software R, version 4.1.2 (R Core Team, 2021), primarily the Comprehensive R Archive Network package “metagen” (Software, 2022). Subsequently, we employed a random-effects model based on the DerSimonian and Laird’s method with an assumption of nonidentical true effect sizes (DerSimonian & Laird, 1986). These results were presented as forest plots. Furthermore, heterogeneity among studies was quantified using Cochran’s Q test and I2 statistics, and a p-value of <0.05 in the Q test or I2 value of >$50\%$ indicated the presence of heterogeneity (Higgins & Thompson, 2002). Subgroup analysis was determine to assess the potential origins of heterogeneity. We did not perform a meta-regression analysis using patient characteristics, as some studies did not provide enough study-level variable information (Pehrsson, Linnersjö & Hammar, 2005; Dzaye et al., 2021). Thus, this method would have been unsuitable, according to the methodological standards for meta-analysis and qualitative systematic reviews (Rao et al., 2017). We investigated the association between CAD and different cancers, including lung, colorectal, breast, liver, and prostate cancers. To assess the robustness of the results, we performed a sensitivity analysis that included only studies with a follow-up time of >1 year. The risk of publication bias was assessed using funnel plots and Egger’s test (Egger et al., 1997). ## Study characteristics and quality assessment Figure 1 presents the article selection flowchart. Initially, we obtained 8,533 articles from databases and by hand searching. Subsequently, we removed duplicates, reviewed titles and abstracts, and retrieved and evaluated 25 full-text articles for eligibility. After excluding articles with duplicate populations or those incompatible with the inclusion criteria, our systematic review included 14 cohort studies, 11 of which were eligible for meta-analysis (Fig. 1). Figure 1: Flow diagram for selection of articles. Table 1 summarizes the general demographic characteristics of the included studies in the systematic review. Of the included studies, only two (Suzuki et al., 2017; Kwak et al., 2020) were conducted in Asia, whereas other studies were from USA or Europe. Four studies included patients with myocardial infarction identified via discharge diagnosis with Internal Classification of Disease (ICD) codes, (Pehrsson, Linnersjö & Hammar, 2005; Malmborg et al., 2018; Rinde et al., 2017; Erichsen et al., 2013) whereas other studies included patients with CAD identified via hospital medical records, discharge diagnosis with ICD codes, or computed tomography scan with coronary artery calcium (CAC) score of >0. The duration of follow-up ranged from <1 year to a maximum of 33 years. Furthermore, we confirmed that the diagnosis of CAD was made before the occurrence of cancer in all included studies. Considering the cancer type, most studies investigated the incidence of all cancers, whereas other studies only assessed specific cancers, such as colorectal cancer, (Dzaye et al., 2021; Erichsen et al., 2013) or cancers specific to men (prostate) or women (Dzaye et al., 2021; Thomas et al., 2012). Regarding the outcomes, a study only reported the incidence rate, (Berton et al., 2018) whereas other studies provided the overall or subgroup effect estimates of RR, OR, and HR with $95\%$ CI. In our study quality assessment, we observed that only one study did not report the items of selection and comparability domain and, as such, did not meet our criteria (Dreyer & Olsen, 1998). All other included studies received at least six of nine stars on the NOS quality assessment scale, indicating high quality. Tables S3 presents the detailed results. ## Results of meta-analysis We pooled 11 studies for meta-analysis, which included >1,321,978 patients; however, one of these studies (Pehrsson, Linnersjö & Hammar, 2005) did not specify the number of participants. Patients with CAD had significantly higher odds of cancer risk than those without CAD (OR = 1.15, $95\%$ CI = [1.08–1.22], I2 = $66\%$; forest plot shown in Fig. 2). Subgroup analyses were performed based on the heterogeneity in the country and CAD type of patients. Patients with CAD had significantly higher odds of cancer risk than those without CAD in non-Asian regions (OR = 1.15, $95\%$ CI = [1.08–1.23], I2 = $67\%$; Fig. S1). Furthermore, Asian patients with CAD showed nonsignificantly higher odds of cancer risk than those without CAD (OR = 1.17, $95\%$ CI = [0.89–1.53], I2 = $67\%$; Fig. S1). We also conducted a subgroup analysis by CAD subtype, which revealed that those with or without myocardial infarction had significantly higher odds of cancer risk among patients with CAD than among those without CAD (OR = 1.11, $95\%$ CI = [1.00–1.23], I2 = $89\%$ and OR = 1.17, $95\%$ CI = [1.08–1.27], I2 = $51\%$, respectively; Fig. S2). Figure 2: Forest plot of incident cancer risk, comparing participants with CAD as those without CAD.CAD, coronary artery disease; CI, confidence interval; OR, odds ratio; se, standard error; TE, treatment effect. ## Subgroup analysis by sex We also performed pooled analyses in a random-effects model based on sex. This analysis was conducted when the studies indicated the odds of cancer risk by individual sex. After pooling seven studies, (Dreyer & Olsen, 1998; Pehrsson, Linnersjö & Hammar, 2005; Malmborg et al., 2018; Rinde et al., 2017; Dzaye et al., 2021; Thomas et al., 2012; Vinter et al., 2017) the overall risk of cancer incidence in men with CAD was higher than that in those without CAD (OR = 1.12, $95\%$ CI = [1.03–1.22], I2 = $61\%$; Fig. 3[1]). Furthermore, after pooling six studies, (Dreyer & Olsen, 1998; Pehrsson, Linnersjö & Hammar, 2005; Malmborg et al., 2018; Rinde et al., 2017; Dzaye et al., 2021; Vinter et al., 2017) women with CAD showed a higher incident cancer risk than those without CAD (OR = 1.08, $95\%$ CI = [1.00–1.16], I2 = $56\%$, Fig. 3[2]). Figure 3: Forest plot of incident cancer risk, comparing participants with CAD as those without CAD by individual gender.(1) Men (2) Women. CAD, coronary artery disease; CI, confidence interval; OR, odds ratio; se, standard error; TE, treatment effect. ## Subgroup analysis by different outcome We determined whether CAD exerted different effects on different types of cancer. Patients with CAD had a significantly higher risk of colorectal and lung cancers than those without CAD (OR = 1.06, $95\%$ CI = [1.03–1.10], I2 = $10\%$; Fig. 4[1]) and (OR = 1.36, $95\%$ CI = [1.15–1.60], I2 = $90\%$, respectively; Fig. 4[2]), as determined after pooling four (Pehrsson, Linnersjö & Hammar, 2005; Kwak et al., 2020; Erichsen et al., 2013; Vinter et al., 2017) and five (Dreyer & Olsen, 1998; Pehrsson, Linnersjö & Hammar, 2005; Malmborg et al., 2018; Kwak et al., 2020; Vinter et al., 2017) studies, respectively. However, according to the odds of breast cancer risk in five studies, (Dreyer & Olsen, 1998; Pehrsson, Linnersjö & Hammar, 2005; Malmborg et al., 2018; Kwak et al., 2020; Vinter et al., 2017) a lower risk was observed among patients with CAD than among those without CAD (OR = 0.86, $95\%$ CI = [0.77–0.97], I2 = $57\%$; Fig. 4[3]). Furthermore, compared with patients without CAD, a nonsignificantly increased risk of prostate and liver cancers was observed in those with CAD (OR = 1.04, $95\%$ CI = [0.94–1.16], I2 = $72\%$; Fig. S3[1] and OR = 1.03, $95\%$ CI = [0.88–1.21], I2 = $59\%$, respectively; Fig. S3[2]), as determined after pooling seven (Dreyer & Olsen, 1998; Pehrsson, Linnersjö & Hammar, 2005; Malmborg et al., 2018; Dzaye et al., 2021; Kwak et al., 2020; Thomas et al., 2012; Vinter et al., 2017) and three (Dreyer & Olsen, 1998; Pehrsson, Linnersjö & Hammar, 2005; Kwak et al., 2020) studies, respectively. Figure 4: Forest plot of incident cancer risk, comparing participants with CAD as those without CAD by individual cancer type.(1) Colorectal cancer (2) Lung cancer (3) Breast cancer. CAD, coronary artery disease; CI, confidence interval; OR, odds ratio; se, standard error; TE, treatment effect. ## Sensitivity analysis and publication bias We analyzed six studies in which all patients had a follow-up time of >1 year (Rinde et al., 2017; Dzaye et al., 2021; Kwak et al., 2020; Erichsen et al., 2013; Handy et al., 2016; Peng et al., 2020). The incident cancer risk was still higher in patients with CAD than in those without CAD (OR = 1.23, $95\%$ CI = [1.08–1.39], I2 = $76\%$; Fig. S4). Funnel plots revealed asymmetry for publication bias, as shown in Fig. S5. In addition, Egger’s test revealed a significant publication bias ($$p \leq 0.06$$). ## Discussion Our meta-analysis revealed that patients with CAD had significantly higher odds of cancer risk than those without CAD among cohort studies. Subgroup analysis indicated that cancer risk was significantly higher in both men and women, those with and without myocardial infarction, and non-Asian patients. Moreover, for specific cancer types, patients with CAD had a higher risk of colorectal and lung cancers, nonsignificantly higher risk of prostate and liver cancers, and lower risk of breast cancer. A previous systematic review of myocardial infarction based on only three studies revealed that the incident cancer risk in the test group was nonsignificantly higher (OR = 1.08, $95\%$ CI = [0.97–1.19]) than that in the control group. However, subgroup analysis revealed that the overall cancer risk was higher in women and during the first 6 months following myocardial infarction diagnosis (Li et al., 2019). Further, our meta-analysis of eleven studies revealed a significantly higher incident cancer risk in patients with CAD with or without myocardial infarction. One of the differences in the outcomes of patients with myocardial infarction is the number of cohort participants included in the meta-analysis. As the 1998 study by Dreyer & Olsen [1998] in Denmark comprised only a small proportion (96,891 people) of the 2013 study by Erichsen (297,523 people), (Erichsen et al., 2013) we included a large cohort instead of a small cohort. Further, our meta-analysis evaluated patients without myocardial infarction via CAC, percutaneous coronary intervention (PCI), or hospital discharge records to comprehensively assess cancer risk in patients with CAD. CAD and incident cancer risk are mainly associated because of the presence of shared risk factors. As summarized in the study by Hasin et al. [ 2016] cancer may be caused by treatment modalities or biological changes related to cardiovascular diseases. Other reviews have also indicated that inflammatory cytokines, such as interleukin(IL)-1, IL-6, IL-10, tumor necrosis factor-α, macrophage migration inhibitory factor, and transforming growth factor-β, are involved in tumor initiation and progression (Amin et al., 2020; Leiva et al., 2021). In addition to inflammation during the development of atherosclerosis and cancer, a recent review revealed that age-related mutations, obesity, smoking, and diabetes are overlapping risk factors between cancer and CAD (Leiva et al., 2021). Additionally, some observational studies have reported that noncardiac causes, such as malignancies, are responsible for most later deaths in patients with myocardial infarction treated with PCI (Pedersen et al., 2014; Spoon et al., 2014). Conversely, some studies have suggested that the increased cancer risk immediately after myocardial infarction can be attributed to other confounding factors, such as surveillance bias, rather than myocardial infarction itself. Patients with myocardial infarction had frequent clinical appointments and underwent more diagnostic examinations, especially in the first few months after the event, which may increase the likelihood of early cancer detection (Malmborg et al., 2018; Li et al., 2019). This situation is not only observed in patients with myocardial infarction but also in those without. Other studies have shown that occult cancers could have occurred before the cardiovascular event if cancer incidence is observed immediately after the start of myocardial infarction follow-up (Hasin, Iakobishvili & Weisz, 2017). In some patients, an underlying malignancy can cause an ischemic stroke. The effects of the coagulation cascade, tumor mucin secretion, infections, and nonbacterial endocarditis may contribute to the mechanisms (Selvik et al., 2015). Thus, occult cancer may also contribute to the development of CAD. However, our sensitivity analysis revealed that patients with CAD continue to have an increased incident cancer risk after >1 year of follow-up, which differs from the meta-analysis based on only two studies reporting that cancer risk is only significant in the first 6 months. Another study revealed that although the cancer risk is the highest in the first year following myocardial infarction, cancer develops over time (Malmborg et al., 2018). According to a recent large-scale cohort study, atherosclerotic cardiovascular disease increases the incident cancer risk after a median follow-up of 1,020 days (Suzuki et al., 2017). Moreover, the risk is increased when patients with CAD concomitantly have aortic and peripheral artery disease with a median follow-up of 3 years (Suzuki et al., 2022). Therefore, CAD may affect long-term cancer incidence. Our study revealed that CAD events increased the risk of lung and colorectal cancers but decreased the risk of breast cancer. We determined that “smoking,” a well-known cause of lung and colorectal cancers, was a common risk factor. This may account for some of our findings that indicate that the risk of both cancers was significantly increased after CAD (Dekker et al., 2019). Another reason for an increase in lung cancer incidence may be that cardiac scanning includes the lungs; thus, lung cancers account for most detected cancers (Vinter et al., 2017). Diabetes is a classic risk factor for CAD and is also related to elevated risk of cancer, especially colorectal cancer (Leiva et al., 2021). A study showed that patients with diabetes had a 20–$38\%$ higher cancer risk than those without diabetes (Yuhara et al., 2011). Moreover, modifiable environmental risk factors, such as obesity, lack of physical activity, and Westernized diet, may predispose individuals to CAD and colorectal cancer (Keum & Giovannucci, 2019). According to two large prospective cohort studies, a high intake of animal fat or processed red meat and low intake of fiber could increase the risk of CAD and colon cancer (Al-Shaar et al., 2020; Willett et al., 1990). One possible explanation for the lower risk of breast cancer in our study is life-long aspirin treatment, as recommended by CAD guidelines, (Qiao et al., 2018) which may also affect carcinogenesis. Large-scale cohort studies have consistently demonstrated the protective effects of low-dose aspirin for treating breast cancers (Qiao et al., 2018; Yang et al., 2017). However, there is limited evidence to support the association between CAD and breast cancer and we cannot exclude the possible selection bias; therefore, more research is warranted in this regard. This is the first study to conduct a comprehensive review and meta-analysis of the association between CAD and incident cancer risk with regard to patients with or without myocardial infarction as well as different cancer types. However, there are some limitations that must be addressed. First, our meta-analysis had significant publication bias, indicating that some nonsignificant studies are not published. This would weaken the positive association between CAD and incident cancer risk observed in our study. However, current evidence was the best available, and all studies, including several population-based cohort studies, were of moderate-to-high quality. Second, not all included studies could distinguish the length of follow-up and different cancer types. Our findings showed that the cancer risk remains elevated even at 1 year of follow-up after a CAD event, which contradicts the findings of the previous two studies (Malmborg et al., 2018; Kwak et al., 2020). According to our subgroup analysis, CAD may have different effects on different cancer types. Additional studies with subgroup analysis of follow-up time and different types of cancer are thus warranted to investigate the association between CAD and incident cancer risk. Third, most studies did not provide data regarding heart failure or left ventricular ejection fraction. Recently, Meijers et al. [ 2018] indicated that heart failure stimulates tumor growth via cardiac-excreted circulating factors. Furthermore, heart failure is associated with cancer incidence (Hasin et al., 2016) and could become a confounding factor in future research. ## Conclusions Our analysis of newly published data suggested an increased risk of incident cancer after a CAD event, particularly for lung and colorectal cancers. This increased risk was observed in men and women with or without myocardial infarction. Although this trend may be attributable to several common risk factors and underlying pathophysiologic mechanisms such as inflammation, patients with a history of CAD are still more likely to develop cancer. As CAD and cancer are the two leading causes of death, treatment of any one disease may affect the occurrence of the other. Therefore, more research is warranted regarding the causes of malignancy. 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--- title: 'Effects of Rapid Maxillary Expansion on Upper Airway Volume in Growing Children: A Three-Dimensional Cone-Beam Computed Tomography Study' journal: Cureus year: 2023 pmcid: PMC9968479 doi: 10.7759/cureus.34274 license: CC BY 3.0 --- # Effects of Rapid Maxillary Expansion on Upper Airway Volume in Growing Children: A Three-Dimensional Cone-Beam Computed Tomography Study ## Abstract Background: Rapid maxillary expansion (RME) is a common orthodontic procedure that widens the maxillary arch to treat moderate to mild overcrowding and transverse skeletal and dental abnormalities. Orthodontic equipment applies lateral tension on posterior maxilla teeth or palate mucosa to the mid-palatal suture. The maxilla may grow transversely when force is applied at right angles to the mid-palatal suture, which is usually inactive in children and adolescents. This study used cone-beam computed tomography (CBCT) and an authorized upper respiratory airway volume measurement approach to compare RME cohort pharyngeal airway volume changes to healthy controls. Materials and Methods: This retrospective analysis included 52 RME patients and 52 healthy controls. The RME category's expansion regimen entailed twisting the screw of expansion on a tooth-attached Hyrax-type expansion equipment by 0.25 mm daily for at least 14 days. After six months, a few RME participants used fixed orthodontic gear. The comparison group used fixed orthodontic appliances for minor malocclusions without extractions (without RME). CBCT scans from 1021 orthodontic patients who visited a dental hospital between 2012 and 2022 were examined. The registry comprised only anonymized photographs. Volume, minimum cross-sectional area (MCA), molar width, and inter-molar width were measured before and after therapy. Results: The control group had 12227.12 mm3 at T0 and 15805.54 mm3 at T1. The control group's T0-T1 volume difference was statistically significant ($$p \leq 0.007$$). The RME group has 12884.84 mm3 at T0 and 17471.08 mm3 at T1. The RME group had a significant volume difference at T0 and T1 ($$p \leq 0.002$$). The volume RME effect was ±1011.92 and statistically insignificant. ( $p \leq 0.05$). MCA in the control group was 126.04 mm2 at T0 and 170.61 mm2 at T1. MCA at T0 and T1 in the control group was statistically significant ($$p \leq 0.041$$). RME group MCA was 126.53 mm3 at T0 and 164.69 mm2 at T1. The RME group had a significant volume difference at T0 and T1 ($$p \leq 0.002$$). The MCA, RME effect was 5.92 and statistically insignificant ($p \leq 0.05$). Both the control and RME groups had statistically significant volume and MCA differences at T0 and T1. However, the intergroup analysis showed no significant differences across the groups. Conclusion: Tooth-borne RME does not affect upper airway or MCA volume in children compared to controls. Upper airway changes were better with younger skeletal ages before treatment. The findings may aid RME for young children. ## Introduction In order to promote regular craniofacial maturation, early identification and intervention are preferred for the dentofacial abnormalities that appear as a consequence of upper airway compression [1]. The conditions of obstructive sleep apnea (OSA), sleep-disrupted breathing, and upper airway architecture have all been researched more frequently, and there is a common opinion that proper care at the beginning of such problems can have a better impact on patients' long-term general health outcomes as well as dental effects [2]. Rapid maxillary expansion (RME) is a frequent orthodontic procedure that increases the width of the maxillary arch to alleviate moderate to mild overcrowding of teeth while addressing transverse skeletal abnormalities and dental abnormalities [3]. Force is placed on the midpalatal suture by applying force in a lateral direction on the teeth present in the posterior region of the maxilla or mucosa of the palate using an orthodontic apparatus. When applied with force at right angles to the suture at the midpalatal region, which is typically a dormant condition in kids and adolescents, growth in the transverse direction in the maxilla may result [4-7]. RME's main goal is to apply force on the maxillary bone [8]. It has long been difficult to comprehend the three-dimensional impact of RME on the upper portion of the respiratory airway. Although auditory rhinometry has been employed, this method is only applicable to the nasal cavities [9-11]. The application of computed tomography has lately been discussed, although there are certain drawbacks, including the requirement that individuals be in a supine posture and a reasonably high dose of radiation [12,13]. It has been demonstrated that putting someone in certain positions alters the airway's volume [14-17]. Cone-beam computed tomography (CBCT) has been demonstrated to be a trustworthy and precise method for evaluating the upper portion of the respiratory airway in the upright position [18]. With easy-to-see markers and only a small amount of image magnification, it can see the edges between the airway spaces and the soft tissues next to them in both children and adults [19]. There are discrepancies in the data and a lack of uniformity across the measuring techniques utilized, according to a recent systematic assessment of earlier CBCT studies examining the variations in the upper portion of airways both before and after administration with RME [20]. The goals of this study were to compare changes in the volume of the pharyngeal airway as well as the minimum cross-sectional area (MCA) in an RME cohort with those in healthy controls using CBCT and an authenticated procedure for measuring the volume of the upper respiratory airway. ## Materials and methods A registry of 1021 patients who visited a dental hospital for orthodontic therapy between 2012 and 2022 was evaluated to obtain their CBCT images. Ethical approval for the study was taken from the Albaha university with ref no: IEC/ALB/$\frac{2022}{11.}$ All photos were anonymized before being included in the registry. The database also gave information about the person's gender, age, anatomical occlusion (based on Angle's classification of malocclusion), and the type of orthodontic therapy they were getting. There were two categories in this retrospective analysis: a category of individuals with RME with 52 participants and a category of healthy controls with 52 participants. For a minimum period of 14 days, the expansion regimen in the category of RME involved turning the screw of expansion on a tooth-attached Hyrax-type appliance for expansion that was 0.25 mm each day. Few RME participants kept using fixed orthodontic appliances after the initial six-month duration of retention. In the comparison group, only fixed orthodontic appliances (no RME) were used to treat small misalignments without extractions. The criteria for inclusion were: [1] RME procedure for unilateral or bilateral crossbite using a tooth-associated Hyrax expansion device; [2] a minimal rise of three mm in the distance between the molars on both sides of the maxilla was observed as a difference between the distance recorded in pre-treatment CBCT scans and the distance recorded in post-treatment CBCT scans, translating into a minimum anticipated orthopedic change of 1.5 mm in the upper jaw; [3] adolescents between the ages of 8 and 15; [4] cutting in regular intercuspal orientation with just an angled Class I molar contact; [5] Baseline CBCT scans and progression comprehensive scanning of the base of the cranium, maxillary bone, and mandibular bone, as well as the first six cervical vertebrae and related airways, is performed during CBCT scans. Criteria for exclusion include prior history of orthodontic therapy, prior history of adenotonsillectomy, prior history of syndromic disorders, history of movement artifacts, prior history of swallowing while scanning and recording, and a treatment program necessitating the extraction of teeth for orthodontic treatment. After using criteria for who to include and who to leave out, the final cohort was made up of 52 patients. Scan protocol: The same radiologist used the CBCT device of an iCAT Next Generation to capture the radiographic CBCT pictures. The settings were KVp of 120 Kv, current of 5 mA, voxel resolution of 0.4 mm, scan duration of 8.9 seconds, and a scanned space measuring 13 cm in height by 16 cm in diameter [21-23]. Image preparation and airway assessment (volume and MCA): The upper and lower margins, as per the previously established margins-which comprised the anterior margins and posterior boundaries of the airway-were specified in order to compute the MCA and volume. This was done to prevent the MCA from being calculated using a partial section caused by the discrepancy between the airway border for volume computation and the plane for computation. Within the set limits, the software automatically estimated the MCA (mm2) and volume (mm3). At the start of the active therapy (T0) and the completion of it, both measurements were performed (T1). Assessment of the transverse effects of RME: For study participants at the start (T0) and end (T1) of treatment, posteroanterior-oriented CBCT-generated cephalometric radiographs were used to measure the width of the maxillary arch and the width of the mandible. In accordance with the procedure outlined by Yoon et al., these images were reconstructed by the digital software of CBCT with a minimum elongation of the radiographic image. To assess the skeletal treatment modifications attained with the RME apparatus, CBCT scans were obtained in a standard setting in RME study participants and then compared to images from the control group [23]. According to Adkins et al. [ 24], the intermolar dimension was determined using CBCT images taken from the extreme palatal side of the maxillary first molars at the position of the cementoenamel junction. Statistical analysis: When put to the test using a Shapiro-Wilks assessment, the data sets from the RME category and control groups were both distributed evenly. A paired t-test was used to assess the dentofacial differences and airway distinctions among the two categories at T0. A multivariate regression model was used to analyze the intragroup and intergroup variations in the quantitative analysis of dentofacial structures and the upper portion of the respiratory airway at the beginning of treatment (T0) and the end of treatment (T1), taking into account the data's longitudinal and nested nature. The predictor variables, namely volume of the upper portion of the airway and MCA of the upper airway, and selected variables like the group of study participants and duration of the treatment procedure, in addition to their interactions, were all included in the fixed effects section of the models. The random effects part of the model was made up of the pairs of people who took part. ## Results Volume in the control group at T0 was 12227.12 mm3, and at T1 was 15805.54 mm3. The difference in volume at T0 and T1 in the control group was quite relevant when statistically analyzed ($$p \leq 0.007$$). Volume in the RME group at T0 was 12884.84 mm3, and at T1 was 17471.08 mm3. The difference in volume at T0 and T1 in the RME group was quite relevant when statistically analyzed ($$p \leq 0.002$$). The RME effect regarding volume was ±1011.92, and it was not relevant when statistically analyzed ($p \leq 0.05$). MCA in the control group at T0 was 126.04 mm2, and at T1 was 170.61 mm2. The difference in MCA at T0 and T1 in the control group was quite relevant when statistically analyzed ($$p \leq 0.041$$). MCA in the RME group at T0 was 126.53 mm3, and at T1 was 164.69 mm2. The difference in volume at T0 and T1 in the RME group was quite relevant when statistically analyzed ($$p \leq 0.002$$). The RME effect regarding MCA was 5.92, and it was not substantially important statistically ($p \leq 0.05$) (Table 1). Table 1 \| Unnamed: 0 \| Volume (mm3) \| Volume (mm3).1 \| Volume (mm3).2 \| MCA (mm3) \| MCA (mm3).1 \| MCA (mm3).2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Control group \| RME group \| RME effect \| Control group \| RME group \| RME effect \| \| T0 \| 12227.12 \| 12884.84 \| \| 126.04 \| 126.53 \| \| \| T1 \| 15805.54 \| 17471.08 \| ±1011.92 \| 170.61 \| 164.69 \| 5.92 \| \| P Value \| 0.007 \| 0.002 \| 0.67 \| 0.041 \| 0.031 \| 0.101 \| \| 95%Confidence Interval \| 13,258.24 to 18,330.62 \| 15,028.06to 19,893.78 \| 2401.10 to 4418.73 \| 131.89 to 207.18 \| 132.97 to 196.40 \| 56.02 to 45.19 \| There were significant variations statistically in intra-group analysis in both the control group and RME group at T0 and T1 regarding volume and MCA. However, there were no substantially important distinctions between the two groups in the intergroup analysis. Maxillary width in the control group at T0 was 59.67 mm, and at T1 was 61.28 mm. The difference in the width of the maxilla at the T0 stage and T1 stage in the control category was statistically significant ($$p \leq 0.005$$). The maxillary width in the RME group at T0 was 59.91 mm, and at T1 was 62.81 mm. The difference in the width of the maxilla at the T0 stage and T1 stage in the RME category was statistically significant ($$p \leq 0.002$$). The RME effect regarding maxillary width was -1.77, and it was not substantially important statistically ($p \leq 0.05$). Maxillary inter-molar width in the control category at the T0 stage was 32.85 mm, and at the T1 stage was 33.73 mm. The difference in the width of the maxilla at the T0 stage and T1 stage in the control category was statistically significant ($$p \leq 0.002$$). Maxillary inter-molar width in the RME category at the T0 stage was 31.42 mm, and at the T1 stage was 34.94 mm. The difference in maxillary width at T0 and T1 in the RME group was statistically significant ($$p \leq 0.002$$). The RME effect regarding maxillary width was +3.83, and it was substantially important statistically ($$p \leq 0.002$$) (Table 2). Table 2 \| Unnamed: 0 \| Maxillary width (mm) \| Maxillary width (mm).1 \| Maxillary width (mm).2 \| Maxillary intermolar width (mm) \| Maxillary intermolar width (mm).1 \| Maxillary intermolar width (mm).2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Control group \| RME group \| RME effect \| Control group \| RME group \| RME effect \| \| T0 \| 59.67 \| 59.91 \| \| 32.85 \| 31.42 \| \| \| T1 \| 61.28 \| 62.81 \| -1.77 \| 33.73 \| 34.94 \| +3.83 \| \| P Value \| 0.005 \| 0.002 \| 0.006 \| 0.002 \| 0.002 \| 0.002 \| \| 95%Confidence Interval \| 59.42 to 62.14 \| 61.95 to 63.68 \| 0.51 to 2.91 \| 33.39 to 34.86 \| 33.26 to 35.74 \| 2.44 to 4.42 \| There were significant variations statistically in intra-group analysis in both the control group and RME group at T0 and T1 regarding the width of the maxillary arch. Unluckily there were no substantially important distinctions between the two groups in the intergroup analysis. There were significant variations statistically in intra-group analysis in both the control group and RME group at T0 and T1 regarding maxillary inter-molar width (mm). There were no substantially important distinctions between the two groups on intergroup analysis. ## Discussion The impacts of RME were examined by Cistulli et al. in a group of 10 individuals who had mild to moderate OSA [6]. All of these patients showed a decrease in the index score for distress in respiration; snoring was reduced in nine of these participants, along with a reduction in sleepiness during the daytime in these nine patients. In seven cases, the index for distress in respiration returned to normal. The authors concluded that RME might be a helpful therapeutic approach for some OSA patients [6]. Understanding the three-dimensional outcomes of RME on the upper portion of the respiratory airway has been a challenging task in the past. Although auditory rhinometry has been used, the nasal cavities are the only places where it may be used. The use of computed tomography has recently been addressed [7,8], although there are certain disadvantages, such as the need for subjects to be in the supine position and relatively large radiation dosage. Putting a person in certain positions has been shown to change the size of their airways [25-30]. Cistulli et al. investigated the effects of RME in a group of 10 people with mild to moderate OSA. Nine of these participants also had a decrease in snoring and daytime tiredness. All of these patients also showed a decrease in the respiratory distress score. Seven people had their respiration scores return to normal after experiencing respiratory distress. The authors concluded that, for some OSA patients, RME would be a beneficial therapy strategy [6]. The difference in volume at T0 and T1 in the control group was quite relevant when statistically analyzed ($$p \leq 0.007$$). The difference in volume at T0 and T1 in the RME group was quite relevant when statistically analyzed ($$p \leq 0.002$$). The RME effect regarding volume was 1011.92, and it was not relevant when statistically analyzed ($p \leq 0.05$). MCA in the control group at T0 was 126.04 mm2, and at T1, it was 170.61 mm2. The difference in MCA at T0 and T1 in the control group was quite relevant when statistically analyzed ($$p \leq 0.041$$). MCA in the RME group at T0 was 126.53 mm3, and at T1, it was 164.69 mm2. The difference in volume at T0 and T1 in the RME group was quite relevant when statistically analyzed ($$p \leq 0.002$$). The RME effect regarding MCA was 5.92 and was not substantially important statistically ($p \leq 0.05$). There were significant variations statistically in intra-group analysis in both the control group and the RME group at T0 and T1 regarding volume and MCA. On intergroup analysis, however, there were no statistically significant differences between the two groups. Early detection and intervention are preferred for dentofacial abnormalities that develop as a result of upper airway compression to promote normal craniofacial maturation. Obstructive sleep apnea (OSA), sleep-disordered breathing, and upper airway architecture have all been studied more often, and it is generally believed that treating these conditions early may enhance patients' long-term dental and general health outcomes [31-35]. Rapid maxillary expansion (RME) is a common orthodontic procedure that widens the maxillary arch to treat transverse skeletal anomalies and dental irregularities, as well as moderate to mild tooth crowding. By exerting force in a lateral direction on the teeth located in the posterior part of the maxilla or the mucosa of the palate with the use of an orthodontic tool, force is applied to the mid-palatal suture. If pressure is put on the suture in the mid-palatal area, which is usually still present in children and teens, it can cause the maxilla to grow in a different direction [36-37]. The difference in maxillary width between the T0 and T1 stages in the control group was statistically significant ($$p \leq 0.005$$) in this study. In the RME category, the difference in maxillary width at T0 and T1 was statistically significant ($$p \leq 0.002$$). The RME effect regarding maxillary width was -1.77, and it was not substantially important statistically. ( $p \leq 0.05$). Maxillary inter-molar width in the control category at the T0 stage was 32.85 mm, and at the T1 stage, it was 33.73 mm. In the control group, the difference in maxillary width at T0 and T1 was statistically significant ($$p \leq 0.002$$). It has been proven that cone-beam computed tomography (CBCT) is a reliable and accurate tool for assessing the upper section of the respiratory airway in the upright position. With easily identifiable markers and little image magnification, it can accurately identify the boundaries between the spaces of the airway and the surrounding soft tissues in both pediatric patients and adult patients. In this study, maxillary inter-molar width in the RME category at the T0 stage was 31.42 mm, and at the T1 stage, it was 34.94 mm. The difference in maxillary width at T0 and T1 in the RME group was statistically significant ($$p \leq 0.002$$). The RME effect regarding maxillary width was 3.83, and it was substantially important statistically. ( $$p \leq 0.002$$). There were significant variations statistically in intra-group analysis in both the control group and the RME group at T0 and T1 regarding the width of the maxillary arch. Unfortunately, the intergroup analysis revealed no statistically significant differences between the two groups. There were significant variations statistically in the intra-group analysis in both the control group and the RME group at T0 and T1 regarding maxillary inter-molar width (mm). Based on the intergroup analysis, there were no statistically significant differences between the two groups. A review of past CBCT studies assessing the alterations in the upper region of the airways both before and after administration with RME revealed differences in the findings and a lack of homogeneity among the measuring methodologies used [38-44]. Anandarajah used CBCT in healthy, untreated individuals to determine a relationship between the volume of the upper section of the airway and the width of the maxillary and mandibular arches. Additionally, he recommended and supported a standardized method for measuring the upper airways [19]. The goal of this study was to measure the volume of the upper respiratory tract using a validated method, calculate the MCA, and compare the results to those of healthy controls using CBCT. Similar to what was found in other CBCT studies, there was no link between the position of the jaw and changes in the volume of the upper airway, or MCA. ## Conclusions When compared to controls, tooth-borne RME is not linked to a substantial change in the volume of the upper airway or MCA in children. When the skeletal age prior to treatment was younger, the impact on the upper airway modifications was more favorable. 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--- title: Application of the New “Points in Range” Metrics in the Assessment of In-Hospital Glycemic Control journal: Cureus year: 2023 pmcid: PMC9968487 doi: 10.7759/cureus.34278 license: CC BY 3.0 --- # Application of the New “Points in Range” Metrics in the Assessment of In-Hospital Glycemic Control ## Abstract Introduction Capillary blood glucose (CBG) monitoring remains the most used testing form in hospitals and allows for “points in range (PIR)” metric calculation. This study was conceived to evaluate the metabolic control in patients with diabetes mellitus (DM) at a hospital through PIR metrics. Methods This was an observational cross-sectional study conducted on October 9, 2020, that included non-critical adults admitted to Centro Hospitalar Universitário do Porto (except pregnant/postpartum women) with DM under CBG monitoring and a minimum of 24 hours of hospitalization. Glycemic control was evaluated by previous day CBG monitoring. Results The study sample consisted of 110 patients with DM ($93.6\%$ type 2) with a median number of CBG tests of 4.00 (1.00) and a median CBG of 166.20 (69.41) mg/dL, SD 41.93 ± 27.20 mg/dL, and variation coefficient of 22.56 ± $12.51\%$. Points below range were $0.5\%$, with $0\%$ below 54 mg/dL. The points in ranges 70-140 mg/dL and 140-180 mg/dL were $32.8\%$ and $22.0\%$, respectively, and the total number of patients with all points in range 70-180 mg/dL was 19 ($17.3\%$), with only 3 ($2.7\%$) having all points in range 140-180 mg/dL and 10 ($9.1\%$) in range 70-140 mg/dL. Regarding points above range (PAR), $29.9\%$ and $14.8\%$ points were at levels 1 and 2 hyperglycemia, respectively, and 15 ($13.6\%$) patients had all points above 180 mg/dL. Correlations were identified between PAR and the total number of CBG assessments (ρ = 0.689, $p \leq 0.001$). Conclusion We conclude that in-hospital glycemic control remains suboptimal: only few have adequate control according to the PIR metrics despite low glycemic variability. PIR metrics are a new, valuable, simple and valid way to take better advantage of CBG monitoring at no added cost. ## Introduction Capillary blood glucose (CBG) and continuous glucose monitoring (CGM) provide different information about the glucose regulatory situation of a certain patient. CBG monitoring provides an accurate static value that reflects glucose concentration in the body’s transport system but does not report on its variations over time. Instead, CGM offers a dynamic measurement of the interstitial fluid glucose levels. These two forms measure glucose levels in different compartments, which can be associated with some degree of discrepancy especially in situations of high glycemic variability [1]. We have been witnessing the exponential growth in forms of CGM worldwide and this has led to the emergence of the concepts of glycemic variability (GV), time in range (TIR), time above range (TAR), time below range (TBR) and other CGM metrics that complement the value of HbA1c [2,3]. In 2019, the Advanced Technologies and Treatments for Diabetes (ATTD) Congress panel published guidance on CGM-based targets for the assessment of glycemic control for different diabetic populations using those metrics [4]. Nonetheless, CBG testing remains the most widely used form of inpatient monitoring because it is associated with lower costs and there are some doubts about CGM accuracy in hospitalized patients. However, despite the usefulness of CBG in daily decision making in hospitalized patients, the evaluation of the global metabolic control in these patients is difficult to assess based on individual CBG testing. During the COVID-19 pandemic, there was a need to develop strategies for the remote monitoring of glucose in hospitalized patients, which led to greater liberalization of the in-hospital temporary use of CGM. After April 2020, when the Food and Drug Administration (FDA) made the use of CGM available in hospitalized COVID-19 patients, several studies sought to assess its accuracy and safety in inpatient settings with favorable results, although there is still not enough evidence to prove the cost-benefit of its more indiscriminate use in this context [5]. Therefore, it is urgent to find alternative ways of obtaining the complementary information provided by CGM in situations where its widespread use is not possible. The analysis of self-monitoring CBG values by Diabetes Management Systems (DMS) led to the perception that those isolated values could be pooled to provide additional information on glycemic control, similarly to what is done with CGM systems. This way, these new CBG monitoring metrics, the “points in range (PIR)”, were created seeking to bypass its limitations compared to CGM but considering that CBG only provides isolated glucose values. The PIR metrics are interpreted similarly to CGM’s TIR and provide additional information on the isolated value of CBG [6]. The primary objective of this study was to describe CBG monitoring in PIR metrics and to assess the metabolic control in patients with diabetes mellitus (DM) in the hospital through PIR metrics. Furthermore, we also intended to compare PIR metrics in the infection/non-infection subgroups and with respect to the length of hospitalization. The results of this study were previously presented as a meeting abstract at the Portuguese Congress of Endocrinology (72nd Annual Meeting of the Portuguese Society of Endocrinology, Diabetes and Metabolism), January 29-31, 2021. ## Materials and methods This was an observational cross-sectional study conducted on October 9, 2020, at Centro Hospitalar Universitário do Porto, a central hospital in Portugal, and included non-critical adult patients (except pregnant/postpartum women) with diagnosis of DM (by consulting the clinical process), hospitalized for a minimum of 24 hours and under CBG monitoring. A total of 31 patients with insufficient clinical information in the process were excluded. The study protocol conformed to the World Medical Association’s Helsinki Declaration and was approved by the Ethics Committee of Centro Hospitalar Universitário do Porto, approval number 2021.079 (065-DEFI/068-CE). Informed consent was waived by the Ethics Committee based on the retrospective nature of the study and full data anonymization. The following data were collected from the electronic clinical record: demographic information, day of hospitalization, main diagnosis (infection/non-infection), type of DM and its microvascular/macrovascular complications, and CBG monitoring of the previous day. To define the total points in each of the intervals, we took into account both the American Diabetes Association (ADA) recommendations regarding glycemic control in hospitalized patients and the CGM-based targets defined in the 2019 ATTD consensus [4,7]. This way, we considered points below range (PBR) as <70 mg/dL, PIR as 70-180 mg/dL and points above range (PAR) as >180 mg/dL, calculated by adding the number of CBG tests in each of the periods. For example, a patient who had the CBG test results 181, 230, 74, 140 mg/dL would have a total of 0 PBR, 2 PIR and 2 PAR. Similarly, the percentage of points per interval for each patient was calculated by dividing the number of CBG tests in each range by its total amount in that 24-hour period and multiplying it by 100. In the previous example, we would have the following percentages: $0\%$ PBR, $50\%$ PIR and $50\%$ PAR. GV was assessed by standard deviation (SD) of the mean glucose value of each patient and the value of coefficient of variation (CV) (calculated by the formula: SD/mean glucose x 100) [3]. An analysis of subgroups was carried out in order to compare PIR metrics in the infection/non-infection groups, and in accordance with the length of hospitalization (categorized as 1-3 days, 4-7 days, 8-14 days, 15-30 days and >30 days). Data analysis was performed using the statistical package IBM SPSS Statistics, version 20.0.0 (IBM Corp., Armonk, NY). Categorical variables were presented as frequencies and percentages, and continuous variables as means and SD or medians and interquartile ranges (IQR) for variables with skewed distributions. Normal distribution was checked using the Shapiro-Wilk test or skewness and kurtosis as appropriate. All reported p-values are two-tailed, with a $p \leq 0.05$ indicating statistical significance. Spearman’s correlation coefficient (ρ) was used to assess the correlation between PIR metrics and other parameters under analysis, namely, the length of hospital stay and the number of CBG tests. The interpretation of correlations’ strength was made using the reference values provided by Bryman and Cramer [8]. For the analysis by main diagnosis and day of hospitalization subgroups, we used Mann-Whitney U-test and Kruskal-Wallis test, respectively. ## Results The sample description is shown in Table 1, consisting of 110 patients with a predominance of males ($60.0\%$), an average age of 72.70 ± 11.49 years and a median hospital stay of 11.00 (17.00) days at the time of data collection. The most frequent type of DM was type 2 in $93.6\%$, and with regard to complications, 67 ($60.9\%$) patients had at least one documented microvascular or macrovascular complication and $41.8\%$ had a diagnosis of infection (Table 1). The median number of CBG tests of the previous day was 4.00 (1.00). Table 1 \| Sample characteristics \| n (%) \| n (%).1 \| \| --- \| --- \| --- \| \| Gender \| \| \| \| Male \| 66 \| (60.0) \| \| Female \| 44 \| (40.0) \| \| Age at time of data collection (years), mean ± SD \| 72.70 ± 11.49 \| 72.70 ± 11.49 \| \| Minimum-maximum \| 32-92 \| 32-92 \| \| Median hospital stay (days), median (IQR) \| 11.00 (17.00) \| 11.00 (17.00) \| \| Day of hospitalization \| \| \| \| 1-3 days \| 22 \| (17.2) \| \| 4-7 days \| 22 \| (17.2) \| \| 8-14 days \| 32 \| (25.0) \| \| 15-30 days \| 30 \| (23.4) \| \| >30 days \| 22 \| (17.2) \| \| DM classification \| \| \| \| Type 1 \| 4 \| (3.6) \| \| Type 2 \| 103 \| (93.6) \| \| Induced by GC \| 2 \| (1.8) \| \| Not yet clarified \| 1 \| (0.9) \| \| Microvascular complications \| \| \| \| 0 \| 68 \| (61.8) \| \| 1 \| 28 \| (25.5) \| \| 2 \| 10 \| (9.1) \| \| 3 \| 4 \| (3.6) \| \| Macrovascular complications \| \| \| \| 0 \| 62 \| (56.4) \| \| 1 \| 36 \| (32.7) \| \| 2 \| 10 \| (9.1) \| \| 3 \| 2 \| (1.8) \| \| Main diagnosis \| \| \| \| Infection \| 46 \| (41.8) \| \| Non-infection \| 64 \| (58.2) \| Table 2 summarizes glycemic control results, including those of the new PIR metric application in the study population. The median blood glucose value was 166.20 (69.41) mg/dL and the mean SD and CV were 41.93 ± 27.20 mg/dL and 22.56 ± $12.51\%$, respectively. PBR were $0.5\%$, with $0\%$ below 54 mg/dL. Points in the intervals 70-139 mg/dL and 140-180 mg/dL were $32.8\%$ and $22.0\%$, respectively. Regarding PAR, $29.9\%$ and $14.8\%$ were at levels 1 and 2 of hyperglycemia, respectively (Table 2). The total number of patients with all points in the range 70-180 mg/dL was 19 ($17.3\%$), with only 3 ($2.7\%$) having all points in the range 140-180 mg/dL and 10 ($9.1\%$) in the range 70-139 mg/dL. In what concerns PAR, the total of patients with all points above 180 mg/dL was 15 ($13.6\%$). Table 2 \| Results \| Unnamed: 1 \| Unnamed: 2 \| \| --- \| --- \| --- \| \| Number of CBG tests, n (%) \| \| \| \| 2 \| 4 \| (3.6) \| \| 3 \| 33 \| (30.0) \| \| 4 \| 58 \| (52.7) \| \| 5 \| 11 \| (10.0) \| \| 6 \| 4 \| (3.6) \| \| Minimum CBG value (mg/dL), median (IQR) \| 124.00 \| (45.00) \| \| Maximum CBG value (mg/dL), mean ± SD \| 226.90 ± 77.25 \| 226.90 ± 77.25 \| \| HbA1c (%), median (IQR) \| 7.20 \| (2.20) \| \| Points per glucose range, n (%) \| \| \| \| <54 mg/dL \| 0 \| \| \| 54-69 mg/dL \| 2 \| (0.5) \| \| 70-139 mg/dL \| 137 \| (32.8) \| \| 140-180 mg/dL \| 92 \| (22.0) \| \| 181-250 mg/dL \| 125 \| (29.9) \| \| >250 mg/dL \| 62 \| (14.8) \| \| Total \| 418 \| (100.0) \| Table 3 shows the results of the correlation analysis. A positive, statistically significant and moderate correlation was identified between the total of PAR and the number of CBG assessments (Table 3). Table 3 \| Unnamed: 0 \| PIR \| PIR.1 \| PAR \| PAR.1 \| \| --- \| --- \| --- \| --- \| --- \| \| \| (70-180 mg/dL) \| (70-180 mg/dL) \| (>180 mg/dL) \| (>180 mg/dL) \| \| \| Correlation coefficient (ρ) \| p value \| Correlation coefficient (ρ) \| p value \| \| Number of CBG tests \| -0.094 \| 0.549 \| 0.684 \| <0.001 \| \| Days of hospitalization \| 0.222 \| 0.152 \| -0.139 \| 0.508 \| Regarding the subgroup analysis by main diagnosis (infection/non-infection), we found a greater number of CBG tests in the infection group, 4.0 (0.0) vs. 3.0 (1.0), $$p \leq 0.011$$, although there were no significant differences in relation to the total PIR, 3.0 (2.0) vs. 3.0 (1.0), $$p \leq 0.910$$, or PAR, 3.5 (2.0) vs. 3.0 (1.0), $$p \leq 0.680.$$ With respect to the length of stay subgroup analysis, no differences were found in terms of the number of CBG tests ($$p \leq 0.062$$), total PIR ($$p \leq 0.167$$) or PAR ($$p \leq 0.199$$). ## Discussion Using the 2019 ATTD consensus, these results showed that inpatient glycemic control remained suboptimal because only a minority of the patients had adequate control according to PIR metrics: the total of points in the range 70-180 mg/dL was $54.8\%$ with a minimum percentage of PBR, which means that hyperglycemia continues to keep us from reaching our glycemic targets. This is supported by the low percentage of patients with all points in the range 70-180 mg/dL, thus indicating that the number of patients with at least one assessment corresponding to hyperglycemia is quite high. We found a positive significant and moderate correlation between CBG tests and PAR. One possible explanation might be the tendency to intensify CBG monitoring in face of poor glycemic control. Yet, the opposite would be expected for PIR, that is, a negative correlation between the number of CBG tests and the total of PIR, which was not verified. Thereby, the results of the subanalysis by main diagnosis are also interpretable along this line of thinking: there was a significantly higher number of CBG tests in the infected group in which worse glycemic control would be expected, but probably not sufficiently higher to reflect a difference in the total PIR/PAR. Besides, the lack of correlation between PIR/PAR and length of stay combined with the absence of significant differences in the subanalysis by day of hospitalization suggests that the length of stay might be a less important factor compared to infection diagnosis to take into account as a potential interfering factor in glycemic control. This was the first study to apply the PIR metrics to inpatients and demonstrate its relevance in a real context for the assessment of glycemic control. Despite the existence of FDA-approved CGM devices for non-critical patients, their widespread use is not possible in clinical practice mainly for reasons of cost [9]. It was proved by this study that the calculation of PIR metrics is a possible, simple and inexpensive way to obtain information complementary to the isolated glucose values obtained by CBG monitoring at no added cost. Nevertheless, in order to take better advantage of this metric, it would be desirable to have correlations between the PIR and CGM metrics and also recommendations regarding the appropriate intervals and the respective percentages of points in each of the intervals, similar to ATTD consensus recommendations [4]. Moreover, given the exponential growth in the number of outpatient CGM users, it would be desirable that these recommendations exist in particular for the inpatient situation. As limitations of this study, the following should be noted: it was cross-sectional in nature that only allowed a punctual assessment of glycemic control by this new metrics; the sample size may still have been insufficient to obtain significant correlations between some variables under study and the PIR metrics. 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--- title: 'Maternal obesity and ovarian failure: is leptin the culprit?' authors: - Yashaswi Sharma - António Miguel Galvão journal: Animal Reproduction year: 2023 pmcid: PMC9968511 doi: 10.1590/1984-3143-AR2023-0007 license: CC BY 4.0 --- # Maternal obesity and ovarian failure: is leptin the culprit? ## Abstract At the time of its discovery and characterization in 1994, leptin was mostly considered a metabolic hormone able to regulate body weight and energy homeostasis. However, in recent years, a great deal of literature has revealed leptin’s pleiotropic nature, through its involvement in numerous physiological contexts including the regulation of the female reproductive tract and ovarian function. Obesity has been largely associated with infertility, and leptin signalling is known to be dysregulated in the ovaries of obese females. Hence, the disruption of ovarian leptin signalling was shown to contribute to the pathophysiology of ovarian failure in obese females, affecting transcriptional programmes in the gamete and somatic cells. This review attempts to uncover the underlying mechanisms contributing to female infertility associated with obesity, as well as to shed light on the role of leptin in the metabolic dysregulation within the follicle, the effects on the oocyte epigenome, and the potential long-term consequence to embryo programming. ## Introduction Obesity is a complex and progressive disease, well known for its ability to result in a wide spectrum of debilitating co-morbidities, including metabolic disease, type 2 diabetes, cardiovascular disease (Hruby and Hu, 2015), various types of endometrial, breast, or colon cancer (Dağ and Dilbaz, 2015), and reproductive disorders (Kyrou et al., 2018). Infertility is recurrently observed in obese women of reproductive age, who usually present menstrual disorders and anovulatory cycles, lower implantation and pregnancy rates, as well as failed assisted reproductive interventions (Dağ and Dilbaz, 2015). Infertility is, therefore, a prominent co-morbidity of obesity, the aetiology of which remains largely understudied. The impairment of reproductive function in obese females occurs at both central and peripheric levels and can affect either the ovaries or the endometrium (Bellver et al., 2007). Literature comprehensively characterises the major readouts associated with ovarian pathology and failure in obese women, which comprise an excessive accumulation of lipids or lipotoxicity in various ovarian components (Wu et al., 2010), increased endoplasmic reticulum stress and apoptosis (Yang et al., 2012), increased inflammation (Robker et al., 2011), altered mitochondrial function and oxidative stress (Igosheva et al., 2010). Thus, the aforementioned features invariably lead to impaired ovulation and reduced oocyte developmental competence (Robker et al., 2009). Overall, obesity poses a clear threat to ovarian function and the quality of the growing gamete; nonetheless, we lack mechanistic insights and understanding of the molecular mechanisms underpinning such detrimental effects at various cellular levels. The deleterious effects of obesity result from a major endocrine imbalance that follows the expansion of fat stores. Amongst several hormones being dysregulated in the course of obesity, leptin, a key bioactive peptide largely secreted from the white adipose tissue (adipokine) (Friedman and Halaas, 1998), has a strong association with both obesity and reproduction (Tong and Xu, 2012). In fact, mice with a homozygous mutation for the leptin-producing gene ob (obese gene), were shown to develop both obesity and infertility (Zhang et al., 1994). Leptin concentrations rise rapidly in circulation in obese specimens, since leptin circulating levels are positively correlated with body fat mass (Considine et al., 1996; Maffei et al., 1995). Initially, leptin was shown to act as an important neuroendocrine regulator of food intake and energy homeostasis (Farooqi and O'Rahilly, 2009; Zhang et al., 2005). Nonetheless, like other adipokines, its pleiotropic actions were soon reflected at various levels, such as the regulation of the immune system, haematopoiesis, angiogenesis (Matarese et al., 2006), cognition and bone metabolism (Dalamaga et al., 2013) and reproduction (Childs et al., 2021). In recent years, leptin has engendered a great deal of interest in its regulatory role in reproductive tract and fertility (Castracane and Henson, 2003). In women, leptin is known to be associated with all stages of reproductive age - puberty, menstrual cycle, pregnancy, as well as menopause. A number of informative and well-conceived reviews have elaborated on the particular roles of leptin at each one of the above-mentioned stages (Brannian and Hansen, 2002; Pérez-Pérez et al., 2015; Wołodko et al., 2021), reiterating the importance of leptin physiological actions in the control of female fertility. Importantly, under physiological conditions, leptin signals within a narrow concentration range, and excessive or insufficient levels of leptin may compromise fertility and ovarian function (Childs et al., 2021). For instance, conditions of hypoleptinemia, such as in hypothalamic amenorrhoea (Miller et al., 1998), have been characterised by anovulation and dysregulation of the oestrous cycle (Chou and Mantzoros, 2014). Interestingly, fertility was shown to be restored in such patients after leptin treatment(Welt et al., 2004). Conversely, hyperleptinemia observed during obesity was also associated with polycystic ovarian syndrome, hypogonadism associated with type 2 diabetes and infertility, (Chou and Mantzoros, 2014) by mechanisms yet to be fully understood. Of particular importance, obese women are known to present high levels of circulating leptin, the state of hyperleptinemia, and often developing insensitivity to exogenous administration of leptin, a state known as leptin resistance. Therefore, hyperleptinemia and leptin resistance are two major features of obesity likely to drive the detrimental effects of energy surplus on ovarian function. This dichotomy in leptin signalling throughout obesity dramatically affects ovarian function. In early obesity, the establishment of a rapid hyperleptinemia and increased leptin signalling may affect ovarian function particularly through the negative impact on folliculogenesis, altered steroid synthesis and secretion in the growing follicle, and oocyte maturation (Brannian and Hansen, 2002). Conversely, the establishment of ovarian leptin resistance observed in late obesity, may result in perturbations in ovulation (Pérez-Pérez et al., 2015) or increased primordial follicle recruitment, leading to reduced reproductive performance and premature ovarian failure (Moslehi et al., 2018). Such a complex set of actions, alongside the inherent intricacies of ovarian function regulation, portrays leptin as a key but challenging signalling system to study during obesity. Consequently, little is known about the impact of altered levels of leptin signalling in the ovary and the gamete. In particular, it is still unclear whether local changes in leptin signalling can directly impact the oocyte epigenome, posing therefore the risk of transmission of such epimutations to the embryo, and potentially jeopardising early embryo development and reprogramming events. Collectively, growth and maturation of the female gamete are highly demanding processes, requiring an optimal interplay between maternal nutritional state and other environmental factors, which will invariable control nuclear, cytoplasmic and epigenetic maturation (He et al., 2021), ultimately ensuring the adequate transfer of genetic and epigenetic information required for embryonic development. Since, the environment in which the oocyte grows and develops critically determines its quality, it is extremely relevant to understand how maternal metabolic performance may affect such environment, both under physiological and pathological conditions. One such promising factors controlling metabolite availability and energetic performance is leptin. The present review, revisits the metabolic roles of leptin in various cellular contexts, which may pose deleterious consequences to oocyte and early embryo development in the context of maternal obesity (Figure 1). It sheds light upon the missing links in literature to better understand the crosstalk between obesity, altered ovarian leptin signalling, and putative consequences for oocyte development, particularly instigating the effects on oocyte metabolome and epigenome and further outcomes for early embryo development. Figure 1: Maternal obesity and the putative impact of altered ovarian leptin signalling on oocyte growth and long-term effects for embryo development and offspring health. With the increase in fat stores in the body, leptin is produced in large amounts leading to hyperleptinemia and subsequent leptin resistance in the ovary. Local perturbations in leptin signalling may lead to changes in oocyte metabolic profile and mitochondrial function, which may critically affect oocyte quality (Ge et al., 2012), affecting the oocyte metabolic and epigenetic legacy which, in turn, may affect early reprogramming events in the embryo and offspring susceptibility to disease in adulthood. Created with BioRender.com. ## Obesity, leptin signaling, and the establishment of leptin resistance in the ovaries Leptin modulates female reproductive tract through endocrine and neuroendocrine mechanisms, but may also locally regulate ovarian activity, particularly controlling steroidogenesis (Hausman et al., 2012), folliculogenesis (Brannian and Hansen, 2002), and luteal function (Galvão et al., 2012). Under physiological conditions, leptin signals through a single-spanning transmembrane protein receptor belonging to the class I cytokine receptor superfamily (Tartaglia et al., 1995). Although at least five isoforms of the receptor have been identified, the canonical pathway involves the activation of Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signalling through the long isoform of the receptor, called leptin receptor b (ObRb) (Banks et al., 2000). The non-canonical signalling pathway includes the activation of insulin receptor substrate (IRS)/phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt) and mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) signalling pathways (Bjørbæk et al., 1997, 2001; Hegyi et al., 2004). With the expansion of fat stores in the course of obesity, the levels of leptin in circulaiton dramatically rise and result in the dysregulation of the aforementioned signalling pathways (Myers et al., 2010). Although ideally it might be expected that leptin acts as a true ‘anti-obesity’ hormone promoting satiety, reducing food intake and increasing energy expenditure (Myers et al., 2010), its ability to modulate adiposity can be forestalled by perturbations in the activation of the signalling pathway, often characterised by hyperleptinemia. The condition termed ‘leptin resistance’ may be mediated by: i) the down-regulation of leptin receptors, ii) the presence of receptor defects, iii) deficiencies in the secretion or circulation of the protein that compromises its bioavailability, or iv) its inability to reach the target tissue, for instance, crossing the blood-brain barrier (Castracane and Henson, 2003). The most common reason for leptin resistance, however, is the failure of the intracellular signalling cascade of the ObRb receptor (Enriori et al., 2007), widely mediated by molecules such as the suppressor-of-cytokine-signaling-3 (SOCS-3) protein (Bjørbaek and Kahn, 2004; Myers, 2004; Rabe et al., 2008) and the phosphotyrosine phosphatase-1B (PTP1B) (Bence et al., 2006; M. G. Myers et al., 2010). The ensuing resistance is specifically termed ‘cellular leptin resistance’. With the hyperactivation of the leptin signalling and the resulting increase in phospho-signal transducer and activator of transcription 3 (pSTAT3), the gene expression of SOCS-3 is upregulated, which in turn blocks tyrosine and Janus kinase-2 (JAK2) phosphorylation in a classic feedback inhibition pathway (Bjørbæk et al., 2000). Similarly, the upregulated expression of PTP1B due to STAT3 signalling, dephosphorylates JAK2 to block leptin signalling (Cheng et al., 2002; M. P. Myers et al., 2001). More recently a number of novel molecules have been described to mediate leptin resistance centrally, such as protein tyrosine phosphatases (PTPs), brain-derived neurotrophic factor (BDNF), myeloid differentiation factor 88 (MyD88), methyl-CpG-binding protein 2 (MeCP2), I-kappa-B kinase epsilon (IKKε), extracellular signal-regulated kinases (ERKs), mitofusin 2 (MFN2), histone deacetylase 5 (HDAC5), withaferin A, c-Jun N-terminal kinases (JNKs), activating transcription factor 4 (ATF4) (J. Liu et al., 2018), each with its own proposed mechanism of action. Ultimately, increased circulating levels of leptin during obesity will culminate with the dysruption of the signalling pathway in an organ specific manner. The state of leptin resistance has unique features, with regard to ovarian function. As shown by studies in mice treated with high fat diet (HFD), leptin resistance sets in phases, in which initially the sensitivity to peripheral leptin injection is maintained, followed by peripheral insensitivity but adequate response to central leptin injections and finally a late stage in which the mice develop central leptin resistance (Enriori et al., 2006). Recently it was also shown that leptin resistance may be organ specific. For instance, we have shown that the establishment of ovarian leptin resistance in mice after 16 weeks of diet induced obesity (DIO), followed an increase in leptin signalling in the ovaries of mice under DIO for 4 weeks (Wołodko et al., 2020). Such organ specific response has also been shown in the hypothalamus (Münzberg et al., 2004; Ozcan et al., 2009) and the liver of both mice and humans (Brabant et al., 2005). Nonetheless, other organs like the heart and kidney are known to maintain its responsiveness to leptin throughout obesity (Mark et al., 2002; Morgan et al., 2008). Thus, the regulation of leptin signalling in the course of obesity seems to be organ dependent, and particularly for the ovaries associated with the levels of obesity and pregression of the disease. Overall, leptin resistance is either an adaptive response or a pathological state (Tups, 2009), which marks the onset of impaired leptin signalling under conditions of leptin excess, such as during obesity. ## Oocyte maturation The successful fertilisation of the female gamete and further embryo development requires the accomplishment of major steps during oogenesis, the nuclear, cytoplasmic, and epigenetic maturation. ( Eppig et al., 2004). The process involves events like meiotic resumption and metaphase II arrest, accumulation of mRNA, proteins, and nutrients which will enable the genomic modifications ensuring correct gene expression programs during embryo development (Eppig et al., 2004; Watson, 2007). Oocyte growth and maturation are, therefore, highly orchestrated events that require an optimal interplay between intrinsic signals and nutritional and environmental factors (Hunt and Hassold, 2008). Among the various factors controlling the development, competence and quality of the female gamete, oocyte metabolism is widely known to play key roles (Sirard, 2011), providing the energy required for meiotic progression, buffering between intracellular redox and osmotic potential and, most importantly, providing the building blocks for growth (Watson, 2007). Thus, oocyte growth requires an active synthesis of metabolites and metabolic enzymes for the regulation of multiple cellular events. The demand for such metabolites and energy substrates is cratered by both the oocyte machinery and the surrounding cumulus cells (CCs) through specialized membrane connections called gap junctions (Russell et al., 2016; Sugiura and Eppig, 2005). Specific processes in the oocyte require a characteristic metabolic milieu for a successful developmental progression. For instance, the maturation of the oocyte was associated with a state of diminished bile acid biosynthesis, decreased levels of polyunsaturated fatty acids (PUFA), but increased availability of nucleotides and one-carbon metabolism (Li et al., 2020). Such metabolic control is mostly dependent upon the activity of intracellular substrates and enzymes present in the oocytes, other intracellular mediators, the transport across the plasma membrane, and nutrient availability from the follicular environment (Kurus et al., 2013). Overall, metabolite availability dictates how efficiently events like oocyte growth, meiosis, or epigenetic programming are coordinated in a developmentally competent oocyte. ## Leptin metabolic roles Leptin has always been closely related to metabolism, with several lines of evidence indicating its regulatory role over carbohydrates, lipids, and protein metabolism (Pereira et al., 2021). Literature has shown that leptin controls glucose homeostasis at different levels, stimulating glucose uptake in the skeletal muscle, heart, and brown adipose tissue (Minokoshi et al., 2012), potently suppressing circulating insulin levels, while increasing gluconeogenesis, decreasing glucagon and attenuating glycogen synthesis (D’souza et al., 2017). Furthermore, leptin has also been shown to regulate lipids and protein metabolism. A recent study on patients with congenital deficiency of leptin showed that treatment with leptin promoted major metabolic changes, such as lipid catabolism involving fatty acid oxidation and cholesterol breakdown (Lawler et al., 2020). The regulatory role of leptin in fatty acid oxidation was also supported by the findings of Kircherlber and colleagues, who reported a negative association between acylcarnitines and acetylcarnitine levels with plasma leptin concentrations (Kirchberg et al., 2017). Another study in the skeletal muscle by Minokoshi and colleagues supported leptin’s direct influence on fatty acid oxidation by reversal of the inhibitory action of carnitine palmitoyltransferase I (CPT-1) (Minokoshi et al., 2012). Similarly, leptin concentrations in the plasma were positively associated with the presence of a number of amino acids, like alanine and asparagine (Kirchberg et al., 2017). Hence, leptin seems control the metabolism of macronutrients. It is, therefore, highly plausible that changes in circulating levels of leptin may promote important changes in the availability of metabolites, their precursors and catabolic products. ## Leptin metabolic roles and the ooocyte The ovaries and the oocyte are no exception with regard to leptin regulatory actions on energy homeostasis. Both the ovarian cells (Lin et al., 2000; Ruiz-Cortes et al., 2000) and the oocytes (Ryan et al., 2002; Cervero et al., 2004) of several animal species and humans are known to express the different forms of leptin receptors including the ObRb mRNA and protein. This suggests the ability of ovarian cells to respond directly to fluctuations in circulating levels of leptin. Therefore, under conditions of altered leptin signalling such as in obesity, the metabolism in ovarian somatic cells, as well as the gamete, are susceptible to dysregulation. ## Glucose metabolism Glucose is essential for oocyte development, and is metabolised in a concerted way between the oocyte and granulosa cells (GCs) by glycolysis, the pentose phosphate pathway (PPP), hexosamine biosynthesis pathway (HBP) or the polyol pathways (Sutton-McDowall et al., 2010). Pyruvate is the preferred energy substrate for the oocytes, but given the low glycolytic rate and capacity for glucose uptake and transport (Harris et al., 2007; Saito et al., 1994), oocytes are dependent upon GCs to access such metabolic intermediates. Pyruvate is also known to be pivotal for oocyte maturation (Johnson et al., 2007) and successful meiotic division. In fact, it was previously corroborated that enhanced tricarboxylic acid (TCA) cycle activity and pyruvate oxidation during this stage favoured the accomplishment of meiosis (Li et al., 2020). Glucose metabolites were also shown to be essential for nucleic acid and purine synthesis, maintenance of redox state, CCs expansion, cell signalling, and the regulation of oocyte nuclear maturation (Sutton-McDowall et al., 2010). The oocyte is however highly sensitive to changes in the availability of glucose, with either high or low levels of glucose resulting in precoucious resumption of nuclear and cytoplasmic maturation, failed fertilization and impaired embryo development (Sutton-McDowall et al., 2010). On one hand, low glucose levels may lead to reduced de novo purine synthesis, depleted hyaluronic acid and low energy availability in the oocytes, while on the other hand, high glucose levels may lead to increased reactive oxygen species (ROS) and untimely maturation of the oocyte (Sutton-McDowall et al., 2010). Moreover, the intrafollicular hyperglycemia that ensues under conditions like diabetes, obesity, and poor diet was shown to be detrimental to oocyte viability in mice (Moley et al., 1998). Specifically concerning obesity, higher body mass index (BMI) was associated with alterations in the follicular fluid composition showing increased follicular insulin, glucose and lactate concentrations (Robker et al., 2009). Furthermore, leptin levels were also shown to increased in the follicular fluid of obese women, suggesting that ovarian follicular environment mirrors the systemic alterations seen during obesity (Mantzoros et al., 2000). Indeed, it has been suggested that with the simultaneous increase in the circulating levels of leptin and glucose, the glucose metabolism is altered in various tissues including ovary and possibly the the oocyte (Silva et al., 2012). Such changes in the metabolism, particularly that of glucose, have also been linked to establishing epigenetic memory, with reports showing glucose-induced alterations of posttranslational modifications to histones, including methylation and acetylation (Pirola et al., 2010). Furthermore, it was also reported the strong crosstalk between leptin and insulin signaling pathways during the regulation of glucose metabolism (Koch et al., 2010). Leptin increase has been shown mitigate the deleterious effects of high glucose on oocyte development, mostly through the downregulation of insulin-like growth factor 1 (IGF1) expression in CCs, and the upregulation of insulin receptor substrate 1 (IRS1) in oocytes (Silva et al., 2012). High leptin was also shown to alter glucose metabolism in cumulus-oocyte-complexes (COCs) by downregulation of glucose transporter 1 (GLUT1) (Silva et al., 2012). Finally, leptin was also shown to improve glycolytic activity in the oocytes through IRS1 upregulation and IGF1 receptor action leading to phosphatidylinositol 3-kinase activation and thereby significantly influencing glucose metabolism in different tissues including the ovary(Poretsky et al., 1999). Collectively, these findings indicate that metabolism of glucose and leptin action are closely interconected in the oocytes. Glucose and its metabolites are indispensable for normal oocyte growth and quality, and therefore any alteration in its metabolism, particularly associated with fluctuations in leptin signalling in obese individuals, may cause decreased competence and subfertility. ## Lipid metabolism Fatty acids, stored in the form of triglycerides, are the major energy reserves in the oocytes. The intracellular lipid levels change throughout oocyte growth and development, especially during maturation, in a species-specific manner (Gu et al., 2015). As reported by a recent study characterising the metabolome of mouse oocytes, a 3- to 4-fold increase in carnitine and palmitoyl-carnitine content was reported in oocytes around the time of meiotic resumption (Li et al., 2020), while lipid-metabolism related products including cholesterol and arachidonic acid were shown to be depleted as the oocytes progress through meiosis(Li et al., 2020). Mitochondrial oxidation follows the breakdown of fatty acids by lipases in the oocyte or CCs and releases acetyl-CoA, which can then enter the TCA cycle to produce energy in the form of adenosine triphosphate (ATP) (Dunning et al., 2014). The importance of oocyte lipid metabolism, especially that of beta-oxidation can be ascertained by a number of studies reporting the reduction of embryo viability (Ferguson and Leese, 2006) and inhibition of meiotic oocyte resumption (Downs et al., 2009) following inhibited beta-oxidation activity. Conversely, enhanced oocyte nuclear and cytoplasmic maturation was confirmed under conditions of active lipid metabolism (Dunning et al., 2011). Furthermore, fatty acid metabolites were also shown to be involved in cell signalling events, regulating oxidative stress, membrane composition, and controlling gene expression in the female gamete (McKeegan and Sturmey, 2011). For instance, diacylglycerol (DAG), which acts as a secondary messenger in the triphosphoinositol (IP3) /DAG pathway, is known to facilitate the activation of protein kinase C (PKC), which has been implied to play specific roles in oocyte development, such as meiotic resumption, spindle organization, and activation (Kalive et al., 2010). Other lipid metabolites such as ceramide, are known to promote meiosis ressumption (Strum et al., 1995), while fatty acids binding to nuclear receptors and transcription factors (Sampath and Ntambi, 2005), have been linked to successful embryo development and female fertility(al Darwich et al., 2010; Cui et al., 2002). Given the crucial roles that lipids play in all stages of normal oocyte development, the importance of an optimal state of lipid metabolism as well as that of its regulator is insurmountable. Leptin is a well established regulator of lipid metabolism, known to stimulate lipolysis and fatty acid oxidation in many cell types (Reidy and Weber, 2000), including the components of the ovarian follicles (Zhang et al., 2015). Leptin is also known to be a key modulator of cellular triacylglycerol content (Reidy and Weber, 2000), which, as previously discussed, is the main energy source described for the oocytes. Moreover, it has also been reported that leptin is an important factor linking fatty acid β-oxidation with oocyte maturation, through the JAK-STAT pathway (Zhang et al., 2015). However, under conditions of obesity, ovaries and oocytes are known to accumulate excessive lipids resulting in increased oxidative stress and ovarian inflammation (Cardozo et al., 2016; Gu et al., 2015). This can be a result of the putative pathogenic role of hyperleptinemia in lipid accumulation (Shen et al., 2019). Although different mechanisms may be at play, the downregulation of master regulators of lipid metabolism like Sterol regulatory element binding protein 1 (SREBP-1c) under high leptin levels has been previously reported(Shen et al., 2019). Such long-term exposure to altered lipid levels may therefore impair the oocyte function or their somatic counterparts (T. Liu et al., 2022). Finally, as shown by studies on polycystic ovarian syndrome (PCOS) (Khan et al., 2021), associated most commonly with obesity, impaired lipogenesis and lipolysis events are commonly seen in the ovaries. This could be also ascribed to leptin resistance during obesity which impairs leptin’s peripheral role in regulating lipid metabolism in cells (Sáinz et al., 2015). Generally, leptin is well known to control lipid oxidation and regulate triglyceride cellular homeostasis, critical for energy generation and the development of the oocyte. Therefore, changes in leptin levels during obesity levels can potentially affect oocyte fatty acid oxidation and energy provision during critical phases of oocyte growth and developmental competence. ## Amino-acid metabolism Oocytes access amino-acids via their unique transport systems like β, L, GLY, xc−, and b0,+ (Pelland et al., 2009). Alternativelly, they also depend on GCs and CCs for the uptake of specific amino acids like L-alanine, glycine, taurine, and lysine. Amino acids are utilised in the oocyte as substrates for energy, for protein synthesis, to facilitate osmosis, and also as redox buffering elements (van Winkle, 2001). Specifically, they are also known to support the development of preimplantation embryos, while in post-implantation embryos, amino acids are known for their roles in fostering viable embryos and supporting early embryo cleavage (van Winkle, 2001). For instance, the addition of glutamine to the oocyte culture medium turned out as an efficient energy substrate, improving oocyte maturation (Songsasen and Wildt, 2007) and initiating meiotic resumption (Downs and Hudson, 2000). Glutamine and other amino acids like aspartate and valine have also been shown to avert polyspermy in pigs (Hong and Lee, 2007) and glycine has been implicated in exerting unique cell volume regulatory mechanisms (Baltz and Tartia, 2010). Typical temporal metabolite profiles of amino acids are evident during the course of oocyte growth and development, with different amino acid subsets increasing in abundance at particular stages of development, while declining during others. For example, a significant increase in the levels of serine, glutamate and histidine was evidenced during meiotic resumption, whereas their availability was shown to be decreased post-maturation (Li et al., 2020). Amino acids may act as key components in the synthesis of de novo purine and pyrimidines, guanosine triphosphate (GTP), nicotinamide adenine dinucleotide (NAD+) and are the sources of carbon and fixed nitrogen (Sturmey et al., 2008). Besides their metabolic regulation and role in protein synthesis, some amino acids were shown to be involved in the regulation of DNA methylation. As an exmple, methionine along with folate and vitamin B12 were shown to be important co-factors that integrate the methylation cycle (Gilbody et al., 2007). Additionally, the Serine-Glycine-One-Carbon (SGOC) pathway, which involves the folate and the methionine cycles, was shown to be upregulated during meiotic resumption of the oocytes (Li et al., 2020), with SGOC fuelling methyltransferase activity (Reina-Campos et al., 2020) and shaping the epigenetic landscape of the oocytes. Hence, protein metabolism is one of the most diverse yet important biochemical process that dictate the developmental potential of the oocyte. Amino acids and their metabolites are equally susceptible to alteration in their normal metabolism under conditions of obesity (Short et al., 2019; Zhou et al., 2013), most likely driven by conditions of hyperleptinemia. As shown by a metabolomic study on lipodystrophy patients receiving leptin treatment, a drastic change in protein and amino acid catabolism was evident leading to overall increased protein turnover (Grewal et al., 2020). The study reported that leptin caused an increase in markers of protein degradation, like gamma-glutamyl amino acids, 3-methylhistidine, and N-acetyl-3-methylhistidine along with metabolites involved in urea cycle (Grewal et al., 2020). This can be particularly detrimental in the context of ovarian funciton and oogenesis, since increased protein catabolism and the resulting presence of high concentrations of ammonium and urea have been shown to reduce embryonic development and promote sustained metabolic stress in the surviving embryos (Sinclair et al., 2000). Also, the altered profiles of plasma amino-acids observed during obesity, can further dysregulate carbohydrate metabolism in oocytes. For instance, high levels of leucine and tyrosine in the plasma are known to increase the levels of glucagon (Rooke et al., 2009) and also stimulate insulin release (Calbet and MacLean, 2002). This is particularly relevant for the environment of the growing gamete, as the free amino acid profile in plasma is broadly similar to follicular fluid free amino acid composition (Orsi et al., 2005). Furthermore, the total free amino acid concentration is proven to be very closely related to oocyte quality (Rooke et al., 2009). In conclusion, increased levels of leptin, or leptin resistance, may be at least partly responsible for the oocyte abnormalities taking place under conditions of metabolic dysregulation during obesity. Overall, in this section we have seen that obesity leads to a state of systemic and local metabolic dysregulation, which is likely to be affected by altered levels of circulating leptin. Through its metabolic actions on various substrates, leptin may alter the levels as well as profiles of available metabolites. In the ovarian context, such events may impede the physiological development of the gamete, through direct actions on oocytes or through the metabolic dysregulation of the surrounding GCs and CCs. In fact, our recent study on the transcriptome of CCs isolated from DIO mice revealed dramatic changes in the expression of genes particularly involved in cellular trafficking and cytoskeleton organisation of the CCs, which were also found in the CCs of mice treated with leptin (Wołodko et al., 2020). Such changes can render the CCs inefficient in supplying the oocytes with the metabolites or metabolic precursors it demands, compromising the oocyte developmental competence. For instance, leptin treatment of COCs during in vitro maturation at 100 ng/ml of concentration, was reported to downregulate the expression of GLUT1 in CCs (Silva et al., 2012). In fact, the oocyte relies on the CCs critical supply of glucose and its metabolites, cholesterol biosynthesis as well as for some amino-acids (Su et al., 2009.). Moreover, the potential of leptin to influence the function of CCs have been also studied in bovine oocytes, where an optimal level of leptin was shown to enhance developmental potential of oocytes via CC-dependent mechanisms (Paula-Lopes et al., 2007). Thus, owing to its systemic and ovarian roles in metabolic homeostasis, fluctuations in leptin circulating levels observed during maternal obesity may affect the oocyte metabolism, quality and fertility outcomes. In a nutshell, the periconceptional period encompassing the active stages of oocyte growth and development is sensitive to metabolite availability in the germ cell. The presence of adequate metabolic substrates at physiological levels ensures that nutritional needs are met, as well as critical events for the quality and competency of the female gamete are maintained. Any form of deficiency or excess of macro- or micro-nutrients or other metabolic substrates during this period can therefore lead to reduced fertility, altered foetal development and compromised long-term offspring health. ## Leptin and the oocyte mitochondrial function Highly acclaimed as the ‘powerhouse of the cell’, mitochondria are indispensable for oocyte growth and development, being a critical indicator of oocyte quality (Schatten et al., 2014). Therefore, we dedicate a chapter specifically to the putative effects of leptin signalling dysregulation on mitochondrial function in oocytes during obesity. The developing oocyte and the surrounding follicular cells are highly reliant on the biosynthetic precursors and the energy produced by mitochondrial oxidative phosphorylation (Qi et al., 2019). Hence, ATP, the main product from oxidative phosphorylation, is particularly important for active transcription and translation, which drives oocyte maturation (Kirillova et al., 2021). Furthermore, optimal mitochondrial function is required for the formation of the meiotic spindles before and during oocyte activation (Benkhalifa et al., 2014), as well as for the maintenance of redox homeostasis (Spinelli and Haigis, 2018). Given the pivotal roles played by the mitochondria, it is critical that oocytes contain a minimum threshold number of mitochondria, and adequate copies of mtDNA (Wai et al., 2010). In other cellular contexts, mitochondria play a central role in the regulation of cell senescence and death, facilitating cell signalling and the biosynthesis of compounds like nucleotides, fatty acids, cholesterol, amino acids, and heme (Spinelli and Haigis, 2018). Importantly, in immature oocytes, mitochondria are initially transcriptionally and bioenergetically silent (Allen and Paula, 2013), which certainly minimises the occurrence of mitochondrial DNA (mtDNA) mutations (Allen and Paula, 2013). Conversely, after fertilization, mitochondrial activation is necessary to protect the oocyte from oxidative damage and support early embryo development (Qi et al., 2019). In fact, mitochondrial inheritance is exclusively maternal and oocyte-derived mitochondria give rise to the entire mitochondrial content present in the various tissues of the offspring (McPherson et al., 2015). As a result, coordinated regulation of mitochondrial activity in oocytes ensures not only the competence of the gamete but also the successful development of the embryo and metabolic performance in the offspring. Numerous reports have recently described the dramatic impact of maternal obesity on oocyte mitochondrial activity. Studies in mice have shown that diet-induced obesity alters oocyte mitochondrial morphology, such as decreased number of cristae and vacuoles (Luzzo et al., 2012), increased mtDNA copy number and higher mitochondrial biogenesis (Igosheva et al., 2010; Luzzo et al., 2012), or changes in mitochondrial potential (Igosheva et al., 2010; Wu et al., 2010). It was also observed that energetic surplus in mothers is associated with increased ROS formation, and altered spatial distribution of mitochondria in the oocyte (Igosheva et al., 2010). Hence, spindle and chromosome alignment defects leading to aneuploidy, failed oocyte maturation, poor fertilization rates and abnormal embryo development frequently reported in obese women may be explained by the underlying mitochondrial dysfunction in the oocytes from obese mothers. Importantly, mitochondrial metabolites were shown to affect gene expression regulation and promote epigenetic changes during oocyte maturation and embryo development (Ge et al., 2015; Matilainen et al., 2017; Whidden et al., 2016). For instance, mitochondrial metabolites of the TCA cycle such as ATP, alpha ketoglutarate (α-KG), and citrate were shown to alter chromatin configuration which was associated with gene expression (Qi et al., 2019). Additionally, citrate is known to facilitate histone acetylation and drive gene expression by changes in chromatin conformation. This was shown to be facilitated by the conversion of citrate into acetyl-CoA, with the donation of the acetyl group to histone acetyltransferases (HATs) (Montgomery et al., 2015). Similarly, α-KG is known to promote DNA demethylation, working as a co-factor of ten-eleven translocation (TET) enzymes, which in turn are known to catalyse the hydroxylation of methylated cytosines in the genome (Qi et al., 2019). With regard to histone methylation, α-KG was shown to act as a crucial co-factor of histone demethylases (HDMs) (Kooistra and Helin, 2012). Another metabolite, the S-adenosyl methionine (SAM), a universal methyl- donor and a common substrate for numerous enzymatic reactions, is known to be originated from the folate cycle and ATP generated by the mitochondria (Qi et al., 2019). In fact, SAM is a critical regulator of DNA methylation as is often utilised as a coenzyme involved in the transfer of methyl groups (Smith and Denu, 2009). As previously reported, the availability of mitochondrial substrates like SAM are known to maintain human embryonic stem cells (hESCs) pluripotency, as decreased levels of SAM in culture resulted in cell differentiation (Sperber et al., 2015). Also, the energy required for the modulation of changes in chromatin configuration and specific binding of the chromatin remodelling complexes is largely ATP-dependent (Flaus and Owen-Hughes, 2011). Finally, acetyl-CoA dependent HAT activity was also shown to control oocyte maturation and the activation of follicular reserve (Yin et al., 2017). This suggests that mitochondria, and the metabolites generated through their activity, not only play major physiological roles in regulating energy homeostasis but also maintain the stability of both genetic and epigenetic signatures in gametes and developing embryos. Of great relevance within the scope of obesity, but largely understudied, is the potential impact of altered local leptin signalling on mitochondrial function in the oocyte from obese mothers. This seems plausible, especially considering that leptin treatment was shown to increase mitochondrial metabolism and ATP production, decrease oxidative stress, promote mtDNA replication, and increase mitophagy, generally affecting mitochondrial function in oocytes (Blanquer-Rossellõ et al., 2015). In addition, it has also been suggested that leptin can influence the routes of mitochondrial ATP production, since the ATP production in db mice lacking functional leptin receptors was less reliant on glycolysis, but rather on beta-oxidation (J. Park et al., 2010). A number of other studies on muscle, endothelial cells, and adipocytes have also revealed the stimulatory role of leptin on fatty acid oxidation, glucose uptake and ROS production (Yamagishi et al., 2001; Minokoshi et al., 2002; Luo et al., 2008), which resulted in increased mitochondrial activity (Henry et al., 2011). Therefore, under the influence of leptin, mitochondrial metabolism appears to be enhanced, with subsequent changes in metabolites and major outcomes for gene expression regulation and epigenetic changes. For instance, the availability of acetyl-CoA is mostly dependent on the rate of mitochondrial metabolism. Under increased metabolism, the actyl-CoA in excess facilitates histone acetylation, which has been associated with active gene transcription (Menzies et al., 2016). Furthermore, glucose derived acetyl-CoA supplies most of the acetyl group for histone H4K16 acetylation (Morrish et al., 2010), as well as to histone acetyltransferases enzymes (Montgomery et al., 2015), the availability of which can be influenced by leptin regulatory effects on mitochondrial activity (Blanquer-Rossellõ et al., 2015; Henry et al., 2011). Thus, the epigenome can be modulated in response to the availability of essential metabolites, which can in turn be regulated by leptin. Mitochondria are one of the most relevant organelles for oocyte development. Obesity clearly affects the oocyte mitochondrial function, leading to mitophagy and impaired oocyte function and quality. Such changes are accompanied by the dysregulation of oocyte gene expression and the epigenetic program and may be determined by local changes in leptin signalling, which can directly modulate oocyte mitochondrial activity and function. Thus, altered leptin signalling and associated impaired mitochondrial function most certainly affect the oocyte quality and contributes to the pathogenesis of ovarian failure and infertility in maternal obesity. ## Leptin and methylation changes We have previously discussed the putative role of leptin on epigenetic actions mostly through the modulation of metabolite availability and mitochondrial function. Despite being scarce, evidences of leptin direct actions on epigenetic machinery start getting noticed. Leptin has been recently associated with changes in DNA methylation, and post-transcriptional histone modifications, as well as the regulation of microRNA (miRNA) (Wróblewski et al., 2019) in various cellular contexts. Therefore, we presently discuss the most up-to-date studies on leptin-mediated epigenetic changes. Studies related to carcinogenesis showed leptin involvement in the modulation of important enzymes controlling epigenetic processes. For instance, in a study on human colon cancer cells, leptin was found to up-regulate the expression of histone deacetylase enzyme sirtuin 1 (SIRT1) (Song et al., 2018). In another study in ovarian cancer, it was reported that leptin modulated HDACs gene expression, in which class I and II HDACs were increased in OVCAR-3 cells, while class II HDAC expression was increased in folliculoma cells (Fiedor et al., 2018). Importantly, animals with dysregulated leptin signalling, the diabetic mouse (db) and obese mouse (ob), showed lower expression of SIRT1 in colon cells, suggesting its involvement in leptin-induced pathogenesis of colon carcinogenesis (Song et al., 2018). In another study, leptin was shown to induce the progression and chemoresistance of pancreatic adenocarcinoma by affecting the levels of HDACs and the miRNAs miR21 and miR200a/c in tumors, promoting cancer cell survival and division by acting as a proliferative factor (Tchio et al., 2016). Finally, a recent study on rat adrenal cells revealed that leptin demethylated the promoter of a cation channel- Trpm7 (transient receptor potential melastatin 7) and also induced posttranslational modifications of histone proteins (H3K4me3, H3K27ac and H3K27me3), leading to increased Trpm7 transcription via LEPRb-dependent STAT3 activation (Yeung et al., 2021). Thus, leptin was recently shown to directly regulate the activity of enzymes controlling histone post-translational modifications. Concerning the direct regulation of DNA methylation, a number of studies have evidenced the ability of leptin to control de novo DNA methylation. Leptin was shown to induce changes in the methylation of CpG sites in the Pomc promoter, the precursor of the melanocyte-stimulating hormone. Indeed, animals treated with leptin during lactation showed hypomethylation of CpG dinucleotides in a specific region of Pomc promoter in the hypothalamus, when exposed to high-fat diet conditions later in life (Palou et al., 2011). This suggests the putative role of leptin in programming, as the treatment with leptin in early life affected the establishment or maintenance of DNA methylation patterns and subsequent gene expression later in life, after an environmental challenge such as obesity (Palou et al., 2018). Furthermore, leptin was shown to drive the obesity-dependent changes in global DNA methylation and gene expression in the adipose tissue of diet-induced or genetically obese mice, with evidence for obesity related global DNA hypomethylation and subsequent increased gene expression (Sonne et al., 2017). Also, the leptin-deficient ob/ob mouse was shown to have increased expression profiles of DNA methyltransferases (DNMT) 3a and 3b in adipose tissue, suggesting the potential role of leptin in the regulation of these de novo DNA methylation enzymes (Kamei et al., 2010; You et al., 2017). Finally, leptin has been also associated with the regulation of various miRNAs, which in turn may regulate gene expression (Derghal et al., 2015; Nakanishi et al., 2009; Sangiao-Alvarellos et al., 2014). Therefore, there is increasing evidence in the literature that supports the direct involvement of leptin in DNA methylation regulation at various cellular levels, with potential consequences for gene expression regulation. Given the evidence here presented, it is expectable that fluctuations in ovarian leptin may affect the oocyte epigenome, through the modulation of its epigenetic machinery. Such effects on DNA de novo methylation could impact not only the oocyte epigenome, quality, and competence but also the embryo and its development. As a matter of fact, the epigenetic program of the germ cells, seems to be affected by leptin, with reports showing a decrease in sperm quality as a result of elevated HDAC1 and HDAC2 expression following leptin administration in rats (Almabhouh et al., 2017). Similarly, leptin treatment was shown to prevent oocyte apoptosis in buffalos against an inhibitor of the first and second-class HDACs called trichostatin A (Reza Shafiei Sheykhani et al., 2016). Hence, under conditions of dysregulated leptin signalling (hyperleptinemia or resistance) during the course of obesity, leptin may well negatively impact the establishment of the epigenetic programme, affecting oocyte development and growth. The need for more studies are therefore justified specifically on the roles of leptin on epigenetic regulations, with particular focus on any inter- or trans-generational consequences that an altered epigenome of germ cell may have, on not only the developing offspring, but also potentially subsequent generations. ## The putative role of leptin in developmental programming It is widely accepted that obesity not only affects maternal health and reproductive outcomes but also exerts deleterious effects on the normal growth and development of the foetus (Guelinckx et al., 2008). Maternal obesity was previously associated with foetal macrosomia (Guelinckx et al., 2008), congenital anomalies (Moore et al., 2000), stillbirth, and perinatal death (Kristensen et al., 2005). On the other hand, postnatal studies revealed the association between maternal obesity, metabolic syndrome, and childhood obesity in the offspring (Stocker and Cawthorne, 2008). The nutritional and health status of the mother has always been known to be critical for foetal growth. However, the precise molecular mechanisms relating offspring predisposition to obesity and associated comorbidities to maternal obesity are still unclear. It is unclear what is the exact contribution of a low oocyte quality, or that of an altered intrauterine environment, to such effects in the offspring. Pregnancy is known to be associated with dramatic developmental plasticity, which through intrauterine adaptations determine the impact of prenatal environment and maternal metabolic performance (Santangeli et al., 2015) on the future health of the offspring (Dunkerton et al., 2022). Nonetheless, several lines of evidence support the contribution of altered gametes, rather than the intrauterine environment, to offspring predisposition to disease. For instance, studies showed that one-cell zygote, and blastocysts, from mice with diabetes retain the ability to result in congenital malformations and growth retardation in the offspring, despite being transferred into healthy pseudo-pregnant female recipients (Wyman et al., 2008). Another similar study employing embryo transfer experiments from mice fed a high-fat diet also claimed that defects observed on the foetus arose prior to the blastocyst stage and were not determined by potential changes in the uterine environment of obese mothers (Luzzo et al., 2012). However, the key question about the maternal factors carried on by the oocyte leading to altered developmental programming in the offspring remains unanswered. We speculate that conditions like hyperleptinemia or leptin resistance in obese mothers may drive such changes in the gamete with potential repercussions to the offspring. Considering the evidence presented throughout this review, supporting the potential roles of leptin in the regulation of oocyte metabolism, mitochondrial function, and epigenetic landscape, it cannot be dismissed that leptin may play a role in determining short- and long-term health outcomes in the offspring. Leptin itself is an important contributor to metabolic disease, as increased leptin and reduced adiponectin levels have been described as a major feature of obesity that contributes to the establishment and maintenance of metabolic syndrome (Frühbeck et al., 2019). Also, environmental cues in early life, especially that of the maternal health and diet likely the state of altered leptin levels, were shown to alter epigenetic regulation in the offspring (Park et al., 2008; Pinney et al., 2011; Pinney and Simmons, 2010; Thompson and Einstein, 2010). Interestingly, studies also supported the notion of such epigenetic control through inheritance via both male and female gametes (Chen et al., 2016; Daxinger and Whitelaw, 2012; Z. J. Ge et al., 2014; Huypens et al., 2016; Rando and Simmons, 2015). Thus, changes in the epigenome and other content of the gametes, can be passed on and affect the health state of the offspring. For instance, a study involving the fertilization of gametes from mice subjected to different dietary treatment, suggested that epigenetic changes in the oocyte and sperm play an important role in the intergenerational transmission of susceptibility to obesity in the offspring (Huypens et al., 2016). Similarly, Chen and coworkers revealed more recently that reduced levels of TET3 dioxygenase in the oocytes from hyperglycemic mothers, could lead to maternally inherited glucose intolerance in the offspring in mice (X. Wu et al., 2022). This was mediated through the potential effect on the zygotic genome reprogramming via TET3-dependent DNA demethylation of genes involved in insulin secretion, sensitizing the offspring to glucose intolerance (X. Wu et al., 2022). Another study seeking to understand the oocyte-mediated effects of maternal obesity on embryo development reported that reduced levels of the Stella protein in oocytes obtained from mice fed high-fat diet, drove the genome-wide changes in methylation in the zygote, culminating in compromised adult metabolic phenotypes (Leong, 2018). Hence, specifically for the oocyte, disrupted metabolism in response to changes in metabolite availability and mitochondrial function, as the result of compromised local leptin signalling, can lead to alterations in the epigenome that may exert a detrimental effect on the developmental programming of the offspring. In light of the intricate set of interactions between epigenetic mechanisms, metabolite availability, and gene expression regulation during embryo development, as well as the established role of leptin mediating such processes, one may anticipate that substantial evidence from studies will be generated in the soon future examining the impact of altered leptin levels on the female gamete and possible consequences for offspring health. Of important note, leptin has been already started to be recognized as a factor capable of affecting developmental programming in other contexts than obesity (Vickers et al., 2005). In fact, an optimal level of leptin in the umbilical cord blood was shown to be key for adequate intrauterine development of the foetus (Sivan et al., 1997), whereas increased leptin levels in maternal obesity were suggested to alter metabolic programming (Karakosta et al., 2013). For instance, leptin has been shown to be necessary for successful trophoblast invasion, having a mitogenic and anti-apoptotic effect in cultured human trophoblast cells (Magariños et al., 2007). Leptin levels have also been inversely correlated with placental weight (Buchbinder et al., 2001). Metabolically, leptin was shown to upregulate placental lipolysis (White et al., 2006) and stimulate the activity of amino acid transporter system A in the placental villi (Jansson et al., 2003), ensuring the adequate transfer of free fatty acids and neutral amino acids to the growing foetus. Leptin has also been suggested to control the intrauterine foetal glucogenic capacity, particularly by inhibition of endogenous glucose production towards term (Forhead et al., 2008). More generally, leptin is considered an important modulator of foetal growth and develpment (Hassink et al., 1997), controlling the proliferation of pancreatic islet cells(Islam et al., 2000), the development and migration of neuronal cells in the cerebral cortex (Udagawa et al., 2007), and the development of foetal adipose tissue and foetal length and body weight (Javaid et al., 2005; Valūnienė et al., 2007; Varvarigou et al., 1999). In fact, the synthesis and circulating levels of leptin in utero is known to be sensitive to changes in nutrients, hormones, or genetic influence (Forhead and Fowden, 2009). Hence, maternal overnutrition was reported to increase gene expression in foetal adipose tissues (Mühlhäusler et al., 2002). As a result, leptin is widely regarded as one of the main hormones capable of modulating the intrauterine environment, controlling foetal growth and development (Forhead and Fowden, 2009). Concerning the putative role of leptin in progmramming, it was shown that leptin can affect the formation and activity of hypothalamic networks in the foetus, which will dictate the regulation of appetite and energy balance in adult life (Bouret and Simerly, 2006; McMillen et al., 2005). Thus, exposure of the foetus to altered leptin levels at any critical period of development may, therefore, have important programming consequences. Additionally, new functions of leptin in milk (Palou et al., 2018) and amniotic fluid (Yau-Qiu et al., 2020) regardig early metabolic programming and metabolic health have also been reported in recent studies, in which animals showed long-term beneficial effects of leptin treatment against metabolic disease when leptin was administered during the lactation period. This portrays leptin as an important factor capable of modulating programming events at various developmental stages in the perinatal period. When reproducing, women transfer through the gametes a complex cargo that comprises not only the genetic code but also the epigenome, proteins, metabolites, and other components relevant for embryo growth and, most importantly, capable of affecting developmental programming. The putative role of leptin in regulating the oocyte metabolome and epigenome renders this adipokine an important factor controlling the legacy of the gamete capable of affecting embryo development. Furthermore, the direct actions of leptin on the intrauterine environment and placentation also account for its putative impact on developmental programming. Finally, programming events can be also established post-natally (McMillen et al., 2005)during lactation, an equally relevant developmental timepoint concerning the hyperleptinemic environment seen during maternal obesity. It however remains, uncharacterised whether reversing leptin signalling in these developmental stages eventually rescues the phenotypic consequences in the offspring. ## Conclusion The obesity epidemic is a global health problem with a profound impact on maternal-foetal health. Maternal obesity not only produces the usual grave outcomes of obesity but also poses significant risks to the development of the offspring both in the short and long run. This is due to the fact that the female gamete develops and matures in a physiologically altered conditions which may have impair the oocyte quality and alters its epigenome and metabolome. Given that the oocyte epigenome has the potential to control initial reprogramming events in the early embryo as well as longer-term metabolic outcomes, the oocyte legacy has the potential ability to affect predisposition to health and disease in the offspring. However, a better understanding of the maternal factors contributing to the alterations in developmental programming in the offspring is of extreme relevance. With growing evidence on the support of leptin in maintaining an optimal metabolic state, and mitochondrial function as well as a normal epigenetic landscape in the oocyte and other cellular contexts, its role as a major modulator of oocyte quality and successful embryo development seems secure. Generally, it is attractive to propose that perturbed leptin signalling observed during obesity, has detrimental effects as early as the oocyte stage, which further predisposes them to embryo developmental abnormalities and even metabolic diseases in the offspring. ## References 1. Al Darwich A, Perreau C, Petit MH, Papillier P, Dupont J, Guillaume D, Mermillod P, Guignot F. Effect of PUFA on embryo cryoresistance, gene expression and AMPKalpha phosphorylation in IVF-derived bovine embryos. 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--- title: Exploring the effectiveness of demand-side retail pharmaceutical expenditure reforms authors: - Michael Berger - Markus Pock - Miriam Reiss - Gerald Röhrling - Thomas Czypionka journal: International Journal of Health Economics and Management year: 2022 pmcid: PMC9968684 doi: 10.1007/s10754-022-09337-6 license: CC BY 4.0 --- # Exploring the effectiveness of demand-side retail pharmaceutical expenditure reforms ## Abstract Increasing expenditures on retail pharmaceuticals bring a critical challenge to the financial stability of healthcare systems worldwide. Policy makers have reacted by introducing a range of measures to control the growth of public pharmaceutical expenditure (PPE). Using panel data on European and non-European OECD member countries from 1990 to 2015, we evaluate the effectiveness of six types of demand-side expenditure control measures including physician-level behaviour measures, system-level price-control measures and substitution measures, alongside a proxy for cost-sharing and add a new dimension to the existing empirical evidence hitherto based on national-level and meta-studies. We use the weighted-average least squares regression framework adapted for estimation with panel-corrected standard errors. Our empirical analysis suggests that direct patient cost-sharing and some—but not all—demand-side measures successfully dampened PPE growth in the past. Cost-sharing schemes stand out as a powerful mechanism to curb PPE growth, but bear a high risk of adverse effects. Other demand-side measures are more limited in effect, though may be more equitable. Due to limitations inherent in the study approach and the data, the results are only explorative. ## Introduction Past decades have seen steady increases in expenditure related to health, which has become a major issue in healthcare systems for many countries in terms of financial stability and sustainability. Expenditure on retail pharmaceuticals is a substantial component of total expenditure on healthcare and is easily traceable, making it a prime candidate for policy action. In 2015, this share was $16.2\%$ on average across the 34 OECD member countries (excluding Chile, New Zealand and Turkey), but variation across countries is high, ranging from $6.8\%$ in Denmark to $29.2\%$ in Hungary, with a $37\%$ coefficient of variation. With novel high-cost pharmaceuticals entering the market (e.g. in cancer care) and new therapies gradually shifting treatment out of the hospital sector (e.g. therapies for Hepatitis C), the pressure on policy makers to align healthcare and (retail) pharmaceutical expenditures with revenues will further increase. Recent trends in pharmaceutical spending in OECD countries are neither homogeneous over time nor across countries. From 2003 to 2015, public pharmaceutical expenditure (PPE) in OECD member countries (excluding Belgium, Chile, Israel, Latvia, Mexico, New Zealand, Turkey and the United Kingdom) increased by $1.1\%$ on average in real terms per year. Splitting the sample in two six-year pre- and post-crisis periods highlights the varying dynamics. While real PPE growth rates were substantial prior to the crisis ($3.6\%$ yearly average), they turned negative on average in the post-crisis period. Figure 1 shows the development of real PPE of selected countries from 2000 to 2015. In terms of controlling costs, some countries performed better than others (with PPE declining in Denmark and Sweden, in contrast to steep growth by $234\%$ in the US). In many countries, PPE growth rates still pressure public budgets. Belloni et al. [ 2016] suggest that future growth of pharmaceutical spending is likely to pick up pace again due to changes in pharmaceutical markets and increased availability of high-cost pharmaceuticals. Against this background, the question of appropriate expenditure control measures beyond direct price controls is again gaining importance. Fig. 1Real PPE growth in selected countries (national currency, constant prices). OECD Health Statistics [2017] The objective of this article is to evaluate the options available to policy makers to safeguard fiscal sustainability of healthcare systems across countries. We add to the existing literature by assessing the effectiveness of expenditure control measures for retail PPE in a cross-country panel setting using a core sample of 10 European OECD member countries (Austria, Belgium, Denmark, Finland, France, Germany, the Netherlands, Sweden, Switzerland, and the United Kingdom). We further use an extended sample of 12 countries (the core sample plus two additional non-European OECD member countries, Canada and the United States) to assess the robustness of the results. Over the last decades, governments have implemented a range of expenditure control measures on pharmaceuticals [for an overview see Vogler [2012]]. Some mainly target drug prices, others quantity and prescription behaviour. An overview of the relevant literature suggests that the evaluations of these control mechanisms for pharmaceutical expenditure have either been done on a national level (Bastida and Mossialos, 2000; Moreno-Torres et al., 2011; Barros and Nunes, 2010; Andersson et al., 2006, 2007; Lee et al., 2012; Fischer et al., 2018) or by meta-studies (Ghislandi et al., 2005; Galizzi et al., 2011; Tele & Groot, 2009; Acosta et al., 2014; Rashidian et al., 2015). Panel studies concerned with rising pharmaceutical expenditure mainly focus on income elasticity without considering policy variables (Okunade & Suraratdecha, 2006; Clemente et al., 2008). By pooling data from multiple countries, statistical inference gains power and findings stand independent of national context. It is imperative to identify and quantify the determinants of PPE growth to properly assess the effectiveness of expenditure control measures. At the same time, the impact of various expenditure control measures that have already been enacted must be evaluated. To the best of our knowledge, this is the first systematic empirical comparison of the effectiveness of pharmaceutical expenditure control measures in a cross-country setting. However, a limitation of this type of study is that the overlapping of several policies within countries at a given time does not allow to follow a clear identification strategy and our results are therefore first and foremost descriptive and hypothesis generating in nature and should not be read as causal. As we are concerned with the financial sustainability of public budgets, we focus our analysis on the part of expenditure borne by public entities. The analysis is further limited to policy reforms targeting retail pharmaceutical spending, i.e. pharmaceutical spending occurring outside hospitals, as the comparable System of Health Accounts methodology accounts for final consumption only. For a complete picture of PPE, pharmaceutical expenditure of the hospital sector would be desirable. However, we justify separate analysis by noting that pharmaceutical expenditure in hospitals need not have the same dynamics as retail pharmaceutical expenditure for several reasons. For instance, the majority of innovative, high-cost pharmaceutical interventions like cancer therapy takes place in a hospital setting. Pharmaceutical expenditure in hospitals may thus depend much more strongly on technological progress than retail pharmaceuticals. Country-specific healthcare regulations further exacerbate the different dynamics. In Austria and Germany, for example, the budget for hospital pharmaceuticals is a lump-sum part of the public hospital funding for diagnosis related treatment and therefore does not respond to direct price-damping measures of the public health funds for retail pharmaceuticals. ## Types of demand-side expenditure control measures Following the Pharmaceutical Pricing and Reimbursement Information (PPRI) reports by the WHOCC (World Health Organisation Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies), the most common measures can be divided into six principal categories presented in Table 1. A graphical representation of the six principal categories as well as their timeline in the country sample of this analysis is provided in the appendix (Fig. 2). Although the practical implementation of the different reform types can differ, it is necessary to group them for cross-country analysis. Additionally, to allow for comparability across countries, only policies that were in place at the national level are considered in this analysis, thereby excluding (a) pilot projects (e.g. pilot projects for e-prescription in Switzerland), (b) policies only in effect at certain healthcare payers (e.g. the pharmaceutical budgets that some Austrian SHI funds implemented, but which were not coordinated on a national scale), and (c) policies only affecting specific groups of pharmaceuticals (e.g. generic substitution scheme in Belgium that is restricted to international non-propriety name (INN)-prescriptions, antibiotics and antimycotics, as well as pharmaceuticals with a reimbursement ceiling). Measures that aim at the type and price of pharmaceuticals enjoy widespread use across OECD member countries. Substitution measures include (i) generic prescription, where physicians do not prescribe specific brands of drugs, but rather prescribe the drug’s generic name. The choice of drug is then left to the dispensing pharmacists. In contrast, with (ii) (compulsory) generic substitution, pharmacists are obliged to substitute a physician’s prescription of a brand drug with a generic drug, if available. Implementation of system-wide price-control measures such as (iii) reference price systems is also fairly common in OECD member countries. Reference price system in the context of our analysis is used in the meaning that pharmaceuticals are grouped according to their use, and one product’s price is set as a reference price. This procedure is often referred to as internal reference pricing. The public health system will then only cover that price. If a more expensive (brand) product is requested, patients have to pay the difference out-of-pocket. Internal reference pricing cannot be applied to (often highly-priced) innovative drugs that have only recently been introduced onto the market and for which no reference is available, potentially making the system less effective (Giuliania et al., 1998).1 In contrast, under external reference pricing, the price of a drug in one or several countries is used to derive a reference price. As such, external reference pricing is a typical pricing policy applied to innovative pharmaceuticals and is in effect in all countries included in the country sample for publicly reimbursed pharmaceuticals, though there is some indication that pharmaceutical producers have in the past tried to outmanoeuvre these mechanisms by systematically delaying dossier submissions in Belgium to avoid the lower Belgian prices to affect prices in other countries (Toumi et al., 2014). Moreover, the exact implementation of external reference pricing varies in practice. Several countries use external reference pricing only as a supplementary or initial pricing policy and Denmark restricts its use to the hospital sector altogether (Rémuzat et al., 2015). To account for this variation and the limitation of the dataset not covering hospital medication, the reference price system policy variable is limited to the form of internal reference pricing described above, which is applied in a more comparable fashion across countries. A similar problem exists for profit margin controls and Health Technology Assessment (HTA) for which an aggregation into policy variables is not possible, as the strategies pursued by countries are too different. Profit control is either set for wholesale or retail, or both, with the size of a fixed mark-up differing strongly between countries [see e.g. Lee et al. [ 2021]], and in countries with established HTA institutes, HTA may play a crucial role in the use of cost-intensive pharmaceuticals, even if there are no legal requirements to consider the results of HTA.Table 1The six principal categories of expenditure control measuresExpenditure control measure categoryCountry (first year measure active in dataset)(i)Generic prescriptionBEL [2005], CAN [1990], FIN [1996], FRA [2002], GER [2002], NLD [1990], SUI [2001], GBR [1990], USA [1990](ii)Generic substitutionCAN [1990], DEN [1991], FIN [2003], FRA [1999], GER [2002], NLD [2003], SWE [2002], SUI [2001], USA [1990](iii)Reference price systemsBEL [2001], DEN [1993], FIN [2009], FRA [2003], GER [1992], NLD [1991], SWE [1993-2002]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{}$$\end{document}∗, SUI [1996](iv)Pharmaceutical budgets for physiciansGER [1993], SWE [1997], GBR [1991](v)Electronic prescriptionDEN [1995], FIN [2010], FRA [2007], NLD [1998], SWE [2003], GBR [2005], USA [2009](vi)Information on prescription behaviourBEL [1996], DEN [2003], FIN [1998], FRA [2007], GER [2003], NLD [1992], SWE [1997], GBR [1991]BEL Belgium, CAN Canada, FIN Finland, FRA France, GBR United Kingdom of Great Britain and Northern Ireland, GER Germany, NLD The Netherlands, SWE Sweden, SUI Switzerland, USA United States of AmericaReference price system only in effect until 2002 Other methods tackle the problem directly at the physician level. Among these physician-level behaviour measures (iv) pharmaceutical budgets grant physicians a certain budget for the prescription of pharmaceuticals. If applied thoroughly, pharmaceutical budgets could potentially be a very effective cost control measure, but soft budget constraints (Kornai, 1986) can undermine the effects. Hard pharmaceutical budgets are, however, difficult to implement in practice. As exceeding a pharmaceutical budget may be medically justified in some instances, resistance by both physician and patients against hard budgets is likely. ( v) Electronic prescription systems support physicians with finding the most economically sensible options among a choice of appropriate drugs. Another approach is to provide physicians with (vi) feedback on their prescription behaviour. By informing physicians about the cost structure and how their prescription behaviour compares to the national or regional average, management of costs and prescription behaviour is incentivised. This is an important measure in conjunction with pharmaceutical budgets. For policy bundles, it may also be possible to accrue an impact beyond that of simple additive effects when there are synergies between expenditure control measures. However, owing to the small sample size available and the limited amount of actually observed policy combinations, a detailed analysis is beyond the scope of our present study. ## Data We use data from the OECD System of Health Accounts (SHA) dataset (OECD, Eurostat, WHO, 2017) in this analysis—with the exception of time series on the prevalence of overweight in adults, which is taken from the World Bank database (The World Bank, 2021). OECD data have the advantage that they are largely comparable across countries due to harmonized calculation methods, but which comes at the expense of a somewhat limited set of variables to choose from. In total, 12 OECD member countries were studied, for which data were sufficiently complete. The core dataset includes ten European countries (Austria, Belgium, Denmark, Finland, France, Germany, the Netherlands, Sweden, Switzerland, and the United Kingdom) while two non-European countries (Canada and the United States) were used to ascertain the robustness of the results in an extended dataset. Data were extracted from the OECD Health Statistics database for the period 1990–2015. As the dataset contained structural breaks and missing values, we performed uniform data cleansing to ensure sufficiently long and complete time series. Missing data points at the beginning or the end of time series were extrapolated with yearly average growth rates of the previous or subsequent periods, respectively. Missing values within time series without a structural break were linearly interpolated. Real structural breaks, for example, caused by a change in the method of calculating health expenditure, were smoothed by replacing the growth rate in the year of the break by the average growth rate of the two years preceding and succeeding the break. As the dependent variable, we use log-differenced PPE per capita in national currency units at GDP prices of 2005. Independent variables cover a range of metric variables that are expected to influence the growth of pharmaceutical expenditure: on the demand side, log-differenced GDP per capita at 2005 prices is taken as a proxy for available income. Life expectancy at birth is used to account for differences in health expenditure owing to the demographic structure. There is some debate in the literature that healthcare expenditure is in principle determined by proximity to death rather than age [see e.g. Zweifel et al. [ 1999], Howdon & Rice [2018]]. However, such information cannot be extracted from macro time series, such as SHA data. Private expenditure on pharmaceuticals (through voluntary schemes or household out-of-pocket payments) as a share of total health expenditure is used as a proxy for private cost-sharing. Although information on private cost-sharing is provided in the SHA framework, this variable could not be used as too many data points were missing. On the supply side, the density of practicing generalist medical practitioners, specialists, physicians in total and pharmacists, and curative (acute) care beds per 1000 inhabitants were taken as resource variables to control for a potential connection between healthcare consumption and the density of healthcare provision through supplier-induced demand [e.g. Léonard et al. [ 2009]], as well as the setting in which healthcare is provided (e.g. the relative importance of specialised physicians in the outpatient sector vis-à-vis primary care physicians, or the extent of inpatient care), both of which may conceivably influence quantity and types of retail pharmaceutical prescriptions. The density of magnetic resonance imaging (MRI) units per 1 million inhabitants is used as a proxy for technological progress. For Belgium, Germany and Switzerland, for which data on the total density of MRI units were not available, we use MRI units in hospitals per 1 million inhabitants instead. As we are not interested in the density of MRI units as such, but only as a proxy for medical technological progress, mixing two time series does not limit our analysis. We control for epidemiological characteristics of the population with the percentage of overweight adults in the population to proxy the chronic disease burden (Kearns et al., 2014), and with the standardized death rate for malignant neoplasms per 100,000 inhabitants, which serves also as a proxy for access to cost-intensive treatments, as is the case with cancer (Kantarjian et al., 2014). As differences in the financing structure of health systems may entail differences in expenditure on health and pharmaceuticals (e.g. through differences in bargaining position of payers vis-à-vis manufacturers, etc.), we further distinguish between countries whose healthcare system is primarily financed through social health insurance (SHI) contributions and those with a primarily tax-funded or otherwise funded healthcare system. For this purpose, we introduce a dummy variable in our regression model that takes the value 1 for SHI countries and 0 otherwise. Moreover, we proxy the role and importance of HTA in a country by a dummy variable that is 1 in case HTA is legislatively required in the process for reimbursement decisions.2 Note that due to the limitations in the accuracy of this variable definition—HTA may still impact reimbursement procedures, even if not explicitly legally binding—the estimated coefficient cannot be interpreted as a policy variable as such, but only as a control variable for a country-characteristic. Finally, six dummy variables were constructed to assess the impact of the different expenditure control measures (see Table 1). Each measure takes the value 1 in each year the measure was in action and 0 otherwise. The summary statistics of the variables for both samples prior to the sequential correction of the error term structure are provided in Table 2.Table 2Summary statistics of the variables prior to the error term correction procedureSummary statisticsVariableObservationsMeanSDMinMaxCore SamplePublic pharmaceutical expenditure (log-diff)2500.0330.073−0.3220.433Private pharmaceutical expenditure (log-diff)250−0.0130.136−1.2591.189GDP (log-diff)2500.0110.023−0.1140.057Density pharmacists (log-diff)2500.0130.023−0.0530.135Density MRI units (log-diff)2500.0830.084−0.0920.445Density GPs (log-diff)2500.0120.025−0.0650.139Density physicians (log-diff)2500.0160.012−0.0240.075Density specialists (log-diff)2500.0230.017−0.0430.089Density (acute) care beds (log-diff)250−0.0190.015−0.0840.023Death rate malignant neoplasms (log-diff)250−0.0110.013−0.0470.031Life expectancy (log-diff)2500.0030.003−0.0060.014Prevalence of overweight in adults (log-diff)2500.0100.0030.0030.024SHI2500.6000.49101Legislatively required HTA2500.3560.48001Generic prescription2500.4960.50101Generic substitution2500.4440.49801Reference price system2500.5480.49901Pharmaceutical budgets for physicians2500.2680.44401Electronic prescription2500.3120.46401Information on prescription behaviour2500.5640.49701Extended samplePublic pharmaceutical expenditure (log-diff)3000.0380.072−0.3220.433Private pharmaceutical expenditure (log-diff)300−0.0100.125−1.2591.189GDP (log-diff)3000.0120.022−0.1140.057Density pharmacists (log-diff)3000.0130.023−0.0770.135Density MRI units (log-diff)3000.0820.084−0.0920.524Density GPs (log-diff)3000.0110.024−0.0650.139Density physicians (log-diff)3000.0140.012−0.0240.075Density specialists (log-diff)3000.0210.017−0.0430.089Density (acute) care beds (log-diff)300−0.0190.016−0.1040.032Death rate malignant neoplasms (log-diff)300−0.0110.012−0.0470.031Life expectancy (log-diff)3000.0030.003−0.0060.014Prevalence of overweight in adults (log-diff)3000.0100.0030.0030.024SHI3000.5000.50101Legislatively required HTA3000.2970.45801Generic prescription3000.5800.49401Generic substitution3000.5370.49901Reference price system3000.4570.49901Pharmaceutical budgets for physicians3000.2230.41701Electronic prescription3000.2830.45101Information on prescription behaviour3000.4700.50001SHI Social health insurance, HTA health technology assessment For the construction of dummy variables indicating policy change with respect to each cost control measure category, diverse sources on the historical development of healthcare systems were used, among which the PPRI reports (DeSwaef & Antonissen, 2008; Peura et al., 2007; Redman & Köping Höggärd, 2007; Palnoch et al., 2007; Stargardt et al., 2008; Thomsen et al., 2008; Vogler et al., 2008), OECD reports (Belloni et al., 2016; Colombo et al., 2006; Moïse & Docteur, 2007) and Health Systems in Transition reports by the European Observatory (Busse & Riesberg, 2004; den Exter et al., 2004; Sandier et al., 2004; Glenngård et al., 2005; Corsens, 2007; Vuorenkoski, 2008; Marchildon, 2013) were the most valuable sources. In addition, experts from the individual countries were contacted for confirmation and clarification. ## Empirical strategy Different expenditure control measures were implemented in different OECD member countries at different times. This allows us to make use of these differences across time and countries to isolate the associations of six types of expenditure control measures (Table 1) with PPE growth from confounding factors in the panel data setting. However, it is important to note that this study design does not allow for a causal interpretation of the estimated coefficients. The nature of the data adds another dimension to the problems of the statistical analysis of this issue: first, an abundance of potential covariates may lead to overfitting of the statistical models. Second, due to incomplete country time series and the resulting sample selection, spatial methods cannot be used to deal with the problem of contemporaneous cross-sectional correlation. We, therefore, propose a study design that allows addressing both issues. ## Error-term correction Levin-Lin-Chu tests (Levin et al., 2002) indicate a unit root in the level time series of the dependent variable.3 The continuous variables are therefore transformed in log-differences. Our statistical framework starts from the time-series cross-sectional model:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} y_{it} = X'_{it} \beta _{1} + D'_{it} \beta _{2} + \varepsilon _{it} \end{aligned}$$\end{document}yit=Xit′β1+Dit′β2+εitwith \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$i = 1$,\ldots, N$$\end{document}$i = 1$,…,N (countries), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$t = 1$,\ldots, T$$\end{document}$t = 1$,…,T (years). The variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_{it}$$\end{document}yit is PPE in log-difference and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X'_{it}$$\end{document}Xit′ is the set of log-differenced regressors given in Table 2, including a common intercept, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D'_{it}$$\end{document}Dit′ is the set of dummy variables. The composite error term \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varepsilon } \sim N(0,\sigma ^{2}\varOmega)$$\end{document}ε∼N(0,σ2Ω), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varOmega $$\end{document}Ω is a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$NT \times NT$$\end{document}NT×NT positive definite matrix, allows for group-wise heteroscedasticity, common first-order serial correlation and time-invariant cross-sectional correlation4:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \varepsilon _{it} = \rho \varepsilon _{i,t-1} + u_{it} \end{aligned}$$\end{document}εit=ρεi,t-1+uitwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\|\rho \|<1$$\end{document}\|ρ\|<1 is the common autocorrelation parameter and the zero-mean innovations \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u_{it}$$\end{document}uit are temporally independent and identically distributed. The error covariance matrix of a balanced panel is given by3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \varOmega = E \left[\varepsilon \varepsilon ' \right] = \varSigma \otimes \varPi \end{aligned}$$\end{document}Ω=Eεε′=Σ⊗Πwith4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \varSigma = \begin{bmatrix} \sigma _{\varepsilon, 11} &{} \sigma _{\varepsilon, 12} &{} \cdots &{} \sigma _{\varepsilon, 1N} \\ \sigma _{\varepsilon, 21} &{} \sigma _{\varepsilon, 22} &{} \cdots &{} \sigma _{\varepsilon, 2N} \\ \vdots &{} \vdots &{} \ddots &{} \vdots \\ \sigma _{\varepsilon, N1} &{} \sigma _{\varepsilon, N2} &{} \cdots &{} \sigma _{\varepsilon, NN} \end{bmatrix} \end{aligned}$$\end{document}Σ=σε,11σε,12⋯σε,1Nσε,21σε,22⋯σε,2N⋮⋮⋱⋮σε,N1σε,N2⋯σε,NNand5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \varPi = \frac{1}{1-\rho ^2} \begin{bmatrix} 1 &{} \rho &{} \rho ^{2} &{} \cdots &{} \rho ^{T-1} \\ \rho &{} 1 &{} \rho &{} \cdots &{} \rho ^{T-2} \\ \rho ^{2} &{} \rho &{} 1 &{} \cdots &{} \rho ^{T-3} \\ \vdots &{} \vdots &{} \vdots &{} \ddots &{} \vdots \\ \rho ^{T-1} &{} \rho ^{T-2} &{} \rho ^{T-3} &{} \cdots &{} 1 \end{bmatrix} \end{aligned}$$\end{document}Π=11-ρ21ρρ2⋯ρT-1ρ1ρ⋯ρT-2ρ2ρ1⋯ρT-3⋮⋮⋮⋱⋮ρT-1ρT-2ρT-3⋯1where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varSigma $$\end{document}Σ is the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N \times N$$\end{document}N×N panel-by-panel covariance matrix and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varPi $$\end{document}Π is the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T \times T$$\end{document}T×T autocorrelation matrix. Following Magnus & De Luca [2016] and Magnus et al. [ 2011], we correct for the nonspherical disturbances by pre-multiplying equation [1] by an estimate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varOmega ^{-\frac{1}{2}}$$\end{document}Ω-12 under the normalization constraint \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$trace(\varOmega)=n$$\end{document}trace(Ω)=n based on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{\varOmega }}={\hat{\varSigma }}\otimes {\hat{\varPi }}$$\end{document}Ω^=Σ^⊗Π^:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {\tilde{y}} = \tilde{X'} \beta _{1} + D' \beta _{2} + {\tilde{\varepsilon }} \end{aligned}$$\end{document}y~=X′~β1+D′β2+ε~ with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{X'} = {\hat{\varOmega }}^{-\frac{1}{2}} X'$$\end{document}X′~=Ω^-12X′, and the independent and identically distributed errors \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{\varepsilon }} = {\hat{\varOmega }}^{-\frac{1}{2}} \varepsilon $$\end{document}ε~=Ω^-12ε, all in the stacked form of dimension NT. Note that we have excluded the dummy variables \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D'$$\end{document}D′ from the transformation to conserve their binary character as the pre-multiplication would otherwise only complicate the interpretation of the coefficient. Moreover, in contrast to the continuous covariates, the autocorrelation and cross-sectional correlation does not affect the policy dummies variables, justifying their exclusion. The estimates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{\rho }}$$\end{document}ρ^ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{\varSigma }}$$\end{document}Σ^ are extracted from an estimation of the full linear model [1] including all continuous and dummy covariates with the PCSE-estimator [Panel-Corrected Standard Errors, see Beck and Katz [1995]], which preserves the Prais–Winsten transformation for autocorrelation but uses a sandwich estimator to incorporate cross-sectional dependence when calculating standard errors. The PCSE-estimator is shown to be the superior estimator in the current setting when the primary concern is hypothesis testing (Moundigbaye et al., 2018). ## Weighted-average least squares estimation As numerous variables are potential candidates for inclusion in the regression model, the issue of model choice is not straight-forward to resolve and can have nonnegligible effects on the statistical properties of the estimators and hence the estimated coefficients (Magnus & Durbin, 1999; Danilov & Magnus, 2004; Moral-Benito, 2015). In this analysis, we use the weighted-average least squares (WALS) approach introduced by Magnus et al. [ 2010] which combines Bayesian and frequentist estimators. We have chosen the WALS approach for this analysis as the combination of these features gives this model averaging estimator an edge over strictly Bayesian and strictly frequentist model averaging estimators: in contrast to strictly Bayesian approaches, theoretical considerations determine the choice of priors in WALS that relate to admissibility, bounded risk, robustness and near-optimality in terms of minimax regret (De Luca et al., 2018). In addition, WALS presents a more explicit and transparent treatment of ignorance in the choice of priors (Magnus et al., 2010). As WALS uses a semiorthogonal transformation of the regressors, the computational burden is greatly reduced compared to other Bayesian or frequentist alternatives. We use the implementation of WALS in STATA by De Luca & Magnus [2011] in combination with a data set containing the pre-multiplied time series as per Eq. [ 6]. A key aspect of the model selection in WALS is the distinction between focus and auxiliary regressors, which was first introduced in Danilov & Magnus [2004]. The inclusion of focus regressors in the model is fixed based on theoretical considerations, while the inclusion of auxiliary regressors is mutable. Model uncertainty within this framework arises since different subsets of variables could be excluded from the model, leading to a trade-off between bias and precision in the estimators of the focus regressors. Thus, model selection takes place over the subset of auxiliary regressors, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{A}$$\end{document}kA, resulting in the model space \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {M}}:= \{ {\mathcal {M}}_{j}, $j = 1$,\ldots,2^{k_{A}} \}$$\end{document}M:={Mj,$j = 1$,…,2kA} with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2^{k_{A}}$$\end{document}2kA possible models. Rewriting equation [6], the model \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {M}}_{j}$$\end{document}Mj can be expressed as:7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {\tilde{y}}= {\tilde{F}}'\beta _{F} + {\tilde{A}}'_{j} \beta _{Aj} + {\tilde{\varepsilon }}_{j} \end{aligned}$$\end{document}y~=F~′βF+A~j′βAj+ε~jwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{F}}_{j}$$\end{document}F~j is the matrix of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{F}$$\end{document}kF (transformed) focus regressors, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{A}}_{j}$$\end{document}A~j is the matrix of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{Aj}$$\end{document}kAj (transformed) auxiliary regressors, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{F}$$\end{document}βF and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{Aj}$$\end{document}βAj are the corresponding parameters, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{\varepsilon }}_{j}$$\end{document}ε~j is the vector of independent and identically distributed errors. We fix the variables GDP, private pharmaceutical expenditure and the dummy for SHI systems as focus regressors. The remaining variables are considered auxiliary regressors resulting in a model space of 524,288 models, or 1125 billion models when including the country- and year-dummies. The policy variables were intentionally not chosen as focus regressors in order to avoid overfitting and ensure parsimony of the resulting model, although these are the main variables of interest. In the key steps of the WALS estimation, the orthogonal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{A} \times k_{A}$$\end{document}kA×kA matrix P and a diagonal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{A} \times k_{A}$$\end{document}kA×kA matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varLambda $$\end{document}Λ are computed such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P'{\tilde{A}}'M_{{\tilde{F}}}{\tilde{A}}P=\varLambda $$\end{document}P′A~′MF~A~P=Λ, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{{\tilde{F}}}=I_{1}-{\tilde{F}}({\tilde{F}}'{\tilde{F}})^{-1}{\tilde{F}}'$$\end{document}MF~=I1-F~(F~′F~)-1F~′ is a symmetric and idempotent matrix (Magnus et al., 2010). Using these matrices \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Z_{{\tilde{A}}}={\tilde{A}}P\varLambda ^{-\frac{1}{2}}$$\end{document}ZA~=A~PΛ-12 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma _{{\tilde{A}}}=\varLambda ^{\frac{1}{2}}P'\beta _{{\tilde{A}}}$$\end{document}γA~=Λ12P′βA~ are defined such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Z'_{{\tilde{A}}}M_{{\tilde{F}}}Z_{{\tilde{A}}}=I_{k_{A}}$$\end{document}ZA~′MF~ZA~=IkA and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Z_{{\tilde{A}}}\gamma _{{\tilde{A}}}={\tilde{A}}\beta _{{\tilde{A}}}$$\end{document}ZA~γA~=A~βA~. Applying the orthogonal transformation to the basic linear regression model [7] and using a Laplace estimator \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{\eta }}_{j}$$\end{document}η^j for the theoretical t-ratio \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eta _{j} = \frac{\gamma _{{\tilde{A}}j}}{\sigma _{\varepsilon }}$$\end{document}ηj=γA~jσε based on the Laplace prior distribution8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \pi (\eta _{j};c)= \frac{c}{2} e^{-c\|\eta _{j}\|} \end{aligned}$$\end{document}π(ηj;c)=c2e-c\|ηj\|with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c=log2$$\end{document}c=log2 such that the prior median of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eta _{j}$$\end{document}ηj is zero and the prior median of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eta _{j}^{2}$$\end{document}ηj2 is one (which reflects the notion of ignorance in the choice of priors), the resulting WALS estimators of the regression parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{{\tilde{F}}}$$\end{document}βF~ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _{{\tilde{A}}}$$\end{document}βA~ are given by9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}&{\tilde{\beta }}_{{\tilde{F}}} = ({\tilde{F}}'{\tilde{F}})^{-1}{\tilde{F}}'({\tilde{y}}-{\tilde{A}}{\tilde{\beta }}_{{\tilde{A}}}) \nonumber \\&{\tilde{\beta }}_{{\tilde{A}}} = s P \varLambda ^{-\frac{1}{2}} {\hat{\eta }} \end{aligned}$$\end{document}β~F~=(F~′F~)-1F~′(y~-A~β~A~)β~A~=sPΛ-12η^where s is derived from the estimator \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{j}^{2}$$\end{document}sj2 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{{\tilde{\varepsilon }}}^{2}$$\end{document}σε~2 in model \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {M}}_{j}$$\end{document}Mj. ## Results Table 3 provides a comparison of the t-ratios of WALS regressions for the two samples with and without country- and year-dummies and Table 4 presents the respective coefficient estimates. Columns [1] and [3] present the results for the core sample of European countries and columns [2] and [4] for the extended sample including Canada and the US. Note that as the WALS estimation is a model averaging technique and therefore, as Magnus and De Luca (2016, p. 118)note, “does not select a single model out of the available set of models”, but rather allows each model to contribute information on the parameters of interest. Accordingly, we present the estimated coefficients of all potential control variables in Tables 3 and 4.Table 3Comparison t-ratios for the core (Europe) and extended sample (Europe \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document}+ Canada and USA)SampleEuropeEurope \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document}+ Canada and USAEuropeEurope + Canada and USADependent variablePublic pharmaceutical expendituret-ratiot-ratiot-ratiot-ratio[1][2][3][4]Private pharmaceutical expenditurea−11.20−11.00−11.15−10.70Constanta1.431.461.690.98GDPa4.372.533.652.86Electronic prescription system−2.41−2.61−0.130.00SHIa−0.91−1.50−0.95−0.25Density MRI units1.671.820.921.54Pharmaceutical budget for physicians−1.00−0.83−1.96−1.29Generic substitution (lag)−1.58−1.70−0.71−1.04Density specialists−0.08−0.810.14−0.36Density generalist medical practitioners−1.27−1.17−1.59−1.96Density physicians0.811.120.751.24Information on prescription behaviour−1.00−1.53−0.58−0.88Death rate malignant neoplasms0.540.450.300.26Density pharmacists0.670.981.011.07Density (acute) care beds−0.10−0.880.30−0.29Reference price system (lag)−0.60−0.48−0.02−0.23Generic prescription−0.310.04−0.450.08Generic substitution1.201.090.950.91Reference price system0.630.640.310.33Life expectancy−0.61−0.06−0.600.30Generic prescription (lag)0.200.090.670.83Prevalence of overweight in adults1.831.901.010.38Legislatively required HTA0.110.08−0.84−0.88N240288240288Country- and time-fixed effectsNoNoYesYesModel space524,288 models1125 billion modelsbNote that the sample size size is reduced by 10 and 12 observations respectively, due to the inclusion of variables with a one-year lagaFixed inclusionbCountry-dummies for CAN, UK and USA omitted because of collinearityTable 4Results of the WALS regression for the core (Europe) and the extended sample (Europe + Canada and USA)Results statistical analysisSampleEuropeEurope \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document}+ Canada and USAEuropeEurope + Canada and USADependent variablePublic pharmaceutical expenditure[1][2][3][4]GDP0.865\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$*$$\end{document}∗∗∗0.509\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗0.953\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$*$$\end{document}∗∗∗0.794\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗∗[0.475, 1.254][0.112, 0.906][0.438, 1.468][0.246, 1.342]Private pharmaceutical expenditure−0.313\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗∗−0.295\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗∗−0.303\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗∗−0.286\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗∗[−0.368, −0.258][−0.348, −0.243][−0.356, −0.249][−0.338, −0.233]SHI−0.00927−0.0164−0.0366−0.00978[−0.0293, 0.0107][−0.0380, 0.00516][−0.113, 0.0396][−0.0860, 0.0665]Legislatively required HT0.001050.000818−0.0131−0.0140[−0.0169, 0.0190][−0.0182, 0.0198][−0.0439, 0.0177][−0.0454, 0.0174]Density pharmacists0.06900.1050.1110.122[−0.135, 0.272][−0.107, 0.318][−0.106, 0.328][−0.103, 0.348]Density MRI units0.0637\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^$$\end{document}∗0.0719\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^$$\end{document}∗0.03710.0739[−0.0117, 0.139][−0.00611, 0.150][−0.0423, 0.117][−0.0206, 0.168]Death rate malignant neoplasms0.1030.09190.05990.0567[−0.275, 0.481][−0.307, 0.490][−0.336, 0.456][−0.365, 0.479]Density physicians0.2180.3110.2020.376[−0.313, 0.749][−0.235, 0.856][−0.327, 0.732][−0.222, 0.974]Density GPs−0.163−0.154−0.196−0.254[−0.415, 0.0894][−0.413, 0.105][−0.440, 0.0471][−0.509, 0.00107]Density specialists−0.0154−0.1530.0236−0.0726[−0.375, 0.344][−0.523, 0.218][−0.317, 0.364][−0.474, 0.329]Density (acute) care beds−0.0201−0.1840.163−0.0657[−0.404, 0.364][−0.594, 0.226][−0.229, 0.555][−0.518, 0.386]Life expectancy−0.569−0.0738−0.7890.477[−2.396, 1.258][−2.557, 2.410][−3.381, 1.803][−2.659, 3.612]Prevalence of overweight in adults1.801\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗1.573\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗1.5320.581[−0.138, 3.740][−0.0554, 3.201][−1.447, 4.511][−2.469, 3.631]Electronic prescription−0.0245\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗−0.0287\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗∗∗−0.00181−0.0000362[−0.0445, −0.00447][−0.0505, −0.00700][−0.0288, 0.0252][−0.0291, 0.0290]Information on prescription behaviour−0.00994−0.0166−0.00778−0.0123[−0.0296, 0.00970][−0.0379, 0.00475][−0.0343, 0.0188][−0.0402, 0.0155]Pharmaceutical budgets for physicians−0.00915−0.00799−0.0578\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗−0.0388[−0.0272, 0.00887][−0.0270, 0.0111][−0.116, 0.000391][−0.0980, 0.0205]Generic prescription−0.007720.00106−0.01150.00209[−0.0574, 0.0420][−0.0502, 0.0523][−0.0614, 0.0384][−0.0507, 0.0549]Generic substitution0.02790.02640.02280.0235[−0.0181, 0.0739][−0.0213, 0.0741][−0.0246, 0.0702][−0.0272, 0.0743]Reference price system0.01290.01400.006060.00680[−0.0274, 0.0533][−0.0290, 0.0570][−0.0328, 0.0449][−0.0344, 0.0480]Generic prescription (lag)0.005000.002380.01700.0224[−0.0454, 0.0554][−0.0498, 0.0545][−0.0328, 0.0668][−0.0308, 0.0756]Generic substitution (lag)−0.0371−0.0417\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗−0.0162−0.0257[−0.0836, 0.00931][−0.0899, 0.00657][−0.0612, 0.0289][−0.0742, 0.0229]Reference price system (lag)−0.0121−0.0105−0.000388−0.00493[−0.0518, 0.0277][−0.0532, 0.0322][−0.0394, 0.0387][−0.0465, 0.0367]Constant0.01910.02220.0691\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$\end{document}∗0.0418[−0.00717, 0.0453][−0.00787, 0.0523][−0.0117, 0.150][−0.0426, 0.126]N240288240288Country- and time-fixed effectsNoNoYesYesNote that the sample size size is reduced by 10 and 12 observations respectively, due to the inclusion of variables with a one-year lag. $95\%$-Confidence intervals in parentheses\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.1$$$\end{document}$p \leq 0.1$, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.01$ $$\end{document}$p \leq 0.01$ The results suggest that the effects most demand-side policy measures are not as clear once compared across countries. Electronic prescription has the highest absolute t-ratios of the policy measures under investigation when not including country- and year-dummies. It shows the strongest consistent estimated coefficient in the WALS regressions with around $2.5\%$ lower annual growth depending on the sample, though both size and significance vanish when including country- and year-dummies. The estimated coefficient for pharmaceutical budgets and generic substitution (lagged by one year are of similar size (roughly $5\%$ lower annual growth), but in contrast to electronic prescription are only significant at the $10\%$-level of confidence in the extended sample without country- and time-fixed effects (pharmaceutical budgets) and the core sample with country- and time-fixed effects (lagged generic substitution), respectively. Information systems on prescription behaviour have relatively high t-ratios in the core sample, but also fail to reach significance and the coefficient is somewhat smaller. It is a noteworthy result that reference price systems and generic prescription have only relatively low t-ratios and hence no statistically significant coefficients. We further find a strong and highly significant association between a higher cost-sharing and PPE growth, suggesting that a $1\%$ increase in private pharmaceutical expenditure growth reduces PPE growth by roughly $0.3\%$. It is of course by itself not a surprising finding that a higher share of private expenditure will ultimately lower the share of public expenditure. However, the size and significance of the coefficient are robust to both the inclusion of non-European countries in the sample as well as the inclusion of country- and year-dummies. Among the control variables, we find a positive association between GDP and PPE growth. The prevalence of overweight in the adult population as a proxy for chronic disease burden, and the density of MRI units as a proxy for technological progress have a minor positive association with PPE growth as well, though only when not including country- and year-dummies. In contrast, a higher density of generalist medical practitioners is associated with lower PPE growth, though the association is only significant in the extended sample when country- and year-dummies are included. For the remaining control variables we do not find statistically significant effects on PPE growth. ## Discussion Rising pharmaceutical expenditure puts a strain on publicly financed healthcare systems, an issue that is expected to again gain relevance in the future after a period of relative calm, not least due to novel therapeutic approaches gradually becoming available (Belloni et al., 2016). We analysed policies enacted in different countries over the course of 25 years in terms of their effectiveness to curb PPE growth for retail pharmaceuticals. Our empirical analysis suggests that reductions in retail PPE growth are achievable, both by patient-level cost-sharing schemes and demand-side control measures. But not all policy measures seem to have been equally successful, and what has worked in one country might not work as well in a different setting. While some of our findings are well in line with the literature like the positive link between GDP and PPE growth (Shaikh & Gandjour, 2019; Clemente et al., 2008; Okunade & Suraratdecha, 2006) or the negative link between patient cost-sharing and PPE growth, our results with respect to demand-side expenditure control measures are more unexpected. Only two physician-level behaviour expenditure control measures, electronic prescription and pharmaceutical budgets, have a statistically significant negative association with PPE growth, though the association depends both on the inclusion of country- and year-dummies in the statistical model as well as the sample choice. For information systems on prescription behaviour, we do not find an association with PPE growth in our analysis. Among system-level substitution measures aiming at promoting the use of generics (i.e. generic substitution and generic prescription), only generic substitution (with a one-year lag) has notable t-ratios and has a significant association with PPE growth in the extended sample when including country- and year-dummies. For system-level price-control measures in the form of reference price systems, no significant association with PPE growth is identified. The latter is a particularly interesting finding, as reference price systems are widely used in OECD countries and studies like Acosta et al. [ 2014] suggest that reference price systems can lead to a reduction of pharmaceutical expenditure in the short term by shifting use from cost-share drugs to reference drugs, although the authors themselves point to the low quality of the evidence. Our results hence suggest that the effectiveness of reforms depends on the timing and the context of their implementation, both of which is not adequately captured in meta-studies. Our results highlight that with a relatively large impact, patient cost-sharing has been an effective policy tool to curb the growth of PPE, though it comes with a certain risk for social equity and increasing costs in other sectors down the line. Over the last years, several countries have increased patient cost-sharing for retail pharmaceuticals, including France and Sweden (Belloni et al., 2016). Patient cost-sharing affects PPE in several ways. First, an obvious effect is that, ceteris paribus, higher cost-sharing increases the share of private expenditure in total expenditure on pharmaceuticals shifting the balance between public and private expenditure. Second, cost-sharing can also influence patients’ consumption of pharmaceuticals, i.e higher cost-sharing could reduce patients’ use of pharmaceuticals. There are two sides to this coin: while in some instances, decreased use of pharmaceuticals can be desirable (for instance, if patients do not ask for antibiotics in cases where use might not be appropriate), adverse effects can occur if adherence to treatment plans is lowered when drugs are less affordable due to higher cost-sharing (Austvoll-Dahlgren et al., 2008). The cost savings for PPE may hence be offset by increased costs of protracted treatment in other health-related budgets, e.g. when more expensive acute care is need that could have otherwise been avoided, as well as in other sectors, e.g. productivity losses through presenteeism or absenteeism. These adverse effects could be counterbalanced as patients might be more inclined to opt for lower-priced pharmaceuticals, i.e. generics, when the level of cost-sharing is high (Belloni et al., 2016). As empirical evidence from Korea suggests, increased cost-sharing can contribute to lowering per patient expenditure on pharmaceuticals without notably affecting utilisation cost-sharing system within a country next to the availability of generic alternatives. It is not an unreasonable conjecture that lump-sum payments per package do not cause such effects. Patient cost-sharing schemes nevertheless bear a high risk of adverse societal effects as the financial burden imposed on patients can be substantial, especially in lower-income countries (Vogler et al., 2019). Hence, patient cost-sharing is far from being a "one-size-fits-all" solution. Accordingly, a wide spectrum of different cost-sharing schemes exists across OECD countries (Barnieh et al., 2014; World Health Organization, 2018). We want to briefly discuss some limitations pertaining to data and study design that we consider important for the interpretation of our results. By aggregating cost control policies into the six principal categories, some informational content is necessarily lost. An example would be that reference price systems differ in details between countries. In our study design, we make a trade-off between informational content and usability of the policy variables to ensure that a meaningful interpretation of the results is possible. Insufficient aggregation limits the power of the statistical inference as the number of observations is reduced. In case policy variables coincide with country variables, the estimated significant policy effects could be masked by unobserved country-specific characteristics. Moreover, although our study design always provides a counter-factual for the different policy measures to be compared against, the estimated effects cannot be interpreted as causal. Several cost containment measures also had to be omitted from the analysis for practical reasons, for instance in case of pilot projects, or policies targeting only specific types of pharmaceuticals, or when the policy itself cannot be observed, like in the case of country-specific price differentials due to rebates. Espin et al. [ 2018] suggest that the latter in particular have a strong impact in the forecasting the growth of pharmaceutical expenditure. Indeed, confidential and complex discounts have become more widespread over recent years, but more often concern speciality drugs than primary care drugs (Morgan et al., 2017). We therefore expect a somehwat lower impact in the context of retail pharmaceuticals, which are the focus of our study. Another important aspect of our study approach is that we are only able to include explicitly formulated policies in contrast to implicit cost-control measures. For instance, a country may not follow a formal generic substitution policy as defined in our study, but could yet otherwise incentivise the use of generics. To a certain extent, these effects should, however, be absorbed by the addition of country-specific dummies in the WALS regression. There may arguably exist additional synergies between cost control measures included in the study and those that could not be included. However, an exploration of these is beyond the scope of our study approach. Along these lines, a potential path for future research would be to account for the fact that there often is not one standard way of implementing a certain type of policy (e.g. indicative versus mandatory generic prescription) and to focus cross-country panel-analysis on specific categories of policies to highlight best-practice examples, which may also allow exploration of synergies between individual policy measures. The sample selection through the requirement of sufficiently complete time series may also distort the estimates given the comparatively small number of countries under investigation. A clear identification of the impact of some policy variables is further limited by relying on a small number of country observations only. In view of the limitations concerning our study design, we stress that our results should be read as conflicting explorative empirical evidence rather than definite evidence in favor of discarding specific policy measures. A further limitation of our approach is the impossibility of distinguishing between PPE reductions due to desired or undesired effects, for instance when the level of patient cost-sharing is increased. Last but not least, we want to stress again that the data used in our analysis do not cover medication dispensed in hospitals, therefore only policies affecting retail pharmaceutical expenditure outside hospitals are analysed and results must be interpreted accordingly. Overall, improving data availability would greatly strengthen the evidence base for policy making. Policy makers must proceed with great care when implementing patient-level cost sharing schemes to ensure social equity. The importance of this aspect is vividly underlined by recent research of the WHO on unmet need and financial hardship in European healthcare systems (Thomson et al., 2019). To maximise impact while minimising adverse effects, reforms must be tailor-made to current circumstances and future developments within a country. That is, whether the level of patient cost-sharing is already high in a country to begin with, or if measures are in place to protect lower-income patients from adverse effects. Against this background, the introduction of demand-side measures targeting behaviour at the physician-level, or prices at the healthcare system-level, or fostering substitution of brand drugs with generics appears a safer choice regarding economic and social equity, even though their expected impact is likely more limited and may not be free of accompanying adverse effects either. ## A Appendix See Appendix Fig. 2.Fig. 2Timeline of the six principal categories of expenditure control measures per country in the data set ## References 1. Acosta A, Ciapponi A, Aaserud M, Vietto V, Austvoll-Dahlgren A, Kösters JP, Vacca C, Machado M, Ayala DHD, Oxman AD. 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--- title: Transcriptional profiling of the developing rat ovary following intrauterine exposure to the endocrine disruptors diethylstilbestrol and ketoconazole authors: - Indusha Kugathas - Hanna K. L. Johansson - Edith Chan Sock Peng - Maryne Toupin - Bertrand Evrard - Thomas A. Darde - Julie Boberg - Monica K. Draskau - Antoine D. Rolland - Séverine Mazaud-Guittot - Frédéric Chalmel - Terje Svingen journal: Archives of Toxicology year: 2023 pmcid: PMC9968686 doi: 10.1007/s00204-023-03442-2 license: CC BY 4.0 --- # Transcriptional profiling of the developing rat ovary following intrauterine exposure to the endocrine disruptors diethylstilbestrol and ketoconazole ## Abstract Exposure to endocrine-disrupting chemicals (EDCs) during development may cause reproductive disorders in women. Although female reproductive endpoints are assessed in rodent toxicity studies, a concern is that typical endpoints are not sensitive enough to detect chemicals of concern to human health. If so, measured endpoints must be improved or new biomarkers of effects included. Herein, we have characterized the dynamic transcriptional landscape of developing rat ovaries exposed to two well-known EDCs, diethylstilbestrol (DES) and ketoconazole (KTZ), by 3’ RNA sequencing. Rats were orally exposed from day 7 of gestation until birth, and from postnatal day 1 until days 6, 14 or 22. Three exposure doses for each chemical were used: 3, 6 and 12 µg/kg bw/day of DES; 3, 6, 12 mg/kg bw/day of KTZ. The transcriptome changed dynamically during perinatal development in control ovaries, with 1137 differentially expressed genes (DEGs) partitioned into 3 broad expression patterns. A cross-species deconvolution strategy based on a mouse ovary developmental cell atlas was used to map any changes to ovarian cellularity across the perinatal period to allow for characterization of actual changes to gene transcript levels. A total of 184 DEGs were observed across dose groups and developmental stages in DES-exposed ovaries, and 111 DEGs in KTZ-exposed ovaries across dose groups and developmental stages. Based on our analyses, we have identified new candidate biomarkers for female reproductive toxicity induced by EDC, including Kcne2, Calb2 and Insl3. ### Supplementary Information The online version contains supplementary material available at 10.1007/s00204-023-03442-2. ## Introduction Early life exposure to endocrine-disrupting chemicals (EDCs) can affect reproductive development and cause disease later in life. This holds true for both male and female reproductive health (Johansson et al. 2017; Skakkebaek 2017), although the evidence for a causal relationship is much stronger for male reproductive disorders. There is, however, increasing evidence to suggest that female reproductive health is more sensitive to EDCs than previously thought (Buck Louis et al. 2011; Johansson et al. 2017), especially within hormone-sensitive developmental stages during perinatal life; in mice and rats after birth (Johansson et al. 2021). Thus, it is critical to include these windows of development in chemical toxicity testing aiming to detect potential endocrine disruption in females. It is also critical that the tests that are used are sensitive enough for female reproductive toxicity, which may currently not be the case. Since rats are often used for in vivo reproductive toxicity testing of chemicals, it is important to consider differences in reproductive development between rats and humans. There are obvious temporal differences in ovarian development between the species, which also means that there are differences in susceptible windows of chemical exposure (Johansson et al. 2017). With regard to chemical safety assessments, a prevailing challenge is that current rodent test guidelines may not be sensitive enough to reveal endocrine-disrupting effects that could pose a risk to women’s reproductive health (Draskau et al. 2021; Johansson et al. 2021; OECD 2018). This is not necessarily because there is a lack of effects, but could instead be that the endpoints being assessed are not sensitive or specific enough for their intended purposes (Johansson et al. 2021). This, coupled with the fact that chemical testing and regulation regimens are aiming at rapidly reducing the use of animal testing for chemical safety assessment, means that we must identify appropriate mechanisms of action in order to employ the correct panel of tests in the future. Diethylstilbestrol (DES) and ketoconazole (KTZ) are well-characterized EDCs. In humans, prenatal exposure to the synthetic estrogen DES increases the incidence of reproductive tract cancers, impairs fertility and causes early menopause in daughters of mothers who have taken DES during pregnancy (Hatch et al. 2006; Hoover et al. 2011; Johansson et al. 2021; Laronda et al. 2012; Palmer et al. 2001; Steiner et al. 2010). KTZ, a pharmaceutical used to treat fungal infections, can perturb steroid hormone synthesis in humans and rodents by inhibiting various cytochrome P450 (CYP) enzymes of the steroidogenesis pathway (Kjærstad et al. 2010; Mason et al. 1987; Munkboel et al. 2019), potentially interfering with both androgen and estrogen synthesis and downstream signaling. KTZ has also been used to reduce the rate of folliculogenesis in women during fertility treatment (Gal et al. 1999; Parsanezhad et al. 2003). However, the mechanisms by which either DES or KTZ cause these adverse female reproductive effects are poorly understood. We recently performed an in vivo rat reproductive toxicity study to address the lack of sensitive markers for female reproductive toxicity (Johansson et al. 2021). Both DES and KTZ were used to answer some of the questions raised above. To complement and expand on this work, we used the Bulk RNA Barcoding-sequencing (BRB-seq) technology on rat developing ovaries to study the impact of perinatal exposure to DES and KTZ at the transcriptional level and to potentially identify biomarkers of exposure which might be used in future chemical testing strategies. We also performed a cross-species deconvolution-based analysis by combining the BRB-seq dataset with a mouse ovary developmental cell atlas composed of three single-cell RNA sequencing datasets. This was done to validate if the transcriptional changes we observed were indeed related to transcriptional variations or simply to changes in the ratio of different cell types that normally occur during ovary development. ## Chemicals Diethylstilbestrol (DES, CAS no. 56-53-1; purity \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}≥ $99\%$) and Ketoconazole (KTZ, CAS no. 65277-42-1; purity $98\%$) were purchased from Sigma-Aldrich and BOC Sciences Inc., USA, respectively. Corn oil was purchased from Sigma-Aldrich (cat.no. C8267-2.5 L) and used as control and vehicle. Solutions used for dosing of animals were stored in glass bottles in the dark at room temperature and continuously stirred during the dosing period. ## Animals and dosing Animal experiments had ethical approval from the Danish Animal Experiments Inspectorate (license number 2015-15-0201-00553) and were overseen by the in-house Animal Welfare committee. All methods were performed in accordance with relevant guidelines and regulations. The in vivo rat study was previously described in Johansson et al. [ 2021]. Briefly, time-mated Sprague–Dawley rats (Crl:CD(SD)) (Charles River Laboratories, Sulzfeld, Germany) were delivered on gestational day (GD) 3, with the day following overnight mating denoted GD1. Dams were weighed and assigned to treatment groups with similar body weight (bw) distributions on GD4. Animals were housed in standard conditions with 12 h light/dark cycles and fed a standard soy- and alfalfa-free diet based on Altromin 1314 (Altromin GmbH, Germany) along with ad libitum tap water in Bisphenol A-free bottles (Polysulfone 700 ml, 84-ACBT0702SU Tecniplast, Italy). Animals were housed in pairs until GD17, thereafter individually. Rat dams were exposed to DES or KTZ from GD7 until birth. Dams, and offspring following birth, were weighed and gavaged each morning with vehicle control or test substances. Impact of KTZ and DES exposures were studied at three distinct postnatal days (PND 6, 14, and 22) using three different doses of 3, 6 and 12 µg/kg bw/day for DES and of 3, 6 and 12 mg/kg bw/day for KTZ. Ovaries from a total of 159 rats were collected for analysis, giving 5–8 replicates for each experimental condition (Fig. 1A). Fig.1Experimental design, principal component analysis and statistical filtration. A Rat dams were orally exposed from gestational day (GD) 7 until birth day, then the offsprings until postnatal day (PND) 6, 14 or 22 via mother’s milk. Exposure doses were 3 (low), 6 (medium) and 12 (high) µg/kg bw/day of DES and 3 (low), 6 (medium) and 12 (high) mg/kg bw/day of KTZ. A total of 156 rat ovaries were collected and sequenced 79 from DES exposure groups and 77 from KTZ exposure groups. B Projection on a two-dimension PCA-based space of preprocessed sample data. Ovaries exposed to DES are represented with squares and those exposed to KTZ with triangles. Three colors differentiate the three different stages, PND6 in blue, PND14 in green and PND22 in pink. A color gradient differentiates the doses within each group; lightest for control group and darkest for highest dose group. The first dimension (Dim 1) explains around $41\%$ of variance, discriminating samples according to postnatal days. C Differentially expressed genes (DEGs) during rat ovary development and after exposures, analyzed by BRB-seq by comparing controls at PND6, PND14 and PND22 and controls vs low, medium, high doses of DES (3, 6 and 12 µg/kg bw/day) or KTZ (3, 6 and 12 mg/kg bw/day). A total of 1254 DEGs were detected by applying 3 filtration steps for each comparison: (i) the detectable cutoff (0.43); (ii) the fold-change cutoff (1.5) and (iii) the adjusted F value (0.05). These genes were then separated into 1137 DEGs during ovary development, 184 DEGs after exposure to DES and 111 DEGs after exposure to KTZ. The number of DEGs found in each condition is indicated in the next row, and the two following rows representing the number of over- or under-expressed DEGs for each condition. Diethylstilbestrol (DES), ketoconazole (KTZ), bodyweight (bw). ( color figure online) ## RNA extraction Total RNA was extracted using an RNA/DNA extraction kit (Qiagen, Germany) following manufacturer’s instructions. For PND6 samples, two ovaries from two littermates were pooled for RNA extraction. For PND14 and PND22, only one ovary from one littermate was used for RNA extraction. RNA quantity was assessed using a NanoDrop™ 8000 Spectrophotometer (Thermo Fisher Scientific), and RNA quality using a 2100 Bioanalyzer Instrument (Agilent Technologies, CA, USA) according to manufacturer’s instructions. Only samples with an RNA integrity number (RIN)-score > 7 were included for sequencing. ## BRB-seq library preparation and sequencing The 3’ Bulk RNA Barcoding and sequencing (BRB-seq) (Alpern et al. 2019) experiments were performed as previously described (Draskau et al. 2021; Giacosa et al. 2021). Briefly, RNAs from ovaries for all treatment groups were distributed onto two 96-well plates, referred to as plate #1 and plate #2. A first step of reverse transcription and template switching reactions was performed using 4 µL total RNA at 2.5 ng/µL and sample-specific barcoded oligo-dT. Subsequently, cDNAs from each plate were pooled, purified and double-strand (ds) cDNAs were synthesized by PCR. The two corresponding sequencing libraries were next built by tagmentation using 50 ng of ds cDNA with the Illumina Nextera XT Kit (Illumina, #FC-131-1024) following the manufacturer’s recommendations. The two resulting libraries were finally sequenced on a NovaSeq sequencer as Paired-End 100 base reads, following Illumina’s instructions by the IntegraGen Company (https://integragen.com/fr/). Image analysis and base calling were performed using RTA 2.7.7 and bcl2fastq 2.17.1.14. Adapter dimer reads were removed using DimerRemover (https://sourceforge.net/projects/dimerremover/). ## Data preprocessing and quality control Briefly, the first reads (R1) contained 16 bases that were required to have a phred quality score higher than 10. Among these, the first 6 bases corresponded to the unique sample-specific barcode that was needed to de-multiplex the sequencing data, while the following 10 bases corresponded to a unique molecular identifier (UMI) that was used for quantification. The second reads (R2) were aligned to the rat reference transcriptome from the UCSC website (release rn6, downloaded in August 2020) using BWA version 0.7.4.4 with the parameter “−l 24”. Reads mapping to several positions in the genome were filtered out from the analysis. After quality control and data preprocessing, a gene count matrix was generated by counting the number of unique UMIs associated with each gene in lines for each sample in columns. The UMI matrix was further normalized with the regularized log (rlog) transformation package implemented in the DeSeq2 package (Love et al. 2014) (Fig. S1A). Raw and preprocessed data were deposited at the GEO repository under the accession number GSE208545 (Edgar et al. 2002). After quality controls, the number of samples was reduced to 156. ## Differential gene expression analysis Principal component analysis (PCA) was performed with the FactoMineR (Lê et al. 2008) package implemented in R v4.0.3. Differentially expressed genes (DEGs) were identified based on the following statistical comparisons: (i) PND6 controls vs PND14 controls vs PND22 controls; (ii) controls vs DES-exposed samples at PND6, 14 and 22 and at each dose; (iii) controls vs KTZ-exposed samples at PND6, 14 and 22 at each dose. To avoid potential batch effects between different plates, the comparison of controls at distinct developmental stages was performed using samples from plate #1 only, as these included more replicates. The comparison of KTZ- and DES-exposed samples with their respective controls was performed using plate #1 and plate #2, respectively. For each comparison, sequential filtration steps (Fig. S1A) were applied: (i) the median gene expression value of all the samples was used as a background cutoff; (ii) a fold-change cutoff of at least 1.5; and, (iii) a statistical filtration with the Linear Models for MicroArray data (LIMMA) package (Smyth 2004) and a p value cutoff of 0.05 adjusted with the Benjamini and Hochberg method (Benjamini and Hochberg 1995). Finally, heatmap representations of DEGs were generated with the R package pheatmap. Spot plots were generated with the FlexDotPlot package (Leonard et al. 2022). The resulting transcriptomic signatures were deposited at the TOXsIgN repository under the accession number TSP1269 (https://toxsign.genouest.org/) (Darde et al. 2018). ## Clustering and functional analysis The resulting lists of DEGs were clustered into distinct gene expression patterns with the K-means algorithms. A Gene Ontology term enrichment analysis was performed with the Annotation Mapping Expression and Network (AMEN) suite (Chalmel and Primig 2008). A specific annotation term was considered significantly enriched in a given gene expression pattern when the False Discovery Rate (FDR)-adjusted p value (Fisher’s exact probability) was ≤ 0.05 and the number of associated genes was \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}≥ 2. KEGG pathways were visualized using the pathview function implemented in R (Luo and Brouwer 2013). ## Assembly of a mouse ovary developmental cell atlas The analysis of three single-cell RNA sequencing (scRNA-seq) datasets (Long et al. 2022; Meinsohn et al. 2021; Wang et al. 2020) was performed using the Seurat v4.0.1 (Hao et al. 2021) package in R (Fig. S1B). Doublets were filtered out independently in each individual sample using the DoubletFinder R package v.2.0.2 (McGinnis et al. 2019). Cells with less than 1000 UMI, 500 genes and more than $15\%$ of mitochondrial content were removed. Data for each individual dataset were normalized using the NormalizeData and the SCTransform (by regressing out for the mitochondrial, ribosomal and cell cycle genes) functions implemented into Seurat. The three datasets were then integrated using the RPCA function implemented into Seurat. A principal component analysis (PCA) was performed with the RunPCA function based on the top-3000 most varying genes by excluding mitochondrial, ribosomal and cell cycle genes. Next, cells were projected into a two-dimensional space with the Uniform Manifold Approximation and Projection (UMAP) method implemented into the RunUMAP function based on the top-50 principal components. Cells were then clustered with the FindNeighbors and FindClusters functions with default parameters. Each cell cluster was associated with ovarian cell types based on known marker genes complemented by markers identified by the R package Presto (Korsunsky et al. 2019). A total of 10 broad cell types corresponding to 36 clusters were identified. ## Deconvolution analysis The MuSiC R package was used for the deconvolution analysis (https://github.com/xuranw/MuSiC) (Wang et al. 2019). MuSiC is a method that utilizes cell type-specific gene expression from scRNA-seq data to characterize cell type compositions from bulk RNA-seq data in complex tissues (Wang et al. 2019). For the subsequent analysis, each bulk sample was deconvoluted to estimate the proportion of each individual cell type described in the mouse ovary developmental cell atlas. Briefly, count matrices of scRNA-seq data and BRB-seq data were used as input data. To assist the deconvolution algorithm with cell type-specific genes conserved across mice and rats, the scRNA-seq matrix was reduced to only variable genes with unique mouse-to-rat ortholog, i.e., one-to-one relationship as described in the set of homologs available on Ensembl v105 (Howe et al. 2021). The deconvolution analysis was performed on each cell cluster described in the mouse ovary developmental cell atlas (Fig. 2A). The proportions of ten broad cell types (including coelomic/surface epithelial cells, interstitial cells, theca cell, granulosa cells, steroidogenic granulosa cells, germ cells, endothelial cells, immune cells, erythrocytes and perivascular cells) were estimated by summing the predicted proportions of their associated cell clusters. Statistical comparisons were performed using the Wilcoxon rank-sum test. Fig.2Transcriptional characterization of perinatal ovary development. A Uniform manifold approximation and projection (UMAP) representation of single-cell atlas of the developing mouse ovary comprising 35,040 cells divided into 36 cell clusters and showing 10 broad ovarian cell types, including germ cells, immune cells, coelomic/surface (C/S) epithelial cells, theca cells, perivascular cells, interstitial cells, erythrocytes, endothelial cells, granulosa cells and steroidogenic granulosa cells. B Boxplot showing predicted cell proportions of the eight most prominent ovarian cell types after deconvolution using single-cell data. Control samples were from the plate containing KTZ-exposed samples (Plate #1). Statistical comparisons were performed using the Wilcoxon rank-sum test, with asterisks indicating statistical significance. ns = non-significant; p ≤ 0.05; p ≤ 0.01; *p ≤ 0.001. Coelomic/surface epithelial cells (C/S epit. cells), interstitial cells (Interst. cells), granulosa cells (Gran. cells), steroidogenic granulosa cells (SG cells), endothelial cells (Endo. cells). C Heatmap representation of DEGs during rat ovary development revealed by BRB-seq through comparisons of control ovaries at PND6, PND14 and PND22. In total, 1137 DEGs were detected and grouped into 3 expression patterns (O1–O3) underpinning rat ovary development. Each row represents a gene and each column a time point. The color code indicates scaled gene expression values. GO terms enrichment analysis revealed biological processes terms significantly associated with each expression pattern, which are indicated on the right with associated number of genes and p value. Besides, enriched cell types based on corresponding markers are provided for each pattern. ## A cross-species deconvolution-based strategy confirms significant changes in ovarian cellularity during perinatal development To further characterize toxicological effects in the developing rat ovaries exposed to DES and KTZ (Johansson et al. 2021), we sought to analyze potential transcriptional changes using the BRB-seq technology. Total RNA from 156 postnatal ovaries from 12 experimental groups were sequenced, comprising 3 dose groups each of DES- and KTZ-exposed rats with respective control groups, at 3 distinct developmental stages: PND6, PND14 and PND22 (Fig. 1A). Following data normalization, the 156 transcriptomes were projected on a 2D PCA-based space whose first component explaining $41\%$ of the variation (x-axis) clearly differentiated samples according to the three developmental stages (Fig. 1B). Notably, the two first components of the PCA analysis could not discriminate between samples based on exposure scenarios. We next performed a cross-species deconvolution analysis to predict the relative proportion of distinct ovarian cell types in each BRB-seq sample. Since rat scRNA-seq datasets describing the developing ovary were not available at the time of the analysis, we instead assembled a mouse ovary developmental cell atlas. This atlas was composed of nine samples ranging from embryonic day 16.5 (E16.5) to adulthood by integrating control samples from three studies (Long et al. 2022; Meinsohn et al. 2021; Wang et al. 2020). After quality controls, the number of detected cells per sample ranged from 610 to 16,202 (Fig. S2A). The median number of detected genes per cell ranged from 917 to 5738. The final scRNA-seq dataset contained 35,040 cells that were projected on a 2D space (UMAP) (Fig. S1B). Cells were next partitioned into 36 cell clusters (termed c1–c36) (Fig. S2B) that were associated with ten broad cell types based on specific marker genes (Fig. S2, panels C–D, Table S3). Germ cells (associated with five cell clusters including c4, c6, c14, c23, and c26) were identified based on the expression of Ddx4, Dazl, Sycp3 and Kit (Fig. S2 C, D). Four clusters (c10, c12, c34, and c36) were classified as coelomic or surface epithelial cells as they expressed canonical markers such as Upk3b and Krt19. Height clusters (c1, c5, c7, c8, c11, c13, c28, and c31) were associated with granulosa cells and two others (c19, c24) with steroidogenic granulosa cells based on the expression of Fst, Foxl2, Nr5a1, Wnt6, and Runx1 as well as genes encoding steroidogenic enzymes (such as Cyp17a1, Cyp19a1, Cyp11a1, Star, and Hsd3b1). Eight clusters (c2, c3, c16, c15, c21, c30, c33, and c35) were associated with interstitial cells among which two corresponded to theca cells (c33, c35) based on the expression of Pdgfra, Tcf21, Dcn and Ptch1. The remaining clusters were classified as endothelial cells based on Cd34 and Pecam1 (c20), perivascular cells based on Pdgfrb and Acta2 (c29), erythrocytes based on Snca and Alas2 (c9, c17, c18, and c27), and finally immune cells based on Ptprc (c22, c25, and c32). With the exception of a difference in the germ cell population at PND14, no significant difference in overall cell numbers were observed between the two BRB-seq plates (#1 and #2), which reflects good reproducibility within the study (Fig. 2B, Fig. S3). When comparing cell populations in control ovaries from plate #1 across developmental stages, a shift in overall cell type proportion for almost all cell populations was detected: (i) a decrease in proportion of germ cells (− $81\%$, 5.8e-4); (ii) a decrease in proportion of coelomic/surface epithelial cells (− $65\%$, 4.1e−3) and interstitial cells (− $66\%$, 5.8e−4) concomitant with, (iii) an increase in the proportion of granulosa (+ $53\%$, 5.8e−4), steroidogenic granulosa (+ $600\%$, 5.8e−4) and theca cells (+ $67\%$, 7.3e−2); and finally, (iv) variations in immune (+ $78\%$, 2.3e−3) and endothelial (+ $340\%$, 4.7e−3) cell populations (Fig. 2B). ## Temporal changes in the rat ovary transcriptome between PND6 and PND22 Statistical comparisons of control ovaries at distinct developmental stages, or control samples to either DES- or KTZ-exposed samples, identified 1254 significantly differentially expressed genes (DEGs) (Fig. 1C, Table S1) among which 1137 were differentially expressed during ovarian development(Figs. 1C, 2C, Table S1). DEGs related to the developing ovary were subsequently clustered into three expression patterns (named O1–O3) showing transcriptional transitions across developmental stages (Fig. 2C). The functional analysis of those expression patterns highlighted 479 enriched biological processes and 22 pathways (Table S2, Fig. 2C). Briefly, the O1 expression pattern displayed highest expression at PND6 and was significantly enriched in genes associated with ‘reproductive process’ (118 genes, adjusted p value 6.5e−12), gamete generation (55, 1.4e−5), ‘oogenesis’ (14, 1.3e−3) and ‘oocyte differentiation’ (12, 6.2e−4), as well as ‘cell cycle’ (84 genes, adjusted p value 5.1e−5), ‘cell division’ (43, 6.6e−6) and ‘meiotic cell cycle’ (27, 1.1e−5). As expected, O1 included well-known oocyte/germline-related genes such as Ddx4, Sycp1, Syce1, Syce2, Hsf2bp, Dazl, Lhx8 and Nlrp5 (Table S2). Functional analysis based on markers identified from scRNA-seq data demonstrated O1 to be significantly enriched for markers of germ cells (220, 1.5e−16), coelomic/surface epithelial cells (103, 6.5e−16), interstitial cells (102, 3.4e−22), granulosa cells (76, 1.2e−4), erythrocytes (40, 1.4e−2), theca cells (39, 9.0e−3), endothelial cells (29, 1.2e−3) and perivascular cells (21, 1.2e−3). Genes belonging to O2 were most highly expressed at PND14, were significantly associated with terms such as ‘response to hormone’ (21, 2.8e−3) or ‘small molecule metabolic process’ (34, 1.2e−7) and were enriched in marker genes for steroidogenic granulosa cells (28, 1.3e−5), granulosa cells (27, 5.4e−8), coelomic/surface epithelial cells (14, 3.1e−2) and immune cells (10, 2.8e−2). Finally, the O3 expression pattern showed highest expression at PND22 and was enriched in biological processes related to ‘response to stress’ (139, 3.3e−7), ‘regulation of apoptotic process’ (69, 1.1e−5), ‘developmental process’ (203, 2.0e−6), ‘cell differentiation’ (134, 5.0e−4), and ‘cell migration’ (64, 2.3e−4). It was enriched with markers for steroidogenic granulosa cells (136, 9.0e−35), granulosa cells (123, 3.5e−42), coelomic/surface epithelial cells (81, 1.3e−17), immune cells (53, 8.0e−14), interstitial cells (51, 9.1e−8), theca cells (50, 4.8e−12) and endothelial cells (22, 5.8e−4) (Table S2, Fig. 2C). ## DES exposure induces significant transcriptional changes in the developing rat ovary The cross-species deconvolution analysis was performed on the DES transcriptomic data (plate #2) to estimate whether DES exposure could induce changes to ovary cellularity. The relative proportion of each cell type was statistically compared between DES samples and their corresponding control samples at each exposure dose (low = 3 µg/kg bw/day; medium = 6; high = 12) and at each developmental stage (PND6, PND14 and PND22) (Fig. S4). With the exception of a decrease of coelomic/surface epithelial cells (−$46\%$, 1.7e−2) and a slight increase of granulosa cells (+ $17\%$, 3.8e−2) at PND6 in the highest dose, this analysis did not reveal any other significant changes. The pairwise comparisons between ovaries exposed to DES and control samples at each developmental stage, and at each dose, identified 184 DEGs (Fig. 1C, Fig. 3A, Table S1) of which 62, 71 and 128 showed significant changes at the low, medium and high doses, respectively. Conversely, out of the 184 DEGs, 91, 85 and 27 genes showed significant changes at PND6, PND14 and PND22, respectively. The resulting DEGs were further clustered into six expression patterns (named D1–6) which were subjected to a functional analysis (Fig. 3A, Table S2). Patterns D1 and D2 comprised 59 genes that were over-expressed following DES exposure, but not enriched in specific biological processes. Patterns D3, D4 and D5 comprised 113 genes that were under-expressed after DES exposure. These DEGs were significantly associated with 178 biological processes and six pathways (Fig. 3A, Table S2), including ‘response to stress’ (42, 3.4e−3), ‘response to external stimulus’ (36, 3.6e−3),’response to hormone’ (35, 2.6e−11) and ‘developmental process’ (62, 9.1e−4). Pattern D6 was composed of genes with a mixed expression pattern; i.e., showing overexpression at PND14 and underexpression at PND22, but were not enriched in specific biological processes. In clusters D1 and D3 at PND6, some gene transcripts stood out with interesting expression profiles specific to DES exposure. In cluster D1, expression of Dusp10 and Homer2 were upregulated in all dose groups, and Tex101 in two dose groups (Fig. 3A, Table S1). In cluster D3, Rspo1 expression was decreased in all dose groups, whereas Sycp3 and Wt1 were downregulated in two dose groups (Fig. 3A, Table S1). These* genes could be potential candidate biomarkers for estrogen-like activities. The functional analysis based on single-cell, cell-specific markers revealed that the under-expressed patterns (D3–5) were significantly enriched for markers of coelomic/surface epithelial cells (27, 1.4e−8), granulosa cells (21, 1.6e−4), interstitial cells (16, 7.1e−4), steroidogenic granulosa cells (22, 4.7e−3), immune cells (10, 2.0e−2) and perivascular cells (5, 4.0 e−2). Together, these results indicate that the decrease of coelomic/surface epithelial cell markers might be due to the slight decrease of the coelomic/surface epithelial cell proportion observed at PND6 with the cross-species deconvolution analysis. Fig. 3Heatmap representation of differentially expressed genes (DEGs) induced by DES and KTZ. A DES exposure induced 184 DEGs in the ovaries at postnatal days 6, 14 and 22 at 3 different exposure doses (3, 6 and 12 µg/kg bw/day), and were grouped into 6 expression patterns (D1–D6). B 111 DEGs induced by KTZ exposure induced 111 DEGs at postnatal days 6, 14 and 22 at 3 different exposure doses (3, 6 and 12 mg/kg bw/day) and were grouped into 8 expression patterns (K1–K8). Each row represents a gene and each column a combination of time and dose. The color scale indicates scaled log2 fold-change values. The matching biological processes and cell types are shown on the right, with the associated number of genes and p value. DEGs that are common between DES and KTZ exposure are indicated in black on the left of the heatmap. ## KTZ exposure induces significant transcriptional changes in the developing rat ovary With the exception of a decrease in interstitial cells at the lowest dose (−$42\%$, 2.9e−2) and at the highest dose (−$58\%$, 1.8e−2) at PND22, the cross-species deconvolution analysis of the KTZ transcriptomic data (plate #1) did not reveal any other significant variations in relative cell proportions (Fig. S5). The transcriptomic analysis identified 111 DEGs showing an altered expression after exposure to KTZ, including 36, 53 and 49 genes at the low, medium and high doses, respectively (Fig. 1C, Fig. 3B, Table S1). The majority of the transcriptional changes were observed at the two highest exposure doses. Among the 111 DEGs, 43, 40 and 40 genes showed an altered expression level at PND6, PND14 and PND22, respectively. Contrary to DES, transcriptional changes induced by KTZ exposure were not linear dose-responses in the current study. The 111 DEGs were next partitioned into eight expression clusters (termed K1–K8) (Fig. 3B). Clusters K1, K2, K3 and K4 comprised 54 genes over-expressed at PND6, PND14, PND14-22 and PND22, respectively. Among the genes specifically altered after exposure to KTZ, Hormad1 in K1 and Sox9 in K4 were consistently over-expressed whatever the dose at PND6, and could therefore be considered as potential candidate biomarkers for exposure to steroidogenic inhibitors. The functional analysis did not reveal any enriched terms. Conversely, expression patterns K5, K6, K7 and K8 included 57 genes under-expressed at PND6, PND14, PND14–22 and PND22, respectively. These DEGs were significantly associated with nine biological processes (Table S2) related to ‘response to hormone’ (14, 3.6e−2), ‘hormone metabolic process’ (7, 2.7e−2) and ‘response to gonadotropin’ (5, 2.7e−2). The cell type functional analysis did not reveal any cell type enrichment. ## Transcriptional signature comparison of DES and KTZ points to candidate biomarkers of exposure We next sought to identify potential biomarkers for sensitive, reliable and reusable endpoints related to female reproductive toxicity that may be used for future chemical hazard identification or safety assessments. The transcriptomic analysis of perinatal ovaries exposed to either DES or KTZ identified 184 and 111 DEGs, respectively. Of these, 35 were affected by both DES and KTZ (Fig. 4A) which were subsequently partitioned into four clusters (termed P1–4) (Fig. 4B, Table S1). Genes belonging to P1 (15 genes) were downregulated after exposure to DES and KTZ. Conversely, cluster P2 (3 genes) included genes that were upregulated. Cluster P3 comprised 10 genes that were predominantly downregulated after exposure to DES, but upregulated after exposure to KTZ. Finally, genes from P4 showed a complex expression pattern for which it is difficult to identify common characteristics. Functional analysis of the 35 shared DEGs identified 64 enriched biological processes (Table S2, Fig. 4B), including ‘lipid metabolic process’ (12, 2.2e−3), ‘hormone metabolic process’ (7, 4.7e−4), ‘response to hormone’ (13, 3.3e−4), ‘gonad development’ (6, 1.2e−2), ‘sex differentiation’ (6, 2.1e−2) or ‘reproductive process’ (11, 2.9e−2). The overwhelming majority of those terms were specifically associated with P1 (Fig. 4B). Pathway analysis revealed that ovarian steroidogenesis was significantly associated with P1 (3, 1.1e−2). Indeed, the expression patterns of the three genes Cyp17a1, Cyp19a1 and Lhcgr of the ovarian steroidogenesis pathway were similarly affected by DES and KTZ exposures, whereas the expression of several other genes encoding for steroidogenic enzymes (i.e., Star, Hsd3b3, Hsd17b1 and Akr1c15) were only altered by one of the two chemicals (Fig. S6). This suggests that expression, and potentially function, of critical steroidogenic enzymes are negatively affected by DES and KTZ exposure during rat ovary development. Among the 35 shared DEGs, 23 were found in the mouse ovary developmental cell atlas, with 13 corresponding to marker genes associated with specific cell type populations (Fig. S7, Table S3). Crem and Stc1 were associated with theca cells, Por and Ptgds with coelomic/surface epithelial cells, Cyp17a1, Cyp19a1, Lhcgr and Dhcr7 with steroidogenic granulosa cells, and Nup214 and Zfp703 with germ cells. Ccn1 is associated with coelomic/surface epithelial cells, interstitial cells and perivascular cells, Drosha with germ and steroidogenic cells, and finally Alas1 with immune and granulosa cells. Importantly, three regulated genes belonging to P2 (Kcne2, Dhrc7, Akr1b7) were identified as being upregulated at several doses and developmental stages in both DES and KTZ. We also identified Insl3 in P1, which tended to be under-expressed whatever the dose of either DES or KTZ. These up- or downregulated genes might be good candidate markers of exposure to investigate in the near future. Fig. 4Identification of shared differentially expressed genes (DEGs) induced by DES and KTZ. A Intersection plot indicating the number of DEGs shared by DES and KTZ over-expressed (D1–D2 and K1–K4), under-expressed (D3–D5 and K5–K8) or mixed expression patterns (D6). B Heatmap representation of the 35 shared DEGs after exposure to DES and KTZ, which were grouped into four expression patterns (P1–P4). The number of DEGs in each cluster is indicated on the left of the heatmap. The matching GO terms are shown on the right with the associated number of genes and p value. The color code indicates scaled log2 fold-change values. Genes that are found in the single-cell data are indicated in bold. Those that were also found as cell type markers are indicated with asterisks ## Discussion With the use of BRB-seq technology, we have analyzed transcriptional changes in the developing rat ovary following exposure to two well-known endocrine disruptors DES and KTZ. These analyses were conducted to complement our previous in vivo toxicity study (Johansson et al. 2021) where we reported on reproductive effect outcomes, but with a specific aim to potentially identify sensitive biomarkers of ovarian dysgenesis. We first performed a cross-species deconvolution analysis to identify changes to rat ovarian cellularity during early postnatal development. The relative proportion of germ cells was drastically reduced between PND6 and PND22, concomitant with a relative decrease in interstitial cells, and an increase in theca cells. These changes in tissue cellularity correspond with known biological events occurring during ovary development (Picut et al. 2015), for instance the death of the first wave of follicles (McGee et al. 1998) and the occurrence of a second wave of follicle formation by around PND14 (Picut et al. 2015). Despite massive follicular atresia, we also observed a relative increase in the granulosa cell proportion, which could be explained by the continuous recruitment of subsequent waves of follicle growth rather than a bias of the deconvolution-based analyses. Notably, cross-species differences between rats and mice could introduce some limitations with respect to deconvolution using the mouse single-cell dataset; however, our approach allowed for a broad classification of genes consistent with known cell types of both species. In addition, it identified steroidogenic granulosa cells, consistent with known profiles of the first waves of follicle growth in rats (Guigon et al. 2003; Mazaud et al. 2002), mice and humans (François et al. 2017), suggesting that cross-species comparisons was a valid approach in this instance. With respect to ovaries from rats exposed to either DES or KTZ, we did not observe any significant changes in overall cell type numbers when compared to control ovaries. Thus, with the exception of coelomic/surface epithelial cell numbers that were slightly decreased at PND6 in the DES-exposed ovaries, we surmised that any observed changes to transcript levels in the exposed ovaries were most likely due to bona fide effects on gene expression and not simply a consequence of changes to tissue cellularity. In other words, we have demonstrated that a cross-species deconvolution approach can confirm a good correspondence between mouse and rat developmental windows between PND0 to PND14 (Cardoso-Moreira et al. 2019). Given the critical importance of the rat model in toxicology, however, it would be valuable to assemble a rat ovary developmental cell atlas to further improve such deconvolution-based analyses. During the course of postnatal ovarian rat development, we found 1137 DEGs divided into 3 expression patterns (O1–3) showing consecutive peak expression at PND6, PND14 and PND22. Each pattern was associated with specific processes including oogenesis and gamete generation in O1 at PND6, response to hormone in O2 and O3 from PND14 onwards, and response to stress and apoptotic process in O3 at PND22. This is consistent with the high relative ratio of oocytes at PND6, the growth of healthy follicles based on a high rate of proliferation of granulosa cells at PND14 with differentiating theca, and with the beginning of follicle atresia at PND22 (Mazaud et al. 2002; Picut et al. 2015). Noteworthy, the PND6-22 window of development encompasses the first waves of follicle growth, which were already shown to be atypical (François et al. 2017; Mazaud et al. 2002). Although not the focus of this study, these developmentally regulated genes display a pattern consistent with what is known about the program underlying ovary development in the rat and thus attest to the robustness of our BRB-seq dataset. Most DEGs induced by DES exposure were significantly differentially expressed at PND6 (91 DEGs) and PND14 (85 DEGs), and less at PND22 (27 DEGs). This suggests either an age-related effect with more marked effect at earlier stages, or a cell type-related effect, considering the evolution of cell type ratios in the postnatal ovary. Our results also indicate dose-dependent effects of DES, with a higher dose corresponding to a higher number of transcriptional alterations in the developing ovary. In addition, the majority of the DEGs were downregulated after DES exposure (113 out of 184 DEGs). Functional analysis of these revealed a direct response to external stimuli, including response to hormone signaling, stress responses and general cell differentiation. In the absence of gross changes in ovarian cellularity, as shown by deconvolution analyses, these responses could be indicative of disrupted perinatal development. Interestingly, several genes whose expression has already been described as atypical in the development of the first waves of follicular growth in immature rats were deregulated at PND14 (Guigon et al. 2003; Mazaud et al. 2002). Genes displaying lower expression levels in exposed ovaries typically associate with the first wave of follicle growth in granulosa cells (e.g., Inhbb, Cyp19a1, and Inhba) and theca cells (Star, Cyp17a1, Cyp11a1, Lhcgr, and Insl3). Since this effect peaked at PND14, it suggests that this first follicular wave may have been altered by DES exposure, which would be consistent with almost $50\%$ of the downregulated genes known to be involved in developmental processes. In general, the early effect on the transcriptome by DES is associated with the enrichment in genes involved in development, which is consistent with the in vivo effects observed on immature rat ovaries (Johansson et al. 2017). Thus, potential biomarkers of estrogenic effects, selected at PND6, could include Homer2, Dusp10, Tex101, Sycp3, Wt1 and Rspo1, as they were all affected by exposure to DES for at least two doses. Compared to DES, KTZ induced a comparable low number of transcriptional changes at PND6 (43 DEGs), PND14 [40] and PND22 [40], with only a few genes intersecting between the groups, i.e., Sox9 and Ccn1, suggesting stage-dependent modes of action. Functional analysis revealed dysregulation of genes involved in the response to hormones such as Cyp17a1, Fdx1, Por, Cyp19a1 and Lhcgr. KTZ also had much less effect on genes associated with atypical expression in the first waves of follicular growth in immature rats than what was observed for DES. For example, Cyp19a1 and Inhba, both of which are strongly expressed in the granulosa cells of the first waves, were not significantly affected by KTZ with the exception of reduced expression of Cyp19a1 at PND22. This may suggest that the first wave of follicular growth is maintained, but may disappear prematurely, or that its steroidogenic function contributing to the E2 spike is affected specifically. Differentiation of theca cells was also affected by KTZ, with under-expression of genes such as Cyp17a1, Fdx1, Por, or Insl3 from PND14. This suggests an effect on the endocrine function of the first waves of follicle growth rather than the growth itself, since the deconvolution analysis revealed little effects on overall cellularity of the ovaries. Such subtle alterations may not massively modify the maturation of the whole ovarian brain axis, and subsequent puberty regulation, in these KTZ-exposed animals (Johansson et al. 2021), consistent with the absence of impact of disrupted first wave of follicular growth on puberty parameters (Mazaud et al. 2002). Promising potential biomarkers of exposure to steroidogenesis inhibitors were identified at PND6, and include the over-expressed Hormad1, which is a critical protein involved in meiosis I checkpoint (Shin et al. 2013) and Sox9, which can be found in adult mouse theca interna cells of pre-antral/antral follicles (Notarnicola et al. 2006) and is also found to be affected after exposure to triticonazole (Draskau et al. 2022). Comparison of both DES and KTZ transcriptomic signatures identified 35 common genes that were further partitioned into four expression patterns (P1–P4). While gene ontology consistently showed an enrichment of genes associated with response to drugs, we also found that most of the common DEGs associated with ovarian steroidogenesis. This suggests that ovarian steroidogenesis is highly sensitive to chemicals in immature rat ovaries, at least at the transcript level. While some genes displayed very distinct transcriptional changes when comparing DES and KTZ exposures (expression patterns P3 and P4), others displayed reasonably similar transcriptional profiles after exposure to DES and KTZ (expression patterns P1 and P2). This makes them promising potential biomarkers of endocrine disruption. Expression pattern P1 included 15 genes downregulated after exposure to DES and KTZ, and it could thus be interpreted as a loss of cellular function. It includes well-known genes encoding for protein involved in steroid biosynthesis (Cyp17a1, Cyp19a1, Fdx1, Lhcgr and Por) (Yazawa et al. 2019), gonad development (Ahsg, Insl3, Lhcgr, and Ptx3) (Chartrain et al. 1984; Fisher et al. 1998; Mack et al. 2000; Scarchilli et al. 2007; Zhang et al. 2001; Zimmermann et al. 1999), or the calcium signaling factor Calb2, which may also be involved in steroidogenesis (Schwaller 2014). Calb2 has for long been known to be expressed in the ovaries of rodents and humans (Bertschy et al. 1998; Lugli et al. 2003; Pohl et al. 1992), but its function remains unclear. Notably, expression mainly localizes to the androgen-producing theca cells (Bertschy et al. 1998; Pohl et al. 1992) and in rats, there is a surge in Calb2 at around PND19 corresponding to theca cell recruitment and activation of the hypothalamus–pituitary–gonadal (HPG) axis (Picut et al. 2015). Correspondingly, Calb2 is expressed by androgen-producing Leydig cells of the testis (Altobelli et al. 2017; Strauss et al. 1994), where it has been suggested to be involved in the regulation of steroidogenesis (Xu et al. 2018). We recently identified Calb2 as a putative biomarker for female reproductive toxicity by performing a proteomics screen on rat ovaries exposed during development to a mixture of environmental chemicals (Johansson et al. 2020). Subsequently, we have observed dysregulated Calb2 expression in fetal rat testis exposed to flusilazole (Draskau et al. 2021). Based on these observations, it would be of interest to scrutinize further if Calb2 could serve as a broad biomarker for gonadal toxicity, especially pertaining to perturbed steroidogenesis and reproductive function. Expression pattern P2 included Kcne2, Dhcr7 and Akr1b7 that were all upregulated in several exposure groups (several doses and developmental stages). Kcne2 encodes an ion transmembrane transport and voltage-gated potassium channel protein (Kundu et al. 2008) involved in cardiac arrhythmia (Abbott 2012; Papanikolaou et al. 2021). While its role during gonad development is not clearly established, several potassium channels are known to participate in the regulation of progesterone secretion (Kim et al. 2020). Dhcr7 encodes an enzyme involved in the cholesterol biosynthesis (Nakanishi et al. 2021). It has been identified as a biomarker of exposure to KTZ and DES in human adult ovarian cortex cultures (Li T et al. Unpublished). Finally, Akr1b7, which is involved in lipid detoxification process (Volle et al. 2004), is a major protein of the vas deferens in rodents (Baumann et al. 2007). Together, Kcne2, and to a lesser extent Dhcr7 and Akr1b7, appear to be robust candidate biomarkers of ED exposure, being all upregulated after exposure to DES and KTZ, while Inls3 was robustly under-expressed after exposure to these drugs, whatever the age analyzed. ## Conclusion We previously showed that developmental exposure to DES and KTZ could induce expected endocrine-disrupting effects in exposed dams and male offspring, but not in female offspring. We surmised that this lack of effect outcomes had as much to do with the measurements being insensitive to detecting endocrine disruption in rodent toxicity studies as with the chemicals not affecting reproductive parameters in female offspring. By combining large-scale transcriptomic screening using BRB-seq with a deconvolution approach employing scRNA-seq datasets to discriminate bona fide changes to gene transcription from changes in ovary cellularity, we found the perinatal rat ovaries (PND 6-22) to be sensitive to perturbation by both DES and KTZ. We identified a suit of potential biomarkers of ovarian dysgenesis, some of which were common between the two chemicals. Many of the genes should be scrutinized further for their potential utility as biomarkers, both in combination and singularly. In particular, we consider Kcne2, Calb2 and Insl3 as highly interesting genes to investigate in additional rodent toxicity studies testing endocrine disruptors for potential impacts on the developing ovaries. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 Workflow of the bulk (A) and single-cell (B) RNA-seq analyses. A) Flowchart of the methods used for the bulk RNA-seq analysis. Blue boxes represent data format. Grey boxes represent methods used to pass each format. B) Flowchart of the methods used for the single-cell RNA-seq analysis. Blue boxes represent data format. Grey boxes represent methods used to pass each format (PDF 84 KB)Supplementary file2 Mouse ovary single-cell atlas data. A) Representation of the number of detected cells per sample (left) and the median number of detected genes per cell for each sample (right). The color gradient increases with the developmental stage. B) Distribution of developmental stages (left) and studies (right) per cluster. C) Spot plot representation of the expression of cell type markers across single-cell clusters. The size of a dot represents the percentage of cells in which a specific gene was detected for a given cluster, while its color represents the scaled expression value, according to the scale bars. D) UMAP representation of the expression of cell type markers. C/S = Coelomic/Surface; SG = Steroidogenic granulosa (PDF 1693 KB)Supplementary file3 Deconvolution analysis of control samples. Boxplots showing predicted proportions of the eight most prominent ovarian cell types after deconvolution of bulk RNA-seq data. Control samples were from the plate containing DES exposed samples (Plate #2). Statistical comparisons were performed using the Wilcoxon rank-sum test, with asterisks indicating statistical significance. ns = non significatif; p ≤ 0.05; p ≤ 0.01; *p ≤ 0.001. Coelomic/Surface epithelial cells (C/S epit. cells), Interstitial cells (Interst. cells), Granulosa cells (Gran. cells), Steroidogenic granulosa* cells (SG cells), Endothelial cells (Endo. cells) (PDF 55 KB)Supplementary file4 Deconvolution analysis on Diethylstilbestrol (DES) exposed samples. Boxplots showing predicted proportions of the eight most prominent ovarian cell types after exposure to DES at PND6, PND14 and PND22. Statistical comparisons were performed on control samples vs low, medium or high doses using Wilcoxon rank-sum test and the asterisks indicate statistical significance. ns = non significatif; p ≤ 0.05; p ≤ 0.01; *p ≤ 0.001 (PDF 103 KB)Supplementary file5 Deconvolution analysis* on Ketoconazole (KTZ) exposed samples. Boxplots showing predicted cell proportions of the eight most prominent ovarian cell types after exposure to KTZ at PND6, PND14 and PND22. Statistical comparisons were performed on control samples vs low, medium or high doses using Wilcoxon rank-sum test and the asterisks indicate statistical significance. ns = non significatif; p ≤ 0.05; p ≤ 0.01; *p ≤ 0.001 (PDF 97 KB)Supplementary file6 Ovarian steroidogenesis* pathway significantly affected by DES and KTZ. The molecules studied are represented in two columns for each gene (DES on the left and KTZ on the right), so that we can follow the impact of the molecules on the expression of genes involved in ovarian steroidogenesis. Genes significantly affected by DES are indicated with an orange circle, while those affected by KTZ are indicated with a green circle. We chose to show the general impact of the molecules regardless of the developmental stage or dose. The color-code indicates log2 fold-change value (PDF 126 KB)Supplementary file7 Representations of scaled single-cell average expression values of the common DEGs. A) Spot plot representation of scaled single-cell average expression values in each cluster of the common DEGs. The size of a dot represents the percentage of cells in which a specific gene was detected for a given cluster, while its color represents the scaled expression value, according to the scale bars. Only genes retrieved in the single-cell study are represented. Those that were also found as cell type markers are indicated with asterisks. B) UMAP representation of scaled single-cell average expression values of the common DEGs. The color represents the scaled expression value, according to the scale bars in (A). Only genes retrieved in the single-cell study are represented. Those that were also found as cell type markers are indicated with asterisks (PDF 15338 KB)Supplementary file8 (XLSX 494 KB)Supplementary file9 (XLSX 110 KB)Supplementary file10 (XLSX 2228 KB) ## References 1. Abbott GW. KCNE2 and the K (+) channel: the tail wagging the dog. Channels (2012) 6 1-10. PMID: 22513486 2. Alpern D, Gardeux V, Russeil J, Mangeat B, Meireles-Filho ACA, Breysse R, Hacker D, Deplancke B. BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing. Genome Biol (2019) 20 71. PMID: 30999927 3. Altobelli GG, Pentimalli F, D’Armiento M, Van Noorden S, Cimini V. 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--- title: Mono(2-ethylhexyl) phthalate induces transcriptomic changes in placental cells based on concentration, fetal sex, and trophoblast cell type authors: - Samantha Lapehn - Scott Houghtaling - Kylia Ahuna - Leena Kadam - James W. MacDonald - Theo K. Bammler - Kaja Z. LeWinn - Leslie Myatt - Sheela Sathyanarayana - Alison G. Paquette journal: Archives of Toxicology year: 2023 pmcid: PMC9968694 doi: 10.1007/s00204-023-03444-0 license: CC BY 4.0 --- # Mono(2-ethylhexyl) phthalate induces transcriptomic changes in placental cells based on concentration, fetal sex, and trophoblast cell type ## Abstract Phthalates are ubiquitous plasticizer chemicals found in consumer products. Exposure to phthalates during pregnancy has been associated with adverse pregnancy and birth outcomes and differences in placental gene expression in human studies. The objective of this research was to evaluate global changes in placental gene expression via RNA sequencing in two placental cell models following exposure to the phthalate metabolite mono(2-ethylhexyl) phthalate (MEHP). HTR-8/SVneo and primary syncytiotrophoblast cells were exposed to three concentrations (1, 90, 180 µM) of MEHP for 24 h with DMSO ($0.1\%$) as a vehicle control. mRNA and lncRNAs were quantified using paired-end RNA sequencing, followed by identification of differentially expressed genes (DEGs), significant KEGG pathways, and enriched transcription factors (TFs). MEHP caused gene expression changes across all concentrations for HTR-8/SVneo and primary syncytiotrophoblast cells. Sex-stratified analysis of primary cells identified different patterns of sensitivity in response to MEHP dose by sex, with male placentas being more responsive to MEHP exposure. Pathway analysis identified 11 KEGG pathways significantly associated with at least one concentration in both cell types. Four ligand-inducible nuclear hormone TFs (PPARG, PPARD, ESR1, AR) were enriched in at least three treatment groups. Overall, we demonstrated that MEHP differentially affects placental gene expression based on concentration, fetal sex, and trophoblast cell type. This study confirms prior studies, as enrichment of nuclear hormone receptor TFs were concordant with previously published mechanisms of phthalate disruption, and generates new hypotheses, as we identified many pathways and genes not previously linked to phthalate exposure. ### Supplementary Information The online version contains supplementary material available at 10.1007/s00204-023-03444-0. ## Introduction Phthalate plasticizers are ubiquitous chemicals in the man-made environment that have been linked with a host of adverse pregnancy outcomes (Lucaccioni et al. 2021). Exposure to phthalates occurs through a variety of sources including personal care products, food packaging, toys, pharmaceuticals, and medical equipment (Tuan Tran et al. 2022). Phthalates have been classified as endocrine-disrupting chemicals due to their ability to interact with hormone receptors and cause changes to hormone concentrations and activity (Sathyanarayana et al. 2014; Engel et al. 2017; Beg and Sheikh 2020). Many studies have explored the association between gestational phthalate exposure and adverse pregnancy and birth outcomes as previously reviewed (Lucaccioni et al. 2021). Specifically, prenatal phthalate exposure has been associated with perinatal health outcomes including decreased anogenital distance in male infants (Swan et al. 2005) and increased odds of preterm birth (Ferguson et al. 2014, 2019). It has also been associated with disruptions to key hormones involved in fetal reproductive development as well as the regulation of parturition, including decreased maternal serum testosterone concentration (Sathyanarayana et al. 2014), increased maternal serum estrone and estradiol concentrations (Sathyanarayana et al. 2017), decreased second trimester corticotropin-releasing hormone (Cathey et al. 2019), and altered human chorionic gonadotropin expression (Adibi et al. 2017).The placenta has been investigated as a regulator of these adverse outcomes in a number of studies, highlighting the need for a better understanding of how phthalates affect placental physiology (Warner et al. 2021). As a fetal organ that is unique to the gestational period, the placenta plays a role in mediating outcomes of pregnancy and fetal development through nutrient and oxygen exchange as well as hormone production and signaling (Burton and Fowden 2015). The placenta is the primary barrier between mother and fetus, and protects the fetus from environmental exposures, such as smoking, air pollution, and chemicals, including endocrine disruptors like phthalates (Vrooman et al. 2016; Everson and Marsit 2018). The effects of maternal phthalate exposure on the placenta have been extensively studied in humans, animals, and cells as reviewed by Warner et al. and Strakovsky and Schantz (Strakovsky and Schantz 2018; Warner et al. 2021). Most of these studies evaluate phthalates by studying their metabolites, as phthalate parent compounds are quickly degraded through a two-step metabolism involving hydrolysis and conjugation followed by excretion in urine (Frederiksen et al. 2007). Due to the rapid metabolic transformation of phthalates, the monoester and subsequent metabolites are the primary species that the fetus is exposed to and believed to cause adverse effects in humans (Zhang et al. 2021). The initial hydrolysis steps of phthalate metabolism are carried out by lipase and esterase enzymes in the intestine and parenchyma, so most in vitro studies of placental exposure to phthalates utilize the metabolites rather than the parent compound (Frederiksen et al. 2007; Strakovsky and Schantz 2018). In addition to being measurable in human urine, phthalates have also been recorded in maternal and cord blood indicating that phthalates are able to cross the placenta and enter fetal circulation (Latini et al. 2003; Li et al. 2013; Maekawa et al. 2017; Caserta et al. 2018). Studies that have directly measured phthalates in maternal and fetal placental perfusate (Mose et al. 2007) or placental tissue (Poole and Wibberley 1977) have also concluded that phthalates can cross the placenta. RNA sequencing is a discovery-based methodology that can reveal genes and pathways perturbed by environmental exposures and be used to study relationships between prenatal exposures and birth or later life health outcomes (Lapehn and Paquette 2022). Recently, we evaluated the maternal urinary concentrations of 16 phthalate metabolites in the second and third trimester of pregnancy with the placental transcriptome at birth in the CANDLE study, a cohort based in Memphis, TN ($$n = 760$$). This study identified 38 differentially expressed genes (DEGs) associated with four phthalate metabolites across the second and third trimester, as well as several fetal sex-specific gene and phthalate associations (Paquette et al. 2021). To date, this has been one of only two studies evaluating the association between urinary phthalate concentrations during pregnancy and placental mRNA or long non-coding RNA (lncRNA) in placentas at birth in humans (Machtinger et al. 2018; Paquette et al. 2021). Mono(2-ethylhexyl) phthalate (MEHP), the most commonly studied metabolite of di(2-ethylhexyl)phthalate (DEHP), showed the highest number of associations with gene expression including ten lncRNAs in Machtinger et al. ( Machtinger et al. 2018). We also identified several sex-specific associations in gene expression in relation to maternal urinary concentrations of MEHP in our prior analysis of phthalate metabolites and the placental transcriptome in the CANDLE study (Paquette et al. 2021). Though these two studies provide strong evidence for phthalate-induced gene expression changes in the placenta, the observational nature of the studies does not allow for causal conclusions. While there have been several in vitro studies on the effects of phthalates on the placenta during pregnancy, most of these studies have been limited in deriving mechanisms of toxicity due to evaluating expression of only a small subset of candidate genes (Tetz et al. 2013, p. 201; Wang et al. 2016; Meruvu et al. 2016b; Adibi et al. 2017; Strakovsky and Schantz 2018; Zhang et al. 2020a; Warner et al. 2021). To date, there has only been a single in vitro study utilizing RNA sequencing technology to evaluate the effect of phthalates in placental cells which used trophoblast stem cells from a rhesus monkey, limiting translation of the results to human pregnancies (Midic et al. 2018). Because epidemiological assessment of the placenta is most commonly performed within bulk placental tissue, in vitro assessment of the placenta presents a unique opportunity to assess the cell type-specific responses to environmental exposures, since the placenta is a heterogenous tissue comprising multiple trophoblast cell types with distinct functions in placental physiology. Cytotrophoblasts (CTBs) and syncytiotrophoblasts (STBs) are both villous trophoblast cells with CTBs serving as precursors that develop into multi-nucleated STBs, which reside as the outer layer of the placental villi where they act as the primary exchange surface of the placenta (Farah et al. 2020). Extravillous trophoblasts (EVTs) are an invasive trophoblast cell type that embed the placenta in the decidual wall and assist in spiral artery remodeling (Farah et al. 2020). This research aims to expand upon the knowledge of phthalate metabolite-induced transcriptome changes in the placenta from human studies by evaluating the causality between phthalate exposure and gene expression changes using in vitro methodology. This study performed RNA sequencing following exposure to phthalate metabolite MEHP, in both immortalized (HTR-8/SVneo) and primary placental cells that are representative of two different placental cell types and trimesters of origin (HTR-8/SVneo: 1st trimester EVTs and primary cells: term syncytiotrophoblasts). We also compare the findings of our differential gene expression analysis and pathway analysis to the results of the CANDLE study to identify similarities in phthalate-induced differences between bulk placental tissue and in vitro models of the placenta. Results of this work will advance knowledge of phthalate-induced changes to the placental transcriptome, while providing additional evidence for cell type-specific responses that cannot be easily assessed in human studies of bulk placental tissue. ## Cell culture The first trimester EVT cell line, HTR-8/SVneo, was obtained from ATCC (#CRL-3271, batch: 70016636, obtained: January 2021). HTR-8/SVneo cells were cultured at 37 °C with $5\%$ CO2 and ambient O2 in six-well tissue culture dishes between passages 4–7 using RPMI-1640 with L-glutamate supplemented with $10\%$ fetal bovine serum (FBS), $1\%$ penicillin–streptomycin (P–S), 1 mM sodium pyruvate, and 10 mM HEPES. The HTR-8/SVneo cells were grown to at least $60\%$ confluence prior to treating with MEHP. Placental villous tissue samples were derived from non-pathological term pregnancies (> 37 weeks gestation) from women who delivered via elective cesarean section in the absence of labor from the Labor and Delivery Unit at Oregon Health & Sciences University (OHSU). Exclusion criteria included maternal BMI > 25, maternal age < 18 or > 40 years, any current pregnancy complications (preeclampsia, gestational diabetes, or chorioamnionitis) and current smokers. Placentas were collected and weighed immediately following cesarean section. Five random samples of tissue (~ 80 g) were collected from each placenta and stored in PBS. The chorionic plate and decidua were removed from each placental sample, leaving only villous tissue, which was thoroughly rinsed in PBS to remove excess blood. Primary cytotrophoblasts were isolated from villous tissue using a protocol adapted from Eis et al. using trypsin/DNAse digestion, followed by density gradient purification (Eis et al. 1995). Isolated cytotrophoblast cells (5–10 × 106 cells /ml) were then frozen in freezing media ($10\%$ DMSO in FBS) and stored in liquid nitrogen. Placentas were collected into a tissue repository under a protocol approved by the OHSU Institutional Review Board with informed consent from the patients. All tissues and clinical data were de-identified before being made available to the investigative team. Primary cytotrophoblast cells were cultured in IMDM with $10\%$ FBS and $1\%$ penicillin/streptomycin. Cells were plated at ~ 1.5 × 106 cells per well in 12-well plates and given 24 h to adhere prior to changing the media to remove non-adherent cells. Syncytialization occurs spontaneously and was confirmed under a light microscope at 48 h (Online Resource 1). Primary syncytiotrophoblast cells were incubated at 37 °C with $5\%$ CO2 and ambient O2 in 12-well plates. ## Phthalate metabolite treatment MEHP (Sigma Aldrich; St. Louis, MO) was prepared in $100\%$ DMSO as a 180 mM stock solution that was stored at – 20 °C prior to treatment. HTR-8/SVneo and primary trophoblast cells were each treated with DMSO ($0.1\%$) or one of three final concentrations (1 µM, 90 µM, and 180 µM) of MEHP with three replicates per concentration. For the primary syncytiotrophoblast cells, cells from three male and three female placentas were used per treatment group with each replicate representing the placenta of a unique individual. There were no statistical differences in maternal BMI, maternal age, or gestational length between male and female samples (Table 1). Cells were incubated with dosed media for 24 h at 37 °C. MEHP was added to cultures at the 48 h time point and incubation continued for an additional 24 h at 37 °C before removal of media and isolation of RNA from cells. Table 1Maternal BMI, maternal age, and gestational length for primary syncytiotrophoblast samplesMaleFemaleMaternal BMI (kg/m2)20.7, 22.3, 24.222.6, 23.3, 24.0Maternal age (years)32.0, 35.0, 39.032.0, 35.0, 35.0Gestational length (weeks)37.9, 38.1, 39.139.0, 39.0, 39.0 ## RNA isolation, library preparation, and sequencing RNA from HTR-8/SVneo cells was isolated by a phenol:chloroform extraction using TRIzol. Primary trophoblast RNA isolation was performed with the Zymo Direct-zol RNA Miniprep kit (Zymo Research, Irvine, CA), following the manufacturer’s instructions after lysing cells in 350µL TRIzol. Library preparation and paired-end RNA sequencing were performed by Novogene, Inc. (Beijing, China). Novogene prepared libraries with high-quality RNA after measuring the RNA integrity number (RIN) > 7 with the Agilent High-Sensitivity RNA Screentape Assay (Agilent Technologies; Santa Clara, CA). Sample library preparation was performed using the SMARTer Stranded Total RNA-Seq Kit v2 (Takara Bio Inc.; Kusatsu, Japan) for primary syncytiotrophoblast cells, followed by paired-end sequencing (150BP) using an Illumina HiSeq with a read depth of 30 million read pairs/sample. Library preparation for HTR-8/SVneo cells was performed using an optimized NEBNext Ultra II kit, followed by paired-end sequencing (150BP) with a NovaSeq 6000 and a read depth of 30 million read pairs/sample. ## RNA sequencing analysis Transcript abundances were estimated using the pseudo-alignment program kallisto with bias correction (Bray et al. 2016) and condensed to Ensembl Gene IDs using tximport (Soneson et al. 2015). Filtering was performed to remove genes with a mean log CPM < 0, resulting in a final dataset which included 13,379 protein coding and lncRNA transcripts for HTR-8/SVneo, 15,784 transcripts for the combined primary syncytiotrophoblasts, 15,182 transcripts for male primary syncytiotrophoblasts, and 16,358 transcripts for female primary syncytiotrophoblasts. Normalization was performed using the trimmed mean of M-values (Robinson and Oshlack 2010). Differentially expressed genes between treatment groups compared to DMSO controls were identified using generalized linear models implemented within edgeR (Chen et al. 2016). Dispersion parameters were estimated using the Cox–Reid method, and then differentially expressed genes were identified using the quasi-likelihood F-tests, which is more robust and produces more reliable error rates when the number of replicates is small (Lun et al. 2016). Regression models for primary cells included precision variables such as RNA integrity number (RIN) and Sample ID, which minimized interindividual variation and sex differences in our full model (not stratified for sex). Genes were considered differentially expressed at a Benjamini–Hochberg false discovery rate (FDR) < 0.05. We also compared the genes that were differentially expressed in our cell models to lists of genes that are over-expressed in the placenta or specific to the placenta via the Human Protein Atlas which categorizes genes based on their degree of tissue specificity or over-expression (Uhlén et al. 2015). ## Pathway analysis Pathway analysis was performed using the self-contained gene set testing method Fry to identify non-disease-associated pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa and Goto 2000; Wu et al. 2010). Pathways with FDR < 0.05 were considered significant. Pathway analysis using rotational gene set testing identified 174 significant (FDR < 0.05) KEGG pathways for 90 µM and 180 µM MEHP across HTR-8/SVneo and primary syncytiotrophoblast cells. Most pathways were significant only within a single dose group (Fig. 3a). In HTR-8/SVneo cells, there were 21 pathways that were significant following both 90 µM and 180 µM MEHP treatment (Online Resource 4), with 16 pathways showing directional concordance (Fig. 3b). The calcium signaling pathway was increased for both concentrations and had the lowest average FDR value. Pathway analysis of primary syncytiotrophoblast cells identified 20 significant pathways for 90 µM MEHP and 2 significant pathways for 180 µM MEHP with a single pathway—ascorbate and aldarate metabolism, significant across the two MEHP concentrations (Fig. 4). A single pathway—terpenoid backbone synthesis—was significantly increased following 180 µM MEHP treatment in the male samples only. Comparing the significant KEGG pathways between HTR-8/SVneo and primary syncytiotrophoblast cells, there were 11 pathways that were significantly altered in relation to at least one MEHP concentration in each cell type (Fig. 5). Of these shared pathways, two (glycerolipid metabolism and regulation of actin cytoskeleton) showed directional concordance across cell types and concentrations. Fig. 3HTR-8/SVneo pathway analysis performed by self-contained gene set testing with Fry using KEGG pathways. a Number and directional concordance of significant (FDR < 0.05) KEGG pathways. Pathways shared across multiple concentrations are indicated by striped shading and pathways unique to a single concentration are indicated by solid shading. Orange bars represent increased pathways and teal bars represent decreased pathways. b Shared KEGG pathways between MEHP 90 µM and MEHP 180 µM annotated by KEGG categoriesFig. 4Significant (FDR < 0.05) KEGG pathways for MEHP 90 µM and 180 µM identified by self-contained gene set testing with Fry for all primary syncytiotrophoblast cellsFig. 5Shared KEGG pathways in HTR-8/SVneo and primary syncytiotrophoblast cells identified through self-contained gene set testing with Fry that were significant (FDR < 0.05) for at least one MEHP concentration in each cell type ## Transcription factor enrichment analysis Transcription factor (TF) enrichment analysis was performed using Enrichr with the ENCODE/ChEA consensus TFs from ChIP-X (Chen et al. 2013; Kuleshov et al. 2016; Xie et al. 2021). TFs with an FDR < 0.05 were considered significant. ## Results We treated two placental cell lines (Primary and HTR-8/SVneo cells) with three different concentrations of MEHP based on a comprehensive review of existing literature surrounding phthalates in the placenta. HTR-8/SVneo was selected for use in this study because it is not derived from a choriocarcinoma, and it represents an extravillous trophoblast phenotype that differs from the other primary syncytiotrophoblast model utilized in this study. A literature review of phthalate exposure in placental cell lines revealed that HTR-8/SVneo is one of the most commonly used cell lines for this work, thus increasing cross-study comparability (Online Resource 2) (Xu 2005; Xu et al. 2006, 2016; Tetz et al. 2013, 2015; Wang et al. 2016; Meruvu et al. 2016a, b; Pérez-Albaladejo et al. 2017; Gao et al. 2017; Petit et al. 2018; Shoaito et al. 2019; Zhang et al. 2020b; Du et al. 2020). To reduce the influence of gestational age and labor status on gene expression in the primary placental cells, we only used cells derived from full-term (> 37 weeks) deliveries by cesarean section. Our sex-stratified analysis was roughly matched on maternal age and BMI showing no significant differences across groups. 90 µM and 180 µM MEHP concentrations were selected to be comparable to previous studies of MEHP exposure in placental cells (Xu 2005; Tetz et al. 2013; Wang et al. 2016; Meruvu et al. 2016a, b; Gao et al. 2017), while the lower MEHP concentration (1 µM) was within the range of MEHP measured in maternal urine in the CANDLE study (1.1 × 10–4 µM to 2.2 µM) (Paquette et al. 2021). The selected concentrations are also in alignment with a recent assessment of placental phthalate concentrations in the CANDLE study, which reported mean placental MEHP concentrations of 20.4 µM in a subset of CANDLE participants ($$n = 50$$) (Liang et al. 2022). ## Differentially expressed genes MEHP treatment in HTR-8/SVneo EVT cells induced a higher number of differentially expressed genes (DEGs, FDR < 0.05) with increasing MEHP concentrations (Fig. 1a). In total, there were 34 DEGs for 1 µM MEHP, 1606 DEGs for 90 µM MEHP, and 3894 DEGs for 180 µM. 63 of the 4091 HTR-8/SVneo DEGs were considered placenta specific, enhanced or enriched, based on the human protein atlas (Online Resource 3). One placenta-enhanced gene (KISS1) was significantly increased across all three MEHP concentrations in the HTR-8/SVneo cell line and was also significant, but with decreased expression at the 180 µM MEHP concentration in the primary cells. Across all three concentrations of MEHP treatment, there were more genes with increased expression than decreased expression compared to DMSO controls. 28 genes were significantly affected by all MEHP concentrations (Fig. 1b). Of these genes, 27 were upregulated, with 18 genes exhibiting a positive dose response (Fig. 1c). Across the DEGs identified at each concentration, 19 lncRNAs were altered with 90 µM treatment, and 77 lncRNAs were altered with 180 µM MEHP treatment. Fig. 1HTR-8/SVneo differentially expressed genes (DEGs). a Number and directionality of DEGs (FDR < 0.05) for MEHP 1 µM, 90 µM, and 180 µM in HTR-8/SVneo cells ($$n = 3$$/concentration). b Venn diagram showing DEG overlap across MEHP concentration groups (FDR < 0.05) in HTR-8/SVneo cells. Created with Biorender.com. c Heatmap of log-fold change (LogFC) for 28 shared DEGs across MEHP 1 µM, 90 µM, and 180 µM concentrations Primary syncytiotrophoblasts were assessed for DEGs in a combined ($$n = 6$$) and sex-stratified analysis ($$n = 3$$ Male, 3 Female). Overall, 552 unique DEGs were associated with at least one of three MEHP concentrations, and 55 of these DEGs were considered enriched, enhanced, or specific to the placenta based on the human protein atlas. Of particular note, there were four genes expressed only in the placenta (based on the HPA) that were significantly decreased by at least one concentration of MEHP in the primary cells including PSG3, PSG9, LGALS13, and PSG8 (Online Resource 3). In the combined analysis, there was also a higher number of DEGs with increasing MEHP concentration (Fig. 2a). The sex-stratified analysis revealed dimorphisms in transcriptional response to MEHP, with male samples having a higher number of DEGs with increased MEHP concentration, while female samples showed a non-monotonic dose response with the highest number of DEGs in the 90 µM group (Fig. 2b). Comparison of DEGs across the combined and sex-stratified analysis revealed 35 DEGs significant only within the male-stratified analysis and 12 DEGs significant only within the female-stratified analysis (Fig. 2c). Three genes (FABP4, STRIP2, HMGCS2) were significantly altered by 90 µM and 180 µM concentrations in the combined and both sex-stratified analyses (Fig. 2c). After treatment with 90 µM MEHP, 11 lncRNAs were statistically significant in the combined analysis and 2 in the male-specific analysis, while after treatment with 180 µM MEHP 7 lncRNAs were statistically significant in the combined and 1 lncRNA in the male-specific analysis. A list of DEGs from each cell type are available in Online Resource 3.Fig. 2Primary syncytiotrophoblast differentially expressed genes (DEGs). a Number and directionality of DEGs (FDR < 0.05) for MEHP 1 µM, 90 µM, and 180 µM in primary syncytiotrophoblast cells ($$n = 6$$/concentration). b Number and directionality of DEGs (FDR < 0.05) for MEHP 1 µM, 90 µM, and 180 µM in primary syncytiotrophoblast cells stratified by sex (female $$n = 3$$/concentration, male $$n = 3$$/concentration). c UpSet plot showing the overlap of DEG groups across full primary syncytiotrophoblast data ($$n = 6$$ 90 µM and 180 µM, $$n = 5$$ 1 µM) and sex-stratified primary syncytiotrophoblast data (female $$n = 3$$, male $$n = 3$$) ## Transcription factor analysis Transcription factor (TF) enrichment analysis was performed using Enrichr, with TFs characterized from the ENCODE/ChEA consensus TF library. Enrichr identified potential TF regulators of our DEG lists by performing an overrepresentation test based on TF binding sites within the target gene list. In HTR-8/SVneo cells, 74 TFs were enriched for DEGs associated with 90 µM MEHP treatment and 80 TFs were significantly enriched for DEGs associated with 180 µM MEHP treatment (Fig. 6a). There were 73 TFs that were significantly enriched from both concentrations, with 5 TFs (HNF4A, AR, ESR1, PPARG, PPARD) defined as ligand-inducible nuclear hormone receptors, based on the IUPHAR/BPS Guide to Pharmacology (Alexander et al. 2021). The top three significant TFs were MAX, MYC, and SIN3A for 90 µM MEHP and MAX, MYC, and NFYB for 180 µM MEHP. A full list of TFs significantly enriched within the HTR-8/SVneo cell analysis can be found in Online Resource 5. In primary syncytiotrophoblasts, 3 TFs were significantly enriched for 90 µM MEHP and 13 were significantly enriched for 180 µM MEHP (Fig. 6b). In the sex-stratified analysis, no TFs were significantly enriched following MEHP treatment in females. The male-stratified analysis, however, identified two significant TFs (PPARG, PPARD) enriched for 90 µM MEHP and six TFs (NFE2L2, PPARG, TCF3, SALL4, GATA1, SMAD4) enriched for genes treated with 180 µM MEHP. There were no TFs enriched after treatment with 1 µM MEHP in either cell type. Fig. 6Transcription factor enrichment analysis by Enrichr using the ENCODE/ChEA consensus TF library for HTR-8/SVneo (a) and primary syncytiotrophoblast cells (b). a Scatterplot of shared TFs for MEHP 90 µM and 180 µM in HTR-8/SVneo cells plotted by percent of downstream gene targets that were identified as significant for each corresponding TF. Orange labeled TFs are nuclear hormone receptors. b Bubble plot of all significant (FDR < 0.05) for primary syncytiotrophoblast TFs Four ligand-inducible nuclear hormone receptor TFs (PPARD, PPARG, ESR1, AR) were significantly enriched following MEHP treatment for at least one MEHP concentration in both cell types. PPARG and PPARD were also enriched for at least one MEHP concentration in the male-stratified analysis. Changes in gene expression associated with DEGs downstream of these TFs in at least two cell type or concentration groups are shown in Fig. 7. PPARG, PPARD, and AR primarily regulated genes which had increased expression after treatment with MEHP, while ESR1 regulated genes which had decreased expression after treatment with MEHP.Fig. 7Heatmap of the LogFC for significant (FDR < 0.05) downstream genes of enriched nuclear hormone receptor TFs (ESR1, PPARG, PPARD, AR) that were in at least two cell/concentration groups. Gray-shaded cells are genes that were not significant for that treatment group and cell line. The full list of genes is presented in Online Resource 3 ## Comparison to the CANDLE cohort In our previous evaluation of associations between the placental transcriptome and phthalate metabolites in the CANDLE cohort ($$n = 760$$), 12 genes were significantly associated (FDR < 0.05) with the second trimester urinary MEHP concentrations in males alone (Paquette et al. 2021). Three of these 12 genes (NEAT1, ANKRD10, PHLDB2) were also significantly associated with MEHP treatment in HTR-8/SVneo and/or primary syncytiotrophoblast cells in this in vitro study of MEHP. There were no KEGG pathways that were associated with MEHP after adjustment for multiple comparisons, but there were 20 pathways associated with MEHP that were marginally significant with an unadjusted p value less than 0.05 (Online Resource 6). Comparing the pathway analysis results of our two cell lines to those from CANDLE, we found that of the 20 pathways ($p \leq 0.05$) for the CANDLE MEHP data, 17 were fully significant (FDR < 0.05) in our analysis in relation to at least one MEHP concentration in HTR-8/SVneo and/or primary syncytiotrophoblast cells. Two of these pathways (Vasopressin-regulated water reabsorption and mitophagy) were found in at least one CANDLE time point and one MEHP concentration for each cell line. For both pathways, there was decreased directional concordance for the CANDLE samples and primary syncytiotrophoblasts, while HTR-8/SVneo cells showed increased expression for these pathways, highlighting potential differences across placental trophoblast subtypes. ## Discussion Phthalate metabolite MEHP’s effects on the placenta have been extensively studied through epidemiological work and candidate gene expression studies; however, only recently have MEHP’s global effects across the placental transcriptome been evaluated through RNA sequencing. To date, there has only been one study that assessed the complete mRNA and lncRNA placental transcriptome in a large human population (Paquette et al. 2021). To our knowledge, the current in vitro study represents the first transcriptome-wide study of the placental response to MEHP using commercial and primary placental cell lines. Through this study, we identified DEGs following exposure to three concentrations of MEHP in two cell types, evaluated the effect of fetal sex on gene expression in primary syncytiotrophoblast cells, contextualized the biological context of DEGs through pathway analysis, and investigated upstream causes of gene expression changes through transcription factor enrichment analysis. Our results highlight that MEHP exposure causes gene expression changes that are unique to MEHP concentration, fetal sex, and placental cell type. MEHP exposure caused substantial changes in gene expression in both HTR-8/SVneo and primary syncytiotrophoblasts with 4,091 and 552 total genes that were affected in each respective cell type. In HTR-8/SVneo cells, there were 28 genes that were significantly affected by MEHP treatment at all three concentrations with all but one (NLRP1) exhibiting increased expression. NLRP1 has not previously been associated with MEHP exposure, but the NLRP1 inflammasome has been implicated as part of a combined inflammation/autophagy response to oxidative stress in HTR-8/SVneo cells (Li et al. 2021). Placental exposure to MEHP or its parent compound DEHP have both been linked with a variety of oxidative stress end points indicating a potential link between MEHP and NLRP1 expression (Martínez-Razo et al. 2021). The three genes with the highest average log fold change after MEHP exposure were MMP1, ESM1, and KRTAP2-3. These three genes, which were not significant in the primary syncytiotrophoblast cells, may be related to extravillous trophoblast cell function captured within the HTR-8/SVneo cells. Two important functions of EVT cells during the first trimester of pregnancy include invasion of the maternal decidua and spiral artery remodeling (Burton and Jauniaux 2015). MMP1 is a matrix metalloproteinase that is involved in the breakdown of extracellular matrices, which may help EVTs to move and embed themselves within the decidual wall (Chahar et al. 2021). KRTAP2-3 is a keratin-associated protein. Keratins are intermediate filament components of the cytoskeleton that help EVT cells invade the decidua and the spiral arteries (Gauster et al. 2013). ESM1 is an endothelial cell-specific molecule, frequently termed endocan that is involved in the process of angiogenesis. Although a specific angiogenic role for ESM1 in the placenta is not known, it could be involved in spiral artery remodeling and its expression levels have been associated with numerous adverse pregnancy outcomes including preeclampsia and gestational diabetes (Chang et al. 2015; Hentschke et al. 2015; Murthi et al. 2016; Cross et al. 2022). Although none of these three genes have previously been associated with MEHP exposure, expression of MMP-9, another member of the matrix metalloproteinase family, was decreased following MEHP exposure in HTR-8/SVneo cells (Gao et al. 2017). In both cell types, the 1 µM MEHP concentration caused the smallest number of gene expression changes; however, not all the genes that were significantly changed at 1 µM were significantly affected in the higher dose groups. In HTR-8/SVneo, there were two genes (DHRS2 and TASOR2) that were only significant after 1 µM and 180 µM MEHP exposure, while one gene (IGFBP5) was only significant in the two lower concentrations (1 µM and 90 µM). IGFBP5 encodes a binding protein of insulin-like growth factors which have been previously demonstrated to promote trophoblast proliferation and invasion of the maternal decidua by EVTs (Crosley et al. 2014). Though there are no studies of MEHP’s effect on IGFBP5 expression, a study of chronic exposure to MEHP’s parent compound, DEHP, performed in Rhesus macaque embryonic stem cells found that DEHP exposure increased the expression of IGFBP5 which matches the directionality of the response seen in the HTR-8/SVneo cells in our study (Midic et al. 2018). Interestingly, all the genes discussed here were directionally concordant for the concentrations where they showed significance and were only significant in one of the two cell types analyzed. We compared the results of the in vitro evaluation of MEHP on the placenta to the prior epidemiological analysis of phthalates on the placenta completed in the CANDLE study (Paquette et al. 2021). The lncRNA NEAT1 was significantly decreased after 90 µM and 180 µM MEHP treatment in HTR-8/SVneo cells as well as increased in the male-specific analysis of the primary syncytiotrophoblasts at 90 µM MEHP. NEAT1 expression in the placenta has previously been associated with other phthalate metabolites, including related DEHP metabolites mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) and mono-(2-ethyl-5-carboxypentyl) phthalate (MECCP) as well as mono(carboxyisooctyl) phthalate (MCIOP) and monomethyl phthalate (MMP), suggesting a potential role for this lncRNA in phthalate-induced gene expression disruption (Machtinger et al. 2018; Paquette et al. 2021). Two additional genes, ANKRD10 and PHLDB2, were significantly decreased following 180 µM MEHP treatment in HTR-8/SVneo cells. For both of these genes. the CANDLE study was their first known association with MEHP exposure (Paquette et al. 2021). None of the other genes significantly associated with MEHP from the CANDLE study were altered after MEHP treatment in the combined or female-specific primary syncytiotrophoblasts, suggesting that the effects we observe may be fetal sex and placental cell type specific. Results of our analysis were also compared to other in vitro studies of MEHP in HTR-8/SVneo or primary syncytiotrophoblast cells. In HTR-8/SVneo cells exposed to 1–200 µM MEHP for 24 h by Gao et al., the activity of MMP9 was decreased (100 and 200 µM) and the protein expression of TIMP-1 was increased (10, 100, 200 µM); however, the mRNA expression levels of both MMP9 and TIMP-1 were not changed (Gao et al. 2017). In our HTR-8/SVneo cells, we noted an increase in TIMP-1 expression (90 and 180 µM) and a decrease in MMP9 expression (180 µM) mirroring the directionality of the activity and protein expression changes from Gao et al. Neither of these genes were affected by MEHP exposure in the primary syncytiotrophoblast cells. Another study of MEHP exposure in HTR-8/SVneo cell line found increased expression of PTGS2; however, these results were not replicated in our study (Tetz et al. 2013). A study of MEHP exposure in primary syncytiotrophoblast cells identified increased expression of CRH and COX-2, but this finding was also not replicated in either of our cell lines (Wang et al. 2016). Fetal sex is an important biological variable in placental omics analyses, and it has been shown to affect gene expression following phthalate exposure (Paquette et al. 2021). In the CANDLE phthalate study, 14 genes with differential expression in females were associated with five phthalate metabolites and 25 genes in males were associated with five phthalate metabolites and DEHP, with MEHP having the highest number of sex-specific findings with 12 associations identified in males (Paquette et al. 2021). The identification of sex-specific differences underscores the importance of considering fetal sex as a contributing variable in analyses of environmental exposures. Using primary syncytiotrophoblast cells presented in this study provides a unique opportunity to assess fetal sex in an in vitro cell model of the placenta and phthalate exposure. Our study identified 35 genes in male placentas and 12 genes in female placentas that were exclusive to the sex-stratified analysis and not present in the full model for primary syncytiotrophoblasts. Of these unique genes in males, there were three (FABP5, MGAT3, NHS) that were significant for both 90 µM and 180 µM concentrations, while female placentas had a single gene (LRP1B) that was significant for both concentrations. While none of these genes have previous associations with MEHP, FABP5 has been associated with DEHP exposure in mice and HepG2 cells, as well as a DEHP-containing phthalate mixture in rats (Stenz et al. 2017; Wei et al. 2017; Scarano et al. 2019). Placental DNA methylation levels of LRP1B, which was significant only in female primary cells, and codes for a low-density lipoprotein receptor, had previously been linked with gestational diabetes mellitus and maternal glucose levels (Houde et al. 2015). Three genes (FABP4, STRIP2, and HMGCS2) were significant at 90 µM and 180 µM MEHP in all three primary syncytiotrophoblast models (male, female, and combined). Of these three genes, two (FABP4 and HMGCS2) are involved in the mechanisms of fatty acid use and the third (HMGCS2) in cytoskeletal organization. In mouse stromal and fat cells, expression of FABP4 increases following MEHP exposure, which matches the directionality of gene expression change seen in the primary syncytiotrophoblast cells (Watt and Schlezinger 2015; Chiang et al. 2016). STRIP2 and HMGCS2 expressions have not previously been associated with MEHP exposure, but expression of genes was decreased in human embryonic stem cells following DEHP exposure, and HMGCS2 expression increased after DEHP treatment in mouse liver tissue (Eveillard et al. 2009; Fang et al. 2019). Identifying transcriptomic differences in the response of male and female placentas to MEHP supports previous research on sex differences in response to phthalates and sex differences in placental adaptation mechanisms. Some of the first widely noted anatomical effects of phthalates were reduced anogenital distance and incomplete testicular descent in male infants (Swan et al. 2005). Since then, additional recent sex-specific findings of phthalate exposure have included differences in body and organ weight in weanling mice exposed prenatally to phthalates (Neier et al. 2019), as well as differences in associations of adverse birth outcomes with mixtures of phthalate metabolites in the PROTECT cohort (Cathey et al. 2022). The placenta is a particularly relevant organ for identifying sex-specific differences in environmental exposures as there are already known differences in the ratio of male vs female fetuses compared to their placental weight, with male fetuses having comparatively smaller placentas with lower reserve capacity than female fetuses, which is understood to be the result of males prioritizing in utero growth more than females (Eriksson et al. 2010; Meakin et al. 2021). This discrepancy in placental reserve capacity could therefore cause some of the sex-specific effects seen in the placenta when faced with environmental exposures, such as phthalates. In HTR-8/SVneo cells, 21 pathways were significantly enriched for genes whose placental expression was altered after both 90 µM and 180 µM MEHP. Of these shared pathways, the top three (based on average FDR) were calcium signaling pathway, spliceosome, and ErbB signaling pathway, all of which exhibited directional concordance for the two dose groups with increased pathway expression. Although there is no published research linking these pathways to MEHP exposure, calcium signaling pathway and ErbB signaling pathway were both enriched for genes reported to be associated with MEHP in the Comparative Toxicogenomics Database (CTD) (Davis et al. 2021). In primary syncytiotrophoblast cells, there was only a single pathway, ascorbate and aldarate metabolism that was identified for both 90 µM and 180 µM MEHP concentrations. This pathway was also significantly enriched for MEHP-associated genes in the CTD, but not otherwise linked to MEHP exposure in the currently available literature (Davis et al. 2021). Across both cell types, there were 11 pathways that were significantly associated with at least one MEHP concentration. Only two of these pathways, glycerolipid metabolism and regulation of actin cytoskeleton, exhibited directional concordance with both pathways having increased expression. Neither pathway had previous experimental evidence of a connection with MEHP, but regulation of actin cytoskeleton was enriched for MEHP-associated genes in the CTD (Davis et al. 2021). The two pathways that were identified for at least one concentration in HTR-8/SVneo, primary syncytiotrophoblasts, and the CANDLE study (vasopressin-regulated water reabsorption and mitophagy) had not previously been associated with MEHP exposure prior to the CANDLE study (Paquette et al. 2021). The actin cytoskeleton is involved in the processes of invading and anchoring the placenta to the decidual wall as well as the syncytialization of villous trophoblasts all rely on cytoskeletal reorganization (Rote et al. 2010; Farah et al. 2020). Alterations in the regulation of the actin cytoskeleton could thus result in impaired placental development and function. Glycerolipid metabolism is also an essential placental process with evidence that lipid accumulation above normal physiological levels induces lipid droplet formation, increases cytokine production, and causes changes to syncytialization and hormone production in the placenta (Pathmaperuma et al. 2010). Of the pathways shared with the CANDLE study (Online Resource 6), the tight junction pathway which was significant in HTR-8/SVneo cells is of particular interest, as tight junctions are a critical component of trophoblast cell function with roles in differentiation of cytotrophoblasts to extravillous trophoblasts or syncytiotrophoblasts, as well as involvement in extravillous trophoblast invasion of the maternal decidua (Adu-Gyamfi et al. 2021). Phthalate disruption of tight junctions has been most extensively studied in testes with a focus on Sertoli cells (Zhang et al. 2008; Sobarzo et al. 2009, 2015; Hu et al. 2014; Kumar et al. 2015). Therefore, this study and the CANDLE study are two of the first to indicate a potential role for phthalate disruption of tight junctions in the placenta. With knowledge of genes that are altered by MEHP exposure, it is next imperative to understand the upstream cause of the phthalate-induced gene expression changes. Phthalates are hypothesized to affect gene expression through direct binding to nuclear steroid hormone receptors which is attributable to the similarity of the phthalate metabolite benzene ring which mimics Ring A of steroid hormone structures (Baker 2014; Beg and Sheikh 2020). Specifically, phthalates may bind to the androgen receptor (AR), estrogen receptors (ERs) and peroxisome proliferator-activated receptors (PPARs), which in addition to being nuclear hormone receptors are also ligand-inducible transcription factors (Engel et al. 2017; Beg and Sheikh 2020). Metabolites of DEHP (including MEHP) have been shown to activate PPARA and PPARG, but do not affect the activity of AR or ERα or ERß (Engel et al. 2017). In this study, MEHP did not alter the activity of AR or ER, but DEHP exposure did inhibit the activity of all three of these receptors (Engel et al. 2017). In the current study, we identified transcription factors that were enriched for downstream gene targets that we identified as associated MEHP treatment using Enrichr. Across both cell types, four of the significant TFs were nuclear hormone receptors (AR, ESR1, PPARG, and PPARD) that have previously been shown to interact with phthalate metabolites. Because most of the research on the interaction between these TFs and phthalates have been performed in other cell types and tissues, it is of particular importance to note that all four of these nuclear hormone receptors have been demonstrated to be expressed in the placenta (Matsuda et al. 2013; Kim et al. 2016; Meakin et al. 2021). While this study was the first of its kind in assessing the transcriptome of two placental cell lines in response to MEHP, there are inherent limitations in generalizing the findings to human populations. Cell line selection is an important decision when completing in vitro placental work, as each of the commercially available cell lines has strengths and weaknesses and represents a unique placental cell type and period of placental development. Several of the most common placental cell lines (BeWo, JEG-3, JAR) are derived from choriocarcinomas which may not be reflective of normal placental function and genetics (Bačenková et al. 2022). The immortalized placental cell line HTR-8/SVneo used in this study represents a first trimester extravillous trophoblast phenotype, while the primary cells represent term syncytiotrophoblast cells with known fetal sex. Given that most epidemiological studies of the placenta are performed in bulk tissue, in vitro assessments of the placenta are critical to understanding the unique roles and responses of placental trophoblast subtypes to environmental exposures. Future research should aim to expand upon the use of primary placental tissue culture, particularly in early pregnancy to understand the intricacies and interplay of cell types present during the crucial developmental window and how they may be altered in response to toxicants. Conversely, in vitro exposure studies introduce limitations not inherent to epidemiological studies, which include the use of standalone chemicals rather than mixtures and the need to select an environmentally relevant dose while taking into consideration differing pathways of exposure and metabolism for each in vitro model. In this study, we treated cells with the monoester metabolite (MEHP) of parent phthalate compound DEHP, as phthalates are known to undergo rapid metabolism in vivo (Zhang et al. 2021), meaning that placental cells in vivo are more likely to be exposed to the metabolite than the parent compound. The two higher MEHP concentrations (90 µM and 180 µM) were selected based on previous in vitro studies of MEHP in placental cell lines (Tetz et al. 2013, 2015; Meruvu et al. 2016a, b) (Online Resource 2). These concentrations are much higher than MEHP concentrations that have been measured in human urine or cord blood (Li et al. 2013; Maekawa et al. 2017). Acute exposure studies, such as the one performed in this paper with only a 24-h exposure length, may need higher than average levels of phthalates to account for the continuous exposure seen in humans. We also selected a lower concentration (1 µM MEHP), which was in line with concentrations used in at least two previous cell studies (Wang et al. 2016; Gao et al. 2017) that also fell in the range of concentration values (1.1 × 10–4 µM to 2.2 µM) measured in maternal urine in the CANDLE cohort (Paquette et al. 2021). Using a lower, more biologically relevant dose is of particular importance for phthalates, as they have been shown to exhibit a non-monotonic dose response that have at times been below the published no observed adverse effect level (NOAEL), underscoring the importance of low-dose phthalate concentrations in experimental research (Hill et al. 2018). Phthalate concentrations can differ substantially between maternal urine and placental measurements. A recent analysis of a subset of CANDLE participants ($$n = 50$$) revealed that concentrations of MEHP quantified within the placenta were up to 800 times higher than average urinary MEHP across the second and third trimesters, and MEHP was the phthalate metabolite with the largest discrepancy between urine and placenta (Liang et al. 2022). This study highlights the importance of selecting a wide range of concentrations for in vitro analyses. Analysis of the placental transcriptome in response to chemical mixtures, to more accurately model human exposures, is an area of research that needs more attention in both epidemiological and in vitro studies (Lapehn and Paquette 2022). Although similarities were identified in DEGs and KEGG pathways for the two placental cell types (HTR-8/SVneo and primary syncytiotrophoblasts), overall, there were more differences. Similarly, there were only a small number of overlapping DEGs identified between this study and the CANDLE study. These differences may be attributable to differences in trophoblast phenotype and/or trimester of origin for sampling or exposure data. The CANDLE study evaluated phthalate concentrations in maternal urine in the second and third trimesters and the associated placental gene expression changes in bulk tissue from term placentas finding more differences overall with respect to second trimester phthalate concentrations and little overlap in DEGs across each time point (Paquette et al. 2021). These findings potentially highlight the second trimester as being a more vulnerable window of time for phthalate exposure (Paquette et al. 2021). The two cell types used in this in vitro assessment of MEHP represented both different trophoblast phenotypes (extravillous trophoblast vs. syncytiotrophoblast) and different trimesters of origin (1st trimester vs 3rd trimester (term)). The primary syncytiotrophoblast cells from term placentas showed lower sensitivity to MEHP based on the total number of DEGs, similar to findings from the CANDLE study, which could suggest decreased transcriptional response at this time point. Future analyses (potentially in animal models) would benefit from evaluating the same trophoblast phenotype across all three trimesters with matched exposure data to elucidate whether measured differences in gene expression are due to vulnerable windows of exposure in specific trimesters or due to differences in trophoblast phenotype and function. This is not currently feasible through in vitro approaches or within human studies. Given the differences in phthalate-induced gene expression changes across these two cell models, we believe that single-cell RNA sequencing of the placenta should be prioritized when feasible. Under circumstances where single-cell approaches are not amenable, we recommend surrogate variable analysis (Leek 2014) or other cellular deconvolution approaches to account for cellular heterogeneity of bulk tissue samples (Campbell et al. 2021). Overall, the results of this study highlight that phthalate metabolite MEHP causes changes to the placental transcriptome that are dependent on placental trophoblast subtype, MEHP concentration, and fetal sex. We identified three genes that were associated with MEHP exposure in both cell and human studies, including an lncRNA transcript (NEAT1) that is involved in transcriptional regulation through paraspeckle formation (Li et al. 2017), and has been shown to promote expression of inflammatory genes (Zhang et al. 2019). This highlights the need to better characterize the roles of these transcripts in human health. Transcription factors with known phthalate interactions (PPARG, PPARD, AR, ESR1) were reported as enriched for both cell types. Beyond these primary findings, this study has also generated an extensive list of mRNAs, lncRNAs, and pathways that are significantly affected by phthalates and should be evaluated through further candidate gene studies. These genes and pathways should be evaluated in particular for potential roles in adverse pregnancy and early life health outcomes. Linking placental gene expression to exposures and outcomes would allow for chemical monitoring and toxicological risk assessment that could eventually lead to identification of placental biomarkers of phthalate toxicity. Additionally, future work should evaluate the role of nuclear hormone receptor TFs in eliciting altered gene expression through identifying genomic binding sites following exposure to MEHP. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (PDF 148 KB) Online Resource 1 Representative image of primary trophoblast cells (Male) at 24 hours (a), 48 hours (b), and 72 hours (c). Syncytialization progresses spontaneously and can be noted by the fused cells in images b and c compared to the independent cells that are not aggregated in image a. Syncytialization was confirmed visually at 48 hours prior to treating with DMSO or phthalates from 48 to 72 hoursSupplementary file2 (DOCX 31 KB) Online Resource 2 Table of recent studies of in vitro phthalate exposure in placental cell lines that highlights length of exposure, phthalate metabolites, and phthalate concentrationSupplementary file3 (XLSX 860 KB) Online Resource 3 Table of differentially expressed genes (DEGs) from each cell type and MEHP treatment groupSupplementary file4 (XLSX 22 KB) Online Resource 4 Full list of significant (FDR<0.05) KEGG pathways for 90µM and 180µM MEHP in HTR-8/SVneo cells. Pathway gene number indicates the size of the KEGG pathwaySupplementary file5 (XLSX 17 KB) Online Resource 5 All significant (FDR<0.05) transcription factors from the Enrichr analysis in HTR-8/SVneo cells sorted by average FDR across doses. Total downstream genes indicate the total number of genes downstream of the transcription factor, whereas DEG number indicates the number of significant genes from a dose group found to be downstream of that TFSupplementary file6 (XLSX 14 KB) Online Resource 6 KEGG pathways ($p \leq 0.05$) for MEHP from the 2nd and 3rd trimester CANDLE study compared to significant KEGG pathways (FDR<0.05) for HTR-8/SVneo and primary syncytiotrophoblasts exposed to 90µM or 180µM MEHP ## References 1. Adibi JJ, Zhao Y, Zhan LV. An investigation of the single and combined phthalate metabolite effects on human chorionic gonadotropin expression in placental cells. Environ Health Perspect (2017.0) 125 107010. DOI: 10.1289/EHP1539 2. Adu-Gyamfi EA, Czika A, Gorleku PN. The involvement of cell adhesion molecules, tight junctions, and gap junctions in human placentation. Reprod Sci Thousand Oaks Calif (2021.0) 28 305-320. DOI: 10.1007/s43032-020-00364-7 3. Alexander SP, Cidlowski JA, Kelly E. 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--- title: Machine learning-based obesity classification considering 3D body scanner measurements authors: - Seungjin Jeon - Minji Kim - Jiwun Yoon - Sangyong Lee - Sekyoung Youm journal: Scientific Reports year: 2023 pmcid: PMC9968712 doi: 10.1038/s41598-023-30434-0 license: CC BY 4.0 --- # Machine learning-based obesity classification considering 3D body scanner measurements ## Abstract Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual’s body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans. ## Introduction Grading a patient’s obesity level is an important activity in healthcare1. Obesity acts as a risk factor for various diseases, including chronic diseases, type-2 diabetes, heart disease, and certain cancers2–7. Knowing that you are obese can motivate you to manage your weight8,9. In addition, intentional weight management can lower the risk of poor health and is associated with the benefit of a lower risk of disease10. However, Body Mass Index (BMI), which represents the criteria for obesity defined by the World Health Organization (WHO), does not accurately classify obesity as it does not sufficiently reflect body-type factors11–13. Nutrition varies between regions and body types14,15. Reflecting the body-type factor requires anthropometric measurements. However, traditional anthropometric methods are impractical because such measuring requires trained experts. As an alternative, research on using a 3D scanner for human body measurement is being actively conducted16–27 as it is a less-invasive method than traditional anthropometric measurements. Computed Tomography (CT) or Dual-energy X-ray absorptiometry (DXA), which is the gold standard for measuring human body fat percent (bf%), involve the risk of exposure to radiation when frequent measurements are taken. Unlike CT or DXA, 3D scanners do not expose the human body to radiation26. In addition, health risks can be analyzed or predicted through various measures, not just a single one. Therefore, in this study, 3D body scan data and DXA data were collected in pairs for Koreans, and obesity classification was performed using anthropometric values obtained from the 3D body scan data. Löffler-Wirth et al. classified the body types of residents in Leipzig, Germany using a 3D scanner17. Body measurements were extracted from 8499 people through a 3D scanner. After dividing this by height, the body types were grouped through Self-organizing Map (SOM). Clustering body types using a Machine Learning methodology through large-scale experiments is meaningful, but it is not scalable because it cannot be paired with clinical information such as DXA. Pleuss et al.25 conducted a sampling study to supplement Löffler-Wirth et al. but the number of samples was quite small. In Ng et al. and Bennett et al. 3D scanner data and DXA showed a strong correlation through statistical analysis; the relationship can be explained but it is difficult to estimate bf% through various factors of the human body16,27. Some studies used machine learning; Harty et al.21 developed a new bf% estimation equation based on 3D human body data and 4C model anthropometric data. Using a decision tree model is useful in that important factors and the criteria necessary for formulas can be distinguished. Lu et al.20 proposed a methodology to predict bf% through a machine learning-based framework after extracting features from 3D body data obtained using a 3D scanner. Although it showed higher accuracy than BMI and BOD POD, the experiment was conducted on relatively few subjects, i.e., 50 men. Although active research is underway on DXA and 3D body scan data, not many machine learning models have been studied. Most obese people do not perceive themselves as obese28,29, which may hinder public health initiatives30. Knowing one's obesity group can serve as a prerequisite for behavior change for health31 and will be helpful for health management and disease prevention. The purpose of our study is to develop a machine learning framework to classify obesity among Koreans based on the bf% of DXA—considered the gold standard—using body measurements extracted from 3D scanners. By selecting input features through a Genetic Algorithm, we not only improve the performance of the Machine Learning model but also observe the selected input features to help in healthcare. ## Materials The collection and use of data used in this study and ethical review were approved by the Institutional Review Board of Korea National Sport University [20220411-021]. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or legal guardians regarding including their information/images in an online open-access publication and paper. We confirmed that informed consent was obtained from all subjects (for participation). This experiment faithfully followed the strict regulations and guidelines of the Institutional Review Board. The dataset used in this study was collected between 2022–04–11 and 2022–06–30. Recruitment was based on BMI to have a BMI distribution similar to Size Korea. There were a total of 160 subjects: 73 women and 87 men. As shown in Fig. 1, the men wore tight-fitting bottoms and swimming caps and the women wore tight-fitting tops and bottoms and swimming caps. The subjects were measured using a 3D scanner, DXA, and BIA. Through this, a total of 160 3D body data, DXA data, and BIA data were obtained. Statistics for the collected data are presented in Table 1.Figure 1Subjects’ attire and posture. Table 1Collected data statistics. GenderStatisticsAgeHeight (cm)Weight (kg)BMI (kg/m2)DXA BF(%)BIA BF(%)Male [87]Average24.07178.1077.9224.6420.1318.45Std4.255.3713.524.008.707.42Min20.00165.5554.3016.225.505.40Max39.00191.83120.9037.6137.4037.00Female [73]Average24.14165.6357.0120.7427.5525.83Std4.544.9610.133.346.946.29Min20.00155.8740.7015.706.7011.70Max37.00173.9989.6032.2546.0045.70 First, we verified whether our 160 data were a fair representation of general Korean anthropometric data. The 8th Anthropometric Data collected between 2020 and 2021 by Size Korea was used for validation32. As our main participants were in their 20 s and 30 s, we used the data of 1547 women and 1306 men in 20 s and 30 s from amongst the 8th Anthropometric Data for accurate comparison. In order to verify whether the anthropometric characteristics of Koreans can be viably represented, a Kolmogorov–Smirnov (KS) test was conducted to confirm that the two sample distributions were identical. This test is useful for determining the difference in variance between two samples. In the case of men, the p value of the KS test came out as 0.926, which is valid at the significance level of 0.01. In the case of women, the p value of the KS test was 0.052, which is valid at the significance level of 0.01. And a t-test was conducted to compare the average BMI within the data we collected with the average BMI within the Size Korea data. For men, the t-test result was a p value of 0.5807, at a significance level of 0.01, so we hypothesized that there is no difference between the average BMI of Korean men in 20 s and 30 s and the average BMI of men we collected. Similarly, for women, the t-test result was a p value of 0.0532, and at a significance level of 0.01, so we hypothesized that there is no difference between the average BMI of Korean women in 20 s and 30 s and the average BMI of women we collected. Thus, we verified that the data we collected was representative of Koreans in 20 s and 30 s. ## 3D scanner The 3D scanner was a PFS-304A model (PMT innovation company, Gyeonggi-do, Korea, PFS software ver. 1.3). Figure 2a shows the 3D scanner used in this study, wherein the camera module rotates 360° through a motor mounted on the top when it scans and subjects can be measured in a stationary position. The subjects’ postures were measured by taking the A-pose recommended by ISO-725033. If the A-pose is not taken (e.g., anthropometry is not performed when the arm is close to or attached to the body), a blind spot is formed and the correct mesh shape is not created. As shown in Fig. 2a, the measurements of the 3D scanner were taken in an indoor lighting environment, and they are summarized in Table 2.Figure 2Equipment used in the experiment. ( a) 3D scanner (PFS-304 of PMT), (b) DXA (Lunar of GE), (c) BIA (Inbody770).Table 2Measurement of 3D mesh data. ValuedescriptionValuedescriptionWCRWaist-to-chest ratioThick thing cirCircumference of the thickest part of the innermost thighWHRWaist-to-hip ratioMiddle thigh cirCircumference of the middle of the thighWHtRWaist-to-height ratioKnee cirHorizontal circumference through the midpoint of the kneecapTHRThigh-to-height ratioCalf cirCircumference of the most convex part of the calfHeight of the back of the neckHeight from floor to the back of the neck in a standing positionArm cirCircumference of the thickest part of the armShoulder heightHeight from floor to the shoulders in a standing positionCS. area of the back of the neckCross-sectional area of the back of the neckChest heightIn a standing position, the height from floor to the top of the nipples and the beginning of the chestShoulder CSCross-sectional area of the shoulder(breast) Chest heightHeight from floor to nipple in standing positionChest areaCross-sectional area of the part passing through the midpoint of the sternumWaist heightHeight from floor to the point in front of the waist in a standing position(breast) Chest areaCross-sectional area of the part passing through the nipple pointNavel heightHeight from the standing position to the navelWaist CSCross-sectional area of the part passing through the front of the waist, the side of the waist, and the back of the waistHeight below the navelThe height from the standing position to the iliac bone below the navelNavel waist CSCross-sectional area of the part passing through navel point, navel level, waist point, navel level, back pointHip heightHeight from floor to hip protrusion in standing positionArea below the navelCross-sectional area of the part that passes through iliac crest below navelGroin heightVertical height from floor to groin (actual leg length)Hip CSCross-sectional area of the part passing through the buttock protrusionThick thigh heightHeight from floor to the thickest part of the thighGroin areaCross-sectional area of the groinMidthigh heightHeight from floor to mid-thighThick thigh areaCross-sectional area of the thickest part of the innermost thighKnee heightVertical height from floor to the top of the shinboneMeddle thigh areaCross-sectional area of the middle part of the thighCalf heightHeight from floor to the point of the thickest part of the calfKnee CSCross-sectional area passing through the midpoint of the kneecapNeck cirCircumference passing under the back of neck and under the shield cartilageCalf CSCross-sectional area of the most convex part of the calfShoulder cirCircumference from the end of the shoulder to the end of the shoulder opposite the back of the neckTotal volumeVolume of total bodyCir. of the chestHorizontal circumference through the midpoint of the sternumShoulder volumeVolume of shoulder(breast)Chest cirHorizontal circumference through nipple pointChest volumeVolume of chestWaist cirHorizontal circumference passing through the point in front of the waist, the point in the side of the waist, and the point in the back of the waistEpigastric volumeVolume of epigastriumNavel waist cirHorizontal circumference passing through navel point, navel level, waist point, navel level, back pointLower abdominal volumeVolume of lower abdomenBelow navel cirHorizontal circumference through the iliac crest below the navelThigh volumeVolume of thighHip cirHorizontal circumference through the buttock protrusionCalf volume Volume of calfGroin cirGroin circumferenceAbdominal volumetricVolume of abdominal ## Dual-energy X-ray absorptiometry (DXA) Lunar (GE Healthcare, Madison Wisconsin, America, EnCore software ver. 13.60.03) in Fig. 2b was used for DXA. DXA is a body component-measuring instrument that measures body fat, lean body mass, and bone mass and has long been regarded as the gold standard for measuring body components. The DXA device was handled and measured by an expert; during measurement, the subject maintained an immobile posture while lying down, and the following body parts were examined: arm (left, right), leg (left, right), trunk (left, right), Android, and Gynoid. BMD, BMC, fat%, fat(g), and lean(g) were obtained through DXA for each body part. Here, BMD represents bone density and BMC represents bone mineral mass. Fat% represents fat percentage, fat (g) represents fat weight, and lean (g) represents weight excluding fat. This study classified obesity in individuals based on the total body fat% (bf%) according to DXA. ## Bioelectrical impedance analysis (BIA) BIA is a method of measuring body composition through the impedance difference between body fat and lean body mass34. As shown in Fig. 2c, Inbody770 was used as the measuring device and the subjects were measured in the same clothing and immobile posture as in the previous experiments. Total Body Water (TBW), IntraCellular Water (ICW), ExtraCellular Water (ECW), protein, minerals, Body Fat percentage (bf%), and Fat-free Mass (FFM) were obtained through BIA. Among them, bf% was used for comparison using the methodology proposed in this study. The obesity group of BIA was divided using the bf% criteria presented in Table 3.Table 3Obesity class standard cutoff. IndexSexUnderweightNormalOverweightObesityBMI (kg/m2)Male/FemaleBMI < 18.518.5 < BMI < 2323 ≤ BMI < 2525 ≤ BMIBIA, DXAMaleBF% < 1010 ≤ BF% ≤ 2020 < BF% ≤ 2525 < BF%FemaleBF% < 2020 ≤ BF% ≤ 2828 < BF% ≤ 3535 < BF% ## Data preprocessing Data obtained through DXA and BIA only used bf%. Based on the bf% obtained from the DXA of the test subjects in the experiment, the obesity class label was derived as per the cutoff in Table 3. The bf% standard cutoff for Obesity was set in accordance with the WHO35 classification, and the standard cutoffs from Lobman et al. were used for the rest, namely underweight, normal, and overweight36. Currently, there is no clear obesity category for DXA37, but the cutoff for obesity was based on $25\%$(bf%) for men and $35\%$(bf%) for women by the Korean Society for Obesity, and the rest of the groups were classified by reference to McArdle and Chang38,39. Therefore, subjects were labeled into four groups: underweight, normal, overweight, and obese. The distribution results of labeling based on bf% measured by DXA Table 3 are depicted in Table 4. As shown in Fig. 3, the data obtained from the 3D scanner extracted body measurements from the mesh data based on five landmarks: the back of the neck, the umbilicus, the groin, and the armpit (left, right).Table 4Subject class distribution. Train setClassMaleFemaleTotalUnderweight8412Normal232346Overweight121224Obese251338Test setClassMaleFemaleTotalUnderweight336Normal6814Overweight5611Obese549Figure 3Sample of the 3D mesh data and standard landmarks for measurement. ## Framework We propose a machine learning-based methodology to classify obesity groups. Figure 4 shows the proposed machine learning-based framework. Body measurements are first obtained from the 3D scanner, and then data preprocessing is performed by matching it with the bf% of DXA and labeling it. After that, the final model is selected through the process of “Choose ML model” and “Feature selection Genetic Algorithm. ”Figure 4Overall framework of this study. ## Choose ML model In data preprocessing, the data in the state of finished preprocessing the 3D body measurements and Sex are used as input values for the Logistic Regression40, Decision Tree41, Random Forest42, Support Vector Machine (SVM)43, Gradient Boosting44, and AdaBoost45 and fivefold cross validation is performed. It is divided into 120 training sets and 40 test sets. Among these models, Accuracy, F1, Recall, and Precision values were compared, and a model with good performance was selected and used as a classifier in the “Feature selection Genetic Algorithm” process. In Table 4, as the quantity of data is small and imbalances exist, the model is selected by referring to Precision, Recall, and F1 score values rather than simply using Accuracy. Accuracy is the ratio of correctly predicted numbers to the total number. Precision is the sum of true positives and false positives and the ratio of true positives. Recall is the sum of true positives and false negatives and the ratio of true positives. F1 score is the harmonic mean of Precision and Recall. Logistic regression predicts the probability of occurrence by using a linear combination between variables of input data, and the result is classified into a specific class. This study used multiclass logistic regression with a cross-entropy function. Decision Tree is the most preferred machine learning model as an explanatory model. It outputs a class in which input data is classified based on input variables through a tree structure; it is a way to perform a query on a node and branching out. Its performance is not as good as other models, and it is vulnerable to overfitting. Random Forest is a machine learning model that uses a bundle of basic decision trees and averages them to compensate for performance. Through this, the performance can be generalized and made more robust against overfitting compared to a decision tree. Support Vector Machine determines the hyperplane to maximize the margin between support vectors; its purpose is to maximize the distance between various classes and to find a hyperplane that has a large difference from the training data to which the hyperplane is closest. The main idea of Gradient Boosting is to connect multiple non-deep decision trees, that is, weak learners. As basic trees can classify some data well, performance improves when trees are added. The loss function is defined and gradient descent is used to supplement the value to be classified by the next tree. AdaBoost stands for Adaptive Boosting. Unlike Gradient Boosting, this model is trained by adding weights to the classified samples. At this time, the learning model is created by adding weights to the next model in the sample that is poorly classified. ## Feature selection Genetic Algorithm This process selects the input features of the previously selected machine learning model through a Genetic Algorithm. Selectively choosing the input features of the machine learning model not only improves the model’s performance but also identifies whether a specific value among the 3D body measurements in Table 2 affects the classification of obesity. While selecting input features, finding the Global Optimum by comparing all sets of input features combinations is practically impossible. Therefore, a meta-heuristic algorithm approach was chosen to find an optimal solution close enough to the Global Optimum. Previous studies have demonstrated that the Genetic Algorithm is superior to other meta-heuristic algorithms in variable selection46,47. In this study, the Genetic Algorithm (GA) was used as a feature selection method. GA takes a meta-heuristic approach to solving complex problems through efficient trial and error48, hence mimicking Charles Darwin's theory of natural selection and mammalian reproduction. In this study, GA aims to find the best input feature through repeated generation reproduction. GA involves six steps. In Step 1, it initializes the combination of chromosomes, i.e., the initial input features, and sets the parameters. These parameters include population and mutation ratio, where population refers to the number of chromosomes in each generation, i.e., the number of combinations of input features. The mutation ratio refers to the ratio of gene mutations among all chromosomes; this corresponds to the ratio of selection of input features. We set the population to 100 and the mutation ratio to $20\%$. Step 2 involves learning each input feature in a Random Forest. In Step 3, fitness was evaluated for the chromosomes of each input feature and the fitness function was used to determine the accuracy. In Step 4, out of the current generation and current chromosomes, we selected excellent chromosomes with Accuracy. In this study, the top $80\%$ were selected as excellent chromosomes. Step 5 involved generating next-generation chromosomes through crossover and mutation. In this case, crossover means mixing the selected adoptive parent chromosomes in half. We set this to stop when the 100th generation was passed, and until then, it was set to return to Step 2 and repeat all intervening steps. In Step 6, we selected the final model, picking the model that generated the highest Accuracy. A total of 100 generations were generated and the input feature of the generation with the highest Accuracy, Recall, Precision, and F1 score was selected. Among the 100 generations, the generation with Accuracy, Recall, Precision, and F1 score of 0.8, 0.767, 0.842, and 0.792 was the highest and the corresponding input feature was selected as the final input feature. Accuracy reached $80\%$ in the 50th epoch, after which it converged or even decreased. Figure 5 shows the flow of accuracy by generation. Table 5 presents the final selected features. Figure 5Accuracy flowchart by generation. Table 5Selected features of the “Feature selection Genetic Algorithm”. ValueValueValueWCRHip cirHip CSGroin heightThick thing cirGroin areaThick thigh heightMiddle thigh cirThick thigh areaMidthigh heightArm cirCalf CSKnee heightShoulder CSTotal volumeCalf heightChest areaShoulder volumeShoulder cirNavel waist CSThigh volume(breast) Chest cirArea below the navelSex ## Results Accuracy, Recall, Precision, and F1 score were calculated using DXA as the Ground Truth for the reference group classification. BIA classified obesity based on the bf% obtained through Inbody770, and BMI classified obesity according to the WHO Asian standard cutoff13. The cutoff for BIA and BMI is listed in Table 3. Table 6 shows the results of classification using the above models; among them, as its Accuracy, Recall, Precision, and F1 score were 0.725, 0.692, 0.661, and 0.78, i.e., all higher than those of other models, Random Forest was selected as the machine learning model in the process. Table 6Results of the choosing the ML model. RankClassifierAccuracyRecallPrecisionF1 score1Random Forest0.7250.6610.7800.6922GradientBoosting0.7000.5990.6550.6093Logistic0.5000.5150.4430.4624SVM0.6500.4770.3830.4125DecisionTree0.4750.4840.4780.4656AdaBoost0.4250.3580.4310.356Significant values are in bold. The performance of the proposed approach is better than that using BMI. In Table 7, there is a difference of 0.271 in Accuracy from 0.529 to 0.8, 0.295 in Recall, 0.384 in Precision, and 0.33 in F1 score. This means that obesity can be classified more comprehensively by reflecting the various dimensions of the human body considering the body type rather than just the BMI, which classifies obesity through simple height and weight. Although BIA is widely used in body composition studies, concerns about its accuracy still exist49,50. As BIA assumes that the percentage of body water is approximately $73\%$ and estimates it accordingly, low accuracy may ensue when the percentage of body water of an individual does not meet these conditions51. Furthermore, depending on the statistical model derived from a specific population, BIA may have differences in gender, age, ethnicity, and so on50. The Pearson’s correlation between bf% of DXA and bf% of BIA in our collected data was 0.95. When evaluated after the obesity classification presented in Table 3, there was a misclassification. BIA scored 0.752, 0.742, 0.751, and 0.739 in Accuracy, Recall, Precision, and F1 scores, respectively. The proposed obesity classification showed an improvement of 0.048, 0.025, 0.091, and 0.053 in Accuracy, Recall, Precision, and F1 scores, respectively, compared to BIA. This shows that the Accuracy, Recall, Precision, and F1 score are 0.075, 0.1, 0.106, and 0.062 higher, respectively, than in the Random Forest model without feature selection. The selected features affect the classification of obesity more than the unselected features. We also compared with the model provided by Tian et al.52. Denotes, Tian's model is a regression model, not a classification model, and we obtained results by dividing it by the criteria of the Table 3. The R2 and RMSE values of Tian's model were 0.56 and 5.74, respectively. Table 7Comparison between our method, BMI, BIA and Tian et al. within the test set. ClassifierAccuracyRecallPrecisionF1 scoreR2/RMSEOurs0.8000.7670.8420.792w/o GA0.7250.6610.7800.692BIA0.7520.7420.7510.739BMI0.5290.4720.4580.462Tian et.al$\frac{0.4720.3790.4480.3210.56}{5.74}$w/o without, GA genetic algorithm. Significant values are in bold. ## Conclusion We collected 3D body scans, DXA, and BIA data pairwise for Korean subjects and used this data to classify obesity in individuals. By using not only 3D body data but also DXA and BIA, we developed a technique for clinically clear obesity judgment that is expandable in terms of healthcare. This study proposes a methodology for classifying obesity using various body measurements through a 3D scanner, unlike the BMI, which classifies obesity solely based on height and weight. The present study specifically considers Korean body types. The proposed methodology showed better performance in classifying obesity than the BMI through the machine learning methodology. It also showed better performance than BIA. Pleuss et al. and Harty et al. conducted analysis using one or two machine learning models, but in this study, six machine learning models were compared to select a model suitable for obesity classification21,25. We performed feature selection through a Genetic Algorithm to identify the measurements of the human body that have an impact on determining obesity. The proposed system showed superior performance over BMI and BIA. It can be used for long-term obesity healthcare monitoring by measuring one's body with a 3D scanner. Furthermore, as future healthcare is predicted to be Predictive, Preventive, Personalized, Participatory (4P)53, a system that classifies obesity as an indicator of health can be utilized as a new healthcare service that satisfies the 4P. Our study has certain limitations. First, there are limits on data. The collected data included males and females in their 20 s and 30 s. As such, it is not common to all age groups. It does not reflect the adult group over 40 years of age. Furthermore, obesity was not evenly distributed, and there was a limited amount of data. Given that the measurement values were extracted and used from the mesh data generated by the 3D human body scanner, it is difficult to ensure that the 3D information was fully used. Second, there are spatial and cost limitations associated with 3D scanners that make them unpopular. Third, it provides users with categorical information rather than continuous information. It does not reflect the continuous variation of human. We intend to collect continuous pair data as future work and conduct experiments in various age groups as well as compare between men and women in their 20 s and 30 s. Furthermore, we intend to conduct experiments on patients with specific diseases or disorders. By referring to previous studies54, we intend to consider using a three-dimensional representation such as curvature. In the Genetic Algorithm selection, selection methods such as elitism and tournament can be introduced to reduce computation complexity and optimize future research55,56. In order to reflect the continuous variability of human beings, we intend to introduce the regression methodology as a future study. We would like to observe the consistency of data obtained from different scanner devices and DXA as future research. We need to confirm this and do future work to validate the data and other models. ## References 1. Zelenytė V. Body size perception, knowledge about obesity and factors associated with lifestyle change among patients, health care professionals and public health experts. BMC Fam. Pract. (2021.0) 22 1-13. DOI: 10.1186/s12875-021-01383-2 2. Gade W, Schmit J, Collins M, Gade J. Beyond obesity: The diagnosis and pathophysiology of metabolic syndrome. Am. Soc. Clin. Lab. Sci. (2010.0) 23 51-61. DOI: 10.29074/ascls.23.1.51 3. Donohoe C, Pidgeon G, Lysaght J, Reynolds J. 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--- title: Gestational age and trajectories of body mass index and height from birth through adolescence in the Danish National Birth Cohort authors: - Johan L. Vinther - Claus T. Ekstrøm - Thorkild I. A. Sørensen - Luise Cederkvist - Deborah A. Lawlor - Anne-Marie Nybo Andersen journal: Scientific Reports year: 2023 pmcid: PMC9968714 doi: 10.1038/s41598-023-30123-y license: CC BY 4.0 --- # Gestational age and trajectories of body mass index and height from birth through adolescence in the Danish National Birth Cohort ## Abstract Preterm birth is associated with smaller body dimensions at birth. The impact on body size in later life, measured by body mass index (BMI) and height, remains unclear. A prospective register-based cohort study with 62,625 singletons from the Danish National Birth Cohort born 1996–2003 for whom information on gestational age (GA) at birth, length or weight at birth, and at least two growth measurements scheduled at the ages of 5 and 12 months, and 7, 11 and 18 years were available. Linear mixed effects with splines, stratified by sex, and adjusted for confounders were used to estimate standardised BMI and height. GA was positively associated with BMI in infancy, but differences between preterm and term children declined with age. By age 7, preterm children had slightly lower BMI than term children, whereas no difference was observed by adolescence (mean difference in BMI z-score − 0.28 to 0.15). GA was strongly associated with height in infancy, but mean differences between individuals born preterm and term declined during childhood. By adolescence, the most preterm individuals remained shorter than their term peers (mean difference in height z-score from − 1.00 to − 0.28). The lower BMI in preterm infants relative to term infants equalizes during childhood, such that by adolescence there is no clear difference. Height is strongly positively associated with GA in early childhood, whilst by end of adolescence individuals born preterm remain slightly shorter than term peers. ## Introduction Preterm birth is one of the leading causes of perinatal mortality and morbidity1, and some evidence suggest associations with long-term health and social outcomes2,3. Exposures during fetal life may influence postnatal growth and cardio-metabolic health4,5. Much literature has demonstrated that birth weight associates with postnatal body mass index (BMI) with positive linear J and U-shaped associations reported6–8. Birth weight is strongly related to gestational age at birth (GA)9, but the extent to which the association between birth weight and later BMI reflects differences in GA is unclear10. Some studies have investigated the associations between GA and BMI and, respectively, height3,11–20 and reported positive associations in childhood13,21, while findings are mixed in adulthood20,22. However, wide variations in study design, mode of data collection, and use of covariates in the studies making it difficult to evaluate the evidence23. Some studies assessed gestational duration as a dichotomous variable reporting all preterm children in same exposure group3,24, while others compared growth of extremely preterm with term, which may hide important differences by degree of preterm3. We identified only two previous publications that reported associations with BMI and height for subgroups of preterm and term born children13,25. In addition, majority of studies have examined the association without repeat measurements of BMI or height; hence, evidence on the longitudinal association between gestational age and BMI and height is limited. A life-course approach that enables tracking of BMI and height during infancy, childhood and in adolescence by where individuals have reached their maximum height and a stabilized BMI may add to the evidence26. The aim of this study was to examine longitudinal associations between length of gestation at birth and trajectories of BMI and height, respectively, from birth through adolescence. We explored sex-specific associations due to natural differences in growth27. ## Study design This is a longitudinal study using data from the Danish National Birth Cohort (DNBC), which includes information on 96,822 live-born children and their mothers28. Baseline information was planned to be collected at 12 and 30 weeks gestation, and follow-ups with information on height and weight were planned to be collected at 18 months and 7, 11 and 18 years. Information is linked with nation-wide registry data at Statistics Denmark29–32 as described below. Details of the DNBC are given elsewhere28. ## Study population Eligible for this study was any live-born singleton in the DNBC with a gestational age of 23–43 completed weeks at birth ($$n = 92$$,615) (Fig. 1). We excluded individuals who emigrated or died ($$n = 1826$$) during follow-up, and excluded individuals with less than two growth measurements after birth and without information on potential confounders: maternal education ($$n = 173$$), maternal pre-pregnancy BMI ($$n = 3894$$) and/or household income ($$n = 286$$). Before defining two specific populations for analysis, individuals with missing information on birth length ($$n = 374$$) or birth length < 30 cm or > 80 cm ($$n = 685$$), and with less than two measurements of height after birth ($$n = 22$$,749) were excluded. Figure 1Flow chart of eligibility and inclusion in the study population in the study. In the sample for height, we excluded three additional individuals with height values ± 5 standard deviations from the mean following recommendations from Vidmar et al. leaving 62,625 individuals for analysis33. In the sample for BMI, we excluded an additional 620 individuals without information on birth weight or with implausible combined values of birth weight and GA ($$n = 26$$), and individuals with less than two measurements of BMI ($$n = 594$$). Lastly, 39 individuals with BMI values ± 5 standard deviations from the mean was excluded, which left 61,969 individuals for analysis33. ## Gestational age Information on GA in days was obtained from the Danish Medical Birth Register29. The GA was reported to the register by the midwife at birth, based on results of ultra sound scans (at the time not part of recommended care, however made on almost $80\%$ of women around week 18), anamnestic information about LMP and cycle length and regularity, and the clinical judgement of the child. GA was converted to completed weeks and categorized into seven groups: extremely preterm (23–27 weeks), very preterm (28–31 weeks), moderately preterm (32–33 weeks), late preterm (34–36 weeks), early term (37–38 weeks), term (39–41 weeks), and post term (42–43 weeks). ## Body mass index and height Information on birth weight and birth length was derived from the Danish Medical Birth Register29. Mothers were interviewed when the child was around 18 months and asked to report the weight and height registered in the ‘Child’s book’ at the preventive child examinations in general practice at scheduled ages of 5 and 12 months (measured at 1–22 months). These were used in the analyses together with height and weight reported by mothers at questionnaire-based follow-ups, scheduled at ages of 7 (measured at 5–7 years) and 11 years (measured at 10–14 years)28. At the 18 year follow-up (in practice at 17–19 years), height and weight were self-reported by the adolescent. For each child, standardized scores (z-scores) of BMI and length at birth, and BMI and height after birth to age 19 years (228 months) were calculated separately for boys and girls. Standardization was done internally using 1-month categories. ## Confounders Confounders were selected a priori based on previous evidence2,34–36. Information on maternal height (cm), maternal pre-pregnancy BMI (kg/m2), and maternal smoking during pregnancy (yes/no) was taken from questionnaires at 12 and 30 weeks of gestation. Information on maternal age at delivery (continuous in years); gestational diabetes, ICD-10: O24 (yes/no); gestational hypertension, ICD-10: O13 (yes/no); and pre-eclampsia, ICD-10: O14 (yes/no) was derived from Danish Medical Birth Registry. Maternal education was obtained from the Danish Population’s Education Register and operationalised as highest ongoing or completed education at child’s birth according to international classification standards37: low [ISCED-2011: 0–2], medium [ISCED-2011: 3–4], high [ISCED-2011: 5–8]). Household income was based on disposable household income extracted from the Income Statistics Register. The variable was divided by an equivalence factor according to household size (available at http://www.oecd.org) and recoded into internal quantiles per year. Birth weight was perceived as an intermediate variable on the causal pathway, hence not included in the models38. ## Statistical analysis Linear-mixed effects models were used to estimate the association between GA in categories and BMI and height z-scores, respectively, from birth through age 19 years (228 months) while adjusting for confounders39. Analyses were performed by sex due to natural differences in growth trajectories27. Linear splines are a commonly used type of regression spline for repeated measurements of non-linear growth trajectories40. Therefore, to best approximate the relationship between GA and the standardized growth measurements, we included linear splines in our models with four internal knots (5, 12, 85 and 134 months) and two boundary knots (at birth and 228 months). The knots were chosen a priori based on amount of available data40. The linear mixed-effects model with linear splines (LME) accommodated for repeated measurements from the same child, without imposing any structure on the correlations among the time points using an unstructured covariance matrix. Also, in the LME models we accounted for clustering of children with same mothers. Age was set to 0 months at birth and included as a continuous variable, and the actual age in months at measurements of BMI and height was included in the models. Furthermore, nearly one-third of individuals was excluded from the eligible sample ($32\%$) (S1 Table), thus by adding inverse probability weights (IPWs) to the LME models we sought to remedy bias from this selection. The IPWs were based on variables that predicted selection into our analysis sample: sex, maternal age, maternal education, maternal smoking during pregnancy, maternal pre-pregnancy BMI, household income, parity, and caesarean section41. The fitted LME models were used to predict sex-specific mean BMI and height z-scores by categories of GA with $95\%$ confidence intervals (CIs) at six ages corresponding to the data collections: birth, 5 months, 1 year, 7 years, 11 years, and 18 years. For prediction, we used the following: mean maternal age at delivery (31.2 years), mean maternal pre-pregnancy BMI (23.5 kg/m2), mean maternal height (1.69 m), maternal education (high), household income (4th quartile), maternal smoking during pregnancy (no), gestational diabetes (no), gestational hypertension (no), and preeclampsia (no). We performed sensitivity analyses (S3–S6 Tables), including a model only adjusting for sociodemographic variables, and models without adjustment of covariates, IPW and clustering. Statistical analyses were carried out using the statistical software R version 4.042 and packages for ‘nlme’ and ‘splines’43. ## Ethics approval All methods were carried out in accordance with relevant guidelines and regulations. The DNBC data collection has been approved by the Regional Scientific Ethical Committee for the Municipalities of Copenhagen and Frederiksberg, and the Danish Data Protection Agency. Informed consent for study participation was obtained from the mother upon enrolment, and confirmed by the adolescent at age 18 years. Approval of the study was obtained from the Danish Data Protection Agency through the joint notification of The Faculty of Health and Medical Sciences at The University of Copenhagen (SUND-2017-09) and the study has furthermore been approved by the DNBC Steering Committee. ## Descriptive statistics Baseline characteristics differed across categories of GA in the analysis sample (Table 1). All categories of preterm infants, relative to term, were more likely to have mothers with low educational level, who smoked during pregnancy, were diagnosed with preeclampsia or gestational hypertension, and delivered by caesarean section. Mean age at timing of measurements for height and weight was similar across categories of GA at all data collections, whereas mean BMI (kg/m2) was similar for preterm and term individuals after age 5 months, whilst height was consistently lower for preterm individuals throughout childhood and adolescence (S2 Table).Table 1Selected characteristics of the study population from children born in the Danish National Birth Cohort (1996–2003), $$n = 62$$,625.Extremely preterm (23–27 weeks)Very preterm (28–31 weeks)Moderately preterm (32–33 weeks)Late preterm (34–36 weeks)Early term (37–38 weeks)Term (39–41 weeks)Post term (42–43 weeks)Total (23–43 weeks)Total N of children (%)34 ($0.1\%$)160 ($0.3\%$)292 ($0.5\%$)2019 ($3.2\%$)9463 ($15.1\%$)45,139 ($72.1\%$)5518 ($8.8\%$)62,625 ($100\%$)Sex (%), female$56.2\%$$48.7\%$$43.4\%$$47.1\%$$47.6\%$$50.3\%$$47.9\%$$49.8\%$Gestational age, mean (SD), days187 [6]213 [8]232 [4]251 [6]267 [4]283 [6]296 [2]280 (11.5)Gestational age, mean (SD), weeks26.2 (0.8)30.0 (1.0)32.7 (0.5)35.4 (0.8)37.7 (0.4)40.0 (0.8)42.0 (0.2)39.6 (1.7)Birth weight, mean (SD), g937 [215]1528 [680]2053 [438]2755 [515]3323 [477]3682 [470]3894 [485]3602 [546]Maternal age at delivery, mean (SD), years32.4 [5]30.6 [4]30.8 [5]30.7 [4]31.3 [4]31.1 [4]31.0 [4]31.1 [4]Maternal smoking in pregnancy (%)$29.4\%$$31.9\%$$32.9\%$$26.7\%$$25.3\%$$24.1\%$$24.7\%$$24.4\%$Maternal height, mean (SD), m1.68 (0.05)1.68 (0.06)1.68 (0.06)1.68 (0.06)1.68 (0.06)1.69 (0.06)1.69 (0.06)1.69 (0.1)Maternal pre-pregnancy BMI, mean23.024.123.723.723.623.524.123.6Maternal pre-pregnancy BMI, (%) < 18.5 kg/m$211.8\%$$3.8\%$$3.8\%$$4.9\%$$5.1\%$$4.1\%$$3.1\%$$4.2\%$ 18.5–25.0 kg/m$267.6\%$$63.7\%$$68.5\%$$65.8\%$$66.9\%$$69.3\%$$64.9\%$$68.4\%$ > 25.0 kg/m$220.6\%$$32.5\%$$27.7\%$$29.4\%$$27.9\%$$26.6\%$$32.0\%$$27.4\%$Maternal highest or ongoing education (%)a Low$20.6\%$$10.6\%$$12.7\%$$10.3\%$$10.4\%$$8.9\%$$8.4\%$$9.1\%$ Medium$32.4\%$$46.9\%$$43.2\%$$46.4\%$$43.5\%$$42.1\%$$42.4\%$$42.5\%$ High$47.1\%$$42.5\%$$44.2\%$$43.3\%$$46.1\%$$49.0\%$$49.2\%$$48.4\%$Equivalised income, quartiles (%) 1 (lowest)$20.6\%$$21.2\%$$24.0\%$$23.0\%$$21.7\%$$21.9\%$$22.4\%$$22.0\%$ $226.5\%$$23.8\%$$26.0\%$$23.5\%$$25.2\%$$25.8\%$$24.9\%$$25.5\%$ $335.3\%$$28.1\%$$24.0\%$$25.7\%$$26.4\%$$26.1\%$$27.1\%$$26.2\%$ 4 (highest)$17.6\%$$26.9\%$$26.0\%$$27.8\%$$26.7\%$$26.2\%$$25.6\%$$26.3\%$Gestational diabetes (%)b$0.0\%$$0.0\%$–$1.8\%$$1.9\%$$0.6\%$$0.1\%$$0.8\%$Gestational hypertension (%)$0.0\%$$8.8\%$$10.6\%$$8.5\%$$6.4\%$$4.5\%$$4.1\%$$4.9\%$Preeclampsia (%)$11.8\%$$20.6\%$$17.1\%$$8.4\%$$3.9\%$$1.7\%$$0.8\%$$2.3\%$Delivery by C-section (%)$54.5\%$$73.2\%$$49.3\%$$31.4\%$$27.5\%$$10.7\%$$16.9\%$$14.8\%$SD standard deviation, BMI body mass index, C-section caesarean section.aISCED 11 is used for classification of maternal educational level.bFor moderately preterm, information on gestational diabetes was omitted due to disclosure control and low numbers (< 5) in cell. ## Trajectories of predicted BMI z-score by categories of GA For extremely, very, moderately and late preterm children, the mean BMI z-score increased noticeably in the first year of life, with catch-up growth (gain in z-score of > 0.67) in preterm infants between birth and 5 months (Table 2, S1 Fig.)44. Accordingly, mean differences in BMI z-score between preterm and term infants attenuated in the first 12 months, though BMI remained lower in preterm than term. Table 2Predicted mean BMI z-score from birth to age 18 years by category of gestational age, $$n = 61$$,969 (estimated from LME-model).AgeStandardized mean BMI z-score by category of gestational age, and mean differences in BMI z-score compared to full term reported with $95\%$ confidence interval (CI)NExtremely preterm23–27 weeksVery preterm28–31 weeksModerately preterm32–33 weeksLate preterm34–36 weeksEarly term37–38 weeksTerm39–41 weeksPost term42–43 weeksBoys Birth31,127 Mean ($95\%$ CI)− 4.90 (− 5.67, − 4.12)− 3.92 (− 4.16, − 3.67)− 2.84 (− 2.99, − 2.69)− 1.62 (− 1.68, − 1.56)− 0.57 (− 0.59, − 0.54)0.11 (0.09, 0.12)0.48 (0.44, 0.51) Mean difference ($95\%$ CI)− 5.00 (− 6.16, − 3.85)4.02 (− 4.39, − 3.65)− 2.95 (− 3.18, − 2.72)− 1.73 (− 1.81, − 1.64)− 0.67 (− 0.72, − 0.63)Ref.0.37 (0.32, 0.43) 5 months24,948 Mean ($95\%$ CI)− 0.47 (− 1.80, 0.86)− 0.80 (− 1.09, − 0.52)− 0.22 (− 0.40, − 0.05)− 0.08 (− 0.15, − 0.02)− 0.04 (− 0.08, − 0.01)− 0.06 (− 0.08, − 0.04)− 0.06 (− 0.10, − 0.02) Mean difference ($95\%$ CI)− 0.40 (− 2.40, 1.60)− 0.74 (− 1.17, − 0.31)− 0.16 (− 0.43, 0.10)− 0.02 (− 0.12, 0.08)0.02 (− 0.03, 0.07)Ref.0.01 (− 0.06, 0.07) 12 months23,634 Mean ($95\%$ CI)− 0.84 (− 1.73, 0.04)− 0.46 (− 0.74, − 0.19)− 0.04 (− 0.21, 0.14)− 0.15 (− 0.22, − 0.09)− 0.06 (− 0.09, − 0.03)− 0.06 (− 0.08, − 0.04)− 0.03 (− 0.07, 0.01) Mean difference ($95\%$ CI)− 0.78 (− 2.12, 0.55)− 0.40 (− 0.82, 0.01)0.02 (− 0.24, 0.29)− 0.09 (− 0.19, 0.00)− 0.00 (− 0.05, 0.05)Ref.0.03 (− 0.03, 0.09) 85 months (7 years)21,257 Mean ($95\%$ CI)− 0.23 (− 1.10, 0.63)− 0.44 (− 0.73, − 0.15)− 0.24 (− 0.43, − 0.06)− 0.11 (− 0.18, − 0.04)− 0.08 (− 0.11, − 0.04)− 0.07 (− 0.09, − 0.05)− 0.01 (− 0.05, 0.03) Mean difference ($95\%$ CI)− 0.17 (− 1.47, 1.14)− 0.37 (− 0.80, 0.06)− 0.18 (− 0.45, 0.10)− 0.04 (− 0.14, 0.07)− 0.01 (− 0.06, 0.04)Ref.0.06 (− 0.01, 0.12) 134 months (11 years)18,049 Mean ($95\%$ CI)− 0.16 (− 1.25, 0.93)0.08 (− 0.26, 0.42)− 0.22 (− 0.43, − 0.02)0.02 (− 0.05, 0.10)− 0.05 (− 0.09, − 0.01)− 0.05 (− 0.07, − 0.03)− 0.02 (− 0.07, 0.03) Mean difference ($95\%$ CI)− 0.10 (− 1.74, 1.53)0.13 (− 0.38, 0.64)− 0.17 (− 0.48, − 0.13)0.08 (− 0.04, 0.19)− 0.00 (− 0.06, 0.06)Ref.0.03 (− 0.04, 0.10) 220 months (18 years)14,261 Mean ($95\%$ CI)0.07 (− 1.20, 1.34)− 0.06 (− 0.47, 0.35)− 0.17 (− 0.41, 0.07)0.03 (− 0.06, 0.13)− 0.06 (− 0.10, − 0.01)− 0.08 (− 0.10, − 0.06)− 0.11 (− 0.17, − 0.05) Mean difference ($95\%$ CI)0.15 (− 1.76, 2.06)0.02 (− 0.60, 0.64)− 0.09 (− 0.45, 0.27)0.11 (− 0.03, 0.26)0.02 (− 0.05, 0.09)Ref.− 0.03 (− 0.10, 0.12)Girls Birth30,842 Mean ($95\%$ CI)− 5.04 (− 5.67, − 4.41)− 4.00 (− 4.24, − 3.76)− 3.01 (− 3.18, − 2.84)− 1.62 (− 1.68, − 1.56)− 0.56 (− 0.59, − 0.53)0.10 (0.08, 0.12)0.43 (0.40, 0.47) Mean difference ($95\%$ CI)− 5.14 (− 6.09, − 4.19)− 4.10 (− 4.46, − 3.73)− 3.11 (− 3.37, − 2.85)− 1.72 (− 1.81, − 1.63)− 0.66 (− 0.70, − 0.62)−0.33 (0.28, 0.39) 5 months24,236 Mean ($95\%$ CI)− 1.09 (− 1.88, − 0.30)− 0.64 (− 0.94, − 0.35)− 0.27 (− 0.47, − 0.07)− 0.10 (− 0.16, − 0.03)− 0.07 (− 0.10, − 0.04)− 0.06 (− 0.07, − 0.03)− 0.04 (− 0.08, − 0.00) Mean difference ($95\%$ CI)− 1.03 (− 2.22, 0.16)− 0.59 (− 1.03, − 0.15)− 0.22 (− 0.52, 0.08)− 0.04 (− 0.15, 0.06)− 0.02 (− 0.07, 0.03)Ref.0.01 (− 0.05, 0.07) 12 months23,001 Mean ($95\%$ CI)− 0.81 (− 1.55, − 0.07)− 0.70 (− 0.98, − 0.42)− 0.24 (− 0.44, − 0.04)− 0.06 (− 0.12, − 0.01)− 0.06 (− 0.09, − 0.03)− 0.06 (− 0.08, − 0.04)− 0.05 (− 0.09, 0.01) Mean difference ($95\%$ CI)− 0.76 (− 1.87, 0.36)− 0.64 (− 1.07, − 0.22)− 0.18 (− 0.48, 0.11)− 0.00 (− 0.10, 0.10)− 0.00 (− 0.05, 0.05)Ref.0.01 (− 0.05, 0.07) 85 months (7 years)20,445 Mean ($95\%$ CI)− 0.73 (− 1.63, 1.17)− 0.32 (− 0.62, − 0.03)− 0.14 (− 0.36, − 0.07)− 0.13 (− 0.21, − 0.05)− 0.05 (− 0.09, − 0.02)− 0.05 (− 0.07, − 0.04)− 0.03 (− 0.08, 0.01) Mean difference ($95\%$ CI)− 0.68 (− 2.04, 0.68)− 0.27 (− 0.71, 0.18)− 0.09 (− 0.41, 0.23)− 0.07 (− 0.19, 0.04)− 0.00 (− 0.05, 0.06)Ref.0.02 (− 0.04, 0.09) 134 months (11 years)18,518 Mean ($95\%$ CI)− 0.47 (− 1.48, 0.54)− 0.11 (− 0.47, 0.24)− 0.12 (− 0.35, 0.16)− 0.04 (− 0.12, 0.04)− 0.02 (− 0.05, 0.02)− 0.05 (− 0.07, − 0.03)− 0.03 (− 0.08, 0.01) Mean difference ($95\%$ CI)− 0.42 (− 1.94, 1.10)− 0.07 (− 0.60, 0.47)− 0.07 (− 0.41, 0.27)− 0.01 (− 0.11, 0.13)0.03 (− 0.03, 0.09)Ref.0.01 (− 0.06, 0.09) 220 months (18 years)19,256 Mean ($95\%$ CI)− 0.36 (− 1.15, 0.44)− 0.16 (− 0.49, 0.18)0.02 (− 0.23, 0.26)− 0.01 (− 0.09, 0.07)− 0.05 (− 0.09, − 0.01)− 0.08 (− 0.10, − 0.05)− 0.04 (− 0.09, 0.01) Mean difference ($95\%$ CI)− 0.28 (− 1.49, 0.92)− 0.08 (− 0.59, 0.43)0.09 (− 0.27, 0.46)0.06 (− 0.06, 0.19)0.02 (− 0.04, 0.08)Ref.0.03 (− 0.05, 0.11)Predicted standardized mean birth length and height are weighted to the eligible sample, accounts for clustering of children with same mothers, and adjusted for the mean in continuous variables and the reference categories in categorical variables: maternal pre-pregnancy BMI (23.5), maternal age at delivery (31.2), maternal education (high), maternal height (1.69), equalized household income (4th quartile), maternal smoking during pregnancy (no), gestational diabetes (no), gestational hypertension (no), preeclampsia (no). The mean does not equal zero (at each age), because the estimates are predicted, and adjusted for covariates, clustering of children with same mothers, and inverse probability weights. By age 7 preterm children had a slightly lower BMI z-score than term children with largest mean differences for very preterm boys (− 0.37, $95\%$ CI: − 0.80 to 0.06) and extremely preterm girls (− 0.68, $95\%$ CI: − 2.04 to 0.68). During adolescence the mean BMI z-score was similar across categories of GA, with the largest mean differences relative to term children observed for extremely preterm girls at both 11 years (− 0.42, $95\%$ CI: − 1.94 to 1.10) and 18 years (− 0.28, $95\%$ CI: − 1.49 to 0.92). Results from the sensitivity analyses with sociodemographic variables were only slightly different with estimates away from the null, while sensitivity analyses without IPW and clustering, respectively, were consistent with the main findings (S3, S4 Tables). ## Trajectories of predicted height z-score by categories of GA Extremely, very, moderately and late preterm children experienced catch-up in height (gain in z-score of > 0.67) within the first year of life (Table 3, S2 Fig.). By age 1 year, preterm children remained shorter than term children with the largest mean differences observed in extremely preterm boys (− 1.22, $95\%$ CI: − 2.41 to 0.03) and girls (− 1.56, $95\%$ CI: − 2.58 to 0.54) relative to term counterparts. Table 3Predicted mean height z-scores from birth to age 18 years by category of gestational age, $$n = 62$$,625 (estimated from LME-model).AgeStandardized mean height z-score by category of gestational age, and mean differences in height z-score compared to full term reported with $95\%$ Confidence Interval (CI)NExtremely preterm23–27 weeksVery preterm28–31 weeksModerately preterm32–33 weeksLate preterm34–36 weeksEarly term37–38 weeksTerm39–41 weeksPost term42–43 weeksBoys Birth31,407 Mean ($95\%$ CI)− 6.40 (− 7.10, − 5.72)− 4.31 (− 4.53, − 4.09)− 2.83 (− 2.97, − 2.68)− 1.39 (− 1.44, − 1.33)− 0.44 (− 0.47, − 0.41)0.17 (0.15, 0.19)0.55 (0.52, 0.59) Mean difference ($95\%$ CI)− 6.56 (− 7.63, − 5.50)− 4.48 (− 4.81, − 4.15)− 2.99 (− 3.21, − 2.78)− 1.56 (− 1.64, − 1.48)− 0.62 (− 0.65, − 0.57)Ref.0.38 (0.33, 0.43) 5 months25,050 Mean ($95\%$ CI)− 2.73 (− 3.89, − 1.57)− 2.37 (− 2.63, − 2.11)− 1.58 (− 1.74, − 1.42)− 0.67 (− 0.73, − 0.61)− 0.22 (− 0.25, − 0.19)0.10 (0.08, 0.12)0.30 (0.26, 0.34) Mean difference ($95\%$ CI)− 2.83 (− 4.58, − 1.08)− 2.47 (− 2.86, − 2.08)− 1.68 (− 1.92, − 1.43)− 0.77 (− 0.86, − 0.68)− 0.32 (− 0.36, − 0.27)Ref.0.20 (0.14, 0.26) 12 months23,714 Mean ($95\%$ CI)− 1.18 (− 1.97, − 0.39)− 0.81 (− 1.07, − 0.56)− 0.56 (− 0.73, − 0.40)− 0.15 (− 0.21, − 0.09)− 0.06 (− 0.09, − 0.03)0.04 (0.02, 0.06)0.16 (0.13, 0.20) Mean difference ($95\%$ CI)− 1.22 (− 2.41, − 0.03)− 0.85 (− 1.23, − 0.48)− 0.60 (− 0.85, − 0.36)− 0.19 (− 0.28, − 0.10)− 0.10 (− 0.14, − 0.05)Ref.0.13 (0.07, 0.18) 85 months (7 years)21,774 Mean ($95\%$ CI)− 0.80 (− 1.58, − 0.02)− 0.14 (− 0.40, 0.11)− 0.28 (− 0.45, − 0.11)− 0.05 (− 0.11, − 0.02)0.00 (− 0.03, 0.04)0.02 (0.00, 0.04)0.07 (0.03, 0.11) Mean difference ($95\%$ CI)− 0.82 (− 1.99, 0.35)− 0.17 (− 0.55, 0.22)− 0.30 (− 0.56, − 0.05)− 0.07 (− 0.17, 0.03)− 0.02 (− 0.07, 0.03)Ref.0.05 (− 0.01, 0.11) 134 months (11 years)18,555 Mean ($95\%$ CI)− 0.65 (− 1.64, 0.33)− 0.11 (− 0.41, 0.19)− 0.26 (− 0.44, − 0.07)− 0.01 (− 0.08, 0.06)0.03 (− 0.00, 0.07)0.02 (− 0.00, 0.03)0.03 (− 0.02, 0.07) Mean difference ($95\%$ CI)− 0.67 (− 2.15, 0.81)− 0.13 (− 0.59, 0.33)− 0.27 (− 0.55, 0.01)− 0.02 (− 0.13, 0.08)0.02 (− 0.04, 0.07)Ref.0.01 (− 0.06, 0.08) 220 months (18 years)14,657 Mean ($95\%$ CI)− 0.53 (− 1.67, 0.62)− 0.35 (− 0.71, 0.01)− 0.13 (− 0.35, 0.09)− 0.07 (− 0.16, 0.02)0.04 (-0.00, 0.08)0.03 (0.01, 0.06)0.05 (− 0.01, 0.10) Mean difference ($95\%$ CI)− 0.56 (− 2.28, 1.16)− 0.39 (− 0.93, 0.16)− 0.16 (− 0.50, 0.17)− 0.10 (− 0.24, 0.03)0.00 (− 0.06, 0.07)Ref.0.01 (− 0.07, 0.09)Girls Birth31,218 Mean ($95\%$ CI)− 6.69 (− 7.25, − 6.13)− 4.64 (− 4.87, − 4.42)− 2.91 (− 3.07, − 2.75)− 1.42 (− 1.48, − 1.36)− 0.42 (− 0.45, − 0.39)0.18 (0.16, 0.20)0.54 (0.50, 0.57) Mean difference ($95\%$ CI)− 6.87 (− 7.72, − 6.03)− 4.82 (− 5.16, − 4.49)− 3.09 (− 3.33, − 2.85)− 1.60 (− 1.69, − 1.52)− 0.60 (− 0.64, − 0.56)Ref.0.36 (0.31, 0.42) 5 months24,341 Mean ($95\%$ CI)− 2.63 (− 3.36, − 1.90)− 2.16 (− 2.43, − 1.90)− 1.46 (− 1.65, − 1.28)− 0.67 (− 0.73, − 0.60)− 0.19 (− 0.22, − 0.16)0.10 (0.08, 0.12)0.29 (0.25, 0.33) Mean difference ($95\%$ CI)− 2.73 (− 3.83, − 1.63)− 2.26 (− 2.67, − 1.86)− 1.57 (− 1.85, − 1.29)− 0.77 (− 0.86, − 0.67)− 0.30 (− 0.34, − 0.25)Ref.0.18 (0.12, 0.24) 12 months23,079 Mean ($95\%$ CI)− 1.50 (− 2.17, − 0.82)− 1.07 (− 1.33, − 0.81)− 0.70 (− 0.88, − 0.51)− 0.24 (− 0.31, − 0.18)− 0.06 (− 0.10, − 0.03)0.06 (0.04, 0.08)0.18 (0.14, 0.22) Mean difference ($95\%$ CI)− 1.56 (− 2.58, 0.54)− 1.13 (− 1.52, − 0.74)− 0.76 (− 1.04, − 0.49)− 0.31 (− 0.40, − 0.21)− 0.13 (− 0.17, − 0.08)Ref.0.12 (0.06, 0.18) 85 months (7 years)21,033 Mean ($95\%$ CI)− 0.86 (− 1.68, − 0.04)− 0.29 (− 0.55, − 0.02)− 0.29 (− 0.49, − 0.10)− 0.06 (− 0.13, 0.01)0.03 (− 0.01, 0.06)0.04 (0.02, 0.06)0.07 (0.03, 0.11) Mean difference ($95\%$ CI)− 0.90 (− 2.14, 0.34)− 0.33 (− 0.72, 0.07)− 0.33 (− 0.63, − 0.04)− 0.10 (− 0.20, 0.01)− 0.01 (− 0.07, 0.04)Ref.0.03 (− 0.03, 0.09) 134 months (11 years)19,011 Mean ($95\%$ CI)− 0.52 (− 1.41, 0.36)− 0.07 (− 0.39, 0.25)− 0.20 (− 0.40, 0.02)− 0.08 (− 0.15, − 0.01)0.02 (− 0.02, 0.06)0.04 (0.02, 0.06)0.06 (0.02, 0.09) Mean difference ($95\%$ CI)− 0.56 (− 1.90, 0.77)− 0.11 (− 0.59, 0.38)− 0.23 (− 0.55, 0.08)− 0.12 (− 0.23, − 0.00)− 0.01 (− 0.07, 0.04)Ref.0.02 (− 0.05, 0.09) 220 months (18 years)20,533 Mean ($95\%$ CI)− 0.93 (− 1.65, − 0.22)− 0.22 (− 0.53, 0.09)− 0.29 (− 0.51, − 0.06)− 0.04 (− 0.12, 0.04)0.04 (0.00, 0.08)0.06 (0.04, 0.08)0.02 (− 0.03, 0.07) Mean difference ($95\%$ CI)− 1.00 (− 2.08, 0.08)− 0.28 (− 0.75, 0.18)− 0.35 (− 0.69, − 0.01)− 0.10 (− 0.22, 0.01)− 0.02 (− 0.08, 0.04)Ref.− 0.04 (− 0.12, 0.03)Predicted standardized mean birth length and height are weighted to the eligible sample, accounts for clustering of children with same mothers, and adjusted for the mean in continuous variables and the reference categories in categorical variables: maternal pre-pregnancy BMI (23.5), maternal age at delivery (31.2), maternal education (high), maternal height (1.69), equalized household income (4th quartile), maternal smoking during pregnancy (no), gestational diabetes (no), gestational hypertension (no), and preeclampsia (no). The mean does not equal zero (at each age), because the estimates are predicted, and adjusted for covariates, clustering of children with same mothers, and inverse probability weights. The mean difference in height z-score between preterm and term attenuated between age 1 year and 7 years, yet extremely, very and moderately preterm remained relatively shorter than term. By age 11 and through age 18 years individuals born preterm remained slightly shorter than term with mean differences being greatest in extremely preterm girls (− 1.00, $95\%$ CI: − 2.08 to 0.08). Results from the sensitivity analyses were overall similar to the main finding, though the analysis with adjustment for sociodemographic variables only scarcely changed the estimates away from the null (S5, S6 Tables). ## Discussion This study investigated trajectories of predicted BMI and height z-scores across categories of GA from birth through 18 years. In the first year of life, BMI and height were lower in preterm than in term infants. Mean difference in BMI and height between preterm and term attenuated during childhood, and continued to decrease towards zero for BMI by adolescence. At 18 years of age, individuals born preterm remained only slightly shorter than children born at term. This study agrees with two recent publications from United Kingdom (UK) ($$n = 475$$) and Australia ($$n = 478$$) showing that a mean difference in BMI between extremely preterm and term is largest in infancy, whilst decreasing during childhood through adolescence21,45. The two studies reported lower mean difference in BMI at age 6 years (− 0.98, $95\%$ CI: − 1.23 to − 0.73) and 8 years (− 0.42, $95\%$ CI: − 0.67 to − 0.18), respectively, which corresponds the magnitude of association for our results in girls at age 7 years (− 0.71, $95\%$ CI: − 2.10 to 0.67). Similar to our findings, the studies from UK and Australia found no difference in BMI for extremely preterm and term adolescents at age 18 and 19 years, respectively. Very and moderately preterm boys and girls remained lighter than term after birth through 7 years in our study. This is in line with findings from a recent cohort study from Brazil ($$n = 3036$$) reporting lower mean BMI in both boys and girls (born ≤ 33 weeks) aged 6 years25. Also, we found that late preterm had similar mean BMI as term already following the first 5 months of life through adolescence, as reported previously in a Chinese study on 7 169 children aged 14 years, and at age 18 years in the study from Brazil46. The studies from Australia, UK, Brazil and China did not adjust for potential confounders such as maternal pre-pregnancy BMI and pre-eclampsia. While the results are in accordance with our findings, we consider it a strength of our study that the estimates are adjusted for key confounders and sex-specific across seven categories of GA contrasting previous publications. We further found evidence to support that GA is positively associated with height in infancy with results indicating a linear association. This is in line with findings from a British study on 18 818 singletons aged 3 and 5 years, despite the authors reporting greater magnitudes of association for each GA-category (23–31 weeks, 32–33 weeks, 34–36 weeks, 37–38 weeks), respectively3. The analyses, however, were not adjusted for maternal height, gestational diabetes, gestational hypertension or preeclampsia. For extremely preterm children, our study suggests that the height remain shorter after birth through adolescence, although the mean differences attenuate with age. These findings agree with results from Australia and UK where extremely preterm was shorter than term peers through infancy, childhood and adolescence compared to term peers21,45. The study from UK reported a lower mean height z-score in 315 extremely preterm aged 6 years (− 0.95, $95\%$ CI: − 1.16 to − 0.73), 11 years (− 0.71, $95\%$ CI: − 0.92 to − 0.50), and 19 years (− 0.81, $95\%$ CI: − 1.14 to − 0.47), respectively, compared with term peers, which corresponds to the likes of the study from Australia. Our findings support that gestational duration affects height in early life, and that this pattern persists and includes the age where individuals reach their maximum height. This contrast previous genetically informed studies challenging that exposures during fetal life is associated with postnatal growth47. A strength of this study is the large sample size, and the ability to assess associations of seven categories of GA with BMI and height at birth and at scheduled ages 5 months, 1, 7, 11 and 18 years. Also, we were able to include maternal anthropometrics and measures of gestational hypertension and diabetes and preeclampsia. A key limitation of our study is missing data on one-third of participants due to loss-to-follow up or lack of reporting child height and weight, or maternal pre-pregnancy BMI. To account for potential biases due to missing data, in the main analyses we accounted for missing data using IPWs and found that results were broadly consistent with results obtained in sensitivity analyses without IPWs (S3–S6 Tables). Lack of growth measurements between age 2 to 5 and 13 to 16 years is also a limitation, which means we are not able to make any inference during two periods of dynamic change in size, namely adiposity rebound and puberty. We relied on parental reporting of child weight and height from 7 to 11 years, and self-report at 18 years. Parental reporting at child height and weight may be prone to systematic error of under- or over-reporting of BMI, but given characteristics of the data collections we do not assume this would affect our overall estimates markedly48. Self-reporting of current BMI is a reasonably valid tool49, hence we neither considered this to bias the results. Our study uses data from a healthy population50 and there is a slightly overrepresentation of socially advantaged families in the study population (S1 Table). However, given the particular magnitudes we do not consider this to affect the validity of internal comparisons within the cohort, while earlier studies have also demonstrated that estimates obtained from the DNBC have been almost identical to those from completely unselected populations50. Also, the sensitivity analyses without IPW were consistent with the main analyses. DNBC is almost entirely comprised of women of Danish ethnic origin, thus replication of our findings in more diverse populations might be useful. Importantly, to understand the causal mechanisms between the associations of GA and BMI and height, respectively, intermediate factors such as birth weight and breast feeding would be relevant to include for investigate in future studies. ## Clinical implications For preterm infants, the largest mean differences in BMI and height relative to term infants appear in the first years of life. However, despite magnitudes of potential clinical relevance in infancy (mean difference > 0.5 z-score)51, findings from our main and sensitivity analyses are reassuring for the growth of preterm individuals with the majority of children reaching similar height and BMI of term peers by end of adolescence Importantly, for a clinical setting these findings should be further considered in combination with maternal characteristics (e.g., socioeconomic position, gestational diabetes and hypertension, preeclampsia, smoking during pregnancy, and comorbidities). ## Conclusion The lower BMI in preterm infants relative to term infants equalizes during childhood, such that by adolescence there is no clear difference. Height is strongly positively associated with GA in early childhood, whilst by end of adolescence preterm individuals remain only slightly shorter than term peers. ## Supplementary Information Supplementary Information. 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--- title: Lentilactobacillus kefiri SGL 13 and Andrographis paniculata alleviate dextran sulfate sodium induced colitis in mice authors: - Laura Manna - Eleonora Rizzi - Eleonora Bafile - Andrea Cappelleri - Massimiliano Ruscica - Chiara Macchi - Michele Podaliri Vulpiani - Romolo Salini - Emanuela Rossi - Concetta Panebianco - Francesco Perri - Valerio Pazienza - Federica Federici journal: Frontiers in Nutrition year: 2023 pmcid: PMC9968723 doi: 10.3389/fnut.2023.1072334 license: CC BY 4.0 --- # Lentilactobacillus kefiri SGL 13 and Andrographis paniculata alleviate dextran sulfate sodium induced colitis in mice ## Abstract ### Introduction Inflammatory bowel diseases (IBD) are chronic inflammatory conditions that typically involve diarrhea, abdominal pain, fatigue, and weight loss, with a dramatic impact on patients’ quality of life. Standard medications are often associated with adverse side effects. Thus, alternative treatments such as probiotics are of great interest. The purpose of the present study was to evaluate the effects of oral administration of Lentilactobacillus kefiri (basonym: Lactobacillus kefiri) SGL 13 and Andrographis paniculata, namely, Paniculin 13™, on dextran sodium sulfate (DSS)- treated C57BL/6J mice. ### Methods Colitis was induced by administering $1.5\%$ DSS in drinking water for 9 days. Forty male mice were divided into four groups, receiving PBS (control), $1.5\%$ DSS, Paniculin 13™ and $1.5\%$ DSS + Paniculin 13™. ### Results The results showed that body weight loss and Disease Activity Index (DAI) score were improved by Paniculin 13™. Moreover, Paniculin 13™ ameliorated DSS-induced dysbiosis, by modulating the gut microbiota composition. The gene expression of MPO, TNFα and iNOS in colon tissue was reduced and these data matched with the histological results, supporting the efficacy of Paniculin 13™ in reducing the inflammatory response. No adverse effects were associated to Paniculin 13™ administration. ### Discussion In conclusion, Paniculin 13™ could be an effective add-on approach to conventional therapies for IBD. ## Introduction Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract that refers mainly to Crohn’s disease (CD) and to ulcerative colitis (UC) [1]. The intestinal inflammation is the result of a combination of immune system disorders, genetic susceptibility, environmental factors, and imbalance of the normal gut microbiota [2, 3]. The etiology of inflammatory bowel disease is still unknown, but the main assumption is that the inflammation is triggered by the altered and pathogenic microbiota in genetically prone subjects. In fact, mouse model studies of IBD demonstrated protection against the development of the pathogenesis in germ free intestinal tract animals, supporting the role of gut flora in the onset of symptoms [4]. Therapeutic approaches for IBD include anti-inflammatory, immunosuppressive, and biologic therapies, largely prescribed in clinical practice [5]. Due to its chronic nature, management of IBD generally requires long-term treatment which often results in side effects or discomfort. Moreover, many patients with IBD are refractory to conventional therapies [6] requiring for alternative treatments. In this context, the role of microbiota can not be underestimated [7]. Indeed, a significant difference in the gut microbiome of healthy subjects and IBD patients has been described [8, 9]. The pattern of dysbiosis most often associated with IBD is a decrease in commensal bacteria diversity, mostly Firmicutes and Bacteroides, and a relative raise of bacterial species belonging to Enterobacteriaceae [10, 11]. Therefore, the modulation of intestinal microbiota, such as by means of fecal bacteria transplantation (FMT) or pro/prebiotics, can restore a healthy gut composition. This represents a valuable alternative and an effective therapeutic option to treat IBD [12]. Among IBD patients, probiotics have gained a great interest due to their feature to alleviate clinical symptoms and to improve the quality of life, either during periods of exacerbation or remission (13–15). In animal model, different strains of lactic acid bacteria, e.g., Bifidobacterium longum [16], Lactococcus lactis [17], Lactobacillus plantarum (recently reclassified as Lactiplantibacillus plantarum) [18], Lactobacillus reuteri (recently renamed Limosilactobacillus reuteri) [19] have been reported to modulate the gut microbiota composition leading to a restored intestinal barrier function. In clinical trials enrolling individuals with IBD, several probiotic strains have alleviated intestinal mucosal inflammation, have reduced colonic myeloperoxidase and fecal calprotectin levels, and have delayed the in-between symptom recurrences [20, 21]. Moreover, natural products derived from plants and herbals, are also increasingly used by IBD patients [22]. A. paniculata, a traditional Chinese medicine with anti-inflammatory, antimicrobial and immunomodulatory properties, is often used to treat gastrointestinal and respiratory infectious diseases [23]. The anti-inflammatory effect of diterpenoids isolated from the plant, as dehydroandrographolide, andrographolide, and neoandrographolide, were tested in vitro studies reporting the down-expression of genes involved in inflammatory cascade and the interfering COX and inflammatory cytokines [24]. Moreover, the effect of andrographolide on the expression of inducibile NO synthase (iNOS) mRNA protein has been investigated, demonstrating that such molecule may reduce the expression of the protein both preventing the de novo synthesis and increasing the protein stability [25]. In addition, a clinical study was conducted using an herbal mixture of A. paniculata, comparing the extract with mesalazine, in patients with mild-to-moderately active ulcerative colitis. No significant difference in remission and clinical response between two groups has been reported, suggesting that the herbal extract A. paniculata may represent an effective alternative to mesalazine in the treatment of ulcerative colitis [26]. Our previous study showed that Lentilactobacillus kefiri SGL 13 exhibited anti-inflammatory and anti-cancer properties in LPS-treated HT-29 cells [27]. Moreover, the anti-inflammatory properties of L. kefiri in the gut has been already demonstrated in mice [28, 29], proving that the administration of a strain of L. kefiri modulates the production of pro- and anti-inflammatory cytokines both downregulating the expression of pro-inflammatory mediators and increasing the anti-inflammatory molecules. In the present study we aimed to exploit the combined action of L. kefiri SGL 13 and A. paniculata, in order to develop an effective alternative treatment for IBD without side effects. For this purpose, the safety of Paniculin 13™ and its efficacy in reducing the severity of IBD, was evaluated in C57Bl/6J mice fed a dextran sulfate sodium (DSS)-induced colitis diet. ## Bacterial strains and growth conditions Lentilactobacillus kefiri SGL 13 (DSM 27331) was obtained from Sintal Dietetics Srl collection. The strain was grown in MRS broth (BD Difco, Franklin Lakes, NJ, USA) in aerobic condition and stored as stock cultures at −80°C in MRS broth supplemented with $15\%$ (v/v) glycerol. In order to prepare L. kefiri SGL 13 biomass, lactobacilli were grown in MRS medium at 37°C and pH 5.5, in a 2 L fermentor (Omnitec srl, Milano, Italy) with an agitation speed of 200 rpm. Cells were harvested by centrifugation (17000 × g, 15 min at 4°C) and then lyophilized in a freeze-drier (Telstar Liobeta 3PS, Spain). After lyophilization, the number of viable cells/g was determined by plate count method. ## Paniculin 13™ formulation Paniculin 13™ was composed of lyophilized L. kefiri SGL 13 (DSM 27331), dry extract of A. paniculata ($50\%$ andrographolide content) and inulin. Each daily dose per mouse consisted of SGL 13 (5 × 108 CFU/kg bw) according to the formula for dose translation [30], A. paniculata (130 mg/kg bw) and inulin (1.25 mg/kg bw). Immediately before starting oral gavage procedure, the powder of Paniculin 13™ was suspended in a suitable volume of sterile phosphate buffered saline (PBS) (dose for each mouse = 0.2 ml/day). To assess the stability of Paniculin 13™, determination of the number of viable cells/vial of SGL 13, was carried out in triplicate by plate count method. The viability was determined on days 7, 14, 28, and 38; that is respectively after 0, 6, 20, and 30 days of Paniculin 13™ oral administration. The tolerance of SGL 13 to gastrointestinal conditions and its adhesion ability to epithelial cells HT-29, which are prerequisites for colonization and functional activity, were determined as described in a previous study [27]. Cells were harvested by centrifugation (17000 × g, 15 min at 4°C) and then lyophilized in a freeze-drier (Telstar Liobeta 3PS, Spain). After lyophilization, the number of viable cells/g was determined by plate count method. ## Animal experimental design Animal experimental procedures were approved by Italian Ministry of Health (approval no. $\frac{229}{2018}$-PR) and were performed in accordance with national regulation for care and use of laboratory animals and following internal procedures of Experimental Zooprophylactic Institute of Abruzzo and Molise (IZSAM) in order to minimize animal suffering and distress. Forty 7 weeks-old C57BL/6J (WT) mice, all male, were obtained from Charles Rivers Laboratories (Wilmington, MA, USA). The number of animals per group was statistically determined and approved by the Italian Ministry of Health. On the basis of well documented safety profile of both A. paniculata (31–33) and L. kefiri [28, 34, 35] and to adhere to the 3Rs rules (Replacement, Reduction and Refinement), we reduced the number of animals required to assess the safety of Paniculin 13™ (G1 and G2). In order to demonstrate the efficacy of Paniculin 13™ in reducing the severity of IBD, we used a larger sample size for experimental groups treated with DSS (G3 and G4), improving the statistical power. Moreover, to maintain the initial assumptions about the minimal differences that the test would be able to highlight as significant, we included extra mice (one for each experimental group) to prevent unexpected deaths. Therefore, animals were randomized into 4 experimental groups: control group called G1 ($$n = 6$$ + 1), Paniculin 13™ group called G2 ($$n = 6$$ + 1), DSS group called G3 ($$n = 12$$ + 1) and DSS + Paniculin 13™ group called G4 ($$n = 12$$ + 1). Mice were housed in collective enriched cages in a controlled environment (12:12 h light/dark cycle, 22 ± 2°C room temperature, 55 ± $5\%$ humidity) and were allowed to acclimate for 7 days. Mice were transferred to individual cages only before fecal samples collection. The whole experimental procedure lasted 38 days. All animals were fed the same diet (2016 Teklad global diet, Envigo) with access to food and water ad libitum. G1 and G3 received the vehicle PBS for 31 consecutive days (days 8–38), G2 and G4 received Paniculin 13™ for 31 consecutive days (days 8–38). Paniculin 13™ and PBS were administered by daily oral gavage (0.2 ml/day). In addition, G3 and G4 received $1.5\%$ (w/v) DSS (DSS for colitis, TdB Consultancy, Sweden, MW 40,000) in drinking water for 9 consecutive days (15–23) to induce colitis, followed by 15 days of washout (days 24–38). Fresh DSS solution was prepared every day and administered to mice. The average amount of DSS solution taken was recorded daily. Tolerability and safety of Paniculin 13™ were assessed. The wellbeing and state of health of mice were observed during the entire experimental period by the animal technologists and care staff, using a clinical score sheet system. As shown in Table 1, a specific pain scale was used in order to minimize animal pain and distress. Different parameters such as general appearance, food/water intake, behavior, nest building, were assessed as indicators of health and welfare in mice (36–39). Body weight was measured weekly for mice of G1 and G2 throughout the whole experimental period and daily during the DSS treatment period for mice of G3 and G4. Moreover, during DSS administration clinical symptoms of colitis were monitored daily such as weight loss, diarrhea and presence of blood in the stool [40, 41]. TABLE 1 \| Appearance \| Score \| \| --- \| --- \| \| <5% weight loss \| 0 \| \| 5–10% weight loss \| 1 \| \| 11–15% weight loss \| 2 \| \| 16–20% weight loss \| 3 \| \| >20% weight loss \| HEP \| \| Reduced grooming activity \| 1 \| \| Pinched skin/dehydration/slight piloerection \| 2 \| \| Strong piloerection \| 3 \| \| Body functions \| Score \| \| Dyspnea \| 2 \| \| Tachypnea \| 1 \| \| Food and water intake \| Score \| \| 25–40% food/water intake reduction (for 3 days) \| 1 \| \| >40% food/water intake reduction (for 3 days) \| 2 \| \| >40% food/water intake reduction (for more than 3 days) \| 3 \| \| Behavior \| Score \| \| Reluctance to move, slightly hunched posture \| 1 \| \| Lethargy, apathy, hunched posture \| 2 \| \| Persistent immobility < 24 h \| 3 \| \| Immobility > 24 h \| HEP \| \| Vocalization on handling \| 1 \| \| Vocalization, tense and nervous on handling, aggressivity \| 2 \| \| Vocalization on moving/spontaneous \| 3 \| \| Nest building \| Score \| \| Nestlet slightly manipulated \| 1 \| \| Nestlet noticeably manipulated \| 2 \| \| No nest \| 3 \| \| Altered intestinal function \| Score \| \| Mild-soft stool and diarrhea \| 1 \| \| Presence of blood on the bedding \| 2 \| \| Rectal bleeding \| HEP \| At the end of the study mice were euthanized by isoflurane inhalation followed by cervical dislocation, spleen and colon were collected and, respectively weighted and measured. Blood sampling (0.5–0.8 ml) were collected from each mouse. Blood glucose was measured by tail puncture using a glucometer (Accu-Chek® Aviva, Roche Diagnostics, Germany) on days 23, 30, 38 for mice of G1 and G2 and on days 18, 23, 30, 38 for mice of G3 and G4. Total cholesterol (TC) triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) were measured at the end of the experiment (day 38) with enzymatic methods [42], using standard biochemical evaluations with Cobas c501 analyzer (Roche Diagnostic, Monza, Italy). The experimental design is depicted in Figure 1. FIGURE 1: Experimental design of the study. Experimental groups (G1, G2, G3, G4); weight determination ●; Paniculin 13™ administration; DSS treatment; DAI score; collection date and time of stools, blood and tissues are indicated in the grid (x). Stool samples on days 18 and 23 were collected only for mice of G3 and G4.* ## Disease Activity Index (DAI) The DAI was calculated by the sum of scores assigned for each DAI category (body weight loss, stool consistency and bleeding) based on scoring system of De Fazio et al. [ 41, 43]. All parameters were scored from day 15 to day 38. Stool samples were collected by placing a single mouse in an empty cage without bedding material for 15 to 30 min and fecal pellet were checked for color, consistency and presence of blood. The criteria for DAI scoring are described in detail below. Body weight loss: body weight loss $0\%$, 0 points; 5–$10\%$, 1 points; 11–$15\%$, 2 points; 16–$20\%$, 3 points; >$20\%$, 4 points. Weight loss percentage was calculated, using the formula: Stool consistency: formed, 0 points; mild-soft, 1 points; very soft, 2 points; watery stool, 3 points. Bleeding: normal color stool, 0 points; brown color, 1 points; reddish color, 2 points; bloody stool, 3 points. The DAI score ranged from 0 to 10 (total score). ## Histological evaluation of colon tissue Mice ($$n = 7$$ for experimental group G1 and G2, and $$n = 8$$ for G3 and G4) were sacrificed on day 38 and the colons were excised immediately, rinsed in sterile PBS, fixed in $10\%$ neutral buffered formalin, trimmed, and routinely processed for histology. After processing, samples were embedded in paraffin and 4 μm thick sections were obtained and stained with Mayer’s hematoxylin and eosin G [41, 43]. Histological slides were then examined with a light microscopy. ## mRNA expression of MPO, TNFα, iNOS and COX-2 in colon tissues Colon specimens were collected after sacrifice from each mouse and total RNA was extracted according to spin column (Qiagen, Milan, Italy). Tissue samples were homogenized by using TissueLyser II (QIAGEN, Milan, Italy) in ice cold commercial lysis buffer (RNeasy Micro Kit, Qiagen) supplemented with RNase-free DNase I (RNase-Free DNase Set, Qiagen) [44, 45]. Reverse transcription-polymerase first-strand cDNA synthesis was performed by using maxima 1strand cDNA synthesis kit (Thermo, Life Technologies, Waltham, MA, USA). qPCR was then performed by using the Kit Thermo SYBR Green/ROX qPCR Master Mix (Thermo, Life Technologies, Waltham, MA, USA) and specific primers for selected genes. The analyses were performed with the 9,600 Biorad Real-Time PCR Detection Systems (Bio-Rad, CA, United States). The developed primer sequences have been listed in Table 2. PCR cycling conditions were as follows: 94°C for 5 min, 40 cycles at 94°C for 15 s, and 60°C for 30 s. Data were expressed as Ct values and used for the relative quantification of targets with the 2–ΔΔCt calculation. TABLE 2 \| Gene \| Forward \| Reverse \| Accession number \| Percentage identity % \| \| --- \| --- \| --- \| --- \| --- \| \| TNFα \| 5′-CGTCGTAGCAAACCACCAAGT-3′ \| 5′-TTGAAGAGAACCTGGGAGTAGACA-3′ \| X02611.1 \| 100 \| \| MPO \| 5′-AGGAGGCCCGGAAGATTGTA-3′ \| 5′-AGACATTGGCGATTCGAGGG-3′ \| NM_010824.2 \| 100 \| \| IFNγ \| 5′-CCTGCGGCCTAGCTCTGA-3′ \| 5′-CCATGAGGAAGAGCTGCAAAG-3′ \| NM_008337.4 \| 100 \| \| iNOS \| 5′-GGCAGCCTGTGAGACCTTTG-3′ \| 5′ -GCATTGGAAGTGAAGCTGTTC- 3′ \| M87039.1 \| 100 \| \| COX-2 \| 5′-GGGTTGCTGGGGGAAGAAATG-3′ \| 5′ -GGTGGCTGTTTTGGTAGGCTG- 3′ \| M64291.1 \| 100 \| ## Characterization of intestinal microbiota Fresh stool samples were collected on days 14, 30 and 38 for G1 and G2 and on days 14, 18, 23, 30 and 38 for G3 and G4. Samples, were immediately stored at −80°C until microbiota analyses. Total bacterial DNA was extracted from 100 mg of pooled fecal material from each group, using QIAamp PowerFecal DNA Kit (QIAGEN, Manchester, UK) with modified protocol [46]. Amplification of V3-V4 region of 16S rDNA was performed by PCR in 25 μL of final volume mix containing 12.5 ng of microbial DNA and 200 nmol/L of S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 primers [47], carrying Illumina overhang adapter sequences. After two amplification steps and their respective purification steps as previously reported [42] amplicons were used to build libraries which were purified and successively pooled at equimolar concentrations (4 nM), denatured, and diluted to 5 p.m. Samples were sequenced on Illumina MiSeq platform using a 2 × 300 bp paired end protocol with $20\%$ of PhiX as run control, according to the manufacturer’s instructions (Illumina, San Diego, CA, USA). Paired-end reads, obtained by sequencing, were analyzed using the 16S Metagenomics GAIA 2.0 tool (Sequentia Biotech, Barcelona, Spain, 2017; Benchmark of Gaia 2.0), which performs the quality control of the reads/pairs (i.e., trimming, clipping and adapter removal) through FastQC and BBDuk. The reads/pairs are mapped with BWA-MEM against the custom databases (based on NCBI). ## Statistical analyses A non-parametric Mann–Whitney test was applied to verify any differences between 2 independent samples. Comparisons between k independent samples were made using the non-parametric Kruskal–Wallis test. If the Kruskal–Wallis test was significant, a post hoc analysis was performed to compare all possible pairs. Specifically, post hoc comparisons were carried out with the Dunn test (using the Bonferroni correction). ## Safety and stability assessment of Paniculin 13™ Prior to randomization, 3 mice were euthanized due to serious injuries. Thus, sample size of each experimental group was as follows: G1 ($$n = 7$$), G2 ($$n = 7$$), G3 ($$n = 13$$), G4 ($$n = 13$$). Throughout the experimental period, neither death nor adverse effects were observed in any of the animals treated with Paniculin 13™ (G2). Paniculin 13™ had no effect on blood glucose at the considered time points (data not shown). Regarding serum lipid profile (Figure 2), no significant differences in HDL-C and TC were found among groups. On the contrary, mice treated with only DSS (G3) and those treated with DSS + Paniculin 13™ (G4), showed higher TG values, compared to mice assigned to G1 and G2, but only the differences between G4 and G1 and G4 and G2 were statistically significant ($p \leq 0.0083$). To support the effectiveness of the association between SGL 13 and A. paniculata (Paniculin 13™), the viability and stability of SGL 13 in the presence of herbal extract was assessed in vitro by plate count. No significant differences in cell concentration were found among the considered time points, namely, day 1, day 14, day 28, and day 38 (10.07 ± 0.08; 10.12 ± 0.03; 10.10 ± 0.02; 10.04 ± 0.04). The results were expressed in log10/vial. FIGURE 2: Lipid profile in mice of G1, G2, G3, and G4 at the end of the experimental period (day 38). TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides. ## Effects of Paniculin 13™ on clinical signs of colitis induced by DSS Mice assigned to DSS group (G3) began to lose weight on day 17, only after 2 days of DSS, with a maximum mean weight loss of $24.35\%$ on day 25. On the contrary, in mice assigned to DSS + Paniculin 13™ group (G4), a first reduction in body weight was observed on day 21, after 6 days of DSS, with a peak of $22.45\%$ on day 25. Mice of G4 showed less body weight loss, compared to those from G3, with a maximum weight loss difference on day 32. Mice of G3 continued to lose weight until day 37 and began to gain weight on day 38. On the other hand, mice of G4 showed a progressive increase in body weight from day 35 to the end of the experimental period, with a maximum mean weight gain of $5.45\%$ on day 38. Overall, all the differences in weight changes between G3 and G4, were statistically significant ($p \leq 0.05$), throughout the whole experimental period (Figure 3A). FIGURE 3: Variation in the average body weight gain percentage between mice of G3 and G4 (A). Disease Activity Index (DAI) score of colitis in 1.5% DSS treated mice G3 and G4 (B). The weekly mean body weight of G1 and G2 was similar during the whole experimental period: G1 day 0 (21.87 ± 1.40), G1 day 15 (23.35 ± 1.07), G1 day 38 (25.33 ± 1.54) and G2 day 0 (20.93 ± 1.28), G2 day 15 (22.76 ± 2.32), G2 day 38 (25.89 ± 2.09). Furthermore, all mice from G3 and G4 showed changes in stool consistency, 7 days after the beginning of $1.5\%$ DSS assumption (day 22). Stool consistency scores were higher in mice of G3, compared those of G4, these last exhibiting only soft or very soft stools. A normal stool consistency was reached starting from day 30 for both G3 and G4. The presence of blood in feces was observed after 8 days of DSS (day 23) and was noticed until day 28 for both mice assigned to G3 and G4. Mice of G3 reached a maximum score of 3 for bleeding (bloody stools), while mice of G4 showed only reddish stools (score of 2). Overall, the most severe clinical signs were observed between day 23 and 26 for all mice, reaching a peak on day 25, with an average DAI score of 9 for mice of G3 and of 7.75 for mice of G4. DAI values were significantly higher in mice of G3 compared to corresponding values of G4 ($p \leq 0.05$), throughout the entire DSS treatment period (day 38). DAI score results are all displayed in Figure 3B. Mice of G3 started to show mild signs of pain and distress (slight piloerection, reluctance to move, reduction in nest building activity) on day 19, only after 4 days of DSS. On the contrary, mice of G4 showed similar signs after 9 days of DSS. Symptoms of colitis became severe at the end of DSS treatment, peaking on day 25, when the maximum weight loss was observed. Consequently, 3 mice of both G3 and G4 died on day 25 and 2 mice died on day 28 in G3 and on day 31 in G4. The mortality rate was $41.7\%$. Nevertheless, mice that survived DSS treatment in G4, showed in general a more rapid improving of health conditions than those of G3. Overall, Paniculin 13™ seems to delay signs and symptoms of colitis, e.g., abdominal pain, stool consistency, presence of blood in stools, and weight loss. ## Effects of Paniculin 13™ on colon length and spleen weight A non-parametric Mann–Whitney test was applied to verify any differences between 2 independent samples. The macroscopic changes associated with DSS-induced colitis classically include shortening of the colon [48], and splenomegaly [49]. As colitis progresses and the epithelium begins to erode, the colon thins and becomes shorter and immune cells infiltrate the lamina propria [40]. Changes in the inflammatory milieu driven by infiltration of immune cells results in an increased spleen weight. We found no significant differences in the mean colon length (cm) among control group G1, Paniculin 13™ group G2 and Paniculin 13™ + DSS group G4 (7.23 ± 0.46, 7.30 ± 0.50, 7.37 ± 0.58). On the contrary, the mean colon length of G3 was shorter (6.36 ± 0.63) than that observed in the other groups, even if this difference was not statistically significant. The mean spleen weight (mg) of mice in G1 and G2 was not significantly different, namely, 60.88 ± 9.65 and 76.83 ± 24.88, respectively. In comparison to G1 and G2, the mean spleen weight of DSS group was higher than that of DSS + Paniculin 13™ group (G3 = 125.01 ± 48.91; G4 = 100.51 ± 31.06), but only the differences between G3 and G1 were significant ($p \leq 0.0083$). ## Histological evaluation of colitis Histopathological examination was carried out on small intestine and colon. Lymphoid aggregates in lamina propria were found in all mice as a part of gut associated lymphoid tissue (GALT). Lymphoid aggregates were classified as small when composed of less than 100 cells and large when composed of 100 or more cells. Histopathological changes in the colon after DSS treatment are shown in Figure 4. Mice of control group (G1) and Paniculin 13™ group (G2) revealed a fully intact colonic epithelium with no inflammatory infiltration. The tissue damage induced by DSS, tended to be limited to the terminal colon. Mice of DSS group (G3) showed moderate cellular infiltration in the colonic mucosa (granulocytes, lymphocytes, macrophages) in association to flattening of the intestinal epithelium. On the contrary, mice of Paniculin 13™ + DSS group (G4) showed reduced post ulcerative areas with a lower inflammatory infiltration and a flattened intestinal epithelium. In both G3 and G4 signs of gut epithelial regeneration were seen at day 38, after 14 days of washout. Overall, supplementation with Paniculin 13™ seems to ameliorate colon tissue injury induced by DSS. FIGURE 4: Differences in histological parameters during experimental colitis induced by DSS. Colon was collected at day 38 from mice of G1 (A), G2 (B), G3 (C), and G4 (D). Colonic sections were stained with hematoxylin and eosin and representative images were captured at 20× magnification. DSS treated mice are G3 and G4 groups. ## Effects of Paniculin 13™ on mRNA expression levels of MPO, TNFα, iNOS and COX-2 Exposure to DSS significantly raised the mRNA levels of MPO (Figure 5A) and TNFα (Figure 5B) in mice treated only with DSS (G3), an effect which was counteracted in G4 receiving DSS + Paniculin 13™, even if a significant difference between G3 and G4 was not reached. In both cases, only the differences between G3 and G2 were found statistically significant ($p \leq 0.0083$). A similar profile of expression was found in the case of iNOS (Figure 5C), where G3 showed significantly higher iNOS mRNA levels ($p \leq 0.0083$) compared to both G2 and G4. FIGURE 5: Pro-inflammatory mediators’ expression in colon tissue in mice of G1, G2, G3, and G4. mRNA levels of MPO (A), TNFα (B), iNOS (C), and COX-2 (D) were all normalized against RPL-19 mRNA. Contrary to previous findings [41, 50], administration of DSS failed to induce COX-2 over-expression, in comparison to untreated mice (Figure 5D). Moreover, a decreased expression of this inflammatory mediator was observed in mice of G2 treated only with Paniculin 13™, namely, significant lower mRNA levels ($p \leq 0.0083$) compared to G1 (control). ## Intestinal microbiota modifications induced by Paniculin 13™ Firmicutes and Bacteroidetes were the most abundant phyla in all samples. DSS induced an increment in inflammatory Proteobacteria which was found 1 week after DSS washout. This effect was mitigated by Paniculin 13™ supplementation (day 30, G3 = $3.09\%$ and G4 = $0.68\%$) (Figure 6A). Going down the taxonomic scale, the families (Figure 6B) of Enterobacteriaceae and Halomonadaceae, both belonging to Proteobacteria, were found enriched in DSS-treated mice, but were reduced by Paniculin 13™ supplementation. In details, Enterobacteriaceae increased upon DSS treatment in G3 (day 23 = $0.03328\%$, day 30 = $1.04478\%$ and day 38 = $0.13112\%$) while remaining less abundant in mice of G4 receiving DSS + Paniculin 13™ (day 23 = $0\%$, day 30 = $0.02845\%$ and day 38 = $0.00698\%$). FIGURE 6: Heatmaps showing the fecal microbiota composition in mice of G1, G2, G3, and G4 at the phylum (A), family (B), genus (C), and species (D) level. As for Halomonadaceae, they were measured during DSS treatment in G3 (day 18 = $0.00812\%$ and day 23 = $0.00792\%$) and in lower amounts in G4 (day 18 = $0.00216\%$ and day 23 = $0.00341\%$), but after DSS washout, they only remained measurable in non-supplemented mice (day 30, G3 = $0.00434\%$ and G4 = $0\%$; day 38, G3 = $0.00334\%$ and G4 = $0\%$). In addition, Bacteroidaceae, previously found increased in mice treated with DSS [51], showed higher levels in G3 (day 18 = $1.03\%$ and day 23 = $1.38\%$) as compared to G4 (day 18 = $0.77\%$ and day 23 = $0.66\%$). Moreover, the Carnobacteriaceae family, which is expanded in IBD patients [52, 53], were detected in G3 during and after DSS exposure, but disappeared in G4 from day 23 to day 30. At the genus level (Figure 6C), a progressive increase in the LPS-producing Bacteroides was observed in DSS-treated mice (G3 day 18, $1.00\%$ and G3 day 23 $1.34\%$) in agreement with previous reports [54]. This increase, however, was mitigated by Paniculin 13™ supplementation in G4 (day 18 = $0.73\%$ and day 23 = $0.66\%$). Among other interesting changes, Acidaminobacter, which is increased in colorectal cancer patients [55], was detected in all time-point of G3 but was completely absent in Paniculin 13™ -supplemented G4. Further Allobaculum, a genus enhancing intestinal barrier by producing short chain fatty acids [56] was measured in G1 and G2, and it remained undetectable in G3 during DSS treatment and after washout, while being recovered in G4. Finally, at the species level (Figure 6D), some pro-inflammatory and colitis-associated bacteria were only measured in G3 but not in G4 during and after DSS treatment, such as Escherichia coli (day 30, G3 = $0.18483\%$ and G4 = $0\%$; day 38 G3 = $0.11359\%$ and G4 = $0\%$), and Enterococcus faecalis (day 23, G3 = $0.01981\%$ and G4 = $0\%$; day 30, G3 = $0.02083\%$ and G4 = $0\%$). On the other hand, some beneficial species, producing short chain fatty acids, resulted enriched in the same comparisons, including Ruminococcus bromii which was absent in G3 but was measured in G4 during DSS treatment (day 18 and day 23) and Eubacterium xylanophilum which was depleted in both groups during DSS treatment but was restored only in DSS + Paniculin 13™ G4 during washout (day 30 and day 38). ## Discussion In the last decade, there has been a growing interest in the use of probiotics for the treatment of intestinal disorders [57]. Current IBD therapies, include treatment with COX-2 inhibitors, corticosteroids, immunomodulators, antibiotics, and biological agents. Long-term use of these conventional drugs can cause several severe side-effects, e.g., nausea, vomiting, headache, sickness, fever, rash, diarrhea, infections [58]. Hence there is a need to discover effective and safe non-pharmacological remedies, as supportive approaches to IBD therapy, able to alleviate clinical symptoms and improve the quality of life of patients [13, 59]. The beneficial properties of L. kefiri SGL 13 have been already investigated on human colon adenocarcinoma cells, HT-29. Upon treatment with SGL 13, the proteomic profile of HT-29 showed a total of 60 differentially expressed proteins compared to untreated cells, an effect apparently correlated with pro-apoptotic and anti-inflammatory pathways, highlighting a potential anti-tumor effect of SGL 13 [27]. Evidence of efficacy is also available for the natural extract A. paniculata, in patients with IBD. The main known components of A. paniculata, the diterpene lactones, principally andrographolide and its derivatives, have been reported to exert anti-inflammatory properties through inhibition of the transcription factor NF-κB. NF-κB activation promoted the increased expression and synthesis of different pro-inflammatory mediators involved in the inflammatory response associated with IBD [60]. In the present investigation, we evaluated the impact of a combination of SGL 13 and A. paniculata herbal extract on a DSS-induced colitis mouse model, demonstrating that different IBD severity indicators were improved. In particular, the administration of Paniculin 13™ for 31 days reduced the weight loss percentage and the DAI score in DSS + Paniculin 13™ group (G4), that showed a reduced spleen weight and a longer colon tract compared to mice treated only with DSS (G3). Moreover, Paniculin 13™ ameliorated the general health status of G4, accelerating their recovery phase after DSS administration. The efficacy of Paniculin 13™ was supported by the histological examination of small intestine and colon sections, also since DSS + Paniculin 13™ group (G4) had reduced post ulcerative areas with a lower inflammatory infiltration and a flattened intestinal epithelium compared to DSS group (G3). These data positively correlate with mRNA expression in colon tissue of MPO, TNFα, iNOS, mediators which play a role in colitis induced by DSS [41]. Tolerability and safety of Paniculin 13™ were demonstrated, since physiological parameters, such as blood glucose and lipid profile, and histological data were similar between control group (G1) and mice treated only with Paniculin 13™ (G2). Moreover, neither death nor adverse effects were observed in mice of G2 compared to G1. Concerning, the gut microbiota composition, in clinical trials, probiotics have been used as a supportive therapy for IBD, for the prevention of dysbiosis associated with long-term antibiotic or immunosuppressive therapies, as well as in the treatment of dysbiosis in patients with newly diagnosed IBD or with exacerbation of the disease [61, 62]. Moreover, a number of different bacterial strains belonging to the genus of Lactobacillus have been used as probiotics for their ability to inhibit the growth of pathogenic bacteria [63]. We found that Paniculin 13™ supplementation affected the microbial community, inducing favorable changes, such as the reduction of some inflammatory bacteria and the enrichment of some beneficial bacteria producing short chains fatty acids. Although we are aware that the single administration with A. paniculata and L. kefiri SGL 13 alone is missing, which could have better clarified the synergistic effect due to the combined treatment, a possible limitation glimpsed, that reduced the statistical significance of our study, relies on the unexpected number of deaths in the groups receiving DSS (G3 and G4) associated with a more severe clinical phenotype, although we used the same protocol published by De Fazio et al. describing a mild model of colitis [41, 43]. This investigation of course represents a preclinical assessment prior to developing further studies to confirm that the oral administration of Paniculin 13™ in patients with gut inflammation associated with dysbiosis is really effective. Overall, this study highlighted the multifunctional properties of Paniculin 13™ supplementation, since dysbiosis, local and systemic inflammation, and tissue damage were simultaneously ameliorated. In conclusion, although further investigations are required, SGL 13 and A. paniculata could be considered a plausible supportive approach to IBD conventional therapy. ## Data availability statement The sequencing datasets presented in this study can be found in the SRA (Sequence Read Archive) of NCBI, accession number: PRJNA899360 (https://www.ebi.ac.uk/ena/browser/home). ## Ethics statement This animal study was reviewed and approved by Italian Ministry of Health (approval no. $\frac{229}{2018}$-PR of $\frac{13}{11}$/2017). ## Author contributions LM, ERi, EB, and FF: conception and design of the study. LM, RS, and CP: data curation. LM, CM, and FF: formal analysis. LM, MR, MPV, and ERo: methodology. LM and EB: project administration. ERi: resources. VP, FP, and FF: supervision. LM, MR, RS, and FF: writing—original draft. ERi, EB, AC, CM, MPV, ERo, CP, and VP: writing—review and editing. All authors contributed to the article and approved the submitted version. ## Conflict of interest LM, ERi, and FF were employed by PNK Farmaceutici S.p.a. EB was employed by Sintal Dietetics S.r.l. Sintal Dietetics S.r.l deposited Lactobacillus kefiri SGL 13 for purposes of European patent and Paniculin 13™ is a trade market of Sintal Dietetics S.r.l. 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 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. M’Koma A. Inflammatory bowel disease: an expanding global health problem.. 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--- title: 'Abuse and humiliation in the delivery room: Prevalence and associated factors of obstetric violence in Ghana' authors: - Abena Asefuaba Yalley - Dare Abioye - Seth Christopher Yaw Appiah - Anke Hoeffler journal: Frontiers in Public Health year: 2023 pmcid: PMC9968731 doi: 10.3389/fpubh.2023.988961 license: CC BY 4.0 --- # Abuse and humiliation in the delivery room: Prevalence and associated factors of obstetric violence in Ghana ## Abstract ### Background Abuse and mistreatment of women during childbirth is a major barrier to facility-based delivery, putting women at risk of avoidable complications, trauma and negative health outcomes including death. We study the prevalence of obstetric violence (OV) and its associated factors in the Ashanti and Western Regions of Ghana. ### Methodology A facility-based cross-sectional survey was conducted in eight public health facilities from September to December 2021. Specifically, close-ended questionnaires were administered to 1,854 women, aged 15–45 who gave birth in the health facilities. The data collected include the sociodemographic attributes of women, their obstetric history and experiences of OV based on the seven typologies according to the categorization by Bowser and Hills. ### Findings We find that about two in every three women ($65.3\%$) experience OV. The most common form of OV is non-confidential care ($35.8\%$), followed by abandoned care ($33.4\%$), non-dignified care ($28.5\%$) and physical abuse ($27.4\%$). Furthermore, $7.7\%$ of women were detained in health facilities for their inability to pay their bills, $7.5\%$ received non-consented care while $11.0\%$ reported discriminated care. A test for associated factors of OV yielded few results. Single women (OR 1.6, $95\%$ CI 1.2–2.2) and women who reported birth complications (OR 3.2, $95\%$ CI 2.4–4.3) were more likely to experience OV compared with married women and women who had no birth complications. In addition, teenage mothers (OR 2.6, $95\%$ CI 1.5–4.5) were more likely to experience physical abuse compared to older mothers. Rural vs. urban location, employment status, gender of birth attendant, type of delivery, time of delivery, the ethnicity of the mothers and their social class were all not statistically significant. ### Conclusion The prevalence of OV in the Ashanti and Western Regions was high and only few variables were strongly associated with OV, suggesting that all women are at risk of abuse. Interventions should aim at promoting alternative birth strategies devoid of violence and changing the organizational culture of violence embedded in the obstetric care in Ghana. ## 1. Introduction Maternal mortality and morbidity remain a major global health challenge and a threat to women's lives worldwide. According to the World Health Organization (WHO) [1], 211 deaths occur in every 100,000 live births as a result of preventable causes associated with pregnancy and childbirth globally with $94\%$ of these deaths occurring in developing countries. Sub-Saharan Africa alone accounts for two-thirds of all maternal deaths worldwide due to poor obstetric care and unskilled birthing/low institutional deliveries (1–4). Ghana ranks high with a maternal mortality ratio of 310 deaths per 100,000 births, which is far above the global target of 70 deaths per 100,000 births [5]. The majority of these deaths are preventable through the provision of high-quality maternal and obstetric services. Increasing the number of skilled birth attendants (SBAs) has been a cornerstone of international efforts to reduce maternal mortality as demonstrable evidence reveals low skilled birth attendance to be closely associated with high maternal mortality [1, 6]. Studies demonstrate that about $70\%$ of maternal and neonatal deaths could be prevented if all deliveries are attended by SBAs [2]. Consequently, there have been intense efforts in Ghana to increase institutional deliveries by strengthening community-based health planning and services, and free healthcare services for pregnant women through the National Health Insurance Scheme since 2008. However, a good number of pregnant women still deliver without skilled health care service. The rate of skilled birth deliveries is between 54 and $63\%$ compared to the 80–$97\%$ of women who utilize antenatal care services [7, 8], indicating a high proportion of Ghanaian women who do not use facility-based services for childbirth, a likely contributor to the high maternal mortality ratio in Ghana. To reduce maternal mortality, there is a need to identify and address barriers that limit access and reduce the quality of obstetric services in the health system. Recent studies on the barriers of institutional birthing have established that experiences of mistreatment and abuse in health facilities are major impediments to their use (9–11). Across the globe, many women are abused during childbirth in health facilities. Although the global prevalence of OV is unknown, several studies have highlighted a gross of abuse and mistreatment associated with facility-based childbirth (12–16). The United Nations acknowledges that OV is widespread and systematic in nature [17]. Vacaflor [18] defines OV as “the violence exercised by health personnel on the body and reproductive processes of women (during pregnancy or childbirth), as expressed through dehumanizing treatment, medicalization abuse, and the conversion of natural processes of reproduction into pathological ones”. Obstetric violence is a relatively new concept in global health scholarship with scholars adopting different terminologies such as “mistreatment and abuse”, “disrespect and abuse” and “dehumanized care” to describe violence during childbirth. “ Mistreatment and abuse” and “disrespect and abuse” helps to categorize distinctly the manifestations of violence while OV as a concept stresses the structural dimensions as a gender-based violence that intersects with institutional violence [19]. All the terms emphasize the harmful impact of violence, the over medicalization of childbirth, violations of women's human rights and its gendered nature. For the purpose of this study, OV is used interchangeably with mistreatment and abuse. WHO classifies mistreatment and abuse during childbirth to include: Negative experiences of obstetric care diminish incentives for institutional delivery and undermine technological equipment and facilities created to ensure optimal healthcare. The United Nations Educational, Scientific and Cultural Organization (UNESCO), in its Universal Declaration of Bioethics and Human Rights, declared that health does not depend solely on scientific and technological research developments, but also on psychosocial and cultural factors [21], thus, stressing the importance of humanized birthing. The WHO regulations on intrapartum care stress respectful and humanized care for all women which includes “dignity, privacy, and confidentiality, ensures freedom from harm and mistreatment and enables informed choice and continuous support during labor and childbirth” [22]. OV counteracts this regulation by violating the bodily integrity of women, right to good health, respect, freedom from discrimination, privacy and choice [20, 23]. It reduces women's satisfaction and trust in health facilities, which subsequently affects their willingness to give birth in facility-based services which provide proper management of birth-related complications. Recent studies on the challenges of facility-based delivery for women in developing countries found that obstetric violence is a significant barrier to utilizing health facilities for childbirth [14]. In Latin American, women cite obstetric violence as the main reason for their failure to reuse health facilities for subsequent pregnancies, which leads to a considerable increase in maternal mortality and morbidity [12]. Mistreatment and abuse also heighten trauma, which can lead to complications and poor health outcomes including death [24]. In recent years, a number of studies have reported on women's experiences of OV in some parts of the world, with a prevalence rate of $33\%$ in Mexico, $44\%$ in Argentina, $15\%$ in India and $17\%$ in the United States (25–28). Whereas, the phenomenon of OV is gaining attention in many countries, in Ghana, only few studies have been conducted on OV and these are mainly qualitative [10, 29, 30], limiting our understanding of the magnitude of OV among Ghanaian women. Studies that estimate the prevalence of OV are imperative for understanding the scope of this kind of violence based on which effective interventions that promote humanized care and minimize maternal mortality can be designed. We conducted a comprehensive literature review on OV and found that only two quantitative studies have been conducted in Ghana [31, 32]. While these studies were useful in providing insight on the prevalence of mistreatment and abuse of women during childbirth in health facilities, they were based on a small sample size restricted to urban centers. Considering the fact that the majority of maternal deaths in Ghana occur among rural women due to low utilization of skilled birthing ($43\%$) compared to $74\%$ among urban women (33–35), there is a critical need for a comprehensive study that inculcates the experiences of rural women. Furthermore, there has been no quantitative study that estimates the prevalence and associated factors of obstetric violence in the Ashanti and Western Regions, the first and third most populous regions in Ghana, that are witnessing a decline in skilled birthing [7, 36]. These gaps in knowledge could potentially impede efforts aimed at reducing maternal mortality in Ghana. In the current study, we employ a larger sample size of 1,854 mothers to examine the prevalence of obstetric violence, the associated factors and the characteristics of the perpetrators in urban and rural communities in Ashanti and Western Regions of Ghana. ## 2.1. Study setting and design A facility-based cross-sectional study was conducted from September to December 2021 in public health facilities in the Western and Ashanti Regions of Ghana. According to the Population and Housing Census conducted in 2021, the Western and Ashanti Regions have ~8 million inhabitants, which corresponds to 25.9 % of the total population [36]. About $75\%$ of women in Ashanti Region and $85\%$ of women in the Western Region use antenatal care services while $53.4\%$ and $53.8\%$ of births take place in health facilities in Ashanti and Western Regions, respectively [7]. The healthcare system in Ghana is classified into three main levels- primary, secondary and tertiary [37]. The primary and secondary level healthcare services are the main points of delivery for most women in Ghana while the tertiary hospitals handle pregnancy and delivery-related complications. Eight health facilities were purposefully selected in urban and rural areas. The health facilities were included if they were public health facilities, were primary or secondary-level health facilities, provided obstetric care and maternal services, and had a high client flow for maternity services. In Ashanti Region, the study was conducted in two health facilities located in the Kumasi Metropolis district- the Maternal and Child Hospital and the Tafo Government Hospital—and two hospitals serving the rural communities—Nkenkaasu Government Hospital and Ejura District Hospital. Empirical data in Western Region was collected in the Kwesimintsim Polyclinic and Essikado Government hospital, the two main hospitals providing maternal care in Sekondi-Takoradi Metropolis and the Agona Nkwanta Health Center and Dixcove Government Hospital located in the rural part of the Western Region. ## 2.2. Study population and sampling The survey involved a convenience sample of women who had given birth in the selected hospitals between January 2020 and December 2021 and accessed immunization services for their babies. The women were eligible to participate if they had given birth in the selected hospitals, were 15 years or older, were residents in either Ashanti and Western region and gave their consent to participate. Women who delivered outside the selected health facilities, employees of the hospitals, those whose last birth had occurred more than 24 months before contact and women who declined participation or consent, were excluded from the study. The cross-sectional survey being reported precedes a violence reduction intervention (unreported) that is to be followed after this study and as such, the sample size estimates are influenced by the overall study design. The sample was estimated based on Cochran's statistical formula for cross sectional studies [38] with the assumption that $50\%$ of women experience mistreatment and abuse during childbirth, $95\%$ confidence level and a relative precision of $5\%$, $10\%$ non-response rate. The minimum sample size for the study participants as guided by the sample size formula (see Supplementary material) was 1,881. After cleaning the data from missing and incomplete entries, the final sample size was 1,854. When disaggregated by facility, the sample size for Maternal and Child hospital was 252, Tafo Government Hospital 275, Nkenkaasu government hospital 147, and Ejura district hospital 173. In the Western Region, the sample size for Kwesimintsim polyclinic was 291, Essikado government hospital 323, while the final sample size for Dixcove government hospital and Agona Nkwanta health center were 168 and 205, respectively. ## 2.3. Data collection procedure We specially hired and trained enumerators for the recruitment of participants and the data collection. Recruiting was done in person at the child immunization centers where women were receiving immunization services for their babies. The women were approached by the enumerators and invited to participate in the study after a brief description of the study. The women who met the eligibility criteria were then provided with comprehensive information on the purpose of the study and those who agreed and gave their consent were enrolled. A structured, close-ended questionnaire was administered for the women digitally using the Survey-To-Go data collection tool that allowed us to conduct surveys offline or online. Questionnaires were administered in English or Akan, depending on the participant's preference. The recruitment of respondents continued until the sample size for each health facility was reached. The outcome variable was OV and was measured by the proportion of respondents who reported at least one form of abuse during their last pregnancy and childbirth. In the absence of a validated questionnaire for OV we designed our own questionnaire, closely based on the seven performance indicators developed by Bowser and Hill [39]. These include physical abuse, non-consented care, non-confidential care, non-dignified care, discrimination, abandonment of care, and detention in facilities. The questionnaire was shared with public health experts for their critical review and a pilot test was conducted with the target population. In total, 35 verification criteria were utilized to measure the indicators of obstetric violence in a composite scale. The fieldwork was monitored throughout the data collection period, meaning that data entry checks were made every day to ensure consistency and reduce errors. ## 2.4. Ethical considerations Ethical approval for this study was obtained from the Ethics Committee of the University of Konstanz, Germany (IRB Statement $\frac{37}{2021}$) and the Ghana Health Service Ethics Review Committee (GHS-ERC $\frac{010}{06}$/21). Further administrative consents were sought from all the directors of medical services in all the hospitals and health centers where the study was conducted. In addition to these, individual consents were granted by all the women who participated in the study before the administration of the questionnaire. The purpose of the study was duly explained to all women and individual consent forms were signed or thumb printed by respondents after a presentation of the information sheet (explaining the purpose of the study, confidentiality, duration of interviews, withdrawal of consent) were made. A translation of the information sheet into Akan was provided. Parental consent was sought for teenage mothers who participated in the study. Some respondents opted to give verbal consent. To ensure confidentiality, individual details such as the names and telephone numbers of all women were not collected. ## 2.5. Outcome variables The survey instrument included questions on seven separate categories of OV: non-dignified care, non-consented care, discriminated care, non-confidential care, neglected care, detention in the health facility and physical abuse, which were all based on respondent's last childbirth experience. For each category, there were several verification criteria which had “Yes” or “No” dichotomized responses. An abuse was considered to have occurred for the specific category if a respondent reported “Yes” to any of the verification criteria under that category. The research instrument included questions on physical violence such as beating, pinching, holding of mouth/legs, stitching without anesthesia and slapping. Within the scope non-dignified care, respondents were asked if they had been verbally abused, shouted or yelled at, mocked, blamed, if their sexual life was disrespected or if they have received offensive criticism or remarks from health workers. Other aspects of the questionnaire asked questions about women's experiences of discriminatory treatments, if vaginal examinations or other medical procedures were conducted without their consent, if their privacy was breached by caregivers while performing vaginal examinations, or if delivery was carried out in the presence of others as well as if they had been ignored when they requested care or support. Finally, we also included questions on bribery, detention of women in health facilities for their inability to pay medical bills or bring the required materials. Prevalence rates were calculated for each category and for “any OV”. ## 2.6. Explanatory variables The questionnaire also captured several demographic characteristics and obstetric history of respondents. The demographic variables included women's age, marital status, occupation, household income, level of education, the number of children, religion and education. Variables on women's obstetric history included antenatal attendance, the time of delivery, type of delivery (vaginal or cesarean section), facility of birth, the sex and qualification of the birth attendant and finally the presence of complications during labor or childbirth. ## 2.7. Data processing and analysis The data were exported to IBM SPSS Statistics Version 28.0 for data processing. The analysis was carried out in two steps. First, we provide some descriptive analysis on the prevalence of obstetric violence before performing multivariate analyses between the potential associated factors and obstetric violence following the model by Bohren et al. [ 31]. The Crude Odds Ratio (COR) and Adjusted Odds Ratios (AOR) were estimated with $95\%$ confidence intervals ($95\%$ CI). All point estimates with a p-value < 0.05 were considered statistically significant. ## 3.1. Sociodemographic characteristics and obstetric history of participants Table 1 provides some descriptive data of the participants. The majority of women are aged 20–34 ($75.6\%$), with teenagers making up a very small part of the sample ($3.9\%$). The majority of women are married ($72.3\%$) and $27.5\%$ of all the mothers reported on the birth of their first child. A minority of the participants received no formal education ($7.8\%$). More than half of the respondents ($60.0\%$) live in a household with a monthly income of <500 Cedis (about 65 US Dollar). The majority of the respondents are Christians ($80.0\%$) and they live in an urban setting ($61.5\%$). Out of the eight health facilities, half were located in the Ashanti region. The majority of births were attended by a midwife ($71.2\%$) and the presence of medical doctors was mostly reported for caesarian sections. The majority of the birth attendants were female ($83.7\%$). Almost one in five women reported birth complications ($18.9\%$) and caesarian sections accounted for one fifth of all deliveries ($21.2\%$). Table 1 \| Unnamed: 0 \| Rural (n = 713) \| Rural (n = 713).1 \| Urban (n = 1,141) \| Urban (n = 1,141).1 \| Total (n = 1,854) \| Total (n = 1,854).1 \| Unnamed: 7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| N \| % \| N \| % \| N \| % \| \| \| Maternal age (years) \| \| \| \| \| \| \| * \| \| 15–19 years \| 31 \| 4.3% \| 41 \| 3.6% \| 72 \| 3.9% \| \| \| 20–24 years \| 176 \| 24.7% \| 171 \| 15.0% \| 347 \| 18.7% \| \| \| 25–29 years \| 225 \| 31.6% \| 358 \| 31.4% \| 583 \| 31.4% \| \| \| 30–34 years \| 163 \| 22.9% \| 310 \| 27.2% \| 473 \| 25.5% \| \| \| 35–39 years \| 90 \| 12.6% \| 204 \| 17.9% \| 294 \| 15.9% \| \| \| 40–44 years \| 25 \| 3.5% \| 51 \| 4.5% \| 76 \| 4.1% \| \| \| 45 years and above \| 3 \| 0.4% \| 6 \| 0.5% \| 9 \| 0.5% \| \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| Single/never married \| 106 \| 14.9% \| 191 \| 16.7% \| 297 \| 16.0% \| \| \| Married \| 510 \| 71.5% \| 831 \| 72.8% \| 1341 \| 72.3% \| \| \| Divorced \| 6 \| 0.8% \| 5 \| 0.4% \| 11 \| 0.6% \| \| \| Widowed \| 2 \| 0.3% \| 4 \| 0.4% \| 6 \| 0.3% \| \| \| Living with partner \| 89 \| 12.5% \| 110 \| 9.6% \| 199 \| 10.7% \| \| \| Education \| \| \| \| \| \| \| * \| \| No formal education/schooling \| 94 \| 13.2% \| 51 \| 4.5% \| 145 \| 7.8% \| \| \| Primary school (did not complete) \| 98 \| 13.7% \| 49 \| 4.3% \| 147 \| 7.9% \| \| \| Primary school (completed) \| 94 \| 13.2% \| 58 \| 5.1% \| 152 \| 8.2% \| \| \| Junior high school (did not complete) \| 77 \| 10.8% \| 79 \| 6.9% \| 156 \| 8.4% \| \| \| Junior high school (completed) \| 144 \| 20.2% \| 346 \| 30.3% \| 490 \| 26.4% \| \| \| Senior high school (did not complete) \| 40 \| 5.6% \| 86 \| 7.5% \| 126 \| 6.8% \| \| \| Senior high school (completed) \| 108 \| 15.1% \| 285 \| 25.0% \| 393 \| 21.2% \| \| \| Tertiary education \| 58 \| 8.1% \| 187 \| 16.4% \| 245 \| 13.2% \| \| \| Number of births \| \| \| \| \| \| \| * \| \| One \| 152 \| 21.3% \| 358 \| 31.4% \| 510 \| 27.5% \| \| \| Two \| 203 \| 28.5% \| 306 \| 26.8% \| 509 \| 27.5% \| \| \| Three \| 164 \| 23.0% \| 243 \| 21.3% \| 407 \| 22.0% \| \| \| Four \| 88 \| 12.3% \| 148 \| 13.0% \| 236 \| 12.7% \| \| \| Five and above \| 106 \| 14.9% \| 86 \| 7.5% \| 192 \| 10.4% \| \| \| Employment status \| \| \| \| \| \| \| \| \| Working in the formal sector \| 70 \| 9.8% \| 178 \| 15.6% \| 248 \| 13.4% \| \| \| Working in the informal sector \| 487 \| 68.3% \| 744 \| 65.2% \| 1231 \| 66.4% \| \| \| Keeping house (Homemaker/Housewife) \| 50 \| 7.0% \| 71 \| 6.2% \| 121 \| 6.5% \| \| \| Looking for work/ Unemployed \| 63 \| 8.8% \| 94 \| 8.2% \| 157 \| 8.5% \| \| \| Schooling/learning a trade \| 43 \| 6.0% \| 54 \| 4.7% \| 97 \| 5.2% \| \| \| Household income \| \| \| \| \| \| \| * \| \| <200 per month \| 182 \| 25.5% \| 207 \| 18.1% \| 389 \| 21.0% \| \| \| 200– <300 \| 122 \| 17.1% \| 173 \| 15.2% \| 295 \| 15.9% \| \| \| 300– <500 \| 174 \| 24.4% \| 254 \| 22.3% \| 428 \| 23.1% \| \| \| 500– <1,000 \| 123 \| 17.3% \| 299 \| 26.2% \| 422 \| 22.8% \| \| \| 1,000– <2,000 \| 96 \| 13.5% \| 155 \| 13.6% \| 251 \| 13.5% \| \| \| 2,000– <5,000 \| 14 \| 2.0% \| 45 \| 3.9% \| 59 \| 3.2% \| \| \| 5,000 and above \| 2 \| 0.3% \| 8 \| 0.7% \| 10 \| 0.5% \| \| \| Religion \| \| \| \| \| \| \| * \| \| Christian \| 496 \| 69.6% \| 988 \| 86.6% \| 1484 \| 80.0% \| \| \| Muslim \| 209 \| 29.3% \| 150 \| 13.1% \| 359 \| 19.4% \| \| \| Traditional religion \| 8 \| 1.1% \| 0 \| 0.0% \| 8 \| 0.4% \| \| \| Others \| 0 \| 0.0% \| 3 \| 0.3% \| 3 \| 0.2% \| \| \| Region \| Region \| Region \| Region \| Region \| Region \| Region \| Region \| \| Ashanti \| 340 \| 47.7% \| 527 \| 46.2% \| 867 \| 46.8% \| \| \| Western \| 373 \| 52.3% \| 614 \| 53.8% \| 987 \| 53.2% \| \| \| Name of facility \| \| \| \| \| \| \| * \| \| Maternal and child hospital \| 0 \| 0.0% \| 252 \| 22.1% \| 252 \| 13.6% \| \| \| Tafo government hospital \| 0 \| 0.0% \| 275 \| 24.1% \| 275 \| 14.8% \| \| \| Nkenkaasu government hospital \| 167 \| 23.4% \| 0 \| 0.0% \| 167 \| 9.0% \| \| \| Ejura district hospital \| 173 \| 24.3% \| 0 \| 0.0% \| 173 \| 9.3% \| \| \| Kwesimintsim polyclinic \| 0 \| 0.0% \| 291 \| 25.5% \| 291 \| 15.7% \| \| \| Essikado government hospital \| 0 \| 0.0% \| 323 \| 28.3% \| 323 \| 17.4% \| \| \| Dixcove government hospital \| 168 \| 23.6% \| 0 \| 0.0% \| 168 \| 9.1% \| \| \| Agona Nkwanta health center \| 205 \| 28.8% \| 0 \| 0.0% \| 205 \| 11.1% \| \| \| Asked for bribery \| \| \| \| \| \| \| * \| \| Yes \| 67 \| 9.4% \| 32 \| 2.8% \| 99 \| 5.3% \| \| \| No \| 646 \| 90.6% \| 1109 \| 97.2% \| 1755 \| 94.7% \| \| \| Type of delivery \| \| \| \| \| \| \| * \| \| Caesarian section \| 91 \| 12.8% \| 302 \| 26.5% \| 1461 \| 78.8% \| \| \| Vaginal delivery \| 622 \| 87.2% \| 839 \| 73.5% \| 393 \| 21.2% \| \| \| Time of delivery \| \| \| \| \| \| \| \| \| Day (6:00 am−6:59 pm) \| 425 \| 59.6% \| 696 \| 61.0% \| 1121 \| 60.5% \| \| \| Night (7:00 pm−5:59 am) \| 288 \| 40.4% \| 445 \| 39.0% \| 733 \| 39.5% \| \| \| Birth attendant \| \| \| \| \| \| \| * \| \| Medical doctor (Gynecologist) \| 96 \| 13.5% \| 303 \| 26.6% \| 399 \| 21.5% \| \| \| Midwife \| 528 \| 74.1% \| 792 \| 69.4% \| 1320 \| 71.2% \| \| \| Nurse \| 81 \| 11.4% \| 45 \| 3.9% \| 126 \| 6.8% \| \| \| Community health nurse \| 8 \| 1.1% \| 1 \| 0.1% \| 9 \| 0.5% \| \| \| Sex of birth attendant \| Sex of birth attendant \| Sex of birth attendant \| Sex of birth attendant \| Sex of birth attendant \| Sex of birth attendant \| Sex of birth attendant \| Sex of birth attendant \| \| Male \| 102 \| 14.3% \| 201 \| 17.6% \| 303 \| 16.3% \| \| \| Female \| 611 \| 85.7% \| 940 \| 82.4% \| 1551 \| 83.7% \| \| \| Birth complication \| Birth complication \| Birth complication \| Birth complication \| Birth complication \| Birth complication \| Birth complication \| Birth complication \| \| Yes \| 145 \| 20.3% \| 205 \| 18.0% \| 350 \| 18.9% \| \| \| No \| 568 \| 79.7% \| 936 \| 82.0% \| 1504 \| 81.1% \| \| ## 3.2. Prevalence and types of abuse during childbirth In Table 2, we report the prevalence rates of OV. The majority of women reported that they experienced at least one form of OV ($65.3\%$). The most common form of OV is non-confidential care or lack of privacy ($35.8\%$), followed by neglected or abandoned care ($33.4\%$), non-dignified care ($28.5\%$) and physical abuse ($27.4\%$). Detention in the health facility was relatively rare ($7.7\%$) as was non-consented care ($7.5\%$). There appears to be little difference by location as women in rural and urban areas report very similar rates. Table 2 \| Unnamed: 0 \| Rural (n = 713) \| Rural (n = 713).1 \| Urban (n = 1,141) \| Urban (n = 1,141).1 \| Total (n = 1,854) \| Total (n = 1,854).1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| N \| % \| N \| % \| N \| % \| \| Any form of obstetric violence \| 453 \| 63.50% \| 757 \| 66.30% \| 1210 \| 65.30% \| \| Non-confidential care/lack of privacy \| 234 \| 32.80% \| 429 \| 37.60% \| 663 \| 35.80% \| \| Neglected or abandoned care \| 208 \| 29.20% \| 411 \| 36.00% \| 619 \| 33.40% \| \| Non-dignified care \| 201 \| 28.20% \| 327 \| 28.70% \| 528 \| 28.50% \| \| Physical abuse \| 208 \| 29.20% \| 300 \| 26.30% \| 508 \| 27.40% \| \| Discriminated care \| 88 \| 12.30% \| 116 \| 10.20% \| 204 \| 11.00% \| \| Detention in the health facility \| 72 \| 10.10% \| 70 \| 6.10% \| 142 \| 7.70% \| \| Non-consented care \| 60 \| 8.40% \| 79 \| 6.90% \| 139 \| 7.50% \| \| Non-confidential care/lack of privacy \| 234 \| 32.80% \| 429 \| 37.60% \| 663 \| 35.8% * \| \| Anyone other than the midwife or doctor present in the delivery room or labor room without consent \| 130 \| 18.20% \| 254 \| 22.30% \| 384 \| 20.70% \| \| Vaginal examinations were performed in the presence of other people \| 14 \| 2.00% \| 86 \| 7.50% \| 100 \| 5.40% \| \| Disclosure of medical information to others without your permission \| 18 \| 2.50% \| 55 \| 4.80% \| 73 \| 3.90% \| \| Not covered with any cloth or any screen to protect your privacy during delivery \| 115 \| 16.20% \| 201 \| 17.60% \| 316 \| 17.00% \| \| Disposing private information about you (loudly) to others \| 16 \| 2.20% \| 77 \| 6.70% \| 93 \| 5.00% \| \| Neglected or abandoned care \| 208 \| 29.20% \| 411 \| 36.00% \| 619 \| 33.4% \| \| Left unattended by midwives when you needed help \| 85 \| 11.90% \| 146 \| 12.80% \| 231 \| 12.50% \| \| Ignored when you requested for care \| 85 \| 11.90% \| 152 \| 13.30% \| 237 \| 12.80% \| \| Ignored when you ask questions \| 85 \| 11.90% \| 123 \| 10.80% \| 208 \| 11.20% \| \| Lack of support \| 72 \| 10.10% \| 141 \| 12.40% \| 213 \| 11.50% \| \| Health workers were unresponsive to your needs \| 80 \| 11.20% \| 144 \| 12.60% \| 224 \| 12.10% \| \| Separated your baby from you without medical justification \| 43 \| 6.00% \| 94 \| 8.20% \| 137 \| 7.4% \| \| Withdrawal of services for inability to provide materials \| 37 \| 5.20% \| 33 \| 2.90% \| 70 \| 3.80% \| \| First body contact with your baby was not performed \| 57 \| 8.00% \| 148 \| 13.00% \| 205 \| 11.10% \| \| Non dignified care \| 201 \| 28.20% \| 327 \| 28.70% \| 528 \| 28.50% \| \| Insults or verbal abuse \| 93 \| 13.00% \| 124 \| 10.90% \| 217 \| 11.70% \| \| Disrespect of my partner/spouse or family member \| 62 \| 8.70% \| 60 \| 5.30% \| 122 \| 6.60% \| \| Disrespect of my sexual life or history \| 15 \| 2.10% \| 27 \| 2.40% \| 42 \| 2.30% \| \| Laughed at me or made fun of me in a demeaning manner \| 16 \| 2.20% \| 55 \| 4.80% \| 71 \| 3.80% \| \| Criticized my personality, body, appearance \| 24 \| 3.30% \| 91 \| 8.50% \| 115 \| 6.20% \| \| Shouting/yelling \| 147 \| 20.60% \| 189 \| 16.60% \| 336 \| 18.10% \| \| Humiliation \| 51 \| 7.20% \| 87 \| 7.60% \| 138 \| 7.40% \| \| Scolding \| 16 \| 2.20% \| 76 \| 6.70% \| 92 \| 5.00% \| \| Blaming \| 28 \| 3.90% \| 93 \| 8.20% \| 121 \| 6.50% \| \| Offensive remarks \| 21 \| 2.90% \| 133 \| 11.70% \| 154 \| 8.30% \| \| Physical Abuse \| 208 \| 29.20% \| 300 \| 26.30% \| 508 \| 27.40% \| \| Beating/hitting \| 20 \| 2.80% \| 38 \| 4.30% \| 58 \| 3.20% \| \| Pinching \| 19 \| 2.70% \| 11 \| 1.00% \| 30 \| 1.60% \| \| Slapping face/thighs/back \| 58 \| 8.10% \| 93 \| 8.20% \| 151 \| 8.10% \| \| Holding your legs \| 47 \| 6.60% \| 35 \| 3.10% \| 82 \| 4.40% \| \| Holding/ covering your mouth \| 1 \| 0.10% \| 7 \| 0.60% \| 8 \| 0.40% \| \| Stitching without anesthesia \| 98 \| 13.70% \| 127 \| 11.10% \| 225 \| 12.10% \| \| Restriction of movement without medical justification \| 43 \| 6.00% \| 84 \| 7.40% \| 127 \| 6.90% \| \| Restriction from reactions to pain/ forcing me to keep quiet when in pain \| 93 \| 13.00% \| 129 \| 11.30% \| 222 \| 12.00% \| \| Discriminated care \| 88 \| 12.30% \| 116 \| 10.20% \| 204 \| 11.00% \| \| Discriminated treatment based on tribe, socio-economic status, HIV/AIDS \| 88 \| 12.30% \| 116 \| 10.20% \| 204 \| 11.00% \| \| Detention in the health facility \| 72 \| 10.10% \| 70 \| 6.10% \| 142 \| 7.7% \| \| Detained in hospital for inability to pay bills \| 54 \| 7.60% \| 51 \| 4.50% \| 105 \| 5.70% \| \| Detained for inability to provide required materials \| 39 \| 5.50% \| 23 \| 2.00% \| 62 \| 3.30% \| \| Asked to sweep/ mop/ or do anything for inability to pay bills \| 1 \| 0.10% \| 7 \| 0.60% \| 8 \| 0.40% \| \| Non consented care \| 60 \| 8.40% \| 79 \| 6.90% \| 139 \| 7.50% \| \| Midwife/medical doctor did not seek approval before beginning any medical procedure on you \| 29 \| 4.10% \| 61 \| 5.30% \| 90 \| 4.90% \| \| Internal examinations (vaginal examinations etc.) performed without approval \| 51 \| 7.20% \| 58 \| 5.10% \| 109 \| 5.90% \| Inspecting the different categories of OV provides the following insights. In the non-confidential care category, the most common complaint was that other people were present in the labor room without consent, $20.7\%$ of all women reporting lack of privacy. In the neglected or abandoned care category the reasons for OV were manifold, ranging from being left unattended to ignoring requests of care to healthcare workers being unresponsive. Non-dignified care was mainly due to the shouting and yelling by staff ($18.1\%$) and being insulted or verbally abused ($11.7\%$). In the physical violence category, the most common complaint was stitching without anesthesia ($12.1\%$). ## 3.3. Factors associated with obstetric violence in Ghana We now turn to the investigation of which characteristics are associated with the risk of experiencing any form of OV. Although a number of studies on the prevalence of OV exist [10, 11, 26, 28], there is yet no standardized model to investigate the correlates of OV. Rather than offer yet another modeling attempt, we chose to follow the recently published study by Bohren et al. [ 31] in order to benchmark our results. As in their study, we find it very difficult to identify variables that are robustly correlated with the experience of OV. Our results in Table 3 suggest that single women are at higher risk of abuse as they are more likely than married women to experience OV (OR 1.6). None of the other characteristics, age, education and first birth, are statistically significant (column 1). We then carried out a number of additional analyses. Since many of our variables are correlated, we take one variable at a time. We first investigate whether rates of OV differ significantly according to facility and we find that rates are significantly higher in the Dixcove Government Hospital, Maternal and Child Hospital and Kwesimintsim Policlinic. We also find that women who were asked for a bribe were more likely to experience OV (column 3, OR 2.4). Women were also significantly less likely to experience OV if the birth was attended by a midwife or medical doctor, as opposed to a nurse or a community health nurse (column 4, OR 0.4 for midwifes and OR 0.5 for doctors). Women who are poorer or those who reported their household income to be below 500 Cedis, were also less likely to experience OV (column 5). Women who reported complications during childbirth were much more likely to report OV, they were twice as likely to report OV compared to women who did not report complications (OR 3.2, column 6). Christians were also more likely to report OV (column 7, OR 1.4,). We also investigated rural vs. urban location, gender of birth attendant, type of delivery (vaginal vs. Caesarian section), time of childbirth (day/night), the ethnicity of the mothers and their social class. None of these variables were statistically significant and we report these result in the Supplementary material. Furthermore, irrespective of which variable was added, the ORs in our baseline model (Table 3, column 1) remained qualitatively similar. Following Bohren et al. [ 31], we also investigate the different forms of OV in Table 4. Although age is generally not significant, we find that teenage mothers are much more likely to experience physical abuse when compared to women aged 30 and over (OR 2.6). Marital status appears to be significantly associated with neglected care, non-confidential care and non-dignified care, single women being at greater risk of experiencing these forms of OV. There is also some suggestive evidence that women with no formal education are at higher risk of experiencing detention, non-consented care and discriminated care. First birth was not statistically significant in any of the models. ## 4. Discussion In this study, we examine the prevalence of obstetric violence and its associated factors in rural and urban areas in Western and Ashanti Regions in Ghana. Like other studies (11, 32, 40–42), we have found the prevalence of OV to be high in health care facilities, the majority of women reported the experience of at least one form of OV ($65.3\%$). However, it is difficult to identify characteristics that make women more vulnerable to OV. We provide some evidence that women who are married, older and have some formal education are less likely to be subjected to OV, but the evidence is not robust across all models we have investigated for different forms of OV. Some of the additional variables we investigated suggest that nurses and community health nurses tend to be more violent toward their patients. Only $7.3\%$ of all births were attended by nurses and community health nurses and these clearly provide worse care for women during birth. The increased risk of violence by nurses and community health nurses could be because they are not trained to provide delivery services but are forced to take up delivery services due to the shortage of midwives especially in rural areas. Hence, they are more likely to use force and abuse due to a lack of skill during birth attendance. Qualitative evidence on the drivers of mistreatments and abuse reveal that health workers view obstetric violence as an essential means to ensure a positive birth outcome for the babies and therefore abuse women to force delivery (43–45). While concerns for the safety of the baby may provide some explanations for abuses during labor and childbirth, this procedure has negative health consequences for the mothers and this could be long term. Furthermore, our study also found that single women were about $60\%$ more likely than married women to be abused during childbirth, revealing how gender constructions of marriage shape women's treatment. Marriage is considered a symbol of responsibility, honor and a prestigious identity for Ghanaian women [46], earning married women more respect in society with single mothers being perceived as irresponsible and somewhat sexually immoral. Our finding is consistent with Bohren et al. [ 31] study on mistreatment during childbirth in Ghana, Guinea, Myanmar and Nigeria, where abuse was much higher among single mothers than married women. A high proportion of women reported that they were shouted at and that they were verbally abused ($29.9\%$). This was more likely if the birth was attended by a midwife than by a medical doctor (see Supplementary material). With a midwife patient ratio of 2.7 per 1,000 patients in Ghana [47], midwives work under undue stress which significantly influences how women are treated. The shouting and verbal abuse is a strong indicator of the inability to cope with stress in the delivery room (31, 48–50). Also, women who experience birth complications are at higher risk of abuse. This could be explained by the long duration of contact that women with complications have with caregivers and longer stays in the health facilities. Long stays in health facilities have been associated with violence especially when violence is inculcated in daily routines of care [51]. Similar evidence was found in Ethiopia where women who faced complications in the labor and birthing process were 1.6 times more likely to be abused [52]. Many women report physical abuse ($27.4\%$) and the most common violation is stitching without anesthesia ($12.4\%$). A similar form abuse has been reported in Mexico [26] and Nigeria [40] although at a relatively lower rate of 4 and $9\%$, respectively. Stitching of the vagina without anesthesia is considered torturous, a human right abuse and against the WHO regulations on intrapartum care [22, 53, 54]. Within the category of physical violence, teenage mothers were at a higher risk, indicating inequalities in the treatment of women during childbirth. Our finding is supported by other studies where teenage mothers were humiliated for their engagement in pre-marital sex [10, 31]. Much of the literature on African health focuses on the rural and urban differences (55–59), but we could find no differences between OV in rural and urban health facilities in Ghana, contrary to the case in India and Ethiopia where women in urban areas reported more abuse than women residing in rural areas [12, 60]. This could probably be due to the fact that violence during birth is institutionalized and normalized as part of maternal care irrespective of where the facilities are located. This has been supported by qualitative studies on delivery room violence, suggesting the normalization of violence in delivery services in Ghanaian health institutions [61, 62]. We found some evidence that women were not given the right treatment although it was given to others in the same facility. This discriminated care was reported by $11.0\%$ of the women. Other studies on OV found relatively higher discriminatory practices ($21\%$) in Ethiopia and $20\%$ in Nigeria [40, 52]. We attempted to investigate the correlates of discrimination further but found no evidence that women were discriminated against due to their ethnic group or social class. Being a member of a religious minority appeared to work in the favor of women, the majority, in this region Christians, were at higher risk of experiencing OV. The picture emerging from our study is that women suffer great harm in the delivery room and that this should be addressed urgently. Our study offers some pointers. As nurses are unlikely to provide care which is free of violence, they should receive additional training before attending to births. Birthing puts women in their most vulnerable physical and mental state and medical staff should receive more training to understand the negative consequences of violence on mothers, the importance of comprehensive care for women's optimal health and that of their newborns. Institutional factors such as high patient to caregiver ratio and lack of medical equipment play a significant role in inducing stress which tends to contribute to abusive treatments. Reducing the workload of caregivers, recruiting more caregivers and providing adequate medical supplies and equipment are important steps deal with the problem. More crucially, there is also the need for structural changes which include training on dignified care, gender norms and underlying socio-cultural factors the shape obstetric violence. Like all forms of gender-based violence, enforcing legal frameworks for maternal care and legal actions against OV are important steps to dealing with this menace. Considering the high prevalence of OV, there is the need for further studies to interrogate institutional and professional interventions to reduce and prevent abuse. From our study, it is evident that sociocultural factors such as gender constructions play a role in shaping women's experiences, hence further studies into the gendered dynamics of obstetric violence in Ghana is recommended. In the present study we only had a few HIV positive women ($1.2\%$) and found no effect of HIV status on OV (results are available upon request). As the sub-sample was too small to draw meaningful conclusions, future studies should examine OV among HIV positive women through purposive sampling. ## 5. Conclusion To summarize, we found a high prevalence rate of OV but there are few significant correlates in our regression analysis. Thus, we cannot point to a group of women that are at particularly high risk and conclude that all women who deliver in any of the eight public health facilities studied in Ghana are at a substantial risk of experiencing OV. This explains the reluctance of women to deliver in facilities and undermines the Ghanaian government's efforts to persuade women to have their babies in health facilities. We also established that there was no significant difference in the experiences of rural and urban women, thus emphasizing the endemic nature of obstetric violence in Ghanaian health institutions. On the other hand, our study also shows that great progress has been made. Health care is free of charge and only a small number of women are asked for a bribe, once a common practice [63]. ## 6. Strengths and limitations Key strengths of our study include the collection of a large sample size and the inclusion of health facilities in rural areas, extending the generalization of our results to the experiences of rural women. To reduce the risk of biases, enumerators were predominantly non-medical staff trained for the interviews. Although studies of this nature could be affected by recall bias, Simkin has demonstrated through her studies that memories from childbirth last up to 20 years and even more if women experience violence [64, 65]. Hence our study is unlikely to suffer from recall bias. However, the study was conducted in the health facilities and this might lead to a risk of underestimation, as women may underreport their experiences out of courtesy or social desirability. Nonetheless, we found OV to be a very serious issue that compromises the health of women in Ghana. ## 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 the Ethics Committee of the University of Konstanz, Germany (IRB Statement $\frac{37}{2021}$) and the Ghana Health Service Ethics Review Committee (GHS-ERC $\frac{010}{06}$/21). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. ## Author contributions AY: conceptualization and design of the study, funding acquisition, methodology, data monitoring, and manuscript preparation. AH: design of study, funding acquisition, and manuscript preparation. DA: data cleaning and analysis. SA: methodology. All authors reviewed the manuscript and the content has been approved for publication. ## 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/fpubh.2023.988961/full#supplementary-material ## References 1. Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. Trends in Maternal Mortality 2000–2017 2. 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--- title: CARD9 deficiency improves the recovery of limb ischemia in mice with ambient fine particulate matter exposure authors: - Qiang Zhu - Xuanyou Liu - Hao Wu - Chunlin Yang - Meifang Wang - Feng Chen - Yuqi Cui - Hong Hao - Michael A. Hill - Zhenguo Liu journal: Frontiers in Cardiovascular Medicine year: 2023 pmcid: PMC9968734 doi: 10.3389/fcvm.2023.1125717 license: CC BY 4.0 --- # CARD9 deficiency improves the recovery of limb ischemia in mice with ambient fine particulate matter exposure ## Abstract ### Background Exposure to fine particulate matter (PM) is a significant risk for cardiovascular diseases largely due to increased reactive oxygen species (ROS) production and inflammation. Caspase recruitment domain (CARD)9 is critically involved in innate immunity and inflammation. The present study was designed to test the hypothesis that CARD9 signaling is critically involved in PM exposure-induced oxidative stress and impaired recovery of limb ischemia. ### Methods and results Critical limb ischemia (CLI) was created in male wildtype C57BL/6 and age matched CARD9 deficient mice with or without PM (average diameter 2.8 μm) exposure. Mice received intranasal PM exposure for 1 month prior to creation of CLI and continued for the duration of the experiment. Blood flow and mechanical function were evaluated in vivo at baseline and days 3, 7, 14, and 21 post CLI. PM exposure significantly increased ROS production, macrophage infiltration, and CARD9 protein expression in ischemic limbs of C57BL/6 mice in association with decreased recovery of blood flow and mechanical function. CARD9 deficiency effectively prevented PM exposure-induced ROS production and macrophage infiltration and preserved the recovery of ischemic limb with increased capillary density. CARD9 deficiency also significantly attenuated PM exposure-induced increase of circulating CD11b+/F$\frac{4}{80}$+ macrophages. ### Conclusion The data indicate that CARD9 signaling plays an important role in PM exposure-induced ROS production and impaired limb recovery following ischemia in mice. ## Introduction Ambient particulate matter (PM) exposure is a significant challenge to public health with significant increase in cardiovascular mortality and morbidity [1]. Based on aerodynamic diameter, PM is categorized as coarse particles with a diameter of ≤10 μm (PM10), fine particles with a diameter of ≤2.5 μm (PM2.5), and ultrafine/nanoparticles with a diameter of ≤0.1 μm (PM0.1) [2]. Epidemiological studies have shown that increased cardiovascular adverse events are largely related to the exposure to PM2.5 and PM0.1 [2, 3]. Peripheral artery disease (PAD) is an important pathological condition that is frequently associated with significant limb ischemia especially in the patients with diabetes mellitus or hyperlipidemia. Unfortunately, very limited treatment options are available for these patients with less desirable outcome, and amputation is often the treatment of choice. The etiology for PAD is complex, and has not been fully understood. A recent study has shown that PM exposure is associated with a cumulative increase of acute limb ischemia (ALI) hospital admissions [4]. PM exposure also attenuates the recovery of limb ischemia in animal studies (5–7). However, the mechanism(s) for the impaired recovery of the ischemic limb is largely undefined. PM2.5 exposure triggers significant systemic inflammatory responses with increased levels of oxidative stress and reactive oxygen species (ROS) formation with release of large amount of pro-inflammatory cytokines including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6 [5, 8]. Caspase recruitment domain-containing protein 9 (CARD9) is abundantly expressed in immune cells such as macrophages and dendritic cells and is critically involved in the regulation of immune cell activation and inflammatory responses (9–11). CARD9 functions as an important upstream activator of pro-inflammatory signaling pathways (NF-kB and p38 MAPK signaling) to regulate the productions of a wide spectrum of inflammatory cytokines including TNF-α, IL-1β, and IL-6 [12, 13], thus playing an essential role in ROS production and oxidative stress. It has been reported that CARD9 signaling is involved in PM exposure-induced pulmonary injury [14]. The present study was designed to test the hypothesis that CARD9-mediated signaling is critically involved in PM exposure-induced ROS production and impaired recovery following limb ischemia. There were two objectives: [1] to determine if PM exposure could attenuate the recovery of circulation and mechanical function of ischemic limb in a mouse model; and [2] to define the role of CARD9-mediated signaling in mediating the effect of PM exposure on ROS production and the recovery of ischemic limb. ## Animals and PM exposure All animal studies were performed in compliance with the “Guide for the Care and Use of Laboratory Animals of US National Institutes of Health.” The animal study protocols were reviewed and approved by the Institutional Animal Care and Use Committee of the University of Missouri-Columbia (Protocol #9227). Male wildtype (WT) C57BL/6 mice (8–12 weeks old) and age-matched CARD9 deficient (CARD9−/−) mice (Jackson Laboratory, USA) were randomly divided into PM exposure and control groups. PM preparations (Standard Reference Materials 2786) were obtained from the National Institute of Standards and Technology (NIST) with an average diameter of 2.8 μm as described [15]. PM was dispersed in PBS (free of endotoxin) by ultrasonication with a concentration of 0.5 μg/μl as detailed in previous publication [16]. Each mouse received 10 μg PM (i.e., 5 μg PM in 10 μl for each nostril with a 5 mins interval) via intranasal instillation every other day (three times per week) for 4 weeks under general anesthesia with $1.5\%$ isoflurane before the surgery of critical limb ischemia (CLI) with continuation of PM exposure until the end of the experiment, with PBS (free of endotoxin) as the control as described [16, 17]. ## Mouse CLI model and evaluations of limb blood flow and mechanical function There were 4 experimental groups with 7 mice in each group: [1] WT-PBS control (WT C57BL/6 mice with CLI and PBS treatment); [2] WT-PM group (WT C57BL/6 mice with CLI and PM treatment); [3] CARD9-PBS group (CARD9−/− mice with CLI and PBS treatment); and [4] CARD9-PM group (CARD9−/− mice with CLI and PM treatment). For surgical induction of CLI, a 3–5 mm incision was made, with minimal tissue disturbance, and the right femoral artery was identified and ligated using a 6-0 silk suture and then transected, under general anesthesia with isoflurane ($1.5\%$) and constant temperature (37 ± 0.5°C) as described [18, 19]. Successful creation of CLI was confirmed by a lack of femoral artery blood flow signal using Laser Doppler perfusion imaging (LDPI, Moor Instruments, Devon, UK). Blood flow recovery of the ischemic limb was evaluated using LDPI as the ratio of blood perfusion in the ischemic (right) limb over the blood perfusion in normal (left) limb before ligation, at 30 mins after ligation and on days 3, 7, 14, and 21 after surgical procedure. LDPI imaging was obtained when the blood flow signal was stable for each mouse at each time point. Recovery of mechanical function of ischemia limb was evaluated through a swimming endurance test and a semi-quantitative assessment of ambulatory impairment and limitation of the ischemic limb (modified clinical standard score) prior to creation of CLI and at 14 and 21 days after ischemic limb surgery as detailed in previous publications [20, 21]. The ischemic limb recovery index was determined as following: 0 (flexing the toes to resist gentle traction on the tail), 1 (plantar flexion), 2 (no plantar flexion, but without dragging), and 3 (foot dragging) as described [18, 21]. ## CD31 immunofluorescent staining and H&E staining Gastrocnemius muscle tissue was collected from the ischemic limb. The tissue was weighed, and carefully prepared for immunofluorescent as well as H&E staining at day 21 after ischemia. Multiple cross sections of the muscle were obtained for each muscle sample, and 3 cross sections were randomly chosen from each muscle preparation for each of the histological examinations (CD31+ capillary density, H&E staining, CD68+/CARD9 double staining, and DHE staining, as detailed below). For CD31 immunofluorescent staining, the frozen sections of 6 μm were fixed with $4\%$ paraformaldehyde for 10 mins after air drying for 15 mins. Sections were subsequently incubated with BSA ($2\%$) for 30 mins at room temperature and the exposed to AF 594 anti-mouse CD31 antibody (Biolegend, 102432) with the dilution factor of 1:400 overnight at 4°C. The preparations were mounted using anti-fading DAPI agent after washing with PBS (3×). Three random fields for each section were imaged with a laser confocal microscope. CD31+ capillary density was evaluated quantitatively with ImageJ software. The frozen sections of the ischemic muscle tissue were examined for muscle morphology and structure using a H&E staining kit (Thermo Scientific, Waltham, US) as per manufacturer's protocol. Three independent fields were imaged for each section with an inverted light microscope. ## CD68 and CARD9 immunofluorescent staining and DHE staining CD68 and CARD9 immunofluorescent staining and dihydroethidium (DHE) staining were performed to evaluate CD68+ macrophage numbers, CARD9 fluorescence intensity and ROS production in the ischemic limb, respectively. For CD68 and CARD9 staining, the preparations were incubated with BSA ($2\%$) for 30 mins at room temperature after 10 mins of fixation with paraformaldehyde ($4\%$) and washing thoroughly with PBS (x3). Then, the samples were exposed to Spark YG 570 anti-mouse CD68 antibody (1:500, Biolegend, 137037) at 4°C overnight. For CARD9 staining, the tissue preparations were permeabilized with Triton X-100 ($0.3\%$, Sigma-Aldrich) for 15 mins after washing with PBS (x3), and then incubated with anti-CARD9 antibody (1:500, Biolegend, 679102) overnight at 4°C. After PBS washing (x3), the samples were exposed to AF 488 goat anti-mouse lgG (H+L) secondary antibody (1:1,000, Invitrogen, A11001) for 2 h at room temperature. After three times of PBS washing, the tissue samples were mounted using anti-fading DAPI agent. For both CD68 and CARD9 staining, the corresponding IgG isotype antibodies with a dilution factor of 1:500 were used as negative controls. Three random fields for each section were imaged using a laser confocal microscope. For DHE staining, the preparations were mixed with DHE (1:1,000, Invitrogen, D1168) for 15 mins at room temperature after air drying and fixation. After washing with PBS (x3), the preparations were mounted using anti-fading DAPI agent, and examined using a laser confocal microscope and quantified with ImageJ software. ## Flow cytometric analysis for macrophages and intracellular ROS level Mouse whole blood was obtained at day 21 after creation of CLI and prepared for flowcytometry analysis with lysis and removal of red blood cells (RBCs) using RBC lysis buffer, as described [22]. For macrophage analysis, the CD11b+/F$\frac{4}{80}$+ cell population was determined as described [23]. Anti-mouse CD11b+ PE-Cy5 and anti-mouse F$\frac{4}{80}$+ FITC antibodies were obtained from Biolegend (San Diego, CA, USA). Careful compensation was performed for the cell populations with corresponding isotype antibodies as controls (PE/Cyanine5 IgG2b, κ isotype antibody for CD11b+ PE-Cy5; FITC IgG1, κ isotype antibody for F$\frac{4}{80}$+ FITC, from Biolegend). Total cell population was gated, and the macrophage population defined as cells double positive for CD11b+/F$\frac{4}{80}$+ as determined by flow cytometry [24]. The level of intracellular ROS was quantitatively evaluated with FITC-ROS detection reagents (Invitrogen) as detailed previously [25]. Cells were mixed with the reagent at 37°C for 10 mins. After washing with PBS (x3), the labeled cells were suspended in warm PBS, and analyzed with flow cytometry. Fluorescence-positive cells were then quantitatively determined using LSRFortessa X-20 (BD Bioscience, CA) and FlowJo (V10) software. ## Western blot analysis Gastrocnemius muscle was collected from the ischemic limb and prepared (via homogenization and lysis) for western blot analysis as detailed previously [26]. The lysates of the muscle tissues were placed on $8\%$ SDS-PAGE gels. After electrophoresis, the preparations were then transferred onto 0.45 μm polyvinylidene difluoride (PVDF) membranes. After blocking with $5\%$ milk in 1 × TBST buffer and incubation with antibody against CARD9 (1:500, Biolegend, 679102) at 4 °C overnight, the PVDF membranes were exposed to the second antibodies for 1 h at room temperature. After brief exposure to electrochemiluminescence (ECL) buffer, the preparations were with an Odyssey Imaging System (Li-Cor Biosciences, Lincoln, NE), and analyzed using ImageJ software. ## Statistical analysis All the data were presented as means ± standard deviation (SD) and analyzed using GraphPad Prism 8.0 software (San Diego, CA, USA). Two groups of data were analyzed using Student's t-test. And multiple groups of data were analyzed using ANOVA followed by Tukey's test for subgroup analysis. The difference was considered statistically significant when a two-tailed p value was <0.05. ## PM exposure significantly attenuated the circulatory and functional recovery of mouse ischemic limb The recovery of blood flow in the ischemic limb was evaluated by measuring the ratio of blood perfusion in the ischemic (right) limb over the blood perfusion in normal (left) limb. As shown in Figures 1A, B, PM exposure significantly decreased the recovery of blood flow in C57BL/6 (WT) mice at days 14 and 21 (PM vs. PBS; $68.4\%$ vs. $83.7\%$ at day 14, and $75.7\%$ vs. $93.6\%$ at day 21, #$p \leq 0.01$). The mechanical function of ischemic limbs was evaluated using a swimming endurance test and a semi-quantitative assessment of ambulatory impairment and limitation of the ischemic limb (modified clinical standard score). As shown in Figures 1C, D, there was a significant decrease in the recovery of mechanical function for mice treated with PM compared to PBS controls at days 14 and 21 (for swimming time: PM vs. PBS; 116 mins vs. 149 mins at day 14, and 148 mins vs. 197 mins at day 21; for limb ischemia recovery index: PM vs. PBS; 2.6 vs. 1.6 at day 14, and 1.9 vs. 1.1 at day 21; #$p \leq 0.05$). Figure 1: Circulatory and functional recovery of the ischemic limb was attenuated in mice with PM exposure. Recovery of blood flow in the ischemic limb was evaluated by measuring the ratio of ischemic (right) limb blood perfusion/normal (left) limb blood perfusion. PM exposure significantly decreased the recovery of blood flow in C57BL/6 (WT) mice at days 14 and 21 [#p < 0.01, (A, B)]. The mechanical function of the ischemic limb was evaluated using a swimming endurance test and a semi-quantitative assessment of ambulatory impairment and limitation of the ischemic limb (modified clinical standard score). As shown in (C, D), there was a significant decrease in the recovery of mechanical function for mice treated with PM compared to control mice with PBS treatment at days 14 and 21 (#p < 0.05). Gastrocnemius samples of the ischemic limb was harvested at day 21 after CLI for ex vivo histological analyses for capillary density, muscular mass, and inflammatory infiltration. As shown in (E, F), CD31+ capillary density was significantly decreased in PM-treated mice compared to PBS-treated mice (#p < 0.01). H&E staining showed that there was a significant increase in inflammatory cell infiltration in the ischemic limb in PM-treated mice compared to PBS-treated mice, while there was no significant change in muscle mass [#p > 0.05, (G, H)]. BL, before ligation; PL, post ligation; Scale bar, 50 μm. n = 7/group. Statistical differences were determined with ANOVA followed by Tukey's post hoc test or Student's two-tailed t-test. Ex vivo histological examinations were conducted to determine the capillary density, muscular mass, and inflammatory cell infiltration in the gastrocnemius muscle of the ischemic limb at day 21 after CLI. As demonstrated in Figures 1E, F, CD31+ capillary density was significantly decreased in PM-treated mice compared to PBS-treated controls (PM vs. PBS: 172 vs. 305, #$p \leq 0.01$). The morphology and structure of ischemic muscle were evaluated using H&E staining and muscle mass measurements. There was a significant increase in inflammatory cell infiltration in the ischemic muscle of PM-treated mice compared to PBS-treated controls without significant changes in muscle mass (Figures 1G, H). ## PM exposure increased the levels of ROS production, macrophage infiltration, and CARD9 protein expression in ischemic limbs Local and circulating levels of intercellular ROS production were evaluated using DHE staining and flow cytometry, respectively. As shown in Figures 2A–D, ROS production in ischemic tissue and circulating monocytes were significantly increased in PM-treated mice (for DHE staining: PM vs. PBS: 2.62 vs. 0.98; for flow cytometry: $32.54\%$ vs. $10.69\%$, #$p \leq 0.01$). Similarly, CD68+ macrophage infiltration in ischemic limbs was evaluated through immunostaining analysis, and circulating CD11b+/F$\frac{4}{80}$+ monocytes were examined using flow cytometry analysis. As demonstrated in Figures 3A–E, PM treatment significantly increased the number of circulating monocytes and regional macrophage infiltration in the ischemic muscle compared to PBS-treated control (for immunostaining: PM vs. PBS: 117.30 vs. 73.50; for flow cytometry: PM vs. PBS: $6.12\%$ vs. $3.85\%$, #$p \leq 0.01$). To determine the effect of PM exposure on CARD9 protein expression in ischemic limbs, the western blot and immunostaining assay for CARD9 protein were performed. It was observed that CARD9 expression was significantly enhanced in the ischemic muscle from PM-treated mice compared to PBS-treated controls (for immunostaining staining assay: PM vs. PBS: 94.67 vs. 59.50, #$p \leq 0.01$, Figures 3A, C; for western blot assay: PM vs. PBS: 1.06 vs. 0.38, #$p \leq 0.01$, Figures 3F, G). Figure 2: PM treatment increased the levels of ROS in blood and ischemic limb in mice. ROS levels in the ischemic limbs were evaluated using DHE staining, while ROS levels in circulating mononuclear cells were assessed using flow cytometry. As shown in (A–D), the ROS levels in the ischemic tissue and circulating mononuclear cells were significantly increased in PM-treated mice over the controls (#p < 0.01). DAPI, 4′, 6-diamidino-2-phenylindole; DHE, dihydroethidium; BL, blood; Scale bar, 50 μm. n = 6/group. Statistical differences were determined with Student's two-tailed t-test. Figure 3: PM exposure increased the numbers of macrophages in blood and ischemic limbs and CARD9 protein level in ischemic limbs. CD68+ macrophage infiltration in ischemic limbs was evaluated using immunostaining assay and circulating CD11b+/ F4/80+ monocytes/macrophages were examined by flow cytometry. As demonstrated in (A, B, D, E), PM treatment significantly increased the levels of CD11b+/F4/80+ cells in blood and CD68+ macrophages infiltration in the ischemic limb compared to PBS-treated control (#p < 0.01, n = 6). Immunostaining and western blot assays showed that CARD9 protein level was significantly increased in the ischemic limb in PM-treated mice compared to PBS-treated controls [#p < 0.01, n = 6, (A, C, F, G)]. DAPI, 4,6-diamidino-2-phenylindole; Scale bar, 50 μm. Statistical differences were determined with Student's two-tailed t-test. ## CARD9 deficiency effectively prevented PM-induced increase of ROS production and macrophage infiltration CARD9 knockout mice were used to repeat the experiments to determine the role of CARD9 signaling in ROS production and macrophage infiltration following PM exposure. Interestingly, PM-induced increases in ROS production (both local and circulating) and macrophage/monocyte infiltration (both local and circulating) were significantly attenuated in CARD9 deficient mice (ROS level using DHE staining: WT-PM vs. CARD9-PM: 2.62 vs. 1.73; ROS level using flow cytometry: WT-PM vs. CARD9-PM: $32.54\%$ vs. $16.92\%$, $p \leq 0.01$, Figures 4A, B, E, F; macrophage infiltration using immunostaining: WT-PM vs. CARD9-PM: 73.33 vs. 51.33; monocyte cell count using flow cytometry: WT-PM vs. CARD9-PM: $6.12\%$ vs. $4.10\%$, $p \leq 0.01$, Figures 4C, D, G, H). Figure 4: CARD9 deficiency effectively prevented PM-induced increase of ROS production and macrophage infiltration in ischemic limbs. Immunostaining and flow cytometry analysis demonstrated that PM-induced increases in the levels of ROS and macrophages/monocytes in blood and ischemic limbs were significantly attenuated in CARD9 deficient mice [p < 0.01 for WT-PM vs. CARD9-PM; #p < 0.01 for WT-PBS vs. WT-PM; &p < 0.05 for CARD9-PBS vs. CARD9-PM, (A–H)]. DAPI, 4′,6-diamidino-2-phenylindole; DHE, dihydroethidium; BL, blood; Scale bar, 50 μm. n = 6/group. Statistical differences were determined by one-way ANOVA with Tukey's post-hoc test.* ## CARD9 deficiency prevented PM-induced impairment in the recovery of the ischemic limb in mice To determine the role of CARD9 in PM exposure-induced decreases in blood flow and impairment of mechanical function, experiments were repeated using CARD9−/− mice. As shown in Figure 5A, CARD9 deficiency effectively prevented the PM-induced impairment of blood flow recovery (WT-PM vs. CARD9-PM: $68.4\%$ vs. $81.3\%$ at day 14, and $75.7\%$ vs. $90.7\%$ at day 21, $p \leq 0.01$). In addition, CARD9 deficiency partially, but significantly, reversed the reduction in mechanical function in ischemic limbs of mice exposed to PM at days 14 and 21 (for swimming time: WT-PM vs. CARD9-PM: 116 mins vs. 139 mins at day 21, WT-PM vs. CARD9-PM: 148 mins vs. 177 mins at day 21; for limb ischemia recovery index: WT-PM vs. CARD9-PM: 2.6 vs. 1.7 at day 14, and 1.9 vs. 1.1 at day 21; $p \leq 0.05$, Figures 5B, C). Interestingly, there was no significant difference in the recovery of blood flow and mechanical function between CARD9−/− mice and WT mice without PM exposure. Ex vivo histological analyses using CD31 immunofluorescent staining and H&E staining demonstrated that CARD9 deficiency significantly improved CD31+ capillary density and decreased inflammatory cell infiltration in ischemic limbs in mice with PM exposure (CD31+ capillary density, WT-PM vs. CARD9-PM: 172 vs. 293, $p \leq 0.01$, Figures 5D–G). Figure 5:* CARD9 deficiency effectively prevented PM-induced impairment in the recovery of blood flow and mechanical function of ischemic limbs, preserved capillary density and attenuated inflammatory infiltration in ischemic limbs. To determine the effect of CARD9 on the recovery of ischemic limb in mice with PM exposure, we performed animal experiment with age-matched CARD9 deficient (CARD9−/−) mice. As shown in (A), CARD9 knockout effectively prevented the PM-induced reduction of blood flow recovery (p < 0.01 for WT-PM vs. CARD9-PM; #p < 0.01 for WT-PBS vs. WT-PM). In the meanwhile, CARD9 knockout partially, but significantly reversed the reduction of mechanical function in ischemic limbs in mice with PM exposure at days 14 and 21 [p < 0.05, #p < 0.05; (B, C)]. Interestingly, there was no significant difference in the recovery of blood flow and mechanical function between CARD9 and WT mice with PBS treatment. Consistent with the recovery of blood flow and mechanical function, ex vivo histological analysis showed that CARD9 deficiency effectively prevented PM-induced decrease of CD31+ capillary density and attenuated inflammatory cell infiltration in ischemic limbs [p < 0.01, #p < 0.01; (D–G)]. DAPI, 4,6-diamidino-2-phenylindole. Scale bar, 50 μm. n = 7/group. Statistical differences were determined with ANOVA followed by Tukey's post-hoc test.* ## Discussion In the present study, we demonstrated: [1] exposure to PM2.5 significantly decreased the recovery of blood flow and mechanical function in limb ischemia associated with increased levels of ROS production, macrophage infiltration and CARD9 protein expression; [2] levels of circulating monocytes and their intracellular ROS were significantly increased in PM-treated mice; [3] CARD9 deficiency effectively prevented PM-induced increases of ROS production and macrophage infiltration, while improving circulatory and functional recovery of ischemic limb in mice with PM exposure; and [4] CARD9 deficiency had no impact on ROS production and the recovery of blood flow and function of the ischemic limb in mice without PM exposure. CARD9-mediated signaling was shown to be involved in diet-induced inflammation and cardiac dysfunction as well as metabolic disorders and ischemia/reperfusion cardiac injury (27–29). The present study revealed for the first time that CARD9-mediated ROS production and macrophage infiltration played an important role in the impairment of ischemic limb recovery in mice with PM exposure. Epidemiological studies have shown that PM exposure increases the risk of CVDs including atherosclerosis, hypertension, arrhythmia, myocardial infarction, and sudden cardiac death (30–33). The mechanisms for increased adverse cardiovascular events associated with PM exposure are complex and clearly multifactorial. However, a common underlying feature that connects PM exposure with CVDs appears to be a significant increase in ROS production and oxidative stress. It is known that PM exposure enhances oxidative stress and inflammatory responses systemically and is associated with various diseases in different organ systems [30, 34, 35]. PM exposure-related ROS may come from multiple sources including directly from the PM particles, and more importantly, from various intracellular sources in response to PM exposure through productions of a variety of inflammatory cytokines [8]. Inflammatory cytokines promote ROS formation through activation of transmembrane NADPH oxidases (NOXs). ROS in turn stimulates the expressions and releases of pro-inflammatory cytokines through activation of NF-κB signaling pathways [36, 37]. We have previously shown that PM exposure enhances ROS levels with increased levels of serum TNF-α, IL-1β, and IL-6 in mice [16, 17]. Treatment with Tempol (a SOD mimic) or N-acetylcysteine (NAC) or concomitant overexpression of human superoxide dismutase (SOD)1, SOD3, and glutathione peroxidase (Gpx-1) effectively prevents PM exposure-induced increases of intracellular ROS and serum inflammatory cytokines including TNF-α, IL-1β, and IL-6 in mice [16, 38, 39]. Monocytes and macrophages are an important source of inflammatory cytokines [40, 41]. Macrophages play an important role in PM-induced inflammation in respiratory and cardiovascular systems [30, 42]. PM exposure significantly enhances inflammatory M1 polarization through ROS-mediated pathway and inhibits anti-inflammatory M2 polarization through a mTOR-dependent mechanism [43]. Further, PM exposure has been shown to increase the release of pro-inflammatory mediators including TNF-α, IL-6, and granulocyte-macrophage colony-stimulating factor (GM-CSF) from lung macrophages into circulation [43, 44]. It has also been reported that acute exposure to PM induces a sustained activation of macrophages in lung, and enhances leukocyte rolling, adhesion, and transmigration, and aggravates experimental myocardial infarction with an increased infarction size [44]. Long-term PM exposure increases macrophage recruitment and lipid content and decreases fibrous cap thickness and SMCs infiltration in atherosclerotic plaques in HFD-fed mice [45]. In the present study, we observed that PM exposure significantly attenuated the recovery of limb ischemia in mice in association with increased numbers of circulating monocytes and infiltration of macrophages into the ischemic muscle. The findings of increased numbers of circulating monocytes and infiltrated macrophages in the ischemic areas are consistent with significant increases in intracellular ROS level in the circulating monocytes and tissue ROS levels in the ischemia muscle in mice exposed to PM. However, how PM exposure could increase macrophage infiltration and ROS production in the ischemic muscle is unclear. It is certainly possible that PM exposure-induced productions of inflammatory cytokines including TNF-α, IL-1β, and IL-6 may significantly contribute to monocyte recruitment to and/or macrophage proliferation into the ischemic area. Consistent with this, cytokines from alveolar macrophages and bronchial epithelial cells may stimulate bone marrow with resultant leukocytosis and activate vascular endothelial cells [41]. In addition, cell surface adhesion molecules including intercellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion protein 1 (VCAM-1) [46, 47], as well as inflammatory cytokine expression [41] are upregulated in endothelium in response to ischemic injury. These adhesion proteins including their soluble forms are important for recruiting monocytes and lymphocytes into circulation and ischemic tissues [41, 48, 49]. Limb ischemia is well known to increase ROS production both systematically and locally in the ischemic area [50, 51]. In the present study, we showed that ischemia-induced ROS production was further increased in mice with PM exposure. Importantly, we also demonstrated that PM exposure significantly increased macrophage infiltration/accumulation in the ischemic muscle and the expression of CARD9 in the ischemic tissue with increased ROS production. CARD9 deficiency had no impact on ischemia-induced macrophage infiltration/accumulation and ROS production in mice without PM exposure. Interestingly, CARD9 deficiency effectively prevented PM exposure-induced enhancement of macrophage infiltration/accumulation and ROS production in the ischemic muscle following PM exposure. These data suggest that PM exposure-induced expression of CARD9 might be critical to ROS production and monocyte/macrophage infiltration in ischemic limbs. However, the mechanism(s) for PM exposure-induced increase in CARD9 expression in the macrophages in the ischemic limb of mice is unclear at this point. It is known that ROS in the lungs enhances the signal transduction of pattern recognition (e.g., TLRs) (52–54), thus increasing the expression of a variety of inflammatory cytokines and chemokines [8]. CARD9 is a crucial molecule that mediate the signaling of TLRs and the activations of MAPK and NF-κB, leading to the productions of many important cytokines including (but not limited to) TNF-α, IL-6, IL-2, IL-12p40 [9, 55]. Studies have shown that CARD9 signaling is critically involved in diet-induced myocardial dysfunction [27, 56], and obesity-associated metabolic disorders [28]. The data from the present study demonstrated that CARD9 signaling was critically involved in ROS production and macrophage infiltration following PM exposure. CARD9-mediated oxidative stress is an important mechanism for the impaired recovery of ischemic limb in mice with PM exposure. PM exposure triggers a significant systemic inflammation with increased levels of oxidative stress and inflammatory cytokines including TNF-α, IL-6, and IL-1β. It is known that CARD9 is expressed in various types of cells with different functions including macrophages, neutrophils, dendritic cells, lymphoid cells, endothelial cells, cardiomyocytes, and microglial cells [29]. However, CARD9 is predominantly expressed in immunoreactive cells especially macrophages and dendritic cells, and is critically involved in the productions of a wide range of cytokines (TNF-α, IL-6, and IL-1β) and chemokines (CXCL1, CXCL2, and CXCL8), which are primarily associated with local and systemic inflammation, oxidative stress, and the development and progression of a variety of diseases including cardiovascular diseases and cancers (57–59). Although many cells can produce inflammatory cytokines, macrophages are an important source for inflammatory cytokines in response to PM exposure [30, 42]. In the present study, we observed that PM exposure significantly increased the numbers of circulating monocytes and infiltration of macrophages in the ischemic limb muscle of mice with increased CARD9 expression and ROS production. CARD9 deficiency effectively prevented PM exposure-induced macrophage infiltration/accumulation and ROS production in the ischemic muscle. Interestingly, CARD9 deficiency had no impact on ROS production and the recovery of blood flow and function of the ischemic limb in mice without PM exposure. These data suggest that CARD9-mediated signaling is essential for the development of inflammation and ROS production in the ischemic limb in response to PM exposure in mice. However, further studies are needed to determine if macrophages or other specific cells (e.g., neutrophils or dendritic cells, or endothelial cells) play a dominant role in PM exposure-induced ROS production and impairment of ischemic limb recovery. Future studies are also needed to determine which inflammatory cytokine(s) contributes critically to PM exposure-induced ROS production and inflammation in the ischemic limb in mice. There were some other limitations in the present study, including [1] No detailed studies to define the complex, and yet critical roles or mechanisms of CARD9-mediated signaling in the pathophysiology of CLI in mice with PM exposure; and [2] No studies to determine if a significant sex difference in the recovery of CLI in mice with PM exposure. There are substantial sex differences in many cardiovascular diseases without well-defined mechanisms. Recently, we observed that there was a significant sex difference in the levels of serum inflammatory cytokines including TNF-α, IL-1β, and IL-6 as well as circulating endothelial progenitor cells (EPCs) in mice after PM exposure [17]. The levels of serum inflammatory cytokines especially TNF-α was significantly lower in female mice with PM exposure than that in age-matched males with preserved level of circulating EPCs independent of female sex hormone estrogen [17]. EPCs are involved in angiogenesis and ischemic limb recovery, thus, it is important to determine if there are significant sex differences in CARD9 expression, ROS production, macrophage infiltration, and the recovery of CLI in mice with PM exposure in future studies. In conclusion, the present study demonstrated that PM exposure significantly decreased the recovery of blood flow and mechanical function in ischemic limbs with increased ROS production, and monocyte/macrophage infiltration through a CARD9-mediated mechanism. The data may provide a potential novel target or strategy for CLI patients with refractory limb ischemia with exposure to PM. ## 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 animal study was reviewed and approved by the Institutional Animal Care and Use Committee of the University of Missouri-Columbia. ## Author contributions ZL and QZ contributed to the study conception and design. QZ, XL, HW, CY, MW, and FC performed the experiments and collected the data. QZ, YC, and HH did the statistical analysis. QZ drafted the manuscript. QZ, MH, and ZL critically reviewed the data and revised the manuscript. ZL supervised the study and provided financial supports. All authors carefully reviewed the manuscript and agreed on publication of the data. ## 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. Rajagopalan S, Landrigan PJ. Pollution and the heart. N Engl J Med. (2021) 385 1881-92. DOI: 10.1056/NEJMra2030281 2. Miller MR, Newby DE. Air pollution and cardiovascular disease: car sick. 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--- title: 'Association between daidzein intake and metabolic associated fatty liver disease: A cross-sectional study from NHANES 2017–2018' authors: - Zheng Yang - Daoqing Gong - Xinxiang He - Fei Huang - Yi Sun - Qinming Hu journal: Frontiers in Nutrition year: 2023 pmcid: PMC9968739 doi: 10.3389/fnut.2023.1113789 license: CC BY 4.0 --- # Association between daidzein intake and metabolic associated fatty liver disease: A cross-sectional study from NHANES 2017–2018 ## Abstract ### Background Metabolic associated fatty liver disease (MAFLD) has become the most common liver disease globally, yet no new drugs have been approved for clinical treatment. Therefore, we investigated the relationship between dietary intake of soy-derived daidzein and MAFLD, to find potentially effective treatments. ### Methods We conducted a cross-sectional study using data from 1,476 participants in National Health and Nutrition Examination Survey (NHANES) from 2017 to 2018 and their associated daidzein intake from the flavonoid database in the USDA Food and Nutrient Database for Dietary Studies (FNDDS). We investigated the relationship between MAFLD status, controlled attenuation parameter (CAP), AST/Platelet Ratio Index (APRI), Fibrosis-4 Index (FIB-4), liver stiffness measurement (LSM), nonalcoholic fatty liver disease (NAFLD) fibrosis score (NFS), hepatic steatosis index (HSI), fatty liver index (FLI), and daidzein intake by adjusting for confounding variables using binary logistic regression models and linear regression models. ### Results In the multivariable-adjusted model II, there was a negative association between daidzein intake and the incidence of MAFLD (OR for Q4 versus Q1 was 0.65, $95\%$ confidence interval [CI] = 0.46–0.91, $$p \leq 0.0114$$, p for trend was 0.0190). CAP was also negatively associated with daidzein intake, β = −0.37, $95\%$ CI: −0.63 to −0.12, $$p \leq 0.0046$$ in model II after adjusting for age, sex, race, marital status, education level, family income-to-poverty ratio (PIR), smoking, and alcohol consumption. Stratified by quartiles of daidzein intake, trend analysis of the relationship between daidzein intake and CAP remained significant (p for trend = 0.0054). In addition, we also found that HSI, FLI, and NFS were negatively correlated with daidzein intake. LSM was negatively related to daidzein intake but had no statistical significance. The correlation between APRI, FIB-4, and daidzein intake was not strong (although $p \leq 0.05$, β values were all 0). ### Conclusion We found that MAFLD prevalence, CAP, HSI, and FLI, all decreased with increased daidzein intake, suggesting that daidzein intake may improve hepatic steatosis. Therefore, dietary patterns of soy food or supplement consumption may be a valuable strategy to reduce the disease burden and the prevalence of MAFLD. ## Introduction Chronic liver disease is the leading cause of both morbidity and mortality worldwide, with 2 million deaths from the liver disease each year [1, 2]. With the improvement of people’s living conditions, dietary patterns, and lifestyle changes, the incidence of non-alcoholic fatty liver disease (NAFLD) has been on the rise year by year and is now the fastest-growing cause of cirrhosis and liver cancer incidence and its associated mortality, resulting in a serious clinical and socio-economic burden (3–5). NAFLD has become the most prevalent chronic liver disease worldwide in the last two decades, and affects about a quarter of the world’s population [6]. In addition to liver involvement, NAFLD is associated with an increased risk of diabetes, cardiovascular disease, cerebrovascular disease, chronic kidney disease, and extrahepatic tumors [7]. In 2020, the international consensus proposed a new concept: metabolic associated fatty liver disease (MAFLD), which emphasized the role of metabolic disorders without excluding patients with an intake of alcohol or other chronic liver diseases and differed significantly from the diagnostic criteria for NAFLD [8, 9]. In the real world, MAFLD diagnostic criteria are more practical than NAFLD diagnostic criteria for recognizing patients with a fatty liver at a high risk of progressive disease [10]. The use of MAFLD criteria is more helpful in identifying and treating fatty liver patients at risk of hepatic fibrosis through non-invasive tests [11, 12]. However, the treatment of NAFLD/MAFLD is limited to lifestyle changes, and there is a lack of clear and effective drugs [13]. Current studies have confirmed that the macronutrient and micronutrient composition of food can promote or prevent MAFLD (14–16). MAFLD is considered to be the liver manifestation of metabolic syndrome, and its occurrence is usually associated with chronic exposure to a nutrient-deficient diet [16]. Wang et al. [ 17] showed that flavonoids can block oxidative stress by inhibiting CYP2E1 activity, thus improving insulin resistance, lipid peroxidation, and endoplasmic reticulum stress to prevent and treat NAFLD. Flavonoids are natural polyphenolic compounds that are widely found in plants and can be classified into several subtypes based on the degree of oxidation, mainly including isoflavones, flavonoids, flavanones, flavanols, flavonols, and flavan-3-ols [18]. Among them, isoflavone compounds belong to phytoestrogens, mainly including daidzein, glycitein, genistein, biochanin A and formononetin [19]. Equol is not a phytoestrogen, but as a metabolite of daidzein, it is sometimes included in the isoflavone class [20]. The main dietary sources of human isoflavones are soybeans and soy products, containing daidzein and genistein [21]. The chemical structure of daidzein is similar to that of mammalian estrogens and can act by replacing or blocking this hormone and its corresponding receptors, thus making daidzein a drug candidate with dual use [22]. This makes daidzein a potential therapeutic option in estrogen-dependent diseases like prostate cancer, breast cancer, diabetes, and cardiovascular disease (23–25). Yamagata et al. [ 26] showed that daidzein had the potential to prevent metabolic syndrome by affecting hypertension, hyperglycemia, dyslipidemia, and atherosclerosis in patients. Another research surveyed the relationship between dietary isoflavone intake and risk of metabolic disorders in 6786 Chinese adults, which showed that total isoflavones, genistein, and daidzein intake were all negatively associated with NAFLD, hyperlipidemia, and hypertension [27]. However, it is necessary for us to study the role of daidzein in MAFLD of different ethnicities. In this study, we prospectively surveyed the relationship between dietary daidzein intake and the incidence of MAFLD, hepatic steatosis, and hepatic fibrosis in participants in the National Health and Nutrition Examination Survey (NHANES) to find a potentially effective treatment for MAFLD. ## Study population The National Health and Nutrition Examination Survey (NHANES) is a continuous cross-sectional sample survey conducted since 1999 by the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention, every 2 years representing a survey cycle. The project is a complex, nationally representative, stratified, multi-stage probabilistic health survey of the non-institutionalized civilian population of the United States. The specific data collection procedures and detailed methodology for NHANES have been described by the National Center for Health Statistics [28]. NHANES data are obtained from interviews, laboratory tests, and physical examinations conducted by trained staff. In our study, we specifically used data from one cycle of NHANES 2017–2018, which evaluated parameters of hepatic steatosis and fibrosis in patients using vibration-controlled transient elastography (VCTE) [29]. Flavonoid intake data for patients in this study are derived from the 2017–2018 Food and Beverage Survey flavonoid values in the NHANES-associated USDA Food and Nutrient Database for Dietary Studies (FNDDS). There are more than 7,000 foods/beverages with flavonoid values in FNDDS 2017–2018, which can calculate the estimated value of flavonoid intake representing the United States population of all ages. Linking these estimates to laboratory data, physical examination, and interview data in NHANES can better investigate the relationship between flavonoid intake and human health [30]. The data and documents used in this study are unidentified data frames that are publicly available on the National Center for Health Statistics [31] and Agricultural Research Service [30] websites. Ethics approval has been granted by the NCHS Ethics Review Committee, and protocol descriptions are available at.1 Written informed consent is required for participants 12 and over, and parental consent is also required for participants under 18 years old. A total of 9,524 participants were recruited into the study during the 2017–2018 NHANES cycle. After excluding 2,853 participants who were not assessed for hepatic steatosis and liver fibrosis using VCTE at baseline, 453 participants who could not be diagnosed with MAFLD due to incomplete information, 499 participants with no daidzein value, and 3,973 participants with a daidzein value of 0, a total of 1,476 participants were finally enrolled in the analysis (Figure 1). Figure 1: Flowchart of the selection process of the participants in NHANES 2017–2018. NHANES, National Health and Nutrition Examination Survey; MAFLD, metabolic associated fatty liver disease. ## Evaluation of daidzein intake The flavonoid database in the FNDDS provides the total intake of 29 individual flavonoids, the total of the 6 major flavonoids, and the total intake of all flavonoids consumed by each participant on day 1 and day 2, respectively. Isoflavones are mainly sourced from soy foods/beverages such as soy milk, soy-based proteinaceous powder, and tofu, as well as isoflavone ingredients that are added to foods/beverages as additives to achieve specific functions. The 2017–2018 FNDDS nutrient values were calculated for each food/beverage based on the ingredient data in FoodData Central [32]. In this study, we selected the mean of daidzein intake on days 1 and 2 for each participant in the flavonoid database to investigate its association with MAFLD incidence, hepatic steatosis, and hepatic fibrosis. ## Definition of MAFLD The diagnosis of MAFLD in this study was by the VCTE measurement of hepatic steatosis and the presence of one of the following three conditions, such as overweight/obesity, diabetes mellitus (DM), or evidence of metabolic abnormalities [9]. Evidence of metabolic abnormalities was defined as at least 2 of the following metabolic risk abnormalities such as [1] waist circumference ≥102 and 88 cm in men and women, respectively, [2] blood pressure ≥ $\frac{130}{85}$ mm Hg or treated with specific medications, [3] plasma triglycerides (TG) ≥1.70 mmol/l or treated with specific medications, [4] high-density lipoprotein cholesterol (HDL-C) <1.0 mmol/l in men and <1.3 mmol/l in women or treated with specific medications, [5] prodromal diabetes (fasting glucose) level 5.6–6.9 mmol/l or hemoglobin A1c 5.7–$6.4\%$, [6] insulin resistance score ≥2.5 as assessed by the homeostatic model, [7] plasma high-sensitivity C-reactive protein level >2 mg/l. ## Non-invasive evaluation of liver disease During the 2017–2018 NHANES cycle, the technicians performed VCTE (Echosens, Paris, France) tests using the FibroScan 502 V2 Touch model after 2 days of professional training and authorization following the liver ultrasound transient elastography operation manual [33]. Technicians obtained at least 10 measurements from participants who fasted for at least 3 h, yielding median controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) values and interquartile spacing for each participant. The higher the CAP value measured, the higher the liver fat content (CAP reference range: 100–400 dB/m); similarly, the higher the LSM value, the more severe the hepatic fibrosis (LSM reference range: 1.5–75 kPa). In this study, we defined CAP ≥248 dB/m as hepatic steatosis according to the published data of a large meta-analysis [34] and the findings of a study of hepatic steatosis in an adolescent population [35]. Eddowes et al. [ 36] showed cutoff values of 8.2 kPa, 9.7 kPa, and 13.6 kPa for F > F2, F > F3, and F=F4, respectively; therefore, we defined progressive hepatic fibrosis as LSM ≥ 9.7 KPa. In the study, we also used serum-based scores for the assessment of non-invasive hepatic steatosis and liver fibrosis to investigate their relationship with daidzein intake. Liver steatosis score includes Fatty liver index (FLI) (FLI is calculated based on TG, gamma glutamyl transferase (GGT), body mass index (BMI), and waist circumference; FLI ≥ 60 is judged as hepatic steatosis) [37] and Hepatic steatosis index (HSI) (calculated based on alanine aminotransferase (ALT), aspartate aminotransferase (AST), BMI, diabetes mellitus (DM), gender; HSI > 36 is judged as NAFLD) [38]. Hepatic fibrosis scores include AST to platelet ratio index (APRI) (results based on AST and platelet (PLT) count, APRI >1.5 indicates significant liver fibrosis) [39], Fibrosis-4 (FIB-4) (results based on ALT, AST, PLT and age, FIB-4 > 2.67 suggests that there are NAFLD patients with grade F3-4 or higher hepatic fibrosis) [40, 41] and NAFLD fibrosis score (NFS) (NFS calculated based on albumin, ALT, AST, PLT count, age, DM or impaired fasting blood glucose, and BMI, with NFS > 0.676 suggesting that NAFLD patients have progressive hepatic fibrosis) [42]; the above scoring models of formulae for calculation are shown in Supplementary Table S1. ## Covariates Demographic, physical examination, and laboratory test information in this study are available from the NHANES database, which mainly include age, sex, race, marital status, education level, economic status, BMI, smoking, drinking, homeostasis model assessment of insulin resistance (HOMA-IR), waist circumference and history of hypertension and diabetes. BMI = weight [kg]/height [m2], classified as <25, 25–30, and ≥30 [43]; HOMA-IR is fasting insulin (μU/mL) × fasting glucose (mmol/L)/22.5 [44]. Hypertension is defined as systolic blood pressure (SBP) ≥ 140 mmHg and diastolic blood pressure (DBP) ≥90 mmHg or the use of antihypertensive drugs. The diagnosis of diabetes mellitus is defined as a self-reported physician diagnosis of diabetes mellitus and/or fasting glucose ≥7.0 mmol/l or glycosylated hemoglobin (HbA1c) ≥$6.5\%$ and/or taking diabetes medications [45]. We defined current smokers as individuals who have smoked more than 100 cigarettes in their lives and currently smoke on some days or every day; never smokers as people who have smoked less than 100 cigarettes in their lives; and former smokers as people who have smoked more than 100 cigarettes in their lives and now do not smoke at all. We assessed drinking status according to the volume and frequency of alcohol consumption in participants’ self-report questionnaires [46]. The races are classified as non-Hispanic white, non-Hispanic black, Mexican-American, and other races. Marital status is divided into married/cohabiting and unmarried. Education level is classified as below high school, high school or the equivalent, and more than high school. Economic status is evaluated by the family income-to-poverty ratio (PIR), which is categorized as <1.0, 1.0–3.0, and >3.0. Laboratory tests in this study included fasting glucose, fasting insulin, HbA1c, total bilirubin (TBIL), AST, ALT, GGT, alkaline phosphatase (ALP), albumin, creatinine, TG, uric acid, HDL-C, total cholesterol (TC), lactate dehydrogenase (LDH), high sensitivity-C-reactive protein (Hs-CRP), hemoglobin (Hb), white blood cell (WBC) counts, and red blood cell (RBC) count. All routine biochemical tests were performed according to NHANES laboratory/medical technician procedure manual standards [47]. ## Statistical analysis All statistical analyses in this study were performed using R software (version 4.2.02) and Empower Stats software (3X&Y Solutions Inc.). The level of significance of the reported statistical results for all analyses was two-tailed, and $p \leq 0.05$ was considered statistically significant. Missing values in the study were processed by directly deleting the participant data if the exposure factor daidzein intake or the outcome indicator MAFLD was missing. The missing values of other confounders are processed by random forest interpolation using the “missForest” R package. In the analysis of participant baseline characteristics, we divided the exposure factor daidzein intake into quartiles and compared statistical differences between quartile groups. We used weighted linear regression analysis to calculate p values for continuous variables, while p values for categorical variables were calculated using a weighted chi-square test. Generalized additive models (GAM) and smoothing curve fitting were used to study whether the exposure variables were nonlinearly related to the outcome variables. The inflection points of the smoothed curves were analyzed by saturation and threshold effects, and the inflection points were calculated by two-stage linear regression analysis. We investigated the relationship between MAFLD status and daidzein intake by multivariate binary logistic regression models, and the relationship between APRI, CAP, LSM, FIB-4, NFS, FLI, HSI, and daidzein intake were assessed, respectively, using multivariate linear regression models. In this study, we constructed three models: crude model: no adjustment for any covariates; model I: adjusted for age, sex, and race; model II: adjusted for covariates of age, sex, race, marital status, education level, PIR, smoking, and alcohol consumption. In addition, we conducted subgroup analyses stratified by variables of interest and used the “forestlater” R package to draw forest plots to show the results of the subgroup analyses. ## Results A total of 1,476 participants were included in this study, with a weighted mean age of 45.24 ± 18.28 years and a weighted ratio of $50\%$ for both males and females. The median level of daidzein intake in our study was 0.18 mg (interquartile range [IQR], 0.02–1.55 mg). Table 1 described the baseline characteristics of participants by daidzein level quartiles. Laboratory findings of participants by quartiles of daidzein intake are shown in Supplementary Table S2. The findings suggest that participants in the third quartile may be younger compared with those in the first quartile of daidzein intake levels (49.00 ± 17.79 years vs. 41.49 ± 19.00 years) and that the daidzein diet intake population concentrated between the ages of 20–65 years. The proportion of daidzein intake was significantly higher among non-Hispanic whites, those with a high school degree or more, those married/cohabiting, and those with higher household income (PIR > 3, i.e., household income above three times the poverty line) than among other populations [e.g., other Hispanics, those below high school level, those unmarried, and those with lower household income (PIR < 1)]. In addition, the proportion of daidzein intake was significantly higher in participants without pre-diabetes, diabetes, and hypertension compared to those with underlying conditions such as these conditions. Table 1 \| Characteristic \| Quartile 1 \| Quartile 2 \| Quartile 3 \| Quartile 4 \| value of p \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristic \| <0.02 \| ≥0.02 to <0.18 \| ≥0.18 to <1.55 \| ≥1.55 \| value of p \| \| No. of participants \| 290 \| 425 \| 377 \| 384 \| \| \| Age (years) \| 49.00 ± 17.79 \| 44.42 ± 17.60 \| 41.49 ± 19.00 \| 46.43 ± 17.97 \| <0.0001 \| \| Age group (%) \| \| \| \| \| <0.0001 \| \| > = 12, <20 \| 4.49 \| 8.85 \| 15.66 \| 5.46 \| \| \| > = 20, <65 \| 72.25 \| 78.78 \| 71.28 \| 75.59 \| \| \| > = 65 \| 23.26 \| 12.36 \| 13.06 \| 18.95 \| \| \| Gender (%) \| \| \| \| \| 0.0056 \| \| Female \| 50.34 \| 48.77 \| 43.50 \| 56.00 \| \| \| Male \| 49.66 \| 51.23 \| 56.50 \| 44.00 \| \| \| Race/ethnicity (%) \| \| \| \| \| 0.0001 \| \| Non-Hispanic white \| 66.70 \| 58.50 \| 60.48 \| 65.23 \| \| \| Non-Hispanic black \| 11.79 \| 7.44 \| 11.18 \| 4.88 \| \| \| Other race - including multi-racial \| 8.42 \| 19.99 \| 11.86 \| 15.86 \| \| \| Mexican American \| 7.63 \| 7.99 \| 9.71 \| 6.43 \| \| \| Other Hispanic \| 5.46 \| 6.08 \| 6.76 \| 7.60 \| \| \| Education level (%) \| \| \| \| \| 0.0005 \| \| Less than high school \| 11.61 \| 13.50 \| 19.22 \| 9.51 \| \| \| High school or equivalent \| 22.00 \| 19.67 \| 25.83 \| 22.42 \| \| \| Above high school \| 66.39 \| 66.83 \| 54.94 \| 68.07 \| \| \| Marital status (%) \| \| \| \| \| 0.0111 \| \| Married/cohabiting \| 63.98 \| 64.62 \| 55.65 \| 66.58 \| \| \| Unmarried \| 36.02 \| 35.38 \| 44.35 \| 33.42 \| \| \| PIR (%) \| \| \| \| \| <0.0001 \| \| PIR < 1 \| 9.58 \| 7.93 \| 17.74 \| 6.75 \| \| \| 1 < =PIR < =3 \| 33.85 \| 31.62 \| 33.73 \| 30.95 \| \| \| PIR > 3 \| 56.57 \| 60.44 \| 48.54 \| 62.30 \| \| \| Smoking status (%) \| \| \| \| \| 0.0006 \| \| Never \| 52.81 \| 66.17 \| 66.93 \| 64.19 \| \| \| Past/current \| 47.19 \| 33.83 \| 33.07 \| 35.81 \| \| \| Alcohol consumption (%) \| \| \| \| \| <0.0001 \| \| Never \| 7.10 \| 11.88 \| 14.99 \| 10.25 \| \| \| Mild \| 63.41 \| 49.50 \| 43.95 \| 51.52 \| \| \| Moderate \| 13.28 \| 16.14 \| 18.22 \| 21.71 \| \| \| Heavy \| 16.21 \| 22.48 \| 22.84 \| 16.52 \| \| \| Waist circumference (cm) \| 97.03 ± 16.67 \| 96.28 ± 17.31 \| 98.78 ± 19.35 \| 96.85 ± 18.50 \| 0.2760 \| \| BMI (kg/m 2 ) \| 28.01 ± 6.68 \| 28.37 ± 7.20 \| 29.23 ± 7.49 \| 28.28 ± 7.29 \| 0.1372 \| \| BMI categories (%) \| \| \| \| \| 0.0131 \| \| BMI < 25 \| 32.46 \| 34.93 \| 33.38 \| 36.74 \| \| \| 25 < =BMI < 30 \| 36.18 \| 33.18 \| 24.83 \| 28.93 \| \| \| BMI > =30 \| 31.36 \| 31.89 \| 41.79 \| 34.33 \| \| \| HOMA_IR \| 4.10 ± 5.95 \| 3.29 ± 3.24 \| 3.91 ± 3.48 \| 3.85 ± 8.97 \| 0.3145 \| \| preDM (%) \| \| \| \| \| 0.0210 \| \| No \| 47.51 \| 55.22 \| 54.31 \| 56.48 \| \| \| preDM \| 33.60 \| 32.90 \| 32.46 \| 33.76 \| \| \| DM \| 18.89 \| 11.88 \| 13.23 \| 9.76 \| \| \| DM (%) \| \| \| \| \| <0.0001 \| \| No \| 77.63 \| 77.58 \| 78.69 \| 85.31 \| \| \| IFG \| 3.48 \| 10.54 \| 8.09 \| 4.93 \| \| \| DM \| 18.89 \| 11.88 \| 13.23 \| 9.76 \| \| \| Hypertension (%) \| \| \| \| \| 0.1359 \| \| No \| 52.28 \| 61.14 \| 56.89 \| 55.99 \| \| \| Yes \| 47.72 \| 38.86 \| 43.11 \| 44.01 \| \| \| NFS \| −1.75 ± 1.53 \| −1.84 ± 1.43 \| −1.97 ± 1.66 \| −1.85 ± 1.35 \| 0.3104 \| \| NFS categories (%) \| \| \| \| \| 0.0446 \| \| NFS < −1.455 \| 59.06 \| 64.36 \| 64.14 \| 63.25 \| \| \| −1.455 = <NFS < 0.676 \| 35.88 \| 30.63 \| 27.37 \| 32.83 \| \| \| NFS > =0.676 \| 5.06 \| 5.01 \| 8.49 \| 3.92 \| \| \| FIB-4 \| 1.04 ± 0.66 \| 0.93 ± 0.51 \| 0.86 ± 0.58 \| 1.02 ± 0.62 \| 0.0001 \| \| FIB-4 categories (%) \| \| \| \| \| 0.0002 \| \| FIB-4 < 1.3 \| 72.90 \| 82.09 \| 84.10 \| 73.08 \| \| \| 1.3 < =FIB-4 < 2.67 \| 24.10 \| 17.08 \| 14.30 \| 25.63 \| \| \| FIB-4 > =2.67 \| 3.00 \| 0.83 \| 1.60 \| 1.29 \| \| \| FLI \| 47.58 ± 32.53 \| 45.39 ± 32.54 \| 47.83 ± 34.81 \| 44.62 ± 30.87 \| 0.4451 \| \| FLI categories (%) \| \| \| \| \| 0.1675 \| \| FLI < 60 \| 60.27 \| 62.17 \| 57.81 \| 65.36 \| \| \| FLI > =60 \| 39.73 \| 37.83 \| 42.19 \| 34.64 \| \| \| HSI \| 36.97 ± 8.42 \| 36.60 ± 9.48 \| 38.03 ± 9.52 \| 36.70 ± 9.22 \| 0.1344 \| \| HSI categories (%) \| \| \| \| \| 0.7269 \| \| HSI < 36 \| 53.27 \| 52.88 \| 49.33 \| 51.40 \| \| \| HSI > =36 \| 46.73 \| 47.12 \| 50.67 \| 48.60 \| \| \| LSM (kPa) \| 5.47 ± 3.90 \| 5.39 ± 4.29 \| 5.65 ± 4.39 \| 5.39 ± 3.67 \| 0.7917 \| \| LSM categories (%) \| \| \| \| \| 0.5451 \| \| LSM < 9.7 \| 95.95 \| 95.91 \| 93.97 \| 95.65 \| \| \| LSM > =9.7 \| 4.05 \| 4.09 \| 6.03 \| 4.35 \| \| \| CAP (dB/m) \| 259.86 ± 58.60 \| 255.28 ± 65.48 \| 258.84 ± 64.57 \| 246.30 ± 59.99 \| 0.0091 \| \| CAP categories (%) \| \| \| \| \| 0.2531 \| \| CAP<248 \| 46.00 \| 52.08 \| 50.23 \| 53.25 \| \| \| CAP> = 248 \| 54.00 \| 47.92 \| 49.77 \| 46.75 \| \| \| APRI \| 0.32 ± 0.22 \| 0.33 ± 0.18 \| 0.31 ± 0.17 \| 0.35 ± 0.26 \| 0.1212 \| \| APRI categories (%) \| \| \| \| \| 0.2619 \| \| < = 0.5 \| 93.62 \| 92.18 \| 91.34 \| 89.90 \| \| \| >0.5, < = 1.5 \| 5.95 \| 7.47 \| 8.62 \| 8.95 \| \| \| >1.5 \| 0.42 \| 0.35 \| 0.05 \| 1.15 \| \| Table 2 showed the relationship between MAFLD incidence and daidzein intake evaluated by three univariate and multivariate binary logistic regression models. In the crude model, there was a negative linear correlation between daidzein intake and MAFLD incidence (OR for Q4 versus Q1 was 0.63, $95\%$ CI: 0.46–0.86, p for trend was 0.0024). Multivariate adjusted model II also revealed a significant negative association between daidzein intake and the incidence of MAFLD with an OR of 0.98 (0.97, 0.99) and a value of p of 0.0039. Similarly, participants in the highest quartile had an OR of 0.65, $95\%$ CI: 0.46–0.86, compared to those in the lowest quartile of daidzein intake, and statistical significance remained. In addition, we observed a dose–response relationship between daidzein intake and MAFLD incidence, and MAFLD incidence decreased with increasing daidzein intake independent of gender (Figures 2A, 3A; Table 3). Table 2 also showed the relationship between CAP, APRI, LSM, FIB-4, NFS, FLI, HSI, and daidzein intake by three different linear regression models. We found that CAP was negatively related to daidzein intake in the crude model. Similar results were present in the model I (after adjusting for age, sex, and race, β = −0.40, $95\%$ CI: −0.66 to −0.14, $$p \leq 0.0024$$) and model II (after adjusting for age, sex, race, education level, marital status, PIR, smoking, and alcohol use, β = −0.37, $95\%$ CI: −0.63 to −0.12, $$p \leq 0.0046$$). Stratified by quartiles of daidzein intake levels, trend analysis of the association between daidzein intake and CAP remained significant (p for trend = 0.0054). We also found a negative correlation between HSI, FLI, NFS, and daidzein intake, and the detailed results were shown in Table 2. In model I, the β values between daidzein intake and HSI, FLI, and NFS were −0.06 ($95\%$ CI: −0.10 to −0.03, $$p \leq 0.0012$$), −0.24 ($95\%$ CI: −0.37 to −0.11, $$p \leq 0.0003$$), −0.01 ($95\%$ CI: −0.01 to 0.00, $$p \leq 0.0005$$), respectively. In model II, the β values between daidzein intake and HSI, FLI, and NFS were −0.06 ($95\%$ CI: −0.10 to −0.02, $$p \leq 0.0027$$), −0.22 ($95\%$ CI: −0.35 to −0.09, $$p \leq 0.0008$$), −0.01 ($95\%$ CI, −0.01to 0.00, $$p \leq 0.0006$$), respectively. LSM was negatively associated with daidzein intake, but $p \leq 0.05$, with no significant statistical differences. The correlation between APRI, FIB-4, and daidzein intake was not strong (although $p \leq 0.05$, all β values were 0). In Figures 2, 3 and Table 3, we further assessed the dose–response relationships between each outcome variable and daidzein intake by generalized additive modeling and smoothed curve fitting. With this dose–response relationship, we also used a log-likelihood ratio-based test to assess the presence of a saturation threshold effect and used a two-step recursive method to determine the inflection point of the smoothing curve. In Table 3, if the log-likelihood ratio test $p \leq 0.05$, it indicates a linear correlation between daidzein intake and the outcome variable, and the curve inflection point is not significant, referring to the results of Model I. If $p \leq 0.05$, it indicates a curvilinear relationship between daidzein intake and the outcome variable, and the inflection point is significantly present, referring to the results of Model II. Therefore, Table 3 and Figure 2 show a linear relationship between MAFLD, CAP, LSM, FIB-4, NFS, FLI, HSI, and daidzein intake, except for APRI, where the presence of two inflection points is a non-linear relationship. In addition, we performed interaction tests for gender and found that the value of ps for all interactions after adjusting for variables were >0.05, which indicated that the relationship between daidzein intake and each outcome variable was not significantly dependent on gender (Figure 3; Table 3). Figure 4 further stratifies by age, sex, race, education level, marital status, PIR, alcohol use, and smoking to determine the independent relationship between MAFLD prevalence and daidzein intake. We observed that higher daidzein intake was related to lower MAFLD prevalence among those aged 20–65 years, married/cohabiting, with less than high school level, with PIR between 1 and 3, and who never smoked (Figure 4). Figure 4: Subgroup analysis of the association between daidzein intake and the prevalence of MAFLD plotted in (A) and (B). MAFLD, metabolic associated fatty liver disease; Q, quartile; PIR, family income-to-poverty ratio. ## Discussion A previous study from China supported a negative association between daidzein intake in isoflavones and the incidence of NAFLD [27]. However, a number of growing evidence has supported that MAFLD diagnostic criteria are more useful than NAFLD for recognizing patients with a fatty liver at high risk of progressive disease (10–12). It was also necessary to study the association of daidzein intake with MAFLD in different ethnic groups. We found a relatively high proportion of daidzein intake among non-Hispanic white participants who had a high school degree or higher, were married/cohabiting, had higher household income, and did not have underlying conditions such as prediabetes, diabetes, and hypertensive disease. We also observed that the incidence of MAFLD decreased with increasing levels of daidzein intake and was not affected by gender, and more specifically, it is likely that this relationship was more significant among those aged 20–65 years, married/cohabiting, with education below high school level, with a PIR between 1 and 3 and never having smoked. We also found that CAP, HSI, FLI, and NFS were negatively associated with daidzein intake, but the correlation between daidzein intake and LSM, APRI, and FIB-4 was not strong. The previous 2001–2004 NHANES study showed that income and race were associated with differences in dietary intake, particularly among individuals from low-income households and non-Hispanic blacks, who consumed less fruits, vegetables, grains, legumes, dairy, etc. [ 48]. Another study from 2011 to 2016 NHANES data demonstrated the association of flavonoid intake in the US population with higher socioeconomic status [49]. Our findings were similar, revealing a higher daidzein intake among non-Hispanic white, higher household income participants. Daidzein is protective against certain diseases related to estrogen regulation like prostate cancer, breast cancer, diabetes, and cardiovascular diseases (23–25). In addition, studies have shown that daidzein has the potential to prevent metabolic syndromes such as cardiovascular disease and diabetes [26], which may well explain the lower risk of diabetes and hypertension in participants with a high percentage of daidzein intake in this study. In mice, daidzein may ameliorate insulin resistance in obesity via direct modulation of hepatic ab initio adipogenesis and insulin signaling, and alter adipocyte metabolism to indirectly control obesity and reduce NAFLD by regulating adipokine expression through PPAR γ [50, 51]. Another study also showed that daidzein improved lipopolysaccharide (LPS)-induced damage to hepatocytes by inhibiting inflammation and oxidative stress in mice [52]. The MAFLD definition emphasizes more metabolic disorders and hepatic steatosis. Thus, the above-mentioned basic research further explains the negative correlation between daidzein intake and the prevalence of MAFLD. A prospective cohort study from Guangzhou, China, included 2,694 participants and assessed the association between dietary flavonoid intake and NAFLD status using a food frequency questionnaire with face-to-face interviews, and revealed that higher flavonoid intake was related to a lower risk of progression of NAFLD in an elderly Chinese population [53]. Our study also showed that the higher the daidzein intake in the middle-aged and elderly population, the more significant the reduction in the incidence of MAFLD. Another study from Tehran showed that education level and marital status were related to cardiovascular risk factors and dietary intake [54], and married women in the Hong Kong research had significantly higher intakes of vegetables, soy products, and fish than single women [55]. Smoking was also considered an independent risk factor for poor prognosis in various chronic liver diseases (56–58). The result of a recent nationally representative cohort study from Thailand showed that smoking also increases the risk of all-cause mortality in patients with NAFLD, and this association was more pronounced in women with NAFLD [59]. These studies also explained well that increased daidzein intake in the nonsmoking population in our study reduced the occurrence of MAFLD. The pathophysiology of NAFLD/MAFLD has evolved from a “first strike” characterized by increased hepatic fat to a “second strike” consisting of adipokines, inflammatory cytokines, oxidative stress, and mitochondrial dysfunction; and the “multiple strikes” hypothesis consisting of insulin resistance, inflammation, lipotoxicity, cytokine imbalance, innate immune activation and microbiota disorders in the background of genetic and environmental factors (60–62). However, the accumulation of hepatic fat caused by insulin resistance remains the first and central striking factor [63]. The current diagnosis of MAFLD is the basis of the presence of hepatic steatosis, which can be assessed clinically by non-invasive (blood biomarkers and imaging) and invasive means (liver biopsy) [64]. Liu et al. [ 65]. evaluated the accuracy of five commonly non-invasive hepatic steatosis algorithms such as FLI, HSI, Visceral Adiposity Index (VAI), NAFLD-liver fat score (NAFLD-LFS), and Steato text (ST) in the diagnosis of MAFLD using NHANES III, and the result showed that FLI had the highest diagnostic performance in the diagnosis of MAFLD. In this study, we also used three non-invasive liver fat assessment methods such as FLI, HSI, and CAP assess the relationship between daidzein intake and MAFLD, and the results showed that FLI, HSI, and CAP were negatively associated with daidzein intake, implying that daidzein may improve hepatic steatosis. Given the current state of treatment for NAFLD/MAFLD, daidzein may be a potential therapeutic option. Recent research has demonstrated that soy protein concentrates and related isoflavones play an important role in lowering blood lipids, reducing hepatic steatosis, and improving the symptoms of NAFLD [66, 67]. A cross-section study of 17,685 US adults also showed a negative association between the consumption of flavonoids and FLI [68]. A randomized controlled trial from Iran found that 8 weeks of soy milk consumption and a low-calorie diet showed significant improvements in blood pressure and insulin resistance-related indicators in patients with NAFLD [69]. Canadian food guidelines also recommend that the diet of soy foods or supplements may be a beneficial strategy to reduce the burden of disease and prevalence of MAFLD [70]. NAFLD can develop from simple steatosis to NASH, progressive hepatic fibrosis, cirrhosis, hepatocellular carcinoma, and liver failure [71, 72]. NAFLD is related to increased mortality from hepatic and cardiovascular events, in which advanced fibrosis is considered an essential predictor of prognosis in NAFLD patients [72, 73]. Liver biopsy is the golden standard for diagnosis of NAFLD fibrosis but is limited by the invasive nature of liver biopsy. Therefore, non-invasive assessments such as VCTE, point shear wave elastography (pSWE), magnetic resonance elastography (MRE), FIB-4, NFS, APRI, and other tests are used for screening fibrosis in NAFLD [74]. In this study, we assessed the relationship between daidzein intake and hepatic fibrosis using four methods, including FIB-4, NFS, APRI, and LSM, and the findings revealed a negative correlation between NFS and daidzein intake levels, but no strong between daidzein intake and LSM, APRI, and FIB-4. There is insufficient evidence for the ability of daidzein to alleviate hepatic fibrosis. A previous study reported a significant inhibitory effect of high-dose soy isoflavones on thioacetamide-induced hepatic fibrosis in rats, which may be related to the inhibition of hepatic stellate cell activation and proliferation [75]. The future may require us to investigate the association between daidzein intake and MAFLD-associated hepatic fibrosis in greater depth. The advantages of our study are the use of representative national NHANES data, the multi-stage stratified sampling and the collection of relevant information by trained technical staff, the relatively large size of the sample included studies and the adjustment for multiple potential confounders to improve the reliability of the findings. However, our study also has some limitations. First, the cross-sectional study design of the study could not conclude whether there was a causal association between daidzein intake and MAFLD; therefore, further validation in a prospective cohort study is necessary. Second, the diagnosis of MAFLD in this study was defined as hepatic steatosis based on a CAP ≥248 dB/m measured by VCTE rather than by liver biopsy, which inevitably leads to a biased diagnosis of MAFLD. Also, non-invasive assessment methods do not perfectly predict the presence of hepatic steatosis and fibrosis in liver biopsies. Third, in our study, we tried to incorporate as many confounding factors as possible that were relevant to the study results, but we still could not completely rule out the possibilities of other confounding factors causing bias in the conclusions. Finally, due to limitations in the design of the USDA Food and Nutrient Dietary Study Database (FNDDS), it does not have data addressing the relationship between daidzein intake and calories, fat, protein, and dietary fiber. Therefore, we also do not know whether the daidzein intake is associated with a reduced intake of calories, fat, and protein. We will further investigate the relationship between daidzein intake and energy, lipid and protein metabolism, and dietary fiber, to elaborate more deeply on the relationship between daidzein intake and MAFLD. ## Conclusion In conclusion, we found that the prevalence of MAFLD decreased with increasing daidzein intake and that CAP, HSI, and FLI were negatively correlated with daidzein intake, suggesting that daidzein intake may have improved hepatic steatosis. Therefore, dietary patterns of soy food or supplement consumption may be a beneficial strategy to reduce the disease burden and prevalence of MAFLD. However, the correlation between daidzein intake and hepatic fibrosis indicators such as LSM, APRI, and FIB-4 was not strong, and we may need to investigate the association between daidzein intake and MAFLD-related hepatic fibrosis more deeply in the future. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Author contributions ZY, YS, and QH conceptualized the study idea and conducted the interpretation, manuscript writing, and final approval. DG, XH, and FH performed the data analysis and collection, as well as the linguistic polishing. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by Jingzhou Science and Technology Bureau Plan Project (2021CC28-04 to ZY) and the Natural Science Foundation of Hubei Province (2019CFB567 to YS). ## 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. 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--- title: Diagnosis potential of subarachnoid hemorrhage using miRNA signatures isolated from plasma-derived extracellular vesicles authors: - Bin Sheng - Niansheng Lai - Tao Tao - Xiangxin Chen - Sen Gao - Qi Zhu - Wei Li - Qingrong Zhang - Chunhua Hang journal: Frontiers in Pharmacology year: 2023 pmcid: PMC9968748 doi: 10.3389/fphar.2023.1090389 license: CC BY 4.0 --- # Diagnosis potential of subarachnoid hemorrhage using miRNA signatures isolated from plasma-derived extracellular vesicles ## Abstract The diagnosis and clinical management of aneurysmal subarachnoid hemorrhage (aSAH) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of disease. We used microRNAs (miRNAs) as diagnostics to characterize plasma extracellular vesicles in aSAH. It is unclear whether they can diagnose and manage aSAH. Next-generation sequencing (NGS) was used to detect the miRNA profile of plasma extracellular vesicles (exosomes) in three patients with SAH and three healthy controls (HCs). We identified four differentially expressed miRNAs and validated the results using quantitative real-time polymerase chain reaction (RT-qPCR) with 113 aSAH patients, 40 HCs, 20 SAH model mice, and 20 sham mice. Exosomal miRNA NGS revealed that six circulating exosomal miRNAs were differentially expressed in patients with aSAH versus HCs and that the levels of four miRNAs (miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p) were differentially significant. After multivariate logistic regression analysis, only miR-369-3p, miR-486-3p, and miR-193b-3p enabled prediction of neurological outcomes. In a mouse model of SAH, greater expression of miR-193b-3p and miR-486-3p remained statistically significant relative to controls, whereas expression levels of miR-369-3p and miR-410-3p were lower. miRNA gene target prediction showed six genes associated with all four of these differentially expressed miRNAs. The circulating exosomes miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p may influence intercellular communication and have potential clinical utility as prognostic biomarkers for aSAH patients. ## Introduction Aneurysmal subarachnoid hemorrhage (aSAH), which generally results from ruptured aneurysms, is a clinical syndrome with nearly $45\%$ mortality and morbidity in approximately 0.1‰ of individuals worldwide every year, and $5\%$ of cases involve subarachnoid hemorrhage in the cerebrovasculature. aSAH occurs in patients aged 50–55 years at more than $5\%$ of the total incidence of stroke (Nasra et al., 2022; Zeineddine et al., 2022). Initial hemorrhaging that causes early brain injury (EBI) and early cerebral vasospasms is a vital prognostic determinant and is possibly associated with delayed cerebral ischemia (DCI) (Sheng et al., 2018a; Takeuchi et al., 2021; Zhang X. et al., 2021). Recently, researchers have found that EBI after SAH may be a leading factor contributing to unfavourable outcome in SAH (Ahn et al., 2022; Tao Q. et al., 2022). Therefore, it is essential to understand the pathophysiological changes in the early phase after aSAH, including microvascular filling defects, inflammation, and microarterial narrowing (Helbok et al., 2015; Huang et al., 2017). Therefore, a reliable, economical, and non-invasive approach is needed to provide screening of patients and contribute to improving the prognoses of patients with aSAH. Extracellular vesicles, including exosomes and microvesicles, are important mediators of intercellular communication and used as biomarkers for disease (Badimon et al., 2022; Clancy and D'Souza-Schorey, 2022). In this study, we detected extracellular lipid vesicles of 30–150 nm in diameter, most of which consisted of exosomes, in plasma from patients with subarachnoid hemorrhage, and the subsequent analyses were primarily in exosomes. Exosomes can cross the blood–brain barrier, where they specialize in long-distance intercellular communication and facilitate the transfer of proteins, lipids, functional mRNAs, and miRNAs for the subsequent expression of proteins at their target cells (Goetzl, 2020; Zhang L. et al., 2021). A similar study reported that exosomes from peripheral blood could be enriched and used for detecting proteins, lipids, and nucleic acids (Kanninen et al., 2016). These circulating exosomal miRNAs could be ideal biomarkers to reflect the pathological progression of aSAH. MicroRNAs (miRNAs) are a family of non-coding RNAs of 17–24 nucleotides that regulate the expression of multiple target genes at the post-transcriptional level (Baluni et al., 2022). Previous studies of miRNAs as biomarkers of aSAH have been reported (Bache et al., 2020; Kalani et al., 2020). Other studies have demonstrated significant differential expression of miRNAs in human cerebrospinal fluid after aSAH (Bache et al., 2017; Stylli et al., 2017). Nevertheless, the variety and characteristics of miRNAs in the plasma of patients with aSAH remain unknown. In this study, we determined the expression profiles of plasma exosomal miRNAs in post-aSAH patients and healthy controls using next-generation sequencing (NGS). We then determined the feasibility of measuring differential expression levels of miRNAs in plasma from aSAH patients and in a SAH mouse model. Finally, we estimated the relevance of differential miRNA expression to prognosis. The purpose of the study was to determine the clinical significance and prognostic value of plasma exosomal miRNAs in aSAH. ## Human subjects and animal studies Study participants were enlisted from the Department of Neurosurgery, The First Affiliated Hospital of Wannan Medical College, China. The study was compliant with the Declaration of Helsinki. All participants or valid proxies signed an informed consent form prior to inclusion. All experiments were approved by the hospital ethics committee (No. 2019–86) and were performed in accordance with the National Institutes of Health Guidelines for the Care and Use of Animals. Healthy adult male C57BL/6 J mice (8–10 weeks) weighing 22–25 g were used in all experiments. The mice were purchased from the Animal Center of Zhejiang Province, China. All the mice were kept in temperature- and humidity-controlled animal quarters with a 12-h light/dark cycle. Every effort was made to minimize the number of animals used, as well as their suffering. ## Study design Patients with aSAH were admitted from April 2020 to February 2021. The study design is shown in Figure 1. First, exosomal miRNA profiles were generated for three sets of plasma (three aSAH patients and three healthy controls) by NGS, and the results were confirmed by RT-qPCR. To analyze the miRNA NGS results, 20 serum samples (10 aSAH and 10 healthy controls) were selected by RT-qPCR. Subsequently, the candidate miRNAs were further verified by RT-qPCR in plasma samples from 113 aSAH patients and 40 healthy controls. Finally, in the SAH mouse model study, the validated miRNAs were tested in the plasma of SAH-model mice. FIGURE 1: Schematic diagram of the study design. Neurological outcomes were evaluated by the modified Rankin Scale (mRS) at one year post-aSAH using outpatient outcomes. At the end of the follow-up period, patients with mRS scores of 0–2 were considered to have good outcomes, and those with mRS scores of 3–6 were considered to have poor outcomes (Limin et al., 2022). ## Sample processing The study involved 193 plasma samples from 113 aSAH patients, 40 healthy controls, 20 SAH mice, and 20 sham control mice. Blood was collected within 24 h of admission, and each sample was obtained in the fasted state. Plasma fractionation was performed within 2 h of obtaining whole blood. The whole blood was centrifuged at 500 rpm and 4°C for 10 min. The upper layers were transferred into RNase/DNase-free 1.5-mL EP tubes and centrifuged further at 3000 rpm at 4°C for 10 min. Plasma aliquots were stored at −80°C for further analysis. ## SAH model Following our previous studies (Tao T. et al., 2022), briefly, animals were anesthetized with $3\%$ inhaled isoflurane and maintained with $1.5\%$ isoflurane during surgery. Meloxicam (5 mg/kg) was administered subcutaneously after anesthesia for pain relief. Mice were placed on a heating pad to maintain their body temperature throughout the procedure, as monitored by an anal temperature sensor. The external carotid artery was ligated and divided slightly at the bifurcation, with an indwelled knot. The vessel was cut close to the fractured end, and a prelabeled monofilament was inserted from the ECA backward, through the internal carotid artery to the middle cerebral artery bifurcation. In the subarachnoid hemorrhage group, the monofilament was quickly inserted into the pointed nylon monofilament (6–0), with a slight breakthrough feeling, and then pulled out; in the sham group, the monofilament was removed directly. The knots were tied to prevent bleeding. One milliliter of saline was injected intraperitoneally. After surgery, the mice were put into the animal postoperative nursing room until they fully awakened; they were fed jelly to provide energy and water. Finally, blood was sampled and subjected to further analysis after SAH mice were sacrificed at the indicated time points. ## Isolation of exosomes from plasma samples Exosomes were isolated from plasma using the exoEasy Maxi Kit (Qiagen, Valencia, CA), following the manufacturer’s instructions. Briefly, syringe filters (EMD Millipore, Burlington, MA) were used to filter plasma samples to exclude particles larger than 0.8 μm. One milliliter of Buffer XBP was added to 1 mL of plasma samples, mixed for 5 min, centrifuged at 500 g for 1 min, and then bound to exoEasy membrane spin columns. The bound EVs were washed with Buffer XWP by centrifugation at 5,000 g for 5 min and eluted with 400 μL Buffer XE by centrifugation at 5,000 g for 5 min to collect the eluates, after which they were ready for further analysis. All processes were performed at room temperature. ## Exosome identification For exosome identification, transmission electron microscopy (TEM, Hitachi HT7700, Tokyo, Japan) was performed to observe the morphologies of precipitated particles. Briefly, we extracted 5 μL of the previous eluates and diluted them to 10 μL. Then, we extracted 10 μL samples onto copper disks for 1 min and used filter paper to remove the floating material. Subsequently, 10 μL phosphotungstic acid was dropped on copper disks for 1 min, and the floating material was removed with a filter paper. After drying for several minutes at room temperature, we examined the samples using TEM. The size and concentration of the particles were measured and analyzed using the NanoFCM instrument (NanoFCM Inc. China). The levels of CD63 and Alix were measured by Western blot, and the exosome biomarkers CD9 and CD81 were measured using the NanoFCM instrument. ## Isolation and concentration measurement of exosome proteins The bicinchoninic acid (BCA) protein assay kit (Beyotime, P0010) was used to measure the concentration of exosomes. Briefly, the isolated exosomes were melted at 37°C. We quickly added equal volumes of RIPA lysis buffer, and then mixed and split on ice for 30 min. We prepared a BCA protein concentration standard sample and then added it to the BCA mixture and mixed. After incubating for 30 min at 37°C, optical density was measured at 570 nm on a microplate reader. The protein concentration of the sample was calculated according to the standard curve. ## Western blot analysis Ten percent sodium dodecyl sulfate–polyacrylamide gels were used to separate molecular weight markers (5 µ/lane, Thermo Scientific, USA) and protein samples (20 µg/lane), which were then electrophoretically transferred onto polyvinylidene difluoride membranes (Millipore Corporation, USA). Then, $5\%$ non-fat milk and primary antibodies were used to block membranes for 1 h at room temperature, and blots were incubated in $5\%$ BSA overnight at 4°C. Mouse anti-CD63 and rabbit anti-Alix (all 1:1,000, Abcam) were used as primary antibodies. Corresponding HRP-conjugated anti-rabbit or anti-mouse (1:10,000, Pierce) secondary antibodies were incubated for another 2 h under the same conditions. Bands were visualized with an enhanced chemiluminescence kit. ## Flow NanoAnalyzer studies We diluted 20 µL exosome to 60 μL and then added 20 µL fluorescent marker antibodies (CD9 and CD81) to 30 µL dilution. We incubated for 30 min at 37°C after mixing. We added 1 mL pre-cooled PBS and then ultracentrifuged for 70 min at 110,000 x g and 4°C. After removing supernatants, we repeated centrifugation. We again removed supernatants, resuspended in 50 µL pre-cooled 1 x PBS, and analyzed using the NanoFCM NanoAnalyzer (NanoFCM, China) as per manufacturer instructions. ## miRNA NGS analysis miRNA profiling of exosomes was performed using NGS. A detailed NGS analysis is described in the Supplementary Material. ## RNA isolation and RT-qPCR of miRNAs Total RNA was extracted from exosomes using the exoRNeasy Serum/Plasma Midi Kit for exosomes (Qiagen, Valencia, CA) and QIAzol for tissues (Qiagen, Valencia, CA), following the manufacturer instructions. cDNA was synthesized using the miRcute Plus miRNA First-Strand cDNA Synthesis Kit (Tiangen Biotech). The quantification of miRNA was performed using the miRcute Plus miRNA qPCR Detection Kit (SYBR Green) according to the manufacturer’s protocol. A detailed experimental protocol is described in the Supplementary Material. ## Statistical analysis Data were analyzed using MedCalc version 15.0 (MedCalc, Belgium). Data were presented using the Mann–Whitney U test. Spearman’s rank correlation coefficient analysis was used to calculate correlations among the variables. Receiver operating characteristic (ROC) curves were constructed to determine the optimal thresholds of miRNAs to predict SAH outcomes. A multivariate logistic regression model was analyzed to determine factors independently predicting mRS, after adjusting for risk factors that reached $p \leq 0.1$ in the univariate analysis. $p \leq 0.05$ was considered significant. ## Exosome characterization TEM was used to evaluate exosome morphology. Exosomes had spherical shapes with sizes of 68.25 ± 13.70 nm and were surrounded by membranes (Figures 2A, B). The levels of CD63 and Alix were more highly expressed in aSAH than in healthy controls (Figure 2C). The identity of exosomes was further validated by quantitating the exosome membrane-associated markers CD9 and CD81 using NanoFCM, and the positivity rates were $8.7\%$ and $7.4\%$ (Figures 2D, E). The sample concentration of exosomes was 9.18 × 107 particles/mL (Figure 2F). There were no obvious differences in size or shape of exosomes between the control and aSAH samples. The quantity and purity of total RNA isolated from exosomes were analyzed on a Bioanalyzer 2100 instrument (Agilent, CA, USA) with RIN number <7.0. FIGURE 2: Characterization of exosomes. (A,B) Transmission electron microscopy of isolated plasma exosomes. Bar, 100 nm. (C) Western blot analysis of Alix and CD63. (D,E) NanoFCM analysis of the positive rate of CD9 and CD81. (F) NanoFCM analysis of exosome concentration. ## Distinct plasma exosomal miRNA profiles in aSAH patients To identify potential biomarkers, six plasma samples were analyzed, including 10 control individuals and 10 patients with aSAH. NGS of these samples yielded 9–15 million reads, corresponding to more than 50,000 different RNA sequences. These were aligned to the reference human genome sequence. Only 746 miRNAs from the 2,139 known miRNAs were considered to be expressed, with raw read count ≥1 in at least one sample. Distinct profile expression was found after measuring the expression spectra of six plasma samples (Supplementary Data; Additional Supplementary File S1). To analyze the differences between the groups, a global statistical analysis was used to detect different miRNA sequences (adjusted p-value <0.05, \|log2 (fold change) \| > 2). Six miRNAs were significantly differentially expressed in plasma exosomes of aSAH patients, compared to control samples: hsa-miR-369-3p, hsa-miR-136-3p, hsa-miR-410-3p, hsa-miR-195-5p, hsa-miR-486-3p, and hsa-miR-193b-3p (Figure 3A and Supplementary Table S1). These data suggest that NGS helped us to identify a group of differentially expressed plasma exosomal miRNAs in aSAH patients. FIGURE 3: Expression profiles of the circulating exosomal miRNAs after aSAH. (A,B) Six differentially miRNAs of NGS were validated in aSAH patients and healthy controls. (C,D) Expression of four miRNAs in aSAH (patients, C), (mice, D), and healthy controls. (E) ROC curves to distinguish aSAH patients from healthy controls. *p < 0.001. ## Validation of NGS data by RT-qPCR in an independent patient cohort To analyze the miRNA NGS results, 20 total serum samples (10 aSAH and 10 healthy controls) were selected. Real-time qPCR and genomic analysis determined that six circulating exosomal miRNAs were differentially expressed and that four of those miRNAs (hsa-miR-369-3p, hsa-miR-410-3p, hsa-miR-193b-3p, and hsa-miR-486-3p) showed significant differential expression in two groups (Figures 3A, B). Subsequent technical confirmation of the significance of the observed differences in miRNA expression, with additional RT-qPCR assays on a larger group of samples (113 aSAH and 40 healthy controls), confirmed that hsa-miR-369-3p, hsa-miR-410-3p, hsa-miR-193b-3p, and hsa-miR-486-3p exhibited significantly altered expression levels after aSAH as compared with healthy controls (Figure 3C). To further explore the significance of the observed differences in exosomal miRNA expression, we performed ROC analysis and found that all these miRNAs provided the best AUCs for discriminating between aSAH patients and controls (Figure 3E). ## miRNA biomarker correlation with clinical activity and clinical outcomes World Federation of Neurosurgical Societies (WFNS) scores were used to assess levels of brain injury after aSAH. Because understanding severity and progression is important for designing future treatments for aSAH patients, these miRNAs levels were analyzed with respect to various groups classified according to disease severity. When admitted to the hospital, patients with WFNS grades I–III were classified as mild, and those with grades IV–V were classified as severe (Luo et al., 2022). As illustrated in Figure 4A, comparison of the severe and mild aSAH patients revealed that the miRNA expression levels of hsa-miR-193b-3p and hsa-miR-486-3p were significantly elevated and that levels of hsa-miR-369-3p and hsa-miR-410-3p were significantly lowered ($p \leq 0.001$). It is important to determine the potential outcomes of aSAH patients at the earliest stages to optimize their treatment. Therefore, the patients were divided into two groups according to their clinical outcomes. As shown in Figure 4B, we clearly see that the levels of hsa-miR-193b-3p and hsa-miR-486-3p were lower in the positive-outcome group than those in the poor-outcome group. However, the levels of hsa-miR-369-3p and hsa-miR-410-3p showed opposite results (both $p \leq 0.001$). These results may improve the determination of the expression of these miRNAs. FIGURE 4: Plasma samples were collected, and miRNAs, analyzed by RT-qPCR. (A) Relative levels of miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p in patients with severe SAH and those with mild SAH. (B) Relative levels of four miRNAs in relation to clinical outcomes. *p < 0.001. Spearman’s correlation coefficient analysis was used to investigate the relationships between the four miRNA levels and WFNS grades. The results revealed that in the aSAH patients, plasma exosomal levels of hsa-miR-369-3p (ρ = −0.645; $p \leq 0.001$; $95\%$ CI: −0.753 to −0.541), hsa-miR-410-3p (ρ = −0.639; $p \leq 0.001$; $95\%$ CI: −0.727 to −0.499), hsa-miR-193b-3p (ρ = 0.868; $p \leq 0.001$; $95\%$ CI: 0.688–0.839), and hsa-miR-486-3p (ρ = 0.862; $p \leq 0.001$; $95\%$ CI: 0.746–0.871) were closely correlated with aSAH severity, as scored by WFNS grade and is shown in Figure 5. FIGURE 5: Relationship between four miRNA levels and WFNS grade. (A) Relationship between miR-369-3p, (A); miR-410-3p, (B); miR-193b-3p, (C); and miR-486-3p, (D) levels and WFNS grade. In univariate analysis, the WFNS grade, Hunt–Hess grade, and Fisher score were identified as prognostic predictive factors 1 year post-aSAH (Table 1). In the multivariate logistic regression models, levels of miR-369-3p ($$p \leq 0.009$$), miR-193b-3p ($$p \leq 0.040$$), and miR-486-3p ($$p \leq 0.012$$) were significantly associated with mRS at 1 year after aSAH. This finding suggests that severe neurological status upon admission and levels of miR-369-3p, miR-193b-3p, and miR-486-3p indicate a high risk of a poor outcome (Table 2). ## miRNA expression in mice after SAH To examine the conservation of miRNA expression, it is necessary to determine whether the miRNAs exhibit significantly altered expression levels in mice after SAH, as compared with human levels, to provide a strong theoretical basis for miRNA-based mechanisms of SAH in mice. Plasma was obtained from the model and sham mice, and levels of the four miRNAs were measured. Increased expression of plasma exosomal miR-193b-3p and miR-486-3p remained statistically significant relative to the controls in this analysis, whereas expression levels of miR-369-3p and miR-410-3p were lower than those of the controls ($p \leq 0.001$). These results were similar to those of human plasma (Figure 3D). ## Identification of the target genes for circulating exosomal miRNAs To characterize the potential functions of the circulating exosomal miRNAs dysregulated in SAH, we analyzed the potential target genes of miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p in protein-coding transcripts. Considering only strong-evidence targets identified using miRanda and TargetScan Release 7.2, six genes were found to be targeted by all four of these differentially expressed miRNAs (Figure 6A). A list of these factors is provided in Supplementary Table S2. FIGURE 6: Circulating exosome candidate miRNA target analysis. (A) Venn diagram of the overlap of putative targets of miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p. (B) KEGG enrichment analysis of putative diseases for these four miRNAs. DIANA Tools provided associations with the KEGG pathway database for each target gene of the four candidate miRNAs. Some identified pathways included terms associated with cancer, the MAPK signaling pathway, focal adhesion, regulation of actin cytoskeleton, axon guidance, autophagy, breast cancer, oxytocin signaling pathway, Hippo signaling, and phospholipase D signaling, among others (Figure 6B). ## Discussion To the best of our knowledge, this study is not the first to identify exosomal miRNAs as potential biomarkers correlating with disease severity and prognosis in patients with aSAH (Kalani et al., 2020). Recently published work from our laboratory and others has described miRNAs using peripheral blood or cerebrospinal fluid samples of aSAH patients (Powers et al., 2016; Bache et al., 2017; Lai et al., 2017; Lopes et al., 2018); nevertheless, no distinctive signatures have been reported for circulating exosomal miRNAs to date. In this study, we observed that circulating exosomal miRNA expression profiles showed distinct patterns between aSAH patients and HCs. We made three major findings. First, the overall strategy is feasible because exosomal miRNAs were detectable using RT-qPCR on cDNA synthesized from a small volume of patient plasma. Second, levels of circulating exosomal miRNA expression were associated with prognosis in aSAH patients. Finally, an SAH model of C57BL/6 J mice can be used to test clinical assumptions in patients with aSAH. We began with a total exosomal miRNA analysis, using NGS to identify changes in the expression of miRNAs. We identified six miRNAs that were differentially expressed among the different groups ($p \leq 0.05$, \|log2 (fold change) \| > 1 and the expression level was not low). The NGS results for hsa-miR-486-3p, hsa-miR-193b-3p, hsa-miR-369-3p, and hsa-miR-410-3p were verified by RT-qPCR experiments. In the validation set, our results revealed that the levels of the four miRNAs were markedly changed in the aSAH patients, compared to the healthy controls, in association with severity and clinical outcomes. Furthermore, these miRNAs distinguished aSAH patients from healthy controls. Exosomes provide novel molecular mechanisms of intercellular communication. Importantly, exosome content is not random but rather depends upon the secreted cell’s status (Raposo and Stoorvogel, 2013). In the present study, high levels of miR-193b-3p and miR-486-3p expression occurred more frequently in circulating exosomes from the SAH models. Nevertheless, exosome levels in other tissues were unclear. A previous study showed that the majority of exosomes in circulation originate from other organs because of the tight regulation of the BBB in molecular transport (Kanninen et al., 2016). The results suggest that in patients with SAH, other organs in the body could also secrete exosomes by neurohumoral regulation. miR-193b-3p has been primarily linked with cancer, chondrocyte metabolism, apoptosis, and autophagy (Meng et al., 2018; Feng et al., 2021; Dinami et al., 2022). Originally, miR-193b-3p was reported to be a tumor suppressor inhibiting several tumor-associated proteins, including the MYB oncogene in T-cell acute lymphoblastic leukemia (Mets et al., 2015) and MORC4 in breast cancer (Yang et al., 2018). In addition, as a novel non-invasive biomarker, extracellular vesicle expression of miR-193b-3p is upregulated in HeLa cancer cells and directly targets HDAC3 (Lin et al., 2018; Meng et al., 2018); it also attenuates neuroinflammation in early brain injury after aSAH in mice (Lai et al., 2020). miR-486-3p was correlated with central nervous system maturation, inflammation, and cancer (Li et al., 2021; Li et al., 2022; Yu et al., 2022). miR-486-3p has been identified as a candidate diagnostic marker for oral tongue squamous cell carcinoma; in addition, it plays a vital role in neuronal differentiation and central nervous system maturation in the brain (Chen et al., 2017; Yu et al., 2018). miR-369-3p and miR-410-3p belong to the miR-379-410 cluster, a large genomic miRNA cluster with brain-specific functions located on chromosome 14 in humans and chromosome 12 in mice (Winter, 2015). miR-379-410 cluster miRNAs regulate neurogenesis and neuronal migration in the developing neocortex by targeting N-cadherin, and their levels correlate with glioblastoma aggressiveness and patient survival (Rago et al., 2014; Shahar et al., 2016). miR-369-3p is downregulated in a variety of other solid tumor tissue types and potentially influences cellular function through diverse pathways (Hao et al., 2017; Zou et al., 2018), and miR-410-3p promotes ground-state pluripotency via inhibition of multi-lineage differentiation and stimulation of self-renewal in embryonic stem cells (Moradi et al., 2017). Associations between miRNA and its target genes were explored using miRanda and TargetScan Release 7.2. Considering only strong-evidence targets, six genes were found to be targeted by four miRNAs. RORA is a member of the NR1 subfamily of nuclear hormone receptors involved in circadian rhythm (Zheng et al., 2018). CASK is a calcium/calmodulin-dependent serine protein kinase involved in intellectual disability (Muthusamy et al., 2017). LCOR is a transcriptional corepressor that interacts with estrogen receptor α and other nuclear receptors (Cao et al., 2017). ZBED6 is a transcriptional repressor that binds to insulin-like growth factor 2, modulating cell proliferation, wound healing, and neuronal differentiation (Wang et al., 2018), and CNKSR3 is a molecular scaffold that coordinates the assembly of a multiprotein ENaC-regulatory complex and hence plays a central role in sodium homeostasis (Soundararajan et al., 2012). Conventional neuroimaging (CT, DSA) has obvious advantages in the diagnosis of aSAH; however, due to the complex pathological mechanisms involved in SAH, a considerable number of patients still die from related complications, even after microsurgical treatment. EBI is the key factor influencing the condition changes and prognoses of patients. Unfortunately, the current clinical symptoms combined with neuroimaging cannot accurately predict and evaluate EBI. Therefore, it is urgent to determine EBI treatment significance and positive outcomes of biomarkers. In our previous studies (Lai et al., 2017; Sheng et al., 2018a; Sheng et al., 2018b), we found that serum microRNAs, as non-invasive biomarkers for the presence and progression of subarachnoid hemorrhage, and high levels of miR-502-5p and miR-1297 can predict and evaluate the prognosis of SAH. In addition, Pedrosa et al. [ 2022] found that the microRNA cerebrospinal fluid profile during the early brain injury period is a biomarker in subarachnoid hemorrhage patients. These findings suggest that circulating miRNAs can be used to assess condition changes and predict prognosis in patients with SAH. In addition, Song et al. 2022) found that miR-340-5p can attenuate EBI caused by SAH-induced neuroinflammation by inhibiting STING. Huang et al. [ 2023] alleviated EBI after SAH by down-regulating miR-26b expression. These studies provide evidence supporting the treatment of post-SAH complications by miRNA. These results indicate that circulating miRNAs, as non-invasive biomarkers, can be used not only to evaluate condition changes and predict the prognosis of SAH patients but also to alleviate brain damage after SAH, which is beyond the reach of conventional neuroimaging. In this study, we detected changes in the expression levels of four circulating exosomal miRNAs after SAH; these expression levels can be used to evaluate condition changes and predict prognosis of patients with SAH. Based on previous studies, we can detect the expression level changes of circulating exosomal miRNAs and regulate their levels or tissue distributions to achieve treatment of SAH. In our previous study (Lai et al., 2020), we used targeted delivery of modified Exo/miR-193-3p in brain tissue to alleviate neurobehavioral impairments and neuroinflammation following SAH. Wang et al. [ 2022] found that exosome-encapsulated microR-140-5p could alleviate neuronal injury by regulating the IGFBP5-mediated PI3K/AKT signaling pathway in SAH. These studies provide broad prospects for the future treatment of SAH by regulating the tissue distribution or modification of circulating exosomal miRNAs for alleviating brain injury after SAH. There are some potential limitations to our study. First, this was a single-center, retrospective study. Therefore, the results may not be generalizable to populations. Second, the patients underwent surgical treatment or drug therapy prior to serum sample collection, which may have induced changes in expression levels of plasma exosomal miRNAs. Third, clinical parameters such as the Hunt and Hess grades vary between institutions and/or individual clinicians; therefore, the results with this small cohort may reflect biases inherent in the acquisition of such clinical data. Clearly, these results require validation in prospective studies performed on larger cohorts from multicenter clinical trials. ## Conclusion Circulating exosomal miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p have potential clinical utility as prognostic biomarkers for SAH patients. These findings suggest that peripherally injecting modified exosomes to deliver miRNAs to the central nervous system in future research could be a promising therapy for regulating neuroinflammation or apoptosis. ## 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: NCBI Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/), GSE222980. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Wannan Medical College. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Wannan Medical College. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions NL performed the miRNA analyses, BS assisted with writing the paper, TT and XC assisted with miRNA analyses, SG and QZ were involved in statistical analyses and writing the paper, and WL and CH conceived the study. QZ and BS revised the paper. ## 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. 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--- title: 'Causal associations between site-specific cancer and diabetes risk: A two-sample Mendelian randomization study' authors: - Rong Xu - Tingjin Zheng - Chaoqun Ouyang - Xiaoming Ding - Chenjin Ge journal: Frontiers in Endocrinology year: 2023 pmcid: PMC9968794 doi: 10.3389/fendo.2023.1110523 license: CC BY 4.0 --- # Causal associations between site-specific cancer and diabetes risk: A two-sample Mendelian randomization study ## Abstract ### Background Both cancer and diabetes are complex chronic diseases that have high economic costs for society. The co-occurrence of these two diseases in people is already well known. The causal effects of diabetes on the development of several malignancies have been established, but the reverse causation of these two diseases (e.g., what type of cancer can cause T2D) has been less investigated. ### Methods Multiple Mendelian randomization (MR) methods, such as the inverse-variance weighted (IVW) method, weighted median method, MR-Egger, and MR pleiotropy residual sum and outlier test, were performed to evaluate the causal association of overall and eight site-specific cancers with diabetes risk using genome-wide association study summary data from different consortia, such as Finngen and UK biobank. ### Results A suggestive level of evidence was observed for the causal association between lymphoid leukaemia and diabetes by using the IVW method in MR analyses ($$P \leq 0.033$$), indicating that lymphoid leukaemia increased diabetes risk with an odds ratio of 1.008 ($95\%$ confidence interval, 1.001-1.014). Sensitivity analyses using MR-Egger and weighted median methods showed consistent direction of the association compared with the IVW method. Overall and seven other site-specific cancers under investigation (i.e., multiple myeloma, non-Hodgkin lymphoma, and cancer of bladder, brain, stomach, lung, and pancreas) were not causally associated with diabetes risk. ### Conclusions The causal relationship between lymphoid leukaemia and diabetes risk points to the necessity of diabetes prevention amongst leukaemia survivors as a strategy for ameliorating the associated disease burden. ## Introduction One of the twenty-first century’s major threats to public health is the elevation of diabetes mellitus prevalence worldwide [1]. An initial stage of insulin resistance and compensatory hyperinsulinemia which contribute to β-cell failure defines type 2 diabetes (T2D) [2]. T2D is characterized by chronic hyperglycaemia, which damages end organs over time [2]. The World Health Organization reports that out of six deaths, one is attributed to cancer, which makes cancer the second primary cause of mortality worldwide [3]. Both cancer and diabetes are complex chronic diseases and have high economic costs for society. The co-occurrence of these two diseases in people has already been reported for more than 50 years [4]. It is presumed that these two diseases may have similar developmental pathways, such as the malfunction of immunological regulation and cytokine activity [5]. Common risk factors, such as obesity, genetic predisposition, and exposure to certain environmental factors, have been identified in the development of cancer and diabetes [5, 6]. Given that abdominal adiposity has been found to promote a proinflammatory condition throughout the body, which increases the risk of cancer and diabetes, obesity has been proposed as one of the underlying reasons for these two diseases [6]. Epidemiological evidence has indicated that several malignancies are more likely to occur in people with T2D [7]. For instance, diabetes significantly increases the relative risk of liver and pancreatic cancer (PC) [8, 9], but less evidence has been observed for other cancers. Because the development of some malignancies can precede and cause T2D, the potential reverse causation of these two diseases should also be considered. For instance, PC is likely to promote the development of T2D [10]. According to a recent study from Korea, cancer can enhance the risk of developing diabetes among cancer survivors, independent of conventional diabetes risk factors [11]. The diabetes risk was most significant in the first two years after cancer diagnosis, and elevated risk was continuously observed for as long as 10 years [11]. Moreover, circulating cytokines aggravate hyperglycaemia in cancer patients by promoting insulin resistance and increasing hepatic gluconeogenesis [12]. A standard tumour marker for PC is a higher level of CA19-9, and elevated serum CA19-9 levels have been related to the severity of inadequate glucose regulation [13, 14]. It has been proposed that survivors of cancer treatment are at higher risk for endocrinopathies, such as diabetes and metabolic syndrome, for the rest of their lives [15]. For example, recent work has revealed that diabetes is more likely to develop in people who survived childhood cancer [15]. In addition, a long latency may exist between cancer treatment and the onset of different treatment-related conditions, emphasizing the necessity for lifelong awareness and monitoring [16]. Observational epidemiological research can be hampered by various potential biases caused by residual confounding [17]. Moreover, the possible reverse causation of the exposure and outcome in these works makes it difficult to determine the direction of the correlations [17]. The Mendelian randomization (MR) method, which uses genetic variants as instrumental variables, can infer the causal effects of exposure on outcomes. Because genetic variations are fixed at birth and normally cannot be modified by outcomes, MR analyses are less affected by reverse causality [18]. Considering that the effects of cancer from different sites on diabetes risk may be different [19], the current study used the MR method to estimate the causal effects of overall and eight site-specific cancers on the risk of diabetes. ## Study design MR examines the causal relationship between exposures and diseases using genetic variants (e.g., single nucleotide polymorphisms [SNPs]) as instrumental variables (IVs). In our analyses, the summary statistics of IVs were taken from genome-wide association study (GWAS) datasets of overall and site-specific cancers. Three requirements should be met for the selection of IVs. First, IVs are not directly associated with outcomes, and they only influence outcomes through exposure. Second, strong correlations exist between IVs and exposure. Third, IVs are not associated with the confounders (no horizontal pleiotropy exists). An MR framework was employed using GWAS summary data from different consortia to evaluate the causal association between overall and eight site-specific cancers and diabetes risk. ## Data sources Summary-level genetic data for overall and site-specific cancers were gathered from Finngen [20], the international lung cancer consortium (ILCCO) [21], the UK biobank (UKB) [22] and the genetic epidemiology research on aging (GERA) [23]. Supplementary Table 1 provides more information on the data sources. GWAS datasets were used to extract the IVs for overall and lung cancer, in which the SNPs reached a genome-wide significance level ($P \leq 5$ × 10–8). We lowered the P value threshold for including SNPs as IVs to $P \leq 1$ × 10-5 if fewer than five IVs were selected (Supplementary Table 1). This threshold-lowering method has been previously adopted in MR studies [24]. SNPs within 10,000 kb of each other were then clumped, with a linkage disequilibrium threshold of R2 > 0.001. The F-statistics of the IVs, an indicator of the ability of the IVs to predict the exposures [25], were estimated, and all exposures had F-statistics higher than 10 (Supplementary Table 2). The GWAS datasets for T2D, as the outcome, were from the Diabetes Meta-analysis of Trans-ethnic Association Studies (DIAMANTE) consortium [26]. ## Statistical analysis The major method used to ascertain the relationships between different types of cancer and diabetes risk was the inverse-variance weighted (IVW) MR method. For sensitivity analyses, the weighted median (WM) method, MR-Egger, and MR pleiotropy residual sum and outlier (MR-PRESSO) test were also conducted. The potential heterogeneity was estimated by Cochrane’s Q statistic, and the potential pleiotropy was assessed by the intercept of the MR-Egger test. Scatter plots were used to present the results of different MR methods. The estimate of the effect of SNPs after removing each SNP one by one was achieved by “leave-one-out” analysis. The causal effects of overall and site-specific cancer were represented using odds ratios (ORs) and $95\%$ confidence intervals (CIs). The statistical significance of the MR analyses was adjusted using Bonferroni correction. The testing results that did not survive Bonferroni correction but had a $P \leq 0.05$ were defined as associations with suggestive level of evidence. R software was used for these analyses, in which the “TwoSampleMR” and “MR-PRESSO” R packages were employed. ## Results We first performed the MR analyses to examine the possible causal association of overall and eight site-specific cancers with diabetes using GWAS summary statistics from various consortia. Detailed information, as well as P threshold for IV selection for each GWAS summary dataset, is given in Supplementary Table 1. The results indicated that none of the tested associations survived Bonferroni correction with a P threshold of $\frac{0.05}{9}$ = 0.006, but a suggestive level of evidence was observed for the causal association between lymphoid leukaemia and diabetes (IVW method, $$P \leq 0.033$$), indicating that lymphoid leukaemia increased diabetes risk, with an OR of 1.008 ($95\%$ CI, 1.001-1.014) (Figures 1, 2; Supplementary Figure 1, Supplementary Table 3). The F-statistic of the IVs used in these analyses ranged from 15.7 to 151.5, with a mean of 25.4, suggesting strong ability of the IVs to predict the exposures (Supplementary Table 2). For the observed causal association between lymphoid leukaemia and diabetes, sensitivity analyses using the MR-Egger and WM methods showed a consistent direction of the association compared with the IVW method. In addition, the leave-one-out sensitivity analysis revealed that the association of lymphoid leukaemia with diabetes became marginally significant after removing several SNPs, including rs147576549, rs17480734, rs59261129, rs61915331, and rs763477, with a P value ranging from 0.050 to 0.072 (Figure 3). Furthermore, no significant heterogeneity or horizontal pleiotropy was detected in the analysis of causality between lymphoid leukaemia and diabetes (Supplementary Tables 4, 5, respectively). MR-PRESSO consistently revealed no outlier IV in the analysis of lymphoid leukaemia, and the results were identical for the analyses of bladder cancer and PC after correcting for the identified outlier SNPs (Supplementary Table 6). Figure 1: The potential causal relationships between site-specific cancer and diabetes risk were examined using various MR methods, including IVW, MR-Egger, and WM. IVW, inverse-variance weighted method; MR, Mendelian randomization; WM, weighted median method; OR, odds ratio. Figure 2: Scatter plots of the MR analyses showing the potential causal associations of site-specific cancer with diabetes. MR, Mendelian randomization; SNP, single nucleotide polymorphism. Figure 3: Leave-one-out analysis as a sensitivity analysis to examine the causal association between lymphoid leukaemia and diabetes. MR, Mendelian randomization; OR, odds ratio. ## Discussion Our study screened the possible causal association of a total of eight site-specific cancers with diabetes using MR methods based on GWAS summary datasets, and we found that lymphoid leukaemia was causally associated with diabetes risk. This observation is also reflected by the results of MR-Egger and WM MR analyses that showed a consistent direction of association. In addition, the MR-Egger intercept test and MR-PRESSO global test revealed that the causal association between lymphoid leukaemia and diabetes was not due to horizontal pleiotropy. A class of deadly hematologic malignancies known as leukaemia is defined by malignant growth of white blood cells and their precursor cell [27]. On the one hand, an increased leukaemia risk has been reported in patients with diabetes. For instance, a study in Sweden showed that patients with T2D had a noticeably higher incidence of leukaemia after hospitalization [28]. Meta-analysis of 11 publications indicated that the OR of leukaemia for people with T2D was estimated to be 1.22 [29]. On the other hand, leukaemia has been proposed as one of the childhood cancers that leads to higher risk of diabetes [30]. Indeed, childhood cancer survivors were more likely to develop diabetes compared with their sibling controls according to one study from the childhood cancer survivor study (CCSS) group [31]. Consistent results were observed in studies conducted in Scandinavia [32] and Canada [33]. Several mechanisms underlying the higher diabetes risk in patients with leukaemia have been proposed. Leukaemia cells can directly infiltrate the pancreas [34], and chemotherapeutic treatment using L-asparagine can also lead to β-cell malfunction, causing hyperglycaemia in acute lymphocytic leukaemia [34], one of the most prevalent cancers among children [35]. For chronic lymphocytic leukaemia, one case report indicated that a patient developed diabetes after being treated with fludarabine and cyclophosphamide therapy, which could potentially disrupt the local immune-regulatory balance [36]. Corticosteroids are normally used as an integral part of combination chemotherapy in leukaemia treatment [37]. However, some complications might arise during the usage of corticosteroids, of which two of the most common are hyperglycaemia and chemotherapy-induced diabetes (CID) [38]. The development of diabetes after abdominal radiation is often linked to damage to the pancreas tail induced by the radiation, which leads to pancreatic insufficiency [39]. For hematopoietic cell transplantation patients suffering from high-risk hematologic cancers, the precondition is normally achieved by total body irradiation (TBI) [40]. The entire body is exposed to radiation during TBI, which affects the hypothalamic-pituitary axis and increases the risk of endocrinopathies (e.g., growth hormone deficiency) in cancer survivors [41]. The risks of developing diabetes have been documented amongst children survivors exposed to TBI treatment, with a 12.6-fold risk ratio compared with their sibling controls [31]. The major pathophysiologic mechanisms that contribute to the post-TBI development of diabetes have been proposed to be insulin resistance and hyperinsulinemia, rather than pancreatic insufficiency [16]. It is also not uncommon for survivors of TBI exposure to present abnormality processes, such as altered adipokines and occurrence of inflammation [42]. CID contributes to poor clinical outcomes in leukaemia patients [43], and the underlying reasons could be multifactorial. One explanation is the increased susceptibility to infections in patients with CID undergoing intensive chemotherapy [44]. Hyperglycaemia and hyperinsulinemia can further stimulate the neoplastic process, leading to unfavourable clinical outcomes in patients with leukaemia and CID [45]. In patients suffering from acute myeloid leukaemia, researchers also reported an alteration in the glucose metabolism signature, which contributes to undesirable clinical outcomes [46]. Thus, early commencement of CID screenings and relevant strategies to reduce its negative impact is advised because cancer survivors have an elevated chance of developing premature cardiovascular morbidity [47]. Further research is warranted to elucidate the complex metabolic abnormality in cancer survivors, which could guide preventive and therapeutic endeavours to improve the quality of life of cancer survivors. The association between cancer and diabetes can be site specific. For example, the risks of developing diabetes have been reported to be comparatively higher for survivors of PC compared with other types of cancers [48]. A significant portion of patients recently diagnosed with PC present hyperglycaemia or T2D [49]. In addition, T2D is alleviated after tumour removal, which reinforces the idea that T2D is related to PC [50]. The risk of diabetes is elevated by PC because it promotes the secretion of insulin that leads to insulin resistance [51]. Furthermore, pancreatic tissue destruction with an accompanying β-cell loss can also occur in patients with PC, which contributes to the development of diabetes [52]. However, the causal effects of PC on T2D subtypes may be different. One MR analysis suggested that PC is causally associated with newly onset T2D but not long-standing T2D [53]. The GWAS summary dataset of T2D used in our MR analysis did not separate subtypes of T2D, and the results indicated no causal association between PC and T2D. Similar to PC, six other site-specific cancers under investigation, including multiple myeloma, non-Hodgkin lymphoma and cancers of the bladder, brain, stomach, and lung, were also not causally associated with diabetes. There were several areas of strength in this study. First, we employed an MR design to reduce the biases that can be introduced by reverse causality and residual confounding in conventional observational studies, which may lead to false-positive results. Second, numerous SNPs were used as IVs for overall and site-specific cancers, which was essential in facilitating the analysis of horizontal pleiotropy. Third, for sensitivity analyses aimed at estimating pleiotropy, several MR methods, such as MR-PRESSO and MR-Egger, were utilized. Lastly, the participants within the initial GWAS were mainly of European descent, which helped to reduce the bias attributable to population stratification. Despite the strengths, there were also several shortcomings in the present study, a key of which was the inability to completely exclude the possible effect of pleiotropy. Additionally, the interpretation of the results was limited to a certain ethnicity because the GWAS summary datasets were of European origin. ## Conclusion This comprehensive MR analysis has established a causal relationship between lymphoid leukaemia and diabetes risk, which points to the necessity of diabetes prevention amongst leukaemia survivors as a strategy for ameliorating the associated disease burden. ## 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 authors. ## Ethics statement The GWAS used in the current work were approved by their relevant review board, and informed consent were collected from all participants. ## Author contributions RX and CG concepted the study. RX, TZ, CO, and XD performed the statistical analyses, and drafted 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/fendo.2023.1110523/full#supplementary-material ## References 1. Zimmet P, Alberti KG, Magliano DJ, Bennett PH. 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--- title: Estimating the causal effects of genetically predicted plasma proteome on heart failure authors: - Jian Yang - Bin Yan - Haoxuan Zhang - Qun Lu - Lihong Yang - Ping Liu - Ling Bai journal: Frontiers in Cardiovascular Medicine year: 2023 pmcid: PMC9968807 doi: 10.3389/fcvm.2023.978918 license: CC BY 4.0 --- # Estimating the causal effects of genetically predicted plasma proteome on heart failure ## Abstract ### Background Heart Failure (HF) is the end-stage cardiovascular syndrome with poor prognosis. Proteomics holds great promise in the discovery of novel biomarkers and therapeutic targets for HF. The aim of this study is to investigate the causal effects of genetically predicted plasma proteome on HF using the Mendelian randomization (MR) approach. ### Methods Summary-level data for the plasma proteome (3,301 healthy individuals) and HF (47,309 cases; 930,014 controls) were extracted from genome-wide association studies (GWASs) of European descent. MR associations were obtained using the inverse variance-weighted (IVW) method, sensitivity analyses, and multivariable MR analyses. ### Results Using single-nucleotide polymorphisms as instrumental variables, 1-SD increase in MET level was associated with an approximately $10\%$ decreased risk of HF (odds ratio [OR]: 0.92; $95\%$ confidence interval [CI]: 0.89 to 0.95; $$p \leq 1.42$$ × 10−6), whereas increases in the levels of CD209 (OR: 1.04; $95\%$ CI: 1.02–1.06; $$p \leq 6.67$$ × 10−6) and USP25 (OR: 1.06; $95\%$ CI: 1.03–1.08; $$p \leq 7.83$$ × 10−6) were associated with an increased risk of HF. The causal associations were robust in sensitivity analyses, and no evidence of pleiotropy was observed. ### Conclusion The study findings suggest that the hepatocyte growth factor/c-MET signaling pathway, dendritic cells-mediated immune processes, and ubiquitin-proteasome system pathway are involved in the pathogenesis of HF. Moreover, the identified proteins have potential to uncover novel therapies for cardiovascular diseases. ## Introduction Heart failure (HF) is a life-threatening clinical syndrome that represents the end stage of various cardiac conditions, including ischemic heart disease, hypertension, and non-ischemic cardiomyopathy [1]. HF is a leading cause of cardiovascular hospitalization and death worldwide, especially in individuals older than 60 years [2, 3]. The common risk factors of HF include hypertension, hypercholesterolaemia, diabetes, obesity, familial history of HF, and psychological agents (4–7). Despite remarkable advances in HF treatment, the prognosis of patients with HF remains poor, and none of the treatments has been proven to be effective for acute HF and HF with preserved ejection fraction [8, 9]. Discovering novel biomarkers for early diagnosis or etiological treatment has always been a central goal for specialists in this field [10]. Current omics techniques, particularly proteomics, are holding a revolution in the search for clinically useful biomarkers for complex human diseases [11, 12]. Proteins are macromolecules with biological functions in organisms and can also serve as intermediate phenotypes for how genetic and non-genetic factors act on diseases. The advent of proteomic technologies has allowed simultaneous quantification of thousands of proteins in human cells, blood, and tissues, in stark contrast to previous biomarker research that focused on single or several protein measurements [13]. Proteomics has been increasingly applied to identify novel biomarkers, reveal pathophysiological mechanisms, and develop novel therapeutic targets for cardiovascular diseases since the late 1990s (14–17). Furthermore, improvements in proteomic techniques and integration with genomics have provided broader application prospects for proteomics. Mendelian randomization (MR) is a genetic epidemiological study design that uses genetic variants as instrumental variables to investigate causal inferences between modifiable exposures and disease outcomes [18]. The MR works analogous to a randomized controlled trial, except that the population is randomly assigned to different levels of exposure by genotypes [19]. Given the fact that genotypes are determined at birth and, therefore, not susceptible to confounding and reverse causation, MR has the potential to provide an unbiased investigation of the causal effect of a modifiable exposure on a disease outcome of interest [20]. Recently, genome-wide association studies (GWASs) have been introduced in the human plasma proteome and have evaluated the associations of single-nucleotide polymorphisms (SNPs) with thousands of proteins, which provides a great opportunity to investigate the causal inferences between the human plasma proteome and HF (21–23). The present study aimed to provide a comprehensive review of the causal effects of genetically predicted human plasma proteome (including 2,994 proteins) on HF by extracting summary-level data from large GWASs. ## Study design We employed an MR study design based on publicly available summary statistics from large-scale GWASs (Figure 1). In this study, genetically predicted human plasma proteomes were used as exposures; genetic associations with HF were selected as primary outcomes; and other outcomes included coronary artery disease (CAD), myocardial infarction (MI), and atrial fibrillation (AF). In addition, the causal associations were tested by adjusting for several specific confounders/mediums, including circulating lipid levels (low-density lipoprotein [LDL], high-density lipoprotein [HDL], triglycerides [TG]), blood pressure traits (systolic blood pressure [SBP] and diastolic blood pressure [DBP]), body mass index (BMI), and type 2 diabetes (T2D). Figure 1: Study design and principal findings of the MR investigation. AF, atrial fibrillation; CAD, coronary artery disease; MI, myocardial infarction. ## Ethical approval Ethical approval and written informed consent were not sought because all datasets included in this study were extracted from publicly available GWASs. ## Study population and data sources Table 1 summarizes the data sources used in MR analysis. Genetic instruments for exposure were taken from a recent GWAS of the human plasma proteome [21]. The study population comprised 3,301 healthy blood donations from 25 centers across England. Proteins were quantified using an aptamer-based SOMAscan assay. Log-transformed protein levels of 3,283 SOMAmers (mapping to 2,994 unique proteins) were tested by adjusting for age, sex, duration between blood draw and processing, and ancestry in GWAS analysis. GWAS summary statistics for HF were derived from the Heart Failure Molecular Epidemiology for Therapeutic Targets (HERMES) Consortium [24], comprising 47,309 cases and 930,014 controls of European ancestry. Cases were recruited according to definite clinical criteria without definition based on the left ventricular ejection fraction. Summary statistics for CAD (60,801 cases and 123,504 controls) and MI (60,801 cases and 123,504 controls) were obtained from the CARDIoGRAMplusC4D Consortium [25]. Summary statistics for AF were obtained from a large GWAS on 65,446 cases and 522,744 controls, of which $84.2\%$ were European [26]. Genetic associations with LDL, HDL, and TG were obtained from the Global Lipids Genetics Consortium that included 188,578 European individuals [28]. Genetic associations with SBP and DBP were obtained from the UK Biobank including 757,601 European individuals [27]. Genetic summary-level data for BMI (694,649 individuals of European ancestry) were obtained from the Lindgren’s group in Oxford University [29], and genetic data for T2D (180,834 cases and 1,159,055 controls) were obtained from the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium [30]. Table 1 \| Variable \| Phenotype \| Sample Size \| Ancestry \| Study \| \| --- \| --- \| --- \| --- \| --- \| \| Exposure \| Plasma proteome (2,994 proteins) \| 3,301 individuals \| European \| (21) \| \| Primary outcome \| Heart failure \| 47,309 cases/930,014 controls \| European \| (24) \| \| Other outcomes \| Coronary artery disease \| 60,801 cases/123,504 controls \| European \| (25) \| \| Other outcomes \| Myocardial infarction \| 43,676 cases/123,504 controls \| European \| (25) \| \| Other outcomes \| Atrial fibrillation \| 65,446 cases/522,744 controls \| Multi-ancestry \| (26) \| \| Confounders \| SBP, DBP \| 757,601 individuals \| European \| (27) \| \| Confounders \| LDL, HDL, TG \| 188,578 individuals \| European \| (28) \| ## Statistical analysis To obtain genetic instruments for the 2,994 plasma proteins, we extracted all SNPs that had reached a significance threshold of $p \leq 1$ × 10−5. Next, we performed a clumping procedure to select for independence, setting a linkage disequilibrium (LD) threshold of r2 < 0.001 in a 10-Mb window in the 1,000 Genomes Project Phase 3 (EUR) reference panel. Proxy SNPs (LD r2 > 0.8) were used when no instrument SNP for predicting protein level was available in the outcome dataset. The strength of each genetic instrument was evaluated using two key parameters: the proportion of variance explained by the SNPs (R2) and the F statistic. The inverse variance-weighted (IVW) method was adopted for the primary MR analysis. The IVW method can be equivalently regarded as a weighted regression of SNP-outcome effects on SNP-exposure effects, with the intercept constrained to zero. However, the IVW estimate is known to suffer from horizontal pleiotropy bias, where any SNP acts on the outcome through pathways other than the exposure. Therefore, several additional MR methods were used to account for such bias, including the weighted median method, which allowed no more than $50\%$ of the SNPs to be invalid instruments [31] and the Egger method, which could detect and adjust for pleiotropy by transforming the intercept to be non-zero [32]. Furthermore, we removed horizontal pleiotropic outliers using the MR-PRESSO method and evaluated the presence of horizontal pleiotropy using the MR-Egger intercept test [33], Cochran Q test [34], and leave-one-out analyses [35]. Multivariable MR analysis was conducted to assess the potential confounding effect of circulating lipids (LDL, HDL, and TG) and blood pressure traits (SBP and DBP). Genetic instruments for each trait were extracted and combined with those for the proteins. The IVW method was employed to correct for confounders in the multivariable MR model. All analyses were performed using the TwoSampleMR and MVMR packages in R software (version 3.6.1; R Foundation for Statistical Computing, Vienna, Austria). Causal estimates are expressed as odds ratios (ORs) with $95\%$ confidence intervals (CIs) per 1-SD increase in quantification of each protein on outcome. Statistical significance was set at a multiple-testing corrected threshold of $p \leq 1.52$ × 10−5 ($\frac{0.05}{3283}$) following the Bonferroni method. ## Main MR analysis The IVW MR analysis identified three proteins that were causally associated with HF (Figure 2; Supplementary Table S1). Using 14 SNPs as instrumental variables (variance explained = $12.5\%$; F statistic = 33.5; Supplementary Table S2), genetically predicted increased levels of MET were associated with approximately $10\%$ decreased risk of HF (IVW OR: 1.16; $95\%$ CI: 1.02 to 1.34; $$p \leq 1.42$$ × 10−6; Figure 3; Supplementary Figure S1). Increased levels of CD209 (IVW OR: 1.04; $95\%$ CI: 1.02 to 1.06; $$p \leq 6.67$$ × 10−6) and USP25 (IVW OR: 1.06; $95\%$ CI: 1.03–1.08; $$p \leq 7.83$$ × 10−6) were associated with increased risk of HF (Figure 3; Supplementary Figures S2, S3), with 22 SNPs explaining $48.0\%$ variance (F statistic = 137.6) of CD209 and 17 SNPs explaining $24.0\%$ variance (F statistic = 66.9) of USP25 (Supplementary Tables S3, S4). Figure 2: Effects of genetically predicted plasma proteome on HF. The red solid line represents the Bonferroni-corrected significant threshold of p = 1.52 × 10−5. The black dotted line represents the suggestive association threshold of p = 0.05. HF, heart failure. Figure 3: Sensitivity analysis of causal associations between identified proteins and Heart Failure. IVW, inverse variance-weighted. ## Sensitivity analysis The causal risk of MET on HF was robust in the sensitivity analysis (weighted median OR: 0.92; $95\%$ CI: 0.85 to 0.99; $$p \leq 0.041$$; MR-Egger OR: 0.90; $95\%$ CI: 0.86 to 0.95; $$p \leq 3.38$$ × 10−5; Figure 3). Horizontal pleiotropy was not observed in the MR-Egger intercept test ($$p \leq 0.973$$), Cochran Q test (Q statistic = 14.4; $$p \leq 0.349$$), or leave-one-out analyses (Supplementary Figure S4). Similar results were found for the causal risk of CD209 on HF (weighted median OR: 1.05; $95\%$ CI: 1.02 to 1.08; $$p \leq 1.31$$ × 10−4; MR-Egger OR: 1.05; $95\%$ CI: 1.03 to 1.07; $$p \leq 1.86$$ × 10−6), and USP25 on HF (weighted median OR: 1.07; $95\%$ CI: 1.03 to 1.10; $$p \leq 1.73$$ × 10−4; MR-Egger OR: 1.07; $95\%$ CI: 1.03 to 1.10; $$p \leq 3.06$$ × 10−5). The MR-Egger intercept test ($p \leq 0.05$) and Cochran’s Q test ($p \leq 0.05$) did not indicate any evidence of pleiotropy for the causal effects of CD209 and USP25 on HF. Leave-one-out analyses suggested that the effect of CD209 on HF was substantially driven by a single SNP rs505922 (Supplementary Figure S5), whereas the effect of USP25 on HF was robust (Supplementary Figure S6). ## Multivariable MR analysis To verify the direct causal effects of MET, CD209, and USP25 on HF, we performed multivariable MR analyses adjusting for common HF risk factors. The causal effects of MET on HF were broadly consistent after adjusting for LDL (OR: 0.93; $95\%$ CI: 0.88 to 0.98, $$p \leq 9.99$$ × 10−3), HDL (OR: 0.89; $95\%$ CI: 0.83 to 0.95, $$p \leq 1.21$$ × 10−3), TG (OR: 0.89; $95\%$ CI: 0.83 to 0.95, $$p \leq 1.43$$ × 10−3), SBP (OR: 0.93; $95\%$ CI: 0.89 to 0.98, $$p \leq 6.69$$ × 10−3), DBP (OR: 0.93; $95\%$ CI: 0.90 to 0.96, $$p \leq 1.42$$ × 10−4), BMI (OR: 0.97; $95\%$ CI: 0.93 to 0.99, $$p \leq 0.036$$), and T2D (OR: 0.94; $95\%$ CI: 0.91 to 0.98, $$p \leq 0.002$$; Table 2). Similar results were observed for the effect of CD209 on HF (OR: 1.04, $95\%$ CI: 1.01 to 1.06 and $$p \leq 5.77$$ × 10−3 for LDL; OR: 1.05, $95\%$ CI: 1.02 to 1.08 and $$p \leq 4.45$$ × 10−3 for HDL; OR: 1.05, $95\%$ CI: 1.02 to 1.08 and $$p \leq 5.43$$ × 10−3 for TG; OR: 1.04, $95\%$ CI: 1.02 to 1.06 and $$p \leq 6.67$$ × 10−6 for SBP; OR: 1.04, $95\%$ CI: 1.02 to 1.07 and $$p \leq 9.86$$ × 10−4 for DBP; OR: 1.02, $95\%$ CI: 1.01 to 1.04 and $$p \leq 0.029$$ for BMI; OR: 1.03, $95\%$ CI: 1.01 to 1.05 and $$p \leq 0.011$$ for TD; Table 2). However, the causal effects of USP25 on HF (OR: 1.02, $95\%$ CI: 0.97 to 1.08 and $$p \leq 0.393$$ for LDL; OR: 1.04, $95\%$ CI: 0.97 to 1.12 and $$p \leq 0.250$$ for HDL; OR: 1.04, $95\%$ CI: 0.97 to 1.12 and $$p \leq 0.296$$ for TG; OR: 1.06, $95\%$ CI: 1.03 to 1.08 and $$p \leq 7.83$$ × 10−6 for SBP; OR: 1.04, $95\%$ CI: 1.01 to 1.08 and $$p \leq 0.016$$ for DBP; OR: 0.99, $95\%$ CI: 0.96 to 1.04 and $$p \leq 0.793$$ for BMI; OR: 1.04, $95\%$ CI: 1.01 to 1.07 and $$p \leq 0.017$$ for TD; Table 2) became non-significant after adjusting for LDL, HDL, TG, or BMI suggesting that circulating lipid traits and BMI might have a confounding effect on the causal association between USP25 and HF. Table 2 \| Model \| MET \| MET.1 \| CD209 \| CD209.1 \| USP25 \| USP25.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Model \| OR(95% CI) \| P \| OR(95% CI) \| P \| OR(95% CI) \| P \| \| Unadjusted model \| 0.92 (0.89,0.95) \| 1.42e-06 \| 1.04 (1.02,1.06) \| 6.67e-06 \| 1.06 (1.03,1.08) \| 7.83e-06 \| \| Adjusted for SBP \| 0.93 (0.89,0.98) \| 6.69e-03 \| 1.04 (1.02,1.07) \| 9.86e-04 \| 1.04 (1.01,1.08) \| 0.016 \| \| Adjusted for DBP \| 0.93 (0.90,0.96) \| 1.42e-04 \| 1.04 (1.02,1.06) \| 5.02e-05 \| 1.04 (1.02,1.07) \| 1.05e-03 \| \| Adjusted for LDL \| 0.93 (0.88,0.98) \| 9.99e-03 \| 1.04 (1.01,1.06) \| 5.77e-03 \| 1.02 (0.97,1.08) \| 0.393 \| \| Adjusted for HDL \| 0.89 (0.83,0.95) \| 1.21e-03 \| 1.05 (1.02,1.08) \| 4.45e-03 \| 1.04 (0.97,1.12) \| 0.250 \| \| Adjusted for TG \| 0.89 (0.83,0.95) \| 1.43e-03 \| 1.05 (1.02,1.08) \| 5.43e-03 \| 1.04 (0.97,1.12) \| 0.296 \| \| Adjusted for BMI \| 0.97 (0.93,0.99) \| 0.036 \| 1.02 (1.01,1.04) \| 0.029 \| 0.99 (0.96,1.04) \| 0.793 \| \| Adjusted for T2D \| 0.94 (0.91,0.98) \| 0.002 \| 1.03 (1.01,1.05) \| 0.011 \| 1.04 (1.01,1.07) \| 0.017 \| ## Associations with other outcomes We further investigated the causal effects of MET, CD209, and USP25 on the three relevant outcomes. Genetically predicted MET levels showed consistent associations with CAD (OR: 0.92; $95\%$ CI: 0.86 to 0.98), MI (OR: 0.85; $95\%$ CI: 0.79 to 0.91), and AF (OR: 0.95; $95\%$ CI: 0.92 to 0.98; Figure 1). Genetically predicted levels of CD209 were associated with CAD (OR: 1.05; $95\%$ CI: 1.03–1.07) and MI (OR: 1.07; $95\%$ CI: 1.04 1.10). Genetically predicted levels of USP25 were associated with AF (OR: 1.03; $95\%$ CI: 1.01 to 1.06). ## Discussion In this comprehensive MR analysis of the effect of the human plasma proteome on HF, we identified three plasma proteins that might have causal associations with HF. Genetically predicted higher level of MET was associated with a decreased risk of HF, whereas higher levels of CD209 and USP25 were associated with an increased risk of HF. The results were robust in alternative MR methods and sensitivity analyses. Multivariable MR analyses showed the effects of MET and CD209 on HF were robust after adjustment for confounding factors, whereas lipid traits (LDL, HDL, and TG) might have a confounding effect on the association between USP25 and HF. Associations with other cardiovascular outcomes suggested that MET might also have causal effects on CAD, MI, and AF, CD209 might have effects on CAD and MI, USP25 might have a causal effect on AF. Several published studies have investigated the association between high-throughput proteomics and HF risk based on prospective cohorts (36–38). However, the approach in our study is significantly different from these previous approaches. First, we implemented an MR study design that made causal inferences from the perspective of genetics. Unlike previous observational studies, the MR study design was able to provide etiological clues for revealing the underlying pathogenesis of HF and was less susceptible to confounding factors, such as dietary habits, medications, and comorbidities. Second, we extracted data from the largest GWAS for HF. With a large sample size (47,309 cases and 930,014 controls) and wide population coverage, the findings of our study are highly powerful and generalizable. In addition, the plasma proteome included in our analysis covered a wide range of approximately 3,000 proteins using the latest proteomic profiling platform with high sample throughput and sensitivity of detection. Third, the proteome could serve as an intermediate phenotype of the genetic risk factors and disease outcome, which might help to uncover the underlying molecular pathways that connect the genome to HF. Our study reported three proteins (MET, CD209, and USP25) that might have causal effects on HF. Interestingly, MET has long been suggested to play a role in cardiovascular disease in previous studies [39, 40]. MET, also known as c-MET, is a hepatocyte growth factor (HGF) receptor. The HGF/c-MET function plays a prominent role in protecting the heart from both acute and chronic insults, including ischemic injury and doxorubicin-induced cardiotoxicity [39]. This mechanism may be involved in enhancing the ability of cardiac stem cells [41], attenuating cardiac hypertrophy, remodeling [42], anti-calcification [43], anti-fibrotic [44], and anti-inflammatory [45]. Consistent with these findings, our results showed that increased levels of MET had a beneficial effect on HF as well as on several other cardiovascular outcomes, thus providing novel clues for uncovering the pathogenesis or drug targets for cardiovascular diseases. CD209 is a pathogen-recognition receptor expressed on the surface of immature dendritic cells (DCs) and is involved in the initiation of the primary immune response. A previous study found significant increases in the level of immature DCs (with CD209 as a marker) in the course of plaque progression in patients with atherosclerosis, especially in those with unstable atherosclerotic lesions [46]. Another study showed that the immature type (CD 209 expression) of DCs was extensively recruited in the ischemic myocardium of patients after acute MI [47]. Furthermore, DCs have been suggested to initiate an immune response against cardiac antigens in the infarcted myocardium, leading to progressive HF [47]. USP25 is a ubiquitin-specific protease, which represents the largest subfamily of deubiquitinating enzymes and plays essential roles in regulating the ubiquitin-proteasome system (UPS) [48]. Actually, previous studies have suggested that the small ubiquitin-related modifier (SUMO) of SERCA2a, a critical ATPase responsible for Ca2+ re-uptake during excitation-contraction coupling, played an essential role in the development of HF [49, 50]. Thus the UPS has the potential to serve as a novel target for future heart failure therapeutics (51–53). Strengths of the study include the MR study design using data from large GWASs, use of comprehensive genomic atlas of the human plasma proteome, validation with multiple sensitivity analysis methods, and evaluation in other cardiovascular outcomes. This study also has several limitations. First, the data of plasma proteome are quantified using an aptamer-based SOMAscan assay. Though the aptamer-based strategy provides a rapid and convenient way of outsourcing protein measurements, some issues can still affect its accuracy, such as altered binding properties by electrical charge changes, protein structure alteration, and batch or plate effects. Second, the exposure-related instrumental variables are selected at a relatively relaxed threshold ($p \leq 1$ × 10−5), rather than the genome-wide significant threshold ($p \leq 5$ × 10−8), since the sample size of GWAS on proteome was not that large and few genome-wide significant SNPs were available for most proteins. Nevertheless, we evaluated the strength of these selected instrumental variables with the variance explained (R2) and the F statistic, and all instrumental variables were effective for declaring causal inferences. Third, the pathogenesis and therapies were much different for HF patients with reduced or preserved left ventricular ejection fraction. However, our study did not able to determine the causal roles of the three proteins on the two HF subtypes. Fourth, although our study identified novel biomarkers that might help to uncover novel drug targets or pathogenesis for HF, further studies were needed to verify the findings and the underlying mechanisms. Finally, the study samples involved in the MR analysis were restricted to European ancestry, further work should be done to verify these findings in other ethnic populations. ## Conclusion This MR investigation of causal associations between genetically predicted plasma proteome and HF found three proteins with causal effects on HF. Increased levels of MET appear to be associated with a lower risk of HF, whereas CD209 and USP25 may be associated with a higher risk of HF. The underlying mechanisms may be involved in the HGF/c-MET signaling pathway, DCs-mediated immune processes, and the UPS pathway. This study provides novel clues for uncovering the pathogenesis or drug targets in HF. ## 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 Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions JY and LB conceptualized and designed the study. JY and BY carried out the initial analyses and drafted the manuscript. HZ helped with the methodology. QL and LY contributed to the interpretation of results. PL and LB critically reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version. ## Funding The study was funded by General Projects of Social Development in Shaanxi Province (No. 2018SF-247). ## 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/fcvm.2023.978918/full#supplementary-material ## References 1. Metra M, Teerlink JR. Heart failure. 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--- title: 'Body composition parameters, immunonutritional indexes, and surgical outcome of pancreatic cancer patients resected after neoadjuvant therapy: A retrospective, multicenter analysis' authors: - Salvatore Paiella - Danila Azzolina - Ilaria Trestini - Giuseppe Malleo - Gennaro Nappo - Claudio Ricci - Carlo Ingaldi - Pier Giuseppe Vacca - Matteo De Pastena - Erica Secchettin - Giulia Zamboni - Laura Maggino - Maria Assunta Corciulo - Marta Sandini - Marco Cereda - Giovanni Capretti - Riccardo Casadei - Claudio Bassi - Giancarlo Mansueto - Dario Gregori - Michele Milella - Alessandro Zerbi - Luca Gianotti - Roberto Salvia journal: Frontiers in Nutrition year: 2023 pmcid: PMC9968808 doi: 10.3389/fnut.2023.1065294 license: CC BY 4.0 --- # Body composition parameters, immunonutritional indexes, and surgical outcome of pancreatic cancer patients resected after neoadjuvant therapy: A retrospective, multicenter analysis ## Abstract ### Background and aims Body composition parameters and immunonutritional indexes provide useful information on the nutritional and inflammatory status of patients. We sought to investigate whether they predict the postoperative outcome in patients with pancreatic cancer (PC) who received neoadjuvant therapy (NAT) and then pancreaticoduodenectomy. ### Methods Data from locally advanced PC patients who underwent NAT followed by pancreaticoduodenectomy between January 2012 and December 2019 in four high-volume institutions were collected retrospectively. Only patients with two available CT scans (before and after NAT) and immunonutritional indexes (before surgery) available were included. Body composition was assessed and immunonutritional indexes collected were: VAT, SAT, SMI, SMA, PLR, NLR, LMR, and PNI. The postoperative outcomes evaluated were overall morbidity (any complication occurring), major complications (Clavien-Dindo ≥ 3), and length of stay. ### Results One hundred twenty-one patients met the inclusion criteria and constituted the study population. The median age at the diagnosis was 64 years (IQR16), and the median BMI was 24 kg/m2 (IQR 4.1). The median time between the two CT-scan examined was 188 days (IQR 48). Skeletal muscle index (SMI) decreased after NAT, with a median delta of −7.8 cm2/m2 ($p \leq 0.05$). Major complications occurred more frequently in patients with a lower pre-NAT SMI ($$p \leq 0.035$$) and in those who gained in subcutaneous adipose tissue (SAT) compartment during NAT ($$p \leq 0.043$$). Patients with a gain in SMI experienced fewer major postoperative complications ($$p \leq 0.002$$). The presence of Low muscle mass after NAT was associated with a longer hospital stay [Beta 5.1, $95\%$CI (1.5, 8.7), $$p \leq 0.006$$]. An increase in SMI from 35 to 40 cm2/m2 was a protective factor with respect to overall postoperative complications [OR 0.43, $95\%$ (CI 0.21, 0.86), $p \leq 0.001$]. None of the immunonutritional indexes investigated predicted the postoperative outcome. ### Conclusion Body composition changes during NAT are associated with surgical outcome in PC patients who receive pancreaticoduodenectomy after NAT. An increase in SMI during NAT should be favored to ameliorate the postoperative outcome. Immunonutritional indexes did not show to be capable of predicting the surgical outcome. ## Introduction Pancreatic cancer (PC) remains a lethal malignancy [1], with a 5-year survival rate of around $30\%$ after surgical resection and multimodal treatment [2]. Furthermore, pancreatic surgery's morbidity and mortality rates are still high [3, 4], making the scenario even more problematic. Pancreatic resections are recognized as one of the most challenging operations due to the magnitude of dissection and resection, the resultant global stress, and the high morbidity rate. Major surgery produces an intense metabolic response and nutritional status changes by activating an inflammatory cascade and releasing stress hormones. Appropriate tissue healing and recovery/maintenance of organ function after such operations necessitate adequate qualitative and quantitative nutritional substrates to be effective. Furthermore, when PC is cephalic, obstructive jaundice is almost invariably present and associated with impaired absorption, nutritional state, and homeostasis [5]. The preoperative identification of patients at risk of malnutrition, and the adoption of nutritional corrective actions, especially in patients receiving systemic therapy before surgery, provides a window of intervention [6] that may mitigate the risk of poor postoperative outcome. Sarcopenia, a progressive decline in skeletal muscle mass, strength, and performance [7], is a direct consequence of impaired nutritional and metabolic status. Based on the patients' populations considered and the cutoff used, the prevalence of sarcopenia in PC patients at diagnosis is variable [8]. Research on the association of sarcopenia with surgical outcomes after pancreatic surgery has produced conflicting results (9–11). Computed tomography (CT) is an accurate tool to quantify whole-body composition [12]; moreover, it is routinely used for staging and restaging of PC. Therefore, it is readily available without additional cost, radiation exposure, or inconvenience to the patient. In PC patients, the effects of neoadjuvant therapy (NAT) on body composition have been increasingly investigated, with contrasting results (13–16). In general, lean muscle mass depletion is typical in patients with energetic imbalance and metabolic derangement and may be the driver of a worse surgical outcome. Chronic systemic inflammation is the theoretical substrate of muscle depletion, sarcopenia, and cachexia [17], and many immunonutritional biochemical parameters have been developed to quantify it [18]. Cutoff values of such immunonutritional indexes might serve as a proxy for immunonutritional impairment. Thus, they may help identify fragile patients with an increased pro-inflammatory status, assign patients to appropriate therapies, and even identify early pre-cachexia by offering a multimodal treatment. Among these indexes, the prognostic nutritional index (PNI) [19], the neutrophil-to-lymphocyte ratio (NLR) [20], the platelet-to-lymphocyte ratio (PLR) [21], and the lymphocyte-to-monocyte ratio (LMR) [22] have all been shown to be predictive of surgical or oncological outcome of PC patients. The current study investigated whether changes in body composition during NAT and multiple preoperative nutritional indexes predict the surgical outcome of locally advanced PC patients who underwent pancreaticoduodenectomy after NAT. ## Study design, patient population, and management The prospective institutional electronic databases of the General and Pancreatic Surgery Unit, Pancreas Institute, University of Verona (Verona, Italy), Milano-Bicocca University at San Gerardo Hospital (Monza, Italy), Pancreatic Surgery Unit, University of Bologna (Bologna, Italy), and of the Pancreatic Surgery Unit of Humanitas University (Milan, Italy) were searched for adult PC patients with NCCN-defined [23] “borderline resectable” or “locally advanced” PC receiving pancreaticoduodenectomy after NAT, between January 2012 and December 2019, of whom two cross-sectional imaging examinations (before and after NAT) and immunonutritional indexes (before surgery) were available. Regarding individual patient management, each Institution managed each case independently but with a common pathway. Briefly, the chemotherapy choice was left at the oncologist's discretion, and regular multidisciplinary reassessments were made. When the tumor shrunk and/or the Ca 199 levels normalized or at least halved, if radical resection was deemed feasible and the patient was fit, surgery was optioned, and the tumor was ultimately resected. The postoperative care was conducted according to the ERAS recommendations [24]. Given this study's retrospective, observational, and anonymous nature, ethical approval was not required. The study was carried out following the Declaration of Helsinki. ## Body composition assessments and definitions Weight and height obtained from the patient's chart were recorded by hospital staff. Body mass index (BMI) was obtained by dividing actual weight by height squared (kg/m2), and the WHO classification was used for interpretation [25]. Skeletal muscle area (SMA), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were analyzed from CT images. A single DICOM image was extracted from pre- (at the time of diagnosis/staging) and post-NAT (at restaging before surgery) CT images at the level of the third lumbar vertebra (L3) [26], an area chosen as the best correlate to whole-body composition [27]. DICOM images were then exported to dedicated software, such as CoreSlicer® [28] (Verona and Milan Centers) and ImageJ [29] (Bologna Center). All software, using pre-established Hounsfield unit (HU) thresholds [30], identified and quantified in cm2 areas of specific tissues as follows: −29– +150 HU for SM, −190– −30 HU for SAT, and −150– −50 HU for VAT. The skeletal muscle index (SMI) was calculated by normalizing the skeletal muscle area to squared height (in m2). Body composition measurements' variation (delta, Δ, calculated as post- minus pre-NAT values) has been calculated. Acknowledging that the evaluation of muscle quality is mandatory to describe the presence of sarcopenia, and this parameter was not evaluated in the present study, the commonly used term “sarcopenia” has been substituted with “Low muscle mass,” referring to the depletion of lean muscle mass, and the cutoff value proposed by Martin et al. [ 31] has been adopted. ## Immunonutritional indexes The immunonutritional indexes were calculated using the laboratory data available at preoperative clinical assessment, typically performed 1–3 weeks before surgery. NLR, PNI, PLR, and LMR were considered continuous variables. ## Surgical outcome Overall morbidity was the main outcome. It was evaluated considering the rate of postoperative complications (any kind). Secondary metrics for surgical outcome evaluation were: ## Statistical analysis Descriptive statistics were used to summarize the data from the study variables. Median and interquartile ranges were considered for continuous variables, while, for categorical ones, absolute and relative frequencies were used to synthesize the data. Comparisons of patient characteristics between independent groups were made by calculating the Wilcoxon rank-sum test for continuous variables and the Chi-square test or Fisher's exact test, wherever appropriate, for categorical ones. The effect of SMI on the primary study endpoint was evaluated via a logistic regression model accounting for non-linear effects by estimating a restricted cubic spline. The models were adjusted for the characteristics of the patients, such as “sex” and “age.” The SMI cutoff was estimated by identifying the inflection point of the morbidity risk prediction curve. The SMI effects on the morbidity risk are reported in intervals of 5 SMI variations around the inflection point. The effect of SMI on the length of stay has been assessed using the ordinary least squares method with a restricted cubic spline. The Huber-White robust standard error sandwich estimator accounted for the correlation within the repeated pre- and post-measurements. The effect of age on SMI has been assessed using the ordinary least squares method with a linear regression model, adjusted for sex. The 1,000 runs bootstrap $95\%$ confidence intervals have been reported for the prediction plots. The univariable linear regression model results, considering the effect of body composition parameters on the length of stay have been also reported with the estimated effects (Beta) and the $95\%$ confidence intervals. Analyses were performed with the R system [33] and the rms libraries [34]. ## Patient characteristics A total of 121 patients met the inclusion criteria and were enrolled in the study. Females and males were almost equally distributed ($50.4\%$/$49.6\%$), the median age at diagnosis was 64 (IQR 16), and the median BMI was 24 kg/m2 (IQR 4.1). At diagnosis, 92 ($76\%$) cases were borderline resectable cancer, and the remaining 29 ($24\%$) were locally advanced. The most common chemotherapy regimen was FOLFIRINOX (Fluorouracil-Folinic Acid-Irinotecan-Oxaliplatin, $45.4\%$), and the median duration of chemotherapy was five cycles (IQR 5). Thirty patients ($24.8\%$) received additional stereotaxic radiation therapy before surgery. At restaging, 63 ($52\%$) and 54 ($44.7\%$) patients had stable and partial/complete responses, respectively. Table 1 reports the general characteristics of the study population, including chemotherapy, surgical, pathologic, and relevant postoperative data. Table 1 \| Variable \| Total, n (%) \| \| --- \| --- \| \| Age (years, mean, SD) \| 61 (10) \| \| Sex (Female) \| 61 (50.4%) \| \| ASA score III-IV, yes \| 24 (19.8%) \| \| CACI >4, yes \| 71 (58.7%) \| \| Diabetes mellitus, yes \| 27 (22.3%) \| \| NLR (median, IQR) \| 2.1 (2) \| \| PLR (median, IQR) \| 140 (59.8) \| \| LMR (median, IQR) \| 2.6 (2) \| \| PNI (median, IQR) \| 41 (4.8) \| \| Albumin (g/L, median, IQR) \| 41 (5.2) \| \| Stage at diagnosis \| Stage at diagnosis \| \| Borderline resectable \| 92 (76) \| \| Locally advanced \| 29 (24) \| \| Tumor size (mm, mean, SD) \| \| \| Pre-neoadjuvant therapy \| 30.6 (8.9) \| \| Post-neoadjuvant therapy \| 24.3 (9.6) \| \| Neoadjuvant therapy scheme \| \| \| FOLFIRINOX \| 55 (45.4) \| \| Gemcitabine/Nab-Paclitaxel \| 33 (27.3) \| \| Other \| 23 (27.3) \| \| Chemotherapy duration (cycles, median, IQR) \| 5 (5) \| \| Time diagnosis to surgery (mo, median, IQR) \| 6 (5) \| \| Vascular resection, yes \| 24 (19.8%) \| \| T-status at pathology \| T-status at pathology \| \| Tx \| 15 (12.4) \| \| T1 \| 31 (25.6) \| \| T2 \| 58 (47.9) \| \| T3 \| 4 (3.3) \| \| T4 \| 13 (10.7) \| \| N-status at pathology \| N-status at pathology \| \| N0 \| 47 (38.8) \| \| N1 \| 46 (38) \| \| N2 \| 28 (23.2) \| \| R0 resection, yes \| 68 (56.2) \| \| Length of stay (days), median (IQR) \| 11 (9) \| \| Postoperative morbidity (overall), yes \| 61 (50.4%) \| \| Major complications (Clavien-Dindo ≥ 3), yes \| 15 (12.4%) \| ## Body composition changes after NAT Table 2 shows the changes in body composition after the completion of NAT. The median time between the two CT scans was 188 days (IQR 48). Before NAT, 36 patients ($32.1\%$) reported low muscle mass, and this percentage increased slightly after NAT ($$n = 41$$, $33.9\%$). Muscle components (SMI) or adipose tissue (VAT) components decreased after NAT (all $p \leq 0.05$). The regression model found that for an increase in age from 54 to 70 years, a decrease in SMI of 5 cm2/m2 is expected [$95\%$CI (−9.9, −0.2), $$p \leq 0.04$$]. Table 2 \| Parameter \| Pre NAT \| Post NAT \| Delta \| 95% CI \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| BMI, kg/m2 \| 24.0 (4.1) \| 23.8 (4.0) \| −0.14 (1.46) \| −1.3, 0.60 \| 0.5 \| \| SMA, cm2 \| 133 (58) \| 134 (51) \| 1.2 (16) \| −12, 8.5 \| 0.7 \| \| SMI, cm2/m2 \| 52 (32) \| 49 (16) \| 0.34 (13.54) \| −13, −2.6 \| 0.003 \| \| VAT, cm2 \| 121 (124) \| 103 (108) \| −8.7 (40.1) \| −30, 9.4 \| <0.001 \| \| SAT, cm2 \| 167 (108) \| 166 (98) \| −8.7 (55.3) \| −29, 9.7 \| 0.054 \| ## Body composition changes and surgical outcome Regarding the main study's outcome, general postoperative complications were not associated with changes in the body compartment (Supplementary Table 1). The SMI effects on the morbidity risk are reported in intervals of 5 SMI variations (30–50) around the 42 SMI inflection point. We found that an increase in SMI from 35 to 40 cm2/m2 reduced the probability of developing any postoperative complications [Log-OR 0.43, $95\%$ CI (0.21, 0.86), $p \leq 0.001$, Figure 1]. As concern major postoperative complications, they occurred more frequently in patients who had a pre-NAT lower SMI ($$p \leq 0.035$$) and a gain in the SAT compartment ($$p \leq 0.043$$), and less frequently in patients who had a gain in SMI ($$p \leq 0.002$$, Table 3). Figure 1: Logistic regression model for postoperative morbidity risk (log-OR) according to SMI (Pre- and Post-NAT) adjusted per gender and age ($p \leq 0.001$, see text). Both linear ($$p \leq 0.01$$) and non-linear ($$p \leq 0.02$$) effects are significant. NAT, Neoadjuvant therapy; SMI, skeletal muscle index (cm2/m2). TABLE_PLACEHOLDER:Table 3 When it comes to the length of stay, an increase in VAT (pre- and post-NAT), and the presence of low muscle mass after NAT were associated with a longer stay [Beta 0.03, $95\%$CI (0.01, 0.05), $$p \leq 0.010$$; Beta 0.04, $95\%$CI (0.02, 0.06), $$p \leq 0.019$$; and Beta 5.1, $95\%$CI (1.5, 8.7), $$p \leq 0.006$$, respectively], while an increase in albumin predicted a shorter stay [Beta −0.24, $95\%$CI (−0.47, −0.02), $$p \leq 0.039$$]; Table 4 shows a selection of the variables of the analysis, while Supplementary Table 3 provides the complete list. Table 4 \| Variable \| Beta \| 95% CI \| p-value \| \| --- \| --- \| --- \| --- \| \| Albumin, g/L \| −0.24 \| −0.47, −0.02 \| 0.039 \| \| NLR \| 0.58 \| −0.17, 1.3 \| 0.13 \| \| PNI \| −0.07 \| −0.42, 0.27 \| 0.7 \| \| PLR \| 0.01 \| −0.01, 0.02 \| 0.4 \| \| LMR \| −0.50 \| −1.5, 0.47 \| 0.3 \| \| SMA pre-NAT, cm2 \| 0.01 \| −0.03, 0.05 \| 0.6 \| \| SMI pre-NAT, cm2/m2 \| −0.01 \| −0.09, 0.07 \| 0.8 \| \| VAT pre-NAT, cm2 \| 0.03 \| 0.01, 0.05 \| 0.010 \| \| SAT pre-NAT, cm2 \| 0.00 \| −0.02, 0.02 \| >0.9 \| \| SMA post-NAT, cm2 \| −0.03 \| −0.07, 0.02 \| 0.2 \| \| SMI post-NAT, cm2/m2 \| −0.07 \| −0.17, 0.04 \| 0.2 \| \| VAT post-NAT, cm2 \| 0.04 \| 0.02, 0.06 \| 0.019 \| \| SAT post-NAT, cm2 \| 0.01 \| −0.01, 0.03 \| 0.4 \| \| Low muscle mass (32) pre-NAT \| 2.3 \| −1.5, 6.1 \| 0.2 \| \| Low muscle mass (32) post-NAT \| 5.1 \| 1.5, 8.7 \| 0.006 \| ## Immunonutritional indexes and surgical outcome None of the immunonutritional indexes proved predictive of a worse postoperative outcome (Table 4, Supplementary Tables 2, 3). In addition, no differences were found when comparing each immunonutritional index in sarcopenic vs. non-sarcopenic patients (data not shown). ## Discussion Body composition analysis and a careful nutritional assessment are invaluable tools that help identify cancer patients at risk of major postoperative complications. PC patients are not an exception. Typically, they are malnourished and sarcopenic, already at diagnosis. In this study, about one-third of the included patients had a low muscle mass at diagnosis, and this rate remained substantially stable after NAT. The absence of a worsening of sarcopenia, reported by other surgical series [9], may be due to the always increased awareness among patients, caregivers, and healthcare providers of the importance of nutritional status in oncology, especially in PC patients (the majority of the present study patients were enrolled during the last year of the study period). Regarding the body composition changes that occur during NAT, it was found that both the muscular and the fat compartments were significantly impacted by NAT. These findings have already been reported for PC patients receiving chemotherapy (14, 35–39), demonstrating energetic dyshomeostasis. Therefore, attention must be paid to the body composition changes that occur during NAT in an attempt to maintain patients' body homeostasis, energetic balance, and appropriate metabolism. Radiological reevaluations performed periodically during NAT allow clinicians to achieve it. When it comes to the study's primary endpoint, while any body composition parameter change did not influence the occurrence of any complications, patients experiencing major complications had a lower pre-NAT SMI value compared with those not facing major complications ($p \leq 0.05$); additionally, patients having a positive delta SMI (those who gained lean muscle mass) were less likely to experience major postoperative complications. The opposite was true for patients gaining subcutaneous fat tissues after NAT that were more exposed to major complications (all $p \leq 0.05$). These results align with the fact that the presence of sarcopenia post-NAT predicts a longer length of stay [11]. That gaining SAT exposed patients to a greater risk of major postoperative complications is not easily explained because, despite being non-statistically significant, a tendency toward fat tissue loss during NAT was found for both VAT and SAT (Table 1). This finding is likely to be clinically meaningless. A longer stay was also associated with high VAT values. This finding may be explainable by some factors or events not collected for this study (surgical site infections and, in general, infectious complications), so that patients with a high component of adipose tissue experience a longer hospitalization and, in general, failure to rescue. Instead, an increase in albumin was associated with a shorter length of stay. This recalls previous reports that associated low preoperative albumin levels with a worse postoperative outcome after pancreatic surgery (43–45). However, other studies did not report the same finding [46], and a recent randomized controlled trial demonstrated that the routine correction of preoperative hypoalbuminemia did not lead to a better postoperative outcome [40]. This study presents a novel dynamic model that can identify patients with the greater benefit of gaining lean muscle mass, namely those who move from an SMI of 35 to an SMI of 40 cm2/m2. This positive change may reduce the odds of experiencing any postoperative complication by about $60\%$. This aspect points attention to the need to identify patients at high risk of postoperative complications, focusing on those with low SMI who can concretely benefit from a tailored nutritional intervention to reduce the probability of postoperative complications, following a nutritional path, and setting a goal. The other fluctuations of the SMI to values >40 did not show any protective factor vs. major postoperative complications, since at these values of the SMI it is likely that the body can better resist surgical stress and sooner reach homeostasis. However, our results need to be confirmed prospectively. Among the immunonutritional values, none predicted the postoperative outcome. This result probably reflects the heterogeneity of the study population when it comes to neutrophil and lymphocyte values with respect to having suffered from inflammatory, infectious events before and close to surgery that could have altered these values in the preoperative period ($65\%$ of patients had a biliary stent, $25\%$ received multiple endoscopic procedures in the biliary tract, and $15\%$ had had cholangitis). Of note, we found that a decrease in SMI has to be expected with the increase in age (Supplementary Figure 1). About one-third of 60-year-old patients are sarcopenic [41], and a decrease in lean muscle mass must be expected at a rate of $15\%$ per decade over 70 years [42]. Considering that the highest peak of PC incidence occurs between 60 and 80 years, our results underline that nutritional evaluation at the time of diagnosis and during NAT may be fundamental, especially in elderly patients. Pre-habilitation regimens based on exercise (aerobic and resistance activity) and nutritional support focused on maximizing energy and protein intake should focus especially on these subgroups of patients to improve the outcome. This study has some limitations. First, its retrospective nature does not allow avoiding a selection bias. Second, while the study covers a long period, there was an imbalance toward the last year, when more than half of the cases were recruited. This may have generated a selection and management bias. Third, it cannot be excluded that the enrolled patients could have received nutritional counseling and support during chemotherapy, thus creating another source of bias. Fourth, the assessment of muscle quality (strength or performance) was not done nor feasible, highlighting that muscle mass was evaluated in terms of quantity (low muscle mass) and not quality. Fifth, comparing the results of the present study with the available literature might be inaccurate, as populations are very heterogeneous in terms of disease stages and treatments. Last, the study population is heterogeneous in terms of neoadjuvant treatment and stage disease, and this may impact the results obtained. ## Conclusions In conclusion, in our experience, the muscle compartment may decrease during NAT, and the delta of variation may provide useful predictive information for the preoperative risk assessment analysis of PC patients undergoing pancreaticoduodenectomy after NAT. For the first time, we identified a subset of patients that may benefit the most from a gain in SMI during NAT, creating a nutritional trajectory to follow and a goal for clinicians to optimize postoperative outcomes. This study failed to prove the ability of the immunonutritional indexes to predict the postoperative outcome; their application may be more appropriate in non-cephalic PC. ## 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 authors. ## Ethics statement Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. ## 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. 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--- title: 'Processed meat, red meat, white meat, and digestive tract cancers: A two-sample Mendelian randomization study' authors: - Zhangjun Yun - Mengdie Nan - Xiao Li - Zhu Liu - Jing Xu - Xiaofeng Du - Qing Dong - Li Hou journal: Frontiers in Nutrition year: 2023 pmcid: PMC9968810 doi: 10.3389/fnut.2023.1078963 license: CC BY 4.0 --- # Processed meat, red meat, white meat, and digestive tract cancers: A two-sample Mendelian randomization study ## Abstract ### Background Previous observational studies suggested inconsistent insights on the associations between meat intake and the risk of digestive tract cancers (DCTs). The causal effect of meat intake on DCTs is unclear. ### Methods Two-sample Mendelian randomization (MR) was performed based on genome-wide association studies (GWAS) summary data from UK Biobank and FinnGen to evaluate the causal effect of meat intake [processed meat, red meat (pork, beef, and lamb), and white meat (poultry)] on DCTs (esophageal, stomach, liver, biliary tract, pancreatic, and colorectal cancers). The causal effects were estimated using a primary analysis that employed inverse-variance weighting (IVW) and complementary analysis that utilized MR-Egger weighted by the median. A sensitivity analysis was conducted using the Cochran Q statistic, a funnel plot, the MR-Egger intercept, and a leave-one-out approach. MR-PRESSO and Radial MR were performed to identify and remove outliers. To demonstrate direct causal effects, multivariable MR (MVMR) was applied. In addition, risk factors were introduced to explore potential mediators of the relationship between exposure and outcome. ### Results The results of the univariable MR analysis indicated that genetically proxied processed meat intake was associated with an increased risk of colorectal cancer [IVW: odds ratio (OR) = 2.12, $95\%$ confidence interval (CI) 1.07–4.19; $$P \leq 0.031$$]. The causal effect is consistent in MVMR (OR = 3.85, $95\%$ CI 1.14–13.04; $$P \leq 0.030$$) after controlling for the influence of other types of exposure. The body mass index and total cholesterol did not mediate the causal effects described above. There was no evidence to support the causal effects of processed meat intake on other cancers, except for colorectal cancer. Similarly, there is no causal association between red meat, white meat intake, and DCTs. ### Conclusions Our study reported that processed meat intake increases the risk of colorectal cancer rather than other DCTs. No causal relationship was observed between red and white meat intake and DCTs. ## 1. Background Digestive tract cancers (DTCs) are a severe threat to human health and a substantial economic burden worldwide because of their high morbidity and mortality. In the 2020 Global Cancer Statistics, the top five most common cancers include two DTCs: colorectal cancer (CRC) and stomach cancer [1]. Similarly, CRC, liver cancer, and stomach cancer are three of the top five cancers in terms of mortality [1]. Studies have revealed that multiple factors mediate DTCs and smoking [2], alcohol consumption [3], obesity [4], and hepatitis B virus infection [5] as potential risk factors for DTCs. Unfortunately, the association between poor dietary habits or nutrition and cancer has received little research attention. Instead, cancer diagnosis and treatment have been the primary focus among scholars. Identifying and eliminating risk factors for cancer is more beneficial to human health than focusing on cancer treatment and diagnosis. High-fat and high-protein diets have recently become mainstream, and the incidence of CRC has risen from fifth to second from 2018 to 2020 worldwide (1, 6–8). The digestive tract is the primary organ that comes into direct contact with food. Moreover, it is pivotal in the process of food digestion and absorption; therefore, a causal relationship undoubtedly exists between dietary habits and DTCs. Numerous studies have reported a possible correlation between meat intake and DTCs, but the results are inconsistent. For example, a cohort study revealed a negative association between red meat intake and stomach cancer rather than esophageal cancer [9]. In contrast, after evaluating and analyzing the quality of 822 published articles about diet and esophageal cancer, Qin and colleagues reported that red and processed meat intake increases the risk of esophageal cancer [10]. Similarly, a recent meta-analysis [11] including 400 participants found that red and processed meat intake, but not poultry, was positively associated with CRC risk. However, Mejborn et al. believed that poultry rather than red and processed meat intake increased CRC risk [12]. Johnston and colleagues reported that few randomized controlled trials (RCTs) had confirmed the association between red meat and CRC risk [13]. Until now, only two RCTs have explored the relationship between red meat and CRC risk. However, both studies presented limited evidence indicating that red meat consumption promoted the risk of CRC [14, 15]. In short, cohort or case-control studies have reported contradictory results regarding the associations of meat intake with DTCs. Such inconsistencies may be due to a lack of standardization in study design; moreover, bias and confounding factors cannot be ruled out [16]. In addition, the existing observational studies could not establish causality and exclude confounding factors owing to methodological deficiencies, causing bias and disagreements [17]. Implementing standard RCTs is difficult because of limitations concerning ethical concerns, time of observation, resources, and cost. Thus, the understanding regarding the causal effect of meat intake on DTCs remains unclear. Mendelian randomization (MR) studies use single nucleotide polymorphisms (SNPs) that are significantly associated with different types of exposure as instrumental variables (IVs) to assess the association between genetically predicted exposures of interest and outcomes [18]. These SNPs are randomly inherited by offspring, providing an analytical approach that simulates an RCT study. As genetic variants before disease onset are randomly assigned at conception, MR studies can rule out confounding factors and prove cause and effect [19]. However, no MR studies explored the potential causal effect of meat intake on DTC risk. Considering that the causal effect of meat intake on DTCs remains unclear, we performed an MR analysis to assess it. This study provided stronger evidence for implementing preventive strategies. ## 2.1. Study design We conducted a two-sample MR based on genome-wide association studies (GWAS) summary data to explore the causal relationship between the intake of five common meat types (processed meat, pork, beef, poultry, and lamb) and six common DTCs (esophageal, stomach, liver, biliary tract, and pancreatic cancers, and CRC). Pork, beef, and lamb are defined as “red meat” and poultry as “white meat.” To avoid sample overlap in exposure and outcome and interference from ethnic differences, we derived GWAS data for both exposure and outcome from the European population but from different cohorts. ## 2.2. IVs for meat intake The study process is presented in Figure 1. Summary data for meat intake from the MRC-IEU UK Biobank OpenGWAS [20] based on the study by Elsworth et al. was used as genetic tools for processed meat intake ($$n = 461$$,981), pork intake ($$n = 460$$,162), beef intake ($$n = 461$$,053), lamb intake ($$n = 460$$,006), and poultry intake ($$n = 461$$,900). Meat intake was defined by the participants' daily meat intake. All participants were Europeans aged between 40 and 69 years who completed the touchscreen Food Frequency Questionnaire (FFQ) on food intake over the last year [21]. Participants had to choose “never” to “once or more daily” for each food intake, and participants with irregular eating habits were excluded [21]. For example, how often do you eat processed meats (such as bacon, ham, sausages, meat pies, kebabs, burgers, and chicken nuggets)? Codes include the following: less than one time a week, one time a week, 2–4 times a week, 5–6 times a week, one time or more daily, do not know, and prefer not to answer. The FFQ records detailed data on the frequency of meat intake (https://biobank.ndph.ox.ac.uk/ukb/label.cgi?id=100052). Moreover, a rigorous MR analysis must satisfy three major assumptions: [1] IVs are strongly associated with the exposure of interest; [2] IVs are independent of outcome-relevant confounders; and [3] IVs are not related to the outcome and can only influence the outcome through risk factors [22]. We reviewed the SNP quality using rigorous filtering guidelines to satisfy the aforementioned assumptions. To enforce the hypothesis, we set three criteria [23]. First, SNPs with $P \leq 5$ × 10−8 were extracted and considered significantly associated with the exposure of interest at the genome-wide level. Second, SNPs were clumped according to the removal of linkage disequilibrium (LD, R2 > 0.001 and within 10,000 kb). Third, to prevent bias from weak IVs, F statistic values were calculated for each SNP to assess the statistical strength of the IVs. SNPs with F < 10 were considered as weak instruments and were removed to ensure that all the SNPs could provide sufficient variation for the corresponding metabolites. To avoid violating hypotheses [2] and [3], the interference of potential confounders and horizontal pleiotropy were excluded (IVs affect outcomes through other exposures rather than the exposure of interest). Thus, each SNP in IVs was examined using a PhenoScanner (www.phenoscanner.medschl.cam.ac.uk), which documented the details of SNP-related genotypes and phenotypes. Furthermore, SNPs associated with potential confounders and outcomes of genome-wide significance were deleted. The MR-Egger method was performed to examine the presence of horizontal pleiotropy in the results [23]. Finally, any ambiguous or palindromic SNPs were removed to ensure the consistency of alleles between the exposure and outcome. Figure 1: A flowchart for the MR analysis of five types of meat intake and digestive tract cancers. PMI, processed meat intake. PI1, pork intake. BI, beef intake. LI, lamb intake. PI2, poultry intake. MVMR, multivariable Mendelian randomization. ## 2.3. GWAS summary data for DTCs GWAS data related to the six DTCs—including esophageal cancer (cases = 410), stomach cancer (cases = 1,054), liver cancer (cases = 518), biliary tract cancer (cases = 187), pancreatic cancer (cases = 1,054), and CRC (cases = 4,957)—were accessed from the Finngen database (https://www.finngen.fi/en/access_results) [24] R7 release on 19 September 2022. The Finngen study cohort included 309,154 participants, after the exclusion of those with indeterminate sex, high genotype deficiency (>$5\%$), excess heterozygosity (±4 SD), and non-Finnish ancestry [24]. Cancer was diagnosed based on the International Classification of Disease codes (8th, 9th, and 10th revisions). ## 2.4. Multivariable MR and risk factors The univariable MR analysis provided compelling evidence for a causal relationship between genetically proxied processed meat intake and CRC. To confirm the actual association between processed meat intake and CRC, we performed reverse MR and multivariable MR (MVMR) analyses. The reverse MR analysis confirmed the absence of a causal effect between the exposure of interest and the outcome. MVMR analysis assesses the direct effect of the exposure of interest on outcomes by controlling for potential effects between exposures [25, 26]. MVMR analysis can prove that processed meat intake can directly affect CRC, independent of other meat intakes. This study performed an MVMR analysis using the multivariable random-effects multiplicative inverse variance weighted method. Moreover, to further explore the potential mechanism through which processed meat intake increases CRC risk, we used mediating variables, such as BMI and total cholesterol (TC), in the analysis. These are widely recognized risk factors for CRC and have been confirmed by previous MR studies for their causal effect on CRC [27, 28]. The GWAS data for BMI and TC were obtained from the Genetic Investigation of Anthropometric Traits [29] consortium and the UK Biobank [30], respectively. To assess whether the BMI or TC could mediate the causal effect of processed meat intake on CRC, processed meat intake was considered exposure, and BMI and TC were considered outcomes while performing the MR analysis. Details of all GWAS data for exposure and outcome are presented in Table 1. Table 1 \| Phenotype \| Consortium \| Sample size \| Ancestry \| GWAS ID \| \| --- \| --- \| --- \| --- \| --- \| \| Processed meat intake \| MRC-IEU \| 461981 \| European \| ukb-b-6324 \| \| Pork intake \| MRC-IEU \| 460162 \| European \| ukb-b-5640 \| \| Beef intake \| MRC-IEU \| 461053 \| European \| ukb-b-2862 \| \| Lamb intake \| MRC-IEU \| 460006 \| European \| ukb-b-14179 \| \| Poultry intake \| MRC-IEU \| 461900 \| European \| ukb-b-8006 \| \| Esophageal cancer \| Finngen \| 239088 \| European \| \| \| Stomach cancer \| Finngen \| 239732 \| European \| \| \| Liver cancer \| Finngen \| 239196 \| European \| \| \| Biliary tract cancer \| Finngen \| 238865 \| European \| \| \| Pancreatic cancer \| Finngen \| 239732 \| European \| \| \| Colorectal cancer \| Finngen \| 243635 \| European \| \| \| Body mass index \| GIANT \| 322154 \| European \| \| \| Total cholesterol \| UK Biobank \| 441016 \| European \| \| ## 2.5. Statistical analysis For a more comprehensive assessment of the causal effect of meat intake on DTCs, we performed an MR analysis using random-effect inverse-variance weighted (IVW), MR-Egger, and weighted median. The aforementioned approaches are based on different assumptions; however, each approach has its own advantages. Our estimates are primarily based on IVW analysis because IVW is under the hypothesis that horizontal pleiotropy is absent for all SNPs, and IVW provides the most accurate assessment under the following premise [31]. Moreover, other MR methods, such as the MR-Egger method and the weighted median, were complementary to IVW to more comprehensively assess the causal relationship between exposure and outcome. Both methods offer a more robust analysis under more generous parameters. The weighted median model allows at least $50\%$ of the SNPs to have no pleiotropy and is affected by outliers to a lesser extent [31]. The MR-Egger model allows for pleiotropy in all genetic instruments, detects horizontal pleiotropy, and allows for greater heterogeneity [32]. Horizontal pleiotropy occurs when exposure-related IVs directly affect outcomes through pathways other than the exposure of interest. To evaluate the robustness and potential biases of our results, we conducted a sensitivity analysis using multiple methods. These methods included the Cochran Q statistic, the MR-PRESSO test, Radial MR, the funnel plot, the MR-Egger intercept, and leave-one-out (LOO) analyses [32, 33]. We first identified any possible heterogeneity in our results by calculating the P-value from the Cochran Q test. We then looked for outliers that may have been affected by pleiotropic bias and removed them using MR-PRESSO and Radial MR [33, 34]. Funnel plots were used to check for any bias in the direction of pleiotropy. We also evaluated horizontal pleiotropy by determining the P-value of the MR-Egger intercept. ## 2.6. Ethical consideration All data in this study are available in publicly available databases. No additional ethical approval was required. ## 3. Results Following the rigorous selection criteria, 21, 10, 12, 25, and 7 SNPs were identified to genetically predict the intake of meat, pork, beef, lamb, and poultry, respectively (Supplementary Table S1). The F statistic values for all of the genetic instruments used in the study were >10, indicating their high quality and reliability. The primary results of the MR analysis were determined based on IVW analysis results. Our findings did not support the causal effect between genetically predicted pork, beef, poultry, and lamb intake and esophageal cancer, stomach cancer, liver cancer, biliary tract cancer, pancreatic cancer, and CRC, with an IVW-derived P-value of >0.05 (Table 2). Surprisingly, we found that only the intake of processed meat had a significant causal effect on CRC (IVW: $P \leq 0.05$), but not on esophageal cancer, stomach cancer, liver cancer, biliary tract cancer, or pancreatic cancer (IVW: $P \leq 0.05$) (Table 2). However, heterogeneity was only detected in the MR analysis of pork intake and CRC, with a Cochran Q test-derived P-value of 0.02. Two outliers (rs2387807, rs3964074) were identified using the MR-PRESSO and Radial MR methods (Supplementary Figure S1). With the deletion of these two outliers and re-application of the MR analysis, heterogeneity became insignificant (Cochran Q test-derived P-value = 0.24). Heterogeneity (Cochran Q test-derived P-value > 0.05) and horizontal pleiotropy (MR-Egger intercept-derived P-value > 0.05) were not detected in any of the other MR analysis results (Table 2). The results of the MR-Egger and weighted median analyses are presented in Supplementary Table S2. Scatter and symmetric funnel plots revealed the absence of pleiotropic bias. The LOO analysis revealed that our estimation results are robust. All scatter plots, funnel plots, and LOO plots are displayed in Supplementary Figures S2–S8. Table 2 \| Exposures \| Outcomes \| No. SNPs \| OR (95% CI) \| P-value \| Heterogeneity \| Pleiotropy \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Processed meat intake \| OC \| 21 \| 2.36 (0.20–27.55) \| 0.493 \| 0.31 \| 0.32 \| \| Pork intake \| OC \| 10 \| 13.09 (0.11–1,605.65) \| 0.295 \| 0.71 \| 0.34 \| \| Beef intake \| OC \| 12 \| 2.39 (0.03–167.21) \| 0.688 \| 0.24 \| 0.73 \| \| Poultry intake \| OC \| 7 \| 0.36 (0.00–63.96) \| 0.696 \| 0.74 \| 0.24 \| \| Lamb intake \| OC \| 25 \| 0.53 (0.03–10.71) \| 0.678 \| 0.44 \| 0.42 \| \| Processed meat intake \| SC \| 21 \| 1.78 (0.42–7.56) \| 0.434 \| 0.65 \| 0.35 \| \| Pork intake \| SC \| 10 \| 0.21 (0.00–9.18) \| 0.415 \| 0.11 \| 0.37 \| \| Beef intake \| SC \| 12 \| 0.94 (0.05–16.71) \| 0.968 \| 0.13 \| 0.26 \| \| Poultry intake \| SC \| 7 \| 12.44 (0.46–332.65) \| 0.133 \| 0.41 \| 0.19 \| \| Lamb intake \| SC \| 25 \| 2.21 (0.26–19.03) \| 0.47 \| 0.13 \| 0.83 \| \| Processed meat intake \| LC \| 21 \| 0.20 (0.03–1.58) \| 0.127 \| 0.97 \| 0.43 \| \| Pork intake \| LC \| 10 \| 9.89 (0.14–722.95) \| 0.295 \| 0.72 \| 0.78 \| \| Beef intake \| LC \| 12 \| 5.61 (0.19–164.18) \| 0.316 \| 0.54 \| 0.2 \| \| Poultry intake \| LC \| 7 \| 0.18 (0.00–96.90) \| 0.595 \| 0.09 \| 0.51 \| \| Lamb intake \| LC \| 25 \| 0.21 (0.01–4.10) \| 0.304 \| 0.06 \| 0.06 \| \| Processed meat intake \| BTC \| 21 \| 2.23 (0.05–97.41) \| 0.678 \| 0.22 \| 0.99 \| \| Pork intake \| BTC \| 10 \| 0.02 (0.00–273.48) \| 0.432 \| 0.07 \| 0.86 \| \| Beef intake \| BTC \| 12 \| 0.13 (0.00–35.42) \| 0.478 \| 0.8 \| 0.55 \| \| Poultry intake \| BTC \| 7 \| 0.30 (0.00–1,535.78) \| 0.782 \| 0.29 \| 0.6 \| \| Lamb intake \| BTC \| 25 \| 0.16 (0.00–20.86) \| 0.456 \| 0.2 \| 0.97 \| \| Processed meat intake \| PC \| 21 \| 0.82 (0.19–3.48) \| 0.787 \| 0.6 \| 0.14 \| \| Pork intake \| PC \| 10 \| 0.19 (0.00–3.91) \| 0.283 \| 0.77 \| 0.38 \| \| Beef intake \| PC \| 12 \| 0.34 (0.02–4.56) \| 0.412 \| 0.27 \| 0.53 \| \| Poultry intake \| PC \| 7 \| 6.34 (0.24–164.80) \| 0.267 \| 0.55 \| 0.55 \| \| Lamb intake \| PC \| 25 \| 1.32 (0.15–11.95) \| 0.804 \| 0.1 \| 0.52 \| \| Processed meat intake \| CRC \| 21 \| 2.12 (1.07–4.19) \| 0.031 \| 0.49 \| 0.38 \| \| Pork intake \| CRC \| 8 \| 0.28 (0.04–1.82) \| 0.181 \| 0.24 \| 0.63 \| \| Beef intake \| CRC \| 12 \| 1.15 (0.32–4.16) \| 0.837 \| 0.2 \| 0.57 \| \| Poultry intake \| CRC \| 7 \| 0.27 (0.04–1.61) \| 0.149 \| 0.22 \| 0.16 \| \| Lamb intake \| CRC \| 25 \| 0.99 (0.36–2.72) \| 0.987 \| 0.14 \| 0.22 \| The IVW analysis revealed that genetically predicted processed meat intake can significantly promote CRC risk [odds ratio (OR) = 2.12, $95\%$ confidence interval (CI) 1.07–4.19; $$P \leq 0.031$$] (Table 2). Each standard deviation (SD) increase in genetically predicted processed meat intake enhanced CRC risk by $112\%$, according to the IVW analysis. However, the MR–Egger (OR = 9.53, $95\%$ CI 0.34–269.39; $$P \leq 0.202$$) and weighted median (OR = 2.51, $95\%$ CI 0.26–24.55; $$P \leq 0.428$$) analyses revealed a consistent direction, but the results were not significant. As mentioned earlier, the three analysis methods were established based on different assumptions, which resulted in inconsistent estimates. However, the IVW analysis results are widely acknowledged to be the most accurate. Meanwhile, the consistency of their directions is not accidental, improving our results' persuasiveness. Moreover, there is no evidence of heterogeneity in the MR analysis results because the Cochran Q test-derived P-value was 0.49. Similarly, the MR–Egger intercept outcome exhibited no horizontal pleiotropy ($$P \leq 0.38$$). Scatter plots did not present significant intercepts, and funnel plots were symmetrical, demonstrating that the results were not heterogeneous or pleiotropic (Supplementary Figures S9A, C). The LOO analysis results suggested that rs4240672, rs203319, rs6765179, rs9809856, rs6786550, and rs2029401 could potentially impact the IVW analysis results (Supplementary Figure S9B). The MVMR analysis also proved that processed meat intake could directly affect CRC without interference from other exposures of interest [OR = 3.85, $95\%$ CI 1.14–13.04; $$P \leq 0.030$$ (Figure 2A)]. Then, we performed a reverse MR analysis, considering CRC as the exposure and processed meat intake as the outcome. No evidence supports the hypothesis that genetically related CRCs can influence processed meat intake (IVW: OR = 1.00, $95\%$ CI 0.99–1.01; $$P \leq 0.666$$) (Figure 2B). As shown in Supplementary Figures S10A–C, the results were not heterogeneous or pleiotropic (P-value of heterogeneity = 0.99; P-value of pleiotropy = 0.99). Thus, no reverse causality existed between the exposure of interest and the outcome. Figure 2: The odds ratio plot of the MR analysis. The odds ratio plot (A) shows estimates of the MVMR analysis from meat intake on CRC in IVW methods when controlling for the other four factors, respectively. The results from IVW, the MR-Egger method, and the weighted median in the univariable MR analysis from CRC to PMI, PMI to BMI, and PMI to TC were displayed in odds ratio plots (B–D). MR, mendelian randomization; MVMR, multivariable Mendelian randomization; IVW, inverse-variance weighted; CRC, colorectal cancer; PMI, processed meat intake; BMI, body mass index; TC, total cholesterol. To further investigate the potential mechanism through which genetically established processed meat intake increases CRC risk, we introduced the mediating variables BMI and TC to explore whether the aforementioned common risk factors violate the causal effect. The Cochran Q test-derived P-values of BMI and TC were 2.752078 × 10−28 and 8.779711 × 10−46, which indicated that the results were heterogeneous. Through the MR-PRESSO and Radial MR analyses, 8 (rs11887120, rs1422192, rs203319, rs2873054, rs4077924, rs4778053, rs7531118, and rs838133) and 10 outliers (rs10454812, rs1422192, rs2873054, rs4240672, rs4778053, rs6010651, rs6786550, rs6961970, rs77165542, and rs838133) were identified, respectively (Supplementary Figures S11A, E). After these outliers were removed, the MR analysis was re-performed. However, evidence supporting a causal relationship between processed meat intake and BMI and TC is limited (Figures 2C, D). Scatter and funnel plots indicate the absence of heterogeneity and pleiotropy (Supplementary Figures S11B, C, F, G). The LOO sensitivity analysis proved the robustness of the results (Supplementary Figures S11D, H). ## 4. Discussion We used multiple MR methods to analyze large-scale GWAS data from the MRC-IEU UK Biobank OpenGWAS and Finngen to investigate the causal effect of genetically proxied meat intake on DTCs. The univariable MR analysis only indicated the negative causal effect of processed meat intake on CRC. This estimate is consistent with that made in the MVMR analysis after multiple corrections for other exposures. BMI and TC do not appear to be potential mediators. Our findings do not support associations between DTCs and other exposure of interest. Evidence indicating the results of the MR analysis with pleiotropy and heterogeneity bias is scarce. Furthermore, observational studies produced controversial conclusions because of the unavoidable interference of confounding factors and reverse causation. However, the MR analysis results positively support the causal effect of processed meat rather than red or white meat intake on CRC, except for a bias due to a more rigorous MR design. Despite the contradictory conclusions, observational studies provided evidence that meat intake is associated with cancer risk [35]. The rapid increase in CRC prevalence and CRC-associated mortality is primarily due to changing diet structures [36]. By constructing a risk model, Parra-Soto and colleagues analyzed the associations between diet and cancer in a study involving a cohort of 409,110 participants with a mean follow-up period of 10.6 years. They revealed that processed meat intake increases the risk of CRC and prostate cancers [37]. Another prospective study of 472,377 UK Biobank participants with a median follow-up of 1.4 years found that people who ate less red meat were less likely to develop CRC and breast cancer [38]. Another large prospective study also concluded that consumption of red and processed meat, and not poultry, promoted CRC risk (hazard ratio = 1.35, $95\%$ CI 0.96–1.88) [39]. Moreover, a meta-analysis of 60 case-control or cohort studies published between 2016 and 2017 found that processed meat consumption increased the risk of colon cancer, but not rectal cancer [40]. RCTs provide more compelling evidence compared with case-control and cohort studies. Until now, only two RCTs explored the associations between red meat (no processed meat) and CRC [14, 15]. The results of these RCTs provided robust evidence that red meat intake has no bearing on CRC risk, which is consistent with the results of our study. Therefore, a causal effect of genetically predicted processed meat, but not red meat, is believed to exist on CRC. However, the potential mechanisms through which processed meat increases CRC risk are poorly defined and may be mediated by the following pathways. Multiple studies confirmed that cancerogenic substances such as polycyclic aromatic hydrocarbons (PAHs), heterocyclic amines (HCAs), N-nitroso compounds (NOCs), and heme iron are formed when meat is processed, such as fried or grilled, at a high temperature for a long time. Creatine and creatinine in meat produce HCAs during high-temperature processing, and more HCAs are produced with an increase in temperature and time [41]. Approximately 25 HCAs have been identified and classified into aminoimidazo-azarenes, and carbolines or pyrolytic HCAs. The aforementioned HCAs are metabolically activated to induce DNA sequence mutations and promote cell proliferation, thereby leading to cancer [42]. Cytochrome P450 (CYPs) enzymes, CYP1A1 and CYP1B1, induce PAHs, covalently binding to DNA to promote DNA sequence variation [43]. Furthermore, not only can high-temperature processing produce NOCs but the heme iron present in meat can also induce the endogenous synthesis of NOCs and genotoxic free radicals [44, 45]. In summary, the aforementioned carcinogenic substances interact with DNA, resulting in genetic mutations that promote cancer development. Therefore, we hypothesize that the causal effect of genetically proxied processed meat intake on CRC may be mediated by the cancerogenic substances produced during meat cooking. In addition, we introduced two obesity-associated phenotypes, such as BMI and TC, to further explore the potential mediators of the causal effects between processed meat and CRC. Strong evidence confirming that chronic inflammation and sex hormone metabolism mediate obesity and cancer is available, with moderate evidence supporting the role of insulin and IGF signaling [46]. Because of the high metabolic activity of adipose tissue, pro-inflammatory factors such as interleukin (IL)-6 and tumor necrosis factor (TNF)-alpha secreted by this tissue can initiate tumor formation [47]. Inflammation from the adipose tissue causes insulin resistance, and insulin triggers cancer through antiapoptotic effects [48, 49]. Moreover, the pro-cancer effects of TC are widely accepted to be mediated through multiple mechanisms, including TC-induced NLRP3 inflammasome activation [50]. However, our findings suggest that the association between processed meat and CRC is independent of BMI and TC. This may be due to the formation of cancerogenic substances due to fatty tissue cleavage during the meat cooking process. Our findings do not support the evidence that the causal effect of meat intake on DTCs is none other than that of processed meat intake on CRC. A previous meta-analysis including 4 cohorts and 31 case-control studies concluded that processed and red meat increases esophageal cancer risk [51]. However, a case-control study presented contradictory results; meat intake was unrelated to esophageal cancer in that study [52]. This inconsistent finding is also observed in other DTCs. As Händel et al. reported, the relationship between processed meat and DCTs varied considerably between cohort and case-control studies [16]. Risk estimates were higher in case-control studies because these studies had more confounding factors. Until now, standard, large-scale RCTs for verifying their true relationship were scarce. Our research has the following advantages. To the best of our knowledge, this study conducted the first MR analysis to explore the causal effects of five common meat intakes on DCTs. The greatest strength of our study is the detection of causal effects through minimal confounding. The consistency in the results of univariable and multivariable MR analyses reinforces the evidence that consumption of genetically proxied processed meat, rather than red and white meat, promotes CRC risk. However, our study has some limitations. This study is based on European populations, and how well the findings fit in other populations remains unclear. Moreover, the moderate relationship between cancer and meat intake may have been overlooked because of the low number of cases. In summary, processed meat intake can directly increase CRC risk, independent of whether red or white meat has been consumed. Advocating for a reduction in processed meat intake is beneficial for the early prevention of CRC. Previous findings have violated the real association due to interference from confounding factors and reverse causation. ## 5. Conclusions Our study revealed that processed meat intake increases the risk of CRC rather than other DCTs. No causal relationship was observed between red and white meat intake and DCTs. ## 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 authors. ## Author contributions Conceptualization, writing—review and editing, supervision, and project administration: LH and QD. Methodology: LH, QD, and ZY. Software and formal analysis: ZY and XL. Validation: ZY, MN, and ZL. Investigation: XD and XL. Resources: MN. Data curation: ZL. Writing—original draft preparation: ZY and MN. Visualization: JX. Funding acquisition: LH. All authors read and agreed to the published version of 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. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1078963/full#supplementary-material ## References 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A. 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--- title: Integrated transcriptomic and metabolomic analyses reveal key metabolic pathways in response to potassium deficiency in coconut (Cocos nucifera L.) seedlings authors: - Lilan Lu - Siting Chen - Weibo Yang - Yi Wu - Yingying Liu - Xinxing Yin - Yaodong Yang - Yanfang Yang journal: Frontiers in Plant Science year: 2023 pmcid: PMC9968814 doi: 10.3389/fpls.2023.1112264 license: CC BY 4.0 --- # Integrated transcriptomic and metabolomic analyses reveal key metabolic pathways in response to potassium deficiency in coconut (Cocos nucifera L.) seedlings ## Abstract Potassium ions (K+) are important for plant growth and crop yield. However, the effects of K+ deficiency on the biomass of coconut seedlings and the mechanism by which K+ deficiency regulates plant growth remain largely unknown. Therefore, in this study, we compared the physiological, transcriptome, and metabolite profiles of coconut seedling leaves under K+-deficient and K+-sufficient conditions using pot hydroponic experiments, RNA-sequencing, and metabolomics technologies. K+ deficiency stress significantly reduced the plant height, biomass, and soil and plant analyzer development value, as well as K content, soluble protein, crude fat, and soluble sugar contents of coconut seedlings. Under K+ deficiency, the leaf malondialdehyde content of coconut seedlings were significantly increased, whereas the proline (Pro) content was significantly reduced. Superoxide dismutase, peroxidase, and catalase activities were significantly reduced. The contents of endogenous hormones such as auxin, gibberellin, and zeatin were significantly decreased, whereas abscisic acid content was significantly increased. RNA-sequencing revealed that compared to the control, there were 1003 differentially expressed genes (DEGs) in the leaves of coconut seedlings under K+ deficiency. Gene Ontology analysis revealed that these DEGs were mainly related to “integral component of membrane,” “plasma membrane,” “nucleus”, “transcription factor activity,” “sequence-specific DNA binding,” and “protein kinase activity.” Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that the DEGs were mainly involved in “MAPK signaling pathway-plant,” “plant hormone signal transduction,” “starch and sucrose metabolism,” “plant-pathogen interaction,” “ABC transporters,” and “glycerophospholipid metabolism.” Metabolomic analysis showed that metabolites related to fatty acids, lipidol, amines, organic acids, amino acids, and flavonoids were generally down-regulated in coconut seedlings under K+ deficiency, whereas metabolites related to phenolic acids, nucleic acids, sugars, and alkaloids were mostly up-regulated. Therefore, coconut seedlings respond to K+ deficiency stress by regulating signal transduction pathways, primary and secondary metabolism, and plant-pathogen interaction. These results confirm the importance of K+ for coconut production, and provide a more in-depth understanding of the response of coconut seedlings to K+ deficiency and a basis for improving K+ utilization efficiency in coconut trees. ## Introduction Potassium (K+) is a major nutrient necessary for plant growth and development, accounting for approximately 2–$10\%$ of the total dry weight of plants (Leigh and Wyn Jones, 1984). K+ plays important roles in enzyme activation, protein synthesis, photosynthesis, turgor, osmotic adjustment, ion homeostasis, and electric neutralization (Römheld and Kirkby, 2010; Kanai et al., 2011; Hafsi et al., 2014), and more than 60 enzymes and several cofactors play direct or indirect roles in these processes (Hawkesford et al., 2012; Vašák and Schnabl, 2016). K+ levels affect the levels of primary and secondary metabolites in plants (Armengaud et al., 2009; Coskun et al., 2017; Chatterjee et al., 2020). Therefore, K+ deficiency negatively affects osmotic pressure, nutrient balance, photosynthesis, and protein synthesis, and compromises plant growth. The soil K+ content in farmlands is low in large areas of the world, and crops cannot effectively use soil mineral elements (Perry et al., 1972; Hafsi et al., 2014). Coconut trees are grown in acidic soils in southern China, where K+ supply is insufficient (Lu et al., 2021). Adding K+ fertilizer can improve crop yield, but increasing the use of K+ fertilizer in agriculture will lead to environmental pollution (Römheld and Kirkby, 2010). Based on K+ fertilizer consumption from 1961 to 2018, the global K+ utilization efficiency of cereal crops was estimated only $19\%$, emphasizing the need to protect this non-renewable natural resource (Dhillon et al., 2019). Therefore, improving crop K+ utilization efficiency is crucial for optimizing fertilization, increasing crop yields, and reducing environmental pollution. To optimize K+ utilization by specific crops, it is important to study the responses and adaptations of crops to K+ deficiency and the underlying mechanisms. K+ deficiency is a common abiotic stress in agricultural practice (Hosseini et al., 2017; Xu et al., 2020). It increases the free sugar content in plants, such as Arabidopsis (leaves and roots) (Armengaud et al., 2009), rice (Ma et al., 2012; Chen et al., 2015), barley (Zeng et al., 2018), potato (Koch et al., 2018), soybean leaves (Cakmak et al., 1994), rape (Pan et al., 2017), beet (Aksu and Altay, 2020), alfalfa root (Jungers et al., 2019), tomato (Sung et al., 2015), and cotton (leaves) (Bednarz and Oosterhuis, 1999; Pettigrew, 1999; Hu et al., 2017). In addition, free amino acids (especially, proline) are excessively accumulated in Arabidopsis (Armengaud et al., 2009), barley (Zeng et al., 2018), tobacco (Ren et al., 2016), cotton (Hu et al., 2017), and rape (Lu et al., 2019) under K+ deficiency. Significant changes in the plant metabolite profile caused by K+ deficiency can lead to metabolic disorders (Hu et al., 2016). Under K+-deficient conditions, plants alter their root structure and root hairs to absorb more nutrients (Jia et al., 2008). Transcriptome analysis has been used to analyze gene maps of the main metabolic pathways in K+-deficient tomato, cotton, wheat, rice, and soybean roots (Ma et al., 2012; Ruan et al., 2015; Singh and Reddy, 2017; Zhao et al., 2018; Yang et al., 2021). These studies have identified transcription factors and genes involved in metabolic pathways (including those encoding carbohydrates, plant hormones, and kinases) that play indispensable roles in maintaining plant growth under K+ deficiency (Hyun et al., 2014; Zhao et al., 2018; Yang et al., 2021). Seedlings can regulate mineral nutrient absorption via the roots (Bari et al., 2006; Tabata et al., 2014; Xiong et al., 2022). High-affinity K+ transporters regulate K+ uptake by cotton roots during K+ deficiency (Wang et al., 2019). Therefore, it is important to analyze the adaptation of plant seedlings to K+ deficiency. Coconut (Cocos nucifera L.) is a perennial palm tree species and a multifunctional tropical crop that is used to produce food, energy, daily necessities, and chemicals. Coconuts can be eaten fresh and are an important high-value tropical fruit and oil crop, as well as a unique renewable, green, and environment-friendly resource in tropical areas (Lu et al., 2021). Coconut trees require a large amount of K+ for growth and development, and coconut water is rich in K+; therefore, the trees have to absorb sufficient K+ from the soil during development. Coconut generally requires additional external organic or chemical K+ fertilization for optimal growth (Malhotra et al., 2017; Baghdadi et al., 2018). K+ deficiency causes abiotic stress in crops, limiting their yield (Zorb et al., 2014; Qin et al., 2019). Plants respond to K+ deficiency at the morphological, physiological, biochemical, and molecular levels (Hafsi et al., 2014). In maize, low K+ levels induce lateral root growth, inducing genes related to nutrient utilization, hormones, and transcription factors (Zhao et al., 2016; Ma et al., 2020). Mild and moderate K+ deficiency causes yellowing and scorching of mature coconut leaves, and in severe cases, new leaves start yellowing and withering, leading to weakened photosynthesis, decreased immunity, disease, and insect infestation of the leaves (Ollagnier and Shi, 1988; Maheswarappa et al., 2014). K+ deficiency also reduces the number of female coconuts, resulting in poor pollination and affecting the development of flowers, young fruits, and fruits (Frem. and He, 1989; Broschat, 2010); lowers the yield and quality of adult trees (Tang, 1997; Kalpana et al., 2008); and causes aging and decay of coconut tree roots (Chen, 2005; Mohandas, 2012a; Mohandas, 2012b). However, although K+ is absorbed via the roots, it is unclear how low K+ concentrations promote root absorption to adapt to K+ deficiency by regulating relevant genes and metabolites in coconut seedling leaves. K+ metabolism is regulated by several genes that are differentially expressed under different K+ conditions. RNA-sequencing (RNA-seq) is an important tool in plant molecular research that uses high-throughput sequencing technologies to sequence cDNA libraries. RNA-seq involves reverse transcription of total RNA in tissues or cells to determine gene expression levels. Transcriptome analysis using next-generation sequencing technologies has been used to study the molecular mechanisms of plant responses to nutritional stresses (Wasaki et al., 2006; Saurabh et al, 2017) Studies have used transcriptome analysis to analyze the response of plants, including rice (Ma et al., 2012), soybean (Wang et al., 2012), sugarcane (Zeng et al., 2015), wheat (Ruan et al., 2015), soybean (Zeng et al., 2015), and wheat (Wang et al., 2019), to K+ stress. Transcriptome analysis improves our understanding of the complex molecular mechanisms of K+ uptake and transformation in plants. Metabolomic analysis is used to detect metabolites in cells or tissues, study the synthesis of all or some metabolites, and elucidate their decomposition or transformation principles (Hall, 2011; Zhang et al., 2020). Abiotic stress can lead to metabolic disorders in plants (Meena et al., 2017). CChanges in small-molecular compounds under mineral nutrient stress have been evaluated. Sung et al. [ 2015] studied the metabolic response of tomato leaves and roots to nitrogen (N), phosphorus (P), and K deficiency and found that nutrient deficiency affected plant amino acid metabolism and energy production. Zhang et al. [ 2020] discovered that α-amino acids and their derivatives, sucrose, and sugar alcohols were significantly increased in cotton seedlings under low-K stress and reflected as damaged cell membranes and abnormal protein metabolism. K+ deficiency reduces the antioxidant capacity of cotton seedlings, leading to metabolic disorders, including an increase in primary metabolites and inhibition of secondary metabolite production. Watanabe et al. [ 2020] found that the levels of anti-stress substances and amino acid substitutes increased under N deficiency, and the metabolism of benzoic acid, erucic acid, and glucuronate in rice leaves was related to low-P stress. Gao et al. [ 2020] found that p-hydroxybenzoic acid, inositol, dinol, and stachyose levels increased in lettuce leaves under N deficiency. Integrated transcriptomic and metabolomic methods are increasingly being applied to reveal molecular mechanisms of environmental stress resistance in different crops based on genetic, physiological, and morphological data (Bowne et al., 2011). This approach has led to the unraveling of the tolerance mechanism of wild soybean seedling roots to low-N stress (Liu et al., 2020), identification of candidate genes possibly involved in oat adaptation to P deficiency (Wang et al., 2018), elucidation of the response of rice carbon and N metabolism to high N (Xin et al., 2019), understanding metabolic changes caused by regulation of P utilization efficiency in rice leaves (Wasaki et al., 2003), regulation of phosphorylated metabolite metabolism in soybean roots in response to P deficiency (Mo et al., 2019), effect of N deficiency on wheat grains during the medium filling stage (Wang et al., 2021), response mechanisms of apple to different P stresses (Sun et al., 2021), regulatory mechanisms of primary and secondary metabolism of peanut roots under N deficiency stress (Yang et al., 2022), and the transcriptional and metabolic responses of maize buds to long-term K+ deficiency (Xiong et al., 2022). At present, few studies have evaluated nutritional stress in palm woody plants using a combination of metabolomic with transcriptomic methods. Coconuts are very important for the development of food, household goods, energy, and industrial resources in Hainan Province, China, and the economic income level of the region. However, few studies have reported the metabolome and transcriptome data of coconuts under K+ stress. As a perennial tropical woody fruit tree, the level of K+ has a significant impact on the growth, yield, and quality of coconut trees (Mao et al., 1999; Malhotra et al., 2017). Thus, identifying the genes related to K metabolism is key to optimizing K application to coconut trees. In contrast to the model plants Arabidopsis, rice, and maize, for which extensive molecular information related to K metabolism research is available (Zhao et al., 2018; Wang et al., 2019; Yang et al., 2021), the effects of K stress and the genes and metabolites involved in coconut tree response to K stress are relativelky unknown. Thus, the identification of genes and metabolites under different K conditions will be of great significance to better understand K metabolism and the related pathways in coconut trees. In this study, the growth and physiological conditions of coconut seedlings under different K+ treatments were analyzed using pot hydroponic experiments. In addition, the effects of K+ deficiency on coconut seedling development were studied using transcription and metabolomics. This study aimed to determine the main effects of K+ deficiency on coconut seedling growth and their transcript and metabolite responses. These findings will be helpful in understanding the physiological adaptability and developmental mechanisms of coconut seedlings under K+ deficiency and provide a theoretical basis for improving the K+ utilization efficiency in coconut breeding. ## Plant growth and treatments Seed fruits were removed from Huangai (Wenye No. 2) coconut seedlings that had grown for 1.5 months after the seed fruit had sprouted and the seedlings were recovered in a nutrition bag for 15 days. Seedlings with similar size, height, and number of leaves were transferred to a hydroponic system in a substrate of non-nutritive quartz sand and vermiculite (20:6) (50 × 37 × 35 cm). The coconut seedlings were randomly divided into three treatment groups (60 seedlings per treatment), with three biological replicates per treatment and 20 seedlings per replicate. After 10 days of preculture, K+ deficiency stress treatment was initiated. The K content of the coconut seedlings was determined based on the standard K content of coconut leaves (Brunin and He, 1965). The seedlings were treated with 0.1 mM KCl (K+ deficiency [K0]) or 4 mM KCl (K+ sufficiency [Kck]); solution concentrations of other nutrients were in accordance with those reported by Hoagland and Arnon [1950]. All nutrient solutions were irrigated every 3 days for 30 days, after which the seedling responses were evaluated. Plant height was recorded and the plants were sampled. Part of the leaves were immediately frozen in liquid nitrogen and stored at –80°C until further physiological, nutrient, metabolic, and transcriptional analyses. ## Measurements of plant height, dry weight, and soil and plant analyzer development values Plant height was measured using a measuring tape (accuracy: 1 mm). The SPAD value of the coconut leaves was measured using a SPAD chlorophyll meter (SPAD-502 Plus, Konica Minolta, Tokyo, Japan). To evaluate the dry weight of coconut seedlings, the fresh stems, leaves, and roots of coconut seedlings were dried at 105°C for 15 min and at 70°C for 72 h and then weighed using an electronic balance (Labpro, Shanghai, China). The dry weight (stems + leaves + roots) of the whole plant was calculated. ## Determination of N, P, and K contents Briefly, 200 mg dried and finely ground coconut leaf sample was transferred into a 100 mL digestion tube, to which, 5 mL H2SO4 and 5 mL HClO4 were added, and the mixture was shaken gently. Then, a curved neck funnel was placed at the mouth of the bottle and the bottle was heated until the digestion solution was colorless or clear, then heated for another 5–10 min, and allowed to cool down. The digestion solution was then transferred into a 100-mL constant volume bottle, diluted with deionized water, and filtered. The filtrate was used to measure N, P, and K contents. For the determination of N, 5 mL filtrate was transferred to a 50-mL volumetric flask, to which 2 mL of 100 g·L–1 sodium tartrate solution was added. This was followed by the addition of 100 g·L–1 KOH solution for acid neutralization, deionized water was added to make up the volume to 40 mL, and then, 2.5 mL Nessler’s reagent was added to the mixture. The mixture was diluted with deionized water and shaken well. In addition, we prepared N (NH4 +-N) standard solutions (2, 5, 10, 20, 40, 60 µg.mL–1) by adding 5 mL blank digestion solution. Color development in the sample and standard solutions was measured at 420 nm using a UV-visible spectrophotometer (UV-1600, Aoyi, Shanghai, China). The measurement for the blank digestion solution was set as the zero point for the instrument. For the determination of P, 5 mL digestion filtrate was transferred into a 50-mL volumetric flask and diluted to 30 mL with deionized water. Then, two drops of dinitrophenol indicator were added, followed by the addition of 4 mol·L–1 NaOH solution until the solution turned yellow; subsequently, one drop of 2 mol·L–1 ($\frac{1}{2}$ H2SO4) was added so that the yellow color of the solution faded. Then, 5 mL molybdenum-antimony reagent was added to the solution, followed by the addition of 50 mL deionized water. P standard solutions (0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0 µg·mL–1) were prepared by adding 5 mL blank digestion solution, using the same procedure. Color development in the sample and standard solutions was measured at 880 nm using the UV-visible spectrophotometer, and the absorbance of the blank solution was set to 0. For the determination of K, 5 mL digestion filtrate was transferred into a 50-mL volumetric flask, and the volume was adjusted using deionized water. K standard solutions (2, 5, 10, 20, 40, 60 µg·mL–1) were prepared by adding 5 mL blank digestion solution, and the sample and standard solutions were analyzed using an atomic absorption spectrophotometer (AA6300F, Shimadzu, Kyoto, Japan), according to the method reported by Bao [2000]. ## Determination of soluble sugar, soluble protein, crude fat, endogenous hormone, proline, and malondialdehyde contents and enzyme activities For CF detection, CF was extracted from fresh leaf tissues using a distillation device. For the determination of auxin (IAA), gibberellin (GA), abscisic acid (ABA), zeatin (ZR), SP, SS, MDA, and Pro contents and superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) activities, 0.1000 g coconut leaf tissue was accurately weighted and mixed with precooled PBS at a weight (g) to volume (mL) ratio of 1:10. The samples were subjected to high-speed grinding and centrifuged at 2500 rpm for 10 min. Then, 50 µL supernatant was used for the measurements. IAA, GA, ABA, ZR, MDA, SP, Pro, SOD, CAT, and POD kits and standards were obtained from the Nanjing Jiancheng Bioengineering Research Institute, and the measurements were performed in strict accordance to the manufacturer’s instructions and following the method reported by Li [2000]. We used 1-cm optical path cuvettes, and blank cuvette was used for setting the baseline. The wavelength was set to 450 nm (IAA, ABA, GA, ZR), 595 nm (SP), 620 nm (SS), 532 nm (MDA), 520 nm (Pro), 550 nm (SOD), 405 nm (CAT), or 420 nm (POD) to measure the absorbance by using the enzyme marker (DG5033A, Nanjing Huadong Electronics Group Medical Equipment). All measurements were carried out within 10 min after adding the termination solution. Based on the absorbance value, the concentration/activity was calculated according to the manufacturer’s formulas. ## RNA extraction and RNA-seq Total RNA was extracted from the frozen samples using the improved cetyltrimethylammonium bromide (CTAB) method. RNA purity and integrity were visually evaluated by agarose gel electrophoresis. The RNA concentration was measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). An Agilent 2100 Bioanalyzer system (Agilent Technologies, Palo Alto, CA, USA) was used to quantify RNA integrity. Library assembly and RNA-seq analysis were performed at Beijing Biomarker Biotechnology Company (Beijing, China) and Beijing Biomarker Cloud Technology Company (Beijing, China). RNA-seq libraries were generated using the NEBNext ® Ultra ™ II RNA Library Prep Kit for Illumina ® (New England Biolabs, Ipswich, MA, USA), and index codes were added to each sample. The libraries were sequenced on an Illumina ® HiSeq2500 platform (Illumina, San Diego, CA, USA). Each sample was sequenced in triplicate. The raw reads were filtered by removing low-quality reads and adapters. The clean reads were mapped to the reference coconut genome (Xiao et al., 2017) (http://creativecommons.org/licenses/by/4.0/) using the transcript splicing-aligned hierarchical index (HISAT 2) program (Kim et al., 2015). Gene functions were annotated using the following databases: NCBI non-redundant protein sequences (Nr), Clusters of Orthologous Groups of proteins (COG/KOG), Swiss-PROT protein sequence database, Kyoto Encyclopedia of Genes and Genomes (KEGG), Homologous protein family (Pfam), and Gene Ontology (GO) (Tatusov et al., 2000; Finn et al., 2013). For each transcript region, the RESM software was used to calculate the Fragments per kilobase of transcript per million fragments mapped (FPKM) (Li and Dewey, 2011). The DESeq software was used to analyze differential gene expression among samples, and the Benjamini–Hochberg method was used to determine significance. DEGs were defined based on \|fold change (FC)\| ≥ 1.5 and $P \leq 0.05$ (Love et al., 2014). The GOseq R software package was used for GO term enrichment analysis of the DEGs (Ashburner et al., 2000; Alexa and Rahnenfuhrer, 2010; Robinson et al., 2010). The KEGG Orthology Based annotation system (KOBAS) software was used for KEGG pathway enrichment analysis of the DEGs (Kanehisa et al., 2004). ## Metabolite analysis Sample preparation and metabolome and data analyses were conducted by Beijing Biomarker Biotechnology Co., Ltd. (http://www.biomarker.com.cn/). Briefly, the frozen coconut leaves were grounded into a powder in liquid N and 100 mg powder was added to a 1.5 mL Eppendorf tube. The samples were extracted with 1.0 mL of $70\%$ aqueous methanol solution at 4°C for 24 h and centrifuged at 10000×g and 4°C for 10 min. The extracts were filtered through 0.22-µm nylon membranes and subjected to liquid chromatography-mass spectrometry (LC-MS) analysis. Quality control samples were prepared by extracting and mixing three duplicate samples from each K treatment. During analysis, each quality control sample was measured together with the corresponding three experimental samples to check the stability of the analysis conditions. An ultraperformance LC-electrospray ionization MS (UPLC-ESI-MS/MS) system (Shimadzu) was used to analyze the metabolic spectra of the leaf extracts (10 µL). Chromatographic separation in water was performed on a UPLC HSS T3 C18 column (2.1 mm × 100 mm, i.d., 1.8 µm) (Waters, Milford, MA, USA) at 40°C. The mobile phase consisted of water containing $0.04\%$ acetic acid (mobile phase A), and acetonitrile containing $0.04\%$ acetic acid (mobile phase B). The linear gradient program for elution was set as follows: 0–11.0 min from $5\%$ to $95\%$ B, 11.0–12.0 min from $95\%$ to $5\%$, and 12.1–15.0 min from $5\%$ to $5\%$. The flow rate of the mobile phase was 0.40 mL/min. An API 4500 QTRAP LC-MS/MS system (AB SCIEX, Framingham, MA, USA) was used for MS and MS/MS analysis. The ESI source parameters were as follows: turbine spray, ion source, source temperature: 550°C; ion spray voltage: 5.5 kV; air curtain gas pressure: 25 pounds per square inch (psi), ion source gas 1 pressure: 55 psi, and gas II pressure: 60 psi. A multiple reaction monitoring experiment was conducted with a 5-psi nitrogen collision gas to obtain the quadrupole scanning results. Metabolites were identified based on a public database of metabolite information and the cloud technology database of Beijing Biomarker Biotechnology Co., Ltd. (Beijing, China). Open databases, including HMDB, MoToDB, MassBank, METLIN, and KNAPSAcK, were used for qualitative analysis of the metabolites identified by the MS. Metabolite structures were analyzed using standard metabolic procedures. The metabolites were quantified using multiple reaction monitoring. All metabolites identified were analyzed using partial least squares discriminant analysis (PLS-DA). Principal component analysis (PCA) and orthogonal PLS-DA (OPLS-DA) were used to identify potential biomarkers. For biomarker selection, variable importance of projection (VIP) ≥1 and folding change (FC) ≥ 2 or ≤ 0.5 were used as criteria to screen for significantly differentially accumulated metabolites (DAMs). ## Integrated metabolome and transcriptome analyses Pearson correlation coefficients (R2) between the metabolome and transcriptome data were calculated. Specifically, the coefficients correlation between log2(FC) for each metabolite and log2(FC) for each transcript were calculated using the Excel program. Items with R2 > 0.8 were selected. Cytoscape (version 2.8.2) was used to visualize the relationship between the metabolome and the transcriptome (Shannon et al., 2003). ## Quantitative real-time PCR analysis RT-qPCR was used to verify the DEGs in coconut seedlings identified by RNA-seq. Gene-specific RT-qPCR primers were designed (Supplementary Table 1). qPCRs were run on a LightCycler® 480II Real-Time system (Roche, Carlsbad, CA, USA) in 96-well plates, using Hieff qPCR SYBR Green Master Mix (Not-Rox) (Yeasen Biotech, Shanghai, China) according to the manufacturer’s instructions. The thermal cycle steps included denaturation at 95°C for 5 min, followed by 40 cycles at 95°C for 10 s and 60°C for 30 s. All RT-qPCR analyses were performed using three technical and three biological replicates. An internal reference gene (β-actin) was used for normalization. The 2–ΔΔCT method (Livak and Schmittgen, 2001) was used to calculate differential target gene expression in reference to the levels in the control group. ## Statistical analysis All experiments consisted of three replicates ($$n = 3$$). Data are expressed as the mean ± standard deviation (SD) of the three replicates. The data were analyzed by one-way analysis of variance (ANOVA). The SPSS software (version 20.0; SPSS, Chicago, IL, USA) was used for statistical analysis. Results were $P \leq 0.05$ considered statistically significant. Charts and data were prepared using Excel 2003. ## Effects of K+ deficiency on plant growth and nutrient, SP, SS, and CF contents of coconut seedlings First, we evaluated the effect of K+ deficiency on the growth of coconut seedlings using pot experiments. The seedlings were exposed to K+ deficiency (0 mM, K0) or K+ sufficiency (4 mM, Kck). The results showed that K+ deficiency significantly ($P \leq 0.05$) reduced the plant height and dry weight of coconut seedlings by $21.83\%$ and $26.45\%$, respectively (Supplementary Table 2). In addition, under K+-deficient conditions, the SP and CF contents decreased by $24.83\%$ and $56.82\%$ ($P \leq 0.05$), respectively, whereas the SS content increased by $33.43\%$ (Figure 1). Under K+-deficient conditions, the leaf K, N, and P contents of coconut seedlings decreased; the K concentration decreased by $48.36\%$ (Supplementary Table 3) and the SPAD value was significantly lower than that in control seedlings (Kck) ($P \leq 0.01$) (Figure 1). Therefore, K+ deficiency seriously affected the biomass, mineral nutrients, photosynthetic indices, and quality of coconut seedlings. Figure 1: Effects of K+ deficiency on coconut seedlings. (A) Phenotype of coconut seedlings after 30 days of K+ treatment. (B) Changes in SPAD, SP, SS, and CF contents under K+ deficiency for 30 consecutive days. P ≤ 0.05, *P ≤ 0.01. Bars represent SDs. ## Effects of K+ deficiency on SPAD values, MDA and pro contents, enzyme activities, and hormone contents K+ deficiency stress had a significant impact on the physiology of the coconut seedlings (Figure 2). Under K+ deficiency stress, the MDA content increased by $29.11\%$, whereas the Pro content decreased by $39.41\%$ ($P \leq 0.05$). Compared with Kck seedlings, in K0 seedlings, CAT, POD, and SOD activities in the leaves were significantly reduced by $16.44\%$, $16.34\%$, and $20.16\%$, respectively ($P \leq 0.05$). In addition, K+ deficiency stress had a significant impact on endogenous hormone levels in coconut seedling leaves; the ABA content increased by $50.68\%$ ($P \leq 0.05$), whereas IAA, ZR, and GA contents decreased by $22.56\%$, $23\%$, and $21.13\%$ ($P \leq 0.05$), respectively (Figure 2). Therefore, K+ deficiency seriously affected the physiological function indices and hormone levels of the coconut seedlings. Figure 2: MDA, Pro, IAA, GA, ABA, and ZR contents and SOD, CAT, and POD activities in K0 vs. Kck. P ≤ 0.05, *P ≤ 0.01. Bars represent SDs. ## Transcriptome response to K+ deficiency A summary of the RNA-seq data is presented in Supplementary Table 4. The FPKM values were higher in Kck than in K0 samples, and an FPKM > 1 was used as a threshold to determine gene expression (Supplementary Figure 1A). The PCA results showed that Kck and K0 samples clustered separately, suggesting significant differences in gene expression between the sample groups. Repeat samples under the Kck and K0 conditions did not strictly cluster together, indicating that differences occurred between different repetitions (Supplementary Figure 1B). A volcano plot showing the significantly up-regulated and down-regulated DEGs between the two groups is shown in Supplementary Figure 1C. RNA-seq detected 20910 genes with appropriate FPKM values (Supplementary Table 5). Under K+ deficiency, 1003 genes were differentially expressed in coconut seedling leaves (at thresholds of \|FC\| ≥ 1.5 and $P \leq 0.01$), including 284 up-regulated DEGs and 719 down-regulated DEGs (Supplementary Figure 1C; Supplementary Table 5). In total, 884 DEGs were functionally annotated, including 249 up-regulated and 635 down-regulated DEGs (Supplementary Table 6). In total, 733 DEGs (Supplementary Figure 2) were assigned GO terms, and GO functional enrichment analysis yielded 55 significantly enriched GO terms (FDR [error detection rate] limited standard ≤ 0.05; DEG No. ≥ 3), including 21 in the biological process (BP) category, 18 in the cellular component (CC) category, and 16 in the molecular function (MF) category (Supplementary Figure 2, Supplementary Table 7). Among the top 20 GO enriched terms, in BP, the major DEGs down-regulated under K+ stress were related to “sesquiterpene biosynthetic process”, “cellular sphingolipid homeostasis”, “negative regulation of ceramide biosynthetic process”, “sphingosine biosynthetic process”, “regulation of jasmonic acid mediated signaling pathway”, and “ceramide metabolic process”. In terms of CC, the down-regulated DEGs were mainly related to “integral component of membrane,” “serine C-palmitoyltransferase complex,” “SPOTS complex,” and “plasma membrane”. As for MF, the major down-regulated DEGs were involved in “cyclase activity”, “protein kinase activity”, “serine C-palmitoyltransferase activity”, “ATP binding”, “magnesium-dependent protein serine/threonine phosphatase activity”, “protein serine/threonine kinase activity”, “protein serine/threonine phosphatase activity”, “calcium ion binding”, “sphingosine-1-phosphate phosphatase activity”, “transferase activity, transferring glycosyl groups”, and “polysaccharide binding”. The up-regulated DEGs were mainly related to “nucleus”, “transcription factor activity, sequence-specific DNA binding”, “sequence-specific DNA binding”, and “DNA binding” (Figure 3; Supplementary Figure 3, Supplementary Table 7A). Figure 3: Top 20 GO pathways enriched in DEGs in K0 vs. Kck, including the three categories of BP, CC, and MF. (A) BP terms enriched in up-regulated DEGs. (B) CC terms enriched in up-regulated DEGs. (C) MF enriched in up-regulated DEGs. (D) BP terms enriched in down-regulated DEGs. (E) CC terms enriched in down-regulated DEGs. (F) MF terms enriched in down-regulated DEGs. The X-axis (GeneNum) represents the number of genes of interest annotated in the entry, the Y-axis indicates each term entry. The color of the column represents the P -value of the hypergeometric test. According to analysis of DEGs enriched in the top 20 GO terms ($P \leq 0.05$; q < 0.05, DEG No. ≥ 3), in BP, DEGs related to sesquiterpene biosynthetic process [7], cellular sphingolipid homeostasis [3], negative regulation of ceramide biosynthetic process [3], sphingosine biosynthetic process [4], regulation of jasmonic acid-mediated signaling pathway [5], and ceramide metabolic process [3] enrichment pathways were down-regulated. Among the six GO terms enriched, the significantly down-regulated DEGs were LOC105044768 (log2FC = –1.93), ORM1 (log2FC = –2.50), Os02g0806900 (log2FC = –2.23), and TIFY9 (log2FC = –1.64). In terms of CC, 236 DEGs were related to the integral component of membrane, and 198 of these were down-regulated, including RBOHC (log2FC = –2.03), CUT1 (log2FC = –2.10), URGT2 (log2FC = –2.23), At4g17280 (log2FC = –2.05), ORM1 (log2FC = –2.50), PXC3 (log2FC = –2.08 to –2.16)), WAK2 (log2FC = –2.08), ABCG39 (log2FC = –2.01), GALT6 (log2FC = –2.35), LRK10 (log2FC = –2.00), LECRK91 (log2FC = –2.48), and EIX2 (log2FC = –1.95). Among the 38 up-regulated DEGs in CC, CML46 (log2FC = 2.23) was significant. Three down-regulated DEGs were related to the serine C-palmitoyltransferase complex, and Os11g0516000 (log2FC = –1.78) was the significantly down-regulated DEG. Three down-regulated DEGs were related to the SPOTS complex, and ORM1 (log2FC = –2.50) was the significantly down-regulated DEG. Sixty-eight DEGs were associated with the plasma membrane, of which 58 were down-regulated and 10 were up-regulated, with the most significantly down-regulated DEGs being ABCG39 (log2FC = –2.01), NSL1 (log2FC = –2.16), and LECRK91 (log2FC = –2.48). Furthermore, 139 DEGs were related to the nucleus, of which 85 were down-regulated and 54 were up-regulated; the significantly down-regulated DEGs were WRKY41 (log2FC = –2.19 to –2.52), WRKY55 (log2FC = –2.22), and ERF026 (log2FC = –2.27 to –2.92). In MF, eight down-regulated DEGs were related to cyclase activity, and LOC105044768 (log2FC = –1.93) was the most significantly down-regulated DEG. Sixty-five DEGs were associated with transcription factors or sequence-specific DNA binding activity, 38 of which were down-regulated; significantly down-regulated DEGs included WRKY41 (log2FC = –2.19 to –2.52), WRKY55 (log2FC = –2.22), and ERF026 (log2FC = –2.27 to –2.92). Forty-nine DEGs were associated with protein kinase activity, of which 43 were down-regulated, with the most significantly down-regulated DEGs being PXC3 (log2FC = –2.08 to –2.16)), WAK2 (log2FC = –2.08), IBS1 (log2FC = –2.14), and LRK10 (log2FC = –2.00). Four down-regulated DEGs were related to serine C-palmitoyltransferase activity, with Os02g06900 (log2FC = –2.23) being the most significantly down-regulated. Eleven DEGs were associated with magnesium-dependent serine/threonine phosphatase activity, nine of which were down-regulated and PLL5 (log2FC = –2.45) was the most significantly down-regulated DEG. Twelve DEGs were associated with protein serine/threonine phosphatase activity. Among them, 10 DEGs were down-regulated, with the most significant one being PLL5 (log2FC = –2.45). A total of 115 DEGs were related to ATP binding, 94 of which were down-regulated; significantly down-regulated DEGs included HSP70-4 (log2FC = –2.09), PXC3 (log2FC = –2.08 to –2.16), WAK2 (log2FC = –2.08), ABCG39 (log2FC = –2.01), IBS1 (log2FC = –2.14), LRK10 (log2FC = –2.00), and LECRK91 (log2FC = –2.48). Forty-seven DEGs were associated with protein serine/threonine kinase activity, of which 39 were down-regulated, and the most significantly down-regulated DEG was LECRK91 (log2FC = –2.48). Seventy-three DEGs were associated with sequence-specific DNA binding, of which 43 were down-regulated, and the significantly down-regulated DEGs were WRKY41 (log2FC = –2.19 to –2.52), WRKY55 (log2FC = –2.22), and ERF026 (log2FC = –2.27 to – 2.92). Twenty-two DEGs were related to calcium ion binding, of which 17 were down-regulated (RBOHC [log2FC = –2.03] and WAK2 [log2FC = –2.08] being significantly down-regulated) and five were up-regulated (CML46 [log2FC = 2.23] being the most significant one). Sixteen DEGs were related to transferase activity, transferring glycosyl groups; of these, 15 were down-regulated, and GALT6 (log2FC = –2.35) was the most significantly down-regulated DEG. Eight DEGs were related to polysaccharide binding, and WAK2 (log2FC = –2.08) was the significantly down-regulated DEG (Supplementary Figure 3, Supplementary Table 7B). To further understand the physiological processes of coconut seedling growth under K+ deficiency, we mapped the DEGs to KEGG metabolic pathways. Among the 884 DEGs, 293 DEGs were allocated to 92 KEGG pathways; 69 up-regulated genes were assigned to 49 KEGG pathways and 224 down-regulated genes were assigned to 76 KEGG pathways (Supplementary Table 8A). In the top 20 enriched KEGG pathways, 11 pathways were significantly down-regulated and three pathways were significantly up-regulated under K+ deficiency ($P \leq 0.05$; Number of genes in one KEGG pathway ≥ 3) (Figure 4; Supplementary Table 8A). K+ stress led to significant up- or down-regulation of genes related to “mitogen-activated protein kinase (MAPK) signaling pathway-plant,” “plant hormone signal transduction,” and “starch and sucrose metabolism”. Pathways related to “plant-pathogen interaction,” “glycerophospholipid metabolism,” “alpha-linolenic acid metabolism,” “endocytosis,” “amino sugar and nucleotide sugar metabolism,” “glycerolipid metabolism,” “phosphatidylinositol signaling system,” “inositol phosphate metabolism,” and “valine, leucine and isoleucine degradation” pathways were down-regulated, whereas genes related to “plant circadian rhythm,” “phenylpropanoid biosynthesis,” “phagosome,” and “protein processing in endoplasmic reticulum” were up-regulated to resist K+ stress (Figures 4, 5). Figure 4: KEGG pathway enrichment of DEGs in K0 vs. Kck. (A) Top 20 KEGG pathways enriched in DEGs in K0 vs. Kck. (B) Top 20 KEGG pathways enriched in up-regulated DEGs in K0 vs. Kck. (C) Top 20 KEGG pathways enriched in down-regulated DEGs in K0 vs. Kck. The X-axis (GeneNum) represents the number of genes of interest annotated in the entry, the Y-axis indicates each pathway entry. The color of the column represents the P-value of the hypergeometric test. (D) Top 20 KEGG pathway enrichment bubble chart of all DEGs in K0 vs. Kck. (E) Bubble chart of the top 20 KEGG pathways enriched up-regulated DEGs in K0 vs. Kck. (F) Bubble chart of the top 20 KEGG pathways enriched in down-regulated DEGs in K0 vs. Kck. The X-axis (GeneRatio) represents the proportion of the gene of interest annotated in the entry to the number of all DEGs, the Y-axis indicates each pathway entry. Dot size represents the number of DEGs annotated in the pathway, and dot color represents the P-value of the hypergeometric test. (G) Scatter plot of top 20 KEGG pathway enrichment of all DEGs in K0 vs. Kck. Each circle represents a KEGG pathway, the Y-axis indicates the name of the pathway, and the X-axis represents the enrichment factor, which is the ratio of DEGs annotated to a certain pathway to total genes annotated to the pathway. The color of the circle represents the q-value, which is the P-value after multiple hypothesis test correction. Figure 5: KEGG pathway enrichment network of DEGs in the top five enriched pathways in K0 vs. Kck. (A) up-regulated DEGs, (B) down-regulated DEGs, and (C) all DEGs. Line colors represent different pathways, and gene node colors represent multiple differences. K+ deficiency stress may affect amino acid and sugar metabolism in coconut seedlings. According to DEG enrichment in the top 20 KEGG pathways, 38 DEGs were related to the MAPK signaling pathway-plant; $95\%$ of these were down-regulated, especially, four genes (RBOHC (id:COCN_GLEAN_10013289, COCN_GLEAN_10013288), Cht10, MPK5, LECRK3) were down-regulated by –1.66 to 2.03 fold (log2FC) under K0 treatment (Figure 6). In contrast, probable calcium-binding protein (CML46) (log2FC = 2.23) was significantly up-regulated. Eighteen DEGs were related to starch and sucrose metabolism, $72\%$ of which were down-regulated, including LECRK91 (log2FC = –2.48), TPP6 (log2FC = –2.48), and SIT2 (log2FC = –1.63) (Figure 6). Thirty-eight DEGs were involved in plant hormone signal transduction, $52\%$ of which were down-regulated and $48\%$ were up-regulated, with TIFY9 (log2FC = –1.64), At5g48380 (log2FC = –1.58), LRK10 (id: COCN_GLEAN_10019058) (log2FC = –1.57) being significantly down-regulated, and SAUR71 (log2FC = 1.82) being significantly up-regulated. Fifty-five DEGs were related to plant-pathogen interaction; $95\%$ of them were down-regulated, including WRKY41 (COCN_GLEAN_10008971, COCN_GLEAN_10008505) (log2FC = –2.25 to –2.19), WRKY55 (log2FC = –2.22), CUT1 (log2FC = –2.10), RBOHC (log2FC = –2.03), EIX2 (log2FC = –1.95), and LRK10 (COCN_GLEAN_10002024) (log2FC = –1.93). Twelve DEGs were related to glycerophospholipid metabolism, and LCAT3 (log2FC = –1.41) was the most significantly down-regulated DEG. Six DEGs were related to alpha-linolenic acid metabolism, all of which were down-regulated; the most significantly down-regulated DEG was Os04g0447100 (log2FC = –1.7 to –2.48). Nine DEGs were related to the circadian rhythm pathway-plant; the most significant DEG was CHS3 (log2FC = 1.37) (Figure 6; Supplementary Table 8B). Figure 6: DEGs related to the MAPK signaling pathway - plant, starch and sucrose metabolism, plant hormone signal transduction, plant-pathogen interaction, glycerophospholipid metabolism, alpha-linolenic acid metabolism, circadian rhythm - plant. Changes in relative expression under K+ deficiency (log2FC based on mean RPKM in K0 vs. Kck) are shown as a color gradient from low (green) to high (red). Log2(FC) > 0 indicates upregulation, log2(FC) < 0 indicates downregulation, and log2(FC) = 0 indicates unchanged expression. ## RNA-Seq validation of the RNA-Seq results by RT-qPCR Thirteen genes were randomly selected to verify the RNA-seq results. The genes down-regulated ($P \leq 0.05$) under K starvation according to RNA-seq included ethylene-responsive transcription factor (ERF026), putrescine hydroxycinnamoyltransferase 1 (PHT1), probable WRKY transcription factor 41 (WRKY41), respiratory burst oxidase homolog protein C (RBOHC), ABC transporter G family member 39 (ABCG39), wall-associated receptor kinase 2 (WAK2), and L-type lectin domain-containing receptor kinase IX.1 (LECRK91). The up-regulated genes included CBL-interacting serine/threonine-protein kinases 14 and 25 (CIPK14, CIPK25), probable calcium-binding protein (CML46), putative potassium transporter 8 (HAK8), auxin-responsive protein (SAUR71), and chlorophyll a-b binding protein 91R (CAB91R). RT-qPCR was used to analyze the relative expression of selected genes. The relative expression changes (log2FC) of the 13 genes in three biological repeats as determined by RT-qPCR correlated well with the RNA-seq results (R2 = 0.987) (Supplementary Figure 4). These results confirmed the accuracy of the RNA-seq analysis in this study. ## Metabolome response to K+ deficiency A total of 227 metabolites were detected in UPLC-MS/MS analysis of the two K+ treatment groups. There were 164 DAMs, including 108 down-regulated and 56 up-regulated DAMs, in the K0 vs. Kck group (Supplementary Table 9A; Figure 7A). In PCA and OPLS-DA of the 164 DAMs, the groups were fairly well-separated (Figures 7B–D). A heatmap of the DAMs between the two groups showed a similar trend (Figure 7E). The repeatability among the three experimental (K0) and three control (Kck) sample groups was good, and the degree of separation between K0 and Kck was significant. The DAMs were classified into seven categories: sugars [8], organic acids [17], amino acids [25], fatty acids [21], amines [12], fatty alcohols [14], nucleic acids [5], flavonoids [17], alkaloids [6], phenolic acids [12], and others [27] (Figure 7F; Supplementary Table 9B). Under K+ deficiency, $66\%$ of the metabolites in coconut seedling leaves were down-regulated, which included $90\%$ fatty acids, $93\%$ fatty alcohols, $67\%$ amines, $71\%$ organic acids, $72\%$ amino acids, $40\%$ nucleic acids, $50\%$ sugars, $65\%$ flavonoids, $50\%$ alkaloids, and $33\%$ phenolic acids. In contrast, $34\%$ of the metabolites were up-regulated, including $67\%$ phenolic acids, $60\%$ nucleic acids, $50\%$ sugars, and $50\%$ alkaloids. Notably, the levels of guanethidine, glycyl-threonine, isoleucyl-asparagine, 11.alpha.-hydroxyprogesterone, mevinolinic acid, tetrahydrodeoxycortisol, C16 sphingosine, and (+-)-lavandulol were 3–5 fold lower under K+ deficiency than under normal potassium fertilization. In contrast, the contents of procyanidin A1, S-nitroso-L-glutathione, PC(18:2(9Z,12Z)/16:0), cinnavalininate, and neohesperidin were 3–5 times higher under K+ deficiency than under normal potassium fertilization (Supplementary Table 9B). Among the 164 DAMs detected, the top 10 up-regulated DAMs were neohesperidin, cinnavalinine, PC(18:2(9Z,12Z)/16:0), S-Nitroso-L-glutathione, procyanidin A1, mevalonic acid, dihydro-5-methyl-2(3H)-thiophenone, 3-Hydroxycoumarin, 2,3’,4,6-tetrahydroxybenzophenone and acetamiprid. The top 10 down-regulated DAMs were (+-)-lavandulol, C16 Sphingosine, tetrahydrodeoxycortisol, mevinolinic acid, 11.alpha.-hydroxyprogesterone, isoleucyl-asparagine, glycyl-threonine, guanethidine, glycine Gln and 4-mercapto-4-methyl-2-pentanone (Supplementary Table 9B, Supplementary Figure 5) Figure 7: Analysis of DAMs in K0 vs. Kck. (A) Volcano plat of the DAMs. Each dot represents a metabolite. The X-axis represents the change in each substance (Log2), the Y-axis represents the P-value of the t-test (Log10), and the scatter size represents the variable importance of projection VIPvalue of the OPLS-DA model. down-regulated DAMs are indicated in blue, up-regulated DAMs are shown in red, and metabolites with insignificant differences are shown in grey. The first five qualitative metabolites were selected and labeled in the figure, based on their P-value. (B) Three-dimensional diagram of PCA. (C) OPLS-DA score model chart. R2X and R2Y represent the interpretation rate of the built model to the X and Y matrices, respectively, wherein the X matrix is the model input, i.e., the metabolite quantitative matrix, the Y matrix is the model output, i.e., the sample grouping matrix, and Q2 represents the prediction ability of the model, i.e., whether the built model can distinguish the correct sample grouping based on metabolite expression. The closer R2Y and Q2 are to 1 in the index, the more stable and reliable the model is. This model can be used to screen DAMs. Generally, Q2 > 0.5 indicates an effective model, and Q2 > 0.9 reflects an excellent model. (D) OPLS-DA model validation chart. The X-axis represents the similarity with the original model, the Y-axis represents the value of R2Y or Q2 (where R2Y and Q2 taken as 1 in the abscissa are the values of the original model), the blue and red points represent R2Y and Q2 of the model after Y replacement, respectively, and the dotted line is the fitted regression line. (E) Clustering heat map of the 164 DAMs. (F) Histogram of classification changes of different metabolites. In KEGG pathway analysis, 227 metabolites (28 DAMs) were annotated to 40 KEGG pathways, and the DAMs (l-isoleucine, 2-oxo-5-methylthiopentanoic acid, l-anserine, spermine, phytosphingosine, 3-ketosphinganine, raffinose, capsidiol, cinnavalininate, and adenine; Supplementary Table 10) were mainly enriched in glucosinolate biosynthesis; beta-alanine metabolism; sphingolipid metabolism; galactose metabolism; valine, leucine, and isoleucine degradation; valine, leucine, and isoleucine biosynthesis; sesquiterpenoid and triterpenoid biosynthesis; tryptophan metabolism; zeatin biosynthesis; and plant hormone signal transduction pathway (Figure 8). Figure 8: Metabolite pathway analysis in K0 vs. Kck. (A) Classification diagram of different metabolite pathways in each group. The X-axis indicates the number of DAMs annotated to a pathway, and the Y-axis indicates the name of the pathway. (B) Enrichment map of DAMs in KEGG pathways. The X-axis represents the ratio of the DAMs in a pathway to all DAMs with pathway annotation. (C) KEGG enrichment network diagram of DAMs. The light-yellow node indicates the pathway, and the small node connected to it indicates the specific metabolite annotated to the pathway. Color depth represents Log2(FC). The figure shows up to five pathways. ## Integrated metabolome and transcriptome analysis revealed crucial pathways involved in the response to K+ deficiency KEGG pathway enrichment analysis of the DEGs and DAMs showed that 28 pathways were enriched in K0/Kck (Supplementary Table 11).Interestingly, among these pathways, those related to plant hormone signal transduction; glycerophospholipid metabolism; valine, leucine, and isoleucine degradation; amino sugar and nucleotide sugar metabolism; alpha-linolenic acid metabolism; folate biosynthesis; ABC transporters; sphingolipid metabolism; phenylpropanoid biosynthesis; and beta-alanine metabolism were significantly enriched under K+ deficiency (Figure 9). Based on the DAMs and DEGs, a screening was carried out according to the correlation coefficient (CC) and the relevant P value, with a screening threshold of \| CC \| > 0.70 and CCP < 0.05. Among nine quadrants, the patterns of DEGs and DAMs were consistent in quadrants 1, 3, 7, and 9 (Supplementary Figure 6A), and the regulation of genes and metabolites was positively or negatively correlated. Correlation analysis, hierarchical clustering, and correlation coefficient matrix heatmaps of the DAMs and DEGs showed a similar trend (Supplementarys Figure 6B,C). There were 1003 DEGs between the two groups, corresponding to 164 differential metabolites. We detected a significant correlation between 995 genes and 164 metabolites ($P \leq 0.01$, R2 > 0.8), identified 22 common KEGG pathways (metabolite and gene correlations), and drew a network diagram for correlation analysis of the metabolites and genes (Figure 10; Supplementary Table 11). Figure 9: Analysis of KEGG pathways significantly enriched in DEGs/DAMs in K0 vs. Kck. (A) Bubble diagram of the top 30 KEGG pathways significantly enriched in DEGs/DAMs. The X-axis represents the Gene Enrich factor, the diff/background of genes in this pathway, and the Y-axis indicates the enrichment pathway. Dot size represents the significance of enrichment of the annotated DAMs in the pathway; the larger the dot, the more enriched it is. Dot color represents the significance of enrichment of DEGs in this pathway; the deeper the color, the more enriched they are. (B) Histogram of the top 30 KEGG pathways with significant enrichment of DEGs/DAMs. Each column represents a KEGG pathway. Yellow represents the transcriptome and blue represents the metabolome. Pathway names are indicated on the Y-axis, and the X- axis indicates the significance of enrichment of each pathway, that is, the FDR value taking the logarithm of the FDR. Figure 10: DEG and DAM correlation network diagram of 22 KEGG pathways in K0 vs. Kck. Circles represent metabolites, boxes represent genes, and values on the line represent correlation coefficients. Positive and negative correlation is indicated in red and green, respectively. The larger the correlation coefficient, the wider the line and the darker the color. The number in each ellipse indicates the ID of the KEGG pathway corresponding to the network diagram. RFS1 and raffinose were enriched in galactose metabolism (Ko00052) and were negatively correlated; RFS1 was up-regulated and raffinose was down-regulated, suggesting that RFS1 may inhibit galactose metabolism. SAMS and DL-methionine sulfur co-enriched in the cysteine and methionine metabolism (Ko00270) pathway and were down-regulated and positively correlated. LOC105059620 and betaine were enriched in the glycine, serine, and threonine metabolism (Ko00260) pathway; both were down-regulated and positively correlated. STYB, PHI-1, and L-Isoleucine co-enriched in the valine, leucine, and isoleucine degradation (Ko00280) pathway, and the genes as well as metabolites were down-regulated, showing a positive correlation. SHM7 and L-Isoleucine co-enriched in the cyanoamino acid metabolism (Ko00460) pathway, and SHM7 was down-regulated and positively correlated with L-Isoleucine. CM2, HM7, RPI2, SAMS, AS, GAPCP2, and L-Isoleucine enriched in amino acid biosynthesis (Ko01230) pathway; CM2, SHM7, RPI2, and SAMS were down-regulated and positively correlated with L-Isoleucine, whereas AS and GAPCP2 were up-regulated and negatively correlated with L-Isoleucine. CM2 and 3-hydroxybenzoate were enriched in the phenylalanine, tyrosine, and tryptophan biosynthesis (Ko00400) pathway, both were down-regulated and positively correlated. ABCG39, ABCB19, ABCG36, L-Isoleucine, betaine, and raffinose were enriched in the ABC transporters (Ko02010) pathway; ABCG39, ABCB19, ABCG36, L-Isoleucine, and betaine were down-regulated; ABCG39 and ABCB19 were positively correlated with L-Isoleucine; ABCG39 and ABCG36 were positively correlated with betaine; and ABCG36 was negatively correlated with raffinose. UGD3, CBR1, PMI1, PHM1, LOC105060583, HXK2, Cht10, GAE1, and N-Glycdyneuramicacid were enriched in the amino sugar and nuclear sugar metabolism (Ko00520) pathway; UGD3, CBR1, PMI1, PHM1, LOC105060583, HXK2, and Cht10 were down-regulated and negatively correlated with N-Glycdyneuramic acid, whereas GAE1 was up-regulated and positively correlated with the genes regulating the synthesis and metabolism of amino acid analogs. Os04g0447100, LCAT3, SDP1, and alpha-Linolenic acid were enriched in the alpha-Linolenic acid metabolism (Ko00592) pathway, all of which were down-regulated and positively correlated, and thus may regulate alpha-Linolenic acid metabolism. FAD2-2 and alpha-Linolenic acid were enriched in the biosynthesis of unsaturated fatty acids (Ko01040) pathway, and both were down-regulated and positively correlated. SDP1 and 15-Deoxy-delta-12,14-PGJ2 were enriched in the arachidonic acid metabolism (Ko00590) pathway; both were down-regulated and positively correlated. SRC2, LCAT3, UGD4, CCT2, LOC105031993, PLD1, PLD2, and LysoPC(18:3(6Z,9Z,12Z)) were enriched in the glycerophospholipid metabolism (Ko00564) pathway; the genes were down-regulated and positively correlated with LysoPC(18:3(6Z,9Z,12Z)), indicating that these genes regulate the synthesis and metabolism of fatty acid analogs. RSH2, GK1, VPD2, and adenine were co-enriched in the purine metabolism (Ko00230) pathway; RSH2, GK1, and adenine were down-regulated and positively correlated, whereas VPD2 was up-regulated and negatively correlated with adenine. RSH2, GK1, and VPD2 can jointly regulate purine metabolism. UGT73C3 and adenine were enriched in the zeatin biosynthesis (Ko00908) pathway, and both were down-regulated and positively correlated. GAD1 and spermine were enriched in the beta-Alanine metabolism pathway (Ko00410); both were down-regulated and positively correlated. GSTU18, GGCT2, and spermine were enriched in the glutathione metabolism pathway (Ko00480) and showed a downward trend; GSTU18 and GGCT2 positively correlated with spermine levels. VPD2 and spermine were enriched in the pantothenate and CoA biosynthesis (Ko00770) pathway and showed a negative correlation. SCL32, MYC2, SAUR36, NLP2, and L-Anserine were enriched in the plant hormone signal transformation (Ko04075) pathway; SCL32, MYC2, and L-Anserine were up-regulated and positively correlated, whereas SAUR36 and NLP2 were down-regulated and negatively correlated with l-anserine; therefore, these genes may regulate glutathione metabolism; purine, zeatin, pantothenate, and CoA biosynthesis; and plant hormone signal transformation. CSE, PHT1, PNC1, PER64, caffeic acid, and chlorogenic acid were enriched in the phenylpropanoid biosynthesis (Ko00940) pathway; CSE, PHT1, and PNC1 were down-regulated, while PER64, caffeic acid, and chlorogenic acid were up-regulated. CSE and PHT1 were negatively correlated with caffeic acid and chlorogenic acid, and PNC1 was negatively correlated with chlorogenic acid; however, PER64 was positively correlated with caffeic acid and chlorogenic acid. PHT1, chlorogenic acid, and neohesperidin were enriched in the flavonoid biosynthesis (Ko00941) pathway, and PHT1 was negatively correlated with chlorogenic acid and neohesperidin. PHT1 and chlorogenic acid were negatively correlated in stilbenoid, diarylheptanoid, and ginger biosynthesis (Ko00945). The results showed that the genes encoding CSE, PHT1, PNC1 and PER64 regulated phyloproponoid, flavonoid, stilbenoid, diarylheptanoid, and ginger biosynthesis. A metabolic network diagram based on 21 DAMs and 133 DEGs under K+ deficiency stress is shown in Figure 11. The network clearly shows that the down-regulated DAMs ($62\%$) and DEGs ($75\%$) were mainly involved in the metabolism of fatty acids, lipids, amino acids, organic acids, amines, and flavonoids, which was in line with the finding that the CF and SF contents were significantly reduced by $56.82\%$ and $24.83\%$, respectively. This may be due to a significant reduction in several intermediates of the tricarboxylic acid cycle under K+ deficiency. The down-regulated metabolites included fat acids related to 15-Deoxy-delta-12,14-PGJ2, LysoPC(18:3(6Z,9Z,12Z)), cis-9-Palmitoleic acid, 3-Hydroxybenzoate, lipidol-related 3-ketosphingonine, phytosphingosine, amino acid-related L-Isoleucine, beta, DL-Methionine sulfur, amine-related spermine, adenine, organic acid-related alpha-Linolenic acid, and flavonoid-related bioprotein. Levels of sugar- and phenolic acid-related metabolites were mostly increased, especially those involved in the pentose phosphate pathway (PPP), such as raffinose, chlorogenic acid, and caffeic acid, which may be due to glycolysis and the synthesis and metabolism of secondary products under K0 conditions to adapt to K stress. Twenty-two KEGG pathways were enriched, including plant hormone signal transport, glycerophospholipid metabolism; valine, leucine, and isoleucine degradation; amino sugar and nuclear sugar metabolism; alpha-Linolenic acid metabolism; folate biosynthesis; ABC transporters; sphingolipid metabolism; phenylpropanoid biosynthesis; and beta-Alanine metabolism. in the metabolic pathways, there was a significant positive correlation between DEGs and DAMs, with most DEGs and DAMs being down-regulated. Figure 11: Profiles of DEGs and DAMs in flavonoid biosynthetic pathways in K0 vs. Kck. The boxes in the pathway represent DEGs or DAMs. Red and green represent up- and down-regulated genes, respectively. Yellow and blue represent up- and down-regulated metabolites, respectively. Boxes of the same color represent the relevant pathway. ## Discussion Mineral nutrients (N, P, and K) are crucial for plant growth and development, and their effects on plant growth, morphology, physiology, transcriptome, and metabolome under different exogenous nutrient conditions have been reported (Pettigrew, 2008; Hafsi et al., 2014; Mo et al., 2019; Wang et al., 2019; Ma et al., 2020; Ding et al., 2021). In our study, the biomass of coconut seedlings significantly decreased under K+ deficiency. We also analyzed the transcriptomic and metabolic changes under long-term K+ deficiency. Most DEGs were related to pathways such as signal regulation and transport, primary and secondary metabolism, and plant-pathogen interactions. DAMs induced by K+ deficiency were mainly involved in primary and secondary metabolism, including the metabolism of fat acids, amino acids, sugars, lipidols, organic acids, amines, flavonoids, phenolic acids, and alkaloids. The results showed that genes related to MAPK signaling, plant hormone signal transduction, starch and sucrose metabolism, plant-pathogen interaction, glycerophospholipid metabolism, and plant circadian rhythm may play an important role in the response of coconut seedling leaves to K+ deficiency (Supplementary Tables 6, 8, 9). ## Developmental responses to K+ deficiency K+ deficiency limited the growth of coconut seedlings and led to significant reductions in whole-plant biomass and fruit yield. The application of K+ fertilizer promotes the growth of coconut trees and increases total biomass and yield, indicating that K+ directly affects the biomass and yield of coconut trees (Mohandas, 2012a). Most studies have focused on the effects of K+ on coconut growth and yield, rather than on the growth, physiology, and metabolic regulation (Chen, 2005). Due to the lack of K+, the height of coconut seedlings decreased, which significantly limited plant growth and reduced dry weight (Supplementary Table 2). This indicates that low-K+ stress significantly reduced seedling biomass. In green plants, photosynthesis is an important process that provides the necessary energy sources for metabolism (Chen et al, 2015; Zhang et al., 2020). Low-K+ stress inhibits the photosynthetic activity of plants, leading to slower metabolism, thus affecting plant growth and development (Kanai et al., 2011; Amanullah. et al., 2016). According KEGG pathway annotation, numerous significantly down-regulated DEGs were involved in plant MAPK signaling, starch and sucrose metabolism, phenylpropanoid biosynthesis, ABC transporters, and photosynthesis (Figures 4, 5; Supplementary Table 8). Therefore, low-K+ stress inhibits the photosynthetic activity, signal transduction and regulation, primary product synthesis, and metabolism of coconut seedlings, thus affecting their growth and development. K+ plays an important role in photosynthesis and ATP generation and severe K+ deficiency suppresses photosynthesis (Kanai et al., 2011; Huang et al., 2013). Roots have low K+ absorption efficiency, which significantly reduces K content in leaves ($48.36\%$ lower than in the control), inhibits photosynthesis, and suppresses plant growth (Supplementary Table 3; Battie-Laclau et al., 2014; Ma et al., 2020). Leaves and stems act as sinks for K+ and carbon assimilation during plant growth, and plant growth retardation acts as a feedback signal to K+ deficiency (Kanai et al., 2007). Similar K+ deficiency responses have been observed in tomato, sugarcane, and Eucalyptus grandis (Hartt, 1969; Kanai et al., 2007; Ployet et al., 2019). Under K+ deficiency, the SPAD value was significantly lower than that in control seedlings, and K+ deficiency reduced plant growth and biomass, which may be due to an increase in reactive oxygen species in coconut seedlings caused by K+ deficiency, leading to a reduction in chlorophyll. The expression of a chlorophyll-related gene (encoding ferredoxin nitrate reductase) was down-regulated in the leaves of coconut seedlings (Figure 1; Supplementary Table 6), which may have contributed to the decrease in the SPAD value of K0 seedlings. The activity of antioxidant enzymes in plant cells is stimulated by environmental stress, and reactive oxygen-scavenging enzymes, such as SOD, CAT, and POD, play an important role in plant antioxidant defense (Rahimizadeh et al., 2007). Under K+ deficiency, the activities of CAT, POD, and SOD in the leaves of coconut seedlings were significantly decreased (Figure 2); this may be due to the decreased expression of genes (IBS1, NSL1, PXC3, ORM1, LOC105044768, Os02g0806900, Os11g0516000) related to the synthesis and metabolism of proteins or proteases under K+ deficiency stress (Supplementary Tables 6, 8). Furthermore, the Pro content was significantly reduced, and genes and metabolites related to purine metabolism were down-regulated, suggesting that the downregulation of Pro metabolism-related genes and metabolites under K+ deficiency significantly reduced the Pro content in coconut leaves. Low-K+ stress also inhibited MDA activity, which may have led to MDA accumulation under K+ deficiency (Figure 2; Supplementary Tables 6, 8). Studies have shown that low K levels can induce lateral root growth in maize via regulating genes involved in nutrient utilization, hormones, and transcription factors (Zhao et al., 2016; Ma et al., 2020). In this study, except for ABA, endogenous hormone levels were significantly decreased under K+ deficiency stress, which is consistent with the finding that most genes involved in plant hormone metabolism were significantly down-regulated, whereas some genes related to aging and abscission were significantly up-regulated (Figure 2; Supplementary Tables 6, 8), indicating that K+ deficiency stress has a strong impact on endogenous hormones in coconut seedling leaves. ## Transcription responses to K+ deficiency Studies have examined the transcriptome spectrum of plant responses to K+ deficiency (Ruan et al., 2015; Zhao et al., 2018; Ployet et al., 2019; Ma et al., 2020; Yang et al., 2021). We analyzed the transcriptional changes in coconut seedling leaves under K+ deficiency and K+ sufficiency, and 1003 DEGs were identified between the two groups. GO enrichment analysis showed that the largest proportion of DEG enrichment was found in the MF category. In the BP category, most of the DEGs down-regulated under K+ deficiency were related to “sesquiterpene biosynthetic process,” “cellular sphingolipid homeostasis,” “negative regulation of ceramide biosynthetic process,” “sphingosine biosynthetic process,” “regulation of jasmonic acid mediated signaling pathway,” and “ceramide metabolic process”. In terms of CC, the down-regulated DEGs were mainly related to “integral component of membrane,” “serine C-palmitoyltransferase complex,” “SPOTS complex,” and “plasma membrane”. In the MF category, the down-regulated DEGs were mostly involved in “cyclase activity,” “protein kinase activity,” “serine C-palmitoyltransferase activity,” “ATP binding,” “magnesium-dependent protein serine/threonine phosphatase activity,” “protein serine/threonine kinase activity,” “protein serine/threonine phosphatase activity,” “calcium ion binding,” “sphingosine-1-phosphate phosphatase activity,” “transferase activity, transferring glycosyl groups,” and “polysaccharide binding,” whereas the up-regulated DEGs were mainly related to “nucleus,” “transcription factor activity, sequence-specific DNA binding,” “sequence-specific DNA binding,” and “DNA binding” (Figure 3). In these enriched GO terms, genes involved in plant hormone signal transduction (TIFY9), plant-pathogen interaction (WRKY41, WRKY55, ERF026), signal transduction mechanisms (WAK2, PLL5), spliceosome and endocytosis (LRK10, Hsp70-4), ABC transporters (ABCG39), starch and sucrose metabolism (LECRK91), MAPK signaling pathway-plant (RBOHC), fatty acid elongation (CUT1), amino acid transport and metabolism (URGT2, GALT6), plant-pathogen interaction (EIX2), and protein kinase/protein domain (IBS1, NSL1, PXC3, ORM1, LOC105044768, Os02g0806900, Os11g0516000) were significantly down-regulated. However, the gene (CML46) encoding a probable calcium-binding protein related to MAPK signaling was significantly up-regulated (Supplementary Table 7). Transcriptome studies on the effects of nutrient stress on crops have shown that DEGs in these processes may play an important role in plant adaptation to P deficiency (Li et al., 2009). In contrast to our findings in coconut, in wheat subjected to low-P stress, DEGs related to amino acid metabolism, photosynthesis, carbohydrate metabolism, and organic acid metabolism were highly up-regulated (Wang J. et al., 2019). The expression of genes related to organic and amino acid metabolism was significantly altered in oats under P deficiency (Wang et al., 2018). In maize seedlings under K+ deficiency stress, MAPK signaling pathway-plant, signal transduction mechanisms, plant hormone signal transduction, and amino acid transport and metabolism were the mainy enriched pathways, and related gene expression was significantly altered (Xiong et al., 2022) In our study, in coconut seedling leaves under K+ deficiency stress, “MAPK signaling pathway-plant”, “signal transduction mechanisms”, “plant hormone signal transduction”, “amino acid transport and metabolism”, “plant-pathogen interaction”, “starch and sucrose metabolism”, “spliceosome”, “endocytosis”, “ABC transporters”, “fatty acid elongation”, and “protein kinase/protein domain” pathways were affected. In particular, genes related to “MAPK signaling pathway-plant”, “signal transduction mechanisms”, “plant hormone signal transduction”, “amino acid transport and metabolism” pathways were significantly down-regulated (Supplementary Tables 7, 8). Genes involved in plant hormone signal transduction affect the content and distribution of plant hormones, thus regulating plant growth (Ma et al., 2020). Therefore, we confirmed that MAPK signaling, plant hormone signal transduction, and transcription factors may be key regulators of the response of coconut seedlings to K+ deficiency. KEGG pathway enrichment analysis showed that K+ deficiency stress affected genes were involved in “MAPK signaling pathway-plant”, “plant hormone signal transduction”, “starch and sucrose metabolism”, “plant-pathogen interaction,” “glycerophospholipid metabolism”, “alpha-Linolenic acid metabolism”, “endocytosis”, “amino sugar and nucleotide sugar metabolism”, “glycerolipid metabolism”, “phosphatidylinositol signaling system”, “inositol phosphate metabolism”, “valine, leucine and isoleucine degradation”, “circadian rhythm-plant”, “phenylpropanoid biosynthesis”, “phagosome” and “protein processing in endoplasmic reticulum” (Figures 4 5; Supplementary Table 8). Under K+ deficiency, genes related to “MAPK signaling pathway-plant”, “plant hormone signal transduction”, “starch and sucrose metabolism” pathways in coconut seedling leaves were significantly modulated. For example, in the MAPK signaling pathway-plant, RBOHC, Cht10, MPK5, and LECRK3 were significantly down-regulated, whereas CML46, encoding a probable calcium-binding protein, was significantly up-regulated. The significantly down-regulated DEGs in plant hormone signal transduction were TIFY9, At5g48380, and LRK10, whereas SAUR71 was significantly up-regulated. The significantly down-regulated DEGs in starch and sucrose metabolism were LECRK91, TPP6, and SIT2. In addition, genes involved in plant-pathogen interactions (WRKY41, WRKY55, RBOHC, CUT1, EIX2), alpha-linolenic acid metabolism (putative lipoxygenase, Os04g0447100), endocytosis (HSP70-4, LRK10), amino sugar and nucleotide sugar metabolism (Cht10), glycerolipid metabolism (LOC105048065), and valine, leucine, and isoleucine degradation (PHI-1) were significantly down-regulated (Supplementary Table 8), indicating that these pathways were suppressed under K+ deficiency stress. However, genes in the circadian rhythm-plant (CHS3) and phenylpropanoid biosynthesis (CAD6) pathways (Supplementary Table 8) were up-regulated, which indicates that these pathways are enriched to resist K+ deficiency stress. Under K+ deficiency, “MAPK signaling pathway-plant”, “plant hormone signal transduction”, “starch and sucrose metabolism”, “plant-pathogen interaction”, “glycerophospholipid metabolism” and “circadian rhythm-plant” were significantly enriched pathways (Figure 5; Supplementary Table 8). The significantly down-regulated genes involved in “MAPK signaling pathway-plant”, “plant hormone signal transduction”, “starch and sucrose metabolism”, “plant-pathogen interaction”, “glycerophospholipid metabolism” are mainly involved in the signal regulation process, transportation and primary metabolism, and the metabolism of disease resistant interacting substances (fat acids, lipidols, amino acids, organic acids, and amines, among others) (Figure 11; Supplementary Table 10). In addition, MAPK signaling is related to aging and apoptosis, which may explain why leaves have yellow or brown edges and tips under K+ deficiency (Sun et al., 2015). This study also showed that the CF content decreased by $56.82\%$ and the SP content decreased by $24.83\%$ under K+ deficiency (Figure 1). Genes related to circadian rhythm and metabolism of phenolic acids, flavonoids, and other substances were significantly up-regulated (Figure 11; Supplementary Table 10), which may be attributed to the fact that coconut seedlings can enhance some of these metabolites to resist K+ deficiency through the autoimmune effect. The genes involved in plant hormone signal transduction affect the content and distribution of plant hormones, thus regulating plant growth (Takahashi et al., 2005; Argyros et al., 2008; Ma et al., 2020). This study showed that relevant genes and metabolites in plant hormone signal transduction were significantly down-regulated, which is consistent with the significantly reduced contents of IAA, GA, and ZR in coconut seedling leaves (Figure 2; Supplementary Table 8). Transcription factors play indispensable roles in regulating the response to biotic and abiotic stresses (Ma et al., 2012; Zhang et al., 2017). In coconut seedling leaves under K+ deficiency, 71 transcription factors were differentially expressed, including WRKY, RLK, AP2/ERF, C2C2, Tify, bHLH, NAC, HB, GRAS, FAR1, MYB, bZIP, and PLATZ; among these, WRKY, RLK, Tify, and NAC were significantly down-regulated, and AP2/ERF, C2C2, MYB, and PLATZ were significantly up-regulated (Supplementary Figure 7). ## Metabolic responses to K+ deficiency Metabonomic analysis showed that DAMs under K+ deficiency were mainly enriched in “glucosinolate biosynthesis,” “beta-Alanine metabolism,” “sphingolipid metabolism,” “galactose metabolism,” “valine, leucine and isoleucine degradation,” “valine, leucine and isoleucine biosynthesis,” “sesquiterpenoid and triterpenoid biosynthesis,” “tryptophan metabolism,” “zeatin biosynthesis” and “plant hormone signal transduction” pathways (Figure 8). DAMs (S-Nitroso-L-glutathione, glutaminyl-Asparagine, N-Glycolylneuraminic acid) related to amino acids were significantly up-regulated, whereas Isoleucyl-Asparagine, Glycyl-Threonine, Gly-Gln, L-Isoleucine, and Lysyl-Lysine were significantly down-regulated. Sugar-related raffinose was significantly up-regulated, whereas C-6 Ceramide was significantly down-regulated. Amine-related DAMs (cinnavalinine, L-anserine, procyanidin A1, and acetamiprid) were significantly up-regulated, whereas 13Z docosamide, anandamide, and adenine were significantly down-regulated. Purpurin, which is related to nuclear acids, was significantly up-regulated, whereas guanethidine was significantly down-regulated. DAMs (mevalonic acid, 2-Oxo-5-methylthiopentanoic acid, caffeic acid, 5-methyltetrahydrofolate) related to organic acids were significantly up-regulated, whereas alpha-lienolenic acid, mevinolinic acid, 9-hydroxy-10E, 12,15Z-Octadecatrienonic acid, 11(Z),14(Z),17(Z)-Ecosatrienoic acid were significantly down-regulated. A DAM (PC(18:2(9Z,12Z)/16:0) related to fatty acids was significantly up-regulated, whereas 13S HpOTrE (gamma), PS(18:2(9Z,12Z)/0:0), LysoPE(18:2(9Z,12Z)/0:0), and PC(18:2(9Z,12Z)/P-16:0) were significantly down-regulated. Nor-psi-tropine associated with lipidol was significantly up-regulated, whereas C16 Sphingosine, 1-Linoleoylglycophorophospholine, (+-)-lavandulol, 6-[5]-ladderane-1-hexanol, and tetrahydrooxygenycortisol were significantly down-regulated. DAMs (neohesperidin, dihydro-5-methyl-2 (3H)-thiophene, 2,3’, 4,6-tetrahydroxybenzophene) related to flavonoids were significantly up-regulated, whereas 11-alpha-hydroxyprogesterone and 4-mercapto-4-methyl-2-pentanone were significantly down-regulated. DAMs (6’’-O-acetylgenistein, chlorogenic acid, and benzaldehyde) associated with phenolic acids were significantly up-regulated, whereas acetal R and MG(0:$\frac{0}{18}$:3(9Z,12Z,15Z)/0:0) were significantly down-regulated. DAMs (3-hydroxycoumari and cytidine 5’-diphosphocholine) related to alkaloids were significantly up-regulated, whereas vanillin was significantly down-regulated (Supplementary Table 9). It can be concluded that K+ deficiency stress greatly affects the synthesis and expression of metabolites, including amino acids, sugars, amines, nucleic acids, fatty acids, flavonoids, phenolic acids, and alkaloids, in the leaves of coconut seedlings. In addition, $66\%$ of the metabolites were down-regulated, which included $90\%$ fatty acids, $93\%$ lipids, $67\%$ amines, $71\%$ organic acids, $72\%$ amino acids, $40\%$ nucleic acids, $50\%$ sugars, $65\%$ flavonoids, $50\%$ alkaloids, and $33\%$ phenolic acids, whereas $34\%$ of the metabolites were up-regulated, including $67\%$ phenolic acids, $60\%$ nucleic acids, $50\%$ sugars, and $50\%$ alkaloids (Figure 7, Supplementary Table 9). In different substances, whether up-or down-regulated, each metabolite, whether up- or down-regulated, plays a different role and plays a very important role in response to low-K+ stress. For example, sugars such as raffinose, radish sugar, and glucosamine are accumulated in low-K+ stress, and are also detected in plants under P or cold stress (Cook et al., 2004; Ding et al., 2021; Xiong et al., 2022). Flavonoids and phenols are major secondary metabolites involved in plant immunity (Wang and Wu, 2013). Under K+ deficiency, flavonoid and phenolic levels changed significantly. RT-qPCR analysis showed that the expression of genes involved in the biosynthesis of flavonoids and phenolic substances (Supplementary Figure 4), which can activate enzymes and play an important role in protein synthesis (Hafsi et al., 2017), changed accordingly. Under K+ deficiency, the contents of CF and SP in coconut seedling leaves were decreased, and metabonomic analysis also showed that $90\%$ of fatty acids and $72\%$ of amino acids were down-regulated (Figure 1; Supplementary Table 9). This is inconsistent with the findings of previous studies that reported increased amino acid accumulation under K+ and P deficiency (Wang et al., 2012, Hernandez et al., 2007; Pant et al., 2015; Mo et al., 2019; Ding et al., 2021; Xiong et al., 2022). This may be because the lack of K+ in coconut seedlings inhibits carbon metabolism and affects amino acid accumulation. In addition, amino acid transport-related genes are reportedly down-regulated in response to K+ deficiency, which is accompanied by suppressed activity of transmembrane transport proteins and ATPases (Supplementary Table 6). ## Comparative transcriptome and metabolome responses to K+ deficiency The comparative analysis of transcriptional and metabolic responses to K+ deficiency showed that DEGs and DAMs were mainly enriched in “plant hormone signal transduction,” “glycerophospholipid metabolism,” “valine, leucine and isoleucine degradation,” “amino sugar and nucleotide sugar metabolism,” “alpha-Linolenic acid metabolism,” “folate biosynthesis,” “ABC transporters,” “sphingolipid metabolism,” “phenylpropanoid biosynthesis” and “beta-alanine metabolism” pathways. In addition, the DEGs and DAMs were largely significantly correlated. For example, STYB, PHI-1, and L-isoleucine were co-enriched in the valine, leucine, and isoleucine degradation (Ko00280) pathway, and both, the genes and metabolites were down-regulated, showing a positive correlation. ABCG39, ABCB19, ABCG36, L-isoleucine, betaine, and raffinose were enriched in the ABC transporter (Ko02010) pathway; ABCG39, ABCB19, ABCG36 L-isoleucine, and betaine were down-regulated; ABCG39 and ABCB19 were positively correlated with L-isoleucine; ABCG39 and ABCG36 were positively correlated with betaine; and ABCG36 was negatively correlated with raffinose. UGD3, CBR1, PMI1, PHM1, LOC105060583, HXK2, Cht10, GAE1, and N-Glycdylneuraminic acid were enriched in the amino sugar and nuclear sugar metabolism (Ko00,520) pathway. UGD3, CBR1, PMI1, PHM1, LOC105060583, HXK2, and Cht10 were down-regulated and negatively correlated with N-Glycdylneuraminicacid, whereas GAE1 was up-regulated and positively correlated with N-Glycdylneuraminicacid (Figure 10; Supplementary Table 11). The synthesis and metabolism of amino acid analogs and ABC transporters have been discussed above (Supplementary Table 11). Amino acids and ABC transporters play important roles in K+ uptake and transport under K+ deficiency (Xie et al., 2020) and regulate the cellular K+ content to promote permeability (Cuin and Shabala, 2007). This study showed that genes and metabolites related to amino acids and ABC transporters were significantly modulated under K+ deficiency stress (Supplementary Tables 8, 9, 11), which indicates that K+ absorption and transport mechanisms are greatly affected by K+ deficiency. Os04g0447100, LCAT3, SDP1, and alpha-Linolenic acid were enriched in the alpha-Linolenic acid metabolism (Ko00592) pathway, and all were down-regulated. Os04g0447100, LCAT3, and SDP1 were positively correlated with alpha-Linolenic acid. Therefore, these genes may regulate alpha-Linolenic acid metabolism. SRC2, LCAT3, UGD4, CCT2, LOC105031993, PLD1, PLD2, and LysoPC(18:3(6Z,9Z,12Z)) were enriched in the glycerophospholipid metabolism (Ko00564) pathway, and the relevant genes and metabolites were down-regulated; however, SRC2, LCAT3, UGD4, CCT2, LOC105031993, PLD1, and PLD2 were positively correlated with LysoPC(18:3(6Z,9Z,12Z)), indicating that these genes and metabolites may regulate the synthesis and metabolism of fatty acid analogs. RSH2, GK1, VPD2, and adenine were co-enriched in the purine metabolism (Ko00230) pathway. RSH2, GK1, and adenine were down-regulated, and RSH2 and GK1 were positively correlated with adenine, whereas VPD2 was up-regulated and negatively correlated with adenine (Figure 10; Supplementary Table S11). Putrescine is a metabolic marker of K+ deficiency in plants and plays an important role in regulating the activity of vacuole channels (Bruggemann et al., 2002) It is also shown to be related to other stresses such as salinity and cold, as well as plant growth (Kou et al., 2018; Guo et al., 2019). Pro and Pro-derived metabolites were significantly decreased under K+ deficiency, and the expression of genes related to their regulation was altered accordingly (Figures 2, 10; Supplementary Tables 10, 11). This is inconsistent with a previous finding, which reported that Pro and Pro-derived metabolites significantly accumulate under K+ deficiency (Xiong et al., 2022). This discrepancy can be attributed to the differences in Pro metabolism and synthesis in response to K+ deficiency in different plant species. GAD1 and spermine were enriched in the beta-alanine metabolism (Ko00,410) pathway, and both were down-regulated and positively correlated. SCL32, MYC2, SAUR36, NLP2, and L-anserine were enriched in the plant hormone signal transformation (Ko04075) pathway. SCL32, MYC2, and L-anserine were up-regulated, of which, SCL32 and MYC2 were positively correlated with L-Anserine; SAUR36 and NLP2 were down-regulated and negatively correlated with L-anserine, indicating that these genes regulate purine, beta-alanine metabolism, and plant hormone signal transduction. CSE, PHT1, PNC1, PER64, caffeic acid, and chlorogenic acid were enriched in the phenolproponoid biosynthesis (Ko00940) pathway; among these, CSE, PHT1, and PNC1 were down-regulated, whereas PER64, caffeic acid and chlorogenic acid were up-regulated, moreover, CSE and PHT1 were negatively correlated with caffeic acid and chlorogenic acid. PNC1 was also negatively correlated with chlorogenic acid, whereas PER64 was positively correlated with caffeic acid. Besides, chlorogenic acid, ALIS1, FLACCA, and biopterin were enriched in the folate biosynthesis pathway. Collectively, these results showed that CSE, PHT1, PNC1, PER64, ALIS1, and FLACCA regulate phenylpropanoid, flavonoid, stilbenoid, diarylheptanoid, and ginger biosynthesis (Figure 10; Supplementary Table 10). ## Conclusion In this study, we compared the growth, nutrient contents, physiology, transcriptome, and metabolites of coconut seedlings under K+ deficiency and K+ sufficiency. The results showed that growth, nutrient contents, antioxidant enzyme activity, and endogenous hormone levels of coconut seedling leaves were affected by K+ deficiency stress. As K+ plays an important role in coconut growth, biomass, quality, yield, and disease resistance, it is essential to improve the K+ utilization efficiency of plants. The identification and further research on DEGs and DAMs under K+ deficiency would lay a foundation for improving K+ utilization efficiency of coconut plants in the future. ## Data availability statement The datasets presented in this study can be found in online repositories. The name of the repository and accession number can be found below: NCBI; PRJNA914120. ## Author contributions LL, YFY and YDY contributed to conception and design of the study. LL, SC and YDY organized the database. LL performed the statistical analysis. LL wrote the first draft of the manuscript. LL, SC, YW, WY, XY and YL wrote sections of 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/fpls.2023.1112264/full#supplementary-material ## References 1. Aksu G., Altay H.. The effects of potassium applications on drought stress in sugar beet: part i. sugar beet quality components. J. Sci. Perspect. 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--- title: Vasoplegic syndrome in patients undergoing heart transplantation authors: - Tong-xin Qin - Yun-tai Yao journal: Frontiers in Surgery year: 2023 pmcid: PMC9968842 doi: 10.3389/fsurg.2023.1114438 license: CC BY 4.0 --- # Vasoplegic syndrome in patients undergoing heart transplantation ## Abstract ### Objectives To summarize the risk factors, onset time, and treatment of vasoplegic syndrome in patients undergoing heart transplantation. ### Methods The PubMed, OVID, CNKI, VIP, and WANFANG databases were searched using the terms “vasoplegic syndrome,” “vasoplegia,” “vasodilatory shock,” and “heart transplant,” to identify eligible studies. Data on patient characteristics, vasoplegic syndrome manifestation, perioperative management, and clinical outcomes were extracted and analyzed. ### Results Nine studies enrolling 12 patients (aged from 7 to 69 years) were included. Nine ($75\%$) patients had nonischemic cardiomyopathy, and three ($25\%$) patients had ischemic cardiomyopathy. The onset time of vasoplegic syndrome varied from intraoperatively to 2 weeks postoperatively. Nine ($75\%$) patients developed various complications. All patients were insensitive to vasoactive agents. ### Conclusions Vasoplegic syndrome can occur at any time during the perioperative period of heart tranplantation, especially after the discontinuation of bypass. Methylene blue, angiotensin II, ascorbic acid, and hydroxocobalamin have been used to treat refractory vasoplegic syndrome. ## Introduction Vasoplegic syndrome (VS) is a common life-threatening complication characterized by severe and persistent systemic arterial hypotension (mean arterial pressure, <50 mmHg), normal or slightly increased cardiac output (cardiac index, >2.5 L/min/m2), low systemic vascular resistance (SVR, <800 dyne/s/cm5), and insensitivity to appropriate fluid resuscitation and high-dose vasopressors [1]. VS occurs in up to $34.8\%$ of patients who undergo heart transplantation (HTX) [2]. The incidence of VS is higher in patients who underwent HTX compared to other forms of cardiac surgery, e.g., off-pump coronary artery bypass graft (CABG) ($2.8\%$) [3], on-pump CABG ($6.9\%$–$26\%$) [3, 4], and aortic valve replacement (AVR) ($20\%$) [5]. Earlier research [6] showed that the incidence of VS is as high as $45\%$ in patients with a ventricular assist device (VAD) at the time of HTX. Chemmalakuzhy et al. [ 7] observed increased risk for early mortality among HTX recipients with VS, with a 30-day mortality rate of $33\%$. This study aimed to summarize the risk factors, onset time, and treatment of VS in patients undergoing HTX. ## Search strategy Relevant case reports were searched using the PubMed and OVID electronic databases from inception until January 14, 2022. Chinese literatures from the CNKI, VIP, and WANFANG databases were also searched. Different combinations of terms that included “vasoplegic syndrome,” “vasoplegia,” “vasodilatory shock,” and “heart transplant” were used in the search strategy. All relevant case reports were included. The exclusion criteria were as follows: (a) non-English and non-Chinese studies; (b) studies based on animal models; and (c) duplicate publications. Each author independently read the titles and abstracts of all the identified reports for eligibility, excluding ineligible reports. The eligibility of the remaining reports for final inclusion was determined by examining the full-text versions of the publications. ## Data abstraction Data of interest from the included case reports were abstracted and tabulated by each author independently: (a) author, year, and journal of publication; (b) total number of patients, age, sex, medical history, number of thoracotomy surgeries, bleeding and coagulopathy or not, postoperative transesophageal echocardiography, treatment of ventricular dysfunction (VD), and complications; (c) onset time, clinical manifestation, and treatment of VS. Disagreements were resolved by discussion between both authors during the process of data abstraction. ## Results As depicted in the flowchart (Figure 1), the database search identified 26 potentially eligible studies. Nine case reports (8–16) describing 12 patients in total were deemed eligible and included. All case reports were written in English. A descriptive analysis of these cases is presented in Table 1. The 12 patients were aged 7–69 years, and included 9 males ($75\%$) and 3 females ($25\%$). Nine ($75\%$) patients (8–11, 13–16) had nonischemic cardiomyopathy, and three ($25\%$) patients [12, 13] had ischemic cardiomyopathy. Eight ($66\%$) patients [10, 11, 13, 15, 16] had undergone preoperative thoracotomy, such as CABG, AVR, VAD, and Fontan operations. Five ($63\%$) patients experienced intraoperative bleeding and coagulopathy intraoperatively due to the formation of dense adhesions between the mediastinum and pericardium. Six ($50\%$) patients (8, 11–14, 16) used a variety of drugs before surgery, including angiotensin-converting enzyme inhibitors (ACEI), angiotensin II (ANG-II) receptor blockers (ARB), diuretics, β-blockers, and milrinone. After the discontinuation of bypass, eight ($66\%$) patients (9, 12–14, 16) developed ventricular dysfunction, and were treated with milrinone, dobutamine, epinephrine, or norepinephrine. Other treatments include the inhalation of nitric oxide or epoprostenol, restarting cardiopulmonary bypass (CPB), and intra-aortic balloon counter-pulsation or extracorporeal membrane oxygenator. Figure 1: Flow diagram of study selection. TABLE_PLACEHOLDER:Table 1 Of the 12 patients, 9 ($75\%$) developed various complications, with 7 ($58\%$) patients having developed some degree of renal dysfunction, respiratory insufficiency, ischemic optic neuropathy, subdural hematoma, thrombocytopenia, liver injury, agitated delirium, serotonin syndrome, or delayed chest closure. Most patients were discharged, but one patient [11] died of multiple organ failure. The time to onset of VS ranged from during CPB to 2 weeks postoperatively; nine ($75\%$) patients experienced VS intraoperatively, and three ($25\%$) patients experienced VS postoperatively (Figure 2). All patients were insensitive to vasoactive agents, developed persistent hypotension, and were subsequently administered methylene blue (MB), hydroxocobalamin, ascorbic acid (AA), and ANG-II. Figure 2: Onset time of vasoplegic syndrome. ICU, intensive care unit; CPB, cardiopulmonary bypass. ## Discussion Several risk factors for VS have been identified, including ACEI, β-blockers, calcium channel blockers, heparin, amiodarone, diabetes mellitus, prolonged CPB, congestive heart failure, and left ventricular ejection fraction <$35\%$ [17, 18]. The preoperative use of VAD in adults is an independent risk factor for VS [6]. In this study, six ($50\%$) [11, 13, 15, 16] patients had used LVAD before surgery. Of the 12 patients, 8 patients [10, 11, 13, 15, 16] had undergone previous thoracotomy. This easily led to dense adhesions between the mediastinum and pericardium, resulting in severe bleeding and coagulation disorders, requiring a large number of blood products and factor replacement. Administration of blood products activates pro-inflammatory mediators during surgery [18]. Packed red blood cells, fresh frozen plasma, and platelet transfusion increase the prevalence of VS [19]. In addition, packed red blood cell transfusion exhibited a dose-dependent increase in the development of VS with each packed red blood cell unit transfused [19]. Milrinone is a powerful inotropic agent commonly used for right ventricular dysfunction, and may exacerbate systemic vasoplegia [20]. Of the 12 patients, 8 (9, 12–14, 16) used milrinone pre- or intraoperatively. A meta-analysis [21] revealed that $38\%$ of patients with New York Heart Association class III heart failure symptoms and $42\%$ of those with class IV symptoms experienced depression. Depression not only increases the incidence of hypertension, coronary heart disease, and diabetes, but also causes chronic inflammation [22, 23]. The mechanism of VS is largely unknown, and study results suggest that VS is correlated with the release of cytokines, such as tumor necrosis factor (TNF) and interleukin-1, which increase nitric oxide (NO) production, resulting in marked relaxation of the vascular smooth muscles [24]. Therefore, the chronic inflammatory state of patients before surgery may be a risk factor for VS. Other chronic inflammation diseases include obesity, obstructive sleep apnea, chronic kidney disease, and smoke (25–28). Eight ($67\%$) patients (9, 11–13, 15) had at least one of these medical histories. The risk factors for VS in the patients undergoing HTX are summarized in Table 2. Of the 12 patients, 9 ($75\%$) experienced VS intraoperatively, including 4 patients before weaning from CPB and five after discontinuation of CPB. The other three patients had VS after arriving at the intensive care unit, and one developed VS 2 weeks post- operatively. Septic shock is considered more likely than VS 2 weeks after surgery. Therefore, the possibility of infection must be ruled out, especially infections of the chest, abdomen, genitourinary system, and bloodstream, which account for >$80\%$ of sepsis cases (29–31). When VS occurs, catecholamines and vasopressin should be used at first. However, high-dose catecholamines may lead to tissue hypoperfusion and myocardial ischemia. Furthermore, prolonged hypotension may have adverse consequences, such as gradual deterioration of ventricular function and decreased urine output. At present, four drugs are used to treat refractory VS (Table 3). MB and hydroxocobalamin increase SVR by inhibiting NO synthase and reducing NO production, inhibiting the activation of soluble guanylyl cyclase, and binding to NO directly (32–35). Of the 12 patients, four were treated with at least two of these drugs. The combination of MB and hydroxocobalamin may be more beneficial than that of MB alone [36, 37]. One study [38] found that MB reduced the duration of VS and mortality. However, a potentially lethal complication of MB is serotonin syndrome, especially in patients taking serotonergic antidepressants. Fentanyl is the most commonly used narcotic analgesics, which reduces serotonin reabsorption; therefore, it should be used cautiously when fentanyl was used during surgery. Hydroxocobalamin, an injectable form of vitamin B12, interferes with dialysis treatment owing to an alarm of blood leak, which can be overcome by continuous renal replacement therapy [39]. AA is an essential cofactor for the endogenous biosynthesis of catecholamines, which cannot be synthesized by humans, and the concentration of AA in patients undergoing cardiac surgery after CPB is low (40–42). One study [43] found that the utilization of vasopressors was reduced when high-dose AA was administered for the treatment of VS after CPB. However, it should be noted that MB and AA cannot be used in patients with glucose-6-phosphate dehydrogenase deficiency to avoid hemolytic anemia. Prolonged exposure to CPB impairs the pulmonary capillary endothelium, thereby limiting the activity of angiotensin-converting enzyme [44]. ANG- II acts directly on blood vessel walls, resulting in vasoconstriction, increased mean arterial pressure antidiuretic hormone secretion, adrenal cortex stimulation, and increased water reabsorption [44, 45]. The adverse effects of ANG- II include thromboembolic events, hypoperfusion from vasoconstrictive actions, and increased pulmonary vascular resistance [46, 47]. VS treatment of during the perioperative period of HTX is shown in Figure 3. Figure 3: Treatment of vasoplegia during perioperative period of HTX. MAP, mean arterial pressure; NE, norepinephrine; DA, dopamine; VP, vasopressin; SVR, systemic vascular resistance; CI, cardiac index; G6PD, glucose-6-phosphate dehydrogenase; MB, methylene blue; AA, ascorbic acid; ANG-II, angiotensin II. TABLE_PLACEHOLDER:Table 3 In addition to the abovementioned four drugs, induced mild hypothermia may be a useful treatment for VS. Earlier studies [48] showed that hypothermia decreases the release of cytokines. Furthermore, mild hypothermia effectively restored SVR and blood pressure within 4 h without adverse effects on pulmonary pressure [49], and improved the response to epinephrine [50] and norepinephrine [51]. Therefore, it may be an excellent prevention and treatment method for VS by avoiding active rewarming after the operation and letting the patient gradually and spontaneously reach normothermia or maintain a 33°C–35°C corporeal temperature for the first 24 h after HTX. However, hypothermia can induce problems, such as cardiac arrhythmia and coagulopathy. Further research is necessary to determine the safety of mild hypothermia for the treatment of VS. In-hospital mortality was more than 2.5-fold higher in patients with ($25\%$) than in patients without VS [52]. Therefore, the prevention of VS is crucial for patients undergoing HTX. Ozal et al. [ 4] reported that those who received preoperative MB had significantly higher postoperative SVR and MAP, and a significantly shorter mean length of stay in intensive care units. A randomized, double-blind, controlled trial showed that tranexamic acid attenuates the development of VS after CPB by blocking fibrinolysis [53]. Further research should prioritize the mechanism and prevention measures for VS in patients undergoing HTX. In summary, several risk factors for VS exist in patients undergoing HTX, including the chronic inflammatory exhibited by some patients before surgery. VS can occur at any time during the perioperative period in patients who underwent HTX, especially after the discontinuation of bypass. MB, ANG- II, hydroxocobalamin, and AA have been used to treat refractory VS. ## 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 Both authors were involved in the analysis and interpretation of the data. YtY designed the research study. TxQ wrote the initial draft of the manuscript. Both authors revised the manuscript and approved the final 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. ## References 1. Fischer GW, Levin MA. Vasoplegia during cardiac surgery: current concepts and management. Semin Thorac Cardiovasc Surg (2010) 22 140-4. DOI: 10.1053/j.semtcvs.2010.09.007 2. Chan JL, Kobashigawa JA, Aintablian TL, Dimbil SJ, Perry PA, Patel JK. Characterizing predictors and severity of vasoplegia syndrome after heart transplantation. 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--- title: Associations of novel serum lipid index with epithelial ovarian cancer chemoresistance and prognosis authors: - Yuan Li - Chunliang Shang - Huamao Liang - Kun Zhang - Yu Wu - Hongyan Guo journal: Frontiers in Oncology year: 2023 pmcid: PMC9968862 doi: 10.3389/fonc.2023.1052760 license: CC BY 4.0 --- # Associations of novel serum lipid index with epithelial ovarian cancer chemoresistance and prognosis ## Abstract ### Purpose To evaluate the relationship between novel serum lipid index and chemoresistance as well as prognosis of epithelial ovarian cancer (EOC). ### Patients and methods Patients’ serum lipid profiles of 249 cases diagnosed with epithelial ovarian cancer, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) as well as their ratios, the novel indicators HDL-C/TC and HDL-C/LDL-C, and clinicopathologic characteristics were retrospectively collected and calculated from January 2016 to January 2020 and correlation between serum lipid index and clinicopathological features such as chemoresistance as well as prognosis were evaluated. ### Results 249 patients pathologically diagnosed EOC who underwent cytoreductive surgery were included in our cohort. The mean age of these patients was 55.20 ± 11.07 years. Binary logistic regression analyses indicated Federation International of Gynecology and Obstetrics (FIGO(stage and HDL-C/TC ratio had significant association with chemoresistance. Univariate analyses demonstrated pathological type, chemoresistance, FIGO stage, neoadjuvant chemotherapy, maintenance treatment, HDL-C/LDL-C ratio, HDL-C/TC ratio were related to Progression-Free Survival (PFS) and Overall Survival (OS) ($P \leq 0.$ 05). Particularly, multivariate analyses indicated that HDL-C/LDL-C ratio was independent protective factors for both PFS and OS. ### Conclusion The complex serum lipid index HDL-C/TC ratio has a significant correlation with chemoresistance. HDL-C/LDL-C ratio is closely related to the clinicopathological characteristics and prognosis of patients with EOC and is an independent protective factor indicating better outcome. ## Introduction Ovarian cancer (OC) is the most lethal malignant tumor of female reproductive system. The standard treatment for ovarian cancer is cytoreductive surgery combined with platinum-based chemotherapy and maintenance therapy based on tumor heterogeneity [1, 2]. Lack of sensitive and effective screening methods, as well as atypical clinical symptoms, leads to initial diagnosis of advanced stage in more than $70\%$ of patients. Although the initial response rate is 70–$80\%$ including 40 to $50\%$ complete response, majority of patients relapse within 2 years with subsequent resistance to chemotherapy, leading to a 5-year survival rate of ovarian cancer patients only $45\%$ with a gradually shortened interval between recurrences [3]. Multidisciplinary management of ovarian cancer patients is key to improve outcome. The introduction of imaging biomarkers, such as those obtained with FDG PET/CT, could help improve patient outcome, by precisely staging and restaging, as well as providing a prognostic value [4]. More serological markers have also become a hotspot in predicting tumor prognosis. Disturbance of lipid metabolism in cancer cells can lead to structural changes of tumor cell membrane, turbulence of intracellular energy metabolism, dysregulation of cell signal transduction and gene expression [5]. As the source and transforming component of lipid metabolism in tumor microenvironment, serum lipids play an important role in tumorigenesis. A greater synthesis or absorption of lipids has been found in a variety of cancers as the raw materials contributing to the growth of cancer cells and the formation of tumors. Reprogramming of lipid metabolism can also affect the development of tumor cells by changing the fluidity of cell membranes and weakening immune function [6]. It is reported that serum lipid levels are closely related to the occurrence and development of malignant tumors such as breast cancer [7], gastric cancer [8] and lung cancer [6]. Mesenchyme-derived adipocytes in the tumor microenvironment can promote tumorigenesis of prostate cancer by releasing fatty acids and related factors [9]. Adipose stem cells derived from the human omentum can promote proliferation, migration, chemoresistance of ovarian cancer cells and radiation resistance in nude mice model of ovarian cancer [10]. There is also a correlation between lipid metabolism in tumor microenvironment and the efficacy of targeted drugs. For example, adipose abundance in breast cancer tumors was significantly correlated with resistance to trastuzumab [11]. The main components of serum lipids are total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). TC is an essential lipid to maintain cell homeostasis and an important part of cell membrane, which is rich in lipid raft and plays a key role in intracellular signal transduction [12]. HDL-C maintains normal cellular cholesterol homeostasis by removing excess cholesterol from cells and transporting it to the liver. When the level of HDL-C is low, the accumulated cholesterol is utilized by cancer cells for membrane formation, thereby promoting the development of cancer. On the contrary, LDL-C is a significant carrier of cholesterol from the liver to tissues throughout the body, providing energy and materials for tumor cell proliferation and development [12]. It has been reported in literatures, TC、TG、HDL-C and LDL-C are independent factors for survival prediction of various tumors such as gastric cancer [13] and breast cancer [14]. Novel complex indicators of lipid metabolism, such as TG/HDL-C ratio, have been proved superior to that of single TG or HDL-C level in predicting survival of patients with triple negative breast cancer [15] and gastric cancer [7]. These results suggest that complex novel indicators might be better predictive index for survival prediction. Correlation between complex lipid indicators and prognosis in patients of ovarian cancer has not been clearly reported. Therefore, this study aimed to explore the correlation between serum lipid levels and chemoresistance as well as prognosis in patients of ovarian cancer. ## Patients 249 patients pathologically diagnosed EOC who underwent cytoreductive surgery at the Department of Obstetrics and Gynecology, Beijing University Third Hospital between January 2016 to January 2020 were retrospectively reviewed in our cohort. Patients were included if they: ① pathologically confirmed to be EOC; ② ovary is the primary lesion of tumor; ③ received cytoreductive surgery combined with regular platinum-based chemotherapy in Peking University Third Hospital; ④ had complete preoperative peripheral blood data and complete follow-up; ⑤ There were no acute or chronic infections and blood system diseases, and not complicated with other tumors (Figure 1 shows the workflow for selection procedure of epithelial ovarian cancer patients). This study was ratified by the Ethics Committee of Peking University Third Hospital. Written informed consent was obtained from all the patients in this study. Figure 1: Workflow for selection procedure of epithelial ovarian cancer patients. ## Follow-up Patients’ age, Federation of International of Gynecologists and Obstetricians (FIGO) stage, pathological type, tumor grade, therapeutic regimen and complications were collected from clinical attendance records. Follow-up began on the day of surgery and was conducted through direct telecommunications and outpatient visits until October 2021. The median follow-up time was 35 months. Follow-up of patients included blood tests, urine tests, computed tomography, and physical examination. Overall survival (OS) is measured from the date of surgery to the date of death from any cause, or the date of last follow-up visit. Progression-free survival (PFS) is identified from the date of surgery to the date of recurrence which was confirmed by imaging evidence or serum CA125 elevation. Platinum-sensitive is defined when patients have a clear response to the initial platinum-based treatment and achieve clinical remission, and progress or recurrence occurs more than 6 months (including 6 months) after the withdrawal of the previous platinum-containing chemotherapy. Platinum-resistant is defined when patients respond to initial chemotherapy but progresses or relapses occurs within 6 months of completion of chemotherapy. ## Statistical analysis Differences of clinicopathological parameters and relative prognostic parameters between groups were evaluated by independent-samples Student’s t-test or Fisher’s exact test according to the distribution of data, and the normal distribution continuous variables were divided into groups by the median. Chi square test and binary logistic regression analyses were used to analyze the relationship between serum lipid indicators and chemoresistance as well as other clinicopathological characteristics of patients. The survival curves were calculated using the Kaplan–Meier method. Survival analyses were conducted by Cox proportional hazards model. All reported p-values were two-sided. $P \leq 0.05$ was considered significant, and $95\%$ CIs were calculated. All analyses were performed using the SPSS Statistics version 26.0 (IBM Corporation, Armonk, NY, USA). ## Baseline clinicopathological characteristics The mean age of these patients was 55.20 ± 11.07 years. There were 161 patients with FIGO stage III to IV, accounting for $64.66\%$of the total number. 222 ($89.16\%$) patients were diagnosed of high-grade EOC. Serous carcinoma was found in 191 cases ($76.71\%$), and other pathological types of epithelial ovarian cancer together were 58 cases ($23.29\%$). There were 56 patients ($22.49\%$) who received neoadjuvant chemotherapy. 57 patients ($22.89\%$) received maintenance therapy after surgery. Among 249 patients included in this study, a total of 70 patients ($28.11\%$) had the clinical characteristic of chemoresistance after chemotherapy. Recurrence occurred in 120 patients ($48.20\%$) and 55patients ($22.09\%$) died during the following. ## Grouping serum lipid single and complex indicators by median We divided normal distribution continuous variables TC, HDL-C, LDL-C, HDL-C/LDL-C, HDL-C/TC into low- or high-level groups by median. With TC’s median of 4.65mmol/L as the boundary, 126 patients were in the low TC group and 123 patients in high TC group. Grouped by HDL-C ‘s median of 1.10mmol/L, there were 127 patients in the low HDL-C group and 122 patients in the high HDL-C group. Divided by the LDL-C’s median of 2.90mmol/L, there were 126 patients in the low LDL-C group and 123 patients in the high LDL-C group. The median of normal distribution continuous variable HDL-C/LDL-C was 0.39, 125 patients were in the low HDL-C/LDL-C group and 124 patients in the high HDL-C/LDL-C group. There were 124 patients in the low HDL-C/TC group and 125 patients in the high HDL-C/TC group with the HDL-C/TC’s median of 0.24 as the boundary. ## Correlation between clinical features and serum blood lipid levels Table 1 shows the correlation between clinical features and serum blood lipid levels. Patients of higher TC groups tend to be suffered from serous ovarian cancer. Lower HDL-C/LDL-C groups were associated with advanced FIGO stage and having complication of diabetes mellitus. Table 1 \| Unnamed: 0 \| High TC group(n=123) \| Low TC group(n = 146) \| χ 2 \| P \| High HDL-C group(n=122) \| Low HDL-C group(n = 127) \| χ 2.1 \| P.1 \| High LDL-C group(n=123 \| Low LDL-C group(n = 126) \| χ 2.2 \| P.2 \| High HDL-C/LDL-C group(n = 124) \| Low HDL-C/LDL-C group(n = 125) \| χ 2.3 \| P.3 \| High HDL-C/TC group(n = 125) \| Low HDL-C/TC group(n = 124) \| χ 2.4 \| P.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Age \| \| \| 0.202 \| 0.65 \| \| \| 0.646 \| 0.09 \| \| \| 0.894 \| 0.66 \| \| \| 0.906 \| 0.34 \| \| \| 2.507 \| 0.11 \| \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| \| serous \| 87 \| 104 \| 4.857 \| 0.03 \| 88 \| 103 \| 0.603 \| 0.09 \| 91 \| 100 \| 0.315 \| 0.74 \| 96 \| 95 \| 0.07 \| 0.79 \| 97 \| 94 \| 0.122 \| 0.74 \| \| non-serous \| 36 \| 22 \| \| \| 34 \| 24 \| \| \| 32 \| 26 \| \| \| 28 \| 30 \| \| \| 28 \| 30 \| \| \| \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| \| I- II \| 50 \| 38 \| 2.998 \| 0.08 \| 55 \| 33 \| 0.002 \| 0.43 \| 45 \| 43 \| 0.898 \| 0.69 \| 53 \| 35 \| 5.92 \| 0.02 \| 52 \| 36 \| 4.303 \| 0.04 \| \| III- IV \| 73 \| 88 \| \| \| 67 \| 94 \| \| \| 78 \| 83 \| \| \| 71 \| 90 \| \| \| 73 \| 88 \| \| \| \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| Tumor grade \| \| high \| 113 \| 109 \| 1.851 \| 0.17 \| 109 \| 113 \| 0.926 \| 1.04 \| 112 \| 110 \| 0.341 \| 0.06 \| 108 \| 114 \| 1.084 \| 0.3 \| 109 \| 113 \| 0.994 \| 0.32 \| \| medium/low \| 10 \| 17 \| \| \| 13 \| 14 \| \| \| 11 \| 16 \| \| \| 16 \| 11 \| \| \| 16 \| 11 \| \| \| \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| chemoresistance \| \| sensitive \| 88 \| 91 \| 0.014 \| 0.91 \| 90 \| 89 \| 1.201 \| 0.52 \| 87 \| 92 \| 0.893 \| 0.69 \| 93 \| 86 \| 1.184 \| 0.28 \| 95 \| 84 \| 2.101 \| 0.15 \| \| resistance \| 35 \| 35 \| \| \| 32 \| 38 \| \| \| 36 \| 34 \| \| \| 31 \| 39 \| \| \| 30 \| 40 \| \| \| \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| \| Yes \| 22 \| 23 \| 0.006 \| 0.94 \| 108 \| 96 \| 0.008 \| 0.4 \| 22 \| 23 \| 0.975 \| 0.94 \| 16 \| 29 \| 4.457 \| 0.04 \| 15 \| 30 \| 0.012 \| 0.43 \| \| No \| 101 \| 103 \| \| \| 14 \| 31 \| \| \| 101 \| 103 \| \| \| 108 \| 96 \| \| \| 110 \| 94 \| \| \| \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| \| Yes \| 40 \| 53 \| 2.422 \| 0.12 \| 40 \| 53 \| 0.681 \| 0.15 \| 40 \| 53 \| 0.664 \| 0.06 \| 42 \| 51 \| 1.277 \| 0.26 \| 42 \| 51 \| 0.219 \| 0.72 \| \| No \| 83 \| 73 \| \| \| 82 \| 74 \| \| \| 83 \| 73 \| \| \| 82 \| 74 \| \| \| 83 \| 73 \| \| \| Figure 2 shows binary logistic regression analyses indicating FIGO stage and HDL-C/TC ratio had significant correction with chemoresistance. Advanced FIGO stage and lower HDL-C/TC ratio mean a tendency to chemoresistance. Figure 2: Correction between serum lipid levels as well as other clinical features and chemoresistance. ## Kaplan–Meier survival curves of patients with ovarian cancer stratified by HDL-C/LDL-C and HDL-C/TC ratios Kaplan–Meier survival analysis showed that patients of higher HDL-C/LDL-C and HDL-C/TC group had longer PFS and OS than those of lower HDL-C/LDL-C group, and the difference was statistically significant (P ≤ 0. 05), as shown in Figures 3, 4. Figure 3: Survival curves of PFS and OS in EOC patients in low/high HDL-C/LDL-C groups. Figure 4: Survival curves of PFS and OS in EOC patients in low/high HDL-C/TC groups. ## Univariate and multivariate analysis of FPS and OS The survival analysis of PFS showed that the patient’s age, FIGO stage, pathological type, chemoresistance, primary treatment, maintenance therapy, HDL-C/LDL-C ratio, HDL-C/TC ratio, and whether having complication of diabetes mellitus were related to PFS ($P \leq 0.$ 05). Cox multivariate analysis showed that chemoresistance, FIGO stage, maintenance therapy, HDL-C/LDL-C ratio, and having complication of diabetes mellitus were independent predictive factors for PFS ($P \leq 0.$ 05), as shown in Tables 2, 3. The survival analysis of OS showed that FIGO stage, pathological type, chemoresistance, primary treatment, maintenance therapy, HDL-C/LDL-C ratio, HDL-C/TC ratio, and whether having complication of diabetes mellitus were related to OS ($P \leq 0.$ 05). Cox multivariate analysis showed that chemoresistance, FIGO stage, maintenance therapy, and HDL-C/LDL-C ratio were independent predictive factors for PFS ($P \leq 0.$ 05), as shown in Tables 4, 5. ## Discussion In recent years, the relevance of lipid metabolism to the tumor microenvironment and cancer development has become a hot topic of research. To meet the demands of rapid growth, tumor cells try their best to obtain nutrients from outside, one of the main sources of energy is lipids in tumor microenvironment. At the same time, lipids are the main raw material of biofilm, which is important for the stability of cancer cell membrane and the reproduction and metastasis of cancers [16]. Serum lipids are the main source of lipids in cancer microenvironment and are therefore closely related to the development of cancer [17]. Studies have shown that in some solid tumors such as gastric cancer [18], lung cancer [19] and colon cancer [20], there are correlations between patients’ serum lipid levels and their clinical characteristics and prognosis. In the study of gynecological malignancies and lipid metabolism, researchers have focused on endometrial cancer [21, 22], which has a strong correlation between lipid metabolism and hormones. There has been no in-depth study of association between serum lipids and prognosis of ovarian cancer. Moreover, most of the studies on serum lipids and cancer just focus on single lipid index such as high/low density lipoproteins cholesterol and have not considered the combined effect of these indicators. It is therefore necessary to further explore the association between dyslipidemia and the clinical characteristics of ovarian cancer. The main indicators of lipid metabolism in peripheral blood are total cholesterol, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol. Cholesterol is an important component of the cell membrane and is essential for maintaining lipid raft stability which promotes cell proliferation [23]. In the literature, patients of prostate cancer and cervical cancer with elevated total serum cholesterol levels have a poor prognosis [24, 25]. Meanwhile, patients of non-small cell lung cancer, gastric cancer, and primary liver cancer with lower total serum cholesterol levels suggest a poor prognosis [11, 26, 27]. A study of 229 patients with ovarian cancer and 233 patients with benign ovarian tumors showed a trend of lower total cholesterol levels in the ovarian cancer group compared to the benign ovarian tumor group, the hypothesis was that the rapid growth of cancer cells requires the involvement of large amounts of total cholesterol, which in turn promotes total cholesterol depletion [14, 28]. However, no studies have reported a correlation between serum total cholesterol levels and the prognosis of ovarian cancer patients. In our research, we found that serum cholesterol levels correlated with the type of pathology of the patients, with serous type of ovarian cancer having higher cholesterol levels than other pathological types. High-density lipoprotein cholesterol is known as a vascular scavenger, which reduces cholesterol levels in cancer cells, peripheral tissues and tumor lessens biofilm raw materials by transporting cholesterol from surrounding tissues and converting it into bile acids for excretion from the intestine, which may antagonize the function of intracellular cholesterol in cancer cells to some extent. It has been reported that elevated high density lipoprotein level in peripheral blood is an independent prognostic factor for patients with gastric liver cancers. Zhang D. found that lower levels of HDL were an independent risk factor for the development of ovarian cancer [29]. In contrast, LDL is primarily responsible for the distribution of cholesterol between extrahepatic tissues and cells, and elevated serum LDL level can lead to increased levels of cholesterol stored in cancer cells, thereby promoting cell proliferation. Andrew J. retrospectively analyzed serum lipid levels and disease-specific survival in 132 patients with epithelial ovarian cancer and concluded that serum LDL levels were negatively associated with patient disease-specific survival [30]. Our study analyzed the relationship between serum HDL, LDL as well as cholesterol levels and the clinical characteristics and prognosis of patients with ovarian cancer and failed to obtain a statistical correlation. Previous literatures as well as the results of our study reveal that there are few studies focusing on the prognostic evaluation of serum lipid levels in ovarian cancer and some conclusions are controversial. Therefore, we firstly introduced the previously reported high-density lipoprotein cholesterol/total cholesterol (HDL-C/TC) and high-density lipoprotein cholesterol/low-density lipoprotein cholesterol (HDL-C/LDL-C) as two composite lipid markers to better investigate association between serum lipid levels and clinical characteristics and prognosis of ovarian cancer. Previous studies have shown that in gastric cancer, HDL-C/TC ratio has better prognostic evaluation than a single HDL-C level [6]. A similar study has demonstrated that TG/HDL was positively correlated with clinical features such as FIGO stage and pathological type of endometrial cancer [15]. Our results show that HDL-C/LDL-C and HDL - C/TC ratios are negatively associated with patients’ FIGO stage. One of the mechanisms may be that a higher HDL-C/TC ratio increases the amount of cholesterol brought into the blood vessels by HDL thus reducing the amount of free cholesterol in cancer cells and weakening the ability of cancer cells to proliferate and metastasize. Using multiple regression analysis, we concluded that the HDL-C/TC ratio was negatively associated with chemoresistance in patients. It has been documented that cholesterol levels are elevated in the tumor microenvironment of ovarian cancer patients which can upregulate the expression of the drug efflux pump proteins ABCG2 and MDR1, as well as cholesterol receptor LXRα/β to reduce the sensitivity of ovarian cancer cells to chemotherapeutic agents [31]. In terms of prognosis, univariate analysis showed that the type of tumor pathology, FIGO stage, chemoresistance, HDL-C/LDL-C, HDL-C/TC, the presence of neoadjuvant chemotherapy and maintenance therapy were associated with progression free survival (PFS) and overall survival (OS) in patients with ovarian cancer. FIGO stage and chemoresistance were independent risk factors for FPS and OS. HDL-C/LDL-C ratio and post-operative maintenance therapy were independent protective factors for FPS. HDL-C/LDL-C ratio was also an independent protective factor for patients’ OS. Patients with higher HDL-C/LDL-C ratio have longer FPS and OS ($p \leq 0.$ 05). The mechanism may be that the conversion and metabolism of cholesterol within ovarian cancer cells affects cell proliferation, chemoresistance and metastasis, which in turn affects patient prognosis. Our study shows that the HDL-C/TC and HDL-C/LDL-C ratios are closely related to the FIGO stage, chemoresistance and prognosis of ovarian cancer patients. Previous studies have suggested that statins such as lovastatin and Fluvastatin may play a potential role in cancer therapy strategy by modulating patients’ serum lipid levels. For example, statin can significantly reduce the risk of breast, colorectal, ovarian, and pancreatic cancer (32–35). In non-small cell lung cancer, lovastatin combined with gefitinib produces a synergistic anti-tumor effect, reducing cancer cell proliferation by inhibiting the epidermal growth factor receptor [36]. Studies on ovarian cancer have found that fluvastatin and cisplatin can synergistically induce apoptosis of ovarian cancer cells by modulating Ras pathway [37]. Based on our findings and relevant literature data, statins are expected to play an important role in the treatment of ovarian cancer in the future [38, 39]. This research is a single-center study with a relatively small sample size, so further multi-center, large-sample studies are needed to verify the relationship between serum lipids and clinical features and prognosis of ovarian cancer. We will conduct further molecular research on the relationship between lipid metabolism and biological behaviors of ovarian cancer cells and closely link them to patient’s serum lipid levels and prognosis, bringing new thinking to the treatment of ovarian cancer. Statins’ role in modulating lipid metabolism of tumor microenvironment therefore changing characteristics of cancer cells requires further investigation. ## Conclusion In conclusion, serum lipid profiles are associated with the chemoresistance and prognosis of epithelial ovarian cancer. Higher HDL-C/LDL-C ratio and higher HDL-C/TC levels were protective factors for epithelial ovarian cancer, indicating novel index for screening and follow-up of ovarian cancer. At present, statins have been used in anti-tumor therapy. With the progress of research, lipid-lowering drugs are expected to play a more important role in improving the prognosis of patients. ## 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 Conceptualization: YL and HG; methodology: YL; software: YL; formal analysis: YL; writing - original draft: YL; 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. ## References 1. Piver MS. Treatment of ovarian cancer at the crossroads: 50 years after single-agent melphalan chemotherapy. 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--- title: Age-to-Glasgow Coma Scale score ratio predicts gastrointestinal bleeding in patients with primary intracerebral hemorrhage authors: - Weizhi Qiu - Chubin Liu - Jinfu Ye - Gang Wang - Fuxing Yang - Zhigang Pan - Weipeng Hu - Hongzhi Gao journal: Frontiers in Neurology year: 2023 pmcid: PMC9968863 doi: 10.3389/fneur.2023.1034865 license: CC BY 4.0 --- # Age-to-Glasgow Coma Scale score ratio predicts gastrointestinal bleeding in patients with primary intracerebral hemorrhage ## Abstract ### Objective Recent clinical studies have demonstrated that advanced age and low initial Glasgow Coma Scale (GCS) score were independent predictors of gastrointestinal bleeding (GIB) in patients with primary intracerebral hemorrhage (ICH). However, used singly, age and GCS score have their respective shortcomings in predicting the occurrence of GIB. This study aimed to investigate the association between the age-to-initial GCS score ratio (AGR) and the risk of GIB following ICH. ### Methods We conducted a single-center, retrospective observational study of consecutive patients presenting with spontaneous primary ICH at our hospital from January 2017 through January 2021. Patients who fulfilled the inclusion and exclusion criteria were categorized into GIB and non-GIB groups. Univariate and multivariate logistic regression analyses were implemented to identify the independent risk factors for the occurrence of GIB, and a multicollinearity test was performed. Furthermore, one-to-one matching was conducted to balance important patient characteristics by the groups' propensity score matching (PSM) analysis. ### Results A total of 786 consecutive patients fulfilled the inclusion/exclusion criteria for the study, and 64 ($8.14\%$) patients experienced GIB after primary ICH. Univariate analysis revealed that patients with GIB were significantly older [64.0 (55.0–71.75) years vs. 57.0 (51.0–66.0) years, $$p \leq 0.001$$] and had a higher AGR [7.32 (5.24–8.96) vs. 5.40 (4.31–7.11), $p \leq 0.001$] and a lower initial GCS score [9.0 (7.0–11.0) vs. 11.0 (8.0–13.0), $p \leq 0.001$]. The multicollinearity test revealed that no multicollinearity was observed in the multivariable models. Multivariate analysis showed that the AGR was a significant independent predictor of GIB [odds ratio (OR) 1.155, $95\%$ confidence interval (CI) 1.041–1.281, $$p \leq 0.007$$], as well as prior anticoagulation or antiplatelet therapy (OR 0.388, $95\%$ CI 0.160–0.940, $$p \leq 0.036$$) and MV used >24 h (OR 0.462, $95\%$ CI 0.252–0.848, $$p \leq 0.013$$). Receiver operating curve (ROC) analysis illustrated that the optimal cutoff value for the AGR as a predictor for GIB in patients with primary ICH was 6.759 [the area under the curve (AUC) was 0.713 with a corresponding sensitivity of $60.94\%$ and specificity of $70.5\%$, $95\%$ CI 0.680–0.745, $p \leq 0.001$]. After 1:1 PSM, the matched GIB group had significantly higher AGR levels compared with the matched non-GIB group [7.47(5.38–9.32) vs. 5.24(4.24–6.40), $p \leq 0.001$]. The ROC analysis indicated an AUC of 0.747 (the sensitivity was $65.62\%$, and the specificity was $75.0\%$, $95\%$ CI 0.662–0.819, $p \leq 0.001$) for AGR levels as an independent predictor of GIB in patients with ICH. In addition, AGR levels were statistically correlated with unfunctional 90-day outcomes. ### Conclusion A higher AGR was associated with an increased risk of GIB and unfunctional 90-day outcomes in patients with primary ICH. ## Introduction Spontaneous primary intracerebral hemorrhage (ICH), a current significant disabling and killer disease worldwide, has the characteristics of high incidence, high prevalence, high mortality, high disability rate, and numerous lethal complications [1, 2]. In addition to ICH severity, the complications of ICH are also associated with increased mortality and poor prognosis. Gastrointestinal bleeding (GIB) is a significant complication of acute ICH, which is significantly associated with an increased risk of mortality, length of stay in the intensive care unit (ICU), and unfavorable outcomes [3, 4]. The reported incidence of GIB after ICH varies widely across studies, ranging from 16 to $54\%$ (3, 5–8). However, previous reports in the literature have revealed that GIB is related to the morbidity of $48.3\%$ and mortality as high as $87.9\%$ following ICH [3, 5]. Therefore, it is critical and crucial to identify people who are at high risk for GIB. Several indicators affecting GIB after acute ICH have been reported, including advanced age, lower Glasgow Coma Scale (GCS) score, ICH volume, and mechanical ventilation (MV) >48 h (3–5, 9). It is worth noting that there are consistent results from previous studies that the most relevant to the occurrence of GIB is neurological status and patient age [4, 5, 9, 10]. Neurological status is typically measured using the GCS score, and clinicians assess the conscious state and brain functions through eye-opening, verbal, and motor responses. Age-related effects have been observed on ICH and GIB following ICH, including the initial physiological response (e.g., the presenting GCS), a particularly computed tomography (CT) finding, and the aggressiveness of neurosurgical management, morbidity, and mortality [5, 11]. The GCS score has the disadvantage that the summed score does not always accurately portray a patient's condition [12]. It is now widely accepted that the GCS may be higher in the elderly than in younger patients for an equivalent anatomic severity of ICH. Despite near-normal GCS appearances, elderly patients may have severe anatomic ICH, with a high risk of subsequent GIB and poor prognosis [3, 5, 7]. Accordingly, age may influence the relationship between the anatomic severity of ICH and neurological conditions measured by the GCS, ultimately affecting the prediction of GIB. Regrettably, most studies have not been adjusted for age and GCS score. To overcome the disadvantages of age and GCS score, we proposed a concept—age-to-GCS score ratio (AGR). The AGR was calculated by dividing age by the GCS score. In addition, a propensity score matching (PSM) analysis was conducted to reduce the potentially confounding elements affecting GIB in patients with ICH. The present study aimed to investigate the predictive capacity of the AGR to identify GIB following ICH, and the AGR is expected to be a more practical tool for predicting GIB. ## Study population Ethics approval for patient information was obtained from the Ethics Committee of the Second Affiliated Hospital of Fujian Medical University. We conducted a single-center, retrospective observational study of consecutive patients presenting with spontaneous primary ICH at our hospital from January 2017 through January 2021. Data were extracted from a prospectively maintained database containing demographic, clinical, operative, and follow-up data. The inclusion criteria were as follows: [1] age equal to or greater than 18 years; [2] an index ICH admission CT scan obtained within 24 hours (h) of symptom onset (for diagnostic confirmation and hematoma localization). Exclusion criteria were the following: [1] age less than 18 years old; [2] evidence of a secondary ICH etiology, including trauma, aneurysm, vascular malformation, moyamoya disease, hemorrhagic transformation of cerebral infarction, brain tumor, or any other cause of secondary ICH; [3] primary intraventricular hemorrhage (IVH); [4] active GIB (including esophageal and gastric variceal) on admission; [5] total gastrectomy; [6] known gastrointestinal (GI) lesions that might bleed (varies, polyps, and tumors); [7] peptic ulcer disease; and 8) historical modified Rankin scale (mRS) > 2. All patients received the standard treatment according to the current ICH management guidelines [13]. An indication for surgery was a midline shift more significant than 5 mm, a large hematoma <30 ml (supratentorial)/10 ml (infratentorial), or neurological impairment [14]. ## Baseline data collection Demographic variables included age and sex. Baseline characteristics related to medical history included the history of hypertension, diabetes mellitus, cigarette smoking, drug alcohol consumption, and prior anticoagulation or antiplatelet therapy as previously described [15, 16]. Clinical neurological status on admission was evaluated with the GCS score, and if the patients were intubated and/or sedated, we used the best pre-intubation and post-resuscitation GCS scores. ICH characteristics recorded include the presence of intraventricular hemorrhage (IVH), ICH location, and hematoma volume. ICH hematoma volumes were determined on the initial CT using the ABC/2 method with an approximation of hematoma to an ellipsoid [17] and were categorized into large hematoma size (≥30 ml) or small hematoma size (<30 ml). In our clinical stroke database, ICH locations on admission CT were categorized as lobar (originating at the cortex and cortical–subcortical junction), deep (e.g., basal, ganglia, and thalami), cerebellar, or brainstem [18, 19]. Peripheral venous blood was extracted per patient via venipuncture within 2 h of admission for laboratory examinations, including blood routine and blood coagulation. Blood coagulation included platelets, activated partial thromboplastin time (aPTT), international normalized ratio (INR), prothrombin time (PT), fibrinogen, and thrombin time. The patients invasively mechanically ventilated for more than 48 h were recorded. The age-to-GCS score ratio (AGR) was calculated by dividing age by the GCS score. ## Outcome assessments The primary outcome was the occurrence of GIB within 14 days of acute ICH onset. GIB was defined as the presence of fresh blood or ground coffee in nasogastric aspirate, hematemesis, melena, blood in the stool, positive occult blood test, or positive fecal occult blood test during hospitalization [20]. The patients received routine intravenous proton pump inhibitors (PPIs) during the hospital stay, 40 mg Q12H to treat GIB or 40 mg QD to prevent GIB [7]. The secondary outcome was functional independence (90-day, mRS 0–2). The functional outcome using the mRS score was assessed 90 days after the onset of ICH or until death, depending on which occurred first. All patients with ICH were followed up via outpatient records, telephone interviews, or WeChat (Tencent, Shenzhen, China) interviews. An mRS score of 0–2 was defined as a good prognosis, whereas an mRS of 3–6 was a poor prognosis. ## Statistical analysis All statistical analyses were conducted with SPSS Statistics 25.0 software (SPSS Inc., Chicago, USA) and Prism 8.3.0 (GraphPad Software, San Diego, CA, USA). Prior to parametric statistical analysis, the data distributions were analyzed for normality using the Kolmogorov–Smirnov test (KS test). Baseline demographics and clinical characteristics were summarized as mean ± standard deviation (SD) for normally distributed continuous variables, median (interquartile range, IQR) for non-normally distributed continuous variables, and frequencies (percentages) for categorical variables. Student's t-test assessed comparisons between two groups for normally distributed variables and the Mann–Whitney U-test for non-normally distributed variables. In addition, comparisons were presented as violin plots with median and quartile values. Differences in categorical data were compared using the chi-square test or Fisher's exact test. Variables potentially associated with $p \leq 0.1$ in univariate analyses were included in the multivariate model. Multicollinearity was assessed using variance inflation factor (VIF) and tolerance before conducting the multivariable logistic regression analysis, and the model was subsequently adjusted to remove factors with obvious multicollinearity. Variables with VIF > 5 or tolerance < 0.2 indicated the existence of multiple collinearities removed from the model [21, 22]. Receiver operating characteristic (ROC) curves were plotted along with the area under the ROC curve (AUC) using MedCalc (MedCalc Software, Ostend, Belgium). The ROC curves were used to determine the optimum cutoff points, whereby sensitivity and specificity were equally weighted. To further account for significant differences in baseline characteristics between non-GIB and GIB groups, we conducted a 1:1 propensity score matching (PSM) analysis. The variables with a $p \leq 0.05$ in the univariate analysis were incorporated into the PSM analysis. A two-sided $p \leq 0.05$ was considered statistically significant for all statistical analyses. ## Results Figure 1 illustrates the study flow diagram and PSM analysis process. A total of 786 consecutive patients fulfilled the inclusion/exclusion criteria for the study, and 64 ($8.14\%$) patients experienced GIB after primary ICH. Baseline demographic and clinical characteristics are summarized in Table 1. The median age of included patients was 58 years (IQR, 52–66), and 497($63.2\%$) patients were male. The median time from illness onset to the first CT examination was approximately 4.0 h (IQR, 3.0–5.0 h), and the median GCS score upon admission was 12.0 (7.0–15.0). A total of 233 ($29.6\%$) patients had an initial hematoma volume more significant than 30 ml. Figure 1: Study flow diagram and propensity score matching analysis process. AC, anticoagulation; AGR, age-to-GCS ratio; AP, antiplatelet; GCS, Glasgow Coma Scale; GIB, gastrointestinal bleeding; ICH, intracerebral hemorrhage; MV, mechanical ventilation. $p \leq 0.05.$* TABLE_PLACEHOLDER:Table 1 Seven hundred eighty-six patients with primary ICH were stratified into two groups: absence (non-GIB group, $$n = 722$$) or presence of GIB (GIB group, $$n = 64$$). Univariate analysis revealed significant differences between the two groups in terms of age, baseline GCS score, baseline volume (BV) ≥ 30 ml, prior anticoagulation or antiplatelet therapy, mechanical ventilation (MV) used >24 h, surgery, and AGR. When compared with patients without GIB, those with GIB were significantly older [64.0 (55.0–71.75) years vs. 57.0 (51.0–66.0) years, $$p \leq 0.001$$] and had a higher AGR [7.32 (5.24–8.96) vs. 5.40 (4.31–7.11), $p \leq 0.001$] and a lower initial GCS score [9.0 (7.0–11.0) vs. 11.0 (8.0–13.0), $p \leq 0.001$] (Table 2, Figures 2A, B). The AGR was positively associated with the occurrence of GIB with an odds ratio (OR) value of 1.220 [$95\%$ confidence interval (CI) 1.122–1.326, $p \leq 0.001$] in the unadjusted model (Figure 2B). Multicollinearity analyses of the included variables were undertaken, and no multicollinearity was observed in the multivariable models as judged by VIF and tolerance (Table 3). We, therefore, carried out a stepwise multivariate analysis incorporating six covariates (Figure 2C). The multivariate analysis results were adjusted for the following confounding factors: platelet, BV ≥ 30 ml, prior anticoagulation or antiplatelet therapy, surgery, and MV used >24 h. After adjustment in the multivariate logistic regression model, AGR level was a significant independent predictor of GIB (OR 1.155, $95\%$ CI 1.041–1.281, $$p \leq 0.007$$), as well as prior anticoagulation or antiplatelet therapy (OR 0.388, $95\%$ CI 0.160–0.940, $$p \leq 0.036$$) and MV used >24 h (OR 0.462, $95\%$ CI 0.252–0.848, $$p \leq 0.013$$) (Figure 2C). However, BV ≥ 30 ml, platelet, and surgery were not independent predictors of GIB ($p \leq 0.05$; Figure 2C). The Hosmer–Lemeshow test was applied to assess goodness-of-fit for the multivariable logistic regression model, indicating that the model was an appropriate fit (χ2 = 11.336, $$p \leq 0.183$$). The resulting ROC curves and associated AUC values of age, GCS score, and AGR are visualized in Figure 3. The optimal cutoff value for AGR as a predictor for GIB in patients with primary ICH was 6.759(the AUC was 0.713 with a corresponding sensitivity of $60.94\%$ and specificity of $70.5\%$, $95\%$ CI 0.680–0.745, $p \leq 0.001$; Figure 3). The AUC of the AGR level and the GCS score were comparable by Z-test ($$p \leq 0.0042$$), and the AUC of AGR was statistically higher than that of the GCS score. We conducted a 1:1 PSM analysis to match patients without GIB with patients with GIB, balancing the differences in the baseline characteristic. The significantly different in BV ≥ 30 ml, prior anticoagulation or antiplatelet therapy, MV used >24 h, and surgery between the two groups was balanced. The PSM analysis identified 64 pairs of patients, with 64 patients in each group. The characteristics of 128 matched patients based on the PSM (64 in each group) are summarized in Table 4. The matched GIB group had significantly higher AGR levels compared with the matched non-GIB group [7.47 (5.38–9.32) vs. 5.24(4.24–6.40), $p \leq 0.001$; Table 4, Figure 4A] in the univariate analysis (Figure 4A). After PSM, ROC analysis indicated an AUC of 0.747(the sensitivity was $65.62\%$, and the specificity was $75.0\%$, $95\%$ CI 0.662–0.819, $p \leq 0.001$) for AGR levels as an independent predictor of GIB in patients with ICH (Figure 4B). The higher AGR level was still an independent predictor of GIB. Interestingly, a significant difference was witnessed between the ICH location and GIB after PSM (Table 4). Patients in the poor prognosis group had a significantly higher AGR level than those in the good prognosis group (Figure 5A). The ROC analysis illustrated the predictive power of AGR (the AUC was 0.814, $95\%$ CI 0.785 to 0.8411, $p \leq 0.0001$; the sensitivity was $74.45\%$, and the specificity was $77.44\%$) for the outcome (Figure 5B), indicating that AGR was a prognostic predictor in patients with ICH. Patients with GIB had a statistically worse prognosis than those without GIB (Tables 2, 4, Figure 5C), either before or after PSM. Figure 5C depicts the 90-day mRS after illness onset for patients with GIB and without GBI. Patients with an AGR > 5.357 had a statistically worse prognosis than patients with an AGR ≤5.357. The distribution of mRS scores is demonstrated in Figure 5D for the two groups of patients, and a statistically significant difference was observed. Figure 5: Association of AGR with outcomes after 1:1 PSM analysis. (A) Violin plot for comparing AGR using Mann–Whitney U-tests between good and poor outcomes after PSM. (B) ROC analysis illustrated the predictive power of AGR (the AUC was 0.814, 95% CI 0.785 to 0.8411, p < 0.0001; the sensitivity was 74.45%, and the specificity was 77.44%) for the outcome after PSM. (C) Distributions of mRS scores at 90 days between the GIB and non-GIB groups. A statistical difference was found between the two groups. (D) Functional outcome at 90 days for patients with lower AGR (AGR > 5.357) and higher AGR (AGR ≤ 5.357). A statistically significant difference was found in the comparison between the two groups. (C, D) Proportions of patients within each score category on the 7-point scale (where 0 indicates no symptoms and 6 indicates death) at 90 days after illness onset. AGR, age-to-GCS ratio; AUC, area under the curve; CI, confidence interval; GCS, Glasgow Coma Scale; GIB, gastrointestinal bleeding; ICH, intracerebral hemorrhage; PSM, propensity score matching; ROC, Receiver operating curve. ## Discussion In the present study, we identified that a higher AGR level was an independent predictor of GIB following primary ICH. Furthermore, AGR was a statistically better predictor than the GCS score, a well-established predictor in predicting GIB. Even in the patients with selected PSM adjusted for the differences in BV ≥ 30 ml, prior anticoagulation or antiplatelet therapy, mechanical ventilation (MV) used >24 h, and surgery (they were balanced in PSM), our study demonstrated a similar conclusion that the higher AGR was still an independent predictor of GIB in patients with ICH. We additionally observed that AGR levels were statistically correlated with unfunctional 90-day outcomes. To the best of our knowledge, this is the first study to propose the concept of AGR and report the potential predictive power of elevated AGR for GIB in patients with ICH. GIB is a severe complication of acute ICH. Identified risk factors for GIB may help clinicians identify the risks of GIB before it develops. In accordance with previous reports, age was an independent predisposing factor for GIB, with a markedly increased risk after age 65 [23] and even more so in ICH. A retrospective review of 808 ICH cases demonstrated that elderly patients with ICH significantly increased the risk for GIB [5]. A study by Chen et al. found that $20.5\%$ of elderly patients with stroke developed GIB [24]. Similarly, accumulating studies have confirmed that advanced age is an independent predictor of GIB occurring after ICH (24–26). Elderly patients with ICH, especially those with other comorbidities (e.g., infection, renal insufficiency, and replacement therapy) (5–7, 9), are more likely to develop GIB. Furthermore, disruption of the axis between the central nervous system and the gastrointestinal system may lead to gastrointestinal bleeding or dyskinesia, which may be more pronounced in elderly patients with ICH [23, 24, 27]. Third, an early study documented that hyperactivity of the vagus nerve after brain injury, including ICH, may induce gastric acid hypersecretion and gastric mucosal damage, ultimately leading to GIB [28]. This condition is more common in elderly patients with cerebral hemorrhage. Numerous studies have previously confirmed that a lower GCS score is statically associated with GIB after ICH. However, the underlying mechanism is less understood. The GCS score has been used to assess the severity of neurological deficits [29], focusing on vital functions of the central nervous system, including eye-opening, language, and motor responses. The GCS score correlates with the level of consciousness. Thus, a decreased GCS score in the acute phase of ICH is associated with neurological impairment. A recent study has reported that impaired consciousness or conscious disturbance (GCS score <8) was the most critical risk factor associated with GIB in patients with stroke [24]. People with impaired consciousness are more likely to experience breaks in the axis between the digestive and nervous systems than those with clear consciousness [24, 27], resulting in GIB. Although multiple factors contribute to the development of GIB, ischemia and reperfusion injury are the primary pathophysiological mechanism leading to GIB [7]. The acute rise in intracranial pressure (ICP) associated with ICH may result in vagal hyperactivity and increased gastric acid secretion [8]. A larger hematoma may increase ICP when ICH occurs. A larger initial intracranial hematoma occurs, followed by an increase in ICP and a decrease in GCS score. A decreased GCS score may be one of the clinical manifestations of elevated ICP. Prior literature has demonstrated that the occurrence of GIB is significantly associated with surrogate markers of increased ICP [8, 30]. In the present study, decreased initial GCS score was observed in the GIB group after ICH, supporting a possible link between GCS score, ICP, and GIB. We speculated that the mechanisms were as follows. First, the raised ICP may cause vagal hyperactivity, leading subsequently to mucosal ischemia and increased gastric acid secretion, resulting in GIB. Second, a sharp increase in ICP may lead to excessive cholinergic activity, which increases gastric acid production. Third, elevated catecholamine concentrations in patients with ICH may cause vasoconstriction and ulceration of the gastrointestinal mucosa, ultimately leading to gastric bleeding [24]. Fourth, disruption of the axis between the central nervous system and the digestive system due to severe stroke (GCS score <8) may increase the risk of mucosal injury in the digestive system [23, 24]. Finally, following ICH stress, especially severe ICH (GCS score <8), reactive oxygen metabolites and various proinflammatory mediators increase [18, 31]. Alternation in these proinflammatory mediators, neutrophils, and mast cells may all potentially contribute to reperfusion-related gastric injury [8]. Our study verifies that an elevated AGR level can predict GIB following ICH. After multivariate and PSM analyses, AGR remained a valuable predictor for GIB following ICH. The ROC curve illustrated that the AUC of AGR was significantly higher than the GCS score (AGR vs. GCS score: $Z = 2.865$, $$p \leq 0.0042$$). The AUC of AGR was slightly higher than that of age without statistical significance ($Z = 1.798$, $$p \leq 0.0722$$). Our study indicated that AGR exhibited better performance than to initial GCS score in predicting GIB in patients with ICH. An interesting finding was that a significant difference was witnessed between the ICH location and GIB after PSM, although a statistically significant difference was not detected before PSM. This finding seemed inconsistent with prior studies [6, 7, 9, 26]. The different results may be related to the different classifications of ICH locations. Further studies are needed to determine the association between ICH location and the occurrence of GIB. Another finding was that AGR levels were statistically correlated with unfunctional 90-day outcomes. Our study has the following limitations. First, this was a single-center retrospective observational study, and multicenter prospective studies should corroborate the findings. Second, the timing of diagnosis and treatment of GIB after ICH onset was not controlled, but it was determined by each attending physician, which might affect the outcome. Third, the alignment of GCS scores between different physicians may impact this study's results. As a retrospective study, however, we could not evaluate this difference precisely. Fourth, the source of GIB could not be determined due to the inability to perform an endoscopy. Fifth, drugs such as glucocorticoids and antibiotics may have affected the results, but we did not assess their effects. Finally, our study did not include the National Institutes of Health Stroke Scale, which measures neurological impairment after stroke, due to the incompleteness of the records. ## Conclusion Higher AGR was associated with an increased risk of GIB and unfunctional 90-day outcomes in patients with primary ICH. Nevertheless, we need further studies with large-sample, multicenter, and prospective clinical trials to validate our results. ## 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 Second Affiliated Hospital of Fujian Medical University. The Ethics Committee waived the requirement of written informed consent for participation. ## Author contributions WQ, CL, and HG designed the study and drafted the manuscript. WQ, CL, FY, and ZP collected and analyzed data. JY and GW helped in the statistical analysis and result interpretation. ZP, WQ, and WH prepared the figures and interpreted the results. HG and WH supervised the study and revised the manuscript, were identified as the guarantor of the article, and taking responsibility for the integrity of the study as a whole. 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. Cordonnier C, Demchuk A, Ziai W, Anderson CS. 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--- title: 'Development of a tailored intervention targeting sedentary behavior and physical activity in people with stroke and diabetes: A qualitative study using a co-creation framework' authors: - Stefan Sjørslev Bodilsen - Mette Aadahl - Troels Wienecke - Trine Hørmann Thomsen journal: Frontiers in Rehabilitation Sciences year: 2023 pmcid: PMC9968882 doi: 10.3389/fresc.2023.1114537 license: CC BY 4.0 --- # Development of a tailored intervention targeting sedentary behavior and physical activity in people with stroke and diabetes: A qualitative study using a co-creation framework ## Abstract ### Purpose Type 2 diabetes and sedentary behavior pose serious health risks in stroke survivors. Using a co-creation framework, this study aimed to develop an intervention in collaboration with stroke survivors with type 2 diabetes, relatives, and cross-sectoral health care professionals to reduce sedentary behavior and increase physical activity. ### Materials and methods This qualitative explorative study used a co-creation framework consisting of a workshop and focus group interviews with stroke survivors with type 2 diabetes ($$n = 3$$), relative ($$n = 1$$), and health care professionals ($$n = 10$$) to develop the intervention. A content analysis was used to analyze data. ### Results The developed “Everyday Life is Rehabilitation” (ELiR) intervention consisted of a tailored 12-week home-based behavior change intervention with two consultations of action planning, goal setting, motivational interviewing, and fatigue management including education on sedentary behavior, physical activity, and fatigue. The intervention has a minimalistic setup using a double-page paper “Everyday Life is Rehabilitation” (ELiR) instrument making it implementable and tangible. ### Conclusions In this study, a theoretical framework was used to develop a tailored 12-week home-based behavior change intervention. Strategies to reduce sedentary behavior and increase physical activity through activities of daily living along with fatigue management in stroke survivors with type 2 diabetes were identified. ## Introduction Stroke and type 2 diabetes mellitus (T2DM) are both common diseases and among the top ten causes of disability worldwide [1] along with being some of the most costly diseases with expenses expected to increase (2–4). Stroke survivors with T2DM are at high risk of poor health and mortality compared to individuals living with only one of these diagnoses [5]. T2DM poses a four times higher risk of stroke [6] and is an independent risk factor for stroke recurrence [5]. Living with several health issues forces individuals to manage multiple negative consequences of their morbidities daily, such as impaired physical function and coordination of numerous interactions with the healthcare system. In addition, up to half of stroke survivors and individuals with T2DM experience fatigue [7, 8] and/or depression [9, 10]. Added up these factors make this group particularly vulnerable, prone to sedentary behavior (SB) [11, 12], poor ability to perform activities of daily living (ADL) [13], and low quality of life (QoL) [13]. SB is associated with cardiovascular disease, T2DM, and premature death [14]. Stroke survivors and individuals with T2DM spend more time with SB, are shown to have lower physical activity (PA) levels, and do not meet general PA guidelines compared to healthy peers [11, 12, 15, 16]. In addition, stroke survivors with T2DM are more sedentary than stroke survivors without T2DM [17]. PA is essential for preventing disability, improving physical function following stroke [18], and reducing mortality and morbidity in individuals with T2DM [19]. WHO has recently emphasized the health benefits of PA and limiting SB for individuals living with disabilities [20]. Due to the health benefits, numerous interventions with different methods and contradicting results focus on reducing SB and increasing PA among stroke survivors or individuals with T2DM patients (21–31). In stroke survivors; no effect of light PA on insulin was reported [32], however low amounts of PA [33] and prolonged periods of SB >90 min were both found to increase HbA1c levels [17]. These results provide an incentive to break up prolonged periods of SB and increase PA, however, this is not easy due to the complexity of the factors influencing SB and PA levels in stroke survivors and individuals with T2DM (34–38). Thus, it is important to explore which components should be included in multicomponent and tailored interventions [24, 39]. In recent years co-creation as a method, has become acknowledged for developing interventions when the development process is supported by behavior change theories [21, 40]. One such theory, the Social Cognitive Theory by Albert Bandura evolves around aspects of the behavior itself along with personal and environmental factors, hereunder action planning and motivation [41]. This theory has previously been used in co-creation processes [22, 26, 27] but co-creation frameworks have not been applied for a population of stroke survivors with T2DM [28]. However, co-creation may be a feasible way to obtain a better understanding of SB and PA behaviors in this population as T2DM, SB and low levels of PA increase the risk of stroke [5, 6, 14], poor post-stroke recovery [13, 42], morbidity, and mortality [18, 19]. Therefore, interventions that aim for beneficial effects of PA in stroke survivors with T2DM are desirable [43]. Using a co-creation framework, this study aimed to develop an intervention in collaboration with stroke survivors with T2DM, relatives, and cross-sectoral health care professionals (HCP) to reduce SB and increase PA. ## Design The co-creation of the intervention was based on the Social Cognitive Theory [41] and followed the framework by Leask et al. [ 44]. Five principles from the systematic approach PRODUCES were included in this framework: [1] Framing the aim of the study; [2] Sampling; [3] Manifesting ownership; [4] Defining the procedure; and [5] Evaluating (the co-creation process) were utilized for this qualitative explorative study throughout a workshop and three focus group interviews. The mix of a workshop and focus group interviews contributes to a diverse understanding and allow participants to discuss and reflect on each other's experiences stimulating group interactions and dynamics [45]. The role of the researchers and confidentiality within the group were clarified before beginning the workshop and focus group interviews. This study was conducted at Neurovascular Center at Zealand University Hospital, Roskilde, Denmark (NC) between $\frac{07}{02}$/2022–$\frac{02}{03}$/2022. This study followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist for reporting qualitative research [46] and the GUIDance for the reporting of intervention Development (GUIDED) [47]. ## Participants Eligible participants were recruited consecutively face to face from the NC. The inclusion criteria for stroke survivors with T2DM were ischemic stroke or intracerebral hemorrhage, diagnosed with T2DM by a specialist prior to their admission to NC, modified Rankin score (mRS) [48] 1–3 at discharge, discharged with a rehabilitation plan within 1–2 hospitalization days, able to ambulate independently, speak and understand Danish, able to give informed consent and motivated to contribute in a workshop and focus group interviews. Exclusion criteria were type 1 diabetes mellitus, dysphasia or cognitive impairments severe enough to preclude informed consent, medically unstable, considered too physically unstable by the clinical team to participate, or discharged to inpatient rehabilitation or a nursing home. The stroke survivors with T2DM were invited before discharge to the workshop and focus group interviews, which took place two to four weeks after discharge. Relatives were recruited as they visited and/or picked up their relatives and included if they were related to an individual with the above-mentioned criteria and were able to speak and understand Danish. Included relatives and patients were not to be related. Author SS engaged with management at NC and municipal rehabilitation centers and obtained permission to approach HCPs to request participation in the workshop and focus group interviews. Two occupational therapists (OTs), two physiotherapists (PTs), two nurses, and two stroke care coordinators working at stroke rehabilitation and linked community services were purposely invited. HCPs were included if they were working in stroke rehabilitation at a hospital or a municipal rehabilitation center, with more than three years of experience in stroke rehabilitation, and were able to speak and understand Danish. ## Workshop The workshop took place in an auditorium with participants seated in a U shape facing a presentation screen at NC. Following the framework [44] the workshop started with describing the purpose, framing the session, and facilitating ownership of the co-creation process by underlining equal status and participation, emphasizing their responsibilities and encouraging openness and control of the process. Subsequently, prepared in a written script, the workshop consisted of open questions and exercises to explore the knowledge and perspectives on SB and PA. Later, picture presentations and scenarios were used to clarify perspectives, feelings, and opinions on lifestyle and rehabilitation. The picture presentation consisted of images of middle-aged people which the research team found reflected the body, PA, quality of life, health, diet, and thoughts on the future. The scenarios were concrete situations from everyday life e.g., on how the participants would break up prolonged SB or implement more movement in their ADL. After a break the participants' overall perspectives were represented and the generalization of results, user-friendliness, and feasibility of the future intervention were discussed and optimized from the participants' perspectives. All discussions were taken in plenary. Author SS functioned as interviewer and facilitated the workshop while co-author TT functioned as a mediator/facilitator and took field notes on general observations, content, and elements for further elaboration in the focus group interviews. The workshop was not audio recorded. ## Focus group interviews For the focus group interviews, a meeting room was used with participants seated at a square table at NC. Participants were divided into three groups, one with stroke survivors with T2DM and relatives and two groups with HCPs. This was done as the stroke survivors with T2DM might feel less comfortable stating their opinions about their admission when HCP were present. Each group participated in one focus group interview. The focus group interviews were semi-structured, using the funnel model starting with broad questions before more specific questions [49], and focused on getting the participants to share and discuss opposite opinions and perspectives. Social dynamics and interactions between the participants were encouraged to create an informal atmosphere and get the participants to contribute actively and express as many different opinions and perspectives as possible [45]. All focus group interviews followed the interview guide (Supplementary file, S1) based on content and field notes from the workshop and previous literature [36, 37] with SS as interviewer and TT as co-interviewer. The guide provided the main structure, however, if relevant topics arose, the participants were encouraged to discuss and elaborate on them. The interviews were audio-recorded and TT took field notes on the atmosphere, interactions, reactions, and reflections. The stroke survivors with T2DM and relatives were asked about their daily living, views on SB, and, motivational factors for PA, and barriers to changing their lifestyle. HCPs were asked about their view on current rehabilitation, organizational factors, areas for improvement, lifestyle changes, and motivators for PA. ## Ethics Ethical approval was obtained from Region Zealand Ethics Committee on $\frac{13}{12}$/2021 (SJ950, EMN-2021-08261). This study complied with the Declaration of Helsinki and the General Data Protection Regulation (GDPR). All participants gave written informed consent and had no prior relation to the researchers or knowledge of this study. ## Analysis The focus group interviews were transcribed verbatim and pseudo-anonymized transcripts were analyzed using the content analysis method by Graneheim and Lundmann [50] alongside field notes. Data were inductively analyzed parallel by SS and TT in a triangulation process. Firstly, by familiarizing themselves with the data from the focus group interviews as a whole. Then separately focusing on manifest content using the complete focus group interview as a unit of analysis and afterwards comparing and agreeing upon the content. Abstracting meaning units into codes where first done separately then compared and agreed upon before continuing doing the same with sub-categories, categories, main categories, and lastly themes (Figure 1). Subsequently, SS and TT met to review consistency of abstraction levels, discuss categories, and condense these into themes for all focus group interviews. As no new coding items emerged when re-reading the meaning units, the analysis process continued with extraction of sub-categories, categories and themes. The research team translated the main themes from Danish into English. For transparency, Figure 1 provides an overview of the methodology and analysis process. Figure 1: Methodology flowchart. ## Enhancing rigor To ensure credibility and provide broad insights, a workshop and focus group interviews with three different participant groups were used. The researcher team was experienced in the field of stroke rehabilitation and/or in conducting qualitative research. The research team consists of SS; male physiotherapist and Ph.D. student with 5 years of experience in stroke rehabilitation, MA; female physiotherapist and clinical professor with more than 20 years of experience in SB, PA, and behavior change, TW; male MD and clinical associate professor with 14 years of experience in stroke and TT; female nurse and post.doc. with 19 years of clinical experience in neurology and 5 years of experience within mixed methods and conduct of everyday life. Collaboratively, before conducting this study, all activities, organization, and analysis were discussed addressing “pre-understanding”. SS and TT conducted the workshop and focus group interviews and critically reflected on data collection and validation of the findings during analysis. To enhance transferability a predefined description of the context and aim of the research and methodological considerations with notes on interpretations and decisions during the analysis were followed [51]. The analysis process was chosen in order to ensure transparency and to strengthen the credibility of the process (Figure 1 and Supplementary file, S2). Dependability was sought through describing methods as well as analytic strategies. To ensure confirmability, authors SB and TT repeatedly reread and reheard the interview material to stay close to the participants’ statements. As a part of this process, triangulation was intensively performed to challenge any pre-assumptions and misinterpretations to ensure trustworthiness [52]. ## Findings Three male stroke survivors with T2DM, one female relative, and 5 HCPs participated in the workshop. The same participants and 5 additional HCPs participated in three separate focus group interviews. One PT and one OT working as a stroke care coordinator were recruited from municipal rehabilitation section and one nurse from the Danish Stroke Association, an organization supporting individuals in life after stroke. The remaining stroke survivors with T2DM, relative, and HCP were recruited from NC. See Table 1 for characteristics of the stroke survivors with T2DM and HCP. The one relative participating in this study was a 76-year-old female, retired nurse, living with a male with an ischemic stroke (mRS of 1 with mild paralyses of his right arm). Table 1 \| Characteristics, Stroke survivors with T2DM \| Participants (n = 3) \| \| --- \| --- \| \| Ischemic stroke, n \| 2 \| \| Intracerebral hemorrhage, n \| 1 \| \| Age in years, mean \| 77 \| \| Male, n \| 3 \| \| Affected right side, n \| 3 \| \| Used a walking aid, n \| 1 \| \| Cohabitants, n \| 2 \| \| Working status retired, n \| 3 \| \| Level of education above high school, n \| 1 \| \| Characteristics, health care professionals \| Participants (n = 10) \| \| Age in years, mean \| 39.9 \| \| Female, n \| 8 \| \| Male, n \| 2 \| \| Physiotherapist, n \| 4 \| \| Occupational therapist, n \| 3 \| \| Nurse, n \| 3 \| \| Level of education above bachelor's degree, n \| 1 \| \| Years of experience in stroke rehabilitation, mean \| 11.1 \| Thirteen stroke survivors with T2DM were eligible. Hereof nine were invited to participate, the remaining four were not invited due to other related examinations. Four stroke survivors with T2DM declined to attend and two dropped out, all describing it as unmanageable and overwhelming in their current situation, for example due to duration of transport to the hospital, time point, and duration of the workshop and focus group interviews. Three relatives were invited to participate in this study, as they were present at the ward. One declined due to work and one dropped out due to the date of the workshop and focus group interviews. Forty-four HCP were assessed for eligibility; thirteen did not meet inclusion criteria. Management at NC and municipal rehabilitation centers asked fifteen random HCPs if they would be interested in participating after which SS informed and invited them. Of the fifteen HCP's three declined to participate since the workshop and focus group interviews took place during their leisure time. Two dropped out due to COVID-19. All participants were recruited from December 2021 to February 2022. The workshop lasted two hours and each of the three focus group interviews lasted approximately one hour. The full interview guide was used in all interviews. However, some items were discussed passionately by the stroke survivors with T2DM, including driving ban after stroke, discharge, sector transition, and the information procedure in the healthcare system, even though these items were not intently emphasized in the interview guide. Time for these discussions was allowed as they served as ice-breaking items/moments and led to new insights. The concurrent analysis collectively for all interviews resulted in five overarching themes [1] Everyday life is rehabilitation, [2] To preserve oneself, [3] Feeling lost in the sector transition, [4] Early initiation of process and tailored rehabilitation, and [5] Environment as support and motive power. Each theme is presented with quotation examples below and in Supplementary file S2 showing steps of the analysis process and abstraction level from meaning units to themes. The theme Everyday life is rehabilitation emerged from participants describing that the best way to ensure PA was to implement it into activities of everyday life. Overall, participants did not want to do more than they already do. Further participants described generic self-managed home-based exercises as overwhelming due to fatigue and lack of motivation and that PA should be rephrased to movement. To preserve oneself reflects a major motivator for the participants to engage in PA. Participants wanted to preserve their appearance both at home and in public and be able to perform their ADL independently. These factors seem to be closely linked to the feeling of being the same person as before the stroke and T2DM diagnosis. Basically, they wanted to keep living the life they knew and valued. However, fatigue was described as a barrier to achieving this. Feeling lost in the sector transition is based on participants and the relative describing information as hard to find and a lack of coordination in the healthcare system upon discharge. All participants called for information that was tangible, easy to understand, access, and could be brought home and across sectors. The HCPs also emphasized the importance of preparing and clarifying that recovering was going to be tough and fatiguing. Early initiation of process and tailored rehabilitation emanates from perspectives that PA had to be initiated early, tailored to the individuals, their specific needs and preferences to be most effective and motivating. The HCPs agreed that rehabilitation had to focus on the process of recovering and returning to their former lives as rehabilitation was not a singular stage. The final theme Environment as support and motive power originates from participants describing the physical environment as affecting the desire to be physically active at the hospital or at home. Moreover, participants stated that other stroke survivors with T2DM, relatives, and HCPs played a central role in providing motivation and emotional support to take care of their health and be less sedentary. ## Building the intervention Based on [1] a narrative review of relevant literature; [2] findings from the present study; [3] consultation with experienced clinicians and within the researcher team the “Everyday Life is Rehabilitation” (ELiR) intervention was developed. The ELiR intervention is a tailored 12-week home-based behavior change intervention delivered on a double-page paper instrument containing [1] action planning and goal setting, [2] motivational interviewing, [3] education on SB, PA, and sector translation, and [4] fatigue management. The instrument will work as a conversational, inspirational, and goal-setting instrument tailored by participants filling in their answers. The intervention consists of two consultations 3–5 days and 6 weeks after discharge between the participant and an HCP in their home. The participant will have the instrument handed out with additional information upon discharge from the hospital allowing them to read the instrument before the first consultation. The intervention was designed to function in hospital or during rehabilitation as an urgent need for a cross-sectoral instrument became apparent through the focus group interviews (Figure 3). Figure 2: Enrolment flowchart. Figure 3: The “Everyday life is Rehabilitation” instrument. Action planning and goal setting were incorporated on the front-page (Figure 3) emerging from the themes “Everyday life is rehabilitation” and “Early initiation of process and tailored rehabilitation” as the participants unanimously stated the need for individualization of rehabilitation to their everyday life and allowing them to decide which ADL will be modifiable to ensure sustainable changes. It was important for the participants to have the actions and goals written for them to be committed to and for HCP to follow up on. Action planning and goal setting were found during the narrative review to be effective in interventions for reducing SB and increasing PA [21, 22, 26, 27] which inspired the use in ELiR. For execution, the front-page (Figure 3) has pictograms with examples of ADL, which could be altered and thereby facilitate more movement, and a section to note three ADL movement actions and goals meaningful to each participant. Motivational interviewing techniques [53] will be used to identify the participant's current SB and PA behavior and the interviewer will help the participant to understand how their behavior affects their health using the instructions (Supplementary file S3). Further, the interviewer focuses on helping the participant describe their motivation for changing their behavior and helping them note their motivation for more movement e.g., staying independent or being able to play with their grandkids. The motivational interviewing was incorporated into the ELiR based on the themes “To preserve oneself” and “Environment as support and motive power” and as it is feasible [22, 26] and effective [54]. This was implemented on the front-page (Figure 3) as the participants agreed it was important to identify motivational factors. Education on SB, PA, and sector transition was needed as the participants described information as hard to find and described feeling lost in the sector transition emerging in the theme “Feeling lost in the sector transition”. The front-page (Figure 3) contains a QR code linking to information on stroke and sector transition. The second-page (Figure 3) has bullet points on how movement positively affects health and fatigue along with a QR code linking to an educational video on fatigue. Fatigue management was included as all participants agreed it was important to handle fatigue as it affects all parts of one's life and behavior and is a major barrier to PA and behavior change as presented in the third quote. However, no high evidence-based fatigue management tool was found [55]. In an attempt to map fatigue tendencies, the second-page (Figure 3) contains 1) a diagram to note ADL that give and drain energy, 2) noting activities that would optimize energy and 3) noting on a clock face time points throughout the day where the participant feels most energized. These elements were implemented into the ELiR based on experiences from an OT with more than 20 years of experience in fatigue management and literature (55–58), which describe written tasks and activity management as effective tools. This may help participants in clarifying what affects them during their everyday along with giving the HPCs an insight into how and when to help them manage their fatigue and behavior change. The ELiR intervention has an appertaining instruction with a concrete guide to standardize the intervention (Supplementary file S3). All aspects of the ELiR intervention were discussed within the researcher team, face-validated with other stroke survivors with T2DM, the same HCPs from the hospital and municipal rehabilitation and an OT with more than 15 years of fatigue management experience. ## Discussion The cross-sectoral ELiR instrument is based on the five identified themes in this study where stroke survivors with T2DM described wanting to do what they used to in order to preserve oneself, that movement should be integrated into ADL for the everyday to be rehabilitation, wanting information and support during the sector transition along with that fatigue should be identified and managed as it was a barrier to movement. By using one instrument and having two consultations, the intervention is relatively minimalistic potentially making it easy to implement and use in a hospital, rehabilitation, or community setting in the future. ## Integrating movement into activities of daily living The participants of this study did not want to change their everyday life, however, stated that reduction of SB and PA should be implemented into their everyday life which are self-contradictory. This may explain the inconsistent methods and results of studies exploring the effect of reducing SB and increasing PA using behavioral or lifestyle interventions in stroke or T2DM populations (21, 29–31). Saunders et al. [ 21] reported in a systematic review that multi-component lifestyle interventions, SB, and PA interventions did not reduce mortality, cerebrovascular events or sedentary time. In a systematic review by Aguiar et al. [ 31] some interventions in stroke populations were effective in improving daily PA when including e.g., aerobic exercise, resistance training, home-based exercise, and health information. This was likewise the case in individuals with T2DM where regular exercise and diet interventions were effective in improving fasting glucose and exercise outcomes [29, 30]. The above-mentioned intervention components, which may not be realistic to implement in a municipal setting, differ from this intervention, which focuses on movement adapted to ADL rather than e.g., aerobic exercise or resistance training. In addition, the ELiR intervention focuses on total PA throughout the day, which was reported to be associated with glucose and insulin sensitivity in stroke survivors [33]. However, further research on SB in stroke survivors is warranted since high-quality studies are missing, as are interventions including action planning, inclusion of the home environment, and education [21] which are elements in the ELiR intervention. ## Fatigue management to reduce sedentary behavior and increase physical activity Previous interventions on SB and PA in individuals with T2DM and stroke survivors were all feasible and safe with elements of tailoring, goal setting, education, and counseling which are similar to the elements in the ELiR intervention. Depression and fatigue are both interdependent and prevalent in up to half of every individual with T2DM and stroke survivors (7–10, 55, 59). Fatigue was reported [34, 35] and described as a barrier to movement by the participants and low levels of PA are associated with a higher risk of post-stroke depression [9]. Hence, tailoring and goal setting of the ELiR intervention is important to help the participants change behavior and implement more movement despite fatigue and by that potentially reduce risks of depression and other related health issues. To do this participants define and note which ADL can be modified to change behavior which facilitates and ensures that the intervention will be as individual as possible making it more likely to be as successful as other interventions. Education on the harm of SB and gain of PA has earlier been used for helping the participant to understand how their lifestyle affects their health [22, 60]. The findings in this study suggest that the participants were fully aware of the harmful effects of their lifestyle. However, participants call for a different approach in tailoring rehabilitation efforts as they described themselves as too fatigued to act and that interventions often not seemed to be incorporable into everyday life. Fatigue was also reported to prevent breaking up prolonged SB and as a barrier to rehabilitation adherence [35, 37, 38, 61, 62]. However, fatigue management has not yet been incorporated effectively into intervention studies in stroke survivors [63] and clinicians depend on their own experiences [57] even though post-stroke fatigue may be aggravated by SB and helped by PA [18]. Therefore, the ELiR intervention focus on the positive effects of breaking up prolonged SB and on encouraging the participants to be aware that every move counts when incorporating PA despite fatigue. This approach is likewise recommended for adults with chronic conditions, which may be more doable than structured PA [20]. ## An operational tool Previously reported studies (21–23, 26, 27, 64) mentioned above have explored more comprehensive interventions compared to the ELiR intervention with regard to equipment and interactions between participants and HCP with limited success. Most interventions used more than two consultations on behavior change strategies, goal setting, education, and supervised training (21–23, 26, 27, 64) in contrast to the ELiR intervention which contains two consultations. This may influence the efficacy of the intervention due to fewer interactions, more dropouts, and participants having to take responsibility for their own health. However, it may also make the intervention more manageable for HCPs, implementable in the clinic, and more achievable for the participants as adherence to PA and home exercise programs are low [61, 62]. The minimalistic scope of the ELiR intervention was prioritized as the HCP stated to need something tangible, user-friendly, and easy to implement into their practice that was not time-consuming and expensive, thus making the rehabilitation centers in municipalities more likely to implement the intervention as a standard approach ## Limitations, strengths, and future directions Different approaches for co-creating interventions have been utilized (25–27), with strengths and limitations to every approach, yet it is important to adapt the comprehensiveness of the co-creation process to the setting of the study [65]. The trustworthiness of the findings in this study was enhanced by using a well-described framework, an interview guide similar to previous studies [36, 37] along with triangulation using multiple qualitative methods for data collection (observations, field notes, and interviews). Further, researchers analyzed the transcribed interviews separately and subsequently synthesized and identified similarities and differences [52]. Of the screened patients admitted to NC, thirteen were eligible which was relatively few due to in-hospital rehabilitation being performed at other hospitals. Only male stroke survivors and one female relative were included which were not necessarily representative of the target population and may not be adequate for reaching data saturation. However, data saturation was not viewed as a necessity for continuing the study as stroke survivors were hard to recruit and the intention was to gain insight into everyday life post-stroke. This small and homogenous representation may create gender-bias and affect the co-creation process by them being underrepresented compared to HCPs possibly causing the stroke survivors statements to be supplementary rather than co-creative. Nevertheless, the three stroke survivors' statements were, in terms of content for the development process, prioritized higher than HCP's statements to compensate for the participant ratio. Thus, the HCP's statements substantiated and supported the statements of the stroke survivors and placed those into a clinical perspective. In future studies it is important to plan the recruitment of participants for the co-creations process, in order to secure the planned representation of e.g., stroke survivors. The workshop and focus group interviews took place two to four weeks after discharge which is within the intervention period enhancing the relevance of perspectives. Participants may represent a resourceful part of the population as co-creation demands high levels of attendance and participation [66]. The representation of HCPs was extensive and adequate with attendance from multiple sectors and professions. The intention is that the ELiR intervention should be individualized based on participants' resources and easy to follow-up and bring to rehabilitation sessions. Thereby, the instrument was co-created to provide a tailored instrument to enhance the efficacy of the rehabilitation and improve communication across sectors. These findings may be transferable to other chronic patients in a similar context even though the findings also represent data exclusive to this patient group. The feasibility and efficacy of the intervention will be tested in subsequent studies to further inform the development and usability. ## Conclusion A theoretical, co-creation framework was systematically used in this study to develop a tailored 12-week home-based behavior change intervention. The process included stroke survivors with T2DM, relative, and HCP's perspectives, and targets the implementation of movement into activities of daily living along with fatigue management in reducing sedentary behavior and increasing physical activity. Stroke survivors with T2DM, relative, and HCPs were actively engaged throughout the co-creation process increasing the likelihood of an acceptable and implementable intervention. ## 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 Region Zealand Ethics Committe. The patients/participants provided their written informed consent to participate in this study. ## Author contributions SB, MA, TW, TT contributed to conception and design of the study. SS and TT performed the workshop, interviews and analysis. All authors contributed in the development of the intervention. SS wrote the first draft of 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/fresc.2023.1114537/full#supplementary-material. ## References 1. 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--- title: 'Effects of resourcefulness on internet game addiction among college students: The mediating role of anxiety and the moderating role of gender' authors: - Yan Zhang - Yun-Ling Zhong - Jing Luo - Jin-Long He - Cen Lin - Jaclene A. Zauszniewski - Jin-Hui Zhou - Ying Chen - Chun-Yan Wu - Shu-Rui Wang - Zheng-Huan Li - Jing Tang - Wan-Ning Li - Jing Wu - Jia-Ming Luo journal: Frontiers in Public Health year: 2023 pmcid: PMC9968884 doi: 10.3389/fpubh.2023.986550 license: CC BY 4.0 --- # Effects of resourcefulness on internet game addiction among college students: The mediating role of anxiety and the moderating role of gender ## Abstract ### Introduction The mechanism of internet game addiction is unclear. Whether anxiety mediates between resourcefulness and internet game addiction and whether gender affect its mediation role have not been studied previously. ### Methods A total of 4,889 college students from a college in southwest China were included in this study to complete the investigation, in which three questionnaires were used for evaluation. ### Results Pearson's correlation analysis indicated a remarkable negative correlation between resourcefulness with internet game addiction and anxiety, as well as a significant positive correlation between anxiety and this addiction. The structural equation model confirmed the mediation role of anxiety. The multi-group analysis confirmed the moderating role of gender in the mediation model. ### Discussion These findings have advanced the results of existing studies, indicating the buffering effect of resourcefulness on internet game addiction and revealing the potential mechanism of this relationship. ## Introduction With the popularity of the internet comes potential problems. There is evidence that some college students are using the internet irrationally and are even addicted to online games [1]. Internet game addiction refers to “the persistent and repeated use of the internet to engage in games that result in impairment of daily life, and the tendency to isolate oneself socially” [2, 3]. This phenomenon is prevalent around the world, with one meta-analysis showing a global prevalence of $3.05\%$ for internet game addiction [4]. Studies have also reported that the prevalence of internet game addiction is $11\%$ in the Chinese college student population [5]. Internet game addiction has been officially listed under ICD-11 (the International Classification of Diseases 11th Revision) and has become a public health issue that can have a range of negative effects on college students. Internet game addiction could lead to sleep insufficiency, depression, academic difficulties, and poor creativity and productivity. It impairs the physiological, psychological and social functioning of college students [6]. “Resourcefulness is a combination of an individual's ability to carry out everyday tasks independently and the ability to seek help from outside sources when appropriate” [7, 8]. It typically comprises of personal and social dimensions. “ Personal resourcefulness” is the ability of an individual to maintain daily life independently, for example by using personal effort or internal resources to achieve goals in the face of potentially adverse stressful situations and stimuli [7]. “ Social resourcefulness” is the ability to seek help from formal or informal sources when an individual is unable to deal with a problem on his or her own [8]. Although there are no previous theories that directly discuss resourcefulness and internet game addiction, Zauszniewski's Theory of Resourcefulness and Quality of Life© may provide support. This theory suggests that resourcefulness has a direct impact on a person's quality of life and that internet game addiction can be considered an indicator of quality of life (8–10). Although this is the first study of personal and social resourcefulness and internet game addiction, there have been empirical studies that have examined the relationship between closely related construct and addictive behavior. For example, two studies have examined the relationship between “learned resourcefulness” and addictive behavior. In these studies, “learned resourcefulness” was operationalized through self-control, which did not consider seeking help from others as a characteristic of resourcefulness [8]. Accordingly, Kennett et al. reported that persons with greater “learned resourcefulness” were better able to change their alcohol-drinking and smoking habits [11]. Bulut and Zeren found that Internet addiction could be predicted by “learned resourcefulness” [12]. In terms of social resourcefulness, Rapp et al. reported that social resourcefulness leads to more social support [13]. According to the main effect model of social support, social support affects internet addiction [14], and this relationship has similarly been tested in empirical studies (15–17). Taken together, the Theory of Resourcefulness and Quality of Life©, the main effect model of social support [14], and related empirical studies all suggest that resourcefulness may be an important variable in predicting internet game addiction. The mechanism of the influence of resourcefulness on internet game addiction is unclear, and whether anxiety plays a mediating role has not been studied before. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model was chosen as the framework for this study. This model emphasizes that addictive behaviors are the consequence of interactions between predisposing factors (Person's characteristics), mediators (affective and cognitive responses) and execution (Figure 1) [18]. In this study, resourcefulness is a relatively stable personal characteristic, and it refers to the ability to learn [19, 20]. Therefore, resourcefulness is considered a predisposing variable. Anxiety is included as a mediating variable. Internet game addiction is considered a dependent variable. Specifically, individuals who are not good at using personal and social resources may face real-world difficulties and a worse emotional state [21], and they may develop internet addiction in regulating emotions [22]. Regarding personal resourcefulness, studies have shown that anxiety and depression mediate between personal resourcefulness and life satisfaction [23]. As for social dimension, social resourcefulness will bring more social support [13]. Social support may indirectly affects human behaviors (e.g., internet addiction and suicide) via emotional state [14]. This is supported by a related empirical study that suggests depression mediates between social support and suicidal ideation [24]. Based on the reasoning above, the relationship between resourcefulness and internet gaming addiction should consider a psychological model that includes anxiety. Figure 1: I-PACE model for internet addiction. The Theory of Resourcefulness and Quality of Life© has four components, i.e., antecedent situational factors (internal demographic characteristics and external environmental factors), process regulators (perceptive, cognitive, affective, motivational, and volitional), resourcefulness (personal resourcefulness and social resourcefulness), and life quality indicators [8]. Resourcefulness can directly influence quality of life, and both anxiety and internet game addiction were conceptualized as quality of life indicators in this study. Zauszniewski et al. mentioned in a previous study resourcefulness influenced anxiety (anxiety as a quality of life indicator) and vice versa (anxiety as a process regulator) [25], and this study focused on the former. Previous studies in caregivers of people with dementia [26], older adults [27], adolescents [28], and pregnant women [29] have found that resourcefulness negatively predicts anxiety, depression. And, anxiety is a major risk factor for Internet addiction, with both cross-sectional and longitudinal studies providing empirical evidence for the relationship [30, 31]. One study found that cognition mediates the relationship between “learned resourcefulness” and adaptive functioning [32]. Another study among college students showed that self-control influenced social anxiety, which in turn led to negative emotions and internet addiction [33]. Thus, anxiety may play a mediating role. In evaluating the factors associated with online game addiction, researchers consider demographic factors. Gender differences in addictive behaviors have been studied and are important evidence in understanding online game addiction. Some studies have noted that compared with females, males have poorer self-control ability and are bad at seeking social support [34], so they may have a higher risk of developing negative emotions and online games [35, 36]. However, the opposite finding still exists, with females scoring higher than males in online gaming addiction [37]. In addition, there are studies that do not find an association between gender and online addiction, possibly due to factors such as the popularity of the internet and the purpose of use [38]. In summary, although no unanimous conclusion has been reached regarding the effect of gender on self-control, anxiety, and internet game addiction, the greater extent suggests that resourcefulness, anxiety, and internet game addiction may change with gender differences, and the mediating role of anxiety may also vary. In summary, this study proposes the hypotheses that: resourcefulness has a predictive effect on internet game addiction, and anxiety is one of the mediating factor. This study also hypothesize that gender moderates the mediating effect, i.e., the mediating role of anxiety vary between male and female groups. ## Participants This is a cross-sectional survey study conducted in Southwest China in October 2022. A convenient sampling method was used to administer a questionnaire to students of clinical medicine, nursing science, medical imagology, clinical medicine of traditional Chinese medicine and western medicine, anesthesiology, pharmacy, preventive medicine, stomatology, optometric medicine and ophthalmology, medical laboratory technology, midwifery, management science, foreign language and culture, biomedical engineering, and athletic rehabilitation. Inclusion criteria: college students at school; signed an informed consent form and voluntarily joined the study. Exclusion criteria: those with severe mental disorders that prevented them from cooperating with the survey; those who did not wish to join the study. The researcher distributed the questionnaire star QR code or link to the subjects through social media platforms, and the participants voluntarily filled in the questionnaire after reading the informed consent form. A total of 5,523 questionnaires were distributed in this study and 4,899 valid questionnaires were collected, with a response rate of $88.7\%$. Among them, 1,758 ($35.9\%$) were male students and 3,141 ($64.1\%$) were female students. The number of freshmen to 5th-year students were: 2,003 ($40.9\%$), 970 ($19.8\%$), 1,219 ($24.9\%$), 574 ($11.7\%$), and 133 ($2.7\%$), respectively. The age of the subjects ranged from 16 to 25 years, with an average age of 19.54 ± 1.46 years. The Ethics Committee of North Sichuan Medical College confirmed that the present study adhered to ethical principles. ## Demographics Several demographic variables were collected for this study: age, grade, major, gender (1 = boy, 2 = girl), place of residence (1 = urban, 2 = rural), and whether or not the child was an only child (1 = parent with one child, 2 = parent with more than one child). ## Resourcefulness Resourcefulness was measured using the Chinese version of the Resourcefulness Scale©, which was the translated version by Lai and Wang et al. [ 39, 40] of RS© developed by Zauszniewski et al. [ 7]. The C-RS© consists of two dimensions of personal resourcefulness (16 items) and social resourcefulness (12 items). The Likert 6-point scale is used, with higher scores indicating higher levels of resourcefulness. The Cronbach's alpha coefficient for the Chinese version of the scale was 0.898, and those for the dimensions were 0.875 and 0.797. ## Anxiety SAS was designed by Zung [41]. This study used the Chinese translation of the SAS to measure anxiety [42]. The scale has 20 items and is rated on a 4-point scale, with higher scores indicating higher levels of anxiety. The Cronbach's alpha coefficient for the Chinese translated version of the SAS in this study was 0.844. ## Internet game addiction Nine diagnostic criteria for internet game disorder were put forward in The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [43, 44]. On this basis, Pontes et al. developed Nine-Item Internet Gaming Disorder Scale (IGDS9-SF) [45]. This study used the revised IGDS9-SF to measure internet game addiction [46, 47]. The scale has nine question items and is scored on a five-point scale, with higher scores associated with higher levels of internet game addiction. The Cronbach's alpha coefficient for the revised IGDS9-SF in this study was 0.902. ## Quality control The research design phase involved forming a research team with psychiatry and psychology professionals to discuss the research design, select survey instruments and determine survey procedures and methods. The data collection phase was conducted in a classroom setting, with the researcher distributing the QR code of wjx.cn through social media platforms and subjects scanning the code to access the web-based questionnaire system. Before subjects completed the survey, they were required to read the purpose of the survey, the method of completion, informed consent, and informed that the survey was anonymous. After consenting, subjects voluntarily participated in the online questionnaire to ensure that the study data were authentic and valid. In the stage of data completion and analysis, researchers exported the data from wjx.cn and imported them into the SPSS24.0 software. The data analysts were trained uniformly, thus ensuring the accuracy of data. ## Data analysis The data were analyzed using SPSS 24.0. Cronbach's alpha coefficient represented the reliability of the questionnaire. Harman's one-factor test was performed to assess the common method bias. Pearson's correlation analysis was conducted to analyze the correlation among variables. A structural equation model was established by Amos 24.0. The mediating effect was tested by the percentile Bootstrap method for bias calibration; the moderating effect was measured by multi-group analysis. Due to the impact of sample size on chi-square values, the model's fitting result was evaluated by comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). According to the researcher's recommendation, CFI and TLI > 0.90 and RMSEA and SRMR < 0.08 were used as the criteria to evaluate the goodness of fit data of the model. ## Common method biases analyses To control the possible common method bias, this study used questionnaires with reverse scoring and different rating scales. The Harman's One-factor Test was used to test for common method bias. The results showed that nine factors with a characteristic root >1. The first factor explained $17.53\%$ of the variation, which was much less than the critical value of $40\%$ [48]. Therefore, there was no significant common method bias in this study. ## Correlation analysis of variables Pearson's correlation analysis was conducted on resourcefulness, anxiety and internet game addiction (Table 1). The results showed that resourcefulness was significantly and negatively correlated with both internet game addiction (r = −0.150, $P \leq 0.001$) and anxiety (r = −0.243, $P \leq 0.001$). Besides, anxiety was significantly and positively correlated with internet game addiction ($r = 0.322$, $P \leq 0.001$). This provided initial support for further testing. Table 1 \| Variable \| M \| SD \| 1 \| 2 \| 3 \| 4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 Gendera \| 0.64 \| 0.48 \| 1 \| \| \| \| \| 2 Resourcefulness \| 3.39 \| 0.53 \| −0.025 \| 1 \| \| \| \| 3 Anxiety \| 1.64 \| 0.36 \| −0.058* \| −0.243* \| 1 \| \| \| 4 Internet game addiction \| 1.74 \| 0.66 \| −0.365* \| −0.150* \| 0.322* \| 1.0 \| ## Analysis of the mediating effect of anxiety To address the problem of latent variables containing multiple observing indexes, this study used a completely random packing method to pack resourcefulness, anxiety, and internet game addiction into 2, 3, and 3 indexes, respectively [49]. A structural equation model was developed with resourcefulness as the independent variable, internet game addiction as the dependent variable, and anxiety as the mediating variable (Figure 2). The results showed that the model fitted well, with all fit indices within a reasonable range (χ2/df = 16.87, CFI = 0.98, TLI = 0.97, SRMR = 0.03, RMSEA = 0.05). Based on these fitting results, the non-parametric percentile Bootstrap method for bias calibration was used to test the mediating effect and evaluate the confidence interval (CI), during which the sampling was repeated 5,000 times. Results showed that resourcefulness negatively predicted internet game addiction (−0.16, $P \leq 0.001$) and anxiety partially mediated the process (β = −0.09, $95\%$ CI = −0.1 to −0.07), accounting for $56.25\%$ of the total effect. The $95\%$ CI did not include 0, which verified the mediating effect of anxiety (Table 2). Figure 2:** Model for the mediating effect of anxiety. *$p \leq 0.001.$ TABLE_PLACEHOLDER:Table 2 ## Analysis of the moderating effect Multi-group analysis was conducted to identify whether the path coefficients differ significantly between females and males. Unconstrained model M1, structural weight model M2, and structural residual model M3 were developed, respectively. As displayed in Table 3, the results showed that the constrained models (M2 and M3) were significantly different from the unconstrained model (M1; $P \leq 0.001$), suggesting significant gender difference. Further comparing the difference in the coefficients, we found gender played a role in the following pathways. Table 3 \| Model \| χ2 \| Df \| CFI \| RMSEA \| Δχ2 \| Δdf \| P \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| M1 \| 320.79 \| 34 \| 0.98 \| 0.04 \| - \| - \| - \| \| M2 \| 350.71 \| 39 \| 0.98 \| 0.04 \| 29.93 \| 5 \| <0.001 \| \| M3 \| 387.1 \| 42 \| 0.98 \| 0.04 \| 66.31 \| 8 \| <0.001 \| From resourcefulness to anxiety, the path coefficients were −0.24 ($p \leq 0.001$) and −0.29 ($p \leq 0.001$) for the male and female groups, respectively. The absolute value of critical ratios for differences between parameters was 4.66 (>1.96), significantly different at the 0.05 level. Resourcefulness could reduce anxiety more effectively in females than in males. From anxiety to internet game addiction, the path coefficients were 0.40 ($p \leq 0.001$) and 0.30 ($p \leq 0.001$) for the male and female groups, respectively. The critical ratios for differences between parameters showed an absolute value of 3.08 (>1.96), significantly different at the 0.05 level. The predictive effect of anxiety on internet game addiction was stronger for males compared to females. ## Discussion This study examined the relationship between resourcefulness, anxiety and internet game addiction among Chinese college students. This is the first time that the potential influence mechanism of resourcefulness on college students' addiction to online games has been discussed. This study extended the results of previous studies on internet game addiction and its internal psychological mechanism. Also, it provided an empirical reference for preventing and intervening such addiction among college students. ## The relationship between resourcefulness and internet game addiction The present study found that resourcefulness significantly and negatively predicted internet game addiction, supporting our hypothesis. That is, having resourcefulness is effective in reducing the risk of internet game addiction, which is similar to previous findings. For example, Bulut and Zeren found that learned resourcefulness reduced online addiction [12]. One possible explanation for this is that resourcefulness is similar to a resilient defense that serves as a protective buffer. Individuals who possess resourcefulness are more resilient, have better coordination and adaptability, and are less likely to become addicted to the Internet [50]. Another possible explanation is that individuals who lack resourcefulness have a poor level of self-control. Their behaviors are mainly controlled by immediate gratification and short-term goals. They tend to seek immediate pleasure and rewards in online games [51, 52]. ## The mediating effect of anxiety The main finding of the present study is that anxiety partially mediated the relationship between resourcefulness and internet game addiction, supporting our hypothesis, which, to our knowledge, has not been directly examined. Thus, our study, to a certain extent, fills the gap in exploring the influence mechanism of resourcefulness on internet game addiction. The mediating role of anxiety can be explained by the I-PACE model, which assumes that the occurrence and development of addictive behaviors result from predisposing variables, affective and cognitive responses, and executive [18]. In this study, resourcefulness serves as the independent variable that influences internet game addiction through anxiety. A person's inability to self-regulate and lack of social support may lead to real-world difficulties and worsen the individual's emotional state [21]. They may seek satisfaction in the virtual online world when regulating their emotions and develop a dependency [53]. Overall, individuals who lack resourcefulness are prone to develop more anxiety. In this instance, they often vent their anxiety by playing online games, which will easily result in addiction to these games. Conversely, individuals who have resourcefulness may have less anxiety and are less vulnerable to internet game addiction. ## The moderating effect of gender Another important finding of this study was that the effect of resourcefulness on anxiety was more obvious in females than in males. The “protective-responsiveness model” states that the effect of a protective factor is stronger when another risk factor is high [54]. Resourcefulness is like an elastic line of defense, performing protective and buffer functions. Studies have shown that females are more emotionally sensitive and may be at higher risk for anxiety (55–57). Thus, the effect of resourcefulness on anxiety is more obvious in females. In addition, the effect of anxiety on internet game addiction was stronger in males than in females. One explanation for this result could be that social norms and expectations of males play a reinforcing role. According to social role theory, males and females play different roles in society. In the Chinese cultural context, males are portrayed as independent, strong and successful, while females are portrayed as docile, affectionate and easy to ask for help. They may behave according to their own understanding of their roles and society's expectations of them [58]. Among people who feel anxious, females may be more inclined to express themselves, shop, and seek interpersonal support, influenced by social roles and norms. Whereas, males may be more inclined to work through it alone, online gaming would be an option. Another possible explanation is that neural mechanisms make the effect of anxiety on internet game addiction more obvious in males. According to the gender difference in neural mechanism, gaming cues will induce a stronger desire in males, so they can easily experience something new through online games. In the meantime, the competitive structure in these games is more attractive to males, which activates the area of the brain related to awards [59, 60]. ## Limitations and implications The findings confirm that resourcefulness can negatively predict internet game addiction, with anxiety playing the meditating role. Besides, gender moderates this mediating effect. These findings to a certain extent enrich the existing research on internet game addiction and its underlying psychological mechanisms. The present study suggests that we should pay more attention to college students' state in playing online games and improve their mental health by enhancing resourcefulness and balancing emotions. Further, we should make greater efforts to evaluate and educate females about their resourcefulness level and males about their emotion management. However, there are some limitations in the present study, which indicates the direction of future research. First, despite the large sample size, more representative samples shall be selected from a wider range. Besides, the large sample size may affect the significance of correlation analysis, as well as mediation, and moderating effects. Second, we cannot conduct causal inference since this is a cross-sectional study. In this respect, a longitudinal tracking study shall be conducted to make a more detailed exploration. Third, there may be some other potential variables that affect the results. For instance, depression may also mediate the association between resourcefulness and internet game addiction. So we shall explore other variables further to provide more references for the intervention and treatment of individuals' internet game addiction. ## Conclusion Resourcefulness can negatively predict the internet game addiction of individuals in a significant way. Resourcefulness can indirectly predict the internet game addiction of individuals through anxiety. Anxiety mediates the relationship of resourcefulness and internet game addiction differently between males and females. ## 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 North Sichuan Medical College. The patients/participants or patients/participants' legal guardians/next of kin provided their written informed consent to participate in this study. ## Author contributions YZ was involved in the study design and the composing of this manuscript. J-ML and Y-LZ provided the subject of this study and critically revised this manuscript. JL, J-LH, and CL searched and reviewed the references. JZ provided Resourcefulness Scale©, constructive comments, and revised the manuscript. J-HZ completed the data analysis. YC and JW modified this manuscript. C-YW, S-RW, Z-HL, JT, and W-NL collected the data. All authors contributed to this 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. ## References 1. Pettorruso M, Valle S, Cavic E, Martinotti G, Giannantonio D, Grant JE. Problematic Internet use (PIU), personality profiles and emotion dysregulation in a cohort of young adults: Trajectories from risky behaviors to addiction. Psychiatry Res. (2020) 289 113036. 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--- title: 'Positive faecal immunochemical test predicts the onset of inflammatory bowel disease: A nationwide, propensity score-matched study' authors: - Eunyoung Lee - Gil Ho Lee - Bumhee Park - Sung Soo Ahn - Choong-Kyun Noh journal: Frontiers in Immunology year: 2023 pmcid: PMC9968927 doi: 10.3389/fimmu.2023.1128736 license: CC BY 4.0 --- # Positive faecal immunochemical test predicts the onset of inflammatory bowel disease: A nationwide, propensity score-matched study ## Abstract ### Background & aims The faecal immunochemical test (FIT), a non-invasive test for screening colorectal cancer (CRC), is being increasingly understood to reflect heightened inflammation. We aimed to investigate the association between abnormal FIT results and onset of inflammatory bowel disease (IBD), a disease characterized with chronic gut mucosal inflammation. ### Methods Participants in the Korean National Cancer Screening Program for CRC between 2009–2013 were analysed and divided into positive and negative FIT result groups. The incidence rates of IBD after screening were calculated after excluding cases of haemorrhoids, CRC, and IBD at baseline. Cox proportional hazard analyses were used to identify independent risk factors for IBD occurrence during follow-up, and 1:2 propensity score matching was performed as a sensitivity analysis. ### Results In total, 229,594 and 815,361 participants were assigned to the positive and negative FIT result groups, respectively. The age- and sex-adjusted incidence rates of IBD in participants with positive and negative test results were 1.72 and 0.50 per 10,000 person-years, respectively. Adjusted Cox analysis revealed that FIT positivity was associated with a significantly higher risk of IBD (hazard ratio 2.93, $95\%$ confidence interval: 2.46, 3.47, $P \leq .001$), which was consistent for both disease subtypes of ulcerative colitis and Crohn’s disease. The results of Kaplan–Meier analysis in the matched population yielded identical findings. ### Conclusions Abnormal FIT results could be a preceding sign of incident IBD in the general population. Those with positive FIT results and suspected IBD symptoms could benefit from regular screening for early disease detection. ## Introduction Inflammatory bowel disease (IBD) is a chronic, potentially life-threatening disorder that affects the digestive system and presents with recurrent episodes of abdominal pain, diarrhoea, haematochezia, fever, and weight loss [1]. According to the inflamed regions, their pattern, and pathologic findings in the gastrointestinal tract, IBD is classified into two different diseases: ulcerative colitis (UC) and Crohn’s disease (CD). The underlying pathogenesis of IBD involves a complex interplay of factors, including dysregulation of intestinal microbiota, host genetic susceptibility, and environmental triggers, resulting in an immunological imbalance, which remains largely unknown (2–4). While IBD has been traditionally considered more common in Western countries, a continuous rise in the incidence of IBD has been reported in recent years, especially in Asia [5, 6]. Therefore, there is a need for diagnostic tests that are useful for early detection of IBD. Immunologic perturbation, particularly in the adaptive immune system, is thought to be a crucial element responsible for the impairment of bowel equilibrium and causing chronic gut inflammation in IBD (7–9). Consequently, mucosal inflammation documented by endoscopy or imaging of the gastrointestinal tract is a typical finding in patients with IBD [10]. The faecal immunochemical test (FIT) is a non-invasive test that measures faecal haemoglobin concentrations using an antibody specific for human haemoglobin, and has been widely used for colorectal cancer (CRC) screening by detecting blood in the faeces [11, 12]; however, since a characteristic feature observed in IBD is the presence of mucosal injury, it is possible that a positive FIT may be an early sign of IBD [13]. Additionally, growing evidence suggests that abnormal FIT results, without a definite focus on bleeding, may reflect underlying systemic inflammation and could be correlated with chronic inflammatory diseases [14]. However, the association between positive FIT results and incident IBD has not yet been determined in the general population. Because of this, we aimed to evaluate whether abnormalities of the gut mucosa, defined as a positive FIT result, are associated with the development of IBD, and to evaluate the risk factors in those who participated in the national program for CRC screening. ## Data source The Korean National Cancer Screening Program (KNCSP) is a national program operated by the South Korean government and is designed to screen for cancers in the stomach, liver, colorectum, breast, and cervix according to specific recommendations [15]. The results of the KNCSP are stored in the National Health Insurance Sharing Service-National Health Information Database (NHIS-NHID); the use of this data is approved for authorised researchers. In South Korea, the NHIS provides medical services covered by the national health insurance for >50 million individuals (approximately $97\%$ of the entire population) [16]. In the KNCSP for screening colorectal cancer (CRC), the government provides an annual faecal immunochemical test (FIT) for individuals aged ≥50 years to screen for CRC. In addition, for those with positive FIT results, the NHIS provides subsequent examinations by either double-contrast barium enema or colonoscopy, based on the preference of the individual. In this study, data of the population who participated in the KNCSP for CRC between 2009–2013, including data from the NHIS, were utilised. and participants were followed up until December 21, 2019. Details of the study design, participants, and data acquisition have been described previously [17]. This study was approved by the institutional review board of Ajou University Hospital (approval No. AJIRB-MED-EXP-20-479). The requirement for obtaining individual informed consent was waived because the entire dataset was anonymised. ## Study design and selection of eligible participants In total, 9,161,668 subjects participated in the KNCSP for CRC between the year of 2009–2013. Among them, those who did not undergo a FIT, had a history of colorectal cancer, and had immune-mediated inflammatory diseases (IBD, rheumatoid arthritis, psoriatic arthritis, and systemic lupus erythematosus) that could influence FIT results were excluded. Of the 8.646,887 participants, FIT positive (FIT [+]) and FIT-negative (FIT [−]) groups were separated by applying a 1:1 matched random sampling according to age and sex. In the FIT (+) group, participants who had undergone colonoscopy as a subsequent evaluation were selected, and colonoscopy findings were categorised according to reports submitted to the KNCSP. Colonoscopy-positive findings were defined as documented gross abnormal mucosal lesions of suspected colon cancer, colon cancer, polyps, diverticulosis, and inflammatory lesions. We excluded subjects diagnosed with haemorrhoids, IBD, or CRC according to the colonoscopy results. In addition, those who were diagnosed with IBD and CRC within 6 and 12 months after undergoing a FIT, based on the tenth revision codes of the International Statistical Classification of Diseases (ICD-10 codes), were excluded, as they could be a missed IBD and CRC [18]. In the FIT (−) group, those diagnosed with IBD within 6 months and CRC within 12 months after screening were also excluded (Figure 1). Figure 1: A Flow Diagram of Selecting the FIT (+) and FIT (-) Group. FIT, faecal immunochemical test; CRC, colorectal cancer; IBD, inflammatory bowel disease. ## Faecal immunochemical tests of the participants Faecal samples from subjects participating in the KNCSP for CRC were collected according to the general instructions provided, and were sent to an assigned centre for analyses, which were reported as positive or negative. Faecal immunochemical test assessment was conducted using qualitative and quantitative methods. For the qualitative method, a commercially available kit was used according to the cut-off values provided in the kit as follows: FOBtest, Humasis Co., Korea (50 ng/mL [10 ug/g]), SD Bioline FOB, SD Co., Korea (30 ng/mL [6 ug/g]), ASAN Easy Test FOB, Asan Pharm Co., Korea (50 ng/mL [10 ug/g]), and OC-Hemocatch Lignt™, Eiken Chemical Co., Japan (50 ng/mL [10 ug/g]). The faecal haemoglobin value was determined by latex agglutination nephelometric immunoassay in a quantitative assay (Eiken Chemical Co.), in which the cut-off value of the corresponding institution was also reported [19, 20]. ## Covariates Those who participated in the CRC screening program completed questionnaires on smoking, alcohol drinking, and physical exercise, and submitted them to the responsible institutions. In addition, data on age, sex, anthropometric measurements including weight, height, and body mass index (BMI), medical and family history, socioeconomic status, and clinical information were collected. The variables used in our analysis were as follows: sex; age; BMI; smoking status (no or yes); alcohol consumption (no or yes); insurance type (medical aid or national health insurance); comorbidities (hypertension, diabetes, or dyslipidaemia); and laboratory results of haemoglobin, total cholesterol, triglyceride, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, aspartate aminotransferase (AST), and alanine aminotransferase (ALT). The normal values of laboratory data were set according to the predefined cut-off values: haemoglobin (male: ≥13 g/dL, female: ≥12 g/dL), total cholesterol (<200 mg/dL), triglyceride (<150 mg/dL), HDL cholesterol (≥60 mg/dL), LDL-cholesterol (<130 mg/dL), AST (≤40 IU/L), and ALT (≤35 IU/L) [21]. ## Definition of inflammatory bowel disease and CRC The outcome of interest was the incidence of inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD). In South Korea, when an individual utilises a medical service covered by national insurance, all medical institutions must provide a record of their healthcare usage, which is collected in the National Health Insurance Sharing Service-National Health Information Database (NHIS-NHID) containing diagnosis codes [22]. To search for cases of IBD and CRC, we first identified CRC and IBD using primary and secondary codes of the ICD-10 codes (C18–20 for CRC and K50–51 for IBD). Furthermore, as the Korean government grants special exemption codes for patients with rare and intractable diseases to subsidise their healthcare expenses for those fulfilling the criteria defined by the National Health Insurance, we used a special exemption code (V code) to ensure an accurate diagnosis of IBD. Thus, in this study, IBD cases that were designated with a special exemption code of V130 (CD) and V131 (UC) were selected [23]. ## Statistical analysis Continuous and categorical variables were presented as a mean (SD) and a number (frequency), respectively. Differences between groups were evaluated using Student’s t-test and chi-squared test for continuous and categorical variables. The follow-up duration was set as the date of initial screening to the diagnosis date of IBD in those diagnosed as IBD, whereas it was defined as the last follow-up in those who did not develop IBD. Crude and age-and sex-adjusted incidence rates per 10,000 person-years were calculated using the number of incident IBD cases and person-time of those at risk. Furthermore, Cox proportional hazard analyses were used to identify independent risk factors for IBD during follow-up. As a sensitivity analysis, 1:2 propensity score matching was performed in order to adjust for the differences in baseline characteristics. Kaplan–Meier analysis and the log-rank test were used to compare the differences in IBD incidence. Statistical analyses were conducted using SAS statistical software (SAS Institute, Cary, NC, USA), and a two-tailed $P \leq .05$ was considered significant. ## Comparison of baseline characteristics between the FIT (+) and FIT (−) groups Among the 1,044,955 participants, 815,361 and 229,594 individuals were divided into FIT (−) and FIT (+) groups, respectively (Table 1). Of the total number of participants, 565,280 ($54.10\%$) were male, the overall mean (SD) age was 62.06 (8.58) years, and the proportion of those aged 50–59 years was the highest, accounting for $43.77\%$ of the participants. In addition, the proportions of current smokers and alcohol drinkers in the total population were $16.1\%$ and $21.13\%$, respectively. There were significant differences between the FIT (−) and FIT (+) groups in the baseline characteristics investigated, including demographic data and laboratory results, with the exception of triglyceride levels ($$P \leq .490$$). Table 1 \| Unnamed: 0 \| Total cohort(n = 1,044,955) \| FIT (−) group(n = 815,361) \| FIT (+) group(n = 229,594) \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| Demographic data \| Demographic data \| Demographic data \| Demographic data \| Demographic data \| \| Sex \| \| \| \| <.001 \| \| Male \| 565,280 (54.10) \| 436,092 (53.48) \| 129,188 (56.27) \| \| \| Female \| 479,675 (45.90) \| 379,269 (46.52) \| 100,406 (43.73) \| \| \| Age at screening, years \| 62.06 ± 8.58 \| 62.4 ± 8.73 \| 60.86 ± 7.90 \| <.001 \| \| 50–59 \| 457,331 (43.77) \| 346,316 (42.47) \| 111,015 (48.35) \| <.001 \| \| 60–69 \| 352,523 (33.74) \| 273,036 (33.49) \| 79,487 (34.62) \| \| \| 70–79 \| 203,606 (19.48) \| 167,777 (20.58) \| 35,829 (15.61) \| \| \| ≥80 \| 31,495 (3.01) \| 28,232 (3.46) \| 3,263 (1.42) \| \| \| BMI, kg/m2 \| 24.14 ± 3.03 \| 24.10 ± 3.03 \| 24.27 ± 2.99 \| <.001 \| \| Smoking status \| \| \| \| <.001 \| \| No \| 754,426 (83.86) \| 592,256 (83.99) \| 162,170 (83.38) \| \| \| Yes \| 145,186 (16.14) \| 112,858 (16.01) \| 32,328 (16.62) \| \| \| Alcohol drinking \| \| \| \| <.001 \| \| No \| 709,427 (78.87) \| 559,665 (79.38) \| 149,762 (77.01) \| \| \| Yes \| 190,055 (21.13) \| 145,337 (20.62) \| 44,718 (22.99) \| \| \| Insurance type \| \| \| \| <.001 \| \| Medical aid \| 44,650 (4.27) \| 35,631 (4.37) \| 9,019 (3.93) \| \| \| National health insurance \| 100,265 (95.73) \| 779,696 (95.63) \| 220,569 (96.07) \| \| \| Underlying comorbidity \| Underlying comorbidity \| Underlying comorbidity \| Underlying comorbidity \| Underlying comorbidity \| \| Hypertension \| \| \| \| <.001 \| \| No \| 656,727 (87.75) \| 518,754 (93.40) \| 137,973 (92.47) \| \| \| Yes \| 47,872 (12.25) \| 36,644 (6.60) \| 11,228 (7.53) \| \| \| Diabetes mellitus \| \| \| \| <.001 \| \| No \| 656,727 (93.21) \| 518,754 (93.40) \| 137,973 (92.47) \| \| \| Yes \| 47,872 (6.79) \| 36,644 (6.60) \| 11,228 (7.53) \| \| \| Dyslipidaemia \| \| \| \| <.001 \| \| No \| 501,128 (67.06) \| 397,006 (67.49) \| 104,122 (65.44) \| \| \| Yes \| 246,187 (32.94) \| 191,200 (32.51) \| 54,987 (34.56) \| \| \| Laboratory results \| Laboratory results \| Laboratory results \| Laboratory results \| Laboratory results \| \| Haemoglobin, g/dL \| 13.84 ± 1.49 \| 13.85 ± 1.49 \| 13.79 ± 1.50 \| <.001 \| \| Total cholesterol, mg/dL \| 198.72 ± 40.78 \| 198.30 ± 40.43 \| 200.10 ± 42.02 \| <.001 \| \| Triglyceride, mg/dL \| 139.19 ± 97.69 \| 139.20 ± 98.12 \| 139.30 ± 96.11 \| .490 \| \| HDL-cholesterol, mg/dL \| 54.11 ± 24.20 \| 54.08 ± 24.40 \| 54.23 ± 23.46 \| .010 \| \| LDL-cholesterol, mg/dL \| 118.27 ± 48.35 \| 117.90 ± 47.51 \| 119.50 ± 51.25 \| <.001 \| \| AST, IU/L \| 27.22 ± 19.77 \| 26.78 ± 19.10 \| 28.81 ± 21.94 \| <.001 \| \| ALT, IU/L \| 25.34 ± 24.51 \| 24.87 ± 22.24 \| 27.05 ± 31.33 \| <.001 \| ## Incidence of IBD during the follow-up period according to FIT positivity A total of 784 participants (incidence rates [IR] $\frac{0.98}{10}$,000 person-years [PY]) were diagnosed with IBD during a mean follow-up period of 7.59 years (SD 1.81). Among those who developed IBDs, 672 (IR $\frac{0.85}{10}$,000 PY) and 126 (IR $\frac{0.16}{10}$,000 PY) were diagnosed with UC and CD, respectively, and 14 patients were diagnosed with both UC and CD. The incidence of UC and CD was higher in the FIT (+) group than in the FIT (−) group, even after adjusting for age and sex (Figure 2). Moreover, the cumulative incidence of IBD in the FIT (+) group was also significantly higher than that in the FIT (−) group in a Kaplan–Meier analysis among subjects with normal haemoglobin values (all $P \leq .001$) (Figure 3). Figure 2: Comparison of IBD incidence rates between the FIT (−) and FIT (+) groups. Values adjusted for age and sex. IBD, inflammatory bowel disease; FIT, faecal immunochemical test; UC, ulcerative colitis; CD, Crohn’s disease; IR, incidence rate per 10,000 person-years.* Figure 3: Cumulative incidence of IBD, UC, and CD according to FIT results in those with normal haemoglobin. The incidences of (A) IBD, (B) UC, and (C) CD were significantly higher in the positive FIT group than in the negative FIT group. IBD, inflammatory bowel disease; UC, ulcerative colitis; CD, Crohn’s disease; FIT, faecal immunochemical test. ## The occurrence of IBD according to different time intervals, sex, and age When the incidence of IBD was categorised according to three different time intervals of <2 years, 2–5 year, and ≥5 years, the incidence of IBD was observed to be the highest within the second year of screening (IR $\frac{0.82}{10}$,000 PY, $95\%$ CI: 0.70, 0.95), which was not affected by disease subtypes. In addition, a trend of decreasing incidence of IBD after screening was demonstrated for both UC and CD. In particular, those in the FIT (+) group were more frequently diagnosed with IBD, as well as UC, during follow-up in the adjusted analyses, but this was not evident in CD (Table 2). Table 2 \| Groups \| Number of incident cases \| Number of incident cases.1 \| Number of incident cases.2 \| Crude IR (95% CI) \| Crude IR (95% CI).1 \| Crude IR (95% CI).2 \| Adjusted IR (95% CI)a \| Adjusted IR (95% CI)a.1 \| Adjusted IR (95% CI)a.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Groups \| <2 year \| 2–5 years \| ≥5 years \| <2 year \| 2–5 years \| ≥5 years \| <2 year \| 2–5 years \| ≥5 years \| \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| IBD (n = 784) \| \| Overall population \| 169 \| 310 \| 305 \| 0.82(0.70, 0.95) \| 0.61(0.55, 0.68) \| 0.39(0.35, 0.44) \| – \| – \| – \| \| FIT (−) group \| 69 \| 164 \| 171 \| 0.43(0.34, 0.54) \| 0.41(0.36, 0.48) \| 0.28(0.25, 0.33) \| 0.36(0.13, 0.99) \| 0.36(0.13, 0.97) \| 0.21(0.11, 0.43) \| \| FIT (+) group \| 100 \| 146 \| 134 \| 2.19(1.80, 2.67) \| 1.30(1.10, 1.53) \| 0.75(0.64, 0.89) \| 2.09(0.76, 5.72) \| 0.58(0.22, 1.58) \| 0.58(0.29, 1.17) \| \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| UC (n = 672) \| \| Overall population \| 151 \| 267 \| 254 \| 0.73(0.62, 0.85) \| 0.53(0.47, 0.59) \| 0.33(0.29, 0.37) \| – \| – \| – \| \| FIT (−) group \| 61 \| 135 \| 133 \| 0.38(0.29, 0.49) \| 0.34(0.29, 0.40) \| 0.22(0.19, 0.26) \| 0.33(0.12, 0.93) \| 0.25(0.07, 0.90) \| 0.15(0.05, 0.43) \| \| FIT (+) group \| 90 \| 132 \| 121 \| 1.97(1.60, 2.42) \| 1.17(0.99, 1.39) \| 0.68(0.57, 0.81) \| 1.82(0.66, 5.05) \| 0.47(0.13, 1.65) \| 0.53(0.19, 1.48) \| \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| CD (n = 126) \| \| Overall population \| 18 \| 48 \| 60 \| 0.09(0.05, 0.14) \| 0.09(0.07, 0.13) \| 0.08(0.06, 0.10) \| – \| – \| – \| \| FIT (−) group \| 8 \| 31 \| 42 \| 0.05(0.02, 0.10) \| 0.08(0.06, 0.11) \| 0.07(0.05, 0.09) \| n/ab \| 0.09(0.02, 0.31) \| 0.07(0.02, 0.25) \| \| FIT (+) group \| 10 \| 17 \| 18 \| 0.22(0.12, 0.41) \| 0.15(0.09, 0.24) \| 0.10(0.06, 0.16) \| n/ab \| 0.09(0.02, 0.30) \| 0.06(0.02, 0.19) \| Analysis of the incidence of IBD based on sex and age showed that the overall incidence was the highest in those aged 50–59 years in both sexes (IR $\frac{1.29}{10}$,000 PY for males and $\frac{0.83}{10}$,000 PY for females), which decreased gradually with age. For disease subtypes, UC was most common in the age group of 50–59 years, CD was most frequently diagnosed in the age group of 60–69 years, and the incidence of UC and CD was consistently higher in males than in females across all age groups (Table 3). Table 3 \| Unnamed: 0 \| IBD \| UC \| CD \| \| --- \| --- \| --- \| --- \| \| Male \| Male \| Male \| Male \| \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| \| 50–59 \| 1.29 (1.17, 1.43) \| 1.19 (1.01, 1.39) \| 0.15 (0.13, 0.18) \| \| 60–69 \| 1.21 (1.10, 1.34) \| 1.05 (0.90, 1.23) \| 0.19 (0.16, 0.22) \| \| 70–79 \| 0.85 (0.77, 0.94) \| 0.70 (0.60, 0.81) \| 0.16 (0.14, 0.19) \| \| ≥80 \| 0.51 (0.46, 0.56) \| 0.40 (0.34, 0.47) \| 0.11 (0.09, 0.13) \| \| Female \| Female \| Female \| Female \| \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| \| 50–59 \| 0.83 (0.75, 0.92) \| 0.70 (0.60, 0.82) \| 0.14 (0.12, 0.16) \| \| 60–69 \| 0.78 (0.70, 0.86) \| 0.62 (0.53, 0.72) \| 0.16 (0.14, 0.19) \| \| 70–79 \| 0.54 (0.49, 0.60) \| 0.41 (0.35, 0.48) \| 0.14 (0.12, 0.17) \| \| ≥80 \| 0.33 (0.29, 0.36) \| 0.24 (0.20, 0.27) \| 0.09 (0.08, 0.11) \| ## Factors associated with the occurrence of IBD The Cox proportional hazard analysis indicated that a positive FIT result (hazard ratio [HR] 2.93, $95\%$ CI: 2.46, 3.47, $P \leq .001$), male sex (HR 1.64, $95\%$ CI: 1.35, 2.00, $P \leq .001$), and abnormal high-density lipoprotein (HDL) cholesterol level (HR 1.40, $95\%$ CI: 1.09, 1.81, $$P \leq .010$$) increased the risk of developing IBD, while an increase in body mass index (BMI; HR 0.92, $95\%$ CI: 0.89, 0.95, $P \leq .001$) and diabetes mellitus (HR 0.63, $95\%$ CI: 0.40, 1.00, $$P \leq .050$$) had a negative association with the incidence of IBD (Table 4). Table 4 \| Unnamed: 0 \| IBD \| IBD.1 \| UC \| UC.1 \| CD \| CD.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| HR (95% CI)a \| P-value \| HR (95% CI)a \| P-value \| HR (95% CI)a \| P-value \| \| Screening result \| Screening result \| Screening result \| Screening result \| Screening result \| Screening result \| Screening result \| \| FIT (−) \| Ref \| \| Ref \| \| Ref \| \| \| FIT (+) \| 2.93 (2.46, 3.47) \| <.001 \| 3.12 (2.60, 3.76) \| <.001 \| 2.04 (1.31, 3.19) \| .002 \| \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| \| Female \| Ref \| \| Ref \| \| Ref \| \| \| Male \| 1.64 (1.35, 2.00) \| <.001 \| 1.79 (1.45, 2.22) \| <.001 \| 1.07 (0.66, 1.75) \| .782 \| \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| \| 50–59 \| 2.67 (0.99, 7.17) \| .052 \| 3.12 (1.00, 9.75) \| .051 \| 1.07 (0.66, 1.75) \| .782 \| \| 60–69 \| 2.46 (0.91, 6.62) \| .076 \| 2.72 (0.87, 8.52) \| .087 \| 1.53 (0.21, 11.21) \| .677 \| \| 70–79 \| 1.83 (0.67, 5.03) \| .241 \| 1.95 (0.61, 6.25) \| .260 \| 1.74 (0.24, 12.82) \| .586 \| \| ≥80 \| Ref \| \| Ref \| \| Ref \| \| \| BMI \| 0.92 (0.89, 0.95) \| <.001 \| 0.92 (0.89, 0.95) \| <.001 \| 0.96 (0.89, 1.04) \| .334 \| \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 0.96 (0.75, 1.22) \| .740 \| 0.85 (0.65, 1.11) \| .237 \| 1.68 (0.94, 2.98) \| .078 \| \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 0.86 (0.68, 1.08) \| .181 \| 0.90 (0.70, 1.14) \| .375 \| 0.73 (0.40, 1.35) \| .319 \| \| Insurance type \| Insurance type \| Insurance type \| Insurance type \| Insurance type \| Insurance type \| Insurance type \| \| Medical aid \| Ref \| \| Ref \| \| n/ab \| \| \| National health insurance \| 0.58 (0.24, 1.4) \| .228 \| 0.49 (0.20, 1.18) \| .112 \| n/ab \| \| \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 0.91 (0.67, 1.22) \| .514 \| 1.00 (0.73, 1.36) \| .978 \| 0.34 (0.11, 1.07) \| .065 \| \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| Diabetes mellitus \| \| No \| Ref \| \| Ref \| \| \| \| \| Yes \| 0.63 (0.4, 1.00) \| .050 \| 0.6 (0.37, 1.00) \| .049 \| 0.96 (0.35, 2.63) \| .930 \| \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 0.86 (0.64, 1.15) \| .306 \| 0.93 (0.68, 1.28) \| .665 \| 0.46 (0.20, 1.05) \| .066 \| \| Abnormal haemoglobin \| Abnormal haemoglobin \| Abnormal haemoglobin \| Abnormal haemoglobin \| Abnormal haemoglobin \| Abnormal haemoglobin \| Abnormal haemoglobin \| \| No \| Ref \| \| n/ab \| \| Ref \| \| \| Yes \| 0.48 (0.07, 3.42) \| .463 \| n/ab \| \| 2.35 (0.32, 17.21) \| .400 \| \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 0.84 (0.60, 1.19) \| .335 \| 0.88 (0.61, 1.27) \| .497 \| 0.52 (0.18, 1.52) \| .228 \| \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 0.85 (0.62, 1.17) \| .320 \| 0.83 (0.59, 1.17) \| .287 \| 1.21 (0.53, 2.78) \| .651 \| \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 1.40 (1.09, 1.81) \| .010 \| 1.32 (1.00, 1.75) \| .054 \| 1.86 (1.03, 3.37) \| .040 \| \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 1.37 (0.99, 1.89) \| .061 \| 1.44 (1.02, 2.03) \| .040 \| 0.91 (0.34, 2.42) \| .855 \| \| Abnormal AST \| Abnormal AST \| Abnormal AST \| Abnormal AST \| Abnormal AST \| Abnormal AST \| Abnormal AST \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 0.91 (0.63, 1.33) \| .629 \| 0.94 (0.64, 1.40) \| .775 \| 0.79 (0.25, 2.46) \| .680 \| \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| \| No \| Ref \| \| Ref \| \| Ref \| \| \| Yes \| 1.08 (0.81, 1.43) \| .596 \| 1.13 (0.84, 1.52) \| .417 \| 0.70 (0.30, 1.62) \| .407 \| In subgroup analyses based on disease subtypes, FIT positivity (HR 3.12, $95\%$ CI: 2.60, 3.76, $P \leq .001$), male sex (HR 1.79, $95\%$ CI: 1.45, 2.22, $P \leq .001$), and abnormal low-density lipoprotein (LDL)-cholesterol level (HR 1.44, $95\%$ CI: 1.02, 2.03; $$P \leq .040$$) were associated with a greater risk of UC; however, the risk of UC decreased following an increase in BMI (HR 0.92, $95\%$ CI: 0.89, 0.95, $P \leq .001$) and diabetes mellitus (HR 0.60, $95\%$ CI: 0.37, 1.00, $$P \leq .049$$). In terms of CD, both FIT positivity (HR 2.04, $95\%$ CI: 1.31, 3.19, $$P \leq .002$$) and abnormal HDL levels (HR 1.86, $95\%$ CI: 1.03, 3.37, $$P \leq .040$$) exhibited an increased risk of developing CD (Table 4). Furthermore, we performed an additional adjusted Cox analysis in participants who underwent colonoscopy after FIT (+) to evaluate the influence of colonoscopy results on IBD occurrence. However, positive colonoscopy results did not significantly influence the occurrence of IBD (Table 5), and only male sex (HR 1.38, $95\%$ CI: 1.03, 1.84; $$P \leq .030$$) and BMI (HR 0.92, $95\%$ CI: 0.88, 0.96; $P \leq .001$) were associated with the incidence of IBD. Table 5 \| Unnamed: 0 \| Univariable analysis \| Univariable analysis.1 \| Multivariable analysis \| Multivariable analysis.1 \| \| --- \| --- \| --- \| --- \| --- \| \| \| HR (95% CI) \| P-value \| HR (95% CI) \| P-value \| \| Findings of colonoscopy \| Findings of colonoscopy \| Findings of colonoscopy \| Findings of colonoscopy \| Findings of colonoscopy \| \| Negative \| Ref \| \| Ref \| \| \| Positive \| 1.18 (0.91,1.53) \| .2054 \| 1.22 (0.93,1.59) \| .151 \| \| Sex \| Sex \| Sex \| Sex \| Sex \| \| Female \| Ref \| \| Ref \| \| \| Male \| 1.33 (1.03,1.73) \| .029 \| 1.38 (1.03,1.84) \| .0297 \| \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| Age at screening, years \| \| 50–59 \| 2.55 (0.36,18.18) \| .3519 \| 3.04 (0.42,21.77) \| .3519 \| \| 60–69 \| 2.05 (0.29,14.74) \| .4753 \| 2.36 (0.33,17.00) \| .4753 \| \| 70–79 \| 1.81 (0.25,13.34) \| .5611 \| 1.99 (0.27,14.72) \| .5611 \| \| ≥80 \| Ref \| \| Ref \| \| \| BMI \| 0.92 (0.88,0.96) \| .0003 \| 0.92 (0.88,0.96) \| .0003 \| \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| \| No \| Ref \| \| Ref \| \| \| Yes \| 1.09 (0.78,1.52) \| .6288 \| 0.85 (0.59,1.23) \| .3935 \| \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| Alcohol drinking \| \| No \| Ref \| \| Ref \| \| \| Yes \| 1.10 (0.81,1.47) \| .5471 \| 1.03 (0.74,1.43) \| .8673 \| \| Insurance type \| Insurance type \| Insurance type \| Insurance type \| Insurance type \| \| Medical aid \| Ref \| \| Ref \| \| \| National health insurance \| 0.37 (0.12,1.15) \| .085 \| 0.58 (0.24, 1.39) \| .220 \| \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| Hypertension \| \| No \| Ref \| \| Ref \| \| \| Yes \| 0.93 (0.62,1.41) \| .7424 \| 1.03 (0.68,1.56) \| .8937 \| \| Diabetes \| Diabetes \| Diabetes \| Diabetes \| Diabetes \| \| No \| Ref \| \| Ref \| \| \| Yes \| 0.45 (0.21,0.95) \| .0365 \| 0.48 (0.22,1.02) \| .0554 \| \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| Dyslipidaemia \| \| No \| Ref \| \| Ref \| \| \| Yes \| 0.83 (0.62,1.11) \| .2006 \| 0.74 (0.49,1.13) \| .1673 \| \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| Abnormal total cholesterol \| \| No \| Ref \| \| Ref \| \| \| Yes \| 0.86 (0.63,1.18) \| .3583 \| 0.83 (0.51,1.36) \| .458 \| \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| Abnormal triglyceride \| \| No \| Ref \| \| Ref \| \| \| Yes \| 0.84 (0.59,1.21) \| .3495 \| 1.07 (0.69,1.68) \| .7561 \| \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| Abnormal HDL-cholesterol \| \| No \| Ref \| \| Ref. \| \| \| Yes \| 1.27 (0.90,1.8) \| .1708 \| 1.44 (0.99,2.09) \| .0583 \| \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| Abnormal LDL-cholesterol \| \| No \| Ref \| \| Ref \| \| \| Yes \| 1.08 (0.79,1.47) \| .6458 \| 1.53 (0.97,2.41) \| .0688 \| \| Abnormal AST \| Abnormal AST \| Abnormal AST \| Abnormal AST \| Abnormal AST \| \| No \| Ref \| \| Ref \| \| \| Yes \| 0.72 (0.45,1.17) \| .1841 \| 0.74 (0.43,1.28) \| .2842 \| \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| Abnormal ALT \| \| No \| Ref \| \| Ref \| \| \| Yes \| 0.86 (0.61,1.22) \| .4083 \| 1.03 (0.68,1.54) \| .8966 \| ## The incidence of IBD in the matched population Given the considerable difference between the characteristics of the FIT (+) and FIT (−) groups, a 1:2 propensity score matching (PSM) was conducted to eliminate the difference. The demographic data and laboratory results were found to be comparable after PSM (Table 6). Kaplan–Meier analysis in this population also demonstrated an elevated risk of IBD in the FIT (+) group compared to that in the FIT (−) group (HR 2.85, $95\%$ CI: 2.34, 3.48, $P \leq .001$). FIT positivity increased the risk of both incident UC (HR 3.17, $95\%$ CI: 2.55, 3.94, $P \leq .001$) and CD (HR 1.68, $95\%$ CI: 1.03, 2.74, $$P \leq .037$$) (Figure 4). ## Discussion Detection of faecal haemoglobin is a widely used method to screen for CRC, and FIT is recommended as a CRC screening test because it is considered to have higher diagnostic performance than the guaiac-based method [24]. There is accumulating evidence linking positive faecal blood with diseases unrelated to CRC, indicating that this finding could reflect greater inflammation in the human body; this has been replicated in a number of studies [14, 25]. Moreover, a previous investigation revealed that the risk of immune-mediated inflammatory disorders, particularly rheumatoid arthritis, is increased in patients with positive FIT results [17]. In our study, by using the data of those who participated in a national CRC screening program, we identified a group of people who had positive FIT results but without evidence of apparent gastrointestinal bleeding. Importantly, we found that positive FIT results were independently associated with the occurrence of UC and CD, which was reproduced in the sensitivity analysis, indicating that FIT abnormalities in the general population could predict the onset of IBD. The association between positive FIT and IBD occurrence observed in our study could be explained by the disruption of local bowel homeostasis and changes in the gut microbiome, which play significant roles in alterations of the immune landscape and imbalance of cytokines found in IBD [26, 27]. First, the breakdown of localised gut homeostasis could shift the balance between pro- and anti-inflammatory mediators, contributing to the maintenance of intestinal mucosal integrity and inducing an abnormal systemic immune response [28]. Second, interference of the balance in the gut microbiota is regarded as essential in inducing intestinal barrier damage and triggering inflammatory responses in IBD [29]. Supporting this, in vivo studies have identified gut dysbiosis as a crucial component leveraging the development of IBD [30]. Collectively, it could be assumed that abnormalities in the gut mucosa, defined as positive FIT results in our study, are associated with the evolution of IBD. Predicting individuals at risk for IBD in the general population prior to disease development is highly challenging, although various risk factors for IBD have been identified [31]. In our study, we demonstrated that FIT positivity confers an increased risk of IBD in the general population, even after excluding cases of IBD that were diagnosed after 6 months of screening. This finding implies that a positive FIT could be a preceding sign of IBD and could be applied to determine the high-risk population for developing IBD prior to the onset of overt disease. Notably, previous studies have reported that FIT can anticipate mucosal healing and has equivalent performance compared to faecal calprotectin [32, 33], which is the most widely adopted test for the identification of disease and quantification of inflammation in the bowel (34–36). In this context, those with a positive FIT result and symptoms that raise a suspicion of IBD might be candidates for regular screening for the presence of intestinal inflammation. Also, FIT could be advantageous as a screening test for IBD in the general population compared to faecal calprotectin, in terms of cost-effectiveness. Our results revealed that $0.06\%$ and $0.01\%$ of participants were diagnosed with UC and CD, respectively, during the mean follow-up of 7.61 years. Although a large variation in the incidence rates of IBD has been reported in the literature, it is generally understood that the incidence of IBD is higher in European countries than in regions of the Eastern world [37]. While the exact incidence of IBD in South Korea is not well understood, a previous study has shown that the estimated incidence of UC and CD is approximately $\frac{4}{100}$,000 and $\frac{2}{100}$,000, respectively, each year [38]. Considering that IBD is relatively common in younger individuals, the incidence of IBD in our study (crude annual incidence rate, $\frac{0.98}{10}$,000) was higher than that reported in the literature. Nonetheless, a higher proportion of participants developing UC than those with CD was also demonstrated, in agreement with current evidence. Notably, the discrepancies in UC and CD incidence compared with the previous study could be relevant to the study design. First, we identified those with positive FIT results and matched them with those showing a negative FIT result, which might have influenced the higher incidence of IBD. Second, the incidence of UC is relatively higher in the elderly than that of CD [39]; because participants enrolled in the national CRC screening program were all aged >50 years, this would have resulted in a substantially higher UC annual incidence rate than CD. The Cox proportional hazard analysis indicated that positive FIT, male sex, low BMI, presence of diabetes mellitus, and abnormal HDL cholesterol level were associated with the risk of subsequent IBD. The positive and negative relationship between age groups and female sex is consistent with the knowledge that IBD frequently occurs in men aged 30–40 years, with a decline in its incidence in the older population [40]. In addition, the association of abnormalities in HDL cholesterol with IBD implies that changes in HDL cholesterol reflect alterations in the immune system, other than cardiovascular events [41]. Also, BMI was inversely associated with the incidence of IBD. While it remains inconclusive whether low BMI confers a risk of developing IBD, population-based studies from Denmark have indicated an increase in IBD among participants with low BMI [42, 43], which may be partly explained as a consequence of chronic inflammation causing cachexia or representing a pre-clinical manifestation of IBD. Interestingly, we confirmed a negative correlation between diabetes mellitus and IBD, particularly UC. While there is a lack of studies exploring the association between diabetes mellitus and IBD, it has been reported that the use of metformin and dipeptidyl peptidase-4 inhibitors, which are the most frequently prescribed drugs for the management of diabetes mellitus in South Korea [44], could mitigate the risk of IBD in patients with diabetes mellitus [45, 46]. In particular, as both drugs inhibit the activation of pro-inflammatory cytokines and improve insulin resistance, it is possible that they could decrease local inflammatory reactions in the intestine [47, 48]. Therefore, the decreased risk of IBD in patients with diabetes mellitus could have been accounted for by the effects of these drugs, although we could not confirm the types of medication in The Korean National Cancer Screening Program (KNCSP) database. Lastly, the lack of association with smoking, a known environmental trigger for IBD, could be related to the fact that smoking status was categorised into current and non-current; additionally, the percentage of smokers was low, and the potential association of smoking may not have been evident because of other confounders [49]. An important strength of our study was that we demonstrated in a nationwide cohort that a positive FIT is associated with an increased the risk of future IBD development. However, our study has some limitations. First, although this was a large-scale study involving those who participated in a CRC screening program, we only had baseline information for the analysis of IBD incidence and the primary aim of CRC screening program was not to screen for IBD. Second, because the enrolled participants were exclusively >50 years of age, the risk of developing IBD among those with positive FIT results in the younger population could not be evaluated. Third, detailed colonoscopic findings could not be utilised in our analyses, and it could not be clarified whether a complete endoscopic examination was performed to exclude other potential causes. This was because full colonoscopy results were not provided because of the potential identification of individuals. Furthermore, whether other imaging tests were undertaken subsequently - such as magnetic resonance imaging small bowel study/a capsule study – could not be confirmed. Fourth, although various genetic and environmental factors, including dietary intake, could affect IBD incidence, such data are not available in the KNCSP and could not be analysed. Fifth, 14 patients were diagnosed with UC and CD simultaneously; although the number of patients was small, this may be an unclassified type of IBD showing overlapping features of UC and CD [50]. However, the details of these patients could not be queried owing to the limitations of the National Health Insurance Sharing Service (NHIS). Finally, while measuring faecal calprotectin levels may be a useful strategy for early detection of IBD in patients with positive FIT and normal colonoscopy, these results were not available in the KNCSP database. ## Conclusion In conclusion, by utilising the data of those who participated in the nationwide screening program for CRC, it was found that a positive FIT is associated with the onset of IBD in the general population. Abnormal FIT results could be a preceding sign of incident IBD, and regular screening may be beneficial, especially in patients with suspected symptoms of IBD. ## Data availability statement The datasets presented in this article are not readily available because the datasets analysed in this study cannot be shared publicly because of national legislation for protection of personal information. However, data are available from the Korea National Health Insurance Sharing Service (contact via https://nhiss.nhis.or.kr, contact: +82-33-736-2432, 2433) for those authorised to access the confidential data. Requests to access the datasets should be directed to https://nhiss.nhis.or.kr. ## Ethics statement The studies involving human participants were reviewed and approved by Ajou University Hospital (approval No. AJIRB-MED-EXP-20-479). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions SSA and C-KN: Conceptualisation. EL, SSA, and C-KN: Methodology. EL, GHL, SSA, and C-KN: Software. EL, SSA, and C-KN: Validation. EL and BP: Formal analysis. EL, SSA, and C-KN: Investigation. EL, GHL, SSA, and C-KN: Resources. EL: Data curation. GHL, SSA and C-KN: Writing—original draft preparation. EL, GHL, BP, SSA, and C-KN: Writing—review and editing. EL and C-KN: Visualisation. BP, SSA and C-KN: Supervision. EL, GHL, BP, SSA, and C-KN: Project administration. 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.2023.1128736/full#supplementary-material ## References 1. Podolsky DK. Inflammatory bowel disease. 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--- title: Transcriptome sequencing of facial adipose tissue reveals alterations in mRNAs of hemifacial microsomia authors: - Bingyang Liu - Wei Liu - Shanbaga Zhao - Lunkun Ma - Tianying Zang - Changjin Huang - Kaiyi Shu - Hengbin Gao - Xiaojun Tang journal: Frontiers in Pediatrics year: 2023 pmcid: PMC9968928 doi: 10.3389/fped.2023.1099841 license: CC BY 4.0 --- # Transcriptome sequencing of facial adipose tissue reveals alterations in mRNAs of hemifacial microsomia ## Abstract Hemifacial microsomia (HFM) is a common congenital malformation of the craniofacial region, including mandibular hypoplasia, microtia, facial palsy and soft tissue deficiencies. However, it remains unclear which specific genes are involved in the pathogenesis of HFM. By identifying differentially expressed genes (DEGs) in deficient facial adipose tissue from HFM patients, we hope to provide a new insight into disease mechanisms from the transcriptome perspective. RNA sequencing (RNA-Seq) was performed with 10 facial adipose tissues from patients of HFM and healthy controls. Differentially expressed genes in HFM were validated by quantitative real-time PCR (qPCR). Functional annotations of the DEGs were analyzed with DESeq2 R package (1.20.0). A total of 1,244 genes were identified as DEGs between HFM patients and matched controls. Bioinformatic analysis predicted that the increased expression of HOXB2 and HAND2 were associated with facial deformity of HFM. Knockdown and overexpression of HOXB2 were achieved with lentiviral vectors. Cell proliferation, migration, and invasion assay was performed with adipose-derived stem cells (ADSC) to confirm the phenotype of HOXB2. We also found that PI3K−Akt signaling pathway and human papillomavirus infection were activated in HFM. In conclusion, we discovered potential genes, pathways and networks in HFM facial adipose tissue, which contributes to a better understanding of the pathogenesis of HFM. ## Introduction Hemifacial microsomia (HFM) is one of the most common congenital craniofacial defects, incidence of which ranges from 1:13,500 to 1:5,600 [1], second only to cleft lip and palate. HFM, which was first described in 1,881 (OMIM 164210), typically affects the external ear, middle ear, mandible and temporomandibular joint, mastication and facial muscles, and other facial soft tissues on the affected side. In some affected patients, anomalies may also include in cardiac, vertebral, and central nervous system in addition to craniofacial anomalies. They significantly affect facial appearance and physiological function, posing a substantial psychological and financial burden on families. The precise etiology and physiopathology of HFM are far from being completely understood and both environmental and genetic factors can be considered to interpret the symptoms of HFM. In terms of environmental factors, the embryo is vulnerable to teratogens, such as thalidomide [2], retinoic acid [3], vasoactive medications, and alcohol [4], which may result in permanent congenital malformations, including HFM. In addition, maternal diabetes [5], multiple gestations, and vaginal bleeding during pregnancy [4] are also major risk factors for HFM. Despite the fact that most cases of HFM are sporadic, familial occurrence suggests a genetic predisposition. Most cases of familial occurrence show an autosomal dominant transmission, which accounts for $2\%$ to $10\%$ of cases [6]. Currently, most studies on HFM are limited to evaluating one or more genes for expression alterations, but systematic research on differentially expressed genes or primary pathways is lacking. As a result, HFM is an enigmatic condition with an unknown etiology and poorly understood pathogenesis. It is essential to understand how disease develops and pathogenesis occurs by interpreting transcriptomes. Reports have not been found about the global transcriptome abnormalities of the facial adipose tissue from patients with HFM. Next-generation sequencing (NGS) is well-established for deciphering the transcriptome using high-throughput and quantitative methods [7]. The aim of our study is to identify differentially expressed genes (DEGs) and molecular pathways in facial adipose tissue from patients with HFM, which will provide new insights into disease mechanisms at the molecular level. ## Patients and clinical samples Patients were diagnosed and recruited based on typical clinical symptoms of mandibular, ear and facial soft tissue hypoplasia, physical examination results, and imaging reports including x-ray and CT reports. All samples were collected from Department of maxillofacial surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College. All participants enrolled in our present study were Han Chinese, Asia population. Six patients with HFM and four healthy controls were enrolled in our study. All patients' adipose tissue samples in our manuscript were collected through scar excision when removing the mandibular distractor surgery. The adipose tissue from controls was also collected through the buccal fat pad resection procedure from patients with facial fat accumulation. After taking adipose tissue away from operating table, we dissected it into small pieces with surgical scissors. Samples were maintained in centrifuge tubes with RNAlater (Ambion Inc) and stored at −80°C as the first part, preserved in cryotub. According to the Declaration of Helsinki Principles, the protocols for the study were approved by the Ethics Review Committee of Plastic Surgery Hospital. Written informed consent was obtained from participants. ## RNA sequencing (RNA-seq) According to the manufacturer's instructions, total RNA was isolated from adipose tissues using the RNeasy Lipid Tissue Mini Kit (QIAGEN). In accordance with the manufacturer's instructions, stranded total RNA LT sample prep kit (Illumina) was used to prepare the compatible library. A Bioanalyzer (Agilent) was used to analyze the quality and concentration of all libraries. Sequencing of mRNA was performed on an Illumina Hiseq 2,500 sequencing system (Illumina), and 150-bp paired-end FASTQ files were generated. ## cDNA preparation and quantitative PCR Total RNA was isolated with the TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer's protocol. The RNA concentration and purity were evaluated with NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific Inc). With 1 µg of RNA in the 20 µl reaction system, reverse transcription reactions were performed with PrimeScript Reverse Transcriptase (Takara Bio) according to the manufacturer's instructions. To validate the confidence of RNA-Seq, several differentially expressed genes were selected and analysed by quantitative real-time PCR (qPCR) utilizing the SYBR Premix EX Taq reagent (Takara) in a QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems). Primers (Supplementary Table S1) were designed for the coding sequences of the candidate genes in Primer 3 software (http://frodo.wi.mit.edu/cgibin/primer3). GAPDH was used as the internal control. qPCR replicates were performed in a final volume of 10 µl containing primers, SYBR Premix EX Taq reagent (Takara) and cDNA templates. All quantitative PCRs were performed for three biological replicates. The relative expression levels of the candidate genes were calculated as the averaged normalized Ct value of each sample compared with the GAPDH Ct value of the corresponding sample based on the 2−ΔΔCt method. ## Detection of differentially expressed genes Our RNA expression levels were estimated using HISAT2 (version 2.1.0). Genome sequence data (hg19, Genome Reference Consortium GRCh37) and annotation data were obtained from UCSC website (https://genome.uscs.edu). With featureCounts (v1.5.0-p3), we calculated transcript counts at the gene level and relative abundances in FPKM (fragments per kilobase of exon per million fragments mapped). We build Principal Component Analysis (PCA) and identified 1,234 differential expressed genes (DEGs) by using DEseq2 (P-value <0.05, log2Foldchange >2) [8]. Heatmap show the gene expression pattern in different groups, all DEGs expression level were scale to −2 and 2 based on Z-score calculation. Diagrams were visualized by ggplot2 in R version 4.1.0. ## Gene annotation: gene ontology and pathway analysis The KEGG (Kyoto Encyclopedia of Genes and Genomes database) and gene ontology (GO) pathway enrichment analysis was performed using ClusterProfiler package from BioConductor (http://www.bioconductor.org/). A hypergeometric exact test was used to classify enrichment GO categories and KEGG pathways. Only terms with a P-value 0.05 were considered enriched. According to the rank of P-value, we display the top 20 GO enriched terms for both upregulated and downregulated genes. ## Cell lines, transfection and construction of lentiviral vectors The buccal fat pad from healthy controls were collected in D-Hanks solution with 1,000 U/L penicillin streptomycin (Solarbio, Beijing, China). An equal volume of $0.25\%$ of EDTA trypsin supplemented with $0.1\%$ of type I collagenase solution was added, and the samples were gently vibrated in a 37°C incubator shaker for 45 min. Cells were collected and centrifuged at 1,000 rpm for 10 min; the supernatant was removed, and the cells were resuspended with DMEM supplemented with $10\%$ FBS. After ﬁltering using a 100-mesh sieve, cells were transferred into a culture dish and incubated in 37°C with $5\%$ CO 2 for 24 h. Cells were observed using a phase-contrast microscope daily. Cell morphology and proliferating rates were recorded, and when cells reached $90\%$–$95\%$ conﬂuence, cells were digested with $0.25\%$ EDTA trypsin and subcultured into several culture dishes. Lentiviral interference vectors, pCMV.GFP&PR.U6 (sh-, interference vector), and lentiviral overexpression vector, VP001-CMV-MSC-3flag-EF1-ZsGreen-T2A-PURO (oe-, overexpression vector), were purchased from Sangon Biotech (Shanghai, China) and General Biosystems (Anhui,China). A lentiviral-based HOXB2 interference vector, a HOXB2 overexpression vector were constructed. Lentiviral infections were performed using protocols available online (www.broadinstitute.org). Next, cells were inoculated into a six-well plate at a density of 5 × 104 cells/ml and infected with NC, si-HOXB2, vector, HOXB2-OE, respectively. ## Cell proliferation, transwell assays For the cell proliferation test, a CCK8 Kit (Solarbio, China) was used. In 96-well plates, cells were seeded in 96-well flat-bottomed plates with each well containing 2,000 cells in 200 µl of culture medium and cultured in ambient environment described above. CCK8 reagent was added into wells, after incubation at 37°C for 24 h in a humidified incubator with $5\%$ CO2, the proliferative ability of the cells was measured at 450 nm. For transwell migration assay, different cells were suspended in 200 µl of DMEM without FBS and seeded on the top chamber of 24-well plate-sized Transwell inserts (Corning Falcon). The lower chambers contained DMEM with $20\%$ FBS. After incubation for 8 h, the inserts were fixed and stained with crystal violet. Cells in the upper chamber were removed with cotton swabs. The average confluence of migrated cells was analyzed by ImageJ according to three random fields captured by 100× microscope. Each experiment was conducted in triplicate. ## Clinical characteristics in the HFM and healthy control groups The 6 patients with HFM enrolled in our study who were aged from 7 to 26. All patients were classified with Pruzansky II and III mandibular hypoplasia, including facial soft tissue deficiencies, shortened mandibular ramus, small glenoid fossa, malformed condyle, preauricular tags, microtia, excluding other concurrent clinical symptoms (epibulbar dermoids and vertebral anomalies). The 4 matched controls were all adults who aged from 20 to 25. The adipose tissue was collected when matched controls underwent buccal fat pad resection. The clinical details of the participants are provided in Supplementary Table S2. ## RNA-seq analysis and identification of differentially expressed genes A transcriptomic analysis of adipose tissue from six patients with HFM and four healthy control was conducted to better understand the pathogenesis of the disease. A total of 28 million read pairs were obtained from each sample and were compared between the HFM and control groups in adipose tissue. The total number of annotated mRNAs identified was 333,341 and 1,244 mRNAs were significantly deregulated (q-value <.05; absolute value Log2 Ratio ≥1) (Figure 1A). A comparison of the adipose tissues from patients with HFM and those from controls demonstrated that 710 genes were up-regulated, and 534 genes were down-regulated. The differentially expressed mRNA data sets were analyzed using principal component analysis and hierarchical clustering. HFM adipose tissue exhibited distinct gene expression profiles compared with control adipose tissue based on PCA (Figure 1B). According to a hierarchical clustering analysis of the differentially expressed genes in adipose tissue from patients with HFM and control subjects, each gene expression pattern clustered separately (Figure 1C). Subsequently, we draw a correlation heatmap between various gene expression (Figure 1D). The results indicated that HFM has a profound impact on the expression of mRNA in the facial adipose tissue. Figure 1:* Differentially expressed genes between facial adipose tissue from paitients with HFM and matched controls identified by RNA-Seq. (A) Volcano plots of genes with differential expression. The x-axis represents the log 2 (fold change), and the y axis represents −log 10 (P-value) calculated by student's t-test. The orange points represent the upregulated genes and blue ones represent the downregulated genes. (B) Principal component analysis (PCA). (C) Hierarchical clustering analysis of genes with differential expression. (D) Heatmap summarizing correlation between control and experiment group in log2 gene expression profiles. ## Validation of mRNA expression To confirm the accuracy of RNA-Seq, five genes including up-regulated (HOXB2, HAND2, COL1A1, MACH1) and down-regulated (SIX2) between adipose tissue from patient with HFM and controls were selected for further validation in additional set of clinical samples. Consistent with our expectation, the validation results of HOXB2 and HAND2 were consistent with the RNASeq data. While no significant difference ($P \leq 0.05$) was detected in the levels of COL1A1, MACH1, SIX2 mRNA, according to the results of quantitative real-time PCR (Figure 2). Figure 2: Validation of genes expression by real-time PCR in other patients and matched controls. (A) HAND2, (B) HOXB2, (C) COL1A1, (D) MAGI1 and (E) SIX-2.P < .05.* ## Functional analysis of differentially expressed genes Gene ontology and pathway analysis of genes were performed to find possible biological alterations related to HFM. A wide range of biological functions and signaling pathways are involved with DEGs. The immune system and the skeletal system exhibited the most significant differences, out of many involved pathways. Based on the enrichment score, we selected the top ten items from GO (Figure. 2). the most significant biological processes related to craniofacial morphogenesis included skeletal system development, ossification, regulation of vasculature development, embryonic skeletal system development and embryonic skeletal system morphogenesis. According to KEGG analysis (Figure. 3), signal pathways including PI3K−Akt signaling pathway and Human papillomavirus infection showed significant differences. The PI3K−Akt signaling pathway was previously shown to be associated with NCCs development [9]. The relationship between human papillomavirus infection and HFM has not been reported yet. Additional studies are required to further investigate these possibilities. Figure 3: The top 10 enrichment GO (gene ontology) pathway analysis. ## Effect of HOXB2 on ADSC To examine the effects of HOXB2 on ADSC proliferation and metastasis, we conducted a series of cell function experiments. HOXB2 overexpression significantly decreased the migration and invasion of ADSC (Figures 4A–D), as well as reduced their proliferation (Supplementary Additional file S1, Figure S2B). On the contrary, Loss of HAND2 further enhanced cell migration and invasion of these ADSC (Supplementary Additional file S1, Figure S2F, S3B–D). Figure 4: The top 10 enrichment KEGG pathway terms. Figure 5: (A–C) overexpression of HOXB2 inhibited ADSC migration. (D–F) Knockdown of HOXB2 expression have enhanced ADSC migratory ability. Figure 6: (A) HOXB2 overexpression inhibited ADSC proliferation. (B) HOXB2 knockdown increased the ADSC proliferation ability. ## Discussion The molecular mechanism underlying HFM remains unclear. Numerous studies revealed that HFM has multiple contributors, including genetic, maternal, and environmental factors [10]. Three main hypotheses have been suggested to explain the pathogenesis of HFM: stapedial artery abnormalities, abnormal development of cranial neural crest cells (CNCCs), and injury of Meckel's cartilage. In this study, RNA-Seq has been used for the first time to analyze the transcriptome of affected facial adipose tissue of HFM in Han Chinese. CNCCs are highly capable of proliferating and diffusing during the embryonic period, which plays a vital role in the growth and development of craniofacial cartilage, bone tissue, as well as smooth muscle, sensory nerve, and adipose tissues [11]. One-third of all congenital anomalies are related to improper development of neural crest cells (NCCs) in humans [12]. The majority of abnormal craniofacial tissue involved in HFM is derivatives of CNCCs. Different parts of CNCCs have specific migration paths, forming the first and second branchial arches. Thus, it is a possible mechanism of HFM that affects the proliferation, migration, and differentiation of CNCCs [10]. To our knowledge, this is the first study using Next-generation sequencing to compare the differential expression of genomic profiles between HFM patients' and healthy controls' facial adipose tissue. Our results indicated that overexpressed of HAND2 and HOXB2 may resulted in the alteration of hemifacial hypoplasia in HFM patients. HAND2 (Heart And Neural Crest Derivatives Expressed 2) is a protein coding gene which plays an important role in limb and branchial arch development [13]. HAND2 expression in the ventral region of the branchial arch is independent of Edn1/Ednra-mediated signals [14]. The expression of HAND2 in wild type embryos is restricted to the distal mandibular mesenchyme, which is downstream of Bmp4 [15, 16]. Studies indicated that overdosed BMP signaling inhibits facial skeletal formation by causing dramatic apoptosis in NCC cells [17]. According to research, high HAND2 expression in NCCs can result in the transformation of the upper jaw into a lower jaw, resulting in the absence of a secondary palate [18]. Funato et al. experiments have indicated HAND2-overexpressed mice showed fragmented temporal bones and malformed middle ear (malleus, incus, gonial bone, and tympanic ring) [18]. HAND2 overexpression caused hypoplastic bone formation in the cranial region, consistent with the observation that HAND2 negatively affects mandibular ossification through direct inhibition of RUNX2, a master transcription factor of osteoblast-specific genes [19]. These experimental results are consistent with the observed clinical manifestations in HFM patients including underdevelopment of the mandible, maxilla, ear, orbit, facial soft tissue. HOX genes, which encode for homeodomain-containing transcription factors, are major inhibitors involved in patterning of animal embryos and craniofacial program carried by CNCC [20, 21]. HOXA2, as other HOX2 paralogs, may cooperate with HOXB2 in the second pharyngeal arch [22]. A prevalent role of HOXA2 as its inactivation in mouse induced a mirror-image duplication of the lower jaw with two Meckel's cartilage [23, 24]. Studies have also shown that patients with mutations in HOXA2 display severe microtia, middle ear deformities and hearing loss [25, 26]. In Animal experiments, the hearing impairment were also observed in HOXB2 mutants [27]. On the other HAND, HOXA2 homozygous mutant embryos contained additional, more extensive ossification centers [23]. Histological observations of HOXB2 mutant mice detected a cartilage rod carrying a proximal protuberance, which could be interpreted as a triplicated malleus [28]. Apart from triplicated malleus, the HOXA2 mutants duplicate all skeletal elements normally derived from the first arch NCC, including ectopic Meckel's cartilage, as well as ectopic incus, malleus, tympanic, and squamous bones [24]. In our experiments, it can be observed that high expression of HOXB2 can significantly reduce ADSC migration and proliferation. Unfortunately, no animal experiments about overexpression of HOXB2 can be found in the literature. Further verification experiments will be conducted in order to confirm the gene function. We could only speculate the overexpression of HOXB2 were possibly in accordance with the clinical symptoms in HFM. For the first time, we identified two mRNAs that may participate in the pathogenesis of HFM, providing a new avenue for future research. Suppressing the expression of genes, such as HOXB2 and HAND2, might be a promising therapeutic approach to HFM. However, there are several limitations in our study. First of all, the sample size is small, and more samples will be collected for further study. In addition, we have not provided experimental evidence to support the role of genes. Furthermore, as the samples were collected with a wide range of ages, a comparison of HFM samples to normal adipose tissue may reveal different genes due to the tissues' developmental stages but not because of differences between them. In summary, it is necessary to further explore the underlying roles of the identified pathways and molecules in HFM. ## Conclusion As a result, we identified 1,275 DEGs in adipose tissue of HFM patients along with key pathways and networks. It is the first study to report that HFM is related to high expression of HAND2and HOXB2, providing an important basis for further mechanistic investigations of HFM. A detailed study of these two genes in HFM pathogenesis will be conducted in the near future. ## 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 below: https://ngdc.cncb.ac.cn/gsa-human/s/QUhld0aT, HRA002742. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics Review Committee of Plastic Surgery Hospital. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. ## Author contributions SZ and LM: Conceptualization, Methodology. CH and TZ: Data curation, Writing- Original draft preparation. KS: Visualization, Investigation. XT: Supervision. BL: Writing- Reviewing and Editing, WL: Surgical instruction, Specimen taking. HG: Bioinformation analysis. 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--- title: Dietary yucca extract and Clostridium butyricum promote growth performance of weaned rabbits by improving nutrient digestibility, intestinal development, and microbial composition authors: - Yuyan Wang - Yan Zhang - Hongjie Ren - Zubo Fan - Xu Yang - Cong Zhang - Yibao Jiang journal: Frontiers in Veterinary Science year: 2023 pmcid: PMC9968931 doi: 10.3389/fvets.2023.1088219 license: CC BY 4.0 --- # Dietary yucca extract and Clostridium butyricum promote growth performance of weaned rabbits by improving nutrient digestibility, intestinal development, and microbial composition ## Abstract Yucca has abundant amounts of polyphenolics, steroidal saponins, and resveratrol and its extract can be used as a feed additive in the animal husbandry, which might contribute to the improvement in the growth and productivity in rabbit production. Hence, the current study aimed to examine the effects of yucca extract alone and in combination with Clostridium butyricum (C. butyricum) on growth performance, nutrient digestibility, muscle quality, and intestinal development of weaned rabbits. A total of 400 40-day-old male rabbits were randomly divided into 4 treatment groups for 40 days: [1] basal diet group, [2] basal diet contained 300 mg/kg of yucca extract, [3] basal diet supplemented with 0.4 × 1010 colony-forming units (CFU)/kg of C. butyricum, and [4] the blend of 0.4 × 1010/kg CFU of C. butyricum and 300 mg/kg of yucca extract. The supplementation of yucca extract or C. butyricum increased body weight (BW) of rabbits depending on the age, the combined addition of yucca extract and C. butyricum significantly increased BW, weight gain, and feed intake, companying with increased the digestibility of crud protein, fiber, phosphorous, and calcium as compared to control diet ($P \leq 0.05$). Furthermore, yucca extract and C. butyricum treatment alone and in combination notably increased the villus high and the ratio of villus high to crypt depth of rabbits ($P \leq 0.05$). The combined supplementation of yucca extract and C. butyricum altered the intestinal microbiota of rabbits, as demonstrated by increased the abundance of beneficial bacteria Ruminococcaceae and decreased the proportion of pathogenic bacteria such as Pseudomonadaceae and S24-7. In addition, the rabbits fed the diet with yucca extract and the blend of yucca extract and C. butyricum had significantly increased pH45min, decreased pressing loss, drip loss, and shears force when compared with rabbits received control diet ($P \leq 0.05$). Diet with C. butyricum or its mixture with yucca extract increased the fat content of meat, while the combined addition of yucca extract and C. butyricum declined the content of fiber in meat ($P \leq 0.05$). Collectively, the combined use of yucca extract and C. butyricum showed better results on growth performance and meat quality, which might be closely associated with the improved intestinal development and cecal microflora of the rabbits. ## 1. Introduction The transformation of weaned rabbits from breast milk to solid feed will cause many physiological and environmental stresses, which could lead to the prevalence and spread of enteric pathogens such as Escherichia coli and coccidia, and finally bring huge economic losses to animal husbandry due to negatively affect the growth performance and feed efficiency, as well as impair the animal welfare of rabbits. Considering the banned use of antibiotic growth promoters, mainly because of the emergence of bacteria resistant to multiple types of antibiotics, the development of antibiotic substitute and alternative feed additives to promote intestinal health, protect the stability of gastrointestinal microorganisms, and improve growth performance is urgent to the producers. Emerging evidence have showed that plants and their extracts exert positive roles in animal growth, immunity, and maintaining animal health [1]. The yucca extract contains saponins, polysaccharides, polyphenols, and other active substances [2], and is generally recognized as a safe feed additive. Studies have confirmed that yuca extract can enhance antioxidant function and immunity, maintain intestinal health, and improve animal growth performance in laying hens [3] and broilers [4]. In addition, a diet with yucca extract can improve feed nutrient utilization, gut microflora, and intestinal barrier function of weaned piglets [5, 6]. Dietary yucca extract was also found to promote female rabbit growth and fecundity through affecting the release of hormones and reducing ovarian resistance to benzene [7, 8]. Of note, the performance of livestock is closely related to gut microbial load, the intestinal barrier, and the activity of the immune system, which could be regulated by probiotics [9]. Clostridium butyricum (C. butyricum), a obligate anaerobic gram-positive probiotics that produces butyric acid [10], is considered to be one of the beneficial bacteria that widely colonized in animal intestines, and is proved to promote growth, strength immune system, and regulate intestinal microbial composition in weaned piglets [11], goats [12], and Peking ducks [13]. These above evidence imply supplementation with yucca extract and/or C. butyricum probably improve the growth performance and health of rabbits. Although single probiotic bacteria or plant extract have been widely applied in animal production, the application of combinational yucca extract and C. butyricum in the rabbit industry is rarely reported. The combined supplementation of probiotics and plant extract could be superior to them individual utilization as different species of probiotics or extract may promote animal health and production performance through different effects on gut microbial composition and cooperative action between different bacterial species and botanical extracts [14]. However, some antagonism roles might be possible regarding the use of probiotics and plant extracts. In this context, diets with yucca extract alone or supplementation of clostridium butyric acid, are being practiced in the pigs [6, 11], poultry [3, 4], and ruminant animal research [12]. The aim of this study, therefore, was to evaluate whether yucca extract and C. butyricum addition in rabbit feed improves growth, nutrient digestibility, meat quality, and intestinal microflora, which may provide some guidelines for the development of antibiotics alternative feed additives in rabbit production. ## 2.1. Ethical statement All experimental protocols were approved by the Animal Care and Use Committee of Henan Agricultural University and the animals were maintained in accordance with office guidelines for the care and use of laboratory animals (approval number: HN20210806). ## 2.2. Yucca extract and bacterial strain Yucca schidigera extract with an active ingredient content of $60\%$ was purchased from Xi'an Lutian Biotechnology Co., Ltd. (Xi'an, China). The C. butyricum was provided by Hubei Greensnow Biological Biotechnology Co., Ltd. (Wuhan, China) and the bacterial concentration reached 1 × 1010 colony-forming units (CFU)/g. ## 2.3. Animals, experimental design, and diets To rule out the effects of gender, the just male rabbits from New Zealand White line (Henan, Jiyuan, China) were used in the present study. The animals were individually housed in metal cages (0.4 × 0.6 × 0.45 m) in a climate-controlled facility. The temperature in the room was 15–20°C with relative humidity was 55–$65\%$ based on normal management practices. The light schedule was 16-h light and 8-h dark throughout the experiment. The rabbits (40 days of age) with mean 1.05 ± 0.02 kg body weight (BW) were randomly into 4 treatment groups with 5 replicates of 20 rabbit each group, i.e., [1] basal diet group (Ctrl), which was formulated according to NRC [1977] rabbit feeding standard and shown in Table 1, [2] the basal diet supplemented with 400 mg C. butyricum per kg diet (0.4 × 1010) CFU/kg diet of C. butyricum, [3] the basal diet with 300 mg yucca schidigera extract per kg diet (YSE), and [4] the basal diet supplemented with both 400 mg C. butyricum and 300 mg yucca schidigera extract per kg diet (C. butyricum + YSE). The dosage of additives in this study were based on previous studies [15, 16]. The rabbits were fed at 07:00 and 18:00 every day to ensure free access to drinking water and feed from 40 to 80 days. Table 1 \| Ingredients, % \| Unnamed: 1 \| Calculated analysis nutrient levels, % \| Unnamed: 3 \| \| --- \| --- \| --- \| --- \| \| Corn \| 17.0 \| Digestible energy (MJ/kg) \| 10.42 \| \| Bran \| 14.0 \| Crud protein \| 14.54 \| \| Soybean meal \| 16.0 \| Crud fiber \| 16.85 \| \| Alfalfa hay \| 4.0 \| Neutral detergent fiber \| 47.04 \| \| Peanut seedling \| 22.0 \| Acid detergent fiber \| 24.78 \| \| Peanut shells \| 9.0 \| Ether extract \| 5.0 \| \| Corn germ meal \| 15.0 \| Ash \| 9.81 \| \| Sodium chloride \| 0.5 \| Calcium \| 1.24 \| \| Calcium hydrogen phosphate \| 0.4 \| Total phosphorus \| 0.64 \| \| Stone powder \| 1.1 \| \| \| \| Premixa \| 1.0 \| \| \| \| Total \| 100.0 \| \| \| ## 2.4. Growth performance During the experiments, the feed intake and BW was recorded every 10 days after 8-h feed withdrawal. The average daily gain (ADG), average daily feed intake (ADFI), and feed to gain ratio (F:G) were calculated by recording the feed intake of rabbits in each pen. In addition, the number of rabbits with diarrhea in each replicate was recorded to calculate the diarrhea rate as following: ## 2.5. Determination of nutrient apparent digestibility Five days before the end of the experiment, 3 rabbits from each replicate were randomly selected for digestion test. After a 3-day adaptation period, excreta from each cage were collected daily for the next 72 h. After each collection of feces, $10\%$ hydrochloric acid was added to excreta nitrogen and stored at −20°C. After dry at 60°C for 72 h and ground to a size that could pass through a 1-mm screen, the feed and fecal samples were analyzed for dry matter (DM), ether extract (EE), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude ash, calcium (Ca), and phosphorus (P), and then their apparent digestibility were determined as previous description [17]. ## 2.6. Sample collection On days 80, five similar BW rabbits from each treatment group were selected and sampled. After sacrification, the longissimus thoracis (LT) muscle from the left side of each carcass was used for the measurement of meat characteristics and composition. The middle segments of duodenum, jejunum, and ileum were stored in $4\%$ paraformaldehyde for morphological analysis. Digesta samples of cecum were collected and stored in liquid N2 for 16S rDNA sequencing. ## 2.7. Muscle quality and nutrient composition The meat characteristics included pH, water holding capacity (WHC) expressed as pressing loss (%), drip loss (%) and cooking loss rate (%), and tenderness, as well as the nutrient composition including CP, ether extract, and crude ash were analyzed based on the standard methods [18]. The pH45min and pH24h values were measured twice on LT at 45 min and 24 h postmortem, respectively, using a TESTO 205 pH acidity tester (Mettler-Toledo International Inc., USA) equipped with an insertion glass electrode. The pH meter was calibrated before measurements using standard phosphate buffers (pH = 4.01 and 7.00) and adjusted to the actual temperature of sample measurement following the instrumental user's manual. The filter-paper press method was used to measure pressing loss. Cored LT samples, 2.523 cm in diameter and 1.0 cm in thickness, were collected and weighed (W1). Subsequently, the meat sample was placed on the pressure gauge platform and pressed to 35 kg (the range of the pressure gauge is about 138 kg) for 5 min. Samples were reweighed (W2) and pressing loss (%) was calculated according to the following equation: (W1–W2)/W1 × $100\%$. Two pieces of about 5 g dorsal muscle were cut into 5 mm × 5 cm strips and weight (W3). Then the meat samples were placed in a water drop loss measuring tube avoiding sticking to the wall. After 24 h of the refrigerator at 4°C, these samples were removed, dried the surface moisture of muscle with filter paper, and weighed (W4), and the water drop loss (%) was calculated according to the following equation: (W3–W4)/W3 × $100\%$. About 100 g of LT was subsampled by cutting 5 × 3 × 2 cm cubes devoid of fat and connective tissue and weighted (W5). Each cube was cooked in a water bath at 80°C until an internal temperature of 70°C was reached. Subsequently, the cooked samples were then cooled at 4°C for 2 h and reweighed (W6). The cooking loss (%) was calculated according to the following equation: (W5–W6)/W5 × $100\%$. Tenderness was measured through the shears to force values and expressed in Newton (N) [19]. Meat sample was putted into a constant temperature water bath at 80°C until the core temperature of the muscle reached 70°C, subsequently cooled at 4°C for overnight. About 6 to 8 1.27-cm-dia cylindrical cores parallel to the muscle fiber orientation were removed from each meat. The peak shear force measurement was obtained for 3–5 core each sample using a Warner-Bratzler meat shear machine (C-LM3B, Tenovo, Beijing, China) and the arithmetic mean was calculated for each meat sample. ## 2.8. Morphological analysis of small intestine The fixed segments of duodenum, jejunum, and ileum were dehydrated, embedded, sliced into 5-μm transects, and stained with hematoxylin and eosin (H&E), and subsequently villus height (VH) and crypt depth (CD) of at least ten well-oriented villi, were measured and the ratio of villus height to crypt depth (V/C) was calculated. The histomorphometry data were taken using a microscope (Nikon Eclipse TS100; Nikon Corporation) and an image analyzer (Media Cybernetics Image Pro-Plus) at a magnification of 400×. ## 2.9. Gut microbiome analysis The caecal content was mixed with lysis buffer which was composed of 40 mM ethylene diamine tetraacetic acid, 50 mM Tris pH 8.3, and 0.75 M sucrose, and then submitted to smooth shaking for 30 min. 200 μl of supernatant were used for the DNA extraction with the QIAamp DNA Stool kit (QIAGEN, Hilden, Germany). The quantity and quality of DNA was detected by using the Nanodrop ND-2000 spectrophotometer (Thermo Fisher Scientific, USA) and $1\%$ agarose gel electrophoresis, respectively. The fusion primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGTATCTAAT-3′) were used to amplify the V3–V4 hypervariable region of the 16S rRNA gene using 200 ng DNA based on the 2-step PCR protocol. Sequencing library was prepared, and high-throughput sequencing was performed using the Illumina platform (Illumina, San Diego, US). Initial screening was conducted for the original off-machine data of high-throughput sequencing according to the sequence quality, and the problem samples were retested. Then the primer fragments of the sequence were removed, and the sequences of unmatched primers were discarded, and the steps of quality control, denoising, splicing and chimerism removal were carried out according to DADA2 analysis process in QIIME software [20]. The alpha diversity was evaluated by calculating Chao1 estimator, Simpson, and Shannon diversity index. Beta-diversity was estimated by calculating the distance of dietary treatments to Ctrl group based on Bray-Curtis dissimilarities. Differentially enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways were also calculated. ## 2.10. Statistical analysis In this study, the statistical power of 0.75 ($75\%$) was obtained when the minimally detectable effect size was 1.0 and the significance level was 0.05. The data obtained were analyzed by the Shapiro-Wilk and Levene's test to assess normal distribution and homogeneity of variances (SPSS 26.0). One-way analysis of variance (ANOVA) by Duncan test for multiple comparisons and Kruskal-Wallis test followed by Dunn's multiple comparisons were performed for normal distribution and non-normal distribution, respectively. Values are given as mean ± standard deviation. $P \leq 0.05$ was considered statistically significant. For the determination of the growth curves of BW of rabbits, three non-linear regression model (von Bertalanffy, logistic, and Gompertz) were assessed based on the coefficient of determination (R2) using the non-linear models (PROC NLIN) of SAS by the Gauss-Newton algorithm (data not shown). The logistic model was finally selected as the optimized model for BW as the equation: BW = a/(1 – b × EXP(–kday)), a is the asymptotic value of modeled trait, b is a constant of integration without biological interpretation, k is the maturity rate. The age of maximal growth rate is on lnb/k day. ## 3.1. Growth performance The effects on the growth performance are showed in Table 2, when compared with Ctrl group, dietary YSE inclusion increased the BW of rabbits on days 50, and C. butyricum or YSE treatment significantly increased the BW of rabbits on 60 days (both $P \leq 0.05$). Of note, diet with C. butyricum and YSE induced a remarkably increase in BW than the Ctrl diet on days 70 and 80 ($P \leq 0.05$), which was consistent with the ADG results ($P \leq 0.05$). Furthermore, based on these growth curve showed in Figures 1A–D, the ages of maximal growth rate of YSE, C. butyricum, and YSE + C. butyricum were earlier than those in Ctrl group, i.e., the age of maximal growth rate was 74.36, 65.50, 65.51, and 70.28 days in Ctrl, YSE, C. butyricum, and YSE + C. butyricum groups, respectively. Regarding feed consumption, the rabbits fed C. butyricum or YSE diet presented a significant increase in ADFI during 40–50 and 51–60 days as compared to those received Ctrl diet. The combined supplementation of C. butyricum and YSE significantly increased the ADFI during the whole study period (40–80 days). Except the decreased ration of feed to gain in YSE and C. butyricum + YSE group, the dietary administration not significantly changed the F/G when compared to Ctrl group. In addition, dietary supplemented with YSE, C. butyricum, or their blend could decrease the diarrhea rate to varying degrees when compared to Ctrl diet. ## 3.2. Nutrient apparent digestibility As illustrated in Table 3, diets with YSE, C. butyricum, or their compound notably increased the digestibility of CP, ADF, and Ca as compared to Ctrl diet (both $P \leq 0.05$). Supplementation of the blend of YSE and C. butyricum also significantly elevated the digestibility of NDF and P ($P \leq 0.05$). There was no significant difference in the digestibility of EE among all groups (Table 3). Table 3* \| Item, % \| Ctrl \| YSE \| C. butyricum \| C. butyricum + YSE \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Ether extract \| 76.78 ± 6.21 \| 79.32 ± 8.39 \| 77.82 ± 9.66 \| 81.19 ± 12.25 \| 0.943 \| \| Crude protein \| 68.12 ± 1.50c \| 69.91 ± 0.78b \| 71.86 ± 0.19a \| 71.13 ± 0.41ab \| 0.004 \| \| Neutral detergent fiber \| 32.98 ± 1.40b \| 34.17 ± 1.33ab \| 34.81 ± 0.52ab \| 35.32 ± 0.35a \| 0.097 \| \| Acid detergent fiber \| 23.19 ± 1.20b \| 27.39 ± 1.17a \| 28.16 ± 2.23a \| 27.22 ± 1.30a \| 0.016 \| \| Ash \| 42.59 ± 0.35ab \| 41.75 ± 1.27b \| 42.91 ± 3.42ab \| 45.72 ± 0.81a \| 0.132 \| \| Calcium \| 58.00 ± 5.41b \| 64.56 ± 2.58a \| 67.69 ± 2.62a \| 65.85 ± 2.11a \| 0.039 \| \| Phosphorus \| 27.42 ± 1.02b \| 31.13 ± 2.10b \| 33.13 ± 2.46ab \| 37.47 ± 4.86a \| 0.019 \| ## 3.3. Small intestinal morphology The diet with YSE or C. butyricum reduced duodenal CD, while it did not apparent change the CD of jejunum and ileum (Figure 2A). Dietary supplementation of both YSE and C.butyricum could decrease the intestinal CD when compared to Ctrl diet. Rabbits fed C. butyricum or YSE had a higher VH and VH/CD of duodenum, jejunum, and ileum than the those fed the Ctrl diet. In particular, the combined supplementation of YSE and C. butyricum contributed higher villus and VH/CD than single YSE or C. butyricum group (Figures 2B, C). Figure 2: Dietary yucca extract (YSE) and C. butyricum promote the development of small intestine in rabbits. (A) Crypt depth and (B) villus height of duodenum, jejunum, and ileum were measured, and (C) the ratio of villi height to crypt depth were calculated based on hematoxylin/eosin (H&E) staining. Scale bar = 100 μm. Values are means and standard deviation (SD) represented by vertical bars. a, bMean values with different letters are significantly different (n = 5; P < 0.05). ## 3.4. Muscle quality and nutrient composition The effects of YSE and C. butyricum on muscle quality and nutritional composition of LT are shown in Table 4. The rabbits in the YSE- and YSE + C. butyricum-supplemented groups had significantly increased pH45min compared with rabbits in the Ctrl group ($P \leq 0.05$). The dietary YSE and C. butyricum supplementation improved the WHC of LT, evidenced by decreased pressing loss and drip loss in YSE, C. butyricum, and YSE + C. butyricum as compared with that in the Ctrl group. In addition, the meat shears force of C. butyricum, and YSE + C. butyricum groups was significantly lower than that of the Ctrl group ($$P \leq 0.033$$). In terms of nutrient composition of meat, diet with C. butyricum or the blend of YSE and C. butyricum increased the EE content of LT, whereas the dietary treatments did not change the content of moisture, CP, and ash in meat when compared with the Ctrl diet ($P \leq 0.05$, Table 4). Table 4 \| Item \| Ctrl \| YSE \| C. butyricum \| C. butyricum + YSE \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Meat quality traits \| Meat quality traits \| Meat quality traits \| Meat quality traits \| Meat quality traits \| Meat quality traits \| \| pH45min \| 6.05 ± 0.13b \| 6.67 ± 0.19a \| 6.28 ± 0.22b \| 6.66 ± 0.19a \| 0 \| \| pH24h \| 5.72 ± 0.15 \| 5.71 ± 0.14 \| 5.68 ± 0.17 \| 5.74 ± 0.04 \| 0.9 \| \| Pressing loss, % \| 29.87 ± 3.81a \| 20.89 ± 4.63b \| 24.37 ± 5.90ab \| 20.42 ± 4.51b \| 0.023 \| \| Drip loss, % \| 4.01 ± 0.38a \| 1.72 ± 0.12c \| 2.82 ± 0.24b \| 1.43 ± 0.54c \| 0 \| \| Cooking loss, % \| 27.21 ± 1.47 \| 25.19 ± 4.41 \| 26.56 ± 3.03 \| 24.97 ± 4.13 \| 0.697 \| \| Shear force value, N \| 41.54 ± 4.00a \| 37.78 ± 2.86ab \| 35.40 ± 4.06b \| 34.33 ± 3.72b \| 0.033 \| \| Meat composition, % \| Meat composition, % \| Meat composition, % \| Meat composition, % \| Meat composition, % \| Meat composition, % \| \| Moisture \| 74.49 ± 0.86 \| 75.19 ± 1.12 \| 74.60 ± 0.98 \| 74.76 ± 0.45 \| 0.633 \| \| Ether extract \| 4.19 ± 1.10b \| 5.18 ± 0.82b \| 6.78 ± 0.86a \| 6.60 ± 0.69a \| 0.001 \| \| Crude protein \| 83.24 ± 1.85 \| 84.05 ± 1.56 \| 83.62 ± 1.21 \| 84.08 ± 1.37 \| 0.793 \| \| Ash \| 4.83 ± 0.25 \| 5.02 ± 0.17 \| 4.84 ± 0.30 \| 4.68 ± 0.29 \| 0.255 \| ## 3.5. Microbiota structure in colonic contents The microbiota in the colonic content is presented in Figure 3. The dietary interferes did not change the alpha diversity, showed by similar Chao1, Simpson, and Shannon indexes (Figures 3A–C; $P \leq 0.05$). The administration of YSE combined C. butyricum induced an apparent difference in microbiota composition of the colonic contents in rabbits (Figure 3D), which mainly comprised Firmicutes, Bacteroidetes, Proteobacteria, and Tenericutes at the phylum level (Figure 3E). At the family level, Ruminococcaceae was the dominant species in the cecal flora of rabbits, with $37.94\%$ abundance in the Ctrl group, $43.11\%$ abundance in the C. butyricum group, $41.97\%$ abundance in YSE group and $45.05\%$ abundance in the combined treatment group (Figures 3F, G). Dietary supplementation with YSE and C. butyricum increased the abundance of beneficial bacteria Ruminococcaceae and decreased the proportion of pathogenic bacteria Pseudomonadaceae and S24-7 (Figures 3G–I). In addition, as illustrated in Figure 4, the metabolic pathways were predicted using KEGG according to the known microbial genome data, and these results showed that dietary YSE or/and C. butyricum mainly affected biosynthesis including amino acid, nucleoside, and fatty acid, etc. Figure 3: Dietary yucca extract (YSE) and C. butyricum on caecal microbiome of rabbits. (A–C) Chao1, Simpson, and Shannon indexes were used to assess alpha diversity, (D) the distance to Ctrl group of caecum microbiome diversity at species level based on Bray-Curtis dissimilarities; the relative abundances of bacterial communities at (E) phylum level and (F) family level, including (G) Ruminococcaceae, (H) Pseudomonadaceae, and (I) S24-7. Values are means and standard deviation (SD) represented by vertical bars. a, bMean values with different letters are significantly different (n = 5; P < 0.05). Figure 4: Functional predictions for the caecal microbiome based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. ## 4. Discussion The physiological and environmental stress of rabbits due to weaning usually occur during the initial postweaning period, which is frequently characterized by transient anorexia, gut microbiota dysbiosis, severe intestinal damage, infections, and diarrhea, compromising the disease resistance of juvenile rabbit [21]. Reasonable nutritional regulation means are very important for the normal growth of weaned rabbits on this. In this study, dietary supplementation with the blend of yucca extracts and C. butyricum had a positive role in growth performance, digestive ability, and meat quality of rabbits, which might involve in the improved intestinal development and intestinal microbiota. Yucca is rich in polyphenols, steroidal saponins and resveratrol, which was often used in food, cosmetics, pharmaceutical and animal feed as a solution or powder due to its properties of antioxidant, anti-inflammatory, antiviral, lowering cholesterol [22]. The addition of yucca extract to feed can promote the growth performance of rabbits [7] and broilers [4, 23]. Additionally, the butyrate produced by C. butyricum is essential for the proper functioning of gut. Treatment with C. butyricum could promote growth and regulate intestinal microbial composition in weaned piglets [11], goats [12], and ducks [13]. Diet with C. butyricum was proved to improve intestinal morphology, gut microbiota, and growth performance of rabbits [24]. Consistent with previous findings, the outcomes of performance indicated that the ages of maximal growth rate of YSE, C. butyricum, and YSE + C. butyricum were earlier than those in Ctrl group. Diet with C. butyricum or yucca extract increased the BW of rabbits depending on the age, while the combined supplementation of C. butyricum and yucca extract in feed significantly increased the BW and ADFI during 40–80 days in the present study, which suggests that dietary supplementation of yucca extract has synergetic effect with C. butyricum and enhanced the growth performance of rabbits. Improving nutrient apparent digestibility might be a key reason to explain these positive roles of dietary combined supplementation yucca extract and C. butyricum in growth performance of rabbits, evidenced by the increased digestibility of CP, ADF, NDF, P, and Ca in YSE + C. butyricum group. Of note, the direct action of the combined supplementation yucca extract and C. butyricum on digestive physiology, regulation of the intestinal development, and remodeling gut microbiota might be closely associated with the improved digestibility of rabbits. It was reported that steroidal saponins in yucca extract could increase the activity of digestive enzymes and have a positive promoting effect on digestive tract [25]. Polyphenols existing yucca extract was found to increase the attention of nutrition in the digestive tract due to their multiple bioactive properties [26]. Therefore, dietary yucca extract addition increased the digestibility of fat and DM of sows during late gestation and lactation [27], as well as improved feed conversion, protein efficiency, and energy efficiency of broilers [28]. In addition, C. butyricum is also pointed to increase the activity of digestive enzymes, and ultimately improving the digestion and absorption of nutrients [29], which might contribute to the feed consumption of rabbits during 40–60 days. Promotion of intestinal development might be another likely explanation for the elevation in nutrient apparent digestibility in the current study. Although exerting immunologic function, intestinal tract mainly involves in digestion and absorption of nutrition. The length and thickness of villi directly affect the absorption area of the intestinal tract, and then affect the absorption and utilization of nutrients by animals [30]. The longer intestinal villi and the shallower crypt depth indicate the stronger absorption capacity of nutrients. It was noticed that dietary yucca extract increased the villus height and the ratio of villus height to crypt depth in ileum of weaned piglets [6]. Administration with C. butyricum as a probiotic in the diet could increase the ratio of villus height to crypt depth of geese [31]. Analogously, in this study, the supplementation alone or combined yucca extract and C. butyricum notably increased the villus height and the ratio of villus height to crypt depth. It probably increases the ability of intestinal digestion and absorption of nutrients and improved the performance of rabbits. Intestinal microbiota plays an important role in the development of the immune system and the maintenance of the intestinal barrier. Dysbiosis of intestinal flora can lead to the destruction of the intestinal barrier and increase the susceptibility to pathogenic microorganism [32]. Dietary yucca extract and C. butyricum induced significant changes in microbial composition as evidenced by a large distance between YSE + C. butyricum and Ctrl groups in this study. At the family level, S24-7, Ruminococcaceae, and Lachnospiraceae were the most abundant in rabbits. S24-7 is negatively correlated with BW and had long-term adverse effects on growth [33], and plays important roles in amino acid metabolism and intestinal mucosal immunity [34]. The decreased relative abundance of S24-7 implied the improved role of the addition of yucca extract and C. butyricum to rabbit feed. The mechanism of antiprotozoal effects of yucca extract is the formation of irreversible complex between saponins and cholesterol [25]. In addition, C. butyricum also decreased the abundance of pathogenic bacteria and increased the abundance of beneficial bacteria to regulate the composition of intestinal flora [11]. Previous data showed that C. butyricum could promote the growth and reproduction of cellulolytic bacteria and fungi in gut, improve cellulase activity and thus increase the digestibility of cellulose and ADF in goats [35]. In this study, dietary supplementation with YSE and C. butyricum increased the abundance of beneficial bacteria Ruminococcaceae and decreased the proportion of pathogenic bacteria Pseudomonadaceae in gut microbiota might be closely related to nutrient digestion in rabbits, especially NDF and ADF. Further studies are needed to confirm the possibility. Meat quality traits such as pH, color, WHC, and tenderness are critical to consumers' initial selection of rabbit meat as well as for final product satisfaction. As a key indicator of the glycolysis rate of muscle glycogen after slaughter, the pH of meat is gradually reduced as the slaughtering time goes on, and too low pH value could cause the meat to be rotten and soft. In this regard, increased pH45min in YSE, C. butyricum, and YSE + C. butyricum diets indicated the positive role of yucca extract and C. butyricum in meat quality of rabbits. It is well-known that lower pH prompts muscle fiber contraction, causing more drip loss [36], thus the increased pH might explain why the enhancement in WHC of meat by the diet contained yucca extract and C. butyricum in the current study, evidenced by lower pressing loss and drip loss. Similarly, feeding the diet with yucca extract could reduce the water loss rate of broiler muscle [37]. Dietary C. butyricum treatment was also found to improve the pH45min of meat in 28-day-old Huanjiang Mini-Pigs [38], and reduce drip loss of pectoral muscle in Peking ducks [13]. In addition, shear force is negatively correlated with the tenderness of the muscle, which is affected by multiple factors including pH, WHC, postmortem proteolysis, meat composition such as fat and fiber [39, 40]. In this study, diet with yucca extract and C. butyricum increased the tenderness of meat in rabbits, which could attribute to higher fat proportion. Of note, the failure of yucca extract to increase muscle fat may be due to the inhibitory effect of saponins on pancreatic lipase activity. Saponins can reduce the fat rate of meat by inhibiting pancreatic lipase activity and delaying muscle fat deposition [41]. Taken together, the combined addition of yucca extract and C. butyricum to feed could improve meat quality of TL in rabbits. ## 5. Conclusions In summary, using yucca extract and C. butyricum as feed additives could increase nutrient digestibility and improve growth performance, which is linked to the alteration in digestive physiology, intestinal development, and gut microbiota. In addition, the combination of yucca extract and C. butyricum in feed has a synergistic effect on meat quality through increasing pH45min, WHC, and tenderness, as well as increasing fat proportion of meat. ## 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 below: https://www.ncbi.nlm.nih.gov/, PRJNA897093. ## Ethics statement The animal study was reviewed and approved by Animal Care and Use Committee of Henan Agricultural University. ## Author contributions YW collected data and wrote manuscripts under the guidance of YJ. YZ, HR, ZF, XY, and CZ helped to collect the literature. All authors contributed to the article and have 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. ## References 1. Fu L, Sun M, Dong W, Zhang G, Han D, Zang J. Effects of compound of hawthorn (. Anim Sci J. (2022) 93 e13790. DOI: 10.1111/asj.13790 2. Bafundo KW, Männer K, Duerr I. The combination of quillaja and yucca saponins in broilers: effects on performance, nutrient digestibility and ileal morphometrics. Br Poult Sci. (2021) 62 589-95. 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--- title: The impact of COVID-19 quarantine on lifestyle indicators in the United Arab Emirates authors: - Sharifa AlBlooshi - Maryam AlFalasi - Zainab Taha - Farid El Ktaibi - Alia Khalid journal: Frontiers in Public Health year: 2023 pmcid: PMC9968935 doi: 10.3389/fpubh.2023.1123894 license: CC BY 4.0 --- # The impact of COVID-19 quarantine on lifestyle indicators in the United Arab Emirates ## Abstract ### Introduction COVID-19 is a virus that has spread rapidly and brought economic and social crises all around the world. The current study aimed to investigate the impact of COVID-19 quarantine on dietary habits, physical activity, food purchasing, smoking, and sleeping patterns in the United Arab Emirates. ### Methods A cross-sectional study was conducted using an online questionnaire between November 1st, 2020 and the end of January 2021. Citizens and residents of the UAE aged ≥ 18 years old were asked to complete an anonymous electronic questionnaire created via Google Forms and distributed on various platforms, such as WhatsApp, Twitter, and email. A total of 1682 subjects participated in the study. ### Results The results included that during the COVID-19 lockdown, more participants ($44.4\%$) reported an increase in weight. This gain seems to be linked to increased food consumption [(Adjusted Odd Ratio) AOR = 1.68, $95\%$ (Confidence Interval) CI = 1.12, 2.54, $$p \leq 0.022$$], decreased physical activity (AOR = 2.25, $95\%$ CI = 1.58, 3.21, $p \leq 0.001$), and increased smoking (AOR = 1.90, $95\%$ CI = 1.04, 3.50, $$p \leq 0.038$$). The groups that were most likely to gain weight included those who consumed more cereals (AOR = 1.67, $95\%$ CI = 1.08, 2.57, $$p \leq 0.011$$), had an increased desire for sweets (AOR = 2.19, $95\%$ CI = 1.50, 3.19, $p \leq 0.001$), and an increased desire for food (hunger) (AOR = 2.19, $95\%$ CI = 1.53, 3.14, $p \leq 0.001$). In contrast, those who exercised more were more likely to lose weight (AOR = 0.61, $95\%$ CI = 0.44, 0.86, $p \leq 0.001$) as well as those who slept over 9 h a day (AOR = 1.90, $95\%$ CI = 0.45, 0.88, $$p \leq 0.006$$). ### Discussion Overall, it is essential to promote healthy habits and methods of healthy diet maintenance during stressful and unusual times when people might find it difficult to put effort into their health. ## 1. Introduction COVID-19 is an infectious disease caused by a newly discovered strain of coronavirus; a type of virus known to cause respiratory infections in humans. This new strain was unknown before December 2019, when an outbreak of pneumonia of unknown cause emerged in Wuhan, China [1]. On March 11th, 2020, the World Health Organization (WHO) [1] declared that COVID-19 is a worldwide pandemic as the disease had spread enormously worldwide. To combat COVID-19, many countries have applied preventive measures such as disinfection procedures and partial or complete lockdowns to slow the spread of the virus. For example, the United Arab Emirates (UAE) health authorities implemented preventative measures to protect public health in line with the WHO rules and regulations. On March 1st, 2020, the UAE authorities applied strict infection control and partial lockdown for more than 6 months, forcing many people to stay home (study and work from home). Nevertheless, such actions may cause a sudden and drastic change in the population's lifestyle. It has been reported that staying at home for a long time may lead to a change in dietary habits, physical activity, and sleep patterns, as well as psychological impact [2]. The economic impact of COVID-19 on diet quality and food security is also a concern. Healthy and nutrient-rich foods have become increasingly unaffordable and inaccessible, especially to people of lower socioeconomic status and informal workers [3]. The alteration of some life aspects and the new routine of people's daily life makes researchers want to study people during this critical period. For example, staying at home while news spreads about the COVID-19 pandemic can generate unpleasant emotions such as boredom, anxiety, and stress. These emotions are linked to increased food intake, especially “comfort” food known to be high in sugar and fat [4]. Such patterns have already been observed in several populations. Studies found that following the lockdown, eating habits and physical activity were negatively impacted (5–15). People reported an increase in main meals, frequent snacking, and experiencing a lack of motivation and control regarding food [5, 6]. Additionally, they also showed reduced levels of physical activity and increased sedentary behavior. This suggests that many individuals cannot maintain appropriate levels of physical activity during quarantine (6–9). Likewise, studies done in the UAE found similar results (11–13). It is essential to determine the extent of such patterns in the UAE to address them and prevent the deterioration of the population's health. As previously stated, the emergence of the COVID-19 pandemic has impacted almost every facet of life, including people's access to food and goods [16]. As a result, many individuals have limited their outings, opting to shop for food online [17]. During the height of the pandemic, many restaurants around the world were shuttered. However, delivery services were still available, which led to an increase in the usage of food delivery applications. Many individuals chose to have their groceries delivered to their homes to avoid crowds (18–22). This was especially true for those of higher income, higher levels of education, and those who find food-related online channels easier to use [16, 23]. Similar patterns have been examined in the Middle East, including the UAE (24–31). However, some populations appear to have some concerns regarding online food shopping. A study done in Brazil found that the preparation method was the primary worry among those who wouldn't rely on food delivery [32]. Other studies also found that many responders were concerned about being unable to check the freshness and quality of the product when purchasing groceries online [26, 33]. In addition, it was determined from a study conducted in Portland, United States, that lower-income consumers are less likely to employ internet delivery services [16]. Furthermore, consumers stated that the complexity of using online tools for purchasing food goods alongside technological issues decreases their proclivity to use or re-use these technologies [23]. Another health aspect that has been influenced by the COVID-19 lockdown is smoking. The self-isolation induced by the COVID-19 pandemic seems to have increased the consumption of cigarettes per day [34]. A study conducted in the UAE reported a rise in smoking in $21\%$ of its 2060 respondents [13]. Similar results have been determined in other studies around the world. This increase may be tied to the heightened stress levels during the COVID-19 pandemic (13, 35–38). However, findings included conflicting results where large percentages of participants did not change their smoking habits, reduce them, or even quit smoking entirely (35–38). A study in Italy attributed the reduction in smoking among its participants to their fear of the COVID-19 mortality risk [35]. Additionally, studies on the COVID-19 pandemic show that many people have been experiencing sleep difficulties that did not exist before the pandemic (39–43). The studies' populations commonly show a reduction in night-time sleeping, an increase in day-time napping, and a shift to a later bedtime [39, 42, 43]. In addition, some people have slept more hours overall, but the quality of their sleep has declined [34, 37]. For example, in a study done in the UAE, decreased sleep was reported among $20.8\%$ of the 2,060 responders [13]. Research also indicates that younger people and women were most likely to report sleep distress that may have arisen due to psychological distress during the COVID-19 pandemic (13, 43–45). The novel coronavirus (COVID-19) pandemic has brought extraordinary challenges in various aspects of life. As a result, the United Arab Emirates has imposed stringent rules, including a lockdown that extended a nationwide daily curfew. Since the UAE is a multinational country, our results would be of great importance to health authorities when revising their health policies in pointing out the consequences on the local Emirati people. Therefore, it is important to investigate the consequences of the COVID-19 pandemic and quarantine on the health of the UAE population to create targeted interventions to improve people's lifestyles following the pandemic and to prevent similar outcomes in the case of emergencies. This is especially needed in the UAE because reports of unhealthy lifestyles were already high before the COVID-19 pandemic [13]. This study aims to investigate the impact of COVID-19 quarantine on several health-related aspects among adults in the UAE. These aspects are dietary habits, physical activity, food purchasing patterns, smoking, and sleeping patterns. ## 2.1. Study design This study used a quantitative cross-sectional study design with a questionnaire as a data collection tool. ## 2.2. Sampling and recruitment This study used snowball sampling. The inclusion criteria were citizens and residents of the UAE aged ≥18 years old, both male and female. Participants were asked to complete an anonymous electronic questionnaire created via Google Forms and distributed on various platforms, such as WhatsApp, Twitter, and email. The questionnaire link was sent in Arabic and English for the participants to use the language they prefer as some preferred English language, this has been concluded from the pilot study. The principal investigator sent the questionnaire via email to students and to other Zayed University (ZU) faculties. It was shared with students and faculties in different colleges at ZU. Moreover, faculties sent it to their students via email and WhatsApp groups, and they were asked to share it with their friends and family members. We used Zayed University email addresses to reach the participants. The questionnaire was available between November 1st 2020 and the end of January 2021. A total of 118 questionnaires with missing answers were removed, and we ended with 1,682 completed questionnaires. ## 2.3. Data collection tool An online questionnaire was designed to assess and explore changes in dietary habits, physical activity, food purchasing, smoking, and sleeping patterns during the COVID-19 pandemic in the UAE. This questionnaire was adopted from another similar study that used the questionnaire as a measurement tool. It has been modified from a survey by Di Renzo, which investigated the impact of the COVID-19 pandemic on eating habits and lifestyle changes among the Italian population aged ≥12 years. The study comprised a structured questionnaire that inquired about demographic information, anthropometric data (reported weight and height); dietary habits information and lifestyle habits information [2]. The survey comprised 3 main sections with 32 questions in total. The platform used was Google Forms, and the link to the questionnaire was shared via WhatsApp, Twitter, Snapchat, and Instagram. The questionnaire was initially developed in English, then translated into Arabic and then pilot-tested with 30 students that were not familiar with the subject (not from the College of Natural and Health Sciences), and the errors were reviewed by the authors. The first section included 10 questions regarding demographic data such as age, gender, nationality, occupation, medical history, weight, and height. The second section had 16 questions regarding dietary habits, such as the type of food consumed, the number of meals, and snacks. This section focused on assessing the participants' dietary intake during the lockdown and whether it underwent any changes. The third section had six questions regarding lifestyle habits such as exercise, smoking, and purchasing daily necessities. Individuals were also asked if their physical activities and weight had changed after the lockdown (after August 2020), COVID-19 lockdown period was defined as March 1st, 2020 to the End of August 2020 as per the Supreme Council for the National Security Emergency Crises and Disasters Management Authority in the UAE in August 2020. ## 2.4. Data analysis Data were analyzed using the Statistical Package for Social Sciences, SPSS Version 27. Frequency distributions and percentages generated descriptive statistics to analyze the general characteristics of the participants. To better understand the relationship (association) between the dependent variable (weight change) and the independent variables (number of daily meals, number of snacks per day, consumption of cereals, consumption of sources of protein, consumption of fruits and vegetables, consumption of sweets and French fries, sense of hunger and satiety, level of physical activity), multiple Chi-square tests or Fisher exact tests were conducted whenever appropriate. Additionally, a contingency table was constructed to detail the food intake of the participants. ## 2.5. Ethical clearance This study was approved by the Research Ethics Committee at Zayed University, UAE (ZU20_137_F) and the Research Ethics Committee at the Ministry of Health and Prevention (MOHAP/DXB-REC/ONN/No$\frac{.147}{2020}$). All study participants provided informed consent at the beginning of the online questionnaire. ## 3.1. Characteristics of the study participants A total of 1,682 participants were included in the analysis. Table 1 presents the general characteristics of the studied population. Most participants were aged 18–29 years old ($69.7\%$), female ($80.9\%$), from Dubai ($51.4\%$), and the Northern Emirates (Sharjah, Ajman, Fujairah, Ras al-Khaimah, and Umm al-Quwain) ($37.8\%$). Approximately half of the participants had a high school education ($51.6\%$), and the rest had a university-level and higher education ($45.4\%$). Around half of the participants were students (46.4), $19.1\%$ were unemployed, and the rest were employed. In addition, the majority had a monthly income of <5,000 AED ($63.2\%$). Table 1 \| Variables \| N \| % \| \| --- \| --- \| --- \| \| Age (years) \| Age (years) \| Age (years) \| \| 18–29 \| 1172 \| 69.7 \| \| 30–39 \| 253 \| 15.1 \| \| 40–49 \| 190 \| 11.3 \| \| 50–59 \| 63 \| 3.7 \| \| ≥ 60 \| 4 \| 0.2 \| \| Sex \| Sex \| Sex \| \| Male \| 322 \| 19.1 \| \| Female \| 1360 \| 80.9 \| \| Emirate \| Emirate \| Emirate \| \| Abu Dhabi \| 181 \| 10.8 \| \| Dubai \| 866 \| 51.4 \| \| Sharjah \| 271 \| 16.1 \| \| Ajman \| 200 \| 11.9 \| \| Umm Al Quwain \| 104 \| 6.2 \| \| Ras Al Khaimah \| 45 \| 2.7 \| \| Fujairah \| 15 \| 0.9 \| \| Employment status \| Employment status \| Employment status \| \| Student \| 780 \| 46.4 \| \| Employed full-time \| 468 \| 27.8 \| \| Employed part-time \| 44 \| 2.6 \| \| Self-employed \| 38 \| 2.3 \| \| Unemployed \| 322 \| 19.1 \| \| Retired \| 30 \| 1.8 \| \| Education level \| Education level \| Education level \| \| Below high school \| 50 \| 3 \| \| High school \| 868 \| 51.6 \| \| University education \| 662 \| 39.3 \| \| Higher education \| 102 \| 6.1 \| \| Income (AED * ) \| Income (AED * ) \| Income (AED * ) \| \| < 2,000 \| 837 \| 49.8 \| \| 2,000–5,000 \| 226 \| 13.4 \| \| 5,000–10,000 \| 99 \| 5.9 \| \| 10,000–20,000 \| 230 \| 13.7 \| \| 20,000–40,000 \| 226 \| 13.4 \| \| More than 40,000 \| 64 \| 3.8 \| ## 3.2. Changes in body weight during the COVID-19 lockdown Figures 1, 2 present the participants' BMI and weight changes, respectively, during the COVID-19 lockdown period which was defined as the 1st of March 2020 to the End of August 2020 as per the Supreme Council for the National Security, National Emergency Crises and Disasters Management Authority in the UAE in August 2020. According to BMI categories, the majority of the participants had a normal weight ($47.1\%$), followed by overweight ($27.6\%$), obese ($15.2\%$), and underweight ($10.1\%$). Regarding weight changes during the COVID-19 lockdown, most participants ($44.4\%$) reported an increase in weight, while $23.3\%$ reported weight loss and $32.3\%$ reported no change. Figure 1: Participants' BMI during the COVID-19 lockdown (n = 1,682). Figure 2: Participants' weight changes during the COVID-19 lockdown (n = 1,682). In Table 2, the changes in weight during the COVID-19 lockdown according to age, gender, level of education, employment status, and monthly income are illustrated. Firstly, there was a significant association between changes in weight during the COVID-19 lockdown and age ($$p \leq 0.002$$). The youngest age group, aged 18–29 had the highest frequency across all weight change categories (gained, lost, and no change), with the highest being for weight loss ($76.8\%$) followed by weight gain ($69.3\%$). Those aged 30–39 had the second highest frequency for weight gain ($15.8\%$), which was also the highest frequency for this age group across the weight change categories. Individuals over 60 made up the lowest frequency across the weight change groups, with the highest being for weight loss ($0.5\%$). Table 2 \| Unnamed: 0 \| Total (n = 1,682) \| Unnamed: 2 \| Weight change \| Unnamed: 4 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Weight loss (n = 392) \| No change (n = 543) \| Weight gain (n = 747) \| \| \| Age \| \| \| \| \| 0.002 \| \| 18–29 \| 1,172 (69.7) \| 301 (76.8) \| 353 (65.0) \| 518 (69.3) \| \| \| 30–39 \| 253 (15.0) \| 51 (13.0) \| 84 (15.5) \| 118 (15.8) \| \| \| 40–49 \| 190 (11.3) \| 24 (6.1) \| 78 (14.4) \| 88 (11.8) \| \| \| 50–59 \| 63 (3.7) \| 14 (3.6) \| 27 (5.0) \| 22 (2.9) \| \| \| Over 60 \| 4 (0.2) \| 2 (0.5) \| 1 (0.2) \| 1 (0.1) \| \| \| Gender \| \| \| \| \| 0.009 \| \| Male \| 322 (19.1) \| 55 (14.0) \| 119 (21.9) \| 148 (19.8) \| \| \| Female \| 1,360 (80.9) \| 337 (86.0) \| 424 (78.1) \| 599 (80.2) \| \| \| Highest level of education \| \| \| \| \| 0.021 \| \| Below high school \| 50 (3.0) \| 10 (2.6) \| 20 (3.7) \| 20 (2.7) \| \| \| High school \| 868 (51.6) \| 207 (52.8) \| 258 (47.5) \| 868 (53.9) \| \| \| Bachelor's degree \| 662 (39.4) \| 161 (41.1) \| 233 (42.9) \| 662 (35.9) \| \| \| Graduate level \| 102 (6.1) \| 14 (3.6) \| 32 (5.9) \| 102 (7.5) \| \| \| Employment status \| \| \| \| \| < 0.001 \| \| Student \| 780 (46.4) \| 225 (57.4) \| 228 (42.0) \| 327 (43.8) \| \| \| Employed full-time \| 468 (27.8) \| 70 (20.7) \| 96 (29.8) \| 225 (30.1) \| \| \| Employed part-time \| 44 (2.6) \| 90 (3.1) \| 157 (1.8) \| 22 (2.9) \| \| \| Self-employed \| 38 (2.3) \| 93 (2.0) \| 173 (2.8) \| 15 (2.0) \| \| \| Unemployed \| 322 (19.1) \| 60 (15.3) \| 117 (21.5) \| 145 (19.4) \| \| \| Retired \| 30 (1.8) \| 6 (1.5) \| 11 (2.0) \| 13 (1.7) \| \| \| Monthly income (AED) \| \| \| \| \| 0.026 \| \| < 2,000 \| 837 (49.8) \| 226 (57.4) \| 260 (47.9) \| 351 (47.0) \| \| \| 2,000–5,000 \| 226 (13.4) \| 51 (13.0) \| 74 (13.6) \| 101 (13.5) \| \| \| 5,000–10,000 \| 99 (5.9) \| 22 (5.6) \| 30 (5.5) \| 47 (6.3) \| \| \| 10,000–20,000 \| 230 (13.7) \| 32 (8.2) \| 80 (14.7) \| 118 (15.8) \| \| \| 20,000–40,000 \| 226 (13.4) \| 45 (11.5) \| 77 (14.2) \| 104 (13.9) \| \| \| More than 40,000 \| 64 (3.8) \| 6 (4.1) \| 22 (4.1) \| 26 (3.5) \| \| Regarding gender, there was a significant association between changes in weight during the COVID-19 lockdown and gender ($$p \leq 0.009$$). Females had the highest frequencies in all weight change categories, with weight loss being the highest ($86.0\%$) followed by weight gain ($80.2\%$). Males made up a bigger percentage of the “no change” category ($21.9\%$) compared to their frequency in weight gain ($19.8\%$) and weight loss ($14.0\%$). Level of education was also significantly associated with changes in weight during the COVID-19 lockdown ($$p \leq 0.021$$). The majority possessed a high school degree ($51.6\%$). This group was the most frequent in all weight change categories, with the highest being in weight gain ($53.9\%$) closely followed by weight loss ($52.8\%$). Those with a bachelor's degree had their highest frequency in the no-change group ($42.9\%$). Those below high school also had their highest frequency in the no-change group ($3.7\%$). Finally, those at the graduate level had their highest frequency in the weight gain group ($7.5\%$). There was a significant association between weight changes during the COVID-19 lockdown and employment status ($p \leq 0.001$). Those who gained weight mainly were students ($43.8\%$), followed by employed full-time ($30.1\%$). The lowest percentage was retired people ($1.7\%$). Among those who lost weight, most were students ($57.4\%$), followed by employed full-time ($20.7\%$). The last were retired ($1.5\%$). Finally, those who experienced no change were mainly students ($42.0\%$), then employed full-time ($29.8\%$). The smallest percentage was retired ($2.0\%$). Finally, monthly income is another variable significantly associated with weight changes during the lockdown ($$p \leq 0.026$$). Those who earned <2,000 AED had the highest frequency in all weight change categories. Among those, the highest was for weight loss ($57.4\%$). The second highest frequency for weight gain, after those who earned <2,000 AED ($45.0\%$), was for those who earned 10,000–20,000 AED ($15.8\%$). This percentage was also the highest for this group across all weight change categories, meaning that this group mostly gained weight. Table 3 illustrates the relationship between changes in dietary habits (such as the frequency of meals, snacks, and the consumption of certain food groups) and the weight change groups (gain, loss, or no change). Approximately $35.7\%$ of those who gained weight during quarantine showed an increase in the number of daily meals. Compared to $14.5\%$ of those who lost weight and $14.5\%$ of those who experienced no change. Similarly, the highest frequency of increased snacking 304 ($40.7\%$) was in individuals who gained weight. Changes in the number of daily meals were significantly associated with changes in weight during the COVID-19 lockdown ($p \leq 0.001$). Table 3* \| Unnamed: 0 \| Total (n = 1,682) \| Unnamed: 2 \| Weight change \| Unnamed: 4 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Weight loss (n = 392) \| No change (n = 543) \| Weight gain (n = 747) \| \| \| The number of daily meals \| \| \| \| \| < 0.001 \| \| Decreased \| 512 (30.4) \| 176 (44.9) \| 133 (24.5) \| 203 (27.2) \| \| \| No change \| 767 (45.6) \| 159 (40.6) \| 331 (61.0) \| 277 (37.1) \| \| \| Increased \| 403 (24.0) \| 57 (14.5) \| 79 (14.5) \| 267 (35.7) \| \| \| The number of snacks per day \| \| \| \| \| < 0.001 \| \| Decreased \| 324 (19.3) \| 145 (37.0) \| 75 (13.8) \| 104 (13.9) \| \| \| No change \| 691 (41.0) \| 137 (34.9) \| 295 (54.3) \| 259 (34.7) \| \| \| Increased \| 471 (28.0) \| 66 (16.8) \| 101 (18.6) \| 304 (40.7) \| \| \| The consumption of cereals per day \| \| \| \| \| < 0.001 \| \| Decreased \| 300 (17.8) \| 138 (35.2) \| 77 (14.2) \| 85 (11.4) \| \| \| No change \| 939 (55.8) \| 181 (46.2) \| 363 (66.9) \| 395 (52.9) \| \| \| Increased \| 331 (19.7) \| 39 (9.9) \| 58 (10.7) \| 234 (31.3) \| \| \| The consumption of sources of protein per day \| \| \| \| \| < 0.001 \| \| Decreased \| 241 (14.3) \| 88 (22.4) \| 66 (12.2) \| 87 (11.6) \| \| \| No change \| 1,053 (62.6) \| 225 (57.4) \| 376 (69.2) \| 452 (60.5) \| \| \| Increased \| 289 (17.2) \| 59 (15.1) \| 63 (11.6) \| 167 (22.4) \| \| \| The consumption of fruits and vegetables per day \| \| \| \| \| < 0.001 \| \| Decreased \| 280 (16.6) \| 78 (19.9) \| 63 (11.6) \| 139 (18.6) \| \| \| No change \| 829 (49.3) \| 166 (42.3) \| 318 (58.6) \| 345 (46.2) \| \| \| Increased \| 434 (25.8) \| 121 (30.9) \| 120 (22.1) \| 193 (25.8) \| \| \| The consumption of sweets and French fries per day \| \| \| \| \| < 0.001 \| \| Decreased \| 334 (19.9) \| 139 (35.5) \| 92 (16.9) \| 103 (13.8) \| \| \| No change \| 672 (40.0) \| 137 (34.9) \| 269 (49.5) \| 266 (35.6) \| \| \| Increased \| 464 (27.6) \| 57 (14.5) \| 87 (16.0) \| 320 (42.8) \| \| \| The sense of hunger and satiety \| \| \| \| \| < 0.001 \| \| Decreased appetite \| 392 (23.3) \| 207 (52.8) \| 89 (16.4) \| 96 (12.9) \| \| \| No change \| 508 (30.2) \| 98 (25.0) \| 280 (51.6) \| 130 (17.4) \| \| \| Increased appetite \| 782 (46.5) \| 87 (22.2) \| 174 (32.0) \| 521 (69.7) \| \| Consuming cereals, protein sources, fruits, and vegetables, sweets, and French fries during quarantine was significantly associated with changes in weight ($p \leq 0.001$ for all). Those who consumed more cereals and protein sources had their highest frequencies in the weight gain group ($31.3\%$ and $22.4\%$, respectively). On the other hand, those who increased their consumption of fruits and vegetables had the highest frequency in the weight loss group ($30.9\%$). Most participants reported no change in their consumption of sweets and French fries ($40.0\%$), while $27.6\%$ reported an increase in consumption. However, $42.8\%$ of those who gained weight reported an increase in consumption of this category. During the quarantine, most participants reported an increase in appetite ($46.5\%$). More than two-thirds ($69.7\%$) of those who gained weight reported an increase in appetite. In contrast, $52.8\%$ of those who lost weight reported a decrease in appetite. Changes in the sense of hunger and satiety were significantly related to changes in weight during the COVID-19 lockdown ($p \leq 0.001$). Table 4 summarizes the changes in physical activity and smoking level compared to weight changes during the lockdown. Among the 1,682 participants, only $21.8\%$ reported increased physical activity, $26.1\%$ decreased physical activity, $22.8\%$ reported no changes, and $29.4\%$ never practiced physical activity. Among those who increased their physical activity ($21.8\%$) during the lockdown, $38\%$ lost weight, and $32\%$ experienced no changes in their weight. On the other hand, among those who decreased their physical activity, $62.2\%$ gained weight, and only $15.9\%$ reported no body weight changes. Moreover, $41\%$ reported no changes in their weight among those who didn't change their physical activity. Weight loss was reported by $23.5\%$ of this group. There was a significant relationship between changes in levels of physical activity and changes in weight before and after lockdown ($p \leq 0.001$). Table 4 \| Unnamed: 0 \| Total (n = 1,682) \| Unnamed: 2 \| Weight change \| Unnamed: 4 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Weight loss (n = 392) \| No change (n = 543) \| Weight gain (n= 747) \| \| \| Level of physical activity \| \| \| \| \| < 0.001 \| \| Decreased \| 439 (26.1) \| 70 (15.9) \| 96 (21.9) \| 273 (62.2) \| \| \| No change \| 383 (22.8) \| 90 (23.5) \| 157 (41.0) \| 136 (35.5) \| \| \| Increased \| 366 (21.8) \| 139 (38.0) \| 117 (32.0) \| 110 (30.1) \| \| \| Smoking change \| \| \| \| \| 0.033 \| \| Decreased \| 44 (2.6) \| 9 (2.3) \| 12 (2.2) \| 23 (3.1) \| \| \| No change \| 1,580 (93.9) \| 374 (95.4) \| 519 (95.6) \| 687 (92) \| \| \| Increased \| 58 (3.4) \| 9 (2.3) \| 101 (2.2) \| 304 (5.0) \| \| Similarly, changes in smoking habits were significantly associated with changes in weight during lockdown ($$p \leq 0.033$$). The majority reported no change in their smoking habits ($93.9\%$). Those who increased their smoking had a higher frequency of weight gain ($5.0\%$) than those who decreased their smoking ($3.1\%$). Table 5 presents the adjusted factors significantly associated with the change in weight. After adjusting for the other confounders, people who increased the number of meals consumed were more likely to gain weight (AOR = 1.68, $95\%$ CI = 1.12, 2.54). While an increase in the cereals consumed was positively associated with the change in weight (AOR = 1.67, $95\%$ CI = 1.08, 2.57), the persons who reduced the number of consumed cereals were more likely to lose weight (AOR = 0.53, $95\%$ CI = 0.35, 0.81). The respondents who reported an increase in their sweets' consumption or their desire for food (hunger) had twice the odds of putting on more weight (AOR = 2.19, $95\%$ CI = 1.50, 3.19) and (AOR = 2.19, $95\%$ CI = 1.53, 3.14), respectively. On the other hand, compared to those who did not face any change in the desire for sweets or food (hunger), the persons who reported a decline in their sweets' consumption or their desire for food (hunger) are more likely to lose weight (AOR = 0.84, $95\%$ CI = 0.58, 1.21) and (AOR = 0.54, $95\%$ CI = 0.36, 0.81), respectively. Table 5 \| Change in weight \| AOR \| 95% CI \| 95% CI.1 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| \| Meals consumed \| Meals consumed \| Meals consumed \| Meals consumed \| Meals consumed \| \| No change \| \| \| \| \| \| Decreased \| 1.08 \| 0.84 \| 1.38 \| 0.566 \| \| Increased \| 1.42 \| 1.05 \| 1.91 \| 0.022 \| \| Snacks \| Snacks \| Snacks \| Snacks \| Snacks \| \| No change \| \| \| \| \| \| Decreased \| 0.72 \| 0.54 \| 0.97 \| 0.031 \| \| Increased \| 1.04 \| 0.78 \| 1.39 \| 0.774 \| \| Cereals \| Cereals \| Cereals \| Cereals \| Cereals \| \| No change \| \| \| \| \| \| Decreased \| 0.63 \| 0.47 \| 0.85 \| 0.002 \| \| Increased \| 1.50 \| 1.10 \| 2.06 \| 0.011 \| \| Sweets and French fries \| Sweets and French fries \| Sweets and French fries \| Sweets and French fries \| Sweets and French fries \| \| No change \| \| \| \| \| \| Decreased \| 0.81 \| 0.61 \| 1.07 \| 0.142 \| \| Increased \| 2.21 \| 1.68 \| 2.90 \| < 0.001 \| \| Sense of hunger and satiety \| Sense of hunger and satiety \| Sense of hunger and satiety \| Sense of hunger and satiety \| Sense of hunger and satiety \| \| No change \| \| \| \| \| \| Decreased \| 0.50 \| 0.37 \| 0.66 \| < 0.001 \| \| Increased \| 2.83 \| 2.18 \| 3.68 \| < 0.001 \| \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| \| No change \| \| \| \| \| \| Decreased \| 1.66 \| 1.29 \| 2.14 \| < 0.001 \| \| Increased \| 0.50 \| 0.39 \| 0.65 \| < 0.001 \| \| Sleeping hours \| Sleeping hours \| Sleeping hours \| Sleeping hours \| Sleeping hours \| \| < 7 h per night \| \| \| \| \| \| 7–9 h per night \| 0.88 \| 0.70 \| 1.12 \| 0.295 \| \| More than 9 h per night \| 1.90 \| 0.45 \| 0.88 \| 0.006 \| \| Smoking change \| Smoking change \| Smoking change \| Smoking change \| Smoking change \| \| No change \| \| \| \| \| \| Decreased \| 1.56 \| 0.83 \| 2.94 \| 0.166 \| \| Increased \| 1.90 \| 1.04 \| 3.50 \| 0.038 \| Physical activity was negatively associated with the change in weight: increasing the level of physical activity was more likely to lead to a loss in weight (AOR = 0.61, $95\%$ CI = 0.44, 0.86). On the contrary, persons who practiced fewer sports activities had twice the odds of gaining weight (AOR = 2.25, $95\%$ CI = 1.58, 3.21). The consumption of protein was not significantly associated with the change in weight. When it comes to sleeping patterns, those who slept more than 9 h per night were more likely to lose weight (AOR = 1.90, $95\%$ CI = 0.45, 0.88). In terms of changes in smoking, those who stated rising in smoking were more likely to gain weight (AOR = 1.90, $95\%$ CI = 1.04, 3.50). ## 4. Discussion The present study aimed to investigate the impact of COVID-19 quarantine on dietary habits, physical activity, food purchasing, smoking, and sleeping patterns in the UAE. Overall, this study has found that quarantine has negatively affected these health-related variables. In terms of weight changes during the COVID-19 lockdown, most participants ($44.4\%$) reported an increase in weight. This weight gain can be attributed to the general decrease in energy expenditure since quarantine limits people's ability to go to work, gyms, parks, and even to practice their regular daily routines. In addition, the emotional distress accompanied by having to remain at home for months, fear of novelty, and the high spread of COVID-19 might have provoked emotional eating and cravings [46]. Other determinants leading to increased weight gain during the lockdown include prior behaviors, dietary habits, physical activity, type of work environment, psychosocial and socioeconomic factors, and co-morbidities [4]. This result agrees with previous studies that evaluated weight gain relating to COVID-19 home confinement [2, 47, 48]. In addition, studies in other countries revealed an increase in caloric intake and indicated weight gain during the COVID-19 lockdown (2, 49–51). However, in this study, $32.3\%$ of participants did not notice any weight change, and $23.3\%$ reported weight loss. This could be due to high levels of awareness, or they may not have been as majorly affected by quarantine. During the lockdown, those who gained weight had the highest frequency of participants who increased the number of their daily meals ($35.7\%$), and the highest frequency of increased snacking ($40.7\%$). This is compared to those who reported losing or no weight change. Further testing using logistic regression also showed that those who increased the number of consumed meals were more likely to gain weight (AOR = 1.68, $95\%$ CI = 1.12, 2.54). Similarly, previous studies reported higher amounts of food intake during lockdown periods in Poland, Italy, and UK [5, 19]. Consuming more cereals during quarantine was significantly associated with increased weight gain ($p \leq 0.001$) and with changes in weight (AOR = 1.67, $95\%$ CI = 1.08, 2.57). Conversely, those who reduced the amount of consumed cereals were more likely to lose weight (AOR = 0.53, $95\%$ CI = 0.35, 0.81). A significant difference was seen between the frequency of sweets and French fries' consumption and weight changes during quarantine ($p \leq 0.001$). The participants who reported increased intake of sweets had double the odds of putting on more weight (AOR = 2.19, $95\%$ CI = 1.50, 3.19). Furthermore, those who experienced a decline in their desire for sweets were more likely to lose weight (AOR = 0.84, $95\%$ CI = 0.58, 1.21) than those who didn't experience a change. This was in line with a previous study in Germany which demonstrated an increase in the consumption of foods that are high in sugar and fat such as sweets, and found that it was a significant determinant of weight changes during quarantine [52]. This may be due to the increased stress caused by the pandemic. People tend to seek “comfort foods” while coping with stressful situations [52]. Furthermore, during home confinement, people tended to stock their kitchens with food to reduce unnecessary grocery trips out of fear of contracting the infection [48]. The availability of large quantities of food for many days might lead to overeating that is not necessarily due to hunger [53]. During the lockdown, changes in hunger and satiety were significantly related to weight changes ($p \leq 0.001$). The current study showed that $69.7\%$ of those who gained weight reported an increase in appetite. In contrast, $52.8\%$ of those who lost weight reported a reduction in appetite. Additionally, those who reported an increase in their desire for food (hunger) had twice the odds of weight gain (AOR = 2.19, $95\%$ CI = 1.53, 3.14). Compared to those who didn't face any change, those who reported a decline in their desire for food (hunger) were more likely to lose weight (AOR = 0.54, $95\%$ CI = 0.36, 0.81). These changes may be due to psychological and environmental stressors, consistent with other studies [46]. Regarding physical activity, the present study revealed that among the 1,682 participants, only $21.8\%$ reported an increase in physical activity, $26.1\%$ decreased their physical activity, $22.8\%$ reported no changes, and $29.4\%$ never practiced physical activity. Among those who gained weight, $36.5\%$ reduced their levels of physical activity, and $30.5\%$ had never practiced physical activity. These findings are consistent with recent studies highlighting many individuals (>$50\%$) who reported changes in their physical activity and an increase in their sedentary behavior [8, 48, 49, 54]. Previous studies have shown that reduced activity and increased sedentary time increase the risk of gaining weight both in general (55–57), and particularly during the COVID-19 pandemic, in both people with normal weight [4, 10, 58, 59] and with obesity [60]. Nevertheless, other studies have found that individuals had increased their physical activity levels during their lockdown periods. For example, $21.5\%$ of those who maintained their weight had increased their level of physical activity, and $22.8\%$ maintained the same level of physical activity. Among those who increased their physical activity ($21.8\%$) during the lockdown, $38\%$ lost weight, whereas $32\%$ experienced no changes in their weight. Physical activity was negatively associated with the change in weight. Increased physical activity increases the likelihood of weight loss (AOR = 0.61, $95\%$ CI = 0.44, 0.86). On the other hand, those with lower physical activity levels had twice the odds of gaining weight (AOR = 2.25, $95\%$ CI = 1.58, 3.21). This may be one of the ways of maintaining healthy behaviors and mitigating the negative impact of lockdown on mood and wellbeing [2, 55]. Exercise positively impacts weight management, overall health, and mental wellbeing. It can improve mood, confidence, body image, motivation, and eating habits [61]. Results regarding smoking included that those who increased their smoking had a higher frequency of weight gain ($5.0\%$) than those who decreased their smoking ($3.1\%$). Results of logistic regression testing found the same association where those who smoked more were more likely to experience weight gain (AOR = 1.90, $95\%$ CI = 1.04, 3.50). A similar study also showed a link between tobacco use and weight gain [62]. Finally, the only finding regarding sleep was that those who sleep more than 9 h per night are more likely to lose weight (AOR = 1.90, $95\%$ CI = 0.45, 0.88). Studies have found an association between sleep loss and irregular sleep, and weight gain during COVID-19 lockdowns around the world [63, 64]. Therefore, it is important to investigate the consequences of the COVID-19 pandemic and quarantine on the health of the UAE population to create targeted interventions to improve people's lifestyles following the pandemic and to prevent similar outcomes in the case of emergencies. This is especially needed in the UAE. This study was subjected to several limitations. The study used snowball sampling by sending the survey to students who introduced an age bias. Most of the participants were women, as the survey was sent to Zayed University students who were mostly female. This makes the sample not representative of the UAE population. Another limitation is that the questionnaire questions were close-ended, meaning there might be other unexplored answers, and the choices might suggested answers that were otherwise not the participants' genuine opinions. Additionally, since it was an online questionnaire, the participants may have had a vague understanding of some questions, or they might have interpreted them differently than intended. However, this was minimized by using clear language and having the questions pilot tested by students that are not familiar with the subject (students not from the College of Natural and Health Sciences). Furthermore, this study did not examine the exact food habits and level of physical activity of the participants. Therefore, the data are lacking in these two areas, we didn't investigate all the components of Eating habits. For example, a Likert-type response of the consumption of specific food items, ranging from never to every day. Overall, the present study found that most of the population has shown weight gain, increased food consumption, and decreased or no change in physical activity. The groups most likely to gain weight included those who consumed more meals and cereals, had an increased desire for sweets, and had an increased desire for food (hunger). In addition, changes in levels of physical activity were significantly associated with changes in weight during the lockdown. Those who exercised more were more likely to lose weight. Participants who slept more than 9 h were also more likely to lose weight. Finally, increases in smoking seem to be tied to weight gain. Interventions and awareness campaigns need to be conducted to encourage a healthier lifestyle for the people of the UAE. Not only is a healthy lifestyle important for immunity, it is also necessary to prevent chronic illnesses such as diabetes and heart disease. Preventing such illnesses has always been a goal of the UAE due to their high prevalence. Finally, further research can be done on the exact food habits and level of physical activity of the UAE population during the lockdown to find areas that need to be targeted. ## 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 This study was approved by the Research Ethics Committee at Zayed University, UAE (ZU20_137_F) and the Research Ethics Committee at the Ministry of Health and Prevention (MOHAP/DXBREC/ONN/No. $\frac{147}{2020}$). All study participants provided informed consent at the beginning of the online questionnaire. The patients/participants provided their written informed consent to participate in this study. ## Author contributions SA designed the study and manuscript writing. SA, MA, and AK recruited the participants and supervised the data collection. FE analyzed the data. SA, MA, ZT, and AK wrote the manuscript. All contributed authors of this original manuscript authorized the final version of the manuscript and read and approved the final version of 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. Sadeghi Dousari A, Taati Moghadam M, Satarzadeh N. COVID-19 (coronavirus disease 2019): a new coronavirus disease. Infection Drug Resist. (2020) 13 2819-28. DOI: 10.2147/IDR.S259279 2. 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--- title: Gut microbiota of white-headed black langurs (Trachypithecus leucocephalus) in responses to habitat fragmentation authors: - Ying Lai - Yanqiong Chen - Jingjin Zheng - Zheng Liu - Dengpan Nong - Jipeng Liang - Youbang Li - Zhonghao Huang journal: Frontiers in Microbiology year: 2023 pmcid: PMC9968942 doi: 10.3389/fmicb.2023.1126257 license: CC BY 4.0 --- # Gut microbiota of white-headed black langurs (Trachypithecus leucocephalus) in responses to habitat fragmentation ## Abstract The white-headed black langur (Trachypithecus leucocephalus) is exclusively distributed in the karst forests and is critically endangered owing to habitat fragmentation. Gut microbiota can provide physiological data for a comprehensive study of the langur’s response to human disturbance in the limestone forest; to date, data on spatial variations in the langurs’ gut microbiota are limited. In this study, we examined intersite variations in the gut microbiota of white-headed black langurs in the Guangxi Chongzuo White-headed Langur National Nature Reserve, China. Our results showed that langurs in the Bapen area with a better habitat had higher gut microbiota diversity. In the Bapen group, the Bacteroidetes ($13.65\%$ ± $9.73\%$ vs. $4.75\%$ ± $4.70\%$) and its representative family, Prevotellaceae, were significantly enriched. In the Banli group, higher relative abundance of Firmicutes ($86.30\%$ ± $8.60\%$ vs. $78.85\%$ ± $10.35\%$) than the Bapen group was observed. Oscillospiraceae ($16.93\%$ ± $5.39\%$ vs. $16.13\%$ ± $3.16\%$), Christensenellaceae ($15.80\%$ ± $4.59\%$ vs. $11.61\%$ ± $3.60\%$), and norank_o__Clostridia_UCG-014 ($17.43\%$ ± $6.64\%$ vs. $9.78\%$ ± $3.83\%$) were increased in comparison with the Bapen group. These intersite variations in microbiota diversity and composition could be accounted for by differences in food resources caused by fragmentation. Furthermore, compared with the Banli group, the community assembly of gut microbiota in the Bapen group was influenced by more deterministic factors and had a higher migration rate, but the difference between the two groups was not significant. This might be attributed to the serious fragmentation of the habitats for both groups. Our findings highlight the importance of gut microbiota response for the integrity of wildlife habitats and the need in using physiological indicators to study the mechanisms by which wildlife responds to human disturbances or ecological variations. ## Introduction Animals commonly depend on physiological regulation in case of being unable to make further adaptations by shifting behavior when facing harsh existential conditions (Wong and Candolin, 2015; Bahram et al., 2016). Of the various physiological expressions, gut microbiota can be considered an effective indicator of the physiology and even the health status of wild animals (McManus et al., 2021). The dynamic balance established between the gut microbiota and host in their long-term interactions is related to energy acquisition and nutritional metabolism (Wu et al., 2011), which affects the immune system (Matson et al., 2021), nervous system (Cryan et al., 2019), and growth (Groer et al., 2014). Intense influences may severely disrupt the gut microbiota, which normally remains relatively stable, causing it to lose basic resistance and resilience and eventually leading to various diseases (Downing and Leibold, 2010; de Mazancourt et al., 2013; Fackelmann et al., 2021). The gut microbiota is heavily dependent on the genetics of the host; however, environmental factors also shape the structures and functions of the gut microbiota, even outweighing the influence of the host’s anatomical and physiological characteristics (Dominguez-Bello et al., 2010; Rothschild et al., 2018). Conspecific animals distributed in habitats of different quality have unique gut microbiota (Amato et al., 2013; Barelli et al., 2015). For example, black howler monkeys (Alouatta pigra; Amato et al., 2013) and Udzungwa red colobus monkeys (Procolobus gordonorum; Barelli et al., 2015) exhibit a lower abundance and diversity of gut microbiota when inhabiting areas with heavier fragmentation compared with those inhabiting habitat with less fragmentation. There are significant differences in gut microbiota of rhesus macaques (Macaca mulatta) distributed in different geographical populations being grouped by altitude conditions, which is manifested by the production of new and unique microbiota (Zhao et al., 2018). Additionally, a decreased diversity in the gut microbiota of primates has been observed in gray–brown mouse lemurs (Microcebus griseorufus), which may be associated with varying degrees of human invasion of each habitat, occurring in Bale monkeys (Chlorocebus djamdjamesis) as well (Trosvik et al., 2018; Wasimuddin et al., 2022). The abundance of beneficial gut microbiota and functional metabolism genes is linked to the health of the species, with those in worse habitats showing a decreasing trend, which indicates that these individuals have a more potential disease risk (Amato et al., 2013; Barelli et al., 2015; Trosvik et al., 2018; Wasimuddin et al., 2022). Furthermore, the differences in the gut microbiota of animals living in different habitats are an outcome of adaptive alterations in response to ecological changes and behavioral adjustments, notably dietary composition (Wu et al., 2011; McManus et al., 2021). By supplying a matrix for a given microbiota, diet plays a key role in shaping the host gut microbiota, including assisting hosts in selecting and expanding their corresponding degradation ability to alter the diversity and composition of the gut microbiota (Wu et al., 2011). For instance, herbivores exhibit a higher gut diversity than carnivores and omnivores owing to their more complex diets (Ley et al., 2008; Szekely et al., 2010; Deehan et al., 2020). Specifically, the Bacteroidetes has genes encoding enzymes that hydrolyze complicated plant polysaccharides (Grondin et al., 2017), and the Firmicutes is considered to have lignin-degrading functions (Liu et al., 2019; Que et al., 2022). The hosts meet their major energy and nutritional demands with the help of gut microbiota that convert foods into short-chain fatty acids (SCFAs; Turnbaugh et al., 2006; Sun et al., 2022). Hence, both phyla are often represented in large proportions in the gut microbiota of herbivores (De Filippis et al., 2016). In the wild primates, a higher relative abundance of Bacteroidetes has been linked to the digestion of high-quality foods, such as fruits, young leaves, and buds, which are distributed more widely in high-quality habitats (Li et al., 2021; Xia et al., 2021). In contrast, a higher abundance of Firmicutes is regarded as a response to low-quality habitats that contain fewer high-quality food resources (Li et al., 2021; Xia et al., 2021), as observed in other typical folivorous primates that must degrade crude cellulose and lignin, such as François’s langur (Trachypithecus francoisi; Chen et al., 2020) and the silvered langur (Trachypithecus cristatus; Le et al., 2019; Que et al., 2022). Therefore, data on relationship between the host’s gut microbiota, diet and habitat could provide insights into adaptation strategy, consequently facilitating effective wildlife conservation. Whether the host is sensitive to the environment touches on the process of microbial community assembly (Zhou and Ning, 2017). The niche and neutral theories are the two theoretical frameworks for understanding microbial community assembly (Dini-Andreote et al., 2015). The neutral theory states that stochastic processes are correlated with the birth, death, migration, and ecological drift of microbiota community, whereas the niche theory argues that deterministic processes are linked to abiotic and biotic factors (Fargione et al., 2003; Zhou et al., 2013). Whether deterministic or stochastic processes are dominant in community assembly has been controversial (Chen et al., 2019; Li et al., 2019). The relative importance of stochastic and deterministic processes can be quantified using the neutral community model (NCM; Sloan et al., 2006). For example, the NCM reveals that deterministic and stochastic processes jointly shape microeukaryotic community assembly in rivers under human disturbance (Gad et al., 2020). However, this assembly in subtropical rivers is shaped by stochastic processes because the water is more complex and changeable (Chen et al., 2019). In contrast, a study on rhesus macaques with different foods in altered seasons confirmed the dominant role of deterministic processes in microbiota assembly owing to environmental filtering (Liu et al., 2022). The variations in the importance of deterministic and stochastic processes in the microbiota are likely habitat-dependent; therefore, investigating the assembly process in biological communities is important to explain the formation and maintenance mechanisms of biodiversity in the microbiota (Zhou and Ning, 2017; Heys et al., 2020). White-headed black langurs (Trachypithecus leucocephalus) are exclusively distributed in the limestone forest in Southwest Guangxi, China, and are listed as critically endangered on the IUCN Red List (Bleisch and Long, 2020). These langurs are leaf-eating animals that prefer young leaves (Huang et al., 2008b). These animals face severe habitat fragmentation aggravated by human disturbance, which is threatening their survival (Huang, 2002; Li and Rogers, 2005). The Guangxi Chongzuo White-headed Langur National Nature Reserve comprises four areas, namely, Dalin, Tuozhu, Banli, and Bapen. This study was conducted in Banli and Bapen. The Banli area is more severely fragmented and has lower plant diversity than the Bapen area because of increased human disturbance (Huang et al., 2008a; Huang et al., 2017). Previous studies have focused on the behavioral ecology of white-headed black langurs (Zhou et al., 2011; Huang et al., 2017; Zhang et al., 2021) and have provided data on their adaptability to habitat fragmentation. However, the mechanism of the langurs’ physiological response to habitat fragmentation remains unclear. Hence, in this study, we analyzed the gut microbiota from 203 fecal samples of white-headed black langurs. We first described the structural features of the gut microbiota and then further compared the intersite variations in the diversity and composition of the gut microbiota. Finally, we assessed the relative importance of the community assembly process of the gut microbiota by applying the NCM. We tested the following predictions:A positive correlation has been found between diet diversity and gut microbiota (Heiman and Greenway, 2016; Frankel et al., 2019). More-fragmented habitats with lower vegetation diversity stimulate Banli group to forage and increase the diversity of their diets (Huang et al., 2017; Zhang et al., 2021). We, thus, predicted that the diversity of the gut microbiota in the Banli group would be higher than that in the Bapen group. Young leaves in fragmented habitats are commonly limited (Zhou et al., 2011). Banli group suffering deeper habitat fragmentation will rely more heavily on low-quality foods, such as mature leaves (Li et al., 2016). We, thus, predicted that langurs living in Banli would have a higher relative abundance of cellulose-degrading bacteria. Lower vegetation diversity may intensify the competition for resources (Li et al., 2022). Banli has lower vegetation diversity (Huang et al., 2008a). We, thus, predicted that the community assembly of the langurs’ gut microbiota in Banli area would be more affected by deterministic processes. ## Study site, study subjects, and sample collection This study was performed in the Guangxi Chongzuo White-headed Langur National Nature Reserve (107°16′53″–107°59′46″E, 22°10′43″–22°36′55″N), which is covered by limestone forest with an altitude ranging from 400 to 600 m (Guangxi Forestry Department, 1993). Fecal samples for this study were collected from nine groups in Banli and Bapen. We collected 155 fecal samples from Banli between December 2020 and January 2021 and June and July 2021. We collected 48 samples from Bapen during July and November 2021 (Supplementary Table 1). White-headed black langurs use caves and/or crevices in cliffs as their permanent sleeping sites and defecate at the cave edges before leaving their sleeping sites in the morning. We collected feces under the cliffs after the langurs had left. While collecting the samples, we wore sterile gloves and used sterilized bamboo sticks to obtain ~3–5 g of the internal part that was uncontaminated and placed it in sterile collection tubes. After making clear marks, we immediately placed the samples in a dry ice box and then transferred them to a −80°C ultra-low–temperature refrigerator for storage until further DNA extraction. ## Ethics approval We collected langur fecal samples with the permission of the Administration Center of Guangxi Chongzuo White-headed Langur National Nature Reserve. This study did not involve any invasive animal tissue procedures. Furthermore, we collected the samples after the langurs left their sleeping sites to avoid any stress reactions caused by the collection. ## DNA extraction, PCR amplification, and sequencing Total DNA was extracted from fecal samples of white-headed black langurs using an E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, America). A NanoDrop 2000 (Thermo Fisher Scientific, America) was used to detect the concentration and purity of the DNA, and $1\%$ agarose gel electrophoresis was used to detect the extraction quality of the DNA. The TransGen AP221-02 reaction system (TransGen, China) comprised 4 μL of 5× FastPfu buffer, 4 μL of 2.5 mM dNTPs, 0.8 μL of forward primer (5 μm) and reverse primer (5 μm), 0.4 μL of FastPfu polymerase, 0.2 μL of BSA, 10 ng of template DNA, and enough ddH2O to bring the total amount of reagent to 20 μL. In this system, highly specific amplification primers were used (338F:5′-ACTCCTACGGGAGGCAGCAG-3′ and 806R:5′-GGACTACHVGGGTWTCTAAT-3′; Mori et al., 2014). The amplified DNA was first denatured at 95°C for 3 min, then subjected to 28 PCR cycles (denaturation at 95°C for 30 s, annealing at 53°C for 30 s, and extension at 72°C for 45 s), and finally extended at 72°C for 10 min to obtain amplification products located in the 16S rDNA V3–V4 region using a PCR amplifier model ABI GeneAmp® 9700 (ABI, America). Three replicates of each sample were run, and products from the same sample were mixed and detected using $2\%$ agarose gel electrophoresis and recovered using an AxyPrep DNA Gel Extraction Kit (Axygen, America). The PCR products were detected and quantified using a Quanti Fluor™-ST Blue Fluorescence Quantification System (Promega, America), after which the corresponding proportion was mixed as per the sequencing volume requirement of each sample. According to the fluorescence quantitative measurement results, a NEXTFLEX Rapid DNA-Seq kit (Bio Scientific, America) was used to construct a sequencing library. The sequencing platform used was an Illumina Miseq PE 300 (Illumina, America). ## Bioinformatics and statistical analysis The following procedures were performed on Majorbio Cloud Platform1. The raw sequences were quality-controlled using Trimmomatic. This step included removing repetitive, low-quality (<20 bp) sequences containing primer linkers and sequences with a high proportion of N and then filtering out reads of <50 bp after quality control. The PE reads obtained from sequencing were spliced with Flash 1.2.112 (Magoč and Salzberg, 2011) according to the overlap relationship. The redundant, low-abundance sequences were removed with Usearch 113 (Edgar, 2010), and the chimeras were removed with UCHIME (Edgar et al., 2011) to obtain valid sequences. The valid sequences of all samples were clustered using Uparse 114 (Edgar, 2013) at a threshold of $97\%$ to obtain the OTUs. The OTU representative sequences were generated using Qiime 1.9.15 (Caporaso et al., 2010), and valid sequences with >$97\%$ similarity to the representative sequences were selected to generate the original OTU tables. The obtained OTUs were compared with the 16S rRNA SILVA 138 bacterial database6 for species classification annotation using RDP Classifier,7 and the classification confidence level was set to $80\%$. The samples were leveled twice using the minimum number of sequences, and only OTUs of bacterial domains were retained, finally yielding the OTU tables for subsequent analysis. Alpha diversity analysis was performed using Mothur 1.30.3.8 The dilution curves were used to determine whether the data for this sequencing were sufficient and reasonable. Values of invSimpson represent the reciprocal of Simpson index, and the variations in values with the Shannon index are proportional to species diversity. The ACE and Chao indexes indicate the species richness of the community. The principal co-ordinate analysis (PCoA) reflects the similarity or variability of the community composition in the samples. The distance between different sample points was calculated using Qiime 1.9.1. Significant differences were analyzed for sample species between two sites using a Wilcoxon rank–sum test with a confidence interval of $95\%$, and the results were expressed as corrected p-values. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, the functional prediction of gut microbiota in fecal samples was performed by PICRUSt 2 (version 2.2.09; Douglas et al., 2020). According to the information obtained from the three levels of the KEGG pathway, the abundance of each level was calculated to obtain the abundance table of KEGG pathway. Intersite differences in functional pathways were analyzed using the Mann–Whitney U-test. The following operations were performed with R 4.1.2: The alpha diversity indexes were converted into the form of log10 (x) (Warton and Hui, 2010). They were visualized as box plots and used for generalized linear mixed model (GLMM) calculations. The GLMM was used to compare intersite differences in the alpha diversity of the gut microbiota of white-headed black langurs. In this model, the geography was set as a fixed factor, the indices as response variables, and the different monkey groups as random factors. The differences between the two models with and without fixed effects were compared using ANOVA to determine the effect of fixed factors on the response variables. We then constructed the NCM based on an OTU table to demonstrate the relative importance of stochastic processes on gut microbiota community assembly (Sloan et al., 2006). This model estimated the migration rate of the gut microbial community (indicated by the m value) and the impact of random effects on the construction process (indicated by R2). The value of m is inversely proportional to the dispersal limitation, which shows that abundant taxa are generally higher than rare taxa because the latter are more likely to disappear from a single host owing to ecological drift. Within the range of 0–1, R2 represents the stochastic effect and the remaining part represents the deterministic effect (1 − R2). Using the randomForest package, massive decision trees were constructed after processing the data according to the random and put-back sampling principle (Breiman, 2001). Objects were classified sequentially to obtain the random forest model. The relative importance of bacterial taxa was expressed using Mean Decrease Gini and ranked, with larger values being more important. To ensure the performance of the random classifier, we performed the following steps: First, $70\%$ fraction of the sample was designated as the training set and the remaining $30\%$ as the test set, which served as the basis for evaluating the model performance. Next, a 10-fold cross-validation was performed and repeated five times to avoid uncertain evaluation results. Eventually, the area under the curve (AUC) values were obtained using the pROC package (Robin et al., 2011), considering values ranging from 0.5 (nonsense classification) to 1 (perfect classification; Robin et al., 2011). ## Variations in gut microbiota composition: Banli group possessed more unique taxa and richer bacterial taxa of Firmicutes than Bapen group After the quality control, a total of 11,291,593 optimized sequences were obtained from 203 fecal samples, with an average of 55,624 ± 13,334 sequences per sample (Supplementary Table 2). The OTU table after flattening revealed 3,091 OTUs, 35 phyla, 384 families, and 778 genera. Good’s coverage estimators for all samples ranged from $98.40\%$ to $99.68\%$ ($99.38\%$ ± $0.22\%$; Supplementary Table 3), which indicated that the sequencing results were representative of the actual occurrence of microbial species in the samples. The Shannon (Supplementary Figure 1A) and Sob curves (Supplementary Figure 1B) showed that the curves of all samples were asymptotic, which signified that the amount of sequencing data was sufficient to reflect the majority of microbial diversity information in the samples. The langurs from the Banli and Bapen groups shared 1878 OTUs, 31 phyla, and 284 families. The Banli group possessed 1,024 unique OTUs and 79 unique families, and the Bapen group exhibited 189 unique OTUs and 21 unique families. Moreover, all groups had two unique phyla (Supplementary Figure 2). At the phylum level, all samples were occupied by Firmicutes (Banli group: $86.30\%$ ± $8.60\%$ vs. Bapen group: $78.85\%$ ± $10.35\%$), Bacteroidetes ($4.75\%$ ± $4.70\%$ vs. $13.65\%$ ± $9.73\%$), and Actinobacteria ($3.88\%$ ± $3.56\%$ vs. $1.82\%$ ± $2.04\%$), which accounted for >$94\%$ of the total relative abundance (Figure 1A). At the family level, the top three included Oscillospiraceae (Banli group: $16.93\%$ ± $5.39\%$ vs. Bapen group: $16.13\%$ ± $3.16\%$), Christensenellaceae ($15.80\%$ ± $4.59\%$ vs. $11.61\%$ ± $3.60\%$), and norank_o__Clostridia_UCG-014 ($17.43\%$ ± $6.64\%$ vs. $9.78\%$ ± $3.83\%$; Figure 1B). Other taxonomic groups at the phylum and genus levels are shown in Supplementary Tables 4, 5. Figure 1: Composition of the gut microbiota at the phylum (A) and family (B) levels. All taxa with a relative abundance of <1% were classified as “others”. The composition difference analysis in the gut microbiota community at the phylum (C) and family (D) levels. Only the top 15 bacterial taxa are displayed. Significant difference was expressed by “” for p < 0.05, “” for p < 0.01, and “” for p < 0.001. Dominant phyla and families in the samples were analyzed for intergroup differences using the Wilcoxon rank–sum test. The results showed that the relative abundance of Firmicutes and Actinobacteria in the Banli group was significantly higher than that in the Bapen group at the phylum level, whereas Bacteroidetes and Proteobacteria in the Bapen group were significantly increased compared with the Banli group (Figure 1C). At the family level, the relative abundances of Christensenellaceae and norank_o__Clostridia_UCG-014 in the microbiota were significantly higher than those of the Bapen group, whereas the relative abundances of UCG-010, Prevotellaceae, and Butyricicoccaceae in the Bapen group were significantly higher than those of the Banli group (Figure 1D). Details of the various species at both taxonomic levels are listed in Supplementary Tables 4, 5. We used a random forest model to construct classifiers to rank the importance of gut microbiota, which promotes the formation of intersite variations. At the phylum level, the most important taxa contributing to intersite variations in the gut microbiota of white-headed black langurs were Bacteroidetes, followed by Proteobacteria and Campylobacterota. At the family level, these variations were accounted for by Butyricoccaceae, Peptococcaceae, and Rikenellaceae. The AUC values of the phylum and family levels were 0.963 and 1.000, respectively, which indicate an exceptional classification (Figure 2). Figure 2: The results of relative importance ranking based on values of Mean Decrease Gini at the phylum (A) and family (B) levels. ROC curve used to test the effect of the classifier of random forest model at the phylum (C) and family (D) levels. The closer the difference between the value of AUC and 1, the better the result of classification. ## Variations in gut microbiota diversity: Bapen group had higher alpha diversity index than Banli group According to the results of alpha diversity analysis, the Shannon index (Banli group: 4.821 ± 0.370 vs. the Bapen group: 4.984 ± 0.264) and invSimpson index (57.575 ± 22.396 vs. 73.890 ± 23.367) of the gut microbiota of langurs in the Banli and Bapen group differed significantly (Shannon: χ2 = 5.792, df = 1, $$p \leq 0.016$$; invSimpson: χ2 = 7.734, df = 1, $$p \leq 0.005$$). However, the ACE index and Chao index did not show significant intersite variations (Figure 3A). The detailed results of mean and SD values and GLMM are shown in Supplementary Tables 3, 6, respectively. Figure 3: Compared with the alpha diversity in gut microbiota between the Banli and Bapen groups (A). Significant difference was expressed by “” for p < 0.05, “” for p < 0.01, and “” for p < 0.001. Regional comparison of gut microbiota beta diversity based on OTUs (Tested by Adonis) (B). Results of the PCoA based on Bray–Curtis showed that the structures of the gut microbiota of langurs differed significantly (R2 = 0.093, $$p \leq 0.001$$) between geographic groups (Figure 3B). ## Variations in the functional profiles of gut microbiota: Banli group had richer functional pathways than Bapen group At KEGG pathway level 1, there were three pathways, namely, cellular processes, environmental information processing and genetic information processing, more enriched in Banli group. The remaining three pathways had no significant difference between the two groups. At KEGG pathway 2, the pathways related to metabolism were more abundant in the Bapen group than in the Banli group. Specifically, metabolism of other amino acids and glycan biosynthesis and metabolism were more enriched in the Bapen group than in the Banli group (Figure 4). Figure 4: Differences in the functional profiles prediction in gut microbiota of white-headed black langurs in pathway level 1 (A) and pathway level 2 (B). Significant difference was expressed by “” for p < 0.05, “” for p < 0.01, and “” for p < 0.001. The orange bar represents Banli group, and the blue one represents Bapen group. ## Community assembly of gut microbiota: Two groups shared similar explanation and migration rate According to the results of the NCM, random effects explained $50.3\%$ (for the Banli group) and $48.1\%$ (for the Bapen group) of the community assembly of the white-headed black langurs’ gut microbiota. The migration rate of gut microbiota was higher in the Bapen group ($m = 0.135$) than in the Banli group ($m = 0.124$). Of the gut microbiota of the Banli group, $59.7\%$ were within the theoretical prediction range of the random effects calculated by the model, whereas $23.6\%$ were above this value and $16.7\%$ were below it. The proportion of gut microbiota in the Bapen group that fell within the interval of theoretical predictions as measured by the model was $78.8\%$, with $13.2\%$ above this limit and $8.0\%$ below it (Figure 5). Figure 5: The quantitative results of stochastic processes in the community assembly of the gut microbiota in Banli group (A) and Bapen group (B). Random effects on the community construction of gut microbiota were expressed by “R2”. The migration rate of species for the whole microbiota community is denoted by “m”. The solid black line indicates the best fitness between the actually occurring frequency of OTUs and the theoretical predicted frequency of the model. Field between the two black dashed lines means the 95% confidence interval, beyond which is an outlier. ## Characteristics of the gut microbiota The predominant taxa in the gut microbiota of white-headed black langurs in the fragmented habitats are Firmicutes and Bacteroidetes. Previous studies found similar results in colobine monkeys, such as Sichuan snub-nosed monkeys (Rhinopithecus roxellana; Su et al., 2016) and Udzungwa red colobus monkeys (Barelli et al., 2015). The sympatric rhesus macaques and François’ langurs also follow a similar pattern (Chen et al., 2020). This result might be related to the fragmentation of its habitat in different degrees. Generally, degradation of habitat quality leads to a scarcity of high-quality food resources, such as fruits (Zhao et al., 2018). To satisfy the basic needs for energy and nutrition, dwelling primates preferentially feed on leaves, including mature ones, using a similar foraging strategy in response to habitat destruction, which consequently shapes the composition of their gut microbiota (Huang et al., 2008b; Wu et al., 2011). Firmicutes and Bacteroidetes likely make a large contribution to the digestion of plant polysaccharides, which are prominent in these primates’ leaf-based diets (Barelli et al., 2015; Su et al., 2016; Chen et al., 2020). Specifically, the Firmicutes is linked to degrade dietary fiber (Sun et al., 2022), converting them to SCFAs that are directly absorbed by the host’s gut wall as energy (Turnbaugh et al., 2006). In addition, the Oscillospiraceae (Firmicutes) exhibits the highest relative abundance in both groups and could be involved in the degradation of mucin (Raimondi et al., 2021). Mucin is a major component of mucus in the gastrointestinal tract, which protects hosts from physical and chemical damage (Raimondi et al., 2021). Adequate mucin may assist in the smooth movement of hard dietary fiber in the gut, reducing the leakage risk of toxic secondary metabolites into the gut mucosa (Raimondi et al., 2021). However, abnormal elevation of mucin may be associated with inflammatory bowel disease (Raimondi et al., 2021). Oscillospiraceae, Christensenellaceae, and norank_o__Clostridia_UCG-014 are considered to be involved in the production of SCFAs (Morotomi et al., 2012; Koeck et al., 2014; Konikoff and Gophna, 2016). Additionally, Ruminococcaceae and Lachnospiraceae belonging to Firmicutes have often been reported in other studies and accounted for a relatively high proportion of our results too, making large contributions to the efficient degradation of plant polysaccharides (Arumugam et al., 2011; Tomova et al., 2019). Bacteroidetes is closely related to the degradation of protein and animal fat (Wu et al., 2011) and can degrade pectin and simple carbohydrates in fruits and other foods (Hale et al., 2019). Prevotellaceae (Bacteroidetes) is closely linked to the proportion of fruits in the animals’ food (Sun et al., 2016). These higher-abundance taxa are essential for the energy budget balance and for maintaining the health of white-headed black langurs in fragmented habitats. The intersite variations of these two phyla in relative abundance can be expressed using F/B (ratio of the relative abundance of Firmicutes and Bacteroidetes; Turnbaugh et al., 2006). The folivorous primates, such as François’s langurs (8.24) (Chen et al., 2020) and Udzungwa red colobus monkeys (6.22) (Barelli et al., 2015), have higher F/B than frugivorous primates, such as red-fronted lemurs (Eulemur rufifrons) (0.98) (Murillo et al., 2022) and brown lemurs (Eulemur fulvus) (0.5) (Greene et al., 2021), as well as omnivorous primates, such as Tibetan macaques (Macaca thibetana) (2.65) (Xia et al., 2021). In this study, the F/B of white-headed black langurs was much higher than that of the aforementioned species (12.34). F/B is positively correlated with the ability to obtain energy (Turnbaugh et al., 2006). The higher F/B of the langurs may be associated with the fragmented habitat because they could have high energy requirements in response to the scarcity of high-quality foods caused by habitat fragmentation in the limestone forest. In folivorous primates, although the major dominant gut microbiota are Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria, there are minor differences in the order of their relative abundance. A tendency may be seen in the white-headed black langurs for the relative abundance of Proteobacteria or Actinobacteria to be higher than that of Bacteroidetes within a given situation (Que et al., 2022). Proteobacteria has been shown to facilitate the digestion of animal proteins (De Filippo et al., 2010). In parallel, this phylum includes many disease-causing genera; hence, its irregular and sharp elevations in the relative abundance frequently serve as the first reflection of an individual’s health status (Shin et al., 2015). Actinobacteria also play an essential role in maintaining gut homeostasis, of which, Bifidobacteria, the most widely recognized, is extensively used as a probiotic and can also assist in the hydrolysis of plant polysaccharides by producing glycosyl hydrolases (Pokusaeva et al., 2011). Gut microbiota increase the plasticity by adjusting their relative abundance to fit survival needs but not by changing the species of high-abundance taxa. ## Intersite variations in gut microbiota Our results indicate that the alpha diversity in the gut microbiota of the Bapen group was higher than that in the Banli group inhabiting more-fragmented sites, which is contrary to Prediction 1. The Banli group exhibits higher diet diversity because langurs in more-fragmented habitats adopt an energy conservation strategy and depend on more species of plants to obtain energy (Huang et al., 2017; Zhang et al., 2021). However, the Banli group did not display a high alpha diversity in the gut microbiota, as expected. This observation is contradictory to the findings of previous studies on black howler monkeys (Amato et al., 2013) and Udzungwa red colobus monkeys (Barelli et al., 2015), which considering gut microbiota diversity is positively corrected with diet diversity. This pattern could be associated with the degree of additional human disturbance suffered by the two groups and another indicator of diversity, namely, evenness. Additional human disturbance rather than habitat fragmentation itself may be responsible for the decrease in the diversity of gut microbiota (Fackelmann et al., 2021). The Banli area has more inhabitants and human activity, including farmland reclamation and felling of trees, encroaching on the home ranges of the langurs gradually (Huang et al., 2008a). This will further affect the langurs’ food resources by reducing the possibility that they eat parts of plants other than the leaves (Huang et al., 2008a). Although the Banli group fed on more kinds of plants, the common behavior of eating leaves may make the intake of nutrients unitary and thereby reduce the diversity of gut microbiota. There is a significant difference in alpha diversity between the two groups; however, the richness did not markedly differ, suggesting that evenness may be contributed to this intersite variation in microbiota. The langurs’ response to severe fragmentation included the addition of lower-quality leaves, such as mature leaves, in their diets (Li et al., 2016). Meanwhile, the langurs are restricted by farmlands in the flat zones of limestone hills; hence, they occupy small home ranges and must increase their consumption of mature leaves (Huang et al., 2017). This behavior has led to the overwhelming dominance of Firmicutes and likely lowered the evenness of gut microbiota of the Banli group. Furthermore, higher vegetation diversity in the Bapen area increases the probability of foraging on fruits or other plant parts, resulting in an increased relative abundance of Bacteroidetes (Wu et al., 2011; Hale et al., 2019) and increased evenness. The langurs in the Banli group had a higher abundance of Firmicutes and significantly differed from the Bapen group, which supports Prediction 2. This result may be related to the fact that young leaves are more likely to be exhausted in the more-fragmented Banli area, forcing the langurs to feed more on mature leaves (Li et al., 2016). Hence, the langurs of the Banli group might have a higher abundance of cellulose-degrading bacteria. Besides, the abundance of Firmicutes is also increased in high-fat, high-protein diet populations as it is closely associated with energy production (Turnbaugh et al., 2006). A greater F/B was found in the Banli group than in the Bapen group (18.07 vs. 5.75). The langurs of the Banli group experience more severer habitat fragmentation, which forces a reduction in home range size that requires them to increase movement time and daily path length to forage (Zhou et al., 2011; Zhang et al., 2021). Furthermore, the langurs in Banli spend more time on feeding compared with individuals in the Bapen group (Huang et al., 2017), which also requires the gut microbiota to efficiently degrade these fibers and convert them into energy. Christensenellaceae forms a symbiotic system with other bacteria distributed in a probiotic that favors the maintenance of intestinal homeostasis (Li et al., 2020), and reduction of norank_o__Clostridia_UCG-014 has been observed in patients with diabetes (Karlsson et al., 2013). Both taxa are benefit to the host health and can breakdown cellulose to produce SCFAs (Morotomi et al., 2012; Koeck et al., 2014), which coincides with the adaptation of the Banli group to a more-fragmented habitat. Butyricicoccaceae contributes the most to intersite variations in the gut microbiota, which is a typical butyric acid-producing family and has significantly higher relative abundance in the microbiota from Banli group. This finding may be related to the differences in the parts of foods consumed by the two groups and the effect of strong interactions between colonies in the gut microbiota (Huang et al., 2017; Jeong and Kim, 2022). Studies have shown that the number of Butyricicoccaceae is negatively correlated with the proliferation of anaerobic methanogens (Jeong and Kim, 2022). The Bapen group eat less-mature leaves, which means that they need fewer methanogens to ferment cellulose in the digestive cavity (the structure of fermentable cellulose, similar to that of ruminants, evolved to accommodate diets rich in leaves; Lambert, 1998), which may lead to an increase in Butyricicoccaceae. Similarly, wild black howler monkeys have more Butyricicoccaceae in their gut microbiota, which is positively correlated with the consumption of young leaves by the hosts (Amato et al., 2015). Notwithstanding the overwhelming advantage of Firmicutes, the contribution of Bacteroidetes to intersite variations could not be ignored. Most members of Bacteroidetes are likely affected by environmental factors because their change in abundance is only subject to a few explanations of host genetic factors (Goodrich et al., 2016). In this study, both Bacteroidetes and Prevotellaceae in the Bapen group were significantly higher than those in the Banli group. Functionally, Bacteroidetes actively participates in carbohydrate metabolism, which is characterized by the presence of the polysaccharide utilization locus, and its encoded proteins engage in the construction of carbohydrate utilization systems (Grondin et al., 2017). For example, during seasons rich in high-quality foods, the guts of rhesus macaques and Tibetan macaques are rich in Bacteroidetes associated with assimilating simple carbohydrates and pectin, of which Prevotellaceae is a representative (Li et al., 2021; Xia et al., 2021). The Bapen group suffered less habitat fragmentation and have larger home ranges (Huang et al., 2008a; Huang et al., 2017). This may allow them to eat higher-quality foods, such as fruits rich in simple carbohydrates, which further promote the colonization of Bacteroidetes. The Banli group had more unique bacterial taxa. Even in poor habitats, the langurs are reluctant to settle for low-quality foods and search for a wider variety of plants with young leaves (Huang et al., 2017). High richness of unique bacterial taxa may increase the speed of recovery to the original state and the ability to maintain dynamic balance when the community is disturbed (Downing and Leibold, 2010; de Mazancourt et al., 2013). In addition, when langurs are deficient in important plant resources and increase the consumption of diversified supply plants that are not usually eaten, increased bacterial populations provided an excellent opportunity for functional redundancy. At this time, unique taxa with low abundance temporarily drive the digestion of these rare foods (Lozupone et al., 2012). This is of great significance for langurs with more interference in maintaining the stability of the gut microbiota because the degree of interference is often closely related to the susceptibility of the animals to diseases (Fackelmann et al., 2021). The functional pathways that were significantly enriched at the KEGG pathway level 1 were detected in the Banli group, which may be related to the fact that they had a larger number of unique gut microbiota than the Banli group. This may address the need for the langurs in the Banli group to adjust to a more fragmented habitat by further expanding the feasibility of functional redundancy to take effect in the gut microbiota, as found in previous study on Rana dybowskii with diarrhoea (Tong et al., 2020). However, there were no significant difference in metabolic pathway. This may be related to the fact that white-headed black langurs maintain a highly foliar diet in limestone forest and the two study sites are highly fragmented (Huang et al., 2008a; Huang et al., 2017). Furthermore, among the metabolism-related pathways at level 2, two pathways linked to protein metabolism that were significantly enriched in the Bapen group. According to the composition of the gut microbiota in Bapen group of the langurs, this is probably because the Bacteroidetes and Proteobacteria closely related to protein degradation were significantly enriched. Further study should be needed. ## Community assembly of the gut microbiota Our results showed the greater influence of the stochastic process in the Banli group, which contradicts Prediction 3. The R2 of the NCM presented little difference between the two groups (both ~$50\%$), which indicates that the community assembly of the gut microbiota of langurs is jointly built on a combination of stochastic and deterministic processes. Our results were similar to those of a study on latitudinal phytoplankton distribution (Chust et al., 2013), which suggests that deterministic or stochastic processes are not the exclusive outcome of community construction. The habitats of both groups have already become extremely fragmented, despite minor differences in fragmentation levels and result in similar environmental selection pressures on the langurs (Huang et al., 2008a). Furthermore, the flexible foraging behavior of langurs in the Banli group might narrow the gap with the physiological response of the langurs in the Bapen group, consequently bringing the processes affecting gut microbial community assembly in these two groups closer. The increased influence of the stochastic processes on the Banli group is grounded in the ascending environmental homogeneity and functional redundancy. Specifically, the small spatial fragments could decrease heterogeneity, reduce environmental preferences, and increase the possibility that stochastic factors will dominate (Bahram et al., 2016). Moreover, the strong functional redundancy caused by the high taxonomic richness increases the influence of stochastic factors (Vellend, 2010; Bahram et al., 2016). As specialists living in limestone forests, white-headed black langurs could have reached a peak in the richness of the gut microbiota for adaptation to poor habitats. The migration rates of the Bapen group were only slightly higher than those of the Banli group. Species migration rates are generally positively correlated with their diversity as they contribute heavily to quantitative dispersal (Mo et al., 2018). More frequent group activities of hosts may shrink the social distance between individuals, which is conducive to the spread of gut microbiota among langurs (Sarkar et al., 2020). Intimate communicative behaviors between individual langurs living in the limestone forest include playing and grooming, with the former occurring more frequently among young langurs and the latter being observed more continually on colder days (Huang et al., 2017; Zheng et al., 2021). The time budgets of these two groups of langurs in grooming are similar, but individuals in the Banli group spend more time playing with each other (Huang et al., 2017). Therefore, compared with the Banli group, the langurs in the Bapen group showed only a weak advantage in the interindividual migration rate of the gut microbiota. More than $50\%$ of the bacterial taxa in the Banli and Bapen groups fell within the $95\%$ confidence interval, which indicates that they were mostly in a stochastic condition. It is still necessary to consider the degree of fragmentation in both sites. Severe fragmentation has resulted in progressively smaller habitat fragments for both groups, which is likely to enhance environmental homogeneity. These similar indices in the NCM not only suggest that deterministic and stochastic processes are of equal importance but also indicate that the habitats are severely fragmented and that population and habitat conservation activities are urgently needed. There should be weakness in current study. Specifically, we focused on the intersite variation in the microbiota, without considering their seasonality. We admit that current results could be weakened by the detailed seasonal comparison; however, we provide preliminary pattern of microbiota composition and structure for the karst dwelling primates, consequently deepening our understanding on the adaptation in responses to habitat fragmentation on the physiological insight. Moreover, information on the function of the microbiota is relatively limited. Further studies at the metagenomic level and the interaction between seasonality and geography should be needed. In summary, intersite variations exist in the structure and diversity of gut microbiota between geographical groups of white-headed black langurs inhabiting various forests under different levels of habitat fragmentation. The gut microbiota diversity of langurs in Banli group with deeper fragmentation was lower, which could be related to increased human disturbance. The Banli group had much higher levels of Firmicutes, which could be related to their consumption of low-quality foods, such as mature leaves. Firmicutes helps the langurs digest dietary fiber and produce large amounts of energy, which helps them adapt to life in the limestone forest. Both groups showed similar values of interpretive degree and were at ~$50\%$ on the community assembly of gut microbiota. This finding indicates that the community assembly is built on a combination of stochastic and deterministic processes, which could be related to the flexible survival strategy of the Banli group narrowing the gap with the Bapen group. We conclude that the white-headed black langurs’ response to changing food resources in habitats with different levels of fragmentation is to adjust the diversity and composition of the gut microbiota. This finding highlights the importance of gut microbiota in the adaptation to habitats and the need for using physiological indicators to study the mechanisms by which wildlife responds to human disturbance and/or ecological variability. ## 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: https://www.ncbi.nlm.nih.gov/, PRJNA904436. ## Ethics statement The animal study was reviewed and approved by Administration Center of Guangxi Chongzuo White-headed Langur National Nature Reserve. ## Author contributions ZH designed the study. YiL and YC analyzed the data. YiL wrote the manuscript. JZ, ZL, DN, and JL collected samples. YoL and ZH revised the manuscript. All authors read and approved the submitted manuscript. ## Funding This work was supported by the National Natural Science Foundation of China (32170488, 31960106, and 31960104). ## 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.2023.1126257/full#supplementary-material ## References 1. Amato K. R., Leigh S. R., Kent A., Mackie R. 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--- title: Positive allosteric GABAA receptor modulation counteracts lipotoxicity-induced gene expression changes in hepatocytes in vitro authors: - Elisabeth Rohbeck - Corinna Niersmann - Karl Köhrer - Thorsten Wachtmeister - Michael Roden - Jürgen Eckel - Tania Romacho journal: Frontiers in Physiology year: 2023 pmcid: PMC9968943 doi: 10.3389/fphys.2023.1106075 license: CC BY 4.0 --- # Positive allosteric GABAA receptor modulation counteracts lipotoxicity-induced gene expression changes in hepatocytes in vitro ## Abstract Introduction: We have previously shown that the novel positive allosteric modulator of the GABAA receptor, HK4, exerts hepatoprotective effects against lipotoxicity-induced apoptosis, DNA damage, inflammation and ER stress in vitro. This might be mediated by downregulated phosphorylation of the transcription factors NF-κB and STAT3. The current study aimed to investigate the effect of HK4 on lipotoxicity-induced hepatocyte injury at the transcriptional level. Methods: HepG2 cells were treated with palmitate (200 μM) in the presence or absence of HK4 (10 μM) for 7 h. Total RNA was isolated and the expression profiles of mRNAs were assessed. Differentially expressed genes were identified and subjected to the DAVID database and Ingenuity Pathway Analysis software for functional and pathway analysis, all under appropriate statistical testing. Results: Transcriptomic analysis showed substantial modifications in gene expression in response to palmitate as lipotoxic stimulus with 1,457 differentially expressed genes affecting lipid metabolism, oxidative phosphorylation, apoptosis, oxidative and ER stress among others. HK4 preincubation resulted in the prevention of palmitate-induced dysregulation by restoring initial gene expression pattern of untreated hepatocytes comprising 456 genes. Out of the 456 genes, 342 genes were upregulated and 114 downregulated by HK4. Enriched pathways analysis of those genes by Ingenuity Pathway Analysis, pointed towards oxidative phosphorylation, mitochondrial dysregulation, protein ubiquitination, apoptosis, and cell cycle regulation as affected pathways. These pathways are regulated by the key upstream regulators TP53, KDM5B, DDX5, CAB39 L and SYVN1, which orchestrate the metabolic and oxidative stress responses including modulation of DNA repair and degradation of ER stress-induced misfolded proteins in the presence or absence of HK4. Discussion: We conclude that HK4 specifically targets mitochondrial respiration, protein ubiquitination, apoptosis and cell cycle. This not only helps to counteract lipotoxic hepatocellular injury through modification of gene expression, but - by targeting transcription factors responsible for DNA repair, cell cycle progression and ER stress - might even prevent lipotoxic mechanisms. These findings suggest that HK4 has a great potential for the treatment of non-alcoholic fatty liver disease (NAFLD). ## 1 Introduction No specific pharmacological therapy has been approved to treat non-alcoholic fatty liver disease (NAFLD), a complex disease with many environmental, but also genetic factors contributing to its origin and progression (Byrne and Targher, 2015; Targher et al., 2021). Several previous studies targeting the GABAergic system have shown promising hepatoprotective effects against liver toxicity in vitro and in vivo (Norikura et al., 2007; Shilpa et al., 2012; Hata et al., 2019). It has been reported that GABA improved mitochondrial function in parallel to an attenuation of apoptotic cell death in mice with severe acute liver injury (Hata et al., 2019). Therefore, we aimed to determine if the positive allosteric modulator (PAM) of the GABAA receptor, HK4, could prevent the deleterious effects of lipotoxicity in hepatocytes. In our recent publication (Rohbeck et al., 2022) we were able to confirm the presence of the GABAA receptor in HepG2, as described by Minuk and colleagues (Minuk et al., 1987; Rohbeck et al., 2022). Moreover, we showed that the GABAA receptor PAM, HK4, could reduce palmitate (PA)-induced DNA fragmentation, inflammation, cell death and especially apoptosis, in vitro (Rohbeck et al., 2022). These effects were mediated by activation of the two transcription factors nuclear factor kappa-light-chain-enhancer of activated B Cells (NF-κB) and signal transducers and activators of transcription 3 (STAT3) (Rohbeck et al., 2022). Those transcription factors control the expression of a large number of downstream genes related to cell proliferation, survival, stress responses and inflammation (He and Karin, 2011). Therefore we hypothesize that besides these two transcription factors, HK4 might also counteract gene expression patterns induced by lipotoxic stimuli such as PA exposure. The development of NAFLD in humans strongly correlates with the dysregulation of transcriptional regulators that affect lipid metabolism (CAR, ChREBP, C/EBPα, FXR, LXR, PPARα/γ/δ, SREBP1c, STAT5), inflammation (c-Jun, C/EBPβ, IRF$\frac{1}{3}$, NF-κB, RELA, SHP, STAT$\frac{1}{3}$), metabolic stress (ATF$\frac{4}{6}$, CYP2E1, eIF2α, IRE1α, Nrf2, Xbp1), and fibrosis (AEBP1, RUNX2, Smad/TGFβ, YAP) (Steensels et al., 2020). Besides the aforementioned transcription factors, there are plenty of genes whose expression correlates positively or negatively with aberrant pathways involved in NAFLD (Greco et al., 2008; Jonas and Schürmann, 2021). Therefore we aimed to identify PA-regulated genes and to further understand the underlying signalling pathways regulated by HK4 as a protection against PA-induced lipotoxicity. In fact, lipid intake enriched in PA, has frequently been associated with obesity, type 2 diabetes mellitus and NAFLD (Hernández et al., 2017). In detail, ingestion of saturated fat rapidly increases energy metabolism, hepatic lipid storage, and insulin resistance (Nowotny et al., 2013). It has been postulated that this metabolic change is accompanied by regulation of hepatic gene expression and signalling that may contribute to the development of NAFLD (Hernández et al., 2017). Thus, several studies have identified candidates that may target transcriptional networks as a response to palmitate (Das et al., 2010; Piccolis et al., 2019; Pérez-Schindler et al., 2022), while other studies identified a typical gene expression signature in NAFLD progression in humans mostly related to lipid metabolism, oxidative phosphorylation, inflammation, endoplasmic reticulum (ER) and oxidative stress and apoptosis pathways (Greco et al., 2008; Ryaboshapkina and Hammar, 2017). RNA-sequencing can further identify and unravel specific gene patterns and involved pathway regulations. Therefore, the global aim of the present study was to further explore the effects of HK4 on palmitate-induced lipotoxicity in hepatocytes at transcriptional level. In detail, we have examined if the previously described preventive effect of HK4 over palmitate can be translated to the transcriptional level by taking well-known transcriptional features of NAFLD into account. ## 2.1 Cell culture HepG2 cells were obtained from Merck KGaA (Darmstadt, Germany; ECACC certified) and cultured according to the corresponding manufacturer’s instructions. During experiments, cells were cultured in serum-free medium. Cells were treated with 200 μM bovine serum albumin-conjugated palmitate (Cayman chemical, Ann Arbor, MI) alone or in combination with 10 μM of HK4 (Taros Chemicals, Dortmund, Germany) up to 30 min prior to palmitate exposure for 7 h. ## 2.2 RNA isolation Total RNA of 5 independent experiments, each one comprising 3 treatments (untreated, PA, PA + HK4), from HepG2 cells were isolated with an automated RNA isolation machine Innupure C16 using an RNA Isolation Kit (Analytic Jena, Jena, Germany) following the manufacturer’s instructions. ## 2.3 3′-RNA-seq analysis The total RNA samples used for 3′-mRNA Seq analyses were quantified by fluorometric measurement using the Qubit device and a RNA High Sensitivity assay (Thermo Fisher Scientific Inc. Massachusetts, USA) and quality measured by capillary electrophoresis using the Fragment Analyzer and the “Total RNA Standard Sensitivity Assay” (Agilent Technologies Inc. Santa Clara, USA). All samples in this study showed high quality in RNA Quality Numbers (RQN 10). 100 ng total RNA per sample were used for library preparation performed according to the manufacturer’s protocol using the QuantSeq 3′-mRNA-Seq Library Prep Kit FWD (Lexogen®, Vienna, Austria). Bead purified libraries were normalized and finally sequenced on the NextSeq550 system (Illumina Inc. San Diego, USA), using single-end sequencing with a read length of 76bp. The bcl2fastq2 tool was used to convert the bcl files to fastq files as well as for adapter trimming and demultiplexing. ## 2.4 Bioinformatic analysis Expression analyses were conducted with CLC Genomics Workbench (version 22.0.1, QIAGEN, Venlo. NL). The reads of all samples were adapter trimmed and quality trimmed (using the default parameters: bases below Q13 were trimmed from the end of the reads, ambiguous nucleotides maximal 2). Mapping was done against the Homo sapiens (GRCh38) genome sequence. After grouping of samples according to their respective experimental condition, multi-group comparisons were made and statistically determined using the in-build algorithm. The resulting p-values were corrected for multiple testing by FDR and Bonferroni-correction. A p-value of ≤0.05 was considered significant. Ingenuity Pathway Analysis (IPA, Qiagen) was performed to understand interaction networks within differentially expressed genes (DEGs). Absolute values of fold change (\|FC\|) ≥ 1.5, p-values ≤0.05 and the Fisher’s exact test for p-values have been considered for the identification of canonical pathways. The activation z-score helps to infer the activation states of implicated biological functions, while values ≥2 indicates an activation and a z-score ≤2 points towards the inhibition of potential upstream regulators or canonical pathways. If meaningful, a trend towards activation or inhibition (\|z-score\| ≥ 1.5) have been considered. Either networks have been designed by selecting corresponding DEGs of mitochondrial respiration or protein ubiquitination pathway or by adapting a proposal of IPA for cell death and cell cycle. DAVID (Database for Annotation, Visualization and Integrated Discovery) Enrichment Analysis was used for the functional annotation of DEGs (2021 online version, https://david.ncifcrf.gov/) (Da Huang et al., 2009; Sherman et al., 2022). To ascertain the gene-enriched pathways and the potential Gene Ontology (GO) classification, terms comprising biological process, molecular functions, and signalling pathways concerning specifically from Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used. The modified Fisher exact p-value (EASE score) ≤ 0.05 are considered strongly enriched. ## 2.5 Statistical analyses Statistical analyses were performed using the GraphPad Prism software (Version 8.0.1, San Diego, USA). Two-way ANOVA (post hoc test: Dunnett’s) were used to assess statistical significant differences. Transcript levels (expressed as reads per kilobase of transcript per million mapped reads (RPKM)) were represented as mean values with standard error of the mean (SEM). p-values ≤0.05 were considered as statistically significant. ## 3.1 Functional annotation of the gene expression profile of hepatocytes under lipotoxicity Hepatocyte lipid metabolism, oxidative phosphorylation, inflammation, ER and oxidative stress as well as apoptosis pathways are altered during NAFLD progression (Byrne and Targher, 2015; Dewidar et al., 2020). In parallel, modular enrichment analysis of our lipotoxicity model showed that PA altered pathways related to lipid metabolism, cell cycle, NAFLD pathways in general, oxidative phosphorylation, apoptosis, oxidative stress response, peroxisome proliferator-activated receptors (PPAR) signalling, tumor protein p53 (TP53) and unfolded protein response (Table 1). Between 77 and 10 DEG are involved in the aforementioned pathways addressed by modular enrichment analysis with an increasing fold enrichment value. TABLE 1 \| Term \| Count \| p-value \| Fold enrichment \| \| --- \| --- \| --- \| --- \| \| Lipid metabolism \| 77 \| 0.0039 \| 1.4 \| \| Cell cycle \| 75 \| 0.00058 \| 1.5 \| \| Non-alcoholic fatty liver disease \| 24 \| 0.0025 \| 1.9 \| \| Oxidative phosphorylation \| 20 \| 0.0093 \| 1.9 \| \| Apoptosis \| 20 \| 0.012 \| 1.8 \| \| Response to oxidative stress \| 17 \| 0.0065 \| 2.1 \| \| PPAR signalling pathway \| 14 \| 0.0057 \| 2.3 \| \| TP53 signalling pathway \| 13 \| 0.012 \| 2.2 \| \| Response to unfolded protein \| 10 \| 0.01 \| 2.7 \| ## 3.2 Treatment with palmitate and HK4 results in differentially regulated gene profiles 3′-mRNA sequencing and expression analysis identified differentially regulated protein-coding genes among untreated HepG2 cells, in PA-treated cells and PA + HK4-treated cells (Figure 1). In a differential expression analysis, genes have been filtered by a p-value ≤0.05 and \|FC\| ≥ 1.5. 1,457 genes were differentially expressed when comparing untreated and PA-treated hepatocytes, while only half of the number of genes [723] remained differentially expressed between PA alone compared to PA in combination with HK4. Interestingly, a much lower number of genes [271] are differently expressed between untreated and PA + HK4. From the 784 upregulated genes in untreated cells compared to PA-treated and the 472 upregulated genes in PA + HK4 compared to PA, 342 genes are identic. Thus, these 342 genes are downregulated when treated with PA and reversibly upregulated in the presence of HK4. In contrast, from 673 downregulated genes in untreated cells compared to PA and 251 downregulated genes in PA + HK4 compared to PA, 114 genes are identic. Thus, 114 genes are upregulated when treated with PA and reversibly downregulated in the presence of HK4. We consider those 456 genes (342 upregulated, 114 downregulated in PA + HK4) as key regulated genes by HK4 to restore initial physiological conditions from lipotoxicity. FIGURE 1: Workflow and overview of the number of genes in untreated, palmitate (PA) or PA + HK4-treated HepG2 cells. After 7 h treatment with 200 μM PA and 10 μM HK4, RNA has been isolated from five independent HepG2 cell lysates per treatment group and 3′-mRNA sequencing has been performed with Illumina NextSeq550 system. Thick arrows indicate up or downregulated genes in the first treatment group of the comparison. Differential expression (DE) of genes have been filtered by p-value ≤0.05 and an absolute value of fold change (\|FC\|) ≥ 1.5. ## 3.3 Determining the transcriptome of palmitate-induced lipotoxicity and restoring of initial gene expression pattern as in untreated hepatocytes by HK4 Hierarchical clustering showed distinctive gene expression profiles of untreated hepatocytes, hepatocytes exposed to PA alone or PA combined with HK4 (Figure 2). A heat map with differentially regulated genes after 7 h treatment, showed an opposite expression pattern in the majority of genes in the PA-treated group compared to untreated and PA + HK4 group. Thus, gene expression pattern of PA + HK4-treated cells is more similar to the untreated group than the PA-treated group, except for one PA + HK4 sample. In total, the heat map considered 1,570 transcripts (Supplementary Figure S1). FIGURE 2: Overview of hierarchical clustering and heat map of transcription profile of HepG2 cells in response to palmitate (PA, pink) and HK4 (blue) or untreated cells (green). Euclidean metric for distance measurement was used. Changes in the abundance of 1,570 individual genes are shown, while each rectangle represents a gene. The intensity of the red and blue colors correlates with the degree of up- and downregulation, respectively. Differentially expressed genes were defined by ANOVA with a p-value ≤0.05 and an absolute fold change ≥1.5. ## 3.4 HK4-mediated modification in gene expression related to mitochondrial respiration, protein ubiquitination, cell cycle and apoptosis are crucial for protecting cells from palmitate-induced lipotoxicity Besides the clustering of genes, we used bioinformatic approaches (IPA) to evaluate the overrepresentation of a group of genes mapping to a specific pathway compared to the total reference set of genes. Thus, we obtained an overview of the biological pathways modified by HK4 in lipotoxic conditions (Figure 3). The 456 DEGs with an up- or downregulation in PA and a reversible regulation in the presence of HK4 were analysed. FIGURE 3: Enriched pathways of 456 differentially expressed genes in untreated or PA + HK4-treated HepG2 cells compared to PA-treated cells. The length of the bars is proportional to the significance of the association between the set of genes and the pathway, expressed by the negative logarithm of the p-value. Only pathways with p ≤ 0.05 (dotted threshold line) are shown. Upregulated pathways in untreated or PA + HK4-treated cells associated with a positive z-score are colored in orange, downregulated pathway with a negative z-score are shown in blue and pathways with no change or an unknown pattern are marked in white or grey. Selected canonical pathways which are subsequently considered in more detail are highlighted in red. Regarding affected canonical pathways in IPA with a \|z-score\| ≥ 2, several DEGs are targeting and activating oxidative phosphorylation (z-score = 3, $$p \leq 0.0004$$, 9 genes), cell cycle control (z-score = 2.24, $$p \leq 0.006$$, 5 genes) or on the contrary inhibiting the caspase-independent cell death granzyme A signalling pathway (z-score = −2.24, $$p \leq 0.016$$, 5 genes) in PA + HK4 or untreated hepatocytes compared to PA-treated cells. While several other pathways related to processes such as senescence (z-score = 1.67, $$p \leq 0.046$$, 11 genes), ferroptosis (z-score = 1.13, $$p \leq 0.005$$, 8 genes), apoptosis (z-score = 0.81, $$p \leq 0.02$$, 6 genes), cell cycle regulation (z-score = −1, $$p \leq 0.02$$, 4 genes) and sirtuin signalling pathway (z-score = −0.7, $$p \leq 0.007$$, 13 genes) were significantly addressed without passing threshold \|z-score\| of 2. Since the underlying network also includes findings without associated directional attributes, several pathways with a considerable number of DEGs show up with unknown, unchanged or controversial pattern. Among those, mitochondrial dysfunction ($$p \leq 0.0007$$, 11 genes) protein ubiquitination ($$p \leq 0.002$$, 14 genes), iron homeostasis ($$p \leq 0.007$$, 8 genes), proline biosynthesis ($$p \leq 0.003$$, 2 genes), glucose degradation ($$p \leq 0.043$$, 2 genes) and granzyme B ($$p \leq 0.043$$, 2 genes) signalling were found. To continue assessing the influence of HK4 on the gene expression profile, associated genes of some of the aforementioned canonical pathways were identified by IPA. Selected canonical pathways were mitochondrial respiration, protein ubiquitination, apoptosis and cell cycle related pathways (highlighted in red in Figure 3). We have taken a closer look at oxidative phosphorylation and mitochondrial dysfunction (clustered together as mitochondrial respiration), as well as protein ubiquitination, since they are the most relevant pathways according to their p-values and number of included genes. Additionally, we have focused on apoptosis, because we previously demonstrated the protective effect of HK4 on PA-induced apoptosis (Rohbeck et al., 2022). Since cell cycle plays a pivotal role in our lipotoxicity model with the highest number of related DEGs (Table 1) and it is closely connected to apoptosis (Caldez et al., 2020), we also took cell cycle into account. We listed genes related to the selected pathways (mitochondrial respiration, protein degradation, apoptosis, and cell cycle) suggested by IPA and DAVID together with the fold change of their expression in Supplementary Table S1. Further identified pathways were ER stress, inflammation and lipid metabolism which display few related DEGs, such as CREB3L3, CHOP, NFKB2, NFKBIL1, IL17RC, ABHD3, PLA2G2A (Supplementary Table S1). Out of the 456 DEGs, 14 genes (1 down-, 13 upregulated) can strongly be linked to mitochondrial respiration, 22 genes (3 down-, 19 upregulated) to protein ubiquitination pathway and 14 genes both to apoptosis (5 down-, 9 upregulated) and cell cycle (2 down-, 12 upregulated). The genes ANAPC10, BIRC3, CDC26, FZR1, SIAH1 and SKP2 are matched to several pathways of IPA (protein ubiquitination and apoptosis or cell cycle) at the same time, since the pathways strongly interact with each other. Therefore, we only depict them once for apoptosis or cell cycle, when plotting RPKM values of all associated genes in Figures 4A–D. Remarkably, in all signalling pathways considerable fewer genes are downregulated in PA + HK4-treated hepatocytes. FIGURE 4: Reads per kilobase of transcript per million mapped reads (RPKM) expression values of genes related to mitochondrial respiration (A), protein ubiquitination (B), apoptosis (C) and cell cycle (D). Genes were selected of the 456 DEGs among untreated (white), palmitate (PA, black) and PA + HK4 (black striped)-treated HepG2 cells and sorted by descending expression values of PA. p-values were calculated by two-way ANOVA; p ≤ 0.001, p ≤ 0.01, p ≤ 0.05 vs. untreated, # p ≤ 0.05 vs. PA + HK4-treated cells. Abbreviations of genes are explained in Supplementary Table S1. Most of the genes associated with mitochondrial respiration (Figure 4A) are coding components of the respiratory chain complexes like the NADH dehydrogenase (NDUFA4, NDUFB3, NDUFB5), the succinate dehydrogenase (ETFA, SDHB, SDHC), the cytochrome bc1 complex (CYB5A), the cytochrome c oxidase (COX7B, COX17) or the ATP synthase (ATP5F1C, ATP6V0A1). Additionally, the pyrophosphatase-coding gene PPA2 and peroxiredoxin 3-coding gene PRDX3 are listed as associated genes of oxidative phosphorylation and mitochondrial dysfunction. While PPA2 regulates energy metabolism of the cell, PRDX3 exerts an antioxidative function. From all 14 genes, only ATP6V0A1 is downregulated in PA + HK4-treated hepatocytes compared to PA-treated cells alone. Genes associated with protein ubiquitination (Figure 4B) depict mainly components of the E3 ubiquitin-protein ligase (COP1, MARCHF5, PELI3, SYVN1, UHRF2), the E2 ubiquitin-conjugated enzyme (UBE2T), the E1 ubiquitin-activating enzyme (ATG7), the proteasome (PSMA2, PSMB8) or as heat shock proteins (DNAJC4, HSPA1B, HSPB11, HSPE1). Furthermore, TAP2 is coding a member of ATP transporter and MED20 and PAN2 are involved in the deubiquitination. In total, only three genes (PELI3, ATG7 and DNAJC4) are downregulated in PA + HK4-treated hepatocytes compared to PA alone. Regarding the genes associated with apoptosis (Figure 4C), 5 genes are downregulated in the presence of HK4 combined with PA compared to PA-treated cells. The most downregulated gene in this pathway, referred to its fold change (Supplementary Table S1), is the anti-apoptotic gene BIRC3, followed by the pro-apoptotic genes HRAS, BAX, TP73 and FASTK. 9 genes (TNFRSF10B, STEAP3, SIAH1, CTSC, BCL2L11, LMNB2, NRAS, DDIAS, DFFB) show an upregulation when treated with PA in combination with HK4. Regarding genes related to cell cycle (Figure 4D), two genes (CDKN1C, FZR1) are downregulated in untreated or PA + HK4-treated cells compared to PA-treated hepatocytes, while 11 genes are upregulated in the presence of PA + HK4. Among those, there are the cell division cycle–related genes (CDCA7, CDC26 and CDC45), CDK7, CHEK2, MCM2 and ORC5, which are mainly regulating the progression steps of the interphase, while ANAPC10 and ESPL1 play a role in the anaphase of the mitosis. PPM1D is a negative regulator of cellular stress responses. All genes of mitochondrial respiration, protein degradation, apoptosis and cell cycle have been included in networks designed with IPA with the purpose to understand how groups of data set molecules might interact (Figures 5A–D). B-cell receptor (BCR) complex, cytochrome-c oxidase, Interferon-α, IKK complex, IL 6 and CCND1, as well as the upstream regulators TP53, TP73, NF-κB, STAT5A, MAPK9, RB1 and DDX5 have been listed as additional nodes to connect the genes of the pathways. FIGURE 5: Gene interaction network map generated with IPA for mitochondrial respiration (A), protein ubiquitination (B), apoptosis (C) and cell cycle (D) pathways. Genes associated with one of the four pathways are represented by nodes with their shape representing the type of molecule/functional class. The causal network results of the 456 DEG identified the master regulator SYVN1 targeting the highest number of molecules in our dataset through the intermediate regulators HTT and TP53 (E). Network includes up and down regulation of downstream dataset molecules. Nodes in red are upregulated in untreated/PA + HK4-treated HepG2 cells, orange nodes are predicted to be activated from Ingenuity knowledge base, green colored nodes show downregulation and blue ones are predicted to be inhibited. Orange lines between the nodes indicate an activating relationship, blue lines an inhibition. Pointed arrowheads indicate that the downstream node is expected to be activated if the upstream node connected to it is activated, while blunt arrowheads indicate that the downstream node is expected to be inhibited if the upstream node that connects to it is activated. Predicted legend is shown on the right side. ## 3.5 Upstream regulators of PA and HK4-modified genes affect DNA repair and cell cycle Upstream regulators cover a spectrum of molecule types (e.g., cytokines, kinases, microRNA, receptors and transcription factors) that affect the expression, transcription, or phosphorylation of other (downstream) molecules. To unravel critical mediators of lipotoxicity upstream of the 456 genes modified by PA and HK4, we identified upstream regulators with two different analyses in the IPA software. The first approach of the upstream regulator analysis (URA) determines likely upstream regulators (Table 2) that are connected to dataset genes through a set of direct or indirect relationships. URA revealed 4 potential upstream regulators of the gene expression profile by PA and HK4 with passing \|z-score\| threshold of 2 (Table 2). Among those potential regulators, 2 are predicted to be activated (CAB39L (z-score = 2), DDX5 (z-score = 2.65)) and 2 to be inhibited (KDM5B (z-score = −2.82), TP53 (z-score = −2,11)) with HK4 pretreatment. The most relevant upstream regulator is the tumor suppressor TP53, affecting 30 targets. The encoded protein responds to diverse ways of cellular stress to regulate expression of target genes, thereby inducing cell cycle arrest, apoptosis, senescence, DNA repair or changes in metabolism (Vogelstein et al., 2000; Marcel et al., 2010). The other transcription regulator, KDM5B, is affecting 8 target molecules from our dataset. Interestingly, KDM5B plays a role in DNA repair and the transcriptional repression of certain tumor suppressor genes (Klein et al., 2014; Li et al., 2014). DDX5, showing the highest activation z-score (2.646), is an established co-activator of TP53 and therefore having a pivotal role in orchestrating the cellular response to DNA damage and repair (Nicol et al., 2013). CAB39L, the last upstream regulator of Table 2, exerts a tumor suppressive effect by inducing apoptosis and cell cycle arrest (Li et al., 2018). TABLE 2 \| Upstream regulator \| Activation z-score \| p-value \| Target molecules in dataset \| Target molecules in dataset.1 \| \| --- \| --- \| --- \| --- \| --- \| \| Upstream regulator \| Activation z-score \| p-value \| Upregulated \| Downregulated \| \| transcription regulator \| \| \| \| \| \| TP53 \| −2,113 \| 453E-04 \| BCL2L11, CDH1, CDK7, CHEK2, CITED2, CPOX, DKK1, DSN1, GLIPR1, HK2, HMGCR, HSPA1A/HSPA1B, MCM2, MPZL2, PFKFB3, PPM1D, PRNP, RAD54B, SIAH1, TAP2, TFRC, TMSB10/TMSB4X, TNFRSF10B \| ATG7, BAX, BIRC3, ELF4, HRAS, PHLDB3, TP73 \| \| KDM5B \| −2,818 \| 167E-03 \| CYB5A, GCA, MCM2, MT1E, OIP5, SNRPG, SPTSSA, TMEM14A \| \| \| Kinase \| \| \| \| \| \| CAB39L \| 2000 \| 859E-03 \| COX17, NDUFA4, NDUFB3, NDUFB5 \| \| \| Enzyme \| \| \| \| \| \| DDX5 \| 2646 \| 449E-05 \| ATP5F1C, COX7B, FH, NDUFA4, NDUFB3, NDUFB5, SDHB \| BAX \| The second approach is the causal network analysis (CNA) connecting upstream regulators to dataset molecules with the advantage of taking paths into account that involve more than one link (i.e., through intermediate regulators). Therefore, the CNA can been seen as a generalization of URA, which are used to generate a more complete picture of possible root causes for the observed expression changes (Krämer et al., 2014). In total, 19 master regulators (data not shown) are passing \|z-score\| threshold of 2. We plotted causal network analysis results for the “root” regulator SYVN1 which affects the most regulators [16] and target molecules [55] in our dataset (Figure 5E). SYVN1 is affecting many biological processes like response to unfolded protein, protein ubiquitination and intrinsic apoptotic signalling pathway in response to ER stress through the two intermediate regulators HTT and TP53. HTT (huntingtin) is known to be involved in Huntington’s disease signalling, but also required for normal development, including vesicle transport, protein trafficking, and transcriptional regulation (Rodriguez-Lebron et al., 2005). The upstream regulator TP53 not only affects 30 target molecules of our dataset in the URA, but it is also predicted to activate or inhibit 12 other upstream regulators (PRKCE, JNK, MEK, NOTCH1, NF-κB (complex), PRKAA1, GLI1, TSC2, TP73, RB1, NPM1, MAPK9). Some important activated upstream regulators by TP53, namely NF-κB, MAPK9, TP73 and RB1 are shown to interact together with STAT5A and DDX5 with selected downstream genes of mitochondrial respiration, protein ubiquitination, apoptosis and cell cycle (Figure 5E). ## 4 Discussion PA is one of the most abundant saturated fatty acids in diet (Murru et al., 2022). Analysing the transcriptome in hepatocytes treated with PA to induce lipotoxicity, shows that short term palmitate exposure induces a specific gene expression profile. Although the effect of lipotoxicity on the induction of transcriptome responses has been analysed in hepatocytes, the impact of the novel GABAA receptor PAM, HK4, on lipotoxicity has not been previously studied. Therefore, the purpose of this study was to describe gene expression changes occurring under conditions resembling lipotoxicity and to analyse the potential protection by HK4. We found that mitochondrial respiration, protein ubiquitination, apoptosis and cell cycle pathways are major targets of HK4, mainly addressed by the upstream regulators SYVN1 and TP53. ## 4.1 Differential gene expression profile of hepatocytes under PA-induced lipotoxicity and its reversion by HK4 Under conditions mimicking hepatic lipotoxicity with PA treatment, 1,457 genes were differentially expressed. In line, in HepG2 cells it has been reported that 776 genes were affected by 1 mM PA after 6 h exposure (Das et al., 2010), while others detected changes in only 11 genes after 24 h exposure of 50 μM PA (Vock et al., 2007). These differences in differential gene expression induced by palmitate are likely due concentration used, treatment duration and/or hepatocyte cell type. A total of 456 genes were differentially expressed in PA treatment compared to untreated cells and reverted by HK4 pre-treatment. Putting these DEGs into pathophysiological context, the canonical pathways in IPA pointed towards most relevant DEGs related to mitochondrial respiration, protein ubiquitination, apoptosis and cell cycle pathways. These pathways are regulated in hepatocytes undergoing an adaptive response similarly to other studies resembling pathophysiology in NAFLD (Das et al., 2010; Mota et al., 2016; Piccolis et al., 2019). Both oxidative phosphorylation and mitochondrial dysfunction, are listed as the top two canonical pathways of our dataset according to their p-value comparing PA alone and in combination with HK4. During NAFLD electron transport chain complex expression and respiratory control ratio are decreased, while β-oxidation and TCA cycle activity are increased (Koliaki and Roden, 2016). This is in line with the assumption that hepatic mitochondria might upregulate their oxidative capacity at the expense of decreased coupling efficiency when transiently adapting to lipid overload (Koliaki et al., 2015; Jelenik et al., 2017). Later on, the loss of mitochondrial adaptation will favor lipid deposition and insulin resistance and in turn accelerate oxidative stress, resulting finally in non-alcoholic steatohepatitis (NASH) with impaired mitochondrial biogenesis (Fromenty and Roden, 2022). We observed that mitochondrial respiration-related genes as nicastrin (NCSTN), NADH dehydrogenase (NDUFA4) and succinate dehydrogenase (SDHB) were downregulated by acute palmitate exposure, exhausted from mitochondrial adaption. In mice fed with a Western style diet, mimicking NAFLD, a reduced succinate-activated respiration has been described, due to reduced SDHB gene expression (Staňková et al., 2021). Reduced half-life of oxidative phosphorylation subunits contributed to mitochondrial impairment in mice with NAFLD (Lee et al., 2018). It has also been described, that impaired activity of the NADH dehydrogenase, due to pathogenic mtDNA mutation, occurs in people with type 2 diabetes mellitus. ( Sharma et al., 2009). However description about the gene expression of specific NADH dehydrogenase subunits (NDUFA4, NDUFB3 or NDUFB5) in the context of NAFLD is lacking. In line with our sequencing data, NCSTN protein is overexpressed in liver in HCC leading to an enhanced cell growth and migration through Notch1 and Akt signalling pathways, however little is known about its protective effect in NAFLD (Li et al., 2020). Therefore, it can be postulated that an upregulation of the novel identified genes NCSTN and NDUFA4, as well as SDHB might be a protective mechanism of HK4 to restore mitochondrial capacity and boost energy supply. The protein ubiquitination pathway is affected in our lipotoxicity model, since dysregulation of ER-protein folding and ER stress response is one of the predominant hallmarks of NAFLD progression. Specifically impaired proliferation and apoptosis in hepatocellular injury is correlated with loss of the proteasome or the inhibition of the ubiquitin-proteasome pathway (Wójcik, 2002). Thus, in the liver of people with NAFLD, inactivation of components of the ubiquitin-proteasome pathway, promotes apoptotic cell death (Joshi-Barve et al., 2003). It has also been suggested, that ATP deficiency due to reduced mitochondrial respiration contributed to inhibition of ubiquitin-proteasome and proper protein degradation and therefore activate mitophagy (Lee et al., 2018). This explains the lower expression of the protein ubiquitination-regulated genes, like HSPA1B and TAP after PA exposure and the protective effect of HK4 by elevating their expression. In line, expression of HSP70 members (like HSPA1B) has been proposed to take over an anti-inflammatory role in tissue-resident macrophages (like Kupffer cells) upon metabolic challenge (Brykczynska et al., 2020). There is no literature on the gene expression of TAP2 in NAFLD. However, TAP2 downregulation has been confirmed in hepatocyte-like cells from obese people, leading to abnormally metabolism and liver regeneration (Li, Y. et al., 2021). Ji and colleagues have shown that palmitic acid could induce mitochondrial-mediated apoptosis in HepG2 by upregulating Bax and downregulating Bcl-2 (Ji et al., 2005; Panasiuk et al., 2006). Our data are in line to what was previously described, but also show that HK4 can prevent the upregulation of Bax and downregulation of Bcl-2 induced by PA. Additionally, the lower expression of BIRC3 in HK4-treated cells might also play a protective role in our cell model, since inhibition of BIRC3 reduced hepatocellular carcinoma and progression of metastases (Fu et al., 2019; Frazzi, 2021). These findings underline the results of our recent publication where HK4 exerted an anti-apoptotic effect in HepG2 cells after 24 h of PA exposure (Rohbeck et al., 2022). Cell division is essential for organismal growth and tissue homeostasis, especially when tissue is damaged in NAFLD (Caldez et al., 2020). Aberrations in cell cycle proteins or their regulators might even lead to HCC (Bisteau et al., 2014). In line, gene expression of CDKN1C in our dataset reflects protein downregulation of CDKN1C in HCC and cirrhotic liver samples (Fornari et al., 2008). Furthermore, decreased levels of Mcm2 as a sign of dysregulated cell cycle progression has been shown in hepatocytes in NAFLD (Dabravolski et al., 2021). Inactivity or deletion of CDC26 might result in a reduced activity of anaphase-promoting complex-cyclosome, thus impairing a variety of cellular processes such as cell division, differentiation, genome stability, energy metabolism, cell death and autophagy (Zhou et al., 2016). Although less is known about the specific expression of cell cycle-related genes in patients with NAFLD, impairment (by gene mutation) and absence of cell cycle-related genes in general repress cell proliferation, which inhibits liver regeneration after acute injury or chronic damage (Caldez et al., 2020). Therefore, we can postulate that cell cycle control also plays a pivotal role in the protective effect of HK4. ## 4.2 The upstream regulators SYVN1 and TP53 control and integrate HK4 protective effects against lipotoxicity The causal network analysis suggests that the E3 ubiquitin-protein ligase synoviolin (SYVN1, aka HDR1) is an upstream regulator. The protein encoded by this gene is involved in the ubiquitin-dependent degradation of misfolded proteins which are accumulated during ER stress and proposed as a liver metabolic regulator and a potential drug target for fatty liver disease and progressed hepatocellular carcinoma (Kikkert et al., 2004; Ji et al., 2021). In cirrhotic liver tissues upregulated SYVN1 has been implicated in the downregulation of NRF2, a transcription factor that combats oxidative stress (Wu et al., 2014; Bathish et al., 2022). Overexpression of SYVN1 has been proposed to ameliorate hepatic steatosis and enhanced insulin sensitivity in db/db mice (Li, K. et al., 2021). Our data suggest that upregulation of SYVN1 by HK4 pretreatment might be one mediator of the protective effect of HK4. On the contrary, other works points towards SYVN1 deletion specifically in the liver as protection against HFD-induced obesity and liver steatosis and insulin resistance in mice (Wei et al., 2018). Genome-wide mRNA sequencing revealed that SYVN1 deficiency reprograms liver metabolic gene expression profiles, including suppressing genes involved in glycogenesis and lipogenesis and upregulating genes involved in glycolysis and fatty acid oxidation (Wei et al., 2018). Our results point towards a negative regulation of SYVN1 over HTT (Yang et al., 2007) and TP53 (Yamasaki et al., 2007). Mutant htt is predicted to inhibit the 26S ubiquitin proteasome system, composed of the 20S catalytic core complex and the 19S regulatory units, resulting in protein aggregation and ER stress (Valera et al., 2005; Qu et al., 2021). Thus an upregulation of the components of the proteasome complex PSMA2 and PSMB8, in the presence of HK4 might lead to proper protein degradation and avoid accumulation of misfolded proteins, which could otherwise cause hepatic steatosis (Valera et al., 2005; Rutkowski et al., 2008). Regarding TP53, it has been described that SYVN1 sequestrates and metabolizes TP53 in the cytoplasm and negatively regulates its cellular level and biological functions, including transcription, cell cycle regulation, senescence, DNA repair and apoptosis (Yamasaki et al., 2007). As a double-edged sword TP53 takes over a dual function in both the aggravation and amelioration of NAFLD (Yan et al., 2018). Evidence also suggests that induction of TP53 improves pathophysiological conditions associated to NAFLD (Gross et al., 2017; Humpton et al., 2022). Thus, TP53 is a relevant upstream regulator in our setting orchestrating cellular signalling, like ER stress response by impaired mitochondrial function through counteraction of RB1 and NF-κB (Johnson et al., 2011). Cells lacking Rb1 exhibit defective mitochondria and decreased oxygen consumption (Váraljai et al., 2015). Most likely, also the upstream regulator DDX5, interacting with TP53, directly regulates transcriptional activity of mitochondrial-related genes by binding at or near their promoter (Xing et al., 2020; Xu et al., 2022). Similar to our results, DDX5 expression is reported to be downregulated in palmitate-stimulated hepatocytes but also in patients with NASH (Zhang et al., 2022). Additionally, modulation of TP53-dependent pathways during prolonged metabolic stress has been also described as linked to the protein ubiquitination pathway (French and Bardag-Gorce, 2005; Lee et al., 2012). In this regard, we can speculate that through inhibition of NF-κB activation (Wadgaonkar et al., 1999), TP53 controls ubiquitination via the upregulation of PSMA2, PSMB8 and TAP2 in HK4-treated cells. Furthermore, we can postulate that TP53 might also control apoptosis either via interaction with TP73 and JNK pathway (MAPK9) or directly interfering with BCL2L11 (Brockhaus and Brüne, 1999; Dhanasekaran and Reddy, 2008; Kunst et al., 2016). TP53, one of the key upstream regulators, is a well-known member of the p53 tumor suppressor family, that can inhibit cell cycle-related genes and arrest cell cycle (Kunst et al., 2016; Engeland, 2022). This might explain, why all cell cycle regulating genes except for its negative regulating inhibitor of cyclin-dependent kinases, CDKN1C, and FZR1 are downregulated in lipotoxic conditions and again upregulated in the presence of HK4 in our setting (Fornari et al., 2008). We have previously shown that the protective effects of HK4 can be mediated by downregulated phosphorylation of NF-κB and STAT3 (Rohbeck et al., 2022). Substantial evidence supports that activation of the transcription factor NF-κB and downstream inflammatory signalling pathways are involved in hepatic insulin resistance (Tilg et al., 2017). In line, upregulated NF-κB gene expression in PA-treated HepG2 cells compared to untreated ($$p \leq 0.004$$) or PA and HK4-co-treated cells ($$p \leq 0.089$$) fits with protein expression analysis of our previous study (Rohbeck et al., 2022). Also in line with our previous study, the current data indicate an involvement of STAT signaling, since STAT5A is differentially expressed in our dataset. STAT5A, likewise our recently recognized mediator of HK4 protective effect, STAT3, is known to control cell survival, differentiation, proliferation, and metabolism in response to extracellular stimuli (Wingelhofer et al., 2018). A very recent study suggests that non-canonical NF-κB activation can attenuate the hepatoprotective JAK2/STAT5 signalling (Vesting et al., 2022). We conclude from our study that mitochondrial respiration, protein ubiquitination, apoptosis and cell cycle are major targets of HK4 to minimize hepatocellular injury under a lipotoxic stimulus as PA. Our findings strongly suggest that transcription factors responsible for DNA repair and ER stress such as TP53 play a central regulatory role in hepatocyte lipotoxicity with this novel drug. Targeting those transcription factors and interfere in gene expression pattern holds great therapeutic potential for HK4 in the treatment of NAFLD. ## 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 below: https://www.ncbi.nlm.nih.gov/, PRJNA902305. ## Author contributions ER, CN, JE, MR, and TR contributed to conception and design of the study. ER performed experiments. KK and TW performed sequencing. ER, KK, and TW performed data analysis. ER, CN, JE, TR interpreted data. ER wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version. ## Conflict of interest Authors ER, CN, and JE are employed by CMR CureDiab Metabolic Research GmbH. 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 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. 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--- title: Association between inflammatory cytokines and symptoms of major depressive disorder in adults authors: - Xue Min - Genwei Wang - Yalian Cui - Peipei Meng - Xiaodong Hu - Sha Liu - Yanfang Wang journal: Frontiers in Immunology year: 2023 pmcid: PMC9968963 doi: 10.3389/fimmu.2023.1110775 license: CC BY 4.0 --- # Association between inflammatory cytokines and symptoms of major depressive disorder in adults ## Abstract ### Objective This study investigated the association between inflammatory cytokines and major depressive disorder. ### Methods Plasma biomarkers were measured by enzyme-linked immunosorbent assay (ELISA). Statistical analysis of baseline biomarkers in the major depression disorder (MDD) group and healthy controls (HC) group, and differences in biomarkers before and after treatment. Spearman analysis was performed to correlate baseline and after treatment MDD biomarkers with the 17-item Hamilton Depression Rating Scale (HAMD-17) total scores. Receiver operator characteristic (ROC) curves were analyzed for the effect of biomarkers on MDD and HC classification and diagnosis. ### Results Tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) levels were significantly higher in the MDD group than in the HC group, while high mobility group protein 1 (HMGB1) levels were significantly lower in the MDD group. The AUCs for HMGB1, TNF-α, and IL-6 were 0.375, 0.733, and 0.783, respectively, according to the ROC curves. MDD patients with brain-derived neurotrophic factor precursor (proBDNF) levels were positively correlated with total HAMD-17 scores. The levels of proBDNF levels were positively correlated with the total HAMD-17 score in male MDD patients, and brain-derived neurotrophic factor (BDNF) and interleukin 18 (IL-18) levels were negatively correlated with the total HAMD-17 score in female MDD patients. ### Conclusion Inflammatory cytokines are associated with the severity of MDD, and TNF-α and IL-6 have the potential as objective biomarkers to aid in the diagnosis of MDD. ## Introduction Major depressive disorder (MDD), or depression is a common and highly disabling mental disorder in which patients have a high risk of disability and a low quality of life [1]. often occurring together with cardiovascular disease, diabetes, and autoimmune diseases [2, 3]. MDD imposes a significant emotional and socioeconomic burden, and The World Health Organization estimates that 322 million people around the world from depression, which is about $4.4\%$ of the global population [4]. MDD is highly heterogeneous in terms of clinical features and pathobiological alterations, which makes $\frac{1}{3}$ of patients unresponsive or ineffective to conventional treatments [5]. Furthermore, as the current diagnosis of MDD is based only on the symptom dimension, this makes the whole diagnostic process somewhat subjective and leads to a considerable risk of misdiagnosis and suboptimal treatment [6]. Exploration of biomarkers as indicators of normal biological processes, pathogenic processes, or drug responses to therapeutic interventions may help identify homogeneous patients with MDD [7], such as those who may be involved in inflammatory phenotypes, so that individualized treatment plans can be developed for these patients to improve treatment rates. The pathogenesis of MDD is extremely complex, and biomarker studies currently involve five biological systems, such as immunoinflammatory, neurotrophic, neurotransmitter, neuroendocrine and metabolic systems. The mechanism of immune inflammation has become a research hotspot in recent years, and since Ur [8] and others proposed the cytokine hypothesis, more and more studies have confirmed that MDD is accompanied by immune abnormalities [9]. Early, Maes et al [10] found elevated inflammatory factors and CRP in patients with depressive disorders. Indoleamine 2, 3-dioxygenase (IDO), which decomposed tryptophan, can be activated by high levels of inflammatory factors. IDO’s metabolite quinolinic acid (QA) presents neuroexcitatory toxicity, leading to the reduction of a large number of markers of neuroplasticity, such as brain-derived neurotrophic factor (BDNF) levels, as well as affecting neurogenesis [11]. This process may be associated with depression-like behavior in patients. In addition, inflammatory cytokines can stimulate the activation of the hypothalamic-pituitary-adrenal (HPA) axis and inhibit negative feedback loops, leading to hyperglucocorticoidemia. Elevated cortisol levels have been repeatedly shown to cause mood symptoms and are thought to be another potential link between inflammation and major depression [12]. An increasing number of studies have also found abnormalities in peripheral biomarkers in MDD both at baseline and after treatment [13, 14], such as cellular inflammatory factors and neurotropism. Also, antidepressants have been shown to reduce peripheral biomarkers such as IL-6, IL-10, and TNF-α [15], and anti-inflammatory drugs combined with antidepressants can reduce biomarkers such as CRP and TNF-α and improve depressive symptoms [16, 17]. Khandaker et al. [ 18] suggested that elevated levels of inflammatory markers and others may be the etiology of MDD. Therefore, their exploration may not only add objective markers for clinicians to diagnose MDD but also obtain data about the effectiveness of treatment. In this study, inflammatory factors, neurotrophic factors, and inflammatory proteins, such as TNF-α, IL-4, IL-6, IL-10, IL-18, IL-23, proBDNF, BDNF, hs-CRP, and HMGB1, were chosen as targets for investigation based on the association between immune inflammation, the nervous system, and MDD in this study [19]. In order to better understand the diagnosis and treatment of MDD, it is important to find diagnostic biomarkers and therapeutic response biomarkers for MDD. ## Participants The 113 study cases were outpatients and inpatients admitted to the Department of Mental Health, First Hospital of Shanxi Medical University, from January 2019 to December 2021, of which 22 patients had longitudinal data after treatment. Inclusion criteria:1) aged 18-55 years old; 2) met the diagnostic criteria of “MDD (current episode)” in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV); 3) the 17-item Hamilton Depression Rating Scale (HAMD-17) ≧ 17 at the time of enrollment; 4) had normal understanding ability; 5) patient meets the diagnosis and is willing to receive antidepressant treatment; 6) patients with a first episode or relapse who have not taken medication in the last month. Exclusion criteria:1) severe physical illnesses that could interfere with the study treatment; 2) pre-existing serious organic brain disease, serious mental illness (schizophrenia, etc.); 3) pregnant or lactating women, or planned pregnancy; 4) no previous convulsion-free electroconvulsive therapy (MECT). Forty-one healthy volunteers recruited from surrounding communities during the same period were selected as the control group. Inclusion and exclusion criteria:1) age 18-55 years old; 2) no blood relationship with the patient; 3) no history of serious physical illness; 4) no history of psychiatric disorders and or family history; 5) non-pregnant or lactating women. This study was approved by the Ethics Committee, and all study participants signed an informed consent form. ## Demographic data and clinical assessment General information on enrolled subjects was collected using the self-administered observation of affective disorders scale. The HAMD-17 scale was used to evaluate the severity of MDD. Semi-structured interviews were conducted with the Mini-International Neuropsychiatric Interview (MINI) Chinese version. ## Treatment Fluoxetine and Fluvoxamine, two members of the selective 5-hydroxytryptamine reuptake inhibitors (SSRIs) class of medications, were primarily utilized for a 6-week therapy period. Each medication’s lowest effective therapeutic dosage, average therapeutic dose, and maximum effective therapeutic dose were given after one, two, and four weeks, respectively. Subjects would be assessed for HAMD-17 scores at baseline and after 6 weeks of treatment by the same follow-up physician. ## Specimen collection, storage, and testing 5-6 ml of fasting venous blood was collected from study subjects at week 0 and week 6, blood samples were centrifuged at 3500 r/min for 10 min. The supernate was separated, extracted, and placed at -80°C for measurement. Marker assays included brain-derived neurotrophic factor precursor (proBDNF), brain-derived neurotrophic factor (BDNF), hypersensitive C-reactive protein (hs-CRP), high mobility group protein 1 (HMGB1), tumor necrosis factor-α (TNF-α), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), interleukin 18 (IL-18), and interleukin 23(IL-23). ## Instruments and reagents The equipment used for the assay was ELx808 ELISA, the thermostat was Sanyo’s MIR-262, and the enzyme-linked immunosorbent assay (ELISA) was used to detect the biomarkers in plasma. [ 1] Kits: BDNF, TNF-α, IL-4, IL-6, IL-10, IL-18, IL-23, and HMGB1 are provided by Cloud-clone corp Wuhan. proBDNF and hs-CRP are provided by Shanghai Jianglai biotechnology. [ 2] Intra-batch coefficient of variation: BDNF, TNF-α, IL-4, IL-6, IL-10, IL-18, IL-23, HMGB1<$10\%$, proBDNF, hs-CRP <$9\%$. [ 3] Coefficient of variation between batches: BDNF, HMGB1, TNF-α, IL-4, IL-6, IL-10, IL-18, IL-23<$12\%$, proBDNF, hs-CRP <$11\%$. [ 4] Detection sensitivity: 0.1ng/mL for proBDNF, 11.3pg/mL for BDNF, 0.1mg/L for hs-CRP, 28.3pg/mL for HMGB1, 6.5pg/mL for TNF-α, 5.9pg/mL for IL-4, 3.2pg/mL for IL-6, 2.3pg/mL for IL-10 The results showed that IL-18 was 5.9 pg/mL and IL-23 was 3.1 pg/mL. ## Statistical analysis All data were statistically analyzed by SPSS26.0. Count data such as gender and marriage were tested by the χ2 test. The normal distribution of measurement data was tested by the Shapiro-Wilk test. The normal distribution was expressed as mean± SD (`x ± s) using the paired t-test and independent sample t-test was used. The non-normal distribution was represented as Median (IQR 25–75) employing the Wilcoxon signed-rank sum test or Mann-Whitney U test was used. Bivariate correlation analysis was performed using Spearman correlation. The ROC curve was employed to analyze the effect of biomarkers on the classification and diagnosis of MDD patients and HC. The scatter plot and ROC curve were plotted by GraphPad Prism 8.0. $p \leq 0.05$ was considered as the level of statistically significant difference. ## Comparison of demographic data There were no statistically significant differences in gender (χ2 = 1.678, $$p \leq 0.194$$) and age (z=-0.127, $$p \leq 0.227$$) between the MDD group and the HC group, while there were statistically significant differences in terms of marriage (χ2 = 16.134, $p \leq 0.001$), education years (z=-2.298, $$p \leq 0.022$$), and in HAMD-17(z=-9.439, $p \leq 0.001$) ($p \leq 0.05$) (Table 1). Table 1 \| Demographic variables \| MDD (N=113)Median (IQR 25–75) \| NC (N=41)Median (IQR 25–75) \| χ2/z \| p-value \| \| --- \| --- \| --- \| --- \| --- \| \| Sex (male/female) \| 42/71 \| 21/20 \| 1.678 \| 0.194 \| \| Marital state (married/unmarried) \| 90/23 \| 19/22 \| 16.134 \| 0.0 \| \| Age (years) \| 27 (24,42) \| 31 (25,35) \| -0.127 \| 0.227 \| \| Education (years) \| 14 (12,15) \| 15 (14,15.5) \| -2.298 \| 0.022 \| \| HAMD-17 \| 20 (18, 23) \| 0 (1, 2) \| -9.439 \| 0.0 \| ## Comparison of baseline biomarkers There were significant differences in plasma HMGB1 (t=-2.359, $$p \leq 0.018$$), TNF-α (t=-4.431, $p \leq 0.001$) and IL-6 (z=-5.372, $p \leq 0.001$) levels between the MDD group and the HC group (Table 2). ROC curve results showed that the AUC of HMGB1, TNF-α and IL-6 were 0.375, 0.733 and 0.783, respectively (Figure 1). ## Correlation between baseline biomarkers and HAMD-17 Spearman correlation analysis showed a positive correlation between PROBDNF levels and total HAMD-17 score in all MDD patients (ρ=0.229, $p \leq 0.05$). After gender grouping, there was a significant positive correlation between proBDNF and HAMD-17 scores in male MDD patients (ρ=0.400, $p \leq 0.05$), a negative correlation between BDNF levels and HAMD-17 scores in female MDD patients (ρ=-0.261, $p \leq 0.05$), and IL-18 levels and HAMD-18 scores in female MDD patients were negatively correlated (ρ=-0.244, $p \leq 0.05$) (Figure 2). Figure 2: (A) Correlation between HAMD-17 and proBDNF in overall MDD patients. (B) Correlation between HAMD-17 and proBDNF in male MDD patients. (C) Correlation between HAMD-17 and BDNF in female MDD patients. (D) Correlation between HAMD-17 and IL-18 in female MDD patients. HAMD-17, 17-item Hamilton Depression Rating Scale proBDNF, brain-derived neurotrophic factor precursor; BDNF, brain-derived neurotrophic factor IL, interleukin. ## Comparison of HAMD-17 baseline and after treatment HAMD-17 score at the baseline of MDD was higher than that at HC, and the difference was statistically significant ($p \leq 0.05$). HAMD-17 score after MDD treatment was lower than baseline, the difference was statistically significant ($p \leq 0.05$). HAMD-17 score after MDD treatment was higher than that of HC, and the difference was statistically significant ($p \leq 0.05$) (Figure 3). Figure 3: HAMD-17: 17-item Hamilton Depression Rating Scale. **p<0.01. ## Comparison of biomarkers baseline and after treatment The levels of plasma TNF-α ($t = 4.580$, $p \leq 0.001$) and IL-6 (z=-2.996, $p \leq 0.001$) in MDD patients baseline and after treatment were higher than those in healthy controls, with no significant difference in biomarkers between baseline and after treatment (Table 3). Table 3 \| Project \| Baseline (N=22)Mean ± SD/Median (IQR 25–75) \| After treatment (N=22)Mean ± SD/Median (IQR 25–75) \| HC (N=41)Mean ± SD/Median (IQR 25–75) \| p-value1 \| p-value2 \| p-value3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| proBDNF (ng/mL) \| 3.018 ± 1.232 \| 2.897 ± 0.856 \| 2.602 ± 1.330 \| 0.668 \| 0.246 \| 0.351 \| \| BDNF (pg/mL) \| 4.510 ± 2.690 \| 4.947 ± 2.262 \| 4.101 ± 2.428 \| 0.592 \| 0.542 \| 0.182 \| \| hs-CRP (pg/mL) \| 2.340 ± 0.681 \| 2.316 ± 0.671 \| 1.941 ± 1.048 \| 0.995 \| 0.126 \| 0.134 \| \| HMGB1 (pg/mL) \| 120.047 ± 28.679 \| 129.190 ± 34.203 \| 130.194 ± 26.379 \| 0.342 \| 0.163 \| 0.897 \| \| TNF-α (pg/mL) \| 13.298 ± 0.464 \| 13.403 ± 0.429 \| 12.901 ± 0.227 \| 0.33 \| 0.0 \| 0.0 \| \| IL-4 (pg/mL) \| 6.971 (5.083, 7.986) \| 7.316 (6.563, 9.545) \| 6.640 (4.081, 9.097) \| 0.1 \| 0.874 \| 0.081 \| \| IL-6 (pg/mL) \| 2.184 (0.411, 2.883) \| 2.494 (2.339, 2.883) \| 0.421 (0.391, 0.572) \| 0.26 \| 0.003 \| 0.0 \| \| IL-10 (pg/mL) \| 3.299 (2.929, 3.561) \| 3.160 (2.957, 3.561) \| 3.189 (2.752, 3.956) \| 0.711 \| 0.823 \| 0.896 \| \| IL-18 (pg/mL) \| 4.185 (2.829, 8.285) \| 3.968 (2.898, 5.958) \| 4.104 (2.196, 5.714) \| 0.638 \| 0.345 \| 0.644 \| \| IL-23 (pg/mL) \| 3.293 (2.724, 4.019) \| 2.776 (2.286, 3.573) \| 2.570 (2.071, 3.195) \| 0.113 \| 0.055 \| 0.664 \| ## Discussion The findings of this study showed that MDD and HC had distinct baseline biomarkers, depression severity was associated with different biomarkers in MDD by gender, and that the levels of biomarkers remained unchanged after treatment. This study discovered statistically significant differences in the levels of HMGB1, TNF-α, and IL-6 in patients with MDD at baseline compared to healthy controls. HMGB1, a late-stage inflammatory factor, can interact with early inflammatory factors such as interleukin and tumor necrosis factor. In addition, it can be released by different cell types such as tissue macrophages, astrocytes, and neurons, acting on microglia Mac-1 to mediate chronic neuroinflammation, leading to progressive neurodegeneration. Some studies have found that HMGB1 is involved in the development of several cognitive-emotional disorders and neurological diseases [20]. According to animal research, raising extracellular HMGB1 levels in the hippocampus promoted depressive-like behavior by regulating microglia activation [21]. Wang et al. [ 22] first reported an increase in both central and peripheral HMGB1 protein levels in a chronic unpredictable mild stress (CUMS)-induced depressive behavior model, while the present study found lower plasma HMGB1 levels in the MDD group than in normal controls at baseline. The differences in the above results are considered to be related to the different selection of study subjects. On the other hand, HMGB1, when released extracellularly, can bind to the surface receptors of intrinsic immune cells and activate a series of intracellular inflammatory response pathways, causing increased synthesis and release of inflammatory factors [23]. Therefore, we hypothesize that the large amount of HMGB1 in MDD in this study bound to immune cell surface receptors, which in turn caused a further elevation of TNF-α and IL-6 levels. It should be noted that HMGB1 levels in the 113 MDDs in this study were lower than those in HC at baseline, whereas the treated 22 MDD patients did not differ significantly from HC before and after treatment. This disparity may be attributed to an insufficient number of patients, since only 22 MDDs’ longitudinal data were followed in this research owing to the high incidence of patient shedding. More patients should be followed up to validate the results. TNF-α is a multifunctional signaling molecule with antiviral and immunomodulatory effects. IL-6 is mainly secreted by monocytes-macrophages, produced by Th2, and has functions such as regulating immune responses. TNF-α and IL-6 induce the production of indoleamine 2,3 -dioxygenase (IDO), leading to a decrease in tryptophan and the production of tryptophan metabolites, which are associated with depression [24]. Several studies have found that plasma TNF-α and IL-6 levels are higher in MDD patients than in normal controls [25, 26]. This is consistent with the results of the present report. The ROC curve results of this study showed that TNF-α and IL-6 can be used to identify and differentiate MDD and HC with good diagnostic and classification effects. Cytokines may be involved in depression pathogenesis by regulating monoamine neurotransmitter metabolism and influencing neuroendocrine function [27], therefore, TNF-α, and IL-6 levels may serve as quantitative indicators of MDD patients in their early stages and as objective biomarkers for the disease High levels of inflammatory factors may mediate the effects of inflammation on the brain and are associated with the autonomic nervous system [19], and high inflammation levels decrease the number of neuroplasticity markers, such as BDNF levels and neurogenesis [11]. In this study, the levels of inflammatory factors TNF-α, and IL-6 were elevated in the MDD group, and BDNF levels were not statistically significant compared to healthy controls, which was consistent with those of Sagud [28]. However, several studies have shown lower BDNF levels in MDD than in controls [29, 30]. In addition, CRP, a typical inflammatory factor, and whose levels can reflect the level of inflammation in the body, was not found to be increased in the present study, which is inconsistent with the findings of Howren et al. [ 31]. The difference in the results of the above studies was considered to be due to the greater heterogeneity of MDD, as well as related to the fact that this study did not control for potential factors such as BMI and smoking. Extracellular proteases can convert proBDNF to mature BDNF, which has opposite biological effects through the neurotrophic factor receptor p75 (p75NTR) and complex kinase receptor B (TrkB), respectively. These receptors are crucial to the pathophysiology of mood disorders and the therapeutic mechanisms of antidepressants and mood stabilizers [32]. The correlation analysis of this study found a positive correlation between proBDNF levels and HAMD-17 scores in MDD patients. In addition, the present study considered various responses based on gender differences, and correlation analysis showed that proBDNF levels were favorably connected with HAMD-17 scores in male MDD patients, but BDNF levels and IL-18 levels were inversely correlated with HAMD-17 scores in female MDD patients. Previous studies have shown that age and gender have a significant effect on plasma cytokine levels [33]. This disparity was attributed to psychological and biological differences [34], as well as the prevalence of female patients with MDD being higher than male patients in many studies [35, 36]. This could be related to estrogen’s effect on women’s immune responses. The intricacy of immunology, neuroinflammation, and MDD is highlighted by these results, which also imply that diverse inflammation indicators may be the most useful tool for patient categorization and that future research on MDD biomarkers should take gender into account. In this study, depressive symptoms were reduced in MDD patients after treatment, but plasma TNF-α and IL-6 levels were not statistically different from baseline or HC, whereas plasma pro-inflammatory factors were reduced in MDD patients treated with conventional antidepressants in a large number of studies but were not significantly different from healthy controls [37]. The variations in the aforementioned findings might be attributed to the heterogeneity of MDD or the various antidepressant medication classes. Serotonergic antidepressants are believed to decrease Th2-mediated immune responses, while norepinephrine antidepressants are considered to suppress Th1-mediated immunological responses, according to Martino et al. [ 38] The 5-hydroxytryptaminergic antidepressants that were mostly utilized in this investigation may not suppress TNF-α and IL-6 that is released by Th1 cells, which may partially account for the lack of substantial changes between baseline and treatment-induced TNF-α and IL-6 levels. Currently, the neurotransmitter functions of norepinephrine and dopamine are the main targets of antidepressant treatment. The above findings suggest that in the search for new therapeutic targets for MDD, the role of inflammation and the immune system in the pathogenesis of MDD is growing, and we should recognize the immune-inflammatory phenotype of MDD to develop the best treatment plan for patients. This study uses plasma, which has certain advantages, such as not being affected by disturbances caused by coagulation and technical problems caused by fibrin. However, there are certain limitations. First, the statistical analysis did not include gender, age, and BMI as covariates between groups, and therefore may have some influence on the results. Second, longitudinal outcomes before and after treatment were compared with the same previous control group and no placebo control group. This is not methodologically optimal. Finally, as a cross-sectional study, it did not elucidate the causal relationship between MDD and biomarkers. In conclusion, the findings suggest that proBDNF, BDNF, and IL-18 are linked to clinical symptoms of MDD, and that TNF-α and IL-6 have the potential to serve as objective biomarkers for the diagnosis of MDD. Further studies on the relationship between more biomarkers and MDD are expected in the future to develop new and tailored diagnostic and therapeutic strategies for MDD patients. ## 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 Scientific research ethics review committee of Shanxi Medical University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions Conceptualization: YW, XM. Data curation: XM, YC, PM, SL. Formal analysis: XM, XH. Funding acquisition: YW. Investigation: XH, YW. Methodology: GW, XM. Supervision: YW, GW. Writing-original draft: XM. 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--- title: Dendrobium officinale alleviates high-fat diet-induced nonalcoholic steatohepatitis by modulating gut microbiota authors: - Gege Tian - Wei Wang - Enrui Xia - Wenhui Chen - Shunzhen Zhang journal: Frontiers in Cellular and Infection Microbiology year: 2023 pmcid: PMC9968977 doi: 10.3389/fcimb.2023.1078447 license: CC BY 4.0 --- # Dendrobium officinale alleviates high-fat diet-induced nonalcoholic steatohepatitis by modulating gut microbiota ## Abstract ### Introduction The gut microbiota plays an important role in the development of nonalcoholic steatohepatitis (NASH). This study investigated the preventive effect of Dendrobium officinale (DO), including whether its effect was related to the gut microbiota, intestinal permeability and liver inflammation. ### Methods A NASH model was established in rats using a high-fat diet (HFD) and gavage with different doses of DO or Atorvastatin Calcium (AT) for 10 weeks. Body weight and body mass index along with liver appearance, weight, index, pathology, and biochemistry were measured to assess the preventive effects of DO on NASH rats. Changes in the gut microbiota were analyzed by 16S rRNA sequencing, and intestinal permeability and liver inflammation were determined to explore the mechanism by which DO treatment prevented NASH. ### Results Pathological and biochemical indexes showed that DO was able to protect rats against HFD-induced hepatic steatosis and inflammation. Results of 16S rRNA sequencing showed that Proteobacteria, Romboutsia, Turicibacter, Lachnoclostridium, Blautia, Ruminococcus_torques_group, Sutterella, Escherichia-Shigella, Prevotella, Alistipes, and Lactobacillus_acidophilus differed significantly at the phylum, genus, and species levels. DO treatment modulated the diversity, richness, and evenness of gut microbiota, downregulated the abundance of the Gram-negative bacteria Proteobacteria, Sutterella, and Escherichia-Shigella, and reduced gut-derived lipopolysaccharide (LPS) levels. DO also restored expression of the tight junction proteins, zona occludens-1 (ZO-1), claudin-1, and occludin in the intestine and ameliorated the increased intestinal permeability caused by HFD, gut microbiota such as Turicibacter, Ruminococcus, Escherichia-Shigella, and Sutterella, and LPS. Lower intestinal permeability reduced LPS delivery to the liver, thus inhibiting TLR4 expression and nuclear factor-kappaB (NF-κB) nuclear translocation, improving liver inflammation. ### Discussion These results suggest that DO may alleviate NASH by regulating the gut microbiota, intestinal permeability, and liver inflammation. ## Introduction Nonalcoholic steatohepatitis (NASH) is the inflammatory subtype of nonalcoholic fatty liver disease (NAFLD), defined by the simultaneous appearance of more than $5\%$ fat accumulation, hepatocyte injury (ballooning), and inflammation, with or without fibrosis (Kanwal et al., 2021). The prevalence of NASH is increasing and is predicted to rise by $63\%$ by 2030 (Estes et al., 2018). More than $20\%$ of NASH patients develop irreversible cirrhosis or hepatocellular carcinoma (HCC) (Sheka et al., 2020). The “multiple hit” hypothesis is the currently accepted explanation of the complex etiology and pathophysiology of NAFLD (Salvoza et al., 2022). The “multiple hit” pathogenesis of NASH is closely related to the composition of the gut microbiota and intestinal permeability which can influence the development of NASH by regulating liver inflammation(Zhu et al., 2021). Effectively controlling NASH is critical to prevent the development of cirrhosis or HCC. While NAFLD-specific drug research is primarily focused on NASH, however, no specific drugs have been approved by the Food and Drug Administration or European Medicines Agency (Fraile et al., 2021). Lifestyle changes such as healthy eating and physical exercise are suggestions for treating NASH, however, these methods are not always reliable. Potent natural products such as Traditional Chinese Medicine (TCM), a conventional and effective therapeutic strategy associated with few side effects, are shown to improve gut microbiota and inhibit NASH progression (Chen M, et al., 2021). Dendrobium officinale (DO), a plant that is widely used as a TCM and homologous food product, contains many compounds, including polysaccharides, phenanthrenes, and bibenzyls (Chen WH, et al., 2021), with a variety of pharmacological effects such as lowering lipid content, regulating gut microbiota, protecting the liver, preventing inflammation and hypoglycemia, and protecting intestinal health (Wang K, et al., 2020; Yang J, et al., 2020; Liu et al., 2021; Fang et al., 2022). DO can also alleviate lipopolysaccharides (LPS)-induced gastric cancer cell injury by inhibiting TLR4 signaling and can reverse intestinal epithelial cell damage by regulating TLR4 (Zhang et al., 2019; Yang K, et al., 2020). The impact of DO on NASH remains unknown. Polysaccharides are the pharmacologically active ingredient of DO and while not easily digested and absorbed, it is able to regulate gut microbiota (Li et al., 2019). Our previous network pharmacological studies also identified TLR4 as a possible target for DO in the treatment of NASH (Supplementary Material). Gut-derived LPS, intestinal permeability, and the receptor TLR4 of LPS are the critical mechanisms by which gut microbiota impact the development of NASH (Xiang et al., 2022). Patients with NASH often have an imbalanced gut microbiota, increased intestinal permeability, high LPS levels, and elevated expression of liver TLR4 (Abdel-Razik et al., 2018; Ghetti et al., 2019; Craven et al., 2020). The gut and liver have bidirectional communication across the portal vein and alterations in the balance of microbial populations and function, known as dysbiosis, can disrupt the intestinal barrier tight junctions (Albillos et al., 2020; Bauer et al., 2022). This morphological alteration leads to increased intestinal permeability and allows the translocation of bacteria and/or bacterial products such as LPS through the portal vein to the liver (Plaza-Díaz et al., 2020). The gut microbiota is a source of Toll-like receptor (TLR) ligands, and compositional changes in the microbiota can increase the delivery of TLR ligands to the liver (Miura and Ohnishi, 2014). TLR4 is widely distributed in liver cells, is involved in several liver diseases, and plays a key role in inflammatory pathogenesis following activation by bacteria and/or bacterial products (Wang Y, et al., 2020). TLR4 is a natural receptor of LPS and LPS-induced activation of TLR4 leads to NF-KB nuclear translocation, promotes the release of proinflammatory factors such as IL-6 and TNF-α, and induces the progression from simple fatty liver disease to NASH (Heida et al., 2021). Indeed, in TLR4 knockout NASH mice, liver inflammation and fibrosis are significantly reduced (Csak et al., 2011). Thus, the current study sought to assess whether DO can regulate gut microbiota, intestinal permeability, and liver inflammation to alleviate NASH. ## Chemicals, reagents, and materials DO powder was purchased from Yunnan Tianbao Betula Biological Resources Development Co., Ltd. (Yunnan, China) and Atorvastatin calcium (AT) tablets were purchased from Beijing Jialin Pharmaceutical Co., Ltd. (Beijing, China). The normal diet was purchased from Jiangsu Medisen Biological Medicine Co., Ltd. (Bei Jing, China). Kits used to measure alanine transaminase (ALT), aspartate transaminase (AST), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and gamma-glutamyl transpeptidase (GGT) and the total protein assay were purchased from the Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Kits used to measure interleukin-6 (IL-6), interleukin 1-β (IL-1β), LPS, tumor necrosis factor-α (TNF-α), and diamine oxidase (DAO) were purchased from the Jiangsu Meimian Industrial Co., Ltd. (Jiangsu, China). D-lactate (D-LA) kit was purchased from Jiangsu Addison Biotechnology Co., Ltd. (Jiangsu, China). Anti-Occludin rabbit pAb, anti-Claudin-1 rabbit pAb, anti- Zona occludens-1 (ZO-1) rabbit pAb, and anti-NF-kB p65 (p 65) rabbit pAb were purchased from Servicebio (Wuhan, China). Ultrapure RNA Kit, cDNA Synthesis Kit, and UltraSYBR Mixture were purchased from CoWin Biosciences (Jiangsu, China). TLR4 rabbit pAb was purchased from Proteintech Group, Inc (Rosemont, USA). Phospho-NF-kB p65 (p-p65) antibody was purchased from Affinity Biosciences Ltd. (OH, USA). Anti-beta actin (β-actin) antibody was obtained from Abcam Inc. (Cambridge, UK). ## Animals and experimental design All experimental procedures followed the guidelines of the Animal Ethics Committee of the Yunnan University of Chinese Medicine (Approval lot: R-062021024). Healthy male Sprague-Dawley (SD) rats (180–200 g; SPF) were provided by Hunan Slike Jingda Laboratory Animal Co., Ltd. (Hunan, China). The rats were maintained in a specific pathogen-free standard environment on the public platform for animal experiments in the Science and Technology Department of the Yunnan University of Chinese Medicine. The rearing temperature was 20–25°C and the relative humidity was $50\%$ ± $10\%$, with 12 hours of alternating light. All experimental rats had free access to distilled water and were fed a normal or a high-fat diet (HFD, $82.5\%$ normal diet, $10\%$ lard, $2\%$ cholesterol, $0.5\%$ sodium cholate, and $5\%$ egg yolk powder). After 1 week of adaptive feeding, the rats were randomized into the following six groups ($$n = 8$$ rats per group): 1) Control group, fed with a normal diet and gavaged with distilled water; 2) HFD group, fed with a HFD and gavaged with distilled water; 3) AT group, fed with a HFD and gavaged with 20 mg/(kg·d) AT; 4) High-dose DO group (DOH), fed with a HFD and gavaged with 1000 mg/(kg·d) DO powder; 5) Middle-dose DO group (DOM), fed with a HFD and gavaged with 500 mg/(kg·d) DO powder; 6) Low-dose DO group (DOL), fed with a HFD and gavaged with 250 mg/(kg·d) DO powder. AT and DO powder were prepared separately using distilled water. Rats were gavaged with the corresponding drug (or distilled water) once a day for 10 weeks. ## Sample collection Body weights were recorded weekly during the experiment. After the last administration at the end of 10 weeks, rat feces were collected from the anus of each rat using sterile EP tubes and immediately preserved in liquid nitrogen. Rats were euthanized after fasting for 12 hours, and liver tissue, small intestine tissue, and serum samples were collected. Serum samples and a portion of both the liver and small intestine tissues were stored at -80°C. ## Serum and hepatic biochemical assay Serum AST, ALT, GGT, TG, TC, LDL-c, HDL-c, and D-LA levels and liver TG, TC, LDL-c, and HDL-c levels were measured using a commercial kit. ## Enzyme-linked immunosorbent assay LPS levels in the liver, serum, and ileum, IL-6, IL-1β, TNF-α levels in the liver and ileum, and DAO levels in the ileum were detected using ELISA kits. ## Histopathological analysis Liver and ileum tissues fixed in $4\%$ paraformaldehyde solution were dehydrated with different concentrations of ethanol, made transparent with xylene, embedded in liquid paraffin, stained with H&E, and sealed with neutral gum. The fixed liver tissue was dehydrated in different concentrations of sucrose solution, embedded in an optimal cutting temperature compound, sliced using a cryostat, stained with oil red O, and sealed with glycerol gelatin. A slide scanning image analysis system (Shenzhen Shengqiang Technology, China) was used to observe the staining of the pathological sections at 400x, and the oil red O-positive area was analyzed by ImageJ software (NIH, Bethesda, MA, USA). ## Western blot TLR4, p-p65 and p65 expression in liver tissues were determined by Western blot. Liver tissue (50 mg) and 0.5 ml RIPA lysate were added to an EP tube, ground for 60 s, and centrifuged at 4°C at 10,000×g for 10 min. BCA protein quantification was used to measure the protein concentration. The protein solution was added to a 5x reduced protein loading buffer at a ratio of 4:1 and denatured in a boiling water bath for 15 min. Electrophoresis was conducted at 80V for 20 min and then at 120V until the bromophenol blue ran to a position 1 cm from the lower end of the glass plate. TLR4 (1:8000), p-p65 (1:1000), p65 (1:1000), and β-Actin (1:2000) were incubated for 60 min and washed with TBST until no skimmed milk powder was present. The universal secondary antibodies (1:5000) were incubated for 60 min at room temperature and washed three times with TBST for 5 min each. Immunoreactive protein bands were visualized with a chemiluminescence HRP substrate using a ChemiDoc XRS image detector (Jena Analytical Instruments AG, Jena, Germany). The blots were analyzed using ImageJ software. ## Immunohistochemistry Sections of paraffin-embedded ileum tissue were deparaffinized and rehydrated. Antigen was repaired using citric acid antigen repair buffer and $3\%$ hydrogen peroxide was used to block any endogenous peroxidase. BSA was added dropwise for serum blocking followed by the addition of ZO-1 (1:1000), occludin (1:1000), or claudin-1 (1:800). After incubating the samples overnight, a secondary antibody was added dropwise. The colour was developed with DBA, the cell nuclei were re-stained, and the samples were dehydrated to seal the slides. A slide scanning image analysis system (Shenzhen Shengqiang Technology, China) was used at 400x to observe the samples. Ultimately, the Image-Pro Plus software (U.S. MEDIA CYBERNETICS) was used to count the mean density and analyze the integrated optical density (IOD) of positive staining. ## DNA extraction and PCR amplification Total microbial genomic DNA was extracted from rat feces samples using the E.Z.N.A.® Stool DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). The quality and concentration of DNA were determined by $1.0\%$ agarose gel electrophoresis and a NanoDrop® ND-2000 spectrophotometer (Thermo Scientific Inc., USA) and kept at -80 °C prior to further use. The hypervariable region V3-V4 of the bacterial 16S rRNA gene were amplified with primer pairs 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R(5’-GGACTACHVGGGTWTCTAAT-3’) by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). The PCR reaction mixture including 4 μL 5 × Fast Pfu buffer, 2 μL 2.5 mM dNTPs, 0.8 μL each primer (5 μM), 0.4 μL Fast Pfu polymerase, 10 ng of template DNA, and ddH2O to a final volume of 20 µL. PCR amplification cycling conditions were as follows: initial denaturation at 95 °C for 3 min, followed by 27 cycles of denaturing at 95 °C for 30 s, annealing at 55 °C for 30 s and extension at 72 °Cfor 45 s, and single extension at 72 °C for 10 min, and end at 4 °C. All samples were amplified in triplicate. The PCR product was extracted from $2\%$ agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega, USA). ## Illumina MiSeq sequencing Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform platform (Illumina, San Diego,USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw sequencing reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA872008). ## Statistical analysis Bioinformatic analysis of the gut microbiota was carried out using the Majorbio Cloud platform (https://cloud.majorbio.com). Based on the OTUs information, Species accumulation curve, rank-abundance, alpha diversity indices including observed OTUs, Chao, Qstat and Smithwilson index were calculated with Mothur v1.30.1.The similarity among the microbial communities in different samples was determined by Principal Component Analysis (PCA) based on Bray-curtis dissimilarity using Vegan v2.5-3 package. The PERMANOVA test was used to assess the percentage of variation explained by the treatment along with its statistical significance using Vegan v2.5-3 package. The linear discriminant analysis (LDA) effect size (LEfSe) (http://huttenhower.sph.harvard.edu/LEfSe) was performed to identify the significantly abundant taxa of bacteria among the different groups (LDA score > 3, $p \leq 0.05$). Statistical analysis was conducted using SPSS software (Version 25, SPSS Inc., Chicago, USA) and graphs were created using GraphPad Prism software (Version 9.4.0, GraphPad Software Inc., CA, USA). Data are shown as the mean ± SD. Normally distributed data were tested by one-way ANOVA followed by the LSD or Dunnett’s test. Other types of data were tested using the non-parametric Mann-Whitney test (Kruskal-Wallis test for multiple groups). Differences were considered statistically significant at a p-value <0.05. ## Quantitative real-time-PCR analysis Total RNA was extracted from liver tissue using Ultrapure RNA Kit and was reverse transcribed into cDNA using cDNA Synthesis Kit. The PCR cycle system was set as follows: at 95°C for 10 min, at 95°C for 15 s, and at 60°C for 60 s, for a total of 40 cycles. A total of 1μL cDNA template was used for PCR amplification using the following primers: TLR4, forward: 5′-CCGCTCTGGCATCATCTTCA-3′, reverse: 5′-TGGGTTTTAGGCGCAGAGTT-3′; GAPDH, forward: 5′-GCCCAGCAAGGATACTGAGA-3′, reverse: 5′-GGTATTCGAGAGAAGGGAGGGC′. ## Effect of DO on the body weight and liver weight of rats HFD rats had a higher food intake than those in the other groups at 4 weeks. While food intake gradually decreased in the last 6 weeks, possibly due to an anorexic reaction caused by overeating the HFD, there was no significant difference in intake among the groups (Figure 1A). The HFD rats gained weight faster than rats in the other groups (Figure 1B). HFD rats had significantly higher body weight (p-value < 0.001), body mass index (p-value <0.05), liver weight (p-value <0.001), and liver index (p-value <0.001) than Control rats. The AT and DO interventions significantly reduced the body weight, body mass index, liver weight, and liver index (p-value <0.05, <0.01, and <0.001, respectively) (Figures 1C–F). In addition, the livers of HFD rats were significantly larger and more yellow than those of the Control, AT, and DO rats, suggesting that HFD rat livers may have accumulated more lipids (Figure 1G). These results indicate that DO effectively inhibited HFD-induced weight gain, lipid deposition, and liver enlargement. Figure 1: (A) Temporal changes in food intake by group; (B) temporal changes in body weight by group; (C) final body weight by group; (D) body mass index by group; (E) liver weight by group; (F) liver index by group; (G) representative photo of a liver from each group. All data are shown as the mean ± SD (n=8). # p < 0.05, ### p < 0.001 vs. the Control group; p < 0.05, p < 0.01, p < 0.001 vs. the HFD group. ## Effect of DO on liver pathology and biochemical parameters After 10 weeks of HFD feeding with or without DO treatment, liver cells from Control rat livers were neatly arranged, the liver cords were clear, and no obvious lipid deposition was observed. In contrast, liver cells from HFD rat livers were disordered, with more extensive and robust steatosis accompanied by intralobular inflammatory foci and balloon-like changes. Inflammation and steatosis were lower in the livers of rats in the DO and AT groups than those in the HFD group (Figure 2A). The NAFLD activity score (NAS), semi-quantitative data used to assess NAFLD progression (NAS >4), showed that NASH was occurring in the HFD group, indicating that a HFD successfully induced NASH. NAS scores were significantly lower following DO and AT treatment (p-value <0.001) (Figure 2C). Liver oil red O staining showed no obvious lipid deposition in Control rats and a large amount of deposition in HFD rats (p-value <0.001). The area of lipid deposition was decreased following DO and AT treatment (p-value <0.01 and <0.001, respectively) (Figures 2B, D). These results supported the success of NASH modeling in this study and indicated that DO treatment reduces lipid accumulation and inflammation in rat livers. Figure 2: Detection of liver pathology and biochemical criteria. (A) Representative H&E stained liver samples by group (400x magnification); (B) representative images of oil red O stained liver samples by group (400x magnification); (C) liver NAS scores by group; (D) oil red O staining area by group; (E) serum lipid content by group; (F) liver lipid content by group; (G) serum AST, ALT and GGT levels by group. All data are shown as the mean ± SD (n =8). ### p < 0.001 vs. the Control group; p < 0.05, p < 0.01, p < 0.001 vs. the HFD group. TC, TG, HDL-c, and LDL-c are clinical indicators used to reflect blood lipid levels and lipid metabolism. HFD significantly increased serum and liver TC, TG, and LDL-c and reduced HDL-c levels (p-value <0.001). These blood and liver lipid indexes were significantly improved following DO and AT treatment (p-value <0.05, <0.01, and <0.001, respectively) (Figures 2E, F). ALT, AST, and GGT are the most used clinical indicators of liver function. When the liver is damaged, hepatocytes produce these proteins, resulting in an increase in serum ALT, AST, and GGT levels and indicating the occurrence of liver disease and inflammation. HFD significantly increased serum ALT, AST, and GGT levels (p-value <0.001) and these indexes were significantly decreased following DO and AT treatment (p-value <0.05, <0.01, and <0.001, respectively) (Figure 2G). ## Effect of DO on the gut microbiota of NASH rats Structural changes in the gut microbiota of rats that received DO treatment were assessed using 16S rRNA sequencing analysis. A total of 1,548,295 sequences were obtained from 30 samples, and 1,033 OTUs were collected at a $97\%$ similarity level. The species accumulation and rank-abundance curves showed that most of the diversity and species were included and the amount of sequencing data was adequate (Figures 3A, B). Figure 3: (A) Species accumulation curve; (B) rank-abundance curve; (C) Chao index by group; (D) Qstat index by group; (E) Smithwilson index by group; (F) PCA plot of microbial communities based on the OTU level. (G) Venn diagram based on the OTU level. Data are shown as the mean ± SD (n=6). ## p < 0.01, ### p < 0.001 vs. the Control group; p < 0.05, *p < 0.01 vs. the HFD group. ## Effect of DO on the Alpha diversity and Beta diversity of rat gut microbiota Alpha diversity was determined using Chao, Qstat, and Smithwilson to calculate the complexity of species diversity in samples. The Chao, Qstat, and Smithwilson indexes describe the richness, diversity, and evenness of the gut microbiota, respectively. In the current study, changes in these indexes are shown in Figures 3C–E. The Chao (p-value <0.01) and Qstat (p-value <0.001) were lower and the Smithwilson (p-value <0.001) was higher in the HFD group than in the Control group, suggesting that a HFD can significantly reduce microbiome richness, diversity, and evenness. The Chao and Qstat increased and the Smithwilson decreased following DO treatment (p-value <0.05 and <0.01, respectively) suggesting that DO may effectively improve the diversity, richness, and evenness of gut microbiota in NASH rats. Beta diversity principal component analysis (PCA) was performed to clarify the effects of HFD and DO intervention on the composition and structure of the gut microbiota. As expected, PCA revealed a clear separation between the Control and HFD groups, and the composition of the gut microbiota exhibited a clear response to DO intervention (Figure 3F). The unique and common OTUs in the Venn diagram more directly indicated the unique species information of each group. Different numbers of OTUs were detected in each group, including 884 in the Control group, 627 in the HFD group, 711 in the DOH group, 702 in the DOM group, and 754 in the DOL group. A total of 462 OTUs were shared by all groups and each group had unique OTUs. These results showed that DO could ameliorate the gut microbiota disorder induced by HFD in NASH rats (Figure 3G). ## Effect of DO on the gut microbiota at the phylum, genus, and species levels To evaluate the effect of DO on the gut microbiota of NASH rats, the microbial abundance at the phylum, genus, and species levels was determined using taxonomic analysis. At the phylum level, 13 phyla were found in five groups, of which Firmicutes and Bacteroidetes accounted for the largest proportion. The abundance of Firmicutes, Bacteroidetes, and Proteobacteria related to NASH was changed (Figure 4A). Proteobacteria were positively correlated with the HFD group (Figure 4B). While the relative abundances of Firmicutes and Proteobacteria were higher in the HFD group than in the Control group ($p \leq 0.05$), Bacteroidetes were lower in the HFD group ($p \leq 0.05$). After DO intervention, the relative abundance of Proteobacteria was significantly decreased in the DOH group ($p \leq 0.05$) (Figure 4C). These results indicated that compared with the Control group, the composition of gut microbiota in the HFD group changed significantly at the phylum level, especially for Proteobacteria. Figure 4: DO treatment modulated the gut microbiota composition at the phylum level. (A) Community abundance at the phylum level (%); (B) heat map of cluster stacking at the phylum level; (C) the relative abundances of Firmicutes, Bacteroidetes, and Proteobacteria. Data are expressed as the mean ± SD (n=6). # p < 0.05 vs. the Control group; p < 0.05 vs. the HFD group.* The abundance of gut microbiota at the genus and species levels also differed by group (Figure 5A, 6A). To identify specific bacterial taxa that arose after DO supplementation, LEfSe analysis (all-against-all) with a 3.0 threshold for discriminative features on the logarithmic LDA scale was performed. In LEfSe, different colours represent different groups. The potentially harmful bacteria, Romboutsia, Turicibacter, Lachnoclostridium, Blautia, Ruminococcus_torques_group, Sutterella, and Escherichia-Shigella were enriched in the HFD group at the genus level (Figure 5B). DO treatment reduced the abundance of Romboutsia, Turicibacter, Lachnoclostridium, Blautia, Ruminococcus_torques_group, Sutterella, and Escherichia-Shigella than the HFD group (p-value <0.05, <0.01, and <0.001, respectively) (Figure 5C). Meanwhile, the abundance of the potentially beneficial bacteria, Prevotella and Alistipes, was significantly lower in the HFD group than in the Control group (p-value <0.05 and <0.001, respectively), and DO intervention increased the levels of these organisms (p-value <0.05) (Figure 5C). At the species level, the probiotic Lactobacillus_acidophilus was significantly enriched in the DOH group (Figure 6B). The HFD group had a significantly lower abundance of Lactobacillus_acidophilus than the Control group (p-value <0.05), and the level of this bacteria increased significantly in the DOH and DOL groups (p-value <0.01) (Figure 6C). Figure 5: DO treatment modulated the composition of the gut microbiome at the genus level. (A) Community abundance at the genus level (%); (B) LEfSe of the gut microbiota at the genus level; (C) relative abundances of Romboutsia, Blautia, Turicibacter, Ruminococcus_torques_group, Lachnoclostridium, Sutterella, Escherichia-Shigella, Prevotella, and Alistipe. Data are expressed as the mean ± SD (n=6). # p < 0.05, ## p< 0.01, ### p < 0.001 vs. the Control group; p < 0.05, p < 0.01, p < 0.001 vs. the HFD group. Figure 6: DO treatment modulated the gut microbiota composition at the species level. (A) Community abundance at the species level (%); (B) LEfSe of the gut microbiota at the species level; (C) relative abundances of Lactobacillus_acidophilus. Data are expressed as the mean ± SD (n=6). # p < 0.05 vs. the Control group; p < 0.01 vs. the HFD group. ## Effect of DO on intestinal permeability of rats The gut microbiota plays an important role in maintaining the integrity of the intestinal mucosal barrier, and intestinal tight junction protein is the principal determinant of intestinal permeability. Each layer of the ileum tissue in the Control and the AT groups was clearly structured, the mucosal epithelium was intact, cell morphology was normal, the intestinal villi were evenly distributed, the intestinal glands were abundant and tightly arranged, and no obvious abnormalities were found. In contrast, there was necrotic cellular debris in the intestinal lumen of the HFD group and the apical epithelium of the intestinal villi was separated from the lamina propria. This separation was much rarer in rats receiving different doses of DO (Figure 7A). Expression of the tight junction proteins, ZO-1, claudin-1, and occludin was significantly lower in the HFD group than in the Control group (p-value <0.001), and all three proteins were significantly increased following DO intervention (p-value<0.05, <0.01, and <0.001, respectively) (Figure 7B). Ileum DAO and serum D-LA levels were significantly higher in the HFD group (p-value <0.001), and DAO and D-LA levels were significantly lower after DO and AT treatment (p-value <0.05, <0.01, and <0.001, respectively) (Figure 7C). Ileum IL-6, TNF-α, and IL-1β levels were all significantly higher in the HFD group than in the Control group (p-value <0.001), and significantly decreased following DO and AT treatment (p-value <0.05, <0.01, and <0.001, respectively) (Figure 7D). The protective effect of DO on intestinal permeability was manifested by increased tight junction protein expression and lower DAO activity and D-LA and inflammatory cytokine production. Figure 7: Effect of DO on intestinal permeability. (A) Representative images of H&E staining of the ileum by group (400x magnification); (B) immunohistochemistry of the ileal tight junction protein by group (400x magnification); (C) ileum DAO and serum D-LA levels by group; (D) ileum IL-6, TNF-α, IL-1β levels by group. All data are shown as the mean ± SD (n=8). ### p < 0.001 vs. the Control group; p < 0.05, p < 0.01, * p < 0.001 vs. the HFD group.* ## Effect of DO on liver inflammation At the phylum level, Proteobacteria were most dramatically changed after 10 weeks of HFD, suggesting that there was a concomitant rise in LPS. Disruption of the gut microbiota and increased intestinal permeability allow LPS to enter the liver. TLR4 and NF-κB are important proteins related to liver inflammation during NASH, and IL-6, IL-1β, and TNF-α were the major inflammatory cytokines induced by TLR4 and NF-κB. LPS levels in the ileum, serum and liver were significantly higher in rats in the HFD group than in the Control group (p-value <0.001) (Figure 8A), and relative TLR4 mRNA expression in the liver was significantly increased (p-value <0.001) (Figure 8B). DO and AT treatment resulted in significantly lower LPS levels in the ileum, serum, and liver (p-value <0.05, <0.01 and <0.001, respectively) and reduced relative TLR4 mRNA expression (p-value <0.001). Protein expression of TLR4 and the p-p65/p65 ratio were significantly higher in the HFD group than in the Control group (p-value <0.001) and both were decreased following DO and AT treatment (p-value <0.05, <0.01, and <0.001, respectively) (Figure 8C). IL-6, IL-1β, and TNF-α levels were significantly higher in the HFD group than in the Control group (p-value <0.001) and were significantly decreased following DO and AT treatment (p-value <0.01 and <0.001, respectively) (Figure 8D). These results suggested that DO was able to attenuate liver inflammation. Figure 8: Impact of DO treatment on liver inflammation. (A) Ileum, serum, and liver LPS levels by group; (B) liver relative TLR4 mRNA expression by group; (C) liver TLR4 protein expression and NF-κB nuclear translocation by group; (D) liver IL-6, TNF-α, IL-1β levels by group. A and D results are shown as the mean ± SD (n=8), and B and C results are shown as the mean ± SD (n=3). ### p < 0.001 vs. the Control group; p < 0.05, p < 0.01, p < 0.001 vs. the HFD group. ## Correlation between gut microbiota and biochemical factors and LPS Spearman correlation analysis was performed to assess the potential correlation between gut microbiota and the levels of biochemical factors and LPS. The abundances of Firmicutes, Proteobacteria, Romboutsia, Turicibacter, Lachnoclostridium, Blautia, Ruminococcus_torques_group, Sutterella, and Escherichia-Shigella were positively correlated with TG, TC, LDL-c, AST, ALT, GGT, and LPS levels. Meanwhile, the abundances of Bacteroides, Lactobacillus_acidophilus, Prevotella, and Alistipes were positively correlated with HDL-c levels (Figure 9A). Figure 9: (A) Heatmap of the Spearman correlation between gut microbiota and the levels of biochemical factors and LPS. The colour intensities represent the degree of the associations. p < 0.05, p < 0.01, p < 0.001. ## Discussion The current study established a HFD-induced NASH model in rats to investigate the effect of DO treatment on NASH and characterize the underlying mechanism caused by changes in the gut microbiota. AT, the positive control, was able to lower serum pro-inflammatory cytokine production, reduce serum cholesterol, hepatic free cholesterol, serum alpha-fetoprotein (AFP) and ALT levels, and ameliorate NASH (Domech et al., 2021; Zhang X, et al., 2021). While HFD changed the community composition of the gut microbiota and caused NASH, DO treatment regulated the gut microbiota and mitigated the disease. NASH is associated with disordered gut microbiota, including decreased richness and diversity. Thus, improving these elements can be used to treat this disease (Boursier et al., 2016; Yan et al., 2022). To investigate the mechanism by which DO treats NASH, 16S rRNA gene sequencing was used to identify the composition of the gut microbiome in different groups of rats. DO treatment was able to prevent NASH by increasing the richness, diversity, and evenness of the gut microbiota. At the phylum level, HFD resulted in a significant increase in the abundance of Firmicutes and Proteobacteria and a significant decrease in the abundance of Bacteroidetes. Firmicutes and Bacteroidetes are involved in energy absorption, and an unbalanced proportion of these bacteria is associated with obesity (Tenorio-Jiménez et al., 2020). Firmicutes were shown to exacerbate NAFLD severity by modulating hepatic lipid metabolism after Firmicutes were isolated from healthy individuals and inoculated into HFD-fed germ-free mice (Chen et al., 2019). NASH patients have a higher abundance of Proteobacteria, Gram-negative bacteria, including the pathogens Escherichia-Shigella and Escherichia-coli, whose outer membrane is composed of LPS (Rizzatti et al., 2017; Delik et al., 2022). In addition, Proteobacteria DNA isolated from morbidly obese patients was associated with severe liver pathology (Sookoian et al., 2020). Changes in gut microbiota promote the development of NASH. While changes in the abundance of Firmicutes and Bacteroides were not statistically significant after DO intervention, the abundance of Proteobacteria decreased significantly. These results suggest that the preventive effect of DO on NASH is associated with the regulation of Proteobacteria. At the genus and species level, changes in microbiota richness and diversity during NASH were primarily associated with Romboutsia (Zeng et al., 2019), Turicibacter, Lachnoclostridium (Li et al., 2022), Blautia (Vallianou et al., 2021), Ruminococcus_torques_group, Sutterella, Escherichia-Shigella, Prevotella, Alistipe, and Lactobacillus_acidophilus. Escherichia-Shigella produces ethanol that can damage the intestinal mucosa and promote liver inflammation (Zhu et al., 2013), Sutterella has pro-inflammatory effects on the gastrointestinal tract (Hiippala et al., 2016), and Escherichia-Shigella and Sutterella, both belonging to the Proteobacteria phylum, induce LPS biosynthesis (Song et al., 2017; Xu et al., 2018). LPS was significantly correlated with NASH in NAFLD patients and this confirmed the importance of dysbiosis during hepatic inflammation, as well as fat deposition (Hegazy et al., 2020). HFD, gut microbiota disorders, and specific physiological concentrations of LPS affect the expression and distribution of tight junctions in the intestinal mucosa and increase intestinal permeability, an early event associated with the development of NASH (Binienda et al., 2020; Chopyk and Grakoui, 2020; Rohr et al., 2020; Stephens and von der Weid, 2020). Intestinal permeability is primarily affected by tight junction proteins such as occludin, claudin-1, and ZO-1 while DAO and D-LA levels reflect the function and permeability of intestinal barriers (Mouries et al., 2019). Low intestinal permeability can prevent antigens, endotoxins, pathogens, and pro-inflammatory substances from infiltrating the body (Maciejewska et al., 2019; Zhang H, et al., 2021). LPS also specifically activates TLR4, an important inflammatory receptor in the liver, which promotes NF-κB nuclear entry and the release of inflammatory cytokines and accelerates the development of NASH (Leng et al., 2022). The current study found that the abundance of the LPS-producing Gram-negative bacteria Proteobacteria was significantly increased in NASH rats and was accompanied by higher levels of intestinal LPS. Bacteroidetes, a Gram-negative bacteria, was negatively correlated with LPS, suggesting that gut-derived LPS is mainly produced by Proteobacteria. Intestinal epithelial cells and tight junctions were damaged while intestinal permeability and inflammation were increased in the HFD group, which may be explained by the significant enrichment of Turicibacter, Ruminococcus, Escherichia-Shigella, and Sutterella, bacteria known to disrupt the intestinal barrier (Hänninen et al., 2018; Li et al., 2020). Changes in intestinal permeability allow gut-derived LPS to enter the liver through the portal vein, increasing LPS levels in the serum and liver. Excess LPS activates liver TLR4, promoting NF-κB nuclear translocation and the release of inflammatory factors. DO treatment protects intestinal epithelial cells and tight junctions from damage, thereby reducing intestinal permeability, inhibiting liver TLR4 and NF-κB activation, and lowering inflammatory cytokine production. The reduction in liver inflammation may be related to the decreased abundance of LPS-producing bacteria, Proteobacteria, Sutterella, and Escherichia-Shigella, the intestinal barrier-disrupting bacteria, Turicibacter, Ruminococcus, and the increased abundance of Lactobacillus_acidophilus following DO treatment (Figure 10). Lactobacillus acidophilus is shown to regulate gut microbiota and intestinal permeability, reduce endotoxemia and inhibit TLR4/NF-κB signaling, attenuating NASH progression (Lee et al., 2021; Chen et al., 2022; Kang et al., 2022). Figure 10: Graphic summary of the study. NASH occurs in the liver, but its pathogenesis is complex. TCM is safe, comprehensive, and effective, the holistic and multi-target function of TCM may thus be an appropriate option for NASH treatment (Zhang et al., 2020). Indeed, the ability of DO to improve NASH by regulating gut microbiota is reflective of the characteristics of TCM. ## Conclusion In summary, findings from the current study indicated that DO could regulate gut microbiota, intestinal permeability, and liver inflammation to alleviate NASH. DO treatment alleviated microbiota dysbiosis and reduced the abundance of the LPS-producing bacteria, Proteobacteria, Sutterella, and Escherichia-Shigella, reduced the abundance of the intestinal barrier-disrupting bacteria, Turicibacter, Ruminococcus, decreased intestinal permeability to reduce the movement of gut-derived LPS from the portal vein blood into the liver, inhibiting hepatic TLR4 activation and NF-κB nuclear translocation, and improving hepatic inflammation and steatosis to prevent NASH. The results also found that Lactobacillus_acidophilus may play a critical role during NASH. Additional follow-up, including sterility testing, is needed to further investigate the effect of DO treatment on gut microbiota with the potential mechanism of action required to prevent NASH. This study may provide theoretical support for the clinical promotion of DO. ## 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 below: NCBI Sequence Read Archive (SRA) database with the accession number PRJNA872008. ## Ethics statement The animal study was reviewed and approved by The Animal Ethics Committee of the Yunnan University of Chinese Medicine. ## Author contributions GT and SZ designed and conceptualized the study. GT analyzed the data, drafted the manuscript, carried out the statistical analysis, and interpreted the data. GT and EX carried out animal experiments. SZ, WW, and WC reviewed experimental protocols and revised 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/fcimb.2023.1078447/full#supplementary-material ## References 1. Abdel-Razik A., Mousa N., Shabana W., Refaey M., Elzehery R., Elhelaly R.. 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--- title: A phenotypic screen of Marfan syndrome iPSC-derived vascular smooth muscle cells uncovers GSK3β as a new target authors: - Hongorzul Davaapil - Madeline McNamara - Alessandra Granata - Robyn G.C. Macrae - Mei Hirano - Martina Fitzek - J.A. Aragon-Martin - Anne Child - David M. Smith - Sanjay Sinha journal: Stem Cell Reports year: 2023 pmcid: PMC9968988 doi: 10.1016/j.stemcr.2022.12.014 license: CC BY 4.0 --- # A phenotypic screen of Marfan syndrome iPSC-derived vascular smooth muscle cells uncovers GSK3β as a new target ## Summary Marfan syndrome (MFS) is a rare connective tissue disorder caused by mutations in FBN1. Patients with MFS notably suffer from aortic aneurysm and dissection. Despite considerable effort, animal models have proven to be poorly predictive for therapeutic intervention in human aortic disease. Patient-derived induced pluripotent stem cells can be differentiated into vascular smooth muscle cells (VSMCs) and recapitulate major features of MFS. We have screened 1,022 small molecules in our in vitro model, exploiting the highly proteolytic nature of MFS VSMCs, and identified 36 effective compounds. Further analysis identified GSK3β as a recurring target in the compound screen. GSK3β inhibition/knockdown did not ameliorate the proliferation defect in MFS-VSMCs but improved MFS-VSMC proteolysis and apoptosis and partially rescued fibrillin-1 deposition. To conclude, we have identified GSK3β as a novel target for MFS, forming the foundation for future work in MFS and other aortic diseases. ## Graphical abstract ## Highlights •Developed an iPSC-based drug screen for MFS and tested 1,022 small molecules•Identified GSK3β as a recurring target among effective small molecules•Validated the outcome of the drug screen using six GSK3β inhibitors and siRNA•GSK3β inhibition/knockdown decreased proteolysis and apoptosis in 4 patient lines ## Abstract Animal models of MFS have not yet been successful in predicting human disease. Sinha and colleagues have developed an iPSC model of MFS and used it as a platform for drug screening. They screened over a thousand compounds and identified GSK3β as a top drug target. This work is the foundation for further screening in MFS and related diseases. ## Introduction Marfan syndrome (MFS) is a rare genetic disorder resulting in multi-system abnormalities. It is caused by deleterious variants in the FBN1 gene, a key extracellular matrix (ECM) protein in connective tissue (Dietz et al., 1992). The cardiovascular effects can be life threatening, as patients can develop thoracic aortic aneurysm and dissection (TAAD), particularly at the aortic root and arch. It is currently thought that the majority of aortic disease is propagated through vascular smooth muscle cells (VSMCs), although there is also evidence of endothelial dysfunction (Chung et al., 2007; Galatioto et al., 2018; Oller et al., 2017). In addition, there is heterogeneity in the embryonic origin of VSMCs present in aorta, which itself has been hypothesized to contribute to disease progression (Majesky, 2007). The current treatment options for patients with MFS are limited to prescription of anti-hypertensives, surgical replacement, or external support (Pepper et al., 2020) of the dilated aortic root—a major procedure with significant risk of morbidity and mortality. The use of the angiotensin II receptor blocker (ARB) losartan in a mouse model of MFS was highly effective in limiting aortic disease progression (Habashi et al., 2006). Unfortunately, following up from this work, numerous clinical trials have concluded that losartan was either not successful or had only modest effects in reducing aortic diameter or improving clinical endpoints in patients (Groenink et al., 2013; Lacro et al., 2014; Milleron et al., 2015; Teixido-Tura et al., 2018). These disappointing results could be attributed to a variety of factors, including insufficient safe dosage (Mullen et al., 2020), fundamental differences between the species, and varying genetic backgrounds. There is therefore a need for alternative approaches to identify novel and effective treatment options for MFS. Induced pluripotent stem cells (iPSCs) can be used to generate any somatic cell type, including lineage-specific VSMCs. We have developed protocols to generate lateral plate mesoderm, neural crest (NC), and paraxial mesoderm-derived VSMCs, which correspond to the aortic root, ascending aorta, and descending aorta, respectively (Cheung et al., 2012, 2014), and it is hypothesized that embryonic lineage may be important in disease susceptibility (MacFarlane et al., 2019; Majesky, 2007). Using this lineage-specific approach, an iPSC-based model of MFS in vitro has been developed (Granata et al., 2017). There, the main features of the aortic phenotype in VSMCs were recapitulated in VSMCs derived from the NC, notably abnormal ECM deposition, increased matrix metalloproteinase (MMP) expression and activity, apoptosis, and abnormal response to mechanical stretch. We identified p38 as a candidate for mediating the MFS phenotype, as p38 inhibition partially rescued the phenotype in vitro (Granata et al., 2017). Here, we describe a medium-throughput, unbiased small molecule (SM) screen to identify novel disease mechanisms and therapeutic targets using iPSC technology. In collaboration with AstraZeneca, we have screened 1,022 SMs on MFS NC-derived VSMCs, herein referred to simply as “VSMCs,” and identified a subset that were found to reduce MMP activity. In particular, we identified that GSK3β SM inhibitors (SMIs) and genetic knockdown improved cellular function, where MFS VSMCs were less proteolytic and showed reduced apoptosis. In addition, we treated three additional MFS patient lines with a GSK3β inhibitor and obtained a consistent outcome, suggesting that this may be a common cellular defect among different MFS patient lines. This work highlights a screening strategy that could be used widely to screen additional SMIs and/or applied to models of other aortic diseases. ## Screen of 1,022 SMs SMs are low-molecular-weight compounds typically around 500 Da in size that can modulate protein binding and activity (Khera and Rajput, 2017; Zhong et al., 2021). Because of their low molecular weight, they are able to penetrate cells more easily than macromolecular drugs, such as antibodies or other proteins. Here, we sought to screen a library of 1,022 SMs to identify any compounds that can ameliorate the disease phenotype of MFS VSMCs. The library of compounds used in this study was obtained from AstraZeneca’s Open Innovation Group. It is composed of 14,000 SMs in total and was recently used for SM screening in an iPSC model of non-alcoholic fatty liver disease (Parafati et al., 2020). These compounds are highly annotated and have information on pIC50 for primary and secondary targets; over 1,700 targets are covered in all. This library is composed of SMIs that target multiple proteins within a given signaling pathway, thereby increasing its capacity to uncover new pathways implicated in disease. As there is significant over-representation of some targets, the library of 14,000 SMIs was selectively narrowed down to 1,022 compounds in order to maintain a broad cohort of targets in a smaller number of compounds for this proof-of-concept work. We designed this phenotypic screen around the highly proteolytic nature of MFS VSMCs, which have elevated MMP expression and secretion (Cui et al., 2021; Granata et al., 2017; Ikonomidis et al., 2006). The patient line used for this study, unless otherwise specified, is the FBN1 C1242Y line, which we have previously characterized (Granata et al., 2017). A fluorescence-quenched gelatin substrate would then be incubated with MMPs from the cell culture medium: cleavage of this substrate would result in a fluorescent signal, which can be detected in a plate reader (Figure 1B). MFS VSMCs were treated for 96 h with 1 μM SM, after which the supernatant was collected and analyzed. Of the 1,022 compounds tested at this concentration for 96 h, 730 were found to be associated with some cell atrophy and/or detachment, which made them poor candidates for proceeding to assay for MMP activity. Of the remaining 292 SMs, 36 were found to successfully reduce MMP activity down to levels comparable to the isogenic corrected control (Corr) VSMCs or MFS VSMCs treated with losartan (Figures 1C and 1D).Figure 1MMP activity-based drug screen(A) Overview of differentiation to NC-VSMCs from iPSCs.(B) MFS VSMCs were treated with 1 μM SMIs for 96 h and cell supernatant collected. This supernatant contains secreted MMPs, which, when incubated with the generic MMP substrate, leads to cleavage and a subsequent fluorescent signal, which can be measured using a plate reader.(C) Of the 1,022 SMIs, the majority were not suitable for further assay at 1 μM.(D) Among the SMIs used in the screen, 36 were found to decrease MMP activity of MFS VSMCs. Corrected VSMCs and MFS VSMCs treated with losartan were used as controls to determine the threshold of sufficient MMP activity reduction. Drug screen was performed as $$n = 2$$ technical replicates. ## GSK3β is a recurring target among the positive hits In order to identify a promising target worthy of further investigation, we analyzed the annotated primary and secondary targets and their associated pIC50 values (Table S1). From the 36 SMIs, we identified 902 unique targets (Figure 2A). Since SMs were used at 1 μM, pIC50 values below 6 are not informative—therefore, drug targets with a pIC50 <6 were not included in our analysis, resulting in 538 unique targets. Figure 2GSK3β is a recurring drug target among the positive hits from the drug screen(A) Outline of how drug targets were filtered. This was performed by removing drug targets with pIC50 <6 and then overall frequency lower than 5.(B) Frequency of drug targets suggests that GSK3β (red) is a recurring target, among others.(C) Heatmap of pIC50 values for all high-frequency drug targets indicates that GSK3β (arrow) is also a high-specificity drug target.(D) Average pIC50 values for each drug target indicates that GSK3β (red) was the most specific target.(E) Primary drug targets among the positive hits from the drug screen. GSK3β (red) is the most recurring primary target. The majority of these targets were protein kinases, as indicated by GO term enrichment (Figure S1A). Interestingly, we identified p38 MAPK inhibitors among our positive hits along with GABA receptor inhibitors, both of which have been found to be effective in MFS by us and others (Figure S1B) (Granata et al., 2017; Hansen et al., 2019). KEGG pathway enrichment analysis indicates that components of the MAPK signal transduction pathway are highly enriched, along with other potentially interesting pathways, such as those linked to focal adhesions (Figure S1C). Out of 538 unique targets, 27 targets were found to be present in 6 or more SMs (Figures 2B; Table S2). We identified that GSK3β is a recurrent and highly specific target, as illustrated by the heatmap (Figure 2C, arrow) and average pIC50 values (Figure 2D, red). Among the negative hits from the SM screen, i.e., compounds that did not produce a beneficial effect in MFS VSMCs, GSK3β was not a top recurring target (Figure S1D). In addition, we also found that GSK3β was the most prominent primary target among the SMIs (Figure 2E). Finally, correlation between positive and negative hits (Figure S2) also demonstrated that GSK3β is a top contender for consideration, which is emphasized by using a more stringent pIC50 threshold (Figure S2). This is particularly important as the pIC50 values from this dataset are derived from isolated enzyme activity assays—it is likely that the activity of compounds inside the cell would be lower. This reinforces our approach to use pIC50 as a cutoff but also suggests that even higher stringency may also be informative. Taken together, we therefore decided to focus our validation on GSK3β. ## GSK3β expression in Corr versus MFS cells We started by assessing the expression of GSK3β in untreated Corr and MFS VSMCs. At the mRNA level, GSK3β expression trended toward an increase ($$p \leq 0.07$$) in MFS cells (Figure 3A). In contrast, total GSK3β expression in MFS VSMCs is decreased compared with the Corr (Figure 3B). Interestingly, it seems that GSK3β activity is decreased, too: there is increased phosphorylation at Ser9 (Figure 3B), which is an inhibitory post-translational modification (Cross et al., 1995). This is supported by the increased amount of β-catenin in MFS cells, indicative of increased signaling through the canonical Wnt pathway. These findings were unexpected as they suggest that GSK3β activity may already be decreased in the MFS cells, yet GSK3β inhibition was identified from the drug screen as effective at reversing the MFS proteolytic phenotype. Figure 3GSK3β expression in MFS VSMCs and its knockdown by siRNA(A) Expression of GSK3α and GSK3β mRNA in corrected and MFS VSMCs.(B) Expression of GSK3β, phospho-GSK3β (Ser9), and β-catenin protein in corrected and MFS VSMCs. GAPDH was used as a loading control.(C) Luciferase assay using a β-catenin reporter construct after 4 h of treatment. For the drug treatment groups, comparisons were performed with MFS DMSO.(D and E) Compared with the scrambled control siRNA, siRNA against GSK3β was effective in knocking down its expression at both the mRNA (D) and protein (E) levels without altering the expression of GSK3α. GAPDH used as the loading control. $$n = 3$$ independent experiments for qPCR, western blotting, and luciferase experiments. Corrected cells ($$n = 4$$ independent experiments) were used for siRNA qPCR analysis, and representative corrected and MFS cells were used for western blotting. Data are represented as mean ± SEM To confirm this, we have performed a reporter assay using the M50 Super 8x TopFlash construct (Veeman et al., 2003), which contains T cell factor (TCF)/lymphoid enhancer factor (LEF) sites upstream of a luciferase reporter as a readout for β-catenin activity and hence the extent of GSK3β inhibition (Cadigan and Waterman, 2012). Even without drug treatment, we noted that there was increased β-catenin signal in MFS VSMCs compared with the corrected line, supporting our western blot findings of increased baseline inhibition of GSK3β (Figure 3C). Furthermore, after 4 h drug treatment, we also observed increased β-catenin activity, indicating that drug treatment does indeed result in further inhibition of GSK3β. Next, in order to help with validation of GSK3β as a target, we used small interfering RNA (siRNA) to knock down the expression of GSK3β. Since SMIs frequently have multiple secondary drug targets, we used a genetic system to also verify and validate the results of our SM screen. Furthermore, knockdown was used instead of CRISPR-mediated deletion as this would not abrogate expression entirely, mimicking the effects more closely of inhibition by SMIs. siRNA-mediated knockdown of GSK3β was successful at reducing the expression of both mRNA and protein (Figures 3D and 3E). We also confirmed that this strategy did not affect the expression of GSK3α (Figure 3E). ## Decreased GSK3β reduces MMP activity and expression We then aimed to confirm the findings of the drug screen. In addition to siRNA-mediated knockdown, we decided to use six SMIs that target GSK3β: three inhibitors identified from the drug screen (6BIO, AZ1, and AZ2), along with three additional compounds (CHIRON, AZ3, and AZ4) for further validation (Table 1). We observed that upon treatment with GSK3β SMI and siRNA, there was mild initial cell death after 24 h, which did not persist thereafter. To validate the results of our screen, we performed in situ zymography, where we cultured cells on DQ gelatin. Similar to the MMP substrate used for the initial screen, DQ gelatin fluoresces when cleaved by MMPs, resulting in deposition of green fluorescence. After 96 h of treatment, cells were imaged, and the data were quantified in an automated and unbiased manner. Our findings indicate that while the MFS VSMCs treated with DMSO or scrambled siRNA exhibited high levels of DQ gelatin degradation, cells treated with the GSK3β inhibitors (1 μM) or siRNA showed less degradation, with levels similar to those of Corr cells (Figures 4A–4C). This successfully recapitulated the decreased matrix degradation observed when cells were treated with doxycycline, losartan, and p38 inhibitor losmapimod (Figure S3). This finding was further supported by decreased expression of MMPs 2 and 9 upon treatment with GSK3β inhibitors (Figures 4D and 4E). In our previous work, we had established that p38 inhibition is beneficial for MFS VSMCs with regards to fibrillin-1 deposition and reduced apoptosis (Granata et al., 2017)—we extend these findings to include its effects on MMP activity in our disease model. Table 1GSK3β SMI used and their pIC50 values for GSK3βDrugOther identifierspIC50ReferenceCHIRONCHIR99021; CT 990219.12Wagman et al., 20046BIO6-bromoindirubin-3-oxime8.6Meijer et al., 2003; Polychronopoulos et al., 2004AZ1SN10585149919.5compound from AstraZenecaAZ2SN10699353788.2compound from AstraZenecaAZ3SN1030101051; AZD10807.9Georgievska et al., 2013AZ4SN102993029010compound from AstraZenecaFigure 4Decreased proteolysis and MMP expression upon disruption of GSK3β(A–C) In situ gelatin degradation assay with either treatment of GSK3β SMI 1 μM (A) or siRNA (B) and quantification (C). $$n = 3$.$(D and E) Analysis of mRNA expression of MMPs (D) 2 and (E) 9 indicate that their expression is decreased following SMI treatment; $$n = 3$$–4 independent experiments. 150 μm scale bars throughout.(F and G) Gelatin zymography following 4 days treatment with DMSO, CHIRON, and AZ3 (F) alongside band quantification (G) for active MMP2, pro-MMP2, and pro-MMP9; $$n = 3$$ independent experiments. In (C–E), comparisons were performed between MFS DMSO and drug treatment groups. Data are represented as mean ± SEM. Cells treated under control condition (DMSO) were also used as controls for Figure S3. Furthermore, to confirm these findings, we have performed gelatin zymography. Here, cell supernatants from corrected and MFS VSMCs were harvested after 4 days of drug treatment. These supernatants were run on a gel containing gelatin to uncover the extent of gelatin degradation by secreted MMPs (Figures 4F and 4G). We noted that without drug treatment, MFS cell supernatants contained notable levels of full-length MMP9, as well as increased full-length and cleaved MMP2. Upon treatment with GSK3β inhibitors CHIRON and AZ3, there was a dramatic reduction in the MMPs in the supernatant, consistent with the findings of the DQ-gelatin assays. Taken together, these results therefore confirm that GSK3β inhibition is beneficial in decreasing the proteolytic nature of MFS VSMCs. ## GSK3β inhibition reduces apoptosis Next, we aimed to see whether GSK3β inhibition could also decrease apoptosis using TUNEL staining. We confirmed that the assay was working by treating cells with DNase I (Figure S4A), and non-GSK3β SMIs (Figure S4B). As before, after treatment for 96 h with SMIs (1 μM) or siRNA against GSK3β, we fixed and stained cells (Figure 5). MFS VSMCs with control treatment had a higher percentage of apoptotic cells compared with the Corr. Treatment with SMIs and siRNA improved this disease phenotype, with the exception of SMI AZ4 (Figure 5). We hypothesize that the off-targets unique to AZ4 (Figure S5; Table S3) may be responsible for its diminished effectiveness in reducing the apoptotic phenotype. There are 8 unique targets: ATR, ALPG, ALPI, ALPL, ALPP, AHR, FLT1, and PRKCI. Of these targets, PRKCI has been shown to be protective against apoptosis (Flum et al., 2018; Murray and Fields, 1997; Xie et al., 2000), and therefore it is plausible that decreased activity of PRKCI in our VMSCs counteracts the beneficial effects of GSK3β inhibition. Nonetheless, the results as a whole suggest that GSK3β inhibition or knockdown is beneficial in MFS VSMCs. Figure 5GSK3β inhibition or knockdown decreases apoptosis in MFS VSMCs(A and B) Cells were treated for 96 h prior to staining for TUNEL (red) and DAPI (blue).(C) Quantification was performed in a blinded and unbiased way using ImageJ and a macro. For the drug treatment groups, comparisons were performed with MFS DMSO. 150 μm scale bars throughout. $$n = 3$$–4 independent experiments. Data are represented as mean ± SEM. Cells treated under control condition (DMSO) were also used as controls for Figure S4. ## Proliferation is unaffected by GSK3β We subsequently aimed to determine whether GSK3β inhibition could improve the proliferation phenotype by performing EdU incorporation analysis. We cultured cells in the presence of EdU for 16 h on the last day of SMI or siRNA treatment. Since the VSMCs we produce are not highly proliferative, we used HS27a cells as a positive control and confirmed that our EdU signal coincided with KI67 staining (Figure S6A). We noted that without treatment, MFS VSMCs had very poor proliferation, consistent with our experience when culturing them. In contrast, isogenic control cells had approximately $10\%$ of cells synthesizing new DNA. Unfortunately, neither treatment with GSK3β SMI nor GSK3β siRNA rescued the proliferation defects (Figure S6). ## GSK3β inhibition reduces proteolysis and apoptosis in three additional MFS patient lines Finally, we also sought to determine whether GSK3β inhibition is also beneficial in additional MFS patient iPSC lines. Three additional lines were used for this validation: DE35, DE37, and DE119 (Table S4). These patients were diagnosed with MFS and experienced an aortic event, either dissection/rupture, or had surgery to replace a part of the aorta. These patient lines were reprogrammed into iPSCs, differentiated into VSMCs and treated with GSK3β-targeting SMI AZ3 at 1 μM as was done previously. AZ3 was selected over the other compounds as we noted it had the fewest off-target effects (Figure S5; Table S3). Cell phenotype was assessed by looking at DQ-gelatin fluorescence and percentage of TUNEL-positive nuclei (Figure 6). The results with these three additional lines support what we have demonstrated with the C1242Y line. In terms of MMP activity, we observed a significant decrease in proteolytic activity after treatment with AZ3 (Figures 6A and 6B). In addition, mRNA expression of MMPs 2 and 9 are also reduced after SMI treatment (Figure 6C), as we had observed previously (Figures 4D and 4E). The apoptotic phenotype of the cells was also reduced after GSK3β inhibition (Figure 6D). Lastly, we wanted to determine whether GSK3β inhibition resulted in any changes in fibrillin-1 deposition. While the MFS patient lines all had abnormal deposition of fibrillin-1, the Corr line displayed uniform and regular fibrils (Figure S7). Upon GSK3β treatment, we observed an improvement in the deposition, although the arrangement of the fibrils is not as regular as in the control. Taken together, this work suggests that GSK3β could be a valuable target to further pursue, showing a beneficial effect in multiple MFS patient lines. Figure 6Inhibition of GSK3β using SMI AZ3 in three additional MFS patient lines is beneficialThree additional patient lines—DE35, DE37, and DE119—were differentiated into NC-VSMCs and treated with AZ3.(A–C) Assays were performed as before to assess the effect of GSK3β inhibition on proteolysis as assessed by DQ-gelatin intensity (A and B) and MMP2 and MMP9 mRNA expression (C).(D and E) Apoptosis in these additional lines was also assayed.150 μm scale bars throughout. $$n = 3$$ independent experiments. Data are represented as mean ± SEM. ## GSK3β activity in aortic aneurysms The role of GSK3β in the development of aortic aneurysms is not entirely clear. There is evidence of increased GSK3β phosphorylation in abdominal aortic aneurysms (AAAs) (Krishna et al., 2017). GSK3β was also identified as a likely regulator of pathogenic mechanisms from the analysis of perivascular adipose tissue of patients with AAA (Piacentini et al., 2020). In this study, we demonstrated that GSK3β inhibition was beneficial in our iPSC-derived MFS VSMCs using both multiple SMIs and a genetic approach. In addition, we have validated that SMI inhibition of GSK3β is beneficial in three additional MFS patient lines, suggesting that this may be a common disease mechanism and not a defect specific to the cell line that we used for the initial screening. GSK3β activity is regulated in an unconventional way compared with most kinases. Many of its targets need to be primed with phosphorylation by another kinase; this post-translational modification will then fit within a groove of GSK3β, allowing it to phosphorylate its target. Inhibitory phosphorylation of GSK3β at the N-terminal Ser9 results in an autologous pseudo-substrate, preventing its binding to primed substrates (Frame et al., 2001). In this study, we have identified that the expression of GSK3β and its phosphorylation at Ser9 is paradoxical, with MFS VSMCs expressing less total GSK3β and having more of the inhibitory Ser9 post-translational modification when compared with the isogenic Corr. Therefore, a big question is why GSK3β inhibition was beneficial despite there being less total GSK3β and more inactivated GSK3β in MFS VSMCs. There may be explanations that account for this paradox. First, Ser9 phosphorylation may not be a direct readout for GSK3β activity. As reviewed thoroughly by the Jope group, there are four main reasons why this may be: [1] not all GSK3β substrates are primed; [2] GSK3β is often found in complex with other proteins, and p-Ser9 does not affect its activity within protein complexes; [3] p-Ser9 does not cause total inactivation of its activity; and [4] its subcellular localization could also impact how p-Ser9 affects activity (Beurel et al., 2015). In addition, the observed levels of GSK3β could also be the cells’ attempt to incompletely compensate for abnormal cell signaling; treatment with SMIs or siRNA would decrease the need for such compensation, thereby reducing some of the disease phenotypes. ## Downstream targets of GSK3β and clinical perspective GSK3β is a kinase with numerous interacting partners. While most kinases have an average of 12 interacting partners, GSK3β is predicted to have over 500 targets (Linding et al., 2009). This is due to the unique mechanisms that regulate the activity and availability of GSK3β in a given cell. In this study, we have demonstrated that inhibition of this multi-target kinase with multiple compounds is beneficial, although GSK3β is not a straightforward enzyme to target in a clinical environment. It is ubiquitous and highly expressed in a number of organs, leading to concerns over toxicity and chronic usage. In addition, recent work has emerged on the relevance of a mesenchymal transition of aortic VSMCs in TAAD formation (Chen et al., 2020; Nolasco et al., 2020); GSK3β may have a role in regulating EMT (Zhou et al., 2004), therefore potentially complicating the use of GSK3b inhibitors in treating aortic disease. Despite these concerns, lithium salts, which include GSK3β as one of their targets (Stambolic et al., 1996), have been used for decades to treat psychiatric disorders (Freland and Beaulieu, 2012), demonstrating the feasibility of long-term GSK3β inhibition at appropriate doses. The therapeutic window of lithium is quite narrow, between 0.4 and 0.8 nmol/L, and doses above this threshold are not well tolerated (Malhi and Berk, 2012). AZD1080, which was also used in our in vitro studies under the name “AZ3” (Table 1), has progressed into phase I clinical trials (Georgievska et al., 2013), although it was subsequently abandoned after finding that it resulted in abnormalities in dog gall bladder at that dosage and did not enter phase II trials (Bhat et al., 2018). Currently, one GSK3β inhibitor, Tideglusib, has successfully gone through phase II trials for myotonic dystrophy (Horrigan et al., 2020). The question of toxicity is particularly important for treating a life-long disease such as MFS. Upon diagnosis with the disease, patients will likely continue drug treatments for the rest of their lives. As such, treatment regimens have to be extremely well tolerated. Losartan was unsuccessful in clinical trials, yet the very similar drug irbesartan retarded the rate of aortic growth compared with the placebo (Mullen et al., 2020). One key difference between losartan and irbesartan is their respective half-lives: the longer half-life of irbesartan increases its bio-availability compared with losartan, suggesting that insufficient dosage could be one of the reasons behind poor performance of losartan in clinical trials. With this in mind, a GSK3β inhibitor alone may not be appropriate for treating MFS—instead, combining GSK3β inhibitors with other drugs, all at lower concentrations, would allow us to target multiple signaling abnormalities while still being well tolerated by patients. This may be particularly important given that some cellular abnormalities, such as proliferation and fibrillin-1 deposition, are not rescued fully by GSK3β inhibition and may benefit from additional compounds to cover those weaknesses. Alternatively, there are likely a multitude of downstream effectors that are currently unknown but may be more specific in the context of MFS and other aortic diseases. Proteomics and other unbiased approaches could be used to identify downstream effectors of the GSK3β pathway in the aorta, which may be more tractable to therapeutic intervention. ## Further optimizing the phenotypic screen Despite recent demonstration of the benefits of angiotensin II receptor blockers, there is still enormous scope for additional therapeutic intervention since irbesartan did not reverse or halt progressive aortic dilatation. Moreover, the experience with losartan suggests that mouse models can be difficult to align with the results of human clinical trials. As a result, screening for potential therapeutic compounds solely in mice is not efficient. Using this human in vitro screen, we have identified GSK3β among other interesting targets. The screen in this assay was performed in a medium-throughput manner where cells were cultured in a 24-well format. Scaling cell culture to 96-well plates for higher throughput was unsuccessful for the initial drug screen; we hypothesize that this is an effect of cell density and insufficient signal-to-noise ratio. We have since managed to perform MMP activity assays in a 96-well format by modifying the culture conditions. It should be noted that MMP activity is quite a non-specific readout for MFS. However, we believe that this is an important feature for our drug screen as it is a straightforward assay that can be used to rapidly eliminate large numbers of uninteresting compounds in an initial screen before more specific and complicated assays are used on the short-listed compounds. This strategy could therefore be used for future studies, allowing us to interrogate even larger libraries of compounds, including the full 14,000 compound library from AstraZeneca; obtain an even larger list of potential targets; improve the power of our compound screens; and identify compounds that are more straightforward to transition into human clinical trials. Our screen has identified a number of compounds that were able to decrease the proteolytic phenotype of MFS. However, even from our relatively modest drug screen of 1,022 compounds, we identified 538 unique putative drug targets where pIC50 values were above 6. In future studies, we aim to interrogate a much larger cohort of SMIs, which in turn will also generate a larger list of potentially drug-able targets. How this large list is managed and how one decides which compounds are worthwhile pursuing will be a challenge. One strategy could be to compare the list of interesting drug targets with lists of SNPs that have been identified in genome-wide studies. Another strategy could be to follow up the compound screen with a genetic screen, utilizing CRISPR for example, to narrow down the list of putative targets even further. When further optimized, we envisage that this screening strategy could be an extremely valuable toolkit for future studies. As discussed above, a combinatorial approach to treating disease could be explored where patient-derived VSMCs are treated with different combinations of drugs, all at doses chosen to minimize in vivo toxicity. Additional patient lines could also be studied to identify signaling pathways that are commonly disrupted—these particular pathways could potentially be very interesting when considering new clinical trials. Finally, it could be expanded toward other aortic diseases that exhibit abnormalities in MMP activity. ## Corresponding author Please contact Sanjay Sinha ([email protected]). ## Materials availability iPSC lines used in this study are available from the lead contact with a completed materials transfer agreement. ## Data and code availability The data supporting the results of this study are available within the main paper and supplemental information. ## Cell culture Isogenic control and patient iPSC lines were derived, cultured, and differentiated as described previously (Cheung et al., 2012; Granata et al., 2017; Serrano et al., 2019). The patient line contains a C1242Y mutation in FBN1, and the original fibroblast line was obtained from Coriell’s cell bank (GM21943), and the isogenic control was generated using CRISPR-Cas9. Additional patient lines (denoted DE35, DE37, and DE119) were obtained from Sonalee Laboratory, St George’s Hospital, London, UK, with the help of Dr. Anne Child. These were received as fibroblasts and were reprogrammed using Sendai Virus v.2.0, as performed previously (Granata et al., 2017) and under research ethics committee approval (11/EE/0053). Briefly, iPSCs were cultured and maintained on vitronectin-XF (Stem Cell Technologies) and E8 media (DMEM/F12 [Gibco]; Insulin-Transferrin-Selenium Supplement [Gibco]; 0.44 μM L-ascorbic acid [Sigma]; $0.05\%$ sodium bicarbonate [Sigma-Aldrich]; 25 ng/mL FGF2 [R&D Systems]; and 1.74 ng/mL transforming growth factor β [TGF-β; Peprotech]). For differentiation, a chemically defined medium (CDM) ($50\%$ IMDM [Gibco]; $50\%$ Ham’s F12 Nutrient Mix [Gibco]; chemically defined lipid concentrate [Life Technologies]; 15 μg/mL transferrin [R&D Systems]; 7 μg/mL insulin [Sigma-Aldrich]; 450 μM monothioglycerol [Sigma-Aldrich]; and 1 mg/mL poly-vinyl alcohol [Sigma-Aldrich]) was supplemented with different cytokine and inhibitors. NC differentiation was initiated by culturing iPSC colonies in FSB media [CDM with 12 ng/mL FGF2 (R&D Systems) and 10 nM SB431542 (R&D Systems)] for 4 days, before being split into single cells and further cultured on $0.1\%$ gelatin-coated plates. These NC were cultured and differentiated into NC-VSMCs in PT media (CDM with 10 ng/mL PDGF-BB [Peprotech] and 2 ng/mL TGF-β [Peprotech]) for 12 days. After differentiation, VSMCs were matured for 2 weeks in DMEM/F12 (Gibco) containing $10\%$ fetal bovine serum (Gibco) before being used in assays (Figure 1A). Although NC-VSMCs are used in this work, we refer to them as “VSMCs” throughout for simplicity. ## SM screen SMs were obtained from AstraZeneca, and 1,022 drugs were selected out of their library of 14,000 compounds (Parafati et al., 2020). These SMs were diluted from 10 mM stock in DMSO to a final concentration of 1 μM in MEF media. Control and MFS VSMCs were seeded onto $0.1\%$ gelatin-coated 24-well plates. The following day, the 96 h treatment with SMIs began, with a medium refresh halfway through. On day 4, cell culture medium was then collected to assay for MMP activity using the SensoLyte 520 Generic MMP Assay Kit Fluorometric (Anaspec) according to the manufacturer’s instructions for protocol B. Briefly, supernatants were incubated with 1 mM AMPA for 3 h at 37°C, and 50 μL was transferred to a 96-well plate. 50 μL of the included MMP substrate solution was added to each well and further incubated for 1 h at room temperature, after which 50 μL Stop Solution was added to terminate the reaction. Fluorescence intensity, corresponding to MMP activity, was measured at Ex/Em = $\frac{490}{520}$ nm on a plate reader. ## siRNA transfection siRNA knockdown was performed in Opti-MEM media (Gibco) and Dharmafect 1 Transfection Reagent (Horizon Discovery). A non-specific siRNA (ON-TARGETplus; Horizon Discovery) was used as a control alongside siRNA against GSK3-β (Invitrogen). Knockdown in wells of a 6-well plate was performed by incubating 40 nM siRNA with Dharmafect 1 for 20 min before applying to cells. The next day, cell culture media was refreshed, and cells were grown for another 3 days before downstream experiments. ## DQ-gelatin assay DQ-gelatin fluorescein conjugate (Invitrogen) was dissolved in water to 0.5 mg/mL and used to coat Ibidi 8-well chambered slides or 96-well plates for 24 h at 4°C. Dishes were washed twice with phosphate-buffered saline (PBS) before seeding 15,000 VSMCs. The following day, cells were treated with either SMIs or transfected with siRNA for 96 h before washing with PBS and fixing in $4\%$ PFA (Alfa Aesar) for 10 min at room temperature. Fixed cells were subsequently imaged using a Zeiss LSM 710 confocal microscope. Resulting images were processed and quantified in ImageJ. DQ-gelatin fluorescence intensity was determined after image processing and thresholding. The number of nuclei was also determined after initial processing and analysis of particles. All described image processing and quantification steps were performed using a macro for automated and unbiased analysis. ## Gelatin zymography VSMCs were seeded in 6-well plates and began treatment with drugs. After 4 days without any media changes, cell supernatants were collected and spun down to remove any debris and floating cells. Supernatant protein content was then quantified using the BCA assay (Pierce) and bovine albumin protein standards. After protein quantification, sample concentrations were normalized prior to mixing with a non-reducing sample buffer. Next, $7.5\%$ SDS-containing polyacrylamide gels with 4 mg/mL porcine skin gelatin (Sigma-Aldrich) were cast using the Bio-Rad system, and 5 μg supernatant was loaded into the wells. Gels were run at 100 V for approximately 2 h before they were incubated in washing buffer ($2.5\%$ Triton X-100, 50 mM Tris-HCl [pH 7.5], 5 mM CaCl2, 1 μM ZnCl2] for 2 × 30 min with gentle agitation at room temperature. Gels were then rinsed in incubation buffer ($1\%$ Triton X-100, 50 mM Tris-HCl [pH 7.5], 5 mM CaCl2, 1 μM ZnCl2) for 10 min, before the incubation buffer was replenished and the gels incubated at 37°C for 24 h with gentle agitation. The gels were then incubated in staining solution ($40\%$ methanol, $10\%$ acetic acid, $0.5\%$ w/v Coomassie blue [Sigma-Aldrich]) for 1 h with agitation before being rinsed in ddH2O and further incubated with destaining solution ($40\%$ methanol, $10\%$ acetic acid) until digested bands became visible. The resulting gel was then scanned, and band intensity was quantified using ImageJ. ## TUNEL staining VSMCs were seeded onto $0.1\%$ gelatin-coated plates and were treated with either SMIs or transfected with siRNA for 96 h. TUNEL staining to identify apoptotic cells was performed using the In Situ Cell Death Detection Kit (Roche) according to the manufacturer’s instructions. Positive controls were obtained by treating cells with 3 U/mL DNase I (Sigma-Aldrich). Tiled images were taken using a Zeiss LSM 710 confocal microscope and quantified in ImageJ using a macro. After image processing, the number of TUNEL-positive nuclei was quantified. ## RNA extraction and qRT-PCR After washing the cells with PBS, RNA extraction was performed from cells growing in 12-well plates using the GenElute Mammalian Total RNA Miniprep Kit (Sigma-Aldrich) according to the manufacturer’s instructions for extraction from adherent cells. After quantification, reverse transcription was performed using the Maxima First Strand cDNA Synthesis Kit (Thermo Scientific). qRT-PCR was performed using SYBR Green (Applied Biosystems) with 5 ng cDNA per sample. Experiments were performed with technical triplicates, and gene expression was determined based on the expression of housekeeping gene GAPDH using the ΔCT quantification method. ## Drug target analysis Drug target analysis was done using R (v.4.0.5) and the following packages: ggplot2, pheatmap, dplyr, tidyr, biomaRt, and clusterProfiler (Durinck et al., 2009; Wickham, 2011; Wickham et al., 2019; Wu et al., 2021; Yu et al., 2012). ## Statistics Statistical significance was determined using an unpaired two-tailed Student’s t test, with p values <0.05 considered to be significant. Significance is shown throughout the manuscript is as follows: ∗$p \leq 0.05$; ∗∗$p \leq 0.01$; ∗∗∗$p \leq 0.001$; ∗∗∗∗$p \leq 0.0001.$ ## Author contributions H.D. conceived and performed experiments and analysis and wrote the paper. M.M. and R.G.C.M. performed the drug screen with supervision and guidance from A.G. M.H. performed blotting experiments. M.F. and D.M.S. assisted with the design of the screen, provided compounds, and assisted with analysis. A.C. provided patient phenotypes and cell lines from patients with classical Marfan syndrome, and J.A.A.-M. provided fibrillin-1 mutations. S.S. conceived and supervised the project. All authors reviewed the manuscript. ## Supplemental information Document S1. Figures S1–S7, Tables S4–S6, and supplemental experimental procedures Table S1. pIC50 values of drug targets from positive hits from the drug screen Table S2. Frequency of drug targets among effective SMs from drug screen Table S3. pIC50 values of drug targets of GSK3β-targeting SMIs used for validation Document S2. Article plus supplemental information ## Conflict of interests 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. ## References 1. Beurel E., Grieco S.F., Jope R.S.. 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--- title: 'Diabetes management in patients undergoing total pancreatectomy: A single center cohort study' authors: - Tianyi Zhao - Yong Fu - Taiping Zhang - Junchao Guo - Quan Liao - Shuoning Song - Yanbei Duo - Yuting Gao - Tao Yuan - Weigang Zhao journal: Frontiers in Endocrinology year: 2023 pmcid: PMC9969079 doi: 10.3389/fendo.2023.1097139 license: CC BY 4.0 --- # Diabetes management in patients undergoing total pancreatectomy: A single center cohort study ## Abstract ### Background Total pancreatectomy (TP) has been increasingly performed in recent years. However, studies on diabetes management after TP during different postoperative periods are still limited. ### Objectives This study aimed to evaluate the glycemic control and insulin therapy of patients undergoing TP during the perioperative and long-term follow-up period. ### Methods Ninety-three patients undergoing TP for diffuse pancreatic tumors from a single center in China were included. Based on preoperative glycemic status, patients were divided into three groups: nondiabetic group (NDG, $$n = 41$$), short-duration diabetic group (SDG, preoperative diabetes duration ≤12 months, $$n = 22$$), and long-duration diabetic group (LDG, preoperative diabetes duration >12 months, $$n = 30$$). Perioperative and long-term follow-up data, including the survival rate, glycemic control, and insulin regimens, were evaluated. Comparative analysis with complete insulin-deficient type 1 diabetes mellitus (T1DM) was conducted. ### Results During hospitalization after TP, glucose values within the target (4.4-10.0 mmol/L) accounted for $43.3\%$ of the total data, and $45.2\%$ of the patients experienced hypoglycemic events. Patients received continuous intravenous insulin infusion during parenteral nutrition at a daily insulin dose of 1.20 ± 0.47 units/kg/day. In the long-term follow-up period, glycosylated hemoglobin A1c levels of 7.43 ± $0.76\%$ in patients following TP, as well as time in range and coefficient of variation assessed by continuous glucose monitoring, were similar to those in patients with T1DM. However, patients after TP had lower daily insulin dose (0.49 ± 0.19 vs 0.65 ± 0.19 units/kg/day, $P \leq 0.001$) and basal insulin percentage (39.4 ± 16.5 vs 43.9 ± $9.9\%$, $$P \leq 0.035$$) than patients with T1DM, so did those using insulin pump therapy. Whether in the perioperative or long-term follow-up period, daily insulin dose was significantly higher in LDG patients than in NDG and SDG patients. ### Conclusions Insulin dose in patients undergoing TP varied according to different postoperative periods. During long-term follow-up, glycemic control and variability following TP were comparable to complete insulin-deficient T1DM but with fewer insulin needs. Preoperative glycemic status should be evaluated as it could guide insulin therapy after TP. ## Introduction More recently, postoperative outcomes following total pancreatectomy (TP) have improved with the advances in surgical techniques, glycemic monitoring, insulin delivery systems, insulin formulations, and pancreatic enzyme preparations (1–5). Recent series also demonstrated that TP was not inferior to pancreaticoduodenectomy regarding mortality, major morbidity, overall quality of life, and long-term survival (6–10). In addition, the major indications for TP have expanded to a range of diffuse pancreatic diseases over decades, encompassing pancreatic carcinoma with repeated positive frozen section margin, intraductal papillary mucinous neoplasm (IPMN), multifocal pancreatic neuroendocrine tumor (PNET), multifocal pancreatic metastases and chronic pancreatitis [4, 9, 11, 12]. Consequently, TP has been performed increasingly in recent years [1, 3, 4, 13]. Diabetes after TP is characterized by the complete deficiency of insulin, pancreatic glucagon, and pancreatic polypeptide. Lund et al. [ 14] described the hormone profiles of 10 TP patients, showing undetectable plasma levels of C-peptide and pancreatic polypeptide but detectable concentrations of gut-derived fasting glucagon. Robust postprandial responses in glucagon induced by the rapid intestinal transit time were also revealed, leading to poor postprandial glucose control in TP patients [14]. Besides, TP for neoplasms usually involves resection of part of the upper gastrointestinal tract, such as the distal stomach and duodenum, resulting in gastrointestinal motility disorders, rapid food transit, and lack of gut hormones, which together with malabsorption and fatty diarrhea due to pancreatic exocrine insufficiency further affects stable nutrient absorption and glycemic stability. Adjuvant chemotherapy, glucocorticoids, long-acting somatostatin analogs, and tyrosine kinase inhibitors for the treatment of underlying disease also had their potential negative influence on glycemic control. Moreover, a retrospective cohort study identified early postoperative fasting blood glucose as one of the independent risk factors for postoperative complications in patients undergoing TP, and high postoperative glycosylated hemoglobin A1c (HbA1c) was associated with poor recurrence-free survival and overall survival [15]. Diabetes after TP has a significant impact on patients’ metabolic status, and good glycemic control is crucial for improving short- and long-term outcomes. However, detailed descriptions of diabetes management following TP during different postoperative periods remain limited. In the present study, we established a large cohort of patients undergoing TP for diffuse pancreatic tumors to summarize the characteristics of diabetes secondary to TP during perioperative and long-term follow-up, including the continuous glucose monitoring (CGM) parameters and insulin pump therapy which were rarely reported before, and compared these with complete insulin-deficient type 1 diabetes mellitus (T1DM). ## Study population Ninety-three patients who underwent TP with at least a 3-month follow-up in Peking Union Medical College Hospital between January 2009 and April 2022 were enrolled in the cohort. Excluded were those who deceased or withdrew within three months after surgery. A total of 10, 28, and 34 cases of TP were performed from 2009 to 2012, 2013 to 2016, and 2017 to 2020, respectively, and 21 cases were conducted since 2021. All 93 patients were candidates for TP due to diffuse pancreatic tumors confirmed by preoperative computed tomography or magnetic resonance imaging. Eighty-seven patients underwent one-stage TP, and six underwent completion TP for tumor occurrence or postoperative bleeding, including four who underwent distal pancreatectomy after pancreaticoduodenectomy and two who underwent pancreaticoduodenectomy after distal pancreatectomy. This study was approved by the Ethics Committee of Peking Union Medical College Hospital (JS-2574). ## Perioperative management Data on demography, pathology, postoperative complications, length of stay, and preoperative glycemic status were collected in the 93 patients underwent TP. All patients were treated with total parenteral nutrition (TPN) and continuous intravenous insulin infusion (CVII) within the first few days after surgery. The insulin infusion rate was started at 0.05-0.10 units/kg/h and was adjusted or discontinued according to the TPN rate and blood glucose levels to reach a target glucose level. Subcutaneous long-acting insulin in combination with CVII was prescribed for some patients with a longer duration of parenteral nutrition. Parenteral nutrition was terminated when the patients resumed enough oral intake. The insulin regimens at different periods (parenteral nutrition, enteral nutrition, and one month after surgery) and daily carbohydrate content of parenteral nutrition were documented. Peripheral blood glucose was measured every 2 to 6 hours, depending on the glucose status. We recorded all the in-hospital glucose data to evaluate the individual’s mean and standard deviation (SD) of blood glucose per day, daily coefficient of variation (CV), maximum blood glucose, minimum blood glucose, and 6 am blood glucose. The target range for blood glucose in the perioperative period was defined as 4.4 to 10.0 mmol/L [16]. Hypoglycemia was a blood glucose value of less than 3.9mmol/L according to the guideline for inpatient glucose management [16]. Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters. The definition of CV (%) was 100 times the mean blood glucose divided by the SD. ## Long-term follow-up management All 93 patients underwent TP were followed up through the endocrinology clinic and telephone visits. Regular follow-up by the surgeon and the oncologist was performed simultaneously. Overall survival of the TP patients with different pathologies from surgery to the date of death or last follow-up was evaluated. Diabetes assessments were available in 80 patients after TP with relatively stable general conditions and primary diseases. Follow-up variables, including HbA1c, fasting C-peptide, creatinine, urinary albumin-to-creatinine ratio, and liver function, were measured in patients underwent TP. Hypoglycemia data were derived from self-monitoring of blood glucose profiles. Seven TP patients underwent CGM for seven days with calibration by 4-point self-monitoring of blood glucose to obtain mean glucose value, SD, CV, time in range (TIR), time above range (TAR), and time below range (TBR), etc. Weight, gastrointestinal symptoms, pancreatic enzymes dose, and events of diabetic chronic complications were recorded. ## Comparative analysis Additional 80 consecutive patients with complete insulin-deficient T1DM (C-peptide <0.05 ng/ml) from the endocrinology clinic between July 2020 and July 2021 were included to evaluate the differences in glycemic control and insulin therapy between patients undergoing TP and patients with T1DM. Patients with T1DM who were under the age of 18, had a BMI over 24 kg/m2, had eating disorders, had renal or liver dysfunction, were pregnant, were treated with steroids, or took oral antidiabetic agents were excluded. ## Statistical analysis Data analyses were performed with SPSS version 25.0. Normally distributed continuous variables were presented as mean ± SD and analyzed with independent samples t test or one-way ANOVA. Nonnormally distributed continuous variables were displayed as medians (interquartile ranges) and compared using Mann-Whitney U test or Kruskal-Wallis H test. Chi-square test or Fisher’s exact test was applied for categorical variables presented as numbers (percentages). Overall survival was assessed by Kaplan-Meier analysis and compared using the logrank test. Statistical significance was considered as a two-tailed P value < 0.05. ## Clinical characteristics of patients undergoing TP A total of 93 patients underwent TP were recruited with a mean age of 59.9 ± 10.8 years, of which 43 ($46.2\%$) were male. Pancreatic malignant and benign tumors were present in 72 ($77.4\%$) and 21 ($22.6\%$) cases, respectively. The most common indications for TP were pancreatic ductal adenocarcinoma and IPMN (including invasive and non-invasive IPMN), accounting for $37.6\%$ and $38.7\%$ of all patients, respectively, followed by $8.6\%$ of PNET and other rare pancreatic neoplasms, including serous cystadenoma, mucous cystadenocarcinoma, carcinosarcoma, ampullary carcinoma, mixed ductal-endocrine carcinoma, cholangiocarcinoma, renal cell carcinoma metastasis, and malignant fibroma metastasis. Postoperative complications occurred in 30 patients ($32.3\%$), mainly infections (21 patients). Patients were divided into three groups according to preoperative glycemic status, including the nondiabetic group (NDG, $$n = 41$$), the short-duration diabetic group (SDG, preoperative diabetes duration ≤12 months, $$n = 22$$), and the long-duration diabetic group (LDG, preoperative diabetes duration >12 months, $$n = 30$$). Age at operation in patients of LDG was higher than those of NDG (62.9 ± 8.1 vs 57.8 ± 11.2 years, $$P \leq 0.038$$) but no significant differences in gender, pathology, postoperative complications, or postoperative length of stay among the three groups. The clinical and pathologic characteristics of TP patients are outlined in Table 1. Table 1 \| Characteristics \| Total \| NDG \| With pre-op DM \| With pre-op DM.1 \| P value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristics \| Total \| NDG \| SDG \| LDG \| P value \| \| N \| 93 (100) \| 41 (44.1) \| 22 (23.7) \| 30 (32.3) \| – \| \| Age at operation (years) \| 59.9 ± 10.8 \| 57.8 ± 11.2 \| 59.5 ± 12.6 \| 62.9 ± 8.1# \| 0.145 \| \| Male \| 43 (46.2) \| 20 (48.8) \| 9 (40.9) \| 14 (46.7) \| 0.835 \| \| Pathology \| \| \| \| \| 0.829 \| \| Pancreatic carcinoma \| \| \| \| \| \| \| Ductal adenocarcinoma \| 35 (37.6) \| 14 (34.1) \| 11 (50.0) \| 10 (33.3) \| – \| \| Acinar cell carcinoma \| 2 (2.2) \| 2 (4.9) \| 0 \| 0 \| – \| \| Adenosquamous carcinoma \| 1 (1.1) \| 0 \| 0 \| 1 (3.3) \| – \| \| IPMN \| \| \| \| \| \| \| Invasive IPMN \| 21 (22.6) \| 9 (22.0) \| 5 (22.7) \| 7 (23.3) \| – \| \| Non-invasive IPMN \| 15 (16.1) \| 5 (12.2) \| 4 (18.2) \| 6 (20.0) \| – \| \| PNET* \| 8 (8.6) \| 4 (9.8) \| 1 (4.5) \| 3 (10.0) \| – \| \| Cystic neoplasms \| \| \| \| \| \| \| Serous cystadenoma \| 2 (2.2) \| 0 \| 1 (4.5) \| 1 (3.3) \| – \| \| Mucous cystadenocarcinoma \| 1 (1.1) \| 1 (2.4) \| 0 \| 0 \| – \| \| Carcinosarcoma \| 2 (2.2) \| 2 (4.9) \| 0 \| 0 \| – \| \| Ampullary carcinoma \| 2 (2.2) \| 1 (2.4) \| 0 \| 1 (3.3) \| – \| \| Mixed ductal-endocrine carcinoma \| 1 (1.1) \| 1 (2.4) \| 0 \| 0 \| – \| \| Cholangiocarcinoma \| 1 (1.1) \| 0 \| 0 \| 1 (3.3) \| – \| \| Renal cell carcinoma metastasis \| 1 (1.1) \| 1 (2.4) \| 0 \| 0 \| – \| \| Malignant fibroma metastasis \| 1 (1.1) \| 1 (2.4) \| 0 \| 0 \| – \| \| Postoperative complications \| 30 (32.3) \| 13 (31.7) \| 7 (31.8) \| 10 (33.3) \| 0.988 \| \| Abdominal infection \| 13 (14.0) \| 6 (14.6) \| 3 (13.6) \| 4 (13.3) \| – \| \| Hospital acquired pneumonia \| 6 (6.5) \| 3 (7.3) \| 3 (13.6) \| 0 \| – \| \| Urinary infection \| 2 (2.2) \| 0 \| 0 \| 2 (6.7) \| – \| \| Catheter related infection \| 1 (1.1) \| 1 (2.4) \| 0 \| 0 \| – \| \| Abdominal hemorrhage \| 9 (9.7) \| 5 (12.2) \| 1 (4.5) \| 3 (10.0) \| – \| \| Delayed gastric emptying \| 2 (2.2) \| 1 (2.4) \| 1 (4.5) \| 0 \| – \| \| Deep venous thrombosis \| 2 (2.2) \| 2 (4.9) \| 0 \| 0 \| – \| \| Acute coronary syndrome \| 2 (2.2) \| 0 \| 0 \| 2 (6.7) \| – \| \| Chyle leak \| 1 (1.1) \| 1 (2.4) \| 0 \| 0 \| – \| \| Postoperative length of stay (days) \| 14 (12, 23) \| 15 (13, 28) \| 13 (11, 19) \| 15 (12, 19) \| 0.318 \| ## Preoperative glycemic status Of the 93 patients, diabetes was present in 52 ($55.9\%$) patients before surgery, and 20 ($38.5\%$) of the 52 had already received insulin therapy. Patients of LDG had higher BMI and proportion of diabetic family history than those of SDG (23.13 ± 3.57 vs 21.27 ± 2.82 kg/m2 and $46.7\%$ vs $13.6\%$, respectively; $P \leq 0.05$). As more patients in LDG had received antidiabetic therapy ($96.7\%$ vs $63.6\%$, $$P \leq 0.003$$), individuals in LDG exhibited lower HbA1c and 2-hour postprandial blood glucose than those in SDG (6.95 ± 1.15 vs 8.09 ± $1.95\%$ and 11.55 ± 3.63 vs 15.87 ± 5.78 mmol/L, respectively; $P \leq 0.01$). None was complicated with diabetic retinopathy or diabetic kidney disease, while five patients with a median preoperative diabetes duration of 5 years had coronary heart disease. The features categorized by preoperative glycemic status are summarized in Table 2. Table 2 \| Characteristics \| Total \| NDG \| With pre-op DM \| With pre-op DM.1 \| P value \| P value.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristics \| Total \| NDG \| SDG \| LDG \| P value \| P value \| \| N, n (%) \| 93 (100) \| 41 (44.1) \| 22 (23.7) \| 30 (32.3) \| – \| \| \| Preoperative glycemic status \| Preoperative glycemic status \| Preoperative glycemic status \| Preoperative glycemic status \| Preoperative glycemic status \| Preoperative glycemic status \| \| \| Age at operation (years), mean ± SD \| 59.9 ± 10.8 \| 57.8 ± 11.2c \| 59.5 ± 12.6 \| 62.9 ± 8.1a \| 0.145 \| \| \| BMI (kg/m2), mean ± SD \| 22.06 ± 3.13 \| 21.70 ± 2.79 \| 21.27 ± 2.82c \| 23.13 ± 3.57b \| 0.063 \| \| \| DM duration (months), median (IQR) \| 36 (6, 60) \| \| 5 (1, 12)c \| 60 (36, 120)b \| < 0.001 \| \| \| Family history of DM, n (%) \| 18 (19.4) \| 1 (2.4)c \| 3 (13.6)c \| 14 (46.7)ab \| < 0.001 \| \| \| FBG (mmol/L), mean ± SD \| 7.02 ± 2.16 \| 5.72 ± 0.81bc \| 7.91 ± 2.54a \| 7.93 ± 2.21a \| < 0.001 \| \| \| 2-h PBG (mmol/L), mean ± SD \| 12.67 ± 5.07 \| 5.30 ± 0.42bc \| 15.87 ± 5.78ac \| 11.55 ± 3.63ab \| < 0.001 \| \| \| HbA1c (%), mean ± SD \| 7.24 ± 1.72 \| 5.48 ± 0.77bc \| 8.09 ± 1.95ac \| 6.95 ± 1.15ab \| 0.007 \| \| \| Antidiabetic medications, n (%) \| 43 (46.2) \| – \| 14 (63.6)c \| 29 (96.7)b \| 0.003 \| \| \| Oral agents only, n (%) \| 23 (24.7) \| – \| 7 (31.8) \| 16 (53.3) \| 0.162 \| \| \| Insulin-containing regimes, n (%) \| 20 (21.5) \| \| 7 (31.8) \| 13 (43.3) \| 0.565 \| \| \| Daily insulin dose (units/kg/day), mean ± SD \| 0.53 ± 0.28 \| – \| 0.42 ± 0.24 \| 0.59 ± 0.29 \| 0.224 \| \| \| CAD, n (%) \| 7 (7.5) \| 2 (4.9) \| 0 \| 5 (16.7) \| 0.056 \| \| \| Stroke, n (%) \| 4 (4.3) \| 0 \| 1 (4.5) \| 3 (10.0) \| 0.080 \| \| \| DKD, n (%) \| – \| – \| 0 \| 0 \| – \| \| \| DR, n (%) \| – \| – \| 0 \| 0 \| – \| \| \| Postoperative hospitalized glucose measurement \| Postoperative hospitalized glucose measurement \| Postoperative hospitalized glucose measurement \| Postoperative hospitalized glucose measurement \| Postoperative hospitalized glucose measurement \| Postoperative hospitalized glucose measurement \| \| \| Mean BG (mmol/L), mean ± SD \| 10.99 ± 1.13 \| 10.96 ± 1.06 \| 11.23 ± 1.09 \| 10.86 ± 1.25 \| 0.497 \| \| \| Maximum BG (mmol/L), mean ± SD \| 21.75 ± 3.19 \| 21.80 ± 3.08 \| 21.68 ± 2.90 \| 21.72 ± 3.63 \| 0.989 \| \| \| Minimum BG (mmol/L), mean ± SD \| 4.25 ± 1.11 \| 4.02 ± 1.06 \| 4.68 ± 1.29 \| 4.22 ± 0.96 \| 0.077 \| \| \| 6 am BG (mmol/L), mean ± SD \| 10.87 ± 1.97 \| 10.62 ± 1.73 \| 11.50 ± 2.43 \| 10.74 ± 1.88 \| 0.218 \| \| \| CV (%), mean ± SD \| 31.54 ± 5.42 \| 32.50 ± 4.94 \| 30.31 ± 5.10 \| 31.13 ± 6.19 \| 0.281 \| \| \| Within the target range (4.4-10 mmol/L), n (%) \| 2698 (43.3) \| 1211 (43.0) \| 569 (41.4) \| 918 (45.1) \| 0.101 \| \| \| Hyperglycemic events \| \| \| \| \| \| \| \| 10.1-13.9 mmol/L, n (%) \| 1976 (31.7) \| 894 (31.8) \| 450 (32.8) \| 632 (31.0) \| 0.560 \| \| \| 14.0-16.7 mmol/L, n (%) \| 775 (12.5) \| 358 (12.7) \| 180 (13.1) \| 237 (11.6) \| 0.367 \| \| \| > 16.7 mmol/L, n (%) \| 610 (9.8) \| 270 (9.6) \| 147 (10.7) \| 193 (9.5) \| 0.437 \| \| \| Hypoglycemic events \| \| \| \| \| \| \| \| 3-3.8 mmol/L, n (%) \| 83 (1.3) \| 42 (1.5) \| 14 (1.0) \| 27 (1.3) \| 0.473 \| \| \| < 3 mmol/L, n (%) \| 10 (0.2) \| 7 (0.2) \| 0 \| 3 (0.1) \| 0.156 \| \| \| Patients with hypoglycemic events, n of patients/total n (%) \| 42/93 (45.2) \| 21/41 (51.2) \| 9/22 (40.9) \| 12/30 (40.0) \| 0.620 \| \| \| 3-3.8 mmol/L, n of patients/total n (%) \| 38/93 (40.9) \| 19/41 (46.3) \| 9/22 (40.9) \| 10/30 (33.3) \| 0.547 \| \| \| < 3 mmol/L, n of patients/total n (%) \| 8/93 (8.6) \| 5/41 (12.2) \| 0 \| 3/30 (10.0) \| 0.311 \| \| \| Postoperative insulin regimen \| Postoperative insulin regimen \| Postoperative insulin regimen \| Postoperative insulin regimen \| Postoperative insulin regimen \| Postoperative insulin regimen \| \| \| Parenteral nutrition \| Parenteral nutrition \| Parenteral nutrition \| Parenteral nutrition \| Parenteral nutrition \| Parenteral nutrition \| \| \| Daily insulin dose (units/kg/day), mean ± SD \| 1.20 ± 0.47 \| 1.07 ± 0.40c \| 1.13 ± 0.34c \| 1.45 ± 0.56ab \| 0.012 \| \| \| Daily insulin dose (units/day), mean ± SD \| 69.03 ± 32.53 \| 60.33 ± 27.55c \| 63.32 ± 23.74c \| 85.09 ± 38.79ab \| 0.003 \| \| \| Insulin dose per 10g carbohydrate (units), mean ± SD \| 5.70 ± 2.56 \| 5.11 ± 2.02c \| 4.93 ± 1.80c \| 7.10 ± 3.15ab \| 0.008 \| \| \| Enteral nutrition \| Enteral nutrition \| Enteral nutrition \| Enteral nutrition \| Enteral nutrition \| Enteral nutrition \| \| \| Daily insulin dose (units/kg/day), mean ± SD \| 0.36 ± 0.13 \| 0.35 ± 0.16 \| 0.34 ± 0.11 \| 0.40 ± 0.11 \| 0.267 \| \| \| Daily insulin dose (units/day), mean ± SD \| 21.19 ± 8.98 \| 20.30 ± 9.90 \| 18.77 ± 7.22 \| 24.05 ± 8.37 \| 0.107 \| \| \| 1-month post-op \| 1-month post-op \| 1-month post-op \| 1-month post-op \| 1-month post-op \| 1-month post-op \| \| \| Daily insulin dose (units/kg/day), mean ± SD \| 0.38 ± 0.12 \| 0.36 ± 0.11c \| 0.35 ± 0.12c \| 0.42 ± 0.12ab \| 0.031 \| \| \| Daily insulin dose (units/day), mean ± SD \| 21.42 ± 8.01 \| 20.22 ± 7.96c \| 18.89 ± 6.98c \| 24.91 ± 7.82ab \| 0.011 \| \| \| Basal percentage (%), median (IQR) \| 40.0 (32.1, 48.7) \| 40.0 (29.4, 47.7) \| 40.0 (33.3, 62.9) \| 36.1 (30.8, 44.7) \| 0.573 \| \| ## Postoperative hospitalized glucose measurement A total of 6224 blood glucose measurements were performed in a median of 14 days postoperative hospital stay. The mean blood glucose was 10.99 ± 1.13 mmol/L, and the 6 am blood glucose was 10.87 ± 1.97 mmol/L. Glucose within the target range (4.4-10.0 mmol/L) and in the range of 4.4-13.9 mmol/L accounted for $43.3\%$ and $75.1\%$ of all glucose data, respectively. The CV calculated from bedside blood glucose monitoring was 31.54 ± $5.42\%$. Hypoglycemic events occurred in 42 patients ($45.2\%$), accounting for $1.5\%$ of all glucose data, mainly of 3.0 to 3.8mmol/L. There was no significant difference among the three groups of NDG, SDG, and LDG with regard to mean blood glucose, maximum blood glucose, minimum blood glucose, 6 am blood glucose, CV, glucose within the target range, and the proportion of hyperglycemic and hypoglycemic events (all $P \leq 0.05$). The detailed postoperative hospitalized glucose measurements are listed in Table 2. ## Postoperative insulin regimen During the first few days after surgery, TPN with a median carbohydrate content of 120 [100, 142] g was given for 12 to 15 hours per day. The daily insulin dose was 1.20 ± 0.47 units/kg/day (69.03 ± 32.53 units/day), and the insulin dose per 10g carbohydrate was 5.70 ± 2.56 units, which were all higher in individuals of LDG than in those of NDG (1.45 ± 0.56 vs 1.07 ± 0.40 units/kg/day, 85.09 ± 38.79 vs 60.33 ± 27.55 units/day, and 7.10 ± 3.15 vs 5.11 ± 2.02 units, respectively; $P \leq 0.05$) and SDG (1.45 ± 0.56 vs 1.13 ± 0.34 units/kg/day, 85.09 ± 38.79 vs 63.32 ± 23.74 units/day, and 7.10 ± 3.15 vs 4.93 ± 1.80 units, respectively; $P \leq 0.05$). No significant changes were found in insulin dose between NDG and SDG (Table 2). Subsequently, a mean long-acting insulin Lantus dose of 0.16 ± 0.58 units/kg/day was added on the basis of CVII therapy in 46 patients with a longer duration of parenteral nutrition. After the combination of basal insulin, the percentage of glucose within the target range (4.4-10.0 mmol/L) increased ($34.4\%$ vs $46.7\%$, $P \leq 0.001$), and that of hyperglycemic events decreased with a slight increase in level 1 hypoglycemia (3.0-3.8 mmol/L) ($0.5\%$ vs $1.5\%$, $P \leq 0.001$) but no change in level 2 hypoglycemia (< 3.0mmol/L) (Figure 1A). Mean blood glucose (12.55 ± 1.48 vs 10.50 ± 1.52 mmol/L, $P \leq 0.001$) significantly decreased, as well as mean 6 am blood glucose (15.61 ± 2.78 vs 9.42 ± 2.74 mmol/L, $P \leq 0.001$) (Figure 1B) and daily insulin dose (1.27 ± 0.48 vs 0.94 ± 0.42 units/kg/day, $P \leq 0.001$) (Figure 1C). Figure 1: Analysis of the glycemic control and daily insulin dose after adding basal insulin to CVII therapy in 46 patients after total pancreatectomy during postoperative parenteral nutrition. Changes in the percentage of glucose data (A), mean BG, mean 6 am BG (B), and daily insulin dose (C). CVII, continuous intravenous insulin infusion. BG, blood glucose. Data are presented as mean ± SD. *P < 0.001. The Insulin regimen was switched from CVII to multiple daily insulin injections (MDI) along with the recovery of enteral nutrition, and the daily insulin dose was significantly reduced to 0.36 ± 0.13 units/kg/day. At one month after TP, the daily insulin dose was 0.38 ± 0.12 units/kg/day, similar to the insulin dose in the enteral nutrition period, and was also higher in patients of LDG than in those of NDG (0.42 ± 0.12 vs 0.36 ± 0.11 units/kg/day, $$P \leq 0.021$$) and SDG (0.42 ± 0.12 vs 0.35 ± 0.12 units/kg/day, $$P \leq 0.027$$). See Table 2 and Figure 2 for details. Figure 2: Comparison of the daily insulin dose between different periods after total pancreatectomy. post-op, postoperation. Data are presented as mean ± SD. *P < 0.001. ## Overall survival of TP The 5-year survival rates for patients with malignant and benign tumors were $42.6\%$ and $86.1\%$, respectively. In the subgroup analysis, patients with ductal adenocarcinoma had a median survival of 24.0 months and the 1-, 3-, and 5-year survival rates of $81.2\%$, $43.5\%$, and $23.3\%$, respectively. Pancreatic ductal adenocarcinoma had the lowest 5-year survival rate ($23.3\%$), followed by invasive IPMN ($53.8\%$), while non-invasive IPMN and PNET had favorable 5-year survival rates of $92.3\%$ and $100\%$, respectively. The estimated long-term overall survival following TP according to pathology is displayed in Figure 3. Figure 3: The Kaplan-Meier survival curve for patients after total pancreatectomy according to underlying diseases. (A) A better survival rate was identified in benign tumors compared with malignant tumors. (B) 1-year survival rates for patients with PDAC, invasive IPMN, non-invasive IPMN, and PNET: 81.2%, 100%, 92.3%, 100%; 3-year: 43.5%, 53.8%, 92.3%, 100%; 5-year: 23.3%, 53.8%, 92.3%, 100%. PDAC, pancreatic ductal adenocarcinoma; IPMN, intraductal papillary mucinous neoplasm; PNET, pancreatic neuroendocrine tumor. ## Glycemic level and variability of patients undergoing TP At a median follow-up of 20 months after TP, patients all recovered normal food intake, and $90.3\%$ were free of fatty diarrhea with a median pancreatic enzyme dosage of 900 [900, 1350] mg per day. However, a mean weight loss of 4.50 ($95\%$ confidence interval, 3.21-5.80) kg was still observed in $69.9\%$ of patients compared to preoperative weight. Serum C-peptide was undetectable, and HbA1c was 7.43 ± $0.76\%$. During the one month before the last follow-up, 47 patients ($58.8\%$) experienced at least one hypoglycemic episode. The respective values for mean glucose levels, SD, TIR, and CV assessed by CGM were 8.61 ± 1.59 mmol/L, 3.27 ± 0.71 mmol/L, 71.92 ± $9.43\%$, and 37.49 ± $8.26\%$. Other CGM measurements are presented in Table 3. Table 3 \| Characteristics \| TP(n = 80) \| T1DM(n = 80) \| P value \| \| --- \| --- \| --- \| --- \| \| Age at last follow-up (years), mean ± SD \| 62.59 ± 10.41 \| 44.01 ± 17.22 \| < 0.001 \| \| Male, n (%) \| 36 (45) \| 30 (37.5) \| 0.422 \| \| Diabetes duration (months), median (IQR) \| 20 (7, 63)* \| 132 (72, 204) \| < 0.001 \| \| BMI (kg/m2), mean ± SD \| 20.33 ± 2.39 \| 20.92 ± 1.76 \| 0.079 \| \| HbA1c (%), mean ± SD \| 7.43 ± 0.76 \| 7.66 ± 1.12 \| 0.128 \| \| C-peptide (ng/ml), median (IQR) \| <0.05 \| <0.05 \| – \| \| Hypoglycemic events, n of patients/total n (%) \| 47/80 (58.8) \| 57/80 (71.3) \| 0.097 \| \| CGM measurements† \| \| \| \| \| Mean glucose value (mmol/L), mean ± SD \| 8.61 ± 1.59 \| 8.25 ± 1.62 \| 0.665 \| \| SD (mmol/L), mean ± SD \| 3.27 ± 0.71 \| 3.24 ± 1.22 \| 0.967 \| \| CV (%), mean ± SD \| 37.49 ± 8.26 \| 38.22 ± 6.94 \| 0.851 \| \| Maximum glucose value (mmol/L), mean ± SD \| 18.80 ± 3.51 \| 17.71 ± 2.56 \| 0.491 \| \| Minimum glucose value (mmol/L), mean ± SD \| 2.93 ± 0.56 \| 3.20 ± 0.69 \| 0.396 \| \| TIR (%) (3.9-10 mmol/L), mean ± SD \| 71.92 ± 9.43 \| 70.19 ± 14.99 \| 0.787 \| \| TAR (%) (>10 mmol/L), mean ± SD \| 23.41 ± 9.78 \| 24.38 ± 14.51 \| 0.877 \| \| TBR (%) (<3.9 mmol/L), mean ± SD \| 4.68 ± 4.54 \| 5.42 ± 3.13 \| 0.707 \| \| Insulin regimen \| \| \| \| \| Daily insulin dose (units/kg/day), mean ± SD \| 0.49 ± 0.19 \| 0.65 ± 0.19 \| < 0.001 \| \| Daily insulin dose (units/day), mean ± SD \| 27.09 ± 11.88 \| 38.74 ± 11.44 \| < 0.001 \| \| Basal percentage (%), mean ± SD \| 39.4 ± 16.5 \| 43.9 ± 9.9 \| 0.035 \| \| Insulin pump‡ \| \| \| \| \| Daily insulin dose (units/kg/day), mean ± SD \| 0.44 ± 0.14 \| 0.66 ± 0.14 \| < 0.001 \| \| Daily insulin dose (units), mean ± SD \| 24.81 ± 8.05 \| 39.93 ± 10.22 \| < 0.001 \| \| Basal percentage (%), mean ± SD \| 44.1 ± 14.5 \| 49.8 ± 8.86 \| 0.138 \| Compared with patients with complete insulin-deficient T1DM, TP patients were older (62.59 ± 10.41 vs 44.01 ± 17.22 years, $P \leq 0.001$) with shorter diabetes duration (20 vs 132 months, $P \leq 0.001$) but similar BMI (20.33 ± 2.39 vs 20.92 ± 1.76 kg/m2, $$P \leq 0.079$$). Comparative analysis revealed no significant difference in HbA1c (7.43 ± 0.76 vs 7.66 ± $1.12\%$, $$P \leq 0.128$$) and proportion of patients experiencing hypoglycemic events (58.8 vs $71.3\%$, $$P \leq 0.097$$) between these two groups. The mean glucose value, TIR, TAR, and TBR obtained by CGM were comparable between TP patients and patients with T1DM (8.61 ± 1.59 vs 8.25 ± 1.62 mmol/L, 71.92 ± 9.43 vs 70.19 ± $14.99\%$, 23.41 ± 9.78 vs 24.38 ± $14.51\%$, and 4.68 ± 4.54 vs 5.42 ± $3.13\%$, respectively; $P \leq 0.05$). The glycemic variability indices, including SD and CV, did not differ significantly among the two groups either (3.27 ± 0.71 vs 3.24 ± 1.22 mmol/L and 37.49 ± 8.26 vs 38.22 ± $6.94\%$, respectively; $P \leq 0.05$). Table 3 provides a summary of the comparison outcomes. ## Diabetes treatment for patients undergoing TP All 80 patients undergoing TP were treated with insulin, 63 of them ($78.8\%$) received the MDI regimen, and only three patients ($3.8\%$) were on premixed insulin injections. Daily insulin dose increased significantly at a median follow-up length of 20 months in comparison with one month after operation (0.49 ± 0.19 vs 0.38 ± 0.12 units/kg/day, $P \leq 0.001$) (Figure 2), with a mean basal insulin proportion of $39.4\%$. Comparison of insulin treatment between TP and T1DM showed that the daily insulin dose and the basal insulin percentage of patients with T1DM was higher than patients after TP (0.65 ± 0.19 vs 0.49 ± 0.19 units/kg/day, 38.74 ± 11.44 vs 27.09 ± 11.88 units/day and 43.9 ± 9.9 vs 39.4 ± $16.5\%$, respectively; $P \leq 0.05$) (Table 3). In subgroup analysis, with comparable postoperative BMI and HbA1c among the three groups, insulin requirements were higher in LDG than in NDG (0.60 ± 0.21 vs 0.42 ± 0.12 units/kg/day, $P \leq 0.001$) and SDG (0.60 ± 0.21 vs 0.42 ± 0.17 units/kg/day, $$P \leq 0.003$$), but were equivalent in the latter two groups. While the daily insulin dose was lower in NDG and SDG than in T1DM (0.42 ± 0.12 vs 0.65 ± 0.19 and 0.42 ± 0.17 vs 0.65 ± 0.19 units/kg/day, respectively; $P \leq 0.001$), it was similar between LDG and T1DM (0.60 ± 0.21 vs 0.65 ± 0.19 units/kg/day, $$P \leq 0.295$$). Glycemic control and treatment in the long-term follow-up period classified by preoperative glycemic status are shown in Table 4. Table 4 \| Characteristics \| Total \| NDG \| With pre-op DM \| With pre-op DM.1 \| P value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristics \| Total \| NDG \| SDG \| LDG \| P value \| \| N \| 80 (100) \| 34 (42.5) \| 17 (21.3) \| 29 (36.3) \| – \| \| Age at last follow-up (years) \| 62.59 ± 10.41 \| 60.53 ± 11.19c \| 61.29 ± 12.10 \| 65.76 ± 7.61a \| 0.079 \| \| BMI (kg/m2) \| 20.33 ± 2.39 \| 20.00 ± 2.31 \| 20.03 ± 2.45 \| 20.89 ± 2.44 \| 0.290 \| \| HbA1c (%) \| 7.43 ± 0.76 \| 7.32 ± 0.83 \| 7.77 ± 0.88 \| 7.36 ± 0.52 \| 0.193 \| \| Insulin regimen \| \| \| \| \| 0.411 \| \| MDI \| 63 (78.8) \| 28 (82.4) \| 12 (70.6) \| 23 (79.3) \| – \| \| Premixed insulin \| 3 (3.8) \| 0 \| 2 (11.8) \| 1 (3.4) \| – \| \| Insulin pump \| 14 (17.5) \| 6 (17.6) \| 3 (17.6) \| 5 (17.2) \| – \| \| Daily insulin dose (units/kg/day) \| 0.49 ± 0.19 \| 0.42 ± 0.12c \| 0.42 ± 0.17c \| 0.60 ± 0.21ab \| < 0.001 \| \| Daily insulin dose (units/day) \| 27.09 ± 11.88 \| 23.00 ± 7.57c \| 22.98 ± 9.41c \| 34.28 ± 14.00ab \| < 0.001 \| \| Basal percentage (%) \| 39.4 ± 16.4 \| 37.2 ± 16.6 \| 45.6 ± 17.4 \| 38.4 ± 15.3 \| 0.208 \| \| Insulin pump \| \| \| \| \| \| \| N \| 14 (100) \| 9 (64.3)* \| 9 (64.3)* \| 5 (35.7) \| – \| \| Daily insulin dose (units/kg/day) \| 0.44 ± 0.14 \| 0.38 ± 0.10 \| 0.38 ± 0.10 \| 0.53 ± 0.15 \| 0.043 \| \| Daily insulin dose (units/day) \| 24.81 ± 8.05 \| 22.02 ± 6.97 \| 22.02 ± 6.97 \| 29.83 ± 8.02 \| 0.081 \| \| Basal percentage (%) \| 44.1 ± 14.5 \| 43.4 ± 13.5 \| 43.4 ± 13.5 \| 45.9 ± 17.2 \| 0.743 \| \| Combined oral antidiabetic agent \| 7 (8.8) \| 1 (2.9) \| 1 (5.9) \| 5 (17.2) \| 0.222 \| \| Metformin \| 3 (3.8) \| 0 \| 0 \| 3 (10.3) \| – \| \| α-glucosidase inhibitors \| 4 (5.0) \| 1 (2.9) \| 1 (5.9) \| 2 (6.9) \| – \| Fourteen patients used insulin pump therapy with a daily insulin dose of 0.44 ± 0.14 units/kg/day and a mean basal insulin proportion of $44.1\%$. Among them, five patients of LDG required more insulin than patients of NDG or SDG (0.53 ± 0.15 vs 0.38 ± 0.10 units/kg/day, $$P \leq 0.043$$) (Table 4). In patients on insulin pump therapy, daily insulin dose was also lower in patients undergoing TP than in those with T1DM (0.44 ± 0.14 vs 0.66 ± 0.14 units/kg/day and 24.81 ± 8.05 vs 39.93 ± 10.22 units, $P \leq 0.001$) (Table 3). The basal insulin infusion rates at all time points of TP patients were significantly lower than patients with T1DM (all $P \leq 0.05$). An increasing trend of insulin infusion rate in the morning could be observed in both TP and T1DM patients (Figure 4). However, no discernible difference was found between the MDI and continuous subcutaneous insulin infusion (CSII) group in terms of HbA1c, the proportion of hypoglycemia events, and insulin dose (7.42 ± 0.74 vs 7.49 ± $0.84\%$, 59.1 vs $57.1\%$ and 0.49 ± 0.20 vs 0.44 ± 14 units/kg/day, $P \leq 0.05$). Figure 4: TP, total pancreatectomy; T1DM, type 1 diabetes mellitus. Data are presented as mean ± SD. All P values were less than 0.05. Seven patients received oral antidiabetic agents in combination with insulin therapy, including three patients treated with metformin and four with α-glycosidase inhibitors. Five of the seven patients were from LDG (Table 4). ## Diabetic complications of patients undergoing TP Two out of eighty patients developed microalbuminuria (urinary albumin-to-creatinine ratio 30-300 mg/g) with normal creatinine levels at 119 months and 96 months after TP, respectively. There was no diabetic retinopathy in any of the 16 patients who underwent ophthalmofundoscopy. All the patients were free of nonfatal myocardial infarction and stroke events throughout the follow-up. No patient needed medical intervention owing to diabetic ketoacidosis, and no deaths in this series were attributed to diabetic complications. ## Discussion This large cohort presented a comprehensive picture of glycemic control, insulin therapy, diabetic complications, and surgical outcomes in patients undergoing TP from perioperative to long-term follow-up periods, providing reference protocols for CVII, MDI, and CSII at different postoperative stages. During hospitalization after TP, the proportion of glucose value within the target range (4.0-10.0 mmol/L) was consistent with the previous research [17], and the hypoglycemic events were rare under close glucose monitoring. Compared with the common insulin dose during the TPN period for patients with diabetes [16], the higher ratio at 5.70 units of regular insulin per 10 g carbohydrate following TP might be attributed to complete insulin deficiency, postoperative stress, and the lack of basal insulin. After adding basal insulin to CVII therapy, the daily insulin dose decreased, and glycemic control further improved with an acceptable hypoglycemia risk, indicating that timely and adequate basal insulin replacement was important during the postoperative parenteral period after TP. From our experience, subcutaneous basal insulin can be administered at 0.1-0.2 units/kg/day on the basis of CVII and adjusted until fasting blood glucose was appropriate. Pre-meal rapid-acting insulin can be given according to the food intake and blood glucose values. Andersen et al. [ 17] reported that parenteral nutrition with insulin treatment after TP improved glycemic control compared with glucose infusion and reduced non-infectious postoperative complications. In our cohort, all patients were given TPN after surgery, and the insulin dose decreased after the gradual transition to enteral nutrition. Guidelines for nutrition support therapy suggest that once tolerance to enteral nutrition improves, the amount of parenteral nutrition should be reduced and discontinued when the patient receives >$60\%$ of total energy from enteral nutrition to minimize the negative effect of hyperglycemia [18]. Further research can be done about the impact of parenteral and enteral nutrition on blood glucose and surgical complications in patients undergoing TP. During the long-term follow-up, we noted the mean HbA1c level of $7.43\%$ after TP was in accordance with recent larger cohorts, which ranged from $7.3\%$ to $7.9\%$ (10, 19–23). Several previous studies reported the incidence of hypoglycemia after TP ranging from $42.0\%$ to $100.0\%$ with a median of 2 occurrences per week or 10 per month (10, 24–28), and body weight loss, low total cholesterol level, strict glycemic control, and using rapid-acting insulin were risk factors for hypoglycemia [28]. We performed a comprehensive evaluation of glycemic control through CGM, confirming great glycemic variability in patients after TP but comparable mean glucose values, TIR, TBR, TAR, and CV to patients with complete insulin-deficient T1DM. Juel et al. [ 29] also reported similar CV and TBR assessed by CGM between TP and T1DM patients but lower TIR, higher TAR, and higher continuous overall net glycemic action per 60 minutes were observed in TP patients. Furthermore, HbA1c, hospitalization rate secondary to hypoglycemia, and impact of diabetes after TP on most domains in quality of life were paralleled to insulin-dependent diabetes from other causes (13, 30–33). Overall, glycemic control after TP can be similar to T1DM under regular follow-up but indeed is influenced by more factors, such as diet recovery, pancreatic enzyme supplementation, and primary disease treatment. With the recovery of food intake and sufficient pancreatin replacement, the mean daily insulin dose at long-term follow-up considerably increased compared to one month after surgery. The mean daily insulin dose was 27.09 ± 11.88 units/day (0.49 ± 0.19 units/kg/day), which was in parallel with that in other published studies varying from 23 to 37 units (8, 10, 23–25, 34, 35) or 0.50 to 0.58 units/kg/day [15, 21, 23, 34]. Our findings, which were also in line with the previous research, showed a marked decrease in daily insulin requirements and basal insulin percentages in patients after TP compared to T1DM with similar BMI and complete insulin deficiency [36, 37]. Moreover, in subgroup analysis compared with T1DM, the decrease in insulin dose was observed only in patients of NDG and SDG but not LDG. The lower daily and basal insulin needs in patients following TP may be attributed to increased peripheral insulin sensitivity, malabsorption, and defect in the counterregulatory mechanism offered by pancreatic glucagon [36, 38]. Patients with preoperative long-duration diabetes had higher daily insulin requirements than those without diabetes or those with preoperative short-duration diabetes during both perioperative and long-term follow-up periods. This phenomenon may reflect that patients with preoperative long-duration diabetes have a certain degree of insulin resistance. The patients with preoperative diabetes all had extensive involvement of the pancreas, making it difficult to distinguish between type 2 diabetes and pancreatogenic diabetes preoperatively. Higher age, BMI, and proportion of diabetes family history were also characteristics of patients with preoperative long-duration diabetes, which could help determine whether patients have greater insulin requirements. Therefore, preoperative identification of glycemic status and diabetes classification in patients planned to undergo TP are important to guide postoperative insulin treatment. In our cohort, TP patients who received insulin pump therapy had similar daily insulin requirements but higher basal insulin percentages compared to the previous literature [36]. The increase in insulin infusion rate early in the morning was also detected in TP patients, suggesting that the dawn phenomenon also existed in patients after TP despite the deficiency of pancreatic glucagon. The dawn phenomenon after TP may derive from other counter-regulatory hormones rather than the pancreatic glucagon, as the levels of cortisol, thyroxine, and growth hormone have been reported to be comparable between patients after TP and patients with T1DM [36]. We identified no difference in glycemic control or insulin dose between the CSII group and the MDI group, but Struyvenberg et al. [ 26] demonstrated a significant reduction in severe hypoglycemic events in the CSII group than in the MDI group. Additionally, it was reported that artificial pancreas, sensor-augmented predictive low-glucose suspend pump and advanced hybrid closed−loop systems were efficacious and safe for perioperative and long-term glycemic control after TP (39–41). Hence, advanced techniques in diabetes care, including CGM, CSII, and new insulin preparations, can potentially allow better glycemic control in patients after TP. However, prospective, randomized controlled clinical trials were still needed. Only a few patients in our cohort received combined oral antidiabetic agents after TP. Several studies have reported other antidiabetic medications for patients after TP. Juel et al. [ 42] disclosed that glucagon-like peptide 1 receptor agonist lixisenatide reduced postprandial plasma glucose excursions in TP patients by decelerating gastric emptying and reducing postprandial responses of gut-derived glucagon. A case report showed that the suppression of extrapancreatic glucagon by octreotide long-acting repeatable improved the hyperglycemia in a TP patient with PNET [43]. Diabetic complications after TP should be taken seriously because patients with benign diseases have a long-life expectancy, as shown in the survival analysis of our cohort. A systematic review of outcomes after TP reported no diabetes-related mortality since 2005 and rare diabetic ketoacidosis [9]. The risk for microvascular complications of pancreatogenic diabetes appeared to be similar to other types of diabetes [44]. In a mayo clinic cohort, end-organ complications after TP developed in $28\%$ of patients during a mean follow-up of 3.8 years [34]. Crippa et al. [ 35] reported diabetic complications in 6 of 45 patients at least 60 months follow-up after TP, including four patients with peripheral vascular disease, one stroke, and one retinopathy. In our study, we observed the development of microalbuminuria in only 2 out of 80 individuals without new-onset cardiovascular disease. The diabetic outcomes after TP require longer follow-ups to clarify. The present study had some limitations. Firstly, the perioperative data were collected retrospectively, so the insulin adjustment protocol at hospitalization was not completely consistent. In the second place, several patients with malignant tumors received adjuvant chemotherapy, tyrosine kinase inhibitors, or somatostatin analog during follow-up, which might have an adverse effect on glycemic control. Thirdly, the selected patients with different pancreatic pathologies increased the heterogeneity of subjects. Finally, longer follow-ups are needed for the perioperative and long-term outcomes. Further prospective randomized controlled trials are required to determine the optimal treatment and glucose targets for patients after TP. ## Conclusions In conclusion, glycemic control following TP could be kept within an acceptable range. Patients undergoing TP usually had high insulin requirements in the postoperative parenteral nutrition period. During long-term follow-up, similar glycemic control and variability but lower insulin needs were observed in patients after TP compared to those with complete insulin-deficient T1DM. Considering preexisting long-duration diabetes was associated with higher insulin requirements postoperatively, we proposed that preoperative glycemic status should be evaluated as it could guide insulin treatment after TP. In addition, both perioperative and long-term multidisciplinary management, including primary disease treatment, intensive diabetes care, adequate pancreatic enzyme supplementation, and nutritional support, have an essential role in improving the short- and long-term outcomes of TP. ## 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 Peking Union Medical College Hospital. The patients/participants provided their written informed consent to participate in this study. ## Author contributions TYZ, WZ, and TY designed the study. TYZ, WZ, TY, YF, TPZ, JG, QL, SS, YD, and YG performed clinical evaluation and management for patients. TYZ, WZ, TY, and YF collected the clinical data. TYZ conducted the statistical analysis and drafted the manuscript. WZ and TY revised 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. ## References 1. Murphy M, Knaus W, Ng S, Hill J, McPhee J, Shah S. Total pancreatectomy: A national study. HPB (Oxford) (2009) 11. DOI: 10.1111/j.1477-2574.2009.00076.x 2. Johnston W, Hoen H, Cassera M, Newell P, Hammill C, Hansen P. 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--- title: Effects of polysaccharides from Lyophyllum decastes (Fr.) Singer on gut microbiota via in vitro-simulated digestion and fermentation authors: - Fangfang Zhang - Ying Xiao - Liang Pan - Ling Yu - Yanfang Liu - Deshun Li - Xiaojie Liu journal: Frontiers in Microbiology year: 2023 pmcid: PMC9969080 doi: 10.3389/fmicb.2023.1083917 license: CC BY 4.0 --- # Effects of polysaccharides from Lyophyllum decastes (Fr.) Singer on gut microbiota via in vitro-simulated digestion and fermentation ## Abstract ### Introduction Lyophyllum decastes (Fr.) Singer polysaccharides (LDSPs) have been verified to possess strong biological properties. However, the effects of LDSPs on intestinal microbes and their metabolites have rarely been addressed. ### Methods The in vitro-simulated saliva-gastrointestinal digestion and human fecal fermentation were used to evaluate the effects of LDSPs on non-digestibility and intestinal microflora regulation in the present study. ### Results The results showed a slight increase in the content of the reducing end of the polysaccharide chain and no obvious change in the molecular weight during in vitro digestion. After 24 h in vitro fermentation, LDSPs were degraded and utilized by human gut microbiota, and LDSPs could be transformed into short-chain fatty acids leading to significant ($p \leq 0.05$) decrease in the pH of the fermentation solution. The digestion did not remarkably affect the overall structure of LDSPs and 16S rRNA analysis revealed distinct shifts in the gut microbial composition and community diversity of the LDSPs-treated cultures, compared with the control group. Notably, the LDSPs group directed a targeted promotion of the abundance of butyrogenic bacteria, including Blautia, Roseburia, and Bacteroides, and an increase in the n-butyrate level. ### Discussion These findings suggest that LDSPs might be a potential prebiotic to provide a health benefit. ## Introduction Lyophyllum decastes (Fr.) Singer, commonly known as the fried chicken mushroom, has been recognized as a culinary delicacy in Asia with potential for the commercial culture and great economic importance (Arana-Gabriel et al., 2018). Apart from excellent flavor and texture, Lyophyllum decastes (Fr.) Singer is currently of great interest due to being a biologically rich source of various active substances (Pokhrel and Ohga, 2007). It is deserved to be mentioned that polysaccharides from Lyophyllum decastes (Fr.) Singer (LDSPs) exhibit a range of possible health benefits, including antioxidant, anticholinesterase, antibacterial, and antidiabetic (Miura et al., 2002; Pushpa and Purushothama, 2010; Tel et al., 2015; Deveci et al., 2021). Increasing evidence demonstrates that the consumption of fungal-derived polysaccharides can result in specific changes in gastrointestinal microbiota. The composition and homeostasis of the gut microbial community have an intense and intimate linkage to human health and play a crucial role in a multitude of physiological functions involving metabolic, immunologic, and protective activities (Slavin, 2013). Polysaccharides derived from Ganoderma lucidum have been shown to alleviate rat Dextran sulfate (DSS)-induced colitis by stimulating the growth of beneficial bacteria, such as Ruminococcus_1, and reducing pathogens, such as Escherichia-Shigella (Xie et al., 2019). In addition, acting as a substrate for gut microorganisms, polysaccharides from fungi can facilitate gut microbiota to produce short-chain fatty acids (SCFAs) providing a health benefit in immune and metabolic disease (Sanna et al., 2019). For instance, Poria cocos polysaccharides significantly enhanced glucose and lipid metabolism and mitigated hepatic steatosis in ob/ob mice by elevating the levels of butyrate and butyrate-producing bacteria Lachnospiraceae and Clostridium (Sun et al., 2019). It was observed that the promising prophylactic and therapeutic properties of fungal polysaccharides might be attributed to their diversified abilities to restore gut microbial balance. Meanwhile, different polysaccharides of fungal origin may possess distinct prebiotic properties, which could be critical to comprehend their modulation of gut microbiota composition (Jayachandran et al., 2018). An in vitro batch culture fermentation study examined the impact of edible mushrooms polysaccharides on aging gut microbiota characteristics, demonstrating that Pleurotus spp. and C. Cylindracea mushrooms induced a significant bifidogenic effect, while P. eryngii mushrooms simulated the growth of Lactobacillus spp. and butyrate-producing bacteria such as F. prausnitzii and E. rectale/Roseburia spp. group (Mitsou et al., 2020). Undeniably, fungal polysaccharides represent a vast and still untapped source assumed to be applied, but there is little known about the regulation of LDSPs on the intestinal microbiome and their metabolites. We hypothesized that LDSPs should be neither digested nor absorbed; therefore, LDSPs could undergo fermentation in the colon and be utilized by the gut microbiota accompanied by changes in SCFAs. In this study, the simulated saliva-gastrointestinal digestion model was conducted to reveal the digestion behavior of LDSPs and the main changes in molecular weight (Mw) and the content of the reducing end of the polysaccharide chain (RC). In vitro anaerobic fermentation was performed, 16S rRNA sequencing was adopted to clarify the effect of LDSPs on gut microbial composition, and their fermentation characteristics were investigated by determining SCFAs production and pH value. This study was intended to provide evidence of the possible digestion and fermentation mechanism of LDSPs for application in new potential prebiotics. ## Materials and reagents Lyophyllum decastes (Fr.) Singer was collected from an agricultural product processing base in Taixing, Jiangsu Province, China. The enzymes, used in this study, including α-amylase (100 U/mg), pepsin (3,000 U/g), and pancreatic enzyme (4,000 U/g), were purchased from Sigma-Aldrich (St. Louis, United States). SCFA standards including acetic, propionic, butyric, valeric, isobutyric, and isovaleric acids were purchased from Aladdin (Shanghai, China). All other chemicals and solvents used were analytical grade. ## Preparation of the LDSPs The Lyophyllum decastes (Fr.) Singer was prepared using the method described by Wu et al. [ 2017] with slight modifications. Hot water extraction was used to obtain polysaccharides from Lyophyllum decastes (Fr.) Singer. In brief, the dried Lyophyllum decastes (Fr.) Singer powders (50.0 g) were extracted three times with ultrapure water (1:15, w/v) at 100°C for 1 h. Then, polysaccharides extracted from Lyophyllum decastes (Fr.) Singer was further concentrated and ultra-filtered (molar mass cutoff, 5.0 kDa) to remove impurities and finally freeze-dried. ## Determination of physiochemical characteristics of LDSPs The protein content, total phenol content, and total carbohydrate content of LDSPs were determined by the Bradford method, Folin–Ciocalteu method, and phenol–sulfuric acid method (Dubois et al., 1951; Bradford, 1976). The monosaccharide composition of LDSPs was according to Wang et al. [ 2019] with some modifications. Here, 2 M aqueous trifluoroacetic acid (TFA) was used to hydrolyze LDSPs at 110°C for 4 h. Then, High-Performance Anion-Exchange Chromatography with Pulsed Amperometric Detection (HPAEC-PAD; Dionex ICS-5000+) equipped with the Dionex CarboPac PA20 analytical column (3 × 150 mm) was conducted to detect monosaccharides in hydrolyzed samples. The Mw of LDSPs was determined using the modified method as previously reported (Chen G. et al., 2017) by high-performance size exclusion chromatography, equipped with a multi-angle laser light scattering and a refractive index detector (HPSEC-MALLS-RID Shimadzu, Kyoto, Japan). The separation of samples was applied on TSK-GEL G6000PWXL and G4000PWXL column (7.8 × 300 mm, TOSOH Crop., Tokyo, Japan). The sodium nitrate solution (0.15 mol/ml) was eluted and the flow rate was 0.5 ml/min, and the injection volume of the sample was 100 μl. ## In vitro-simulated salivary-gastrointestinal digestion The simulated in vitro digestion was performed according to the previously published procedures (Brodkorb et al., 2019) with minor modifications. First, 25 mg of LDSPs were dissolved in 5 ml of ultrapure water. Subsequently, 4 ml of simulated salivary fluid (SSF) was preheated at 37°C in a water bath and then added into a 5 ml of LDSPs solution (10 mg/ml), CaCl2 solution (0.025 ml, 0.3 M), and α-amylase solution (75 U/ml, 0.5 ml). Ultrapure water was added to supplement the solution to 10 ml to mix LDSPs solution with SSF to achieve a final ratio of 1:1 (wt/wt). In addition, the ultrapure water and inulin solution (10 mg/ml) added to the simulated digestion medium were, respectively, used as the blank control (CON) group and positive control (INU); then, each group was kept in a 37°C shaking bath for 5 min. Thereafter, the pH was adjusted to 3.0 with HCl (6 mol/L). Afterward, 8 ml of simulated gastric fluid (SGF) was preheated at 37°C in a water bath, followed by the addition of the previous stage of 10 ml simulated digestive fluid, CaCl2 solution (0.01 ml, 0.3 M), pepsin solution (2000 U/ml, 1 ml), and ultrapure water to supplement the solution to 20 ml to achieve a final ratio of 1:1 (wt/wt). The simulated digestion samples were incubated at 37°C. During the digestion process, an equal volume of simulated gastric fluid was taken out at the time points of 0, 1, 2, and 4 h for further analysis. Next, the pH was adjusted to 7.0 with NaOH (6 mol/L). Next, 12 ml of simulated intestinal fluid (SIF) was preheated in a 37°C water bath, followed by the addition of the previous stage-simulated digestive fluid, bile salt solution (2.5 ml, 10 mM), CaCl2 solution (0.04 ml, 0.3 M), a pancreatic enzyme solution (0.5 g, 100 U/ml), and ultrapure water to supplement it to 40 ml. During the digestion process, an equal volume of simulated gastric fluid was taken out at the time points of 0, 1, 2, 4, and 6 h for further analysis. ## Collection and preparation of microbiota inoculums and in vitro fermentation The in vitro fermentation was performed based on the previous method with minor modifications (Lam et al., 2018). First, fresh fecal samples were collected from six healthy volunteers (three women and three men, 20–30 years of age) who maintained a regular diet and had not received antibiotics or prebiotic treatment for 3 months. The fecal samples were immediately mixed with sterilized 0.1 M phosphate-buffered saline (pH 7.0) to produce a $10\%$ (w/v) fecal suspension. Then, 2.0 g of peptone, 2.0 g of yeast extract, 0.5 g of cysteine-HCl, 0.1 g of NaCl, 2.0 g of NaHCO3, 0.04 g of K2HPO4, 0.04 g of KH2PO4, 0.01 g of MgSO4 7H2O, 0.01 g of CaCl2 6H2O, 0.02 g of hemin, 0.5 g of bile salt, 2.0 ml of Tween 80, 10 μl of vitamin K1, and 1.0 ml of resazurin $1\%$ (w/v) were dissolved in 1 L ultrapure water to obtain a basic medium. Finally, LDSPs were selected as $1\%$ (w/v) carbon sources, and the medium without carbon sources and $1\%$ (w/v) inulin were used as a blank control (CON) and positive control (INU). The medium was adjusted to pH 7.0 using 1 mol−1 HCl and placed in an anaerobic chamber at 37°C overnight to pre-reduce the media. Here, 1.0 ml of $10\%$ (w/v) fecal slurry was added to a 9.0 ml medium and placed in the 37°C anaerobic chamber and incubated for 0, 6, 12, and 24 h. Then, the samples were collected immediately and stored at −80°C for further analysis. ## Determinations of LDSPs variations during in vitro digestion and fermentation The reducing end of the polysaccharide chain (RC) contents of the digestion and fermentation products were analyzed by the dinitrosalicylic acid (DNS) method using glucose as the standard (Li et al., 2020). The Mw and residual carbohydrate of the digestion and fermentation products were determined according to the previous method. ## The pH value and SCFAs analysis during in vitro fermentation The pH of the fermentation system was measured with a standard pH meter (DELTA320, Mettler Toledo Co., Ltd. Shanghai, China). SCFAs were extracted by absolute ether following the method of Bai et al. [ 2021] and were analyzed by gas chromatography–mass spectrometry (GC–MS)-TQ8040 (Shimadzu, Kyoto, Japan) equipped with SH-Rtx®-WAX column (30 m × 0.25 mm i. d.; film thickness 0.25 μm). In brief, the fermented solution was centrifuged at 6000 g for 10 min. The 20 μl of $10\%$ H2SO4 was added to acidify 500 μl of supernatant, and 500 μl of absolute ether was used to extract SCFAs. The mixtures were then centrifuged at 8000 g for 10 min at 4°C, and the phases were separated. The supernatant was taken and filtered using a 0.20 μm filter into a sample injection bottle. The temperature increased raised to 140°C at 7.5°C/min and held for 4 min, which then raised to 200°C at 60°C/min. Carrier gas helium was employed, the flow rate was 2.0 ml/min, the full scan mode in the m/z range was 20.0–300.0, and the injection volume was 1 μl. SCFA concentration was determined by the external standard method with corresponding standards. ## Analysis of the gut microbiota After fermentation of 24 h, high-throughput sequencing technology of bacterial 16S rRNA was implemented to investigate the impact of polysaccharides on the gut microbiota. Each sample of 24 h blank control fermentation group (CON group), 24 h inulin fermentation group (INU group), and 24 h Lyophyllum decastes (Fr.) Singer fermentation group (LDSPs group) was extracted using Qiagen QIAamp Fast DNA Stool Mini Kit, according to the manufacturer’s instructions. The DNA extraction from all samples was visualized on $1\%$ agarose gel electrophoresis. PCR amplification was performed using TransStart FastPfu DNA Polymerase, and the amplicons were purified using the AxyPrep DNA gel extraction kit (Axygen Bioscience, Union City, United States) and quantified using QuantiFluor™-ST fluorometer (Promega, Madison, United States). The major PCR products from the V3–V4 region of the 16S rRNA gene amplified with primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R(5′-GGACTACHVGGGTWTCTAAT-3′) were sequenced on the Illumina Miseq platform by Shanghai Majorbio Bio-pharm Technology Co. Ltd. (Shanghai, China). ## Statistical analysis The microbiological data were analyzed on the online platform Majorbio Cloud Platform.1 First, the double-ended reads were quality-controlled and filtered according to the sequencing quality, and the optimization data after quality control (QC) splicing are obtained according to the overlapping relationship between the double-ended reads. Then, the sequence noise reduction method (DADA2/Deblur) is used to process the optimization data to obtain Amplicon Sequence Variant (ASV) representing sequence and abundance information. All data, including alpha diversity, beta diversity, and the examination of the bacterial taxonomic compositions, were obtained from ASV. By using one-way ANOVA, the microbiota substantial difference analysis was obtained. The other experiments were carried out 5-fold. Data were expressed as mean ± standard deviation (SD). Statistical analysis was carried out using SPSS (Version 17.0, Chicago, United States). One-way analysis of variance (ANOVA) followed by Tukey’s test at a $5\%$ confidence level was used to calculate the significant difference. ## Preliminary characterization of LDSPs The basic physical and chemical properties of purified LDSPs are shown in Table 1. As shown in Table 1, the total carbohydrate content, total phenol content, and protein content of LDSPs were 90.51, 3.82, and $1.95\%$, respectively. Moreover, the monosaccharide composition of LDSPs was glucose ($82.14\%$), fructose ($14.20\%$), galactose ($2.49\%$), mannose ($0.82\%$), fucose ($0.20\%$), arabinose ($0.10\%$), and rhamnose ($0.04\%$). These results indicated that the main monosaccharide of LDSPs was glucose. HPGPC chromatogram showed the two peaks with an average molecular weight of 4.59 × 107 Da and 4.30 × 106 Da (1.14:1). Table 1 \| Unnamed: 0 \| Parameters \| Value \| \| --- \| --- \| --- \| \| Essential component (%) \| Carbohydrate \| 90.51 ± 0.12% \| \| Essential component (%) \| Phenol \| 3.82 ± 0.09% \| \| Essential component (%) \| Protein \| 1.95 ± 0.03% \| \| Monosaccharide composition (%) \| Glucose \| 82.14 ± 0.03% \| \| Monosaccharide composition (%) \| Arabinose \| 0.10 ± 0.01% \| \| Monosaccharide composition (%) \| Fucose \| 0.20 ± 0.02% \| \| Monosaccharide composition (%) \| Rhamnose \| 0.04 ± 0.00% \| \| Monosaccharide composition (%) \| Galactose \| 2.49 ± 0.01% \| \| Monosaccharide composition (%) \| Fructose \| 14.20 ± 0.02% \| \| Monosaccharide composition (%) \| Mannose \| 0.82 ± 0.00% \| ## Changes in the reducing end of the polysaccharide chain (RC) contents released from LDSPs Salivary amylase and the severe pH in the simulated gastric fluid may play important roles in the digestion of non-starch polysaccharides (Zhang et al., 2021). Moreover, due to the presence of pancreatin and high concentration of bile acids, there may be changes in the chemical composition of polysaccharides after intestine digestion. After hydrolyzed by digestive fluid, the glycosidic of polysaccharides bonds is destroyed accompanied by the increase in reducing end. As shown in Table 2, the contents of RC in LDSPs did not change significantly after simulated saliva digestion, indicating that LDSPs were not affected by salivary amylase. During simulated gastric digestion and intestinal digestion, both LDSPs and INU were partially degraded, but RC content increased significantly ($p \leq 0.05$) in the INU group compared to the LDSPs group. Overall, the structures of LDSPs were not significantly influenced by simulated saliva-gastrointestinal fluid, so they could be neither digested nor absorbed and directed to the colon to be utilized by gut microbiota. Table 2 \| Unnamed: 0 \| Time \| Reducing end of polysaccharide chain content (mg/mL) \| Reducing end of polysaccharide chain content (mg/mL).1 \| Reducing end of polysaccharide chain content (mg/mL).2 \| \| --- \| --- \| --- \| --- \| --- \| \| \| Time \| CON \| INU \| LDSPs \| \| Saliva digestion \| 0 min \| 0.122 ± 0.004a \| 0.701 ± 0.005a \| 0.145 ± 0.004a \| \| Saliva digestion \| 5 min \| 0.132 ± 0.012a \| 0.709 ± 0.001a \| 0.146 ± 0.001a \| \| Gastric digestion \| 0 h \| 0.148 ± 0.005a \| 0.389 ± 0.001c \| 0.159 ± 0.001b \| \| Gastric digestion \| 2 h \| 0.149 ± 0.002a \| 0.591 ± 0.006b \| 0.161 ± 0.005b \| \| Gastric digestion \| 4 h \| 0.155 ± 0.009a \| 0.808 ± 0.002a \| 0.165 ± 0.008a \| \| Intestinal digestion \| 0 h \| 0.108 ± 0.002a \| 0.342 ± 0.001b \| 0.116 ± 0.005c \| \| Intestinal digestion \| 2 h \| 0.110 ± 0.003a \| 0.346 ± 0.002b \| 0.118 ± 0.005bc \| \| Intestinal digestion \| 4 h \| 0.112 ± 0.013a \| 0.350 ± 0.001b \| 0.120 ± 0.001ab \| \| Intestinal digestion \| 6 h \| 0.109 ± 0.002a \| 0.370 ± 0.001a \| 0.121 ± 0.001a \| ## Changes in molecular weight The digestion properties of LDSPs are also related to their changes in molecular weights (Wu et al., 2018). The molecular weight distributions of two peaks during digestion are shown in Table 3 and Supplementary Figure 1A. After in vitro-simulated digestion, the molecular weights of LDSPs did not change significantly ($p \leq 0.05$), in agreement with the changes in RC, which indicated that LDSPs were indigestible during in vitro digestion. Table 3 \| Samples \| Peak 1 \| Peak 2 \| \| --- \| --- \| --- \| \| Samples \| Mw (Da) \| Mw (Da) \| \| LDSPs \| 4.59× 107 ± 0.04a \| 4.30 × 106 ± 0.15a \| \| LDSPs-S \| 4.52× 107 ± 0.12a \| 4.25 × 106 ± 0.09a \| \| LDSPs-G \| 4.46× 107 ± 0.23a \| 4.21 × 106 ± 0.19a \| \| LDSPs-I \| 4.41× 107 ± 0.09a \| 4.19 × 106 ± 0.13a \| \| LDSPs-6 h \| 4.08× 107 ± 0.14b \| 3.96 × 106 ± 0.18b \| \| LDSPs-12 h \| 3.37× 106 ± 0.16c \| 3.55× 105 ± 0.09c \| \| LDSPs-24 h \| 2.35× 106 ± 0.10d \| 2.03× 105 ± 0.24d \| The possible changes in molecular weight of LDSPs during in vitro fermentation were further investigated. Table 3 and Supplementary Figure 1B show the Mw changes in two peaks in LDSPs, and the Mw of LDSPs showed a slight decrease during 0–6 h and marginally decreased after 12 h of fermentation. These observations demonstrated that the microbiota went through a lag period in the initial 12 h and then degraded LDSPs quickly into polysaccharide chains with reducing ends for proliferation. ## Changes in the RC and residual carbohydrate Lyophyllum decastes (Fr.) Singer was subjected to an in vitro fermentation model inoculated with human fecal microbiota. Recently, the accumulating evidence shows that large polysaccharides are indigestible in saliva-gastrointestinal fluid for the lack of corresponding enzymes but can be degraded by the intestinal microbiota to increase the reducing end of the polysaccharide chain (Guo et al., 2021). As shown in Figure 1C, the RC and residual carbohydrate content were detected at 6, 12, and 24 h of fermentation. The LDSPs group showed a significant ($p \leq 0.05$) increase in the contents of RC during 0–12 h fermentation and a decrease after 12 h of fermentation. Combined with the sharp ($p \leq 0.05$) reduction in residual carbohydrates in Figure 1B, it was clear that the LDSPs were degraded into polysaccharide chains with reducing ends by the gut microbiota before the first 12 h. In addition, the degradation rate of LDSPs was higher than the utilization rate, and with the rapid proliferation of microbiota, the polysaccharide chains with reducing ends were easily utilized by gut microbes; therefore, RC was gradually decreased during the 12–24 h of fermentation. Indeed, the total residual carbohydrate remarkably ($p \leq 0.05$) decreased to 39.31 and $81.59\%$ in INU and LDSPs groups, respectively, after the in vitro fermentation for 24 h, suggesting that they can be partially or fully hydrolyzed by intestinal microbiota, which is determined by the physicochemical properties of polysaccharides (Hu et al., 2018). Figure 1: pH value (A), residual carbohydrate (B), and reducing end of polysaccharide chain content (C) during in vitro fermentation, respectively. Data are expressed as mean ± SD (n = 5), and a–d mean significantly different (p < 0.05) by a Tukey test in the same group with different time points. ## Effects of LDSPs on gut microbiota In the present work, the high-throughput sequencing analysis was conducted on samples after 24 h fecal fermentation to reveal the effect of the indigestible LDSPs on the microbial structure. The average Good’s coverage was $99.28\%$, indicating the 16S rRNA sequences identified in this study likely represent the majority of bacterial sequences present in the samples. Diversity data analysis of 60 samples was completed and obtained ranging from 3,530,423 to 1,472,821,519 reads, with an average sequence length of 418 bp. The Sobs index, Shannon index, Simpson index, hierarchical clustering analysis, and principal co-ordinate analysis (PCoA) are shown in Figure 2. The rarefaction curves for the Sobs index exhibits the numbers of observed species per sample, and each sample reached plateaus, indicating that the majority of the sequencing was already sufficient (Figure 2A). Both the community diversity and richness in the LDSPs group significantly decreased after 24 h of fermentation compared with the initial sample group (OR) but higher than that in the 24 h blank group (CON) and the 24 h inulin supplement group (INU). Furthermore, the PCoA is an important analytical method for β-diversity. The PCoA score plot was used to reveal the microbiota shifted in both LDSPs and INU groups (Figure 2F). The total alternation of principal component 1 (PC1; $55.87\%$) and principal component 2 (PC2; $36.91\%$) was $92.78\%$. PCoA results show that both the LDSPs group and the INU group were far away from the CON group, and the outcome of hierarchical clustering analysis (Figure 2E) was in line with the PCoA results, indicating that LDSPs and INU could significantly affect the structure of the microbial community. Figure 2: Alpha diversity analysis using the Student’s t-test for the Ace (A), Chao (B), Shannon (C), and Simpson indices (D) and β-diversity analysis using UPGMA for hierarchical clustering (E) and ANOSIM for PCoA (F) on ASV level of gut microbiota in groups (n = 5) after 24 h of fermentation. p < 0.05, p < 0.01, and p < 0.001, respectively; OR, the initial sample group, CON, the blank control (no additional carbon source supplement), INU, the positive control (INU supplement), and LDSPs, the experimental group (LDSPs supplement); ANOSIM, analysis of similarities; PCoA, principal co-ordinate analysis. At the phylum level, the dominant bacterial communities comprised Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes (Figure 3A). Although the LDSPs group ($85.75\%$) was noticeably richer in the relative abundance of Firmicutes compared with the CON group ($27.66\%$), the Bacteroidetes to Firmicutes (B/F) ratio in the LDSPs group was significantly ($p \leq 0.05$) upregulated with $97\%$ compared with the CON group (Figure 3B). Bacteroidetes is one of the major gut bacteria that could degrade polysaccharides, and the increased B/F ratio could alleviate obesity, which was considered one of the essential biological indicators (Ai et al., 2017). In the control group, an unsuitable ratio between protein and carbohydrate could result in increases in the number of potential pathogens due to disruption of the homeostasis of the gut micro-ecosystem with a higher abundance of Proteobacteria (Zhao et al., 2018). In addition, the LDSPs ($4.10\%$) and INU ($0.02\%$) also remarkably decrease the relative abundance of Proteobacteria compared with CON ($65.28\%$), which might be attributed to the fact that LDSPs and INU could inhibit pathogens belonging to Proteobacteria such as Escherichia-Shigella. Actinobacteria, represented by the major probiotic bacteria Bifidobacterium, significantly increased in the INU group, compared with the CON group. Figure 3: Relative abundance of gut microbiota community at the phylum (A) and genus (C) levels, Bacteroidetes/Firmicutes (B/F) ratios (B), and the comparison of microbiota from phylum level to genus level among the CON, INU, and LDSPs groups based on linear discriminant analysis effect size (LEfSe; D) after 24 h of fermentation. p < 0.05, p < 0.01, and p < 0.001, respectively; OR, the initial sample group, CON, the blank control (no additional carbon source supplement), INU, the positive control (INU supplement), and LDSPs, the experimental group (LDSPs supplement). As shown in Figure 3C, three groups displayed different gut microbiota distributions at the genus level. The CON group was mainly composed of Escherichia-Shigella ($23.72\%$), unclassified_f_Enterobacteriacece ($30.02\%$), Enterobacter ($5.51\%$), Klebsiella ($6.02\%$), Bifidobacterium ($5.94\%$), and Phascolarctobacterium ($3.17\%$). However, Megamonas ($70.30\%$) and Bifidobacterium ($23.22\%$) became the dominant microbiota for the INU group after 24 h of fermentation, indicating that Megamonas might be the principal gut microbiota to degrade and utilize INU, similar to the result reported in a previous study (Chen B. D. et al., 2017). The LDSPs group also possessed higher levels of Blautia ($39.59\%$), Faecalibacterium ($9.69\%$), Dorea ($3.62\%$), and Subdoligranulum ($3.99\%$) than that of the CON group. The increasing works have suggested that Blautia is a kind of gut microbiota to promote the production of butyric acid (Cantu-Jungles et al., 2018). The linear discriminant analysis effect size (LEfSe) in Figure 3D indicates the abundance of significantly different bacteria from the phylum level to the genus level in the CON, INU, and LDSPs groups. Prevotella has been reported to play an important role in glucose homeostasis and host metabolization (Si et al., 2017), and it was observed at a higher level in the LDSPs group than that in the CON and INU groups in this study. In addition, the increased Prevotella might be associated with the consumption of dietary fiber or carbohydrates (Kovatcheva-Datchary et al., 2015). Furthermore, clostridium and Lachnospiraceae, in the Firmicutes phylum, presented large increases in the LDSPs ferments and are known to be responsible for most of the butyrate produced in the human gut (Louis and Flint, 2009). The relative abundance of Bifidobacterium significantly increased in the INU group, which has been proved recently that could control serum cholesterol levels, prevent intestinal diseases, and modulate the immune system (Di Gioia et al., 2014). However, the lower abundance of Bifidobacterium in the LDSPs group, which might be a poor utilization of LDSPs by bifidobacterial, is consistent with the report that Bifidobacterium was not found in in vitro fermentation of polysaccharides from Fuzhuan brick tea (Chen G. et al., 2017). The probiotics can be broadly defined, and probiotics are live bacteria and yeasts, which are beneficial to human health when administrated in a viable form and in adequate amounts (Valdes et al., 2018). These results suggested that the commensal bacteria in the intestinal tract could break down indigestible LDSPs and that LDSPs could regulate gut microbiota dysbiosis by supporting the growth of beneficial bacteria and suppressing the proliferation of harmful bacteria, while the effects of LDSPs and INU on retarding dysbiosis were quite different. ## Effects of LDSPs on PH and SCFAs Fermentation of polysaccharides by microbes in the colon results in the production of SCFAs, and SCFAs are involved in the reduction of gut pH levels (Thandapilly et al., 2018). As shown in Figure 1A, the pH values of the INU group and the LDSPs group were significantly ($p \leq 0.05$) lower than the 0 h after 24 h of fermentation. Furthermore, the pH values decreased with significant differences between the INU and LDSPs groups ($p \leq 0.05$), which might be related to their diverse chemical structure. A previous study showed that lower pH of the intestinal tract could promote the growth of probiotics and inhibit the reproduction of pathogens (Shoukat and Sorrentino, 2021). The concentrations of acetic acid, propionic acid, n-butyric acid, i-butyric acid, n-valeric acid, and i-valeric acid produced during in vitro fecal fermentation are shown in Table 4. Both INU and LDSPs groups noticeably promote the production of SCFAs, especially for the significant increase ($p \leq 0.05$) in propionic acid and n-butyric acid in the LDSPs group. The increased level of acetic acid was observed in the INU group. Table 4 \| SCFAs(mmol/L) \| Time \| CON \| INU \| LDSPs \| \| --- \| --- \| --- \| --- \| --- \| \| Acetic acid \| 0 \| 2.76 ± 0.10d, A \| 2.76 ± 0.10d, A \| 2.76 ± 0.10d, A \| \| Acetic acid \| 6 \| 5.02 ± 0.12a, C \| 7.18 ± 0.17c, B \| 20.72 ± 0.05b, A \| \| Acetic acid \| 12 \| 4.57 ± 0.05c, C \| 11.75 ± 0.11b, B \| 20.09 ± 0.13c, A \| \| Acetic acid \| 24 \| 4.90 ± 0.23b, C \| 28.64 ± 0.17a, B \| 18.70 ± 0.01a, A \| \| Propionic acid \| 0 \| 0.86 ± 0.03d, A \| 0.86 ± 0.03d, A \| 0.86 ± 0.03d, A \| \| Propionic acid \| 6 \| 1.75 ± 0.10c, C \| 10.16 ± 0.29c, B \| 14.60 ± 0.09c, A \| \| Propionic acid \| 12 \| 3.06 ± 0.03b, C \| 11.71 ± 0.40b, B \| 17.78 ± 0.14b, A \| \| Propionic acid \| 24 \| 4.43 ± 0.02a, C \| 15.78 ± 0.20a, B \| 26.31 ± 0.16a, A \| \| n-Butyric acid \| 0 \| 0.76 ± 0.02c, A \| 0.76 ± 0.02d, A \| 0.76 ± 0.02d, A \| \| n-Butyric acid \| 6 \| 0.47 ± 0.05d, C \| 1.17 ± 0.13b, B \| 15.32 ± 0.18c, A \| \| n-Butyric acid \| 12 \| 1.80 ± 0.15a, C \| 1.57 ± 0.04a, B \| 34.44 ± 0.04b, A \| \| n-Butyric acid \| 24 \| 0.89 ± 0.07b, C \| 1.12 ± 0.01c, B \| 45.10 ± 0.29a, A \| \| i-Butyric acid \| 0 \| ND \| ND \| ND \| \| i-Butyric acid \| 6 \| 0.06 ± 0.03b, B \| ND \| 0.12 ± 0.05b, A \| \| i-Butyric acid \| 12 \| 0.20 ± 0.11a, A \| 0.03 ± 0.01, B \| 0.32 ± 0.01a, A \| \| i-Butyric acid \| 24 \| 0.19 ± 0.06a, B \| 0.13 ± 0.03, B \| 0.40 ± 0.09a, A \| \| n-Valeric acid \| 0 \| ND \| ND \| ND \| \| n-Valeric acid \| 6 \| 0.21 ± 0.37c, A \| ND \| ND \| \| n-Valeric acid \| 12 \| 0.76 ± 0.66b, C \| 0.60 ± 0.21b, B \| 2.97 ± 0.19a, A \| \| n-Valeric acid \| 24 \| 1.83 ± 0.15a, B \| 1.18 ± 0.23a, A \| 1.91 ± 0.10b, A \| \| i-Valeric acid \| 0 \| ND \| ND \| ND \| \| i-Valeric acid \| 6 \| ND \| ND \| ND \| \| i-Valeric acid \| 12 \| ND \| 1.00 ± 0.05a, B \| 1.42 ± 0.31a, A \| \| i-Valeric acid \| 24 \| ND \| ND \| ND \| \| Total acid \| 0 \| 4.39 ± 0.12d, A \| 4.39 ± 0.12d, A \| 4.39 ± 0.12d, A \| \| Total acid \| 6 \| 7.46 ± 0.13c, C \| 18.51 ± 0.08c, B \| 50.64 ± 0.05c, A \| \| Total acid \| 12 \| 10.19 ± 0.16b, C \| 26.63 ± 0.19b, B \| 76.69 ± 0.09b, A \| \| Total acid \| 24 \| 12.05 ± 0.03a, C \| 46.72 ± 0.21a, B \| 92.02 ± 0.13a, A \| Short-chain fatty acids have various positive effects on human health as prebiotic metabolites, and it has been confirmed that acetic acid is absorbed in the brain, heart, and peripheral tissues as an energy source (Kimura, 2014). Propionic acid produced in the intestinal tract improves tissue insulin sensitivity and suppresses cholesterol synthesis in the liver (Ding et al., 2019). In addition, butyric acid is the key to maintaining intestinal barrier integrity by providing energy for colonic epithelial cells and can also affect the host gene regulation, cell differentiation, and cell apoptosis (Ziegler et al., 2016). Moreover, the very high-level concentration of n-butyric acid exhibited in the LDSPs group might be primarily attributed to the relatively high abundance of Firmicutes according to previous reports (Fu et al., 2019). Correlation analysis was conducted to further identify the relationship between the gut microbiota community and SCFAs. As shown in Figure 4, the level of butyrate has a significantly ($p \leq 0.05$) positive correlation to the abundance of Blautia, Faecalibacterium, and Bacteroides, which belong to members of butyrogenic Clostridium cluster XIVa (Cantu-Jungles et al., 2018). Faecalibacterium produces butyrate which is required for colonic epithelium repair and Treg cell production and plays a crucial role in human health (Faintuch and Faintuch, 2019). Moreover, the concentration of propionic acid was enhanced by the increase in the abundance of Collinsella. A previous study reported that the Collinsella genus correlated closely with the production of pro-inflammatory cytokine IL-17A and could ameliorate the permeability of the gut (Chen B. D. et al., 2017). The increases in the relative abundance of Lachnospiraceae and Prevotella in LDSPs was along with the promotion of total acid. Lachnospiraceae substantially involves the production of SCFAs that are positively correlated to the integrity of the epithelial barrier and immune activation, and its reduction in abundance is found in patients with Parkinson’s disease (Keshavarzian et al., 2020). The results provided strong evidence that LDSPs could exert health benefits by simulating the targeted abundance of beneficial bacteria to produce SCFAs. Figure 4: Heatmap analysis for correlation between gut microbiota community and SCFAs. p < 0.05, p < 0.01, and p < 0.001, respectively; CON, the blank control (no additional carbon source supplement), INU, the positive control (INU supplement), and LDSPs, the experimental group (LDSPs supplement); SCFAs, short-chain fatty acids. ## Conclusion In conclusion, we found that LDSPs were indigestible under simulated saliva-gastrointestinal digestion conditions and degraded and utilized by human gut microbiota after 24 h in vitro fermentation, resulting in a prominent increase in the concentration of SCFAs and a decrease in pH. Remarkably, the production of propionic acid and n-butyric acid after LDSPs fermentation causally correlated with the enrichments of Prevotella, Blautia, and Lachnospiraceae. Therefore, LDSPs may be a potential prebiotic for health benefits. ## Data availability statement The data presented in the study are deposited in the NCBI repository (https://www.ncbi.nlm.nih.gov/bioproject/), accession number PRJNA930120. ## Author contributions FZ: experimental studies, data analysis, and writing. YX: experiment design, conceptualization, project administration, and revision. LP: data curation and statistical analysis. LY: experiment design and resources. YL: funding acquisition and conceptualization. DL: experimental studies. XL: statistical analysis. All authors contributed to the article and approved the submitted version. ## Funding This study was supported by the Shanghai Agriculture Applied Technology Development Program, China (grant no. X2021-02-08-00-12-F00797), and the Earmarked Fund for China Agriculture Research System, China (Grant No. CARS-20). ## Conflict 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 study. ## 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.2023.1083917/full#supplementary-material ## References 1. Ai C., Ma N., Sun X., Duan M., Wu S., Yang J.. 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--- title: Blood flow perfusion in visual pathway detected by arterial spin labeling magnetic resonance imaging for differential diagnosis of ocular ischemic syndrome authors: - Yanan Chen - Xue Feng - Yingxiang Huang - Lu Zhao - Xi Chen - Shuqi Qin - Jiao Sun - Jing Jing - Xiaolei Zhang - Yanling Wang journal: Frontiers in Neuroscience year: 2023 pmcid: PMC9969084 doi: 10.3389/fnins.2023.1121490 license: CC BY 4.0 --- # Blood flow perfusion in visual pathway detected by arterial spin labeling magnetic resonance imaging for differential diagnosis of ocular ischemic syndrome ## Abstract ### Background Ocular ischemic syndrome (OIS), attributable to chronic hypoperfusion caused by marked carotid stenosis, is one of the important factors that cause ocular neurodegenerative diseases such as optic atrophy. The current study aimed to detect blood flow perfusion in a visual pathway by arterial spin labeling (ASL) and magnetic resonance imaging (MRI) for the differential diagnosis of OIS. ### Methods This diagnostic, cross-sectional study at a single institution was performed to detect blood flow perfusion in a visual pathway based on 3D pseudocontinuous ASL (3D-pCASL) using 3.0T MRI. A total of 91 participants (91 eyes) consisting of 30 eyes with OIS and 61 eyes with noncarotid artery stenosis-related retinal vascular diseases (39 eyes with diabetic retinopathy and 22 eyes with high myopic retinopathy) were consecutively included. Blood flow perfusion values in visual pathways derived from regions of interest in ASL images, including the retinal-choroidal complex, the intraorbital segments of the optic nerve, the tractus optics, and the visual center, were obtained and compared with arm-retinal circulation time and retinal circulation time derived from fundus fluorescein angiography (FFA). Receiver operating characteristic (ROC) curve analyses and the intraclass correlation coefficient (ICC) were performed to evaluate the accuracy and consistency. ### Results Patients with OIS had the lowest blood flow perfusion values in the visual pathway (all $p \leq 0.05$). The relative intraorbital segments of optic nerve blood flow values at post-labeling delays (PLDs) of 1.5 s (area under the curve, AUC = 0.832) and the relative retinal–choroidal complex blood flow values at PLDs of 2.5 s (AUC = 0.805) were effective for the differential diagnosis of OIS. The ICC of the blood flow values derived from the retinal–choroidal complex and the intraorbital segments of the optic nerve between the two observers showed satisfactory concordance (all ICC > 0.932, $p \leq 0.001$). The adverse reaction rates of ASL and FFA were 2.20 and $3.30\%$, respectively. ### Conclusion 3D-pCASL showed that the participants with OIS had lower blood flow perfusion values in the visual pathway, which presented satisfactory accuracy, reproducibility, and safety. It is a noninvasive and comprehensive differential diagnostic tool to assess blood flow perfusion in a visual pathway for the differential diagnosis of OIS. ## Introduction Marked stenosis or occlusion of the common or internal carotid arteries may cause ocular hypoperfusion (Lee et al., 2022) and/or cerebral hypoperfusion (Lineback et al., 2022). Ocular ischemic syndrome (OIS), attributable to chronic ocular hypoperfusion, is one of the important factors that cause ocular neurodegenerative diseases (Mester et al., 2009), such as optic atrophy (Battista et al., 2022). Ocular ischemic syndrome (OIS) describes ocular symptoms and signs attributable to ocular hypoperfusion caused by marked stenosis or occlusion of the common or internal carotid arteries (Terelak-Borys et al., 2012). It was first described by Hedges [1962], with their findings such as peripheral dot and blot hemorrhages and dilated retinal veins attributed to retinal hypoxia induced by carotid artery insufficiency (Casalino et al., 2017). It is a blinding and disabling disease (Hung and Chang, 2017) and has diverse clinical manifestations accompanied by asymptomatic injury (Mendrinos et al., 2010). It is usually asymptomatic but has potentially blinding abilities (Hung and Chang, 2017). The diagnosis of OIS can portend life-threatening cerebrovascular and cardiovascular complications (Mendrinos et al., 2010). The mortality rate of patients with OIS is $40\%$ within 5 years from onset (Mills, 1989), and the most common causes of death are cardiac disease and stroke (Avery et al., 2019). The diagnosis of OIS is critical for saving visual function and improving the chances of survival. The identification of the OIS and its various clinical manifestations presents an interdisciplinary challenge. In addition to OIS, there are also ischemic mechanisms present in retinal vascular diseases related to noncarotid artery stenosis, such as diabetic retinopathy (DR) and high myopia (HM) retinopathy (Steigerwalt et al., 2009). It was reported that the thinning of the choroid contributes more to the measured decreased chorioretinal perfusion than slowed arterial filling time (Vaghefi et al., 2017). Previous studies confirmed the ischemic mechanisms in DR and HM retinopathy. DR is a well-recognized ocular ischemic disease which is a microvascular complication of diabetes (Stolte and Fang, 2020). Mudaliar et al. reported that hyperglycemia causes retinal damage through complex metabolic pathways, leading to vascular damage, oxidative stress, capillary ischemia, and retinal tissue hypoxia. A growing body of evidence (Steigerwalt et al., 2009) suggests that HM is associated with decreased ocular blood flow (BF), the complications of which may contribute to severe visual loss. A recent study has shown that the aberrant blood perfusion of the cerebellum detected by ASL in patients with HM indicates a new understanding of brain abnormalities and brain plasticity (Wang et al., 2020). Identifying a clinical distinction between OIS, which can potentially imply being affected by lethal disease and noncarotid artery stenosis-related retinal vascular disease, is essential and difficult. Therefore, reliable diagnostic biomarkers are needed. The traditional imaging modality for assessing ocular blood perfusion is fundus fluorescein angiography (FFA) (Terelak-Borys et al., 2012). Its invasive examination process relies on sodium fluorescein, an orange water-soluble dye, which is not applicable to all patients. Arterial spin labeling (ASL) magnetic resonance imaging (MRI) has been widely used in cerebrovascular disease (Scelsi et al., 2018). ASL allows magnetically labeled water protons from arterial blood as an endogenous diffusible tracer that disperses from the vascular system into neighboring tissues (Kitajima and Uetani, 2023). Voxel blood flow was quantified in mL/100 mL/min (Valentin et al., 2022). Anatomy and functionality are all important factors affecting tissue perfusion (Vaghefi et al., 2017). Therefore, we set the DR group in terms of arterial filling time and the HM group in terms of tissue volume. This diagnostic test study was designed to detect blood flow perfusion in a visual pathway by ASL-MRI and explore an accurate, reproducible, and safe diagnostic tool for the differential diagnosis of OIS. ## Study design and participants In this cross-sectional study, 91 participants (91 eyes) with retinal vascular diseases were prospectively and consecutively enrolled from November 2018 to November 2021. Participants included 30 patients with carotid artery stenosis (30 eyes with OIS) and 61 controls with noncarotid artery stenosis-related retinal vascular diseases (39 eyes with DR and 22 eyes with high myopic retinopathy). The diagnostic criteria of OIS (Luo et al., 2018) are as follows: [1] the stenosis of the ipsilateral (to the affected eye) internal carotid artery (ICA) was >$50\%$; [2] abnormal ocular symptoms and/or signs which cannot be explained by other ocular diseases; [3] FFA with the following signs: arm-choroidal circulation time>15 s, arm-retinal circulation time (ARCT) >18 s, and retinal circulation time (RCT) >11 s. The subjects that satisfied the first criterion and any of the two criteria in [2] or [3] led to a diagnosis of OIS (Lauria et al., 2020). The diagnostic criteria for DR (Fransen et al., 2002) are based on the international clinical DR severity grading standard established by the American Academy of Ophthalmology in 2002 (Nawaz et al., 2019; Flaxel et al., 2020). The diagnostic criteria for high myopic retinopathy are based on three key factors: atrophy (A), traction (T), and neovascularization (N), which is named the ATN classification system (Ruiz-Medrano et al., 2019). The inclusion criteria were defined as follows: [1] patients with OIS; [2] patients with DR with severity greater than or equal to mild non-proliferative diabetic retinopathy; [3] patients with high myopic retinopathy with severity graded as A0-A4/T0-T3/N0-N2s. The exclusion criteria were defined as follows: a history of other ocular diseases: glaucoma, uveitis, ocular trauma, or intraocular surgery; other types of retinal vascular diseases: retinal artery occlusion, retinal vein occlusion, retinal macroaneurysms, hypertensive retinopathy; MRI ineligibility (de Keizer and te Strake, 1986): claustrophobia or the presence of a cardiac pacemaker, joint replacement, or other implanted metal devices; MR images with visible artifacts; FFA ineligibility (Awan and Yang, 2006): hypersensitivity to sodium fluorescein and liver and kidney dysfunction; ocular diseases that diminished the quality of fundus image: serious cataract and vitreous hemorrhage. We excluded those who were implanted with metal devices ($$n = 1$$), had hypersensitivity to sodium fluorescein ($$n = 1$$), and whose MR and FFA images were of poor quality ($$n = 2$$). The study was approved by the Medical Research Ethics Committee of Beijing Friendship Hospital, Capital Medical University (NO.2018-P2-185-02). All participants provided informed consent according to the Declaration of Helsinki. ## Clinical ophthalmic examination All subjects underwent slit-lamp, optical coherence tomography (OCT, Heidelberg Spectralis), and FFA (Spectralis hra) examinations (Figure 1). OCT was used to measure the central macular retinal thickness in conventional mode. OCT measured the central macular choroidal thickness in the enhanced depth imaging (EDI) mode. FFA examinations were performed according to the requirements of the patient's condition. Allergy tests were carried out, and the subjects with negative results underwent a puncture of the median cubital vein and were injected with sodium fluorescein contrast medium. We collected the ARCT, RCT, capillary non-perfusion (NP) area, neovascularization (NV), retinal vascular staining, microaneurysms, and other fluorescein angiography signs. The same experienced technician completed each examination. Figure 1: Clinical ophthalmic examinations. Fundus fluorescein angiography (A–C). Optical coherence tomography (D–G). The arm-retinal circulation time is 24.69 s showing the delayed retinal arierial filling (A). The venous phase starts at 34.45 s, showing delayed retinal venous filling, which indicates that the retinal circulation time is 9.76 s (B). Late retinal vascular staining (C). The infrared image and the central macular choroidal thickness in enhanced depth imaging mode (D, E). The infrared image and the central macular retinal thickness in conventional mode (F, G). ## ASL image acquisition All subjects underwent a 3.0T MRI scan using a Philips Ingenia 3.0T scanner equipped with a 16-channel head coil. T1 and T2 weighted images, diffusion-weighted images, and 3D time-of-flight MR angiography images were obtained before the ASL sequence, and scanning time summed up to 20 min. Foam pads were placed at the sides of the subject's head to minimize head motion, and earplugs were used to reduce noise. During the MRI scan, subjects were instructed to close their eyes and stay relaxed to reduce eye movement. The BF in the visual pathway was determined using the 3D pseudo-continuous ASL (3D-pCASL) technique, with the scan parameters as follows: gradient and spin echo sequence, post-labeling delay (PLD) = 1.5 s (repetition time [TR] = 3903 ms, echo time [TE] = 11 ms), PLD = 2.5 s (TR = 4903 ms, TE = 11 ms), bandwidth in echo-planar imaging = 2899.7 Hz, label distance = 90 mm, flip angle (FA) = 90°, slice thickness = 6 mm, number of slices = 20, slice gap = 0, slice orientation=transverse, field of view (FOV) = 240 × 240 mm, acquisition matrix = 64 × 64, number of excitations (NEX) = 3. ## ASL data quantification Blood perfusion maps were automatically obtained using the default process by the dedicated workstation (IntelliSpace Portal Release v.7.0.4.20175, Philips), and the data were derived from the blood perfusion maps. The regions of interest (ROIs) derived from the retinal-choroidal complex, the intraorbital segments of the optic nerve, the tractus opticus, and the visual center (Figure 2) were drawn by a neurologist (10 years of experience) and an ophthalmologist (10 years of experience), respectively, and clinical information was reviewed in a blinded fashion. The specific location of the retina-choroid complex, the orbital segment of the optic nerve, the optic tract, and the visual center were based on T1 and T2 weighted images. The unified criteria for drawing ROIs were as follows: The ROIs were all subrounded. The area of ROI of the retinal-choroidal complex, the intraorbital segments of the optic nerve, and the tractus opticus were 0.3 cm2; the area of ROI of the gyrus lingual, the cuneus, and the occipital lobe was 2 cm2, and the average BF value was taken as the BF of the visual center. The relative BF (rBF) value was defined as rBF = affected BF/healthy BF (Muir and Duong, 2011). The results of the measurements were retrieved from the two observers and calculated as the average value. Figure 2: Examples of different patterns in MRI images. T2 weighted images (A–D). Arterial spin labeling (ASL) images at post-labeling delay (PLD) of 1.5 s (E–H). Regions of interest derived from the retinal-choroidal complex (A, E); the intraorbital segments of the optic nerve (B, F); the tractus opticus (C, G); the visual center (D, H); ROIs were all marked by red circles. ## Statistical analysis Sample size considerations included the rarity of the OIS. This study hypothesizes that the area under the curve (AUC) of the BF perfusion values in a visual pathway is >0.5. Our pre-test showed that the AUC was >0.8. According to the following parameters, α= 0.05, β = 0.1, the power was calculated using PASS11.0 software, which was >$90\%$, proving that the sample size was adequate. Statistical analyses were performed using SPSS statistical software (version 26.0, SPSS) and GraphPad Prism software (version 6.0c, GraphPad Inc). Continuous variables were presented as mean ± standard deviation. A one-way ANOVA was used to analyze the differences among groups. Categorical variables were analyzed using Chi-square tests. Receiver operating characteristic (ROC) curve analyses were performed, and the AUC was applied to evaluate accuracy. A intraclass correlation coefficient (ICC) was performed to evaluate the consistency of BF values reported by the two observers; an ICC of >0.75 indicated satisfactory concordance. Statistical significance was accepted as a two-sided test with an alpha level of 0.05. A P-value of < 0.05 was considered statistically significant. ## Demographics and ocular characteristics A total of 91 participants (mean [SD] age, 61.0 [10.0] years; 37 [$40.7\%$] women) had 91 eyes with retinal vascular diseases, including 30 patients (30 eyes) with OIS after carotid artery stenosis and 61 controls with noncarotid artery stenosis-related retinal vascular diseases, which included 39 patients (39 eyes) with DR and 22 patients (22 eyes) with high myopic retinopathy. There were differences in age ($F = 8.97$, $p \leq 0.001$), with the predominant gender being male (χ2 = 16.54, $p \leq 0.001$) among the three groups. Subjects with OIS and high myopic retinopathy showed thinner central macular retinal thickness ($F = 4.98$, $$p \leq 0.009$$); subjects with high myopic retinopathy showed the thinnest central macular choroidal thickness ($F = 42.65$, $p \leq 0.001$). There were no significant differences in ARCT among the three groups ($F = 1.40$, $$p \leq 0.253$$). The differences among the three groups in the RCT were significant. The subjects with OIS showed the highest RCT values ($F = 3.75$, $$p \leq 0.027$$). The differences in the rates of capillary non-perfusion and neovascularization among the three groups were significant. The subjects with DR showed the highest rates of capillary non-perfusion (χ2 = 27.66, $p \leq 0.001$) and neovascularization (χ2 = 22.00, $p \leq 0.001$). The demographics and clinical characteristics of each group are represented in Table 1. Table 1 \| Variable \| Total (n = 91) \| OIS (n = 30) \| DR (n = 39) \| HM (n = 22) \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Gender, female/male, n (%) \| 37(40.7)/54(59.3) \| 5(16.7)/25(83.3) \| 16(41.0)/23(59.0) \| 16(72.7)/6(27.3) \| < 0.001 \| \| Age, years, mean (SD) \| 61.0 (10.0) \| 66.6 (8.3) \| 59.3 (7.8) \| 56.3 (12.3) \| < 0.001 \| \| OCT \| OCT \| OCT \| OCT \| OCT \| OCT \| \| Central macular retinal thickness, μm, mean (SD) \| 265.90 (122.81) \| 223.47 (30.12) \| 309.33 (142.75) \| 242.50(145.31) \| 0.009 \| \| Central macular choroidal thickness, μm, mean (SD) \| 211.84 (93.55) \| 243.41 (61.80) \| 252.61 (73.53) \| 94.48 (60.43) \| < 0.001 \| \| FFA \| FFA \| FFA \| FFA \| FFA \| FFA \| \| ARCT, seconds, mean (SD) \| 18.00 (5.51) \| 19.30 (6.60) \| 17.61 (4.68) \| 16.87 (5.12) \| 0.253 \| \| RCT, seconds, mean (SD) \| 3.87 (4.37) \| 5.60 (7.18) \| 3.06 (1.19) \| 2.89 (0.89) \| 0.027 \| \| Capillary non-perfusion, n (%) \| 22 (24.2) \| 2 (6.7) \| 20 (51.3) \| 0 (0) \| < 0.001 \| \| Neovascularization, n (%) \| 24 (26.4) \| 3(10) \| 20(51.3) \| 1(4.5) \| < 0.001 \| ## ASL characteristics based on ROI analysis There were significant differences among the three groups in detectable BF values of the visual pathway at PLDs of 1.5 and 2.5 s, including the BF values of the retinal–choroidal complex ($F = 4.065$, $$p \leq 0.020$$; $F = 4.923$, $$p \leq 0.009$$), the intraorbital segments of the optic nerve ($F = 10.873$, $p \leq 0.001$; $F = 3.907$, $$p \leq 0.024$$), the tractus opticus ($F = 13.617$, $p \leq 0.001$; $F = 3.738$, $$p \leq 0.028$$), and the visual center ($F = 11.057$, $p \leq 0.001$; $F = 4.012$, $$p \leq 0.022$$) (Table 2). Subjects with OIS had the lowest BF perfusion values in the visual pathway at PLD of 1.5 and 2.5 s among the three groups (all $p \leq 0.05$). Subjects with DR were presented with lower BF perfusion values in the intraorbital segments of the optic nerve, the tractus opticus, and the visual center at a PLD of 1.5 s (all $p \leq 0.05$). Subjects with high myopic retinopathy were presented with lower BF perfusion values in the retinal–choroidal complex at a PLD of 2.5 s (all $p \leq 0.05$). Most of the perfusion values in the visual pathway increased from PLD 1.5 s to PLD 2.5 s (Figure 3). ## Accuracy of ASL in the differential diagnosis of OIS The accuracy of ASL in the diagnosis of OIS was evaluated using the ROC curve analysis (Figure 4). The BF values of the retinal–choroidal complex at a PLD of 1.5 s [AUC:0.669; $95\%$ confidence interval (CI) 0.55–0.79; $$p \leq 0.01$$] were estimated by comparison with the ARCT of the gold standard FFA-based diagnosis of delayed retinal arterial filling. The relative intraorbital segments of optic nerve BF values at PLDs of 1.5 s (AUC:0.832; $95\%$CI 0.74–0.93; $p \leq 0.001$), with a cutoff point of 0.79 (sensitivity:$76.7\%$; specificity:$85.2\%$), and the relative retinal–choroidal complex BF values at PLDs of 2.5 s (AUC:0.805; $95\%$CI 0.70–0.92; $p \leq 0.001$), with a cutoff point of 0.78 (sensitivity:$73.3\%$; specificity:$83.6\%$), were effective predictors for the differential diagnosis of OIS. Figure 4: Receiver operating characteristic curves. The area under curve (AUC) showing the accuracy of the values of blood flow perfusion in the visual pathway, identified using arterial spin labeling (ASL) for diagnosis of the delayed retinal arterial filling (A, D). The AUC shows the accuracy of the values of blood flow perfusion in the visual pathway, identified using ASL for diagnosis of OIS (B, C, E, F). BF, blood flow; rBF, relative blood flow; ARCT, arm-retinal circulation time; OIS, ocular ischemic syndrome. ## Concordance between observers in ASL There was concordance between the two observers, with an ICC of 0.932 ($95\%$ CI 0.897–0.955, $p \leq 0.001$) at PLDs of 1.5 s and 0.974 ($95\%$CI 0.956–0.984, $p \leq 0.001$) at PLDs of 2.5 s for the retinal–choroidal complex. The ICC of the BF values of the intraorbital segments of optic nerve BF between the two observers was 0.972 ($95\%$CI 0.956–0.982, $p \leq 0.001$) at PLDs of 1.5 s and 0.984 ($95\%$CI 0.974–0.990, $p \leq 0.001$) at PLDs of 2.5 s. The ICC of the BF values of the optic tract and the visual center at PLDs of 1.5 s and PLD of 2.5 s were all more than 0.984 (all $p \leq 0.001$). ## Safety of ASL and FFA Of the 91 subjects, two patients felt uncomfortable due to the claustrophobic space of the MRI, and three patients developed a mild rash due to the sodium fluorescein contrast agent. The adverse reaction rates of ASL and FFA were 2.20 and $3.30\%$, respectively. There was no significant difference in the safety between ASL and FFA ($p \leq 0.001$), but ASL was noninvasive and, independent of contrast media, showed better convenience. ## Discussion In our study, subjects with OIS tended to be older, with a male predominance, keeping with characteristics described in the literature (Xiang and Zou, 2020). The characteristics of the disease made complete matching impossible. Previous studies (Vaghefi et al., 2017) showed that, in addition to the rate of BF, the volume of the vascular tissue may be one of the important factors that will influence the perfusion of the eye in ASL. Central macular retinal and choroidal thickness measured by EDI-OCT can be the surrogate biomarker of the vascular tissue, which is known to decrease with increasing age (Ikuno et al., 2011). Our study showed that subjects with OIS and high myopic retinopathy showed thinner central macular retinal thickness compared to subjects with DR, which was consistent with the characteristics of the disease reported in the earlier literature (Brito et al., 2015). Another finding was that subjects with HM presented with the thinnest central macular choroidal thickness compared with the other two groups, which was in keeping with previous studies (Fang et al., 2019). The blood supply of the visual pathway is from the ophthalmic artery, the middle cerebral artery, and the posterior cerebral artery (Abhinav et al., 2020). A study (Dan et al., 2019) assessed resting cerebral blood flow changes in patients with retinitis pigmentosa using a pseudo-continuous ASL and found that altered cerebral BF may cause trans-synaptic retrograde degeneration of the visual pathway in patients with retinitis pigmentosa. We also found some interesting results: The subjects with DR were presented with lower BF perfusion values in the intraorbital segments of the optic nerve, the tractus opticus, and the visual center at PLDs of 1.5 s. A previous study (Wong et al., 2020) showed the association between DR and an increased risk of stroke, which indicated that the larger cerebrovascular implications are caused by the microvascular pathology inherent to DR. Therefore, we speculated that the BF perfusion of the visual pathway in patients with DR was affected by systemic diseases. The results of the present study also showed that the subjects with high myopic retinopathy presented with lower BF perfusion values in the retinal–choroidal complex at PLDs of 2.5 s, which further confirmed that the volume of the vascular tissue is another factor that will affect the perfusion of the posterior pole in ASL. ASL is used to evaluate the tissue perfusion rate. Tissue perfusion—the exchange of water and nutrients with tissues—occurs over the entire length of capillaries (Zhu et al., 2022). ASL basically “tracks” the water molecules in the blood from the arterial cavity to the tissue capillary bed and treats the water molecules as a freely diffusible tracer. ASL can easily occur through magnetization reversal or saturation of blood and water molecules in the blood supply artery along the Z-axis (Moran et al., 2022). After labeling, the time to wait for the blood to enter the tissue is called the PLD time or the reversal time of some specific ASL technology. Select the delay time so that the image can be obtained, ideally when the water molecules and tissues are magnetized and exchanged. Arterial blood labeling is realized through the combination of pulse and gradient to reverse the longitudinal magnetization of blood-water protons (Iutaka et al., 2023). The accuracy of ASL perfusion evaluation is essential to diagnosing OIS. As the primary cause of OIS, the stenosis or occlusion of the common or internal carotid arteries is easy to ignore, and it is a necessary condition for diagnosing OIS (Mendrinos et al., 2010). The most specific (but not the most sensitive) fluorescein angiography sign of OIS is prolonged retinal filling time, known as ARCT, which is present in approximately $60\%$ of patients with OIS (Terelak-Borys et al., 2012). The most sensitive (but not the most specific) fluorescein angiography sign of OIS is prolonged RCT, which is present in $95\%$ of patients with OIS (Brown and Magargal, 1988). The BF values of the retinal–choroidal complex at PLDs of 1.5 s were estimated by comparison with the ARCT of the gold standard FFA-based diagnosis of delayed retinal arterial filling in our study. The result of an AUC of 0.669 was not satisfactory. However, the results of the rBF value were satisfactory. In previous studies of cerebral blood flow perfusion, the relative cerebral perfusion value (Iutaka et al., 2023) was more concerning than the absolute value (Salisbury et al., 2022). However, a delayed arterial filling time is not diagnostic for ocular ischemia (Hung and Chang, 2017). In Vaghefi's report (Vaghefi et al., 2017), they attempted to quantify the chorioretinal blood perfusion in patients with a clinical diagnosis of retinal ischemia using ASL. They speculated that ocular ischemia may be due to tissue volume and arterial flow, but only four participants without blood perfusion of the visual pathway were evaluated in their study. The reproducibility of ASL perfusion evaluation is necessary for clinical application in diagnosing OIS. In our study, the ICC of the BF values derived from the retinal–choroidal complex and the intraorbital segments of the optic nerve between the two observers at PLDs of 1.5 and 2.5 s showed satisfactory concordance. A previous study (Khanal et al., 2019) demonstrated the high intraday and interday repeatability in the quantitative ASL-MRI measurements of retinal–choroidal complex blood perfusion. However, their study did not evaluate other blood perfusion values in the visual pathway. When we suspect that the patient has ocular hypoperfusion, we should combine ASL with FFA to make a comprehensive judgment. When ASL is applied in the eye, the blood perfusion in the posterior part of the eye will be measured, and the low perfusion of the visual pathway can be presented, which will help to understand the factors affecting the changes in the blood perfusion of the visual pathway and the changes in the blood perfusion of the eye caused by carotid artery stenosis. The safety and convenience of the clinical application of ASL in the differential diagnosis of OIS may be attractive to ophthalmologists compared with traditional ophthalmic examinations. However, FFA is the gold standard for diagnosing retinal vascular diseases. The limitations of FFA in itself affect the clinical application. In our study, two patients felt uncomfortable due to the claustrophobic space of the MRI, and three patients developed a mild rash due to the sodium fluorescein contrast agent. Although there was no significant difference in the safety of ASL and FFA, ASL was noninvasive and showed more advantages, independent of contrast media. ## Conclusion In conclusion, 3D-pCASL showed the participants with OIS had lower blood flow perfusion values in the visual pathway, which presented satisfactory accuracy, reproducibility, and safety. It is a noninvasive and comprehensive diagnostic tool to assess blood flow perfusion in a visual pathway for the differential diagnosis of OIS. ## Limitations The limitations of this study are as follows. The spatial resolution of images is larger than the areas of intraorbital ROIs and the tractus opticus, which are determined by the size of the study organ. ASL of white matter, particularly small white matter tracts, has always been problematic, even when this study used the contralateral side as an internal reference. However, we attempted to include complete clinical data for analysis to explore OIS's noninvasive differential diagnosis 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 authors. ## Ethics statement The studies involving human participants were reviewed and approved by the Medical Research Ethics Committee of Beijing Friendship Hospital, Capital Medical University (NO.2018-P2-185-02). The patients/participants provided their written informed consent to participate in this study. ## Author contributions XZ and YW supervised the present study. YC and XF performed the analysis and wrote the manuscript. YH, LZ, XC, SQ, and JS helped to collect the clinical data. JJ contributed to the image processing. 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. ## References 1. Abhinav K., Gimenez P., Tyler M., Patel Z. M., Fernandez-Miranda J. C.. Endoscopic endonasal approach for resection of suprasellar hemangioblastoma: selective pituitary sacrifice and use of indocyanine dye: 2-dimensional operative video. Oper Neurosurg. (Hagerstown) (2020) 20 E46-E47. DOI: 10.1093/ons/opaa250 2. Avery M. 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--- title: Systemic inflammatory markers in patients with polyneuropathies authors: - Patricia García-Fernández - Klemens Höfflin - Antonia Rausch - Katharina Strommer - Astrid Neumann - Nadine Cebulla - Ann-Kristin Reinhold - Heike Rittner - Nurcan Üçeyler - Claudia Sommer journal: Frontiers in Immunology year: 2023 pmcid: PMC9969086 doi: 10.3389/fimmu.2023.1067714 license: CC BY 4.0 --- # Systemic inflammatory markers in patients with polyneuropathies ## Abstract ### Introduction In patients with peripheral neuropathies (PNP), neuropathic pain is present in $50\%$ of the cases, independent of the etiology. The pathophysiology of pain is poorly understood, and inflammatory processes have been found to be involved in neuro-degeneration, -regeneration and pain. While previous studies have found a local upregulation of inflammatory mediators in patients with PNP, there is a high variability described in the cytokines present systemically in sera and cerebrospinal fluid (CSF). We hypothesized that the development of PNP and neuropathic pain is associated with enhanced systemic inflammation. ### Methods To test our hypothesis, we performed a comprehensive analysis of the protein, lipid and gene expression of different pro- and anti-inflammatory markers in blood and CSF from patients with PNP and controls. ### Results While we found differences between PNP and controls in specific cytokines or lipids, such as CCL2 or oleoylcarnitine, PNP patients and controls did not present major differences in systemic inflammatory markers in general. IL-10 and CCL2 levels were related to measures of axonal damage and neuropathic pain. Lastly, we describe a strong interaction between inflammation and neurodegeneration at the nerve roots in a specific subgroup of PNP patients with blood-CSF barrier dysfunction. ### Conclusion In patients with PNP systemic inflammatory, markers in blood or CSF do not differ from controls in general, but specific cytokines or lipids do. Our findings further highlight the importance of CSF analysis in patients with peripheral neuropathies. ## Introduction Polyneuropathy (PNP) is a term to describe a group of diseases with peripheral nerve dysfunction of various etiologies. Symptoms may affect the motor, sensory and/or autonomic system, and in $50\%$ of the cases, patients experience neuropathic pain. Why some patients with PNP have pain and others not, is unknown, and even PNPs with the same etiology may be painful or painless [1]. The available treatments have modest efficacy in reducing pain and present considerable side effects [2, 3]. One approach toward better symptom control and potentially causative treatment might be to find common pathophysiologic pathways in PNP of different etiologies. Although merely $14\%$-$20\%$ of PNP have a definite immune-related cause [4], inflammatory processes have been found to be involved in neuro-degeneration, -regeneration and pain in neuropathies of different origin (5–7). One of the main pro-inflammatory pathways that are activated upon damage is toll-like receptor (TLR) 4 mediated. Stimulation of TLR4 results in the activation of the nuclear factor k B (NFkB) pathway and the release of inflammatory cytokines, including tumor necrosis factor-alpha (TNF-α), interleukin (IL)-1β, IL-6, IL-8, IL-10, or chemokines such as the CC-chemokine ligand (CCL) 2 [8]. This pathway can in turn be regulated by other mediators. For instance, the NAD-dependent deacetylase sirtuin 1 (SIRT1) has been suggested to be able to inhibit the pro-inflammatory cascade by deacetylating the p65 subunit of NFkB [9]. Fractalkine (Cx3CL1) is an algesic chemokine which is cleaved from neurons and activates glial cells [10]. Furthermore, small fragments of RNA, or microRNAs (miR), can regulate the expression of genes coding for pro- and anti-inflammatory proteins and have been found altered in different neuropathies of the central or peripheral nervous system, nociception and pain [11]. While many miRs might be involved in the development of neuropathic pain, miR-146a-5p, miR-132-3p and miR-155-5p participate in the regulation of the NFkB pathway and have been described altered in patients with painful PNP (12–14). In addition, alterations in ion channels such as voltage gated sodium channels or those of the transient receptor potential cation channel subfamily like TRPV1 might lead to enhanced excitatory responses in nociceptors and promote axonal degeneration and the release of pro-inflammatory mediators, exacerbating the immune response [15]. Furthermore, TRPV1 is expressed in blood mononuclear cells, and a direct role in cytokine production and immune responses has been proposed [16, 17]. Several lipid compounds, such as prostaglandins (PGs) or thromboxanes, are known to be involved in inflammation and pain, and can in turn be upregulated upon inflammation or tissue injury. Moreover, high levels of PGs have been described in serum from patients with peripheral neuropathies, as well as in the spinal cord of animal models with peripheral injury [18, 19]. In fact, non-steroidal anti-inflammatory drugs (NSAIDs), a very common treatment against pain, have anti-inflammatory and analgesic properties mediated by blocking the synthesis of PGs and thromboxanes [18, 20, 21]. Short-chain and long-chain acylcarnitines can be found upstream their synthesis pathway and have been reported upregulated in sera, nerve and spinal cord in animal models of neurodegeneration, in particular, palmitoylcarnitine (C16:0) and oleoylcarnitine (C18:1) (20, 22–24). In PNP, signs and symptoms are typically length-dependent and thus more severe in the distal extremities of the body, such as feet and hands, than in the proximal regions or in the torso. Previous studies found a local upregulation of inflammatory mediators such as IL-2, IL-6, IL-8 or IL-10 in skin or nerve from patients with PNP [5, 25]. The study of local inflammation, especially in the nerve, is difficult in humans, since it implies an invasive nerve biopsy, while systemic samples, such as blood and cerebrospinal fluid (CSF), are drawn and analyzed routinely. Since several inflammatory cytokines have been seen upregulated locally in patients with PNP, we studied whether some of these markers might be found systemically in sera or CSF. Different studies have shown a high variability in the cytokines present in serum from patients with PNP or nerve root compression, associated with the severity and symptoms of the neuropathy (26–31). Furthermore, extensive studies have shown that the CSF is directly altered by diseases of the central nervous system, while in diseases of the peripheral nervous system (PNS), differences in the levels of cytokines in CSF have yet to be explained. A factor that can influence these levels is the integrity of the barrier between blood and CSF, also called blood-CSF-barrier (B-CSF barrier) (32–34) or blood brain barrier (BBB) [35]. We hypothesized that PNP is associated with enhanced systemic inflammation that may correlate with the severity of the disease and the development of neuropathic pain. To test our hypothesis, we performed a comprehensive analysis of the protein, lipid, and gene expression of selected pro- and anti-inflammatory markers in blood and CSF from patients with PNP and controls. Furthermore, we measured the levels of neurofilament light-chain (NFL), as a cytoskeletal protein expressed by neurons and released upon cell damage, to assess the level of neurodegeneration present in patients with PNP [36], and to correlate NFL levels with the degree of inflammation. We also aimed to identify new systemic markers that might be important in the pursuit of a more accurate diagnosis and the prediction of neuropathic pain. ## Patient recruitment and diagnostic assessment Between 2019 and 2021, 28 patients with PNP were prospectively recruited at the Department of Neurology, University of Würzburg, Germany, where they were seen for diagnostic work-up. In addition, 10 patients with acute headaches of unclear etiology were used as disease controls for the CSF analysis, and serum data from twelve healthy volunteers were included in the analysis to determine the inflammatory state of the headache group. Our study was approved by the Würzburg Medical Faculty Ethics Committee (# $\frac{15}{19}$ and # $\frac{242}{17}$) and all participants gave written informed consent prior to inclusion. Diagnoses were based on history taking and neurological examination, laboratory studies, and nerve conduction examinations. All patients underwent laboratory tests including full blood count, electrolytes, kidney and liver function tests, C-reactive protein, thyroid stimulating hormone, vitamin B12, HbA1c, oral glucose tolerance test (OGTT), screening for autoimmune antibodies (i.e., ANA, ENA, ANCA, anti-ganglioside antibodies), and lumbar puncture. Electrophysiological assessment with nerve conduction studies of the affected nerves including tibial motor nerve and sural sensory nerve was performed in all PNP patients. To rate the severity of the neuropathy, we used standardized scales, including the modified Toronto clinical neuropathy score (mTCNS), the overall disability sum score (ODSS) and the Medical Research Council-sumscore (MRC-sumscore). Depression was assessed with the “Allgemeine Depressionsskala” (ADS) [37]. Pain was evaluated by a numerical rating scale (NRS) from 0 (no pain) to 10 (worst pain), the neuropathic pain symptom inventory (NPSI) and the graded chronic pain scale (GCPS). After the diagnostic work-up, the examining neurologists categorized the neuropathies into a mild [1], moderate [2] or severe [3] clinical phenotype. ## Sample collection From each patient, venous blood was drawn in the morning between 8 a.m. and 9 a.m. into S-Monovette® Serum-Gel Tubes (Sarstedt, Nümbrecht, Germany) and Tempus™ Blood RNA Tubes (Thermo Fisher Scientific, Waltham, MA, USA). For the collection of sera, whole blood was left to clot for 30 min at room temperature and later centrifuged for 10 min at 1200 g. Supernatant was aliquoted and stored at -20°C until further analysis. Tempus™ Blood RNA Tubes were immediately shaken for 30 s to lyse the cells and stabilize the RNA and stored at -80°C until extraction. CSF samples were obtained after a lumbar puncture at the L$\frac{4}{5}$ level. The amount of CSF taken varied between 10-15 ml. Samples of 2 ml were aliquoted and subsequently stored at -20 °C. ## Gene expression analysis RNA was extracted from Tempus™ Blood RNA Tubes following the manufacturer’s protocol [38, 39] from the MagMAX™ for Stabilized Blood Tubes RNA Isolation Kit (Thermo Fisher Scientific, Waltham, MA, USA). RNA quality and quantity were assessed with a NanoDrop™ One (Thermo Fisher Scientific, Waltham, MA, USA), and RNA was stored at -80°C. For cDNA synthesis from mRNA, TaqMan Reverse Transcription reagents (Thermo Fisher Scientific, Waltham, MA, USA) were used. 250 ng mRNA of each sample were pre-incubated with 5 µl random hexamer at 85°C for 3 min. Next, 10 μL 10× PCR buffer, 22 μL MgCl2, 20 μL deoxyribonucleoside triphosphate, 6.25 μL multiscribe reverse transcriptase and 2 μL RNase inhibitor were added per sample. Lastly, reaction was performed under these conditions: annealing (25°C, 10 min), reverse transcription (48°C, 60 min), and enzyme inactivation (95°C, 5 min). For miRNA, reverse transcription was carried out with the miRCURY LNA RT Kit (Qiagen, Hilden, Germany). 10 ng of RNA were mixed with 2 μL of 5x reaction buffer, 5 μL of nuclease free water and 1 μL of enzyme mix, per sample. Reaction was performed using the following program: reverse transcription (42°C, 60 min) and enzyme deactivation (95°C, 5 min). Reactions were carried out on a PRISM 7700 Cycler (Applied Biosystems, Waltham, MA, USA) and transcribed cDNA was stored at -20°C until further analysis. Real time qPCR of mRNA and miRNA targets was performed to analyze gene expression using the StepOnePlus Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA). For mRNA, RT-qPCR was carried out with TaqMan qRT-PCR reagents (all Thermo Fisher Scientific, Waltham, MA, USA) and pre-designed assays. For target normalization, different endogenous controls were used: ribosomal protein L13a (RPL13A), actin beta (ACTb), Hydroxymethylbilane Synthase (HMBS) and TATA-Box binding protein (TBP) were tested. TBP was the most stable across groups, and thus selected as suitable endogenous control. For each reaction, 3.5 µl cDNA (8.75 ng cDNA) was mixed with 0.5 µl nuclease free water, 5 µl Fast Advanced Mastermix and 0.5 µl TBP primer and 0.5 µl target primer (see list of primers in Table 1). Table 1 \| Taqman Primer* \| Assay Number \| \| --- \| --- \| \| TRPV1 \| Hs00218912_m1 \| \| TLR4 \| Hs00152939_m1 \| \| SIRT1 \| Hs01009006_m1 \| \| TNFα \| Hs00174128_m1 \| \| IL-1β \| Hs00174097_m1 \| \| IL-6 \| Hs00174131_m1 \| \| IL-8 \| Hs00174103_m1 \| \| CCL2 \| Hs00234140_m1 \| \| IL-10 \| Hs00174086_m1 \| \| TBP \| Hs00427620_m1 \| \| SYBR Green Primer# \| Assay Number \| \| hsa-miR-132-3p \| YP00206035 \| \| hsa-miR-146a-5p \| YP00204688 \| \| hsa-miR-155-5p \| YP00204308 \| \| hsa-miR-16-5p \| YP00205702 \| \| 5s rRNA \| YP00203906 \| For miRNA, the miRCURY LNA SYBR Green PCR Kit (Qiagen, Hilden, Germany) and pre-designed miRCURY LNA miR PCR assays (Qiagen, Hilden, Germany) were used. Based on previous experience from our group [12, 13] and on the recommendations by TaqMan Advanced miRNA Assays (https://assets.thermofisher.com/TFS-Assets/GSD/Reference-Materials/identifying-mirna-normalizers-white-paper.pdf), the ribosomal RNA 5s and hsa-miR-16 were tested as endogenous controls. Due to differences found in the expression of 5s between groups, hsa-miR-16 was selected as suitable endogenous control, based on its comparability and standard deviation across groups and samples. Each miRNA was run adding 5 µl 2× miRCURY SYBR Green Master Mix with 1 µl ROX per 50 µl and 1 µl primer (see list of primers in Table 1) to 4 µl of 1:80 diluted cDNA. Each mRNA and miRNA was amplified in triplicates and relative quantitation (RQ) values were obtained by the StepOnePlus™ Software v2.3 (Thermo Fisher Scientific, Waltham, MA, USA) using interplate calibrators through the 2-ΔΔCt method. ## Protein analysis Cytokine levels were measured in serum and CSF using the Ella™ technology (ProteinSimple, San Jose, Cal, USA). Ella™ is a fully automated cartridge-based system that allows you to perform multiple sample, multi-analyte immunoassays with the specificity of a traditional single-plex ELISA (enzyme-linked immunosorbent assay). Samples were thawed on ice and more than two freeze-thaw cycles were avoided. Samples were diluted 1:1 in the appropriate sample diluent from each kit and 50 µl were added to each sample well of the Simple Plex™ cartridge, after 1 ml of washing buffer was added to their corresponding wells. Each sample was measured in triplicates, and the levels of each cytokine were displayed in pg/ml. ## Lipid analysis Serum and CSF samples were quantitatively analyzed for their levels of the endogenous metabolites carnitine (CAR), palmitoylcarnitine (PC) and oleoylcarnitine (OC) by LC-MS/MS (liquid chromatography coupled with tandem mass spectrometry). All samples were thawed on ice for the first time for this analysis, in order to avoid freeze-thaw cycles, and processed within two years upon collection. Serum samples were analyzed at a neat dilution and after applying a 1:10 dilution step in $70\%$ (v/v) ethanol. For sample preparation, 50 µl of serum or CSF samples were mixed with 150 µl internal standard solution (100 ng/ml acetylcarnitine-d3 in acetonitrile). Samples were centrifuged (13000 rpm, 1.5 min) and the supernatant was used for analysis. Chromatographic separation was performed with a UHPLC System (1290 Infinity series, Agilent) using a Waters Acquity UHPL CSH™ Fluoro-Phenyl (75 × 2.1 mm, 1.7 µm) column. The injection volume was 2 µl and the flow rate was set to 0.3 mL/min. For the chromatographic separation a gradient was run using $0.1\%$ formic acid in water (solvent A) and $0.1\%$ formic acid in acetonitrile (solvent B). Detection was performed by means of a triple quadrupole mass spectrometer (API 4000®, Sciex) in multiple reaction monitoring (MRM) mode. Measurements were carried out in the positive electrospray ionization (ESI) mode. The LC-MS/MS system was operated with the Software Analyst®, Version 1.7.1 (Sciex). Calibration samples were prepared in $30\%$ acetonitrile and covered calibration ranges of 5 - 500 ng/ml for PC and OC and 5 - 1500 ng/ml for CAR. To verify method performance at medium analyte concentrations, QC samples with a concentration of 50.00 ng/ml CAR, OC and PC were prepared in $30\%$ acetonitrile or in a surrogate matrix comparable to the analyzed biological samples ($2\%$ bovine serum albumin (BSA) or PBS buffer pH 7.4 diluted 1:10 in $30\%$ acetonitrile were used to mimic serum or CSF, respectively). Correlation coefficients of 0.9996 (CAR, OC) and 0.9992 (PC) were obtained. Accuracies of the calibration samples were found within +/-$15\%$ or within +/-$20\%$ for the lowest calibration level, respectively. Mean accuracies for all QC samples were found between $87.07\%$ and $109.77\%$ with CVs < $15\%$, indicating an acceptable method performance for all matrices investigated. Further analytical information can be found in the Supplementary Material. ## Statistical analysis and visualization Statistical analysis was performed in SPSS 27 (IBM, Armonk, NY, USA), where the Shapiro-Wilk test was used to determine the normal distribution of the data. For parametric data, a T-test was used for comparison between two groups and a Pearson test was performed for correlations. In non-parametric data, the Mann–Whitney U Test was applied for comparison of two groups, and the Spearman test was used for correlations. Data results were plotted in GraphPad Prism 9 (GraphPad Software, Inc., La Jolla, CA, USA) for visualization. Graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence. ## Clinical characteristics of patient cohort Baseline characteristics of the study group and the diagnostic subgroups are summarized in Table 2. Patients were included if they presented for diagnostic work-up for their PNP, including lumbar puncture. To be included, they further needed to have either no pain (NRS = 0) or pain ≥ 4 at the time of admission. Patients with an NRS between one and three were excluded from the cohort. After applying the exclusion criteria, twenty-eight patients with PNP of different etiologies were included [median body max index (BMI) 28.3, range 19.2-35.1; median age 54.5 years, range 20–80]. The median disease duration was 2.5 years (range 0.02–29 years). Nine patients were diagnosed with an inflammatory neuropathy including non-systemic vasculitis (six patients), chronic inflammatory demyelinating polyneuropathy (CIDP) (two patients) and multifocal motor neuropathy (MMN) (one patient). Nineteen patients were classified as non-inflammatory, including idiopathic neuropathy (nine patients), hereditary neuropathy (five patients), a neuropathy caused by vitamin B deficiency (three patients) or diabetic neuropathy (two patients). Table 2 \| Item \| Number (% of entire group) \| \| --- \| --- \| \| M, F (N) \| 22, 6 \| \| Median BMI (range) \| 28.3 (19.2-35.1) \| \| Median age (range) \| 54.5 years (20-80) \| \| Median disease duration (range in years) \| 2.5 years (0.02-29) \| \| Diagnostic subgroups (N and % of entire group): \| Diagnostic subgroups (N and % of entire group): \| \| Idiopathic neuropathy \| 9 (32.1%) \| \| Vasculitic neuropathy \| 6 (21.4%) \| \| Hereditary neuropathy \| 5 (17.9%) \| \| Vit B1/B6/B9/B12 deficiency \| 3 (10.7%) \| \| Diabetic neuropathy \| 2 (7.1%) \| \| CIDP \| 2 (7.1%) \| \| Multifocal motor neuropathy (MMN) \| 1 (3.6%) \| \| Analysis subgroups (N): \| Analysis subgroups (N): \| \| Painless, painful \| 14, 14 \| \| Inflammatory, non-inflammatory neuropathy \| 9, 19 \| \| Patients with treatment, without treatment (N): \| 15, 13 \| \| Immunosupressory/immunomodulatory treatment (Corticoids, immunoglobulins, NSAIDs) \| 6 \| \| Pain treatment# \| 12 \| \| Anti-neuropathic analgesics \| 11 \| \| Opioids \| 4 \| Patients were classified as painless when they presented an NRS = 0 (fourteen patients; $50\%$), and as painful with an NRS ≥ 4 (fourteen patients; $50\%$). From the full cohort of PNP patients, at the time of inclusion fifteen patients had been treated for their neuropathy with either an immunosuppressive/immunomodulatory drug (six patients) and/or pain treatment (twelve patients), while thirteen received no treatment. Further laboratory and electrophysiological data are given in Table 3. The median CRP value in serum was 0.24 mg/dl (range 0-2.37 mg/dl). In CSF, patients presented a median of 2 leukocytes/µl (range 0-6 cells/µl) and a median total protein of 46.3 mg/dl (range 19.3-194.6 mg/dl). Nerve conduction studies showed a median sural nerve sensory nerve action potential (SNAP) of 4.9 µV (range 2.5-12.6 µV) with a median nerve conduction velocity (NCV) of 45.9 m/s (range 32.4-57.3 m/s), and in the tibial nerve a median compound motor action potential (CMAP) upon stimulation at the ankle of 5.4 mV (range 0.1-23.4 mV) and a median nerve conduction velocity (NCV) of 38.3 m/s (range 26.8-52.2 m/s). Table 3* \| Item \| Normal range \| PNP \| AH \| UH \| \| --- \| --- \| --- \| --- \| --- \| \| N \| \| 28 \| 10 \| 5 \| \| Serum findings \| Serum findings \| Serum findings \| Serum findings \| Serum findings \| \| Median CRP (mg/dl) (range) \| ≤0.5 \| 0.24 (0-2.37) \| 0.23 (0-11.39) \| 0 (0-0.34) \| \| Median NFL (pg/ml) (range) \| \| 27.35 (8.3-2856)/## \| 10.80 (3.04-48.90) \| 4.92 (3.04-26.90) \| \| Lumbar puncture \| Lumbar puncture \| Lumbar puncture \| Lumbar puncture \| Lumbar puncture \| \| Median cells/µl (range) \| ≤4 \| 2 (0-6) \| 0.5 (0-3) \| 0 (0-3) \| \| Median CSF protein (mg/dl) (range) \| ≤50 \| 46.3 (19.3-194.6) \| 27.6 (16.9-44) \| 30.1 (21.2-38.8) \| \| Median NFL (pg/ml) (range) \| \| 627.0 (155.0-8535)/## \| 291.0 (134.0-489.0) \| 199.0 (123.0-413.0) \| \| Median NFL Ratio (CSF/Serum) \| \| 26.14 (1.9-75.9) \| 28.53 (8.4-66.1) \| 56.53 (15.35-66.06) \| \| Electrophysiology \| Electrophysiology \| Electrophysiology \| Electrophysiology \| Electrophysiology \| \| Sural nerve \| Sural nerve \| Sural nerve \| Sural nerve \| Sural nerve \| \| Median SNAP (µV) (range) \| ≥5/10 (Age ≥65y/<65y) \| 4.9 (2.5-12.6) \| \| \| \| Median NCV(m/s) (range) \| ≥40 \| 45.9 (32.4-57.3) \| \| \| \| Tibial nerve \| Tibial nerve \| Tibial nerve \| Tibial nerve \| Tibial nerve \| \| Median Distal CMAP (mV) (range) \| ≥10 \| 5.4 (0.1-23.4) \| \| \| \| Median NCV (m/s) (range) \| ≥40 \| 38.3 (26.8-52.2) \| \| \| ## Control group Patients that presented with acute headaches of unclear etiology (AH) and had a lumbar puncture to exclude meningitis or a subarachnoid bleed were used as controls for the CSF data. Only patients that had less than four leukocytes/µl in their CSF and normal protein values were included. After full clinical work-up, five of these patients had some signs of inflammation (elevated CRP, $$n = 3$$; sinusitis, $$n = 1$$; or elevated ANA and ANCA titers, $$n = 1$$). Thus, the AH group was divided into two groups (unclear headache, UH, and inflammatory headache, IH), and only patients with UH were further used as controls for inflammatory markers (Table 4, Figure 1A). Twelve healthy volunteers (HC) were included as controls for NFL and inflammatory markers in serum. HC and AH did not differ in age or NFL levels in pg/ml in serum (Figures 1B, C), and in both groups a positive correlation between age and NFL was present (HC, $p \leq 0.05$; AH, $p \leq 0.01$) (Figure 1D). When separating AH into UH and IH, NFL levels did not differ between groups, but IL-6 was higher in IH in comparison to HC ($p \leq 0.001$) (Figure 1E). These results supported our decision to continue the study on inflammatory markers with the UH exclusively, while the whole cohort (AH) was used for the NFL analysis. ## NFL levels in serum and CSF from PNP patients NFL was measured in serum and CSF in twenty-eight patients with PNP in comparison to ten age-matched AH (Figures 2A, B). PNP patients had higher serum levels of NFL than AH ($p \leq 0.01$) (Figure 2C). There was a positive correlation between age and NFL levels in AH ($p \leq 0.01$) but not in PNP patients (Figure 2D). NFL levels correlated negatively to sural nerve SNAP in PNP patients (Figure 2E), which translated into higher levels of NFL in patients with abnormal SNAP values (< 5 µV) in comparison to those with normal SNAP (≥ 5 µV) ($p \leq 0.01$) (Figure 2F). Figure 2:* NFL levels in serum from patients with PNP. (A) Visual representation of the analyzed 10 AH (green) and 28 PNP patients (red). (B) Age values in AH and PNP. (C) Log 10 of NFL levels in pg/ml between AH and PNP. (D) Correlation between age and log 10 of NFL levels in pg/ml in AH and PNP. (E) Correlation between log10 of NFL levels in pg/ml and SNAP. (F) Log10 of NFL levels in pg/ml between PNP patients with normal (≥ 5µV) (pink) and abnormal (< 5µV) (purple) sural nerve SNAP. ns, not significant; , p < 0.05; *, p < 0.01. All graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence. The same analysis was performed with CSF samples (Figure 3A) and we observed higher levels of NFL in PNP patients in comparison to AH ($p \leq 0.001$) (Figure 3B). Again, a correlation between age and NFL was present in AH ($p \leq 0.001$) but not in PNP patients (Figure 3C). Furthermore, our results showed a negative correlation between the levels of NFL in CSF and the MRC sumscore (Figure 3D), therefore suggesting an involvement of NFL release in the severity of the neuropathy. Figure 3: NFL levels in CSF from patients with PNP. (A) Visual representation of the analyzed 10 AH (green) and 28 PNP patients (red). (B) Log 10 of NFL levels in pg/ml between AH and PNP. (C) Correlation between age and log 10 of NFL levels in pg/ml in AH and PNP. (D) Correlation between the levels of NFL levels in pg/ml (Log10) and the MRC sumscore in PNP patients. ns, not significant; , p < 0.05; **, p < 0.001. All graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence. ## Gene expression of pro- and anti-inflammatory markers in whole blood In twenty-seven out of twenty-eight PNP patients and five UH, we analyzed the gene expression of the receptors TRPV1 and TLR4; the deacetylase SIRT1; the pro-inflammatory cytokines TNFα, IL-1β, IL-6 and IL-8, and chemokine CCL2; the anti-inflammatory cytokine IL-10; and the microRNAs miR-146a-5p, miR-132-3p and miR-155-5p in whole blood (Figure 4A). The expression results of PNP patients, with painful and painless PNP, and inflammatory and non-inflammatory PNP are displayed as fold change in comparison to UH in Table 5. Comparisons between PNP patients and controls, as well as between painful and painless PNP subgroups, and inflammatory and non-inflammatory did not show any differences in the expression of these components. Figure 4: *Relative gene expression of pro- and anti-inflammatory markers in whole blood from patients with PNP. (A) Visual representation of the analyzed 5 UH (light green) and 27 out of 28 PNP patients (red), further divided into 18 PNP patients with normal (≤0.5mg/dl, pink) and seven with increased (> 0.5 mg/dl, purple) CRP levels in serum, and into 16 PNP patients with normal (≤ 50 mg/dl, pink) and seven with abnormal (> 50 mg/dl, purple) protein levels in CSF. (B) Relative expression of IL-10 in PNP patients with increased CRP in comparison to those with normal values. Relative expression of TNFα (C) and miR-155 (D) in PNP patients with normal and abnormal protein levels in CSF. (E) Volcano plot of the Spearman multivariate correlations between each studied target and the neuropathy scores. , $p \leq 0.05$; , $p \leq 0.01.$ All graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence. TABLE_PLACEHOLDER:Table 5 Interestingly, PNP patients were next grouped into patients with and without systemic inflammation based on increased CRP levels in the serum (cut-off 0.5 mg/dl). Patients with systemic inflammation had a higher expression of IL-10 in comparison to those with normal CRP levels (≤0.5 mg/dl) ($p \leq 0.001$) (Figure 4B) and a positive correlation was found between CRP levels and IL-10 expression ($p \leq 0.01$) (Figure 4E). Furthermore, PNP patients with high CSF total protein (> 50 mg/dl) also presented lower expression of TNFα (Figure 4C) and higher expression of miR-155 (Figure 4D) than those with normal levels (≤ 50 mg/dl). Moreover, a multivariate correlation analysis (Figure 4E) showed that CCL2 correlated positively with the duration of the disease ($p \leq 0.001$) while negatively with several neuropathy scores such as sural nerve SNAP amplitudes ($p \leq 0.01$) or GCPS, indicating a relation between inflammation and axonal degeneration. ## Cytokine protein levels in sera As indicators of systemic inflammation, we analyzed the levels of the pro-inflammatory cytokines TNFα, IL-1β, IL-6 and IL-8, the chemokine CCL2, and the anti-inflammatory cytokine IL-10 in sera from twenty-eight patients with PNP and five UH (Figure 5A). Furthermore, we measured the levels of nerve growth factor beta (NGF-β) in sera, since it is involved in nociceptive processing and a target of novel analgesics [40]. The CSF from the same cohort was available and we measured the levels of the pro-inflammatory cytokines IL-6 and IL-8, and the chemokines CCL2 and fractalkine (Cx3CL1). TNFα, IL-1β and IL-10 were not detected in these samples (data not shown). All results are detailed in Table 6. Figure 5: Cytokine levels in serum from patients with PNP. (A) Visual representation of the analyzed 5 UH (light green) and 28 PNP patients (red). (B) CCL2 levels in pg/ml in serum from UH and PNP patients, further divided between those without (pink) and with pain treatment (purple). (C) IL-10 levels in pg/ml in serum from PNP patients with painless (pink) and painful (purple) neuropathies. (D) Volcano plot of the Spearman multivariate correlations between each studied target and the neuropathy scores. , $p \leq 0.05$; *, $p \leq 0.01.$ All graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence. TABLE_PLACEHOLDER:Table 6 CCL2 was present in higher levels in serum in PNP in comparison to UH, while no differences were discovered for the other analyzed markers. CCL2 was specifically upregulated in PNP patients under analgesic treatment ($p \leq 0.05$, Figure 5B). No differences were discovered between the PNP subgroups inflammatory and non-inflammatory. On the other hand, patients with a painful PNP had higher levels of IL-10 in sera than those with no pain ($p \leq 0.01$, Figure 5C). A multivariate analysis showed that the levels of IL-10 positively correlated with the pain scores in NRS ($p \leq 0.01$), NPSI ($p \leq 0.05$) and GCPS ($p \leq 0.05$) (Figure 5D). ## Analysis of the relation of proteins between sera and CSF in PNP patients The proteins present in CSF, such as NFL or inflammatory components, may be intrathecally produced or have infiltrated from the blood vessels due to a permeabilization of the B-CSF barrier. In order to study the origin of NFL and cytokines in CSF, we compared the levels of albumin between CSF and serum (albumin ratio in CSF/in serum: QAlb) and divided the patients in subgroups according to the B-CSF barrier integrity: Normal B-CSF barrier integrity (QAlb < 0.007) and mild (0.007 < QAlb < 0.01), moderate (0.01 < QAlb < 0.02) and severe (QAlb > 0.02) B-CSF barrier dysfunction (Figure S1A) [41]. Due to a low number of patients with severe B-CSF barrier dysfunction [2], moderate and severe groups were analyzed together. A moderate/severe B-CSF barrier dysfunction was present more often in inflammatory neuropathies than normal or mild dysfunction (Figure S1B). Furthermore, the albumin ratio (QAlb) correlated with the severity of the clinical phenotype (Figure S1C). As expected, the levels of total protein in CSF increased as the B-CSF barrier dysfunction became more severe (Figure S1D), thus indicating a higher permeabilization or a relation between an intrathecal production and a B-CSF barrier dysfunction. NFL measurements in serum and CSF in the different subgroups of twenty-eight PNP patients and ten AH (Figure 6A) showed higher levels in those patients with moderate/severe B-CSF barrier dysfunction in comparison to those with a normal B-CSF barrier integrity and to AH (Figures 6B, C). This resulted in a constant QNFL (NFL ratio in CSF/serum) among subgroups and between PNP and AH (Figure 6D). Furthermore, NFL in serum and CSF correlated positively with the severity of the clinical phenotype (Figure 6E), suggesting that the release of NFL might be a consequence of the severe neurodegeneration. Figure 6: Levels of NFL between CSF and serum from patients with PNP. (A) Visual representation of the analyzed 5 AH (green) and 28 PNP patients (red). Levels of NFL in serum (B) and CSF (C) in patients with normal B-CSF barrier function, or mild, moderate and severe B-CSF barrier dysfunction. (D). NFL ratio between CSF and serum (QNFL) in AH and the subgroups of PNP patients according to their B-CSF barrier dysfunction. (E) Correlation between the levels of NFL in serum () and CSF () and the subjective severity. (F) Correlations between the levels of IL-8 and NFL in CSF in pg/ml. ns, not significant; , p < 0.05; , p < 0.01; , p < 0.001; **, p < 0.0001. All graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence. Interestingly, we found a correlation between the levels of NFL and IL-8 in CSF (Figure 6F), thus indicating a relation between neurodegeneration and inflammation. With this discovery, we decided to study the levels of cytokines between CSF and serum, in order to determine their origin, in the different subgroups of twenty-eight PNP patients and five UH (Figure 7A). This analysis showed that PNP patients with a moderate/severe B-CSF barrier dysfunction present higher levels of IL-6 and IL-8 in CSF and a higher QIL-6 and QCCL2 (cytokine ratio in CSF/in serum), than those with a normal B-CSF barrier integrity (Figure 7B). A multivariate analysis showed that the levels of IL-6 in CSF correlated with the overall severity, thus indicating a relation between the production or release of IL-6 and severe PNP (Figure 7C). Interestingly, a second multivariate analysis showed that the levels of most cytokines analyzed in CSF correlated with each other (Figure 7D), therefore suggesting a common stimulus triggering their production, or their involvement in a common pathway Figure 7: Comparison of cytokine levels between CSF and serum from patients with PNP. (A) Visual representation of the analyzed 5 UH (light green) and 28 PNP patients (red). (B) Levels of IL-6, IL-8 and CCL2 in serum () and CSF () in patients with normal B-CSF barrier function, or mild, moderate and severe B-CSF barrier dysfunction. (C) Volcano plot of the Spearman multivariate correlations between each cytokine in CSF and the neuropathy scores (C), and among each cytokine in CSF (D) /# p < 0.05; ##, p < 0.01. All graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence.* ## Acyl-carnitine levels in serum and CSF from PNP patients In order to complete our study with the analysis of lipid compounds involved in pro-inflammatory pathways, we performed LC-MS to measure the levels of carnitine, palmitoylcarnitine and oleoylcarnitine in serum and CSF from twenty-three PNP patients and five UH (Figure 8A). While carnitine and palmitoylcarnitine in serum did not differ between PNP and UH, oleoylcarnitine was found in higher levels in patients with PNP in comparison to UH (Figure 8B). Carnitine was also not different in CSF between PNP patients and UH (Figure 8C). Palmitoylcarnitine and oleoylcarnitine were not detected in CSF samples (data not shown). Figure 8: Levels of pro-inflammatory lipids in serum and CSF from patients with PNP. (A) Visual representation of the analyzed 5 UH (light green) and 23 out of 28 PNP patients (red), further divided into 15 PNP patients with normal (≤ 50 mg/dl) (pink) and 8 with an abnormal (> 50 mg/dl) (purple) CSF total protein. (B) Levels of carnitine, palmitoylcarnitine and oleoylcarnitine in ng/ml in serum from UH and PNP patients. (C) Levels of carnitine in ng/ml in CSF from UH and PNP patients. (D) Levels of carnitine in ng/ml in CSF (left) and its ratio (serum/CSF) (right) in PNP patients with normal and abnormal CSF total protein. Correlations can be found between oleoylcarnitine and palmitoylcarnitine (ng/ml) in serum from UH and PNP patients (E). (F) Volcano plot of the Spearman multivariate correlations between each carnitine and the neuropathy scores. p < 0.05; , p < 0.01; , p < 0.001; **, p < 0.0001. All graphical images were incorporated from Smart Servier Medical Art, https://smart.servier.com/, under the Creative common Attribution 3.0 Unported Licence. Our results also showed that PNP patients with high CSF protein (>50mg/dl) had higher levels of carnitine in CSF and a lower serum/CSF ratio than those with normal protein levels ($p \leq 0.001$) (Figure 8D). Furthermore, we observed that the levels of palmitoylcarnitine and oleoylcarnitine in serum positively correlated in patients with PNP ($p \leq 0.0001$) and in UH ($p \leq 0.001$) (Figure 8E). Interestingly, a multivariate analysis found a positive correlation between the levels of carnitine in serum and the mTCNS ($p \leq 0.001$) as well as between the levels of carnitine in serum and the overall severity (Figure 8F). ## Discussion In this study, we investigated the gene expression, protein and lipid levels of different pro-inflammatory markers in blood and CSF from patients with PNP, in comparison to a control group of patients with acute headaches of unclear etiology. Our results showed that, contrary to our initial hypothesis of PNP being associated with enhanced systemic inflammation, PNP patients and disease controls did not present major differences in systemic inflammatory markers. Receptors of great interest in the fields of inflammation and pain, TLR4 and TRPV1, were not informative in our cohort. While only CCL2 and oleoylcarnitine were present in higher levels in sera from PNP patients than in controls, the levels of CCL2 were associated to pain treatment. Oleoylcarnitine, as one of the long-chain acylcarnitines found upstream of the synthesis of prostaglandins and thromboxanes, has been described upregulated in patients with systemic inflammation, as well as in different neuronal tissues in models of neurodegeneration [20, 22, 24]. This suggests that the levels of oleoylcarnitine indicate an inflammatory process or neurodegeneration taking place in patients with PNP. Since it did not correlate with any neuropathy scores, we postulate that oleoylcarnitine is upregulated in all patients with PNP, and it might be of interest to explore as a potential early diagnostic marker in larger groups of different types of PNP versus controls. Secondly, we hypothesized that a systemic inflammation may correlate with the severity of the disease and the development of neuropathic pain. Our study showed that IL-10 was the only inflammatory mediator consistently upregulated at the gene and protein level in patients with severe pain, in comparison to those without pain. IL-10 is understood as an anti-inflammatory cytokine, mainly produced by anti-inflammatory macrophages, and secreted to suppress pro-inflammatory responses and maintain tissue homeostasis [42]. In patients with different neuropathies, levels of IL-10 have been described downregulated in serum and CSF and negatively correlating with pain scores, therefore suggesting an increased systemic inflammation (26–30). Our study, on the other hand, showed a positive correlation between IL-10 and CRP, confirming the involvement of IL-10 in inflammatory responses, potentially in a counter-regulatory function. Our results are in accordance with previous studies where higher levels of IL-10 were reported in serum from patients with neuropathies [27, 43, 44]. This suggests that the higher expression of IL-10 might act as a compensatory mechanism against the inflammation triggered by neuropathy-specific processes. However, high levels of IL-10 as well as of IL-10 expressing blood mononuclear cells have been found related to large nerve fiber sensory and motor axonal damage, as well as motor nerve demyelination [28, 45]. Therefore, we cannot exclude the option that the overexpression of IL-10 might also play a direct role in the pathogenesis of nerve fiber damage Severe neuropathy indicated by a low SNAP correlated with high gene expression of CCL2. CCL2 and its receptor CCR2 can cause hyperalgesia through the upregulation of cation channels (46–48). In addition, CCL2, also named monocyte chemoattractant protein-1 (MCP-1), is directly involved in the migration and infiltration of monocytes, memory T lymphocytes, and natural killer (NK) cells, therefore also promoting local inflammatory processes [49]. High systemic levels of CCL2 might thus correlate with the development of neuropathic symptoms and could serve as a severity marker of the neuropathy. Furthermore, the high levels of CCL2 might also be due to the treatment of pain as previously mentioned. More specifically, PNP patients with the highest levels of CCL2 were those that had been treated with opioids. This could either be indicate of a molecular interaction or simply support that severe neuropathy is more likely to be painful and therefore being properly treated. Kaminski et al. provided evidence for a molecular interaction because inhibition of opioid receptors led to a downregulation of CCL2 [50]. On the other hand, high levels of CCL2 can inhibit the activation of opioid receptors, thus attenuating analgesia [51, 52]. This suggests that the high CCL2 levels might be a compensatory effect from the opioid treatment. Further studies need to elucidate the role of CCL2 in the development of PNP and its symptoms. Interestingly, we found that patients with a severe B-CSF barrier dysfunction, determined by the CSF/serum albumin ratio [41], also presented higher levels of NFL in serum and CSF, as well as higher levels of IL-6 and IL-8 in CSF. NFL constitutes one of the subunits of the neurofilament that forms the cytoskeleton in neurons. Neurofilaments are especially abundant in large myelinated axons, while relatively scarce in dendrites, and their release has been described as a marker of neurodegeneration or upon neuroaxonal damage (36, 53–59). As expected, NFL levels in CSF and serum of our PNP group were increased in comparison to the controls, indicating neurodegeneration. This increase seemed to be present especially in those PNP patients with B-CSF barrier dysfunction, both in serum and CSF, while no difference was found in PNP patients with a normal B-CSF barrier and controls. The correlation we found between the albumin ratio (QAlb) and the severity of the neuropathy may indicate that patients with a moderate to severe B-CSF barrier dysfunction also present more severe neuropathy and thus, more neurodegeneration, explaining the higher levels of NFL in these patients. Furthermore, while a break of the B-CSF barrier would allow the exchange of proteins between serum and CSF, the levels of NFL were consistently higher in CSF than in sera, thus indicating an intrathecal production. Since we found higher NFL levels in serum related with a decreased SNAP, and in accordance with previous studies (59–62), we believe that in diseases of the PNS, NFL in serum could come from the degeneration of peripheral axons. Since the prognosis of a neuropathy is often uncertain in an individual patient, NFL in serum could be used prospectively to monitor progress and eventually to detect unexpected accelerations of the progression. In CSF, on the other hand, differences in the levels of NFL in peripheral neuropathies have yet to be explained. In 2018, Axelsson et al. described for the first time higher NFL levels in CSF in patients with Guillain-Barré syndrome (GBS), a neuropathy of the PNS, than in controls. This increase was postulated to be due to axonal damage of nerve roots, which are surrounded by CSF in the subarachnoid space of the spinal cord [63]. This idea has been restated in a more recent study in acute and chronic inflammatory polyneuropathies [57]. Later in 2018, similar levels of NFL were found in CSF by Mariotto et al. in a cohort of acquired peripheral neuropathies. This study, however, discusses a possible ongoing axonal damage in both the CNS and PNS, and that the levels of NFL might be affected by a disrupted blood nerve barrier [64]. Following the idea raised by Axelsson et al., we postulate that neurodegeneration takes place at the nerve roots, which constitute the very beginning of the PNS, leading to the intrathecal release of NFL. Recent studies have also found a lowered NFL CSF/Serum ratio in patients with peripheral neuropathies, therefore proposing peripheral axonal damage contributing to higher levels of NFL in serum [64, 65]. Our results showed a constant CSF/serum ratio among PNP subgroups and controls, suggesting a constant increment of NFL in both tissues and therefore neurodegeneration taking place at the nerve roots as well as in peripheral nerves. Interestingly, patients with a severe B-CSF barrier dysfunction also presented higher levels of IL-6 and IL-8 in CSF, while the levels in sera remained constant, therefore causing an increment of their CSF/serum ratio. Since 1993, high levels of IL-6 and IL-8 have been reported in CSF of patients with GBS and CIDP. This study as well as more recent ones suggest a prominent intrathecal activation of cells of the monocyte/macrophage lineage, leading to the intrathecal production of the cytokines (66–68). Following a similar line of thought, we postulate that inflammation is present at the level of the nerve roots and leads to the release of pro-inflammatory cytokines directly to the CSF. Furthermore, this inflammation may cause neuronal damage and disruption of the axonal cell membrane, inducing the releases of NFL into the CSF compartment [69]. The finding of a strong correlation between the levels of IL-8 and NFL in CSF supports this assumption and leads to the question whether they are simultaneously released from the same cell type, or whether they consecutively induce each other’s release. Moreover, the levels of IL-6 in CSF correlated with the severity of neuropathy, thus indicating that the inflammation at the nerve roots might be the cause or consequence of the neurodegeneration. This would indicate that patients with more severe neuropathic symptoms would present an affection of the nerve roots and a break of the B-CSF barrier, supporting the importance of CSF analysis as a diagnostic tool for patients with PNP [70]. Being an invasive procedure, lumbar puncture to obtain CSF is not without risks, and patients need to sign informed consent, however, the risk of headache, the most frequent adverse effect of lumbar puncture, can be markedly reduced by that use of an atraumatic needle [71]. Other adverse effects are extremely rare when the lumbar puncture is properly performed. Although our study includes the analysis of a large number of pro- and anti-inflammatory markers, non-biased omics-based analysis might be necessary to identify all involved markers and elucidate the specific pathways taking place in PNP. Furthermore, our results are limited by the very well characterized but low number of recruited patients and the variance in etiologies. A larger cohort might help separating the patients into males and females and into diagnostic subgroups that could lead to clearer results. We conclude that in patients with PNP systemic inflammatory markers in blood or CSF do not differ from controls in general, but specific cytokines or lipids do. Nevertheless, we found several indications of a correlation between inflammation and the neuropathy severity and symptoms. In particular, we described a strong interaction between inflammation and neurodegeneration at the nerve roots in a specific subgroup of PNP patients with B-CSF barrier dysfunction, which highlights the importance of CSF analysis in patients with peripheral neuropathies. We believe that the diagnostic marker panel provided in our study may help improving patient stratification not only to increase diagnostic validity, but also to guide treatment decisions. ## 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 Würzburg Medical Faculty Ethics Committee (# $\frac{15}{19}$ and # $\frac{242}{17}$). The patients/participants provided their written informed consent to participate in this study. ## Author contributions PG-F, NÜ and CS contributed to conception and design of the study. AR performed the gene expression analysis. KH and NC performed the protein analysis. KS and AN performed the lipid analysis. KH, NC, A-KR, HR, NÜ and CS contributed to recruitment of patients and collection of clinical data. A-KR and HR provided samples from healthy controls. PG-F organized the database and performed the statistical analysis. PG-F wrote the first draft of the manuscript. KS and AN wrote a section of the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest Author CS has been a consultant for Merz, Omega, Ipsen and Bayer on the subject of neuropathic pain. She has given educational talks for GSK and Pfizer. Authors KS and AN are employed by Bionorica research GmbH. 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 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. 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--- title: 'Circulating autotaxin levels in healthy teenagers: Data from the Vitados cohort' authors: - Marie-Noëlle Méaux - Maitena Regnier - Aurélie Portefaix - Olivier Borel - Candide Alioli - Olivier Peyruchaud - Mélanie Legrand - Justine Bacchetta journal: Frontiers in Pediatrics year: 2023 pmcid: PMC9969100 doi: 10.3389/fped.2023.1094705 license: CC BY 4.0 --- # Circulating autotaxin levels in healthy teenagers: Data from the Vitados cohort ## Abstract Autotaxin (ATX) is a secreted enzyme with a lysophospholipase D activity, mainly secreted by adipocytes and widely expressed. Its major function is to convert lysophosphatidylcholine (LPC) into lysophosphatidic acid (LPA), an essential bioactive lipid involved in multiple cell processes. The ATX-LPA axis is increasingly studied because of its involvement in numerous pathological conditions, more specifically in inflammatory or neoplastic diseases, and in obesity. Circulating ATX levels gradually increase with the stage of some pathologies, such as liver fibrosis, thus making them a potentially interesting non-invasive marker for fibrosis estimation. Normal circulating levels of ATX have been established in healthy adults, but no data exist at the pediatric age. The aim of our study is to describe the physiological concentrations of circulating ATX levels in healthy teenagers through a secondary analysis of the VITADOS cohort. Our study included 38 teenagers of Caucasian origin (12 males, 26 females). Their median age was 13 years for males and 14 years for females, ranging from Tanner 1 to 5. BMI was at the 25th percentile for males and 54th percentile for females, and median blood pressure was normal. ATX median levels were 1,049 (450–2201) ng/ml. There was no difference in ATX levels between sexes in teenagers, which was in contrast to the male and female differences described in the adult population. ATX levels significantly decreased with age and pubertal status, reaching adult levels at the end of puberty. Our study also suggested positive correlations between ATX levels and blood pressure (BP), lipid metabolism, and bone biomarkers. However, except for LDL cholesterol, these factors were also significantly correlated with age, which might be a confounding factor. Still, a correlation between ATX and diastolic BP was described in obese adult patients. No correlation was found between ATX levels and inflammatory marker C-reactive protein (CRP), Body Mass Index (BMI), and biomarkers of phosphate/calcium metabolism. In conclusion, our study is the first to describe the decline in ATX levels with puberty and the physiological concentrations of ATX levels in healthy teenagers. It will be of utmost importance when performing clinical studies in children with chronic diseases to keep these kinetics in mind, as circulating ATX might become a non-invasive prognostic biomarker in pediatric chronic diseases. ## Introduction Autotaxin (ATX), also known as ENPP2 (ecto-nucleotide pyrophosphatase phosphodiesterase family member 2), is a secreted enzyme with a lysophospholipase D activity (1–4). ATX is the only secreted member of the ENPP family [5], synthesized by different cell types and mainly by adipocytes [6]. ATX is found in numerous biological fluids and tissues [7], including blood, with a short circulating half-life due to a quick clearance by the liver [8]. Its pleiotropic expression likely reflects its role in multiple physiological processes. ATX converts lysophosphatidylcholine (LPC) into lysophosphatidic acid (LPA) [1], the smallest bioactive glycerophopholipid [9]. LPA binds to at least six protein G-coupled receptors, LPA 1–6 [10], that are differentially expressed in tissues with distinct and overlapping biological responses, thus explaining the large range of cellular processes involving LPA [6]. LPA is one of the smallest glycerophospholipids of the organism [5] and a structural component of cellular membranes [11]. It is also a major bioactive lipid acting as a signaling molecule involved in multiple cell processes including survival, migration, and proliferation, among almost all cell types [9]. LPA and ATX are crucial in different systems, notably reproduction (ovarian function, embryo implantation, maintenance of pregnancy) [10, 12, 13], development of the central nervous system [14], immunity [15, 16], inflammation [17], and lipid metabolism [18]. A ubiquitous deletion of ATX leads to early embryonic lethality in mice [19], notably because of defective vasculogenesis [20, 21] and central nervous system development [14]. With regard to inflammation, ATX transcription is enhanced by pro-inflammatory cytokines such as tumor necrosis factor alpha (TNFα) in hepatocytes through the nuclear factor kappa beta (NFkB) signaling pathway [10, 22] and IL-6 in adipocytes [23]. Alternatively, ATX will induce the production of pro-inflammatory cytokines, thus maintaining inflammation [17]. With regard to lipid metabolism, the adipose tissue is one of the main sources of ATX [6, 24], and ATX production by adipocytes is increased in the presence of pro-inflammatory cytokines [23]. ATX increases pre-adipocyte proliferation [6], but its role in pre-adipocyte differentiation is more controversial, with a probably inhibitory effect [18]. Through this pathway, the ATX–LPA axis contributes to obesity and insulin resistance [18]. Thus, the ATX–LPA axis is involved in various physiological processes, but it is also linked to several pathological conditions, especially inflammatory and neoplastic diseases [17, 25]. The assessment of circulating ATX levels might therefore be interesting for clinical applications. Studies have shown that measurement of serum ATX antigen level is a reliable indicator of ATX activity in human serum samples [26, 27] and thus can be used for clinical investigation. Circulating levels of ATX have been reported in healthy adults: ATX levels are significantly greater in women (625–1,323 ng/ml) than in men (438–914 ng/ml) [13, 26, 28]. ATX levels are also negatively correlated with age in adult men, but it is not correlated with age in women [26]. Moreover, the serum phospholipase D/ATX activity gradually increases during pregnancy [4, 29] and so do ATX levels: serum ATX antigen is correlated to gestational week [13], increasing from 1,961 ± 450 ng/ml at the first semester to 5150 ± 2,143 ng/ml at the third trimester of pregnancy, with a further decrease immediately after delivery. ATX levels have also been described in pathological conditions such as inflammatory or hepatic diseases. For example, it gradually increases with fibrosis stage in chronic hepatitis C, and even correlates in biliary atresia with histological fibrosis grades, increasing from 1,080 ng/ml at F0 grade to 2,500 ng/ml at F4 grade [27, 28, 30]. ATX serum levels are also influenced by the metabolic status of the individuals and correlate with insulin resistance in obese patients and with their body mass index (BMI) [31]. A recent study even revealed a rapid and lasting decrease in ATX levels after bariatric surgery in obese patients, reinforcing the connection between ATX and lipid metabolism [32]. However, no data have been published on the pediatric age. Thus, the aim of our work is to describe the physiological concentrations of circulating ATX levels in healthy teenagers through a secondary analysis of the VITADOS cohort [33] and to screen for potential correlations with other circulating biomarkers as a basis for further research on the pathological role of ATX in chronic diseases affecting teenagers. ## Population The initial aim of the Vitados cohort (NCT01832623) was to assess native vitamin D [25-(OH)-D] status in a general French population of healthy Caucasian teenagers in association with their bone and cardiovascular status; it allowed to describe normal values for the main phosphate/calcium and bone biomarkers in the total cohort of 100 included healthy teenagers depending on sex and puberty [30]. Exclusion criteria were the following: walking disability, past or ongoing treatment by growth hormone (rhGH) therapy, past intake of oral corticosteroids (for more than 3 months), ongoing treatment with corticosteroids or calcineurin inhibitors, chronic disease with a likely effect on growth (and notably chronic parenteral nutrition, chronic inflammatory disease, systemic disease, chronic renal insufficiency, diabetes mellitus), acute ongoing severe disease (and notably infection or cancer), pregnancy, and an intake of acetylsalicylic acid or anti-inflammatory drugs within the last 3 weeks. Here, we were able to work on the remaining sera of 38 subjects from this cohort. Demographic, physical, and biochemical data were recorded. Height and weight were presented as the standard deviation score (SDS) for age and sex, and BMI was presented as the percentile for age and sex. The Tanner stage was assessed by an experienced physician. Systolic and diastolic blood pressure (SBP and DBP) were expressed in terms of percentile according to age, sex, and height [34]. ## Blood samples Morning fasting samples were obtained. Because of the peculiar evolution of phosphate levels across the pediatric age, phosphate levels were expressed as the standard deviation score (SDS) for age [35]. Renal function was estimated using the 2009 Schwartz formula to calculate an estimated glomerular filtration rate (eGFR) [36]. All assays used for standard biochemical assessments were ones that were previously described [33]. ## ATX measurements with ELISA assays To measure ATX levels in serum, we used ATX sandwich ELISA kits manufactured by Echelon Biosciences (K-5600). Samples were placed on an ATX detection plate (K-5601), and an anti-ATX antibody (K-5603) was used and revealed by a secondary detector (K-SEC7). The revelation was made by using absorbance assays. The normal human serum level of ATX described in this kit is 589–1,135 ng/ml. A volume of 10 µl was used and every sample was assessed in duplicate. ## Statistical analysis The results were presented as median (min−max). Non-parametric tests were performed: Mann–Whitney tests, Kruskall–Wallis tests for multiple comparisons followed by Dunn's tests, and Spearman tests for correlation. A p-value below 0.05 was considered statistically significant. Statistical analyses and figures were performed with Graphpad Prism 8. ## Ethical committee The VITADOS study was approved by the Comité de Protection des Personnes Lyon Sud Est II; all patients and parents (or legal guardians) gave their consent after written information. ## Description of the cohort and results on ATX levels The demographic and biological characteristics of the subgroup of the VITADOS cohort studied here are given in Table 1. Table 1 \| Unnamed: 0 \| Males (n = 12) \| Females (n = 26) \| \| --- \| --- \| --- \| \| Age (years) \| 13.3 (10.2–17.8) \| 14.2 (10.5–17.8) \| \| Tanner stage \| Tanner stage \| Tanner stage \| \| Tanner 1 \| 3 \| 1 \| \| Tanner 2 \| 3 \| 4 \| \| Tanner 3 \| 2 \| 6 \| \| Tanner 4 \| 2 \| 7 \| \| Tanner 5 \| 2 \| 8 \| \| Height (cm) \| 161 (136–187) \| 158 (143–180) \| \| Height (SD) \| 0.9 (−2.4–2.7) \| 0.7 (−1.0–4.0) \| \| Weight (kg) \| 55 (26–77) \| 47 (32–67) \| \| Weight (SD) \| 0.2 (−1.7–5.4) \| 0.8 (−1.0–2.4) \| \| BMI (percentile) \| 25 (3–99) \| 54 (20–97) \| \| SBP (percentile) \| 30 (5–88) \| 29 (0–94) \| \| DBP (percentile) \| 34 (9–93) \| 32 (2–92) \| \| Creatinine (µmol/L) \| 55 (43–94) \| 54 (40–80) \| \| eGFR (ml/min/1.73 m2) \| 107 (71–129) \| 107 (74–141) \| \| Calcium (mmol/L) \| 2.42 (2.24–2.57) \| 2.37 (2.20–2.53) \| \| Phosphate (mmol/L) \| 1.40 (1.08–1.60) \| 1.38 (0.93–1.61) \| \| Phosphate SDS \| −0.6 (−1.6–0.4) \| −0.6 (−2.3–0.5) \| \| PTH (ng/L) \| 17 (10–24) \| 17 (11–29) \| \| ALP (UI/L) \| 229 (84–524) \| 176 (54–305) \| \| 25OHD (nmol/L)* \| 55 (30–66) \| 65 (48–129) \| \| 1,25(OH)2D3 (pmol/L) \| 123 (98–173) \| 142 (96–206) \| \| FGF23 (UI/L) \| 62 (43–98) \| 74 (58–106) \| \| ALP (UI/L) \| 229 (84–524) \| 176 (55–305) \| \| BAP (UI/L) \| 62 (17–194) \| 57 (11–110) \| \| CTX (µmol/L)* \| 1,746 (1442–2410) \| 1,414 (652–2260) \| \| OCN (µg/L) \| 71 (40–160) \| 64 (27–238) \| \| Total Ch (mmol/L) \| 4.2 (3.3–5.1) \| 4.3 (2.3–5.7) \| \| LDL Ch (mmol/L) \| 2.4 (1.7–3.2) \| 2.5 (0.8–4.0) \| \| HDL Ch (mmol/L) \| 1.2 (0.8–2.1) \| 1.5 (0.9–2.3) \| \| TG (mmol/L) \| 0.7 (0.3–1.9) \| 0.6 (0.3–1.4) \| \| ATX (ng/ml) \| 1,109 (521–1455) \| 977 (450–2201) \| The ATX median levels were 1,049 (450–2,201) ng/ml. There was no difference in the ATX levels in terms of sex, but these levels significantly decreased with age and pubertal status, as illustrated in Figure 1, Table 2. There was no relation either with regard to normalized height or body weight or with regard to BMI percentile. Figure 1: *Autotaxin is negatively associated with age and pubertal status. (A) Correlation tests were conducted between ATX circulating levels (ng/ml) and age Spearman correlation test: r = −0.47, IC$95\%$ = −0.69−(−)0.16, $$p \leq 0.0034$$ *. (B) Correlation tests were performed between ATX circulating levels (ng/ml) and the Tanner stage. Kruskal–Wallis test: $$p \leq 0.0039.$$ p (T2 vs. T5) = 0.025 , p (T2 vs. T4) = 0.049 . TABLE_PLACEHOLDER:Table 2 ## ATX and cardiovascular markers A positive correlation was found between ATX levels and diastolic blood pressure ($$p \leq 0.003$$), but later, these also correlated with age. ATX also positively correlated with circulating total and LDL cholesterol ($r = 0.46$, $$p \leq 0.005$$ and $r = 0.33$, $$p \leq 0.043$$, respectively). Age was negatively correlated with total cholesterol but not with LDL cholesterol. These results are shown in Figure 2. Figure 2: Autotaxin and age are positively correlated with cardiovascular risk and fat metabolism markers. (A) Correlation tests were conducted between ATX circulating levels (ng/ml) and systolic blood pressure (SBP, percentile). Spearman correlation test: r = 0.34, IC95% = 0.01–0.61, p = 0.04. (B) Correlation tests were performed between ATX circulating levels (ng/ml) and diastolic blood pressure (DBP, percentile). Spearman correlation test: r = 0.34, IC95% = 0.01–0.61, p = 0.04. (C) Correlation tests were done between ATX circulating levels (ng/ml) and body mass index (percentile). Spearman correlation test: non-significant. (D) Correlation tests were carried out between ATX circulating levels (ng/ml) and total cholesterol (Total Ch) (mmol/L). Spearman correlation test: r = 0.46, IC95% = 0.14–0.68, p = 0.0046. (E) Correlation tests were done between ATX circulating levels (ng/ml) and LDL cholesterol (LDL Ch) (mmol/L). Spearman correlation test: r = 0.33, IC95% = 0.01–0.59, p = 0.0433. (F) Correlation tests were performed between ATX circulating levels (ng/ml) and HDL cholesterol (HDL Ch) (mmol/L). Spearman correlation test: non-significant. (G) Correlation tests were done between ATX circulating levels (ng/ml) and triglyceride levels (TG) (mmol/L). Spearman correlation test: non-significant. Age is correlated with these for blood factors and the Spearman correlation tests: Age and SBP: non-significant. Age and DBP: r = −0.4369, IC95% = −0.67 – (−)0.13, p = 0.0061. Age and per BMI: r = 0.0168, IC95% = −0.31–0.34, p = 0.9204 non-significant. Age and total Ch: r = −0.3670, IC95% = −0.62 – (−)0.04, p = 0.0234. Age and LDL Ch: r = −0.2200, IC95% = −0.61–0.12, p = 0.1845 non-significant. Age and HDL Ch: r = −0.3649, IC95% = −0.62 – (−)0.014, p = 0.0243. Age and TG: r = 0.2168, IC95% = −0.12–0.51, p = 0.1911 non-significant.* ## ATX and phosphate/calcium and bone biomarker metabolism There was no correlation between ATX and calcium, phosphate SDS, PTH, 25OHD, 1,25(OH)2D3, FGF23, or urinary calcium/creatinine ratio. However, ATX levels were statistically associated with the markers of bone turnover: alkaline phosphatase (ALP), bone ALP (BAP), C-terminal fraction of type 1 collagen (CTX), and osteocalcin (OCN). However, all these factors were also significantly correlated with age. These results are shown in Figure 3. Figure 3: Autotaxin is positively correlated with bone turnover markers. (A) Correlation tests were done between ATX circulating levels (ng/ml) and ALP (UI/L, alkaline phosphatases). Spearman correlation test: r = 0.051, IC95% = 0.22–0.72, p = 0.0011. (B) Correlation tests were performed between ATX circulating levels (ng/ml) and bone ALP (UI/L, bone alkaline phosphatases). Spearman correlation test: r = 0.47, IC95% = 0.17–0.69, p = 0.0029. (C) Correlation tests were done between ATX circulating levels (ng/ml) and CTX (µmol/L, C terminal fraction of type 1 collagen). Spearman correlation test: r = 0.42, IC95% = 0.11–0.66, p = 0.0086. (D) Correlation tests were done between ATX circulating levels (ng/ml) and OCN (µg/L, osteocalcine). Spearman correlation test: r = 0.45, IC95% = 0.15–0.68, p = 0.0043. Age was also correlated with these biomarkers and the Spearman correlation tests: Age and ALP: r = −0.7944, IC95% = −0.89 – (−)0.63, p < 0.0001. Age and BAP: r = −0.8111, IC95% = −0.90 – (−)0.66, p < 0.0001. Age and CTX: r = −0.4945, IC95% = −0.71 – (−)0.20, p = 0.0016. Age and OCN: r = −0.7694, IC95% = −0.88 – (−)0.59, p < 0.0001. In contrast, no correlation was found between the osteocyte-synthesized sclerostin and ATX or age ($$p \leq 0.13$$ and 0.07, respectively). ## ATX and inflammation, kidney function No correlation was found between the inflammatory marker C-reactive protein (CRP) and with eGFR. ## Multivariate analysis By using a backward multivariate analysis including age, ALP (as a marker of bone formation), and CTX (as a marker of bone resorption) in the model, nothing remained significantly associated with ATX. ## Discussion The main objective of this study is to describe normal ATX values in healthy teenagers aged 10–18 years, since data are lacking in this age group. This is of utmost importance for future studies in the field and notably in children with chronic diseases such as chronic inflammatory states or obesity. Indeed, in adults, the deregulation of the ATX–LPA axis has been recently shown in chronic pathological conditions, especially in neoplastic and inflammatory diseases with increased ATX levels [2, 17, 25, 37]. Indeed, ATX was first described in 1992 as an autocrine motility factor in human melanoma [38]. Since then, ATX was found overexpressed and implicated in many different cancers such as hepatocellular carcinoma [22], breast cancer [39], glioblastoma [40, 41], Hodgkin lymphoma [42], and non-small-cell lung cancer [43], with a growing evidence that it is directly involved in tumor progression, invasiveness, and dissemination through the production of LPA [2, 5, 44]. However, ATX is also closely linked to inflammation: its levels increase in many inflammatory diseases such as pulmonary fibrosis [45, 46], rheumatoid arthritis [47], or chronic inflammatory bowel diseases [48]. In murine models, conditional genetic deletions of ATX or of the LPA-receptor LPA-R1, as well as pharmacological ATX inhibition [45, 47, 49], lead to attenuated inflammation, thus suggesting the contribution of the ATX–LPA axis in the pathogenesis of these diseases. Because of its synthesis by hepatocytes, ATX is also deregulated in liver diseases: in patients with chronic fibrosis due to hepatitis C, circulating ATX levels increase gradually with fibrosis stage [27, 28]. In biliary atresia (BA), ATX levels correlate with histological fibrosis grades [30], increasing from 1,080 ng/ml at F0 grade to 2,500 ng/ml at F4 grade, thus being a potential interesting non-invasive marker for liver fibrosis estimation. In that setting, it is interesting to note that this study was performed in 35 patients at a median age of 10.6 years, and that the results obtained at the F0 stages were similar to those we report here in healthy subjects at a similar age. Another pediatric study was performed directly on liver specimens from infants with BA undergoing Kasai operation ($$n = 20$$) and compared with samples from infants who underwent liver biopsy for another reason ($$n = 14$$); interestingly, this study found that mRNA and protein expression of ATX were increased in BA livers [50]. High hepatic ATX expression at the time of Kasai operation was associated with liver fibrosis and outcome in BA, suggesting that ATX may serve as a prognostic biomarker in this infantile disease [50]. Eventually, ATX levels are associated with cirrhosis grade whatever its cause may be, along with its complications (e.g., hepatic encephalopathy, esophageal varices, and portal hypertensive gastropathy), and it is an independent predictor of overall survival in this population, as demonstrated in a longitudinal cohort of 270 adult patients with liver cirrhosis followed until death, liver transplantation, or last contact [51]. Reference values in healthy adult controls were 258 ± 40 ng/ml in this study [51]. The ATX–LPA axis is also linked to lipid metabolism, as previously described, including stimulation of pre-adipocytes proliferation. In mice, adipocyte ATX expression is increased in genetically obese mice in correlation with their insulin resistance state [52]. The heterozygous model or adipose-specific knockdown of ATX is associated with attenuated diet-induced obesity and decreased insulin resistance [53], likely through LPAR1 [54]. In humans, adipocyte ATX expression is enhanced in subjects with insulin resistance [52], and serum ATX levels correlate with insulin resistance in obese patients [31]. Thus, ATX closely links with obesity and insulin resistance both in humans and in mice, with growing evidence of its involvement in the impaired glucose homeostasis of diet-induced obesity. This suggests a potential future therapeutic target of ATX and LPAR1 for the treatment of overweight or diabetes-related metabolic diseases. Interestingly, visceral fat ATX expression was found to be increased in obese female patients ($$n = 27$$) compared with non-obese patients ($$n = 10$$) in a previous study [55], which also describes a correlation between ATX and diastolic arterial BP in obese patients, similarly to our present study finding a correlation of ATX levels and diastolic BP in teenagers. However, in our study, this correlation is also found between ATX levels and age, which could then be a confounding factor. Lastly, the ATX–LPA axis is involved in various other pathological conditions, especially cardiovascular diseases, such as the development of atherosclerosis through the accumulation of LPA in atherosclerotic plaques [56] and the LPA-mediated adventitial mast cell activation leading to vascular inflammation and plaque instability [57]. Its involvement in various pathological processes suggests that ATX might be an interesting prognostic biomarker and also a potential target for the treatment of numerous diseases. Indeed, the inhibition of ATX or LPAR1 is a potential therapeutic strategy in cancer and inflammatory diseases, with promising preclinical results with a good safety profile to date [10, 58]. Here, we found no differences in ATX levels between sexes, contrary to what is observed in adults where ATX levels are significantly higher in women. ATX levels significantly decrease with age and pubertal status, reaching 714 (521–1,259) ng/ml in males and 862 (450–1,327) ng/ml in females at the Tanner stage 4–5, which is consistent with the adult values in the literature previously described. Moreover, as mentioned earlier, a previous study also described decreased ATX levels with age among male adults [26] but not among women. Thus, we can hypothesize a potential interaction between sexual hormones and ATX levels because of the sex difference in adults on the one hand and because of the modification of ATX levels during pregnancy on the other hand. Here, we show that, similarly to other biomarkers [and notably phosphate levels [59]], ATX levels decrease along puberty in healthy teenagers. To our knowledge, this is the first description of a modification of ATX levels with puberty in humans. The fact that ATX is deregulated in some gynecological conditions reinforces the hypothesis of an association between sexual hormones and ATX: indeed, in endometrial carcinomas, ATX mRNA expression is higher in neoplastic cells that are positive for estrogen receptor (ER) than in ER-negative neoplastic cells [60]. Moreover, in endometrial cancer, estrogens stimulate ATX expression, and the ATX-LPA axis is involved in estrogen cell proliferation through the MAPK-ERK signaling pathway [60]. This pilot study also suggested positive correlations between ATX levels and blood pressure, lipid metabolism, and bone biomarkers but not between biomarkers of phosphate/calcium metabolism. These results should be confirmed in larger studies, especially because age could be a confounding factor in these bivariate analyses. Still, a correlation between ATX and diastolic BP has been described in obese adult patients [55]. Alternatively, the positive correlation between ATX and LDL cholesterol, which seems independent of age, is consistent with the implication of the ATX-LPA axis in lipid metabolism. This study has several strengths, including a well-phenotyped prospective transversal cohort of healthy pediatric subjects. We measured the biomarkers of bone and phosphate/calcium metabolism using the most recent available assays and were, therefore, able to provide data depending on sex and pubertal status. According to the guidelines from the Clinical and Laboratory Standards Institute (CLSI), 120 healthy subjects per group are required to establish pediatric reference values, with a minimum of 20 subjects per group to validate existing data. As such, we cannot consider that here we provide reference values, especially because we had serum sufficient for only a subset of the VITADOS subjects. Thus, we may have lacked the power to demonstrate differences according to sex. In conclusion, we are the first to describe the decline in ATX levels with puberty. It will be of utmost importance to keep these kinetics in mind when performing clinical studies in children with chronic diseases, as circulating ATX might become a non-invasive prognostic biomarker in pediatric chronic diseases. ## 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 Hospices Civils de Lyon. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. ## Author contributions M-NM and MR performed experiments, analyzed data, prepared the figures, and wrote the paper; AP, OB, and ML performed experiments and analyzed data; OP and JB provided funding, conceptual advice, coordinated the study, and edited the manuscript. All authors have read and agreed to the published version of the manuscript. 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--- title: Mendelian randomization study shows a causal effect of asthma on epilepsy risk authors: - Peng Tang - Xingzhi Guo - Li Chong - Rui Li journal: Frontiers in Immunology year: 2023 pmcid: PMC9969112 doi: 10.3389/fimmu.2023.1071580 license: CC BY 4.0 --- # Mendelian randomization study shows a causal effect of asthma on epilepsy risk ## Abstract ### Objective The relationship between asthma and epilepsy in observational studies is controversial. The purpose of this Mendelian randomization (MR) study is to investigate whether asthma causally contributes to epilepsy susceptibility. ### Methods Independent genetic variants strongly ($P \leq 5$E-08) associated with asthma were from a recent meta-analysis of genome-wide association studies on 408,442 participants. Two independent summary statistics of epilepsy obtained from the International League Against Epilepsy Consortium (ILAEC, Ncases=15,212, and Ncontrols=29,677) and FinnGen Consortium (Ncases=6,260 and Ncontrols=176,107) were used in the discovery and replication stage, respectively. Several sensitivity analyses and heterogeneity analyses were further conducted to assess the stability of the estimates. ### Results Using the inverse-variance weighted approach, genetic predisposition to asthma was associated with an elevated risk of epilepsy in the discovery stage (ILAEC: odds ratio [OR]=1.112, $95\%$ confidence intervals [CI]= 1.023-1.209, $$P \leq 0.012$$), but not verified in the replication stage (FinnGen: OR=1.021, $95\%$CI= 0.896–1.163, $$P \leq 0.753$$). However, a further meta-analysis of both ILAEC and FinnGen showed a similar result (OR=1.085, $95\%$ CI: 1.012-1.164, $$P \leq 0.022$$). There were no causal associations between the age onset of asthma and epilepsy. Sensitivity analyses yielded consistent causal estimates. ### Conclusion The present MR study suggests that asthma is associated with an increased risk of epilepsy independent of the age onset of asthma. Further studies are warranted to explain the underlying mechanisms of this association. ## Introduction Asthma is one of the most common chronic respiratory disorders [1], affecting affects over 300 million people worldwide and bringing a huge economic and social burden [2]. Accumulating evidence has shown that inflammation might be involved in the pathogenesis of asthma [3] and individuals with brain inflammation have a likelihood of being predisposed to epileptogenesis [4, 5]. These findings have drawn much attention to exploring the association of asthma with epilepsy. Indeed, two previously published population-based studies of adults revealed that patients with epilepsy were often accompanied by physical comorbidities such as asthma [6, 7]. In addition, numerous case-control studies have announced that the prevalence of asthma was related to higher odds of epilepsy either in children [8] or in adults [9]. These data suggest that asthma might be associated with high susceptibility to epilepsy. However, data from other case-control studies have displayed discordant findings, with a retrospective study among children suggesting that idiopathic epilepsy is not etiologically connected with asthma [10]. Furthermore, observational studies cannot prove the causal inference due to their sensitivities to residual confounding and reverse causality. Mendelian randomization (MR), using genetic connections to inquire about the causal impact of a risk factor on an outcome [11], is an effective method for gaging causal inference. This approach can not only limit reverse causality but also greatly reduce the likelihood of residual confounding [12]. Based on the inconsistent findings of the aforementioned retrospective cohort studies, we undertook a 2-sample MR approach to assess whether asthma causally contributed to an increased risk of epilepsy. ## Study design and data source Independent single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) were selected as instrumental variables (IV). This MR study aimed to satisfy the three primary assumptions described in detail in Figure 1. Assumption 1 (Relevance), SNPs significantly ($P \leq 5$E-08) associated with asthma. Assumption 2 (Independence), SNPs not associated with confounding factors that correlated with both asthma and epilepsy, including atopic dermatitis [13], celiac disease [14], inflammatory bowel disease [15], rheumatoid arthritis [16], hypothyroidism [17], migraine [18], multiple sclerosis [19], educational attainment [20], and body mass [21]. Assumption 3 (Exclusivity), SNPs affected epilepsy susceptibility directly through asthma and are not associated with epilepsy ($P \leq 1$E-05). Figure 1: Three corresponding principal assumptions in this 2-sample Mendelian randomization study. Red stars mean that genetic variants are not associated with confounding factors and the outcome. The summary statistics of asthma were from the latest large-scale GWAS meta-analysis of 408,442 Europeans from the UK Biobank [22]. For childhood-onset and adult-onset asthma [23], there were 314,633 and 327,253 participants of European descent from the UK Biobank, respectively. For epilepsy, two independent summary statistics of epilepsy from the International League Against Epilepsy Consortium (ILAEC) and the FinnGen Consortium were included in this MR study. The summary statistics from the ILAEC contained 15,212 cases and 29,677 normal controls [24], and a total of 6,260 epilepsy cases and 176,107 normal controls of European descent were obtained from the FinnGen Consortium [25]. Since samples from the ILAEC had a higher proportion of cases ($33.9\%$) than those from the FinnGen Consortium ($3.5\%$), we used the datasets of ILAEC and FinnGen Consortium in the discovery stage and replication stage, respectively. Epilepsy was diagnosed by epilepsy specialists based on electroencephalography, magnetic resonance imaging, and clinical history. Table 1 includes a detailed summary of the study including source publications (Table 1). Table 1 \| Phenotype \| Author \| Year \| Sample size (N) \| SNP(N) \| PMID \| URL (Data Download) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Asthma \| Valette et al. \| 2021.0 \| 408442.0 \| 34551291.0 \| 34103634 \| https://www.ebi.ac.uk/gwas/downloads/summary-statistics \| \| Asthma (adult-onset) \| Ferreira et al. \| 2019.0 \| 327253.0 \| 8949308.0 \| 30929738 \| https://www.ebi.ac.uk/gwas/downloads/summary-statistics \| \| Asthma (childhood-onset) \| Ferreira et al. \| 2019.0 \| 314633.0 \| 8984776.0 \| 30929738 \| https://www.ebi.ac.uk/gwas/downloads/summary-statistics \| \| Epilepsy \| \| \| \| \| \| \| \| ILAEC \| Abou-Khalil et al. \| 2018.0 \| 44889.0 \| 4880492.0 \| 30531953 \| https://gwas.mrcieu.ac.uk/files/ieu-b-8/ieu-b-8.vcf.gz \| \| FinnGen \| FinnGen project \| 2021.0 \| 182367.0 \| 16380349.0 \| – \| https://finngen.gitbook.io/documentation/data-download \| ## Instruments selection Those SNPs passing the genome-wide significance threshold ($P \leq 5$E–08) were selected as IVs, which were clumped according to the linkage disequilibrium structure (1000 Genomes Project of European, r2<0.01 within 10000 kb). In addition, SNPs associated with epilepsy with a P value lower than 1E–05 were excluded from the IV before MR analysis. Meanwhile, IVs associated with the confounders described above were also removed from the MR analysis. SNPs absent from the epilepsy GWAS datasets will be replaced with overlapping proxy SNPs (r2 = 0.8). To strengthen the robustness of the estimates, SNPs with a minor allele frequency of less than 0.3 were also removed. All harmonized SNPs for each exposure-outcome pair were archived (Supplementary Data Sheet). ## Mendelian randomization analysis The TwoSampleMR package (version 0.5.6) was applied in the present Mendelian randomization analysis [26]. The inverse-variance weighted (IVW) method was used as the default method to calculate causal estimates between asthma and epilepsy. Meanwhile, we also employed weighted median, MR–Egger regression, weighted mode, simple median, maximum likelihood, and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) as sensitivity analyses to validate the estimates [27]. MR-PRESSO test could identify horizontal pleiotropic outliers and evaluate the potential pleiotropic effects of the genetic variants selected as IV. MR–Egger intercept test was also applied to measure the horizontal pleiotropy. In addition, F-statistics were also calculated to assess the instrumental strength as previously described [28], and F values of more than 10 were found to avoid bias from weak instruments. A meta-analysis based on ILAEC and FinnGen was also conducted to calculate the overall causal estimates using the meta package (version 5.2.0). A fixed-effect model was applied to combine the estimates if there was obvious heterogeneity ($P \leq 0.05$ or I2<$50\%$), otherwise, a random-effect model was employed [29]. There is yet a lack of consensus regarding the best strategy for multiple test correction [30, 31], where multiple testing for different outcomes might increase the risk of Type I error, while adjustment for multiple comparisons could increase the risk of type II errors. To balance the type I and type II errors, we followed the strategy reported previously by Ronald J. Feise via conducting independent Bonferroni correction for each outcome assessed [30]. Since two independent GWAS datasets for epilepsy were included in this study, a P-value < 0.025 after Bonferroni correction ($\frac{0.05}{2}$) was considered statistically significant. Meanwhile, a P-value < 0.05 was considered suggestive of a causal association. All statistical analyses were performed in R software (version 4.1.3), and the meta package (version 5.2.0) and forestploter package (version 0.1.5) was employed in drawing forest plots. ## Results Using the IVW method, genetically predicted asthma was associated with an increased risk of epilepsy in the discovery stage (ILAEC: OR = 1.112, $95\%$ CI: 1.023-1.209, $$P \leq 0.012$$). Directional consistent results were obtained in sensitivity analyses using simple median, weighted median, maximum likelihood, and MR-PRESSO approaches (Figure 2A). In the replication stage, estimates of the FinnGen dataset showed the same trend direction as the results of ILAEC (Figure 2B). No obvious causal effects of childhood-onset asthma and adult-onset asthma on epilepsy were found in both the discovery stage and replication stage (Figure 2). There was no obvious pleiotropy observed in the MR-Egger intercept test, but potential pleiotropy of childhood-onset asthma on epilepsy ($$P \leq 0.037$$) in the discovery stage was observed in the MR-PRESSO test (Table 2). Cochran-Q test also showed heterogeneity in evaluating the causal association between childhood-onset asthma and epilepsy in the discovery stage (Table 2). The corrected estimate after removing the outlier (rs1893380) identified by the MR-PRESSO test showed a similar result, suggesting good stability. All the F-statistic values were larger than 10 across the MR study, indicating good instrumental strength. Figure 2: Forest plots of Mendelian randomization analyses show the causal effects of asthma on epilepsy. Six different methods, including IVW, weighted mode, weighted median, MR-Egger regression, MR-PRESSO, simple median, and maximum likelihood were used to evaluate the causal effect of asthma on epilepsy. (A, B) showed the causal effect of asthma on epilepsy in the discovery stage and replication stage, respectively. IVW, inverse variance weighed MR-PRESSO, MR-Pleiotropy RESidual Sum, and Outlier. TABLE_PLACEHOLDER:Table 2 A further meta-analysis of ILAEC and FinnGen also showed a causal effect of asthma on epilepsy (OR = 1.085, $95\%$ CI: 1.012-1.164, $$P \leq 0.022$$), which was validated in a sensitivity analysis using other approaches (Figure 3; Supplementary Figure S1). The meta-analysis results from both the fixed-effect model and the random-effect were largely consistent across different statistical methods (Figure 3, Supplementary Figure S1). Figure 3: Forest plots of meta-analysis on ILAEC and FinnGen epilepsy GWAS datasets show the causal effects of asthma on epilepsy. The inverse variance weighted, weighted median, and MR-Egger regression were used to evaluate the causal effects of asthma on epilepsy. ## Discussion In this study, we took advantage of the 2-sample MR method to analyze the causal relationship between asthma and epilepsy. The main results consistently suggested that asthma was associated with a higher risk of epilepsy. Furthermore, several sensitivity analyses were used based on their different underlying assumptions and similar results were observed, which further strengthened the credibility of the results. Previous reports have investigated the relationship between asthma and epilepsy, but the results were inconsistent. A population-based study found that most adult patients with epilepsy presently have symptomatic asthma [6]. Meanwhile, a U.S. National Health Interview Survey found that adult patients with epilepsy were more often to record physical comorbidities like asthma [7]. Previous studies among US children aged 0-17 years reported that the lifetime prevalence of asthma was related to a higher risk of epilepsy (2.30 [1.50-3.52]) [8]. Similarly, a recent cohort study including 150,827 asthma patients showed that the asthma patients had an increased risk of epilepsy than health controls (hazard ratio=1.39) [9]. All these findings indicated that asthma was associated with the risk of epilepsy, which was consistent with the results of our MR study based on data from the ILAEC and FinnGen Consortium. Although an early study among children suggested that there was no etiological relationship between asthma and epilepsy, the result may be attributed to small samples [10]. The underlying mechanism mediating the association between asthma and epilepsy remains largely unknown. The potential reasons connecting asthma and epilepsy are anoxia and hypocapnia owing to repeated asthma attacks. In addition, chronic inflammation is a common pathological feature shared by asthma and epilepsy [3, 32, 33]. Previous studies demonstrated that circulating cytokines might penetrate through the blood-brain barrier and then result in chronic neuroinflammation and neuronal damage, eventually increasing the susceptibility to epileptogenesis [4, 5, 34]. Moreover, emerging evidence shows that the respiratory system has a tight relationship with the central nervous system, which goes beyond the classically known connections such as blood supply and oxygen saturation. Studies showed that respiratory system diseases such as asthma [35] and chronic obstructive pulmonary disease [36] might increase the risk of stroke, which was a risk factor for epilepsy. In addition, clinical data suggested that chronic obstructive pulmonary disease was associated with an increased risk for the development of seizures in patients with stroke [37]. Although oxygen desaturation may be one of the risk factors for epilepsy in asthma patients [38], further work is needed to explore the exact mechanisms by which asthma causes an increased risk of epilepsy. Asthma can be divided into childhood-onset asthma and adult-onset asthma based on the age of onset. Childhood-onset asthma may be related to genetic factors [39, 40], perinatal factors [41], or respiratory infections [42], while adult-onset asthma may be related to environmental and occupational factors such as obesity and smoking [43]. Even though the mechanisms contributing to childhood-onset and adult-onset asthma might be different, our MR study found no causal associations between the age onset of asthma and epilepsy. These data suggested that asthma causally increased the risk of epilepsy independent of the age onset of asthma. The potential reason for these unexpected results might be due to the small sample size of childhood-onset and adult-onset asthma, which might lead to lower statistical power. It is worth noting that the proportion of cases with asthma was $13.8\%$, while in childhood-onset asthmatic and adult-onset asthma were $4.4\%$ and $8.1\%$, respectively. In addition, although the causal relationship was not significant for childhood-onset asthma and adult-onset asthma on epilepsy, most of the OR values were larger than 1, suggesting a potential risk effect of childhood-onset asthma and adult-onset asthma on epilepsy. This study has some limitations: first, the nonlinear connection between asthma and the risk of epilepsy cannot be eliminated due to the linear effect assumption in MR analysis. Second, although no evidence of pleiotropy was detected in the MR-Egger intercept test, potential pleiotropy was observed between childhood-onset asthma and epilepsy ($$P \leq 0.037$$) in the MR-PRESSO test. Third, there was obvious heterogeneity between childhood-onset asthma and epilepsy from ILAEC datasets, which might be due to the mixed population of ILAEC (531 and 147 individuals of Asian and African descent, respectively). Fourth, our study is mainly based on Europeans, thus generalization of the findings to other ethnic groups needs to be cautious. Fifth, to better fulfill the independence assumption for the MR study, we used a relatively stringent way to exclude the SNPs associated with potential confounders of epilepsy from the IVs, which might weaken the statistical power of the MR study. Sixth, due to individual data not being publically available, we were unable to properly account for the potential sample overlap between the GWAS datasets of asthma and epilepsy, which might lead to bias in the overall estimates. Finally, there are other possible unmeasured and residual confounding factors like many other epidemiological studies, which might drive the bias of the overall estimates. For example, as asthma was caused due to an overactive immune response [44], many instrumental variables for asthma were associated with peripheral blood cells (Supplementary Data Sheets, 7). Although previous studies suggested that inflammatory factors were also implicated in epilepsy [33], however, asthma, characterized by chronic inflammation and bronchial hyperresponsiveness, is a disease strongly related to the inflammatory response [45]. If all instrumental variables related to peripheral blood cells were excluded, the number of instrumental variables would be dramatically reduced. Thus, like other previously published MR studies on asthma [46, 47], the SNPs related to peripheral blood cells were not removed from the instrumental variables in our MR study, which could not rule out the potential influence of inflammatory factors on the causal relationship between asthma and epilepsy. In conclusion, the present MR study suggests that asthma is associated with an increased risk of epilepsy independent of the age onset of asthma. Further studies are warranted to investigate the potential mechanism mediating the causal effect of asthma on epilepsy. ## 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 authors. ## Author contributions PT, XG, and RL conceived and designed the project. PT, XG, and LC collected and analyzed the data. XG and PT drafted the manuscript. RL revised the manuscript. All authors approved the final version of 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. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1071580/full#supplementary-material ## References 1. von Mutius E, Smits HH. Primary prevention of asthma: from risk and protective factors to targeted strategies for prevention. Lancet. (2020) 396. 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--- title: Propensity score matching comparisons of postoperative complications and morbidity between digestive tract reconstruction methods after gastrectomy in gastric cancer patients with visceral obesity authors: - Chenchen Mao - Miaofang Xiao - Jian Chen - Jian Wen - Hui Yang - Wentao Cai - Jingwei Zheng - Xinxin Chen - Xiaofeng Xing - Xiangyang Xue - Xian Shen - Sini Wang journal: Frontiers in Oncology year: 2023 pmcid: PMC9969132 doi: 10.3389/fonc.2022.1072127 license: CC BY 4.0 --- # Propensity score matching comparisons of postoperative complications and morbidity between digestive tract reconstruction methods after gastrectomy in gastric cancer patients with visceral obesity ## Abstract ### Background Few studies have compared the prognosis of different reconstruction methods after gastrectomy for gastric cancer (GC) patients with obesity. The aim of the present study was to compare postoperative complications and overall survival (OS) between the following reconstruction methods: Billroth I (B-I), Billroth II (B-II), and Roux-en-Y (R-Y) after gastrectomy for GC patients with visceral obesity (VO). ### Methods We performed a double-institutional dataset study of 578 patients who underwent radical gastrectomy with B-I, B-II, and R-Y reconstructions between 2014 and 2016. VO was defined as a visceral fat area at the level of the umbilicus greater than 100 cm2. Propensity score-matching analysis was performed to balance the significant variables. Postoperative complications and OS were compared between the techniques. ### Results VO was determined in 245 patients, of which 95, 36, and 114 underwent B-I, B-II, and R-Y reconstructions, respectively. B-II and R-Y were fused into the Non-B-I group due to the similar incidence of overall postoperative complications and OS. Therefore, 108 patients were enrolled after matching. The overall postoperative complications incidence and overall operative time in the B-I group were significantly lower than those in the non-B-I group. Further, multivariable analysis showed that B-I reconstruction was an independent protective factor for overall postoperative complications (odds ratio (OR) 0.366, $$P \leq 0.017$$). However, no statistical difference in OS was found between the two groups (hazard ratio (HR) 0.644, $$P \leq 0.216$$). ### Conclusions B-I reconstruction was associated with decreased overall postoperative complications, rather than OS, in GC patients with VO who underwent gastrectomy. ## Introduction Gastric cancer (GC) is one of the most common cancer types and the leading cause of cancer-related mortality worldwide [1]. Although various treatments such as chemotherapy, chemoradiotherapy, targeted therapy, and immunotherapy have been developed [2, 3], radical gastrectomy remains the most effective. Reconstruction methods such as Billroth I (B-I), Billroth II (B-II), and Roux-en-Y (R-Y) are commonly used after gastrectomy [4, 5]. The choice of the reconstruction method is mostly based on the patient’s condition and the surgeon’s preference. Previous studies have compared B-I and R-Y reconstruction methods with inconsistent results; while some of the studies demonstrated that B-I reconstruction was preferable with decreasing overall postoperative complications [6] and morbidity [7], another one showed no significant differences between the two groups in the long-term patients’ quality of life and the incidence of the postoperative complications [8, 9]. Moreover, an additional study reported that R-Y reconstruction does not have greater postoperative complications than B-II does [10]. Thus, the selection of the most appropriate reconstruction method after gastrectomy remains controversial. Obesity, whether determined by body mass index (BMI) or visceral fat area measurements, has been reported to be associated with a higher incidence of postoperative complications for GC [11, 12]. Indeed, the narrow operation space and exposure difficulty attributed to the abdominal wall’s obesity-related hypertrophy and the greater omentum increase the difficulty of specimen isolation and digestive tract reconstruction. Our previous study [13] revealed that laparoscopic gastrectomy significantly decreased the rate of these postoperative complications of GC patients with visceral obesity (VO) owing to the advantages concerning the visual field and operating space. Similarly, different methods of digestive tract reconstruction have different requirements related to the degree of tissue dissociation, leading to differences in operation time and possible tissue injury resulting from the traction applied during the operation, which may also greatly affect the incidence of intraoperative and postoperative complications, especially in VO patients [14]. Despite all this, few studies focused on the prognosis of GC after digestive tract reconstruction. In this study, we used propensity-score-matching (PSM) to balance the significant variables strictly and further compared the incidence of postoperative complications and survival upon different digestive tract reconstructions after gastrectomy in GC patients with obesity. In this way, we aim to obtain clinical evidence for selecting the most appropriate digestive tract reconstruction after gastrectomy in VO patients. ## Study design and patient population Clinical data of 578 GC patients who underwent curative gastrectomy and D2 lymph node dissection at the Gastrointestinal Surgical Departments of the Second Affiliated Hospital of Wenzhou Medical University and the First Affiliated Hospital of Wenzhou Medical University in China were retrospectively collected between January 2014 and December 2016. Patients were enrolled for analysis based on the following criteria: [1] underwent gastrectomy and confirmed as gastric adenocarcinoma by postoperative pathology; [2] older than 18 years. The exclusion criteria were as follows: [1] lack of imaging data; [2] lack of clinical data; [3] underwent palliative or emergency surgery; [4] received preoperative neoadjuvant chemotherapy or radiotherapy; [5] accompanied with severe immune, blood, or endocrine disease; [6] GC concurrent with other malignant tumors. The outline of this study is summarized in Figure 1. Figure 1: Flow diagram of the study process. ## Baseline data collection For each patient enrolled in this study, demographic details, including age, sex, BMI, American Society of Anesthesiologists (ASA) grade, abdominal operation history, and NRS 2002 score, were collected, along with details on the operation, such as tumor location, tumor differentiation, pathological classification, and histopathologic staging according to TNM staging (AJCC Cancer Staging System, 8th ed). Additionally, postoperative complications were defined as adverse events occurring within 90 days after surgery, according to the Clavien–Dindo classification system [15]. Patients with more than two complications were classified as having multiple complications. Postoperative hospital stay, hospitalization costs, and OS were also recorded. ## Computed tomography-based measurement of visceral fat area Preoperatively, all patients underwent computed tomography (CT) of the general abdominal cavity. A single scan in a cross-section at the level of the umbilicus was selected to quantify the degree of visceral fat. Visceral fat was measured under a threshold of -140 to -50 as reported in the previous studies [16, 17]. The total fat area was calculated using a dedicated processing system (version 3.0.11.3, BN17 32-bit; INFINITT Healthcare Co. Ltd., Seoul, South Korea). VO was determined as having a visceral fat area (VFA) of more than 100 cm2 [18, 19]. ## PSM and statistical analyses PSM was performed to balance the significant variables in the following analyses strictly. Propensity scores were generated using a logistic regression model on covariates with differences before matching: age, tumor location, TNM stage, combined organ resection, previous abdominal surgery and laparoscopic gastrectomy. PSM was performed in a 1:1 ratio using a 0.03 caliper width, and the resulting score-matched pairs were used in subsequent analyses. The two matched groups were evaluated for the study endpoints. Means and standard deviations were used for all continuous data, and numbers and percentages were calculated for all categorical data. Intergroup differences in clinicopathological variables were analyzed using the chi-square test or Fisher’s exact test for categorical data and the Mann–Whitney U test for continuous data. We also performed conditional logistic regression analyses after the relevant prognostic variables were defined using univariate analysis. Overall survival (OS) was determined as the time between the diagnosis and death or the last follow-up date. Kaplan–Meier and log-rank tests were performed to estimate and compare survival rates, respectively. The Cox proportional hazard model was performed to estimate the risk ratio in the univariate and multivariate analyses, and the results were expressed as hazard ratios (HRs) with $95\%$ confidence intervals (CIs). Statistical significance was set at $P \leq 0.05.$ All statistical analyses were performed using SPSS version 22.0 (SPSS Inc., Chicago, IL, USA) and R version 3.0.1 (http://www.Rproject.org). ## Patient characteristics We included 245 patients with VO and GC in this study. A total of 95 patients ($38.78\%$) underwent B-I reconstruction, 36 patients ($14.69\%$) underwent B-II reconstruction, and 114 patients ($46.53\%$) underwent R-Y reconstruction. Patients who underwent B-I reconstruction showed a better prognosis outcome than those in the other two groups, as well as the lowest rate of postoperative complications (Table 1) and best OS (Figure 2). However, as summarized in Table 1, B-I reconstruction was more likely to be performed in patients who underwent laparoscopic gastrectomy ($$P \leq 0.002$$) and primary focus resection only ($$P \leq 0.002$$). Furthermore, patients who underwent B-I reconstruction were classified as having a lower TNM stage ($P \leq 0.001$). Considering the limited sample size and differences in clinical characteristics, we fused the B-II and R-Y groups into the non-B-I group. PSM was further performed to minimize selection bias, resulting in the clinicopathological characteristics of the Non-B-I and B-I groups ($$n = 59$$ for each group) being well balanced (Table 2). ## Surgical outcomes and postoperative course Further analyses were performed using data from 118 patients after PSM. The operation time for the B-I group (193.88 ± 48.16 min) was significantly shorter than for the Non-B-I group (212.95 ± 48.54 min, $$P \leq 0.034$$) and the postoperative hospital stay was shorter (15.22 ± 7.13 days VS 18.29 ± 9.58 days, $$P \leq 0.050$$) in the B-I group. As for the hospitalization costs, no significant differences were found between the two groups (68142.02 ± 26214.52 Yuan vs. 66320.71 ± 17834.16 Yuan, $$P \leq 0.660$$). The overall incidence of postoperative complications was significantly lower in the B-I group ($25.42\%$, $\frac{15}{59}$) than in the non-B-I group ($45.76\%$, $\frac{27}{59}$) ($$P \leq 0.021$$). Further analyses showed that both surgical and medical complications incidence tended to be lower in the B-I group, although the difference was not statistically significant. ( Table 3). Table 3 \| Factors \| Unmatched \| Unmatched.1 \| Unmatched.2 \| Unmatched.3 \| Matched \| Matched.1 \| Matched.2 \| Matched.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Factors \| Total (n=245) \| B-I (n=95) \| Non-B-I (n=150) \| P \| Total (n=118) \| B-I (n=59) \| Non-B-I (n=59) \| P \| \| Operative time, (X ± SD), min \| 206.84 ± 50.23 \| 190.13 ± 48.01 \| 217.30 ± 48.92 \| <0.001* \| 203.42 ± 49.09 \| 193.88 ± 48.16 \| 212.95 ± 48.54 \| 0.034* \| \| Postoperative hospital stays, (X ± SD), days \| 15.85 ± 8.62 \| 14.30 ± 7.65 \| 16.83 ± 9.07 \| 0.020* \| 16.75 ± 8.55 \| 15.22 ± 7.13 \| 18.29 ± 9.58 \| 0.050* \| \| Hospitalization costs, (X ± SD), yuan \| 65915.22 ± 32021.88 \| 63459.53 ± 26949.49 \| 67470.49 ± 34852.73 \| 0.313 \| 67231.36 ± 22342.10 \| 68142.02 ± 26214.52 \| 66320.71 ± 17834.16 \| 0.660 \| \| Total complications a \| 89 (36.32%) \| 23 (24.21%) \| 66 (44.00%) \| 0.002* \| 42 (35.59%) \| 15 (25.42%) \| 27 (45.76%) \| 0.021* \| \| Clavien-Dindo grade \| \| \| \| \| \| \| \| \| \| Grade I \| 8 (3.27%) \| 0 (0.00%) \| 8 (5.33%) \| 0.025* \| 5 (4.24%) \| 0 (0.00%) \| 5 (8.47%) \| 0.068 \| \| Grade II \| 58 (23.67%) \| 16 (16.84%) \| 42 (28.00%) \| 0.045* \| 26 (22.03%) \| 10 (16.95%) \| 16 (27.12%) \| 0.183 \| \| Grade III \| 15 (6.12%) \| 4 (4.21%) \| 11 (7.33%) \| 0.321 \| 7 (5.93%) \| 3 (5.08%) \| 4 (6.78%) \| 1.000 \| \| Grade IV \| 8 (3.27%) \| 3 (3.16%) \| 5 (3.33%) \| 1.000 \| 4 (3.39%) \| 2 (3.39%) \| 2 (3.39%) \| 1.000 \| \| Detail of complications \| \| \| \| \| \| \| \| \| \| Surgical complications \| 40 (16.33%) \| 10 (10.53%) \| 30 (20.00%) \| 0.051 \| 18 (15.25%) \| 7 (11.86%) \| 11 (18.64%) \| 0.306 \| \| Gastrointestinal dysfunction \| 7 (2.86%) \| 0 (0.00%) \| 7 (4.67%) \| 0.081 \| 4 (3.39%) \| 0 (0.00%) \| 4 (6.78%) \| 0.127 \| \| Intestinal obstruction \| 3 (1.22%) \| 1 (1.05%) \| 2 (1.33%) \| 1.000 \| 3 (2.54%) \| 1 (1.69%) \| 2 (3.39%) \| 1.000 \| \| Anastomotic leakage \| 2 (0.82%) \| 0 (0.00%) \| 2 (1.33%) \| 0.523 \| 1 (0.85%) \| 0 (0.00%) \| 1 (1.69%) \| 1.000 \| \| Severe wound infection \| 5 (2.04%) \| 1 (1.05%) \| 4 (2.67%) \| 0.684 \| 2 (1.69%) \| 1 (1.69%) \| 1 (1.69%) \| 1.000 \| \| Intra-abdominal infection \| 15 (6.12%) \| 5 (5.26%) \| 10 (6.67%) \| 0.655 \| 5 (4.24%) \| 3 (5.08%) \| 2 (3.39%) \| 1.000 \| \| Intra-abdominal Bleeding \| 8 (3.27%) \| 3 (3.16%) \| 5 (3.33%) \| 1.000 \| 3 (2.54%) \| 2 (3.39%) \| 1 (1.69%) \| 1.000 \| \| Medical complications \| 49 (20.00%) \| 13 (13.68%) \| 36 (24.00%) \| 0.049* \| 17 (14.41%) \| 5 (8.47%) \| 12 (20.34%) \| 0.066 \| \| Pleural and peritoneal effusion \| 13 (5.31%) \| 4 (4.21%) \| 9 (6.00%) \| 0.543 \| 6 (5.08%) \| 2 (3.39%) \| 4 (6.78%) \| 0.675 \| \| Pulmonary complications \| 14 (5.71%) \| 4 (4.21%) \| 10 (6.67%) \| 0.420 \| 5 (4.24%) \| 1 (1.69%) \| 4 (6.78%) \| 0.361 \| \| Venous thrombosis \| 8 (3.27%) \| 2 (2.11%) \| 6 (4.00%) \| 0.657 \| 4 (3.39%) \| 2 (3.39%) \| 2 (3.39%) \| 1.000 \| \| Hypoalbuminemia \| 3 (1.22%) \| 0 (0.00%) \| 3 (2.00%) \| 0.429 \| 2 (1.69%) \| 0 (0.00%) \| 2 (3.39%) \| 0.476 \| \| Multiple complications \| 11 (4.49%) \| 3 (3.16%) \| 8 (5.33%) \| 0.423 \| 7 (5.93%) \| 3 (5.08%) \| 4 (6.78%) \| 1.000 \| ## Risk factors for postoperative complications A risk analysis of the overall postoperative complications was performed to investigate the risk factors for postoperative complications. Univariate and multivariate analyses of the factors associated with overall postoperative complications before and after PSM are summarized in Table 4. Univariate analysis revealed that open surgery, age≥65 years, and non-BI reconstruction were significant risk factors. Moreover, laparoscopic surgery (odds ratio [OR] 0.263; $95\%$ CI 0.116-0.597; $$P \leq 0.001$$) and B-I reconstruction (OR 0.502; $95\%$ CI 0.278-0.908; $$P \leq 0.023$$) were identified as independent protective factors in the multivariable analysis before PSM. After PSM, it was also found that laparoscopic surgery (OR 0.099; $95\%$ CI 0.022-0.454; $$P \leq 0.003$$) and B-I reconstructions (OR 0.366; $95\%$ CI 0.161-0.833; $$P \leq 0.017$$) independently associated with the less rate of postoperative complications. Table 4 \| Factors \| Un-matched \| Un-matched.1 \| Un-matched.2 \| Un-matched.3 \| Matched \| Matched.1 \| Matched.2 \| Matched.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Factors \| Univariate analysis \| Univariate analysis \| Multivariate analysis \| Multivariate analysis \| Univariate analysis \| Univariate analysis \| Multivariate analysis \| Multivariate analysis \| \| Factors \| OR (95% CI) \| P \| OR (95% CI) \| P \| OR (95% CI) \| P \| OR (95% CI) \| P \| \| Gender \| \| 0.824 \| \| \| \| 0.556 \| \| \| \| Female \| 1 \| \| \| \| 1 \| \| \| \| \| Male \| 1.073 (0.577-1.993) \| \| \| \| 1.310 (0.534-3.210) \| \| \| \| \| Age (y) \| \| 0.043* \| \| 0.270 \| \| 0.278 \| \| \| \| ≤65 \| 1 \| \| 1 \| \| 1 \| \| \| \| \| >65 \| 1.736 (1.017-2.964) \| \| 1.374 (0.782-2.414) \| \| 1.537 (0.707-3.338) \| \| \| \| \| NRS 2002 score \| \| 0.235 \| \| \| \| 0.322 \| \| \| \| 1-2 \| 1 \| \| \| \| 1 \| \| \| \| \| 3-4 \| 1.316 (0.722-2.397) \| \| \| \| 1.440 (0.603-3.440) \| \| \| \| \| 5-6 \| 2.250 (0.823-6.152) \| \| \| \| 2.770 (0.667-10.923) \| \| \| \| \| ASA grade \| \| 0.351 \| \| \| \| 0.104 \| \| \| \| 1-2 \| 1 \| \| \| \| 1 \| \| \| \| \| 3-4 \| 1.352 (0.717-2.550) \| \| \| \| 2.033 (0.865-4.780) \| \| \| \| \| Hypertension \| \| 0.927 \| \| \| \| 0.520 \| \| \| \| No \| 1 \| \| \| \| 1 \| \| \| \| \| Yes \| 0.975 (0.567-1.676) \| \| \| \| 0.768 (0.344-1.716) \| \| \| \| \| Diabetes mellitus \| \| 0.289 \| \| \| \| 0.148 \| \| \| \| No \| 1 \| \| \| \| 1 \| \| \| \| \| Yes \| 1.412 (0.746-2.671) \| \| \| \| 1.938 (0.790-4.754) \| \| \| \| \| Previous abdominal surgery \| \| 0.655 \| \| \| \| 0.397 \| \| \| \| No \| 1 \| \| \| \| 1 \| \| \| \| \| Yes \| 1.195 (0.547-2.611) \| \| \| \| 1.553 (0.561-4.296) \| \| \| \| \| Tumor location \| \| 0.256 \| \| \| \| 0.173 \| \| \| \| Antrum \| 1 \| \| \| \| 1 \| \| \| \| \| Body \| 0.840 (0.419-1.685) \| \| \| \| 0.067 (0.002-2.063) \| \| \| \| \| Cardia \| 0.849 (0.902-3.793) \| \| \| \| 0.661 (0.040-10.885) \| \| \| \| \| Total \| 0.616 (0.120-3.161) \| \| \| \| 1.000 (0.020-50.397) \| \| \| \| \| Differentiated degree \| \| 0.692 \| \| \| \| 0.504 \| \| \| \| Differentiated \| 1 \| \| \| \| 1 \| \| \| \| \| Undifferentiated \| 0.715 (0.282-1,810) \| \| \| \| 0.464 (0.120-1.795) \| \| \| \| \| Signet ring carcinoma \| 1.157 (0.552-2.423) \| \| \| \| 1.093 (0.422-2.829) \| \| \| \| \| Pathological type \| \| 0.443 \| \| \| \| 0.547 \| \| \| \| Ulcerative type \| 1 \| \| \| \| 1 \| \| \| \| \| Non-ulcerative type \| 1.383 (0.604-3.116) \| \| \| \| 1.529 (0.383-6.104) \| \| \| \| \| TNM stage \| \| \| \| \| \| 0.621 \| \| \| \| I \| 1 \| 0.693 \| \| \| 1 \| \| \| \| \| II \| 1.340 (0.677-2.654) \| \| \| \| 0.947 (0.352-2.549) \| \| \| \| \| III \| 1.196 (0.647-2.210) \| \| \| \| 0.667 (0.272-1.634) \| \| \| \| \| Laparoscopic gastrectomy \| \| \| \| \| \| 0.004* \| \| \| \| No \| 1 \| <0.001* \| 1 \| 0.001* \| 1 \| \| 1 \| 0.003* \| \| Yes \| 0.216 (0.097-0.481) \| \| 0.263 (0.116-0.597) \| \| 0.108 (0.024-0.486) \| \| 0.099 (0.022-0.454) \| \| \| B-I \| \| 0.002* \| \| 0.023* \| \| 0.022* \| \| 0.017* \| \| No \| 1 \| \| 1 \| \| 1 \| \| 1 \| \| \| Yes \| 0.407 (0.230-0.719) \| \| 0.502 (0.278-0.908) \| \| 0.404 (0.186-0.880) \| \| 0.366 (0.161-0.833) \| \| ## Risk factors for OS As shown in Figure 3, patients in the B-I group had better outcomes than those in the non-B-I group before PSM ($$P \leq 0.002$$). Further evaluation of the potential factors influencing OS revealed that it was affected by age (HR 2.435, $95\%$ CI 1.435-4.132, $$P \leq 0.001$$), NRS score (NRS 3-4: HR 1.100, $95\%$ CI 0.634-1.907; NRS 5-6: HR 3.886, $95\%$ CI 1.978-7.635, $P \leq 0.001$), tumor location (Body: HR 0.946, $95\%$ CI 0.483-1.851; Cardia: HR 1.652, $95\%$ CI 0.895-3.050; Total: HR 4.850, $95\%$ CI 2.042-11.521, $$P \leq 0.002$$), tumor TNM stage (TNM stage II: HR 2.447, $95\%$ CI 1.026-5.834; TNM stage III: HR 6.332, $95\%$ CI 2.986-13.426, $P \leq 0.001$), laparoscopic surgery (HR 0.173, $95\%$ CI 0.063-0.474, $$P \leq 0.001$$) and B-I reconstructions (HR 0.419, $95\%$ CI 0.239-0.733, $$P \leq 0.002$$) on univariate analysis. Among them, only tumor TNM stage (TNM stage II: HR 1.897, $95\%$ CI 0.777-4.629; TNM stage III: HR 4.544, $95\%$ CI 2.077-9.944, $P \leq 0.001$) and laparoscopic surgery (HR 0.270, $95\%$ CI 0.095-0.765, $$P \leq 0.014$$) were independently associated with OS (Table 5). Figure 3: Five-year overall survival curve calculated using the Kaplan-Meier method comparing B-I and non-B-1. (A) Before PSM; (B) After PSM. TABLE_PLACEHOLDER:Table 5 We further compared the OS rates between the two groups after PSM. There was no significant difference in OS between the two groups ($$P \leq 0.231$$, Figure 3). ## Discussion BMI, conveniently calculated as the patient’s weight divided by the square of height, has been broadly used as an indicator of obesity [18, 20]. However, BMI cannot distinguish the fat distribution in the abdominal cavity [13]. Recently, studies proposed that visceral fat was a better tool for predicting surgical outcomes [21, 22]. Considering the priority of VO over BMI in estimating visceral fat, we used CT-based VFA for determining VO. In this way, we focused on patients with VO to compare postoperative complications and OS upon different post-gastrectomy reconstruction methods using PSM. We found that the overall postoperative complications in the B-I group were significantly lower than those in the non-B-I group, while no differences in OS were found between the different reconstruction methods. In addition, BI reconstruction was found to be a strong independent protective factor for postoperative complications. It is known that B-I reconstruction is most commonly performed due to its technical simplicity and intervention in a single anastomotic site, as well as the preservation of the physiological path [23, 24]. In contrast, B-II and R-Y reconstructions are more applicable because they are simpler techniques after distal gastrectomy. Although these techniques for digestive tract reconstructions have already been compared in their short-term complications and long-term prognoses [7, 25], no consistent results have been reached so far, and the preferred reconstruction method remains controversial. In addition, some aspects, such as which anastomosis is more appropriate for patients with VO and GC have not been explored, and this is the first study evaluating this factor. Here, the B-I group had a decreased incidence of overall postoperative complications. After further performing PSM to balance the deviation of tumor characteristics and the patients’ general condition, we also found that B-I reconstruction was still an independent protective factor for postoperative complications, consistent with a previous multi-institutional study [6]. In addition, the difference was mainly concentrated in mild postoperative complications, as most patients with complications were treated conservatively, and severe complications were unusual among the patients. However, although we found that nearly all complications had a lower incidence in the B-I group, no statistical difference was found, possibly due to the small sample size after matching. Additionally, considering the small sample size, we took patients underwent total gastrectomy and distal gastrectomy together rather than analyzed separately. However, since the patients underwent total gastrectomy were relatively few, our results are mainly representative of distal gastrectomy. All in all, a large-scale study is needed to characterize this aspect further. Another concern in selecting reconstruction methods is the long-term prognosis, as it has been shown [26, 27] that a delay in chemotherapy due to postoperative complications adversely affects the OS of patients with GC. Previous studies showed a non-significant difference in the 5-year OS rates between the B-I, B-II, and R-Y reconstruction groups [25]. Similarly, we found that B-I reconstruction yielded oncologic outcomes comparable to those of non-B-I reconstruction. However, our results demonstrated that OS was significantly longer in patients who underwent B-I reconstruction before PSM. This may be attributed to the TNM stage, which greatly affects the prognosis and differs between the groups, as patients undergoing B-I reconstruction showed a lower TNM stage. As reported in a previous study [25], although the range of surgical dissociation varied, gastrectomy and lymph node dissection for GC were so standardized that the digestive tract reconstruction method did not affect the number of retrieved lymph nodes or the prognosis. Therefore, it is more acceptable that B-I reconstruction was not an independent risk factor for OS. To the best of our knowledge, this is the first study to evaluate the short- and long-term outcomes of different reconstruction methods in GC patients with VO using PSM analysis. However, this study had several limitations. First, this was not a randomized controlled trial, and inherent selection biases exist that can be adjusted but not completely eliminated using PSM. Additionally, this was not a multicenter study, and our results may not be directly applicable to other populations. Finally, although the patients’ baseline data was well balanced after PSM, the small sample size may greatly limit our conclusions as patients underwent laparoscopic or open surgery, total gastrectomy or distal gastrectomy should be discussed separately as surgical method may also greatly affected the postoperative complications, thus a large-scale study is necessary. ## Conclusions We compared the incidence of postoperative complications between different reconstruction methods among GC patients with VO and found that B-I reconstruction can reduce the incidence of postoperative complications, thus promoting postoperative recovery. However, no significant differences in OS were found among the three reconstruction methods. Last, because of its implementation facility compared to the other two approaches, B-I reconstruction may be considered a better choice for patients with GC with VO. ## 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 authors. ## Ethics statement All procedures involving human participants performed in the study followed the Helsinki declaration and was approved by the Ethics Committee of the Second Affiliated Hospital of Wenzhou Medical University in China. All individual participants included in the study signed informed consent to participate in the study. ## Author contributions XYX, XS and SW were the study designs. CM, HY and JC collected and analyzed data. CM, MX and XFX wrote the manuscript and interpreted it. WC, JW, JZ and XC revised the paper. The authors read and approved the final 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. ## References 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin (2021) 71. DOI: 10.3322/caac.21660 2. Cats A, Jansen EPM, van Grieken NCT, Sikorska K, Lind P, Nordsmark M. 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--- title: 'Lack of bidirectional association between C-reactive protein and depressive symptoms in middle-aged and older adults: Results from a nationally representative prospective cohort study' authors: - Xiaohui Li - You Nie - Biru Chang journal: Frontiers in Psychology year: 2023 pmcid: PMC9969160 doi: 10.3389/fpsyg.2023.1095150 license: CC BY 4.0 --- # Lack of bidirectional association between C-reactive protein and depressive symptoms in middle-aged and older adults: Results from a nationally representative prospective cohort study ## Abstract Depression is associated with low quality of life and increased health burdens for middle-aged and older adults in resource-limited settings. Although inflammation plays an etiological role in the development and progression of depression, the directionality of the inflammation-depression relationship is unclear, especially in non-Western populations. To examine this relationship among community-dwelling Chinese middle-aged and older adults, we obtained data from the 2011, 2013, and 2015 China Health and Retirement Longitudinal Study (CHARLS). The participants were aged 45 years or above at baseline in 2011 and completed the follow-up survey in 2013 and 2015. Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), and the C-reactive protein (CRP) level was used to measure individual inflammation levels. Cross-lagged regression analyses examined the inflammation-depression relationship. Cross-group analyses were performed to test for model invariance across the sexes. Pearson’s correlations revealed no concurrent correlations between depression and CRP for both 2011 and 2015 (ps > 0.05, ranging 0.07–0.36) studies. Cross-lagged regression path analyses revealed that the paths from baseline CRP to depression in 2013 (ßstd = −0.01, $$p \leq 0.80$$), from baseline CRP to depression in 2015 (ßstd = 0.02, $$p \leq 0.47$$), from baseline depression to CRP in 2015 (ßstd = −0.02, $$p \leq 0.40$$), and from depression at 2013 to CRP in 2015 (ßstd = 0.03, $$p \leq 0.31$$) were not statistically significant. Additionally, the autoregressive model did not vary across the sexes (△χ2 = 78.75, df = 54, $$p \leq 0.02$$, △ comparative fit index (CFI) <0.01). We failed to find a bidirectional association between the CRP levels and depressive symptoms in our sample. ## Introduction Depression, associated with low quality of life and increased health burdens, is a disabling and prevailing mental health issue (Sivertsen et al., 2015; Wilkinson et al., 2018), with more than 264 million people affected worldwide (James et al., 2018). Middle-aged and older adults living in resource-limited settings (e.g., China) are disproportionally affected by depression and physical disability (James et al., 2018). Although several theories have been used to interpret the potential association between depression and physical health, the inflammation process has drawn considerable attention from psychologists and psychiatrists (Raison et al., 2006; Miller et al., 2009; Slavich and Irwin, 2014). Several theories have interpreted the bidirectional association of depression and inflammation, and the macrophage theory of depression hypothesizes that the onset and progression of depression are associated with the amount of pro-inflammatory cytokines secreted by the macrophages (Smith, 1991). Inflammation plays an etiologic role in the development and progression of depression, probably by altering the neurotransmitter metabolism or hypothalamic–pituitary–adrenal (HPA) axis function (Raison et al., 2006). HPA hyperactivity and the autonomic nervous system have also been used to explain how depression affects the inflammation processes (Kop and Gottdiener, 2005). Previous meta-analytic studies reported that older participants with depression demonstrated a high level of dysregulation of HPA axis activity through several mechanisms, including physical illnesses (e.g., diabetes, cardiovascular disease, and autoimmune diseases), alterations in the central nervous system (CNS), and immune-endocrinological alterations. These proposed biological mechanisms suggest a bidirectional path of the depression-inflammation association. Additionally, inflammation may trigger all-out-effort sickness behaviors, many of which may be associated with depressive symptoms (Hart, 1988). At the same time, depression could be prospectively associated with inflammation via behavioral changes (such as exercise, diet, and substance use) that are associated with inflammation, simply increasing experienced stress (which might increase inflammation). One of the commonly and frequently studied inflammation biomarkers is C-reactive protein (CRP). It is an acute phase plasma protein with a short plasma half-life and a relatively robust and reliable response to inflammation, making CRP an ideal marker of inflammation. The high-sensitivity test for CRP, called low-reactive protein (LRP, L-CRP, or hs-CRP), more accurately measures very low levels of CRP, and static sampling of CRP has been widely used in clinical studies to predict diseases, such as cancer and depression (Coventry et al., 2009; Felger et al., 2020; Nehring et al., 2022). In the development, progression and treatment of depression, an increasing number of researchers have focused on the association between CRP levels and depressive symptoms [including major depressive disorder (MDD) and treatment-resistant depression (TRD)] (Orsolini et al., 2022). Several meta-analyses have explored the association between CRP and depression in participants with either major or probable depression and found significant but small effect sizes (Howren et al., 2009; Valkanova et al., 2013; Haapakoski et al., 2015; Horn et al., 2018). More importantly, when considering the study quality of the original studies, the effect size was strikingly attenuated and nonsignificant in the most recent meta-analysis (Horn et al., 2018). Whether this association exists in studies conducted among middle-aged and older adults is highly debatable (Penninx et al., 2003; Almeida et al., 2007; Bremmer et al., 2008; Pan et al., 2008; Stewart et al., 2008; Surtees et al., 2008; Almeida et al., 2009; Elovainio et al., 2009; Milaneschi et al., 2009; Su et al., 2009; Forti et al., 2010; Slopen et al., 2010; Bjerkeset et al., 2011; Häfner et al., 2011; Morris et al., 2011; Baune et al., 2012; Smagula et al., 2014; Zoga et al., 2014; Song et al., 2015; Tully et al., 2015; de Menezes et al., 2017). Although most studies show a significant positive relationship in either depressed or nondepressed older adults, some well-conducted studies failed to replicate these results (Almeida et al., 2009; Su et al., 2009; Forti et al., 2010; Morris et al., 2011; Baune et al., 2012; Häfner et al., 2012; de Menezes et al., 2017). Most studies lack [1] a random sampling method, which may result in potential sampling bias; [2] longitudinal study design, which is needed for assessing the temporal relationship and bi-direction; [3] statistical strictness in choosing confounders, and [4] detailed report of data cleaning strategies. The present study investigated the relationship between hs-CRP levels and depression scores in middle-aged and older adults using longitudinal analysis in a nationally representative cohort. We used available data from the China Health and Retirement Longitudinal Study (CHARLS), which was designed to describe and prospectively delineate the social-, economic-and functional status of middle-aged and older adults. The CHARLS database provides a unique opportunity to examine the temporal and bidirectional relationship between depression and hs-CRP in middle-aged and older adults over 5 years while controlling for various socio-economic, anthropometric, physiologic, and psychosocial factors. ## Sample recruitment and dataset This study’s data came from three waves of the China Health and Retirement Longitudinal Study (CHARLS 2011, 2013, and 2015), including depression and CRP assessments. This nationally representative sample recruited participants aged 45 years or above from 450 communities and 150 counties/districts, including 17,705 respondents from 10,257 households. A baseline survey was conducted between June 2011 and March 2012. Follow-up interviews were conducted between 2013 and 2015. The overall response rate for this survey at the baseline was $80.5\%$. More detailed information on CHARLS has been published previously (Zhao et al., 2014) and can also be found at.1 At baseline and follow-up assessment, 5,092 participants had no missing data on depression and CRP levels. Supplementary Figure S1 presents the detailed participant selection procedure. ## Assessment of depression The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) was used to measure depression levels. Radlo developed the original CESD, a reliable and valid screening tool in many previous community-based studies of the elderly (Radloff, 1977; Beekman et al., 1995, 1997; Lewinsohn et al., 1997). Researchers have developed various shorter forms of the CESD to simplify this scale and improve its sensitivity (Kohout et al., 1993). More recently, a 10-item short version of the CESD (CESD-10), derived from an analysis of item-total correlation (Andresen et al., 1994), proved to be comparably accurate to the original CES-D and is a reliable and valid depression assessment tool for older participants in different countries (Boey, 1999; Li et al., 2018, 2019). Each item was scored from 0 to 3, with a total CESD-10 score ranging from 0 to 30 (Li et al., 2018). In the present study, the Cronbach’s Alphas for CESD-10 ranged between 0.80 and 0.81 in 2011, 2013, and 2015. ## Assessment of CRP Based on the standard protocol, medically trained staff from the Chinese Center for Disease Control and Prevention (China CDC) collected venous blood at baseline and follow-up visits, mostly at centralized locations. All participants were asked to fast overnight, and over $92\%$ of the participants who gave blood did fast. CRP levels were measured using an immunoturbidimetric assay at the Youanmen Center for the Clinical Laboratory of Capital Medical University. The within-assay coefficient was <$1.3\%$, and the between-assay coefficient was <$5.7\%$, with a detection limit of 0.1–20 mg/l. Additionally, the assay kits and methods for CRP assessment were kept constant at all the time points. ## Control variables According to previous suggestions (Horn et al., 2018), several additional variables of socio-demographic characteristics, health and behavior, and current medical treatment relevant to the relationship between depression and CRP were measured at the baseline visits. These variables included: age (birth year), sex (1 = male, 2 = female), education level (1 = below high school, 2 = high school or above), hukou status (1 = agricultural, 2 = non-agricultural hukou), marital status (1 = married, 2 = separated, divorced and widowed, 3 = never married), BMI (calculated as weight in kilograms divided by height in meters squared), smoking status (0 = nonsmoker, 1 = current smoker), alcohol status (0 = nondrinker, 1 = current drinker), and current medical treatment (including dyslipidemia, hypertension, and diabetes). All of these variables were coded as a binary variable (0 = no treatment, 1 = treatment). Demographic characteristics and behavioral and health indicator parameters were all influential factors for confounding variables. Table 1 presents the detailed descriptive statistics for these variables. Table 1 \| Variables \| 2011 \| 2013 \| 2015 \| \| --- \| --- \| --- \| --- \| \| Variables \| M (SD)/n (%) \| M (SD)/n (%) \| M (SD)/n (%) \| \| Demographic characteristic variables \| Demographic characteristic variables \| Demographic characteristic variables \| Demographic characteristic variables \| \| Age (years) % \| 58.79 (8.49) \| 60.79 (8.49) \| 62.79 (8.49) \| \| Sex \| Sex \| Sex \| Sex \| \| Male \| 2,367 (46.5) \| 2,367 (46.5) \| 2,367 (46.5) \| \| Female \| 2,721 (53.5) \| 2,721 (53.5) \| 2,721 (53.5) \| \| Hukou \| Hukou \| Hukou \| Hukou \| \| Agricultural \| 4,183 (82.2) \| 4,183 (82.2) \| 4,183 (82.2) \| \| Non-agricultural \| 907 (17.8) \| 907 (17.8) \| 907 (17.8) \| \| Education level \| Education level \| Education level \| Education level \| \| Below high school \| 4,536 (89.1) \| 4,536 (89.1) \| 4,536 (89.1) \| \| High school or above \| 555 (10.9) \| 555 (10.9) \| 555 (10.9) \| \| Marital status \| Marital status \| Marital status \| Marital status \| \| Married \| 4,628 (90.9) \| 2,367 (46.5) \| 4,466 (87.7) \| \| Separated, divorced and widowed \| 433 (8.5) \| 510 (10.0) \| 594 (11.7) \| \| Never married \| 31 (0.6) \| 35 (0.7) \| 32 (0.6) \| \| Health and behavior variables \| Health and behavior variables \| Health and behavior variables \| Health and behavior variables \| \| BMI % \| 23.81 (3.94) \| 24.09 (3.93) \| 24.07 (4.03) \| \| Smoking status \| \| \| \| \| No \| 3,557 (69.9) \| 3,557 (69.9) \| 3,706 (72.8) \| \| Yes \| 1,534 (30.1) \| 1,534 (30.1) \| 1,384 (27.2) \| \| Alcohol status \| Alcohol status \| Alcohol status \| Alcohol status \| \| No \| 3,381 (66.4) \| 3,381 (66.4) \| 3,333 (65.5) \| \| Yes \| 1711 (33.6) \| 1711 (33.6) \| 1758 (34.5) \| \| Current medical treatment \| Current medical treatment \| Current medical treatment \| Current medical treatment \| \| Anti-dyslipidemia \| \| \| \| \| No \| 4,796 (94.2) \| 4,732 (92.9) \| 4,633 (91.0) \| \| Yes \| 296 (5.8) \| 360 (7.1) \| 459 (9.0) \| \| Anti-hypertension \| Anti-hypertension \| Anti-hypertension \| Anti-hypertension \| \| No \| 4,116 (80.8) \| 3,891 (76.4) \| 3,734 (73.3) \| \| Yes \| 976 (19.2) \| 1,201 (23.6) \| 1,358 (26.7) \| \| Anti-diabetes \| Anti-diabetes \| Anti-diabetes \| Anti-diabetes \| \| No \| 4,895 (96.1) \| 4,813 (94.5) \| 4,775 (93.8) \| \| Yes \| 197 (3.9) \| 279 (5.5) \| 317 (6.2) \| \| Preliminary variables \| Preliminary variables \| Preliminary variables \| Preliminary variables \| \| CRP % \| 1.50 (1.55) \| - \| 1.93 (1.76) \| \| Depression % \| 8.26 (6.24) \| 8.13 (6.01) \| 8.14 (6.36) \| ## Statistical analyses We adopted SPSS 25.02 to analyze the normality, missing effects by comparing the analytic sample and the total sample, the analytic and complete T1 samples, and the analytic and lost samples; correlations among control variables, CRP, and depression; and the between-group differences in CRP and depression and their effect size. Additionally, we adopted the Mplus 7.40 to conduct auto-regressive and cross-lag modeling (Muthén and Muthén, 1998–2012). Sample difference comparisons were conducted for socio-demographic characteristics, health and behaviors, current medical treatment, CRPs and depression at both time points. With reference to previous reports (Stewart et al., 2009; Horn et al., 2018), we constructed four models to examine the longitudinal relations between CRP and depression: [1] the baseline model, namely the autoregressive model, reporting synchronous relations and all stability coefficients; [2] two unidirectional models, namely the depression main-effect model and CRP main-effects model; and [3] the reciprocal model that hypothesized depression and CRP could affect each other at different time points. Following Byrne’s recommendations (Hayes-Ryan and Byrne, 2012), we adopted multiple indices to evaluate the goodness of fit for cross-lagged models: the Comparative Fit Index (CFI) with values higher than 0.90 indicative of an acceptable fit; the Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMR), with values below 0.08 indicative of an acceptable fit. Additionally, to examine the between-group differences between potential models (female vs. male), we followed Cheung and Rensvold’s suggestion that the changes in CFI (△CFI) less than 0.01 indicate invariance between the different models (Cheung and Rensvold, 2002). Finally, the effect sizes for t-tests were computed with Cohen’s d using 0.2, 0.5, and 0.8 as lower bounds for small, medium, and large effects, respectively; the effect sizes for χ2 test were computed with Cramer’s V and the effect sizes for F tests were computed with η2 using 0.01, 0.059, and 0.138 as lower bounds for small, medium, and large effects (Cohen, 1988; Ehrminger et al., 2019). If the size of the missing effects was small, we considered our dataset to be a nonbiased sample. ## Preliminary analyses First, the normal distribution tests demonstrated that the values of skewness and kurtosis for depression across 2011, 2013, and 2015 ranged from 0.84 to 0.97 and from 0.17 to 0.79, respectively; the values of skewness and kurtosis for CRP across 2011 and 2015 ranged from 1.96 to 2.34 and from 4.29 to 6.25, respectively (detailed information in Supplementary Table S1). Second, sample difference comparisons indicated [1] no significant difference between the sample with no missing data in 2011 and the total sample compliance with inclusion criteria; [2] no significant differences between the sample with no missing data in 2011 and the analytic sample but age; and [3] no significant difference between the analytic sample and the lost sample but age, marital status, and anti-hypertension. These results indicate that older participants were inclined to be lost. Supplementary Tables S2–S4 provide the detailed information. Third, regarding the effects of control variables on CRP and depression (Table 2), we found that most control variables were significantly correlated with depression and CRP in 2011 and 2015 (ps < 0.05, ranging from 0.00 to 0.04). Table 2 \| Control variables \| Categories \| CRP at 2011 \| Depression at 2011 \| Depression at 2013 \| CRP at 2015 \| Depression at 2015 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Control variables \| Categories \| M (SD) \| M (SD) \| M (SD) \| M (SD) \| M (SD) \| \| Sex \| Male \| 1.56 (1.61) \| 7.25 (5.79) \| 6.91 (5.17) \| 1.95 (1.81) \| 7.00 (5.87) \| \| Sex \| Female \| 1.46 (1.49) \| 9.14 (6.49) \| 8.67 (6.02) \| 1.92 (1.71) \| 9.13 (6.61) \| \| Hukou status \| Agricultural hukou \| 1.47 (1.54) \| 8.62 (6.35) \| 8.16 (5.81) \| 1.92 (1.74) \| 8.54 (6.45) \| \| Hukou status \| Non-agricultural hukou \| 1.64 (1.60) \| 6.63 (5.44) \| 6.45 (4.96) \| 2.02 (1.85) \| 6.31 (5.60) \| \| Education level \| Below high school \| 1.50 (1.54) \| 8.54 (6.32) \| 8.05 (5.76) \| 1.95 (1.77) \| 8.42 (6.43) \| \| Education level \| High school or above \| 1.57 (1.62) \| 5.96 (4.96) \| 6.18 (4.96) \| 1.84 (1.69) \| 5.83 (5.21) \| \| Marital status \| Married \| 1.49 (1.53) \| 8.08 (6.16) \| 5.61 (0.08) \| 1.92 (1.75) \| 6.22 (0.09) \| \| Marital status \| Separated, divorced and widowed \| 1.68 (1.69) \| 9.93 (6.72) \| 6.29 (0.28) \| 1.96 (1.80) \| 7.14 (0.29) \| \| Marital status \| Never married \| 1.59 (1.79) \| 11.84 (6.67) \| 6.78 (1.15) \| 2.67 (2.48) \| 7.15 (1.26) \| \| Smoking status \| Nonsmoker \| 1.48 (1.52) \| 8.53 (6.31) \| 8.08 (5.81) \| 1.94 (1.74) \| 8.40 (6.44) \| \| Smoking status \| Smoker \| 1.55 (1.63) \| 7.63 (6.02) \| 7.33 (5.43) \| 1.93 (1.81) \| 7.44 (6.10) \| \| Alcohol status \| Nondrinker \| 1.51 (1.53) \| 8.61 (6.32) \| 8.26 (5.81) \| 1.97 (1.77) \| 8.56 (6.46) \| \| Alcohol status \| Drinker \| 1.49 (1.59) \| 7.58 (6.03) \| 7.04 (5.42) \| 1.87 (1.74) \| 7.33 (6.11) \| \| Anti-dyslipidemia \| No \| 1.49 (1.54) \| 8.20 (6.22) \| 7.73 (5.65) \| 1.90 (1.75) \| 7.98 (6.27) \| \| Anti-dyslipidemia \| Yes \| 1.80 (1.63) \| 9.29 (6.52) \| 9.48 (6.22) \| 2.31 (1.79) \| 9.74 (7.06) \| \| Anti-hypertension \| No \| 1.43 (1.50) \| 8.08 (6.21) \| 7.65 (5.60) \| 1.81 (1.71) \| 7.85 (6.22) \| \| Anti-hypertension \| Yes \| 1.84 (1.69) \| 9.02 (6.30) \| 8.50 (6.00) \| 2.27 (1.84) \| 8.94 (6.68) \| \| Anti-diabetes \| No \| 1.49 (1.54) \| 8.24 (6.24) \| 7.80 (5.71) \| 1.90 (1.74) \| 8.07 (6.35) \| \| Anti-diabetes \| Yes \| 1.76 (1.70) \| 8.85 (6.32) \| 8.68 (5.64) \| 2.38 (1.98) \| 9.15 (6.58) \| Fourth, regarding the concurrent correlation (Table 3), we found that depression was not concurrently associated with CRP levels in 2011 (r = −0.01, $$p \leq 0.36$$). Similarly, four-year later depression was not related to four-year later CRP ($r = 0.03$, $$p \leq 0.07$$). Age and BMI were positively associated with CRP and depression in 2011, BMI was positively associated with depression in 2013, age and BMI were positively associated with CRP in 2015, and BMI was negatively associated with depression in 2015. Table 3 \| Unnamed: 0 \| Variables at 2011 \| Variables at 2011.1 \| Variables at 2011.2 \| Variables at 2011.3 \| Variables at 2013 \| Variables at 2013.1 \| Variables at 2013.2 \| Variables at 2015 \| Variables at 2015.1 \| Variables at 2015.2 \| Variables at 2015.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Age \| BMI \| CRP \| Depression \| Age \| BMI \| CRP \| Age \| BMI \| CRP \| Depression \| \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| Variables at 2011 \| \| Age \| 1 \| \| \| \| \| \| \| \| \| \| \| \| BMI \| −0.16 \| 1 \| \| \| \| \| \| \| \| \| \| \| CRP \| 0.09 \| 0.02 \| 1 \| \| \| \| \| \| \| \| \| \| Depression \| 0.05 \| −0.09 \| −0.01 \| 1 \| \| \| \| \| \| \| \| \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| Variables at 2013 \| \| Age \| 1.00 \| −0.16 \| 0.09 \| 0.05 \| 1 \| \| \| \| \| \| \| \| BMI \| −0.18 \| 0.76 \| 0.13 \| −0.05 \| −0.18 \| 1 \| \| \| \| \| \| \| Depression \| −0.01 \| −0.06 \| −0.01 \| 0.51 \| −0.01 \| 0.05 \| 1 \| \| \| \| \| \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| Variables at 2015 \| \| Age \| 1.00 \| −0.16 \| 0.09 \| 0.05 \| 1.00 \| −0.18 \| −0.01 \| 1 \| \| \| \| \| BMI \| −0.17 \| 0.74 \| 0.12 \| −0.07 \| −0.17 \| 0.77 \| −0.06 \| −0.17 \| 1 \| \| \| \| CRP \| 0.06 \| 0.15 \| 0.31 \| 0.01 \| 0.06 \| 0.18 \| 0.01 \| 0.06 \| 0.18 \| 1 \| \| \| Depression \| 0.01 \| −0.05 \| −0.01 \| 0.49 \| 0.01 \| −0.04* \| 0.55 \| 0.01 \| −0.05 \| 0.03 \| 1 \| ## Cross-lagged analyses Although the autoregressive, CRP main-effect, depression main-effect, and reciprocal models yielded a good fit to the data across all fit indices, the other three models did not significantly differ from the autoregressive model (△χ2s > 0.00, ps > 0.05, △CFIs<0.01). Table 4 and Figure 1 provide more detailed results of these analyses. In the autoregressive model, baseline CRP was positively associated with CRP in 2015 (ßstd = 0.26, $p \leq 0.01$), baseline depression prospectively predict depression in 2013 (ßstd = 0.47, $p \leq 0.01$), and depression in 2013 prospectively predicted depression in 2015 (ßstd = 0.49, $p \leq 0.01$). Based on the autoregressive model, the path from baseline CRP to depression in 2013 (ßstd = −0.01, $$p \leq 0.81$$) and the path from baseline CRP to depression in 2015 (ßstd = 0.02, $$p \leq 0.47$$) were not significant in the CRP main-effect model, suggesting that baseline CRP could not prospectively predict depression at further time points after controlling all synchronous relations and all stability coefficients. Based on the autoregressive model, the path from baseline depression to CRP in 2015 (ßstd = −0.02, $$p \leq 0.41$$) and the path from depression in 2013 to CRP in 2015 (ßstd = 0.03, $$p \leq 0.30$$) were not significant in the depression main-effect model, suggesting that depression could not prospectively predict CRP at further time points after controlling all synchronous relations and all stability coefficients. Based on the autoregressive model, the paths from baseline CRP to depression in 2013 (ßstd = −0.01, $$p \leq 0.80$$), from baseline CRP to depression in 2015 (ßstd = 0.02, $$p \leq 0.47$$) were not significant in the CRP main-effect model, from baseline depression to CRP in 2015 (ßstd = −0.02, $$p \leq 0.40$$), and from depression in 2013 to CRP in 2015 (ßstd = 0.03, $$p \leq 0.31$$) were not significant in the CRP-depression reciprocal model, suggesting that CRP and depression could not prospectively predict each other at further time points after controlling all synchronous relations and all stability coefficients. Cross-group analyses were also conducted to determine whether sex moderated any of the observed relationships. Again, the difference tests indicated that the autoregressive model did not vary significantly across the sexes (△χ2 = 78.75, df = 54, $$p \leq 0.02$$, △CFI < 0.01). ## Discussion This study investigated whether the inflammatory marker, CRP, could prospectively predict symptoms of depression in middle-aged and older adults using a prospective design of community and rural dwellings, accounting for a range of potential confounders. CRP was not significantly associated with depression both cross-sectionally and longitudinally in the largest sample. Consistent with the results of previous studies in some Asian regions, such as Islamabad, there was a lack of association between CRP and depression influenced by racial/ethnic, genetic, and environmental factors, and population-based assessments of associations between physiological processes or social integration should consider these variables (Chapman and Santos-Lozada, 2020; Zavos et al., 2022). Contrary to other reports, we did not find this association with gender modification either (Danner et al., 2003; Morris et al., 2011; Vogelzangs et al., 2012; Song et al., 2015; Köhler-Forsberg et al., 2017). In fact, it is unclear from existing studies whether CRP levels directly contribute to the onset and progression of depression. Notably, BMI was negatively associated with depression in 2015 in our study, consistent with previous studies reported in Korea and China, and both studies demonstrated a negative correlation between BMI and depressive symptoms (Qin et al., 2017; Lee et al., 2019). This is contrary to the majority of findings from Western studies that reported a significant association between clinical overweighted or obesity and depression (Luppino et al., 2010; Daly, 2013; Preiss et al., 2013). Similarly, studies on other Asian populations have reported that being overweight or obese prevents depression. As previously reported, overweight individuals in Hong Kong and Bangladesh are less likely to have depressive symptoms, and obesity is an independent protective factor for depressive symptoms (Li et al., 2004). This counterintuitive epidemiological finding appears to be consistent with the “obesity paradox” theory, which reflects the relationship between obesity and reduced mortality compared with normal weight (Adams et al., 2006). However, the CESD-10 was used in the study to primarily reflect an individual’s experience of depression, and abnormalities in BMI (underweight and overweight) may affect a person’s body satisfaction and self-esteem, which in turn may affect the test results (Richard et al., 2016). However, our study found no significant association between CRP level and depression both cross-sectionally and longitudinally. This is in contrast to most findings from Western studies, which reported a significantly higher prevalence of MDD in individuals with high CRP levels, and this correlation appears to be more prominent in younger adults than in older patients (Jung and Kang, 2019; Milaneschi et al., 2021; Orsolini et al., 2022). The cause of elevated CRP may be another potential mechanism of action for neurovascular injury induced by dysregulation of peripheral myeloid cells, pro-inflammatory cytokines and complement pathways (Menard et al., 2017; Horn et al., 2018). Additionally, elevated CRP levels and pro-inflammatory activity can drive inflammation through microglia and astrocyte activation (McKim et al., 2018; Wesselingh et al., 2019). Our results lend partial support to studies on middle-aged and older adults that found no association between elevated CRP and higher odds of depression before and after adjusting for demographic, socio-economic, lifestyle, and prior medical history variables (Pan et al., 2008; Stewart et al., 2008, 2009; Song et al., 2015; de Menezes et al., 2017). Possible interpretations can be made based on two aspects: sampling methods and depression types. Some studies adopted a convenience sample, which may have caused selection bias. Our study adopted a four-stage random sampling method across China, minimizing the potential bias and magnifying the sample representativeness of older Chinese participants. Our results partially replicated studies conducted in some East Asian populations, who share similar lifestyles, such as a sample of 3,289 community residents from Beijing and Shanghai (Pan et al., 2008) and a sample of 569 from a rural community in Korea (Song et al., 2015). However, two studies that adopted a random sampling method, were conducted in Whites, and found a unidirectional association, which suggests that race may play a role in this association (Gimeno et al., 2009; Zalli et al., 2016). Owing to the highly mixed results in this field, ethnic and racial differences should also be considered in future studies. Third, CRP-depression may only exist in people living with atypical depression rather than melancholic depression (Glaus et al., 2014; Lamers et al., 2016), which reflects that depression subtypes may have unique but different biological pathways. Further studies are needed to verify the neurobiological pathways of the CRP-depression association in different subtypes. The strengths and limitations of this study should be mentioned. The major power is the strictness of the study design (random sampling method, prospective cohort, and large sample size), which supports high internal and external validity. However, this study had several limitations. First, we did not collect other pro-inflammatory biomarkers, like interleukin-6 (IL-6), which might be longitudinally associated with future depression status (Stewart et al., 2009). Second, the CESD-10 is not a commonly used assessment of depression in previous reports, limiting the comparability of our results to those of other studies. Third, CRP was collected in two waves, which limits our ability to assess the dynamic interaction between CRP and depression. Fourth, the interval between participant visits is too long, which may cause dampening effects of potential associations or stronger effects due to long-term interactions between variables. ## Conclusion In summary, we failed to find a bidirectional association between CRP levels and depressive symptoms in well-represented middle-aged and older Chinese adults. Additionally, we did not observe any sex-related modifications. Future studies should validate the role of multifaceted pro-inflammatory biomarkers in predicting depression and should also consider racial/ethnic, genetic, and environmental factors for the lack of association between CRP and depression and the use of commonly used depression scales and smaller time intervals to make the results comparable. ## 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 Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions BC formulated the research questions, designed the study, supervised the data cleaning. XL wrote the paper. BC and YN carried out data consolidation, cleaning, and preliminary analysis. XL, BC, and YN were responsible for principal analysis and result interpretation. XL and BC were responsible for the revision of 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/fpsyg.2023.1095150/full#supplementary-material ## References 1. Adams K. 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--- title: Impact of Different Crystalloids on the Blood Glucose Levels of Nondiabetic Patients Undergoing Major Elective Surgeries journal: Cureus year: 2023 pmcid: PMC9969174 doi: 10.7759/cureus.34294 license: CC BY 3.0 --- # Impact of Different Crystalloids on the Blood Glucose Levels of Nondiabetic Patients Undergoing Major Elective Surgeries ## Abstract Background This study aimed to compare intraoperative blood sugar level fluctuations between a group of patients receiving Ringer's lactate (RL) fluid as maintenance fluid and another group receiving $0.45\%$ dextrose normal saline with 20 mmol/liter potassium. Materials and methods This randomized double-blind study was conducted on 68 nondiabetic patients undergoing elective major surgeries at R. Laxminarayanappa Jalappa Hospital, Sri Devaraj Urs Medical College, Kolar, during the academic year from Jan 2021 to May 2022. Informed consent was obtained from these patients concerning their participation in this study. There were two groups of patients: Ringer lactate (RL) was administered to group A, and $0.45\%$ dextrose normal saline and 20 mmol/L potassium chloride (KCl) were administered to group B. The vitals and blood glucose levels were measured among the patients. A p-value of 0.05 was considered statistically important. Results The mean age of the patients was found to be 43.60 ± 15 years, with comparable age and gender distribution between the groups. On comparison of the mean blood glucose levels immediately after induction was not important between the groups. The mean levels were comparable between the groups ($p \leq 0.05$). After completion of the surgery, the mean blood glucose level significantly increased in group B patients when compared to those in group A ($p \leq 0.05$). Conclusion The study found a substantial increase in intraoperative blood glucose levels among patients receiving $0.45\%$ dextrose normal saline with 20 mmol/liter potassium instead of RL solution as maintenance fluid. ## Introduction Maintaining a circulating plasma volume ensures organ perfusion and oxygen delivery to tissues, which is the aim of perioperative fluid treatment [1]. Increased catabolic activity results from an increase in the production of catabolic hormones such as cortisol and glucagon. Neoglucogenesis and hyperglycemia are elevated because of these endocrine and metabolic alterations; hence, the prevalence of hyperglycemia can be used to evaluate this stress response [2]. Insulin treatment has been shown to reduce endothelial activity, preserve hepatic mitochondria, increase glucose absorption, increase circulatory lipid levels and decrease inflammatory markers [3]. The hyperglycemic response may be influenced by the kind of anesthesia used during surgery. It has been shown that as compared to local and epidural analgesia, general anesthesia causes higher blood glucose concentrations [4,5]. Intraoperative glucose management is essential for enhancing surgical results, even in nondiabetic patients. Tight glycemic control has an economic benefit because it has been shown to improve patient outcomes related to the length of stay, stroke, renal failure, and mortality. In this context, we examined how different maintenance fluids affected levels of glucose in nondiabetic persons following major surgery. ## Materials and methods After obtaining the Institutional Ethics Committee's approval (SDUMC/KLR/$\frac{609}{2020}$-21 dated $\frac{24}{12}$/2020), a randomized double-blind study was designed. A total of 68 nondiabetic patients undergoing major elective surgeries at R. Laxminarayanappa Jalappa Hospital, Kolar, India, were enrolled as study subjects. Informed consent was obtained from all of the study participants. The inclusion criteria for this study were patients aged 20 to 60 years, both genders, an American Society of Anaesthesiologists physical status 1 or 2, and patients who would undergo major elective surgery. The duration of recruitment was from Jan 2021 to May 2022. Patients with uncontrolled glycaemic profiles, uncompensated systemic illness (hepato-renal, cardiovascular, and endocrine), alcohol/substance abuse issues, smokers and morbid obesity, and serum potassium levels >5 Meq/l were excluded from this study. Randomization was conducted based on computerized randomization codes, and two groups were formed. Sampling procedure Detailed clinical histories of the patients were obtained, and routine investigations were checked. Once the patient was shifted to OT, their capillary blood glucose (CBG) level was noted, and monitoring was initiated. The patients were pre-medicated with 0.2mg glycopyrrolate, 1mg midazolam, pantoprazole 40mg, and ondansetron 4mg IV before induction of anesthesia. Induction was carried out with propofol 2mg per kilogram, and tracheal intubation was facilitated by succinylcholine 1mg per kg. Anaesthesia was maintained with $66\%$ N2O and sevoflurane in $33\%$ O2 on controlled ventilation. The patients were separated into two groups: group A who received Ringer's lactate (RL), and group B who received $0.45\%$ dextrose with normal saline and KCL 20 mmol/L. The labels of the IV fluids of each patient were wrapped by an OT staff member and given to the anesthetist who attended the surgery to perform the study, and the person who conducted this study did not get to know which fluid was given either. Thus, double blinding was achieved. Based on their body weight and overnight fasting, the hourly infusion rate was calculated, which was balanced out by the maintenance fluid using the 4:2:1 method. Mops and a suction bottle were used to roughly determine Segar's blood loss and the patients' plasma loss. Additionally, the patients' comfort levels were observed. CBG was assessed following surgery. Statistical analysis The sample size was calculated based on a study by Khetarpal et al. [ 6]. The collected data were coded and entered into an Excel database (Microsoft, Redmond, Washington). All of the qualitative/categorical measures, such as gender and the American Society of Anesthesiologists (ASA) physical status, were summarised as frequency and percentage and analyzed using the Chi-squared test. Independent sample t-test, Mann-Whitney U-test, and a Chi-squared test/Fisher's exact test were considered appropriate to interpret the results. Quantitative variables such as weight, blood pressure, and heart rate were summarised as mean and standard deviation and analyzed using unpaired Student's t-test. A p-value of <0.05 was considered as statistically significant and analyzed using SPSS v21 operating on Windows 10 (IBM Inc., Armonk, New York). ## Results In the present study, a total of 68 participants were included after obtaining their informed consent. The patients were then randomly distributed into the following two groups: group A who received RL, and group B who received $0.45\%$ dextrose normal saline and potassium chloride 20 mmol/L. The mean age of the patients was found to be 43.60±15.3 years, with a minimum of 20 and maximum age of 70 years (Table 1). Table 1 \| Unnamed: 0 \| N \| Minimum \| Maximum \| Mean \| SD \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Age \| 68 \| 20 \| 70 \| 43.6 \| 15.3 \| The mean ages between the groups were comparable, with no significant differences between them ($p \leq 0.05$), as shown in Table 2. Table 2 \| Unnamed: 0 \| Group A \| Group A.1 \| Group B \| Group B.1 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| SD \| Mean \| SD \| p-value \| \| Age \| 45.43 \| 14.91 \| 42.15 \| 15.61 \| 0.385 \| On the assessment of the vitals between the groups, it was found that there was comparable heart rate, blood pressure, mean arterial pressure, and baseline blood glucose between the group's preoperative periods (Table 3). Table 3 \| Unnamed: 0 \| Group A \| Group A.1 \| Group B \| Group B.1 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| SD \| Mean \| SD \| p-value \| \| Pre-op PR \| 88.1 \| 8.7 \| 92.0 \| 10.9 \| 0.110 \| \| Pre-op SBP \| 125 \| 8 \| 124 \| 8 \| 0.557 \| \| Pre-op DBP \| 79.9 \| 8.7 \| 80.3 \| 7.9 \| 0.835 \| \| Pre-op MAP \| 94.0 \| 7.5 \| 94.3 \| 7.1 \| 0.857 \| \| Baseline CBG \| 98.0 \| 4.6 \| 95.6 \| 7.0 \| 0.08 \| On comparing the mean blood glucose levels immediately after induction, it was found to be not significant between the groups. Moreover, the mean levels were comparable between the groups ($p \leq 0.05$), as shown in Table 4. Table 4 \| Unnamed: 0 \| Group A \| Group A.1 \| Group B \| Group B.1 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| SD \| Mean \| SD \| p-value \| \| Immediate after induction CBG \| 98.4 \| 3.5 \| 99.4 \| 6.8 \| 0.474 \| After completion of the surgery, the mean blood glucose level was significantly increased in the patients in group B compared to those in group A ($p \leq 0.05$), as shown in Table 5. Table 5 \| Unnamed: 0 \| Group A \| Group A.1 \| Group B \| Group B.1 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| SD \| Mean \| SD \| p-value \| \| After completion of surgery CBG \| 100.3 \| 5.0 \| 120.5 \| 12.1 \| 0.001* \| ## Discussion Even in nondiabetic persons, intraoperative glycemic management plays an essential role in enhancing surgical outcomes. Perioperative hyperglycemia has a complicated etiology: physiologic stress increases sympathetic activation, which raises levels of glucagon, catecholamines, growth hormone, and cortisol [7-9]. An increase in endogenous glucose synthesis by gluconeogenesis (mainly hepatic) and glycogenolysis is brought on by the rise in counter-regulatory hormones. Under typical physiological circumstances, skeletal, cardiac, adipose (GLUT4), and liver (GLUT2) insulin-mediated glucose absorption in peripheral tissues, together with a reduction in hepatic glucose production, carefully maintains glucose homeostasis. [ 10,11]. The current evidence-based practice recommends that intraoperative fluid management be tailored for two key therapeutic contexts: in low-risk patients undergoing low-risk or ambulatory surgery, high-volume crystalloid infusions of 20-30 ml kilogram improve ambulatory anesthetic outcomes, including pain, nausea, dizziness, and preparedness for the street. However, a 'restrictive' hydration regimen appears to be beneficial for high-risk individuals undergoing major surgery. Patients' risk of developing post-operative infections will increase as a result of intraoperative hyperglycemia. In comparison to patients without the condition, those with intraoperative hyperglycemia had a higher rate of operative site infection. The majority of nondiabetic individuals undergoing moderate- to high-risk surgery experienced intraoperative hyperglycemia. Therefore, the risk factors for intraoperative hyperglycemia must be determined [12]. In this study, intraoperative blood glucose level changes were compared between patients receiving RL as a maintenance fluid and a control group receiving $0.45\%$ dextrose NaCl. The mean age of the patients was found to be 43.60±15.3 years, with a minimum of 20 and maximum age of 70 years. The patients were randomly assigned to one of two groups: Group A received RL, while Group B was given $0.45\%$ dextrose normal saline with potassium chloride. The mean ages and gender distributions among the groups were comparable. As in the case of the present study, Kaur et al. documented no significant difference in mean age between the groups, and comparable gender distribution was also noted [13]. In another study by Chin et al., the mean age of patients was 38 years, with equal gender distribution [14]. Mean arterial pressure, HR, blood pressure, and baseline plasma glucose levels were comparable when the vital signs were compared between the groups. Additionally, the mean blood sugar levels between the groups did not significantly differ right after induction. The mean levels were similar amongst the groups ($p \leq 0.05$). Following surgery, group B patients' mean blood glucose levels were significantly higher than those of group A patients ($p \leq 0.05$). Chin et al. discovered a considerable difference in plasma glucose levels across groups an hour after infusion, even though $33\%$ of patients receiving dextrose saline had plasma glucose levels of 8 mmol/l. However, nondiabetic persons can also experience considerable, albeit brief, hyperglycemia after a small 500 ml dose [14]. According to Maitra et al., only $63\%$ of patients in group B (RL) had at least one episode of hyperglycemia, compared to $29\%$ in group A ($0.45\%$ sodium chloride with $5\%$ dextrose). Group B ingested much more insulin than group A to maintain normoglycemia. The relative risk of getting hyperglycemic in group B individuals was 2.172. In group B, the number necessary to induce injury, i.e., hyperglycemia, was 2.941. Stress-induced hyperglycemia is prevalent in nondiabetic persons after major non-cardiac surgery, and the risk increases when RL fluid is substituted for maintenance fluid treatment. Much greater doses of human normal insulin are needed in patients receiving dextrose-containing saline as maintenance fluid to achieve normoglycemia with intravenous bolus [15]. Despite the low-calorie load in a study by Chin et al., 500 mL of $5\%$ dextrose in $0.9\%$ normal saline resulted in severe hyperglycemia. However, this was only temporary, and its clinical importance is unknown. Nonetheless, acute hyperglycemia has been linked to paired phagocytosis by polymorphonuclear leukocytes, complement system malfunction, and increased sympathoadrenergic activity. Furthermore, perioperative hyperglycemia has also been linked to glycosuria, longer hospital admissions, more frequent wound infections, ischemic episodes, and lower survival over two years in the context of coronary artery bypass graft surgery. RL solution can be used as an alternative in nondiabetic persons undergoing major surgery and is most likely a different IV fluid for perioperative care. Within the first few hours after surgery and throughout the intraoperative period, CBG levels in group 2 rose considerably ($p \leq 0.001$). According to the current study, patients who received $0.45\%$ Dextrose NaCl with 20 mmol/L potassium as their maintenance fluid had considerably higher intraoperative blood glucose levels than patients who received RL fluid as their maintenance fluid. Researchers discovered that patients who utilized RL as maintenance fluids had a lower incidence of hyperglycemia. ## Conclusions It is surmised that stress-induced hyperglycemic response is common in nondiabetic persons undergoing major surgery. According to the current study, patients who received $0.45\%$ dextrose normal saline with 20 mmol/liter potassium as their maintenance fluid instead of RL solution had considerably higher intraoperative blood glucose levels. In patients receiving dextrose-containing saline as maintenance fluid, soluble human regular insulin was required to achieve normal CBG levels by IV bolus dose. Intraoperative glucose management is essential for enhancing surgical results, even in nondiabetic patients. Tight glycemic control has an economic benefit because it has been shown to improve patient results such as length of stay, stroke, renal failure, and mortality. The study found that patients who used RL as maintenance fluid had a decreased incidence of hyperglycemia. ## References 1. Doherty M, Buggy DJ. Intraoperative fluids: how much is too much?. Br J Anaesth (2012) 109 69-79. PMID: 22661747 2. Turina M, Fry DE, Polk HC Jr. Acute hyperglycemia and the innate immune system: clinical, cellular, and molecular aspects. Crit Care Med (2005) 33 1624-1633. PMID: 16003073 3. Lipshutz AK, Gropper MA. Perioperative glycemic control: an evidence-based review. Anesthesiology (2009) 110 408-421. PMID: 19194167 4. Rao AM, Indra P. A comparative study of blood glucose levels under general anaesthesia in non diabetic and controlled diabetic patients. IOSR J Dent Med Sci (2015) 14 51-56 5. Pflug AE, Halter JB. Effect of spinal anesthesia on adrenergic tone and the neuroendocrine responses to surgical stress in humans. Anesthesiology (1981) 55 120-126. PMID: 7258714 6. Khetarpal R, Chatrath V, Kaur J, Bala A, Singh H. Impact of different intravenous fluids on blood glucose levels in nondiabetic patients undergoing elective major noncardiac surgeries. Anesth Essays Res (2016) 10 425-431. PMID: 27746527 7. Barth E, Albuszies G, Baumgart K. Glucose metabolism and catecholamines. Crit Care Med (2007) 35 0-18 8. Cely CM, Arora P, Quartin AA, Kett DH, Schein RM. Relationship of baseline glucose homeostasis to hyperglycemia during medical critical illness. Chest (2004) 126 879-887. PMID: 15364770 9. McGuinness OP, Shau V, Benson EM. Role of epinephrine and norepinephrine in the metabolic response to stress hormone infusion in the conscious dog. Am J Physiol (1997) 273 0-81 10. Zauner A, Nimmerrichter P, Anderwald C. Severity of insulin resistance in critically ill medical patients. Metabolism (2007) 56 1-5. PMID: 17161218 11. Lang CH, Dobrescu C, Mészáros K. Insulin-mediated glucose uptake by individual tissues during sepsis. Metabolism (1990) 39 1096-1107. PMID: 2215256 12. Sermkasemsin V, Rungreungvanich M, Apinyachon W, Sangasilpa I, Srichot W, Pisitsak C. Incidence and risk factors of intraoperative hyperglycemia in non-diabetic patients: a prospective observational study. BMC Anesthesiol (2022) 22 287. PMID: 36088294 13. Kaur N, Shenoy BK, Govindswamy S, Iyer SS. Effect of 0.9% saline and Ringer‘s lactate on mean change in blood glucose levels under spinal anaesthesia among diabetic patients. Sri Lankan J Anaesthesiol (2020) 28 31-35 14. Chin KJ, Macachor J, Ong KC, Ong BC. A comparison of 5% dextrose in 0.9% normal saline versus non-dextrose-containing crystalloids as the initial intravenous replacement fluid in elective surgery. Anaesth Intensive Care (2006) 34 613-617. PMID: 17061636 15. Maitra S, Kirtania J, Pal S, Bhattacharjee S, Layek A, Ray S. Intraoperative blood glucose levels in nondiabetic patients undergoing elective major surgery under general anaesthesia receiving different crystalloid solutions for maintenance fluid. Anesth Essays Res (2013) 7 183-188. PMID: 25885830
--- title: 'Burden of ovarian cancer in China from 1990 to 2030: A systematic analysis and comparison with the global level' authors: - Ying Wang - Zhi Wang - Zihui Zhang - Haoyu Wang - Jiaxin Peng - Li Hong journal: Frontiers in Public Health year: 2023 pmcid: PMC9969192 doi: 10.3389/fpubh.2023.1136596 license: CC BY 4.0 --- # Burden of ovarian cancer in China from 1990 to 2030: A systematic analysis and comparison with the global level ## Abstract ### Introduction Ovarian cancer (OC) is one of the major diseases threatening women's health and life. Estimating the burden trends and risk factors of OC can help develop effective management and prevention measures. However, there is a lack of comprehensive analysis concerning the burden and risk factors of OC in China. In this study, we aimed to assess and predict the burden trends of OC in China from 1990 to 2030, and make a comparison with the global level. ### Methods We extracted prevalence, incidence, mortality, disability-adjusted life years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) data from the Global Burden of Disease Study 2019 (GBD 2019) and characterized OC burden in China by year and age. OC epidemiological characteristics were interpreted by conducting joinpoint and Bayesian age-period-cohort analysis. We also described risk factors, and predicted OC burden from 2019 to 2030 using Bayesian age-period-cohort model. ### Results In China, there were about 196,000 cases, 45,000 new cases and 29,000 deaths owing to OC in 2019. The age-standardized rates (ASRs) of prevalence, incidence and mortality have increased by $105.98\%$, $79.19\%$, and $58.93\%$ respectively by 1990. In the next decade, OC burden in China will continue to rise with a higher rate than the global level. The OC burden in women under 20 years of age is slowing down, while the burden in women over 40 years of age is getting more severe, especially in postmenopausal and older women. High fasting plasma glucose is the major factor contributing the most to OC burden in China, and high body-mass index has surpassed occupational exposure to asbestos to be the second risk factor. OC burden from 2016 to 2019 in China has increased faster than ever before, indicates an urgent need to develop effective interventions. ### Conclusion The burden of OC in China has shown an obvious upward trend in the past 30 years, and the increase rate accelerated significantly in recent 5 years. In the next decade, OC burden in China will continue to rise with a higher rate than the global level. Popularizing screening methods, optimizing the quality of clinical diagnosis and treatment, and promoting healthy lifestyle are critical measures to improve this problem. ## 1. Introduction OC is the most lethal malignancy of female reproductive system with poor prognosis. Unfortunately, $70\%$ of OC patients are found to be in the advanced stage, and the 5-year survival rate is only $47.4\%$ [1]. High recurrence rate and chemotherapy resistance rate are the main reasons for the low survival rate. According to the latest data, OC ranks the 8th in incidence and mortality rate among women cancer in high-income areas, while it ranks the third among women in low- and middle-income areas, only after breast cancer and cervical cancer [2]. China is a middle-income country with a population of 1.4 billion. It's reported that OC has replaced uterine cancer and became the second leading cause of death in gynecological cancer in China [3]. Many factors, such as population aging, economic development, lifestyle changes, and medical condition improvement, can affect the evaluation of OC burden. Therefore, understanding the current situation and future trends of OC burden is critical to develop appropriate prevention and control measures. Although there have been articles describing and assessing trends in the global burden of OC [4, 5], there are few comprehensive analyses of OC burden in China. Wang et al. has analyzed the disease burden trends of three major gynecological cancers including cervical cancer, endometrial cancer and OC in China, but only the mortality rates were analyzed [3]. There is still a lack of rounded analysis including prevalence, incidence, mortality, DALYs, YLDs, YLLs and related risk factors of OC burden in China. Using the data from GDB 2019, this study comprehensively described the temporal trend and age distribution of OC burden in China by discussing prevalence, incidence, mortality, DALYs, YLLs and YLDs from 1990 to 2019. We also used joinpoint regression method to figure out the trends in specific periods. Furthermore, we explored the effects of age, period and cohort factors on OC burden in China by using age-period-cohort analysis. Finally, we assessed the trends in attributable risk factors affecting OC mortality and DALYs, and projected trends in OC incidence and mortality over the next decade. This study has also compared the OC burden in China and the global level, which will provide more reference for the formulation and implementation of OC prevention and control policies in China. ## 2.1. Data sources Data in our study was obtained from GBD 2019 (http://ghdx.healthdata.org/gbd-results-tool). GBD 2019 contains comprehensive data of disease burden for 369 diseases and injuries with 87 risk factors in 204 territories and countries from 1990 to 2019. Data resources, disease classification, statistical models and measures to optimize data quality have been described previously in detail (6–8). Specifically, we downloaded the following data for subsequent analysis: [1] Age-specific data for OC on prevalence, incidence, deaths, DALYs, YLDs, and YLLs as absolute numbers, crude rates and ASRs including $95\%$ confidence interval ($95\%$ CI), annually from 1990 to 2019 in China and the global level; [2] Age-specific population data from 1990 to 2019 and projected population data from 2020 to 2030 in China and the global level; [3] Associated risk factors attributable to OC from 1990 to 2019 in China and the global level. ## 2.2.1. Descriptive analysis We performed a descriptive analysis of both the temporal and age trends of OC burden in China compared with the global level. We also analyzed the temporal trends of risk factors contribute to OC from 1990 to 2019 by accessing population attributable proportion (PAF). All data used in descriptive analysis were prepared by using Microsoft Excel 2019, and statistical analysis were performed by using R software (version 4.2.2). A statistically significant p was <0.05. ## 2.2.2. Joinpoint regression analysis Joinpoint regression model was applied to evaluate temporal trend of OC burden in certain periods. This model has the advantage of avoiding the non-objectivity of typical trend analysis based on linear trends, as it estimates the changing pattern of age-adjusted rates by the least square method. In this model, the inflection points of the moving trend were figured out by summing up residual squares between the actual value and the estimated value using grid search method. The Monte Carlo permutation method was used to test whether the changes were significant. We determined the change trend of OC burden in certain periods by comparing annual percentage change (APC) which was calculated by two steps: [1] y = α + β x+ ε, in which y = ln (rate), x = year, β is the estimated value of the slope, and ε was the error term; [2] APC=100[exp(β) – 1]. Average annual percentage change (AAPC) was calculated to estimate overall OC burden trend from 1990 to 2019. APC > 0 means an increasing trend, while APC < 0 means a decreasing trend, and so does AAPC. This model was created by Joinpoint software (version 4.9.1.0). ## 2.2.3. Age-period-cohort analysis We used age-period-cohort model to explain the effects of age, period, and cohort on OC incidence and mortality. In this model, Net Drift reflects the overall time trend in disease rates which is similar to AAPC, the difference lies on that Net Drift takes into account both the effects attributed to period and cohort. Local drifts were used to estimate the average annual percentage changes of OC incidence and mortality rates for certain age groups. Age effect mainly means the effect of disease rate changes with age, and it is characterized by Longitudinal Age Curves which stands for the age-related natural course of the disease rate. Period effect reflects the influence of social, economic and cultural environment changes in different periods on the disease rate. Cohort effect refers to changes in disease rates due to different levels of exposure to risk factors among different generations. Relative risk (RR) was applied to evaluate the period and cohort effects. An intrinsic estimator based on Poisson distributions was conducted to access disease parameters in this model to overcome the multicollinearity problem between age, period, and cohort. To avoid overlapping information in adjacent queues, the age, period, and time interval of the queues must be equal. Thus, the ages were defined as 5–9, 10–14,…90–94, 95–100, and the incidence and mortality rates every 5 years were calculated. Age-period-cohort analysis was performed by Age Period Cohort Tool (http://analysistools.nci.nih.gov/apc/) [9]. Bayesian age-period-cohort (BAPC) model has been proved to have the highest coverage (with $95\%$ CI) and it is well-fit for analyzing predictions of age-stratified cancer rates [10, 11]. Based on age-specific population data from 1990 to 2019, projected population data from 2020 to 2030, and GBD world population age standard which is specified in Appendix Table 13 of a GBD 2019 [6], we applied BAPC model to forecast OC incidence and mortality rates for the next 10 years. The BAPC models were built from R packages INLA (www.r-inla.org) and BAPC (http://r-forge.r-project.org/). ## 3.1. Temporal trends of OC burden in China from 1990 to 2019 In China, there were about 196,000 incident cases, 45,000 new cases for OC and 29,000 deaths due to OC in 2019. Compared to 1990, the crude rates of prevalence, incidence, and mortality in 2019 were all significantly increased by approximately 3 times. In addition, DALYs, YLDs, and YLLs of OC were 835,000, 25,000, and 810,000 years, respectively, and the crude rates of them in 2019 were 2.53, 3.14, and 2.51 times higher than those in 1990 (Supplementary Table 1). In 2019, the age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), age-standardized DALYs rate (ASDR), and the ASRs of YLDs and YLLs of OC were 10.15, 2.29, 1.43, 40.53, 1.25, and 39.28 (per 100,000 population) in China, which have increased by ~$105.98\%$, $79.19\%$, $58.93\%$, $47.88\%$, $91.78\%$, and $46.81\%$, respectively from 1990 to 2019, while the global burden of OC has remained roughly the same from 1990 to 2019 (Table 1). Table 1 \| Measure \| China \| China.1 \| China.2 \| Global \| Global.1 \| Global.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Measure \| ASRs (95% CI) in 1990 \| ASRs (95% CI) in 2019 \| Change of ASRs (95% CI) \| ASRs (95% CI) in 1990 \| ASRs (95% CI) in 2019 \| Change of ASRs (95% CI) \| \| Prevalence \| 4.93 (3.84, 6.67) \| 10.15 (7.52, 12.99) \| 1.06 (0.27, 1.90) \| 12.72 (11.72, 14.38) \| 14.56 (12.85, 16.37) \| 0.14 (−0.03, 0.30) \| \| Incidence \| 1.28 (1.00, 1.79) \| 2.29 (1.68, 2.88) \| 0.79 (0.08, 1.53) \| 3.42 (3.17, 3.84) \| 3.58 (3.17, 4.00) \| 0.04 (−0.11, 0.18) \| \| Mortality \| 0.91 (0.70, 1.34) \| 1.43 (1.04, 1.81) \| 0.58 (−0.10, 1.28) \| 2.50 (2.32, 2.80) \| 2.43 (2.15, 2.66) \| −0.03 (−0.16, 0.08) \| \| DALYs \| 27.40 (21.18, 38.49) \| 40.53 (29.96, 51.58) \| 0.48 (−0.10, 1.13) \| 64.73 (59.29, 74.27) \| 64.34 (56.42, 71.52) \| −0.01 (−0.16, 0.12) \| \| YLDs \| 0.65 (0.42, 0.99) \| 1.25 (0.80, 1.76) \| 0.92 (0.15, 1.71) \| 1.72 (1.23, 2.22) \| 1.86 (1.34, 2.40) \| 0.08 (−0.08, 0.23) \| \| YLLs \| 26.75 (20.59, 37.66) \| 39.28 (28.98, 50.34) \| 0.47 (−0.11, 1.13) \| 63.01 (57.52, 72.45) \| 62.49 (54.96, 69.27) \| −0.01 (−0.16, 0.11) \| We also compared the trend of OC burden as numbers and ASRs in China and the global level year by year. Generally, the numbers of prevalence, incidence, mortality, DALYs, YLLs, and YLDs of OC in both China and the global level kept on increasing. The difference lies on that, while the ASRs of all these burden indicators in the global level generally hold steady from 1990 to 2019, the ASRs in China have risen apparently. It's noting that OC burden in China has ever remained stable from 2014 to 2016, but after that, the ASRs of all the burden indicators in China continued to increase at a faster pace than ever before (Figure 1, Supplementary Figure 1). Therefore, though the current ASRs of these disease burden indicators in China were still lower than those in the global level, they are likely to exceed the global level in the future if no measures are taken. Figure 1: The numbers and age-standardized rates (per 100,000 population) of ovarian cancer prevalence, incidence, mortality, and disability-adjusted life-years (DALYs) from 1990 to 2019 in China and the global level. The bar chart represents numbers and the broken line chart represents age-standardized rates. ## 3.2. Burden trends of OC in different age groups in China from 1990 to 2019 In 2019, the numbers of prevalence, incidence, mortality, DALYs, YLDs and YLLs of OC in most age groups especially those over 40 years of age increased significantly compared to 1990, except for those under the age of 20 of whom the indicators remained stable. The numbers of prevalence, incidence, DALYs, YLLs, and YLDs all peaked between 50 and 54 years of age [Prevalence number: 31,690 ($95\%$ CI: 40,480, 22,730); incidence number: 6,650 ($95\%$ CI: 4,780, 8,520); DALYs number: 142,120 ($95\%$ CI: 104,370, 184,600); YLDs number: 4,080 ($95\%$ CI: 2,580, 5,930); YLLs number: 138,040 ($95\%$ CI: 101,150, 180,110)], while the numbers of mortality peaked between 65 and 69 years of age [4,660 ($95\%$ CI: 3,060, 6,010)] (Figure 2, Supplementary Figure 2). Figure 2: The numbers and crude rates of ovarian cancer prevalence, incidence and mortality in 2019 compared with 1990 in China. Besides, the crude rates of prevalence, incidence, and YLDs in women under 20 years of age, and the crude rates of mortality, DALYs, and YLLs in women under 40 years of age in 2019 were similar with those in 1990. Importantly, the OC burden in women over 40was increased to a large extent. The crude rates (per 100,000 population) of prevalence, incidence, and YLDs peaked between 55 and 59 years of age [27.41 ($95\%$ CI: 18.83, 35.45)], between 70 and 74 years of age [8.50 ($95\%$ CI: 5.48, 10.73)], and between 65 and 69 years of age [4.08 ($95\%$ CI: 2.33, 5.96)], respectively, while DALYs and YLLs both peaked between 65 and 69 years of age [DALYs rate: 158.08 ($95\%$ CI: 103.39, 203,19); YLLs rate: 154.00 ($95\%$ CI: 101.07, 198.70)]. In addition, the crude rates of mortality increased with age. The crude rates of mortality in OC patients over 95 years of age reached as high as 13.57 ($95\%$ CI: 9.67, 17.00) (Figure 2, Supplementary Figure 2). Thus, the OC burden in postmenopausal women and older women has increased a lot from 1990 to 2019 in China, while the OC burden in younger women remained relatively unchanged. Next, we compared the trends in age distribution of OC burden in China and the global level. Consistent with former results, age distribution of OC burden in the global level remained relatively stable from 1990 to 2019. In China, age distribution of OC burden exhibited a dynamic pattern. Over the past three decades, proportion of OC prevalence, incidence, mortality and DALYs in women under 40 years of age have apparently decreased year by year, while proportion of women over 50 years old showed a trend of increase year by year. As for women aged 40 to 49, their share of OC burden in all ages fluctuated over time. Age distribution of OC YLDs, and YLLs number showed similar trends (Figure 3). Figure 3: The trends in age distribution of ovarian cancer burden in China and the global level from 1990 to 2019. ## 3.3. Joinpoint regression analysis Joinpoint regression analysis shows that the ASRs of OC burden indicators in China were all on the rise (Figure 4), while the OC burden in the global level was relatively stable (Supplementary Figure 3). Over three decades, the AAPCs of ASPR, ASIR, ASMR, ASDR, ASRs of YLDs and YLLs of OC in China were 2.56 ($95\%$ CI: 2.37, 2.75), 2.08 ($95\%$ CI: 1.94, 2.22), 1.60 ($95\%$ CI: 1.42, 1.78), 1.38 ($95\%$ CI: 1.18, 1.59), 2.32 ($95\%$ CI: 2.16, 2.47), 1.38 ($95\%$ CI: 1.20, 1.55), respectively, while the AAPCs in the global level fluctuated around zero (Table 2). Although the growing trend of ASPR from 2011 to 2016, ASMR from 2001 to 2016, and ASR of YLLs from 2004 to 2008 in China have moderated, it is noting that the increase rates of all the OC burden indicators were significantly accelerated from 2016 to 2019 (Figure 4), with APCs of ASPR, ASIR, ASMR, ASDR, ASRs of YLDs and YLLs were 4.07 ($95\%$ CI: 2.71, 5.45), 3.13 ($95\%$ CI: 1.88, 4.38), 2.66 ($95\%$ CI: 1.65, 3.68), 2.96 ($95\%$ CI: 1.67, 4.28), 3.46 ($95\%$ CI: 2.41, 4.53), 2.68 ($95\%$ CI: 1.44, 3.93), respectively (Table 2). Figure 4: Joinpoint regression analysis of age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), age-standardized disability-adjusted life-years rate (ASDR), age-standardized years lived with disability rate (YLDs rate) and age-standardized years of life lost rate (YLLs rate) in China from 1990 to 2019. An asterisk indicates that the annual percentage change is statistically significantly different from zero at the α = 0.05 level. TABLE_PLACEHOLDER:Table 2 ## 3.4. Age-period-cohort analysis for OC incidence and mortality rates in China The Net Drift (%) of OC incidence and mortality rates (per 100,000 population) in China are respectively 1.61 ($95\%$ CI: 1.43, 1.79) and 0.67 ($95\%$ CI: 0.50, 0.84), indicating that OC incidence and mortality rates in China were on the rise. The results shows that OC incidence rate of those younger than 12.5 years old and the mortality rate of those younger than 42.5 years old showed overall decreasing trends, while the incidence rate of those over 12.5 years old and the mortality rate of those over 42.5 years old showed overall decreasing trends (Supplementary Table 2). When the period and cohort effects were controlled, the incidence and mortality rates of OC in China increased with age (Figures 5A, D). For the period effect, the period RR of both incidence rate and mortality rate of OC kept increasing from 1990 to 2019 (Figures 5B, E). As for cohort effect, we found that the RR of OC incidence rate kept increasing in the cohort born before 1990, and then was gradually reduced by year of birth in the cohort born after 1990. Similarly, the RR of OC mortality rate first exhibited an increasing trend by year of birth, and then decreased, with the highest RR appeared at population born in 1960 (Figures 5C, F). Figure 5: Age-period-cohort analysis for incidence and mortality rates of ovarian cancer in China. (A) Age effect for incidence rate. (B) Period effect for incidence rate. (C) Cohort effect for incidence rate. (D) Age effect for mortality rate. (E) Period effect for mortality rate. (F) Cohort effect for mortality rate. ## 3.5. Trends of associated risk factors of OC from 1990 to 2019 GBD 2019 identified high fasting plasma glucose, occupational exposure to asbestos, and high body-mass index as three main risk factors for OC mortality and DALYs. In China, the largest contribution came from high fasting plasma glucose, with the PAF increasing rapidly from 2000 to 2005, and then fluctuating around $6.25\%$. The PAF of high fasting plasma glucose in the global level from 1990 to 2019 has risen year by year and was always higher than that of China. In addition, the PAF of high body-mass index has steadily increased year by year and has surpassed occupational exposure to asbestos to become the second largest contribution factor in China. Its growth rate is higher than that of the global level and is likely to catch up in the future. As for occupational exposure to asbestos, its contribution in China fluctuated from 1990 to 2019, while the global contribution has gradually decreased (Figure 6). Specific data is list in Supplementary Tables 3, 4. Figure 6: Population attributable proportion (PAF, %) of associated risk factors for ovarian cancer mortality and disability-adjusted life-years (DALYs) from 1990 to 2019. ## 3.6. The prediction of OC incidence and mortality rates from 2020 to 2030 BAPC analysis results show that OC incidence and mortality rates will increase year by year in both China and the global level in the next decade. In 2030, the ASRs of OC incidence and mortality in China are expected to reach 2.96 (per 100,000 population) and 1.69 (per 100,000 population), respectively, with an increase of $29.26\%$ and $18.18\%$ compared with 2019. The number of incidence and mortality of OC in China will reach 72,700 and 46,100, respectively, increasing by $59.90\%$ and $58.66\%$ compared to 2019. At the same time, the ASRs of OC incidence and mortality in the global level are expected to reach 3.94 and 2.54 (per 100,000 population), respectively, with an increase of $10.06\%$ and $4.53\%$ compared to 2019. As for the global level, the numbers of OC incidence and mortality are expected to reach 410,000 and 274,200, with an increase of $39.28\%$ and $38.24\%$ by 2019, respectively (Figure 7). Annual data for OC incidence and mortality rates from 2019 to 2030 are shown in Supplementary Table 5. Above all, the burden of OC in China will become more and more serious in the next decade if there is no intervention, and the growth rate of which is significantly higher than the global level. Figure 7: The prediction of ovarian cancer incidence and mortality rates for the next 10 years in China and the global level. ## 4. Discussion Based on GBD 2019 data, we analyzed and compared the trend of OC burden in China and the global level from 1990 to 2030. In this study, we applied the joinpoint regression method to analyze OC burden trend in China for the first time, and firstly analyzed the changing trend of attributable risk factors for OC. The findings in our work will provide more reference for developing prevention and management measures for OC in China. From 1990 to 2019, the numbers and crude rates of OC prevalence, incidence, mortality, DALYs, YLDs, YLLs have increased significantly in China. Although the overall global burden was also on the rise during the same period, the growth rate of OC burden in China was much higher than that in the global level, and the ASRs of OC burden indicators in the global level leveled off from 1990 to 2019. This is consistent with the results of a previous study [4], which reported that China has surpassed the United States to rank the first in terms of OC prevalence, mortality and DALYs numbers in the world. Over the past 30 years, the change trend of OC burden varies significantly across different sociodemographic index (SDI) regions. It's reported that OC prevalence, mortality and DALYs rates in high SDI regions like Western Europe, North America and Australia had a negative change, while those in low-middle SDI regions such as Asia, Latin America and Africa exhibited an increased change [4]. The possible reason is that policy makers in high SDI regions pay more attention to the construction of advanced health care and environment, while in low-middle SDI regions, more attention is being paid to infrastructure improvement. The fact that the ASRs of these OC burden indicators were all less than the crude rates indicates that the significant increase of OC burden in China is partly owing to population growth and aging. In addition, the apparent increase in prevalence rate may be the result of the incidence rate being much greater than the mortality rate. In terms of mortality, our results are inconsistent with another previous study [3]. This is possibly due to the result of GBD 2019 data algorithm optimization and update, which proved the necessity of our study. On the whole, the burden of OC in women younger than 20 years old showed a downward trend, while those in women older than 40 years old increased significantly in China from 1990 to 2019. Hormone levels plummet in women after menopause, which is associated with an increased burden of OC [12]. In general, OC patients before the age of 40 are mostly found in stage I and have a good prognosis, while those after the age of 40 are usually found at above stage II, and the prognosis is poor [13]. In addition, the older individuals have deteriorated physical functions and are susceptible to a variety of underlying diseases [14]. These may be the vital reasons for the high burden of OC in postmenopausal women and older women. From the perspective of age distribution, the proportion of OC burden in women under 40 years old showed a downward trend, while the proportion of women over 50 years old showed a significant upward trend from 1990 to 2019. And the proportion of women aged 40 to 49 showed a fluctuating trend. These results suggest that the younger trend of OC burden in China is not obvious. On the contrary, with the aging of the population, reducing OC burden in postmenopausal women and older women should become the focus of OC management and strategies in China. It is worth noting that the OC burden in China had a slowing trend from 2014 to 2016, but after 2016, the growth trend increased significantly and even exceeded the previous maximum growth rate. Joinpoint regression analysis showed that the ASRs of various indicators of OC burden in China were also on the rise, and their AAPCs were about 1.38–2.56. In contrast, all indicators in the global level remained stable, and the AAPCs are close to zero. In China, there was a slowdown in the prevalence rate from 2011 to 2016 and the mortality rate from 2001 to 2016, but from 2016 to 2019 the growth rate of various indicators returned to the previous level or even became faster than ever before. This may be the result of increase in patient visit rate and OC detection rate due to the popularization of health education, medical insurance and advanced medical equipment. As for the global level, the relatively stable pattern of OC burden may be the result of the opposite trend between high SDI regions and low-middle SDI regions. BAPC projection of OC burden from 2019 to 2030 shows that OC incidence and mortality rates will keep on increasing in both China and the global level. But the burden in China will grow much faster than the global level. Therefore, the current situation of OC burden in China is severe, and it is urgent to find out the causes and take effective intervention measures. We further analyzed the effects of age, period and cohort factors on OC incidence and mortality rates in China using the age-period-cohort method. From 1990 to 2019, the Net Drifts of OC incidence and mortality rates are both above zero, which verifies the grim situation of rapid growth of OC burden in China again. As expected, the age effect on OC incidence and mortality rates continuously increased with age. Similarly, the period effect on OC incidence rate showed an increasing trend during the whole period, indicating that the period effect was an important factor in the increasing trend of OC burden. Over the past 30 years, the economy, medical facility and life quality in China have improved a lot. At the same time, the pace of life has become faster, the mental pressure has gradually increased, and the lifestyle has changed fundamentally. In terms of eating habits, people used to eat enough vegetables rich in dietary fiber and vitamins. In recent years, obesity, diabetes, and sedentary behavior have become more prevalent. It's reported that dietary fiber [15] and vitamin intake [16] were inversely associated with OC risk, while obesity [17], diabetes [18], and sedentary behavior [19] are high risk factors for OC. In addition, studies have shown that duration of pregnancy [20] and breastfeeding [21] are negatively correlated with the risk of OC, so the fertility policy in China started in the 1980s may be one of the reasons for the increased risk of OC, but this remains to be verified. The cohort effect on OC incidence rate showed an increase trend in the cohort born before1990 and then exhibited a decrease trend in the cohort born after 1990. Similarly, the cohort effect on OC mortality rate showed an increase trend in the cohort born before 1960 and then exhibited a decrease trend in the cohort born after 1960. Poor environment, low socioeconomic levels and malnutrition in early childhood can profoundly and adversely affect health status, which may lead to higher OC risks in adulthood. The downward trend in the cohort effect may be a result of economic development, environmental improvement, universal access to health education, and improved medical conditions. According to GBD 2019, high fasting plasma glucose is the major factor contributing the most to OC burden in China and the global level. The PAF of high fasting plasma glucose remains stable in recent years. High level of glucose provides sufficient energy for rapidly proliferation in cancer cells and promotes cancer development by activating insulin-like growth factor-1 receptor signaling pathways, altering programmed death receptor pathways, and modulating immune detection (22–25). It has been shown that diabetes is positively associated with OC mortality, and that hypoglycemic drugs such as metformin reduce the risk of OC in women [18, 26]. A high body-mass index indicates obesity which could alter hormone levels and ovulation function and has been proved a major risk factor for OC [17]. In China, the PAF of high body-mass index increased year by year with a faster growth rate than the global level from 1990 to 2019, and has surpassed occupational exposure to asbestos as the second most common attributing factor of OC. Therefore, it is especially necessary to promote healthy diet and strengthen physical exercise to prevent the occurrence of diabetes and obesity. Asbestos has been proved to cause OC by the International Agency for Research on Cancer. It's reported that up to twice the amount of asbestos fibers in the ovarian tissue of women with exposed family members compared to women without exposure [27]. The asbestos fibers (without being able to be excreted) in the ovarian tissues lead to persistent local inflammation at the origin of tissue lesions and genetic and epigenetic alterations, which may promote ovarian tumorigenesis [28, 29]. Asbestos has been classified as a Group 1 carcinogen by the World Health Organization since 1987 and has been banned to use in more than 60 countries [30]. Thus, the PAF of occupational exposure to asbestos in the global level has decreased gradually, and is expected to be lower than high body-mass index. However, China has yet to completely prohibit the use of asbestos. As a result, occupational exposure to asbestos has become another major risk factor for OC and its PAF has remained relatively stable over the past 30 years. Finding alternative materials and using protective equipment to avoid direct exposure to asbestos may be beneficial to reduce the OC burden in China. In recent years, China has made many efforts to control OC burden. Since $70\%$ of OC patients are found in advanced stages due to its insidious onset, early detection and prevention are vital measures. Tumor suppressor genes BRCA$\frac{1}{2}$ play a role in maintaining genome stability by repairing double-strand DNA breaks. The pathogenic mutation in BRCA$\frac{1}{2}$ damages the function of BRCA$\frac{1}{2}$, leading to an increased risk of OC [31]. Currently, women with a family history of OC in China are encouraged to be screened for the BRCA$\frac{1}{2}$ mutation to detect OC and start target treatment earlier. Some experts recommend the combination of serum CA125 (an epithelial carcinoma marker) and transvaginal ultrasound as an early screening method for OC in general women, but its effectiveness needs to be further demonstrated. To further promote quality control of clinical treatment, China has issued the latest quality control index for standardized diagnosis and treatment of primary ovarian cancer in 2022[32]. We believe that the burden of OC in China will develop in a better direction in the future. There are still some limitations in our study. Although GBD 2019 employs various adjusted methods to reduce data bias and the reliability has been confirmed by previous studies, these data are not direct measurements after all, which may lead to the inevitable bias [33, 34]. Secondly, there are at least 50 risk factors of OC that have been reported so far [35], but the types of risk factors in GBD 2019 are limited and the analysis is not comprehensive enough. Thirdly, obvious differences in geographical environment, economic level and lifestyle are existing among provinces and regions in China. However, the corresponding data available for assessment is of lack so that this study did not specifically analyze the spatial distribution of OC burden in China. In the future, more efforts should be made to create and supplement OC burden data for provinces in China for deeper analysis. In addition, the COVID-19 outbreak has made a huge impact on society [36, 37], and trends in OC burden may change to some extent, but our projections do not take this into account. In conclusion, the burden of OC in China has shown an obvious upward trend in the past 30 years, and the increase rate accelerated significantly in recent 5 years. In the next decade, OC burden in China will continue to rise with a higher rate than the global level. We found that the OC burden in people under 20 years of age is slowing down, while the OC burden in people over 40 years of age is getting more severe, especially in postmenopausal women and older women. Thus, these people would be the focus of efforts to OC management and prevention measures. Popularizing screening methods for OC, optimizing the quality of clinical diagnosis and treatment, promoting healthy lifestyle and reducing exposure to carcinogens will help reduce the future burden of OC in China. ## 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 Study concept and design: LH and YW. Data collection and quality control: HW and JP. Data analysis, construction of figures and tables, manuscript draft, and results interpretation: YW. Critical revision of the manuscript for important intellectual content: LH, ZW, and ZZ. 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/fpubh.2023.1136596/full#supplementary-material ## References 1. Stewart C, Ralyea C, Lockwood S. 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--- title: The relative and interactive effects of urinary multiple metals exposure on hyperuricemia among urban elderly in China authors: - Chao Huang - Erwei Gao - Feng Xiao - Qiongzhen Wu - Wei Liu - Yi Luo - Xiaohu Ren - Xiao Chen - Kaiwu He - Haiyan Huang - Qian Sun - Desheng Wu - Jianjun Liu journal: Frontiers in Public Health year: 2023 pmcid: PMC9969194 doi: 10.3389/fpubh.2023.1015202 license: CC BY 4.0 --- # The relative and interactive effects of urinary multiple metals exposure on hyperuricemia among urban elderly in China ## Abstract ### Objective Independent and interactive effects of multiple metals levels in urine on the risk of hyperuricemia (HUA) in the elderly were investigated. ### Methods A total of 6,508 individuals from the baseline population of the Shenzhen aging-related disorder cohort were included in this study. We detected urinary concentrations of 24 metals using inductively coupled plasma mass spectrometry, fitted unconditional logistic regression models, and the least absolute shrinkage and selection operator regression models for the selection of metals as well as unconditional stepwise logistic regression models and restricted cubic spline logistic regression models for assessing the associations of urinary metals and HUA risk, and finally applied generalized linear models to determine the interaction with urinary metals on the risk of HUA. ### Results Unconditional stepwise logistic regression models showed the association between urinary vanadium, iron, nickel, zinc, or arsenic and HUA risk (all $P \leq 0.05$). We revealed a negative linear dose–response relationship between urinary iron levels and HUA risk (Poverall < 0.001, Pnonliner = 0.682), a positive linear dose–response relationship between urinary zinc levels and HUA risk (Poverall < 0.001, Pnonliner = 0.513), and an additive interaction relationship between urinary low-iron and high-zinc levels and HUA risk (RERI = 0.31, $95\%$ CI: 0.03–0.59; AP = 0.18, $95\%$CI: 0.02–0.34; $S = 1.76$, $95\%$CI: 1.69–3.49). ### Conclusion Urinary vanadium, iron, nickel, zinc, or arsenic levels were associated with HUA risk, and the additive interaction of low-iron (<78.56 μg/L) and high-zinc (≥385.39 μg/L) levels may lead to a higher risk of HUA. ## Introduction Hyperuricemia (HUA) is the second most common metabolic disease in China; it has been a concern because of its association with cardiovascular diseases and chronic kidney diseases (1–3). HUA is defined as a serum uric acid (SUA) level of ≥ 7.0 mg/dl for men and ≥ 6.0 mg/dl for women. A meta-analysis of 59 studies on HUA in China during 1995–2010 suggested that the crude prevalence of HUA in the Chinese population was 13.3, $19.4\%$ for men and $7.9\%$ for women [3], and the age-standardized prevalence of HUA in the Chinese population increased with age from $9.5\%$ in individuals aged 60–64 years to $21.9\%$ in those aged 80+ years [4]. Individuals with higher SUA were at high risk for hypertension, metabolic syndrome, acute myocardial infarction, and Alzheimer's disease (5–8). Moreover, a relationship between SUA levels and traditional risk factors including gender, age, dietary habit, and body mass index (BMI) was found [9]; however, limited evidence of non-traditional risk factors for elevated serum SUA levels is available in the literature. Multiple metals can enter the body through the inhalation of air, tobacco smoking, ingestion of drinking water and food, and skin contact, which may induce pathological responses as well as many diseases, such as cardiovascular disease, cancer, and kidney disease (10–13). Evidence indicated that environmental exposure to metals is related to HUA in American and Chinese adults (14–17) and multiple metals exposure can cause metabolic disorders and cognitive impairment in elderly Chinese persons [18, 19]. Based on the data from the National Health and Nutrition Examination Survey (NHANES) during the period from 2003 to 2010, Kuo et al. found that HUA risk was 1.84 times higher in men with total urinary arsenic (As) of ≤ 4.2 μg/L than those with total urinary As of >17.3 μg/L [14]. A recent cross-sectional study in Shenzhen, China regarding routine physical examination data ($$n = 1$$,406, ranging in age from 31 to 91 years) suggested a positive dose–response relationship between plasma levels of zinc (Zn) or As and HUA risk [15]. Several studies in Changsha, China showed that among 6,212 adults aged above 40 years, serum copper (Cu) levels showed a positive relationship with HUA risk after adjusting for potential confounders (age, gender, BMI, smoking, drinking, education, occupation, hypertension, and diabetes) [16], and among 2,120 adults aged 20–75 years, only women with total blood lead (Pb, >126 μg/L) had a 2.19-fold higher risk of HUA [17]. Nevertheless, literature regarding the adverse effects of multiple metals co-exposure on HUA is limited. Certain metals could show synergistic and antagonistic interactions with other metals on human health by promoting or inhibiting the absorption of other metals [20]. The NHANES (2011–2016) study revealed that whole-blood Pb showed a synergistic interaction with blood manganese on the reduced bone mineral density [21]. A recent study ($$n = 2$$,882 individuals with a mean age of 65.58 years) from the Dongfeng-Tongji cohort indicated that higher plasma concentrations of selenium (Se) with Zn decreased the positive association between plasma Cu and C-reactive protein [22]. However, many studies on plasma or serum levels of metals indicated the differences in metal concentrations in various biological samples and their biological significance. For example, urinary cadmium (Cd) concentrations reflect a long-term accumulation of Cd in the kidneys [23], while blood Cd concentrations mainly reflect recent exposure to Cd [24]. Therefore, the relationship between metal concentrations in humans and HUA risk cannot be fully explained by blood metal concentrations. We measured urinary metal concentrations [25] and SUA levels to explore the association between urinary levels of multiple metals with hyperuricemia risk in elderly residents in Shenzhen. ## Subjects This cross-sectional study is based on the baseline data from the Shenzhen aging-related disorder cohort [26]. The baseline population consisted of 9,411 elderly residents (≥ 60 years) with a Shenzhen household registration from the 51 community rehabilitation centers in a district of Shenzhen by random cluster sampling methods, during the period from July 2017 to November 2018. They participated in a health questionnaire and physical examination. To investigate the association between urinary metals levels and HUA risk, we first excluded 36 individuals with self-reported kidney diseases and 1,022 individuals with an estimated glomerular filtration rate (eGFR) of <60 ml/min per 1.73 m2 from the baseline population. Thereafter, we excluded 1,845 individuals who missed data on educational information ($$n = 75$$), marital status ($$n = 123$$), passive smoking status ($$n = 71$$), drinking status ($$n = 16$$), hypertension ($$n = 14$$), hyperlipidemia ($$n = 14$$), diabetes ($$n = 23$$), kidney diseases ($$n = 10$$), BMI ($$n = 88$$), and serum creatinine ($$n = 21$$), as well as 1,390 individuals without urinary metal values. Finally, 6,508 individuals were included in this study. The research protocol was approved by the Medical Ethics Research Committee of Shenzhen Center for Disease Control and Prevention (approval numbers: R2017001 and R2018020). Each participant signed an informed consent form before engaging in the study. ## Data collection We collected data on the health questionnaire from the participants through the trained investigators in face-to-face interviews. The health questionnaire contained the following items: general demographics, personal and family health histories, tobacco smoking, and alcohol drinking. In this study, HUA was defined as SUA of >420 μmol/L in male participants and >360 μmol/L in female participants [27]. Current smoking was defined as those who smoked at least one cigarette per day for more than 6 months; quit smoking was defined as those who had quit smoking at the time of the survey; the rest were considered as never smoking. Current drinking was defined as those who drank at least once a week for over 6 months; quit drinking was defined as those who had quit drinking at the time of the survey; the rest were considered never drinking. Hypertension was defined as systolic blood pressure (SBP) of ≥140 mmHg, diastolic blood pressure (DBP) of ≥90 mmHg, previously diagnosed patients with hypertension patients, or antihypertensive drug use. Diabetes was defined as fasting blood glucose concentration of ≥7.0 mmol/L, previously diagnosed diabetes, or hypoglycemic drug use. Hyperlipidemia was defined as total cholesterol of ≥5.18 mmol/L, triglyceride of ≥1.7 mmol/L, low-density lipoprotein cholesterol of ≥3.37 mmol/L, high-density lipoprotein cholesterol of ≤ 1.0 mmol/L, or previously diagnosed hyperlipidemia or lipid-lowering drug use. eGFR was calculated according to the formula recommended by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) (2009 edition) [28]: serum creatinine (Scr) ≤ 0.7 mg/dL, eGFR = 144 × (Scr/0.7)−0.329 × (0.993) age; Scr > 0.7 mg/dL, eGFR = 144 × (Scr/0.7)−1.209 × (0.993) age for female participants; Scr ≤ 0.9 mg/dL, eGFR = 141 × (Scr/0.9)−0.411 × (0.993) age; Scr > 0.9 mg/dL, eGFR = 141 × (Scr/0.9)−1.209 × (0.993) age for male participants. Data were expressed in ml/min per 1.73 m2. ## Urinary metal concentrations We measured 24 urinary metals in urine samples, including lithium, beryllium (Be), aluminum (Al), titanium, vanadium (V), chromium (Cr), manganese, iron (Fe), cobalt, nickel (Ni), Cu, Zn, As, Se, rubidium, strontium, molybdenum (Mo), Cd, indium (In), tin, antimony (Sb), barium, thallium (Tl), and Pb using inductively coupled plasma-mass spectrometry (ICP-MS, NEXION 300X. PerkinElmer Inc. Waltham, Massachusetts, USA). Briefly, urine samples were thawed at room temperature and then centrifuged (4,200 rpm × 10 min) at room temperature. Afterward, 0.5 ml of supernatant from each urine sample was added into a 15 ml polypropylene tube, and then 4.5 ml of $2\%$ nitric acid solution (10 times the diluted urine sample) was added. To assess the accuracy of the measurements, SeronomTM Trace Elements Urine L-1 (Sero Incorporated Company. Billingstad, Norway), SeronomTM Trace Elements Urine L-2 (Sero Incorporated Company. Billingstad, Norway), and Trace Elements in Natural Water (SRM1640a) (National Institute of Standards and Technology. Gaithersburg, Maryland, USA) were added as quality control samples for each batch. As shown in Supplementary Table 1, the range values of 66.04–$152.86\%$ in urinary metals were considered acceptable for spike recoveries. The limits of detection (LOD) ranged from 0 to 1.66 μg/L for urinary Fe and from 0 to 0.13 μg/L for urinary Zn. The limits of quantification (LOQ) ranged from 0.01 to 5.54 μg/L for urinary Fe and from 0 to 0.44 μg/L for urinary Zn. Urinary concentrations of Be, In, and Sb were excluded from further analysis because the values of Be, In, and Sb in more than $80\%$ of individuals were below the corresponding LOD. Values of urinary metals below the LOQ were replaced by LOQ/2. ## Statistical analysis A Student's t-test, Mann-Whitney U-test, and Chi-square test were correspondingly used to compare normally, non-normally continuous (including eGFR, urine creatinine, and urinary metals concentrations), and categorical variables (including age, gender, education level, marital status, active smoking status, passive smoking status, drinking status, hypertension, diabetes, hyperlipidemia, and BMI) between the non-hyperuricemia and hyperuricemia groups. Values of urinary metals were log10-transformed before analysis to approximately normal distributions. Values of Spearman's rank correlations coefficient were calculated among the 21 urinary metal concentrations. We identified individual urinary metals by unconditional logistic regression models or LASSO regression models for further analysis. When constructing unconditional logistic regression models, the participants were divided into four subgroups (i.e., ≤ P25 as the reference group, P25, P50, and P75) according to the quartile values of urinary concentrations of individual metal, after adjusting for potential confounders, including age (<67 or ≥67 years old), gender (male or female), education level (<9, ≥9, or ≥13 years), marital status (married or other marital status), active smoking status (never, quit, or current), passive smoking status (yes or no), drinking status (never, quit, or current), hypertension (yes or no), diabetes (yes or no), hyperlipidemia (yes or no), BMI (<24 or ≥24 kg/m2), estimated glomerular filtration rate (eGFR: mL/min per 1.73 m2), and urine creatinine (μmol/L). The median value in each metal quartile (log10-transformed urinary metal value) was entered into the logistic regression model as a continuous variable. In the LASSO regression model, 10-fold cross-validation was used to select metals based on the lambda (λ) parameter with minimum mean square error (minimum MSE). Identified metals by both logistical regression models and LASSO regression were included in unconditional stepwise logistic regression models (enter = 0.05 and remove = 0.10). Herein, we adjusted for the same potential confounders in both logistical regression models and LASSO regression. RCS logistic regression models were constructed to analyze the dose–response relationship between urinary metals levels and HUA risk. The knots were set to the 10th, 50th, and 90th percentiles of each metal value, and the 25th percentiles of each metal were set as the corresponding reference value. We also used multiple linear regression models to evaluate the associations between urinary metals and SUA levels. Generalized linear model (GLM) was used to evaluate the additive interaction of urinary metals on the risk of HUA. Individuals were classified into high (≥ median) and low (<median) subgroups based on urinary metals values. Relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (S) were used to assess the additive interaction between urinary metals concentrations and HUA risk [29]. A regression tree is a machine-learning algorithm known to detect multiple interactions between covariates [30]. We used a regression tree to explore multiple interactions between urinary metals and SUA levels. Subgroup analyses of age, gender, BMI, hypertension, diabetes, or hyperlipidemia were conducted. Individuals were divided into high- and low-metal subgroups according to urinary median values (log10-transformed) of urinary metals. The interaction was examined by adding an interaction term between a specific metal and the stratification variable and adjusted for the same confounders in unconditional logistic regression models. All data were analyzed using Statistical Program for Social Sciences 17.0 (SPSS Inc., Chicago, Illinois, USA). LASSO regression analysis was performed with R 4.2 (Lucent Technologies, USA) “glmnet” package. RCS analysis was conducted using SAS 9.2 (SAS Institute Inc., Cary, North Carolina, USA) (RCS_Reg macro) [31]. Statistical significance was defined as a P-value of < 0.05 (two-tailed). ## Participants characteristics As shown in Table 1, among the 6,508 participants, 2,731 were men and 3,777 were women, and 4,147 were in the non-HUA subgroup and 2,361 were in the HUA subgroup. When compared with individuals in the non-HUA subgroup, those in the HUA subgroup had lower urinary levels of lithium, V, Cr, Fe, Ni, strontium, or Mo as well as higher urinary levels of Zn or As (all $P \leq 0.05$). As shown in Supplementary Figure 1, Spearman correlation analysis revealed the correlations among 21 metals with each other (all $P \leq 0.05$), wherein Se showed a strong correlation with titanium, Zn, As, rubidium, and Mo (the corresponding correlation coefficients: 0.76, 0.71, 0.76, 0.74, and 0.74, all $P \leq 0.05$). However, the correlation of V with Al was weak (correlation coefficient: 0.13, $P \leq 0.05$). Table 1 \| Variable \| Non-hyperuricemia \| Hyperuricemia \| p-value \| \| --- \| --- \| --- \| --- \| \| Age (<67/≥67 years, n %) \| 2,149/1,998 (51.8/48.2) \| 1,180/1,181 (50.0/50.0) \| >0.05a \| \| Gender (male/female, n, %) \| 1,702/2,445 (41.0/59.0) \| 1,029/1,332 (43.6/56.4) \| <0.05a \| \| Education level (years, n %) \| \| \| >0.05a \| \| <9 \| 1,808 (43.6) \| 1,039 (44.0) \| \| \| 9- \| 1,411 (34.0) \| 815 (34.5) \| \| \| ≥13 \| 928 (22.4) \| 507 (21.5) \| \| \| Marital status (married/others, n, %) \| 3,640/507(87.8/12.2) \| 2,081/280 (88.1/11.9) \| >0.05a \| \| Active smoking status (n, %) \| \| \| <0.05a \| \| Never \| 3,365 (81.1) \| 1,840 (77.9) \| \| \| Quit \| 390 (9.4) \| 271 (11.5) \| \| \| Current \| 392 (9.5) \| 250 (10.6) \| \| \| Passive smoking status (yes/no, n, %) \| 464/3,683 (11.2/88.8) \| 265/2,096 (11.2/88.8) \| >0.05a \| \| Drinking status (n, %) \| \| \| <0.05a \| \| Never \| 3,586 (86.5) \| 1,970 (83.4) \| \| \| Quit \| 109 (2.6) \| 59 (2.5) \| \| \| Current \| 452 (10.9) \| 332 (14.1) \| \| \| Hypertension (yes/no, n, %) \| 2,186/1,961 (52.7/47.3) \| 1,468/893 (62.2/37.8) \| <0.05a \| \| Diabetes (yes/no, n, %) \| 845/3302 (20.4/79.6) \| 490/1,871 (20.8/79.2) \| <0.05a \| \| Hyperlipidemia (yes, no) \| 3,014/1,133 (72.7/27.3) \| 1,915/446 (81.1/18.9) \| >0.05a \| \| BMI (kg/m2, n, %) \| \| \| <0.05a \| \| <24 \| 2,207 (53.2) \| 829 (35.1) \| \| \| ≥24 \| 1,940 (46.8) \| 1,532 (64.9) \| \| \| eGFR (mL/min per 1.73 m2, mean ± SD) \| 82.08 ± 10.5 \| 77.63 ± 10.19 \| >0.05b \| \| Urine creatinine (μmol/L, median, IQRs) \| 8,399 (4,840, 13,053) \| 8,209 (4,878, 13,002) \| >0.05c \| \| Urinary metal concentration (μg/L, median, IQRs) \| Urinary metal concentration (μg/L, median, IQRs) \| Urinary metal concentration (μg/L, median, IQRs) \| Urinary metal concentration (μg/L, median, IQRs) \| \| Lithium \| 18.78 (10.98, 29.15) \| 18.24 (10.53, 28.34) \| <0.05c \| \| Aluminum \| 24.63 (12.62, 41.37) \| 24.09 (12.44, 41.10) \| >0.05c \| \| Titanium \| 235.10 (136.62, 366.74) \| 237.97 (134.44, 359.33) \| >0.05c \| \| Vanadium \| 2.90 (1.79, 4.15) \| 2.66 (1.62, 3.76) \| <0.05c \| \| Chromium \| 1.57 (0.94, 2.31) \| 1.50 (0.88, 2.17) \| <0.05c \| \| Manganese \| 0.52 (0.26, 0.93) \| 0.52 (0.26, 0.95) \| >0.05c \| \| Iron \| 81.44 (50.90, 123.96) \| 72.51 (43.07, 110.72) \| <0.05c \| \| Cobalt \| 0.21 (0.08, 0.38) \| 0.20 (0.08, 0.38) \| >0.05c \| \| Nickel \| 2.41 (0.08, 0.38) \| 2.25 (1.23, 3.87) \| <0.05c \| \| Copper \| 8.67 (4.84, 13.88) \| 8.61 (4.68, 13.82) \| >0.05c \| \| Zinc \| 374.63 (190.48, 650.51) \| 408.70 (216.57, 689.45) \| <0.05c \| \| Arsenic \| 46.94 (23.21, 90.76) \| 52.52 (25.59, 101.12) \| <0.05c \| \| Selenium \| 31.15 (17.14, 49.94) \| 31.10 (17.52, 49.83) \| >0.05c \| \| Rubidium \| 1,799.86 (1,040.79, 2,785.02) \| 1,755.76 (1,050.24, 2,687.16) \| >0.05c \| \| Strontium \| 90.76 (45.71, 159.16) \| 82.12 (43.02, 148.68) \| <0.05c \| \| Molybdenum \| 47.79 (26.27, 77.69) \| 45.25 (24.67, 73.65) \| <0.05c \| \| Cadmium \| 1.14 (0.56, 2.08) \| 1.17 (0.57, 2.09) \| >0.05c \| \| Tin \| 9.96 (6.32, 15.16) \| 10.13 (6.40, 15.17) \| >0.05c \| \| Barium \| 1.86 (0.99, 3.08) \| 1.85 (1.01, 3.09) \| >0.05c \| \| Thallium \| 0.55 (0.28, 0.91) \| 0.57 (0.29, 0.91) \| >0.05c \| \| Lead \| 1.01 (0.57, 1.64) \| 0.97 (0.55, 1.66) \| >0.05c \| ## Urinary levels of individual metals and HUA risk As shown in Supplementary Table 2, unconditional logistic regression models suggested the association between urinary V (OR = 0.67, $95\%$CI: 0.57–0.78), Cr (OR = 0.78, $95\%$CI: 0.66–0.92), Fe (OR = 0.64, $95\%$CI: 0.55–0.75), Ni (OR = 0.81, $95\%$CI: 0.68–0.95), Zn (OR = 1.36, $95\%$CI: 1.14–1.63), and As levels (OR = 1.46, $95\%$CI: 1.23–1.72) and HUA risk (all Ptrend < 0.05), after adjusting for potential confounders (age, gender, education level, marital status, active smoking status, passive smoking status, drinking status, hypertension, diabetes, hyperlipidemia, BMI, eGFR, and urine creatinine). As shown in Supplementary Figure 2, LASSO regression determined the optimal λ (−4.50) through 10-fold cross-validation based on the minimum MSE. After adjusting for the same confounders in unconditional logistic regression models, urinary metals, V, Fe, Ni, Zn, As, and Mo, were selected as optimal predictors according to LASSO regression models. As shown in Figure 1, we incorporated V, Cr, Fe, Ni, Zn, As, and Mo into the unconditional stepwise logistic regression models, after adjusting for the same confounders in unconditional logistic regression models and found that urinary V (OR = 0.70, $95\%$CI: 0.58–0.84), Fe (OR = 0.56, $95\%$CI: 0.47–0.68), and Ni (OR = 0.71, $95\%$CI: 0.58–0.86) levels were negatively associated with HUA risk (all Ptrend < 0.05), but urinary Zn (OR = 1.92, $95\%$CI: 1.54–2.39) and As levels (OR = 1.75, $95\%$CI: 1.45–2.11) were positively associated with HUA risk (all Ptrend < 0.05). As shown in Figure 2, multiple linear regression models showed a lower SUA level of 34.27 ($95\%$CI: −41.32 to −27.22) for a 1-SD increment in log-transformed Fe and a higher SUA level of 34.58 (27.67–41.48) for a 1-SD increment in log-transformed Zn ($P \leq 0.001$). Figure 1: Association of an IQR increase in urinary metal concentrations of vanadium, iron, nickel, zinc, and arsenic with hyperuricemia risk (Odds ratio and 95% confidence interval). Unconditional stepwise logistic regression models were performed and adjusted for age, gender, education level, marital status, active smoking status, passive smoking status, drinking status, hypertension, diabetes, hyperlipidemia, BMI, eGFR, and urine creatinine. Figure 2: Association of a 1-SD increase in log-transformed urinary metal concentrations of vanadium, iron, nickel, zinc, and arsenic with SUA levels (β and 95% confidence interval). Multiple linear regression models were performed and adjusted for age, gender, education level, marital status, active smoking status, passive smoking status, drinking status, hypertension, diabetes, hyperlipidemia, BMI, eGFR, and urine creatinine. As shown in Table 2, we found that Fe was negatively associated with HUA risk in all subgroups (all Ptrend < 0.05). We also found positive associations between the highest quartile (the 75th quartile) of urinary Zn levels and HUA risk in the subgroups of gender, age, BMI, hypertension, diabetes (no), and hyperlipidemia (yes) (all Ptrend < 0.05). Table 2 \| Subgroup \| Quartiles of urinary metals levels (μg/L) \| Quartiles of urinary metals levels (μg/L).1 \| Quartiles of urinary metals levels (μg/L).2 \| Quartiles of urinary metals levels (μg/L).3 \| p-trenda \| p-interactionb \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| ≤P25 \| P25- \| P50- \| P75- \| \| \| \| Vanadium \| Vanadium \| Vanadium \| Vanadium \| Vanadium \| Vanadium \| Vanadium \| \| Gender \| \| \| \| \| \| 0.278 \| \| Male (n = 2,731) \| 1.00 (ref) \| 0.85 (0.66, 1.10) \| 0.81 (0.62, 1.07) \| 0.60 (0.45, 0.80) \| 0.003 \| \| \| Female (n = 3,777) \| 1.00 (ref) \| 1.01 (0.81, 1.25) \| 0.94 (0.75, 1.18) \| 0.79 (0.62, 1.01) \| 0.142 \| \| \| Age (years) \| \| \| \| \| \| 0.583 \| \| <67 (n = 3,329) \| 1.00 (ref) \| 1.22 (0.96, 1.54) \| 1.23 (0.88, 1.44) \| 0.92 (0.71, 1.20) \| 0.062 \| \| \| ≥ 67 (n = 3,179) \| 1.00 (ref) \| 0.72 (0.57, 0.90) \| 0.68 (0.53, 0.87) \| 0.53 (0.41, 0.70) \| 0.000 \| \| \| BMI (kg/m2) \| \| \| \| \| \| 0.610 \| \| <24 (n = 3,036) \| 1.00 (ref) \| 1.04 (0.81, 1.34) \| 0.92 (0.70, 1.20) \| 0.87 (0.65, 1.17) \| 0.544 \| \| \| ≥ 24 (n = 3,472) \| 1.00 (ref) \| 0.85 (0.68, 1.06) \| 0.82 (0.65, 1.04) \| 0.59 (0.46, 0.76) \| 0.000 \| \| \| Hypertension \| \| \| \| \| \| 0.953 \| \| Yes (n = 3,654) \| 1.00 (ref) \| 1.05 (0.85, 1.30) \| 0.94 (0.75, 1.18) \| 0.71 (0.56, 0.91) \| 0.003 \| \| \| No (n = 2,854) \| 1.00 (ref) \| 0.76 (0.59, 0.99) \| 0.79 (0.59, 1.04) \| 0.67 (0.50, 0.90) \| 0.061 \| \| \| Diabetes \| \| \| \| \| \| 0.272 \| \| Yes (n = 1,335) \| 1.00 (ref) \| 1.01 (0.71, 1.42) \| 0.86 (0.59, 1.26) \| 0.67 (0.44, 1.01) \| 0.142 \| \| \| No (n = 5,173) \| 1.00 (ref) \| 0.91 (0.75, 1.09) \| 0.87 (0.72, 1.07) \| 0.71 (0.57, 0.87) \| 0.007 \| \| \| Hyperlipidemia \| \| \| \| \| \| 0.408 \| \| Yes (n = 4,929) \| 1.00 (ref) \| 1.05 (0.87, 1.26) \| 0.93 (0.77, 1.14) \| 0.72 (0.58, 0.89) \| 0.001 \| \| \| No (n = 1,579) \| 1.00 (ref) \| 0.61 (0.43, 0.87) \| 0.69 (0.47, 0.99) \| 0.62 (0.42, 0.92) \| 0.038 \| \| \| Iron \| Iron \| Iron \| Iron \| Iron \| Iron \| Iron \| \| Gender \| \| \| \| \| \| 0.618 \| \| Male (n = 2,731) \| 1.00 (ref) \| 0.75 (0.60, 0.96) \| 0.74 (0.57, 0.96) \| 0.57 (0.43, 0.77) \| 0.003 \| \| \| Female (n = 3,777) \| 1.00 (ref) \| 0.68 (0.54, 0.85) \| 0.63 (0.50, 0.80) \| 0.56 (0.44, 0.72) \| 0.000 \| \| \| Age (years) \| \| \| \| \| \| 0.544 \| \| <67 (n = 3,329) \| 1.00 (ref) \| 0.62 (0.49, 0.77) \| 0.56 (0.44, 0.72) \| 0.53 (0.41, 0.70) \| 0.000 \| \| \| ≥ 67 (n = 3,179) \| 1.00 (ref) \| 0.81 (0.64, 1.01) \| 0.81 (0.63, 1.03) \| 0.59 (0.45, 0.78) \| 0.002 \| \| \| BMI (kg/m2) \| \| \| \| \| \| 0.286 \| \| <24 (n = 3,036) \| 1.00 (ref) \| 0.72 (0.57, 0.93) \| 0.62 (0.47, 0.81) \| 0.50 (0.38, 0.68) \| 0.000 \| \| \| ≥ 24 (n = 3,472) \| 1.00 (ref) \| 0.69 (0.56, 0.85) \| 0.71 (0.56, 0.89) \| 0.61 (0.48, 0.79) \| 0.001 \| \| \| Hypertension \| \| \| \| \| \| 0.305 \| \| Yes (n = 3,654) \| 1.00 (ref) \| 0.75 (0.61, 0.93) \| 0.74 (0.60, 0.93) \| 0.59 (0.46, 0.75) \| 0.000 \| \| \| No (n = 2,854) \| 1.00 (ref) \| 0.66 (0.51, 0.86) \| 0.59 (0.44, 0.78) \| 0.53 (0.40, 0.72) \| 0.000 \| \| \| Diabetes \| \| \| \| \| \| 0.932 \| \| Yes (n = 1,335) \| 1.00 (ref) \| 0.71 (0.50, 1.01) \| 0.78 (0.53, 1.14) \| 0.51 (0.34, 0.78) \| 0.013 \| \| \| No (n = 5,173) \| 1.00 (ref) \| 0.72 (0.60, 0.86) \| 0.65 (0.54, 0.79) \| 0.58 (0.47, 0.72) \| 0.000 \| \| \| Hyperlipidemia \| \| \| \| \| \| 0.816 \| \| Yes (n = 4,929) \| 1.00 (ref) \| 0.73 (0.61, 0.88) \| 0.69 (0.57, 0.84) \| 0.58 (0.47, 0.72) \| 0.000 \| \| \| No (n = 1,579) \| 1.00 (ref) \| 0.64 (0.46, 0.91) \| 0.65 (0.45, 0.93) \| 0.54 (0.36, 0.81) \| 0.017 \| \| \| Nickel \| Nickel \| Nickel \| Nickel \| Nickel \| Nickel \| Nickel \| \| Gender \| \| \| \| \| \| 0.874 \| \| Male (n = 2,731) \| 1.00 (ref) \| 0.81 (0.62, 1.06) \| 0.80 (0.59, 1.07) \| 0.74 (0.54, 1.02) \| 0.299 \| \| \| Female (n = 3,777) \| 1.00 (ref) \| 0.87 (0.70, 1.08) \| 0.71 (0.56, 0.91) \| 0.70 (0.54, 0.90) \| 0.017 \| \| \| Age (years) \| \| \| \| \| \| 0.594 \| \| <67 (n = 3,329) \| 1.00 (ref) \| 0.67 (0.53, 0.85) \| 0.62 (0.48, 0.80) \| 0.67 (0.51, 0.89) \| 0.001 \| \| \| ≥ 67 (n = 3,179) \| 1.00 (ref) \| 1.10 (0.86, 1.40) \| 0.89 (0.68, 1.16) \| 0.76 (0.57, 1.01) \| 0.022 \| \| \| BMI (kg/m2) \| \| \| \| \| \| 0.042 \| \| <24 (n = 3,036) \| 1.00 (ref) \| 0.89 (0.69, 1.16) \| 0.90 (0.68, 1.20) \| 0.81 (0.60, 1.01) \| 0.584 \| \| \| ≥ 24 (n = 3,472) \| 1.00 (ref) \| 0.81 (0.65, 1.02) \| 0.62 (0.49, 0.79) \| 0.63 (0.49, 0.82) \| 0.001 \| \| \| Hypertension \| \| \| \| \| \| 0.516 \| \| Yes (n = 3,654) \| 1.00 (ref) \| 0.86 (0.69, 1.08) \| 0.72 (0.56, 0.91) \| 0.73 (0.56, 0.94) \| 0.036 \| \| \| No (n = 2,854) \| 1.00 (ref) \| 0.82 (0.64, 1.07) \| 0.76 (0.57, 1.01) \| 0.68 (0.50, 0.92) \| 0.092 \| \| \| Diabetes \| \| \| \| \| \| 0.865 \| \| Yes (n = 1,335) \| 1.00 (ref) \| 0.69 (0.47, 1.01) \| 0.67 (0.44, 1.01) \| 0.75 (0.49, 1.14) \| 0.206 \| \| \| No (n = 5,173) \| 1.00 (ref) \| 0.88 (0.73, 1.07) \| 0.75 (0.61, 0.93) \| 0.69 (0.55, 0.86) \| 0.006 \| \| \| Hyperlipidemia \| \| \| \| \| \| 0.062 \| \| Yes (n = 4,929) \| 1.00 (ref) \| 0.84 (0.70, 1.01) \| 0.68 (0.55, 0.84) \| 0.67 (0.53, 0.83) \| 0.001 \| \| \| No (n = 1,579) \| 1.00 (ref) \| 0.90 (0.61, 1.32) \| 0.91 (0.61, 1.36) \| 0.87 (0.56, 1.33) \| 0.926 \| \| \| Zinc \| Zinc \| Zinc \| Zinc \| Zinc \| Zinc \| Zinc \| \| Gender \| \| \| \| \| \| 0.137 \| \| Male (n = 2,731) \| 1.00 (ref) \| 1.30 (0.95, 1.76) \| 1.50 (1.08, 2.07) \| 1.68 (1.17, 2.40) \| 0.038 \| \| \| Female (n = 3,777) \| 1.00 (ref) \| 1.45 (1.17, 1.80) \| 1.63 (1.28, 2.08) \| 2.02 (1.52, 2.70) \| 0.000 \| \| \| Age (years) \| \| \| \| \| \| 0.725 \| \| <67 (n = 3,329) \| 1.00 (ref) \| 1.30 (1.02, 1.66) \| 1.52 (1.16, 1.98) \| 1.92 (1.41, 2.62) \| 0.001 \| \| \| ≥ 67 (n = 3,179) \| 1.00 (ref) \| 1.51 (1.18, 1.93) \| 1.75 (1.33, 2.30) \| 1.89 (1.38, 2.58) \| 0.000 \| \| \| BMI (kg/m2) \| \| \| \| \| \| 0.556 \| \| <24 (n = 3,036) \| 1.00 (ref) \| 1.40 (1.07, 1.83) \| 1.46 (1.08, 1.96) \| 1.61 (1.14, 2.27) \| 0.035 \| \| \| ≥ 24 (n = 3,472) \| 1.00 (ref) \| 1.38 (1.10, 1.73) \| 1.74 (1.35, 2.24) \| 2.12 (1.60, 2.83) \| 0.000 \| \| \| Hypertension \| \| \| \| \| \| 0.077 \| \| Yes (n = 3,654) \| 1.00 (ref) \| 1.36 (1.08, 1.70) \| 1.76 (1.38, 2.25) \| 1.91 (1.44, 2.54) \| 0.000 \| \| \| No (n = 2,854) \| 1.00 (ref) \| 1.47 (1.12, 1.94) \| 1.45 (1.07, 1.97) \| 1.93 (1.36, 2.73) \| 0.003 \| \| \| Diabetes \| \| \| \| \| \| 0.114 \| \| Yes (n = 1,335) \| 1.00 (ref) \| 1.16 (0.74, 1.81) \| 1.40 (0.88, 2.22) \| 1.46 (0.89, 2.39) \| 0.432 \| \| \| No (n = 5,173) \| 1.00 (ref) \| 1.43 (1.19, 1.73) \| 1.59 (1.29, 1.97) \| 1.99 (1.56, 2.55) \| 0.000 \| \| \| Hyperlipidemia \| \| \| \| \| \| 0.973 \| \| Yes (n = 4,929) \| 1.00 (ref) \| 1.36 (1.13, 1.67) \| 1.66 (1.34, 2.06) \| 1.99 (1.56, 2.54) \| 0.000 \| \| \| No (n = 1,579) \| 1.00 (ref) \| 1.41 (0.96, 2.06) \| 1.40 (0.91, 2.14) \| 1.59 (0.97, 2.60) \| 0.267 \| \| \| Arsenic \| Arsenic \| Arsenic \| Arsenic \| Arsenic \| Arsenic \| Arsenic \| \| Gender \| \| \| \| \| \| 0.741 \| \| Male (n = 2,731) \| 1.00 (ref) \| 1.23 (0.93, 1.61) \| 1.44 (1.08, 1.91) \| 1.84 (1.36, 2.49) \| 0.001 \| \| \| Female (n = 3,777) \| 1.00 (ref) \| 1.10 (0.89, 1.38) \| 1.52 (1.20, 1.93) \| 1.68 (1.32, 2.16) \| 0.000 \| \| \| Age (years) \| \| \| \| \| \| 0.177 \| \| <67 (n = 3,329) \| 1.00 (ref) \| 1.12 (0.88, 1.43) \| 1.35 (1.04, 1.75) \| 1.56 (1.20, 2.03) \| 0.005 \| \| \| ≥ 67 (n = 3,179) \| 1.00 (ref) \| 1.17 (0.92, 1.49) \| 1.62 (1.25, 2.08) \| 2.00 (1.52, 2.63) \| 0.000 \| \| \| BMI (kg/m2) \| \| \| \| \| \| 0.442 \| \| <24 (n = 3,036) \| 1.00 (ref) \| 1.25 (0.96, 1.63) \| 1.33 (1.01, 1.76) \| 1.72 (1.29, 2.31) \| 0.003 \| \| \| ≥ 24 (n = 3,472) \| 1.00 (ref) \| 1.10 (0.88, 1.38) \| 1.67 (1.31, 2.13) \| 1.82 (1.41, 2.34) \| 0.000 \| \| \| Hypertension \| \| \| \| \| \| 0.955 \| \| Yes (n = 3,654) \| 1.00 (ref) \| 1.13 (0.91, 1.41) \| 1.44 (1.14, 1.82) \| 1.60 (1.25, 2.05) \| 0.001 \| \| \| No (n = 2,854) \| 1.00 (ref) \| 1.16 (0.88, 1.52) \| 1.56 (1.18, 2.07) \| 2.02 (1.50, 2.72) \| 0.000 \| \| \| Diabetes \| \| \| \| \| \| 0.340 \| \| Yes (n = 1,335) \| 1.00 (ref) \| 1.17 (0.80, 1.71) \| 1.60 (1.08, 2.36) \| 1.53 (1.01, 2.31) \| 0.072 \| \| \| No (n = 5,173) \| 1.00 (ref) \| 1.16 (0.96, 1.41) \| 1.49 (1.21, 1.82) \| 1.84 (1.49, 2.29) \| 0.000 \| \| \| Hyperlipidemia \| \| \| \| \| \| 0.169 \| \| Yes (n = 4,929) \| 1.00 (ref) \| 1.07 (0.88, 1.29) \| 1.37 (1.12, 1.68) \| 1.64 (1.32, 2.02) \| 0.000 \| \| \| No (n = 1,579) \| 1.00 (ref) \| 1.47 (0.99, 2.16) \| 1.99 (1.33, 2.98) \| 2.26 (1.47, 2.47) \| 0.001 \| \| ## Dose–response relationship of urinary metals with HUA risk As shown in Figure 3, after adjusting for the same confounders in unconditional logistic regression models, RCS logistic regression models showed that there was a negative non-linear dose–response relationship between urinary V levels and HUA risk (Poverall < 0.001, Pnonliner = 0.008), but a negative linear dose–response relationship between both urinary Fe (Poverall < 0.001, Pnonliner = 0.682) and Ni levels (Poverall = 0.009, Pnonliner = 0.953) and HUA risk. A positive linear relationship was found between urinary Zn (Poverall < 0.001, Pnonliner = 0.513), or As levels (Poverall < 0.001, Pnonliner = 0.743) and HUA risk. Figure 3: A restricted cubic spline regression model with three knots (the 10th, 50th, and 90th percentile) for urinary metals levels and hyperuricemia risk. (A) V (vanadium); (B) Fe (iron); (C) Ni (nickel); (D) Zn (zinc); (E) As (arsenic). The X-axis indicates the log10-transformed urinary metal concentrations. Odds ratios (OR) and 95% confidence intervals were estimated, and metal concentrations (log10-transformed) at the 25th percentile were used as the reference value. All of the restricted cubic spline regression models were constructed after adjusting for age, gender, education level, marital status, active smoking status, passive smoking status, drinking status, hypertension, diabetes, hyperlipidemia, BMI, eGFR, and urine creatinine. ## Effect of additive interaction of Fe and Zn on HUA risk As shown in Table 3, GLM showed an additive interaction between urinary low-Fe (<78.56 μg/L) and high-Zn (≥ 385.39 μg/L) levels on an increased risk of HUA (RERI = 0.31, $95\%$CI: 0.03–0.59; AP = 0.18, $95\%$CI: 0.02–0.34; $S = 1.76$, $95\%$CI: 1.69–3.49). However, no interaction between the other metals on HUA risk was found. As shown in Figure 4, the regression tree showed urinary Zn levels of ≥ 312.33 μg/L and urinary Fe levels of <102.25 were likely to have the highest concentrations of SUA (Node 7). ## Discussion We found that higher urinary V, Fe, and Ni levels were linked to a lower risk of HUA in addition to the positive association of urinary Zn and As levels with HUA risk. Moreover, the additive interaction between low-iron (<78.56 μg/L) and high-zinc (≥385.39 μg/L) levels greatly increased the risk of HUA in elderly adults. We also found that the median urinary level of As was 48.92 μg/L, which was 5.9-fold higher than that of individuals aged >20 years ($$n = 5$$,632) from the NHANES 2003–2010 [14] and 3.2-fold higher than that of individuals aged 44.9–56.0 years ($$n = 1$$,335) from the Study of Women's Health Across the Nation [32]. In addition, median urinary V levels (2.81 μg/L) of the individuals were 1.9-fold higher than that of older adults aged >60 years ($$n = 3$$,814) in Anhui province, China [33]. In addition, median urinary levels of Fe (78.56 μg/L) or Zn (385.39 μg/L) levels were higher than that ($$n = 3$$,272, 54.67 μg/L for Fe, 310.94 μg/L for Zn) of individuals from the Wuhan-Zhuhai cohort [34]. The reasons may be related to gender, age, region, environmental exposure, lifestyle, and population size. In the present study, older adults (aged ≥60 years) who have lived in Shenzhen since the early stage of the city's construction tend to have had a longer exposure to metals in the environment and seafood because Shenzhen is a coastal city in South China. Previous studies revealed consistent results to support the positive association between blood Fe concentrations and HUA risk. For example, a cross-sectional and longitudinal study in the employees of Zhenhai Refining and Chemical Company, Ningbo, China ($$n = 10$$,074) [35] indicated that exposure to high serum ferritin (SF) levels was linked to an increased risk of HUA (HR = 1.65, $95\%$CI: 1.38–1.96) after adjusting for age and gender. Results from the 2009 China Health and Nutrition Study ($$n = 7$$,946) [36] revealed that individuals with the highest quartile (the 75th quartile: 237.8 μg/L) of SF levels were at higher risk for HUA (OR = 3.09, $95\%$CI: 2.45–3.89), as compared with those in the lowest quartile (the 25th quartile: 20.3 μg/L). A recent study conducted in Xiangya Hospital, Central South University, Changsha, China [37] reported a link between serum Fe (OR = 1.56, $95\%$CI: 1.14– 2.13) or SF (OR = 2.25, $95\%$CI: 1.54–3.29) concentrations and the prevalence of HUA in adults ($$n = 2$$,824, aged 52.2 ± 7.2 years); however, we found a negative association between urinary Fe levels and HUA risk, which may be due to a difference in biological significances between urinary Fe and blood Fe concentrations. Because urinary Fe concentrations generally represent the levels of Fe in the mucosal cells of the urinary tract and the circulating Fe (transfer Fe protein) [38]. When blood Fe concentration is too high, oxidative damage to the renal tubules can be induced, resulting in decreased renal tubular reabsorption of transferrin and UA, increased excretions of UA and urinary iron, and decreased SUA levels [39, 40]. A recent study on multiple metals (13 blood metals) exposure and HUA risk from Shenzhen city, China ($$n = 1$$,406, aged from 31 to 91 years) [15] reported that there was no association between plasma Fe levels and HUA risk (median of plasma Fe level: non-HUA = 1,697.50 μmol/L, HUA = 1,697.48 μmol/L). The reason may be that the interactions of multiple metals may weaken the effect of plasma Fe on HUA. The association between Fe exposure and HUA was related to several factors, including detected concentrations of Fe in different biological samples, regions, and species. Some controversial results about the relationship between Zn levels (plasma Zn or dietary Zn intake) and HUA risk have been reported in previous studies [15, 41, 42]. We found a positive linear dose–response relationship between urinary Zn levels with HUA risk, which is inconsistent with the previous findings that dietary Zn intake was inversely linked to HUA risk [41, 42]. In the individuals ($$n = 24$$,975, aged ≥ 20 years) from the NHANES 2001–2014, dietary Zn intake was found to be inversely correlated with HUA risk [41], the same finding was found in adults ($$n = 5$$,168, aged ≥ 40 years) from the Department of Health Examination Center, Xiangya Hospital, Changsha, China [42]. We note the consistent finding of a positive correlation between Zn exposure and HUA risk after comparing the findings in a multiple-metal exposure study ($$n = 1$$,406, mean age: 58.89 ± 9.54 years) on plasma Zn [15] and this study; both studies were conducted in older adults in Shenzhen, China. Nevertheless, there were differences in the types of metals (13 metals vs. 21 metals), biological samples (plasma or urinary), and measurement methods for Zn concentrations in the individuals. Lack of zinc will lead to cardiovascular disease, growth restriction, and increased cancer susceptibility [43], while excessive zinc can produce toxic effects [44, 45]. We suppose that if there is an appropriate dose of zinc concentrations, deficiency or excess will be caused varying degrees of hazards. The elderly in coastal cities from southern China may have excessive zinc due to environmental exposure and seafood diets. In addition, the interactions of multiple metals may enhance the risk of Zn to HUA. Overall, the relationship between Zn and HUA risk is still inconclusive, and further studies are needed to validate this finding. Subgroup analysis suggested that individuals with BMI (≥24), hyperlipidemia (yes), and urinary Zn (≥385.39 μg/L) were at higher risk of HUA. The reasons for this may be that Zn transporters are differentially expressed in various tissues of the body, and obesity and other diseases can increase the accumulation of Zn in adipose tissue (one of the most important Zn sources) and can reduce the Zn concentration in blood [46]. A recent study at Yaroslavl State University, Yaroslavl, Russia ($$n = 395$$, aged from 20 to 60 years) [47] indicated that urine Zn levels in obese individuals ($$n = 196$$) were $18\%$ higher than that in lean individuals ($$n = 199$$). In obese individuals, the internal balance of plasma Zn and the process of muscle metabolism may be changed, leading to increased excretion of Zn by urine [48]. We revealed the additive interaction between urinary low-Fe (<78.56 μg/L) and high-Zn (≥385.39 μg/L) levels on the risk of HUA, but previous studies on this were very limited. The interaction between Fe and Zn in the human body absorption process has been widely studied, but the results were inconsistent. Previous studies suggested that the absorptions of Fe and Zn exhibit a competitive inhibition [49, 50]. Solomons and Jacob performed a study on assessing iron–zinc interaction with the increasing proportion of Fe and Zn in cola beverage (the ratios of Fe:Zn were 1:1, 2:1, and 3:1) [49], and the results showed that plasma Zn concentrations were decreased. A community-based randomized controlled trial in Indonesia explored the interactions between Fe and Zn in infants [50], and they found that the combination of iron–zinc supplements was not as effective as a single supplement. Additional studies have found a positive interaction between Fe and Zn in human absorption (51–53). A 6-month randomized, double-blind trial investigated the effect of Zn supplementation on the biochemical status of Fe in individuals aged 55–75 ($$n = 188$$) and 70–85 ($$n = 199$$) years old [51], suggesting that 15 or 30 mg/d Zn supplementation significantly increased serum Zn levels and urinary Zn excretion, but had no effect on Fe status. A randomized single-blind placebo-controlled trial of pregnant women in the United Kingdom [52] indicated that dietary Fe supplementation (100 mg Fe/d) had no detectable adverse effect on Zn metabolism and increased Zn absorption efficiency in late pregnancy. A randomized controlled trial in Bangladesh reported that the combination of Fe and Zn had the same effect as single administration on reducing diarrhea, hospitalization, or improving Fe status [53]. Animal experiments indicated that the interactions between Fe and Zn may depend on their ratios. For instance, no significant inhibition of Zn absorption was found in the digestion and absorption of Zn sulfate (100 μmol/L) in rats (ranging from 0 to 1,000 μmol Fe/L) in the presence of Fe gluconate when the ratio of Fe to Zn was <2:1, and dose-dependent inhibition of Zn absorption between 2:1 and 5:1 reached a plateau beyond this ratio [54]. Based on urinary Zn levels, we have two hypotheses. One is Zn deficiency [55]: the absorption of Fe in the body also inhibits the absorption of Zn, and the content of Zn excreted in urine increases. In addition, the study population was over 60 years old, and the reduction of Fe accumulation and Zn absorption due to aging may be associated with HUA risk [56]. The other hypothesis is that there was excess Zn [57]. The study population is located in the coastal areas, the intake of zinc-rich seafood is higher, and the antagonism of the Fe and Zn interaction is much smaller with the increased Zn intake. However, the current research on the iron–zinc interaction and the risk of HUA is very limited, further mechanistic studies are needed regarding the effect of iron-zinc interaction on HUA risk. There are several strengths in this study. First, we explored the association between urinary multi-metal levels and HUA risk in a large sample size ($$n = 6$$,508) after adjusting for the traditional confounding factors such as gender, age, BMI, hypertension, and diabetes. Second, we used both LASSO regression and logistics regression for metal selection and RCS logistic regression and GLM for assessing the dose–response relationship and interactions between urinary metals and HUA risk. However, there are still some limitations in this study. First, we did not assess the dietary exposure of individuals, whereas dietary intake is an important factor related to metal exposure in the body. Second, we only detected urinary metal concentrations, which is a limitation in assessing metal exposure of the body, because day-to-day variability of urinary metals concentrations and creatinine excretion of the body can result in measurement errors. But, it cannot deny the role of urinary sample, because of the high amount of metal excretion via urine, and non-invasive and convenience in collection of urine sample. Finally, the cross-sectional study is unable to identify the causal relationship between multiple metals exposure levels of the body and HUA. Further prospective studies are needed to validate the findings. ## Conclusion We found a negative association between higher levels of urinary V, Fe, and Ni and HUA risk and a positive association between urinary Zn and As and HUA risk. Additive interaction of low-Fe (<78.56 μg/L) and high-Zn (≥385.39 μg/L) levels are related to a higher risk of HUA. The findings indicated the potential importance of Zn and Fe exposure in the body in the prevention of elevated SUA levels and HUA risk. ## 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 research protocol was approved by the Medical Ethics Research Committee of Shenzhen Center for Disease Control and Prevention (approval numbers: R2017001 and R2018020). The patients/participants provided their written informed consent to participate in this study. ## Author contributions CH and EG: conceptualization and methodology. CH, EG, JL, WL, and YL: formal analysis. XR and QW: data curation. CH, EG, and FX: writing—original draft preparation. CH and JL: writing—review and editing. DW and QS: supervision. XC and KH: project administration. HH and JL: funding acquisition. All authors have read and agreed to the published version of 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. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1015202/full#supplementary-material ## References 1. Obermayr RP, Temml C, Gutjahr G, Knechtelsdorfer M, Oberbauer R, Klauser-Braun R. Elevated uric acid increases the risk for kidney disease. 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--- title: Fear, anxiety, and production in laying hens with healed keel bone fractures authors: - J.L. Edgar - Y. Omi - F. Booth - N. Mackie - G. Richards - J. Tarlton journal: Poultry Science year: 2023 pmcid: PMC9969248 doi: 10.1016/j.psj.2023.102514 license: CC BY 4.0 --- # Fear, anxiety, and production in laying hens with healed keel bone fractures ## Abstract For laying hens, the immediate aftermath and healing period of a keel fracture (KF) is characterized by reduced ability to perform species-specific behavior, access resources, and pain. However, the longer-term impacts, once the fracture is completely healed, are less clear. As well as acute pain and behavioral changes, a negative experience can shape future responses to putatively threatening stimuli, raising future fear, and anxiety levels during husbandry-related events. We aimed to determine whether hens that had previously sustained keel bone fractures, but were now outside of the peak age range for new fractures, showed higher fear and anxiety levels compared to intact hens. We also determined if healed keel bone fractures were associated with reduced production, changes in behavior and resource use. One hundred and fifty hens with a palpation score of 1 (“KF”) and 150 hens with a palpation score of 0 (keel fracture free, “KFF”) were selected from a commercial farm at 63 wk of age and housed in 6 groups (3 × KF and 3 × KFF). We compared production (hen weight and feed consumption, egg quantity, quality and weight, floor eggs, shell thickness, and weight) and home pen behavior (behaviors and transitional movements) in both groups. Finally, we measured the responses of KF ($$n = 75$$) and KFF ($$n = 75$$) during tonic immobility, novel arena, and novel object tests. KF and KFF hens did not differ in their responses to the tonic immobility, novel arena, and novel object tests, nor were there differences between the 2 groups in home pen behavior and transitional movements. KFF birds were lighter and laid eggs with less eggshell membrane compared KF birds, but no differences were found between KF and KFF in any other production measures. We found no evidence that healed KFs were associated with detrimental welfare effects in laying hens, but further work is required to determine the mechanisms and implications of the lower body weight and egg shell membrane. ## INTRODUCTION Keel bone damage (KBD) is one of the most prevalent problems for commercial laying hens (Riber et al., 2018). The high calcium requirements of continuous egg production in modern layer breeds have led to weakened bones and a high susceptibility to bone fractures (see Riber et al., 2018 for a review), and appear to be especially evident in brown layer genetic lines (Eusemann et al., 2018). The occurrence of keel bone damage is dependent upon both the overall system type and the design of internal housing structures (Wilkins et al., 2011). A recent systematic review of prevalence (at >49 wk of age) across system types suggested a mean fracture prevalence from $23\%$ in conventional cages (range: 0–$85\%$) to $63\%$ in single tier systems (range 33–$100\%$) (Rufener and Makagon, 2020). The occurrence and immediate aftermath of bone fracture is acutely painful in animals (Minville et al., 2008; Bove et al., 2009; Nasr et al., 2012b). The inflammatory response to fracture can trigger peripheral and central sensitization manifested as hyperalgesia both during and after healing (Bove et al., 2009). In humans, mechanical stimulation of the periosteum (connective tissue covering the bones) is a potent trigger of pain (Santy and Mackintosh, 2001). In hens following a fracture incident, significant soft callus formation is apparent after 2 to 3 wk, with hard callus formation usually completing the healing process after 4 to 6 wk (Richards et al., 2011). Studies have shown effects of keel bone fracture in the early stage of inflammation and repair. These include decreased vertical locomotion (Rentsch et al., 2019) and staying in the nestbox for longer after egg laying (Gebhardt-Henrich and Fröhlich, 2015). However, there has been little work on long-term consequences once the fracture is fully healed. The most dramatic increase in prevalence of KBD is between the onset and peak of lay (25–35 wk of age) (Harlander-Matauschek et al., 2015), with susceptibility stabilizing after 49 wk of age (Toscano et al., 2018). The majority of studies into the effects of “healed” keel bone damage have focused on this time period (reviewed in Rufener and Makagon, 2020). However, with reports of some fractures ($16\%$) taking several months to heal (Baur et al., 2020), studies of so-called healed fractures may involve birds that are not completely healed, and, because they utilize birds in the peak fracture age range, responses could also be confounded with additional new fractures (which may not necessarily be detectable by palpation; Baur et al., 2020). Whether chronic pain arises following the healing process remains largely unclear but, given the high prevalence of keel bone fracture, this question is of profound relevance to hen welfare and production. Studies that examined the effects of keel fractures (KFs) during the peak susceptibility period for fractures (i.e., those that might be confounded by new fractures) have shown behavioral differences between birds with KFs and control birds. In behavioral tests, 33- to 42-wk-old hens with healed fractures were slower to negotiate a walkway obstacle and made less frequent visits to, and were slower to fly down from, an aerial perch (Nasr et al., 2012a). Other studies comparing the spontaneous behavior of birds with and without KFs had mixed results. Rufener and colleagues (Rufener et al., 2019) investigated use and transitions between 5 zones of a multitier aviary (litter, lower tier, nestboxes, top tier, and wintergarden) in both Lohman brown and Lohman Leghorn birds (21–61 wk old). With increasing keel bone fracture severity, Lohmann brown birds spent more time at the top tier and nestboxes and less time in the litter and lower tier. However, fractures were not linked to the total number of transitions and there was no effect of keel bone status on any aspect of mobility for the Lohmann Leghorns. Rentsch et al. found that, although new fractures decreased vertical locomotion, healed fractures did not affect mobility and neither new nor old fractures were associated with walking pace on ramps (37–39 wk) (Rentsch et al., 2019). Therefore, the effects of truly healed fractures on the birds remain unknown. Studies involving hens beyond the peak fracture age range are best placed to determine the effect of healed KFs, but there have been mixed results and very few studies. In a longitudinal study throughout the whole laying period, Richards et al. reported a reduction in the use of (raised) popholes among hens that had sustained keel bone fractures, indicating reduced mobility as a result of fracture, especially during colder temperatures (Richards et al., 2012). Another study looked at behavior of 70-wk-old hens (i.e., using birds that are likely to have healed fractures) and found that of all the inactive behaviors, only standing behavior was significantly different between fractured and nonfractured birds (Casey-Trott and Widowski, 2016). Fractured hens spent more time perching and resting on the perches, but the authors attributed this to the fact that hens that use perches are more likely to sustain keel damage. Both studies showed some behavioral differences associated with healed KFs, but it is not clear whether this is attributable to the subjective experience of chronic pain or more simply, a reduction in mobility as a result of physical and physiological changes to the tissue in an around the keel. The avian keel bone provides an anchor to which the muscles for wing motion are attached (Riber et al., 2018) and hard callous formation in the area is likely to reduce control of movement, confounding the use of behavior as an indicator of pain for KF studies. Drug studies offer the potential to investigate whether healed fractures are currently painful but have produced mixed results and again have often used young birds that may not have been fully healed. For 35-wk-old birds with KFs, the latency to fly down from a perch was reduced by the opioid, butorphanol (Nasr et al., 2012b), but not by the NSAIDs meloxicam and carprofen (Nasr et al., 2015). Using a conditioned place preference paradigm, Nasr showed that 40-wk hens with recently healed fractures preferred an environment that they associated with receiving an opioid analgesic, whereas fracture-free hens did not show the same preference, suggesting the hens with KFs experienced a shift to more positive affective state induced by the opioid, which the authors suggested provides evidence that healed KFs caused pain (Nasr et al., 2013). Rentsch et al. found that 37- to 39-wk fractured hens receiving paracetamol performed less rapid comfort behaviors, but only when the fracture had a visible gap and not when it was healed (Rentsch et al., 2019), indicating that only the “new” fractures were painful. Although there are some mixed results, the previous studies generally demonstrated effects of KFs which may have welfare implications in terms of a reduced ability to perform species-specific behavior, access resources, and may point to a subjective experience of pain. However, since many of the studies used birds still within the peak fracture window, it is likely that fractures are not fully healed and/or the birds sustained new fractures during the study. Hence the effect of healed fractures on behavior and welfare remains unclear. Additionally, it is not clear whether behavioral changes such as changes in resource use are attributable to the subjective experience of chronic pain or more simply, a reduction in mobility as a result of physical and physiological changes to the tissue in an around the keel. An additionally highly welfare-relevant but little-studied potential long-term effect of KF is the effect on fear and anxiety. An acutely negative experience such as sustaining a bone fracture has the potential to shape future responses to potentially threatening stimuli, raising general fear, and anxiety levels to husbandry-related events. If this is the case, then sustaining a keel bone fracture will have long-term welfare implications for how birds cope with putatively negative aspects of the commercial environment. Indeed, there is empirical evidence that chronic pain leads to heightened fear and anxiety in animal models. Rats showed increased anxiety-like behaviors including decreased locomotion in an open field test, induced by chronic inflammatory pain (Parent et al., 2012) and a mouse model of neuropathic pain showed anxiety-like behavior in an exploratory (hole board) task (Sieberg et al., 2018). One recent study showed that recent keel bone fractures were associated with an increased duration of tonic immobility and an increased latency to approach a novel object (Wei et al., 2022). Since previous negative experiences in animals act as predictors for future events, and chronic pain is associated with anxiety in other animal models, we hypothesized that hens with healed KFs would be more fearful and anxious than intact hens. Additionally, since most studies measuring changes in behavior, resources use and transitions were from hens within the peak fracture age (Nasr et al., 2012a; Rentsch et al., 2019; Rufener et al., 2019), we also aimed to determine if hens with healed fractures also showed such changes in behavior. We hypothesized that, since hard callous formation on keel bone is likely to reduce control of movement, hens with healed KFs would show a less behaviors requiring use of the muscles anchored to the keel (wingflapping, dustbathing, transitions to perches) compared to keel fracture-free (KFF) birds. ## Ethics This project was carried out following ethical approval by the University of Bristol (UIN/$\frac{18}{011}$) and in accordance with the institutional animal care and use committee (IACUC). All hens were rehomed after the study. ## Animals and Housing Three hundred female Hyline Brown hens were obtained from a commercial-free range farm at 63 wk of age. We selected the age of the study birds to be long past the period of maximum fracture risk (Harlander-Matauschek et al., 2015; Toscano et al., 2018) so the majority of sustained fractures were fully healed and our data were less likely to be confounded by new breaks. At the farm, hens were randomly selected from different locations within the house and were palpated by a trained assessor until 150 hens with a palpation score of 1 (“KF” group) and 150 hens with a palpation score of 0 (“KFF” group) had been selected. Palpation involved collecting the hens, one-by-one in an upright position. To enable the keel area to be exposed for examination, hens were then gently maneuvered to be held by both legs to invert the bird, resting the hen against the researcher's body to reduce pressure on the legs. Palpation involved running 2 fingers down the side of the keel bone, feeling for callus formation indicative of previous damage; with particular attention being paid to the tip of the keel. This was carried out by a trained and validated researcher. Prior training involved practicing on hundreds of dead birds, which were palpated and dissected to validate the accuracy of the overall scoring system (as per Wilkins et al., 2004). Hens with minor fractures which by palpation could not be confidently placed into either category were not included in the study. Hens were transported to Bristol Veterinary School in poultry crates, according to Defra transport regulations. Upon arrival each hen was leg tagged for identification and red mite powder was applied to the feathers before the hen was randomly allocated to their KF or KFF home pen. Three home pens contained KFF birds (50 birds in each) and 3 home pens contained KF birds (50 birds in each). Each of the 6 home pens (4.2 m × 4.4 m × 2.15 m L × W × H) were made up of a litter area and a single tier raised slatted area which was accessible by a slatted ramp (see Figure 1). The raised slatted area contained a nestbox unit (with 4 nestboxes) and 2 metal perches (3.5 cm diameter) at 2 different heights (50 cm and 105 cm above the raised slatted area). Ad libitum layers mash was provided in 4 freestanding poultry feeders; 2 on the raised slatted area and 2 on the litter area. The pens were subject to a 12L:12D lighting schedule. Figure 1Home pen. Figure 1 ## Production Measures (Wk 1–4) The hens were weighed upon arrival and production data were collected on a weekly basis (1 egg collection day per week) as follows: Egg quality: Eggs were categorized into “first” and “second” eggs by examining their external appearance for 3 traits; calcification, deformity and breaks. ## Floor Eggs: The Number of Eggs Laid Outside the Nestboxes Egg weight: Ten eggs from each pen were randomly selected and individual weights were recorded once a week (wk 1–4). Eggshell thickness: From each pen, 5 eggs were randomly selected and labeled. The content of the egg was removed and the remaining eggshell with membrane were washed and left to dry overnight in an incubator (37°C). The following day, the shell thickness was measured using a Vernier caliper with accuracy of 0.001 mm, taken as the mean of measures from 3 points equally distributed around the equator of the eggshell. Eggshell weights: Eggshells were placed into a furnace set at 700°C for 2 h. This process involved removal of eggshell membrane and other organic material, thereby resulting in accurate eggshell mineral measures. Pre- and postashed eggshell weights were recorded. Keel bone score: Hens were palpated by a trained assessor so that any KFF hens that sustained a new fracture could be excluded from the individual-level measures. By the end of the study period, 33 out of the 150 intact birds had sustained a new fracture. These were excluded from the individual-level measures, but kept in their pens to maintain the group size and social group. ## Home Pen Behavior Recording (Wk 3) Home pen behavior was recorded using instantaneous samples at 10 timepoints, 3 times of day: i) morning (09.00–10:00), ii) afternoon (16.00–17:00), and iii) evening (18.20–19:20). Every 5 min, during each of these timepoints a trained observer recorded the number of hens performing behaviors in Table 1, and the number of hens in each location in the pen (see Figure 1).Table 1Behavioral ethogram (adapted from Nicol et al., 2009).Table 1BehaviorDescriptionAggressionBird directs threat or pecks toward head region of conspecific, sometimes followed by a chaseBody maintenanceWing flapping, dustbathing, and preening (as above)DrinkingPecking and swallowing water from the drinker lineDust bathingPerformed on the litter area in sitting position, consists of series of behaviors including bill-raking, vertical wing-shaking, litter scratching, and laying on the side and head/body-rubbingFailed landingAn ascending hen fail to perch, or descending hen colliding with furniture, ground/wall-surface or other hen(s) upon landingFeedingPecking and ingesting layers mash from the feederForagingPecking and scratching the ground with beak and legsPreeningSelf-maintaining and manipulation of feathers of the wing and body with the beak. A hen may be sitting, standing, or perchingPeck at enrichmentPecks at any part of the hanging cabbageTransitionMovement from one area to anotherStandingBody off the floor with straight legs, upright neck and headSittingBody against the floor with legs tucked underneath (not dustbathing)Walking/runningTaking steps on the pen floorWing flap/Body shakeWing(s) stretched and flapped multiple times, with/out tail wagging In addition, continuous observation was used to determine the frequency of the birds’ transitional movements between areas, along with the number of occurrences of rare behaviors (aggression, wing flapping, failed landing), for a 30-min period in the morning, afternoon and evening. A total of 11 transitions recorded were Litter-Slats (L-S), Slats-Low perch (S-LP), Low perch-Low perch (LP-LP), Slats-Nest box (inside) (S-NBin), Slats-Nest box (on) (S-NBon), Nest box-Low perch (NB-LP), Nest box-High perch (NB-HP), SlatsHigh Perch (S-HP), Low perch-Litter (LP-Litter), Low perch-High perch (LP-HP), and High perch-Litter (HP-L). ## Fear and Anxiety Tests (Wk 4 and 5) Twenty-five hens per pen (total of 75 KF and 75KFF) were randomly selected to take part in the fear and anxiety tests. The selected hens were used as focal birds for all tests. For all tests, 2 testing rooms of identical size were used (2.9 m × 1.9 m × 2.15 m L × W × H). Testing was conducted between 09:00 and 16:00 with the testing order balanced across the pens. We used 3 well-validated tests which provided outcome measures in the form of fear and/or anxiety-type responses to 3 different situations (Forkman et al., 2007). The tonic immobility test is a measure of the hens’ innate, antipredator fear response; a response known to be stimulated by, and ameliorated by habituation to, human handling (Jones and Faure, 1981; Bryan Jones and Waddington, 1993; Forkman et al., 2007). The novel arena test provides a measure of the general anxiety of the bird with a strong effect of social isolation/dependence, while the novel object test incorporates neophobic response (Forkman et al., 2007). Hens isolated in a test arena exhibit behaviors thought to be representative of the internal conflict between minimizing detection by predators (e.g., freezing behavior) and the need to regroup with conspecifics (e.g., movement, vocalizations, and escape attempts) (Gallup and Suarez, 1980). Tonic Immobility Tests (Wk 4). Each hen was collected from their home pen and brought into one of the test rooms. To attempt to induce tonic immobility, the researcher placed the bird into a wooden cradle and gently placed their hands over the head and body of the hen. The hen was restrained in this position for 20 s, after which time the experimenter's hands were slowly moved from the bird. If the bird self-righted within 10 s of release, the induction procedure was repeated, with a maximum of 3 attempts. The number of induction attempts required and the duration of TI (if it occurred) were recorded for each bird. The maximum duration of TI was set at 180 s, at which point the bird was gently removed from the cradle and encouraged to self-right. After TI testing, all birds were keel-palpated for their keel damage status validation at the end of the TI test. If a change in KF status occurred, the current bird was disregarded and a replacement bird was randomly selected from the same pen, and testing commenced with this bird. Novel Arena Tests (Wk 5). A square novel arena was set up in each test room (1.8 m × 1.8 m × 1.2 m L × W × H). This arena was made from white plastic, with a mesh lid. Each hen was collected from their home pen and immediately placed in the center of the arena. The experimenter withdrew from the room and behavior was recorded for 5 min by an overhead camera. The following variables were assessed from the video recordings: latency to first movement (s), latency to first alarm call(s), number of alarm calls, number of defecations, number of escape attempts. First movement was defined as 2 or more steps in rapid (within 3 s) succession. An escape attempt was defined as the hen attempting to jump and/or fly out of the test arena. Further to the behavioral responses, total distance the hen traveled (cm) and the maximum distance the hen reached from the start point (cm) were calculated by computer software, based on kernelized correlation filters (Henriques et al., 2015). Novel Object Test (Wk 5). Immediately after the novel arena test, the experimenter entered the test arena, moved the bird to the side of the arena nearest the entrance, and placed a novel object in the center of the arena before leaving the room. The novel object was an inflated plastic turtle toy (24 cm L). For a period of 5 min, the following behavioral responses were assessed from video recordings: duration of freezing (both with and without head movement) (s), latency to first alarm call (s), number of alarm calls, number of defecations, number of pecks at the object, number of escape attempts. Total distance traveled (cm) was also analyzed as for the novel arena test. ## Statistical Analyses All data were tested for normality and normality of residuals, and a logarithm or arcsine square root transformation was applied if required. For all tests with multiple measures, Bonferroni correction was applied (see Table 2, Table 3, Table 4 for required level of significance). All analysis was performed using IBM SPSS 24.Table 2Responses of hens to the fear and anxiety tests according to keel fracture status, and effect of keel fracture and pen (required P values: tonic immobility $$P \leq 0.025$$, novel area $$P \leq 0.016$$, novel object $$P \leq 0.007$$).Table 2Keel ractureKeel fracture-freeEffect of keel fractureEffect of penMeasureMeanSEMeanSEFPFPdfTonic immobilityNo of attempts2.220.092.220.920.030.965.770.151, 151Mean duration (s)64.527.4975.707.763.140.229.090.101, 151Novel arenaFirst movement (s)178.5713.00196.9412.900.090.770.890.491, 144First call (s)214.3214.80187.8314.872.860.090.880.501, 139No of calls1.250.271.760.282.190.141.090.371, 144Total distance (cm)894.94110.57817.97109.300.630.431.720.131, 144Novel objectTime spent freezing (s)37.265.0038.134.830.330.572.070.071, 137First call (s)190.5615.87202.2415.131.790.180.790.561, 135No of calls5.464.211.390.250.060.910.570.721, 137No of object pecks0.300.130.040.043.290.071.060.391, 137No of defecations0.270.050.210.062.120.150.840.531, 136No of escape attempts0.030.020.010.011.060.311.040.401, 136Total distance (cm)1410.75157.321290.00152.520.010.931.980.091, 137Table 3Mean (±SE) percentage of hens recorded in locations and performing behaviors according to keel fracture status, and effect of keel fracture, time of day, and interaction effects (required P values: location $$P \leq 0.007$$, behavior $$P \leq 0.006$$, rare behaviors $$P \leq 0.008$$).Table 3Keel fractureKeel fracture-freeEffect of keel fractureEffect of time of dayKeel fracture × time of dayMeasureMeanSEMeanSEFPFPFPdfLocation (% of hens)Litter38.581.5345.581.494.980.090.920.440.940.431, 4Slats39.861.5036.341.503.230.156.820.020.700.531, 4Low perch9.541.597.571.420.400.5641.29<0.001⁎0.800.481, 4High perch1.360.591.660.590.270.6311.880.004⁎0.250.781, 4On drinker line1.310.380.730.250.510.520.200.820.160.861, 4In nestbox7.031.406.621.070.020.9016.970.001⁎0.880.451, 4On nestbox2.300.401.490.391.200.347.540.010.900.441, 4Behavior (% of hens)Body maintenance6.720.969.242.3111.980.0310.000.014.540.051, 4Feed28.691.7229.192.180.090.7810.990.005⁎0.350.711, 4Drink5.740.565.380.640.180.692.920.110.260.781, 4Forage10.322.3011.282.550.350.5933.62<0.001⁎0.870.451, 4Sit6.321.057.211.561.260.3312.750.003⁎1.060.391, 4Stand21.531.9117.281.043.350.147.200.020.590.581, 4Walk/run8.560.807.910.832.280.2124.41<0.001⁎0.030.971, 4Rare behaviors (number)Aggression3.671.383.110.700.0950.773.080.100.860.461, 4Failed landing0.670.240.560.240.10.771.880.210.470.641, 4Wing flap16.671.8813.890.893.050.165.710.032.110.181, 4⁎Indicate statistically significant effects. Table 4Mean (±SE) number of transitions according to keel fracture status, as well as the effect of keel fracture, time of day, and interaction effects (required P value = 0.004).Table 4Keel fractureKeel fracture-freeEffect of keel fractureEffect of time of dayKeel fracture × time of dayMeasureMeanSEMeanSEFPFPFPdfNumber of transitionsLitter-Slats115.0013.25125.3315.600.880.40227.90<0.001⁎2.360.161, 4Slats-Low perch38.448.4329.226.472.920.1643.79<0.001⁎1.120.371, 4Low perch-Low perch16.333.3214.783.860.040.8415.970.002⁎0.830.471, 4Slats-Nest box (inside)2.110.811.440.780.230.662.400.202.400.201, 4Slats-Nest box3.441.193.670.760.020.901.830.220.830.431, 4Nest box-Low perch21.444.5014.674.311.410.3016.920.001⁎0.060.951, 4Nest box-High perch10.782.718.333.460.450.5412.100.004⁎0.840.471, 4Slats-High Perch0.220.150.220.222.000.231.400.300.600.571, 4Low perch-Litter0.220.220.1110.1110.200.680.600.571.400.301, 4Low perch-High perch0.000.000.440.24160.0164.000.024.000.021, 4High perch-Litter0.220.220.110.110.200.683.000.543.000.541, 4Total transitions208.2229.91198.3327.270.190.69181.05<0.001⁎1.450.291, 4⁎Indicate statistically significant effects. Fear and Anxiety Tests. For TI data, a univariate general linear model (GLM) was used to compare duration of TI and the number of induction attempts needed between treatment groups (KF and KFF), with keel fracture status (KFS) as a fixed factor and pen as a random effect. For NA and NO tests, frequencies of behaviors (number of alarm calls, escape attempts, defecation, object pecking (for NO)) were compared between KF and KFF using a univariate GLM, with KFS as a fixed factor and pen as a random effect. Similarly, the effect of both KFS and pen was determined on latency to first move, latency to first vocalization, duration of freezing, total distance traveled during NA and NO tests and maximum distance traveled during NA test. Home-Pen Behaviors and Hen Location. Frequency counts of the number of hens performing each behavior and at each location within the ethogram were converted into the mean (of the 10 scans) percentage of hens performing each behavior and at each location. The number of transitions and rare behaviors were recorded for each pen at each time of day. A repeated measures GLM was performed, with time of day (am, pm, and evening) as a within subjects factor and treatment (KF and KFF) as a between subjects factor. Pen was included as a random effect. Productivity Measures. Hen weight, feed consumption per hen/day, egg shell thickness, egg shell weight, and percentage of egg shell membrane of KF and KFF hens were compared using a One-way GLM with treatment (KF and KFF) used as a fixed effect and pen as a random factor. The number of eggs per hen/day, percentage of first quality eggs, percentage of floor eggs and egg weight of KF and KFF were compared using a repeated measures GLM with date as a within subjects factor and treatment (KF and KFF) as a between subjects factor. ## Fear and Anxiety Tests There was no effect of KFS or pen on the hens’ responses during the tonic immobility test, novel arena test, and novel object test (see Table 2 for means and other statistics). ## Home Pen Behavior Statistics relating to behavior and location of the birds is shown in Table 3. There was no effect of KFS on behavior, location, and transitions, and no interaction effect between KFS and time of day on these measures. However, there was an effect of time of day on use of the high perch ($F = 11.88$, $$P \leq 0.004$$), low perch ($F = 41.29$, $P \leq 0.001$), and in the nestbox ($F = 16.97$, $$P \leq 0.001$$) (see Figure 2). Specifically, more birds were observed on the high perch in the evening when compared to the morning ($$P \leq 0.016$$). More birds were observed on the low perch in the evening when compared to the morning ($$P \leq 0.001$$) and afternoon ($$P \leq 0.004$$). More birds were observed inside the nestbox in the morning when compared to the afternoon ($P \leq 0.001$) and evening ($$P \leq 0.001$$).Figure 2Percentage of birds at locations according to keel fracture status and time of day. Figure 2 There was also an effect of time of day on sitting ($F = 12.75$, $$P \leq 0.003$$), walking/running ($F = 24.41$, $P \leq 0.001$), feeding ($F = 10.99$, $$P \leq 0.005$$), and foraging ($F = 33.62$, $P \leq 0.001$) (see Figure 3).Figure 3Percentage of birds performing behaviors according to keel fracture status and time of day. Figure 3 There was an effect of time of day on the total number of transitions between the litter and slats ($F = 227.90$, $P \leq 0.001$), slats and low perch ($F = 43.79$, $P \leq 0.001$), low perch and (other) low perch ($F = 15.97$, $$P \leq .002$$), nest box and low perch ($F = 16.92$, $$P \leq 0.001$$), nestbox and high perch ($F = 12.10$, $$P \leq 0.004$$) as well as the total number of transitions ($F = 181.05$, $P \leq 0.001$) (see Table 4). ## Production Table 5 shows the statistics relating to production. There was an effect of KFS on hen weight, with KFF hens being heavier than KF hens ($F = 5.94$, $$P \leq 0.015$$). There was an effect of KFS on egg shell membrane, with KFF birds laying eggs with higher egg shell membrane weight than KF birds ($F = 31.51$, $P \leq 0.001$) (see Figure 4).Table 5Production data according to keel fracture status, and effect of date and interaction effects (if appropriate) (required P values; hen weight $$P \leq 0.05$$, feed consumption $$P \leq 0.05$$, egg production $$P \leq 0.008$$).Table 5Keel fractureKeel fracture-freeEffect of keel fracturedfEffect of datedfInteraction effectMeasureMeanSEMeanSEFPFPFPdfHen weight (kg)1.940.022.010.015.940.015⁎1, 289Feed consumption/hen/week (g)0.150.000.150.000.010.921, 42.000.031⁎1, 32.000.191, 3Egg productionProportion of 1st quality0.920.030.880.021.150.341, 40.020.881, 31.500.291, 3Proportion of floor eggs0.020.010.010.010.490.521, 41.830.251, 30.270.631, 3Egg weight (g)66.460.7565.430.762.490.121, 582.000.121, 30.460.711, 3No of eggs per hen/day0.910.010.890.020.800.421, 422.15<0.001⁎1, 190.350.991, 19Egg shell thickness (mm)0.400.000.400.010.000.981, 54Egg shell weight (g)6.100.056.200.030.460.501, 54Egg shell membrane (%)8.150.5311.791.1631.51<0.0011, 54⁎Indicate statistically significant effects. Figure 4Hen weight and egg shell membrane according to keel fracture status. Figure 4 There was no effect of keel bone fracture status on any other measured egg quality data, including egg shell thickness and egg shell weight, and effect of KFS nor date on the on proportion of first quality eggs, floor eggs, egg weight, number of eggs per hen/day, and feed consumption per hen/week. ## DISCUSSION We aimed to determine whether hens with healed keel bone fractures showed higher levels of fear and anxiety compared to hens with intact keels. We hypothesized that hens with a healed keel bone fracture would be more fearful and anxious than intact hens. Contrary to this hypothesis, we found that hens with healed KFs did not differ to intact hens in their responses during the tonic immobility, novel arena, and novel object tests. All 3 fear tests measure responses to commercially relevant stimuli, since on farms the birds are likely to encounter novel stimuli and situations (e.g., changing environments, new people entering the house, potential predators). The lack of effect of KFS on the birds’ responses during these tests indicates that, after healing, KFs are not associated with chronic pain that manifests in increased fear and/or anxiety within these contexts, nor has their experience of sustaining a keel bone fracture resulted in heightened fear or anxiety to these stimuli. This finding is in keeping with Armstrong et al., who recently found that downregulation of adult hippocampal neurogenesis—a biomarker of chronic stress—occurred following a KF, but crucially, this only persisted for 3 to 4 wk after the fracture event (Armstrong et al., 2020). In determining the relationship between KFS and spontaneous bird behavior, we also found no effect of KFS on transitions between different areas of the pen. This finding is in keeping some studies that showed no link between healed fractures and general transitions between areas (Rentsch et al., 2019), although in contrast to others that showed some reduced vertical movements (Nasr et al., 2012a; Richards et al., 2012). It has been hypothesized that keel bone fracture reduces the hens ability to move around their environment due to reduced mobility and/or pain. Our home pens were set up so that birds needed to traverse (up and down) the ramp to access food, water, and nestboxes but that it was less essential that they traversed between the low and high perches (because there were no essential resources there). If the healed KF were causing pain and/or reduced mobility we would expect to see no differences between KF and KFF in transitions up the ramp (to access essential resources) but differences between the groups in transitions between the high and low perches, or on the top of the nestboxes (where there are no essential resources). Since we found no differences between KF and KFF groups in both types of transitions, we can conclude that the healed KFs did not cause pain and reduced mobility that manifested in ability to access essential and nonessential resources. We found no other effects of healed KFS on behaviors or use of the resources between our groups. This included body maintenance (preening and dustbathing); an activity that requires vigorous movement of the body, which might be restricted due to reduced mobility and/or pain. In addition, one would expect walking and foraging (in the litter) to be affected by keel bone status, but we found no such association. However, there remains the possibility that our study design failed to pick up keel bone effects on behavior. In terms of production measures, we found that hens with healed KFs were lighter than the control hens. This could be caused by a residual effect from increased energy demands caused by the immediate aftermath of energy and nutrients being redirected toward the fracture, or a residual effect from behavioral changes immediately following the fracture event. However, our study design, whereby we used birds that had already sustained fractures means that we cannot infer causality and lighter birds might have been more likely to break their keels in the first place due to less tissue protecting the keel or because of some behavioral features that caused both characteristics. This raises the question as to whether we should have selected control birds that were matched for weight with a matched KF bird, but this selection would also have led to bias in our study sample, by only selecting birds at the heavier end of the KF mean weight. There was no effect of KFS on the measures of egg quality, including the number of first and second quality eggs, number of floor eggs, egg weight, and shell thickness. Nasr et al. found that hens with healed KFs laid eggs with lower eggshell weight compared to intact hens, and that KF severity had a negative relationship with egg weight and surface area (Nasr et al., 2012a). It would be hypothesized that tissue damage would redirect energy and resources, particularly calcium, away from egg development. In fact, we did find a significant effect of KFS on egg shell weight lost in the ashing process. The ashing process removes organic matter from the egg shell; much of which is in the egg shell membranes. The 2 egg shell membranes are essential for the formation of eggshell and then act to retain the albumen and prevent penetration of bacteria (Nakano et al., 2003). The organic matter of eggshell and shell membranes contains proteins as major constituents with small amounts of carbohydrates and lipids (Burley and Vadehra, 1989). The finding that the birds with a healed KF had reduced egg shell membrane compared to intact birds could be caused by the residual effects of increased metabolic requirements of the damaged bone. During the healing process, energy, nutrients, and minerals that are usually used in egg production are redirected to the bone healing process (Thiruvenkadan et al., 2010), although an effect on the egg shell membrane has, to our knowledge, not been investigated or reported before. Since the egg shell membrane plays an important role in protecting the egg contents, further research is required to determine the prevalence and the effects this has on egg and chick quality. If KFs chronically reduce egg shell membrane quantity and quality then there may be implications in layer breeder farms in the form of reduced hatchability and quality of chicks. Our use of palpation and categorization of birds into binary categories (which facilitated housing birds together in multiple groups of the same category) meant that we were not able to take into account the complexities of the KF such as precise location, severity and type. These factors can be determined using radiography (Rentsch et al., 2019) but were not deemed practical in the current study due to the need for collecting large numbers of birds from a commercial farm. Instead, we opted for palpation by assessors who had been trained using validation with dissection (as per Wilkins et al., 2004). In addition, we have added further info on the hens with minor fractures which by palpation could not be confidently placed into either category were not included in the study. Additionally, the nature of our study, which involved collecting birds after KFs had occurred and had healed, means that we are not able to infer causality; an issue also raised by previous authors (e.g., Riber et al., 2018). Individual differences in behavior or fear may already be evident before fracture. For example, birds that are more fearful, may be more likely to suffer a KF. For the current study, all birds in a room were either fracture score 1 or 0—this allowed the observation of group-level behavior but the grouping of birds all the same fracture status may result in different social and behavioral dynamics compared to on a commercial farm. As was expected, during the study period, some of the intact birds sustained keel bone fractures (33 out of the 150 intact birds by the end of the study period). As our primary aim was to determine the relationship between KFS and fear/anxiety, these birds were excluded from the individual-level fear tests, although they remained as part of the social group for the group-level data, since we decided it was important to retain a stable social group. To determine the association between KF and spontaneous behavior, a more robust future study might involve individual monitoring of the birds to take measures before and after keel bone fracture. However, individual monitoring of the large numbers of birds required to ensure sufficient birds transition from KFF to KF would be difficult and costly. The design of our study meant that we were able to monitor group-level productivity and behavior as well as individual level fear levels in birds with mostly old fractures. Rentsch et al. investigated behavior before and after KF and found no differences in activity (vertical locomotion, walking pace, rapid comfort behavior, and feather maintenance combined) between birds that later sustained a fracture and those that didn't (Rentsch et al., 2019), suggesting the fracture event is causal for the behavior rather than the other way around. Since we found no difference in fear or anxiety responses between the 2 groups, we can conclude that healed fractures are unlikely to have had an effect on long-term fear and anxiety levels in our birds. ## CONCLUSIONS Previous literature has demonstrated that KFs are associated with pain and behavioral changes persisting for weeks after the fracture. We found no evidence that, once healed, KFs were associated with detrimental welfare in laying hens. However, further work is required to determine the mechanisms and implications of the reduced body weight and egg shell membrane. ## DISCLOSURES The authors declare no conflict of interest. ## References 1. Armstrong E.A., Rufener C., Toscano M.J., Eastham J.E., Guy J.H., Sandilands V., Boswell T., Smulders T.V.. Keel bone fractures induce a depressive-like state in laying hens. Sci. Rep. (2020) 10 3007. PMID: 32080271 2. Baur S., Rufener C., Toscano M.J., Geissbühler U.. Radiographic evaluation of keel bone damage in laying hens—morphologic and temporal observations in a longitudinal study. Front. Vet. Sci. (2020) 7 129. PMID: 32226794 3. Bove S.E., Flatters S.J.L., Inglis J.J., Mantyh P.W.. New advances in musculoskeletal pain. Brain Res. Rev. (2009) 60 187-201. PMID: 19166876 4. Bryan Jones R., Waddington D.. Attenuation of the domestic chick's fear of human beings via regular handling: in search of a sensitive period. Appl. Anim. Behav. Sci. (1993) 36 185-195 5. 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--- title: Osmoadaptive GLP-1R signalling in hypothalamic neurones inhibits antidiuretic hormone synthesis and release authors: - Michael P. Greenwood - Mingkwan Greenwood - Soledad Bárez-López - Joe W. Hawkins - Katherine Short - Danijela Tatovic - David Murphy journal: Molecular Metabolism year: 2023 pmcid: PMC9969259 doi: 10.1016/j.molmet.2023.101692 license: CC BY 4.0 --- # Osmoadaptive GLP-1R signalling in hypothalamic neurones inhibits antidiuretic hormone synthesis and release ## Abstract ### Objectives The excessive release of the antidiuretic hormone vasopressin is implicated in many diseases including cardiovascular disease, diabetes, obesity, and metabolic syndrome. Once thought to be elevated as a consequence of diseases, data now supports a more causative role. We have previously identified CREB3L1 as a transcription factor that co-ordinates vasopressin synthesis and release in the hypothalamus. The objective here was to identify mechanisms orchestrated by CREB3L1 that co-ordinate vasopressin release. ### Methods We mined Creb3l1 knockdown SON RNA-seq data to identify downstream target genes. We proceeded to investigate the expression of these genes and associated pathways in the supraoptic nucleus of the hypothalamus in response to physiological and pharmacological stimulation. We used viruses to selectively knockdown gene expression in the supraoptic nucleus and assessed physiological and metabolic parameters. We adopted a phosphoproteomics strategy to investigate mechanisms that facilitate hormone release by the pituitary gland. ### Results We discovered glucagon like peptide 1 receptor (Glp1r) as a downstream target gene and found increased expression in stimulated vasopressin neurones. Selective knockdown of supraoptic nucleus Glp1rs resulted in decreased food intake and body weight. Treatment with GLP-1R agonist liraglutide decreased vasopressin synthesis and release. Quantitative phosphoproteomics of the pituitary neurointermediate lobe revealed that liraglutide initiates hyperphosphorylation of presynapse active zone proteins that control vasopressin exocytosis. ### Conclusion In summary, we show that GLP-1R signalling inhibits the vasopressin system. Our data advises that hydration status may influence the pharmacodynamics of GLP-1R agonists so should be considered in current therapeutic strategies. ## Highlights •Identification of Creb3l1 as a regulator of Glp1r expression in the hypothalamus.•Physiological stimulation of the HNS increases Glp1r expression in AVP neurones.•Knockdown of SON Glp1rs decreases food intake, body weight, and HNS outflows.•GLP-1R agonist liraglutide inhibits hypothalamic Avp synthesis.•Quantitative phosphoproteomics of the pituitary gland reveals possible mechanisms. ## Introduction There has been a resurgence in interest in the mechanism controlling hypothalamo-neurohypophysial system (HNS) hormone release stemming from clinical associations with obesity and metabolic syndrome [1]. The HNS comprises large magnocellular neurones (MCNs) in the paraventricular nucleus (PVN) and supraoptic nucleus (SON) that make the antidiuretic hormone vasopressin (AVP) and oxytocin (OXT) and release them peripherally into the blood circulation from nerve terminals located in the posterior lobe of the pituitary gland (PP), and within the brain from dendrites and axon collaterals [2]. By signalling at specific G-protein-coupled receptors (GPCRs) in the periphery and centrally, AVP and OXT can regulate physiological processes crucial for the maintenance of homeostasis. The most recognised roles of AVP in the periphery are the regulation body fluid and cardiovascular homeostasis and the control of blood glucose [3]. OXT is best known for its roles in lactation and parturition and beyond this regulates renal sodium excretion, glucose and insulin homeostasis, gastric motility, eating behaviours, lipid metabolism, and osteogenesis [4]. Interestingly, translational human studies are showing strong therapeutic potential for OXT as a treatment for diabetes and obesity in its own right [4]. There is also increasing experimental, pharmacological, and epidemiological evidence that supports a causative role for sustained increases in circulating AVP in the development of diseases, suggesting the AVP system as a prognostic marker and thus a potential therapeutic target [[5], [6], [7], [8], [9], [10], [11]]. How do feeding signals engage MCNs? When we eat a meal, we ingest food and water, and this has profound effects on osmolality in our body. It has been shown that food intake stimulates the brain which rapidly stimulates thirst to increase water uptake leading to the activation of AVP and OXT MCNs in the SON [12,13]. How does water engage MCNs? A lack of water intake increases plasma osmolality and activates AVP and OXT MCNs in the SON resulting in increased AVP and OXT synthesis and release [14]. The relationship between water intake or hydration status and metabolic health is one that is relatively recent and needs to be better understood. It has been proposed that people who drink less water have a greater chance of developing diabetes and this is related to higher AVP circulating levels [15]. Furthermore, many studies have found that increased AVP release (assessed by measuring the surrogate precursor product copeptin) predicts insulin resistance and the onset of type 2 diabetes and major cardiovascular events in metabolic syndrome patients [[16], [17], [18], [19]]. Thus, it is essential to better understand the signalling mechanism that facilitate HNS hormone release. We have found that transcription factor CREB3L1 is crucial for regulating hypothalamic AVP and OXT synthesis and secretion [[20], [21], [22], [23], [24], [25]]. We recently showed that CREB3L1 plays a more fundamental cellular role as a regulator of MCN protein synthesis and secretion in order to meet dynamically changing physiological demands [26]. In this study, we have mined our SON Creb3l1 knockdown (KD) transcriptomic dataset and identified dramatically decreased expression of several GPCRs included the glucagon like peptide 1 receptor (GLP-1R). This receptor is activated by the endogenous ligand glucagon like peptide 1 (GLP-1). There are two major sites of GLP-1 production in our bodies. When food and fluids enter the gut, they stimulate the release of GLP-1 (encoded by the preproglucagon gene, Gcg) from intestinal L-cells into the circulation. A separate GLP-1 circuit of neuronal origin originates from the nucleus tractus solitarius (NTS) that targets GLP-1Rs in the brain [27]. GLP-1 is involved in glucose homeostasis via its stimulation of insulin secretion from the pancreas, cardiovascular function, gut motility, and is a physiological satiety factor [27]. The SON receives projections from GLP-1 synthesising neurones in the NTS and these innervate MCNs in the SON [28]. An HNS GLP-1R circuit has been described but a large void remains regarding assessment of function. In this study, we have investigated GLP-1R expression in different models of HNS activation. To determine the physiological role of the MCN GLP-1R population we delivered viruses to KD its expression exclusively in the SON. Using several experimental models both in vivo and ex vivo including studies with GLP-1R agonist (GLP-1RA) liraglutide, we show that SON GLP-1Rs regulate HNS activity and likely hormone release both basally and following physiological stimulation. By quantitative phosphoproteomics of the pituitary neurointermediate lobe (NIL) after acute liraglutide treatment we have identified hyperphosphorylation events in several proteins located in active zone of the nerve terminal that control hormone release. This new information has the potential to influence current clinical practice with GLP-1RAs. ## Animals All experiments were performed under Home Office UK licences $\frac{30}{3278}$ and PP9294977 held under, and in strict accordance with, the provisions of the UK Animals (Scientific Procedures) Act [1986]; they had also been approved by the University of Bristol Animal Welfare and Ethical Review Board. Animal sample sizes were calculated by making an estimate of variability from previous experiments that we have performed with two groups, using similar approaches, under similar conditions, in rats. These data provide an estimate of the expected standard deviation of the primary outcome and then power calculations in GPower 3.1 have been used to calculate the sample size for the experimental group [29]. In some cases, the same animal was able to serve as its own control with control virus injected into one SON and experimental virus into the other (referred to as SONs per group). All studies were performed with time-matched experimental controls run in parallel and sampled on the same day. For animal studies with two groups, randomisation was performed by the flip of a coin to determine which group the animal was assigned. Viral injections for each animal study were completed in 3 days. In our previous studies injections were missed in approximately $10\%$ of animals so numbers were accordingly increased. Animals to be injected were handled daily. Male Sprague Dawley rats weighing 250–274 g were purchased from commercial supplier Envigo (RRID: 70508). Rats were housed under a 12:12 light/dark cycle at a temperature of 21–22 °C and a relative humidity of 40–$50\%$ with food and water ad libitum unless stated. Through the University of Bristol Animal Management Information System, animals are randomly assigned to cages by Animal Services Unit staff. The number of cages, and the number of animals per cage, is pre-set by the investigator on AMIS, but the allocation is independently performed. Rats were housed in groups of 3–4 for a 1–2-week period of acclimation before experimentation. After surgical procedures animals were singly housed (Techniplast, 1290 conventional rat cages). Cages contained sawdust, bedding material, cardboard tubes, and wooden chews for enrichment. In some experimental series rats were placed in metabolic cages (Techniplast) to allow for precise daily measures of food and water intake alongside urine output. Plastic chew toys (discs) were suspended from the cage lid and actively gnawed providing enrichment to their environment. Measures of food, water, and urine were performed by weight. Nighttime measures and injections were performed under dim red lighting. ## Activation of the HNS Water deprivation (WD) - For WD protocols drinking water was removed for 1–3 days. In a single study water was returned for 4 h after 3 days of WD. Salt loading - For salt loading protocols drinking water was replaced with $2\%$ (w/v) NaCl in drinking water for 1 or 7 days. Hypertonic saline injection - To acutely activate the HNS a single IP injection of 1.5 ml/100 g body weight of 1.5 M NaCl solution was performed. Animals were randomly allocated into one of six groups, control, 10, 30, 60, 120, and 240 min. After injection animals were placed back in their home cages, and food, but not water, was returned for the duration of the experiment. Lactation - Female rats with litters were purchased from commercial supplier Envigo and acclimatised to the animal facility for 3 days. Animals were killed immediately after the removal of pups (postnatal day 8–9), to ensure a state of lactation was active. Singly housed females without litters were used as controls. ## GLP-1RA studies Liraglutide (Tocris, $\frac{6517}{1}$) was prepared in normal saline to a concentration of 100 μg/ml. Injections of 100 μg/kg body weight or normal saline vehicle were performed IP using a 30-gauge insulin syringe (BD). The dose of liraglutide and route of administration has previously been described in the rat [[30], [31], [32]]. ## GLP-1RA study series 1 – Liraglutide control Animals were singly housed 3 days before experimentation. Injections were performed during the final 30 min of the light phase before lights off. Animals were killed 2, 4, 12, and 24 h after injection of liraglutide or vehicle. Food and water intake were recorded for the period of study. ## GLP-1RA study series 2 - Liraglutide 4-hour WD Animals were singly housed for 3 days prior to experimentation. Water bottles were removed from cages at +4 h into the dark phase. After 44 h without drinking water, animals were injected with liraglutide or vehicle. Animals were killed 4 h after injection. Food intake was recorded. ## GLP-1RA study series 3 – Liraglutide prolonged dehydration Animals were placed in metabolic cages (Techniplast) to allow for precise daily measures of food and water intake and the collection of urine samples. Animals were acclimatised to metabolic cages for 2 days before basal measures of food and water intake were recorded. The first of a series of injections, liraglutide or vehicle, were performed at the end of the light phase on day 5 and then every 12 h thereafter. This bi-daily delivery regime has been described based on the half-life of liraglutide in the rat [[33], [34], [35]]. Water bottles were removed after the first injection and not returned. Measures of food and urine were performed for the dark and light phases. Urine samples were collected in 1.5 ml tubes and stored at 4 °C. Animals were killed 60 h after the first injection. ## Series 4 – Liraglutide NIL phosphoproteomics To reduce animal variability that can be introduced by the ingestion of food and fluid, water and food were removed approximately 4 h before injection of liraglutide or vehicle and were not returned. Animals were injected with liraglutide or vehicle in the last hour of the light phase. Animals were killed 30 min later and before lights off. ## Plasma and hormone measures Rats were humanely killed by striking the cranium and then immediately decapitated with a small animal guillotine (Harvard Apparatus). Trunk blood was collected in potassium ethylenediamine tetraacetic acid (EDTA)-coated tubes (BD, 368860) and centrifuged at 1600×g for 20 min at 4 °C. Plasma for hormone measures was collected in 1 ml aliquots and snap frozen in liquid nitrogen before storage at −80 °C. Brains were rapidly removed from the cranium and placed into a chilled rodent brain matrix (ASI Instruments, RBM-4000c) on ice for separation of the fore and hind brain regions. The brain was placed cut edge down onto aluminium foil resting on pellets of dry ice and immediately covered with powdered dry ice (within 3 min of decapitation). Brains were wrapped in foil and stored at −80 °C. Pituitaries were placed in 1.5 ml tubes containing 0.5 ml of 0.1 M hydrochloric acid and also stored at −80 °C. Plasma and urine osmolality measures were performed by freezing point depression using a Roebling micro-osmometer (Camlab). Plasma glucose concentrations were determined in duplicate by glucose assay (Abcam, ab102517). Plasma hormone measures: Plasma concentrations of copeptin, OXT, and GLP-1 were determined by ELISA (Copeptin, MyBioSource, MBS724037; OXT, Enzo Life Sciences, ADI-901-153 A, RRID:AB_2815012; GLP-1, Sigma-Aldrich, RAB0201). The extraction method for OXT was reverse phase C18 columns (Phenomenex) to capture peptides according to manufacturer's protocols. Samples were eluted and then dried under a gentle flow of nitrogen gas. Samples were reconstituted by vortexing in assay buffer and assayed immediately in duplicate in accordance with manufacturer's protocols. Copeptin and GLP-1 assays were performed in duplicate on whole plasma according to manufactures instructions. Pituitary hormone measures were performed as described previously [26]. The signal was detected on an iMark microplate absorbance reader (Bio-Rad Laboratories). ## Cells and treatments Human Embryonic Kidney cells HEK293T/17 (ATTC, CRL-11268, RRID: CVCL_1926) and Neuro 2a cells N2a (ATTC, CCL-131, RRID: CVCL_0470) were cultured in DMEM (Sigma, D6546) supplemented with $10\%$ (v/v) heat-inactivated foetal bovine serum (Sigma-Aldrich; F9665), 2 mM l-glutamine (Gibco, 25030) and 100 unit/ml of penicillin-streptomycin (Gibco, 15140). Cells were incubated at 37 °C in a humidified incubator with $5\%$ (v/v) CO2. Cells were seeded onto tissue culture plates to $60\%$–$70\%$ confluence for experiments. ## Luciferase assays The rat Glp1r promoter region −3563 to −1 bp was amplified from rat liver genomic DNA with restriction sites included in primers for restriction digestion and cloning into compatible sites of minimal promoter plasmid pGL4.10 (Promega, E6651). Primers details are found in Supplemental Table 4. Luciferase assays were performed as described previously using Dual Luciferase Reporter Assay System (Promega, E1910) [25]. ## Virus production and validation Three Glp1r shRNAs were designed using BLOCK-iT™ RNAi Designer (Thermo Fisher Scientific, Supplemental Table 4) and knockdown efficiency was validated in HEK293T/17 cells overexpressing a rat Glp1r cDNA. The most efficient shRNA target sequence was GCACGCATGAAGTCATCTTTG with $88.1\%$ KD. Glp1r and non-targeting short hairpin RNAs (shRNAs) were cloned into pGFP-A-shAAV (OriGene) as previously described [26]. Adeno-associated virus (AAV) particles (AAV$\frac{1}{2}$) were produced using a helper free packaging system (Cell Biolabs, VPK-402 and VPK-421) and prepared to titres of 1 × 1012 (AAV$\frac{1}{2}$-shRNA constructs) genome copies per ml in phosphate buffered saline (PBS) for injection as described previously [24]. The production of Creb3l1 shRNAs has previously been described [24]. ## Stereotaxic injections of virus into the SON and PVN Stereotaxic injections were performed as described previously [26]. The success of viral injections was verified at the end of each study by visualisation of the fluorescent reporter in cryostat cut sections or qRT-PCR [26]. ## Isolation of RNA and qRT-PCR The collection and processing of brains by punching has previously been described [21,25,26]. For relative quantification of gene expression, the 2−ΔΔCT method was employed [36]. The internal control genes used for these analyses were the housekeeping genes Rpl19 [[21], [22], [23]] and Sdha (NTS). The changes in NTS Gcg expression were also found using Rpl19 as the housekeeping gene. qRT-PCR primer sequences can be found in Supplemental Table 4. ## SON and NIL explants Control and WD rats were killed by striking of the cranium and brains were rapidly removed and placed into a rodent brain matrix (ASI Instruments, RBM-4000C) chilled on ice. A 2 mm coronal brain slice excised between two razor blades and placed onto a 10 cm upturned petri dish chilled on ice. A 2 mm in diameter sample corer (Fine Science Tools, 18035–02) was used to collect SONs. Individual SONs were placed into 500 μl of cold Krebs slice solution (120 mM NaCl, 5 mM KCl, 2.5 mM CaCl2, 1.2 mM NaH2PO4, 1.2 mM MgSO4, 25 mM NaHCO3, 5.5 mM glucose, pH 7.4) for 1–1.5 h at 4 °C. The osmolality of the slice solution was adjusted using hypertonic saline solution to match the plasma osmolality of control and WD rats to 300 and 315 mOsmol/kgH2O, respectively. Individual SONs were moved using a spoon spatula to a 96-well plate containing 250 μl of warm slice solution per well for SONs or 2 ml per well for NILs. One SON for each animal was treated with 100 nM liraglutide (Tocris, $\frac{6517}{1}$) and the other vehicle (saline) for 4 h incubated at 37 °C in a humidified incubator with $5\%$ (v/v) CO2. NILs were split in half with a sterile scalpel blade and one half was treated with vehicle and the other half with 100 nM liraglutide for 30 min in a 37 °C water bath set at 25 rpm. This concentration of liraglutide is commonly used for in vitro studies [37,38]. The SON solution was removed after 4 h and replaced with 200 μl of Qiazol reagent. Tissue samples in Qiazol were transferred to 1.5 ml Biomasher tubes (Takara Bio, Cat No. 9791 A) and disrupted for 15 s with a Biomasher homogeniser. Total RNA was extracted for qRT-PCR. NIL samples were removed from Krebs solution and immediately frozen on dry ice in 0.5 ml tubes. ## RNAscope Frozen brains were sliced into 16 μm (SON) or 20 μm (NTS) coronal sections in a cryostat. A multiplex RNAscope assay was performed using the RNAscope Multiplex Fluorescent Reagent Kit (Advanced Cell Diagnostics, 320850) in accordance with manufacturer's guidelines. RNAscope probes used in this study were purchased from Advanced Cell Diagnostics; Rn-Glp1r-C3 (315221-C3), Rn-Avp-C2 (401421-C2), Rn-Gcg-C2 (315471 C2), Rn-Oxt [479631], and Rn-Fos [403591]. Images were captured using a Leica SP5-II confocal laser scanning microscope attached to a DMI 6000 inverted epifluorescence microscope with Leica acquisition software for SON. Images for data analysis were acquired with a Leica SP5-II AOBS confocal laser scanning microscope attached to a Leica DMI 6000 inverted epifluorescence microscope using a 63× PL APO CS lens with a 3.4-zoom factor. Quantification of Glp1r RNA dots in the nucleus (DAPI labelling close to either AVP- or OXT-positive cytoplasm) or cytoplasm (AVP or OXT labelling) of AVP or OXT neurones was performed using a modular workflow plugin for Fiji created by Dr Stephen J Cross from the Wolfson Bioimaging Facility of the University of Bristol, as described [20]. All combinations with Gcg were captured using a DMI6000 inverted epifluorescence microscope with Photometrics Prime 95 B sCMOS camera and Leica LAS-X acquisition software. ## Immunofluorescence Rat perfusion and brain processing was performed as described previously [26]. Slices were washed three times for 5 min each in PBS. For antibodies requiring antigen retrieval (AR), sections were incubated in 1 ml of sodium citrate buffer (10 mM, pH6) in 1.5 ml tubes placed in a 90 °C water bath for 30 min. After 20 min cooling at room temperature, sections were removed from AR buffer and washed three times for 5 min each in PBS. Slices were blocked and permeabilised in $3\%$ (w/v) bovine serum albumen (BSA: Sigma-Aldrich, A7906) prepared in $0.3\%$ (v/v) triton-X100/PBS (PBS-T) for 30 min at RT. Primary antibodies were prepared in $1\%$ (w/v) BSA/PBS-T and incubated at 4 °C for 48–72 h. After three washes for 5 min each in PBS, sections were incubated in darkness (covered in aluminium foil) with Alexa Fluor secondary antibodies made in donkey (Thermo Fisher Scientific) prepared in $1\%$ (w/v) BSA/PBS-T for 1 h. Sections were washed three times for 5 min each with PBS with the inclusion of a minute incubation in DAPI (2-(4-amidinophenyl)-6-indolecarbamidine dihydrochloride, 1 μg/ml) prepared in PBS before the final wash. All incubations were performed with gentle rocking in 24-well plates. Slice were mounted onto glass slides with $0.5\%$ (w/v) gelatine (Sigma-Aldrich, G9382) solution and coverslipped with ProLong Gold antifade mounting media (Thermo Fisher Scientific). The primary antibodies used were GLP-1R (1:500; Abcam, ab218532, RRID:AB_2864762), GLP-1 (1:500; Novus Biologicals, NBP2-23558, RRID:AB_2895594), CREB3L1 (1:500; R&D Systems, AF4080, RRID:AB_2086044), neurophysin I (PS41, RRID: AB_2313960) and neurophysin II (PS38, RRID: AB_2315026) (1:200, both kind gifts from Professor Harold Gainer), OXT (1:5000; Peninsula Laboratories, T-5021), FOS (1:25000, Chemicon, Ab-5 (4–17) PC38, RRID:AB_2106755), S100B (1:500, Sigma-Aldrich, S2532, RRID:AB_477499), and tGFP (OriGene, TA150041, RRID:AB_2622256). All combinations with GLP-1R, CREB3L1, GLP-1 required AR. Images were captured using a DMI6000 inverted epifluorescence microscope with Photometrics Prime 95 B sCMOS camera and Leica LAS-X acquisition software. Images being compared were imaged with the same parameters at the same time and were from the same perfusion collection and same immunostaining run as the reference controls. ## Western blotting A 1-mm micropunch (Fine Scientific Tools) was used to collect SON, PVN, organum vasculosum of the lamina terminalis, subfornical organ, area postrema (AP), median preoptic nucleus, arcuate nucleus and nucleus of the solitary tract (NTS) samples from 100 μm coronal sections in a cryostat according to the rat brain atlas by Paxinos and Watson (fifth edition). A 1.5 mm sample core of 1 mm in diameter was collected for the central amygdala. The NIL was dissected from the anterior pituitary at the time of collection and stored at −80 °C. Tubes were removed from dry ice and 80 μl of ice-cold radioimmunoprecipitation assay (RIPA) buffer was added (50 mM Tris-HCl, pH 7.6; 150 mM NaCl, $1\%$ Nonidet P-40 substitute, $0.5\%$ sodium deoxycholate, $0.1\%$ sodium dodecyl sulfate, 1 mM ethylenediaminetetraacetic acid) supplemented with 1 mM protease inhibitor phenylmethylsulfonyl fluoride, protease inhibitor cocktail (Sigma-Aldrich, P8340), and PhosSTOP phosphatase inhibitor cocktail (Roche, 4906845001). Samples were immediately vortexed before being sonicated in tubes kept on iced water for 10 s of sonication (MSE Soniprep 150). Samples were sonicated for 3 rounds of 10 s with intervals on ice of approximately 5 min. Samples were maintained on ice for an additional 30 min, vortexing every 10 min. To remove cellular debris samples were centrifuged at 10000×g for 15 min at 4 °C. The supernatant was removed and stored at −80 °C. Protein samples were prepared to 1 × Laemmli buffer solution ($2\%$ sodium dodecyl sulfate, $10\%$ glycerol, $5\%$ 2-mercaptoethanol, $0.002\%$ bromophenol blue and 0.125 M Tris HCl, pH 6.8). Unless stated samples were not denatured. For semiquantitative analysis of protein levels, 20 μg/lane of total protein (determined in duplicate by Bio-Rad Protein Assay with BSA as standards) was loaded for control and WD samples. To assess GLP-1R expression in different brain structures total protein concentration was determined and $25\%$ of the total protein extract was loaded for each sample, which were collected from a control and WD animal. Proteins were fractionated on $10\%$ sodium dodecyl sulfate polyacrylamide gels and transferred to Immobilon®-P PVDF Membrane (MERCK). For molecular weight estimation one lane on each blot was loaded with Novex Sharp Pre-Stained Protein Standard (Life Technologies, LC5800). Membranes were incubated in $5\%$ Amersham ECL Blocking Agent (RPN2125) in Tris-buffered saline (TBST, 150 mM NaCl; 20 mM Tris-HCl, pH 7.6) with $0.1\%$ Tween 20 for 1 h. All blots were firstly probed for the GLP-1R (1:2000, Abcam, ab218532, RRID:AB_2864762) with the antibody prepared in $1\%$ blocking agent. All other primary antibodies; phosphorylated p$\frac{44}{42}$ mitogen-activated protein kinase (MAPK) (1:2500; Cell Signaling Technology, 4370 T, RRID:AB_2315112), total p$\frac{44}{42}$ MAPK (1:1000; Cell Signaling Technology, 4696 S, RRID:AB_390780), β Tubulin (1:10000; Covance, MMS-489 P, RRID:AB_10096105), β Actin (1:10000, Proteintech, 66009-1-Ig, RRID:AB_2687938), SNAP25 (phospho-T138) (1:500, Stratech, ORB163730), SNAP25 (1:1000, Santa Cruz Biotechnology, sc-20038, RRID:AB_628264) were prepared in $5\%$ BSA prepared in TBST. After overnight incubation at 4 °C with gentle rocking, membranes were washed three times for 10 min each with TBST. Membranes were incubated for 1 h with secondary antibodies, anti-rabbit (Sigma-Aldrich, A0545, RRID:AB_257896) or anti-mouse (Sigma-Aldrich, A9044, RRID:AB_258431) conjugated with horseradish peroxidase, prepared in the primary antibody dilution buffer (1:20000) with gentle rocking. Membranes were washed three times for 10 min each with TBST. The signal was detected by chemiluminescence using WESTAR® Supernova HRP Detection Substrate (Geneflow, K1-0068) for the GLP-1R and SuperSignal™ West Dura Extended Duration Substrate for other antibodies (Thermo Fisher Scientific, 34075) using a Syngene G:Box imaging system. The immunoblots were stripped in Restore™ Western Blot Stripping Buffer (Thermo Fisher Scientific) for 15 min after probing for phosphorylated p$\frac{44}{42}$ MAPK before assessing total p$\frac{44}{42}$ MAPK abundance. Band intensities were determined using Quantity One (Bio-Rad, Hercules, CA, USA). ## Protein extraction for phosphoproteomics The NIL was separated from the anterior lobe of the pituitary by blunt dissection and frozen in a 0.5 ml tube within 90 s of decapitation on dry ice. Total proteins were extracted in RIPA buffer (50 mM Tris-HCl, pH 7.6; 150 mM NaCl; $0.1\%$ sodium dodecyl sulfate; $0.5\%$ sodium deoxycholate; $1\%$ Nonidet P-40; 1 mM EDTA) supplemented with 1 mM PMSF, pierce protease inhibitor cocktail (Thermo Fisher Scientific, A32963) and phosphatase inhibitor cocktail (Thermo Fisher Scientific, A32957). Lysis buffer (80 μl/sample) was added and samples which were immediately sonicated in tubes kept on iced water for 12 s of sonication (MSE Soniprep 150). Samples were sonicated for 3 rounds of 12 s with intervals on ice of approximately 5 min. Samples we maintained on ice for an additional 30 min, vortexing every 5 min. To remove cellular debris samples were centrifuged at 10000×g for 20 min at 4 °C. The supernatant was removed and stored at −80 °C. Protein concentrations were determined in triplicate by Bradford assay with BSA standards using an iMark microplate absorbance reader (Bio-Rad Laboratories). ## TMT labelling and phosphopeptide enrichment Aliquots of 100 μg of each sample were digested with trypsin (2.5 μg trypsin per 100 μg protein; 37 °C, overnight), labelled with Tandem Mass Tag (TMTpro) sixteen plex reagents according to the manufacturer's protocol (Thermo Fisher Scientific, Loughborough, LE11 5RG, UK) and the labelled samples pooled. For the phospho proteome analysis, the TMT-labelled pooled sample was desalted using a SepPak cartridge (Waters, Milford, Massachusetts, USA). Eluate from the SepPak cartridge was evaporated to dryness and subjected to TiO2-based phosphopeptide enrichment according to the manufacturer's instructions (Pierce). The flow-through and washes from the TiO2-based enrichment were then subjected to FeNTA-based phosphopeptide enrichment according to the manufacturer's instructions (Pierce). The phospho-enriched samples were again evaporated to dryness and then resuspended in $1\%$ formic acid prior to analysis by nano-LC MSMS using an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific). ## Nano-LC mass spectrometry Phospho-enriched fractions (Phospho-proteome analysis) were further fractionated using an Ultimate 3000 nano-LC system in line with an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific). In brief, peptides in $1\%$ (vol/vol) formic acid were injected onto an Acclaim PepMap C18 nano-trap column (Thermo Scientific). After washing with $0.5\%$ (vol/vol) acetonitrile $0.1\%$ (vol/vol) formic acid peptides were resolved on a 250 mm × 75 μm Acclaim PepMap C18 reverse phase analytical column (Thermo Scientific) over a 150 min organic gradient, using 7 gradient segments (1–$6\%$ solvent B over 1 min, 6–$15\%$ B over 58 min, 15–$32\%$B over 58 min, 32–$40\%$ B over 5 min, 40–$90\%$B over 1 min, held at $90\%$ B for 6 min and then reduced to $1\%$ B over 1 min) with a flow rate of 300 nl per minute. Solvent A was $0.1\%$ formic acid and solvent B was aqueous $80\%$ acetonitrile in $0.1\%$ formic acid. Peptides were ionized by nano-electrospray ionization at 2.0 kV using a stainless-steel emitter with an internal diameter of 30 μm (Thermo Fisher Scientific) and a capillary temperature of 300 °C. All spectra were acquired using an Orbitrap Fusion Lumos mass spectrometer controlled by Xcalibur 3.0 software (Thermo Fisher Scientific) and operated in data-dependent acquisition mode using an SPS-MS3 workflow. FTMS1 spectra were collected at a resolution of 120000, with an automatic gain control (AGC) target of 200000 and a max injection time of 50 ms. Precursors were filtered with an intensity threshold of 5000, according to charge state (to include charge states 2–7) and with monoisotopic peak determination set to Peptide. Previously interrogated precursors were excluded using a dynamic window (60s +/−10 ppm). The MS2 precursors were isolated with a quadrupole isolation window of 0.7 m/z. ITMS2 spectra were collected with an AGC target of 10000, max injection time of 70 ms and CID collision energy of $35\%$. For FTMS3 analysis, the Orbitrap was operated at 50000 resolution with an AGC target of 50000 and a max injection time of 105 ms. Precursors were fragmented by high energy collision dissociation (HCD) at a normalised collision energy of $60\%$ to ensure maximal TMT reporter ion yield. Synchronous Precursor Selection (SPS) was enabled to include up to 10 MS2 fragment ions in the FTMS3 scan. ## Data processing The raw data files were processed and quantified using Proteome Discoverer software v2.1 (Thermo Fisher Scientific) and searched against the UniProt Rat database (downloaded July 2021: 35859 entries) using the SEQUEST HT algorithm. Peptide precursor mass tolerance was set at 10 ppm, and MS/MS tolerance was set at 0.6 Da. Search criteria included oxidation of methionine (+15.995 Da), acetylation of the protein N-terminus (+42.011 Da) and Methionine loss plus acetylation of the protein N-terminus (−89.03 Da) as variable modifications and carbamidomethylation of cysteine (+57.0214) and the addition of the TMTpro mass tag (+304.207) to peptide N-termini and lysine as fixed modifications, phosphorylation of serine, threonine and tyrosine (+79.966) were included as variable modification. Searches were performed with full tryptic digestion and a maximum of 2 missed cleavages were allowed. ## Phosphopeptide abundance processing The phosphorylation status of identified peptide spectral matches (PSMs) was determined by PD2.4 and the site of phosphorylation predicted by PD2.4 using the PhosphoRS module. Phosphorylation sites predicted by PhosphoRS with greater than $70\%$ confidence were taken as the likely phosphorylation site, and phosphopeptides identified with identical sequences and predicted phosphorylation sites, were combined to provide improved quantitation and confidence. The number of PSMs used to calculate the phosphosite abundance is shown in the “Contributing PSMs” column in Supplemental Table 2. Where a peptide is predicted to be phosphorylated (based on its mass), but the software is unable to assign the site, the site is listed as “Ambiguous”. Where multiple phosphorylation events are unable to be located to specific sites within a peptide, the word “Ambiguous” is repeated the corresponding number of times. As peptides can often be matched to multiple proteins, the list of proteins to which each peptide matched was searched against the list of master proteins in the Total Protein analysis, and if a matching protein was identified, this protein was used as the master protein for that peptide. ## Statistical analysis Image brightness and contrast adjustments were made to the whole image in Leica LAS X software and matched for comparisons. Statistical analyses and plotting of data were performed in GraphPad Prism version 9. Statistical differences between two experimental groups were evaluated using independent-sample unpaired t tests. One-way ANOVA with Tukey's or Dunnett's post hoc tests were used to determine the difference between more than two samples with only a single influencing factor. Two-way ANOVA with a Šídák post hoc test was used to compare control and experimental values at each timepoint in physiological studies. Two-way ANOVA with Tukey's post hoc test was used to compare every mean with every other mean. Statistical analysis of qRT-PCR data was performed using delta Ct values. A Grubbs' test was performed in GraphPad to identify any significant outliers with an Alpha = 0.05. Viral injection misses confirmed by qRT-PCR or through the visualisation of the GFP reporter expression were not included in analyses. Two animals were removed from liraglutide experimental series 4 after 36 h as they did not show the classic decrease in food intake or urine output on day 1 (0–12 h) of liraglutide treatment but did on day 2 (24–36 h). This is likely due to the first injection being missed as documented for the IP injection route [39]. Statistical significance for phosphoproteomics was then determined using Welch's t tests between the conditions of interest. Since it has been discussed that the use of multiple testing corrected false discover rate (FDR) may be too blunt and restrictive for proteomic analysis [18], especially when analysing such a heterogeneous and complex tissue as the brain [[19], [20], [21], [22], [23], [24]], we considered uncorrected p ≤ 0.05 as differentially expressed phosphosites in our NIL analysis. Data are presented as the mean ± SEM where p ≤ 0.05 was considered statistically significant. ## Gene ontology and pathways analysis Transcriptomic gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses were performed in ShinyGO v.76 [40]. GO and KEGG analysis for SON Creb3l1 KD RNA-seq genes with log2 fold change less than < -1 were performed by comparing with a background of all expressed genes by RNA-seq filtered for a baseMean >10. Phosphoproteomics gene ontologies were performed using SynGO [41]. In SynGO we used brain expressed genes as the background list and terms were identified as significant if they were enriched at $1\%$ FDR. ## Data availability We mined data from SON Creb3l1 KD RNA-seq which has been banked in NCBI's Gene Expression Omnibus under SuperSeries GSE200402 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE200402). All proteomic data has been deposited at the ProteomeXchange Consortium via the PRIDE partner repository with dataset identifier PXD037495. Full western blot lane images can be found in (supplemental file 1). All novel materials and raw data are available to the community upon request to the corresponding author. Data will be made available on request. ## SON Creb3l1 KD RNA-seq identifies several GPCRs as Creb3l1 target genes In this study, we asked about downstream gene targets of CREB3L1 in MCNs of the hypothalamus. The experimental protocol for bilateral AAV delivery into SONs is shown in Figure 1A. We focused on genes significantly reduced in expression by ≤ −1 log fold-change in RNA-seq data of Creb3l1 KD vs. control SONs (Supplemental Table 1 [26]). We show a volcano plot of differentially expressed genes (Figure 1B). To identify gene categories and pathways that might be regulated by CREB3L1, we performed pathway analysis using GO and KEGG (Figure 1C, Supplemental Table 1). A single enriched term in GO: Cellular Component was identified which was Extracellular Region. Four enriched terms were identified in GO: Molecular Function with Hormone Activity and Receptor Ligand Activity being the most significantly enriched. There were no significant GO terms identified for Biological Process. Analysis by KEGG returned a single enriched pathway, KEGG: Neuroactive Ligand–Receptor Interaction. We next asked about downregulated genes in this KEGG pathway. We identified several GPCRs including growth hormone releasing hormone receptor (Ghrhr), Glp1r, oxytocin receptor (Oxtr), and cholecystokinin B receptor (Cckbr) (Figure 1D). The RNA-seq basemean for Ghrhr was much lower than for other GPCRs so was not investigated further. By qRT-PCR we confirmed significant decreases to Cckbr ($t = 3.407$, $$p \leq 0.005$$), Glp1r ($t = 6.198$, $p \leq 0.001$), and Oxtr ($t = 3.268$, $$p \leq 0.007$$) expression in KD SONs (Figure 1E). We further show that Creb3l1 KD in the PVN decreased Oxtr expression ($t = 4.615$, $$p \leq 0.004$$) (Figure 1E). Of this GPCR trio the Glp1r was most strikingly decreased in the SON making it our primary target for further investigation. Figure 1Identification of the Glp1r as a target for regulation by transcription factor CREB3L1. A, outline of the experimental protocol. AAVs expressing control or Creb3l1 specific shRNAs were stereotaxically injected into individual SONs of the same animal. SONs were collected for RNA-seq 3 weeks after AAV delivery. B, volcano plot showing differentially expressed genes (DEGs) in Creb3l1 KD SONs. DEGs are displayed as red (upregulated) and ≤ −1 log2 fold change dark blue (downregulated) dots. The top 10 most DEGs genes by log2 fold change are displayed. C, RNA-seq gene ontology analysis showing enriched terms for Cellular Component (CC), Molecular Function (MF) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways for DEGs ≤ −1 log2 fold change. D, bar chart of the gene components of the KEGG pathway Neuroactive ligand–receptor interaction affected by Creb3l1 KD displayed as log2 fold change. GPCRs are shown as blue bars. E, relative mRNA expression was investigated by qRT-PCR in the SON and PVN of Creb3l1 KD animals. F, dual immunostaining of CREB3L1 and GLP-1R in control and Creb3l1 KD PVN and SON of 3-day WD rats. G, luciferase reporter vector constructed for the rat Glp1r promotor. H, luciferase reporter assays were performed in HEK293T and N2a cells in the presence of CREB3L1CA. Luciferase expression was normalised to the expression of the renilla luciferase control reporter vector and to luciferase expression in control treated cells for each cell-line. Values are means + SEM of $$n = 7$$ SONs per group, $$n = 4$$–5 animals per group (PVN) or $$n = 3$$ per group for cell studies. OC, optic chiasm; 3 V, third ventricle. ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001. Scale bars = 50 μm. Figure 1 To investigate any relationship between GLP-1R and CREB3L1 expression, immunostaining was performed in Creb3l1 KD animals following water deprivation (WD), a stimulus that robustly increases CREB3L1 expression in AVP MCNs [21]. There was co-expression of CREB3L1 and GLP-1R in PVN and SON MCNs of control virus injected SONs, but expression of both proteins markedly decreased in KD SONs (Figure 1F). Indeed, KD dramatically reduced GLP-1R expression in MCNs of the SON and PVN, but interestingly expression in parvocellular neurones was preserved [42], as was the expression of CREB3L1 in astrocytes [21]. This preserved expression in astrocytes likely reflects the significantly higher tropism of $\frac{1}{2}$ viral serotypes for neurones [43]. Further, we have shown that CREB3L1 is expressed predominantly in the MCN component of the PVN [20,21]. This suggested that CREB3L1 regulation of Glp1r expression was specific to the MCN cell population, where CREB3L1 is highly expressed and further increases in expression in response to a rise in plasma osmolality [21]. This suggested a novel CREB3L1 mediated Glp1r regulatory transcriptional activation pathway in these cells. To test if CREB3L1 could be a transcription factor of the Glp1r gene, we created a rat Glp1r promoter luciferase construct (Figure 1G). This region of the Glp1r promoter contains a consensus CREB3L1 binding site between −2434 and −2429 bp [44]. The overexpression of a constitutively active CREB3L1 mutant (CREB3L1CA [45]) significantly increased promoter activity in HEK293T ($t = 13.46$, $p \leq 0.001$) and N2a ($t = 10.62$, $p \leq 0.001$) cells in agreement with CREB3L1 being a transcriptional regulator of the Glp1r gene. ## Physiological stimulation of the HNS increases Glp1r expression in AVP neurones We next took advantage of archived samples in the lab to investigate PVN and SON Glp1r expression in different physiological conditions that elicit increases in AVP and OXT synthesis and release. WD and salt loading (SL) represent classic models to investigate HNS activity [14]. There were significant (one-way ANOVA) alterations to Glp1r expression in the SON following acute (F2,15 = 5.679, $$p \leq 0.015$$) and chronic (F2,15 = 80.05, $p \leq 0.001$) osmotic challenges (Figure 2A). Dunnett's multiple comparisons test showed that WD and SL significantly increased Glp1r expression (1 d WD, $$p \leq 0.040$$; 1 d SL, $$p \leq 0.012$$; 3 d WD, $p \leq 0.001$; 7 d SL, $p \leq 0.001$). There were also significant (one-way ANOVA) differences in Glp1r expression in the PVN following acute (F2,14 = 4.973, $$p \leq 0.023$$) and chronic (F2,15 = 73.78, p = $p \leq 0.001$) osmotic challenges (Figure 2A). Dunnett's multiple comparisons test showed that WD and SL significantly increased Glp1r expression (1 d SL, $$p \leq 0.014$$; 3 d WD, $p \leq 0.001$; 7 d SL, $p \leq 0.001$). To strengthen a relationship between HNS activity and Glp1r expression, we considered PVNs and SONs from lactating rats, a model that increases AVP and OXT release [2,46]. We found significantly higher Glp1r expression in SONs of lactating rats ($t = 6.466$, $p \leq 0.001$), but no change in the PVN (Figure 2B). The expression of Creb3l1 was significantly increased the SON ($t = 6.384$, $p \leq 0.001$) and also the PVN ($t = 2.844$, $$p \leq 0.016$$) in lactating rats (Figure 2B). In addition, we show that Glp1r expression is not increased by a short-term rise in plasma osmolality mediated by acute hypertonic saline injection (Figure 2C).Figure 2Activation of the HNS increases Glp1r expression in Avp MCNs. A, relative mRNA expression of Glp1r was investigated by qRT-PCR in the SON and PVN of 1 day WD and 1 day salt loaded (SL) male rats. B, relative mRNA expression of Creb3l1 and Glp1r was investigated by qRT-PCR in the SON and PVN of female control and lactating rats. C, relative mRNA expression of Glp1r was investigated by qRT-PCR in the SON and PVN of male rats following a single injection of hypertonic saline. D, qRT-PCR analysis of Glp1r mRNA expression in AVP and OXT punches in control and 3-day WD rat PVN. E, fluorescent in situ hybridisation to mark the distribution of Avp (blue), Oxt (red) and Glp1r (white) mRNAs in control and 3-day WD SON MCNs. F, Glp1r RNAscope in situ hybridization in control and 3-day WD SONs. Graphs show gene expression as a function of Glp1r mRNA dots/cytoplasm (Cyto) or nucleus (Nuc) of AVP and OXT neurones in control and WD conditions. Values are means + SEM of $$n = 4$$–7 animals per group. ∗p ≤ 0.05, ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001. Scale bar = 100 μm. Figure 2 Our next step was to identify cell populations in the PVN and SON where Glp1r is expressed in control SON and following stimulation. We chose WD as the stimulus due to our extensive knowledge of this physiological manipulation. The architecture of the rat PVN allows for enrichment of AVP and OXT neuronal populations by tissue punching [25]. We found increased Glp1r expression in AVP enriched punch samples following WD (Figure 2D). In addition, we performed RNAscope to visualise the location of the Glp1r mRNA together with Avp and Oxt in the SON (Figure 2E). Analysis of control SONs showed that the Glp1r mRNA is located in cytosolic and nuclear compartments of AVP and OXT MCNs with no difference between cell-types (Figure 2F). However, WD significantly increased Glp1r mRNA abundance in the cytoplasm of only AVP MCNs ($t = 4.664$, $$p \leq 0.002$$). The result of this was significantly more Glp1r mRNA in AVP compared to OXT MCNs in WD ($t = 3.855$, $$p \leq 0.005$$). Thus, we establish that increased Glp1r expression in WD is the result of increased expression by AVP MCNs. ## WD exclusively increases GLP-1R expression in MCN cell compartments – cell body and nerve terminals We next looked at GLP-1R protein expression in the PVN and SON as well as several other brain regions that express this receptor [42]. It is important to note that we and others have carried out robust and exhaustive validation of the GLP-1R antibody for immunostaining [47,48]. The study of GLP-1R expression in several brain regions suggested that increased expression was unique to AVP MCNs in WD (Figure 3A, Supplemental Figure 1). We further identified alterations to compartments that comprise the HNS, cell soma, median eminence (ME), and posterior pituitary (PP). In addition, we show that GLP-1R expression is increased by 1 day of WD in the PVN and SON. We next performed western blots to validate these observations. Again, it is important to mention that GLP-1R detection by western blotting has been the subject of some debate in the literature. With this in consideration, further antibody controls were carried out looking at N-linked glycosylation of GLP-1R immunoreactive bands (Supplemental Figure 2). The strength of our approach was the application of several techniques to investigate the expression of the GLP-1R at the mRNA and protein levels for a single brain region. We identify GLP-1R immunoreactive bands in the SON and PVN with changes to band intensity consistent with results from in situ hybridisation and immunostaining approaches (Figure 3B). The high GLP-1R expression in the arcuate nucleus, NTS, and AP are supported by the literature [42]. GLP-1R immunoreactive bands in the SON ($t = 3.921$, $$p \leq 0.003$$) and NIL ($t = 4.640$, $$p \leq 0.001$$) were significantly increased by WD (Figure 3C). The increased molecular weight of the GLP-1R immunoreactive bands is consistent with this receptor being glycosylated [49]. Importantly, there was no detectable expression of the Glp1r mRNA in NIL cDNA samples for our previous study as determined here by qRT-PCR [24]. We thus establish increased GLP-1R expression in AVP MCNs in WD (Figure. 3D).Figure 3Discrete changes in GLP-1R expression in the SON and PVN of the WD rat brain. A, immunostaining of GLP-1Rs in the subfornical organ (SFO), SON, PVN, arcuate nucleus (Arc), NTS, area postrema (AP), median eminence (ME), and NIL of control and 3-day WD rats. Lower 4 panels are for 1 day WD. Inset are panels showing AVP staining (red) in the PVN of control and 1 day and 3-day WD animals. The white broken circle indicates the location of the PVN magnocellular bundle that is highly enriched with AVP expressing MCNs in the rat. The boxed regions of the ME in the Arc images have been expanded to visualise the staining of axons. B, western blot of GLP-1R immunoreactive bands in total protein extracts from GLP-1R expressing brain areas from control and 3-day WD rats. The ∗ in this panel indicates non-specific bands confirmed by receptor KD. Other marked bands have been confirmed by receptor KD. A separate image of the NIL captured at lower intensity is shown. Actin is shown as the housekeeping gene. C, densitometry analysis of GLP-1R immunoreactive bands in the SON and NIL. D, summary diagram of GLP-1R expression in control and WD AVP MCNs. Integrated into this diagram are changes to the synthesis and secretion of AVP in WD. OC, optic chiasm; 3 V, third ventricle; AP, anterior pituitary; IL, intermediate lobe. Values are means + SEM of $$n = 5$$–6 animals per group. ∗∗p ≤ 0.01. Scale bars = 50 μm. Figure 3 ## The effects of WD on central and peripheral GLP-1 systems We next asked about the effects of WD on central and peripheral GLP-1 systems that likely target HNS GLP-1Rs as shown in Figure 4A. We first asked about active GLP-1 expression in afferent fibers in the PVN and SON using a validated antibody [48]. Immunoreactive GLP-1 positive fibers were found in close proximity to MCNs (Figure 4B) as previously described [28]. The expression and distribution of GLP-1 positive fibers was similar in control and WD rats whilst receptor was increased (Figure 4C). We next moved to determine the location of Gcg neurones in the rat NTS by in situ hybridization (Figure 4D). In agreement with reports by others [50], Glp1r expression was found in a separate population of cells to Gcg in the NTS. We found by qRT-PCR that WD significantly decreased Gcg mRNA expression in the NTS ($t = 3.101$, $$p \leq 0.010$$) whilst Glp1r and Fos expression was not altered (Figure 4E). It is known that WD increases FOS expression by some neurones in the NTS [51]. We asked if Fos increased in Gcg neurones. We show that WD does not increase Fos expression in Gcg neurones in the NTS compared to control and rehydration, rather expression appeared to decrease (Figure 4F). In our recent SON proteomics study GCG abundance was similarly not altered by WD ([52], Figure 4G). This is consistent with the decrease to Gcg expression. As GLP-1Rs located in the PP possibly receive inputs from circulating GLP-1, we measured total plasma GLP-1 in rats WD for 2 and 3-days (Figure 4H). Measures of total plasma GLP-1 were performed as blood samples were not collected in the presence of dipeptidyl peptidase IV. In the periphery McKay et al. [ 53] reported no change in gut Gcg expression or total plasma GLP-1 in 1-day WD rats consistent with our findings with more prolonged WD. Thus, central and peripheral GLP-1 inputs to the HNS appear not to be significantly altered by WD.Figure 4GLP-1 inputs to the HNS. A, schematic showing the central (NTS) and peripheral (gut) sources of endogenous GLP-1 capable of signalling the MCN GLP-1R populations. B, immunostaining of active GLP-1 containing afferent fibers in the proximity of AVP and OXT neurones in the PVN and SON. C, immunostaining of active GLP-1 containing afferent fibres in the proximity of GLP-1R positive neurones in the PVN and SON of control and WD animals. D, in situ hybridisation of Gcg (green) and Glp1r (red) expressing cells in the rat NTS. E, qRT-PCR analysis of Gcg, Glp1r, and Fos mRNA expression in control and 3-day WD NTS samples. F, in situ hybridisation of Fos (white) and Gcg (green) expressing cells in the rat NTS in control, WD, and WD + 4 h rehydration. Arrow heads indicate Gcg positive neruones. G, total protein raw abundance of GCG in the SON according to LC-MS/MS between control and 2-day WD rats. H, plasma levels of total GLP-1 in samples from 2-day and 3-day WD rats. Values are means + SEM of $$n = 5$$–8 animals per group. OC, optic chiasm; 3 V, third ventricle; cc, central canal. ∗∗p ≤ 0.01. Scale bars = 100 μm. Figure 4 ## Important physiological roles for the SON population of GLP-1Rs To investigate MCN GLP-1Rs functions we used AAVs to deliver Glp1r specific shRNAs to rat SONs for gene KD and non-targeting shRNAs as controls (Figure 5A). Importantly, this represents the first study to selectively target SON Glp1rs to investigate in vivo functions. We measured food intake (Figure 5B), fluid intake (Figure 5C) and body weight (Figure 5D). A two-way ANOVA revealed that KD (viral treatment) significantly altered food (F1,60 = 18.76, $p \leq 0.001$), water intake (F1,60 = 4.473, $$p \leq 0.036$$) and body weight (F1,180 = 36.91, $p \leq 0.001$). Sidak's multiple comparisons found that KD significantly altered body weight from week 6 compared to control (wk6, $$p \leq 0.021$$; wk7, $$p \leq 0.003$$; wk8, $$p \leq 0.003$$). We and others have shown that peptide stores in the pituitary gland are a reliable metric for determining changes to synthesis/secretion throughout experimental protocols [26,[54], [55], [56]]. We found depleted stores of AVP ($t = 2.476$, $$p \leq 0.033$$) and OXT ($t = 3.683$, $$p \leq 0.004$$) in KD rats (Figure 5E). This likely indicates increased hormone release. However, end point measures of plasma copeptin and OXT were not altered (copeptin, con = 73.9 ± 5.9 pg/ml, Glp1r = 73.6 ± 3.8 pg/ml; OXT, con 8.97 ± 3.74, Glp1r = 7.47 ± 1.97). We next asked about signalling pathways altered in KD SONs. The phosphorylation of (ERK)-$\frac{1}{2}$ is a pathway that is activated by GLP-1R signalling in the brain [57] as well as being activated in the SON and NIL by WD (Supplemental Figure 3 [30]). To confirm KD we probed blots for GLP-1R expression (Figure 5F). Immunoreactive GLP-1R bands in the SON were significantly diminished in KD samples ($t = 5.811$, $$p \leq 0.001$$). Moreover, Glp1r KD significantly decreased ERK-2 phosphorylation ($t = 3.542$, $$p \leq 0.012$$) compared to controls (Figure 5G). Immunostaining for phosphorylated ERK$\frac{1}{2}$ in KD SONs showed diminished expression in MCNs in the SON (Figure 5H). Further, investigation of phosphorylated ERK$\frac{1}{2}$ in PP nerve terminals of a KD SON also suggested a decrease in ERK$\frac{1}{2}$ phosphorylation in the pituitary (Supplemental Figure 4). Thus, GLP-1R signalling by ERK$\frac{1}{2}$ provides a potential signalling pathway to instruct hormone release from soma and dendrites and PP nerve terminals. Therefore, we establish important roles for SON GLP-1 signalling in the regulation of ingestive behaviour which is likely mediated by altered HNS hormone release. Figure 5Assessment of Glp1r KD in the SON reveals changes to ingestive behaviour, MCN signalling pathways, and HNS outflows. A, outline of the experimental protocol involving viral vector deliver of specific shRNAs to KD Glp1r expression bilaterally in the SON. The success of gene KD was confirmed by diminished GLP-1R immunostaining in the SON. Food (B) and water intake (C) and body weight (D) were monitored for 8 weeks after viral delivery. E, endpoint measures of AVP and OXT pituitary stores determined by ELISA in control and KD animals. F, immunoblots of GLP-1R, Tubulin, pERK and tERK immunoreactive bands in SON total protein extracts from control and Glp1r KD SONs. G, densitometry analysis of immunoreactive bands from F. H, immunostaining for pERK in the SON of control and Glp1r KD rats. Values are means + SEM of $$n = 5$$–8 animals per group. OC, optic chiasm. ∗p ≤ 0.05, ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001. Scale bars = 50 μm. Figure 5 ## GLP-1RA liraglutide inhibits Avp synthesis both basally and in response to WD We next asked about the effects of GLP-1R activation in control and WD states (Figure 6A). In rodents, GLP-1RA liraglutide inhibits food and water intake [53] as we show here in Figure 6B. A two-way ANOVA showed that liraglutide treatment had a significant effect on food (F1,22 = 32.13, $p \leq 0.001$) and water intake (F1,22 = 59.99, $p \leq 0.001$). Sidak's multiple comparisons found that liraglutide significantly altered food intake at 12 h ($p \leq 0.001$) and water intake after 4 ($$p \leq 0.025$$), 12 ($p \leq 0.001$) and 24 ($$p \leq 0.030$$) hours. We next looked at plasma copeptin levels (Figure 6C). A two-way ANOVA showed that liraglutide treatment did not significantly change plasma copeptin. It is worth mentioning that plasma copeptin levels were significantly reduced as determined by t-test at the 2-hour timepoint ($t = 8.694$, $p \leq 0.001$). We next looked at gene expression in the SON (Figure 6D). A two-way ANOVA showed that hnAvp, Fos, and Creb3l1 expression were significantly altered by liraglutide treatment (hnAvp, F1,22 = 16.40, $p \leq 0.001$; Fos, F1,22 = 5.915, $$p \leq 0.024$$; Creb3l1, F1,22 = 5.635, $$p \leq 0.027$$). Sidak's multiple comparisons test showed that liraglutide significantly reduced hnAvp ($p \leq 0.001$) and Fos ($p \leq 0.001$) expression at 4 h and Creb3l1 ($$p \leq 0.0153$$) at 12 h.Figure 6GLP-1RA liraglutide inhibits Avp synthesis and Fos expression in the SONs of control and WD rats. A, outlines of the experimental protocols for liraglutide or vehicle treatment basally and during WD. B, Endpoint measures of food intake and water intake. C, plasma copeptin 2, 4, 12, and 24 h after a IP injection of liraglutide compared to time-matched vehicle injected controls. D, Relative mRNA expression of Avp, hnAvp, Fos, Glp1r, and Creb3l1 in the SONs of rats 2, 4, 12, and 24 h after IP injection of liraglutide compared to time-matched vehicle injected controls. The data has been normalised to the 2-hour vehicle control group. E, relative mRNA expression of Avp, hnAvp, Fos, Glp1r, and Creb3l1 in the SONs of WD rats 4 h after a single IP injection of liraglutide compared to time-matched vehicle injected controls. F, immunostaining of FOS expression in the SON of WD rats 4 h after a single intraperitoneal injection of liraglutide compared to time-matched vehicle injected controls. G, graphical representation of SON explant isolation for ex vivo experimentation. H, relative mRNA expression of Avp and hnAvp in SON explants isolated from control and 3-day WD rats treated with vehicle or liraglutide in the media for 4 h. The data has been normalised to the 4-hour vehicle control group. Values are means + SEM of $$n = 3$$–5 animals per group or $$n = 8$$–10 SONs per group. OC, optic chiasm. ∗p ≤ 0.05, ∗∗∗p ≤ 0.001. Scale bars = 50 μm. Figure 6 The next step was to investigate gene expression responses to liraglutide in WD. We chose 4 h liraglutide treatment based on findings in the control state. We show significantly decreased hnAvp ($t = 5.332$, $p \leq 0.001$) and Fos expression ($t = 3.081$, $$p \leq 0.015$$), and in contrast increased Avp expression ($t = 2.400$, $$p \leq 0.043$$), but no change to Glp1r or Creb3l1 expression (Figure 6E). It is well known that WD increases hnAvp expression in the SON to meet demands for increased synthesis [21]. This decrease in hnAvp is unexpected as WD presents a physiological state that requires increased AVP synthesis and release. We further show decreased FOS expression suggesting a decrease in neuronal activity (Figure 6F). We next asked specifically about the MCN GLP-1R population in this response. To address this, we isolated SONs to remove inputs and treated them with vehicle or liraglutide (Figure 6G). A two-way ANOVA revealed that liraglutide significantly alters Avp (F1,33 = 12.28, $$p \leq 0.001$$) and hnAvp RNA expression (F1,33 = 7.758, $$p \leq 0.009$$) (Figure 6H). Tukey's multiple comparisons test showed that SON cultures retained properties of WD SONs in cluture with Avp ($$p \leq 0.034$$) and hnAvp ($$p \leq 0.011$$) expression being significantly elevated above controls. Moreover, the addition of liraglutide to culture media significantly decreased Avp ($$p \leq 0.014$$) and hnAvp ($$p \leq 0.038$$) expression in WD SONs. Therefore, we establish that GLP-1R activation inhibits Avp synthesis and this may be mediated by direct targeting of MCN GLP-1Rs expressed at the cell soma. ## GLP-1RA liraglutide increases PP stores of AVP in WD despite increased renal fluid loss We next investigated the physiological response to liraglutide in WD rats as outlined in our experimental protocol (Figure 7A). Total food intake was significantly reduced ($t = 4.117$, $$p \leq 0.003$$) by liraglutide due to significantly decreased ($t = 4.933$, $$p \leq 0.001$$) dark phase food intake (Figure 7B). In the light phase food intake was significantly increased ($t = 3.723$, $$p \leq 0.006$$). We analysed daily food intake in 12-hour bins to study dark and light phase feeding patterns (Figure 7C). Liraglutide decreased food intake during the dark phase from 0 to 12 ($t = 6.813$, $$p \leq 0.001$$) and 24–36 ($t = 4.251$, $$p \leq 0.003$$) hours, but not 48–60 h. In contrast, liraglutide increased light phase food intake from 12 to 24 ($t = 3.067$, $$p \leq 0.015$$) and 36–48 ($t = 4.251$, $$p \leq 0.003$$) hours. The presentation of these data as cumulative food intake showed that liraglutide disrupted feeding and fasting periods (Figure 7D). There was no difference in body weight between the groups (Supplemental Figure 5).Figure 7Liraglutide treatment alters the physiological response to WD. A, outline of the experimental protocol. Animals were housed in metabolic cages for precise daily measures of food intake and urine output. Animals were injected with liraglutide from the onset and throughout WD. B, total food intake during the 60-hour WD protocol and separation of these measures into the dark and light phases of the light cycle. C, measures of food intake separated into 12-hour bins corresponding to the dark and light phases of the light cycle. D, graph displaying cumulative food intake. E, total urine output during the 60-hour WD protocol and separation of these measures into the dark and light phases of the light cycle. F, measures of urine output separated into 12-hour bins corresponding to the dark and light phases of the light cycle. G, graph displaying cumulative urine output. H, measures of urine osmolality separated into 12-hour bins corresponding to the dark and light phases of the light cycle. I, endpoint plasma glucose measures in control and liraglutide treatment. J, endpoint measures of plasma copeptin in control and liraglutide treated animals measured by ELISA. K, AVP pituitary stores were determined by ELISA in control and KD animals. L, relative mRNA expression of Avp, hnAvp, Fos, Glp1r, and Creb3l1 in the SONs in control and liraglutide treated animals. M, relative mRNA expression of Gcg, Glp1r, and Fos in the NTS in control and liraglutide treated animals. Values are means + SEM of $$n = 4$$–6 animals per group. ∗p ≤ 0.05, ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001.Figure 7 Many renal actions of GLP-1RAs have been described including diuresis and natriuresis [58]. AVP mediated renal water conservation is crucial during WD. We found increased ($t = 5.268$, $p \leq 0.001$) urine output in liraglutide treated animals. This was due to significantly increased ($t = 6.321$, $p \leq 0.001$) urine output during the dark phase (Figure 7E) where urine output significantly increased from 0 to 12 ($t = 6.344$, $p \leq 0.001$) and 48–60 ($t = 3.085$, $$p \leq 0.015$$) hours (Figure 7F). These data have also been presented as cumulative urine output (Figure 7G). We next asked about urine concentration (Figure 7H). Liraglutide treatment lowered urine osmolality at all timepoints except 36–48 h (0–12, $t = 12.28$, $p \leq 0.001$; 12–24, $t = 3.997$, $$p \leq 0.004$$; 24–36, $t = 2.692$, $$p \leq 0.0274$$; 48–60, $t = 2.678$, $$p \leq 0.028$$). We then asked about plasma parameters. We found significantly lowered plasma glucose levels in liraglutide treated animals (Figure 7I; $t = 4.416$, $$p \leq 0.002$$). There were no differences in plasma protein (con 44.77 ± 1.31 mg/ml and liraglutide 43.53 ± 0.92 mg/ml), osmolality (con 328.67 ± 1.05 and liraglutide 325 ± 1.10) or copeptin compared to vehicle controls (Figure 7J). However, we found significantly more AVP ($t = 3.658$, $$p \leq 0.006$$) stored in pituitaries of liraglutide treated animals (Figure 7K). This suggested a reduction in AVP secretion during the course of WD. Interestingly, the expression of Glp1r ($t = 2.833$, $$p \leq 0.022$$) and Creb3l1 ($t = 2.666$, $$p \leq 0.029$$) were reduced in SONs of liraglutide treated animals (Figure 7L). In contrast, Glp1r expression was increased ($t = 4.065$, $$p \leq 0.004$$) in the NTS by liraglutide treatment (Figure 7M). Thus, we establish changes to AVP release and altered GLP-1R expression by liraglutide in WD. ## NIL phosphoproteomics reveals changes to the phosphorylation of proteins that control vesicle exocytosis from presynaptic nerve terminals Our data so far suggested that activation of GLP-1R leads to the inhibition of AVP synthesis and secretion. To investigate signalling events in nerve terminals of the PP the site of AVP and OXT release, which expresses an abundance of GLP-1Rs [59], we performed a phophoproteomics screen of the NIL after acute liraglutide injection as outlined in our experimental protocol (Figure 8A) The proteomics data can be found in Supplemental Table 2. We identified changes to the phosphorylation status of 45 proteins all of which were hyperphosphorylated (Figure 8B). We validated increased synaptosome associated protein 25 (SNAP25 T138) phosphorylation by western blotting (Figure 8C), in agreement with the phosphoproteomics data (Figure 8D). We next used SynGO to detail synapse specific changes in the NIL (Figure 8E, Supplemental Table 3). We identified three enriched terms in SynGO: Cellular Component with Presynapse being most significantly enriched. We identified three enriched terms in SynGo: Biological Process with Synaptic Vesicle Exocytosis the most significantly enriched. Genes in these terms are tabled in Figure 8F. We next asked about phosphosite changes to other proteins in SynGO terms (Figure 8G). We found hyperphosphorylation events for CASK (S395) a scaffolding protein with a role in synaptic transmembrane protein anchoring and ion channel trafficking, regulator of voltage-gated L-type calcium channels CACNB1 (S547), Stxbp5 (S693) a protein that inhibits membrane fusion between transport vesicles and the plasma membrane, CASKIN1 (S827, T829) a brain-specific adaptor protein for CASK, PCLO (S2514) a scaffold protein of the presynaptic cytomatrix at the active zone which is the place in the synapse where neurotransmitters are released (Figure 8H). We next isolated NILs to remove central and peripheral inputs to focus on signalling by GLP-1Rs expressed in PP nerve terminals. Treatment with liraglutide for 30 min significantly increased ($t = 2.430$, $$p \leq 0.032$$) SNAP25 phosphorylation at T138 (Figure 8I). Thus, these phosphorylation events may be regulated by directly targeting the nerve terminal GLP-1R population in the PP.Figure 8The liraglutide treated NIL phosphoproteome reveals phosphosite changes to components of the SNARE complex that facilitate vesicular exocytosis. A, outline of the experimental protocol. B, volcano plot of phosphoprotein changes in rat NILs after liraglutide treatment compared to vehicle controls. Selected protein names have been highlighted. C, immunoblot of pSNAP25 and SNAP25 immunoreactive bands in the NIL after liraglutide treatment compared to vehicle controls. Densitometry analysis of immunoreactive bands for pSNAP25 and SNAP25. D, phospho protein raw abundance of pSNAP25 in the NIL according to LC-MS/MS between control and 30-minute liraglutide treated rats. E, analysis of phophoproteomics hyperphosphorylated proteins by SynGO. Figures displaying colour coded enriched terms by Q value for the ontology terms Cellular Component and Biological Process. All level terms identified have been labelled. F, bar chart showing SynGO enriched terms for Cellular Component and Biological Process displayed a -log10 p adjusted value. G, table of hyperphosphorylated proteins containing the overrepresented terms in SynGo ontologies Cellular Component and Biological Process. H, mapping of selected phosphosites undergoing hyperphosphorylation in response to 30 min liraglutide treatment compared to vehicle controls. Blues stars indicate the identification of a novel phosphosite for CASKIN1. I, immunoblots of pSNAP25 and SNAP25 immunoreactive bands in the NIL after liraglutide treatment ex vivo compared to vehicle controls. Densitometry analysis of immunoreactive bands for pSNAP25 and SNAP25 from ex vivo NILs. Values are means + SEM of $$n = 5$$ animals per group or $$n = 7$$ half pituitaries per group. ∗p ≤ 0.05, ∗∗p ≤ 0.01.Figure 8 ## Discussion It has been known for decades that receptors for the satiety peptide GLP-1 are abundant throughout the HNS, of rodents and humans [59]. Yet, a large void has remained regarding assessment of their function. Here, we have discovered increased GLP-1R expression in response to physiological stimuli that activate MCNs and increase HNS hormone release. This provided a new and exciting dynamic to assess central and peripheral signaling of the HNS axis by GLP-1 and GLP-1RA liraglutide. The mechanisms regulating AVP and OXT release have remained rudimentary due to a lack of reliable and precise measurements of circulating levels in vivo, with copeptin that is released in an equimolar mode to AVP most used [5]. We, and others, have shown PP stores of AVP and OXT provide a robust measure for changes to secretion, with activation and inhibition of AVP neurones expected to reduce and increase PP stores, respectively [26,[54], [55], [56]]. The KD of SON GLP-1Rs depleted stores of AVP and OXT, and likely increased release. In the 90's, Larsen and colleagues delivered GLP-1 into the rat brain and observed increased AVP release [60], a response reproduced by others [61]. However, Zueco and colleagues reported a decrease in AVP secretion following intravenous administration of GLP-1 in rats [60]. Based on our targeted KD approach and liraglutide treatment, we suggest that activation of MCN GLP-1R population serves to inhibit HNS hormone release. Several populations of GLP-1Rs in the brain influence feeding behaviour. In this study, changes to feeding are likely the result of altered HNS hormone release. Indeed, central, and peripheral administration of OXT reduce food intake. However, the role of endogenous OXT in feeding behaviours is far more complex and the subject of dispute [62]. Interestingly, vagal afferents express OXT receptors which are required for peripheral OXT-induced eating suppression and represent the major vagal input to NTS GCG neurones [[63], [64], [65]]. This circuit duly provides an OXT feedback mechanism to regulate the activity of MCNs by central GLP-1 circuits innervating the SON. There is also recent evidence that AVP has a role in feeding. The chemogenetic activation of AVP neurones in rats and mice leads to reduction in food intake [66,67]. Therefore, changes to both HNS hormones may contribute to the decrease in food intake in our GLP-1R KD animals as a result of increased release. The creation of long-acting GLP-1RAs for the treatment of diabetes and obesity has provided a “pharmacological GLP-1 circuit’’ to study GLP-1R functions. When delivered peripherally in rodents long-acting GLP-1RAs including exendin-4, liraglutide, and semaglutide access receptors in the periphery including those in the highly vascularized PP, circumventricular entities of the brain, and within the PVN and SON where MCN cell bodies reside [[68], [69], [70]]. In this study, liraglutide inhibited Avp synthesis in control and WD SONs and isolated SONs. This further supported GLP-1 as an inhibitor of HNS activity. We next asked about the physiological effects of liraglutide in WD. Several studies in humans and rodents have shown that GLP-1 and GLP-1RA act on the kidney to induce diuresis and natriuresis [58]. In this study, this response persisted during WD. Our data suggests that decreased AVP release contributes to liraglutide-induced diuresis in the rat. Furthermore, we show that liraglutide decreased plasma glucose levels in WD animals. Liraglutide is known to work by stimulating the secretion of insulin whilst supressing glucagon secretion in a glucose-dependent manner and has been reported not to induce hypoglycaemia [71]. Interestingly AVP is known to stimulate glucagon secretion in rodents and humans and a recent study utilising stimulatory DREADDs to activate SON AVP neurones reported increased circulating copeptin, glucagon and blood glucose levels which were blocked by glucagon receptor and V1bR antagonists [72]. Therefore, the observed decrease in blood glucose may result from reduced AVP release. Thus, we provide a link between the AVP system, hydration status, glucoregulatory health, and liraglutide treatment that may be of clinical importance. To investigate processes within PP nerve terminals, we performed a phophoproteomics screen of the NIL following acute liraglutide administration. We identified changes to the phosphorylation status of constituents of the SNARE complex that mediates vesicle exocytosis and hormone release [73]. SNAP25 is required for the fusion of synaptic vesicles with the plasma membrane to regulate exocytosis. The phosphorylation of SNAP25 at T138 regulates vesicle priming by inhibiting the assembly of the SNARE complex which is important for neuropeptide release [74,75]. SNAP25 interacts with STXBP5 to regulate AVP secretion in an in vitro system and both proteins are highly expressed in PP nerve terminals [76]. Furthermore, synaptic release occurs from a small section of the presynapse nerve terminal called the active zone [77]. Our data shows that liraglutide alters the phosphorylation status of several proteins including PCLO, CASK and CASKIN1 which are scaffold proteins that organise the active zone of the presynapse [78]. In addition, calcium in the PP nerve terminal is crucial for neuropeptide release and the phosphoproteomics data suggests liraglutide induced changes to calcium signalling pathways in the NIL. CASK interacts with N, P/Q, and L-type voltage gated calcium channels as well as several adapter proteins [[79], [80], [81], [82]]. The phosphorylation of L-type calcium channel subunit CACNB1 which regulates channel activity was also increased, and these channels are recognized as part of the molecular machinery for voltage-induced calcium release from internal stores in pituitary nerve terminals [83]. We further identify changes to the phosphorylation of calcium/calmodulin dependent protein kinase kinase 1 (CAMKK1) in the NIL. The hyperphosphorylation of S458 has been shown to decrease CAMMK1 activity and the presence of calcium/calmodulin has been shown to supress the phosphorylation of this site [84,85]. Taken together, these data suggest that peripheral GLP-1 can regulate AVP release by GLP-1R signalling in PP nerve terminals. In this study there are some limitations. The dual KD of GLP-1Rs in both AVP and OXT MCNs prevents the association of physiological changes to a single cell-type. The anatomical complexity of the HNS which can receive inputs from peripheral and central sources of GLP-1 also means that we do not yet fully understand the contribution of the separate GLP-1 inputs to HNS hormone release. This is something that is difficult to achieve in an intact system. We opted to measure copeptin in this study due to its superior stability to AVP [86]. However, we do not yet know the relationship between circulating AVP and copeptin following liraglutide treatment. It is known that liraglutide alters numerous metabolic parameters as well as causing diuresis and natriuresis which may alter this balance. In humans, non-specific increases in copeptin have been detected in acute settings such as hyponatraemia, suggesting that it may not always be an appropriate surrogate for AVP release [87]. To collect samples for phosphoproteomics we minimised the post-mortem sampling time to preserve phosphorylation events [88]. Thus, we must acknowledge that our samples also contain melanotrophs of the intermediate lobe which is difficult to quickly and cleanly remove from the PP. ## Conclusions In summary, we provide new insight and understanding into the role a distinct population of GLP-1R expressing neurones in the brain, an area of timely translational importance, as these receptors are drug targets for treatment of metabolic diseases and new combinational therapies are being formulated to improve treatment regimens. Thus, our findings are of direct clinical relevance and may have implications for risk assessment. For example, suboptimal glycaemic control and consequent dehydration are common during intercurrent illness in people with diabetes. Liraglutide is often used in the treatment of type 2 diabetes. If proven to be the case in humans, the effect on GLP-1RA on HNS hormone release could potentially worsen dehydration and should be held off during intercurrent illness. Some of the common side effects of liraglutide including nausea, which incidentally can be caused by altered AVP release [89], as well as vomiting and diarrhoea which require increased AVP release to prevent dehydration and acute kidney injury, with manufacturers and doctors already stipulating the need to maintain fluid intake. This new understanding may help with treatment regimens to alleviate some of the common contraindications of GLP-1RAs. Our findings further suggest that targeting HNS GLP-1Rs to reduce AVP secretion is one mechanism that may prevent the development of diseases associated with sustained increases in AVP release. ## Author contributions MPG., MG., DT., and DM: Conceptualization. MPG., MG., SBL., and DM: Methodology. MPG., MG., SBL: Formal Analysis. MPG., MG., SBL., JWH., and KS: Investigation. MPG., MG., and SBL: Supervision. MPG: Writing – Original Draft. All authors: Writing – Review & Editing. MPG., MG., and SBL: Data Curation. MPG., DT., and DM: Funding Acquisition. MPG., and DM: Project administration. ## conflict of interest None. ## Supplementary data The following is the Supplementary data to this article:Multimedia component 1Multimedia component 1 ## References 1. Ding C., Magkos F.. Oxytocin and vasopressin systems in obesity and metabolic health: mechanisms and perspectives. Curr Obes Rep (2019) 8 301-316. PMID: 31240613 2. Brown C.H., Bains J.S., Ludwig M., Stern J.E.. 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--- title: 'Mapping sociodemographic and geographical differences in human papillomavirus non-vaccination among young girls in Sweden' authors: - Maria Wemrell - Raquel Perez Vicente - Juan Merlo journal: Scandinavian Journal of Public Health year: 2022 pmcid: PMC9969304 doi: 10.1177/14034948221075410 license: CC BY 4.0 --- # Mapping sociodemographic and geographical differences in human papillomavirus non-vaccination among young girls in Sweden ## Abstract ### Aims: Human papillomavirus (HPV) vaccination plays a key role in the prevention of cervical cancer. Yet, disparities in HPV vaccination in Sweden have persisted. Previous research on such disparities has typically focused on singular sociodemographic variables and measures of average risk. Using a multi-categorical approach and drawing on intersectionality theory, this study aimed to provide a more precise mapping of HPV non-vaccination among girls in different sociodemographic groups and geographical areas in Sweden during 2013–2020. ### Methods: Using nationwide register data, we conducted a multi-categorical analysis of individual heterogeneity and discriminatory accuracy complemented by a multilevel geographical analysis. We mapped HPV non-vaccination prevalence across 54 strata defined by parental income, education and country of birth, and urban versus rural place of residence. We also disentangled municipal and regional influences on HPV non-vaccination. ### Results: HPV non-vaccination was more common in groups with a low income, a low education and an immigration background, whereas among those with an immigration background, the association between income, education and HPV non-vaccination was more complex. Geographical differences were found between municipalities. However, the discriminatory accuracy of the sociodemographic and geographical groups was weak, and $50\%$ of the non-vaccination cases were observed in eight strata, of which some are among those with low risk. ### Conclusions: Our findings underscore the importance of universal yet tailored approaches, including providing adequate information about HPV vaccination in Swedish and other languages, and of health-care professionals displaying sensitivity to patients’ and parents’ questions or needs. ## Introduction In order to prevent human papillomavirus (HPV) infections and cervical cancer [1], in 2010, Sweden introduced a school-based, free-of-charge quadrivalent HPV vaccination programme for girls. Since then, HPV vaccination has been shown to reduce the population-level risk of invasive cervical cancer substantially [2]. In 2020, boys were included in the programme. While HPV vaccine coverage varies between countries [3], in Sweden, it is comparatively high at around $80\%$ [4]. The national goal of $90\%$ coverage has not been reached, however. Alongside efforts towards improved understanding of attitudes and decision-making processes surrounding the HPV vaccine [5–7], including concerns about vaccine hesitancy [5,8], research has pointed to socio-economic differences in HPV vaccine uptake [9,10]. While in Sweden disparities have largely been augmented through the school-based HPV vaccination programme, differences pertaining to income, education and country of birth have persisted [9]. This is of particular concern, since low socio-economic position and immigration status are associated with a higher risk of non-attendance to cervical screening [11,12] and of incidence and mortality in cervical cancer, although the risk of cervical cancer is lower in some immigrant groups [13,14]. Geographical differences in HPV vaccination in Sweden have also been documented [4]. Still, studies of disparities in HPV vaccination uptake in Sweden are relatively sparse and mainly investigate the effects of singular socio-economic or geographical dimensions [9]. This may oversee differences between multidimensional socio-economic strata and at different geographical levels, discernible through multilevel and multi-categorical analyses. Therefore, this study aimed to provide an improved understanding of how combined sociodemographic and geographical dimensions affect HPV vaccination uptake in Sweden. Our study draws on intersectionality theory [15], which is increasingly being used in population health research [16], as it enables an understanding of how combined socio-economic dimensions affect the outcome of interest. Intersectionality theory builds on the fundamental insight that different axes of social differentiation, including sex/gender, country of birth/racialisation and income/class, should not be understood as separate but as interwoven. Noted potential contributions of an intersectional perspective to social epidemiology include an increased specificity in the mapping of health inequalities through providing information about multiple strata defined by combinations of demographic and socio-economic dimensions (i.e. variables) [16]. Moreover, an intersectional perspective promotes the direction of focus towards societal structures and dynamics giving rise to health disparities [16]. We applied an analysis of individual heterogeneity and discriminatory accuracy (AIHDA), which is suitable for the multi-categorical study of health disparities [17], and complemented it with a geographical multilevel analysis [18,19] to disentangle the influence of municipalities and regions on HPV non-vaccination. Measures of discriminatory accuracy (DA) provide information about the ability of the categorisation at hand to distinguish between individuals with and without the outcome, depending on the presence of individual heterogeneity within groups. Such assessment can mitigate simplification or essentialisation of differences between groups and stigmatisation of groups with higher average risks. It may also prevent false expectations in low-risk groups and ineffective interventions due to over- or undertreatment [19]. ## Aim Using multi-categorical AIHDA, complemented by a multilevel geographical analysis, we aimed to provide an improved mapping of the sociodemographic and geographic distribution of HPV vaccine uptake in Sweden. ## Study population After approval by the Swedish Ethical Review Authority (no. 2020-05688), the National Vaccination Register (NVR) administered by the Public Health Agency was linked to the Register of the Total Swedish Population (TPR) and the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA), which provides demographic and socio-economic information. The latter two are administered by Statistics Sweden, who performed the record linkage. Our study population consisted of all girls between two and seven years of age living in Sweden on 31 December 2010 ($$n = 315$$,652). Each age group was followed up during the period in which they were 10–12 years old (i.e. those who were seven years old in 2010 were followed up in 2013–2015, etc). We excluded those who died ($$n = 95$$) or emigrated ($$n = 1134$$) during the follow-up period and those with missing information (on parental education, $$n = 2767$$). The final study population consisted of 311,656 girls ($98.7\%$ of the original sample). ## Assessment of variables Following the school-based HPV vaccination programme, all girls are offered the HPV vaccination in the fifth school year, when they are 10–12 years old. Our outcome variable assessed whether the included girls received at least one vaccination in time (yes vs. no). We computed a cumulative measure of individualised equivalised disposable family income by using information on absolute income for the years 2000, 2005 and 2010. For each of the three years, incomes were categorised into 25 groups by quantiles using the complete Swedish population. These groups were summed up, assigning to each individual a value between 3 (always in the lowest income group) and 75 (always in the highest income group). This cumulative income measure was divided into low, medium or high income by tertiles. The parental educational achievement variable distinguished between girls who had, or had not, at least one parent with a tertiary education (i.e. high vs. low education). The parental country of birth variable was categorised into native, mixed or immigrant based on whether both, only one or neither of the girls’ parents were born in Sweden. Parents with missing information were considered as immigrants, as all those born in Sweden are registered as such. Place of residence was based on the location where the vaccination was administered, according to categories provided by Statistics Sweden (1–9) as big city (1–3), small city (4–5) and rural (6–9). The multi-categorical variable was constructed through all possible combinations of the explanatory variables (3×2×3×3), forming 54 multi-categorical strata. Girls whose parents had a high income and education and were born in Sweden, living in a big city, were used as the reference stratum in the analyses. In the multilevel analysis, we identified the municipality and county where the vaccination was administered. ## Multi-categorical and geographical analyses Following a stepwise analytical approach described previously [18], we performed an AIHDA [17], which considers measures of average risk alongside measures of variance and DA. We first performed a logistic regression modelling HPV non-vaccination as a function of individual socio-economic variables (Model 1). Thereafter, we constructed a model (Model 2) including the same information but using the multi-categorical variable. The purpose of this second model was to provide a detailed mapping of HPV non-vaccination across the 54 strata. In a final step, Model 1 was expanded using geographical information consisting of random effects for the county and municipality levels (Model 3). This multilevel analysis provided information about geographical differences in HPV non-vaccination, adjusted for the socio-economic variables. ## Measures of average risk Associations were expressed as odds ratios (ORs). We also computed stratum-specific prevalence rates or absolute risks (ARs). We calculated $99\%$ confidence intervals (CIs) to minimise the problem of multiple comparisons. ## Assessment of DA We assessed the DA of the regression models, that is, the predictive accuracy or the ability of the categorisations used in the models to distinguish between individuals who received HPV vaccination or not, by computing the area under the receiver operator characteristics curve (AUC) [18]. The curve was obtained by plotting the true-positive fraction against the false-positive fraction for binary classification thresholds of predicted risk. The AUC values range from 0.5 to 1, where 1 represents perfect discrimination and 0.5 indicates an absence of predictive accuracy. The DA can be classified as absent or very weak (AUC=0.5–0.6), weak (AUC >0.6–⩽0.7), strong (AUC >0.7–⩽0.8) or very strong (AUC >0.8) [20]. A weak DA may result from the existence of many false-positives, in this case of many non-vaccinated people in low-risk strata. We calculated the incremental change in the AUC value (Δ-AUC), which quantifies improvements in DA yielded by a model compared to a previous one. If any statistical interaction of effects were identified in the multi-categorical variable, the AUC of Model 2 would be higher than that of Model 1 and the Δ-AUC>0 [17]. In Model 3, an Δ-AUC>0 would suggest the existence of a general contextual effect on HPV non-vaccination risk over and above the individual sociodemographic variables. In Model 3, we also calculated the intraclass correlation (ICC), which measured the share of the total individual variance in the latent propensity of HPV non-vaccination that existed at the municipality and county levels. The ICC is also a measure of DA [21]. IBM SPSS Statistics for Windows v22 (IBM Corp., Armonk, NY) for PC and MLwiN v3.00 called from within Stata v14.1 (StataCorp, College Station, TX) were used to conduct the analyses. The multilevel estimations were performed using Markov chain Monte Carlo methods. ## Results Overall, as shown in Table I, $18.8\%$ of the population did not receive any HPV vaccination on time. The probability of HPV non-vaccination was, on average, somewhat higher among girls whose parents had a low rather than a high education, and this probability increased as parental income decreased (Tables I and II). Non-vaccination was more common among girls with one or two parents born outside of Sweden compared to girls with parents born in Sweden, and among girls living in rural areas compared to those in big cities. Table I. \| Unnamed: 0 \| Unnamed: 1 \| HPV non-vaccination, n (%) \| \| --- \| --- \| --- \| \| Total \| \| 58,640 (18.8) \| \| Income \| Low \| 28,702 (49.0) \| \| Income \| Medium \| 18,444 (31.5) \| \| Income \| High \| 11,494 (19.6) \| \| Parental education \| Low \| 33,760 (57.6) \| \| Parental education \| High \| 24,880 (42.4) \| \| Parental country of birth \| Native \| 35,683 (60.9) \| \| Parental country of birth \| Mixed \| 9041 (15.4) \| \| Parental country of birth \| Immigrant \| 13,916 (23.7) \| \| Place of residence \| Big cities \| 36,176 (61.7) \| \| Place of residence \| Small cities \| 15,416 (26.3) \| \| Place of residence \| Rural \| 7048 (12.0) \| The multi-categorical analysis (Table II, Model 2) yielded only a slight increase in the AUC compared to Model 1, indicating the existence of a weak interaction of effects in the multiplicative scale. Moreover, it provides a more detailed map of HPV non-vaccination (Table III and Supplemental Tables S1, S2 and S3). The highest risk, 3.5 times higher than that of girls with native parents with high income and education residing in big cities (AR=0.11), was found among girls whose parents had a low income, high education and an immigrant background, living in a rural area (AR=0.39). Among girls with parents born in Sweden, a social gradient was present, as non-vaccination was less common among those with high parental income and education compared to those with a medium or low income and low education. Among girls with parents born elsewhere, the distribution was more complex. Of the 18 high-education strata, in 13, non-vaccination was more common than in the corresponding (income; place of residence) low-education strata. Of those comprising parental immigration background and high or medium income, three strata showed a higher non-vaccination prevalence than the corresponding medium- or low-income strata. The five strata with the highest prevalence (Table III) comprised girls whose parents had a low income, an immigrant or mixed background, and, in four cases, a high education. However, the DA of Model 2 was weak (AUC=0.610). In fact, while the non-vaccination prevalence was 2.7 times higher in the five highest-risk strata than in the five lowest-risk strata, the number of cases was 2.9 times higher in the lowest-risk than in the highest-risk strata. The five lowest-risk strata included $19\%$ of all non-vaccination cases, whereas the corresponding number for the five highest-risk strata was $7\%$. Half of the non-vaccination cases were found in eight strata (Table III). The geographical analysis (Table II, Model 3, and Tables IV and V), adjusted for the socio-economic variables, shows that the geographical information only adds a very slight increase in the AUC of model 2 (Δ-AUC=0.024). It also shows that $4\%$ of the adjusted differences in the propensity of non-vaccination were located at the municipality level. The differences between counties were without relevance (ICC=$0.57\%$). The AR of 25.19 in the highest-risk municipality was 5.2 times higher than the AR of 4.88 in the lowest-risk municipality. The five highest-risk municipalities included $1.8\%$ of all the adjusted number of cases in the population, while the corresponding number for the five lowest-risk municipalities was $0.5\%$. Of all the 290 municipalities, 37 shared $50\%$ of all adjusted cases of HPV non-vaccination. ## Discussion This nationwide register study shows between-group disparities in the average prevalence of HPV non-vaccination in Sweden. Girls with parents born outside of Sweden and with a lower income or education had a higher probability of non-vaccination than those whose parents had a high income and education and were born in Sweden. This is in alignment with previous research indicating a higher probability of non-vaccination among groups with low income, low education and immigration background [9]. However, our more detailed multi-categorical mapping shows that non-vaccination prevalence was 3.5 times higher among girls whose parents had a low income, high education and immigration background, living in a rural area, compared to the reference stratum. Moreover, among further heterogeneities, we observed that while a social gradient was found among girls with parents born in Sweden, among those with parents both outside of Sweden, several strata with higher income or education showed a higher non-vaccination prevalence than those with lower income or education. This heterogeneous association between income, education and HPV vaccination uptake corresponds to some degree with previous research indicating a higher degree of HPV vaccine hesitancy among highly educated parents in Sweden [7], although high parental education is also associated with higher HPV vaccination uptake [9]. This study confirms the latter, but not in immigrated groups. Meanwhile, and as expected, we observed geographical differences in average risk, most notably at the municipality level. In a Swedish study of disparities in cervical screening associated with place of birth, these were largely explained by socio-economic rather than cultural or language factors [12]. The results of the present study suggest that such socio-economic aspects likely interact with other issues. A study of attitudes towards HPV vaccination and cervical screening among immigrated women [22] points to barriers including language problems and a lack of knowledge about HPV and about navigation within the Swedish health-care system [22]. Lack of knowledge or information about HPV is not isolated to immigrated populations, however [5,8,23], and it is unclear whether these factors explain the higher non-vaccination prevalence in groups with higher education or income. Other factors associated with HPV non-vaccination in Sweden [5,6] and elsewhere [8] involve trust in vaccinations, health-care providers and the pharmaceutical industry [5,6,8,23]. This issue is actualised in the contemporary context where health-related information is often sought online, where content may contradict information provided by established health-care institutions [24]. Other factors noted to influence decision making include concerns with the vaccine’s safety or efficacy [8] or perceived (in)compatibility with sexual or other ways of life [5,8,23], concern that vaccination may be conducive to risky behaviour through creating a false sense of security [8], and practices or attitudes of health-care professionals [6]. In addition, it may be worth noting that foregone health care among immigrant groups has been associated with experiences of discrimination [25], which may impact levels of trust [26]. Furthermore, it should also be observed that while the risk of cervical cancer is lower in some immigrant groups [13], this should not impact the universal vaccination coverage goal. Meanwhile, a central contribution of the AIHDA approach is the assessments of the DA of the categorisations used, without which measures of average risk may convey a risk of stigmatisation of ‘high-risk’ groups, of creating false expectations in ‘low-risk’ groups and of ineffective interventions due to over- or undertreatment [21]. Our analysis shows that the DA of the geographical and socio-economic information was weak. In fact, and paradoxically [27], many cases of non-vaccination occurred in groups with low average risk. This low DA co-exists with a universal vaccination programme which has alleviated previously greater disparities by largely benefitting less privileged groups in Sweden [9]. In that context, our results suggest that interventions to improve HPV vaccination should be directed to the whole population, while targeted or tailored intervention considering circumstances or characteristics of groups may simultaneously be warranted. Thus, we underline the importance of providing adequate information about HPV vaccination in Swedish and other languages [22,23], potentially in combination with other locally designed interventions [28], and of health-care professionals displaying sensitivity to patients’ or parents’ questions or needs [22,23] while avoiding forms of interaction which may discourage trust and confidence [26]. Any targeted interventions should be evaluated with both specificity and sensitivity in mind. While the existence of false-positives may be a lesser problem than that of false-negatives, the former can actualise the issue of stigmatisation. It should furthermore be noted that an increased uptake of HPV vaccination among boys has been predicted to improve the resilience of HPV infection prevention overall [29]. ## Limitations Being observational, this study does not enable the drawing of conclusions about causal relationships. In addition, some multi-categorical strata were rather small, which is reflected in the wide CIs conveying a limited reliability of some point estimates. Furthermore, the categorisation based on country of birth can be seen as simplistic and insufficient, as it disregards large heterogeneities within the group [30] by conflating, for example, immigrants from Nordic countries with refugees from other continents. Disaggregation into more distinct areas of origin proved difficult, however, as this would considerably reduce the strata size. Furthermore, while the HPV vaccination programme today includes boys [29], it did not do so during the study period, which is why our study only includes girls. ## Conclusions This study corroborates previous findings by indicating a higher prevalence of HPV non-vaccination among girls with immigrated parents with low income and low education who live in rural areas, while also showing geographical differences between municipalities. We provide, however, a more precise mapping of socio-economic disparities in HPV non-vaccination in Sweden. We also observed that the DA of the categorisations used was low, and that many cases of non-vaccination are found in low-risk groups. These findings suggest that interventions aiming to increase HPV vaccination should be directed towards the whole population, while simultaneously considering characteristics of particular groups. We underline the importance of providing adequate information about HPV vaccination in Swedish and other languages, and of health-care professionals displaying sensitivity to patients’ questions or needs while avoiding forms of communication which may discourage trust and confidence. ## References 1. Walboomers JM, Jacobs MV, Manos MM. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol (1999) 189 2. Lei J, Ploner A, Elfström KM. HPV vaccination and the risk of invasive cervical cancer. New Engl J Med (2020) 383 3. 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Eur J Public Health (2015) 25
--- title: 'Does the socioeconomic positioned neighbourhood matter? Norwegian adolescents’ perceptions of barriers and facilitators for physical activity' authors: - Hanne Hennig Havdal - Elisabeth Fosse - MEkdes Kebede Gebremariam - Karien Stronks - Oddbjørn Klomsten Andersen - Nanna Lien journal: Scandinavian Journal of Public Health year: 2022 pmcid: PMC9969305 doi: 10.1177/14034948211066673 license: CC BY 4.0 --- # Does the socioeconomic positioned neighbourhood matter? Norwegian adolescents’ perceptions of barriers and facilitators for physical activity ## Abstract ### Background and aims: A higher proportion of adolescents from lower socioeconomic position families tend to be less physically active than their counterparts from higher socioeconomic position families. More research is needed to understand the causes of these differences, particularly the influence of the neighbourhood environment. This qualitative study aims to explore how adolescents and their parents from higher and lower socioeconomic neighbourhoods perceive the social, organisational and physical environment influencing adolescents’ physical activity behaviours. ### Method: We conducted six semi-structured focus groups with 35 13–14-year-olds and eight interviews with some of their parents. The interviewees were recruited from one higher and two lower socioeconomic neighbourhoods in Oslo, Norway. Theme-based coding was used for analysis, and the results discussed in light of an ecological framework. ### Results: The results indicate that factors like social norms in a neighbourhood could shape adolescents’ physical activity behaviour, and a social norm of an active lifestyle seemed to be an essential facilitator in the higher socioeconomic neighbourhood. Higher availability of physical activity and high parental engagement seemed to facilitate higher physical activity in this neighbourhood. In the lower socioeconomic neighbourhoods, the availability of local organised physical activity and volunteer engagement from parents varied. Programmes from the municipality and volunteer organisations seemed to influence and be essential for adolescents’ physical activity behaviour in these neighbourhoods. ### Conclusions: The results illustrate the complexity of behaviour and environment interaction, and a limitation in explaining the phenomenon by focusing primarily on the individual level rather than an ecological perspective. ## Introduction Physical activity (PA) among adolescents has a beneficial impact on health-related quality of life [1] and overall health and weight [2,3]. Adolescence is defined as the time between the ages of 10 and 19 [4], and is considered a critical period for addressing energy-related behaviour such as PA [5]. For adolescents PA includes organised and unorganised leisure time physical activities (LTPAs). These are activities such as sports, exercising and recreational walking, which are not required as essential activities in their everyday living and are performed as a choice of the person [6]. Most adolescents are insufficiently active, and the proportion not meeting the recommendations of a minimum of 60 minutes of PA of moderate-to-vigorous intensity each day [7] increases with age [8-10]. Adolescents from families of lower socioeconomic position (SEP) tend to be less physically active than their counterparts from higher SEP families, both worldwide [11,12] and in Norway [8,13]. The individual’s SEP is usually determined through achieved educational level, occupation and/or income [14,15], and whether one looks at educational level or income [16], the higher the position, the better the health [14]. Levelling these inequalities is the aim of several policies and one of the main goals in the Norwegian Public Health Act [16,17]. Social inequalities are higher than expected in Norway compared with other European countries [18,19], and these are more present in Oslo with documented inequalities between the east and west side of the city [20,21]. A social gradient is found in PA levels among adolescents, particularly in organised sports participation [22,23]. Approximately $75\%$ of adolescents in the higher SEP urban-districts in the western parts are regularly involved in LTPA, compared with around $45\%$ of adolescents in the lower SEP urban-districts in the eastern parts [23]. Even though social inequalities are present in Oslo, the adolescents themselves, regardless of where they live, score relatively equally on loneliness, bullying, life quality, self-esteem, ailments, and how satisfied they are with their parents [23]. Hence, research on adolescents, SEP and LTPA needs to look beyond just individual behaviour to understand how environmental factors interact and influence LTPA [12,24,25]. Ecological models are suited to study LTPA and complex issues like social inequalities [26], and could increase understanding by focusing on the widely distributed causes that are not solely an individual responsibility instead of the victim-blaming ideology of harmful behaviours [27,28]. An ecological model could include five levels: intrapersonal, interpersonal/cultural/social (social level for the rest of the paper), organisational, the physical environment and policy [26]. Norwegian adolescents tend to spend more of their everyday life in their local neighbourhood than adults [29], and the environment they grow up in and its surroundings may influence life choices and possibilities, as is recognised in current strategies to level out social inequalities [15]. Neighbourhoods vary considerably across and within countries, and are a mix of social, physical and organisational structures [29]. Individuals living in the same environment, such as a neighbourhood, who experience the same surroundings or share the same socioeconomic background, tend to adopt similar behaviour within social or environmental patterns [30]. Research on social-level determinants associated with adolescents’ PA behaviour and SEP influence is limited and needs more attention [22,31]. Family resources, immigrant origin, and social norms from peers and parents can only partially explain these differences [22,31]. A qualitative study from Sweden of adolescents from multicultural, lower SEP neighbourhoods described parents’ nagging and demanding as PA barriers and desired parental support and engagement [32]. Existing research of adolescents’ PA behaviour and their built environment has mixed results [33], providing a knowledge gap due to large differences between environments and an unclear possible influence of SEP [25,33]. A systematic review of qualitative studies that explored adolescents’ perspectives on the barriers to and facilitators of PA stresses that much of the research either does not consider SEP in the analysis, or that only adolescents in higher or lower SEP neighbourhoods are presented [34]. Adolescents living in a lower SEP neighbourhood in Amsterdam expressed in a qualitative study that safety and distance to PA were perceived as environmental barriers for PA involvement [35]. Swedish adolescent girls in lower SEP neighbourhoods said that fewer PA opportunities for girls was a reason for not participating in organised PA [36]. Complementing the present mixed results on adolescents’ PA behaviour and social inequalities with qualitative research will provide more in-depth knowledge. Hence, this qualitative study aims to explore how adolescents, and some of their parents, from higher and lower SEP neighbourhoods in Oslo perceive their environment to influence adolescents’ LTPA behaviours. The environment includes the social, organisational and physical environments, and policy level in an ecological view. Specifically, we explore perceived barriers and facilitators that influence adolescents’ LTPA behaviour and whether these barriers and facilitators appear to differ by neighbourhood SEP. Special attention was given to the social level, including friends, family and the neighbourhood’s culture, and the organisational and physical environment levels, including organisation, availability and LTPA facilities in the neighbourhood. Last, we briefly explore the policy level, including plans and opportunities facilitated by the municipality. ## Methods Before data collection, the Norwegian Centre for Research Data (NSD) approved the project’s data protection. All focus groups and interviews with parents started with a presentation of the project and the main topics, the project’s ethical principles (i.e. confidentiality, the interviewees’ rights and possibilities of leaving with no questions asked) and a space for questions [37,38]. Adolescent participation required signed informed consent from the parents, and the participating parents signed an informed consent form for the personal interviews. ## Recruitment of participants As a divided city in terms of social inequalities [20,21], Oslo was considered a relevant place. To select diverse urban-districts from which more local neighbourhoods would be included, publicly available data on sociodemographic characteristics of the urban-districts of Oslo were explored [39]. The prioritised criteria were life expectancy, educational level, mean income, and adolescents’ participation in organisations, clubs, teams or associations. Three areas were selected, reflecting diversity in SEP (Table I). Table I. \| Areas in Oslo \| Life expectancy (men) (years) \| Lower educational levela (<13 years) (%) \| Mean income (NOKb) \| Organisation participation – lower secondary schoolc (%) \| \| --- \| --- \| --- \| --- \| --- \| \| Northeast (three urban-districts) \| 77.4–80.2 \| 67–73 \| 361 000–384 000 \| 43–48 \| \| Southeast (two urban-districts) \| 80.8–81 \| 50–56 \| 381 000–472 000 \| 54–62 \| \| West (three urban-districts) \| 82.4–83.6 \| 36–38 \| 583 000–791 000 \| 73–78 \| In the three selected areas, west, south-east and north-east, 32 lower secondary schools were located [40]. By using Google maps, the researcher examined the schools’ surroundings, the neighbourhood and the activity facilities. Twelve schools had food and activity facilities surrounding the adolescents’ school within walking distance (~30 min) and were contacted for participation. Out of the twelve contacted schools, three responded positively to participation: one located in the north-east of Oslo, one in the south-east and one on the west side. At the schools, the 8th-grade students were invited, and written information letters with consent forms were provided for those interested in participating: one for the parents and an age-adjusted letter for the adolescents. The letter for the parents also included information and questions about participating in personal interviews. Before conducting any interviews or focus groups, the researcher walked around in each of the three included neighbourhoods one time. This was done a few days before the focus groups interviews but did not follow a qualitative observational methodology. These observations provided the interviewer with a better understanding of the neighbourhoods and recognition of places mentioned by the interviewees, thus easing follow-up questions during the interviews. ## Focus groups with adolescents When recruiting the adolescents, we were interested in the adolescents’ perceptions of their local neighbourhood collectively as a focus group and not through their individual status. Personal information beyond gender, age and parental educational level were therefore not collected. In total, 35 adolescents, aged 13–14 years, agreed to participate, resulting in two focus groups at each school, six in total. In the consent form, parents reported the age and gender of their adolescent in response to open questions. In addition, parental educational level was collected to make sure the participants mirrored their neighbourhood. Education level varies between the sub-districts in Oslo (Table I) and is often used to determine SEP [14,15]. The parents were asked to tick the highest achieved educational level: primary school/lower secondary school, upper secondary school, vocational training or university/university college. These were grouped into categories of lower and higher educational level (Table II). Lower educational level includes primary and lower secondary school (10 years), upper secondary school (13 years) and vocational training. Higher educational level includes university and university college (Bachelor’s, Master’s or higher). The educational level of the adolescents’ parents mirrored their overall area statistics shown in Table I. Table II. \| Area \| Participants \| Participants.1 \| Parental educational levela,b \| Parental educational levela,b.1 \| \| --- \| --- \| --- \| --- \| --- \| \| Northeast 1 \| 4 boys \| 3 girls \| 9 lower \| 3 higher \| \| Northeast 2 \| 2 boys \| 4 girls \| 8 lower \| 4 higher \| \| Southeast 3 \| 2 boys \| 4 girls \| 4 lower \| 7 higher \| \| Southeast 4 \| \| 3 girls \| 5 lower \| 1 higher \| \| West 5 \| 2 boys \| 4 girls \| 2 lower \| 9 higher \| \| West 6 \| 2 boys \| 5 girls \| 2 lower \| 12 higher \| ## Interviews with parents The present study includes interviews with some of the adolescents’ parents. Adolescence is a transition period to more independence from parents, while friends and peers typically become more influential [41]. Thus, the purpose of including parents was to get their view as being an important part of adolescents’ lives and to provide a broader picture of the neighbourhood and its possible influence. The interviews included eight individual parents, two men and six women, age range 40–65 years, with five having a higher educational and three a lower educational level (Table III). Seven parents were recruited through their children’s participation in the focus groups. In addition, one mother from the south-east neighbourhood was recruited through snowballing. Recruitment of parents with lower educational level was more challenging, and snowballing was therefore perceived as a reasonable strategy. The included mother had lower educational level, she lived in the same neighbourhood as the other parents and had a daughter in 8th grade at the included school who did not participate in the focus groups. Table III. \| Area \| Participants \| Age (years) \| Parental educational levela \| Adolescent \| \| --- \| --- \| --- \| --- \| --- \| \| North-east \| Mother 1 \| 40 \| Lower \| Daughter \| \| North-east \| Father 1 \| 61 \| Higher \| Son \| \| South-east \| Mother 2 \| 41 \| Higher \| Son \| \| South-east \| Mother 3 \| 49 \| Lower \| Daughter \| \| South-east \| Mother 4 \| 51 \| Lower \| Daughterb \| \| West \| Mother 5 \| 46 \| Higher \| Son \| \| West \| Mother 6 \| 42 \| Higher \| Daughter \| \| West \| Father 2 \| 65 \| Higher \| Daughter \| ## Materials A semi-structured interview guide for the adolescents was developed. The main topics included questions about LTPA and opportunities in their neighbourhood, both organised and unorganised. There were also questions about parental engagement, adolescents’ engagement in organised LTPA, and facilitators for and barriers to being engaged in any LTPA. The interview guide was pre-tested for clarity, inputs and length with a gender-mixed group of five 8th graders. Based on the questions asked of the adolescents, a semi-structured interview guide for the parents was developed. The interview guide started with a section on dietary behaviour before LTPA. The first question on PA was one all could relate to: ‘picture a new girl or boy moving to this neighbourhood. What type of physical activity would you recommend or describe as the most frequently used in this neighbourhood?’. The main topics about LTPA included organised and unorganised LTPA opportunities in their neighbourhood, adolescents’ engagement in organised LTPA, parental engagement, and facilitators and barriers for engaging in LTPA in their neighbourhood. Dietary behaviour was also covered during the interview, but these results are reported separately [42]. ## Procedure The focus group interviews with the adolescents were conducted at their school during school hours in March and April 2019. Interviews with the parents were conducted at a place and time of their choice in May 2019. The same moderator, with a background in public health nutrition, facilitated all interviews, which were audiotaped and lasted approximately 1 hour. ## Data analysis All interviews were transcribed verbatim using the program f4transkript and were checked multiple times for accuracy and verification by listening to the interviews several times. The transcribing and analysis process started during data collection. The interviews with the parents and adolescents were analysed separately using the same analytic method. The analysis was performed using a descriptive approach of systematic text condensation through theme-based coding. The method aims to present the participants’ own experience expressed through their own words, rather than looking for other underlying meanings of what was said [43,44]. The method describes four analytic steps [43,44]: [1] obtaining an overall impression, [2] identifying meaning-units, [3] abstracting the content in the meaning-units and [4] summarising the overall meaning. All transcribed interviews were read thoroughly in the first step with an open mind and a bird’s-eye view. Preliminary themes that emerged from the text were noted and there was a high focus on the participants’ stories. In the second step, the interviews were read more systematically, and meaning-units were identified and coded. In this step, themes that were mentioned by several interviewees or repeatedly mentioned by participants in one area and not in others were noted. Also, new themes that appeared spontaneously from the interviews and were not covered by the interview guide were noted. Larger bits of text were highlighted in this process to ensure that no valuable text was discarded. In the third step, meaning-units were systematised, highlighted, and critically considered in light of the research questions. There was a continuous reviewing of the themes and meaning-units and returning to the original empirical materials for insurance and verifications. In the last step, re-contextualisation and text development of the findings were essential. A continual return to the original empirical material for assurance and verifications was important. HHH and EF read all interviews and had thorough discussions during all four steps providing validations of the findings. ## Results The results from the focus groups and interviews of parents in the three neighbourhoods in Oslo, the higher SEP neighbourhood in the west, and the two lower SEP neighbourhoods in south-east and north-east, are presented as barriers and facilitators within an ecological framework. First, the themes at the social level are described before themes on the organisational and physical environmental level are presented. Last, a few results on the policy level are described. The two eastern neighbourhoods showed similarities, and these are therefore often presented together as ‘east’. To avoid an individual focus, the results are collectively presented, exemplified through individual voices. ## Social norms An essential facilitator in the higher SEP neighbourhood in the west seemed to be a social norm of an active lifestyle. The interviewed adolescents in this neighbourhood described how almost everybody they knew was involved in one or several organised LTPA. One boy said: ‘I can’t think of anybody who doesn’t do any physical activity’. The three interviewed parents confirmed this by describing the typical family from this area as an active one where the whole family is engaged in various sports activities depending on the season. I think it’s kind of an image for a good west-side family to go cross-country skiing and downhill skiing and to be active in one way or another. Sailing in the summer, water skiing... people post pictures of their activities. It’s not like ‘in our family, we sleep till twelve every Saturday, and then we lie on the couch’. I am sure many people do, but that is not what you are posting on social media. Many people love to tell how active they are.(Mother 5 – higher educational level, west) The three parents in the west described how the adolescents in their neighbourhood showed engagement in being visible on social media through sports performances or portraying themselves as active and interesting through social media posts. I think social media is a very important part... there is one girl who is very good at her sport. Posting pictures all the time and has almost become a mini celebrity at her school. But she’s like, she’s good at it. So it’s like, they have this need to show that they have talent, that they are good at things, and then it’s almost like they have to post it. [...] It is somewhat cool to look fit, and it is cool to show that you are exercising. Two of the girls are supposed to run every morning before school, and they don’t. But at least they make sure to take a picture of it [the times they do].(Mother 6 – higher educational level, west) In the focus group interviews, adolescents from the western higher SEP neighbourhood voiced how they juggled prioritising and organising their lives, including organised LTPA. Several of them said that being with friends was important, but this was often prioritised during the weekends when they could relax and have one day without any plans, just to be with friends and ‘chill out’. Some mentioned that they would never drop a sports practice to be with friends, and they would schedule their activity before their homework. However, the adolescents also talked about being stressed if they did not exercise. (Focus group 5, west) In the two eastern, lower SEP neighbourhoods, the perceptions and norms seemed different. When asked what most adolescents did after school hours, several described hanging around in the neighbourhood, visiting the mall or a fast-food restaurant, or just walking around. One boy in the south-east said: ‘Most people just hang with friends and walk around’. The interviewees in these two neighbourhoods did not describe engaging in organised LTPA, highly occupying themselves or organising everything around an activity in the same way as the adolescents in the higher SEP neighbourhood. On the other hand, some of the boys in the eastern, lower SEP neighbourhoods described how they could play unorganised football with friends, and the girls said they would often watch this. In the south-east neighbourhood, the adolescents described how fewer girls were involved in organised LTPA, and they felt it could be strange and awkward to start an activity alone with no other girls present. ## Parental engagement The adolescents in the higher SEP neighbourhood in west described parents in their neighbourhood as highly engaged and even too involved in the adolescents’ organised LTPA. At the same time, they liked having support from their parents. For example, one girl said, ‘Dad wants me to be... or I want to be good, so he helps me with that in a way’ – showing both the push and the support of the parental engagement. The adolescents also told stories during the focus groups interviews of angry parent-coaches and parents demanding too much. In terms of choosing [a sport] favourably from a young age, some [parents] get pretty angry at the kids if they don’t perform.(Boy 1, focus group 6, west) The three interviewed parents in the higher SEP neighbourhood described how many parents in their neighbourhood had a high focus on and expectation of their child’s performance and dedication to their activities. They would often document and share their children’s performance on, for example, social media, showing other parents their active lives as a family and their children’s accomplishments. I think a lot of parents are concerned that their children should do something, and some are also focused on how the children should perform and show it off. Post movies or talk about their kids being good.(Mother 5 – higher educational level, west) The adolescents in the two lower SEP neighbourhoods in the east did not mention parental engagement to the same degree as the adolescents in the higher SEP neighbourhood. It varied and was challenging to grasp the engagement. Some of the adolescents described how parents could drive them to and watch matches but were not directly involved; others described the influence of the cultural background. (Focus group 2, north-east) The parents in the two lower SEP neighbourhoods described some of the same perceptions and said that generally, only a few parents in their neighbourhood were engaged, although those who did were highly involved. The mother from the south-east thought of several reasons for this, such as cultural aspects, work hours or not having a driving licence. The father from the north-east almost felt unique through his involvement. (Mother 2 – higher educational level, south-east) (Father 1 – higher educational level, north-east) ## Becoming the best at an early age Dividing adolescents into different teams, classified by skills and performance, so-called A- and B-teams, was eagerly discussed in the western, higher SEP neighbourhood’s two focus groups. This was not mentioned in the eastern, lower SEP neighbourhoods. The adolescents in the higher SEP neighbourhood discussed how this could be a facilitator for the good ones, but a barrier for those who only want to exercise without competing or becoming a champion. At the age of 13–14, the adolescents felt that they had to make that choice. (Focus group 6, west) One barrier mentioned in the higher SEP neighbourhood in the west was the challenge to change or start a new organised LTPA after the age of 10–14. This was grounded in the sports clubs focusing on forming talents at an early age, starting at a beginner’s level with a younger group, and feeling insecure about their own abilities. I would think that to start with a new sport when you are 13–14 years old, it is a bit vulnerable because they have just become good, or almost, and started to perfecting. Because the worst thing she knows is to feel silly or dumb in a way, and then it might be challenging to get into an established environment.(Mother 2 –high educational level, west) ## Labelling As the adolescents in the two lower SEP neighbourhoods described a general attitude of not being engaged in an organised LTPA, engagement barriers were explored further. The adolescents in the north-east neighbourhood mentioned how they could be labelled or bullied if they participated in an organised LTPA that was not socially accepted. For example, the girls in this neighbourhood described how they often would not wear shorts in the summer even though they wanted to, because they could get labelled as ‘whores’. Both boys and girls could be bullied for their chosen activity. (Focus group 2, north-east) ## Unorganised activities The researcher’s brief observations when walking around in the three included neighbourhoods showed how all neighbourhoods had the forest surrounding Oslo close by with opportunities for outdoor recreation and unorganised activities. The higher SEP neighbourhood in the west was dominated by detached houses often accompanied by extensive gardens, many of them containing trampolines, and the school was surrounded by a green lawn. In the lower SEP neighbourhoods in the east, grassy open spaces or football fields surrounded the two included schools. Especially the northeast lower SEP neighbourhood had facilities for several different types of outdoor activities. Parents in all three neighbourhoods perceived adolescents to be generally sedentary. The interviewed parents in the higher SEP neighbourhood in west talked about how they never saw children playing in the streets or using the neighbourhood for unorganised outdoor activities like they used to do when they were young. The adolescents in this neighbourhood did not describe any unorganised activity as part of their everyday lives. We did have basketball goals here and we will hang them up again. So they can play some ball and such. So there is some activity. But not like in my childhood, it was probably more common to meet outside in the neighbourhood and play. It was a lot of fun right. It is very organised today, and it takes some of that creativity away. It means that once you have some spare time, you may want to do something other than exercise. You do so much exercise already.(Father 2 – high education, west) In the lower SEP neighbourhoods in the east, the interviewed parents described how adolescents in their neighbourhood occasionally used the football fields to meet and play. This was also expressed by the adolescents themselves and especially the boys. ## Diverse availability of organised sport In the higher SEP neighbourhood in the west, the absence of unorganised activity was in contrast to the topic of organised LTPA. The three parents and adolescents said they found it hard to think of one organised sport that was unavailable in their neighbourhood or other nearby urban-districts easily accessible by public transport. In the two lower neighbourhoods, the adolescents mentioned several organised sports options, but fewer opportunities for organised teams for girls and differences in engagement between boys and girls. If girls in these neighbourhoods wanted to practise sports activities such as football or handball, they would often have to travel to other urban-districts, which several said they felt was too far and they would have preferred activities in their local neighbourhood. (Focus group 3, south-east) ## Programmes from the municipality In the two lower SEP neighbourhoods, the interviewed parents said that Oslo municipality had provided programmes for the urban-districts to help the neighbourhoods, through facilities, volunteers and opportunities, to have a more active lifestyle. The programme was still running in the north-east neighbourhood, providing the neighbourhood with different facilities, whereas it was stopped in the south-east neighbourhood. (Mother 3 – lower educational level, south-east) The parents in the lower SEP neighbourhoods felt that these programmes had made a difference in facilitating organised LTPA and making the environment more available for activity. For example, in the north-east neighbourhood, the interviewed adolescents and the two parents talked eagerly about how many football fields they had and how they could borrow sports equipment for free from the municipality. This was especially important for the winter activities such as cross-country or downhill skiing, since a lot of the adolescents in this neighbourhood did not own a pair of skis. (Focus group 1, north-east) ## Discussion This qualitative study’s results show how several factors could influence PA behaviour separately and interact with each other. The discussion structure follows the themes from the results, and presents the interactions at different levels in an ecological model. We primarily focus on the social, organisational and physical environment, but do also include the policy levels. In Oslo, around $73\%$ of children aged between 0 and 17 years live together with two parents, while $20\%$ only live with one parent. Around $19\%$ of families have one or two children, while only $3\%$ have three or more children. Both parents are typically working, as almost $70\%$ of the inhabitants aged 15–70 years in Oslo are employed, though fewer in the eastern part of the city [39]. Norwegian culture as expressed through outdoor recreation activities such as hiking and skiing is robust, and reflected in the expression: ‘Norwegians are born with their skis on’. These activities are often executed during weekends or holidays, and symbolise the Norwegian identity and family tradition for many [45,46]. The neighbourhoods’ social norms seemed to shape adolescents’ PA behaviour, and several factors appeared to interact with these norms. The social norms expressed in the higher SEP neighbourhood in the west could be driven by the described cultural norms in Norway of an active lifestyle. Factors like portraying a dedication to organised LTPA, the pressure of becoming the best, and organising and juggling their lives to include homework, LTPA and friends are essential. These norms of an active lifestyle were also expressed through the parents’ comments of portraying a healthy family. For example, skiing together as a family reflects the typical family from the west side of Oslo. The lower SEP neighbourhoods in the east were described as multicultural, and the typical Norwegian culture of outdoor recreational activities could thus be less normative. For many adolescents, outdoor activities such as skiing or hiking in the woods might need to be learned, and a recent report encourages municipalities to focus on this in their work in reducing social inequalities in PA [47]. Thus, resolving a PA barrier could require changes on different levels of the ecological model simultaneously. It could be detected on the social level, but behavioural changes require policy actions concerning opportunities to learn activities in the municipalities. The norms could also be interpreted in light of the mentioned barriers in the lower SEP neighbourhoods in the east, like bullying and labelling, that appeared to lower social acceptance of LTPA, hence lowering the participation in LTPA. The young generation is often referred to as ‘Generation Achievement’, causing several to feel pressure to perform on, for example, social media and in organised LTPA [48]. Social media can be challenging for adolescents, contributing to lower self-esteem, whereas using it to self-portray and peer-identify might positively support PA behaviour [49]. Around half of the Norwegian 15-year-olds spend 2 hours or more daily on social media [9]. In our study, social media seemed to be one of the factors driving the social norms of an active lifestyle in the higher SEP neighbourhood in the west by providing a platform for continually sharing results and performances. This could, of course, also be an essential factor in the lower SEP neighbourhood and might transcend some of the neighbourhood differences, but in our study, the adolescents and parents in the lower SEP neighbourhoods did not describe the use of social media as a driver for LTPA. Social media has become a central part of our environment through communication, information sharing and marketing. Considering the social-level findings, this could be a platform for municipalities, volunteers, etc., which would be found at the highest level in the ecological model. Providing targeted information about LTPA options, increasing the focus on having fun and less pressure on becoming the best could be essential for adolescents from both higher and lower SEP neighbourhoods, and could be an initiative that targets all groups, as recommended, to level PA inequalities [15]. The availability of organised LTPA is on the physical environmental level in the ecological model. However, the results from this study indicate that availability could be influenced by other levels and factors in the model. The first is the influence of the norms on the social level. In the higher SEP neighbourhood in the west, the high availability of organised LTPA options seemed to be influenced by the norms for an active lifestyle that consequently could lead to a higher demand for a broad spectrum of LTPA opportunities in the neighbourhood. Several of the largest sports clubs offering skiing lessons or football are located in the higher SEP urban-districts on the west side of Oslo [50]. The mean income is higher, making economic barriers for attending organised LTPA fewer. As a result, being physically active is more feasible for adolescents in these parts of the city. Another finding indicating the need to explore several levels in an ecological model simultaneously is found in the lower SEP neighbourhoods in the east, and how girls provided the impression of being less involved in organised LTPA due to several barriers they expressed as present in their local neighbourhood. In other research, girls have tended to be somewhat less active than boys in sports, and girls with a minority background are more often than other adolescents less active and never having participated in organised LTPA during childhood [8]. Barriers, such as expected voluntary parental engagement, economic resources, prioritising school work, and cultural or religious clothing, have been mentioned in previous research [51,52]. Our study indicated barriers at the social level in the ecological model expressed by the girls in these two neighbourhoods through variation in parental approval for activities, labelling from peers and clothing options. Barriers connected to organised LTPA availability in their local neighbourhood and parental engagement were found at the organisational and physical environmental level in the ecological model. This exemplifies how barriers could interact on several levels. For example, when girls perceive social barriers and consequently do not participate in organised LTPA, this could interact with the organisational and physical environmental level by leading to fewer organised LTPA options. Thus the involvement of volunteers and programmes from the municipality could perhaps help adolescents, especially girls, attend organised LTPA in these neighbourhoods. The availability of organised LTPA at the ecological model’s organisational and physical environmental levels is also closely connected to parental engagement at the social level in the ecological model. In Norway, the organisation of organised LTPA is highly dependent on volunteer participation and engagement from parents, and usually takes place outside school. This differs from other countries and may vary locally across neighbourhoods, urban-districts and municipalities [53]. Parents are perceived to be essential for support in adolescents’ organised LTPA [54] while, on the other hand, youth LTPA is suggested to be too organised, serious and expensive, and demands more from parents, which again could lead to a culture of exclusion [55]. Participating in organised LTPA is more challenging if you do not have parental support, and it is challenging to have a football team in the neighbourhood without parental involvement. In this study, parental engagement as a phenomenon in the neighbourhood was the focus, and not the direct connection between one parent and his/her daughter/son. In the lower SEP neighbourhoods in the east, the parental engagement varied and the present study suggests that parental engagement could be influenced by several aspects and possible barriers such as family, religious and work situations. Therefore, the described support from volunteers and municipalities, located at the policy level in an ecological model, could be important and make a difference to involvement in these neighbourhoods. The mentioned services that lend out sports equipment are often run by the municipalities and/or volunteers to provide youth with opportunities to try different activities and even out social inequalities [56]. These municipalities’ or volunteers’ services or programmes could counteract the high costs of many LTPA activities that lead to exclusion from youth LTPA. These programmes or facilities could be essential for low-income families, numerous children or children attending several organised LTPAs [55]. ## Strengths and limitations A strength of this study is the inclusion of parents as well as adolescents from diverse neighbourhoods. By including parents, the data were enriched through their reflections, placing the neighbourhood in a bigger context. The included adolescents and their parents mirrored the population in the area where they lived satisfactorily, based on information about parental educational level. Still, the presence of a few more parents with lower educational levels could have provided the study with richer data. In general, it could be questioned whether only involved and engaged interviewees participated. Still, the interviewees were considered to be sincere and spoke openly about both barriers and facilitators. As described by Malterud [44], data saturation was considered to have been met in the focus groups because there was an exhausting and no new data supported refinement of categories in the information between the two groups within each neighbourhood. Data saturation was also considered to have been met with the interviewed parents, but no parents with a lower educational level were interviewed in the higher SEP neighbourhood, representing a limitation of the data. Focus groups, as a method, have been considered valuable with adolescents aged 11–14 years if discussions target personal experiences [57]. As adolescents can be vulnerable, especially in the meeting of a grown-up, authoritative researcher, all ethical principles were specified thoroughly, making sure everybody knew their rights and options with regard to the ethical principles [37,38]. The moderator was aware of the position as a researcher in public health, but the interviews were considered not to be affected by this as the interviewees talked freely about both healthy and unhealthy behaviour. Adolescents could be uncomfortable in revealing information, or the groups’ dynamics could be influenced by peers or gender mix [57]. This was carefully considered and discussed after the test-interview with a gender-mixed group and was not considered an issue during the interviews, but rather a strength. The use of a semi-structured interview guide allowed the list of topics and questions to arise naturally and provided the moderator with some flexibility. Most of the included adolescents talked enthusiastically and had more to say than time was given, so the focus group was considered to be a suitable method. Even though one moderator and one assistant are recommended for focus groups [58], the project did not have the resources for this. Therefore, detailed field notes of the interviews were written immediately after finishing to make sure as much information as possible was captured. The moderator also described all non-verbal body language verbally during the interviews: ‘you two are nodding’, ‘you are laughing’. Nevertheless, this could be considered a limitation. ## Conclusion The present study results indicate that the perceived barriers and facilitators for LTPA varied between the socioeconomic neighbourhoods and that several factors could affect adolescents’ PA levels. Factors such as social norms in a neighbourhood could shape adolescents’ PA behaviour, and a social norm of an active lifestyle seemed to be an essential facilitator in the higher SEP neighbourhood. Higher availability and high parental engagement did also seem to facilitate higher PA in this neighbourhood. In the lower SEP neighbourhoods, the availability of local organised PA and volunteer participation and engagement from parents varied. Programmes from the municipality and volunteers seemed to influence and be essential for adolescents’ PA behaviour in these neighbourhoods. The results illustrate a limitation in explaining the phenomenon if the focus is primarily at the individual level in an ecological model and not at several levels at the same time. The results indicate that adolescents in the different neighbourhoods could need different facilitation for LTPA. Research into this could be helpful and provide targeted measures at all levels to increase adolescent PA levels. 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--- title: Can counter-advertising exposing alcohol sponsorship and harms influence sport spectators’ support for alcohol policies? An experimental study authors: - Maree Scully - Helen Dixon - Emily Brennan - Jeff Niederdeppe - Kerry O’Brien - Simone Pettigrew - Brian Vandenberg - Melanie Wakefield journal: BMC Public Health year: 2023 pmcid: PMC9969365 doi: 10.1186/s12889-023-15250-5 license: CC BY 4.0 --- # Can counter-advertising exposing alcohol sponsorship and harms influence sport spectators’ support for alcohol policies? An experimental study ## Abstract ### Background Exposure to alcohol advertising and sponsorship through elite sport is associated with harmful use of alcohol. Owing to strong financial and cultural ties between alcohol and sport in Australia, policy action to restrict alcohol sport sponsorship is unlikely to occur without strong public support for change. This study tested whether exposure to counter-advertising exposing industry marketing of harmful products—a technique shown to be effective in tobacco control—promotes higher support for policy change and less favourable beliefs about the alcohol industry among sport spectators. ### Methods A sample of 1,075 Australian adults aged 18–49 years who planned to watch an National Rugby League (NRL) State of Origin series game, featuring prominent alcohol sponsorship, was recruited through an online panel and randomly assigned to one of three conditions: control (neutral advertisement); counter-advertisement exposing alcohol harms; counter-advertisement exposing alcohol sponsorship and harms. Participants completed a pre-test questionnaire and viewed their assigned counter-advertisement multiple times in the 5–7 days before the NRL game. Within four days of watching the game, participants completed post-test measures. ### Results Compared to both the control advertisement and the counter-advertisement exposing alcohol harms, participants who viewed the counter-advertisement exposing alcohol sponsorship and harms were significantly more likely to indicate support for each of four policies aimed at restricting sports-related alcohol marketing, including the complete removal of alcohol sponsorship from sport ($51\%$ vs. $32\%$ and $37\%$). They were also significantly less likely to agree with statements such as “alcohol companies should be allowed to sponsor sport since their products are legal” ($39\%$ vs. $63\%$ and $60\%$) and significantly less likely to report liking alcohol companies in general ($38\%$ vs. $59\%$ and $54\%$). There were no significant differences in policy support or industry beliefs between participants who saw the counter-advertisement exposing alcohol harms and those who saw the control advertisement. ### Conclusion Counter-advertising employing messages that expose and critique the intent and impact of pervasive alcohol sponsorship in sport has potential to bolster public support for policies targeting alcohol sport sponsorship, diminish beliefs supportive of alcohol industry marketing strategies and enhance negative views of alcohol companies and their marketing practices. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-15250-5. ## Background Alcohol companies invest heavily in sponsorship of elite sport, with 30 of the leading alcoholic beverage brands globally spending a combined $764.5 million on sport sponsorship in 2018 [1]. In Australia, many of the national sporting organisations, competitions, events and teams have sponsorship arrangements with alcohol brands, particularly in popular sports such as Australian rules football (AFL), rugby league (NRL) and cricket, which account for approximately two-thirds of all alcohol sponsorships [2]. Elite sport sponsorship achieves high reach and strong engagement through live spectatorship as well as those watching on television or other screens [3]. While spectator participation levels at sporting events have been impacted by the COVID-19 pandemic, around one in five Australians aged 15 and over was projected to attend at least one sporting event in 2022 [4]. Additionally, over three-quarters of Australians aged 14 years and older watch some form of sport on television [5], with one survey estimating that a total of 60 million hours of sporting content is consumed by Australians at home per week [6]. Sponsorship of sport is an especially powerful promotional tool for alcohol companies as it can transfer positive image attributes from the sport to the brand and/or product, and can potentially neutralise negative associations (e.g., health and social harms of drinking) [7]. In addition, exposure to alcohol advertising and sponsorship messaging in elite sport promotes increased levels of consumption (including among children) [8], which undermines public health efforts to reduce harmful effects of alcohol consumption in the community. At present, there are very few restrictions on alcohol advertising, and no restriction of alcohol brand sponsorship of sport, under current legislation and regulatory codes in Australia. For example, while the Commercial Television Industry Code of Practice limits the broadcast of advertisements (ads) for alcoholic products on television to mature and adult viewing classification periods, alcohol brand sponsorship and alcohol product advertising during live sporting events and sports programs on weekends and public holidays are notable exemptions to these time-based controls [9]. Further, the Alcohol Beverages Advertising Code (ABAC), an industry self-regulation code, sets standards for the responsible content and placement of alcohol marketing (e.g., print, outdoor, digital, social media, cinema, television, radio, packaging, point of sale materials, alcohol brand extensions to non-alcohol beverage products and marketing collateral) in Australia [10]. These standards include prohibiting alcohol marketing that has strong or evident appeal to minors or is directed at minors through its placement, depicts alcohol misuse, or portrays the consumption or presence of alcohol as contributing to success or achievement. However, the ABAC explicitly does not apply to sponsorship. The inadequacy of industry self-regulation codes in protecting vulnerable populations was highlighted in an international systematic review, including studies conducted in Australia, which found that content violations are common and that youth are exposed disproportionately to alcohol marketing [11]. Exploitation of the live sport loophole is also evident, with an analysis of free-to-air television alcohol advertising in Australia for 2012 documenting that $87\%$ of alcohol ads during the daytime (6am-8:29pm) were placed in sport TV programming [12]. This same study also observed a higher mean number of alcohol ads per hour in sport TV programs during which such advertising was shown than in those non-sport TV programs during which alcohol advertising was aired. Given the embedded financial and cultural association between alcohol and sport in Australia, government implementation of controls of alcohol sponsorship of sport is unlikely to occur without strong public support, a known driver of advocacy success and policy action [13, 14]. Recent surveys indicate that around half of Australians support alcohol sponsorships being removed from elite sport [15, 16], suggesting there is considerable scope for improvement. One strategy that could be used by public health advocates to increase public support for policy action in this area is the use of counter-advertisements (hereafter referred to as counter-ads) exposing deceptive or predatory industry practices (e.g., targeting marketing to youth) [17]. Counter-ads focusing on the tobacco industry and its conduct have been shown to be effective in shifting beliefs about the tobacco industry and increasing support for tobacco control policy [18, 19]. While some counter-advertising campaigns critiquing the alcohol industry have run in Australia [20, 21], the United States and the United Kingdom [17], no systematic evaluations have been reported to our knowledge. As such, there is insufficient empirical evidence concerning the efficacy of counter-advertising exposing industry marketing practices in mobilising public support for policies restricting alcohol sponsorship of sport. The present study aimed to address this gap by testing whether spectators who are shown a counter-ad exposing alcohol sponsorship and harms before viewing an alcohol-sponsored sporting event report (i) higher post-event support for policy restricting sports-related alcohol marketing and (ii) less favourable beliefs about the alcohol industry, compared to spectators shown a control ad. We also tested the counter-ad exposing alcohol sponsorship and harms against a traditional alcohol counter-ad (exposing harms associated with alcohol use) to assess the relative effectiveness of each style of counter-ad on spectators’ level of policy support and beliefs about the alcohol industry. ## Design and participants The study design was a pre-post, between-subjects experiment comprising three counter-advertising conditions: (A) control (neutral ad); (B) counter-ad exposing alcohol harms; (C) counter-ad exposing alcohol sponsorship and harms. A sample of Australian adults was recruited by Ipsos from their non-probability online panel (and panel partners). Panel members were eligible to participate if they were aged 18–49 years and planned to watch an upcoming 2021 NRL State of Origin game. The NRL State of Origin series is an elite sporting event in Australia that features extensive alcohol marketing [22]. For example, the 2021 series included alcohol sponsor brand logos on both competing teams’ player uniforms (XXXX beer for the Queensland Maroons and Tooheys New lager for the NSW Blues) and large, superimposed sponsor brand logos on the playing field during the television broadcasts (Victoria Bitter (VB) as the official beer sponsor of the State of Origin). General information about the study was provided to panellists (i.e., that it was about drink products) to obtain their informed consent to participate; however, there was no mention of who had commissioned the survey (Cancer Council Victoria) until the debrief at the end of the study. Following confirmation of their study eligibility, participants completed a pre-test (baseline) questionnaire, viewed a 30-second version of their assigned counter-ad twice and then reported their cognitive and emotional responses to the ad. Participants were randomly allocated to counter-advertising condition using a least-filled quota pre-programmed procedure set up in the backend of the baseline survey by Ipsos. For each condition, gender (male/female) and age (18–$\frac{34}{35}$–49 years) quotas were applied to achieve a relatively even distribution of participant characteristics in each condition at baseline. An alcohol consumption screening question was also asked of participants at the start of the baseline survey to obtain an approximate $\frac{80}{20}$ split of at least monthly drinkers (cf. irregular/non-drinkers). Participants were invited to complete a short exposure task on each of the intervening days between the baseline survey and the game, which was intended to increase their dose of exposure to the assigned ad. Each day, the task rotated between exposing participants to either a 15- or 30-second version of their assigned counter-ad before asking them to answer a single rating question. To test for effects of the counter-ads on support for policies restricting sports-related alcohol marketing and beliefs about the alcohol industry, participants completed a post-test (follow-up) survey within four days of watching the game. Based on previous experimental studies testing audience responses to sport sponsorship [23, 24], counter-advertising [25, 26] and/or anti-industry media content [27], we expected our intervention to produce small effect sizes (i.e., Cohen’s $d = 0.22$–0.35). Thus, to detect group differences of this magnitude with power of 0.80 ($p \leq 0.05$), we aimed to achieve a minimum of $$n = 326$$ participants per condition in the final sample. ## Counter-advertising intervention The counter-ad exposing alcohol harms was from a government-developed educational campaign titled Know When to Say When, which ranked highly (top $25\%$) in terms of motivating reduced drinking in a previous message testing study of 83 existing alcohol harm reduction ads [28]. It depicted real-life scenarios highlighting the social harms of excessive alcohol consumption for the drinker as well as how it affects others around them (e.g., spilling drinks on strangers, getting into a physical fight with friends, losing licence for drink driving). The counter-ad exposing alcohol sponsorship and harms was developed by a creative agency (Three Wise Men) following mixed-methods testing of potential concepts with the target audience. The counter-ad depicted scenes of children going to a sporting event or sitting down to watch sport at home and highlighted the routine exposure they receive to alcohol sponsorship in these settings (e.g., via banners/signage around the sports ground, logos on the field, advertising during the telecast). A male voiceover explains that “Our kids are in training. And who’s training them? The alcohol industry. Alcohol sponsorship covers the sports grounds they go to and is promoted during the sport they watch on TV. They’re being trained to think that sport and alcohol go hand-in-hand. But alcohol causes at least seven types of cancer and 2000 cancer deaths every year. If the alcohol giants keep sponsoring sport, the harm will continue to the next generation. Isn’t it time to kick alcohol sponsorship out of sport?” The end-frame included the tagline “Kick alcohol sponsorship out of sport”, and underneath that was the logo for the Cancer Council (a well-known and respected Australian charity). The neutral ad (control condition) was an existing ad promoting a laptop computer. ## Measures The set of outcomes that form the basis of this paper are described below. Other domains measured in the baseline and/or follow-up surveys (e.g., brand awareness; sponsorship recall and recognition; image-based similarity; event-sponsor fit; brand attitudes, preferences and purchase intentions; alcohol harm beliefs; alcohol attitudes; next week drinking intentions) are the focus of a separate paper (manuscript under review). ## Responses to counter-advertisement Immediately following counter-ad exposure at baseline, participants rated their cognitive (e.g., believability, relevance to them), motivational (i.e., felt motivated to reduce the amount of alcohol I drink) and emotional (e.g., confusion, surprise) responses to their assigned counter-ad using questions adapted from previous studies [25, 28–31]. Responses were recorded on rating scales ranging from 1 = ‘strongly disagree’ to 7 = ‘strongly agree’ (cognitive) or 1 = ‘not at all’ to 7 = ‘extremely’ (motivational and emotional). ## Policy support At follow-up, participants indicated their level of support (1 = ‘strongly oppose’ to 7 = ‘strongly support’) for four proposed policies aimed at restricting sports-related alcohol marketing. Responses were collapsed into ‘support’ (5–7) and ‘neutral/oppose’ (1–4) categories. ## Beliefs about the alcohol industry At follow-up, participants indicated the extent to which they agreed or disagreed (1 = ‘strongly disagree’ to 7 = ‘strongly agree’) with three positively framed and two negatively framed statements about alcohol companies. Responses were collapsed into ‘agree’ (5–7) or ‘neutral/disagree’ (1–4) categories. Participants also provided a rating of how they feel about alcohol companies in general on a scale ranging from 1 = ‘I don’t like them at all’ to 7 = ‘I like them a lot’, with responses collapsed into ‘like’ (5–7) and ‘neutral/dislike’ (1–4) categories. ## Baseline characteristics Participants recorded their gender, age, residential postcode, highest level of educational attainment, parental status and frequency of drinking alcohol over the last 12 months. Socio-economic status (SES) was determined according to the Australian Bureau of Statistic’s Index of Relative Socio-Economic Disadvantage ranking for Australia using participants’ residential postcodes [32]. ## Statistical analysis Data were analysed using Stata/MP V.16.1 (StataCorp, College Station, Texas). One-way analyses of variance with Bonferroni-adjusted post hoc pairwise comparisons were used to test for differences in participants’ cognitive, motivational and emotional responses to the counter-ad (measured at baseline) by condition. Overall differences in participants’ level of support for each policy proposal were assessed using McNemar’s test. Separate logistic regressions were conducted to test for differences by condition in the proportion of participants who were in support of each policy proposal, agreed with each alcohol industry belief statement and reported liking alcohol companies in general (all outcomes measured at follow-up). Where a significant ($p \leq 0.05$) omnibus test for condition was found, pairwise differences were assessed with a Bonferroni correction applied for multiple comparisons. All models controlled for days elapsed between surveys, dose of advertising exposure and game number. Sensitivity analyses were conducted using the original, continuous versions of the policy support and industry belief variables (see Supplementary Material 1). As the pattern of results was generally comparable to those found when using the dichotomous versions of these variables, for ease of interpretation, only the latter are presented in text. ## Sample characteristics The final sample comprised $$n = 1$$,075 eligible adults who were recruited and completed data collection (baseline survey, short exposure tasks, follow-up survey) between 2 June and 18 July 2021 (see Fig. 1 for CONSORT diagram). Participants who did not complete at least two of the short exposure tasks ($$n = 421$$) or who subsequently reported not watching any of the State of Origin game ($$n = 113$$) were excluded a priori from the final sample. On average, participants completed four out of a possible six short exposure tasks ($M = 4.3$, SD = 1.1) and had an overall dose of advertising exposure across the baseline survey and tasks totalling around two and half minutes ($M = 157.6$ s, SD = 25.6 s, range = 90–195 s). There was no differential attrition across conditions at follow-up. A summary of the demographic profile of the final sample by condition is provided in Table 1. Table 1Sample characteristics by counter-advertising condition ($$n = 1075$$)Counter-advertising conditionTotal($$n = 1075$$)Control ad($$n = 356$$)Counter-adexposingalcohol harms($$n = 367$$)Counter-adexposingalcohol sponsorship and harms($$n = 352$$)%%%% Gender Male52.051.753.450.9 Female48.048.346.649.1 Age 18–34 years48.749.247.749.4 35–49 years51.350.852.350.6 M (SD) 34.59 (7.72)34.24 (7.92)35.01 (7.67)34.50 (7.56) Highest level of education completed Non-tertiary45.844.749.343.2 Tertiary54.255.350.756.8 SES (area-based) # Low SES23.524.722.123.7 Medium SES35.935.736.535.4 High SES40.639.641.440.9 Parent (any aged child) No32.534.632.430.4 Yes67.565.467.669.6 Frequency of drinking alcohol over last 12 months At least weekly66.166.366.565.6 At least monthly (but less than weekly)23.422.822.625.0 Less than monthly / Never10.411.010.99.4Notes: Percentages are rounded so may not sum to $100\%$. All sample characteristics were assessed at baseline# SES was determined according to the Australian Bureau of Statistics' Index of Relative Socio-Economic Disadvantage ranking for Australia using participants' home postcode [32]. Participants who resided in a postcode ranked in the bottom third of the index were categorised as low SES, those in the middle third of the index as medium SES and those in the upper third as high SES. SES information is missing for 2 participants as they provided invalid postcodes Fig. 1CONSORT flow diagram ## Responses to counter-advertisement at baseline As shown in Table 2, both counter-ads were rated significantly higher than the control ad on all cognitive response measures, with the exception that ratings of relevance did not differ significantly for the alcohol harms counter-ad. Participants rated the counter-ad exposing alcohol sponsorship and harms significantly higher than the counter-ad exposing alcohol harms on perceived personal relevance ($M = 5.09$ vs. $M = 3.94$, $p \leq 0.001$), making them stop and think ($M = 5.46$ vs. $M = 5.10$, $$p \leq 0.012$$) and teaching them something new ($M = 5.07$ vs. $M = 3.99$, $p \leq 0.001$), whereas both counter-ads were rated similarly on ease of understanding, believability and being likely to prompt discussion with others. Table 2Participants’ cognitive, motivational and emotional responses to counter-advertisement at baseline ($$n = 1075$$)Counter-advertising conditionControl ad($$n = 356$$)Counter-adexposingalcohol harms($$n = 367$$)Counter-ad exposing alcohol sponsorship and harms($$n = 352$$) M SD M SD M SD Cognitive responses Easy to understand4.331.916.23a1.08 6.33 a 1.05 Believable4.381.70 6.03 a 1.16 5.90a1.29 Relevant to me3.841.853.941.92 5.09 ab 1.68 Made me stop and think3.721.795.10a1.62 5.46 ab 1.60 Would talk to others about3.331.914.98a1.65 5.10 a 1.69 Taught me something new3.461.883.99a1.87 5.07 ab 1.81 Motivational response Reduce the amount of alcohol I consume2.131.78 4.45 a 1.93 4.28a2.03 Emotional responses Surprised3.031.873.151.79 4.28 ab 1.70 Reassured3.001.81 3.76 a 1.81 3.48a1.77 Worried2.051.473.55a1.82 4.26 ab 1.75 Encouraged3.261.924.08a1.80 4.40 a 1.80 Amused3.041.812.771.75 3.01 1.85 Confused 3.89 bc 2.04 1.971.522.201.58 Bored 3.93 bc 1.90 2.441.652.621.70oxy_comment_start comment="The footnotes have gotten mixed up in your reordering of the tables. Thus, we have fixed these to match how the tables are currently being shown in the proof. However, please note that our requested changes to the proof in the methods section will mean that the order of the tables in text will revert to how it was in our submitted manuscript. After you have made our requested changes, can you please double-check that the footnotes to each table are correctly matching? If possible, it would be great if we could be sent an updated proof to confirm this for ourselves. "Notesoxy_comment_end: Boldfaced figures highlight the counter-advertisement that produced the strongest response among participants. Pairwise differences were assessed using one-way analysis of variance with Bonferroni correction. a Significantly higher than control ad at $p \leq 0.05$; b Significantly higher than counter-ad exposing alcohol harms at $p \leq 0.05$; c Significantly higher than counter-ad exposing alcohol sponsorship at $p \leq 0.05$ Compared to seeing the control ad ($M = 2.13$), viewing either counter-ad made participants feel more motivated to reduce the amount of alcohol they consume, although these scores were only around the mid-point of the scale (i.e., $M = 4.28$–4.45). In general, participants’ emotional responses to the counter-ads were moderate (means ranged from 1.97 (‘confused’) to 4.40 (‘encouraged’) on the 7-point scale). However, the counter-ad exposing alcohol sponsorship and harms elicited stronger feelings of surprise ($M = 4.28$ vs. $M = 3.15$, $p \leq 0.001$) and worry ($M = 4.26$ vs. $M = 3.55$, $p \leq 0.001$) than the counter-ad exposing alcohol harms. ## Policy support at follow-up Across all conditions, participants showed stronger support for a ban on alcohol during sporting broadcasts at times when children watch TV ($63\%$) than a ban on alcohol advertising at sports grounds ($47\%$; McNemar’s χ2[1] = 113.88, $p \leq 0.001$), a policy preventing professional sporting organisations and teams from entering into new sponsorship arrangements with alcohol companies ($44\%$; McNemar’s χ2[1] = 134.40, $p \leq 0.001$) or the complete removal of alcohol sponsorship from sport ($40\%$; McNemar’s χ2[1] = 187.72, $p \leq 0.001$). Compared to both the control ad and the counter-ad exposing alcohol harms, participants who viewed the counter-ad exposing alcohol sponsorship and harms were significantly more likely to indicate support for each of the four proposed policies aimed at restricting sports-related alcohol marketing (see Table 3). Whereas participants who saw the counter-ad exposing alcohol harms recorded similar levels of support for the respective policies to those who saw the control ad. Table 3Effects of counter-advertising condition on policy support and beliefs about alcohol industry marketing at follow-up ($$n = 1075$$)Counter-advertising conditionOmnibus test for conditionControl ad($$n = 356$$)Counter-ad exposing alcohol harms($$n = 367$$)Counter-ad exposing alcohol sponsorship and harms($$n = 352$$)%%% Policy support (% support) Complete removal of alcohol sponsorship from sport32.336.5 51.4 ab χ2[2] = 29.03, $p \leq 0.001$ Ban on alcohol advertising at sports grounds37.942.2 59.9 ab χ2[2] = 38.55, $p \leq 0.001$ Ban on alcohol advertising during sporting broadcasts at times when children watch TV (i.e., before 8:30pm)54.559.9 74.7 ab χ2[2] = 32.61, $p \leq 0.001$ Policy preventing sporting organisations and teams from entering into new sponsorship arrangements with alcohol companies36.840.6 53.4 ab χ2[2] = 22.22, $p \leq 0.001$ Beliefs supportive of alcohol industry marketing (% agree) Alcohol companies make a positive contribution to the community through sport sponsorship54.850.1 37.8 ab χ2[2] = 21.11, $p \leq 0.001$ Alcohol companies behave in socially responsible ways42.441.4 28.1 ab χ2[2] = 19.98, $p \leq 0.001$ Alcohol companies should be allowed to sponsor sport since their products are legal63.260.5 38.9 ab χ2[2] = 49.18, $p \leq 0.001$ Beliefs opposing alcohol industry marketing (% agree) Alcohol companies are training children to think that sport goes hand-in-hand with alcohol53.156.4 68.2 ab χ2[2] = 19.90, $p \leq 0.001$ Alcohol companies will stop at nothing to sell their products58.755.6 66.8 b χ2[2] = 9.93, $$p \leq 0.007$$ Overall belief about alcohol companies (% like) 59.054.5 38.1 ab χ2[2] = 33.41, $p \leq 0.001$oxy_comment_start comment="As noted above, the footnotes have gotten mixed up in your reordering of the tables. Thus, we have fixed these to match how the tables are currently being shown in the proof. However, please note that our requested changes to the proof in the methods section will mean that the order of the tables in text will revert to how it was in our submitted manuscript. After you have made our requested changes, can you please double-check that the footnotes to each table are correctly matching? If possible, it would be great if we could be sent an updated proof to confirm this for ourselves. "Notesoxy_comment_end: Boldfaced figures highlight the counter-advertisement that produced the highest (policy support, beliefs opposing alcohol industry marketing) or lowest (beliefs supportive of alcohol industry marketing, overall belief about alcohol companies) percentage among participants. Logistic regression models controlled for days elapsed between surveys, dose of advertising exposure and game number. Where the omnibus test for counter-advertising condition was significant ($p \leq 0.05$), pairwise differences were assessed with a Bonferroni correction applied. a Significant difference compared to control ad at $p \leq 0.05$; b Significant difference compared to counter-ad exposing alcohol harms at $p \leq 0.05$ ## Beliefs about the alcohol industry at follow-up As shown in Table 3, participants who viewed the counter-ad exposing alcohol sponsorship and harms were significantly less likely to agree with each of the three statements supportive of alcohol industry marketing compared to participants who viewed either the control ad or the counter-ad exposing alcohol harms. They were also significantly more likely to agree with the statement opposing alcohol industry marketing, ‘alcohol companies are training children to think that sport goes hand-in-hand with alcohol’, which was a key message of this counter-ad ($68\%$ vs. $53\%$ and $56\%$ respectively). However, agreement that ‘alcohol companies will stop at nothing to sell their products’ was only significantly higher among participants who viewed the counter-ad exposing alcohol sponsorship and harms ($67\%$) in comparison to those who viewed the counter-ad exposing alcohol harms ($56\%$), and not the control ad ($59\%$). The percentage of participants who reported liking alcohol companies in general was significantly lower among those who saw the counter-ad exposing alcohol sponsorship and harms ($38\%$) compared to participants who saw either the control ad ($59\%$) or the counter-ad exposing alcohol harms ($54\%$). ## Discussion Findings from the present study indicate that counter-advertising exposing alcohol sponsorship and harms has potential to bolster public support for policies to restrict sports-related alcohol marketing, diminish beliefs supportive of alcohol industry marketing and enhance negative views of alcohol companies and their marketing practices. These effects were observed in comparison to both a control ad and a counter-ad highlighting the social harms of excessive alcohol consumption, indicating that it was the specific emphasis on exposing the questionable logic of allowing alcohol companies to promote their product to children during sport that made the counter-ad exposing alcohol sponsorship and harms impactful. For the four assessed policies to restrict sports-related alcohol marketing, the counter-ad exposing alcohol sponsorship and harms succeeded in boosting support by at least $16\%$ points compared to the control ad and at least $12\%$ points compared to the alcohol harms counter-ad. This is notable given that a previous study gauging public support for 14 alcohol control initiatives across seven countries (Australia, Canada, China, India, New Zealand, United Kingdom, United States) found that support for policies restricting alcohol advertising and sponsorship was typically lower than support for policies related to product labelling and consumer education [15]. Identifying strategies, such as counter-advertising exposing harmful industry marketing practices, that can contribute to redressing this discrepancy is particularly important as robust marketing restrictions are one of the most effective and cost-effective approaches to reducing alcohol-related harm [33–35]. Across the whole sample, the highest level of support among participants was for the policy framed around protecting children (i.e., a ban on alcohol advertising during sporting broadcasts at times when children watch TV), a finding that aligns with past research showing that proposed alcohol control measures that aim to protect young people are better supported by adults than measures targeting the general population [36, 37]. The level of support each policy received from participants in our control condition was quite low in comparison to Australian population surveys. For example, just $32\%$ of control participants were in favour of banning alcohol sponsorship from sport compared to $53\%$ of people aged 14 and over surveyed in the 2019 National Drug Strategy Household Survey [16]. However, this pattern is not altogether surprising as our sample only included younger adults (ages 18–49 years), who have been found to be less supportive of alcohol control policies than older adults [15, 38]. Furthermore, they were a sub-group of younger adult sports spectators, who likely had heavy prior exposure to alcohol sponsorship of sport which could have made them more accepting of this alcohol marketing practice. The fact that exposure to our counter-ad exposing alcohol sponsorship and harms was able to produce such significant increases in policy support among this cohort of young adult sport spectators is encouraging; however, further research is needed to determine if similar effect sizes for this intervention can be replicated in population groups that are already more accepting of government implementing policies to restrict sports-related alcohol marketing. The counter-ad exposing alcohol sponsorship and harms was effective at dampening specific beliefs supportive of alcohol industry marketing practices, heightening specific beliefs opposing alcohol industry marketing and making participants view alcohol companies less favourably overall. The effect size for this counter-ad detracting from the belief that alcohol companies should be allowed to sponsor sport because their products are legal was particularly large, with percentage point differences of over $20\%$ compared to both the control ad and the counter-ad exposing alcohol harms. These observed shifts in participants’ alcohol industry beliefs in response to seeing counter-advertising exposing and critiquing industry marketing practices are in line with results from a naturalistic experiment where exposure to a movie denormalising the tobacco industry (The Insider) promoted more negative perceptions about the industry’s business conduct and less community acceptance of the tobacco industry [27]. They are also consistent with evaluations of the ‘Truth’ campaign in the United States—a youth-focused anti-smoking mass media campaign spotlighting tobacco industry manipulation—that showed associations between campaign exposure and anti-industry beliefs among adolescents [39–41]. Detecting strong effects of our counter-ad exposing alcohol sponsorship and harms on participants’ perceptions of alcohol companies is encouraging, given a demonstrated link between tobacco industry denormalisation beliefs and quitting intentions in adult smokers [42]. Future studies are needed, though, to determine if less favourable beliefs about the alcohol industry are related to reduced drinking intentions. Assessment of participants’ cognitive and emotional responses to their assigned counter-ad indicated that the counter-ad exposing alcohol sponsorship and harms was perceived as more relevant, thought-provoking and educational (i.e., teaching them something new) than the counter-ad exposing alcohol harms, and also tended to elicit greater surprise and worry. This may reflect participants having had minimal to no prior exposure to public health messages highlighting alcohol industry manipulation tactics, with (to our knowledge) only one previous Australian campaign having used this approach (i.e., Foundation for Alcohol Research and Education’s “Alcohol Truth” social media campaign) [20, 21]. Conversely, messages addressing short-term harms of alcohol have frequently been employed in alcohol harm reduction campaigns [43]; thus, the more familiar theme and tone of the counter-ad exposing alcohol harms could at least partly explain why this counter-ad did not engender as strong a response from participants on these particular measures. Some study limitations should be acknowledged. First, we only tested a single example of a counter-ad exposing industry marketing practices that in addition to drawing attention to the alcohol industry’s use of sport sponsorship to promote alcohol to children also included mention of a long-term alcohol harm (i.e., cancer) that did not feature in the counter-ad exposing alcohol harms. While this was done to provide context as to why Cancer Council was advocating for the removal of alcohol sponsorship from elite sport, it is not possible to disentangle to what extent the primary (i.e., industry targeting alcohol marketing to youth through sport sponsorship) and secondary (i.e., link between alcohol and cancer) messages of the counter-ad each contributed to the effects we observed. Experimental studies testing multiple examples of counter-advertising exposing alcohol industry practices (including ones without secondary messages) in comparison to other styles of counter-advertising (including ones that focus solely on cancer as a long-term alcohol harm) could provide insight into the features of alcohol counter-ads that most contribute to effectiveness in garnering support for policy change. Second, as this was a naturalistic experiment based around the NRL State of Origin series, our sample was restricted to sport spectators who intended to, and then did watch an event where alcohol sponsorship is typically prominent. An average of 66.29 min (SD = 7.62) of alcohol marketing (including sponsorship) was observed during the two hours of televised coverage of each State of Origin game in 2012 [22], and it is likely that a similar level of exposure occurred during the 2021 series with both teams continuing to feature alcohol sponsor brand logos on their player uniforms. Consequently, participants may have been more likely to be swayed by the counter-ad exposing alcohol sponsorship and harms than non-sport spectators, or those who watch sporting events where alcohol sponsorship is less prominent, given that these participants had the opportunity to see first-hand the widespread promotion of alcohol during the game. Real-world research investigating how the wider population, with varying levels of exposure to alcohol sponsorship of sport, respond to counter-advertising targeting alcohol companies’ use of sport to market their harmful products is an important next step. Key strengths of the current study include the addition of short exposure tasks that ensured participants received a minimum of four exposures to their assigned counter-ad over a week (i.e., two during the baseline survey and at least two subsequent tasks) to better mimic an actual mass media campaign, and the use of a professionally produced, broadcast-quality counter-ad exposing alcohol sponsorship and harms developed following formative research with the target audience. ## Conclusion These study findings suggest that counter-advertising exposing industry marketing of harmful products offers a promising avenue for increasing public support for regulatory change in relation to alcohol sponsorship of elite sport and shifting beliefs about the alcohol industry and the acceptability of its marketing practices. Scaling up such counter-advertising to gain wider population exposure would be relatively inexpensive to implement per capita, when compared to the huge social and economic costs of alcohol use in Australia [44]. While the resources available to public health organisations to develop and air counter-advertising campaigns are small relative to the commercial weight of the alcohol industry, this study demonstrates that comparatively brief exposure to counter-advertising with well-designed communications can be impactful. Potential concerns from broadcasters—who benefit financially from alcohol marketing—about running counter-ads that are critical of the alcohol industry could be overcome by disseminating these messages through other digital and social media channels. This type of message delivery strategy has been successfully employed by a 2018-19 Truth anti-e-cigarette campaign (that included anti-industry themes and aired almost exclusively over digital platforms), which achieved high levels of campaign awareness and was associated with higher levels of anti-industry sentiment among the target audience [45]. Thus, such obstacles should not discourage public health organisations from pursuing counter-advertising that exposes and critiques the intent and impact of pervasive alcohol sponsorship in sport given its potential to bolster public support for policy reform in this area. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. 1.Forsdick S. Bud Light and Heineken top spending list of alcohol sponsorship in sport [https://www.ns-businesshub.com/business/alcohol-sponsorship-in-sport/] 2. 2.For Purpose. 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--- title: Analyzing gender differentials in dietary diversity across urban and peri-urban areas of Hyderabad, India authors: - Kiran Suryasai Marla - Ravula Padmaja journal: BMC Nutrition year: 2023 pmcid: PMC9969366 doi: 10.1186/s40795-023-00692-2 license: CC BY 4.0 --- # Analyzing gender differentials in dietary diversity across urban and peri-urban areas of Hyderabad, India ## Abstract ### Background India’s recent increase in urbanization alongside with feminization of rural agriculture could increase the existing gender disparities in dietary diversity. With many rural men migrating to urban areas, women have increased domestic burdens as well as productive burdens such as making informed crop production decisions so household members consume a diverse diet. Given the rapid and recent onset of this phenomenon, there is a need to explore gender differentials in diet diversity across urban and rural areas to assess if certain populations are being disproportionately impacted by this trend. There are limited established quantitative studies discussing this gender disparity with respect to urbanization. Therefore, this paper compares dietary diversity among adult men, adult women, adolescent males, and adolescent females in urban and peri-urban locations. The authors also assess if various sociodemographic factors correlate with dietary diversity. ### Methods Analyses were conducted on dietary diversity data collected by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) from selected urban (1108 individuals) and peri-urban (808 individuals) locations of Hyderabad, India. The total sample size of the population is $$n = 1816$$: 660 adult males, 662 adult females, 205 adolescent males, and 289 adolescent females. ### Results Adult women and adolescent females have a higher diet disparity between peri-urban and urban areas when compared to adult males and adolescent males. Multivariate analyses followed by post hoc multiple comparisons testing further support that peri-urban adult women consume a less diverse diet compared to their urban counterparts and less than other peri-urban adult men and adolescent women. It was also found that marital status, type of household card owned, and the highest degree of education are statistically significant correlators of an individual’s dietary diversity. ### Conclusions Given that urbanization could negatively impact already vulnerable populations such as peri-urban adult women, who play a key role in children’s nutrition, it is important to provide support to these populations. This paper suggests it is possible to do so through government subsidization of peri-urban farmers to grow more diverse crops, fortifying easily accessible foods with commonly lacking micronutrients, including Vitamin A, folic acid, and iron, market access, and affordable prices. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40795-023-00692-2. ## Background Food insecurity is particularly high in India, as it is ranked 80 out of 104 countries on the Global Hunger Index. Furthermore, India is 130 out of 155 countries on the Gender Inequality Index, and it is clear there are existing gender disparities in food insecurity [1]. Approximately half of the women of reproductive age have anemia, resulting from iron deficiency, while this issue is prevalent in only $23\%$ of men [2]. Malnutrition in women is important to focus on because inadequate health of the mother can lead to inadequate nutrition of a baby, which is correlated with childhood/adolescent stunting and other life-altering side effects such as the increased risk of adult chronic disease [3]. Dietary diversity is a useful measure of one’s food security. Dietary diversity corresponds to a $0.7\%$ increase in per capita caloric availability [4]. Therefore, this measure can be used to both assess energy intake along with micronutrient levels. Using dietary diversity to quantify food insecurity allows researchers to explore the different factors that may contribute to discrepancies in nutritional access across various populations. Urbanization has not been researched in-depth with respect to its impacts on food security. India’s rate of urbanization recently increased to $2.33\%$ [5]. This rapid increase in rate could lead to disparities in food access across urban and rural areas. Current literature suggests a gendered disparity can be partly attributed to India’s feminization of agriculture, a trend prevalent in many developing countries. While many young men are moving from rural to urban areas, rural women are continuing to do domestic work along with having the role of the temporary household head [6]. Most rural households manage farms jointly, with women in charge of post-harvesting duties through family labor such as cooking and men in charge of crop production labor as well as crop production decision-making [7]. The additional responsibilities for women due to rural men leaving include increased physical labor and ensuring that agricultural decisions are made such that they are still consuming a nutritionally adequate diet [8]. There exist qualitative studies comparing these food systems. For instance, it was found that rural female farmers have more difficulty accessing resources needed for agriculture compared to male farmers [9]. However, there is limited literature quantifying gender disparities in diet diversity across urban and peri-urban areas, particularly in the context of rapid urbanization and the feminization of agriculture. This study investigates differentials in dietary diversity across urban and peri-urban areas in Telangana, India between adult males, adult females, adolescent males, and adolescent females. The paper also explores other possible factors that could be used as indicators of diet diversity, including marital status, whether an individual attended school, their highest degree of education, and the type of household card. The authors hypothesize that adult women residing in peri-urban locations in Hyderabad are consuming less diverse diets compared to other urban adult women and compared to other peri-urban adult males. We also hypothesize that marital status, highest educational degree received, and BMI range are relevant indicators of dietary diversity, as these sociodemographic factors are statistically correlated with an individual’s dietary diversity score. ## Methods Figure 1 above details the conceptual framework for this paper. This study uses location and member group as two independent categorical variables to support that peri-urban adult women are consuming a less diverse diet. Individuals’ locations were categorized as either urban or peri-urban and their member groups as adult male, adult females, adolescent males, or adolescent females. Member group includes both gender and age differentials, as shown above. The four sociodemographic variables that were explored as potential indicators of dietary diversity are highest educational degree, type of household card (a measure of socioeconomic status), BMI range, and marital status. Levels within each variable are detailed below in Table 1.Fig. 1Conceptual framework showing linkages between the independent variables-location and gender with the dependent variable-diet diversityTable 1Variables for multivariate regressionHighest educational degreeType of household cardBMI rangeMarital statusNo schoolBPL - Below Poverty LineUnderweight - BMI < 18.5MarriedPrimary level (classes 1-5)APL - Above Poverty LineNormal - 18.5 ≤ BMI < 23Single/Never marriedSecondary level (classes 6-10)Other - No Ration CardOverweight - 23 ≤ BMI < 25WidowedTechnical or vocational trainingObese - BMI ≥ 25IntermediateGraduate /University (B Sc/BA/B Com/B Tech)PostgraduateOther ## Data collection Data was collected by other team members based on guidelines described in the Urban Sprawl Project Data Collection Documentation report [10]. Individual participants from randomly selected households were surveyed on their consumption of food groups based on a 24-hour recall basis along with sociodemographic information. Other information that was collected can be found in the Urban Sprawl Documentation report. Not all factors were included in the analysis due to a small sample size or lack of relevance. Refer to Supplementary Tables 1 and 2 in Appendix A [see Additional file 1] for other information collected. Within each household, three to four members were selected such that there were an adult male and female (20-65 years), adolescents (11-15 years), and children (3-5 years). Four sites in Hyderabad, India were chosen for data collection, two of which are urban areas and the other two peri-urban. Table 2 reflects household information of locations surveyed. Data was collected every 6 months three separate times. This project explores the data from the first round of data collection. Table 2Locations of households surveyedLocationUrban/Peri-urbanNumber of householdsAfzalgunjUrban55RamachandrapuramPeri-urban61Sri Sai nagar (Bachupally)Peri-urban57BegumpetUrban45ChandrayanguttaUrban46Langerguda/GaganpahadUrban45Thimmaiguda (Gowraram)Peri-urban35KPHB Phase7Peri-urban60LingampallyPeri-urban60MadhapurUrban45MallapurPeri-urban31Sai nagar (Nagole)Peri-urban35PocharamPeri-urban32TolichowkiUrban55Total Number of Households662 ## Dietary diversity scores (DDS) Dietary diversity scores were computed based on surveys conducted by trained enumerators. The FAO details ten food groups reflecting micronutrient adequacy for women that were used in this paper to indicate the dietary diversity level of individual adult males, adult females, adolescent males, and adolescent females. Consumption of these food groups is quantified through Minimum Dietary Diversity for Women (MDD-W). This metric is recommended to be used for adult women and adolescent girls of reproductive age [11]. Given the lack of a validated metric to assess dietary diversity for men, the authors used MDD-W to assess and compare the adequacy of diet for adult females, adult females, adolescent males, and adolescent females. This type of assessment was replicated in other research studies. An example involves a paper that also used MDD-W to develop an association between intra-household dietary diversity and crop and income diversity among men and women in the same household [12]. Another study comparing men’s and women’s diets living in the rural-urban interface also used the same DDS metric for their quantitative analysis [13]. MDD-W cannot be used to assess children’s diet diversity, as another metric is used for this analysis. Therefore, children are not included in this dataset. The total sample size of the population is $$n = 1816$$: 660 adult males, 662 adult females, 205 adolescent males, and 289 adolescent females. One thousand one hundred eight individuals lived in peri-urban areas, and 808 individuals lived in urban areas. Sample sizes for each member group and location are listed below in Table 3.Table 3Two-way table of members groups across urban and peri-urban regionsPeri-urbanUrbanTotalAdult male369291660Adult female371291662Adolescent male12085205Adolescent female148141289Total10088081816Values are sample sizes The ten food groups underlying MDD-W are 1) Grains, white roots and tubers, and plantains 2) Pulses (beans, peas, and lentils) 3) Nuts and seeds 4) Dairy 5) Meat, poultry, and fish 6) Eggs 7) Dark green leafy vegetables 8) Other vitamin A-rich fruits and vegetables 9) Other vegetables 10) Other fruits. Consuming less than $50\%$ of food groups, or 5 groups, is defined as below minimally adequate diet diversity [11]. Raw data including all dietary diversity information collected is provided in Additional file 2 as an excel sheet. ## Data analysis The authors analyzed data collected by enumerators via R, Jamovi, and Microsoft Excel. Based on the consumption of each food group, dietary diversity scores were computed. If consumed, a score of 1 was assigned to the individual for the corresponding food group. If it was not consumed, then a score of 0 was assigned. Total dietary diversity scores were computed by adding food consumption scores for each food group. Computed scores were then validated by confirming that DDS scores were between 0 and 10. Microsoft excel sheets with collected data were imported to RStudio for data cleaning and transposing using the “dplyr” and “tidyverse” packages in RStudio so that all indicators of interest (ex. member group, location, marital status, etc.) were available in one data frame. Then, the data frame was uploaded to Jamovi, a free open-source software. Jamovi was also used to create Descriptive tables, which include count, mean, standard deviation, maximum, and minimum. After identifying possible indicators of DDS differentials among participants in Jamovi, the data frame was then analyzed in RStudio to conduct more complex analyses. A questionnaire was also generated via Google Forms to assess the food environment of a literate population in Patancheru, separate from the surveyed members in this study. This was used to assess the frequency of food group consumption, food accessibility, and attitudes toward nutritional health. The data collected through this survey was not included in this report. ## Statistical model The authors provided a histogram to visualize the consumption of food groups across various member groups. The authors then tabulated a comparison of the proportion of member groups living in urban and peri-urban areas who are below a minimally adequate diet diversity level (DDS < 5). A fisher exact test was conducted to assess if differences in proportions across urban and rural areas were significant for each member group. An alpha significance of 0.05 was used. Other papers used a similar analysis method to compare proportions of populations consuming an adequate diet [14–16]. To fully establish the significance and effect of disparities among member groups and locations, a multivariate analysis was conducted to develop a bivariate association across the two independent variables. As shown in Fig. 2, these two variables are assessed together using a factorial design; each member group (4 groups) was tested to assess if the location makes a significant impact on DDS, and individuals living in each location (2 locations) were tested to assess if the member group they identify as makes a significant impact on DDS.Fig. 2Factorial design of ANOVA The mean differences in DDS between two groups were compared via an independent sample t-test, while more than two means were compared via one-way analysis of variance (ANOVA). When conducting repeated sample t-tests, Bonferroni’s multiple testing correction was applied to develop an adjusted alpha-value by dividing the initial alpha value (0.05) by the number of comparisons made to output the corrected α. ANOVA tests were reported with post-hoc Tukey HSD tests to identify which member groups were statistically impacted by which location, and vice versa. Results outputted an adjusted p-value so α = 0.05 was used. After running statistical analyses to support the hypothesis that adult females are being disproportionately impacted in peri-urban areas, multivariate ordinary least squares regression was conducted on other relevant socio-demographic information collected: highest educational degree, type of household card, BMI range, and marital status. Each independent variable was used to formulate correlation coefficients with the dependent variable DDS. A p-value of less than 0.05 is defined as statistically significant for the model as a whole and levels within each independent variable. ## Results After the initial analysis was done on the consumption of each of the food groups consumed by the different member groups in a household, Fig. 3 indicates that the top three most consumed food groups were food groups 1 (Grains, white root tubers, and plantains), 4 (dairy), and 9 (other vegetables). The least consumed groups were food groups 7 (dark green leafy vegetables), 10 (other fruits), and 8 (other vitamin A-rich fruits and vegetables). A descriptive table breakdown of average food group consumption by each member group is provided in Supplementary Table 3 in Appendix B [see Additional file 1]. A score of 1 is defined as an individual consuming the food group, while 0 is defined as a lack of consumption. Fig. 3Histogram of average food group score of each member group ## Diet diversity in urban and peri-urban areas - a gendered analysis Minimum diet diversity, which is defined as consuming below $50\%$ of total food groups on a given day, was compared across member groups and locations in Table 4. The results of the fisher exact test indicate that adult females had a significant disparity in their proportion of population below minimum diet quality when comparing urban and peri-urban counterparts ($p \leq 0.001$). All other member groups do not have a significant difference in proportion ($p \leq 0.05$).Table 4Fisher’s exact test results of population proportions below minimum diet diversityMember groupPeri-urbanUrbanP-valueAdult male60.97657.7320.425Adult female65.36857.732 < 0.001Adolescent male56.66760.0000.668Adolescent female57.43249.6450.196Values are population percentages below a minimum dietary diversity (MDD < 5)$p \leq 0.001$ An analysis of variance was then conducted to assess how location impacts each of the different member group’s (adult male, adult female, adolescent male, adolescent female) DDS and how the DDS of individuals living in each of the two locations (urban and peri-urban) are impacted by identifying as a particular member group. This factorial design is visualized in the methods section above in Fig. 2. Table 5 indicates the results of the multivariate analysis. Bonferroni’s multiple testing correction was applied. Across the four member groups, there were four independent sample t-tests conducted comparing DDS of those living in peri-urban and urban areas. Therefore, the adjusted cutoff for significance was α = 0.0125. Adjusted p-values were used for Tukey HSD tests when comparing more than two means. Table 5Analysis of variance of DDS with post hoc Tukey HSD resultsDietary diversity scoresCharacteristicCategoryMeanSDMinimumMaximumMember Group Adult malePeri-urban4.3301.18428Urban4.3471.18418 Adult femalePeri-urban4.1001.20728Urban4.3471.17428 Adolescent malePeri-urban4.2921.33128Urban4.2711.18927 Adolescent femalePeri-urban4.5201.34329Urban4.5041.37128Location UrbanAdult male4.347a1.18418Adult female4.347a1.17428Adolescent male4.271a1.18927Adolescent female4.504a1.37128 Peri-urbanAdult male4.330a1.18428Adult female4.100b1.20728Adolescent male4.292a,b1.33128Adolescent female4.520a1.34329Mean values were significantly different as determined by independent t-tests ($p \leq 0.0125$)a,bMean values with different superscript letters were significantly different as determined by ANOVA with post hoc Tukey HSD test (adjusted $p \leq 0.05$) Based on the results, only adult females’ DDS are statistically impacted by their location, with peri-urban members having a lower dietary diversity; Peri-urban and urban adult women had different mean DDS of 4.100 and 4.347, respectively, with a significant p-value. Subscripts indicate specific pairwise comparisons of DDS between member groups living in rural and urban areas. Mean DDS of all member groups residing in urban areas are statistically similar, while this is not the case for those living in peri-urban areas; Peri-urban adult women (Mean DDS = 4.100) have a statistically different (adjusted $p \leq 0.05$) dietary diversity than peri-urban adult males (Mean DDS = 4.330) and adolescent females (Mean DDS =4.520). Adult women residing in peri-urban areas again have lower mean DDS. Urban adult women have statistically similar DDS compared to other urban member groups. ## Exploring indicators of dietary diversity Multivariable regression was conducted to assess the value of other socio-demographic variables as potential correlators of dietary diversity. The variables assessed in this model were BMI range, type of household card received, marital status, and highest educational degree earned (Table 6). Not all levels listed in Table 1 were included in the regression due to a low sample size (n ≤ 10). A full descriptive breakdown of sample size, mean DDS, and SD of each sociodemographic variable analyzed can be found in Supplementary Table 4.Table 6Association between the socio-demographic variables and DDSβ CoefficientsStandard errorP-valueBMI Range UnderweightReference Normal0.1660.1020.105 Overweight0.2450.1330.066* Obese0.4000.110< 0.001*Type of Household Card Other - No Ration CardReference BPL0.1560.1050.138 APL0.4580.1690.007Marital Status Single/Never MarriedReference Married−0.3150.0970.001Highest Educational Degree Primary LevelReference Secondary Level−0.0680.0830.409 Intermediate−0.0270.1380.844 Graduate/University0.3420.1510.024 Postgraduate0.1220.2240.586 Adjusted F3.236 P-value< 0.001* $p \leq 0.05$ $p \leq 0.001$ Regression results indicate that BMI, type of household card, and highest educational degree are positively associated with dietary diversity. Those with a higher BMI relative to the underweight group have a higher dietary diversity score (β = 0.235, SE = 0.133 for overweight and β = 0.400, SE = 0.110 for obese). Individuals holding a governmental APL household card consume a more diverse than those without access to a ration card (β = 0.458, SE = 0.169). Compared to single/never married participants, married participants have a lower diet diversity (β = − 0.315, SE = 0.097). Members who completed a graduate/university level education consume a more diverse diet than those who have only completed a primary level of education (β = 0.342, SE = 0.151). The overall regression model has a p-value less than 0.001 ($F = 3.236$). ## Discussion The results presented in the paper indicate that while every participant consumed Food Group 1, only about $15\%$ consumed dark green leafy vegetables, other fruits, and other Vitamin A-rich fruits and vegetables. A lack of these food groups can lead to adverse health effects, including vision loss, skin issues, and immune system deficiencies [17]. These results are also corroborated by health initiatives that have taken place in India. The government implemented a Vitamin-A supplementation policy in response to the large healthcare burden India’s system is facing due to this deficiency, particularly nutritional blindness from Vitamin A. This key nutrient is mainly found in all three of the least consumed food groups from the population studied. In fact, many of those living in South India also face additional multiple micronutrient deficiencies in folic acid and Vitamin C, which are also all generally consumed in the diet through the three lowest consumed food groups [18]. These findings are also consistent with focus group discussions conducted by the enumerators, as many participants mentioned that dark green leafy vegetables and fruit were particularly expensive and had significantly increased in cost during the COVID-19 pandemic. Another study focusing on diets during the COVID-19 pandemic further corroborated these findings, as it was found that women and households altogether have been spending less on non-staples, which include non-grain foods. The study discusses a shift away from vegetables and meats towards more affordable staples such as cereals, which is seen from the $100\%$ consumption in food group 1, due to the “disproportionate increase in nonstaples prices compared to staple foods.” [ 19]. The results of the multivariate analysis specifically quantified the gender disparity by assessing differences in DDS between member groups and locations. Post-hoc multiple testing via pairwise Tukey t-test further supported the hypothesis because it indicates two layers of disparity among adult females; they are the only member group to have a locational disparity across urban and peri-urban areas, with peri-urban adult females consuming a less diverse diet. And within peri-urban areas, adult females are consuming a less diverse diet than their adult male counterparts. As mentioned, there are limited research studies assessing bivariate associations of assessing member groups and location, specifically with respect to urbanization and dietary diversity. However, a study researching diet diversity between women and men found a similar trend; peri-urban populations had a lower dietary diversity score compared to their urban counterparts. Particularly, women living in peri-urban and peri-urban areas had the unhealthiest food consumption and lowest diet diversity, while this was not true for men [18]. Other studies that have rural-urban or gendered comparisons in dietary diversity have followed a similar analysis method of ANOVA with post-hoc Tukey tests [20–22]. Other researchers assessing rural obesity found statistically significant associations between rising obesity in India and the urbanization of its rural spaces [23]. A third study also researching urban household food security in South Africa concluded that there was a lower average number of micronutrients consumed in rural areas and this disparity is closely associated with increased urbanization. The authors generalized this increasing disparity to other developing countries facing similar geopolitical trends, such as India [24]. Although the paper did not assess gender disparity and only focused on locational disparity, the results of this study alongside existing limited literature support the need to provide a support system for adult women within peri-urban areas; this population particularly needs the resources to control their agricultural output since many men are rapidly leaving peri-urban areas along with their previous farming responsibilities [25]. The regression suggests the sociodemographic factors marital status, highest educational degree, BMI range, and type of household card are statistically significant variables that impact dietary diversity. Findings are also supported by several other research studies whose analyses indicate these factors are correlated with DDS [26]. Although the four variables have statistically notable impacts on DDS, the multivariable linear regression model as a whole cannot be used to accurately predict dietary diversity scores. Diet quality is impacted by a multitude of biological, social, economic, and psychological factors, some of which include appetite, genetic predispositions, mood, stress, and culture. It is not possible to incorporate all factors that accurately account for the variance in an individual’s DDS [27]. Nonetheless, it is crucial to identify the factors that significantly impact one’s diet quality for a given population. There are several limitations to our paper. First, the authors used MDD-W to assess the dietary diversity of adult and adolescent males because there are not any standardized measures to assess the dietary diversity of men. Other researchers have also been using this metric to compare these member groups’ dietary diversity. However, future research could analyze the validity of comparing men’s and women’s diet quality with the ten food groups detailed in this study. Second, this paper reports the results of a cross-sectional study. Although the analysis with the literature review provides correlation conclusions, this research design cannot be used to establish a cause-and-effect relationship between urbanization and increasing gender disparities in dietary diversity. We suggest that future research analyzes the long-term effects of urbanization by following the same community members for several years and collecting dietary data. Third, there was a low sample size across several levels within various sociodemographic variables such as marital status, and educational degree received. A large sample dataset (n > 1800) was aggregated to account for several levels. However, to provide comprehensive pairwise comparisons between categories within each variable, more data should be systematically collected on individuals with these characteristics. ## Conclusions Based on the data collected and analysis conducted, there is a lack of consumption of dark green leafy vegetables and vitamin A-rich fruit and vegetables, which could lead to micronutrient deficiencies and adverse health effects, an already prevalent issue in this area of India. Since every member in the study had consumed grains, white root tubers, and plantains on a given day, it could be beneficial to implement fortification of these staples with Vitamin A, folic acid, iron, and other micronutrients individuals are lacking due to low consumption of these food groups. Fortifying easily accessible foods such as grains and white root tubers would allow community members to consume these important nutrients while mitigating financial and accessibility constraints. GAIN (Global Alliance for Improved Nutrition) already has existing programs and partnerships with India such as the implementation of large-scale food fortification and distribution of better-quality food to children in school [28]. The development of a program through GAIN to help specifically address the fortification of peri-urban Indian communities could help mitigate the negative dietary impacts of urbanization. Given that GAIN already has a partnership with India and has a mission focused on addressing diet, this intervention could be effective. The authors also suggest that the Indian government supports peri-urban farmers, particularly women, who locally grow and sell crops containing under-consumed food groups such as dark green leafy vegetables and other Vitamin A-rich foods. A significant reason participants stated they were not consuming these food groups was the lack of affordability. Subsidizing a portion of farming costs would incentivize farmers to grow more of these crops and sell them at a lower cost, which could increase accessibility for peri-urban community members. There are significant disparities in diet diversity between peri-urban and urban women/adolescent females, whose population percentage under a minimum diet diversity is already significantly higher compared to men. Therefore, it is crucial to assure that the undernourished in peri-urban areas, specifically women and adolescent females, are not left behind during this mass migration to urban areas. After synthesizing the conclusions of other researchers studying this topic with the results of this paper, the authors conclude that developing programs with existing partnerships/alliances as well as government subsidization of farmers can help counteract the negative impacts of urbanization and ensure resources for these vulnerable populations to access a healthy, nutritious diet for generations moving forward [20, 29, 30]. ## Supplementary Information Additional file 1: Appendix A. Nutritional Status Information Collected. Supplementary Table 1. Anthropometric Readings Collected. Supplementary Table 2. Morbidity Patterns Collected. Appendix B. Descriptive Tables. Supplementary Table 3. Descriptive Table of Food Group Consumption by Member Group. Supplementary Table 4. Descriptive Table of Sociodemographic Variable Levels. Additional file 2: Raw Dietary Diversity Data Collected. Raw Dietary Diversity Data Collected. 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--- title: COVID-19 versus applied infection control policies in a Major Transplant Center in Iran authors: - Mojtaba Shafiekhani - Tahmoores Niknam - Seyed Ahmad Tara - Parviz Mardani - Khatereh Mirzad Jahromi - Sedigheh Jafarian - Sara Arabsheybani - Halimeh Negahban - Majid Hamzehnejadi - Zahra Zare - Khadijeh Ghaedi Ghalini - Ali Ghasemnezhad - Mahmoud Akbari - Reza Shahriarirad - Seyed Ali MalekHosseini journal: 'Cost Effectiveness and Resource Allocation : C/E' year: 2023 pmcid: PMC9969367 doi: 10.1186/s12962-023-00427-x license: CC BY 4.0 --- # COVID-19 versus applied infection control policies in a Major Transplant Center in Iran ## Abstract ### Background Since Shiraz Transplant Center is one of the major transplant centers in Iran and the Middle East, this study was conducted to evaluate outcomes of the applied policies on COVID-19 detection and management. ### Methods During 4 months from March to June 2020, patient's data diagnosed with the impression of COVID-19 were extracted and evaluated based on demographic and clinical features, along with the length of hospital stay and expenses. ### Results Our data demonstrated that a total of 190 individuals, with a median age of 58, were diagnosed with COVID-19 during the mentioned period. Among these, 21 patients had a positive PCR test and 56 patients had clinical symptoms in favor of COVID-19. Also, 113 ($59\%$) patients were classified as mild based on clinical evidence and were treated on an outpatient basis. Furthermore, 81 out of 450 cases ($18\%$) of the healthcare workers at our center had either PCR of clinical features in favor of COVID-19. The mortality rate of our study was $11\%$ and diabetes mellitus, hypertension were considered risk factors for obtaining COVID-19 infection. The direct cost of treatment and management of patients with COVID-19 amounted to 2,067,730,919 IRR, which considering the 77 patients admitted to Gary Zone per capita direct cost of treatment each patient was 26,853,648 IRR. ### Conclusion We demonstrated that the COVID-19 pandemic had a noticeable influence on our transplant center in aspects of delaying surgery and increased hospital costs and burden. However, by implanting proper protocols, we were able to was able to provide early detection for COVID-19 and apply necessary treatment and prevention protocols to safeguard the patients under its coverage, especially immunocompromised patients. ## Introduction In late December 2019, China reported an outbreak of viral pneumonia in Wuhan, Hubei Province, China, which spread rapidly to other areas [1, 2]. The novel coronavirus disease 2019 (Also known as SARS-CoV-2 or COVID-19) is a global concern and has become a significant health problem since the number of infected cases and affected countries has escalated rapidly [3]. The outbreak of coronavirus in Iran was officially confirmed on February 19, 2020 [4]. On March 11, 2020, the World Health Organization (WHO) confirmed COVID-19 a pandemic. As of August 29, 2020, over twenty-five million cases of COVID have been reported with a death toll of around 843,000 patients and only around seventeen million recovered cases in 213 countries and territories worldwide. Among the top-ranking countries, Iran has placed in the twelfth position with over 371,000 confirmed cases and over 21,000 deaths [5]. Although months into the pandemic, there are still various aspects of the virus which remain unknown resulting in challenges in detecting and treating the disease; therefore, many medical centers with large numbers of patients are requiring special care settings and subsequently impose high work pressure on medical staff and increase medical costs causing a significant burden on health care systems. Meanwhile, patients with special clinical conditions such as receiving a transplanted organ or waiting for a transplant due to the weakened condition of their immune system are more prone to infection and its complications [6, 7], therefore, centers providing service and care for these patients must adopt appropriate and consistent a strategy [8]. Recent case series from the UK and Italy have reported a mortality of 7–$25\%$ of COVID-19 in post-transplant patients, adding to the concern that these patients suffer significantly adverse outcomes [9]. Akdur and associates reported that the disease course for kidney transplant recipients infected with SAR-CoV-2 was worse than shown in the normal population. Compared with the overall mortality rate in the United States of $1\%$ to $5\%$ (and up to $15\%$ in patients over 70 years of age), the mortality rate in their centers was $28\%$ in solid-organ transplant recipients [10]. In this study, we aim to study aspects of COVID-19 and experiences in this field in Shiraz Organ Transplant Hospital as one of the largest transplant centers in Asia from March to June 2020. ## Study design and data collection This retrospective study was conducted in Shiraz Transplant Hospital, Shiraz, Fars, Iran affiliated by Shiraz University of Medical Sciences as one of the largest centers for solid organ transplantation with more than 500 liver transplantation and 300 kidney transplantations annually and more than 300 hospital beds and 22 active wards. The center also covers a variety of additional wards such as internal medicine, cardiology, pulmonology, gastrointestinal, and other surgical wards. The study timeline was during a 4-month period from March to June 2020 in which all patients diagnosed with COVID-19 were enrolled either based on SARS-CoV-2 positive real-time polymerase chain reaction (RT-PCR), or clinical manifestations along with radiographic findings with hospitalized for more than 48 h. Demographic information of patients along with the course of disease and array of signs and symptoms, medical history, clinical and radiographic findings, length of hospital stay, and clinical outcome of all patients were extracted from the medical records of the patients and evaluated accordingly. ## Screening and triage With the establishment of a fever clinic at the entrance of the hospital, which consists of two nurses and a general practitioner on a 24-h basis, all staff, patients, and patient companions were first assessed by a digital thermometer on a daily basis. In terms of fever or suspicious clinical symptoms, the physician will then first perform a physical examination and obtain a persist medical and contact history, and in suspicious cases, referred to an infectious disease specialist residing in the hospital for further evaluation. RT-PCR was used to confirm suspected cases. RT-PCR assays performed following the protocol established by the WHO [11]. All suspected patients were initially evaluated by internal medicine and infectious disease specialists and based on the severity of clinical symptoms, radiographic images, and PCR test results were divided into three categories: mild, moderate, and severe [12], and if decided by internal medicine and infectious disease specialists, they were transferred to the quarantine ward for patients with COVID-19 (The Gray-Zone). All patients were evaluated daily by transplant surgeons, internal medicine, infectious, pulmonary, and pharmacotherapy specialists, and the necessary medical or surgical interventions were done for management and treatment. ## Transplantation and surgeries policies With the widespread of this virus in the country and Fars province, according to the instructions issued by the Ministry of Health, all non-emergency surgeries were suspended and only emergency surgeries were performed, which in these cases in our hospital, candidates will undergo a full screening including obtaining medical and contact history, full physical examination, SARS-CoV-2 PCR, and if required radiographic evaluations by a general physician which is normally conducted only for surgical cases. In the case of transplant surgery, the donor and recipient of the solid organ must have a negative PCR test as well as the absence of any radiographic findings or suspicious symptoms in favor of COVID-19 during the past 48 h before transplant, which will be evaluated by a team of infectious disease specialists, transplant surgeons, and internal medicine specialists. Furthermore, donors who were among residents with a high prevalence of COVID-19 and considered as a “red areas” according to the announcement of the Ministry of Health were not eligible for organ donation in the initial two months. The center also set up a virtual clinic to monitor transplanted patients or waiting for solid transplant who weren’t admitted at our center, so that all patients are virtually visited daily by transplant surgeons and answered their inquiries if needed. ## Management and treatment of COVID-19 In mild patients which were dischargeable based on the infectious and internal medicine specialist's opinion and followed on an outpatient basis, instructions for receiving hydroxychloroquine and related health tips related to self-quarantine for 14 days were provided. They were also daily telephone monitoring by nurses specialized in infection control for the initial 10 days, and if the disease progressed were re-visited at our clinic. In moderate and severe patients, depending on the underlying disease, received treatment regimen consisting of hydroxychloroquine along with Lopinavir/Ritonavir based on the recommended protocol ordered by the Health Ministry of Iran at the time of detection of infection [13, 14]. In transplant recipients, the basis of pharmacotherapy was based on the principles mentioned by Mirjalili et al. [ 15]. The condition for the discharge of the patient or transfer from the “Grey Zone" to other wards was the improvement of clinical symptoms, the presence of two negative PCR tests with an interval of 48 h, and respiratory rate below 22 with oxygen saturation percentage (SPO$2\%$) above $95\%$ without the need for oxygen therapy or ventilation. ## Statistical analysis Categorical variables were described as frequency rates and percentages, and continuous variables were described using mean, median, and interquartile range (IQR) values. Means for continuous variables were compared using independent group t-tests when the data were normally distributed; otherwise, the Mann–Whitney test was used. Data (non-normal distribution) from repeated measures were compared using the generalized linear mixed model. Proportions for categorical variables were compared using the χ2 test, although the Fisher exact test was used when the data were limited. All statistical analyzes were performed using SPSS (Statistical Package for the Social Sciences) version 26.0 software (SPSS Inc). For unadjusted comparisons, a 2-sided α of less than 0.05 was considered statistically significant. ## Results During the study period, 190 patients suspected of having COVID-19 were diagnosed based on clinical signs, radiographic findings, and PCR results with a median age of 58 (IQR: 44–66.25). Among these, 21 patients had a positive PCR test for COVID-19 and 56 patients had clinical symptoms along with highly suspicious radiographic findings for COVID-19, who were admitted to the gray zone ward. Furthermore, 113 patients ($59.47\%$) were classified as mild based on clinical evidence and were treated on an outpatient basis and 77 patients ($40.52\%$) accounted for as moderate and severe. Table 1 shows the demographic and clinical information related to patients admitted to the Gray zone ($$n = 77$$) by patients with a positive and negative PCR test. Table 1Demographic and clinical features of admitted COVID-19 patients in Shiraz Transplant Center ($$n = 77$$)VariableCOVID-19 PCR Positive (%) $$n = 21$$COVID-19 PCR negative patients but highly suspicious (%)$$n = 56$$Age group < 403 (14.3)7 (10.7) 40–6010 (47.6)17 (28.6) > 608 (38.1)32 (57.1)Gender Male12 (57.1)38 (67.9) Female9 (42.9)18 (32.1)Residence (Fars province) Shiraz (capital of Fars)9 (42.9)34 (60.7) Other Cities12 (57.1)22 (39.2)Comorbid diseases Diabetes mellitus6 (28.5)17 (30.4) Cirrhosis5 (23.8)14 [25] Hypertension4 (19.0)18 (32.1) Ischemic heart disease2 (9.5)1 (1.8) Encephalopathy1 (9.5)5 (8.9) Asthma1 (4.8)0 [0] Ascites1 (4.8)3 (5.4) End-stage renal disease1 (4.8)7 (12.5) Chronic obstructive pulmonary disease0 [0]4 (7.1) Deep venous thrombosis0 [0]1 (1.8)Symptoms Fever11 (52.4)17 (30.4) Cough6 (28.6)13 (23.2) Dyspnea6 (28.6)20 (35.7) Vomiting5 (23.8)8 (14.3) Diarrhea3 (14.3)6 (10.7) Headache3 (14.3)2 (3.6) Pharyngitis0 [0]0 [0] Rhinorrhea0 [0]0 [0] Chest pain0 [0]0 [0] Malaise0 [0]0 [0]Transplantation Status Liver transplant5 (23.8)4 (7.1) Kidney transplant3 (14.3)4 (7.1) Candidate for transplant8 (38.1)15 (26.8)Time after transplant (months) 1–30 [0]2 [25] 3–62 (22.2)2 [25] 6–123 (33.3)1 (12.5) > 124 (44.4)3 (37.5) COVID-19 Coronavirus disease 2019, PCR Polymerase chain reaction. The mean age of patients was 55.8 (± 16.2) for PCR positive and 54.28 (± 17.2) for the clinical suspicious group, with no significant correlation among the two groups ($$P \leq 0.70$$). The results of quantitative and qualitative factors as risk factors for COVID 19 based on regression analysis are shown that the presence of diabetes mellitus, hypertension, and transplantation were the three risk factors for COVID-19 in patients in our study. Also, a mortality rate of 21 ($11\%$) cases was reported in our center in which 16 ($76.19\%$) had positive PCR for COVID-19. As demonstrated in Table 1, 16 transplant patients (7 kidney transplant and 9 liver transplant patients) are seen in patients with positive and highly suspicious PCR tests, $44\%$ of which have been transplanted more than 12 months ago and 23 patients were on the transplant waiting list. According to the policies adopted in the hospital, which was mentioned in the method section, the number of patients admitted during the study period had decreased by $44\%$ compared to the same period last year, and in this regard, the number of general surgeries performed had decreased by $20\%$, liver transplantations $50.5\%$ and kidney transplantations by $22.48\%$ compared to the same period last year. At the time of the study, approximately $33\%$ of the hospital's medical and administrative staff (450 cases) had been screened at least once for COVID-19 disease by PCR testing in which 14 hospital staff (including 3 physicians, 8 nurses, and 3 staff members) had a positive PCR result and 67 cases had a clinical symptom suggestive of COVID-19 with negative PCR. Among these groups, 51 ($65.43\%$) reported close contact with a patient with COVID-19 during their work process. It is also worth mentioning that all of the personnel reported having access to personal protective equipment (PPE). Based on the results extracted from the hospital database until the end of this study, the direct cost of treatment and management of patients with COVID-19 (admitted in Gary Zone) amounted to 2,067,730,919 IRR (approximately 51,000 USD), which considering the 77 patients admitted to Gary Zone per capita direct cost of treatment each patient was 26,853,648 IRR. Furthermore, the cost of purchasing PPE and disinfectants during this period was 8,420,016,600 Rials. ## Discussion With the ongoing COVID-19 pandemic, transplant patients and those with end-stage organ failure are in a particularly vulnerable position and are impacted by the disease from various aspects. Among the problems transplant patients endure one could name that elective surgeries such as live donor transplant procedures have been put on hold in many countries [16, 17]. Also, a decrease in the number of donor transplants, where the procedure is established, continues in some countries, albeit with modified donor and recipient criteria, in an attempt to reduce the risk of COVID-19 transmission or infection after transplantation [17, 18]. Additionally, administered immunosuppression drugs in these patients increase the risk of disease contraction and progression [6, 8]. With the current situation, adopted policies will result in a loss of transplant opportunities and therefore a substantial increase in the number of waiting list patients with also a significant impact on patient and hospitals, such as an increase in dialysis capacity and provision for kidney transplant patients, with additional inevitable morbidity and mortality; Hence, caring for patients who are at higher risk of disease progression is vital during this distinct period. Our study demonstrated that $59.47\%$patients had a mild presentation of COVID-19. In a similar study, Pereia et al. [ 19] reported 90 patients in an epicenter in the United States, among these, twenty‐two ($24\%$) had mild, 41 ($46\%$) moderate, and 27 ($30\%$) severe disease. The higher rate of mild patients in our study can be contributed to the implanted policies for screening and early detection of the disease, which subsequently resulted in a higher detection rate along with identifying patients at early stages of the disease, also some studies declared transplanted patients due to their immunosuppression status presented with less severe symptoms [20, 21]. However, relying on molecular detection of the disease rather than the clinical presentations for commencing treatment is still a controversial issue [22]. The mortality rate in our study was $11\%$ which was higher than the reported rate of the province ($8\%$) [23]. Also, Pacual et al. reported a fatality rate of about $45.8\%$ during the first 60 days after kidney transplantation [24]. The mortality rate varies among studies which may be due to the patients included and comorbidities, which in our case the major cause of mortality was ARDS. So, special attention should be given to decelerate the progression of the disease and avoid reaching severe phases. Diabetes mellitus and hypertension were the most detected comorbidities in our study, which was also reported in other studies [19, 23], which has been reported to be linked with ACE2-increasing drug treatment in these patients [25]. Regarding personnel and healthcare worker infection rates, 81 out of 450 cases ($18\%$) had either PCR or clinical features in favor of COVID-19. Based on PCR results, only 14 ($3.1\%$) had positive PCR tests, which was lower than a previous study in our province which demonstrated an infection rate of $5.6\%$ among healthcare workers [26]. Chu et al. [ 27] reported a rate of 57 cases during 5 weeks by the means of clinical presentations and based on WHO interim guidance [28], our study also demonstrated a higher detection rate by exploiting clinical presentations, in which 67 cases were detected by this method. Whether molecular or clinical features should be used in the context of detecting COVID-19 cases is still a matter of debate which reports highlight employing clinical features due to the high rate of PCR false negatives [22]. Furthermore, the highest group at risk of infection was nurses which accounted for 8 out of 14 ($57.1\%$) positive PCRs for COVID-19 in our study versus a $51.3\%$ rate in a previous study in Fars [26]. This fact may be due to that nurses have more patient contact in comparison with other healthcare workers which increases the risk of infection [29, 30]. Although safety measures and self-protection equipment were provided for all health care workers, these populations are still at risk and should be routinely screen to provide early detection and treatment. This is while a person working in Iran typically earns around 44,800,000 IRR per month (11,300,000 IRR lowest average) [31], while based on our data, the average cost for COVID-19 treatment among the patients in our study was around 10,800,000 IRR, which is $23\%$ to $95\%$ of an average individual’s salary. Other studies have reported that a maximum cost of 20,000 USD for COVID-19 inpatient admissions [25]. This is aside from the substantial cost of purchasing PPE and disinfectants during this period imposed on the hospital along with the decreased number of elective surgeries which subsequently results in a significant reduction in the income for hospitals, which is mentioned in other studies [32, 33]. Therefore, these facts, along with other studies, demonstrate that the global health issue caused by the COVID-19 pandemic not only impacts the individual’s health, but also the economy, mental status, and other various aspects [25, 34–36], therefore considering all these factors is vital in going on with this global pandemic. Although this study provides optimistic evidence of controlling the disease, some limitations remain. Our report was during the initial four months of the pandemic in Iran, and long-term follow-up of the discharged patients are not available. This is while the disease is still persistent in the country and many centers and individuals are still struggling with its impact, therefore precise evaluating management and therapeutic options is vital for optimizing the ultimate results. ## Conclusion We demonstrated that the COVID-19 pandemic had a noticeable influence on our transplant center in aspects of delaying surgery and increased hospital costs and burden. 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--- title: Protective effects of pentoxifylline against chlorine-induced acute lung injury in rats authors: - Meng-meng Liu - Jiang-zheng Liu - Chen-qian Zhao - Peng Guo - Zhao Wang - Hao Wu - Weihua Yu - Rui Liu - Chun-xu Hai - Xiao-di Zhang journal: BMC Pharmacology & Toxicology year: 2023 pmcid: PMC9969370 doi: 10.1186/s40360-023-00645-2 license: CC BY 4.0 --- # Protective effects of pentoxifylline against chlorine-induced acute lung injury in rats ## Abstract ### Objective Chlorine is a chemical threat agent that can be harmful to humans. Inhalation of high levels of chlorine can lead to acute lung injury (ALI). Currently, there is no satisfactory treatment, and effective antidote is urgently needed. Pentoxifylline (PTX), a methylxanthine derivative and nonspecific phosphodiesterase inhibitor, is widely used for the treatment of vascular disorders. The present study was aimed to investigate the inhibitory effects of PTX on chlorine-induced ALI in rats. ### Methods Adult male Sprague-Dawley rats were exposed to 400 ppm Cl2 for 5 min. The histopathological examination was carried out and intracellular reactive oxygen species (ROS) levels were measured by the confocal laser scanning system. Subsequently, to evaluate the effect of PTX, a dose of 100 mg/kg was administered. The activities of superoxide dismutase (SOD) and the contents of malondialdehyde (MDA), glutathione (GSH), oxidized glutathione (GSSG) and lactate dehydrogenase (LDH) were determined by using commercial kits according to the manufacturer’s instructions. Western blot assay was used to detect the protein expressions of SOD1, SOD2, catalase (CAT), hypoxia-inducible factor (HIF)-1α, vascular endothelial growth factor (VEGF), occludin, E-cadherin, bcl-xl, LC 3, Beclin 1, PTEN-induced putative kinase 1 (PINK 1) and Parkin. ### Results The histopathological examination demonstrated that chlorine could destroy the lung structure with hemorrhage, alveolar collapse, and inflammatory infiltration. ROS accumulation was significantly higher in the lungs of rats suffering from inhaling chlorine ($P \leq 0.05$). PTX markedly reduced concentrations of MAD and GSSG, while increased GSH ($P \leq 0.05$). The protein expression levels of SOD1 and CAT also decreased ($P \leq 0.05$). Furthermore, the activity of LDH in rats treated with PTX was significantly decreased compared to those of non-treated group ($P \leq 0.05$). Additionally, the results also showed that PTX exerted an inhibition effect on protein expressions of HIF-1α, VEGF and occludin, and increased the level of E-cadherin ($P \leq 0.05$). While the up-regulation of Beclin 1, LC 3II/I, Bcl-xl, and Parkin both in the lung tissues and mitochondria, were found in PTX treated rats ($P \leq 0.05$). The other protein levels were decreased when treated with PTX ($P \leq 0.05$). ### Conclusion PTX could ameliorate chlorine-induced lung injury via inhibition effects on oxidative stress, hypoxia and autophagy, thus suggesting that PTX could serve as a potential therapeutic approach for ALI. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40360-023-00645-2. ## Introduction Chlorine, as a respiratory irritant, is widely used in numerous industrial processes, such as plastics, synthetic fibers, dyes, pesticides, and pharmaceutical manufacturing [1–3]. Injuries due to chlorine exposure are usually the result of accidents at swimming pools and the mixing of household agents [4]. Moreover, as a traditional chemical weapon, chlorine is still considered a terrorist threat [5–7]. In World War I, German troops released more than 150 tons of chlorine on April 22, 1915, in Ieper of Belgium. This attack killed up to 5000 and caused injuries on both sides [8]. No matter accidental or deliberate, the release of chlorine poses a significant threat to public health [9–11]. Low concentration chlorine acts as an eye and oral mucous membrane irritant [12], but at the high level, it may induce damage to the lung, even resulting in acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Although there have been a number of therapeutic interventions recognized over the past couple of years, there is still no specific antidote against chlorine poisoning [13]. Searching for novel drugs remains urgently. Reactive oxygen (ROS) is known to contribute to the pathogenesis of ALI/ARDS, which may cause the endothelial and epithelial barrier dysfunctions [14, 15]. Through upregulating the expression of adhesion molecules, ROS may amplify the tissue damage and pulmonary edema. In a rats model of LPS-induced ALI, Duan et al. found that inhibited ROS might decrease the expression of adhesion molecules (ICAM-1 and VCAM-1), then attenuated ALI [16]. Hypoxemia is one of the main features of ALI, which is predominantly governed by hypoxia-inducible factor (HIF) [17]. HIF-1 is an oxygen-dependent transcriptional activator that is widely expressed in tissue during hypoxia [18]. HIF-1α, the oxygen-regulated subunit of HIF-1, has been identified to play an important pathophysiological role in maintaining oxygen homeostasis. Under normal conditions, HIF-1α is degraded by ubiquitin-dependent proteasomal. When under hypoxia, the HIF-1α subunit is stabilized and accumulates in the nucleus, and then regulates diverse processes [19]. Moreover, HIF-1 binds to hypoxia-response elements and initiates transcription of various hypoxia-adaptive genes, such as vascular endothelial growth factor (VEGF). As the most potent endothelial specific mitogen, VEGF recruits endothelial cell into hypoxic foci to regulate its function [20]. Our group previously reported that HIF-1α and VEGF levels increased in rat lung tissue after phosgene exposure [21, 22]. Li et al. showed that emodin alleviated pulmonary inflammation in rats with LPS-induced ALI by inhibiting the mTOR/HIF-1α/VEGF signaling pathway [23]. E-cadherin is a transmembrane glycoprotein which presents on the lateral surfaces of epithelial cells and functions as a cell adhesion [24]. Loss of E-cadherin is associated with lung diseases such as asthma and chronic obstructive pulmonary disease [25]. HIF-1α signaling pathway may play a key role in the development of ALI. However, the effects of PTX on hypoxia signaling pathway still need to be explored. Autophagy is a process of cell self-renewal that is dependent on the degradation of the cytoplasmic proteins or organelles of lysosomes [26]. Extensive work has been performed to confirm that autophagy is involved in the occurrence and development of ALI [27]. In the early stages (1 h and 2 h) of ALI induced by LPS, autophagy reached a peak at 2 h. As the ALI process progressed, autophagy decreased in a time-dependently manner [28]. The role of autophagy in ALI is still unclear. In cecal ligation and puncture (CLP)-induced septic mice, the emergence of autophagy alleviated the cytokine excessive release and lung injury, describing a protective role [29]. In vivo, autophagy aggravated oxidative stress in alveolar epithelial cells in H9N2 influenza virus infection [30]. However, there is no direct evidence for the effect of autophagy on chlorine-induced ALI. Historically, the nonselective phosphodiesterase inhibitor pentoxifylline (PTX) is reported for clinical use since 1972 and the LD50 oral dosage in rats is 1170 mg/kg. Because there appears no serious drug–drug interaction, PTX can be used easily in vascular disease, including peripheral vascular disease, cerebrovascular disease, and a number of other conditions involving a defective regional microcirculation [31]. These beneficial effects are thought to be due to its anti-inflammatory properties by inhibiting the production of tumor necrosis factors [32]. More importantly, it is widely reported that PTX has been shown to inhibit liver ischemia/reperfusion injury, abdominal compartment syndrome, and intermittent hypobaric hypoxia in experimental animals due to its antioxidant function [33, 34]. Recent research also implied therapeutic effects of PTX on a model of acid-induced ALI and endotoxin-induced ALI [35–38]. Furthermore, Mostafa-Hedeab et al. reported that PTX might exert a protective effects in COVID-19 [39]. While, as an effective drug candidate in the treatment of ALI induced by chlorine, it still needs to be explored. Thus, this study was aimed to investigate the potential effects of PTX on ALI induced by chlorine. Here, the status of oxidative stress, hypoxia, as well as autophagy in lung tissues were analyzed. The characteristics of “New use of old drugs” can be reflected on PTX. ## Chemicals and reagents Chlorine was obtained from Jinghua Gas Co., Ltd. (Changzhou, China). Pentoxifylline was provided by Sigma (St. Louis, MO). Kits for detecting the activity of lactate dehydrogenase (LDH), superoxide dismutase (SOD), malondialdehyde (MDA), glutathione (GSH) and oxidized glutathione (GSSG) were supplied by Nanjing Jiancheng Bio-Engineering Institute Co., Ltd. Primary antibodies against VEGF, PTEN-induced putative kinase 1 (PINK1), Parkin and cytochrome-c oxidase subunit IV (COX IV) were brought from Santa Cruz Biotechnology (Santa Cruz, CA). Antibodies against occludin and E-cadherin were bought from Abcam (Cambridge, MA, USA). Antibodies against SOD 2 and Beclin 1 were bought from CUSABIO BIOTECH CO., Ltd. (Wuhan, China). Antibodies against HIF-1α, catalase (CAT), bcl-xl and β-actin were bought from Merck Millipore Technology (Burlington, MA), Proteintech Co., Ltd., (Wuhan, China), Cell Signaling Technology (Boston, USA) and Sigma (St. Louis, MO) respectively. Dihydroethidium (DHE) was purchased from Beyotime Co., Ltd. (Shanghai, China). ## Animals and experimental design Adult male Sprague-Dawley rats (4–6 weeks old, weighing 200–220 g) were provided by the Experimental Animal Center of the Fourth Military Medical University. The animals were housed in cages (6 rats per cage) under a permanent temperature of 20–25 °C and a 12 h light/dark cycle. All the rats were allowed free access to food and water. Efforts were made to minimize animal suffering. In this study, animal model of ALI was induced by inhaling chlorine (400 ppm) for 5 min. After validation of ALI model, according to a random number table, the rats were assigned to four experimental groups (6 rats/group). [ 1] normal control (NC) group; [2] chlorine group;[3] chlorine + PTX group; [4] PTX group. In addition, rats in the PTX and chlorine +PTX groups were intragastrically administrated with PTX (100 mg/kg) 30 min before chlorine exposure and treatment 15 min after chlorine exposure. The NC and chlorine-treated groups were orally administered with equal amounts of normal saline at the same time. ## Histologic examination The middle right lung lobes of the rats were fixed in $4\%$ formaldehyde for 24 h. After dehydrated, the sections were embedded in paraffin and sliced at 3 μm. Following deparaffinized and dehydrated, the sectioned tissues were stained with hematoxylin (5 min) and eosin (1–2 min) (H&E). A light microscope (BX51; Olympus Corporation, Japan) was used to observe the extent of histological lung injury. ## Detection of ROS formation According to the previously described method [38], the intracellular ROS level was detected using the fluorescent dye DHE. Then, the tissue was collected and incubated for 30 min at 37 °C in the dark with 10 μM DHE and 10 μM Hoechst. After washed 3 times with PBS, the tissues were immediately observed by a laser scanning confocal microscopy (FV10i; Olympus Corporation, Japan). ## Preparation of bronchoalveolar lavage fluid (BALF) The BALF in the lungs was collected as per Liu et al. [ 40]. In brief, rats were euthanized with intraperitoneal pentobarbital sodium, then the bronchus and lung were exposed. A 3-mm endotracheal cannula was inserted into their trachea. After ligating the hilum of right lung, the left lung was lavaged with 5 mL ice-cold normal saline, which was which retrieved, and the recovery rate was > $90\%$. The BALF samples were centrifuged (2000 r/min and 4 °C for 10 min) to pellet the cells. Supernatants were removed and stored at − 80 °C. ## Determination of LDH A commercial kit was used to determine the amount of LDH release following the manufacturer’s protocol. Briefly, the samples were transferred to 96-well plates and incubated at 37 °C for 15 min in the presence of 1 mg/ml NADH. Then 2,4-dinitrophenylhydrazine was added to the samples at 37 °C for another 15 min. The reaction was stopped by addition of 0.4 M NaOH. Data was determined as the absorbance at 450 nm using a spectrophotometric microplate reader. ## Determination of levels of MDA, SOD, GSH, and GSSG The contents of MDA, SOD, GSH and GSSG in serum were determined according to the Kit commercial instructions. ## Western blotting The experimental procedure of Western blot analysis was carried out as Guo et al. [ 41]. Lung tissues were stored at − 80 °C immediately after rats were sacrificed. Tissue samples (100 mg) were ground with a homogenizer in 1 mL of RIPA lysis buffer with 1 mM PMSF and protease inhibitor. Then the homogenate was centrifuged for 20 min at 14400 r/min at 4 °C to collect supernatant. A bicinchoninic acid (BCA) assay (Thermo Scientifc, MA, USA) was applied to determine the protein concentration. After mixed with loading buffer, the supernatants were heated at 100 °C for 10 min at a ratio of 1:1. Equal amounts of the total proteins from each sample were separated by 6–$15\%$ SDS-PAGE and transferred onto polyvinylidene difluoride membranes (PVDF; EMD Millipore, Burlington, MA, USA). After blocked with $5\%$ skim milk for 2 h, the blotted membranes were washed with $0.1\%$ Tween-TBS (TBST), and subsequently incubated with the primary antibodies at 4 °C overnight. Then the membranes were washed with TBST buffer three times and incubated with the corresponding secondary antibodies at room temperature for 1 h. After washed with TBST again, the bands were visualized by an enhanced chemiluminescent (ECL) reagent (Thermo Scientifc, MA, USA). ## Statistical analysis All data were expressed as mean ± standard deviation (SD) and analyzed with a one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. All the analyses were assessed using the SPSS 13.0. $P \leq 0.05$ was considered statistically significant. ## Effect of chlorine on the histological changes and ROS accumulation First, H&E staining was performed to observe the abnormalities of gross features in the lungs after chlorine exposure under a light microscope. As shown in Fig. 1A-D, the rats in the NC group displayed normal appearance and no other histological alteration was observed. In contrast, the lung tissues collected from the group expose to chlorine exhibited marked histopathologic changes, such as alveolar wall thinness, edema, hemorrhage and interstitial infiltration by neutrophils. The airway pathology led to abnormalities in the lung parenchyma with alternating areas of emphysema and atelectasis. Thus, the ALI model had been successfully constructed. Applying this model, the ROS accumulation was measured by DHE. This probe was oxidized to form intermediate probe-derived radicals that were successively oxidized to generate the corresponding fluorescent products [17]. The results demonstrated that ROS level was significantly increased in chlorine-treated group ($P \leq 0.05$) (Fig. 1E-F).Fig. 1Pathologic changes and ROS accumulation in rats exposed to chlorine. A H&E staining in the lungs of rats in NC group (× 200); B H&E staining in the lungs of rats in NC group (× 400); C H&E staining in the lungs of rats in chlorine group (× 200); D H&E staining in the lungs of rats in chlorine group (× 400); E Confocal microscopy of the lung tissue (× 600); F ROS production measured using DHE. Data are presented as mean ± S.D. ($$n = 3$$). * $P \leq 0.05$ compared with the normal group. H&E: hematoxylin and eosin; NC: normal control; DHE: Dihydroethidium ## Effects of PTX on levels of MDA, GSH, GSSG and SOD To investigate the effects of PTX on oxidative stress, the expressions of biomarkers for oxidative stress, such as MDA, GSH, GSSG and SOD were detected by commercial assay kits. It was confirmed that the levels of MDA, SOD and GSSG in chlorine treated rats were up-regulated ($P \leq 0.05$) when compared with the NC group, while the effects of PTX administration were pronounced ($P \leq 0.05$), except SOD activity. Moreover, expose to chlorine decreased the levels of GSH and GSH/GSSG ratio ($P \leq 0.05$). Administration of PTX to animals remarkably up-regulated these indexes as compared with rats in the model group ($P \leq 0.05$) (Fig. 2).Fig. 2The effect of PTX on the content of MDA, SOD, GSH, GSSG and GSH/GSSG ratio. A The content of MDA, B the level of SOD, C the content of GSH, D the content of GSSG and E GSH/GSSG ratio. * $P \leq 0.05$ compared with the normal group. # $P \leq 0.05$ compared with chlorine-treated group. NC: normal control; MDA: malondialdehyde; SOD: superoxide dismutase; GSH: Glutathione; GSSG: oxidized glutathione; PTX: pentoxifylline ## Changes in the protein expression levels of SOD1, SOD2 and CAT Since regulation of antioxidases may be able to protect against oxidative stress, the present study further investigated whether PTX could affect the expressions of antioxidases. Therefore, the protein expressions levels of SOD 1, SOD 2 and CAT were determined. The western-blot analysis demonstrated that SOD 1 and CAT were markedly up-regulated in the chlorine-treated group compared with the NC group ($P \leq 0.05$) (Fig. 3). Treatment with PTX could inhibit these expressions ($P \leq 0.05$). Interestingly, chlorine did not affect the expression of SOD 2.Fig. 3Effect of PTX on SOD1, SOD2 and CAT protein expression in rat lung tissue following chlorine exposure. A The protein expression levels were determined by western blot analysis. B Densitometric analyses of protein expression levels corresponding to (A). * $P \leq 0.05$ compared with the normal group. # $P \leq 0.05$ compared with chlorine-treated group. NC: normal control; PTX: pentoxifylline; MDA: malondialdehyde; SOD: superoxide dismutase; CAT: catalase ## Effect of PTX on the expression of LDH As LDH release is positively related to cellular damage, the level of LDH was measured to calculate the degree of ALI. It was found that secretion levels of LDH both in serum and BALF were significantly increased following chlorine induction compared with the NC group, while treatments with PTX reduced increased LDH levels ($P \leq 0.05$) (Fig. 4).Fig. 4The effect of PTX on the level of LDH. The content of LDH in BALF (A) and serum (B). Data are presented as mean ± S.D. ($$n = 6$$). * $P \leq 0.05$ compared with the normal group. # $P \leq 0.05$ compared with chlorine-treated group. NC: normal control; LDH: lactic dehydrogenase; BALF: Bronchoalveolar lavage fluid; PTX: pentoxifylline ## Effect of PTX on hypoxia signaling pathway Since hypoxemia is considered as a significant character of ALI and hypoxia activates the hypoxia signaling pathway [17], the protein expressions of HIF-1α, VEGF, occludin and E-cadherin were determined. After exposure to chlorine, expressions of HIF-1α, VEGF and occludin were significantly up-regulated in the chlorine group compared to the NC group ($P \leq 0.05$). Administration of PTX caused a significant decrease in these indicators ($P \leq 0.05$). In addition, the use of PTX resulted in up-regulated expression of E-cadherin ($P \leq 0.05$), compared with the chlorine group (Fig. 5).Fig. 5Effect of PTX on HIF-1α/VEGF signaling pathway in ALI induced by chlorine. A The protein expression levels were determined by western blot analysis. B Densitometric analyses of protein expression levels corresponding to (A). * $P \leq 0.05$ compared with the normal group. # $P \leq 0.05$ compared with chlorine-treated group. NC: normal control; PTX: pentoxifylline; HIF-1α: Hypoxia-Inducible Factor-1α ## Effect of PTX on autophagy To explore whether the protective effect of PTX was associated with autophagy, we detected the level of several key autophagy-related proteins. The results demonstrated that inhaled chlorine significantly down-regulated the ratio of LC3 II/LC3 I and the expression of Beclin-1 and increased the expression of Bcl-xl ($P \leq 0.05$). Following treatment with PTX, the protein expression levels were obviously attenuated ($P \leq 0.05$). To further investigate mitophagy, we searched the expressions of PINK1 and Parkin in the lung tissue. Interestingly, treatment with PTX could promote the expression of PINK1, however, inhibited the Parkin expression in the lung tissue ($P \leq 0.05$). Because PINK1 selectively accumulates on the surface of damaged mitochondria and initiates the mitophagic process, we examined the expressions of PINK1 and Parkin in the cytoplasm and mitochondria respectively. The results showed that the PINK1 protein expression both in cytoplasm and mitochondria were significantly increased ($P \leq 0.05$). The Parkin protein expression in cytoplasm increased while decreased in mitochondria ($P \leq 0.05$) (Fig. 6).Fig. 6Effect of PTX on autophagy in ALI induced by chlorine. A, C, E and G The protein expression levels were determined by western blot analysis. B, D, F and H Densitometric analyses of protein expression levels corresponding to (A, C, E and G) respectively. * $P \leq 0.05$ compared with the normal group. # $P \leq 0.05$ compared with chlorine-treated group. NC: normal control; PTX: pentoxifylline; PINK1: PTEN Induced Putative Kinase 1 ## Discussion Chlorine is a highly reactive oxidizing toxic gas which is produced globally, such as water purification, bleaching of paper, industrial manufacture of several chemicals, and for many other purposes [8]. Chlorine gas has been used as a chemical weapon since World War I. The easy availability and inherent toxicity make it attractive to aggressors willing to disrupt infrastructure or cause mass panic and casualties. Inhalation of chlorine can produce a range of acute pulmonary effects, including impaired lung function, inflammatory reactions, increase of epithelial permeability, and airway hyperresponsiveness [42]. After inhaling chlorine, the features of ALI may be epithelial cell death, inflammation, pulmonary edema, hypoxemia, and pulmonary function abnormalities, which are key aspects in animal models and human clinical studies [43]. In present chlorine-exposed rat model, we observed epithelial damage, alveolar injury and inflammation, which agreed with previous studies in several animal models [44, 45]. We also noticed pulmonary edema and ROS accumulation 3 h after chlorine exposure. These data from this study combined with our previous findings clearly suggested that rat model for chlorine-induced ALI is produced successfully. Currently, anti-inflammatory drugs remain an effective therapy for ALI. Treatment with glucocorticoids, such as dexamethasone, led to significant improvement of lung functions and to reduced inflammation [46]. In clinical use for 30 past years, PTX has been licensed for use in peripheral vascular disease. It increases the deformability of erythrocytes, reduce blood viscosity, and inhibit fibrotic progression [47, 48]. Recent researches also demonstrated that PTX exerted beneficial effects in treating erectile dysfunction, hearing loss, Peyronie’s disease and osteoradionecrosis [49–52]. As well, in a prospective study, PTX inhibits COVID-19 severity by reduction of IL-6 and c-reactive protein (CRP) and improved prognosis of patients when combined with antioxidants [53]. In the present study, from the perspective of oxidative stress and the early research, we explored the intervention effects of PTX. Due to oxidative stress plays a crucial role in the development of ALI [16], we measured oxidative stress markers. As an indicator of lipid peroxidation, MDA is produced in oxidative cellular damage which indicates that ROS is overproduced [54]. PTX treatment caused a significant decrease in lung tissue levels of MDA. To further investigate related mechanisms, we determined antioxidant enzymes and antioxidants. SOD is one of the major intracellular antioxidant enzymes that induces superoxide anions (O2−) free radical to hydrogen peroxide (H2O2). Then, H2O2 can be reduced by converting to H2O in the presence of CAT [55]. As one of the nature antioxidants, GSH plays important roles in reducing the tissues from damage via detoxifying electrophiles, scavenging ROS, maintaining the essential thiol status of proteins, and providing a reservoir for cysteine. During ROS formation, GSH is converted to GSH disulfide (GSSG). In the current study, PTX also suppressed the levels of SOD1, GSSG and expression of CAT, and enhanced the GSH level and GSH/GSSG ratio to protect against pulmonary injury. However, there appeared no obvious effects on SOD activity when treated with PTX. By detecting the protein expressions of SOD1 and SOD2, we found that the expression level of SOD2 protein showed no significant difference between groups. Thus, the failure of PTX to reverse SOD activity may be related to the stable expression of SOD2. Based on the observation, we suggested that PTX has a beneficial antioxidative effect on ALI induced by chlorine. Since the activity level of LDH reflects the degree of cell injury, subsequently, we selected it as a functional indicator of ALI. The results showed that PTX treatment ameliorated the level of the LDH both in BALF and serum effectively, indicating the protective effect of PTX in lung injury induced by chlorine. Hypoxia is closely related to oxidative stress in inflammatory lung diseases [56]. In response to hypoxia, HIF-1α binds to the hypoxia response element of the erythropoietin gene and controls the hypoxic induction of HIF-1-mediated gene transcription. In addition, as a transcriptional heterodimer, HIF-1exerts a vital pathophysiological role in oxygen homeostasis [57]. Jahani et al. considered that hypoxia might be a key feature of COVID-19 launching activation of HIF-1 [58]. Under hypoxic conditions, ROS released from the mitochondrial electron transport chain can participate in the regulation of HIF-1 activity [59]. In this study, we showed that PTX directly reversed overexpression of HIF-1α. The classical HIF-1α/VEGF signaling pathway also exerts an important role in the pathogeneses of ALI and pulmonary edema. Besides, HIF-1α induces and activates the overexpression of the VEGF gene, which consequently affects the expression of tight junction proteins and adhesion molecules [60, 61]. The present study found that PTX significantly inhibited the overexpression of VEGF and occludin accompanied by the upregulation of E-cadherin, which were in agreement with previous researches [62, 63]. These findings showed that the ROS/HIF-1α/VEGF signaling pathway in the lung tissues of rat models in chlorine-induced ALI was activated. Autophagy, one type of cell death, is a mechanism for cell self-protection and self-renewal which relies on lysosomes to degrade their own organelles or proteins. As a major cellular defense against oxidative stress, autophagy is an intracellular digestion system that works as an inducible adaptive response to ALI. ROS may activate autophagy, and then facilitate cellular adaptation and diminish the damaged macromolecules and dysfunctional organelles [64]. However, the role of autophagy in the mechanism of ALI has been controversial. In a diabetic rat model, when treated with autophagy inhibitor 3-methyladenine, the results showed more serious ALI [65]. Numerous regulators like LC3 II and Beclin 1 play important role in process of autophagy induction during lung injury. After binding to the lipid derivative phosphatidylethanolamine, LC 3 I is converted to form LC 3 II, which enables fusion with the lysosomes. In addition, the ratio of LC 3 I/LC 3 II is used as an indicator of autophagy. As a part of a Class III PI3K complex, Beclin 1 takes part in autophagosome formation though assembling around cargo in a vesicle and combining with lysosome [66]. As our results demonstrated, PTX enhanced the expression of LC3 II and Beclin 1 accompanied by the reducing of Bcl-xl, suggesting that autophagy exerted a protective role in ALI induced by chlorine. Because mitochondria are considered as the main contributor of reactive oxygen species, the removal of damaged mitochondria by mitophagy plays important role in cellular antioxidant defenses [67]. PINK1, as a mitochondrially targeted serine–threonine kinase, takes part in mitochondrial quality control. Under normal conditions, PINK 1 maintains low basal levels though importing into the mitochondrial intermembrane space and rapidly degraded when combined with the presenilin-associated rhomboid-like protein (PARL) and the proteasome. When mitochondria are depolarization, PINK1 accumulates on the mitochondrial outer membrane (OMM) and results in recruitment of Parkin from the cytosol, then activates mitophagy [68]. Subsequently, the expressions of PINK1 and Parkin were performed to investigate the potential mechanism. Interestingly, PTX had been shown to inhibit the expression of PINK1, but increased the expression of Parkin. After separating mitochondria and cytoplasm, we found that the expression of PINK1 and Parkin in mitochondria showed similar trends with these expression in lung tissues. However, PTX treatment reduced the expression of PINK1 and Parkin in cytoplasm compared to the chlorine group. 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--- title: Developing expert international consensus statements for opioid-sparing analgesia using the Delphi method authors: - Daniel Da Der Sng - Giulia Uitenbosch - Hans D. de Boer - Hugo Nogueira Carvalho - Juan P. Cata - Gabor Erdoes - Luc Heytens - Fernande Jane Lois - Paolo Pelosi - Anne-Françoise Rousseau - Patrice Forget - David Nesvadba - Sadegh Abdolmohammadi - Sadegh Abdolmohammadi - Gebrehiwot Asfaw - Daniel Benhamou - Gilbert Blaise - Philippe Cuvillon - Mohamed El Tahan - Emmanuel Feldano - Paul Fettes - Gabriele Finco - Michael Fitzpatrick - Atul Kapila - Callum Kaye - Vikas Kaura - Helen May - Patrick Meybohm - Ulrike Stamer - Daniel Taylor - Marc Van De Velde - Benoit Van Pee journal: BMC Anesthesiology year: 2023 pmcid: PMC9969386 doi: 10.1186/s12871-023-01995-4 license: CC BY 4.0 --- # Developing expert international consensus statements for opioid-sparing analgesia using the Delphi method ## Abstract ### Introduction The management of postoperative pain in anaesthesia is evolving with a deeper understanding of associating multiple modalities and analgesic medications. However, the motivations and barriers regarding the adoption of opioid-sparing analgesia are not well known. ### Methods We designed a modified Delphi survey to explore the perspectives and opinions of expert panellists with regard to opioid-sparing multimodal analgesia. 29 anaesthetists underwent an evolving three-round questionnaire to determine the level of agreement on certain aspects of multimodal analgesia, with the last round deciding if each statement was a priority. ### Results The results were aggregated and a consensus, defined as achievement of over $75\%$ on the Likert scale, was reached for five out of eight statements. The panellists agreed there was a strong body of evidence supporting opioid-sparing multimodal analgesia. However, there existed multiple barriers to widespread adoption, foremost the lack of training and education, as well as the reluctance to change existing practices. Practical issues such as cost effectiveness, increased workload, or the lack of supply of anaesthetic agents were not perceived to be as critical in preventing adoption. ### Conclusion Thus, a focus on developing specific guidelines for multimodal analgesia and addressing gaps in education may improve the adoption of opioid-sparing analgesia. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12871-023-01995-4. ## Introduction Postoperative pain remains, at least partially, an unresolved issue. As approaches to pain management improve, it is clear that opioids alone are not the solution to managing postoperative pain. Over the 21.st century, advancements in the understanding of opioid-based medications, coupled with improved accessibility, have led to more liberal opioid prescriptions. For instance, in the UK, a $34\%$ increase in the prescriptions of opioids was noted between 1998 and 2016 [1]. The over-reliance on opioids has opened the doors to over-prescription, which may lead to worrying outcomes [2]. While they are effective for short-term pain relief, opioids have a propensity to be misused. The costs of over-prescription of opioids have a great economic and human toll, including addiction, dependency, overdose and even death [3–5]. Solutions to mitigating the side effects of opioid over-prescription include increasing the adoption of opioid-sparing approaches to analgesia [6]. A multimodal approach to anaesthesia involves a combination of various opioid-sparing and antinociceptive agents, for example the use of locoregional anaesthesia, alpha-2 agonists and anti-inflammatory drugs [7]. These act on multiple pain pathways and receptors, and may have additive or synergistic effects for pain relief. It is worth noting that multimodal analgesia does not need to be opioid-free, but has the objective of minimising the side effects of opioids and improving pain control. In fact, the Royal College of Anaesthetists (RCoA) made a stand that multimodal analgesia has proven to be opioid-sparing and provides superior pain relief. At present, the RCoA openly encourages the use of opioid-sparing analgesia techniques and opioid-sparing adjuvants [8]. While the multimodal approach is arguably a more complex technique and is currently typically proposed for a niche patient group (e.g., individuals at high risk of moderate-to-severe postoperative pain, sleep-related breathing disorders or pre-existent opioid-related misuse), the assurance of superior pain relief alone invites the question as to why opioid-sparing analgesia is not more widely practiced [9]. Other additional benefits, such as improved patient satisfaction, shortened recovery times and improved pain control in certain surgical procedures, propels the curiosity [10]. To date, there are few published Randomised Controlled Trials (RCTs) related to reasons and opinions of the use of opioid-sparing analgesia. As a result, clinical practice variations in the management of opioid-sparing analgesia have resulted in unclear optimal therapeutic management directions. This may be due to individual preferences and the local practices of anaesthetists. Given the dearth of evidence, the objective of this project is to use a Delphi process to achieve a consensus of the reasons and opinions of the use of multimodal opioid-sparing analgesia. ## Methodology model We employed a modified Delphi survey on opioid-sparing analgesia, which was part of a larger project focusing on reasons behind the selection of specific anaesthetic techniques. A modified Delphi survey is broadly defined as a multi-round survey that is anonymised, with a structured information flow and regular feedback [11]. The Delphi technique is often employed to create a robust consensus in healthcare research. Two primary advantages of the Delphi method are as follows: firstly, it preserves the anonymity of the panellists, allowing for unrestricted expression of opinions, thereby preventing discussion from being dominated by strong personalities. Secondly, regular feedback with aggregated responses from previous rounds creates opportunities for participants to change their minds or admit errors in prior judgements [12]. We hope that establishing a group consensus will lead to stronger conclusions and a more balanced viewpoint. In addition to this, the survey was done remotely, overcoming social distancing restrictions due to COVID-19. This also allowed for a more extensive global reach and a wide range of panellists’ contributions. For the first two rounds, the panellists were presented with eight statements based on our initial research on multimodal analgesia, seeking their perspectives on the strength of evidence as well as potential obstacles to adoption in the post-operative setting [13–15]. They could indicate their level of agreement on a five-point Likert scale. This differs from the classical Delphi approach, which often uses an unstructured first round [16]. The final results of the survey and the expert clinical practice statements were circulated among the experts. The manuscript was then circulated among the experts for editing and approval before it was submitted for publication. Figure 1 details the process of the Delphi study. Fig. 1Delphi study flow chart ## Panellist recruitment We recruited panellists by reaching out to members of the major anaesthetic organisations including the European Association of Cardiothoracic Anaesthesiology and Intensive Care (EACTAIC), European Society of Anaesthesiology and Intensive Care (ESAIC), UK Society for Intravenous Anaesthesia (SIVA), and the European Society for Regional Anaesthesia and Pain Therapy (ESRA). To prevent selection bias, we attempted to gather a diverse group of individuals representing the various subspecialties and anaesthetic organisations. 104 anaesthetic organisations and expert individuals were contacted for representatives who would be keen on participating in the survey, and the geographical distribution of the 29 panellists are shown in Fig. 2 below. As opinions on opioid-sparing techniques may vary between geographical locations and professional settings, the panellists were informed beforehand that they did not represent their respective organisations, but rather individual perspectives based on their personal experience. Fig. 2Geographical distribution of countries represented by the experts ## Data collection structure A total of 31 panellists responded positively to complete the survey. Study data was collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at the University of Aberdeen. REDCap is a secure, web-based application designed to support data capture for research studies, providing: 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources [17]. The application ensured the anonymity of data collection, provided a structured framework for providing feedback to the panellists, and facilitated the sending of automated emails and reminders for each Delphi round. ## Data collection process In Round 1, the panellists were requested to select their level of agreement based on a 5-point Likert scale, ranging from ‘Not at all’ to ‘Very much’. Using free-text format, panellists were also invited to propose new statements describing what they felt would be the largest obstacles to the adoption of opioid-sparing analgesia. These new statements were taken into consideration and incorporated into the statement pool in the second round. Additionally, specific panellist demographics were collected (e.g. country of practice, years of practice since qualification, areas of interest) which are partially included in Table 1.Table 1Panellist profileCategoryNo of PanellistsHospital Grade Tertiary21 ($72.4\%$) Secondary8 ($27.6\%$)Years of Anaesthetic practice 0–106 ($20.6\%$) 11–2012 ($41.4\%$) > 2011 ($37.9\%$) In the subsequent second and third rounds, panellists were presented their initial choices in the context of the aggregated and anonymised responses of all panellists, and thereafter given the choice to change their selection or retain the same position. In the third round, the statements were modified to determine if the respective elements were a priority in the use of opioid-sparing analgesia via a Yes/No question format. Notably, one statement which experienced a greater than $10\%$ change from the previous round was retained in the survey to ensure the stability of the consensus. ## Data analysis A consensus was defined as $75\%$ of panellists agreeing somewhat/very much or disagreeing not much/not at all. This is a commonly accepted threshold within Delphi studies [18]. Stability was taken as attained if the variation between each Delphi round was $10\%$ or less. ## Round 1 Twenty-nine out of the 31 invited panellists participated in the Delphi survey. There were two individuals who initially accepted the invitation to participate but eventually did not respond. Panellists indicated their country of practice, number of years of practice, as well as the professional setting where they practice. Eight items were presented. All 29 panellists completed the survey and 24 provided additional comments. ## Round 2 All 29 panellists that completed Round 1 participated in Round 2. Based on the feedback in Round 1, three out of the eight statements were modified according to their inputs. The final statements are detailed in Table 2.Table 2Statements and resultsFinal Agreement% change1There is a strong body of evidence supporting the use of opioid-sparing techniques$79.3\%$ + $6.9\%$2Whether opioid-sparing techniques may be cost effective is an important aspect for me$51.7\%$ + $6.9\%$3Whether opioid-sparing techniques and/or multimodal analgesia is the norm in my context and/or recommended in the locally used guidelines is important in my practice$82.6\%$$0\%$4The lack of training/education for some techniques possibly useful in multimodal analgesia is a key reason anaesthesiologists may not use it$92.6\%$ + $6.4\%$5I feel confident in administering any opioid sparing technique I need$75.8\%$$0\%$6Leadership and/or more specific guidelines for the application of multimodal analgesia will help my practice$79.3\%$ + $10.3\%$7The use of multimodal analgesia, or opioid-sparing techniques, is impractical (time consuming/workload) in my practice (whatever the reason)$10.3\%$$0\%$8The lack of supply of certain analgesic agents restricts my practice of multimodal analgesia$34.5\%$$0\%$ ## Round 3 Twenty-eight out of 29 panellists from Round 2 participated in Round 3. We used the predetermined $75\%$ agreement threshold to determine which of the statements to prioritise. Following this, there were five questions to prioritise, and the level of agreement ranged from $85.7\%$ to $96.4\%$. The results are detailed in Table 3.Table 3Prioritisation of statements (sorted by highest percentage)StatementsPrioritisation in AgreementDo you think training/education is a priority for the use of opioid-sparing/multimodal analgesia?$96.4\%$Do you think confidence in administering opioid sparing techniques is a priority that determines the use of opioid-sparing analgesia?$96.4\%$Do you think the strength of evidence is a priority in determining the use of opioid-sparing analgesia?$89.3\%$Do you think more leadership and more specific guidelines for the application of multimodal analgesia is a priority for the adoption of multimodal analgesia?$89.3\%$Do you think the locally adopted practices and recommendations are a priority when determining the use of opioid-sparing analgesia/multimodal analgesia?$85.7\%$ ## Discussion The Delphi survey reached a strong agreement on five different statements, which were all considered by the panellists as priorities with regard to the adoption of opioid-sparing analgesia. With each survey round, we gained insight into the perspectives of anaesthetists who seemed to be generally in favour of opioid-sparing/multimodal analgesia. There was a strong consensus that there is a robust body of evidence behind the multimodal analgesic approach, with $79.3\%$ in agreement. $89.3\%$ of the panellists believed that the strength of evidence is a priority determining the use of opioid-sparing analgesia. However, there appear to be significant barriers to widespread adoption. At $92.4\%$, the lack of training and education reached the strongest consensus that it was likely a key factor preventing anaesthetists from using multimodal analgesia. Similarly, this gap in education was identified as the highest priority that determined the use of opioid-sparing analgesia, at $96.4\%$. This correlated to the results of the first round, where $41.4\%$ panellists commented that inadequate knowledge and training for these techniques was the main obstacle in the adoption of multimodal analgesia. These findings suggest that since it is believed that there is a strong evidence base for opioid-sparing analgesia, more should be done to introduce these techniques into specialty training programs to build up experience. Gaps in the curriculum could be identified and addressed, with a greater emphasis on multimodal analgesia. Despite identifying the lack of education as an obstacle, the panellists themselves felt confident in administering opioid sparing analgesia, with $75.8\%$ of panellists believing they could administer any technique they may require. This may be partially explained by the panellist selection process, which included many experienced leaders in their field. $79.4\%$ of panellists had over 10 years of practice after full qualification. This finding highlights that while training does exist for multimodal anaesthetic techniques, it is presently not accessible to everyone. Facilitating access and education may thus lead to a better application of current guidelines and improve patient outcomes for pain relief. One recommendation from the panellists was that more leadership and specific guidelines for multimodal analgesia could increase the adoption of these techniques, with $89.3\%$ of the participants believing it should be a priority. There was a $79.3\%$ consensus that more specific guidelines and leadership would improve their own practice. More structured administration procedures and clear communication may be a potential means to encourage more healthcare professionals to adopt less mainstream techniques and build confidence in expanding their range of skills. Interestingly, despite initial research postulating that cost may be an obstacle for multimodal analgesia as compared to opiate-based drugs, our survey did not surface a consensus to suggest so [19]. Only $51.7\%$ of panellists agreed with the statement that the cost-effectiveness of opioid-sparing techniques was of importance to them. Likewise, the logistical complications of opioid-sparing techniques, such as technique and the lack of supply of analgesic agents, were not considered to be a major factor restricting practice ($34.5\%$ of panellists in agreement). This finding may be attributed to differences arising from the structure of healthcare systems the panellists practice in, given that a majority are from the UK and Belgium. To reduce potential inequities, this area merits further exploration for a deeper understanding of the intricacies. Another noteworthy finding is that the panellists deemed multimodal opioid-sparing analgesia as feasible to put into practice. There was a strong consensus of $79.3\%$ disagreeing that multimodal analgesia was impractical (e.g. more time consuming, creating a greater workload for anaesthetists). These practical aspects thus seem satisfactory at present, and may not require large restructuring and investment. Apart from our pre-determined statements, the survey uncovered other factors that may warrant further investigation. Panellists identified several barriers to the widespread uptake of multimodal analgesia in the free-text portion of Round 1 – for example, $24.1\%$ of panellists raised concerns surrounding the reluctance to change their practice and resistance to disrupting the status quo. As these barriers are more deep-seated in nature, a local or national project vis-à-vis an international initiative may tackle them more incisively. Raising awareness of the benefits of and evidence backing multimodal opioid-sparing analgesia whilst addressing localised concerns may kickstart the gears of change. ## Limitations The study was not without its limitations. Firstly, it was challenging to fully integrate all inputs from the free-text portion of the survey. This is a common shortcoming associated with the brevity and potential lack of clarity of online platforms. While discussions may be less rich than a focus group format, the online Delphi method was more practical and international-reaching. It also facilitated more robust discussion as survey responses were kept anonymous. Secondly, certain survey questions were flagged by panellists as being open to interpretation. This may be due to the international nature of the project, with a significant proportion of panellists being non-native English speakers. Lastly, a large proportion of panellists were from the UK and Belgium despite our best efforts to have a wide selection of international participants for adequate representation of geographic differences and subspecialties. The panel further composed mostly of anaesthetists at the mid- to late- career stage, with the overall panellist number tending towards the smaller bound. While these factors may limit the generalisability of the findings, they simultaneously draw attention to other potential differences in perspectives on barriers to adopting multimodal analgesia plausibly attributed to recent educational shifts in anaesthesia techniques. This suggests an interesting comparison for future exploration. ## Conclusion This project explored the perspectives of anaesthetists regarding opioid-sparing analgesia. Practical considerations appeared to be less of an obstacle, as cost effectiveness, practicalities of time management or an increased workload were not regarded as significant problems. However more deep-seated and time-resistant barriers still remain, such as the reluctance to adopt new practices and reform existing practices. The lack of leadership, education, and training were also identified as obstacles. Evidently, developing specific guidelines for multimodal analgesia and addressing gaps in education are critical first steps to encourage the general adoption of opioid-sparing analgesia. ## Supplementary Information Additional file 1. ## References 1. 1.Curtis H. Opioid prescribing trends and geographical variation in England, 1998–2018: a retrospective database study. Lancet Psychiatry. 2018;6(2):140–50. 2. Levy N. UK recommendations on opioid stewardship. BMJ (2021.0) 372 m4901. DOI: 10.1136/bmj.m4901 3. 3.Macfarlane GJ. The epidemiology of regular opioid use and its association with mortality: Prospective cohort study of 466 486 UK biobank participants. Lancet. 2020;21:100321. 4. Bladini A. A review of potential adverse effects of long-term opioid therapy: a practitioner’s guide. Prim Care Companion CNS Disord. (2012.0) 14 PCC.11m01326. PMID: 23106029 5. Benyamin R. Opioid complications and side effects. Pain Physician (2008.0) 11 S105-S120. DOI: 10.36076/ppj.2008/11/S105 6. Quinlan J. Perioperative opioid use and misuse. Clin Med (Lond) (2019.0) 19 441-445. DOI: 10.7861/clinmed.2019.0227 7. Young A. Recent advances in multimodal analgesia. Anesthesiol Clin (2012.0) 30 91-100. DOI: 10.1016/j.anclin.2011.12.002 8. 8.Wilkinson P. Surgery and opioids: best practice guidelines 2021. Faculty of Pain Medicine and Royal College of Anaesthetists, 2021;8. 9. Sultana A. Special indications for opioid free anaesthesia and analgesia, patient and procedure related: including obesity, sleep apnoea, chronic obstructive pulmonary disease, complex regional pain syndromes, opioid addiction and cancer surgery. Best Pract Res Clin Anaesthesiol (2017.0) 31 547-560. DOI: 10.1016/j.bpa.2017.11.002 10. Kurd M. The role of multimodal analgesia in spine surgery. J Am Acad Orthop Surg (2017.0) 25 260-268. DOI: 10.5435/JAAOS-D-16-00049 11. Okoli C. The Delphi method as a research tool: an example, design considerations and applications. Inf Manag (2004.0) 42 15-29. DOI: 10.1016/j.im.2003.11.002 12. Vogel C. A Delphi study to build consensus on the definition and use of big data in obesity research. Int J Obes (2019.0) 43 2573-2586. DOI: 10.1038/s41366-018-0313-9 13. Kuusniemi K, Pöyhiä R. Present-day challenges and future solutions in postoperative pain management: results from PainForum 2014. J Pain Res (2016.0) 9 25-36. PMID: 26893579 14. He Y, Chen W, Qin L. The Intraoperative Adherence to Multimodal Analgesia of Anesthesiologists: A Retrospective Study. Pain Ther (2022.0) 11 575-589. DOI: 10.1007/s40122-022-00367-z 15. Hyland SJ, Brockhaus KK, Vincent WR, Spence NZ, Lucki MM, Howkins MJ, Cleary RK. Perioperative Pain Management and Opioid Stewardship: A Practical Guide. Healthcare (2021.0) 9 333. DOI: 10.3390/healthcare9030333 16. Skulmoski GJ. The Delphi method for graduate research. J Inf Technol Educ (2007.0) 6 1-7 17. Harris P. Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform (2009.0) 42 377-381. DOI: 10.1016/j.jbi.2008.08.010 18. Slade S. Standardised method for reporting exercise programmes: protocol for a modified Delphi study. BMJ Open (2014.0) 4 e006682. DOI: 10.1136/bmjopen-2014-006682 19. Shao B. Opioid-sparing multimodal analgesia after selective dorsal rhizotomy. Hosp Pediatr (2020.0) 10 84-89. DOI: 10.1542/hpeds.2019-0016
--- title: 'Ethnic disparities in COVID-19 outcomes: a multinational cohort study of 20 million individuals from England and Canada' authors: - Francesco Zaccardi - Pui San Tan - Baiju R. Shah - Karl Everett - Ash Kieran Clift - Martina Patone - Defne Saatci - Carol Coupland - Simon J. Griffin - Kamlesh Khunti - Hajira Dambha-Miller - Julia Hippisley-Cox journal: BMC Public Health year: 2023 pmcid: PMC9969387 doi: 10.1186/s12889-023-15223-8 license: CC BY 4.0 --- # Ethnic disparities in COVID-19 outcomes: a multinational cohort study of 20 million individuals from England and Canada ## Abstract ### Background Heterogeneous studies have demonstrated ethnic inequalities in the risk of SARS-CoV-2 infection and adverse COVID-19 outcomes. This study evaluates the association between ethnicity and COVID-19 outcomes in two large population-based cohorts from England and Canada and investigates potential explanatory factors for ethnic patterning of severe outcomes. ### Methods We identified adults aged 18 to 99 years in the QResearch primary care (England) and Ontario (Canada) healthcare administrative population-based datasets (start of follow-up: 24th and 25th Jan 2020 in England and Canada, respectively; end of follow-up: 31st Oct and 30th Sept 2020, respectively). We harmonised the definitions and the design of two cohorts to investigate associations between ethnicity and COVID-19-related death, hospitalisation, and intensive care (ICU) admission, adjusted for confounders, and combined the estimates obtained from survival analyses. We calculated the ‘percentage of excess risk mediated’ by these risk factors in the QResearch cohort. ### Results There were 9.83 million adults in the QResearch cohort (11,597 deaths; 21,917 hospitalisations; 2932 ICU admissions) and 10.27 million adults in the Ontario cohort (951 deaths; 5132 hospitalisations; 1191 ICU admissions). Compared to the general population, pooled random-effects estimates showed that South Asian ethnicity was associated with an increased risk of COVID-19 death (hazard ratio: 1.63, $95\%$ CI: 1.09-2.44), hospitalisation (1.53; 1.32-1.76), and ICU admission (1.67; 1.23-2.28). Associations with ethnic groups were consistent across levels of deprivation. In QResearch, sociodemographic, lifestyle, and clinical factors accounted for $42.9\%$ (South Asian) and $39.4\%$ (Black) of the excess risk of COVID-19 death. ### Conclusion International population-level analyses demonstrate clear ethnic inequalities in COVID-19 risks. Policymakers should be cognisant of the increased risks in some ethnic populations and design equitable health policy as the pandemic continues. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-15223-8. ## Introduction There have been almost 450 million SARS-CoV-2 infections and 6 million deaths (as of March 2022) worldwide since the novel coronavirus disease 2019 (COVID-19) pandemic emerged [1]. Several studies have demonstrated ethnic inequalities in the risk of infection and adverse outcomes, which has generated substantial concern [2–5]. In the United Kingdom (UK), compared to White individuals, men from all backgrounds other than Chinese, and women from any ethnic group other than Bangladeshi, Chinese or mixed ethnicity, had an increased risk of COVID-19 mortality when accounting for differences in demographics, socioeconomic status, and comorbidities [6]. Notably, in the UK Black African men and women were over 2 times as likely to die from COVID-19 than those of White ethnicity [6]. Other large-scale epidemiological analyses from the UK demonstrated that those from South Asian, Black, and ‘Mixed’ ethnic groups had increased rates of COVID-19 death compared to the White group [2]. However, the evidence from other health systems such, as the United States, is conflicting [7], and data on COVID-19 cases and mortality in Canada by ethnicity are more limited [8, 9]. The mechanisms driving the inequalities are unclear but have been posited to be related to a complex and interrelated patterning of multiple factors, including medical factors – such as comorbidities and medication use – as well as social determinants, including cultural, behavioural, and occupational factors, and structural inequalities [10]. The presence of comorbidities has been associated with both a higher risk of SARS-CoV-2 infection and worse outcomes in individuals with COVID-19 [11, 12], while medications (i.e., certain glucose-lowering [13] or immune-modifying drugs [14]) have been linked to either an increased or reduced risk of COVID-19 outcomes. Among the social factors, poor living and working conditions, low income, health literacy, poverty, or exposure to air pollution have all been associated with COVID-19 infectivity and mortality [15–17]. Robustly ascertaining the comparative contributory effects has been difficult to untangle, and one study [18] has sought to quantify potential mediators, rather than reporting ‘overall’ effects [2, 4, 19–21]. Establishing a nuanced understanding of ethnic inequalities in COVID-19-related outcomes is needed to reduce the burden of COVID-19 and may permit rapid public health interventions should modifiable factors be identified. Here, we carried out two observational studies with harmonised designs to reduce the bias due to heterogeneous definitions of exposures, outcomes, and confounders, in the UK (England) and Ontario to quantify the associations between ethnicity and COVID-19 severity and explore potential modifiable and non-modifiable explanatory factors. We then sought to synthesise these cohort-level estimates using a meta-analysis. ## QResearch QResearch database (version 45) comprises individuals registered across 1321 general practices covering $18\%$ of the English population with linkages of primary care data to hospitalisation, intensive care (ICU) admission, and mortality data. For this study, we included 9,828,099 adults aged 18 to 99 years contributing to the QResearch database with at least 12 months of continuous prior registration. The study period ran from the date of the first confirmed SARS-CoV-2 infection in England (24th January 2020, start of follow-up) until 31st October 2020 (end of follow-up), the occurrence of outcome, or death, whichever occurred earlier. ## Ontario The second data source is the population-level healthcare administrative data in Ontario, Canada’s most populous and most ethnically diverse province. These data include the entire population of Ontario (currently 14.5 million, representing nearly $40\%$ of the Canadian population) and are linked to sociodemographic information, hospital, and ICU admissions; in this investigation, 10,273,496 people aged over 18 years were included. The study period ran from the 25th January 2020 (start of follow-up) to 30th September 2020 (end of follow-up), the occurrence of outcome, or death, whichever occurred earlier. ## Ethnicity and COVID-19 outcome: pooled analysis In the first analysis, we explored the association between self-reported ethnicity and COVID-19 related death, hospitalisation, and ICU admission: these outcomes were slightly different in the QResearch and Ontario cohorts as based on country-specific definitions. For QResearch, outcomes included: (a) COVID-19 death, defined as either confirmed or suspected COVID-19 on death certificate, or a death from any cause with a confirmed positive SARS-CoV-2 test in the immediately preceding 28 days; (b) Hospitalisation due to COVID-19, defined as an admission with confirmed or suspected COVID-19 (as per ICD-10 codes U07.1 and U07.2), or new hospitalisation with a positive SARS-CoV-2 test in the immediately preceding 14 days; (c) ICU admission due to COVID-19, defined as admission to ICU with confirmed or suspected SARS-CoV-2 test in the preceding 28 days. In the Ontario database, outcomes were defined as: (a) COVID-19 death, defined as any death with a confirmed positive SARS-CoV-2 test in the immediately preceding 28 days; (b) Hospitalisation due to COVID-19, defined as an admission with confirmed or suspected COVID-19 (as per ICD-10 codes U07.1 and U07.2), or with a positive SARS-CoV-2 test between 28 days prior to and 14 days after the admission date; (c) ICU admission due to COVID-19, defined as a hospital admission that included ICU stay with confirmed or suspected COVID-19 (as per ICD-10 codes), or with a positive SARS-CoV-2 test between 28 days prior to and 14 days after the admission date. We utilised a 3-level ethnicity classification comprised South Asian (Indian, Bangladeshi, Pakistani), Chinese, and ‘General Population’ (all other ethnic groups, $87.5\%$ White in this cohort), based on the UK Office for National Statistics Census ethnic classification. In the Ontario linked healthcare administrative database, ethnicity was ascertained based on surnames, using lists that have been previously validated in this population to identify the two largest ethnic groups in Canada: South Asian and Chinese [22]. The positive predictive values for this approach to identifying ethnicity, when compared to self-reported ethnicity, are high: $89.3\%$ for South Asians and $91.9\%$ for Chinese; specificity $99.7\%$ for both. People whose surnames were not on either list were labelled as ‘General population’ (all other ethnic groups, approximately $80\%$ White). ‘ General Population’ was used as the reference category for analyses. The analyses were adjusted for demographic, clinical, and lifestyle factors (Supplementary Material; Tables S1 and S2); estimates of the associations between ethnicity and each of the three outcomes obtained in the QResearch and Ontario cohorts were combined in a two-stage random-effects meta-analysis. Further details on the definitions of the population and confounders are reported in the Supplementary Material. ## Percentage of excess risk mediated by risk factors The contribution of possible ‘risk factor’ classes to the increased relative risks in different ethnic groups was quantified in the QResearch data as the ‘percentage of excess risk mediated’ (PERM) [23]. By evaluating the change in the magnitude of the exposure-outcome association in models with different confounders, this analysis helps clarify the extent to which a confounder (or a set of confounders) accounts for the association between ethnicity and COVID-19 outcome. For the PERM analyses, we defined 5-level ethnic groups as Mixed ethnicity, South Asian, Black, and ‘Other’ ethnic groups; hazard ratios (HRs), relative to White, were estimated separately for each of the three outcomes and the following set of confounders: ‘minimally adjusted’ model (age, sex, and region); household and social factors; comorbidities; lifestyle factors (including BMI); and ‘maximally adjusted’ model. ## Statistical analyses Country-specific baseline socio-demographic and clinical characteristics were summarised using descriptive statistics by COVID-19 related hospitalisation, ICU admission, and mortality. In QResearch, survival analyses to evaluate the adjusted association of 3-level ethnicity with COVID-19 outcomes, accounting for clustering of practices (robust standard error), were performed with the Royston-Parmar model [24, 25]. We performed multiple imputation to replace missing values for ethnicity ($20.2\%$ missing), deprivation ($0.6\%$), BMI ($16.7\%$), and smoking status ($5.2\%$) using chained equations under the missing at random assumption. These variables were modelled following a multinomial logistic model for ethnicity, ordinal logistic model for smoking and alcohol, and truncated regression for BMI. Results from five imputations were pooled using Rubin’s rules [26]. Complete case analyses and time-varying associations were performed as sensitivity analyses. Cox survival regressions were conducted to evaluate the adjusted associations of ethnicity with COVID-19 outcomes in the Ontario database; as the only missing data were a small number of people missing deprivation data ($0.2\%$), we used complete cases regressions. The proportional hazards assumption was checked in both survival analyses by plotting log-log plot. HRs from maximally adjusted models were used as the common measure of association across QResearch and Ontario cohorts and combined with the DerSimonian-Laird random-effects method in a two-stage meta-analysis; heterogeneity was assessed with I 2. In QResearch, we applied the following formula to estimate PERM using the HR across imputed datasets was:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{PERM}=100\frac{\ \left[\textrm{HR}\left(\textrm{age},\textrm{sex},\textrm{region}\right)\kern0.5em -\kern0.5em \textrm{HR}\left(\textrm{age},\textrm{sex},\textrm{region}+\textrm{risk}\ \textrm{factor}\ \textrm{group}\right)\right]}{\left[\textrm{HR}\left(\textrm{age},\textrm{sex},\textrm{region}\right)-1\right]\kern0.5em }$$\end{document}PERM=100HRage,sex,region-HRage,sex,region+riskfactorgroupHRage,sex,region-1 The PERM was also calculated for ‘maximal adjustment’ in each of the non-White ethnic groups to assess the extent to which inequalities were potentially attributable to the large set of measured adjustment factors. All p-values are two sided and nominal statistical significance was considered at $p \leq 0.05.$ We used Stata v.17 for the QResearch statistical analyses and SAS v.9.4 for the Ontario analyses. We followed current guidance for conducting and reporting observational studies using routinely collected health data (RECORD checklist in the Supplementary Material). ## Patient and public involvement reporting Two public representatives advised on interest and appropriateness of the research questions, were involved in writing the protocol for the wider study, and input on lay-summaries describing the planned study. ## Study populations In the QResearch cohort, there were 9,828,099 individuals; during follow-up, 11,597 COVID-19 deaths, 21,917 hospitalisations and 2932 ICU admissions occurred; in the Ontario cohort, corresponding figures were 10,273,496 individuals, 951 COVID-19 deaths, 5132 hospitalisations, and 1191 ICU admissions (Table 1). Ethnicity data and their classifications are summarised in Table 1 and characteristics stratified by ethnicity are provided in Tables S1 -S2.Table 1Baseline sociodemographic, clinical characteristics and outcomes in the QResearch and Ontario cohortsQResearchOntarioWholecohortCOVID-19deathCOVID-19hospitalisationCOVID-19ICU admissionWholecohortCOVID-19deathCOVID-19hospitalisationCOVID-19ICU admissionSubjects (N) Age (yr)9,828,09911,59721,917293210,273,49695151321191 Mean (SD)47.6 (18.8)80.8 (11.7)69.0 (18.0)60.9 (13.8)48.7 (18.5)77.7 (12.9)66.6 (18.0)62.9 (14.1) 18-291,988,204 (20.2)20 (0.2)649 (3.0)67 (2.3)1,927,688 (18.8)≤5 (≤0.5)202 (3.9)30 (2.5) 30-391,907,376 (19.4)48 (0.4)1067 (4.9)160 (5.5)1,764,197 (17.2)6 (0.6)260 (5.1)44 (3.7) 40-491,620,268 (16.5)125 (1.1)1744 (8.0)339 (11.6)1,684,114 (16.4)21 (2.2)417 (8.1)116 (9.7) 50-591,602,608 (16.3)519 (4.5)2881 (13.1)711 (24.2)1,834,451 (17.9)53 (5.6)815 (15.9)263 (22.1) 60-691,196,452 (12.2)1110 (9.6)3402 (15.5)822 (28.0)1,509,133 (14.7)152 (16.0)992 (19.3)336 (28.2) 70-79922,198 (9.4)2414 (20.8)4401 (20.1)611 (20.8)973,829 (9.5)206 (21.7)980 (19.1)262 (22.0) 80-89471,167 (4.8)4574 (39.4)5485 (25.0)204 (7.0)464,253 (4.5)332 (34.9)1012 (19.7)119 (10.0) 90-99119,826 (1.2)2787 (24.0)2288 (10.4)18 (0.6)115,831 (1.1)≥172 (≥18.1)454 (8.8)21 (1.8)Sex Female4,934,876 (50.2)5154 (44.4)9694 (44.2)917 (31.3)5,301,576 (51.6)435 (45.7)2429 (47.3)440 (36.9) Male4,893,223 (49.8)6443 (55.6)12,223 (55.8)2015 (68.7)4,971,920 (48.4)516 (54.3)2703 (52.7)751 (63.1)Ethnicity (3 levels) General population a 7,160,034 (72.9)8955 (77.2)16,597 (75.7)2167 (73.9)9,180,377 (89.4)850 (89.4)4649 (90.6)1042 (87.5) South Asian585,810 (6.0)582 (5.0)1825 (8.3)353 (12.0)454,694 (4.4)58 (6.1)273 (5.3)72 (6.0) Chinese96,391 (1.0)32 (0.3)77 (0.4)15 (0.5)638,425 (6.2)43 (4.5)210 (4.1)77 (6.5) Not recorded1,985,864 (20.2)2028 (17.5)3418 (15.6)397 (13.5)––––Ethnicity (5 levels) White6,264,009 (63.7)8131 (70.1)13,904 (63.4)1603 (54.7)–––– Mixed145,291 (1.5)77 (0.7)311 (1.4)64 (2.2)–––– South Asian585,810 (6.0)582 (5.0)1825 (8.3)353 (12.0)–––– Black407,604 (4.1)550 (4.7)1546 (7.1)302 (10.3)–––– Other439,521 (4.5)229 (2.0)913 (4.2)213 (7.3)–––– Not recorded1,985,864 (20.2)2028 (17.5)3418 (15.6)397 (13.5)––––Deprivation Quintile 1 – Least deprived2,217,549 (22.6)2462 (21.2)4128 (18.8)474 (16.2)2,496,985 (24.3)124 (13.0)812 (15.8)174 (14.6) Quintile 22,143,852 (21.8)2489 (21.5)4371 (19.9)521 (17.8)2,152,782 (21.0)154 (16.2)832 (16.2)191 (16.0) Quintile 31,941,638 (19.8)2650 (22.9)4570 (20.9)621 (21.2)1,897,167 (18.5)187 (19.7)961 (18.7)219 (18.4) Quintile 41,792,050 (18.2)2107 (18.2)4460 (20.3)599 (20.4)1,813,916 (17.7)198 (20.8)958 (18.7)263 (22.1) Quintile 5 – Most deprived1,676,662 (17.1)1860 (16.0)4311 (19.7)711 (24.2)1,912,646 (18.6)288 (30.3)1569 (30.6)344 (28.9) Not recorded56,348 (0.6)29 (0.3)77 (0.4)6 (0.2)––––Home type Neither9,749,068 (99.2)8600 (74.2)20,040 (91.4)2884 (98.4)–––– Care home60,971 (0.6)2980 (25.7)1819 (8.3)37 (1.3)67,212 (0.7)334 (35.1)862 (16.8)92 (7.7) Homeless18,060 (0.2)17 (0.1)58 (0.3)11 (0.4)––––Household size 1 person3,490,475 (35.5)4556 (39.3)9472 (43.2)1169 (39.9)–––– 2 people2,532,390 (25.8)2361 (20.4)5409 (24.7)792 (27.0)–––– 3-5 people3,277,375 (33.3)1401 (12.1)4509 (20.6)801 (27.3)–––– 6-9 people388,358 (4.0)634 (5.5)994 (4.5)135 (4.6)–––– 10 or more139,501 (1.4)2645 (22.8)1533 (7.0)35 (1.2)––––Body mass Index (kg/m2) < 18.5272,673 (2.8)586 (5.1)529 (2.4)24 (0.8)–––– 18.5-253,228,461 (32.8)3728 (32.1)5255 (24.0)445 (15.2)–––– 25-302,709,699 (27.6)3401 (29.3)6955 (31.7)916 (31.2)–––– 30-351,260,163 (12.8)1739 (15.0)4430 (20.2)733 (25.0)–––– 35-40477,760 (4.9)753 (6.5)2012 (9.2)397 (13.5)–––– ≥ 40239,768 (2.4)394 (3.4)1299 (5.9)258 (8.8)––––Not recorded1,639,575 (16.7)996 (8.6)1437 (6.6)159 (5.4)––––Smoking Non-smoker5,619,707 (57.2)6077 (52.4)12,252 (55.9)1667 (56.9)–––– Ex-smoker2,064,126 (21.0)4499 (38.8)7722 (35.2)1014 (34.6)–––– Current smoker1,633,590 (16.6)742 (6.4)1655 (7.6)226 (7.7)––––Not recorded510,676 (5.2)279 (2.4)288 (1.3)25 (0.9)––––Comorbidities Asthma1,342,685 (13.7)1567 (13.5)3749 (17.1)482 (16.4)1,607,620 (15.6)178 (18.7)1007 (19.6)245 (20.6) COPD224,949 (2.3)1578 (13.6)2523 (11.5)206 (7.0)252,577 (2.5)162 (17.0)572 (11.1)109 (9.2) Hypertension1,643,338 (16.7)6941 (59.9)10,876 (49.6)1329 (45.3)2,735,573 (26.6)771 (81.1)3269 (63.7)713 (59.9) Coronary heart disease342,108 (3.5)2640 (22.8)3620 (16.5)349 (11.9)311,012 (3.0)144 (15.1)558 (10.9)127 (10.7) Stroke208,496 (2.1)2268 (19.6)2736 (12.5)137 (4.7)81,829 (0.8)70 (7.4)277 (5.4)42 (3.5) Atrial fibrillation234,637 (2.4)2311 (19.9)2863 (13.1)162 (5.5)179,709 (1.7)162 (17.0)567 (11.0)87 (7.3) Congestive cardiac failure113,411 (1.2)1519 (13.1)1966 (9.0)124 (4.2)241,392 (2.3)242 (25.4)834 (16.3)154 (12.9) Diabetes671,376 (6.8)3593 (31.0)6312 (28.8)941 (32.1)1,315,449 (12.8)490 (51.5)2062 (40.2)510 (42.8) Chronic kidney disease b 383,449 (3.9)3839 (33.1)5026 (22.9)398 (13.6)267,379 (2.6)246 (25.9)878 (17.1)218 (18.3) Severe mental illness1,091,954 (11.1)2038 (17.6)3850 (17.6)455 (15.5)–––– Parkinson’s disease25,054 (0.3)421 (3.6)450 (2.1)14 (0.5)–––– Epilepsy130,251 (1.3)409 (3.5)726 (3.3)71 (2.4)–––– Dementia98,591 (1.0)3733 (32.2)2614 (11.9)26 (0.9)171,844 (1.7)392 (41.2)1129 (22.0)109 (9.2) Rare neurological diseases29,814 (0.3)122 (1.1)201 (0.9)22 (0.8)–––– Learning disability174,757 (1.8)615 (5.3)967 (4.4)95 (3.2)–––– Cerebral palsy10,892 (0.1)26 (0.2)62 (0.3)10 (0.3)–––– Pulmonary hypertension/fibrosis16,820 (0.2)227 (2.0)316 (1.4)24 (0.8)–––– Rheumatoid arthritis/SLE c 96,286 (1.0)361 (3.1)659 (3.0)72 (2.5)119,127 (1.2)33 (3.5)133 (2.6)29 (2.4) Liver cirrhosis/NAFLD182,026 (1.9)418 (3.6)1097 (5.0)185 (6.3)53,399 (0.5)24 (2.5)115 (2.2)29 (2.4) Sickle cell disease3546 (0.0)7 (0.1)36 (0.2)9 (0.3)2161 (0.0)≤5 (≤0.5)8 (0.2)≤5 (≤0.4) VTE/PVD234,713 (2.4)1669 (14.4)2431 (11.1)206 (7.0)–––– Cancer d 69,259 (0.7)545 (4.7)765 (3.5)96 (3.3)3,102,868 (30.2)493 (51.8)2251 (43.9)462 (38.8) Immunosuppression116,317 (1.2)544 (4.7)1079 (4.9)191 (6.5)–––– Transplant (marrow/solid)11,202 (0.1)58 (0.5)160 (0.7)44 (1.5)13,599 (0.1)6 (0.6)41 (0.8)15 (1.3) Crohn’s/colitis––––84,287 (0.8)≤5 (≤0.5)35 (0.7)9 (0.8) HIV––––19,272 (0.2)≤5 (≤0.5)21 (0.4)≤5 (≤0.4) aPeople not South Asian and Chinese - Ontario: approximately $80\%$ White; QResearch; White, Other Asian, Black African, Black Caribbean, and Other bChronic kidney disease stage 3-5 in QResearch. c Rheumatoid arthritis alone in Ontario cohort. d Blood/respiratory cancer in QResearch, all cancer types in Ontario cohortCOPD – chronic obstructive pulmonary disease; SLE – systemic lupus erythematosus; NAFLD – Non-alcoholic fatty liver disease; VTE – venous thromboembolism; PVD – peripheral vascular disease; HIV – human immunodeficiency virusCells less than 5 are suppressed ## Cohort studies and meta-analyses In QResearch, South Asian ethnicity was associated with increased rates of COVID-19 mortality (HR: 1.35; $95\%$ CI: 1.20, 1.51; Fig. 1 and S1), hospitalisation (1.63; 1.51, 1.75; Fig. 1 and S2), and ICU admission (1.93; 1.67, 2.25; Fig. 1 and S3) compared to the general population group; corresponding estimates in Ontario were 2.04 (1.56, 2.68) for mortality, 1.41 (1.24, 1.59) for hospitalisation, and 1.41 (1.10, 1.79) for ICU admission. In the same maximally adjusted models, in QResearch there was no evidence of increased rates of COVID-19 mortality (HR: 1.12; 0.75, 1.66), hospitalisation (0.86; 0.67, 1.11), or ICU admission (1.20; 0.68, 2.11) in Chinese ethnic group compared to the general population group, whilst in Ontario the HRs were 0.92 (0.67, 1.25) for mortality, 0.79 (0.69, 0.91) for hospitalisation, and 1.29 (1.02, 1.63) for ICU admission. For all three outcomes, the direction of associations was similar for most of the confounders available in both the QResearch and Ontario cohorts, indicating an increased risk associated with the presence of medical conditions and a progressively higher risk in older people and larger households (Fig. S1-S3). In the QResearch cohort, complete case estimations were largely similar to those of the main analyses using multiple imputation (Fig. S4); time-varying associations by ethnic groups are presented in Fig. S5.Fig. 1Cohort-level meta-analysis of individual participant data from QResearch and Ontario. Estimates and number of events and participants are shown following multiple imputation in QResearch cohort and for complete-case analysis in Ontario cohort. The reference ethnic group is “general population”, including: [1] people not South Asian and Chinese in Ontario (approximately $80\%$ White); [2] White, Other Asian, Black African, Black Caribbean, and Other in QResearch Combining estimates for South Asian ethnicity across QResearch and Ontario cohorts resulted in a random-effects HR of 1.63 (1.09, 2.44) for COVID-19 related mortality, with considerable heterogeneity between the two estimates (I 2 $86.9\%$; Fig. 1). Corresponding estimates for hospitalisation and ICU admission were 1.53 (1.32, 1.76) and 1.67 (1.23, 2.28), with considerable heterogeneity: I 2 $75.4\%$ and I2 $74.9\%$, respectively. The pooled random-effects HRs comparing Chinese ethnicity to the general population were 0.99 (0.77, 1.26) for mortality, 0.81 (0.72, 0.91) for hospitalisation, and 1.28 (1.03, 1.58) for ICU admission; there was no evidence of heterogeneity for all three outcomes (I2 $0\%$; Fig. 1). There was no clear trend in the mortality, hospitalisation, or ICU admission HRs comparing ethnic groups across levels of deprivation (Fig. S6). ## Percentage of excess risk mediated by risk factor classes (QResearch) The percentage of excess risk mediated by separate groups of potential attributable factors across the entirety of follow-up in QResearch is reported in Table 2. We estimated that approximately 20-$30\%$ of the excess risk of COVID-19-related hospitalisation in non-White ethnic groups may be mediated by household size/status and deprivation; and that differences in comorbidity prevalence may mediate up to approximately $20\%$ of excess risk (in South Asian). For COVID-19-related ICU admission, adjustment for comorbidities accounted for up to approximately $30\%$ of the excess risk, whereas maximal adjustment accounted for up to approximately $40\%$ of the excess risk (in Black ethnic group). Differences in smoking habits and BMI did not appear to mediate any degree of excess risk of COVID-19-related death in any non-White ethnic group. Maximal adjustment accounted for $42.9\%$ (South Asian) and $39.4\%$ (Black) of the excess risks of death. Therefore, the majority of excess risk in non-White groups may not be accounted for the range of sociodemographic, lifestyle, and comorbidity factors considered in this analysis. Table 2Percentage of excess risk mediated in COVID-19-related outcomes in ethnic minority groups (QResearch cohort)Mixed raceCOVID-19-related deathHR$95\%$ CIPERM$95\%$ CIMinimal adjustment1.190.961.47.........Household and social factors1.080.871.3357.9-73.7168.4Comorbidities1.110.901.3742.1-94.7152.6Lifestyle and BMI1.220.981.50.........Maximal adjustment1.070.871.3363.2-73.7168.4 COVID-19-related hospitalisation Minimal adjustment1.831.632.06......... Household and social factors1.651.471.8621.7-3.643.4 Comorbidities1.781.581.996.0-19.330.1 Lifestyle and BMI1.871.672.10......... Maximal adjustment1.681.491.8818.1-6.041.0 COVID-19-related ICU admission Minimal adjustment2.582.003.32......... Household and social factors2.331.803.0115.8-27.249.4 Comorbidities2.411.863.1110.8-33.545.6 Lifestyle and BMI2.682.073.45......... Maximal adjustment2.351.823.0414.6-29.148.1 South Asian COVID-19-related death HR $95\%$ CI PERM $95\%$ CI Minimal adjustment1.561.401.74......... Household and social factors1.321.181.4842.914.367.9 Comorbidities1.411.271.5826.8-3.651.8 Lifestyle and BMI1.641.471.83......... Maximal adjustment1.321.171.4842.914.369.6 COVID-19-related hospitalisation Minimal adjustment2.081.962.22......... Household and social factors1.821.711.9324.113.934.3 Comorbidities1.871.751.9919.48.330.6 Lifestyle and BMI2.222.082.36......... Maximal adjustment1.781.661.9027.816.738.9 COVID-19-related ICU admission Minimal adjustment2.822.473.23......... Household and social factors2.522.212.8716.5-2.733.5 Comorbidities2.251.952.5831.313.247.8 Lifestyle and BMI3.142.753.60......... Maximal adjustment2.382.072.7324.24.941.2 Black COVID-19-related death HR $95\%$ CI PERM $95\%$ CI Minimal adjustment1.711.541.90......... Household and social factors1.411.271.5642.321.162.0 Comorbidities1.551.391.7222.5-1.445.1 Lifestyle and BMI1.781.601.98......... Maximal adjustment1.431.281.5939.416.960.6 COVID-19-related hospitalisation Minimal adjustment2.202.062.34......... Household and social factors1.831.721.9430.821.740.0 Comorbidities2.061.932.1911.70.822.5 Lifestyle and BMI2.101.972.248.3-3.319.2 Maximal adjustment1.751.651.8637.528.345.8 COVID-19-related ICU admission Minimal adjustment2.952.593.35......... Household and social factors2.492.192.8423.65.639.0 Comorbidities2.572.252.9419.50.535.9 Lifestyle and BMI2.712.383.1012.3-7.729.2 Maximal adjustment2.191.912.5239.022.153.3 Other ethnic group COVID-19-related death HR $95\%$ CI PERM $95\%$ CI Minimal adjustment1.130.981.31......... Household and social factors1.030.891.1976.9-46.2184.6 Comorbidities1.201.031.39......... Lifestyle and BMI1.181.011.37......... Maximal adjustment1.140.991.33......... COVID-19-related hospitalisation Minimal adjustment1.681.561.81......... Household and social factors1.511.401.6325.07.441.2 Comorbidities1.751.621.89......... Lifestyle and BMI1.851.711.99......... Maximal adjustment1.711.591.85......... COVID-19-related ICU admission Minimal adjustment2.542.162.98......... Household and social factors2.301.962.7015.6-10.437.7 Comorbidities2.432.072.867.1-20.830.5 Lifestyle and BMI3.012.563.53......... Maximal adjustment2.612.223.07.........Percentage of excess risk mediated (PERM; relative to White adults) by distinct classes of confounders by ethnic groups during the study period. These are compared to a minimally adjusted model, which accounted for age, sex and geographical region. Results denote hazard ratios with $95\%$ confidence intervals derived from flexible parametric survival models in the multiple imputed database (9,828,099 individuals; 11597 deaths; 21917 hospitalisations; 2932 ICU admissions). ## Discussion In this international study of population-level healthcare databases covering over 20 million individuals, we showed that adults of South Asian background had a $63\%$ increased risk of COVID-19 mortality, $53\%$ increased risk of COVID-19-related hospital admission, and $67\%$ increased risk of ICU admission overall compared to the general population in England and Ontario. This compares to $28\%$ of increased risk of ICU admission in Chinese, with no evidence of increased mortality and hospitalisation risks. In England, sociodemographic, lifestyle, and clinical factors accounted for approximately $40\%$ of excess risks of COVID-19 death. Our results are consistent with other UK population-level analyses derived from data using combinations of different IT systems, which also reported similar estimates of risk in non-White ethnic groups [2]. In this respect, it is important to note that the risks of COVID-19 outcomes estimated in QResearch across ethnic groups, and combined with the results from Ontario, should be considered in view of some variations in the magnitude of associations between ethnicity and COVID-19 outcomes both between waves and within the same wave; more importantly, the public health implications of these variations are primarily determined by the country- and region-specific change in the absolute risk of each outcome over time [27]. Whilst it is increasingly established in the literature that non-white ethnicity is associated with increased risk of severe COVID-19 outcomes, the degree to which modifiable and other factors may contribute to this risk in different ethnic groups is poorly understood. Some ethnic communities may be disadvantaged as living in poorer socioeconomic environments where the risk of infection and worse outcomes is higher, including overcrowded multigenerational houses or occupations with a high degree of public contact [2, 18]; at the same time, biological factors have been suggested to play a role as well, such as an unfavourable metabolic-inflammatory milieu (i.e., obesity, multimorbidity) [11, 20, 28]. In our investigation, rather than reporting summary effect estimates after full or serial adjustment, our approach in the QResearch also included assessment of relative contribution of potential attributable factors and suggests that there may be heterogeneity in the mechanistic underpinnings the increased risks in different ethnic groups. Our study found that the sociodemographic, lifestyle, and clinical factors considered in this investigation accounted for approximately $40\%$ of excess risks of COVID-19 death. Hence, further research should investigate whether other factors, not captured in our data, may explain the proportion of excess risks in some ethnic groups and possible causal pathways in the COVID-19 syndemic [29]. It is possible, in fact, that ethnic differences are at least in part the epiphenomenon of a complex network of other risk factors associated with a higher risk of COVID-19 outcomes, including overcrowding and occupation [30]. Our study analysed in greater detail the differential effects of deprivation within ethnic groups, as well as the relative contributions of different factors to the increased risks in non-White groups, given the suggested interplay between ethnicity and deprivation on the risk of COVID-19 outcomes [31]. Our results also expand and clarify the evidence base regarding ethnic inequalities in COVID-19 outcomes in several ways. First, in contrast to evidence generated using data only from those attending hospitals or registered with providers within fragmented healthcare systems investigating the role of sociodemographic and clinical characteristics on the risk of outcomes across ethnic groups [27, 32], our population-level approach examined the relevant risk trajectories and avoided conditioning on positive tests or other intermediates [33]. Second, much of the available evidence about ethnicity and COVID-19 related outcomes is highly heterogeneous in terms of study designs, population, definitions of outcomes/exposures, confounders adjusted for (if any), and settings (geographical and healthcare system). This negatively affected individual study interpretation but also limited the cohesive synthesis of evidence via meta-analytical approaches due to significant within- and between-study heterogeneity [4, 34]. We explicitly sought to harmonise analytical approaches to facilitate pooling of robust estimates from multiple geographical units, namely different nations (England and Canada). Other key strengths of our study include the use of two large, population-level and representative healthcare databases without selection bias, which possess individual-level linkages across the healthcare network enabling accurate ascertainment of exposures, confounders, and outcomes. Our flexible harmonisation of definitions and analytical approaches facilitated cohort-level meta-analysis of results from both main study databases; we also used the Royston-Parmar survival model which allowed us to explore whether the association between ethnicity and COVID-19 related outcomes changed across the first and second wave. Lastly, we investigated the possible mediation role of some factors in explaining the increased risk observed across ethnic groups in UK. In this respect, it should be noted that different methods exist to investigate mediation (including the possibility to account for intermediate confounding) [35]; furthermore, while the difference between a confounder and mediator is well-known, the same factors may be considered mediators in some context and confounders in others, or even in the same context by different investigators [36], further highlighting the complex interactions among multiple factors in determining the health status. Moreover, some potential mediators have not been included in our analyses (i.e., education, employment status, income). As such, our PERM results should be considered explorative and no definitive causal inference can be derived from them: it is plausible that the comparative causal role of these factors would be different in heterogeneous healthcare systems and societies. Our study has also some limitations, including the inability to further disaggregate ethnicity into more granular groups in Ontario; lack of recorded other information that may be relevant to disease risks (such as occupation, which is relevant to SARS-CoV-2 exposure, and detailed household composition) [37]; the risk of residual confounding, which affects every observational analysis and hampers a conclusive causal interpretation; missing data, which were addressed assuming a missing at random mechanism, yet previous evidence would indicate that ethnicity could be missing not at random: [38] however, the complete-case analysis for the latter scenario [39] resulted in estimates virtually identical to those obtained using multiple imputation; and the potential variations in the ascertainment of COVID-19 infections over time, between countries, and among ethnic groups [40]. Furthermore, the contribution of potential attributable factors was explored only in the QResearch cohort as several of these factors were not available in the Ontario administrative data. Evidence from large-scale cohort studies in England and Canada and from meta-analyses provide robust evidence of ethnic inequalities in COVID-19 outcomes. Not only do these persist despite accounting for potential sociodemographic and clinical confounders but the risks in individual ethnic groups have varied during the pandemic. The currently unexplainable proportion of excess risks in non-White groups requires careful consideration of economic, healthcare system, and other factors to guide public health strategy to protect everyone as the pandemic progresses globally. ## Supplementary Information Additional file 1. ## References 1. 1.Coronavirus cases: Reported Cases and Deaths by Country, Territory, or Conveyance. https://www.worldometers.info/coronavirus/ (Accessed 3 Aug 2021). 2. 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--- title: 'Discovery of a Novel Potent and Selective HSD17B13 Inhibitor, BI-3231, a Well-Characterized Chemical Probe Available for Open Science' authors: - Sven Thamm - Marina K. Willwacher - Gary E. Aspnes - Tom Bretschneider - Nicholas F. Brown - Silke Buschbom-Helmke - Thomas Fox - Emanuele M. Gargano - Daniel Grabowski - Christoph Hoenke - Damian Matera - Katja Mueck - Stefan Peters - Sophia Reindl - Doris Riether - Matthias Schmid - Christofer S. Tautermann - Aaron M. Teitelbaum - Cornelius Trünkle - Thomas Veser - Martin Winter - Lars Wortmann journal: Journal of Medicinal Chemistry year: 2023 pmcid: PMC9969402 doi: 10.1021/acs.jmedchem.2c01884 license: CC BY 4.0 --- # Discovery of a Novel Potent and Selective HSD17B13 Inhibitor, BI-3231, a Well-Characterized Chemical Probe Available for Open Science ## Abstract Genome-wide association studies in patients revealed HSD17B13 as a potential new target for the treatment of nonalcoholic steatohepatitis (NASH) and other liver diseases. However, the physiological function and the disease-relevant substrate of HSD17B13 remain unknown. In addition, no suitable chemical probe for HSD17B13 has been published yet. Herein, we report the identification of the novel potent and selective HSD17B13 inhibitor BI-3231. Through high-throughput screening (HTS), using estradiol as substrate, compound 1 was identified and selected for subsequent optimization resulting in compound 45 (BI-3231). In addition to the characterization of compound 45 for its functional, physicochemical, and drug metabolism and pharmacokinetic (DMPK) properties, NAD+ dependency was investigated. To support Open Science, the chemical HSD17B13 probe BI-3231 will be available to the scientific community for free via the opnMe platform, and thus can help to elucidate the pharmacology of HSD17B13. ## Introduction The unrelenting rise in the worldwide prevalence of obesity, metabolic syndrome and Type 2 diabetes1,2 engenders an increasing burden of associated complications and co-morbidities including cardiovascular disease, nephropathy, neuropathy, retinopathies and nonalcoholic fatty liver disease (NAFLD).3,4 The increased incidence of NAFLD that may progress to nonalcoholic steatohepatitis (NASH) and cirrhosis represents a looming critical burden on clinical and economic resources.3 Presenting initially as a silent accumulation of neutral lipids in the liver, disease progression is characterized by development of severe hepatic inflammation and advancing fibrosis, with an elevated risk of hepatocellular carcinoma (HCC) and ultimate loss of liver function (end-stage liver disease, ESLD).5 Currently, liver transplant is the only option for patients with ESLD. Thus, there is a compelling interest in identifying novel drug targets that may lead to more widely applicable pharmacological therapies to halt or reverse liver disease progression. One of these potential drug targets is HSD17B13 (hydroxysteroid 17ß-dehydrogenase 13), a lipid-droplet associated member of the family of 17ß-hydroxysteroid dehydrogenases (HSD17B), that collectively acts on a range of lipid substrates.6 A link between HSD17B13 and liver disease was first indicated by genome-wide association studies (GWAS) that revealed a strong association between a loss-of-function (LoF) SNP rs72613567 and levels of serum alanine aminotransferase (ALT), a clinical biomarker of liver dysfunction.7 The initial observation has been reinforced by multiple studies of diverse cohorts, demonstrating an association between this and other LoF SNPs and risk for NASH, alcoholic liver disease, cirrhosis, and hepatocellular carcinoma.6−16 Primarily expressed in hepatocytes,17 HSD17B13 is upregulated in the liver of NAFLD patients18 and, preclinically, AAV-mediated HSD17B13 overexpression in mouse liver promoted lipid accumulation, indicating a strong association of HSD17B13 with fatty acid metabolism.18 Direct clinical support for HSD17B13 as a therapeutic target was generated when a hepatocyte-directed small interfering RNA (siRNA) designed to deplete HSD17B13 in human liver was found to decrease serum alanine aminotransferase (ALT) activity in five patients with presumed NAFLD.19 However, both the physiological function and the disease-relevant substrate of this enzyme are still unclear. Several substrates were identified, including steroids (e.g., estradiol) and other bioactive lipids (e.g., leukotriene B4), using an in vitro enzyme assay system in which NAD+ (nicotinamide adenine dinucleotide, oxidized form) acted as co-substrate.7 Thus, numerous lines of evidence suggest HSD17B13 is a promising target for pharmacological treatment of NASH. The lack of well-characterized small-molecule HSD17B13 modulators in the literature triggered our discovery efforts toward the identification and optimization of potent and selective small-molecule inhibitors, described in the present study. ## Substrate Selection for High-Throughput Screening The disease-relevant substrate of HSD17B13 is unknown, but approaches for the identification of HSD17B13 inhibitors using purified enzyme and known substrates were recently published.20 To evaluate a potential risk of substrate-biased inhibitors,21−23 we tested a small subset of compounds predictive for our full-diversity library, using purified human HSD17B13 enzyme and estradiol or leukotriene B4 (LTB4) as substrate in the presence of NAD+. We obtained a strong correlation between LTB4 and estradiol %CTL values at 10 μM compound concentration (Figure 1). Based on these results, we concluded the absence of a substrate bias and selected estradiol as substrate for the high-throughput screening campaign, due to its advantages in handling. Figure 1: Evaluation of the risk for substrate-biased hits. Percent of control values (%CTL) of a diverse set of 175 compounds assayed at 10 μM in the human HSD17B13 enzyme assay using LTB4 or estradiol as substrates (Pearson r = 0.93; linear regression (r2 = 0.87) indicated by solid line). ## High-Throughput Screening and Profiling of Screening Hit 1 For the discovery of small-molecule inhibitors of HSD17B13, we screened ∼1.1 million compounds from Boehringer Ingelheim’s full-diversity library against the enzymatic activity of human HSD17B13 in the presence of estradiol and NAD+ on our fully automated, high-throughput screening compatible matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF-MS) platform24−26 (see Supporting Information, Figure S1). Beyond well-known steroid-like and steroid-derived inhibitors of HSD17B13,27 we identified and confirmed a phenol cluster, and selected alkynyl phenol 1 with an IC50 value of 1.4 μM as a starting point for further evaluation. Notably, other examples of phenol-derived inhibitors of HSD17B13 were reported in the recent patent literature.28−34 To identify potential liabilities of screening hit 1, we thoroughly profiled this compound in several in vitro assays (Figure 2). Compound 1 revealed a moderate activity in the enzymatic human and mouse HSD17B13 enzymatic assays with good selectivity versus the phylogenetically closest related isoform HSD17B11 (Figure 3) and showed moderate activity in the human HSD17B13 cellular assay. We also tested compound 1 in the presence of retinol instead of estradiol in the human HSD17B13 enzymatic assay and again confirmed the absence of a substrate bias (IC50,retinol = 2.4 ± 0.1 μM vs IC50,estradiol = 1.4 ± 0.7 μM). In addition, 1 showed a good balance between solubility and lipophilicity, high permeability, and no inhibition of cytochrome P450 enzymes. While 1 demonstrated a high metabolic stability in liver microsomes, low metabolic stability in hepatocytes pointed toward a significant contribution of phase II metabolism. Metabolite identification of phenol 1 confirmed a strong phase II metabolism,35 leading to $70\%$ glucuronidation and $30\%$ sulfation of the parent phenol 1 after incubation with human hepatocytes (Figure 2). Figure 2: In vitro pharmacological, drug metabolism and pharmacokinetic (DMPK), and physicochemical properties of compound 1. Metabolic clearance values are upscaled from in vitro assays to reflect the in vivo situation. Abbreviations are described at the end of the manuscript. Figure 3: Sequence similarities (calculated using the alignment tool in MOE36) in % between HSD17B13 and other members of the short-chain dehydrogenase/reductase (SDR) family indicate HSD17B11 as the closest homolog. ## Structure–Activity Relationship (SAR) Investigations and Optimization of Screening Hit 1 We began our hit optimization by addressing the identified liabilities of the strong phase II metabolism and reactive metabolite formation of screening hit 1.35 As phase II metabolism is a well-known liability for phenols,37,38 we envisaged to replace this moiety with a variety of suitable bioisosteres.39 Unfortunately, all our attempts resulted in a complete loss of HSD17B13 activity (selected examples shown in Table 1). In parallel, we aimed to improve the ligand efficiency of screening hit 1.40,41 Removal of the annulated five-membered ring of the xanthine in the north increased the ligand efficiency (LE) as well as the lipophilic efficiency (LipE)42 from 0.35 (1, Table 1) to 0.40 (12, Table 2) and from 4.85 [1] to 5.07 [12], respectively. With this more attractive compound in hand, we further focused our optimization efforts on metabolic stability. Structural alerts can support medicinal chemistry design teams to raise awareness and to assess the risk of certain structural motifs.43 Alkynes, for example, have an increased risk of cytochrome P450 mediated formation of reactive metabolites.44,45 Indeed, the formation of reactive metabolites was identified in a GSH adduct formation assay after incubation with human liver microsomes.46 Therefore, we explored the replacement of the linear central part of the molecule. As shown in Table 2, the alkyne moiety could be exchanged by several heteroaromatic groups. In particular, five-membered heterocycles such as thiadiazole 13 and thiazole 14 were able to significantly boost hHSD17B13 activity in the enzymatic as well as the cellular assays. Compared to most five-membered heterocycles, the corresponding six-membered heterocycles, exemplified by compound 22, were less active. Having removed one potential metabolic hotspot, we resumed our SAR activities around the phenol moiety to mitigate the phase II metabolism. With its well-balanced profile, compound 14 served as the basis for a systematic investigation of additional substitutions on the southern phenol (Table 3). Starting with the 2-position, small substituents such as halogens or methyl were tolerated (compare compounds 14 and 23–25). Interestingly, the substituents in compounds 24 (chloro) and 25 (methyl) have a similar steric demand but differ in their electron-withdrawing properties and thus modulate the pKa value of the adjacent phenol. The fact that 24 was over 100-fold more potent than 25 in the enzyme assay indicated that increasing acidity of the phenol OH is beneficial for potency. However, different dihedral angles of the chloro and methyl derivatives might also contribute to the observed potency changes. A similar trend was observed for substitution of the 6-position (compare compounds 14 and 26–29). Halogens such as fluoro [26] and chloro [27] boosted the potency in the human HSD17B13 enzyme assay, but larger [28] or more polar [29] electron-withdrawing groups led to significantly higher IC50 values. We note that substituents in the 5- and 4-position of the phenol showed similar trends (compounds 30–$\frac{32}{33}$ in Table 3). In these positions, not only halogens (see compounds 30, 33) but also slightly larger [31] residues were tolerated. Finally, we investigated double halogen substitutions (compounds 34–36, 38 in Table 3), in which a 2,6-difluoro substitution (compound 34) turned out to be optimal to achieve double-digit nanomolar potency in the enzymatic human and mouse and the cellular human HSD17B13 assays. The low metabolic stability in human hepatocytes of compound 14 (Table 3) could be slightly improved (compound 34) but remained dissatisfying. Next, we investigated the northern part of the lead series (Table 4) in combination with the best five-membered heterocycles (thiadiazole 13 and thiazole 14, Table 2) and the optimized 2,6-difluorophenol moiety (see 34, Table 3). All synthesized compounds (39–48, Table 4) showed single-digit nanomolar potency in the human HSD17B13 enzyme assay which translated well into a double-digit nanomolar potency in the cellular human HSD17B13 assay. Achieving a potency range in the enzymatic human and mouse HSD17B13 assays, where the IC50 values were in a similar range as the enzyme concentration and thereby hitting the assay wall,47 compound optimization was guided by the respective Ki values for tight binding inhibition using Morrison’s equation.48,49 Overall, in vitro metabolic stability in human and mouse hepatocytes remained moderate. Due to its promising profile, compound 45 (BI-3231) was selected for further in vitro profiling, focusing mainly on the investigation of on-target binding behavior, elucidation of mode of inhibition and DMPK characterization. ## In Vitro Profiling of Compound 45 (BI-3231) Compound 45 was profiled in several in vitro assays (see Figure 4). It revealed a single-digit nanomolar activity on the human and the mouse HSD17B13 enzyme (based on Ki values), which translated well into a double-digit nanomolar activity in the human HSD17B13 cellular assay. Furthermore, excellent selectivity versus the structurally related homolog HSD17B11 was achieved (Figures 3 and 4) as well as good selectivity in a commercial SafetyScreen44 panel, Cerep (see Supporting Information, Table S2). With a clog P of 1.3 and a topological polar surface area (TPSA) of 90, 45 exhibited a good balance between polarity and lipophilicity resulting in good aqueous solubility and high permeability in the Caco-2 assay. With no inhibition of cytochrome P450 and hERG, the safety and DDI victim profile for 45 looked favorable. In addition, no GSH adducts after metabolic activation with human liver microsomes were identified.46 Compared to screening hit 1 (Figure 2), compound 45 demonstrated high metabolic stability in liver microsomes and improved (but still moderate) metabolic stability in hepatocytes (Figure 4). Phenotyping of the close analogue 23 (Figure 5) revealed that UGT1A9 is the main mediator for glucuronidation and can explain the observed differences between metabolic stability in liver microsomes and hepatocytes. Figure 4: In vitro pharmacological, DMPK and physicochemical properties of 45 (BI-3231). Metabolic clearance values are upscaled from in vitro assays to reflect the in vivo situation. Abbreviations are described at the end of the manuscript. Real IC50 value unclear due to limits of the assay wall; Ki values (NAD+) should be used for comparison.* Figure 5: UGT phenotyping of compound 23 (Table 3) revealed UGT1A9 as the main driver for glucuronidation. ## In Vivo Profiling of Compound 45 (BI-3231) Due to its promising in vitro profile, compound 45 (BI-3231) was subsequently subjected to mouse and rat PK studies, as well as tissue distribution and excretion studies to further elucidate the fate of 45in vivo. Plasma pharmacokinetics in mice after intravenous and oral administration was characterized by a biphasic and rapid plasma clearance which exceeded the hepatic blood flow and low oral bioavailability. Systemic bioavailability could be significantly increased through subcutaneous administration, avoiding hepatic first-pass effects after oral absorption of 45 (Figure 6A), suggesting the involvement of hepatic uptake transporters in the in vivo disposition of 45,50 which is not reflected by in vitro suspension hepatocyte clearance. While no mechanistic studies were performed to elucidate the contribution of a specific hepatic transporter protein, functional investigation of tissue exposure after intravenous application revealed a strong accumulation of 45 (BI-3231) in liver compared to plasma and other tissues (Figure 6B). As the target protein HSD17B13 is primarily expressed and located in hepatocytes,17 we wanted to understand the tissue pharmacokinetics as well as the underlying mechanism of the observed liver accumulation. Therefore, we determined liver and plasma exposure of 45 in mice time-dependently after single oral administration over 72 h (Figure 7) and observed extensive exposure and retention in the liver compared to plasma. Physicochemical properties of 45 (like acidity, low molecular weight, low lipophilicity and low protein binding) can be indicators for involvement of OATPs or OATs in hepatic drug disposition,51,52 as the dissociated form of 45 (corresponding phenolate anion) carries a negative charge. In addition, phenolic compounds are susceptible to phase II metabolic conjugation (i.e., glucuronidation, sulfation) in the liver,53 followed by excretion to the hepatic bile ducts, often mediated by apical transport mechanisms while constant OAT/OATP mediated reuptake of such conjugates from systemic circulation occurs.54,55 Subsequently, they often undergo enterohepatic circulation. A close analogue of 45 (compound 23) has been demonstrated to undergo mainly UGT1A9 mediated glucuronidation, leading to loss of HSD17B13 inhibitory activity (Figure 5). As the bile excreted fraction of compound 45 is not available for interaction with an intracellular target, it was important to understand the biliary excretion of the compound in the context of extensive liver accumulation in more detail. For this purpose, plasma pharmacokinetics after i.v. administration and biliary excretion of parent compound 45 as well as the respective glucuronide was assessed in rats, revealing its glucuronidation and biliary excretion (Figure 8) as major contributors to the observed in vivo clearance. Figure 6: In vivo pharmacokinetics and tissue distribution of 45 (BI-3231) in mice (n = 3, standard deviation (SD) indicated by error bars). (A) Plasma pharmacokinetics after intravenous and oral administration in mice was characterized by a biphasic and rapid plasma clearance that exceeded the hepatic blood flow and a low oral bioavailability of 10%. Bioavailability was significantly increased by subcutaneous dosing. Relevant systemic exposure corresponding to >10-fold in vitro mouse Ki in unbound plasma concentration could be maintained over 8 h in mice. (B) Tissue exposure 1 h after i.v. administration indicated extensive hepatic accumulation compared to plasma and other tissues, despite comparable in vitro tissue binding properties (PPB = 77.5%, TBliver = 87.1%, TBkidney = 77.8%, TBlung = 70.4%). Figure 7: Plasma and liver pharmacokinetics in mice after single oral administration of 50 μmol/kg 45 (BI-3231) showing extensive compound distribution and retention in the liver compared to plasma (n = 3, SD indicated by error bars). Figure 8: In vivo pharmacokinetic and bile excretion studies of 45 (BI-3231) in rats (n = 3, SD indicated by error bars). Characteristic biphasic and rapid plasma clearance was also observed in rats after intravenous administration. Biliary excretion of parent compound and its glucuronide was identified as major contributor to the overall in vivo plasma clearance with ∼50% of the administered dose being rapidly eliminated via the bile within the first hour of the study. In summary, in depth in vivo pharmacokinetic profiling of compound 45 (BI-3231) in rodents revealed a more pronounced plasma clearance than expected from in vitro hepatocyte studies with a biphasic profile, which had previously been observed for other phenolic structures.56,57 As a large fraction of the administered dose has been found in the form of glucuronide in bile fluid, involvement of enterohepatic circulation seems the likely driver for the terminally flat PK. While 45 was rapidly cleared from plasma, considerable hepatic exposure was maintained over 48 h. However, impacts of in vivo tissue binding, biliary excretion and potential involvement of transporter mediated hepatic uptake complicate a quantitative assessment of the fraction that is available for direct target interaction. Since it is unclear to which extent the hepatic enrichment of compound 45 (BI-3231) beneficially contributes to the inhibition of HSD17B13, reliable methods to assess direct in vivo HSD17B13 target engagement need to be established. We conclude that substantial multiples of in vitro pharmacologically active concentrations could be achieved and maintained systemically in mice using conventional dosing approaches, potentially enabling further in vivo characterization and the study of pharmacodynamic effects of 45 (BI-3231) in subchronic murine models of NASH. Knowing the in vivo PK profile of compound 45 (BI-3231), we focused further efforts on the in vitro characterization of this promising HSD17B13 inhibitor to elucidate its binding properties and mode of inhibition. ## On-Target Binding and Mode of Inhibition of Compound 45 (BI-3231) We tested compound 45 for its binding properties on the recombinant human HSD17B13 enzyme via Thermal Shift Assay experiments (nanoDSF) to confirm on-target binding.58 In the presence of NAD+, the melting temperature of HSD17B13 treated with 5 μM BI-3231 was significantly higher than the dimethyl sulfoxide (DMSO) control (Tm shift = 16.7 K), confirming specific binding to human HSD17B13 (Figure 9, shown in dark green and dark red). Surprisingly, no thermal stabilization of HSD17B13 could be observed with NAD+ alone. The stabilizing effect of BI-3231 is highly dependent on the presence of NAD+ (Figure 9), indicating that the ligand binding pocket might only be formed after NAD+ has bound, suggesting an ordered bi–bi mechanism.59 Figure 9: NAD+ dependency of compound 45 (BI-3231) binding: hHSD17B13 melting curves from Thermal Shift Assay experiment (nanoDSF) in the presence of 2% DMSO or BI-3231 at increasing NAD+ concentrations (0, 0.5, and 5 mM) showing thermal stabilization by BI-3231 only in the presence of NAD+. Inset: corresponding melting temperatures (n = 4, SD indicated by error bars). To further investigate the NAD+ dependency of the phenol lead class, we performed cross-titrations of test compounds and NAD+ at varying concentrations in the human HSD17B13 enzyme assay, while keeping estradiol constant at the highest, practically feasible concentration. In these experiments, we observed a significant NAD+-dependent decrease of the IC50 values for the phenols (Figure 10A) and compound 45 (BI-3231) (Figure 10B), illustrating an uncompetitive mode of inhibition.49,60−62 One might expect that NAD+ concentrations greater than Km would lead to even lower IC50 values, but with an enzyme concentration of 1 nM in the human HSD17B13 assay, the activity of 45 (BI-3231) is most probably beyond the detection limit (“assay wall”)47 so that its IC50 for NAD+ concentrations greater than Km reflects the upper limit to its real potency. Figure 10: Importance of NAD+ for potency of the phenol lead class and compound 45 (BI-3231). (A) Log IC50 values of representative compounds of the phenol lead class assayed in the presence of NAD+ at concentrations at Km and 6-fold Km (linear regression indicated by solid line (r2 = 0.95), 1:1 correlation indicated by dotted line). (B) IC50 values of compound 45 plotted against [NAD+]/Km. Decreasing IC50 values (n = 2, SD indicated by error bars) at increasing [NAD+]/Km values (logistic regression indicated by solid line, r2 = 0.96), indicate an uncompetitive mode of inhibition of 45 against NAD+. Our experimental data showed the importance of NAD+ for binding and potency of the phenol lead class including compound 45 (BI-3231) and motivated us for computational modeling approaches potentially explaining our observations. ## Computational Modeling: Binding Hypothesis for Compound 45 (BI-3231) The NAD+ dependency of the phenol class including compound 45 (BI-3231) is supported by a computational homology model revealing the interaction of 45 with NAD+. Based on functional data and the homology model, we postulate that the positively charged NAD+ in the co-factor binding pocket leads to an increased binding affinity of the spatially adjacent negatively charged phenol 45 (Figure 11), resulting in a NAD+ dependency not only for binding, but also for inhibition of the enzymatic activity of HSD17B13. Figure 11: Binding hypothesis for compound 45 (BI-3231). Postulated interaction of 45 (cyan) and NAD+ (dark cyan). The phenol group of 45 interacts with Ser172 and Tyr185 from the catalytical triad (gray residues), thereby inducing charge transfer and dispersion interactions (green arrow) between 45 and NAD+. ## HSD17B13 Inhibitor 45 (BI-3231) as Chemical Probe for Open Science The public availability of well-characterized chemical probe molecules can help to elucidate pharmacology and mode of action of a target of interest. In recent years, the “Structural Genomics Consortium” (SGC) and its partners have made a concerted effort to define clear criteria for high-quality chemical probes.63−65 Once accepted, the SGC66 and other platforms such as opnMe67,68 or EUbOPEN69 provide information of in-depth profiled compounds to the scientific community and support Open Science by free worldwide shipments of chemical probe samples. An ambition of the SGC and its partners is to discover a pharmacological modulator for every protein in the human proteome by the year 2035 (“Target 2035”).70,71 Therefore, we are pleased to report the discovery of the novel potent and selective HSD17B13 inhibitor BI-3231 (compound 45). Together with BI-0955 (compound 49, Figure 12A), which can be used as an inactive control (Figure 12B), we make BI-3231 as well-characterized chemical probe available to the worldwide scientific community via the opnMe platform.67,68 Figure 12: (A) Inactive HSD17B13 control compound 49 (BI-0955). (B) Dose–response curves of BI-0955 (49) and BI-3231 (45) in the hHSD17B13 enzyme and cellular assays (all n ≥ 3; SD indicated by error bars; solid lines show fitting of a four-parameter logistical equation). ## Syntheses of Screening Hit 1 and Chemical Probe 45 (BI-3231) Screening hit 1 was synthesized as outlined in Scheme 1. Commercially available 1,7-dimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione (=paraxanthine, 1A) was alkylated with propargyl bromide and subsequently reacted with 3-iodo phenol under Sonogashira conditions to furnish compound 1. Scheme 1: Synthesis of Screening Hit 1Reagents and conditions: (a) propargyl bromide, potassium carbonate, N,N-dimethylformamide (DMF), 70% yield; (b) 3-iodo phenol, copper(I) iodide triethylamine, tetrakis(triphenylphosphine)palladium(0), DMF, 36% yield. As outlined in Scheme 2, chemical probe 45 (BI-3231) was synthesized in four steps starting from commercially available 45A. Alcohol 45A could be converted to the corresponding mesylate 45B, which was directly used for the alkylation of thymine 45C giving rise to intermediate 45D. The synthesis of 45 (BI-3231) was completed by alkylation of the free NH of 45D with ethyl iodide followed by Suzuki coupling with boronic acid 45F. The boronic acid 45F itself was prepared in three steps as depicted in Scheme 2. Scheme 2: Synthesis of HSD17B13 Chemical Probe 45 (BI-3231)Reagents and conditions: (a) MeSO2Cl, NEt3, CH2Cl2, 90% yield; (b) N,O-bis(trimethylsilyl)acetamide, MeCN, 76% yield; (c) EtI, K2CO3, DMF, 69% yield; (d) [bis(2,6-di-3-pentylphenyl)imidazol-2-ylidene](3-chloropyridyl)palladium(II) dichloride, EtOH, water, 51% yield; (e) 1-(chloromethyl)-4-methoxybenzene, K2CO3, MeCN, quant. yield; (f) n-BuLi, tetrahydrofuran (THF), −78 °C, trimethyl borate, then 4 M HCl, 91% yield; (g) trifluoroacetic acid (TFA), CH2Cl2, 69% yield. ## Conclusions BI-3231 (compound 45) is the first potent and selective chemical probe reported for HSD17B13, a potential new target for the treatment of NASH and other liver diseases. With a high-throughput screening campaign, specific HSD17B13 inhibitors were identified. The weakly active compound 1 was subsequently optimized resulting in 45 (BI-3231), with improved functional and physicochemical properties as well as an improved DMPK profile. The phenol lead series, including BI-3231, showed a strong NAD+ dependency for binding and inhibition of HSD17B13. BI-3231 was investigated in pharmacokinetic studies revealing a disconnect between in vitro and in vivo clearance while showing extensive liver tissue accumulation.72 We note that pronounced phase II metabolic biotransformation may limit its use in metabolically competent systems. Nonetheless, due to its improved overall profile, we suggest the well-characterized specific HSD17B13 inhibitor BI-3231 as a valuable chemical probe to further elucidate the biological function of HSD17B13. As the in vivo pharmacokinetic/pharmacodynamic (PK/PD) relationship of BI-3231 including target engagement biomarkers for HSD17B13 inhibition are not known, further in vivo evaluation in relevant models of NASH is required. Given the high clearance and short half-life of BI-3231, a tailored approach such as multiple daily administrations or the development of an extended-release formulation might be needed to maintain relevant target exposure in subchronic animal models. To support further studies on HSD17B13 via Open Science, BI-3231 will be available for free to the scientific community through the opnMe67,68 platform (Please place your free order for BI-3231 here: https://opnme.com/molecules/hsd17b13-inhibitor-bi-3231). BI-3231 could also be used as a potential starting point for the synthesis of Proteolysis Targeting Chimeras (PROTACs)73 which would allow us to compare phenotypes resulting from inhibition versus degradation of HSD17B13. ## Compound Synthesis All commercially available chemicals were used as received from their commercial supplier. Anhydrous solvents were either purchased or prepared according to standard procedures74 and stored over molecular sieves under argon. Unless stated otherwise, all reactions were carried out in oven-dried (at 120 °C) glassware under an inert atmosphere of argon. A Biotage Initiator Classic microwave reactor was used for reactions conducted in a microwave oven. Reactions were monitored by thin-layer chromatography (TLC) on aluminum-backed plates coated with Merck Kieselgel 60 F 254 with visualization under UV light at 254 nm, and with high-performance liquid chromatography–mass spectrometry (HPLC-MS) analysis (for HPLC-MS methods, see Supporting Information, Table S1). Unless stated otherwise, crude products were purified by flash column chromatography on silica (using a Biotage IsoleraOne, Biotage IsoleraFour or CombiFlash Teledyne Isco system) or by (semi)-preparative reversed-phase HPLC (Agilent or Waters). Unless specified otherwise, the purity of all final compounds was determined to be ≥$95\%$ by liquid chromatography–mass spectrometry (LC–MS). Nuclear magnetic resonance (NMR) spectra were recorded at room temperature (22 ± 1 °C), on a Bruker Avance 400 spectrometer with tetramethylsilane as an internal reference. Chemical shifts δ are reported in parts per million (ppm). 1H NMR spectra were referenced to the residual partially nondeuterated solvent signal of DMSO (δ = 2.50 ppm). Coupling constants J are reported in Hz, and splitting patterns are described as br = broad, s = singlet, d = doublet, t = triplet, q = quartet, quin = quintet and m = multiplet. High-resolution mass spectra were recorded on a Thermo Scientific LTQ Orbitrap XL using electrospray ionization in positive ion mode (ESI+). MarvinSketch software version 20.19.1 was used to generate compound names. ## 1,7-Dimethyl-3-(prop-2-yn-1-yl)-2,3,6,7-tetrahydro-1H-purine-2,6-dione (1B) Step a (Scheme 1): To a stirred solution of 1,7-dimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione (1A, 10.0 g, 56.0 mmol, commercially available, CAS-RN: [611-59-6]) in DMF (100 mL) was added potassium carbonate (15.3 g, 111 mmol), and the resulting reaction mixture was stirred at rt for 10 min. Next, propargyl bromide (9.91 g, 83.0 mmol, CAS-RN: [106-96-7]) was added and the resulting mixture was stirred at rt for 15 h. The reaction mixture was filtered, diluted with EtOAc (350 mL), and washed with ice water (2–3×). The organic layer was dried over Na2SO4, filtered, and concentrated under reduced pressure to yield the crude product as a liquid. The crude liquid was washed with n-pentane (3×) to obtain the pure compound 1B (8.50 g, $70\%$ yield). LC-MS (method 1): tR = 0.41 min; MS (ESI+): m/$z = 219$ [M + H]+. ## 3-[3-(3-Hydroxyphenyl)prop-2-yn-1-yl]-1,7-dimethyl-2,3,6,7-tetrahydro-1H-purine-2,6-dione (1) Step b (Scheme 1): A solution of 1B (0.60 g, 3.00 mmol) in DMF (20 mL) was degassed with argon gas for 10 min. Then, 3-iodo phenol (1.21 g, 5.00 mmol CAS-RN: [626-02-8]), copper(I) iodide (21.0 mg, 0.11 mmol) and triethylamine (1.11 g, 11.0 mmol) were added, and the reaction mixture was degassed again. Tetrakis(triphenylphosphine)-palladium[0] (191 mg, 0.17 mmol) was added, and the reaction mixture was heated to 100 °C for 6 h. The reaction was monitored by TLC ($10\%$ MeOH in dichloromethane (DCM)). Upon completion, the reaction mixture was filtered, concentrated under reduced pressure, and the residue was purified by flash column chromatography (silica gel 100–200 mesh, 2–$3\%$ MeOH in DCM) to obtain pure compound 1 (0.31 g, $36\%$ yield). LC-MS (method 4): tR = 1.77 min; MS (ESI+): m/$z = 311$ [M + H]+. 1H NMR (400 MHz, DMSO-d6) δ ppm: 3.25 (s, 3H), 3.90 (s, 3H), 4.97 (s, 2H), 6.71–6.82 (m, 3H), 7.14 (t, $J = 7.86$ Hz, 1H), 8.06 (s, 1H), 9.60 (s, 1H). HRMS (ESI, [M + H]+): calcd for C16H15N4O3: 311.1139, found: 311.1140. ## 1-{[5-(2,4-Difluoro-3-hydroxyphenyl)-1,3,4-thiadiazol-2-yl]methyl}-3-ethyl-5-methyl-1,2,3,4-tetrahydropyrimidine-2,4-dione (45, BI-3231) Step a (Scheme 2): (5-Bromo-1,3,4-thiadiazol-2-yl)methanol (45A, 1.00 g, 5.13 mmol, commercially available, CAS-RN: [1339055-00-3]) was dissolved in DCM (30 mL) and triethylamine (1.10 mL, 7.89 mmol). Then, methane sulfonyl chloride (0.60 mL, 7.75 mmol) was added dropwise and the reaction mixture was stirred at rt for 1 h. The mixture was partitioned between an aqueous solution of citric acid and DCM. The organic layer was separated and concentrated under reduced pressure to furnish (5-bromo-1,3,4-thiadiazol-2-yl)methyl methanesulfonate (45B, 1.26 g, $90\%$ yield). The crude product was used in the next step without further purification. Step b (Scheme 2): Thymine 45C (500 mg, 3.97 mmol, commercially available, CAS-RN: [65-71-4]) was suspended in acetonitrile (ACN, 15 mL), N,O-bis(trimethylsilyl) acetamide (2.42 mL, 9.90 mmol) was added, and the mixture was stirred at rt for 4 h. 45B (1.20 g, 4.39 mmol) was dissolved in ACN (10 mL) and added to the reaction mixture. Tetrabutylammonium iodide (300 mg, 0.81 mmol) was added, and the resulting mixture was stirred at 80 °C for 6 h, then cooled to rt, and the reaction was carefully quenched with water (30 mL). The precipitate was filtered and washed with water, then with ACN (2 × 1 mL) followed by diethyl ether (2 × 5 mL). The crude product was dried at 60 °C for 1 h to yield 1-[(5-bromo-1,3,4-thiadiazol-2-yl)methyl]-5-methyl-1,2,3,4-tetrahydro-pyrimidine-2,4-dione (45D, 910 mg, $76\%$ yield). LC-MS (method 3): tR = 0.67 min; MS (ESI+): m/$z = 303$ [M + H]+. The crude product was used in the next step without further purification. Step c (Scheme 2): 45D (900 mg, 2.97 mmol) was dissolved in DMF (5 mL). Potassium carbonate (820 mg, 5.93 mmol) and iodoethane (360 μL, 4.48 mmol) were added, and the resulting mixture was stirred at 70 °C for 2 h. Next, the reaction mixture was cooled to rt and poured on water (30 mL), stirred for additional 10 min, and then filtered. The crude product was washed with water followed by MeOH (2 × 1 mL) and diethyl ether (2 × 3 mL), before being dried at 60 °C in the drying chamber to provide 1-[(5-bromo-1,3,4-thiadiazol-2-yl)methyl]-3-ethyl-5-methyl-1,2,3,4-tetrahydropyrimidine-2,4-dione (45E, 680 mg, $69\%$ yield). The crude product was used in the next step without further purification. Step e (Scheme 2): 2,6-Difluorophenol 45G (50.0 g, 384 mmol, commercially available, CAS-RN: [28177-48-2]) was dissolved in ACN (600 mL). Potassium carbonate (81.2 g, 588 mmol) was added, followed by addition of 1-(chloromethyl)-4-methoxybenzene (54.2 mL, 400 mmol, commercially available, CAS-RN: [824-94-2]). The pale brown suspension was stirred at 70 °C for 90 min and then at rt overnight. The reaction mixture was filtered and concentrated under reduced pressure. The resulting light brown oil was dissolved in EtOAc (600 mL) and washed with $50\%$ saturated aq. NaHCO3 (2 × 200 mL). The organic portion was dried over MgSO4, filtered, and concentrated to yield 45H (106 g, quantitative yield). The product was re-dried: dissolved in DCM, dried over Na2SO4, filtered, concentrated under reduced pressure, and stored under vacuum for 24 h. This material was used in the next step without further purification. Step f (Scheme 2): 45H (12.0 g, 48.0 mmol) was dissolved in anhydrous THF (200 mL) and cooled in an acetone/dry ice bath (internal temperature −74 °C). n-Butyllithium (2.5 M, 25.0 mL, 62.4 mmol) was added dropwise over a period of 15 min (internal temperature kept below −70 °C). After the addition, the solution was stirred at −74 °C for 1 h. Trimethyl borate (7.49 mL, 67.2 mmol) was added dropwise to the reaction mixture, and stirring was continued at −70 °C for 10 min, before the cooling bath was removed, and the reaction mixture was stirred for 1 h warming to rt. The reaction was slowly quenched with aq. HCl (4 M, 10 mL), and the resulting mixture was diluted with EtOAc and water. The layers were separated, and the aqueous layer was further extracted with EtOAc. The combined organic layers were dried over MgSO4, filtered, and concentrated under reduced pressure giving rise to {2,4-difluoro-3-[(4-methoxyphenyl)methoxy]phenyl}boronic acid (45I, 16 g, $91\%$ yield) as a yellow oil/solid mixture. The crude product was used in the next step without further purification. Step g (Scheme 2): 45I (30.0 g, 91.8 mmol) was suspended in DCM (200 mL). Then, TFA (20.0 mL, 259.2 mmol) was added dropwise (reaction became a solution, then reformed a precipitate). The resulting mixture was stirred at rt for 1 h. A purple suspension formed, the solid was collected by suction filtration, and dried under vacuum to give (2,4-difluoro-3-hydroxyphenyl)boronic acid (45F, 11 g, $69\%$ yield). LC-MS (method 2): tR = 0.25 min. The product was used for step d without further purification. Step d (Scheme 2): 45E (58.0 mg, 0.18 mmol), 45F (46.0 mg, 0.26 mmol), and cesium carbonate (143 mg, 0.44 mmol) were suspended in EtOH (2 mL) and H2O (0.5 mL). Pd-PEPPSI 2Me-IPent Cl (7.40 mg, 0.01 mmol) was added, and the resulting mixture was stirred at 80 °C for 75 min. The reaction mixture was diluted with DMF (1 mL), filtered, acidified with TFA, and purified by preparative HPLC (Sunfire C18, ACN, H2O/TFA) to give 45 (34 mg, $51\%$ yield). LC-MS (method 3): tR = 0.90 min; MS (ESI+): m/$z = 381$ [M + H]+. 1H NMR (400 MHz, DMSO-d6) δ ppm: 1.10 (t, $J = 7.03$ Hz, 3H), 1.84 (d, $J = 1.01$ Hz, 3H), 3.87 (q, $J = 6.97$ Hz, 2H), 5.42 (s, 2H), 7.25 (td, $J = 9.57$, 1.77 Hz, 1H), 7.64 (ddd, $J = 8.93$, 7.54, 5.83 Hz, 1H), 7.79 (q, $J = 1.14$ Hz, 1H), 10.75 (s, 1H). HRMS (ESI, [M + H]+): calcd for C16H15F2N4O3S: 381.0827, found: 381.0826. ## Computational Modeling Procedures Due to the lack of a high-resolution crystal structure of HSD17B13, we generated a homology model for SAR explanation and compound design. An X-ray-structure of the close homolog HSD17B11 is available (pdb code: 1YB1); however, the substrate binding pocket in this structure is in a closed state. The same holds true for the Alphafold2 model of HSD17B13, making it not useful for our purposes. Therefore, we decided to employ an estradiol/NADP+ bound structure (pdb code: 1FDU)75 of the more distantly related HSD17B1 as template because both, B1 and B13, bind estradiol/estrone as substrates, yielding a fair chance of a rather accurate model of the ligand binding site. Based on the sequence alignment (see Supporting Information, Figure S2), we generated the homology model using MOE36 (version 2020.09) with standard settings, followed by a constrained minimization (backbone fixed) using the Amber10:EHT force field to resolve potential remaining strains in the structure. The difference in the co-factors of template and target (NADP+ vs NAD+) is not regarded as an issue in model building as the phosphate pocket is solvent exposed, not being composed of conserved structural elements. In particular, the phosphate binding pocket is not conserved between B1 and B13, explaining the co-factor specificity for the isoforms. Compound 45 was optimized by DFT (ωB97XD/cc-pVDZ, with Gaussian16), and its lowest energy conformer was manually placed into the binding site so that the phenol points toward the catalytical triad of HSD17B13 and the northern part is facing the solvent, reflecting the observed SAR. A final constrained energy minimization in MOE36 led to the binding mode shown in Figure 11. ## Protein Production Full-length human HSD17B13 (Uniprot ID: Q7Z5P4), human HSD17B11 (Uniprot ID: Q8NBQ5) and mouse HSD17B13 (Uniprot ID: Q8VCR2) were recombinantly expressed with C-terminal Histidine-tag in HEK293 cells. Expression was performed at Immunoprecise, Netherlands. Cell pellets were lysed in 25 mM Tris pH 7.5, 500 mM NaCl, 10 mM Imidazole, 0.5 mM TCEP, $5\%$ Glycerin, $0.3\%$ Triton-X supplemented with EDTA-free Complete Protease Inhibitor (Roche) and DNase I (Roche) by sonification. After centrifugation at 50.000 rpm at 4 °C for 1 h, the supernatants containing the HSD17 proteins were purified by Nickel affinity chromatography on a HisTrap column (Cytiva) in 25 mM Tris pH 7.5, 500 mM NaCl, 10 mM Imidazole, 0.5 mM TCEP, $0.01\%$ LMNG and eluted by an imidazole gradient with a final concentration of 500 mM. Fractions containing the purified HSD17 proteins were further purified on a Superdex 200 size exclusion column (Cytiva) in PBS supplemented with 500 mM NaCl. The pure protein was then concentrated to the desired concentration using an Amicon filter devise with a cutoff of 10 kDa. Proteins were stored at −80 °C. ## Human HSD17B13 Enzyme Activity Assay for High-Throughput Screening via MALDI-TOF MS Enzymatic reactions were set up in assay buffer containing 100 mM TRIS pH 7.5, 100 mM NaCl, 0.5 mM EDTA, 0.1 mM TCEP, $0.05\%$ BSA, and $0.001\%$ Tween20. First, 50 nL of test compound (final concentration: 5 or 50 μg/mL) or DMSO was placed into the wells of a 1536-well assay plate using a CyBio Well vario (Analytik Jena, Jena, Germany) liquid handling unit equipped with a capillary head. For dose–response experiments, 8-fold dilution series of compound solutions were prepared in DMSO in 1:3.16 dilution steps starting from 10 mM or 5 mg/mL stock solutions, respectively. Next, 2.5 μL of 2× concentrated human recombinant HSD17B13 enzyme in assay buffer (final concentration: 50 nM, columns 1–46) or plain assay buffer (columns 47 + 48) were added by a Certus Flex Micro Dispenser (Gyger, CH). The plates were then incubated for 10 min in a humidified incubator at 24 °C. Subsequently, 2.5 μL of substrate mixture (final concentration: estradiol 30 μM and NAD+ 0.5 mM, row 1–48) were added to each well. The reactions were mixed for 30 s at 1000 rpm and subsequently incubated for 40 min in a humidified incubator at 24 °C. After incubation, the enzymatic reaction was stopped and derivatization initiated by adding 1 μL of internal standard d4-estrone (final concentration: 0.7 μM) together with 2 μL of Girard’s Reagent P (final concentration: 12.5 mM, dissolved in methanol:formic acid 9:1 v/v). Dispensing steps were executed with the aid of a Certus Flex Micro Dispenser (Gyger, CH). The plates were then sealed with an adhesive foil, mixed for 30 s at 1000 rpm, and stored at room temperature until preparation of the MALDI target plates. Time for derivatization should be >10 h to assure full conjugate formation (in most cases plates were stored overnight prior to MALDI target-plate preparation). Each 1536-well assay plate contained high (no compound; columns 45–46) and low (no compound and assay buffer instead of enzyme; columns 47–48) controls to assess compound-related activity loss of the enzyme. Sample preparation was performed as described previously with slight modifications.26 Briefly, a saturated solution of α-cyano-4-hydroxycinnamic acid (HCCA) was prepared in $50\%$ ACN and $50\%$ water (TA50, v/v) containing $0.05\%$ TFA. The CyBio Well vario liquid handling system (Analytik Jena, GER) equipped with ceramic tips and operated in 1536-well format was employed to conduct double-layer spotting providing highly homogeneous spot shapes. Here, 100 nL of matrix solution was spotted onto plain steel MALDI target plates and dried in a vacuum chamber. Subsequently, assay plates were centrifuged at 1000 rpm for 60 s and the seals were removed before 100 nL of matrix solution and 100 nL of sample were aspirated successively from the matrix reservoir and the assay plate, respectively, and dispensed together onto the dried matrix spots. The MALDI target plate was then dried under vacuum and stored until analysis. Finally, samples were washed on-target by transferring 0.3 μL of $0.1\%$ TFA (v/v)/10 mM ammonium dihydrogenphosphate onto each spot with subsequent removal after 2 s of incubation. ## MALDI-TOF-MS-Based Estrone Measurements and Data Analysis Mass spectra were acquired with a rapifleX MALDI-TOF/TOF instrument from Bruker Daltonics including a Smartbeam 3D laser. FlexControl (v 4.0), FlexAnalysis (v4.0), and MALDI Pharma Pulse (v 2.2) were used for MS acquisition and data analysis. Target plates were loaded onto an Orbitor RS (Thermo Scientific) robotic system controlled by the laboratory automation software Momentum (v 4.2.3, Thermo Scientific) and automatically inserted into the MALDI-TOF device. Mass spectra were acquired in the mass range of m/z 380–500, respectively, to cover product and internal standard of the enzyme assay. Therefore, 1000 laser shots per sample spot were accumulated in positive ionization mode. The laser power was adjusted manually before every start of a batch process to reach a sufficient signal intensity for the internal standard. The acquired spectra were processed with a centroid peak detection set to a signal-to-noise ratio of S/$$n = 3$$ and a Gaussian smoothing (0.02 m/z; 1 cycle). Internal calibration was performed using the monoisotopic peak of the internal standard for the respective assays: [d4-estrone-GP]+ = 408.2584. MALDI-TOF data, processed with flexAnalysis or MALDI Pharma Pulse, were exported as a comma delimited (.csv) file. Datasets were further processed with either GraphPad Prism (v9.00; GraphPad Software, La Jolla, CA) or in-house laboratory information management system (LIMS) software. HSD17B13 activity was tracked by analyzing the measured intensity for the enzymatic product ([estrone-GP]+ = 404.2333) as well as for the corresponding internal standard ([d4-estrone-GP]+ = 408.2584). The signal ratio of the reaction product to the respective internal standard was calculated to diminish variations ascribed to the sample preparation and MALDI-TOF analysis. Average control values were calculated and set to $100\%$ activity (high controls) and $0\%$ activity (low controls) while the response values of compound-containing wells were normalized against the controls and expressed as percentage of control (PoC). The assignment of compounds to the corresponding measurements was achieved by software-aided deconvolution of every 1536-well assay plate to the corresponding 384-well substance plates. Determination of compound potencies was obtained by fitting the dose–response data to a four-parameter logistical equation. ## Enzyme Activity Assays for Compound Profiling and Mode of Inhibition Studies via RapidFire MS All enzymatic reactions were performed in assay buffer containing 100 mM TRIS, 100 mM sodium chloride, 0.5 mM EDTA, $0.1\%$ TCEP, $0.05\%$ protease and fatty acid free BSA fraction V and $0.001\%$ Tween20. Compounds were serially diluted in $100\%$ DMSO and 50 nL spotted on a 384-well, PP, V-bottom microtiter plate (Greiner, Cat# 781280) by a Labcyte Echo 55× ($1\%$ DMSO final concentration in the assay). Experiments to select the high-throughput screening substrate were performed using commercially available recombinant human HSD17B13 (transcript variant A, OriGene cat# TP313132; final conc. 228 nM). Compound profiling and mode of inhibition studies were performed using purified, recombinant human HSD17B13 (final conc. 1 nM), human HSD17B11 (final conc. 35 nM) and mouse HSD17B13 (final conc. 50 nM) using estradiol (final conc. 30 μM) or LTB4 (in DMSO, final conc. 30 μM) as substrates and NAD+ (final conc. 0.5 mM for human HSD17B13 and human HSD17B11, 10 mM for mouse HSD17B13) as co-substrate. Compounds were also tested on the human HSD17B13 enzyme (final conc. 50 nM) using retinol (final conc. 30 μM; retinol stocks in $100\%$ DMSO + $10\%$ BHT prepared under anaerobic conditions in the BACTRON 900-2 anaerobic chamber). Substrate and co-substrate concentrations represent their experimentally determined Km values. The assays were performed with the following protocol: 6 μL of diluted purified recombinant protein were added to each well of the compound-spotted microtiter plate and incubated for 15 min at room temperature (RT). After this incubation, 6 μL of substrate/co-substrate-mix were added to the compound-enzyme mix and incubated for 4 h at RT. Enzymatic reaction was stopped and analytes derivatized by adding 1 μL of analyte-specific internal standard (final. conc. 50 nM) and 2.4 μL of Girard’s Reagent P (GP) (final conc. 6.5 mM) dissolved in $90\%$ Methanol and $10\%$ formic acid to the reaction mixtures followed by an overnight incubation at RT. For the measurements of estrone, D4-estrone; for oxo-LTB4, arachidonic acid; and for retinal, D6-retinal were used as internal standards. To increase the sample volume, 70 μL of dH2O was added to the samples before the analyte levels had been measured via RapidFire MS. ## RapidFire MS/MS-Based Estrone, Oxo-LTB4, and Retinal Measurements The analytical sample handling was performed by a RapidFire autosampler system (Agilent, Waldbronn, Germany) coupled to a triple quadrupole mass spectrometer (Triple Quad 6500, AB Sciex Germany GmbH, Darmstadt, Germany). Liquid sample was aspirated by a vacuum pump into a 10 μL sample loop for 250 ms and subsequently flushed for 3000 ms onto a C18 cartridge for estrone and oxo-LTB4 (Agilent, Waldbronn, Germany) and a C4 cartridge for retinal with mobile phase A (for estrone: $99.9\%$ water, $0.09\%$ acetic acid, $0.01\%$ TFA, flow rate 1.5 mL/min. For oxo-LTB4: 1 L water + 50 μL of $25\%$ NH3, flow rate 1.5 mL/min. For retinal: $99.5\%$ water, $0.49\%$ acetic acid, $0.01\%$ TFA, flow rate 1.5 mL/min). The analyte was backflushed from the cartridge for 3000 ms with mobile phase B (for estrone: 475 mL of methanol, 475 mL of ACN, 50 mL of water, 90 μL of acetic acid, 10 μL of TFA, flow rate 1.25 mL/min. For oxo-LTB4: 475 mL of methanol, 475 mL of acetonitrile, 50 mL of water, 50 μL of $25\%$ NH3, flow rate 1.5 mL/min. For retinal: $49.75\%$ methanol, $49.75\%$ acetonitrile, $0.49\%$ acetic acid, $0.01\%$ TFA, flow rate 1.25 mL/min) and flushed into the mass spectrometer for detection in MRM mode. The MRM transition for estrone-GP was Q1/Q3: $\frac{404.1}{157.1}$ Da (declustering potential 27 V, collision energy 43 V) and for the internal standard D4-estrone-GP Q1/Q3: $\frac{408.1}{159.1}$ Da (declustering potential 27 V, collision energy 43 V). The mass spectrometer was operated in positive ionization mode (curtain gas 35 Au, collision gas medium, ion spray voltage 4200 V, temperature 550 °C, ion source gas 1 65 Au, ion source gas 2 80 Au). The MRM transition for the oxo-LTB4 was Q1/Q3: $\frac{336}{195.1}$ Da (declustering potential 27 V, collision energy 43 V) and for the internal standard arachidonic acid Q1/Q3: $\frac{303.2}{259.3}$ Da (declustering potential 27 V, collision energy 43 V). The MRM transition for retinal-GP3 was Q1/Q3: $\frac{418.3}{94.9}$ (declustering potential 10 V, collision energy 23 V) and for the internal standard D6-retinal-GP Q1/Q3: $\frac{424.3}{136.9}$ (declustering potential 66 V, collision energy 39 V). Dwell time for all analytes and each MRM transition was 25 ms and pause time between MRMs was 5 ms. For oxo-LTB4 and retinal: the mass spectrometer was operated in negative ionization mode (curtain gas 35 Au, collision gas medium, ion spray voltage 4200 V, temperature 550 °C, ion source gas 1 65 Au, ion source gas 2 80 Au). The solvent delivery setup of the RapidFire system consists of two continuously running and isocratically operating HPLC pumps (G1310A, Agilent, Waldbronn, Germany) and one binary HPLC pump channel B (G4220A, Agilent, Waldbronn, Germany). ## Data Analysis MS data processing was performed in GMSU (Alpharetta, GA), and peak area ratio analyte/internal standard was reported for IC50 calculation. Area under the curve values were uploaded to our data analysis software (Megalab Software, in-house development) and peak area ratios were calculated (analyte/internal standard). We normalized the peak area ratio data by assigning negative control values (all assay components) to $100\%$ and positive control values (no enzyme) to $0\%$. For IC50 determination, we used a four-parameter logistical equation. Ki values were calculated using Morrison equation for tight binding (GraphPad Prism 9.3.1, GraphPad Software, San Diego, California). Based on repeated independent measurements ($$n = 58$$) of an internal assay reference, the IC50 values determined in the hHSD17B13 enzyme assay showed a variability of a factor of ±2.2. ## Mode of Inhibition Studies The Km value of NAD+ on human HSD17B13 was experimentally determined (Km = 1.4 ± 0.2 mM; $$n = 3$$) in the hHSD17B13 enzyme assay. To elucidate the mode of inhibition, compounds were tested in a dose-responsive manner at [NAD+]/Km ratios ranging from 0.05 to 3, keeping the substrate estradiol constant at the highest practically achievable concentration of 100 μM. IC50 values had been plotted as a function of the ratio [NAD+]/Km and the curve pattern of logistic regressions had been interpreted accordingly.49,60−62 ## Cellular Human HSD17B13 Activity Assay and Cell Viability Custom-made stably overexpressing hHSD17B13-Myc/DDK HEK293 cells (LakePharma, Inc.) and estradiol were prepared in serum free Dulbecco’s modified Eagle’s medium (DMEM) medium containing $10\%$ heat inactivated FBS, 1× Glutamax, and 1× sodium pyruvate; 24 h prior to compound testing, 25 μL of a 0.4 × 106 cells/mL dilution were seeded on 384-well Microplate (culture plate, PerkinElmer, Cat# 6007680). Compounds were serially diluted in $100\%$ DMSO and 50 nL of the compound dilution were spotted on the preseeded cell plate by a Labcyte Echo 55× ($1\%$ DMSO in the Assay) and incubated for 30 min at 37 °C in a humidified incubator (rH = $95\%$, CO2 = $5\%$). After that incubation step, 25 μL of a 60 μM estradiol dilution were added to each well of the microtiter plate and incubated for 3 h at 37 °C in the humidified incubator. Finally, 20 μL of supernatant were taken and 2.5 μL of d4-estrone as an internal standard (final conc. 50 nM) were added. To derivatize the analytes, 5 μL of Girard’s Reagent P (6.5 mM final) dissolved in $90\%$ methanol and $10\%$ formic acid were added to the samples followed by overnight incubation at RT before adding 60 μL of dH2O to increase the sample volume for the RapidFire MS/MS measurements of estrone levels as described previously. To exclude impact on cell viability as cause for reduced estrone levels, we also performed a CellTiter-Glo Luminescent Cell Viability Assay (CellTiter-Glo, Promega, Cat# G9242) with the remaining cell samples of the initial assay plate, according to manufacturer protocol. Luminescence was measured using a PHERAstar FSX (BMG Labtech, Ortenberg, Germany). ## Data analysis Raw data were normalized by assigning negative control values (all assay components) to $100\%$ and positive control values (no cells) to $0\%$. For IC50 determination, we used a four-parameter logistical equation. ## Differential Scanning Fluorimetry (nanoDSF) The Prometheus NT.48 instrument (NanoTemper Technologies) was used to determine the melting temperatures. hHSD17B13 (10 μM) was preincubated with NAD+ (final conc. 0, 0.5 or 5 mM) and DMSO (final conc. of $2\%$) or compound 45 (final conc. 100 μM). The capillaries were filled with 10 μL of sample and placed on the sample holder. Four independent measurements were performed for each sample. A temperature gradient of 1 °C·min–1 from 25 to 95 °C was applied, and the intrinsic protein fluorescence at 330 and 350 nm was recorded. The melting point was determined as the maximum of the first derivative of the melting curve. ## Metabolic Stability in Liver Microsomes The metabolic degradation of the test compound was assayed at 37 °C with pooled liver microsomes. The final incubation volume of 60 μL per time point contained TRIS buffer pH 7.6 at RT (0.1 M), magnesium chloride (5 mM), microsomal protein (0.5–2 mg/mL), and the test compound at a final concentration of 1 μM. Following a short preincubation period at 37 °C, the reactions were initiated by addition of β-nicotinamide adenine dinucleotide phosphate, reduced form (nicotinamide adenine dinucleotide phosphate (NADPH), 1 mM), and terminated by transferring an aliquot into solvent after different time points. The quenched incubations were pelleted by centrifugation (10,000g, 5 min). An aliquot of the supernatant was assayed by LC-MS/MS for the amount of remaining parent compound. The half-life was determined by the slope of the semilogarithmic plot of the concentration–time profile. The intrinsic clearance (CLint) was calculated by considering the amount of protein in the incubation: CLint [μL/min/mg protein] = (Ln 2/(half-life [min] × protein content [mg/mL])) × 1000. ## Metabolic Stability in Human/Mouse Hepatocytes An assay in human hepatocytes was performed to assess the metabolic stability of compounds. The metabolic degradation of a test compound was assayed in a human and mouse hepatocyte suspension. After recovery from cryopreservation, hepatocytes were diluted in DMEM (supplemented with 3.5 μg glucagon/500 mL, 2.5 mg insulin/500 mL, 3.75 mg hydrocortisone/500 mL, $5\%$ species serum) to obtain a final cell density of 1.0 × 106 cells/mL or 4.0 × 106 cells/mL, depending on the metabolic turnover rate of the test compound. Following a 30 min preincubation in a cell culture incubator (37 °C, $10\%$ CO2), test compound solution was spiked into the hepatocyte suspension, resulting in a final test compound concentration of 1 μM and a final DMSO concentration of $0.05\%$. The cell suspension was incubated at 37 °C (cell culture incubator, horizontal shaker) and samples were removed from the incubation after 0, 0.5, 1, 2, 4, and 6 h. Samples were quenched with acetonitrile (containing internal standard) and pelleted by centrifugation. The supernatant was transferred to a 96-deep well plate, and prepared for analysis of decline of parent compound by HPLC-MS/MS. The percentage of remaining test compound was calculated using the peak area ratio (test compound/internal standard) of each incubation time point relative to the time point 0 peak area ratio. The log-transformed data were plotted versus incubation time, and the absolute value of the slope obtained by linear regression analysis was used to estimate in vitro half-life. In vitro intrinsic clearance (CLint) was calculated from in vitro half-life and scaled to whole liver using a hepatocellularity of 120 × 106 cells/g liver, a human liver per body weight of 25.7 g liver/kg as well as in vitro incubation parameters. Hepatic in vivo blood clearance (CL) was predicted according to the well-stirred liver model considering an average liver blood flow (QH) of species. Results were expressed as percentage of hepatic blood flow: QH [%] = CL [mL/min/kg]/hepatic blood flow [mL/min/kg]. ## Plasma Protein Binding (Human and Mouse), Tissue Binding Equilibrium dialysis (ED) technique with Dianorm Teflon dialysis cells (micro 0.2) was used to determine the approximate in vitro fractional binding of test compounds to plasma proteins. Each cell consists of a donor and an acceptor chamber, separated by an ultrathin semipermeable membrane with a 5 kDa molecular weight cutoff. Stock solutions for each test compound were prepared in DMSO at 1 mM and diluted to a final concentration of 1.0 μM. The subsequent dialysis solutions were prepared in pooled human or mouse plasma (with NaEDTA) from male and female donors. Aliquots of 200 μL dialysis buffer (100 mM potassium phosphate, pH 7.4) were dispensed into the buffer chamber. Aliquots of 200 μL test compound dialysis solution were dispensed into the plasma chambers. Incubation was carried out for 2 h under rotation at 37 °C. At the end of the dialysis period, the dialysate was transferred into reaction tubes. The tubes for the buffer fraction contained 0.2 mL of ACN/water ($\frac{80}{20}$). Aliquots of 25 μL of the plasma dialysate were transferred into deep well plates and mixed with 25 μL of ACN/water ($\frac{80}{20}$), 25 μL of buffer, 25 μL of calibration solution, and 25 μL of Internal Standard solution. Protein precipitation was done by adding 200 μL of ACN. Aliquots of 50 μL of the buffer dialysate were transferred into deep well plates and mixed with 25 μL of blank plasma, 25 μL of Internal Standard solution, and 200 μL of ACN. Samples were measured on HPLC-MS/MS–Systems and evaluated with Analyst-Software to determine plasma protein binding (PPB [%]) and fraction unbound in the donor chamber. Tissue binding was determined via equilibrium dialysis in analogy to PPB in mouse liver, kidney, and lung tissue homogenates (0.165 mg/mL tissue in PBS pH 7.4) at a test compound concentration of 1 μM. ## Caco-2 Permeability For the measurement of permeability across polarized, confluent human cancer colon carcinoma cells 2 (Caco-2), cell monolayers grown on permeable filter supports were used as an in vitro absorption model. Apparent permeability coefficients (PE) of the compounds across the Caco-2 monolayers were measured (pH 7.2, 37 °C) in apical-to-basal (AB) (absorptive) and basal-to-apical (BA) (secretory) transport direction. Identical or similar permeabilities in both transport directions indicate passive permeation, vectorial permeability points to additional active transport mechanisms. Caco-2 cells (1–2 × 105 cells/cm2 area) are seeded on filter inserts (Costar transwell polycarbonate or PET filters, 0.4 μm pore size) and cultured (DMEM) for 10–25 days. Compounds were dissolved in appropriate solvent (DMSO, 10 mM stock solutions). Stock solutions were diluted with HTP-4 buffer (128.13 mM NaCl, 5.36 mM KCl, 1 mM MgSO4, 1.8 mM CaCl2, 4.17 mM NaHCO3, 1.19 mM Na2HPO4·7H2O, 0.41 mM NaH2PO4·H2O, 15 mM HEPES, 20 mM glucose, pH 7.2) to prepare the transport solutions (10 μM compound, final DMSO ≤ $0.5\%$). The transport solution (TL) was applied to the apical or basolateral donor side for measuring A–B or B–A permeability (3 filter replicates), respectively. The receiver side contains HTP-4 buffer supplemented with $0.25\%$ BSA. Samples were collected at the start and end of experiment from the donor and at various time intervals for up to 2 h also from the receiver side for concentration measurement by LC-MS/MS. Sampled receiver volumes were replaced with fresh receiver solution. ## CYP Inhibition The inhibition of the conversion of a specific substrate to its metabolite was assessed at 37 °C using human liver microsomes and to determine the inhibition of cytochrome P450 isoenzymes by a test compound. For the following cytochrome P450 isoenzymes, turnover of the respective substrates was monitored: CYP3A4: Midazolam; CYP2D6: Dextromethorphan; CYP2C8: Amodiaquine; CYP2C9: Diclofenac; CYP2C19: Mephenytoin; CYP2B6: Bupropion; CYP1A2: Tacrine. The final incubation volume contained TRIS buffer (0.1 M), MgCl2 (5 mM), human liver microsomes dependent on the P450 isoenzyme measured (ranging from 0.05 to 0.5 mg/mL), and the individual substrate for each isoenzyme (ranging from 1 to 80 μM). The effect of the test compound on substrate turnover was determined at five concentrations in duplicate (e.g., highest concentration 50 μM with subsequent serial 1:4 dilutions) or without test compound (high control). Following a short preincubation period, reactions were started with the co-factor (NADPH, 1 mM) and stopped by cooling the incubation down to 8 °C, followed by addition of one volume of acetonitrile. An internal standard solution is added after quenching of incubations. Peak area of analyte and internal standard is determined via LC-MS/MS. The resulting peak area ratio of analyte to internal standard in these incubations is compared to a control activity containing no test compound to determine the inhibitory IC50. ## Mechanism-Based CYP3A4 Inhibition Time-dependent inhibition of CYP3A4 was assessed in human liver microsomes using midazolam as a substrate. The test compounds and water control (wells without test compound) were preincubated in the presence of NADPH (1 mM) with human liver microsomes (0.2 mg/mL) at a concentration of 0, 5 and 25 μM for 0, 10 and 30 min. After preincubation, the incubate was diluted 1:10 (to 0.02 mg/mL) and the substrate midazolam (15 μM) was added for the main incubation (10 min). The main incubation was quenched with acetonitrile, and the formation of hydroxy midazolam was quantified via LC/MS-MS. The formation of hydroxy midazolam from the 30 min preincubation relative to the formation from the 0 min preincubation was used as a readout. Values of less than $100\%$ mean that the substrate midazolam is metabolized to a lower extent upon 30 min preincubation compared to 0 min preincubation. In general, low effects upon 30 min preincubation are desirable. ## UGT Phenotyping Microsomes from baculovirus insect cells expressing human UGT isoforms (Supersomes) were obtained from Corning GmbH, Germany. These included microsomes from baculovirus-infected insect cells expressing UGT 1A1, 1A3, 1A4, 1A6, 1A9, 2B7, and 2B15 Supersomes (total protein content 5 mg/mL) prepared from cells without the human liver UGT cDNA insert. Microsomes were stored at −80 °C until used for experiments. Microsomal preparations were diluted in 0.1 M TRIS pH 7.4 buffer to a final protein concentration of 1 mg/mL. Compound solutions with a final concentration of 10 μM were prepared from 1 mM DMSO stock solution in double-distilled H2O. UDPGA (25 mM) (uridine-diphosphate-glucuronic acid), 50 mM Saccharolacton, and 50 μg/mL Alamethicin stock solutions were prepared in double-distilled H2O. For the experiment, 10 μL of 1 mg/mL microsomal preparations, 27.5 μL of H2O, 22.5 μL of 400 mM TRIS pH $\frac{7.5}{40}$ mM MgCl2 buffer, 10 μL of Saccharolacton stock solution, and 10 μL of Alamethicin stock solution were mixed and preincubated for 5 min at 4 °C. Subsequently, 10 μL of 10 μM compound solution and 10 μL of 25 mM UDPGA solution were added to achieve a total of 100 μL incubation volume. The reaction was started by heating to 37 °C and stopped after 15 and 30 min, respectively, by cooling to 4 °C and adding 50 μL of $33\%$ ACN in H2O. Samples were centrifuged at 4000 rpm, 4 °C, and supernatants were transferred to 96-well plates for quantification of parent compound depletion and glucuronide formation via HPLC-MS/MS. ## Pharmacokinetics in Mice and Rats Pharmacokinetic studies were performed in male C57BL6/N mice (Janvier, France; mean body weight 25 g) to evaluate the pharmacokinetic properties and tissue distribution of test compounds. A compound suspension ($0.5\%$ Natrosol solution with $0.015\%$ Tween-80) was dosed orally by gavage or subcutaneously to mice at a dose of 50 or 80 μmol/kg, respectively. Blood samples from mice (20 μL) were taken via puncture of the hindleg vein (V. saphena) at several time points post application, anticoagulated, and centrifuged. Compound distribution to the liver was determined in separate studies at multiple terminal timepoints following oral administration. Plasma and tissue samples were stored at −20 °C prior to bioanalysis. For bioanalysis, plasma protein was precipitated with acetonitrile. Tissue samples were transferred to Precellys vials, and three parts of acetonitrile/methanol (1:1) and one part of acidified water were added for glucuronide stabilization prior to homogenization. Homogenates were centrifuged and supernatant was collected for bioanalysis. The concentration of the administered compound in plasma and tissue samples was quantified via high-performance liquid chromatography coupled with tandem mass spectrometry. In addition, pharmacokinetics following intravenous injection to the tail vein (solution with $20\%$ HP-ß-cyclodextrin, 5 μmol/kg) were conducted in male C57BL6/N mice as well as male Han Wistar rats (Janvier, France; mean body weight 270 g) accordingly. Blood samples from rats (50 μL) were taken via puncture of the sublingual vein in short-term isoflurane anesthesia. Tissue distribution was determined terminally 1 h after administering a second intravenous dose. Pharmacokinetic parameters (AUC, oral bioavailability, Vss, Clearance) were calculated using noncompartmental analysis methods. ## Bile Excretion Studies in Rats Bile excretion studies were conducted in male Han Wistar rats (Janvier, France; mean body weight 270 g). Studies were performed under constant intratracheal intubation anesthesia using isoflurane over 6 h. Vital signs (breathing, body temperature, heart rate, blood pressure) were monitored. A constant rate infusion of ringer-acetate solution (2 mL/h) was applied via catheterization of the V. jugolaris to maintain physiologic hydration over the course of the study. After surgical incision of the abdominal cavity, the bile duct (Ductus choledochus) was ligated, catheterized, and fixated. The compound was delivered intravenously at 5 μmol/kg (solution with $20\%$ HP-ß-cyclodextrin) to the tail vein. Subsequently, bile flow was collected in constantly cooled (4 °C) picovials in 1 h intervals over the study duration of 6 h. At the end of the study, the animals were euthanized. For the quantitative assessment of parent compound and corresponding glucuronide/sulfate conjugate biliary excretion, 5 μL of bile sample was incubated with 5 μL of ammonium acetate buffer in the presence and absence of Helix Pomatia-ß-Glucuronidase (≥100,000 units/mL, 1:4 dilution in buffer) for 1 h at 37 °C under constant shaking. As a positive control for enzymatic conjugate cleavage, 20 μM phenolphthalein-glucuronide and phenolphthalein-sulfate solutions in rat bile were incubated in parallel, resulting in a complete turnover to phenolphthalein after 1 h. The enzymatic digestion was stopped by adding 150 μL of ACN containing internal standard. All samples were centrifuged, and 100 μL of $0.1\%$ HCOOH solution was added to 20 μL of supernatant and quantified via HPLC-MS/MS. All animal experiments were approved by the local German authorities (Regierungspräsidium Tübingen, Baden-Württemberg, Germany) and conducted in compliance with the German and European Animal Welfare Acts. ## Determination of Water Solubility from DMSO Stock Solutions The aqueous solubility of the test compound was determined by comparing the amount dissolved in buffer to the amount in an acetonitrile/water (1:1) solution. Starting from a 10 mM DMSO stock solution, aliquots were diluted with acetonitrile/water (1:1) or buffer, respectively. 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--- title: Nicotinamide mononucleotides alleviated neurological impairment via anti-neuroinflammation in traumatic brain injury authors: - Xiaolu Zhu - Jin Cheng - Jiangtao Yu - Ruining Liu - Haoli Ma - Yan Zhao journal: International Journal of Medical Sciences year: 2023 pmcid: PMC9969499 doi: 10.7150/ijms.80942 license: CC BY 4.0 --- # Nicotinamide mononucleotides alleviated neurological impairment via anti-neuroinflammation in traumatic brain injury ## Abstract Traumatic brain injury (TBI) is one of the main factors of death and disability in adults with a high incidence worldwide. Nervous system injury, as the most common and serious secondary injury after TBI, determines the prognosis of TBI patients. NAD+ has been confirmed to have neuroprotective effects in neurodegenerative diseases, but its role in TBI remains to be explored. In our study, nicotinamide mononucleotides (NMN), a direct precursor of NAD+, was used to explore the specific role of NAD+ in rats with TBI. Our results showed that NMN administration markedly attenuated histological damages, neuronal death, brain edema, and improved neurological and cognitive deficits in TBI rats. Moreover, NMN treatment significantly suppressed activated astrocytes and microglia after TBI, and further inhibited the expressions of inflammatory factor. Besides, RNA sequencing was used to access the differently expressed genes (DEGs) and their enriched (Kyoto Encyclopedia of Genes and Genomes) KEGG pathways between Sham, TBI, and TBI+NMN. We found that 1589 genes were significantly changed in TBI and 792 genes were reversed by NMN administration. For example, inflammatory factor CCL2, toll like receptors TLR2 and TLR4, proinflammatory cytokines IL-6, IL-11 and IL1rn which were activated after TBI and were decreased by NMN treatment. GO analysis also demonstrated that inflammatory response was the most significant biological process reversed by NMN treatment. Moreover, the reversed DEGs were typically enriched in NF-Kappa B signaling pathway, Jak-STAT signaling pathway and TNF signaling pathway. Taken together, our data showed that NMN alleviated neurological impairment via anti-neuroinflammation in traumatic brain injury and the mechanisms may involve TLR$\frac{2}{4}$-NF-κB signaling. ## Introduction Traumatic brain injury (TBI), as the most common cause of death in trauma centers, is also one of the major causes of death and disability in adults worldwide 1, 2. Globally, the annual incidence rate of TBI is about 50 million 3, and more than 700,000 TBI incidents have been reported every year in China, of which severe TBI led to more than a quarter of the mortality rate and half of the adverse consequences 4. Car crashes, falls, acceleration/deceleration and assaults are common causes of TBI. Therefore, the only possible treatment of primary injury is prevention, such as reducing dependence on motor vehicles and wearing helmets properly 5, 6. Secondary injury in the seconds, minutes, hours, days is caused by a complex series of pathophysiological reactions, such as excitotoxicity, mitochondrial dysfunction, apoptosis, autophagy and inflammation, which will cause irreversible damage to neurons 7, 8. There are several follow-up managements for TBI. At present, surgery such as debridement and inhibition of intracranial pressure target for penetrating TBI of acute stage 3; drugs like diuretics and anticonvulsants used for preventing hydrocephalus and seizures in modest TBI 9; physical therapy can relieve muscle spasms and contractions in mild TBI 10, but their effects on secondary injuries are limited 11. Previous study has shown that Aucubin can reduce the inflammatory in mice after TBI to exert neuroprotective effect 12. Omega-3 can reduce the loss of neurons by reducing the expression of inflammatory and apoptotic factors 13. NAD+, as a key coenzyme in eukaryotic organisms, determines hundreds of enzymatic reactions 14. In the most basic energy metabolism process, reduced and oxidized NAD are indispensable factors as hydrogen ion transfer carriers 15. In addition, NAD+ can also indirectly regulate the process of anti-stress, cell growth, anti-inflammatory and anti-aging processes by regulating the function of Sirtuins factor which has NAD+ dependent protein deacetylation activity 16. Previous studies have shown that the decline of NAD+ level will also lead to heart failure and Alzheimer's disease 17. In vivo models of excitotoxic injury, the death of neurons is usually accompanied by a decrease in NAD+ levels 18. In addition, the study showed that moderate and severe TBI could lead to a decrease in the level of NAD+ in the brain 19. Nicotinamide mononucleotides (NMN) is the direct precursor of NAD+. It has been used in many medical researches because it can rapidly and effectively increase the level of NAD+ 14. NMN is currently being used in the research of various neurological diseases, and has been observed to have neuroprotective properties and improve neurological function 20-24. In cerebral ischemia mice, NMN showed neuroprotective effects though improving mitochondrial metabolism and alleviating oxidative stress injury 24. Besides, NMN can also reduce the loss of neurons and inhibit the decline of cognitive function in AD mice 25. Therefore, we propose the hypothesis that NMN may improve the behavior function as well as memory ability and alleviate the neurological damage of moderate-to-severe TBI rats. Through the treatment of Sprague-Dawley rats with TBI, the behavioral and pathological changes of rats were observed, and transcriptome sequencing of hippocampal tissues of rats was conducted to explore the role of NMN in TBI rats and the pathways which might be involved, hoping to find a new treatment for TBI. ## Animals A total of 66 male Sprague Dawley rats, weighing 280-320g (7 weeks), were purchased from Vital River Laboratory Animal Technology (Beijing, China). The rats were housed in the Animal Experimental Center of Zhongnan Hospital at Wuhan University under 12 hours light/dark cycles with temperature controlled at 25 °C ± 2 °C. Rats had free access to standard laboratory diet and water. All animal experiments were carried out in accordance with the guidelines for experimentation with lab animals established by the Animal Experiment Center and Ethics Committee of Zhongnan Hospital of Wuhan University. ## TBI procedure Controlled cortical impact (CCI) device (Custom Design & Fabrication, USA) was employed to establish the rat model of a moderate-to-severe traumatic brain injury as described previously 26, 27. After one week of rest, the animals were assigned randomly to one of three groups: sham group, TBI group and TBI+NMN group. Diet was stopped 12 hours before surgery. $3\%$ Pentobarbital sodium (50mg/kg, i.p.) was injected to anesthetize rats. The scalp of rats was shaved and disinfected. The skull was exposed by incision in the middle of the skin of head. A craniotomy with a diameter of 5 mm was drilled in the right parietal region, the caudal side of the coronal and right side of the sagittal suture. The rats were placed on CCI device and their heads were fixed - the impactor was directed at the bone window and contacted with the meninges, and the parameter velocity was 5 m/s, dwell time was 200 ms and impact depth was 3 mm. The sham group received the same treatment except for impact. Analgesics and antibiotics were applied after surgery. ## The detailed experimental arrangement All animals were randomly divided into three groups, Sham, TBI and TBI+NMN (22 rats in each group). One hour after the operation, the TBI group was not given drugs, while the TBI+NMN group were injected with NMN and sham group were injected with vehicle (PBS). The brain tissues of some rats were collected 24 hours after TBI for sequencing, RT-qPCR, brain water content and blood brain barrier tests. The tissues were directly put into liquid nitrogen and then transferred to -80 °C for storage. The neurological deficits of the remaining rats were measured by mNSS score at 1,3,5 and 7 days after TBI. Morris water maze test was conducted from the 3rd to the 8th day. On the 8th day, the brain was removed and stored in $4\%$ paraformaldehyde solution (Fig. 1). ## NMN administration NMN was dissolved in Phosphate Buffer Saline (PBS) with a concentration of 20 mg/ml. Rats were injected with NMN (MCE, USA., HY-F0004, 43.75mg/kg, i.p.) or vehicle (PBS, same volume, i.p.) 1 hours after TBI impact experiment 28. ## Nissl staining After the rats were euthanized 8 days after TBI, normal saline and $4\%$ paraformaldehyde were perfused into the left atrium to flush out the blood and fix the tissues. Three rats brain tissue in each group were obtained and fixed in paraformaldehyde. The paraffin blocks were sliced with a thickness of 10 μm. The brain was cut into three coronal sections to observe the structure of hippocampal CA1, CA3 and DG regions. After dewaxing, the slices were dyed in $0.1\%$ cresol violet solution for 10 minutes, then washed in distilled water for three times, and dehydrated in $95\%$ ethanol. Observe and collect images under Olympus microscope 29. Image J software was used for processing. ## mNSS Score On the days 1, 3, 5 and 7 days after TBI, the neurological function was scored using modified neurological severity score (mNSS), and 6 rats in each group were tested each time. 30. mNSS is a comprehensive test including motor, sensory, balance and reflex (normal score, 0, maximum score, 18 points). The final points were 13-18 for severe injury, 7-12 for moderate injury, and 1-6 for mild injury. The higher the score, the more severe the nerve damage of rats. The test was conducted in double blindness and scored by three professional researchers. ## Morris water maze test Morris water maze (MWM) test was performed on 5 rats in sham group, 6 rats in TBI and TBI+NMN groups. Animals were subjected to the Morris water maze test for the purpose of assessing spatial learning and memory 31. The rats were put into a vat of inky water with a diameter of 2.1 meters. The tank is delimited four quadrants, and a platform is placed in one quadrant, which is 1cm below the water surface. On the 3rd to 7th days after TBI, all rats were tested four times a day. Each time, one rat was placed in a different quadrant, and the time spent to find the platform after it was placed in water was recorded. If it exceeds 120 seconds, guide the rat to find the platform and allow it to stay on the platform for 30 seconds. Remove the platform on the sixth day, and recorded the escape latency of the rats. Each stage of the experiment was recorded by the automatic tracking system (Xmaze™, Xinruan Information Technology Co., Shanghai, China). ## Brain water content Three rats per group were euthanized by giving $3\%$ pentobarbital (150 mg/kg, i.p.). Brain tissues of rats were obtained after 24h post-TBI, removing the olfactory bulb and cerebellum. The brain was divided into ipsilateral (damaged) and contralateral (control) hemispheres, immediately weighed to obtain wet weight. Then drying samples for 24 hours at 100 °C in the oven to obtain the dry weight. The calculation formula of brain water content is as follows, (wet weight - dry weight)/ wet weight$100\%$ 32. ## Immunofluorescence staining The brains of 3 rats in each group used for immunofluorescence were taken from the rats killed after neurological function test. The brain samples were dissected in $4\%$ paraformaldehyde (24h, 4 °C), transferred to $30\%$ sucrose solution for dehydration (72h, 4 °C), and then frozen (- 80 °C). The frozen brain tissues were then sliced at a thickness of 10 μm coronal brain sections with cryostat microtome (Leica Microsystems, Germany). The slices were stored in a -80 °C freezer until analysis. Three sections were taken from each rat for immunofluorescence staining, and one of the sections with the best effect was shown. The brain tissue sections were placed in a repair box filled with antigen repair buffer (0.01M citric acid buffer, pH 6.0), heated to 100 °C and incubated for 15 minutes for antigen repair. Sample washed with PBS (PH 7.4) for three times, and add goat serum at RT for 30 minutes for nonspecific binding sites blocking. Then add GFAP, IBA-1, NEUN (Abcam, ab7260, ab178847, ab177487, 1:500) primary antibody and incubate them in a 4 °C wet box overnight. After PBST washing, sections were incubated with second antibody in a wet box at 37 °C for 1 hour. NEUN incubated sections, with a drip concentration of 20 μg/ml protease K solution was incubated at room temperature for 20 minutes, and then the apoptosis detection kit (Vazyme, A113-03) was used for tunel staining. DAPI (Beyotime, C1002, 1:1000) was used for the nuclear counterstaining. Fluorescence images were captured with a fluorescence microscope (Olympus, Japan) and fluorescence intensity was quantified with Image J. ## RNA-seq analysis RNA-seq analysis was performed on 4 rats in each group. Euthanized rats were perfused $0.9\%$ NaCl, and the brain tissues were removed. Bilateral hippocampus was separated on ice, directly put into liquid nitrogen, and transferred to - 80 °C refrigerator for preservation. Total RNA of hippocampus was extracted and purified by using Trizol reagent (Thermofisher, 15596018) based on the protocol provided by the manufacturer. RNA samples quantity and purity were analysis of Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent, CA, USA, 5067-1511), high-quality RNA samples with RIN number > 7.0 were used to construct cDNA library. Finally, we use illumina Novaseq™ 6000 (LC Bio Technology CO., Ltd. Hangzhou, China) conducts double ended sequencing (PE150) according to the supplier's recommended protocol. To get high quality clean reads, reads were further filtered by Cutadapt, removing reads containing adapters, poly A and poly G, and unknown nucleotides bases. Then sequence quality was verified using FastQC. The reference genome was mapped using HISAT2 software. StringTie (1.3.1) was used to reconstruct transcripts and calculate the FPKM value of all gene expression levels in each sample. The differentially expressed mRNAs were used for GO enrichment and KEGG enrichment analysis, screened with \| log2 (fold change) \| > 0.5 and p value < 0.01. OmicStudio (https://www.omicstudio.cn/) was applied to perform the heatmap, venny analysis, GO and KEGG enrichment analysis. ## RT-qPCR confirmation To confirm the accurate RNA sequencing, Real-Time PCR was performed on 6 rats each group. The experiments were performed on a CFX Connect Real-Time PCR System (Bio-Rad; CFX Maestro 1.0 software). The target RNA and TB Green Premix Ex Taq II (Tli RNaseH Plus) (RR820A, TaKaRa, Japan) were subjected to the system. All procedures were performed according to standard protocols. Primers used in this study were listed in Table 1. ## Statistical analysis All data were expressed as mean ± standard deviation (SD). Difference between two groups was tested by Student's t test, and a one- or two-way analysis of variance (ANOVA) was performed for multiple groups. Values of $P \leq 0.05$ were considered to indicate a statistical different. Analyses were performed using the SPSS software (V25.0, IBM, United States). ## NMN treatment reduced the neurological damage and improved neurological functions after TBI To explore the neuroprotective effect of NMN on TBI, we first examined the survival of nerve cells. As shown by Nissl staining (Fig. 2A), neuron structure was clear and Nissl bodies were evenly distributed in sham group, but the structure of neurons was disappeared after TBI operation. However, less loss of neurons combined with normal structure were showed in the TBI+NMN group Compared to TBI group, indicating that NMN treatment reduced the neurological damage in hippocampal CA1 area after TBI. Quantitative statistics also indicated that the numbers of Nissl-positive cells in sham group was 97 ± 14 cells/field, while the TBI group was far less than sham group ($P \leq 0.01$), which was 24±17 cells/field. And the numbers of Nissl-positive cells in TBI+NMN group was significantly increased compared to TBI group, which was 71 ± 13 cells/field ($P \leq 0.05$) (Fig. 2B). In addition, the measurement of brain water content also demonstrated that the brain edema after TBI was also alleviated by NMN treatment, as the brain water content in the TBI+NMN group was significantly lower than TBI group (TBI+NMN vs TBI, $79.97\%$ ± $0.16\%$ vs $80.41\%$ ± $0.19\%$, $P \leq 0.05$) (Fig. 2C). In order to observe whether NMN administration affect the neurological functions after TBI, we conducted tests to measure mNSS scores and performed behavioral experiments using the Morris water maze. At 1, 3, 5, and 7 days after TBI, the mNSS scores of TBI group were significantly higher than those of sham group (all $P \leq 0.001$). But after NMN treatment, the mNSS scores were significantly reduced. ( $P \leq 0.05$) (Fig. 2E). We also performed MWM tests at 3-8 days after surgery to investigate the effects of NMN on spatial learning and memory ability after TBI. On the first day of training, there was no evidently difference in the escape latency of the three groups. However, from 4 to 7 days after TBI, the escape latency of the TBI group was significantly higher than the sham group ($P \leq 0.01$ on days 4 and 7; $P \leq 0.001$ on days 5 and 6). Consistent with the mNSS score results, NMN treatment rats showed a significantly lower escape latency compared to TBI group during this period ($P \leq 0.05$) (Fig. 2F). Moreover, the latency in TBI+NMN group was also lower than TBI group on the test day of MWM ($P \leq 0.01$) (Fig. 2G). Taken together, these results strongly suggest that NMN has significant therapeutic effect against neurological injury and neurobehavioral damage after TBI. ## NMN reduced the numbers of reactive astrocytes and microglia, and inhibited the transcription level of TNF-α and IL-1β In the behavioral experiment completed, NMN treatment showed a neuroprotective effect on TBI. Previous studies showed the neuroinflammation, which is closely related to the activation of microglia and astrocyte activation, is one of the important mechanisms of TBI secondary injury 33-35. Therefore, we further administrated whether NMN affected the activation of glial cells after TBI. The activated astrocytes maker GFAP and the microglia maker Iba-1 immunostaining showed that NMN treatment significantly reduced the TBI-induced activated astrocytes and microglia in the CA1 area of hippocampus, and the quantitative analysis of fluorescence intensity showed the same result ($P \leq 0.05$) (Fig. 3A-C). TNF-α and IL-1β as representative inflammatory factor in TBI, their expression level reflects the degree of inflammatory response. We detected the expressions of TNF-α and IL-1β by RT-qPCR. The finding illustrated that the expression of TNF-α and IL-1β genes in hippocampus of TBI+NMN group was significantly lower than TBI group ($P \leq 0.05$) (Fig. 3D). These results indicated NMN can relieve the inflammatory response after TBI. ## NMN decreased neuronal apoptosis after TBI The improvement of neurological functions after TBI is not only related to the reduction of inflammation, but also related to the damage of neurons directly. Therefore, we next assessed neuronal apoptosis by TUNEL assay. Brain samples were taken for double immunofluorescence staining to detect the co-expression of TUNEL and NEUN in CA1 area (Fig. 4A). Consistent with the neuroinflammation result, the NEUN fluorescence intensity in hippocampus were decreased ($P \leq 0.05$) and the TUNEL fluorescence intensity were increased after TBI compared with the sham group ($P \leq 0.05$). And NMN administration significantly decreased the fluorescence intensity of TUNEL after TBI ($P \leq 0.05$). The fluorescence intensity of NEUN in TBI+NMN group also increased significantly compared with the TBI group (Fig. 4B, C). These results demonstrated that NMN treatment was able to reduce neuronal apoptosis. ## Hippocampal transcriptome analysis of three groups To further probe the related mechanism of neuroprotective effect of NMN after TBI, transcriptome sequencing was performed on hippocampus of Sham, TBI, and TBI+NMN groups. The sequencing results showed that there were 1589 differentially expressed genes (DEGs) in TBI vs sham, including 1123 up-regulated and 466 down-regulated, and 157 up-regulated and 697 down-regulated were accessed due to the effect of NMN treatment (\|log2FC\| > 1 and $p \leq 0.01$) (Fig. 5A and Table S1). Of the 1589 DEGs genes that appeared after TBI, 792 were reversed in the TBI+NMN group, including 656 up-regulated and 136 down-regulated ones (Fig. 5B and Table S1). Hierarchical cluster analysis of 792 reversed genes shows that gene expression has been significantly reversed after TBI and treatment (Fig. 5D). Gene ontology (GO) and KEGG enrichment analysis of these common differential genes were carried out to explore the function of them. The 792 reversed DEGs were enriched in inflammation related GO items, such as inflammatory response, positive regulation of cell migration, positive regulation of tumor necrosis factor production, immune response, cellular response to tumor necrosis factor, positive regulation of ERK1 and ERK2 cascade and cellular response to interleukin-1 (Fig. 5C and Table S2). They were also enriched in Cytokine-cytokine receptor interaction, TNF signaling pathway, Phagosome, Chemokine signaling pathway, NF-kappa B signaling pathway, Cell adhesion molecules, JAK-STAT signaling pathway, PI3K-Akt signaling pathway and some immunity-related pathway (Fig. 5E and Table S2). The above results suggested that NMN may exert neuroprotective effects via influencing inflammation and immunity related signaling pathways. At last, in order to confirm the reliability of RNA sequencing results, a series of genes were selected to detect their expression levels using RT-qPCR. As showed in Fig. 6, 10 genes (Ccl2, Trl2, Trl4, Stat3, Il11, Il6, Il1rn, Zfp36, Casp4, Hmox1, and Ccn1) were up-regulated in TBI group compared to sham group, and were significantly down-regulated in TBI+NMN group compared to TBI group. Moreover, Hes5 was down-regulated in TBI group compared to sham group and reversed by NMN treatment. The sequencing results and PCR results were mutually verified. ## Discussion TBI is initially caused by mechanical damage, but its secondary damage is complex and diverse. Substantial evidence suggested that cellular cascade of inflammation participates in secondary brain injury 36-39. However, short-term inflammation after TBI is protective, while long-term, intense inflammation can damage the brain 40. And a number of studies have reported that NAD+ play critical roles in many biological processes including metabolism, inflammation, and stress and damage response resulting from its consuming enzymes 41, 42. Our study demonstrated that the administration of NMN, a NAD+ intermediates, is capable of alleviating TBI-associated neurological impairment by mitigating neuronal inflammation. In the present study, we first examined the effect of NMN treatment on the pathological changes after TBI. We found that NMN reduced the death of nerve cells, maintained the structure of hippocampus, and alleviated the brain edema after TBI, which proved that NMN can effectively reduce brain damage caused by TBI. Previous studies have shown that the degree of neuronal death in the hippocampus due to contusion, inflammation, edema and other reasons affects the postoperative recovery of TBI 43, 44. Thus, we further investigated the neurological function of NMN in TBI rats. We found that NMN-treated TBI rats substantially got lower mNSS scores and lower escape latency compared to TBI rats. Zhao's study reported that NAD+ ameliorate cognitive impairment in chronic cerebral hypoperfusion (CCH) models 45, which is consistent with our results. These findings proved that NMN can play a protective role in brain neurons after TBI. Astrocytes and microglia which play crucial roles in the inflammatory response of the central nervous system can lead to neurodegenerative diseases 46. According to our findings, decreased GFAP and Iba-1were observed under NMN administration. Pro-inflammatory response astrocytes induce proinflammatory factors (e.g., IL-1β, TNF-α) that are known to have deleterious functions 47. Besides, microglia were reported have ability to reduce clearance effect, which leads to the aggravation of nerve cell damage 48. Moreover, NAD+ treatment have been reported to reduce CCH-induced microglia and subsequent production of pro-inflammatory factors 45. Similarly, TBI rats in our study exhibited activated TNF-α and IL-1β that were mitigated by NMN treatment. Additionally, Post-traumatic neuroinflammation have been reported to significantly contribute to the neuronal death observed after neurotrauma 49. TUNEL assay in our study also demonstrated that NMN treatment decreased TBI-induced neuronal apoptosis. Given these results, we hypothesized that the improvement of neurological function in TBI rats may be result from the fact that NMN alleviated neuroinflammation. To further investigated the related mechanism of NMN in TBI, we identified differentially expressed genes in TBI by RNA sequencing and explored genes and enrichment pathways for NMN reversal. We found 1589 genes were changed in TBI and 792 genes were reversed by NMN administration. And GO analysis showed that the different reversed genes were strongly related to inflammatory response, which was consistent to our experiment findings. Our data proved that MNM treatment reversed the elevated CCL2 which induced by TBI. Zhao et al. reported CCL2 which was derived from microglia, astrocytes and neurons can aggravate tissue damage by conducting secondary inflammatory response 50. In addition, the reversal of proinflammatory cytokines such as IL-6, IL-11 and IL1rn also proved that NMN could reduce the level of inflammation after TBI. Moreover, we also found that Toll-like receptors TLR2 and TLR4 were also reduced by NMN treatment. Tu et al. found that inhibition of TLR$\frac{2}{4}$-NF-κB signaling reduced brain damage and protected nerves 51, 52. Lin et al. also revealed that TLR$\frac{2}{4}$-NF-κB signaling has an inhibitory effect in the later stage of TBI 53. Wan et al. reported NMN prevented DIC in rats by inhibiting NLRP3 inflammatory body activation 54. These findings were in agreement with our results. Similarly, KEGG analysis also showed that the NF-kappa B signaling pathway, Jak-STAT signaling pathway, TNF signaling pathway were significantly enriched. Besides, present studies have revealed that oxidative and inflammation are indispensable in TBI 55, 56. The increased NAD+ can preserve cellular respiration. reduce oxidative stress, and further alleviate inflammation 57. ## Conclusion In summary, this study provide evidence that NMN administration alleviated TBI-associated neurological impairment by anti-neuroinflammation bioactivities. Besides, the underlying molecular mechanisms of these beneficial effects may involve TLR$\frac{2}{4}$-NF-κB signaling. Our data might provide a novel therapeutic strategy for TBI. However, the precise mechanism on NMN still needs to be explored in further study. ## Funding statement This study was supported by the 2021 Medical Research Project from Health Bureau of Wuchang District of Wuhan, 2020 Annual Funding for Discipline Construction from Zhongnan Hospital of Wuhan University, and Discipline Cultivation Funding from Zhongnan Hospital of Wuhan University. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. ## Ethics statement The animal study was reviewed and approved by Institutional Animal Care and Use Committee of Wuhan University (ZN2021192). ## Author contributions XZ, JC, HM and YZ designed the project; XZ and JC performed the experiments; XZ, JC, HM, JY collected and analyzed the data; XZ, JC, HM prepared the draft; HM and YZ revised the paper. ## Data availability The datasets showed in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://www.ncbi.nlm.nih.gov/bioproject/902029, PRJNA902029. ## References 1. Ghajar J. Traumatic brain injury. The Lancet* (2000.0) 356 923-9 2. Callcut RA, Kornblith LZ, Conroy AS, Robles AJ, Meizoso JP, Namias N. The why and how our trauma patients die: A prospective Multicenter Western Trauma Association study. 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--- title: 'Liver Injury in Patients with COVID-19: A Retrospective Study' authors: - Yun Feng - Yaping Liu - Qian Zhao - Jiao Zhu - Xiaona Kang - Chen Mi - Peijie Li - Weizhi Li - Guifang Lu - Ai Jia - Shuixiang He - Hongxia Li journal: International Journal of Medical Sciences year: 2023 pmcid: PMC9969505 doi: 10.7150/ijms.81214 license: CC BY 4.0 --- # Liver Injury in Patients with COVID-19: A Retrospective Study ## Abstract Objectives: The objective of this study is to explore the incidence, characteristics, risk factors, and prognosis of liver injury in patients with COVID-19. Methods: We collected clinical data of 384 cases of COVID-19 and retrospectively analyzed the incidence, characteristics, and risk factors of liver injury of the patients. In addition, we followed the patient two months after discharge. Results: A total of $23.7\%$ of the patients with COVID-19 had liver injury, with higher serum AST ($P \leq 0.001$), ALT ($P \leq 0.001$), ALP ($$P \leq 0.004$$), GGT ($P \leq 0.001$), total bilirubin ($$P \leq 0.002$$), indirect bilirubin ($$P \leq 0.025$$) and direct bilirubin ($P \leq 0.001$) than the control group. The median serum AST and ALT of COVID-19 patients with liver injury were mildly elevated. Risk factors of liver injury in COVID-19 patients were age ($$P \leq 0.001$$), history of liver diseases ($$P \leq 0.002$$), alcoholic abuse ($$P \leq 0.036$$), body mass index ($$P \leq 0.037$$), severity of COVID-19 ($P \leq 0.001$), C-reactive protein ($P \leq 0.001$), erythrocyte sedimentation rate ($P \leq 0.001$), Qing-Fei-Pai-Du-Tang treatment ($$P \leq 0.032$$), mechanical ventilation ($P \leq 0.001$), and ICU admission ($P \leq 0.001$). Most of the patients ($92.3\%$) with liver injury were treated with hepatoprotective drugs. $95.6\%$ of the patients returned to normal liver function tests at 2 months after discharge. Conclusions: Liver injury was commen in COVID-19 patients with risk factors, most of them have mild elevations in transaminases, and conservative treatment has a good short-term prognosis. ## Introduction The 2019 novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 has threatend public health globally. Moreover, rapidly emerging variants of concern have shown greater transmissibility, including alpha (B.1.1.7, UK), beta (B.1.351, South Africa), gamma (B.1.1.28.1, Brazil), Delta (B1.1.617, India), and Omicron (B.1.1.529, South Africa) 1. Among them, Delta is the "fastest and fittest" variant 2. For the past months, we have suffered from the emerging pandemic of Delta and Omicron. Although COVID-19 patients mainly present with respiratory symptoms, previous reports have shown that patients with COVID-19 are often complicated by liver injury, with the incidence varied from $15\%$ to $53\%$ 3-6. We also found that some COVID-19 patients had liver injury in our work. The causes of liver injury in COVID-19 patients include virus direct attack, cytokine storm, systemic inflammatory response, immune response, hepatic cell ischemia-reperfusion injury, hepatic disseminated intravascular coagulation, hepatotoxicity of drugs, and underlying liver disease, etc. 4, 7-9. Our study retrospectively analyzed the incidence, characteristics, risk factors, and prognosis of liver injury in patients with COVID-19. ## Study objects and groups Our study sample consisted of 384 hospitalized patients of COVID-19 diagnosed between December 9th, 2021 to February 5th, 2022 in Xi'an city, situated in central China. Our inclusion criteria were the presence of the following: positive real-time PCR of severe acute respiratory syndrome coronavirus 2 delta (B1.1.617, India) variants, and our exclusion criteria were the presence of: failure of cooperating with follow-up. All subjects except the deceased received follow-up 2 months after discharge. We broke down the sample into the liver injury group and control group. The liver injury group included COVID-19 patients with abnormal liver function tests above the upper limit of normal during hospitalization. The cases that had normal liver function tests were placed in the control group. ## Clinical Data collection We collected and analyzed all the clinical and demographic data of the study objects. Demographic data included sex and age. Clinical data of the patient's baseline conditions included history of liver diseases, other underlying diseases, statin use, alcoholic abuse, and body mass index. COVID-19 related clinical data included type of COVID-19, fever, gastrointestinal symptoms, liver function tests, minimum white blood cell, minimum lymphocyte, maximum D-dimer, maximum prothrombin time, maximum activated partial thromboplastin time, maximum C-reactive protein, maximum erythrocyte sedimentation rate during hospitalization, and therapies, such as Chinese traditional medicine, acetaminophen, low molecular weight heparin, COVID-19 neutralizing antibody, hepatoprotective drugs. Mechanical ventilation, intensive care unit (ICU) admission, and death were prognostic indicators. For liver function tests, we mainly focus on maximum serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-Glutamyl transferase (GGT), total bilirubin, indirect bilirubin and direct bilirubin during hospitalization. ## Statistical analysis We used SPSS 24.0 (SPSS Inc., Chicago, IL, USA) for statistical analysis of the data. Categorical variables were expressed as the number of cases (percentage) and analyzed by Pearson χ2 tests between groups. Continuous variables conformed to normal distribution were represented by mean ± standard deviation and analyzed by Student's t-tests and ANOVA tests. Continuous variables didn't conform to normal distribution were presented as median (interquartile range) and analyzed by Mann-Whitney U-tests. $P \leq 0.05$ means significant statistical difference. ## Risk Factors of Liver Injury in Baseline Conditions of COVID-19 Patients Our study enrolled a total of 384 patients with COVID-19. Table 1 shows the baseline conditions of COVID-19 patients, and patients in the liver injury group and the control group. The patients' average age was 40.6 ± 19.5 years (0 to 87 years). The ratio of male to female patients was 1:1.08. Only 27 ($7.0\%$) patients had history of liver diseases, including nonalcoholic fatty liver disease ($3.4\%$), alcoholic fatty liver disease ($1.8\%$), chronic hepatitis B ($1.6\%$), chronic viral hepatitis C ($0.3\%$), and liver cirrhosis ($0.3\%$). Seven patients ($1.8\%$) were found to have a history of alcohol abuse, meeting the diagnostic criteria for alcoholic liver disease. As many as 68 ($17.7\%$) patients had history of cardiovascular diseases, including hypertension ($8.1\%$), coronary heart disease ($6.3\%$), peripheral atherosclerosis ($3.4\%$), arrhythmia ($2.9\%$), congenital heart disease ($0.3\%$), rheumatic heart disease ($0.3\%$). Thirty-nine patients ($10.2\%$) had hyperlipidemia. Among the patients with cardiovascular diseases or hyperlipidemia, 25 ($6.5\%$) patients had a history of statin use, which may lead to liver injury. We also calculated the patients' body mass index. Some patients were overweight ($18.0\%$) and obese ($5.7\%$), which may be related to nonalcoholic fatty liver disease, hyperlipidemia, hypertension, and diabetes. COVID-19 patients with liver injury were older than those without liver injury (46.7±18.2 vs 38.7±19.5, $$P \leq 0.001$$). Furthermore, COVID-19 patients with liver injury were more likely to have a history of liver diseases ($14.3\%$ vs $4.8\%$, $$P \leq 0.002$$), alcoholic abuse ($4.4\%$ vs $0.9\%$, $$P \leq 0.036$$), and overweight and obese ($28.6\%$ vs $16.4\%$, $$P \leq 0.037$$) compared with the patients without liver injury (Table 1). Sex, history of cardiovascular diseases, hyperlipidemia, and statin use were not statistically different between the liver injury group and control group. Therefore, Age, history of liver diseases, alcoholic abuse, and overweight and obese were risk factors of liver injury in patients with COVID-19. ## Liver Function Tests of Liver Injury in COVID-19 Patients We included all COVID-19 patients with abnormal liver function tests during hospitalization as patients with liver injury. The median AST (63 [55] vs 19 [13], $P \leq 0.001$), ALT (68 [62] vs 21 [13], $P \leq 0.001$) of COVID-19 patients with liver injury were significantly higher than those of the control group, with the median higher than the upper limit of the reference range, but not more than twice the upper limit (Table 1). The median GGT (31 [26] vs 16 [12], $P \leq 0.001$), ALP (67 [51] vs 58 [49], $$P \leq 0.004$$), total bilirubin (13.1 (10.5) vs 13.1 (9.2), $$P \leq 0.002$$), indirect bilirubin (9.9 (7.8) vs 10.0 (7.1), $$P \leq 0.025$$), and direct bilirubin (3.5 (2.4) vs 2.4 (1.6), $P \leq 0.001$) of COVID-19 patients with liver injury also significantly increased than those of the control group, still within the reference range. ## Correlation Between Liver Injury and the Severity of COVID-19 Most of the COVID-19 patients were common type ($54.2\%$) and mild type ($43.8\%$) (Table 1). Severe type and critical type of COVID-19 accounted for only $2.1\%$ of all the study objects. Severe and critical type of COVID-19 were more likely to complicated with liver injury ($P \leq 0.001$). $8.8\%$ of the COVID-19 patients with liver injury were severe or critical type. However, none of the COVID-19 patients without liver injury were severe or critical type. ## Risk Factors of Liver Injury in Clinical characteristics Related to COVID-19 As shown in Table 1, fever was a common symptom in $45.1\%$ of patients. In addition, $32.8\%$ of patients had gastrointestinal symptoms, including anorexia, abdominal discomfort, abdominal pain, bloating, nausea, vomiting, acid reflux, diarrhea, and constipation, etc. Fever and gastrointestinal symptoms did not differ in COVID-19 patients liver injury or not. The median minimum WBC during hospitalization in COVID-19 patients with liver injury was 3.48×109/L, which was slightly below the lower limit of the normal reference range. However, we found no statistical difference in WBC between the liver injury group and control group. The lymphocytes, D-dimer, prothrombin time, and activated partial thromboplastin time were similar in both groups. The C-reactive protein of the COVID-19 patients were higher than the upper limit of the normal reference value, as well as the erythrocyte sedimentation rate of the patients with liver injury. Moreover, patients with liver injury showed significantly higher C-reactive protein (14.2 (10.8) vs 9.3 (4.5), $P \leq 0.001$) and erythrocyte sedimentation rate (21 [17] vs 17 [10], $P \leq 0.001$) compared with the patients without liver injury. Results show liver damage correlates with severity of inflammation in patients Therefore, comparison of laboratory tests between the two groups showed that liver injury correlated with the severity of inflammation in COVID-19 patients. ## Correlation Between Liver Injury and Treatment of COVID-19 Up to $88.8\%$ of the COVID-19 patients received Chinese traditional medicine treatment, including Lian-Hua-Qing-Wen ($88.8\%$), Qing-Fei-Pai-Du-Tang ($75.0\%$), Huo-Xiang-Zheng-Qi ($18.5\%$), Xuan-Fei-Bai-Du-Tang ($7.0\%$), Shen-Qi-Shi-Yi-Wei ($3.6\%$), Qiang-Li-Pi-Pa ($8.6\%$), Su-Huang-Zhi-Ke ($6.0\%$), Lin-Yang-Jiao ($14.3\%$), and Others ($22.1\%$) (Table 1). $74.2\%$ of the COVID-19 patients received low molecular weight heparin treatment. Some patients with fever took acetaminophen ($32.8\%$). Neutralizing antibody therapy was used in only a very small number of patients ($2.9\%$). We found that patients who took Qing-Fei-Pai-Du-Tang orally were more likely to have liver injury than those who did not take it (76 ($83.5\%$) vs 212 ($72.4\%$), $$P \leq 0.032$$). There was no statistical difference between the patients with liver injury and those without liver injury whether or not to use Chinese traditional medicine, other Chinese traditional medicines except Qing-Fei-Pai-Du-Tang, low molecular weight heparin, and acetaminophen. ## Correlation Between Liver Injury and Clinical Outcomes of COVID-19 Of all our study subjects, 8 ($2.1\%$) patients were admitted to the ICU and received mechanical ventilation. We found that all the 8 patients had liver injury. Mechanical ventilation (8 ($8.8\%$) vs 0 ($0.0\%$), $P \leq 0.001$) and ICU admission (8 ($8.8\%$) vs 0 ($0.0\%$), $P \leq 0.001$) were more common in patients with liver injury than controls (Table 1). One patient ($0.3\%$) died from respiratory failure, chronic obstructive pulmonary disease, and acute coronary syndrome. There was no significant difference in mortality between patients with and without liver injury. The length of hospital stay was similar between the two groups. ## Treatment and Short-term Prognosis of Liver Injury in COVID-19 Patients All patients with liver injury were discontinued from statins and Chinese traditional medicine. Most of the patients ($92.3\%$) with liver injury were treated with hepatoprotective drugs, significantly higher than the control group (84 ($92.3\%$) vs 0 ($0.0\%$), $P \leq 0.001$). Hepatoprotective drugs included compound glycyrrhizin, diammonium glycyrrhizinate, polyene phosphatidyl choline, silymarin, bicyclol, reduced glutathione, Ademetionine1,4-Butanedisulfonate, etc. We found that $35.2\%$ ($\frac{32}{91}$) of the COVID-19 patients with liver injury returned to normal liver function tests before discharge. Patients whose liver function tests had not returned to normal at the time of discharge needed to continue taking hepatoprotective drugs. In addition, patients with a history of liver disease also received treatment tailored to the cause, including antiviral drugs, a low-fat diet, exercise, and abstinence from alcohol. We also instructed patients to review liver function tests 2 weeks and 2 months after discharge. The follow-up results showed that $83.5\%$ ($\frac{76}{91}$) of the COVID-19 patients with liver injury during hospitalization had normal liver function tests at 2 weeks after discharge, and a total of $95.6\%$ ($\frac{87}{91}$) of them returned to normal at 2 months after discharge. The remaining four patients with abnormal liver function tests at discharge all had a history of liver disease, including liver cirrhosis, hepatitis C, and alcoholic liver disease. The median total number of days patients took hepatoprotective drugs was 19, from hospitalization to 2 months after discharge. ## Discussion We found a high risk of liver injury for patients with COVID-19 from our retrospective study. Up to $23.7\%$ of COVID-19 patients had liver injury. Liver function tests in COVID-19 patients with liver injury were characterized by mildly elevated serum AST and ALT. GGT, ALP, total bilirubin, indirect bilirubin, and direct bilirubin were also higher in patients with liver injury. COVID-19 patients with older age, history of liver diseases, alcoholic abuse, overweight and obese were more likely to have liver injury. liver injury also associated with severe and critical COVID-19, increased C-reactive protein, elevated erythrocyte sedimentation rate, mechanical ventilation, ICU admission, and Qing-Fei-Pai-Du-Tang treatment. The prognosis of COVID-19 complicated with liver injury was generally good, with $95.6\%$ of patients returning to normal liver function tests 2 months after discharge. Clinicians should pay attention to monitoring the liver function tests for timely detection and appropriate treatment, since more than $\frac{1}{5}$ of COVID-19 patients had liver injury. The incidence of liver injury in COVID-19 patients caused by Delta variant infection in our study was consistent with previous reports 3-6. Most patients with COVID-19 complicated with liver injury showed mildly elevated transaminases, and some patients had elevated GGT, ALP, and total bilirubin, and decreased ALB. Approximately $60\%$ of the COVID-19 patients with liver injury had slightly elevated AST and ALT, with the values between 1-2 times the upper limit of normal 10. Elevated GGT, ALP, and total bilirubin, and decreased albumin were also observed in some COVID-19 patients 10, 11. Total bilirubin was normal or mildly elevated in most COVID-19 patients 10. Moreover, we draw lessons from our study to notice the liver function tests particularly in patients with risk factors including old age, history of liver diseases, alcoholic abuse, overweight and obese, severe and critical type of COVID-19, mechanical ventilation, ICU admission, high inflammatory indexes, and Qing-Fei-Pai-Du-Tang treatment. Drugs that may lead to liver injury should be avoided in patients with risk factors of liver injury. Multiple factors make elderly patients with COVID-19 more likely to have liver injury. According to previous reports, elderly patients are at higher risk of developing severe and critical COVID-19 than younger patient 12, 13, and are more likely to use statins and other drugs that may cause liver injury due to underlying cardiovascular and cerebrovascular diseases, hypertension, diabetes, hyperlipidemia, etc. 14. It is also obvious that COVID-19 patients with underlying liver disease are more likely to have abnormal liver function tests, which has been frequently mentioned 15. In addition, elderly patients with a history of liver disease may have a longer course of disease, more severe disease, worse liver function, and poorer prognosis 16. It is also well understood that alcohol abuse is a risk factor for liver injury in patients with COVID-19, since it's the cause of alcoholic liver disease. Studies have reported that alcohol related liver disease patients are particularly vulnerable to severe acute respiratory syndrome coronavirus 2 infection and had worse prognosis 17. Overweight and obese are also risk factors for severe and critical COVID-19, and are common components of metabolic syndrome with nonalcoholic fatty liver disease 12, 13. In addition to these underlying risk factors, liver injury is closely related to the severity of the COVID-19 disease itself. Meta-analyses have found that COVID-19 patients with acute liver injury had higher odds risk of suffering from severe disease compared with those without acute liver injury 18, 19. Severe and critical type of COVID-19 were associated with high levels of inflammatory indexes, mechanical ventilation, and ICU admission 4, 5, 20. Therefore, high C-reactive protein and erythrocyte sedimentation rate, mechanical ventilation, and ICU admission are also risk factors for liver injury in patients with COVID-19. These risk factors are associated with the pathogenesis of liver injury complicated in COVID-19, including viral attack, systemic inflammatory response, cytokine storm, coagulation dysfunction, endothelial damage, and ischemia-reperfusion injury 4, 7-9. Drug-induced liver injury is also one of the non-negligible causes of liver injury in COVID-19 21. Common drugs that cause liver injury in COVID-19 patients include lopinavir, ritonavir, oseltamivir, antipyretic drugs, and some Chinese traditional patent medicines 21, 22. Qing-Fei-Pai-Du-Tang, a Chinese traditional medicine formula that including 21 herbs, has proven its effectiveness in mild and common types of COVID-19 23, 24. No other studies have shown that Qing-Fei-Pai-Du-Tang treatment increases the risk of liver injury in COVID-19 patients. Therefore, the risk of liver injury caused by Qing-Fei-Pai-Du-Tang may require further large-sample cohort studies for to explore. The application of Qing-Fei-Pai-Du-Tang in the treatment of COVID-19 may need further optimization and normalization of the course, dosage, or compatibility, etc. Most COVID-19 patients with liver injury were treated conservatively, including hepatoprotective drugs, and discontinuation of Chinese traditional medicine and statins, and achieved good curative effects and short-term prognosis. Patients with underlying liver diseases also require treatment for both causes and complications. Previous studies have also reported that patients with COVID-19 complicated with liver injury have a good prognosis, expect those with underlying liver diseases 25. COVID-19 patients with chronic liver disease had an increased risk of mortality (risk ratio 2.8), in which liver cirrhosis had the highest risk of mortality (risk ratio 4.6) 26. Alcoholic liver disease and hepatocellular carcinoma are also associated with high mortality in COVID-19 patients 26-28. Chronic viral hepatitis and nonalcoholic fatty liver disease have also been shown to be negative prognostic factors in COVID-19 patients 29, 30. Although the short-term prognosis of patients with liver injury in COVID-19 is good, it is still unclear whether liver injury complicated in COVID-19 has long-term effects on the liver. The primary advantage of our study is that it is a large cohort study of liver injury in COVID-19 patients caused by Delta variant infection. Moreover, our study comprehensively analyzed the characteristics of liver function tests and risk factors of liver injury. Furthermore, we performed short-term follow-up and prognostic analysis of patients. There are also some limitations to the study. It's a single-center retrospective study. Most of the COVID-19 patients of mild and common types received Chinese traditional medicine-based comprehensive treatment. We have not performed long-term follow-up of the patients. ## Conclusions In summary, we find that liver injury was common in COVID-19, characterized by mild AST and ALT elevations that respond well to medication. We should pay attention to the monitoring of liver biochemical indexes in COVID-19 patients with risk factors for liver injury, and try to avoid the use of drugs that may cause liver injury. ## Funding Statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. ## Ethics Approval Statement This work was approved by Ethics Committee of The First Affiliated Hospital of Xi'an Jiao Tong University (XJTU1AF2022LSK-001) and complied with the Declaration of Helsinki. ## Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. ## Author Contributions Yun Feng and Hongxia Li contributed to conception and design of the study. Yun Feng, Yaping Liu, Chen Mi, Peijie Li, Weizhi Li, Guifang Lu, and Hongxia Li participated in the diagnosis and treatment of patients. Qian Zhao, Jiao Zhu, Xiaona Kang, and Ai Jia participated in data collection, statistics and analysis. Yun Feng was responsible for drafting the manuscript. Hongxia Li and Shuixiang He have supervised the whole process of the research and revised the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version. ## References 1. Hirabara SM, Serdan T, Gorjao R, Masi LN, Pithon-Curi TC, Covas DT. SARS-COV-2 Variants: Differences and Potential of Immune Evasion[J]. Front Cell Infect Microbiol (2021) 11 781429.. PMID: 35118007 2. Twohig KA, Nyberg T, Zaidi A, Thelwall S, Sinnathamby MA, Aliabadi S. Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study[J]. LANCET INFECT DIS (2022) 22 35-42. PMID: 34461056 3. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y. 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--- title: Significant impact of body mass index on the relationship between increased white blood cell count and new-onset diabetes authors: - Chieh-Yu Hsieh - Wen-Hsien Lee - Yi-Hsueh Liu - Chun-Chi Lu - Szu-Chia Chen - Ho-Ming Su journal: International Journal of Medical Sciences year: 2023 pmcid: PMC9969508 doi: 10.7150/ijms.80207 license: CC BY 4.0 --- # Significant impact of body mass index on the relationship between increased white blood cell count and new-onset diabetes ## Abstract An elevated white blood cell (WBC) count has been linked to incident diabetes. WBC count has been positively associated with body mass index (BMI), and elevated BMI has been reported to be a strong predictor of future diabetes. Hence, the association of increased WBC count with the subsequent development of diabetes may be mediated by increased BMI. This study was designed to address this issue. We selected subjects from the 104,451 participants enrolled from 2012 to 2018 in the Taiwan Biobank. We only included those with complete data at baseline and follow-up and those without diabetes at baseline. Finally, 24,514 participants were enrolled in this study. During an average 3.88 years of follow-up, 248 ($1.0\%$) of the participants had new-onset diabetes. After adjusting for demographic, clinical, and biochemical parameters, increased WBC count was associated with new-onset diabetes in all of these participants (p ≤ 0.024). After further adjustment for BMI, the association became insignificant ($$p \leq 0.096$$). In addition, subgroup analysis of 23,430 subjects with a normal WBC count (range: 3500-10500/µl) demonstrated that increased WBC count was significantly associated with new-onset diabetes after adjusting for demographic, clinical, and biochemical parameters (p ≤ 0.016). After further adjustment for BMI, this association was attenuated ($$p \leq 0.050$$). In conclusion, our results showed that BMI had a significant impact on the relationship between increased WBC count and new-onset diabetes in all study participants, and BMI also attenuated the association in those with a normal WBC count. Hence, the association between increased WBC count and the future development of diabetes may be mediated by BMI. ## Introduction Chronic inflammation has been demonstrated to play a key role in the pathogenesis of type 2 diabetes mellitus (DM) 1. Inflammation itself has been shown to cause insulin resistance 2 and promote β-cell death 3. Previous studies have suggested an association between white blood cell (WBC) count, a non-specific parameter of inflammation, and incident DM 4,5. However, in contrast to these findings, Chao et al. found that measurements of plasma markers of systemic inflammation, including WBC count, contributed little additional value in predicting the risk of DM 6. In addition, several studies have demonstrated a significant link between C-reactive protein (CRP), a useful marker of inflammation, and incident DM after adjusting for obesity indexes 7,8, whereas other studies have argued that such associations may be chiefly mediated by increased adiposity 6. Thorand et al. reported that the association between an increased CRP level with future DM risk became insignificant after adjusting for body mass index (BMI) 9. Hence, BMI may have a significant influence on the relationship between chronic inflammation and future development of DM. WBC count has been positively associated with BMI 10,11, and increased BMI has been shown to be a strong predictor of future DM in various populations, including 29,136 Swedish twins 12, 211,833 Chinese adults >20 years old 13, and 88,305 Japanese subjects 14. Hence, the association of increased WBC count with the subsequent development of DM may be mediated by increased BMI. This study was designed to evaluate whether BMI has a significant impact on the relationship between WBC count and new-onset DM. ## Study Population Our study subjects were collected from the Taiwan Biobank (TWB), a general population-based research database including cancer-free residents aged 30-70 years selected from 31 recruitment stations in Taiwan since 2008. Details of the TWB have been described previously 15,16. The methodologies of data collection from all participants in the TWB were identical and followed a standardized process. Details about the TWB can be found on its official website (https://taiwanview.twbiobank.org.tw/index). Written informed consent was obtained from all enrolled participants, and this study was conducted according to the Declaration of Helsinki. This study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20180242) on March 8, 2018. We selected study subjects from the 104,451 participants enrolled from 2012 to 2018 in the TWB. Demographic data including age, sex, smoking history, and history of DM and hypertension, were obtained from a face-to-face interview with TWB investigators. BMI, systolic blood pressure, resting heart rate, and overnight fasting blood chemistry parameters, including fasting blood glucose, total cholesterol, triglycerides, uric acid, serum creatinine, hematocrit, WBC count, platelet count, and hemoglobin A1c (HbA1c) were collected. All of these data were acquired at baseline and at a mean ± standard deviation follow-up period of 3.88 ± 1.16 years. The enrolled participants were followed up after 2-4 years. Information including the results of a questionnaire, physical examination and blood examination were collected upon enrollment and at the follow-up visit. We only selected participants with complete data at baseline and follow-up ($$n = 27$$,209). Patients with DM at baseline ($$n = 2695$$) were excluded. Finally, 24,514 participants without DM at baseline (8392 men and 16,122 women) were included in this study. ## Definition of subjects without diabetes Subjects who had not received anti-diabetic medications, had no past history of DM, and whose fasting blood glucose was less than 126 mg/dl and HbA1c was less than $6.5\%$ were considered not to have DM. ## Statistical analysis SPSS 22.0 for Windows (SPSS Inc. Chicago, USA) is used to perform statistical analysis. Data are expressed as mean (standard deviation) or number (percentage), as applicable. Differences in continuous and categorical variables between groups are compared using the independent samples t-test and chi-square test, respectively. Normality tests are done to analyze the distribution of data collected for each group using the Kolmogorov-Smirnov test. Homogeneity of variance is tested with Levene's test (Levene's test is used to assess the equality of variance along with an independent sample t-test). Univariate binary logistic analysis is used to identify the factors associated with new-onset DM. Statistically significant variables in univariate binary logistic analysis are selected into multivariate binary logistic analysis, which is performed using a modified stepwise procedure in four modeling steps. The first model consists of age and sex. The second model adds significant clinical risk factors in the univariate analysis (hypertension, smoking history, systolic blood pressure, and heart rate). The third step adds significant laboratory data in the univariate analysis (fasting blood glucose, HbA1c, eGFR, uric acid, triglycerides, and hematocrit). The final step adds BMI to the model. Multivariate linear regression analysis is used to identify the major determinants of baseline WBC count. The results of binary logistic analysis are expressed as odds ratio (OR) and $95\%$ confidence interval (CI). The results of linear regression analysis are expressed as standardized coefficient β. Receiver operating characteristic (ROC) curve analysis and areas under the ROC curves (AUCs) are used to assess the performance and predictive ability of WBC count for new-onset DM. Optimal cutoff values are those with the highest Youden index, or equivalently, the highest sensitivity + specificity. The POWER procedure using SAS statistical software (version 9.4, SAS Institute, Cary, NC, USA) performs power and sample size analyses. Mediation analysis is performed using the CAUSALMED procedure, new in SAS/STAT 14.3. A two-tailed p value less than 0.05 was considered statistically significant. ## Results Table 1 shows comparisons of baseline characteristics between the participants with and without new-onset DM in all 24,514 subjects. Compared to those without new-onset DM, those with new-onset DM were older, more predominantly male, had a higher prevalence of hypertension history, higher prevalence of smoking history, higher systolic blood pressure and heart rate, higher fasting blood glucose, HbA1c, uric acid, triglycerides, WBC count, hematocrit, and BMI, and lower estimated glomerular filtration rate (eGFR) at baseline. Table 2 shows the ORs of variables associated with new-onset DM in univariate binary logistic analysis in all study participants. Older age, male sex, hypertension history, smoking history, increased systolic blood pressure, heart rate, fasting blood glucose, HbA1c, uric acid, triglycerides, WBC count, hematocrit and BMI, and decreased eGFR were associated with new-onset DM. Table 3 shows the ORs for the association of WBC count with new-onset DM in multivariate binary logistic analysis in all study participants. The mean follow-up period was 3.88 ± 1.16 years in all patients, during which 248 ($1.0\%$) subjects developed DM. Increased WBC count was significantly associated with new-onset DM in the age- and sex-adjusted model ($$p \leq 0.001$$) and in the multivariate model adjusting for age, sex, hypertension, smoking history, systolic blood pressure, and heart rate ($$p \leq 0.002$$). This relationship remained significant after further adjustments for significant laboratory data in the univariate analysis, including fasting blood glucose, HbA1c, eGFR, uric acid, triglycerides, and hematocrit ($$p \leq 0.024$$). However, the relationship became insignificant after further adjustment for BMI ($$p \leq 0.096$$). We have performed post hoc power analysis. This investigation at the predictor variable (WBC) had a statistical power of $84\%$ to detect an OR of 1.118 at α=0.10. We have further performed mediation analysis. The 'Percentage Mediated' change is $4.21\%$ ($$p \leq 0.0775$$). When the interaction term is included, the 'Percentage Mediated' changes slightly from $4.21\%$ ($$p \leq 0.0775$$) for the model without this term) to $4.48\%$ ($$p \leq 0.0675$$). The percentage due to interaction is not significant ($$p \leq 0.362$$). Table 4 shows the ORs for the association of WBC count with new-onset DM in multivariate binary logistic analysis in different subgroups of participants. In the subgroup of 23,430 participants with a normal WBC count (range, 3500-10500/µl17), increased WBC count was significantly associated with new-onset DM in the unadjusted model ($p \leq 0.001$), in the age- and sex-adjusted model ($p \leq 0.001$), and in the multivariate model adjusting for age, sex, hypertension, smoking history, systolic blood pressure, and heart rate ($p \leq 0.001$). This relationship remained significant after further adjustments for significant laboratory data in the univariate analysis, including fasting blood glucose, HbA1c, eGFR, uric acid, triglycerides, and hematocrit ($$p \leq 0.016$$). However, the relationship was attenuated after further adjustment for BMI ($$p \leq 0.050$$). In the subgroup of participants with BMI > 25 kg/m2 ($$n = 8064$$), increased WBC count was significantly associated with new-onset DM in the unadjusted model ($$p \leq 0.009$$), in the age- and sex-adjusted model ($$p \leq 0.001$$), and in the multivariate model adjusting for age, sex, hypertension, smoking history, systolic blood pressure, and heart rate ($$p \leq 0.009$$). However, the relationship became borderline significant after further adjustments for significant laboratory data in the univariate analysis, including fasting blood glucose, HbA1c, eGFR, uric acid, triglycerides, and hematocrit ($$p \leq 0.059$$). Moreover, the relationship became insignificant after further adjustment for BMI ($$p \leq 0.112$$). In the subgroup of participants with BMI ≤ 25 kg/m2 ($$n = 16$$,450), increased WBC count was not associated with new-onset DM in the unadjusted model ($$p \leq 0.246$$). Table 5 shows the standardized coefficients of variables associated with WBC count in univariate and multivariate linear regression analyses in all study participants. Results of the multivariate analysis showed that increased WBC count was significantly associated with younger age, female sex, presence of hypertension, smoking history, increased systolic blood pressure, heart rate, HbA1c, uric acid, triglycerides, hematocrit, platelet count and BMI, and decreased total cholesterol. The performance (ROC curves) and predictive ability (AUCs) of WBC count to identify new-onset DM were analyzed. The AUC of WBC count was 0.584 ($95\%$ CI: 0.548-0.619, $p \leq 0.001$). The cutoff value of WBC count was 5950/µl, and the sensitivity and specificity of this cutoff value were $56.0\%$ and $55.9\%$, respectively. ## Discussion In this study, we evaluated the association of WBC count with new-onset DM in 24,514 non-diabetic subjects during a mean 3.88 years of follow-up. We found that increased WBC count was significantly associated with new-onset DM in unadjusted and several multivariate models, but that this association became insignificant after further adjustment for BMI in all study participants. BMI had a great impact on the relationship between increased WBC count and new-onset DM. In addition, we performed subgroup analyses, and found that in the participants with normal WBC count, increased WBC count could predict new-onset DM in the unadjusted and all multivariate models. Further adjustment for BMI did not alter the association between increased WBC count and new-onset DM. In addition, in the participants with BMI > 25 kg/m2, WBC count was positively associated with new-onset DM in the unadjusted and several multivariate models, but this association became insignificant after further adjustment for BMI. Hence, BMI had also a significant impact on the relationship between increased WBC count and new-onset DM in the participants with BMI > 25 kg/m2. Finally, in the participants with BMI ≤ 25 kg/m2, WBC count could not predict new-onset DM, even in the unadjusted model. The results of previous studies have been inconsistent with regards to whether increased WBC count contributes to DM prediction models independently of obesity 4,5 or whether elevated WBC count only reflects an increase in adipose tissue mass 6. Increased BMI has been shown to be an essential contributor to DM through insulin resistance and islet β-cell failure 2,3,18,19. Kashima et al. reported that increased WBC count was predictive of type 2 DM, and that the combination of increased WBC count and BMI augmented the risk of DM, regardless of whether BMI was high or low 4. In addition, Gu et al. reported that WBC count could be used as an indicator to identify whether or not obesity led to an increased risk of DM 20. In contrast, Oda demonstrated that WBC count could not independently predict incident DM in a Japanese health screening population in whom obesity was not prevalent 21. Chao et al. evaluated the utility of inflammation markers to predict the risk of type 2 DM in 93,676 women aged 50 to 79 years, and found that beyond traditional risk factors, WBC count contributed relatively little additional predictive value 6. Hence, whether or not WBC count is a useful predictor of incident DM is unclear. Twig et al. assessed whether WBC count was an independent risk factor for DM among 185,354 young healthy adults using a multivariate model adjusted for age, BMI, family history of diabetes, physical activity, and fasting glucose and triglycerides levels, and revealed a $7.6\%$ increase in incident DM for every 1000/µl increase in WBC count They further found that after controlling for risk factors, BMI was the primary contributor to the variation in the multivariate models for the prediction of incident DM 22. This finding is similar to ours, in that the significant relationship between increased WBC count and new-onset DM became insignificant after adjusting for BMI in all study participants and in the subgroup of participants with BMI > 25 kg/m2. In addition, Twig et al. also found that WBC count was not associated with an increased risk of DM in lean and normoglycemic men with BMI < 25 kg/m2 22, which is also consistent with our subgroup analysis, i.e. no association between WBC count and new-onset DM in the subjects with BMI ≤ 25 kg/m2. We postulate that the pathogenesis may be characterized by an increased BMI leading to inflammation, as expressed by an increased WBC, resulting in impaired glucose tolerance. However, mediation analysis reveals that the percentage due to interaction is not significant, which means that the interpretation of the results is not significant due to the mediation of WBC and is not drastically different from those of the analysis with no interaction. Further studies are still needed to elucidate the causal relationship, and clarify whether WBC is an independent risk factor for the development of DM, as it is difficult to determine whether WBC is a pathogenic mediator or a marker. Leukocytosis, a common laboratory finding, can be caused by infections and inflammatory processes 23, physical and emotional stress 24,25, and some medications such as corticosteroids, lithium, and beta agonists 26-28. Leukopenia is probably caused by certain medications 29, autoimmune diseases 30 and neoplasia 30. We performed a subgroup analysis of 23,430 subjects with normal WBC count (range, 3500-10500/µl) and found that although the relationship was also attenuated after adjusting for BMI, increased WBC count was still a useful predictor of the future development of DM in the multivariate analysis. Hence, after excluding subjects with abnormally low and high WBC counts, our results demonstrated that increased WBC count was useful in predicting the future development of DM. Previous studies have reported a positive association between WBC count and metabolic syndrome as well as hypertriglyceridemia, low HDL-cholesterol, high fasting glucose, and all components of the metabolic syndrome 31-33. In this study, we similarly demonstrated that increased WBC count was significantly associated with the presence of hypertension and increased systolic blood pressure, HbA1c, triglycerides, and BMI. Chen et al. examined the association between WBC count and risk of coronary heart disease in middle-aged and elderly patients with hyperuricemia, and found a significant reverse correlation between tertiles of WBC count and age 34. In the present study, we also found that WBC count was negatively correlated with age in the multivariate analysis. An elevated resting heart rate has also been associated with an increased WBC count 35,36, which is consistent with our findings. In addition, Liu et al. found that WBC count was positively associated with uric acid, and that this association was independent of conventional risk factors 37. Our results also showed that WBC count was positively correlated with uric acid. There were several limitations to this study. First, our study participants were included from the TWB, which does not include information on medications. The use of anti-hypertensive medications, lipid lowering agents, and hypouricemic agents would have influenced the values of blood pressure, resting heart rate, lipid profile, and uric acid. Therefore, we could not exclude the impact of such medications on our results. Second, we also lacked data on differential WBC count (lymphocytes, monocytes, neutrophils, etc.), and clusters of differentiation (CD) (CD4, CD8, CD14, CD16, CD20, CD45, etc.), so we could not analyze the relationship between differential count of WBC and CD with new-onset DM. In addition, although we used inflammation to link WBC and DM, we lacked CRP data to validate the relationship. Further longitudinal studies are warranted to investigate the relationships among differential WBC count, CD, and CRP with new-onset DM. Finally, high WBC count may reflect acute infection, tissue damage, and other inflammatory conditions, and low WBC count be caused by other comorbidities or malnutrition, which were not recorded in our data set. In conclusion, our results showed that BMI had a significant impact on the relationship between increased WBC count and new-onset DM. In all study subjects and in the obese group, BMI had a significant impact on association between increased WBC count and new-onset DM, and BMI also attenuated the association in those with a normal WBC count. Hence, the association between increased WBC count and the future development of diabetes may be mediated by BMI. ## Funding This work was supported partially by the Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan and by Kaohsiung Medical University Research Center Grant (KMU-TC111A01 and KMUTC111IFSP01), and the grant from Kaohsiung Municipal Hsiao-Kang Hospital (kmhk-108-002). ## Institutional Review Board Statement The study protocol was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (number: KMUHIRB-E(I)-20210058). All participants provided informed consent before study enrollment. ## References 1. Poznyak A, Grechko AV, Poggio P, Myasoedova VA, Alfieri V, Orekhov AN. 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--- title: Cardiovascular and lipid-lowering effects of a marine lipoprotein extract in a high-fat diet-induced obesity mouse model authors: - Iván Carrera - Lola Corzo - Vinogran Naidoo - Olaia Martínez-Iglesias - Ramón Cacabelos journal: International Journal of Medical Sciences year: 2023 pmcid: PMC9969509 doi: 10.7150/ijms.80727 license: CC BY 4.0 --- # Cardiovascular and lipid-lowering effects of a marine lipoprotein extract in a high-fat diet-induced obesity mouse model ## Abstract Obesity is a major health challenge worldwide, with implications for diabetes, hypertension and cardiovascular disease (CVD). Regular consumption of dark-meat fish is linked to a lower incidence of CVD and associated metabolic disorders due to the presence of long-chain omega-3 fatty acid ethyl esters in fish oils. The aim of the present study was to determine whether a marine compound like a sardine lipoprotein extract (RCI-1502), regulates fat accumulation in the heart of a high-fat diet-induced (HFD) mouse model of obesity. To investigate its effects in the heart and liver, we conducted a randomized, 12-week placebo-controlled study in which we analyzed the expression of vascular inflammation markers, obesity biochemical patterns and related CVD pathologies. Male HFD-fed mice treated with a RCI-1502-supplemented diet showed reduced body weight, abdominal fat tissue and pericardial fat pad mass density without systemic toxicity. RCI-1502 significantly reduced triacylglyceride, low-density lipoprotein and total-cholesterol concentrations in serum, but increased HDL-cholesterol levels. Our data show that RCI-1502 is beneficial for reducing obesity associated with a long-term HFD, possibly by exerting a protective effect on lipidic homeostasis, indicated also by histopathological analysis. These results collectively indicate that RCI-1502 acts as a cardiovascular therapeutic nutraceutical agent, which modulates fat-induced inflammation and improves metabolic health. ## Introduction Obesity is a risk factor for various cardiovascular disease (CVD) subtypes, including coronary heart disease (CHD), heart failure, and stroke. These pathologies are linked to hypertension 1,2 and low-grade inflammation 3, considered to be the primary mechanisms behind the adverse effects of obesity 4-6. High levels of low-density lipoprotein triacylglycerol (LDL-TG) dysregulate high-density lipoprotein (HDL) metabolism, by increasing cholesteryl ester transfer protein levels and promoting the breakdown of TG-rich HDL in serum 7. The accumulation of TG-rich lipoproteins, therefore, plays a major role in atherogenesis, including foam cell formation, endothelial and vascular inflammation 8. Hypertriacylglycerolemia, a condition related to abnormal lipid metabolism, is associated with visceral obesity and is a risk factor for coronary artery disease 9-11. Although the metabolic mechanism is not fully understood, omega-3 fatty acid ethyl esters (ω-3 FAEEs) reduce LDL-TG levels in serum, preventing dysregulation associated with hypertriacylglycerolemia 12,13. The two main omega-3 FAEEs from fish oil, krill oil or marine microalgae are the eicosapentaenoic acid (EPA) and the docosahexaenoic acid (DHA). Both of them are linked to beneficial cardiovascular clinical effects, such as the increasing hepatic flux of fatty acids from dietary sources 14, lowering blood triglycerides 15 and total cholesterol 16, inhibiting LDL-C oxidation 17, protecting endothelial function and reducing hepatic secretion of LDL-apoB-100 in serum 18 which may be effective for treating obesity-related dyslipidemia. Fatty liver disease (FLD), which is closely associated with obesity, is linked to a variety of extrahepatic metabolic syndrome (MetS)-linked diseases, such as type 2 diabetes and CVD 1,2,19. In individuals with obesity, there is a strong correlation between increased hepatic fat content and subclinical atherosclerosis and cardiac function alterations, besides other risk factors 20,21. The prevalence of MetS and pre-diabetes pathology is increased in patients with FLD; this may be a determinant factor of liver impairment (liver fibrosis, hepatic inflammation, and liver cell death) and adipose insulin sensitivity 22. A decrease in the mitochondrial fatty acid beta-oxidation rate, deficient intake of triglycerides as LDL, an exponential increase in endogenous fatty acid synthesis or enhanced delivery of fatty acids to the liver 23 are all potential pathophysiologic mechanisms in FLD. Obesity-induced inflammatory mechanisms are complex, involving several cellular components and mediators 1. A prolonged high-fat diet (HFD) in mice increases adiposity, causes dysregulation of systemic T helper type 1 cells by altering the Th1/Th2 cell ratio, and induces secretion of inflammatory cytokines, such as monocyte chemoattractant protein 1 (MCP-1), tumor necrosis factor alpha (TNF-α) and interleukin 1-beta (IL-1β) 4,6. These processes, and the huge infiltration of natural killer- and cytotoxic CD8+ T cells into adipose tissue are primarily responsible for macrophage recruitment and a state of chronic inflammation in obese mice 24. The current study aims include not only contributions to new knowledge of pathological effects of obesity on mice metabolism, but also the use of a new natural marine bioactive compound capable of modulating cardiovascular risk and the related immune response 25-30. Here we hypothesized that supplementing mice on a high-fat diet with RCI-1502 (HFD/CS mice) would decrease their TC and LDL levels since it contains 60-$80\%$ lipoproteins and polyunsaturated fatty acid (EPA and DHA content) 25,31. The main strength of this study is the wide range of experimental analysis focused on the metabolic syndrome features by using multi-technical approaches such as immunological markers, biochemical determinations and behavioral evaluation. This preclinical HFD animal models seem to mimic the mechanisms that induce obesity and MetS in humans 32; and thus, it's used in this study for preclinical testing in order to address the role of diet, etiology, pathophysiology and possible future therapeutic interventions. ## Ethics statement All experimental procedures were performed in accordance with the European Community Law ($\frac{86}{609}$/EEC), European Union Directive $\frac{2016}{63}$/EU and the Spanish Royal Decree (R.D. $\frac{1201}{2005}$) and were approved by the Ethics Committee of the EuroEspes Research Center (permit number: EB/2015-037). ## Experimental animals Male wild-type C57BL/6 mice ($$n = 54$$) were bred from colonies, established from mice originally donated by Dr. M. Lakshmana (Florida International University, USA). The mice were housed under a 12 h light/12 h dark cycle in a room at 22 ± 0.5 °C with 40-$50\%$ humidity, and had free access to food and water. Based on data obtained in previous studies concerning the sex-dimorphic effect on obesity response and in order to assure a minimum variation on the baseline-obesity timeline in HFD mice, we use only males in this study. ## Preparation of the marine lipoprotein extract (RCI-1502) RCI-1502, a lipoprotein complex of marine origin, was extracted from the muscle of the marine species S. pilchardus (European sardine, Clupeidae family) by non-denaturing biotechnological processes, which preserves the natural properties of its active ingredients. This patent process is based on the lyophilization method, in which water is removed from a product after it is frozen and placed under a vacuum, allowing the ice to change directly from solid to vapor without passing through a liquid phase. RCI-1502 has a high protein content and saturated fatty acids (mainly palmitic acid), monounsaturated fatty acids (primarily oleic and palmitoleic acids), polyunsaturated fatty acids (mainly eicosapentaenoic, eicosatetraenoic, and docosahexaenoic acids); it is also rich in vitamins (principally B5 and C) and minerals (mainly potassium, phosphorus and calcium), as detailed in Table 1. RCI-1502 was prepared as pellet biscuits by combining RCI-1502 powdered extract ($50\%$) with diet wheat and Milli-Q water ($10\%$; Millipore), and left to dry overnight at 34 °C. The food pellets were stored in air-tight containers at 4 °C. ## High-fat diet (HFD)-induced mouse model of obesity The addition of corn oil (composed of $99\%$ triacylglycerol, with polyunsaturated fatty acid (PUFA) $59\%$, monounsaturated fatty acid $24\%$, and saturated fatty acid (SFA) $13\%$) to the food pellets represented the HFD supplementation vehicle, although its ad libitum ingestion does not allow exact control of the dose that each animal receives, only a percentage in relation to the total ingested in the diet. To avoid possible rancidity and oxidation of the compounds researchers performed daily feed exchanges. The eight-week supplementation based on oil administration included in diet (Table 2 and 3), induced a ranged from 50 to $60\%$ of kcal from fat, while in the control groups, they ranged from 10 to $15\%$. According to recent C57BL/6 mice studies where obesity was induced, an hypercaloric diet with almost $60\%$ of the calories being from lipids are considered effective in promoting obesity and metabolic changes associated with the disease 33. The diet/supplementation regimen (Table 2), according to the nutritional composition of the different diets (Table 3) was performed during eight weeks. Male mice ($$n = 54$$; 6-8 weeks old) were randomly divided into six groups of nine animals each, according to the following supplementations: [1] Group (Gr) A: High-fat diet before and after supplementation with RCI-1502 (HFDba); [2] Gr B: High-fat diet after supplementation with RCI-1502 (HFDa); [3] Gr C: High-fat diet before supplementation with RCI-1502 (HFDb/RCI-1502); [4] Gr D: Normal diet before and after RCI-1502 mice supplementation, as toxicity control (Diet); [5] Gr E: High-fat diet only (HFD); and [6] Gr F: Normal diet as negative control. Male mice with body weights <15 g or >25 g at six-weeks of age, were not included in the study. The same, measured, quantities of food pellets were administered to control and treated mice. In order to show that the amount of HFD reached the minimum obesity biochemical parameters and so to be compared among the different HFD-groups, a quantification study was performed as a pilot experiment (Supplementary data). The health of the animals was monitored twice a day, and their weights assessed once per week. ## Blood collection and sample preparation At the end of the experimental period, serum was collected directly from the ventricle, centrifuged at 4.500 rpm, the blood serum was collected and stored at -80 °C. This cardiac puncture method is a suitable technique to obtain a single, large, good quality sample from a mouse under deep terminal anesthesia (intraperitoneal injection of Avertin (Sigma-Aldrich), as required for metabolic, lipidic, hepatic, nutritional, oxidative and cardiovascular biomarker measurements. Animals under anesthesia were transcardially perfused with $0.9\%$ NaCl followed by $4\%$ paraformaldehyde (PFA, Cat. # 43368 Alfa Aesar™, Germany). Hearts and livers were removed and placed into $4\%$ PFA for 48 h. The tissues were then immersed in 0.1 M phosphate buffer (PB, pH 7.4) for 12 h, cryoprotected with $30\%$ sucrose in PB, embedded in optimal cutting temperature (OCT) compound (Tissue Tek, Torrance, CA) and frozen with liquid nitrogen-cooled isopentane. Parallel series of transverse sections (18 μm-thick) were cut on a cryostat, mounted on Superfrost Plus slides (Menzel Glasser, Madison, WI), and stored at room temperature for histopathological analysis. ## Histology and immunohistochemistry Routine histology was performed on Sudan Black (SB) and hematoxylin and eosin (HE)-stained sections to identify and characterize morphological changes that occur during the cardiovascular disease process, such as vacuolar degeneration, major myofibril changes, adiposomes, and lipid vacuolization 28. To detect the expression of complementary cardiovascular histopathological markers we used immunohistochemistry. Commercially available antibodies were used, such as mouse monoclonal anti-VCAM-1 (1:400; Affymetrix eBioscience) in heart samples. To eliminate endogenous peroxidases, sections of heart and liver were pretreated with H2O2 in phosphate-buffered saline (PBS, pH 7.4) at 37 °C for 15 min. The tissues were rinsed twice in 0.05 M tris-buffered saline (TBS) containing $0.1\%$ Tween-20 (TBS-T, pH 7.4) for 10 min each, and endogenous avidin-binding activity blocked (Vector kit) for 30 min. The sections were incubated overnight at 4 °C with MOM blocking buffer and primary antibodies previously described. The mouse-on-mouse peroxidase immunodetection system (MOM Kit; Vector) was used to eliminate any nonspecific binding of anti-mouse secondary antibodies with the endogenous mouse immunoglobulins in the tissue, according to the manufacturer's instructions. The sections were rinsed in TBS-T (three times for 10 min), incubated in goat anti-rabbit (Dako) or goat anti-mouse (Dako) secondary antibodies for 1 h at room temperature, washed in TBS-T (three times for 10 min) and then incubated for 30 min with the avidin-biotin-peroxidase complex (Vectastain; Vector). Signal detection was performed with 3,3-diaminobenzidine (Sigma-Aldrich) as the chromogen and hydrogen peroxide as the oxidant. Negative controls were included by omitting the primary or secondary antibodies. Sections were then dehydrated in a graded series of ethanol washes, and coverslipped with a rapid embedding agent (Eukitt; Fluka). Images were visualized on a microscope (Olympus BX50) and digital images obtained (DP-10; objective 10x lens, Olympus). The photographs were adjusted only for brightness and contrast with Corel Photo-Paint (Corel, Ottawa, Canada). Immunohistochemical hallmarks were classified as positive for VCAM-1 if cytoplasmic staining was detected by using area/pixel analysis software (Pixcavator 4) to quantify the number of pixels inside the outer boundary of each cell body; this aided quantification of the density of immunofluorescence cell markers relative to background. Two different observers independently evaluated the experimental group slides in a double-blinded manner; both achieved a high level of concordance. ## Locomotor activity and body mass percentage To examine the effects of HFD and RCI-1502 consumption on mice behavior, a rotarod apparatus (IITC Life Science, Inc., Woodland Hills, CA) was used to assess motor coordination and balance 34. The main reason for evaluating the locomotor activity was to address the correlation between the beneficial of the CS supplementation and the motor coordination/agility, in order to reinforce the data of the health effects on this obesity mice models. The instrument consists of a circular rod turning at a constant speed (15 rpm) where animals will try to remain on the rod rather than fall, while the time of fall is recorded. The rotarod accelerated from 1 rpm to 15 rpm over 90 sec, with each trial again lasting a maximum of 120 sec. Trials ended when mice either fell off the rod or clung to the rod as it made two complete rotations. To achieve a basal level of performance, each mouse underwent three days of training prior to injury, in which three trials per day conducted, with a 10 min interval between trials. The rotarod test was performed at the end of each week. Individual scores from three trials were averaged and evaluated relative to their baseline latencies (Fig. 1). The body mass index of each experimental mouse was determined by calculating the mass (g)/height (cm) ratio. The abdominal and pericardial fat percentages were determined by removing and weighing the fat depots around the abdominal cavity and the heart, following euthanasia. ## Serum lipids and specific cardiovascular markers After sacrifice, blood serum was collected from each group of mice for biochemical analysis (Table 4). The quantification of serum biomarkers related to renal function (urea and creatinine), vascular risk [triglycerides (TG), High-density lipoprotein (HDL) cholesterol, Low-density lipoprotein (LDL) cholesterol, total cholesterol (TC), homocysteine (HCY)], hepatic injury [glutamate oxaloacetate transaminase (GOT) and glutamine-pyruvate transaminase (GPT)], nutritional status (albumin) and antioxidant status (TAS) was obtained by using commercial reagents at the automated UV-visible spectrophotometer Cobas Mira Plus Analyzer (ROCHE Diagnostics, Basel, Switzerland). Complementary vascular markers [lipoprotein (a) (Lpa) and hypersensitive-C reactive protein (hs-CRP)] were analyzed from blood serum using the commercial mouse-specific ELISA kits (MyBiosurce, San Diego, USA and Elabscience, Hubei, China, respectively). Quantification was conducted using the best-fit equation of standards in Curve Expert software 2.5. ## Histamine determinations Liver samples for histamine determination were homogenized in 0.5 mL perchloric acid ($2\%$) by ultrasonic cell disruption. Sample homogenates were centrifuged at 12,500 rpm for 30 min. The pellet was discarded and the supernatant was stored at -40 °C for analysis. Histamine levels were measured by high performance liquid chromatography (HPLC) using a stainless-steel column packed with a cation exchanger (TSKgel SP-2SW, 5 μm particle size) and automated Shore's OPA fluorometric detection system equipped with a chromatographic system (Agilent 1100 series). The fluorescence intensity was measured at 450 nm with excitation at 360 nm in a spectrofluorometer (Agilent 1100 series). Under these conditions the retention time for histamine was 14.0±1.0 min. Homogenized liver samples (20 μL) were injected directly into the HPLC column. The minimum detection limit of the system was 0.05 pmol, and the intra- and inter-assay coefficients of variations were 2-$6\%$ and 7-$11\%$, respectively. Substances causing interference in Shore's OPA reaction, such as ammonia, histidine, spermine, and spermidine, were separated in the column; as their fluorescence intensities were short, histamine was determined by injecting a perchloric acid tissue extracts directly into the column eliminating the need for prior purification. ## Statistical analysis Statistical analysis was performed using SPSS software (Version 23.0, Chicago, USA). Data were tested for normality using Shapiro-Wilk test. Differences between treated groups were compared with the Kruskal-Wallis test followed by the Mann-Whitney U-test. Data are presented as standard error of the mean (SEM); a p value <0.05 indicates statistical significance. ## RCI-1502 reduces the body weight of HFD mice without compromising locomotor activity The HFD caused a significant increase in cumulative body weight of mice ($p \leq 0.05$) when compared with controls and other treated groups. Mice body weight significantly increased ($p \leq 0.05$) due to the high-fat diet, with a cumulative body weight gain (Fig. 1A). RCI-1502-supplemented HFD mice (Gr A-C) had significantly reduced body weight (Fig. 1A-C). The mean cumulative body weight gain over eight weeks was 20-$25\%$ lower in HFD/CS mice ($p \leq 0.05$) and 25-$30\%$ lower in diet/CS mice ($p \leq 0.01$) than in HFD mice (Fig. 1A-C). Quantification data from the abdominal (Fig. 1B) and pericardial fat pads (Fig. 1C) exhibit a clear correlation, where the HFD group notably show a significant difference compared with the other experimental groups. We tested the hypothesis that the anthropometrical body mass index (BMI) may identify obesity and may predict its adverse effects on lipid profile in mice. The results showed that after 8 weeks of dietary supplementation, the final body weight, body mass gain and BMI were higher in HFD mice than in any other experimental group. The BMI reached the highest value in HFD at 8 weeks of supplementation (3.39 ± 0.3), correlated with a large increase in body weight (∼$25\%$), in comparison with control group (2.57 ± 0.02) ($P \leq 0.01$), indicating a clear pattern of obesity in HFD treated group. Locomotor activity remained the same between the HFD-CS groups; however, mice on the HFD diet displayed significantly less locomotor activity during the entire study period than RCI-1502 (Gr A-C) and control animals (Fig. 1D; $p \leq 0.05$). Furthermore, HFD-treated mice showed poor locomotor activity and motor coordination than RCI-1502 and control treated mice. However, rotarod performance was not significantly different between HFD/CS and control mice (Fig. 1D). ## RCI-1502 improve the serum lipid profiles of HFD mice We quantified serum biomarkers related to renal function, vascular risk, hepatic injury, nutritional status and antioxidant capacity in all groups of mice (Table 4). HFD-treated mice had significantly higher levels of most of these biomarkers versus RCI-1502 (CS) and control mice. Among the HFD-CS groups, there was a notable difference in the serum levels of renal function, vascular risk, hepatic injury and antioxidant parameters (as indicated in blold at table 4). Compared to HFD-treated mice, RCI-1502 treated group A (HFDba/CS) showed a significantly low levels of vascular risk parmeters such as HCY (4.1 µmol/L; $p \leq 0.05$), Lp(a) (1.5 ng/mL; $p \leq 0.05$), HS-RCP (1.5 mg/dL; $p \leq 0.05$), whereas RCI-1502 treated group B (HFDa/CS) showed a significantly low levels of renal function parameters [Urea (36 mg/dL; $p \leq 0.05$), Creatinine (0.32 mg/dL; $p \leq 0.05$)], hepatic injury [GOT (97 U/L; $p \leq 0.05$), GPT (23.7 U/L; $p \leq 0.05$)] and some vascular biomarkers [LDL-CHOL 8.1 mg/dL; TC/HDL-CHOL 1.6 mg/dL $p \leq 0.05$)]. The group C (HFDb/CS) showed significant decreased levels of cholesterol biomarkers (TC 97,2 mg/dL; HDL-CHOL 57 mg/dL). Between experimental groups, Cholesterol levels were significantly different between HFD-treated mice fed and RCI-1502-supplemented mice ($p \leq 0.05$; Table 4). Serum levels of atherogenic (vascular risk and nutritional biomarkers) and obesity-related parameters (renal function, hepatic injury and antioxidant biomarkers) showed significant differences between HFD mice versus all RCI-1502-treated animals (Table 4; $p \leq 0.05$). Present data show that among the RCI-1502-treated groups (Gr A-C), the beneficial effect of adding RCI-1502 in the diet before and after the HFD period (Gr A) was higher than just before (Gr B) or after (Gr C) the HFD period (Table 4). ## RCI-1502 reduces Cardiac and hepatic lipid accumulation HFD supplementation significantly induced both cardiac and hepatic lipid accumulation revealed by an increase in pericardial fat area, lipid droplet density and liver steatosis (Fig. 2) compared to HFD/CS treated mice (Gr A-C). Histological analysis of pericardial fat pad mass, myocardium and liver tissues in mice supplemented with RCI-1502 (representative of the six supplementation groups) revealed a reduction in cardio- and cerebrovascular pathological hallmarks (Fig. 2). In Sudan Black B-stained adipose tissue of the pericardium (Fig. 2A-F), there were large and numerous lipid droplets (adiposomes) in HFD-treated mice (Gr E; Fig. 2E), contrasting sharply with the reduced density of small adiposomes in HFD/CS treated mice (Gr A-C; Fig. 2A-C). Among the different HFD/CS supplementations, supplementation of RCI-1502 before and after a HFD feeding regimen reduced the appearance of new adiposomes while maintaining their density. Similar results were observed in mice supplemented with RCI-1502 combined with a normal diet (Gr D; Fig. 2D), although with a reduced density of adiposomes. Hematoxylin and eosin staining in myocardial tissue sections revealed a similar protective pattern of RCI-1502 when supplemented prior to a HFD diet (Gr C; Fig. 2I). Similar lipid accumulation patterns were observed in cardiac tissues of groups A (HFDba/CS) and B (HFDa/CS), where some vacuolar degeneration was observed in myocardial tissue (Fig. 2G,H). However, HFD-treated mice showed severe vacuolar degeneration and massive alteration of myofibrils in the heart (Gr E; Fig. 2K), mainly characterized by the fragmentation of cardiac muscle bundles. This pathologic pattern contrasts with normal myocardial tissue structure in control mice (Gr D and F; Fig. 2J,L), showing homogeneously interconnected, anastomosing, muscle fibers. Moreover, histological examination of the heart in control and RCI-1502-treated mice showed negative or slight immune reactions to VCAM-1 (Gr A-D, and F; Fig. 2M-P,R), a marker of vascular inflammation. HFD-treated mice showed a dramatic increase in VCAM-1 immunoreactivity within the endothelial layers of the heart (Gr E, Fig. 2Q); VCAM-1 staining was notably higher in HFD-treated mice than mice treated with RCI-1502 (Fig. 2). Among the HFD/CS-treated groups (Gr A-C), mice supplemented with RCI-1502 after having been on an HFD-feeding regimen (Gr B), showed less VCAM-I immunoreactive density in the endothelial layer of the heart than in groups A and C (Fig. 2M-R). Histological examination of sections of mice liver (Fig. 2S-X) revealed that controls and HFD/CS-treated mice had normal cellular architecture with radially arranged hepatocytes around the centrilobular vein (Gr. A-C, D and F; Figs. 3S-V,X); however, some lipid vacuolization was observed in HFD/CS groups A and C (Figs. 3S,U). This pattern sharply contrasted with that in sections from HFD-treated mice where a high density of lipid vacuolization, fatty hepatocyte degeneration and disintegration of hepatic cords was found; this indicated liver injury due to the high accumulation of cholesterol (Gr. E, Fig. 2W). Quantification of the mean area of vacuolization among HFD/CS treated groups showed relative higher rates in group B than in groups A and C versus control groups (Figs. 2S-X). ## RCI-1502 reduces histamine accumulation levels in liver Histamine levels were analyzed in liver mice samples of all mice groups to determine the inflammation data of RCI-1502 in HFD-fed mice (Fig. 3). HFD mice exhibited high histamine concentrations compared with the HFD mice treated with RCI-1502. Statistically significant differences were detected between HFDba/CS and HFD mice groups (Fig. 3; $p \leq 0.05$). ## Discussion The aim of this study was whether RCI-1502, a sardine-derived extract, as a part of a HFD, can help prevent the development of obesity and related metabolic and immunological disturbances (Fig. 4). To assess this question, HFD-fed mice were used 4,33-36 to measure several histological, immunological and metabolic biomarkers known to be associated with obesity and its adverse health effects. We show that RCI-1502 effectively regulates inflammation and reduces lipid accumulation caused by a sustained HFD regimen. ## The effects of RCI-1502 on HFD-associated weight gain in mice The HFD obesity mouse model used in this study caused an increase in weight, development of hyperlipidemia, and alteration of pro-inflammatory markers 4,6,35,37-39. Our data in mice show that the weight gain induced by a HFD was significantly reduced by supplementing their diet with RCI-1502; the HFD also induced the regulation of serum biomarkers associated with obesity and CVD. These results agree with previous reports on liver 40 and cerebrovascular dysfunction 41-43 showing that high intake of omega-3 fatty acids found in fish oil 44 reduce HFD-induced weight gain in mouse models of obesity. Fish-oil extracts improve adipose tissue storage (dyslipidemia) and secretory functions of adipose tissue, and reduce insulin resistance, hepatic steatosis and inflammation 45. Furthermore, fat mass rates are decreased in rodents 46,47 and humans 48,49 consuming diets with fish oil (ω-3 FAEE) supplementation, probably due to mitochondrial-induced changes by these specific fatty acids 50. Adipose tissue, generated by a HFD, may modulate its effect in serum by providing intrinsic adiponectins, leptins and n-3 fatty acids. These findings are in line with animal model experiments conducted by our group 25-31 and others. RCI-1502 potentially blocks the excessive release of free fatty acids into the circulating serum, avoiding ectopic lipid deposition, thereby preventing obesity in mice. The beneficial effects of RCI-1502 in the regulation of lipid homeostasis and obesity prevention were also analyzed with tests of motor behavior in all groups of mice. Our results align with previously recorded weights, where the pathophysiological progression of lipid accumulation due to HFD was efficiently prevented by RCI-1502 supplementation; this contributed to normal motor behavioral scores in RCI-1502-treated groups. The improved results in motor performance, observed in mice treated with RCI-1502 supplementation before HFD (Gr B), contrasted slightly with two other RCI-1502-treated groups (Gr A and C), and contrast markedly with HFD-fed mice (Gr E). All motor behavior and obesity measured parameters were strongly correlated, demonstrating a significant link between body weight gain and motor performance. These findings, similar to those described elsewhere 54,55, confirmed that overweight (obesity) differentially affects the risk for functional limitations and disabilities in motor performance and behavior 56. ## Effects of RCI-1502 on HFD-associated cardiovascular pathology in mice The present study showed that RCI-1502 prevented the development of cardiovascular pathologies. Cardiovascular pathophysiological hallmarks are among the diagnostic criteria for metabolic syndrome, commonly associated with obesity, diabetes mellitus 57, chronic kidney disease and hypercholesterolaemia 58. Our data demonstrate that supplementing a HFD diet with RCI-1502 minimizes the pathological damage associated with the cardiovascular system, and prevents the development of cardiovascular lipid accumulation by acting as a lipoprotection agent. These findings agree with other reports that show that ω-3 fatty acid (fish oil) supplementation decreases lipoprotein triacylglycerol secretion 59, modulates serum levels of adiponectin and leptin 45 and improves several features of metabolic syndrome associated with CVD 44,60-62. Fish oil significantly reduces platelet aggregation, high-fat diet-induced steatosis, and serum lipid levels caused by eating a diet high in animal models. This reduction is due to the bioactivity of ω-3 fatty acids, specifically eicosapentaenoic acid (EPA) and docosahexahexaenoic Acid (DHA) that confer cardiovascular protection by blocking multiple atherogenic processes 62-66. Furthermore, dietary supplement of pure ethyl EPA and fish oil extract are effective antithrombotic agents by limiting platelet reactivity 67. Nevertheless, our study reveals for the first time that RCI-1502 clearly prevents the progression of atherosclerosis plaques as indicated by the specific biomarkers analyzed. In particular, VCAM-1 immunoreactivity was nearly tripled by HFD in the present study, and slightly detected in mice receiving a HFD supplemented with RCI-1502. The reduction of immunoreactivity to VCAM-1 in cardiovascular tissues from RCI-1502 fed mice, agrees with our previous reports 25, 29, since VCAM-1 is an early biomarker of vascular inflammation induced by atherosclerotic plaque accumulation 39. Progressive atheroma plaque accumulation may play a critical role as an initiator of obesity-associated inflammation, blood flow obstruction, peripheral thromboses, and myocardial infarction 68. Understanding what triggers the development of atheromas involves multiple factors such as lipoprotein content, blood sugar levels and hypertension 69. Our present data highlight the effect of fish-derived lipoproteins, such as leptin, as active agents against atheroma progression in animal models of obesity, as also reported elsewhere 70. Mice treated with RCI-1502 showed significant improvements in cardiovascular performance. This lipoprotein marine extract shows beneficial hypolipemic effects against the accumulation of lipid bodies in the cardiovascular system. Supplementation prior to HFD consumption provides lipo-cardioprotection to cardiovascular tissues via leptin bioactivity. ## Effects of RCI-1502 on HFD-associated liver pathology in mice A high cholesterol-diet causes hypercholesterolemia. This lipid imbalance induces lipolysis and releases free fatty acids into the serum, where it is converted into triglycerides in the liver. If left untreated, this can lead to liver necrosis and cirrhosis. In this study, the liver was also analyzed since it is the first organ to metabolize ingested cholesterol and is therefore the main target of the lipoxidative damage caused by an imbalance between generated free radicals and an ineffective antioxidant defense mechanism 71. Our findings are consistent with other studies that found lipid hepatotoxicity in HFD mice, resulting in severe steatosis and concomitant hepatocyte necrosis (data not shown) 72,73. The lipoprotective effect of RCI-1502 in the liver may be directly related to its bioactive properties such as regulation of fat accumulation, antiplatelet, absence of inflammatory and oxidative effects 44,66,72,74-76. Among the RCI-1502-treated groups, previous RCI-1502 administration induce a notable improvement in terms of hepato-lipidic degeneration, which is probably associated with the protective effects of intrinsic RCI-1502 fish-derived extract components. In particular, some of these components in the bioactive extract, such as fish-derived lipoproteins, have a crucial effect on fatty acids metabolism 77, 78, lipid homeostasis 79 and hepatic lipid accumulation 80,81, as well as an acute immunoregulatory role 29. Adiponectin is the most abundant lipoprotein secreted by adipocytes, and is a key component in the interrelationship between adiposity, insulin resistance and inflammation in the liver 82. RCI-1502 may alleviate dyslipidemia by stimulating phosphorylation and adenosin monophosphate kinase activation, exerting direct effects on vascular endothelium, decreasing the inflammatory response, and improving insulin sensitivity and glucose tolerance 83. Together, our findings confirm that adiponectin fish-derived lipoproteins prevent hepatosteatosis by reducing hepatic lipid accumulation through the regulation of lipogenesis and oxidative stress in obesity animal models 84. ## Effects of RCI-1502 on HFD metabolic syndrome in mice Metabolic syndrome could constitute the previous stage to the development of CVD and diabetes mellitus. This study addresses how cardio-inflammatory processes, triggered by a high-fat diet, can be evaluated by analyzing blood biomarkers from a cardiovascular-related pre-clinical prediction panel. Specifically, 11 different groups of serum biomarkers were analyzed to evaluate their association with metabolic syndrome [urea, hs-RCP, cholesterol (COL, HDL, LDL), TG, HCY, Lpa, GOT, GPT and albumin] as these are the most analyzed inflammatory biomarkers in the CVD field. Our results suggest that the prevention of chronic metabolic disease by RCI-1502 may be mediated by an active anti-inflammatory action. The increase in urea levels in serum, a catabolite of endogenous protein biomarker 85, correlates directly with high levels of toxic nitrogen metabolites, produced during the catabolism of proteins and amino acids, indicating a metabolic disorder 86. Mice in HFD/CS groups A and B showed similar levels of urea in serum as controls (Diet) and lower levels than group C and HFD-fed mice. The high levels of urea in HFDb/CS group A, compared to controls, may be due to the metabolic disorder effect of the HFD period prior to RCI-1502 supplementation, triggering the accumulation of this endogenous catabolite. Serum creatinine is a critical indicator of renal health 87; creatinine blood levels rise when filtration in the kidney is impaired. All HFD/CS-treated mice showed similar levels of serum creatinine to control groups (Diet), and lower levels than did HFD-fed mice. The high serum levels of creatinine in the HFD-fed mice, compared with controls and HFD/CS, suggest an increased likelihood of developing renal disorder or systemic readjustment due to HFD-induced metabolic effects, reinforcing the modulating effect of RCI-1502 against these adverse events. In the past decades, compelling evidence support the reduction of total cholesterol (TC) and LDL as effective in preventing CVD events 88. Moreover, cholesterol meta-analysis reports confirmed the dose-dependent reduction in CVD morbidity and mortality achieved by lowering LDL levels in blood serum 89. All HFD/CS-treated mice showed similar pattern levels of cholesterol biomarkers in serum to control groups (Diet), while HFD mice showed the opposite pattern, where TC, LDL and TC/HDL display high concentration levels. The absence of hypercholesterolemia in mice from groups A-C, compared to HFD-fed mice group, may be due to the modulating effect of RCI-1502 supplementation; RCI-1502 may prevent the accumulation of cholesterol in the vascular system, suggesting that it may act as a lipoprotective agent against atherosclerosis risk and CVD. Triglycerides, are significant and independent risk factors for CVD 90. A typical CVD pattern in patients are elevated triglyceride levels accompanied by low HDL cholesterol 91. All HFD/CS-treated mice showed lower levels of triglycerides in serum than control groups (Diet), whereas HFD mice had high levels of triglycerides. The most significant difference was observed in groups A and C, probably due to the modulating effect of RCI-1502 supplementation against the two main sources of plasma triglycerides: exogenous (i.e., from dietary fat) and endogenous (from the liver), carried in very-low-density lipoprotein (VLDL) particles. RCI-1502 supplementation in a HFD diet prevents increases in serum triglyceride concentrations that contribute to an increased risk of CVD. Such increases are linked to obesity, metabolic syndrome, proinflammatory, pancreatitis, and prothrombotic biomarkers. High levels of hyper-homocysteine (HCY) in serum induce the expression in vascular cells of numerous proinflammatory agents such as MCP-1, IL-8, VCAM-1, E-selectin, and CXCL16 and its receptor CXCR6155 94,95. HCY levels were lower in all HFD/CS-treated animals than in controls (Diet), with HFD mice exhibiting high HCY concentrations. Among HFD/CS-treated mice, the most significant difference was observed in mice from group A. This reduction may probably be due to the regulating effect of RCI-1502 in response to vascular inflammation and endothelial dysfunction. Meta-analysis studies showed that lipoprotein (a), an independent proatherogenic and prothrombotic risk factor, was directly associated with incident coronary heart disease and ischemic stroke 96. Mice from HFD/CS groups A and B showed similar levels of lipoprotein (a) in serum to control group (Diet) and significantly lower levels than group C and HFD-fed mice. When compared to controls, HFDba/CS mice (group A) had the lowest levels of the lipoprotein biomarker; this may be attributed to the extensive therapy period with RCI-1502, which prevented endogenous vascular buildup of LDL-cholesterol and platelet accumulation. Concentrations of hypersensitive C-reactive protein (hs-CRP), a serum biomarker of inflammation, are elevated in response to tissue injury and atherosclerotic CVD risk 97,98. In patients, HS-CRP levels rapidly increase as a result of stroke 99, although, a wide range of inflammatory conditions may also trigger this response. All HFD/CS-treated animals had CRP levels in their serum that were comparable to control groups (Diet) but lower than HFD-fed mice. High hs-CRP serum levels in HFD-fed animals, compared to controls and HFD/CS-treated mice, suggest a greater risk of developing CVD or ischemic events, which support the modulating effect of RCI-1502 against these metabolic events. HS-CRP levels in HFDba/CS-treated mice were similar to control levels, while mice from the other HFD/CS-treated groups showed slightly higher levels, but not as high as in HFD-fed mice. Therefore, the modulating effect of CS may be due to its extensive period of supplementation, regulating vascular and hepatic lipid metabolism. Blood GOT activity represents the capacity of brain glutamate degradation, and is an efficient and novel neuroprotective tool against ischemic stroke 100. All HFD/CS-treated mice had lower levels of GOT in serum than control groups (Diet), while HFD mice had very high GOT concentrations. Among the HFD/CS-treated mice, the most significant difference was observed in groups A and B, probably due to the modulating effect of RCI-1502 in glutamate metabolism. The GPT is a valuable screening biomarker to detect hepatic necrotic processes, such as asymptomatic viral hepatitis and non-alcoholic fatty liver disease. Additional factors that affect serum GPT levels include body mass index, total cholesterol and triglyceride levels 101. HFD/CS-treated mice showed similar levels of serum GPT as controls (Diet), while HFD mice had very high concentrations of GPT. These data may indicate that RCI-1502 supplementation regimen positively affects hepatic metabolism, even when altered by a HFD, suggesting a preventive effect by RCI-1502 on hepatic injury. Ischemia-modified albumin is a biomarker for CVD; high levels of albumin is associated with cardio-metabolic risks and may be a sign of microvascular dysfunction in individuals with metabolic syndrome 102. HFD/CS-treated mice had slightly higher levels of serum albumin than controls (Diet), while HFD mice had high concentrations of serum albumin. This pattern was expected since this biomarker also assesses nutritional status, and is affected by the high-fat composition of the supplementation. However, HFD/CS mice showed similar levels of serum albumin compared with control mice, indicating a metabolic regulatory function of RCI-1502. The total antioxidant capacity was measured by analyzing small antioxidant molecules in serum 103. Oxidative stress, in the development of visceral adipose tissue, subclinical atherosclerosis and metabolic syndrome are well-known 104. All HFD/CS-treated mice showed significantly high serum levels of TAS versus control groups (Diet), while HFD-fed mice had very low levels of TAS. Among the HFD/CS-treated groups, the most significant difference was detected in group A; these mice had strongly elevated TAS levels, probably due to the antioxidant induction effect of RCI-1502 through its capability to counteract reactive oxygen species (ROS). Taken together, the present study found that RCI-1502 drastically reduced the adverse effects of a HFD by significantly modulating the main cardiovascular biomarkers in serum such as LDL, HDL, CT, TG or hs-RCP, among others (see Table 4). Fat-modified diets reduced total cholesterol and triglyceride levels, but there was no evidence of effects on weight, body mass index, LDL or HDL cholesterol that contributes to the development of metabolic syndrome 105. However, by adding RCI-1502 to a HFD, classical CVD risk factors were regulated within normal control levels, which improved the body mass index, renal function, hepatic metabolism and antioxidant status. ## Conclusions Dietary high-fat content can cause obesity by activating various metabolic processes, which can alter fat oxidation and deposition rates, resulting in metabolic syndrome and macrovascular complications. Correction of saturated fat consumption (hypertriacylglycerolemia) in combination with hypolipemic lipoproteins (leptin, adiponectin) may lower the risk of CVD in obese individuals. A sardine-derived bioproduct (RCI-1502) reduces or prevents metabolic disturbances associated with the development of obesity in HFD mice models by regulating lipid homeostasis, particularly by acting against systemic inflammation, hepatic steatosis, and cardiovascular lipid accumulation. More research is required to better understand the molecular metabolic mechanisms involved, particular the role of lipoproteins in altering metabolic signaling after food supplementation with fish extracts. ## Institutional Review Board Statement All experimental procedures were performed in accordance with the European Community Law ($\frac{86}{609}$/EEC), European Union Directive $\frac{2016}{63}$/EU and the Spanish Royal Decree (R.D. $\frac{1201}{2005}$), and following the approval of the Ethics Committee of the EuroEspes Research Center. ## Author Contributions Conceptualization, methodology and validation, R.C. and I.C.; Investigation, resources, data curation and writing—original draft preparation I.C., L.C., O.M.I. and V.N; Software and writing—review and editing I.C., V.N. and L.C. All authors have reviewed the manuscript and have agreed with all of the contents. ## References 1. Ouchi N, Parker JL, Lugus JJ, Walsh K. Adipokines in inflammation and metabolic disease. 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--- title: Effects of inoculation with active microorganisms derived from adult goats on growth performance, gut microbiota and serum metabolome in newborn lambs authors: - Lin Fu - Liaochuan Wang - Li Liu - Li Zhang - Ziyao Zhou - Yan Zhou - Gaofu Wang - Juan J. Loor - Peng Zhou - Xianwen Dong journal: Frontiers in Microbiology year: 2023 pmcid: PMC9969556 doi: 10.3389/fmicb.2023.1128271 license: CC BY 4.0 --- # Effects of inoculation with active microorganisms derived from adult goats on growth performance, gut microbiota and serum metabolome in newborn lambs ## Abstract This study evaluated the effects of inoculation with adult goat ruminal fluid on growth, health, gut microbiota and serum metabolism in lambs during the first 15 days of life. Twenty four Youzhou dark newborn lambs were selected and randomly distributed across 3 treatments ($$n = 8$$/group): autoclaved goat milk inoculated with 20 mL sterilized normal saline (CON), autoclaved goat milk inoculated with 20 mL fresh ruminal fluid (RF) and autoclaved goat milk inoculated with 20 mL autoclaved ruminal fluid (ARF). Results showed that RF inoculation was more effective at promoting recovery of body weight. Compared with CON, greater serum concentrations of ALP, CHOL, HDL and LAC in the RF group suggested a better health status in lambs. The relative abundance of Akkermansia and Escherichia-Shigella in gut was lower in the RF group, whereas the relative abundance of Rikenellaceae_RC9_gut_group tended to increase. Metabolomics analysis shown that RF stimulated the metabolism of bile acids, small peptides, fatty acids and Trimethylamine-N-Oxide, which were found the correlation relationship with gut microorganisms. Overall, our study demonstrated that ruminal fluid inoculation with active microorganisms had a beneficial impact on growth, health and overall metabolism partly through modulating the gut microbial community. ## Introduction The proper management of the pre-ruminant animal is important for ensuring they are able to face the weaning and early post-weaning periods without experiencing excessive stress. This represents one important pillar of a successful cattle and small ruminant industry. Intestinal microorganisms are important for regulating the health and development in the pre-wean ruminant animal. For example, intestinal bacterial imbalance is associated with disordered nutrient digestion and absorption, growth retardation, diarrhea, dehydration, and death (Signorini et al., 2012; Lyoo et al., 2018). Therefore, research focused on the establishment of an optimal intestinal microbiota would be beneficial in terms of promoting the development and health of pre-wean ruminants. The microflora in ruminants is not fully developed at the moment of birth (Baldwin et al., 2004). Microbial colonization in pre-ruminans is divided into three stages: [1] the first 2–3 days of life are the initial colonization stage of “pioneer” bacteria, characterized by a unique profile relative to later stages of growth (Haenel, 2010). A large proportion of the bacterial community at this stage is parthenogenic and exclusively anaerobic but aerobic bacteria are also present (Minato et al., 1992); [2] the transition from colostrum to mature milk or milk replacer during which the ruminal epithelium microbial community and the ruminal environment change significantly with a dramatic decrease in aerobic and parthenogenic anaerobic bacteria (Rey et al., 2012); and [3] from day 14 to day 28 or at weaning, where bacterial communities no longer exhibit significant time-related changes, but rather change based on feeding behavior and growth (Meale et al., 2016). Bacterial species and abundance stabilize at the phylum level by the 15 day of age, and the bacterial community structure reaches a certain level of stability in about 3–4 weeks (Guzman et al., 2015). The ruminal microbiota digest fodder and other plant materials to produce volatile fatty acids (VFAs) and microbial crude protein (MCP), both of which provide energy and amino acids for ruminants (Koike and Kobayashi, 2009; Mccann et al., 2014). These are essential for maintenance of health, digestive efficiency, and growth performance in the growing ruminant animal (Morrison et al., 2010). Therefore, the first 15 days after birth are an important stage where the establishment of microbial flora can be regulated. In a previous study, inoculation with a ruminal fluid increased the weight of calves during weaning partly due to a microbial profile that enhanced starch digestion (Ziolecki et al., 1984). Direct-fed microorganisms were reported to improve immunity of calves by balancing gut microbe profiles, thus, helping them cope with stressful conditions (Krehbiel et al., 2003). Mechanistically, the gut microbiota can stimulate immune responses through its effect on digestion and metabolism (Anand and Mande, 2018). For example, it has been demonstrated that microbiota can regulate metabolism of glutamate and creatine, which in turn impacts the immune response (Ding et al., 2021). Therefore, accumulating evidence highlights that the establishment of the microflora is important in newborn ruminants and can influence their metabolism. Some studies reported that the original composition of the rumen returned to a pre-intervention state after exogenous microbiota interventions were discontinued (Weimer, 2015). In spite of this, there are data indicating that exogenous microbiota transplantation could improve ruminal fermentation and digestion. To our knowledge, few studies have focused on the effect of ruminal microbial transplantation within 15 days after birth on development and health (Jami et al., 2013). Thus, we hypothesized that inoculation with ruminal fluid in newborn lambs could promote growth and health through its effect on the establishment of an “ideal” gut microbiota. Such benefits are reflected in blood metabolite profiles. To address this hypothesis, Youzhou dark newborn lambs were inoculated with sterilized normal saline (CON), fresh ruminal fluid (RF) with active microorganisms or autoclaved ruminal fluid (ARF) without active microorganisms. Growth performance, health status, gut microbiota and blood metabolomics were performed to evaluate treatment effects during the first 15 days after birth. ## Materials and methods The study procedures and use animals were approved by the Ethics Committee in Chongqing Academy of animal sciences (approval number: xky-20180716, 9 June 2021). ## Ruminal inoculum preparation Six healthy Youzhou dark adult goats (three female and three male, ~30 kg) were used as sources of ruminal fluid. All goats were fed with the same diet for 3 weeks prior to ruminal fluid collection ~2 h post-feeding via oral tubing, strained into a container through four layers of cheesecloth and then pooled into a composited sample. All the composited ruminal fluid was divide into two samples. One sample was directly stored at −80°C and served as the “fresh” ruminal fluid (RF). The other portion of sample was sterilized at 205.8 KPa and 132°C for 10 min and served as the autoclaved Ruminal Fluid (ARF), which was subsequently stored at −80°C. All original samples were kept at −80°C until use as the inoculum. ## Animals and treatments A total of 24 Youzhou dark newborn lambs were selected and randomly assigned to one of 3 treatments for a feeding period of 15 days in individual pens. During the initial 48 h after birth, all lambs received colostrum from their mothers, and were then separated from their mothers immediately. Treatments consisted of [1] sterilized goat milk inoculated with sterilized normal saline as the control group (CON); [2] sterilized goat milk inoculated with RF (RF) and [3] sterilized goat milk inoculated with ARF (ARF). Sterilized goat milk was fed 4 times (each time ~100 mL) daily (0800, 1200, 1600 and 2000 h) to ensure that all lambs had adequate nutrition. From day 3 to 7 after birth, 20 mL ruminal fluid or autoclaved ruminal fluid was maintained at 39°C in a prewarmed thermostat water bath. It was then inoculated via the esophagus of each lamb using a soft stomach tube and a 20 mL syringe 2 h after the morning feeding (inoculate once a day, a total of 20 mL). The experiment lasted 15 days, and the all lambs were slaughtered on day 16. ## Growth performance and sampling The live weight (LW) of each lamb was recorded every 3 days before the morning feeding during the experimental period (15 days). Average daily gain (ADG) was calculated as (final LW–initial LW)/days on study. On the 16th day of the experimental period, a blood sample (~5 mL) was collected from the jugular vein and then placed into blood collection tubes. Blood samples were centrifuged at 2,500 rpm at 4°C for 15 min to obtain serum and then stored at −80°C until analysis. Fecal samples from each lamb were collected on the 16th day of the experimental period and stored at −80°C until gut microbiota profiling. ## Blood biochemical indices determination An automatic biochemical analyzer (Beckman Coulter AU680) was used to perform blood biochemical analysis. Briefly, assays included colorimetric kits (modified kinetic Jaffe method), turbidimetry, latex agglutination, homogeneous EIA or indirect ISE according to the Beckman Coulter AU680 analyzer specifications. ## 16S rRNA sequencing of gut microbiota Total DNA was extracted from 1 g feces using the MoBio PowerSoil DNA Isolation Kit (12855-50, MoBio, United States) according to the manufacturer’s instructions. The quantity and quality of DNA were measured using NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, United States) Then DNA integrity was determined with $1\%$ agarose gel electrophoresis. Subsequently, the V3-V4 hypervariable region of the 16S rRNA gene was PCR-amplified with the universal primer pair 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Liu et al., 2022). The PCR amplification was based on the protocol of Chan et al. [ 2015]. Briefly, 10 bp barcode sequence was added to the 5′ end of the forward and reverse primers (provided by Allwegene Company, Beijing). PCR amplification was performed with a 25 μL reaction system including 12.5 μL Taq PCR MasterMix (2×), 3 μL BSA (2 ng/μL), 1 μL (5 μM) forward primer, 1 μL (5 μM) reverse primer, 2 μL dDNA (The total amount of DNA added is 30 ng) and 5.5 μL ddH2O. Thermal cycling parameters were as follows: 95°C for 5 min, 28 cycles of 95°C for 45 s, 55°C for 50 s, 72°C for 45 s and final extension 72°C for 10 min. Purification of PCR products was carried out using Agencourt AMPure XP Kit (Beckman, Brea, CA, United States). Real-time PCR was used for PCR product quantification. Deep sequencing was carried out using IllluminaMiSeq PE300 platform at Allwegene Company (Beijing, China). Image analysis, base calling, and error estimation were performed using Illumina Analysis Pipeline Version 2.6. Read qualification was performed using Illumina Analysis Pipeline Version 2.6. The low-quality sequences with length < 230 bp, average Phred scores <20 and ambiguous bases or false matches to primer sequences and barcode tags were removed. High-quality sequences were clustered into operational taxonomic units (OTUs) at a similarity level of $97\%$ using Uparse algorithm of Vsearch (v2.7.1) software. The Ribosomal Database Project (RDP) Classifier tool was used to conduct OTU taxonomic classification into different taxonomic groups against the SILVA128 database. The rarefaction curve generation and richness and diversity indices calculation were performed using QIIME (version 1.8.0) based on the OTU information. ## Metabolite extraction Metabolomics was performed at Allwegene Company (Beijing, China). Briefly, a total of 20 μL of sample was transferred to an Eppendorf tube. A volume of 80 μL extraction solvent was added (acetonitrile: methanol = 1: 1, containing isotopically-labeled internal standard mixture). The mixture was sonicated in an ice-water bath for 10 min and incubated at −40°C for 1 h to precipitate proteins. The samples were centrifuged at 4°C, 12,000g for 15 min, then supernatant fluid was transferred into a EP tube new glass vial for UHPLC–MS–MS analysis. QC samples were created by merging equal aliquots of supernatant fluid from each sample. ## UHPLC-MS-MS analysis UHPLC-MS-MS analysis was performed using a UHPLC system (Vanquish, Thermo Fisher Scientific) with a UPLC BEH amide column (2.1 mm 100 mm, 1.7 m) coupled to a Q Exactive HFX mass spectrometer (Orbitrap MS, Thermal). Mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonia (pH = 9.75). The autosampler was set to 2 L injection volume at 4°C. The QE HFX mass spectrometer with the acquisition software information-dependent acquisition (IDA) mode (Xcalibur, Thermo) was used for obtaining MS/MS spectra. The ESI conditions were: Sheath gas flow of 30 Arb, auxiliary gas flow of 25 Arb, capillary temperature of 350°C, and full MS resolution of 60,000. Metabolomics analysis was performed based on our previous studies with slight modifications (Dong et al., 2018; Fu et al., 2021). Briefly, principle component analysis (PCA) and (orthogonal) partial least-squares-discriminant analysis (OPLS-DA) were performed using the R package metaX to monitor the reproducibility of the instrument and the differential analysis of metabolic characteristics. Parameters R2Y and Q2 were >0.5 indicating a robust model with prominent predictive ability. Metabolites with variable importance for projection (VIP) values exceeding 1 and $p \leq 0.05$ were selected as the important metabolites between the comparison of two groups. ## Statistical analysis Statistical analysis of weight indices, blood biochemical indices and diversity and relative abundance of gut microbiota were performed using GraphPad Prism 7.0 (GraphPad Software). All data are presented as means ± SEM. The KS normality test was performed to estimate data normality. Statistical differences between groups was assessed using one-way analysis of variance (ANOVA), and then multiple comparisons analysis was performed with a Tukey post-hoc test. Data with non-normal distribution was analyzed using the Kruskal–Wallis test and then the Dunn’s multiple comparison test. $p \leq 0.05$ was considered as significant difference. The correlation between the gut microbiota and serum metabolites was analyzed using Spearman correlations with the R program package. The coefficients $p \leq 0.05$ were considered significant. The R language GGPlot package was used to draw a correlation heat map. ## Growth performance The effects of inoculation with ruminal fluid (RF), autoclaved ruminal fluid (ARF) or sterilized physiological saline (CON) on growth performance of newborn lambs are presented in Figure 1 and Supplementary Table 1. The RF resulted in the lowest weight loss compared with CON and ARF. Moreover, for all groups the trend of weight change was to decrease first followed by an increase. Interesting, RF lambs stopped losing weigth on day 9 after birth, and then followed recovery. This was 3 days earlier than CON and ARF groups. Figure 1: Trend of body weight change during postnatal 15 days in lambs inoculated with ruminal fluid (RF), autoclaved ruminal fluid (ARF) or sterilized physiological saline (CON). ## Blood biochemical indicators The effects of inoculation with ruminal fluid (RF), autoclaved ruminal fluid (ARF) or sterilized physiological saline (CON) on blood biochemical indices are shown in Table 1. Compared with CON, feeding RF resulted in a greater ($p \leq 0.05$) blood concentration of ALP, CHOL, HDL, and LAC. Moreover, a greater ($p \leq 0.05$) blood concentration of ALP was also observed in RF relative to ARF. Compared with CON, ARF led to lower ($p \leq 0.05$) blood concentration of DBIL. No significant differences in concentrations of ALT, AST, GGT, AST/ALT, TAB, TP, ALB, GLO, A/G, TBIL, IDBIL, CHE, BUN, CREA, TG, LDL, CRP, GLU and LDH were observed among all groups. Table 1 \| Item \| CON \| RF \| ARF \| SEM \| Value of p \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| ALT (U/L) \| 9.60 \| 11.25 \| 10.50 \| 0.764 \| 0.1144 \| \| AST (U/L) \| 79.60 \| 104.20 \| 91.00 \| 11.14 \| 0.1077 \| \| GGT (U/L) \| 63.80 \| 67.40 \| 71.00 \| 12.62 \| 0.8388 \| \| ALP (U/L) \| 350.00a \| 580.50b \| 257.20a \| 77.62 \| 0.0032 \| \| AST/ALT \| 8.00 \| 10.00 \| 9.75 \| 1.548 \| 0.3577 \| \| TBA (μmol/L) \| 8.10 \| 15.68 \| 15.26 \| 4.289 \| 0.1483 \| \| TP (g/L) \| 71.98 \| 71.76 \| 71.70 \| 4.358 \| 0.9974 \| \| ALB (g/L) \| 31.95 \| 32.72 \| 32.46 \| 1.803 \| 0.909 \| \| GLO (g/L) \| 40.03 \| 39.04 \| 39.24 \| 4.504 \| 0.97 \| \| A/G \| 0.82 \| 0.88 \| 0.84 \| 0.13 \| 0.8785 \| \| TBIL (μmol/L) \| 2.50 \| 2.140 \| 1.94 \| 0.415 \| 0.3823 \| \| DBIL (μmol/L) \| 1.02a \| 0.700ab \| 0.65b \| 0.144 \| 0.036 \| \| IDBIL (μmol/L) \| 1.48 \| 1.56 \| 1.28 \| 0.322 \| 0.6675 \| \| CHE (U/L) \| 183.30 \| 177.00 \| 185.60 \| 7.414 \| 0.501 \| \| BUN (mmol/L) \| 11.21 \| 13.34 \| 12.88 \| 1.232 \| 0.195 \| \| CREA (μmol/L) \| 49.53 \| 51.26 \| 63.36 \| 7.196 \| 0.1406 \| \| CHOL (mmol/L) \| 5.21a \| 7.16b \| 6.182ab \| 0.484 \| 0.0038 \| \| TG (mmol/L) \| 0.16 \| 0.27 \| 0.23 \| 0.046 \| 0.0896 \| \| HDL (mmol/L) \| 1.99a \| 2.72b \| 2.45ab \| 0.265 \| 0.0393 \| \| LDL (mmol/L) \| 2.82 \| 3.81 \| 4.07 \| 0.601 \| 0.1041 \| \| CRP (mg/L) \| 4.53 \| 4.36 \| 4.78 \| 0.409 \| 0.5874 \| \| GLU (mmol/L) \| 2.81 \| 2.73 \| 3.72 \| 0.748 \| 0.3566 \| \| LAC (mmol/L) \| 7.02a \| 10.33b \| 10.17b \| 0.727 \| 0.0004 \| \| LDH (U/L) \| 394.30 \| 439.00 \| 339.50 \| 39.2 \| 0.0725 \| ## Gut microbiota Under the similarity threshold of $97\%$, 1,342 OTUs were obtained and the calculated good’s coverage were no <$99\%$ for all samples. Venn diagrams showed that the number of OTUs in the RF group was greater compared with other groups. ( Figure 2A). The Chao1 index, Shannon index and Simpson index were used to estimate the Alpha diversity of gut microbiota. Compared with the other groups, the Chao1 index indicated that feeding RF substantially increased the heterogeneity of the gut microbiota (Figure 2B). However, no significant difference among the groups emerged in the Shannon index and Simpson index (Figures 2C,D). The PCA analysis was used to compare the microbial community composition and distribution similarity of each group. According to the distance and separation of each sample in each group in the figure, it is evident that the CON group was markedly different from those of the RF group, and there were differences between the CON group and the ARF group (Figure 2E). Interestingly, the microbial composition was significantly different from that of the original samples (RF-Original and ARF-Original), and the original samples were similar in microbial composition. Figure 2: Gut Microbiota diversity analysis in 15-day old lambs after inoculation with ruminal fluid or autoclaved ruminal fluid. (A) Unique and shared intestinal operational taxonomic units (OTUs) for each group are shown in the Venn diagram. Alpha diversity (Chao1index, Shannon index, Simpson index) (B–D); beta diversity indicated by principal component analysis (PCA) plot (E). Data are presented as means ± SD. n = 8, p < 0.01, **p < 0.0001, ns indicates no significance. We then focused on microbiota abundance at the phylum and genus levels. At the phylum level, Bacteroidetes, Firmicutes, Verrucomicrobiota and Proteobacteria were the 4 most dominant bacterial phyla in the three groups (Figure 3A). The statistical analysis showed that, at the phylum level, the gut microbiota in three groups were dominated by Firmicutes, Bacteroidota, Proteobacteria, Synergistota, Fusobacteriota, Verrucomicrobiota, and Actinobacteriota, while the relative abundance of other genera was below $1\%$ (Figure 3A). The statistical analysis of abundant genera (with relative abundance >$0.01\%$) showed that the relative abundance of Verrucomicrobiota was significantly lower ($p \leq 0.05$), while the relative abundance of Synergistota was significantly higher ($p \leq 0.05$) in the RF group than those in the CON group (Figures 3B,C). The abundance of Synergistota in the RF group was higher than that in other groups (Figure 3C). More details about differential microbiota abundance are shown in Supplementary Figure 1. The relative abundance of Fusobacteriota in the ARF group was significantly higher than that in the CON group (Figure 3D). Figure 3: Relative abundance of microbiota at the phylum level. (A) Microbiota taxonomic profiling of intestinal microbiota from different groups at the phylum level with the abundance of the microbiota is more than 1%. (B) Ratio of Verrucomicrobiota in the three groups. (C) Ratio of Synergistota in the three groups. (D) Ratio of Fusobacteriota in the three groups. n = 8, p < 0.05, p < 0.01, p < 0.001, **p < 0.0001, ns indicates no significance. The analysis of the relative abundance of the bacterial genera, 29 out of the 46 genus identified showed significant differences based on the inoculation treatment. Bacteroides, Christensenellaceae_R−7_group, Porphyromonas and Alloprevotella were the common genera dominated in the gut microbiota of the lambs (Figure 4A; Supplementary Figure 2). Our results revealed that 13 genera were significantly more predominant in the RF than CON (Figure 4E; Supplementary Figure 2). They included the Rikenellaceae_RC9_gut_group, Bacteroides, Brachymonas, Peptostreptococcus, Petrimonas, Phascolarctobacterium, Pseudomonas, and others. The relative abundance of Actinomyces, the Escherichia-Shigella, the Eubacterium_nodatum_group, Parabacteroides and Akkermansia was significantly lower ($p \leq 0.05$) in the RF group than those in the CON group (Figures 4B–G). Figure 4: Relative abundance of microbiota at the genera level. (A) Microbiota taxonomic profiling of intestinal microbiota from different groups at the genera level which the abundance of the microbiota is more than 1%. (B) Ratio of Escherichia-Shigella in the three groups. (C) Ratio of Actinomyces in the three groups. (D) Ratio of Eubacterium_nodatum_group in the three groups. (E) Ratio of Rikenellaceae_RC9_gut_group in the three groups. (F) Ratio of Parabacteroides in the three groups. (G) Ratio of Clostridiales_bacterium_canine_oral_taxon_082 in the three groups. (H) Ratio of Akkermansia in the three groups. n = 8, p < 0.05, p < 0.01, p < 0.001, **p < 0.0001, ns indicates no significance. ## Serum metabolic profiles According to the results obtained, a total of 807 metabolites were identified and quantified. All samples were analyzed with a $95\%$ confidence interval. We performed PCA score plot to visualize the overall change of metabolites, the results showed differences between the three groups (Figures 5A–C). The OPLS-DA revealed a clear separation between the CON and the RF groups in the plot, which indicated that the serum metabolic profiles of the RF group were distinct from that of the CON group (Figure 5D). The other two figures also reflect similar results (Figures 5E,F). In the OPLS-DA model, the parameter R2Y was 0.998, and the Q2 value was 0.89 (Supplementary Figure 3), indicating a good degree of reliability and predictive ability of the model used. The OPLS-DA model involved 200 random permutations and combination experiments on the data to avoid over-fitting. A good model is obtained when all replacement models of R2 and Q2 values are lower than the original values of R2 and Q2. The study results suggested that the OPLS-DA model was not over fitted as demonstrated by the R2 and Q2 values in all permutated models being lower than the value of the original R2 and Q2 models (Figures 5G–I). Figure 5: Metabolomics of PCA analysis, CON group (green) and RF group (red) (A), OPLS-DA score chart, CON group (green) and ARF group (red) (B), OPLS-DA score chart, RF group (green) and ARF group (red) (C). OPLS-DA score chart, CON group (green) and RF group (red) (D), OPLS-DA score chart, CON group (green) and ARF group (red) (E), OPLS-DA score chart, RF group (green) and ARF group (red) (F). Permutation test of OPLS-DA (G–I). The results from the screening of different metabolites were visualized in the form of volcano plots to quickly assess the difference and statistical significance in the metabolic expression levels for the CON, RF and ARF group (Figures 6A–C). Fifty-one different metabolites were observed in CON vs. RF, including 22 decreased and 29 increased metabolites (Figure 6A); 94 different metabolites in CON vs. ARF, including 9 decreased and 85 increased metabolites (Figure 6B) and 57 different metabolites in RF vs. ARF, including 9 decreased and 48 increased metabolites (Figure 6C). The top 20 important differential metabolites with VIP > 1.5 and $p \leq 0.05$ are depicted in VIP plots (Figures 6D–F). Compared with CON, inoculation with ruminal fluid increased the plasma relative concentrations of Chenodeoxycholic Acid, Proline-Hydroxyproline, 3-hydroxyphenylacetic acid and FFA(15:1), whereas it decreased the plasma relative concentrations of 21-Deoxycortisol, 20-COOH-AA, Glu-Met, Tetradecanedioic acid, 9(S)-HpOTrE and Trimethylamine-N-Oxide (Figure 6D). Compared with CON, inoculated with autoclaved rumen fluid increased the plasma relative concentrations of Hippuric Acid, Caffeic Acid, Arachidyl-glycine, Xanthosine, Oxypurinol and 5-Hydroxyindole-3-Acetic Acid, whereas it decreased the plasma relative concentrations of Carnitine C12:1(Figure 6E). Compared with RF, inoculated with autoclaved rumen fluid increased the plasma relative concentrations of Phenylacetyl-L-Glutamine, DL-Leucine, Cis-L-3-hydroxyproline, 6-Aminocaproic-Acid, 20-COOH-AA, Caffeic Acid, Arachidyl-glycine, 21-Deoxycortisol and Carnitine C5:0, whereas it decreased the plasma relative concentrations of Carnitine C12:1(Figure 6F).More details about differential metabolites are shown in Supplementary Tables S1–S3. Figure 6: Differential metabolites in lamb serum inoculated with ruminal fluid. Volcano plots of the affected metabolites in CON VS RF (A), CON VS ARF (B) and RF VS ARF (C). Each point represents a metabolite. The green dots in the figure represent the down-regulated differential metabolites, the red dots represent the up-regulated differential metabolites, and the gray dots represent the metabolites without difference. Metabolites are ranked by variable importance in projection analysis (VIP) of respective groups: CON group and RF group (D), CON group and ARF group (E) and RF group and ARF group (F); the top 20 important metabolites were arranged from top to bottom according to intracellular concentration. The red box represents an up-regulated concentration of the molecule and the green box represents down-regulated concentration. ## Correlations between serum metabolic profiles and gut microbiota Using the Spearman rank correlation coefficients, we displayed the results in the heatmap chart and evaluated the correlation. Compared with CON, at the phylum level, we found that Verrucomicrobiota was strongly correlated with most metabolites and Synergistota was negatively correlated with Tetradecanedioic acid (Figure 7A). At the genus level, a strong correlation between 12 differential genera and 24 differential metabolites at the genera level was observed in the CON and RF groups (Figure 7B). Akkermansia was negatively correlated with Chenodeoxycholic Acid and various amino acids, and was positively correlated with organic acid and its derivatives. The abundance of Pathogenic bacteria such as Escherichia-Shigella and Actinomyces decreased after inoculation with ruminal fluid and was associated with various amino acids, bile acids and other metabolites. Rikenellaceae_RC9_gut_group was negatively correlated with Trimethylamine-N-Oxide, and significantly positively correlated with the level of Chenodeoxycholic Acid and Lithocholic acid in serum. Eubacterium_nodatum_group, Filifactor, Parabacteroides, Clostridiales_bacterium_canine_oral_taxon_082, Porphyromonas and other genera were also correlated with many metabolites. Figure 7: The correlation between gut microbiota and serum metabolites. At the phylum or genera level in the CON and RF groups (A,B). At the phylum or genera level in the CON and ARF groups (C,D). Red represents positive correlation, and green represents negative correlation. p < 0.05, *p < 0.01. By taking the intersection of CON and ARF, 94 differential metabolites were obtained and narrowed down to 30 different metabolites (Figures 7C,D). At the phylum level, Verrucomicrobiota was negatively correlated with significantly different metabolites, Fusobacteriota was positively correlated with significantly different metabolites (Figure 7C). The change in Akkermansia, Porphyromonas and NK4A214_group, Escherichia-Shigella and Parabacteroides levels was associated with the serum levels of significantly different metabolites. Other microbiota were positively correlated with significantly different metabolites (Figure 7D). These results indicated that these differential microbiota were closely associated with, and might contribute to, the altered serum metabolic profiles in response to inoculation with ruminal fluid or autoclaved ruminal fluid. In order to better understand the metabolic regulation relationship, we have drawn a metabolic network diagram (Figure 8). Figure 8: Correlation networks of fecal metabolites in CON, RF, and CON groups. Compared with the latter group, among the metabolites in the former group, the red symbol represents a significant increase, the white symbol represents no difference, and the blue symbol represents a decrease. ## Effect of ruminal fluid inoculation on weight of newborn lambs Growth rate of lambs can potentially affect production performance in adulthood (Sato et al., 2010). Some early studies found that ruminal inoculation improved average daily gain (Pounden and Hibbs, 1949). A potential mechanism for such an effect was proposed to be a benefit of microorganism inoculation on amylase activity (Ziolecka et al., 1984). In contrast, other studies reported no significant improvements in weight gain due to ruminal fluid inoculation (De Barbieri et al., 2015). Although we did not detect a significant effect on weight gain over time, given that postpartum stress could lead to weight loss after birth in the newborn, the trend to promote a faster weight recovery in the RF group suggested a benefit of active microbial inoculation. ## Effect of ruminal fluid inoculation on blood biochemical parameters Blood parameters are important to evaluate the health status of animals in response to nutrition (Weingand et al., 1996). The lack of difference for most of the blood parameters related to liver and kidney function and inflammation suggested that ruminal fluid inoculation had no negative influence on the health of newborn lambs. It is noteworthy that an increase of ALP, CHOL, and HDL was observed in the RF group. A greater concentration of ALP was observed in newborn infants experiencing active bone growth (Devyatkin et al., 2021). Thus, it is plausible to speculate that fresh ruminal fluid inoculation with active microorganisms was beneficial to the development of newborn lambs. CHOL is one of the important blood indices to estimate lipoprotein metabolism. A previous study found that multi-strain probiotic increased serum CHOL in neonatal dairy calves (Guo et al., 2022), which was similar to our study. However, a low blood concentration of CHOL was also observed in a previous study with spray-dried ruminal fluid inoculation (Rezai Sarteshnizi et al., 2020). Combined with the greater HDL blood content in RF relative to CON, the greater CHOL might be helpful in preventing lipid peroxidation and stimulating the synthesis of anti-inflammatory cytokines (such as interleukin-10) (Mertens and Holvoet, 2001). We speculated that active microorganism inoculation could potential regulate lipid metabolism and inflammatory reactions to influence the health of newborn lambs. Overall, the blood biochemical parameters suggested that microorganism inoculation had no negative effect on health, and could potentially regulate lipid metabolism and inflammatory reactions. ## The effect of ruminal fluid inoculation on gut microbiota Gut microbiota is significantly different from the ruminal microbiota (Godoy-Vitorino et al., 2012), but data suggest that ruminal fluid inoculation still could regulate the gut microflora (Ji et al., 2018). In the present work, a distinct microbiota cluster of CON, RF and ARF was observed using PCA analysis, suggesting that ruminal fluid inoculation could influence the establishment of microflora in newborn lambs. The greater alpha-diversity index (Chao 1) in RF compared with CON suggested that ruminal fluid inoculation with active microorganisms could improve the diversity of the gut microbiota. A similar result was also observed in a previous study with goats (Abo-Donia et al., 2011). The establishment of microbiota after birth might involve a sophisticated process (Wang et al., 2016). In the current study, we illustrated the change of gut microbial taxonomical composition with ruminal fluid inoculation at the phylum and genus levels in newborn lambs. In the ruminant, Proteobacteria, Bacteroidetes and Firmicutes were confirmed as the dominant phyla with important functions in maintaining health and production (Sadet et al., 2007). Therefore, our results with the greatest relative abundance of Proteobacteria, Bacteroidetes and Firmicutes suggested that ruminal fluid inoculation could accelerate the establishment of ruminal microbiota in newborn lambs. In our study, Verrucomicrobia was found at a lower proportion in lambs exclusively inoculated with ruminal fluid and were represented exclusively by the genus Akkermansia, in agreement with earlier studies (Saleem et al., 2012). As previously reported, the relative abundance of Verrucomicrobia was significantly higher in milk-fed lambs (Wang et al., 2016). The present study may indirectly suggest that ruminal fluid transplantation promotes the establishment of specific microbial population in lambs in order to better adapt to the transition from milk to solid feed. Fusobacteria is a phylum of anaerobic Gram-negative bacilli, with a shuttle-shaped morphology, commonly found in the oral cavity (Gupta and Sethi, 2014). The view that Fusobacterium is involved in animal infection within a certain range has long been accepted by many researchers (Roberts, 2000). Our results indicated that inoculation with ruminal fluid in lambs may also contribute to health by reducing the number of pathogenic bacteria. Synergistota is a bacterial phylum consisting of gram-negative anaerobes (Belibasakis et al., 2013). End-products of Synergistota are mainly acetate, propionate and isobutyrate (Kang et al., 2020). SCFA are the main source of energy for ruminants to maintain normal growth. At the genus level, most of the Rikenellaceae_RC9_gut_group was upregulated. The Rikenellaceae_RC9_gut_group is the dominant bacteria in the intestine, associated with mucin degradation (Fan et al., 2020), and is significantly in a negative correlation with obesity (Arnoriaga-Rodríguez et al., 2020; Suzuki et al., 2020). It plays an important role in intestinal mucosal health. Moreover, the relative abundance of Rikenellaceae_RC9_gut_group was positively correlated with host feed utilization, volatile fatty acid and short-chain fatty acid metabolism (Derakhshani et al., 2017; Li et al., 2019) and significantly negatively correlated with the expression of inflammation-related immune genes such as insulin levels and interferon IFNγ (Gomez-Arango et al., 2016). Parabacteroides generate acetate to mitigate heparanase-exacerbated acute pancreatitis by reducing neutrophil infiltration (Lei et al., 2021). Acetate is the main SCFA. Some species of Parabacteroides Significant reduce in severity of intestinal inflammation in murine models of acute and chronic colitis through dextran sulfate sodium (Kverka et al., 2011). SCFA can regulate the function of multiple systems, such as the intestinal, neurological, endocrine and hematological systems, and there is considerable evidence that SCFAs play an important role in the maintenance of intestinal health and the prevention and improvement of many non-communicable diseases, including cancer (Doble et al., 2016). However, due to the interaction of microflora, the treatment in this study reduced the concentration of Parabacteroides. In this study, most of the Escherichia-Shigella was significantly downregulated, Escherichia-Shigella is a conditional pathogenic bacteria (Qi et al., 2021). On the first day after birth, the rectum was invaded by Escherichia-Shigella (Alipour et al., 2018). Recent research suggests that the artificial feeding modal can increase the number of potential pathogens such as Escherichia-Shigella and slow the establishment of the anaerobic environment and anaerobic microorganism (Bi et al., 2019). Actinomyces can use carbohydrates to produce fatty acids and are classified as conditional pathogenic bacteria (Hall, 2008). Eubacterium_nodatum_group has the ability to Catabolic carbohydrates to SCFAs (mainly butyrate; Barcenilla et al., 2000). Eubacterium_ nodatum_ Group is common in oral cavity with periodontitis and other diseases, and is considered as pathogenic bacteria (Hill et al., 1987). The results varied a lot from one study to another, and there is no consistent conclusion about the Akkermansia. Akkermansia, produce SCFA by degrading mucin, are closely contacted with immunity (Ganesh et al., 2013). Increasing abundance of Akkermansia muciniphila could decreased body fat mass, increase glucose homeostasis, and lower adipose tissue inflammation (Everard et al., 2013; Dao et al., 2016). Recent research suggests that milk positively affected their growth (Wang et al., 2018). This may further prove that inoculation with ruminal fluid promotes the maturation of lamb microbes in order to better adapt to the transition from milk to solid feed. The previous studies have found that high-doses of heme iron intake significantly increased the abundance of Akkermansia in the intestinal tract of mice, but damaged the intestinal mucus layer (van Hylckama Vlieg et al., 2011). The results of the present study showed that inoculation with ruminal fluid could change bacteria of the Fusobacteria, Akkermansia, Eubacterium_nodatum_group, and the like. The effect was positively correlated with microbial activity in ruminal fluid. These results support the notion that active gut microbiota may inhibit the growth of pathogens, promote the maturation of growth-promoting microbes by altering the diversity and composition of the gut microbiota. ## The effect of ruminal fluid inoculation on serum metabolites and correlations with serum metabolic profiles Microbiota in the gut regulate metabolic reactions such as the production of bile acids, amino acid and fatty acids, which are essential for the health of the animal (Nicholson et al., 2012). In this study, the composition of serum metabolites in the RF group was different from the CON and ARF groups. Through the serum metabolomics analysis, it was concluded that the metabolism of bile acids (Chenodeoxycholic acid, Chaps, Lithocholic acid) was greatly influenced by feeding RF. Bile acids participate in the regulation of lipid and dextrose metabolism, and play a key role in regulating hepatic metabolic pathways (Hylemon et al., 2009). The bile acids may help control growth and composition of gut microbiota through FXR and TGR-5, which also helps protect the gut microbiota from inflammation (du et al., 2020). Previous research suggested that obeticholic acid, a derivative of chenodeoxycholic acid, can increase bile acid homeostasis, lower the expression of TNF-α, IL-6, and IL-1β, suggesting that bile acids alleviate inflammation (Xiong et al., 2017). Dietary chenodeoxycholic acid can improve growth performance and intestinal health by changing blood metabolism and intestinal bacteria in weaned piglets (Song et al., 2021). Lithocholic acid is formed by bacterial action of chenodeoxycholic acid salt and is usually combined with glycine or taurine (Bazzoli et al., 1982). Spearman correlation analysis between the affected metabolites showed that the elevated serum levels of chenodeoxycholic acid and Lithocholic acid were negatively correlated with Escherichia-Shigella, Fusobacterium, Actinomyces, Porphyromonas, Eubacterium_ nodatum_ Group and Akkermansia, and positively correlated with Rikenellaceae_RC9_gut_group. The results suggested that tumor gastric juice transplantation may improve the stability of the body by regulating the level of bile acid metabolism or regulating the composition and quantity of intestinal microbiota. The end products of protein digestion in the digestive tract are often mostly small peptides rather than free amino acids, which are absorbed intact and enter the circulation as dipeptides or tripeptides (Rérat et al., 1988). Studies have shown that insufficient peptide in the ruminal fluid of dairy cows is the main factor limiting the growth of microorganisms, and small peptides are key factors for maximum growth efficiency of microorganisms (Brooks et al., 2012). Small peptides can promote the reproduction of beneficial microorganisms in the digestive tract and improve the synthesis of microbial proteins. Our results revealed an increasing trend of small peptides (such as proline-Hydroxyproline, His-Ala, Ile-Ala, and Pro-Leu), the correlation analysis showed that the elevated serum levels of small peptides was negatively correlated with Escherichia-Shigella, Fusobacterium and Eubacterium_nodatum_group. The tricarboxylic acid cycle is one of the most important energy metabolism pathways, our results on correlation network analysis showed that some small peptides may indirectly participate in the tricarboxylic acid cycle, i.e., enter the cycle after hydrolysis into free amino acids. The above results demonstrated that feeding RF promoted peptide absorption by enhancing the absorption rate of small peptides or change the absorbable amount of small peptides by reducing the abundance of Escherichia Shigella and other bacteria, thereby increasing the concentration of small peptides in the serum, thereby enhancing the metabolism of the body and promoting Rikenellaceae_RC9_gut_group. Xanthine and xanthoside are related to purine and pyrimidine metabolism, and the changes of these metabolites are related to microbial apoptosis (Fujihara and Shem, 2011; Wang et al., 2012). In this study, the contents of xanthine and xanthoside in serum of lambs increased, and were negatively correlated with Escherichia Shigella. Therefore, we speculate that the increase of xanthine and xanthoside content is related to the decrease of Escherichia Shigella. When hepatic glycogen, a quick source of glucose for muscle, is depleted, adipose tissue lipolysis into free fatty acids provides these compounds to tissues as a source of energy (Cree-Green et al., 2017). The content in serum is very low under physiological condition, but the content increases abnormally in diabetes, severe liver dysfunction, hyperthyroidism and other diseases (Wolf et al., 2016). From the weight gain results, we can speculate that the feeding RF promoted feed digestion and nutrient absorption which could have favored liver glycogen synthesis and even glucose utilization for oxidation without the need to induce adipose tissue lipolysis. Free fatty acids are also one of the substances that contribute to oxidative stress (Jiang and Yan, 2022). Oxidized lipids are metabolites of polyunsaturated fatty acids (arachidonic acid, linoleic acid, alpha-linolenic acid) that undergo auto-oxidation or are generated by the action of specific enzymes (Tang et al., 2021). 20-COOH-AA is produced by a series of enzymatic oxidation reactions of arachidonic acid. Oxidative stress is a physiological state in which there is an imbalance between oxidation and antioxidant action in the body. Oxidized lipids serve as the material base in which unsaturated fatty acids are oxidized to form oxidative metabolites under oxidative stress, thus, affecting normal functions (Tsubone et al., 2019). Trimethylamine-N-Oxide is the promoter of atherosclerosis and participates in oxidative damage, endothelial dysfunction and other diseases (Moraes et al., 2015); Injury of vascular endothelial cells can be induced by increasing vascular oxidative stress, and mitochondrial dysfunction can also be induced by oxidative stress in mice (Li et al., 2018; Brunt et al., 2020). Plasma corticosterone is thought to be the main glucocorticoid involved in the regulation of the stress response in rodents (Mizobe et al., 1997). A striking association between serum corticosterone was observed during acute or repeated restraints, chronic unpredictable stress or heat stress (Gong et al., 2015). In the current study, the concentration of Free fatty acids, Oxidized lipids, Trimethylamine-N-Oxide and corticosterone in the RF group were downregulated compared with the other groups, and the blood biochemical indices showed that the concentration of HDL, which had the effect of antagonizing lipid peroxidation, was increased. It has been reported that probiotics can alleviate lipid accumulation, regulate blood lipid levels, increase the concentration of HDL-C, slow down the formation of oxidized lipids, and antagonize oxidative lipid damage in the liver (Nido et al., 2016). In our study, Free fatty acids, Oxidized lipids, Trimethylamine-N-Oxide, Corticosterone was negatively correlated with intestinal available bacteria such as Rikenellaceae_RC9_gut_group, and positively correlated with pathogenic bacteria such as Escherichia Shigella, Actinomyces, Porphyromonas. It has been pointed out that the disturbance of intestinal microbiota is one of the stressors leading to stress, and a stable microbiota can reduce the stress response of the body to a certain extent. Based on the above results, we speculate that the transplantation of rumen juice will promote the stabilization of intestinal microbiota, thereby reducing the levels of lipid oxide, trimethylamine oxide, cortisol, etc., to some extent, it can antagonize the stress response of lambs and improve the blood lipid level of lambs. We found that RF treatment altered the levels of metabolites in lamb serum, with the metabolites differing in expression from the ARF group, and these changes were related to intestinal microorganisms. We speculate that the sterilized ruminal fluid was rich in protein, fatty acids and other nutrients, thus, promoting the development of the microbial community of the lambs and influencing the metabolites. The difference in the results of the combined RF and ARF groups suggested that active microorganisms in the ruminal were the main factors that contributed to the differences observed. ## Conclusion This study demonstrated that ruminal fluid inoculation with active microorganisms could accelerate weight recovery and maintain health in newborn lambs through regulating the establishment of gut microbiota. The proposed mechanism of the overall effects of the ruminal fluid inoculation on gut microbiota and metabolism is schematically represented in Figure 9. In general, this management strategy could be useful for promoting the development and alleviate the postpartum stress in newborn lambs. Figure 9: The proposed schematic diagram of the overall effects of the ruminal fluid inoculation on the gut microbial composition, function, and metabolites of lambs. ## 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: NCBI - PRJNA923048; MetaboLights - MTBLS6998. ## Ethics statement The animal study was reviewed and approved by Ethics Committee in Chongqing Academy of animal sciences (approval number: xky-20180716, 9 June 2021). ## Author contributions LF, LW, and LL: data analysis and original draft writing. LZ, GW, and PZ: conducted the experiments and sample collection. JL and ZZ: draft reviewing. XD: experimental design, supervision, and original draft writing and reviewing. All authors contributed to the article and approved the submitted version. ## Funding This study was supported by the National Natural Science Foundation of China (grant number 32002206, XD), Key Project of Chongqing Natural Science Foundation (grant number cstc2020jcyj–zdxmX0005, XD), the General Projects of Chongqing Natural Science Foundation (grant number cstc2021jcyj–msxmX1143, LL) and the Chongqing Performance Incentive Guide Special Project (20522, XD). ## 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. 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--- title: Erianin Induces Ferroptosis of Renal Cancer Stem Cells via Promoting ALOX12/P53 mRNA N6-methyladenosine Modification authors: - Hongliang Shen - Zixiang Geng - Xiaoli Nie - Te Liu journal: Journal of Cancer year: 2023 pmcid: PMC9969579 doi: 10.7150/jca.81027 license: CC BY 4.0 --- # Erianin Induces Ferroptosis of Renal Cancer Stem Cells via Promoting ALOX12/P53 mRNA N6-methyladenosine Modification ## Abstract Renal cell carcinoma (RCC) is the most common type of primary renal parenchymal malignancy in adults, with a high degree of malignancy and poor prognosis. Human renal cancer stem cells (HuRCSCs) are reported to be the main cause of drug resistance, metastasis, recurrence, and poor prognosis. Erianin is a low molecular-weight bibenzyl natural product extracted from Dendrobium chrysotoxum, which inhibits the in vitro and in vivo activity of a variety of cancer cells. However, the molecular mechanisms of Erianin's therapeutic effect on HuRCSCs are unknown. Here, we isolated CD44+/CD105+ HuRCSCs from patients with renal cell carcinoma. The experiments confirmed that Erianin significantly inhibited the proliferation, invasion, angiogenesis, and tumorigenesis of HuRCSCs, and induced oxidative stress injury and Fe2+ accumulation. qRT-PCR and western blotting showed that Erianin significantly reduced the expression levels of cellular Ferroptosis protective factors, and upregulated the expression of METTL3 and downregulated that of FTO. Dot blotting results indicated that Erianin significantly upregulated the mRNA N6-methyladenosine (m6A) modification of HuRCSCs. The results of RNA immunoprecipitation-PCR also indicated that Erianin significantly enhanced the m6A modification level of the 3' untranslated region of ALOX12 and P53 mRNA in HuRCSCs, resulting in increased stability, prolonged half-life, and improved translation activity. In addition, clinical data analysis showed that the expression of FTO correlated negatively with adverse events in patient with renal cell carcinoma. Thus, this study suggested that Erianin can induce Ferroptosis in renal cancer stem cells by promoting N6-methyladenosine modification of ALOX12/P53 mRNA, ultimately achieving a therapeutic effect on renal cancer. ## Introduction Renal cell carcinoma (RCC) is the most common type of primary renal parenchymal malignant tumor in adults. It originates from the proximal convoluted tubule epithelial system. The incidence of RCC in the genitourinary system is second only to bladder cancer, accounting for about 2-$3\%$ of adult tumors and 80-90 % of renal malignant tumors 1-3. RCC has high malignancy and poor prognosis, and is a serious threat to human health 1-3. The occurrence of RCC includes a series of pathological and molecular changes in clinical malignant tumors of renal organs 1-3. Clear cell carcinoma and papillary carcinoma (category 1 and 2) account for the majority of RCC 1-3. Recent studies have found that RCC contains stem cell subsets with strong proliferation, invasion, drug resistance, and metastasis abilities, which are termed renal cell carcinoma stem cells (HuRCSCs), which are likely to be the main source of metastasis, recurrence, and poor prognosis in patients with RCC 4, 5. In a previous study, we found that the compound fisetin suppressed Tet methylcytosine dioxygenase 1 (TET1) expression and reduced the 5hmC modification in specific loci in the promoters of CCNY (encoding cyclin Y)/CDK16 (encoding cyclin dependent kinase 16) in HuRSCs, which inhibited the transcription of these genes, causing cell cycle arrest and ultimately inhibiting renal cancer stem cell activity 4. Meanwhile, we reported that suppressed expression of the long non-coding RNA HOTAIR inhibited proliferation and tumorigenicity of renal carcinoma cells 5. Thus, epigenetic regulation significantly affects the malignant degree of renal cancer cells. Erianin is a low-molecular-weight bibenzyl natural product extracted from Dendrobium chrysotoxum 6-8. Initial reports found that Erianin can be used as an antipyretic and analgesic agent to inhibit indoleamine 2,3-dioxygenase-induced tumor angiogenesis 7. Later studies found that Erianin can inhibit tumor cell cycle progression and induce tumor death by inhibiting BCL2 apoptosis regulator (Bcl-2) and extracellular regulated kinase (ERK)$\frac{1}{2}$ and promoting BCL2 associated X, apoptosis regulator (Bax) and caspase-3 expression 9-11. Erianin can inhibit the proliferation and induce apoptosis of colon cancer, bladder cancer, liver cancer, gastric cancer and melanoma 9, 12. Ferroptosis is a novel iron‑dependent programmed cell death 13-16. The action of ferrous iron or esterase catalyzes the high expression of unsaturated fatty acids causing lipid peroxidation could induce ferroptosis 15, 17-20. Currently, many studies have reported that some bioactive compouns or small molecule drugs can significantly inhibit cancer cells' self-proliferation, division, and invasion activities via promoting ferroptosis 13, 14, 16, 17, 19. Chen et al. found that the natural product, Erianin, exerted its anticancer effects by inducing Ca2+/calmodulin (CaM)-dependent Ferroptosis and inhibiting cell migration, and Erianin might serve as a prospective compound to treat lung cancer 21. However, it has not been reported whether Erianin can induce the occurrence of Ferroptosis in HuRCSCs and its underlying molecular biological mechanism. RNA N-6 methyladenosine (m6A) is a methylation modification of N atoms at position 6 of RNA adenine 22-27. RNA m6A methylation modification exists widely in most eukaryotic species (from yeast, plant, and fruit fly to mammals) and in viral mRNA, playing a key role in posttranscriptional mRNA regulation and metabolism 22-27. Methyltransferase 14, N6-adenosine-methyltransferase subunit (METTL14) and methyltransferase 3, N6-adenosine-methyltransferase complex catalytic subunit (METTL3) are two components of m6A methyltransferase complexes. These two proteins can form stable complexes at a ratio of 1:1 to complete RNA m6A modification, belonging to the “Writers” 22-27. The fat mass and obesity-associated protein (FTO) removes methylation of RNA m6A, acting as an “Eraser” 22-27. Therefore, RNA m6A modification is a dynamic and reversible enzymatic reaction 22-27. Studies have suggested that the RNA m6A modification can improve the stability of mRNA, increase its transcription and translation activities, promote tumor occurrence and invasion, and improve the reprogramming efficiency of stem cells 22-27. However, the regulatory mechanism of dynamic m6A modification of RNA during ferroptosis has not been determined. Based on the above evidence, we aimed to isolate CD44+/CD105+ HuRCSCs from tissue samples of patients with RCC. In vitro and in vivo, we confirmed that Erianin, through gene mRNA m6A modification of key gene enzyme expression, regulated ferroptosis-related gene mRNA m6A modification and its mRNA stability, eventually inducing HuRCSCs ferroptosis via epigenetic mechanism. ## Materials and Methods A detailed description of all materials and methods can be found in supplementary data. ## CD44+/CD105+ HuRCSCs isolation and culture CD44+/CD105+ HuRCSCs were isolated according to a previously published methods 4. Briefly, human RCC tissues from four patients were digested using trypsin (containing $0.02\%$ EDTA-Na) at 37 °C for 30 minutes and the reaction was terminated using cell culture medium containing $15\%$ fetal bovine serum (FBS). The volume of the cell suspension was adjusted and 4 μl fluorescein isothiocyanate (FITC)-labelled rabbit anti‑human CD44 monoclonal antibody and Cy3-labelled rabbit anti-human CD105+ antibody (eBioscience, San Diego, CA, USA) were added to 100 μl of cell suspension and incubated in the dark at 4 °C for 30 minutes. Pre-cooled phosphate buffered saline (PBS) was used to readjust the volume of the cell suspension to 500 μl. A flow cytometer (BD FACSAria, BD Biosciences San Jose, CA, USA) was used to select CD44+/CD105+ HuRCSCs. All cells were resuspended in complete cancer stem cell culture medium: Dulbecco's modified Eagle's medium (DMEM:F12 (HyClone, Logan, UT, USA), supplemented with 10 ng/mL basic fibroblast growth factor, 10 ng/mL epidermal growth factor, 5 μg/mL insulin, $1\%$ bovine serum albumin (BSA) and $5\%$ knockout serum replacement (KnockOut SR) (all from Gibco, Grand Island, NY, USA). The study protocol was approved by the Regional Ethics Committee of Shanghai Geriatric Institute of Chinese Medicine, Shanghai University of Traditional Chinese Medicine (Permission No.: SHAGE-E-202114), in accordance with the 2008 Helsinki declaration. ## 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium-bromide (MTT) assay Briefly, 2000 cells/ml of each group were seeded in a 96-well plate. After 24 h, 10 μl of MTT solution (Sigma-Aldrich, St. Louis, MO, USA) was added to each group of cells and incubated at 37 °C for 3 h. The medium was discarded, 150 μl of dimethyl sulfoxide (DMSO) (Sigma-Aldrich) was added to each well, and the plate was shaken for 15 s to mix well. The culture plate was placed in a microplate reader to record the absorbance value at 450 nm. The formula for calculating the cell proliferation inhibition rate (%) is (1-OD value of experimental group of cells - blank/OD value of control group of cells - blank) × $100\%$. ## RNA extraction and Quantitative real-time reverse transcription PCR (qRT-PCR) According to the instructions of the RNAprep pure Tissue Kit (TIANGEN Biotech (Beijing) Co., Ltd., Beijing, China), about 20 mg of human tissue samples were taken, added with 800 μl lysis buffer, ground, and homogenized. The supernatant was retained, added with 200 μl of chloroform, mixed by inversion, and centrifuged at 4 °C, 13 400 × g for 15 min. Two volumes of anhydrous ethanol times were added to the supernatant, mixed by inversion, and centrifuged at 4 °C, 13400 × g, for 30 min. RNA pellet was resuspended with 500 μl 75 % ethanol centrifuged at 4 °C, 13400 × g for 5 min. All the liquid was removed and the RNA pellet was fully dissolved in 300 μl of diethyl pyrocarbonate (DEPC) water. The ratio of OD260/OD280 (generally controlled between 1.8 and 2.0) was detected for 1 μl of the RNA solution to determine the purity and total concentration of RNA. Total RNA was treated with DNase I (Sigma-Aldrich), quantified, and reverse transcribed into cDNA using the ReverTra Ace-α First Strand cDNA Synthesis Kit (TOYOBO). The qRT-PCR was performed with a RealPlex4 real-time PCR detection system from Eppendorf Co. Ltd. (Germany). SYBR Green Real-Time PCR Master Mix (TOYOBO) was used as the fluorescent dye in the nucleic acid amplification. qRT-PCR was completed with 40 amplification cycles as follows: denaturation at 95 °C for 15s, annealing at 58 °C for 30s, and extension at 72 °C for 42s. The relative gene expression levelswere calculated using the 2-ΔΔCt method (ΔCt = Ct_genes - Ct_18sRNA; ΔΔCt = ΔCt_all_groups - ΔCt_blankcontrol_group). The mRNA expression levels were normalised to the expression level of 18s rRNA. ## Western blotting In brief, the total proteins of each group were subjected to $12\%$ denaturing sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE), and transferred to a polyvinylidene fluoride (PVDF) membrane (Millipore, Bedford, MA, USA) after completion. After blocking and washing, primary antibodies were added and incubated at 37 °C for 45 min. After sufficient washing, the secondary antibodies were added incubated at 37 °C for 45 min. The membrane was washed four times with Tris-buffered saline-Tween20 (TBST) at room temperature for 14 min each time. Then, Sigma-Aldrich Chemical was added and the immunoreactive protein bands were developed using an Enhanced Chemiluminescence (ECL) kit (Pierce Biotechnology, Rockford, IL, USA). ## In vivo xenograft experiments BALB/Cnu/nu mice aged 6-7 weeks and weighing about 20 g were used in the experiment. The BALB/Cnu/nu mice were administered with approximately 1 × 105 cells in the log phase. Each experimental group consisted of four mice. After 2 months, the mice were sacrificed, and their tumors were excised. The tumour weight was measured and the tumor volume was calculated according to the formula: Tumor volume (mm3) = (wh2)/2, where w is the longest axis (mm) and h is the shortest axis (mm). The animal study was performed at the Shanghai University of Traditional Chinese Medicine with approval from the Institutional Animal Care and Use Committee in accordance with the institutional guidelines. And, all animal experiments complyed with the ARRIVE guidelines and were carried out in accordance with the National Institutes of Health guide for the care and use of Laboratory animals (NIH Publications No. 8023, revised 1978). ## Bioinformatic prediction and analysis A total of 522 patients with renal cell carcinoma patients(T) and 99 non-renal cell carcinoma patients (N) from the Gene Expression Profiling Interactive Analysis (http://gepia.cancer-pku.cn/index.html), GEPIA, were included in the study patient cohorts. The data for the above patient cohorts were used in gene expression profile analysis, pathological stage plot analysis, multiple gene comparison analysis, and gene correlation analysis using the GEPIA online tool. ## Statistical analysis Each experiment was performed as least three times; data are presented as the mean ± the standard error (SE) where applicable. Differences were evaluated using Student's t-tests. P values < 0.05 were considered statistically significant. With respect tothe ANOVA and limma options, genes with a \|log2FC\| cutoff > 1 and q < 0.01 relative to pre-set thresholds were considered to be differently expressed genes (DEGs). ## Erianin significantly reduced the in vitro activity of HuRCSCs Erianin is an active substance from Dendrobium chrysotoxum, comprising a low molecular weight bibenzyl natural product (Figure 1A). According to the previous study 21, the Erianin concentration of 50 nM was used to treated to HuRCSCs. The results of the MTT assay showed that Erianin treatment inhibited the proliferation of HuRCSCs significantly increased in a treatment time-dependent manner (Figure 1B). The results of flow cytometry showed that Erianin significantly increased the apoptosis rate of HuRCSCs (Figure 1C). The results of the Transwell chamber experiment showed that Erianin significantly inhibited the migration of HuRCSCs into the external matrix (Figure 1D). Moreover, the results of the matrix gel angiogenesis experiment also showed that Erianin could significantly weaken the ability of HUVECs to form blood vessels in the matrix gel (Figure 1E). In addition, the biochemical test results showed that after Erianin treatment of HuRCSCs, the concentrations of intracellular lactic acid, lipid peroxide (LPO) and Fe2+ were increased, and the concentrations of pyruvic acid and total glutathione (T-GSH) were decreased (Figure 1F). Meanwhile, the MTT assay was used to determine the weakening effect on ferroptosis of Ferrostatin-1 (Ferr-1, antagonist of ferroptosis) combined with Erianin treatment for HuRCSCs. The MTT results showed that the cell inhibition rate of Erianin + Ferr-1 treatment group was significantly lower than it in only Erianin treatment group (Figure 1G). The BrdU incorporation assay and cell immunofluorescence staining combined with flow cytometry is used to determine the cell proliferation and necroptosis of Erianin treatment of HuRCSCs. The results indicated that the precentage of BrdU+ HuRCSCs (the biomarker of cell proliferation) of Erianin treated group was significantly lower than it in Erianin + Ferr-1 treated group or DMSO group (Figure 1H). The results revealed that the precentage of Caspase-1+ HuRCSCs (the biomarker of necroptosis) of Erianin treated group was significantly higher than it in Erianin + Ferr-1 treated group or DMSO group (Figure 1I). These results showed that Erianin significantly reduced the in vitro activity of HuRCSCs by inducing oxidative stress injury and weakening their energy metabolism activity. ## Erianin promotes the high expression of genes related to ferroptosis in HuRCSCs The qRT-PCR results showed that after Erianin treatment of HuRCSCs, the mRNA expression levels of intracellular ferroptosis inhibitory genes GPX4, AIFM2 (also known as FSP1 (encoding atlastin apoptosis inducing factor mitochondria associated 2)), IREB2 (encoding iron responsive element binding protein 2), GSS (encoding glutathione synthetase), SLC7A11 (encoding solute carrier family 7 member 11), SQS (encoding squalene synthase), and CS (encoding citrate synthase) were significantly lower than those in the control group (DMSO treatment group) (Figure 2A, 2B). The western blotting results also showed that after Erianin treatment of HuRCSCs, the levels of intracellular ferroptosis inhibitory proteins GPX4, ferritin heavy chain 1 (FTH1), and SLC7A11 were significantly lower than those in the DMSO control group, while the levels of ferroptosis promoting proteins, prostaglandin-endoperoxide synthase 2 (PTGS2) and iron responsive element binding protein 2 (IRP2), were significantly increased (Figure 2C).The experimental data suggested that Erianin could significantly increase the expression of positive regulatory factors for ferroptosis of HuRCSCs, but inhibited the expression of negative regulatory factors. ## Erianin promotes m6A methylation modification of the mRNA encoding the key regulatory factors ALOX12 and p53 in the whole RNA and in the ferroptosis signal transduction pathway of HuRCSCs First, we detected the differences in the expression levels of RNA m6A methylation writing enzymes, erasing enzymes, reading enzymes, and translation enzymes in each group. The qRT-PCR results showed that after Erianin treatment of HuRCSCs, the expression levels of METTL3, WTAP (encoding WT1 associated protein), NSUN2 (encoding NOP2/Sun RNA methyltransferase 2), DNMT2 (encoding DNA methyltransferase-2) and other RNA methylation “Writers” were significantly higher than those in the control group, while the expression level of the RNA methylation “Eraser” FTO was significantly decreased (Figure 3A). Dot blotting results showed that the overall mRNA m6A modification level of HuRCSCs treated with Erianin was significantly higher than that of the control group (Figure 3B). In addition, the results of RIP-PCR showed that after HuRCSCs were treated with Erianin, the specific products of 3' untranslated region (UTR) of ALOX12 (encoding arachidonate 12‑Lipoxygenase, 12S Type) and P53 (encoding tumor protein p53) mRNA could be amplified by PCR in the complex associated with anti-m6A antibody (Figure 3C). In the control group, in the complex associated with anti-m6A antibody, it was almost impossible to PCR amplify the specific products of the 3' UTRs of the above factors (Figure 3C). Finally, the western blotting results showed that the levels of METTL3, ALOX12, and p53 proteins in HuRCSCs treated with Erianin were significantly higher than those in the control group, while the level of FTO showed the opposite trend (Figure 3D). In addition, in order to confirm the correlation between ALOX12 and METTL3 induced RNA m6A modification and cell ferroptosis, the siRNAs were used to knockdown the expression of endogenous ALOX12 and METTL3 in HuRCSCs (Figure 3E). The results of MTT assay showed that the cell inhibition rate of siAlox12+Erianin treatment group was significantly lower than it in control group (siMock+Erianin), and the cell inhibition rate of siMettl3+Erianin treatment group was also significantly lower than it in control group (Figure 3F). Besides, the MTT assay was used to determine the influencing effect on ferroptosis of Erastin (agonist of ferroptosis) combined with siRNAs treatment for HuRCSCs. The results showed that the cell inhibition rate of siAlox12+Erastin or siMettl3+Erastin treatment group was significantly lower than it in siMock+Erastin treatment group (Figure 3F). Collectively, these results showed that on the one hand, Erianin could increase the overall RNA m6A methylation level of HuRCSCs by promoting the expression of RNA m6A methylases, and on the other hand, Erianin could increase its stability and expression level by promoting m6A methylation at specific sites in the 3' UTR of ALOX12 and P53 mRNA, the key regulators of the ferroptosis pathway. ## Erianin inhibits the tumorigenicity of HuRCSCs in vivo by promoting ferroptosis-related protein expression HuRCSCs were inoculated onto the back of nude mice, and Erianin was injected intraperitoneally every 2 days. The nude mice were sacrificed around the ninth week. Naked eye observation showed that the tumors on the back of the nude mice in the Erianin injection group were significantly smaller than those in the control group (Figure 4A). Tumor tissue was isolated from the back of nude mice in each group. The weight and volume of the tumor tissue from the Erianin intervention group were significantly lower than those in the control group (Figure 4B). H & E staining showed that although the two groups of tumors were consistent with the pathological characteristics of clear cell renal cell carcinoma, the tumors from the Erianin intervention group had obvious vascular rupture and cell swelling (Figure 4C). The results of biochemical test showed that the concentration of tissue lipid peroxide (LPO) on Erianin intervention group was elevated significantly compared to it in the control group (Figure 4D). Immunohistochemical staining showed that the expression levels of marker of proliferation Ki-67 (Ki67) and GPX4 in the Erianin intervention group were significantly lower than those in the control group, while the expression levels of ALOX12 and METTL3 were significantly higher than those in the control group (Figure 4E). The experimental results suggested that Erianin promoted the expression of ferroptosis-related proteins and weakened the tumorigenicity of HuRCSCs in nude mice by regulating the expression of m6A methylation modification enzymes. ## Correlation between FTO gene expression and clinical prognosis of renal cell carcinoma Bioinformatic analysis of 522 tumor tissues from patients with clear cell renal cell carcinoma and 99 tissue samples from non-tumor diseases in the online database GEPIA (http://gepia.cancer-pku.cn/) showed that the transcription copy numbers of FTO and P53 in tumor tissues were significantly higher than those in normal tissues (Figure 5A), while the transcription copy number of ALOX12 in tumor tissues was slightly lower than that in normal tissues (Figure 5A). The mRNA expression levels of FTO and P53 in tumor tissue samples were significantly higher than those in normal control group samples, while the mRNA expression level of ALOX12 showed the opposite results (Figure 5B). However, there was no statistically significant difference in the expression levels of METTL3, FTO, ALOX12, P53 and other genes among all the stages of renal cell carcinoma (Figure 5C). In addition, the statistical results of the survival curve of patients with tumors indicated that the survival period of patients with RCC with high FTO expression was significantly longer than that of patients with low FTO expression (Figure 5D). Therefore, clinical data analysis showed that the expression level of FTO correlated positively with the survival patients with RCC. ## Discussion To date, many studies have pointed out that tumor tissue contains a special group of cell subsets, namely cancer stem cells 1, 4, 5, 28-30. Cancer stem cells have strong proliferation and invasion abilities, increased tolerance to chemotherapeutic drugs, and easily induce tumor metastasis and recurrence, which directly promotes poor prognosis of patients with tumors 1, 4, 5, 28. In vivo and in vitro experiments have shown that the drug resistance of cancer stem cells is much stronger than that of ordinary tumor cells, and they are resistant to a wide range of chemotherapeutic drugs, involving, for example, platinum drugs, paclitaxel, and gemcitabine 29, 30. Inhibition of the drug resistance of cancer stem cells is the key to blocking tumor cell reactivation and tumor recurrence. The limitations of traditional chemotherapeutic drugs in killing cancer stem cells have led many researchers to investigate natural products, hoping to find substances that can inhibit cancer stem cells. In the present study, we chose to investigate Erianin. Cell‑based experiments showed that Erianin significantly inhibited the proliferation and invasion of RCC stem cells in vitro and in vivo. These encouraging results suggested that Erianin has potential as an effective small molecule drug to inhibit cancer stem cells. However, it is necessary to clarify the molecular biological mechanism by which Erianin inhibits cancer stem cells. In this study, we approached this from two directions. On the one hand, we attempted to clarify the mechanism by which Erianin promoted ferroptosis of RCC stem cells. The occurrence of ferroptosis is closely related to the levels of GPX4, AIFM2, Fe2+, glutamine and lipid peroxide (LPO). Our results suggested that Erianin inhibits GPX4 expression and promotes LPO and Fe2+ accumulation in RCC stem cells, i.e., Erianin might promote drug toxicity by inducing ferroptosis in RCC stem cells. We also referred to recent studies on ferroptosis. For example, Chu et al. found that p53 activates ALOX12 indirectly via transcriptional repression of SLC7A11, resulting in ALOX12-dependent ferroptosis upon reactive oxygen species (ROS) stress 31. This prompted us to investigate the effects of Erianin on ALOX12 and p53 expression. We confirmed that the expression levels of ALOX12 and p53 in Erianin-treated cells increased significantly. We speculated that the mechanism by Erianin induces ferroptosis of RCC stem cells is consistent with the results of the study of Chu et al. Furthermore, Chu et al. reported that ALOX12 was dispensable for ferroptosis induced by erastin or GPX4 inhibitors 31. Thus, their study identified an ALOX12-mediated ferroptosis pathway that was critical for p53-dependent tumor suppression 31. According to the results reported by Chu et al., an increase in ALOX12 and a decrease in GPX4 can induce ferroptosis, and both are relatively independent pathways. However, our study found that Erianin could inhibit the expression of GPX4 and promote the expression of ALOX12 in RCC stem cells. This result suggested that Erianin-induced ferroptosis in cancer stem cells is likely to involve multiple pathways and targets. On the other hand, this study explained the epigenetic mechanism by which Erianin maintained the stable expression of members of the ALOX12/p53 signaling axis and promoted ferroptosis in HuRCSCs at the level of RNA methylation modification. RNA N-6 methyladenosine (m6A) is a methylation modification that occurs on the sixth nitrogen atom (N) of RNA adenine. M6A methylation modification of RNA exists widely in most eukaryotic species (from yeast and plants to fruit flies and mammals) and in viral mRNA, and plays a key role in posttranscriptional mRNA regulation and metabolism 32-36. The m6A methyltransferases METTL14 and METTL3 are two components of the m6A methyltransferase complexes. These two proteins can form stable complexes at a ratio of 1:1 to complete RNA m6A modification, belonging to the “Writers” group of enzymes 35, 37-39. FTO removes m6A methylation of RNA, acting as an “Eraser” 16, 19, 35, 37-40. Therefore, RNA m6A modification is a dynamic, reversible enzymatic reaction 35, 37-39. Studies have suggested that RNA m6A modification can improve the stability of mRNA, increase its transcription and translation activities, promote tumor occurrence and invasion, and improve the reprogramming efficiency of stem cells 32-39. Meyer et al. and Dominissini et al. used the same m6A-specific binding immunoprecipitation high-throughput sequencing method to determine human and mouse genes, respectively, and studied the distribution of RNA m6A in the whole transcriptome. The results showed that the RNA m6A modification was mainly distributed near the termination codons of the 3'-UTR and coding region (CDS) of mRNA, which proved that the distribution of the m6A modification in human and mouse was highly conserved, and the RNA m6A modification could improve the stability of mRNA 26, 32, 33, 37. Further studies showed that m6A was mainly enriched in the vicinity of mRNA termination codon, 3' UTRs, and exons of mRNA internal minister, and the main conserved sequences were G (m6A) C ($70\%$) or A (m6A) C ($30\%$) 32, 34, 40. There are some reports regarding the mechanism of ferroptosis induced by m6A. Ma et al. reported that the m6A reader YT521-B homology containing 2 (YTHDC2) is a powerful endogenous ferroptosis inducer and targeting the solute carrier 3A2 (SLC3A2) subunit of system XC- is essential for this process 17. Meanwhile, Shen et al. found that the m6A modification appears to trigger autophagy activation by stabilizing BECN1 mRNA (encoding beclin 1), which might be the potential mechanism for m6A modification-enhanced ferroptosis of hepatic stellate cells 16. In addition, YTH N6‑methyladenosine RNA binding protein 1 (YTHDF1) was identified as a key m6A reader protein for BECN1 mRNA stability, and knockdown of YTHDF1 prevented BECN1 plasmid-induced ferroptosis of hepatic stellate cells 16. In addition, Sun et al. indicated that nuclear factor kappa B (NF-κB) activating protein (NKAP), an RNA‑binding protein, could protect glioblastoma cells from ferroptosis by promoting SLC7A11 mRNA splicing in an m6A-dependent manner 20. However, whether Erianin can induce changes in the overall RNA m6A modification of tumor cells has not been reported. According to the above clues, we detected the expression of m6A modification-related enzymes before and after Erianin treatment of HuRCSCs. The results suggested that the methylation transferase METTL3 was highly expressed and the methylation erase FTO was poorly expressed. This result suggested that Erianin was likely to promote the overall methylation modification of tumor cell RNA. Subsequently, we detected the m6A modification in the specific region of the 3' UTR of the mRNAs encoding key factors in the of ALOX12/p53 signaling axis, which is closely related to ferroptosis. We found that m6A modification in the 3' UTR specific regions of ALOX12 and P53 mRNA was very low in the control group, whereas the m6A modification level of the above genes in tumor cells increased significantly after Erianin treatment (Figure 6). Therefore, we believe that Erianin induces an increase in the m6A methylation modification of multiple genes by promoting the expression of m6A methylation transferase METTL3 in tumor cells. ## Author Contributions All authors contributed to the study conception and design. 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--- title: Bioinformatic analysis of the obesity paradox and possible associated factors in colorectal cancer using TCGA cohorts authors: - Dong Min Lim - Hyunsu Lee - Kisang Eom - Yun Hak Kim - Shin Kim journal: Journal of Cancer year: 2023 pmcid: PMC9969588 doi: 10.7150/jca.80977 license: CC BY 4.0 --- # Bioinformatic analysis of the obesity paradox and possible associated factors in colorectal cancer using TCGA cohorts ## Abstract Colorectal cancer (CRC) is a common malignancy worldwide and the second leading cause of cancer-related deaths. Obesity is an important determinant of CRC incidence; however, obese patients have also shown better long-term survival than non-obese patients, suggesting that the development and progression of CRC are associated with different mechanisms. This study compares the expression of genes, tumor-infiltrating immune cells, and intestinal microbiota between high- and low-body mass index (BMI) patients at the time of CRC diagnosis. The results revealed that high-BMI patients with CRC have better prognosis, higher levels of resting CD4+ T cells, lower levels of T follicular helper cells, and different levels of intratumoral microbiota than low-BMI patients. Our study highlights that tumor-infiltrating immune cells and intratumoral microbe diversity are major features of the obesity paradox in CRC. ## Introduction Colorectal cancer (CRC) is the third-most diagnosed cancer worldwide and the second leading cause of cancer-related deaths. According to Xi and Xu 1, the proportion of CRC incidence and related deaths are expected to increase substantially by 2040 because of the impacts of a westernized diet and modern sedentary lifestyles. Although obesity is considered to be an important risk factor of CRC 2, 3, contradictory reports have been obtained regarding its role in CRC progression, and it has also been associated with increased survival rates in CRC patients 4-6. Being overweight or obese is known to increase the risk for various chronic diseases, such as cancer or cardiovascular disease 7, 8. However, in various consumptive chronic diseases - several cancers and tuberculosis - lower body mass index (BMI) has been associated with worse prognosis 9-12. This phenomenon in which the prognosis of overweight patients is superior to that of underweight and normal weight patients is known as the “obesity paradox” 13. Although obesity was associated with greater overall mortality in cancer patients, obese patients with lung cancer, renal cell carcinoma, melanoma, and CRC had better prognoses than under- or normal weight patients with the same conditions 14-20. Given that obesity is a confirmed risk factor for CRC and metabolic syndromes 21, the mechanism underlying the obesity paradox in cancer remains ambiguous 22. Tumor-infiltrating immune cells (TIICs) play an important role in tumor development and determining clinical outcomes 23. TIICs are promising biomarkers for the diagnosis and prognosis of non-metastatic CRC 24, and have achieved greater prognostic performance than histopathological methods 25. We hypothesize that the evaluation of TIICs could elucidate the molecular mechanisms associated with the obesity paradox in CRC. The gut microbiome plays a crucial role in the local and systemic immunomodulation of various diseases, including tuberculosis, cardiovascular disease, and cancer 26-28. A previous report showed a strong relationship between the intake of certain bacteria and the inhibition of colon cancer progression 29, which is achieved via intestinal homeostasis and immune regulation 30, 31. Although the relationship between obesity and gut microbiota composition has been widely investigated 32, the complex and dynamic relationships between gut microbiota, obesity, and CRC remain unclear. This study describes the obesity paradox of CRC in cohorts of The Cancer Genome Atlas (TCGA) and investigates the associated characteristics of TIICs and intratumoral microbiome using whole genome and RNA sequencing analyses. ## Data acquisition and pre-processing Gene expression, methylation, and clinical data were downloaded from TCGA-COAD (colorectal adenocarcinoma) and TCGA-READ (rectal adenocarcinoma) databases. Data are available at the Genomic Data Commons data portal 33 (https://portal.gdc.cancer.gov). Overall survival (OS) data were downloaded from the UCSC Xena Browser 34 (http://xena.ucsc.edu). BMI was calculated as weight divided by height squared (kg/m2) and categorized based on WHO classifications (high-BMI group, BMI ≥ 30; low-BMI group, BMI ≤ 25). Asian patient data were excluded from TCGA cohorts because the Asia-Pacific BMI classification differs from that of WHO. We investigated the intratumoral microbiome using the Kraken analysis, as previously described 35. The Kraken algorithm is a rapid and highly accurate program for assigning taxonomic labels to metagenomic sequences using k-mers alignment 36. We downloaded microbiome data for CRC patients from an online data repository (ftp://ftp.microbio.me/pub/cancer_microbiome_analysis/TCGA/Kraken/). We specifically used intratumoral microbiome abundance (“Kraken-TCGA-Voom-SNM-Plate-Center-Filtering-Data.csv”) and clinical data (“Metadata-TCGA-Kraken-17625-Samples.csv”). Pre-processing of data was performed using R software (v4.1.1) 37. Patient information is listed in Table 1. ## Differentially expressed gene (DEG) analysis We used the edgeR package (v3.34.1) in R to identify DEGs 38. First, DEGs were identified for primary tumor samples of the high- and low-BMI patient groups. We used the Benjamini-Hochberg adjusted $P \leq 0.05$ and \|log2 fold change\| > 0.05 to identify genes with upregulated and downregulated expressions. We filtered unexpressed and low counts using the edgeR function filterByExpr, with the minimum count required for at least some samples = 10, minimum total count required = 15, and minimum proportion of samples in the smallest group that expressed the gene = 0.7. The trimmed mean of the M-values were normalized and analyzed using the edgeR function glmQLFTest. A quasi-likelihood test, which fits the data to a quasi-likelihood negative binomial generalized log-linear model, was used to perform gene-specific analyses for a given coefficient or contrast. The EnhancedVolcano package (v1.10.0) in R was used to visualize the DEGs 39. ## Protein-protein interaction (PPI) network construction and module selection To identify the pathways and functions associated with the DEGs, PPI networks were constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database (http://www.string-db.org) 40 and Cytoscape software (v3.8.2) 41. The Molecular Complex Detection (MCODE) plugin was used for module selection with the following parameters 42: degree cutoff, 2 × cluster finding; node score cutoff, 0.2 × K-core = 2 ×; maximum depth, 100. ## Functional analysis of DEGs DEG pathways were further assessed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the ClusterProfiler package (v4.0.5) in R 43, and GO and KEGG enrichment analyses were performed using the enrichment plot (v1.12.3) R package 44. Significance was set at $P \leq 0.05.$ ## Analysis of TIICs based on machine learning We estimated the proportions of infiltrating immune cell types in each sample using bulk RNA sequencing data, which was analyzed using the CIBERSORTx algorithm 45. CIBERSORTx is a machine learning algorithm that extends the CIBERSORT framework to infer cell-type-specific gene expression profiles without physical cell isolation. We included 22 immune cell subtypes parsed from the gene signature matrix LM22 and 1,000 permutations of the CIBERSORTx web portal (http://cibersortx.stanford.edu/) with bulk-mode batch correction. ## Linear discriminant analysis effect size (LEfSe) To identify unique microbial signatures in the CRC samples between the high- and low-BMI patients, we analyzed the Kraken-TCGA datasets using a linear discriminant analysis in the Galaxy web application (http://huttenhower.sph.harvard.edu/galaxy) 46. To identify taxa with significantly differential abundance, we used the factorial Kruskal-Wallis test for classes and pairwise Wilcoxon test for subclasses, with the significance set at $P \leq 0.05.$ The threshold for the logarithmic LDA score for discriminative features was set at 2. ## Differential methylation probes (DMPs) Methylation data (downloaded from TCGA database) included 159 CRC patients (79 high and 80 low BMI samples). We used the ChAMP package (v2.24.0) to identify the DMPs between the high- and low-BMI groups 47. A beta-mixture quantile normalization was performed to correct the probe design bias in Illumina 450k DNA methylation data. Batch effects were corrected using the ChAMP function ComBat to reduce technical variation. We selected the cutoff values $P \leq 0.05$ and \|deltaBeta\| > 0.05 to define hypermethylated and hypomethylated genes. ## Statistical analysis OS rate was compared between the high- and low-BMI groups using Kaplan-Meier survival curves and a log-rank P-value. Hazard ratios (HRs) and $95\%$ confidence intervals (CIs) were estimated using a Cox proportional hazards model to investigate the association between patient survival and multiple predictors. A Schoenfeld individual test was performed to confirm that the assumptions of the Cox proportional hazards model were met (Supplementary Figure S1). The Wilcoxon signed-rank test was used to compare the mean difference in the immune cell fractions of CRC patients. Venn diagrams were generated using Venny software 48. All statistical analyses and visualizations were performed using R software 37. ## BMI and patient survival The Kaplan-Meier survival curves revealed that OS rates were lower in the low-BMI than in the high-BMI CRC patient group (log-rank $$P \leq 0.028$$, Figure 1). According to the Cox proportional hazards model, patient survival in the low-BMI group was significantly affected by age, sex, and TNM stage (HR = 2.49, $95\%$ CI: 1.06-5.9, $$P \leq 0.037$$; Table 2). ## Molecular differences between high- and low-BMI groups in CRC patients We identified 569 DEGs (298 upregulated and 271 downregulated in the high-BMI group) between the high- and low-BMI groups. Volcano plots and a heatmap of the 569 DEGs are shown in Figure 2A and 2B. From the PPI network (Figure 2C-D), we identified significant modules of genes with upregulated and downregulated expressions using MCODE. In the PPI network of the 298 genes with upregulated expressions, the module with the highest MCODE score contained six genes (HIST1H1B, HIST1H1C, HIST1H2AI, HIST1H2BC, HIST1H2BH, and HIST1H3J; Figure 2E). The second-most significant module comprised 12 genes (ABCA4, ALDH3A1, CNGA1, CNGA3, GSTA1, GSTM1, HPGDS, USH2A, FRZB, FZD9, WNT4, and WNT7B; Figure 2F). In the PPI network of the 271 genes with downregulated expressions, the most significant module comprised 14 genes (ALOX15B, CXCR2, CYP2C9, CYP2E1, GFAP, IGF1, IL1A, IL1RN, MAPT, OSM, PLA2G2A, PLA2G4A, PTGS2, and UGT1A6; Figure 2G) and the second-most significant module comprised seven genes (MYH4, MYH7B, MYLPF, SCN5A, TCAP, TRIM54, and TTN; Figure 2H). ## Functional enrichment analysis of DEGs To identify the functional factors of the highly networked protein groups, we performed GO and KEGG enrichment analyses to determine the biological roles of the 39 genes (upregulated: 18, downregulated: 21) comprising the significant modules in the PPI network (Figure 3). The GO analysis considered three major categories: biological process (BP), cellular component (CC), and molecular function (MF). The top GO categories of the 18 genes with upregulated expressions were visual perception, prostanoid metabolic process, prostaglandin metabolic process, photoreceptor cell cilium, and glutathione transferase activity (Figure 3A). The top GO categories of the top 21 genes with downregulated expressions were arachidonic acid (AA) metabolic process, long-chain fatty acid metabolic process, unsaturated fatty acid metabolic process, contractile fiber, and structure constituent of muscle (Figure 3B). According to the KEGG pathway analysis, the 18 genes genes with upregulated expressions were primarily associated with hepatocellular carcinoma, cytochrome P450, Wnt signaling pathway, glutathione metabolism, basal cell carcinoma, DNA adducts, melanogenesis, signaling pathways regulating pluripotency of stem cells, and breast cancer (Figure 3C); the 21 genes with downregulated expressions were primarily associated with AA metabolism, linoleic acid metabolism, DNA adducts, serotonergic synapse, MAPK signaling pathway, cytokine receptor interaction, ovarian steroidogenesis, metabolism of cytochrome P450, and alpha-linolenic acid metabolism (Figure 3D). ## Investigation of DNA methylation-driven DEGs We identified 5,684 DMPs (274 hypermethylated and 5,410 hypomethylated genes in the high-BMI group) between the high- and low-BMI groups. We investigated the overlapping DEGs (Figure 2A) and DMPs between the high- and low-BMI groups (Figure 4A and 4B) and identified 86 upregulated and hypomethylated genes and 10 downregulated and hypermethylated genes. Figure 4C shows the PPI network of the 86 upregulated-hypomethylated genes. The module with the highest MCODE contained four genes (FRZB, FZD9, WNT4, and WNT7B; Figure 4D); the module with the second-highest MCODE comprised 10 genes (ABCA4, CLCA1, CNGA3, FCGBP, FOLR1, MUC16, PI3, REG3A, USH2A, and WFDC2; Figure 4E). The PPI network of the 10 downregulated-hypermethylated genes did not show statistically significant results. The top GO categories of the 14 upregulated-hypomethylated genes were the Wnt signaling pathway, Golgi lumen, actin-based cell projection, and Wnt-protein binding (Figure 4F). In the KEGG enrichment analysis, the 14 upregulated-hypomethylated genes were associated with Wnt signaling pathway, basal cell carcinoma, melanogenesis, signaling pathways regulating pluripotency of stem cells, breast cancer, gastric cancer, mTOR signaling pathway, Cushing syndrome, Hippo signaling pathway, and hepatocellular carcinoma (Figure 4G). ## Differences in TIICs between high- and low-BMI groups in CRC patients Using the CIBERSORTx algorithm, we estimated the relative abundance of 22 immune cells from the bulk tumor RNA sequencing data. Figure 5 shows the differences between the 22 TIICs according to the BMI groups. When compared with that in the low-BMI group, we found that resting CD4+ T cells were more abundant ($$P \leq 0.032$$, Wilcoxon's rank test) and T follicular helper (Tfh) cells were less abundant in the high-BMI group ($$P \leq 0.043$$, Wilcoxon's rank test). Spearman correlation analysis showed that CNGA3, GSTA1, HPGDS, FRZB, and WNT4 were significantly correlated with resting CD4+ T cells. IL1RN, OSM, and PLA2G2A were significantly correlated with T follicular helper (Tfh) cells (Supplementary Figures S2 and S3). Univariate and multivariate Cox regression analyses between BMI and TIIC of CRC patients are shown in Supplementary Table S1. ## Investigation of unique microbial signatures between BMI groups We divided 184 CRC samples into 23 DNA whole genome sequencing (seven high- and 16 low-BMI samples) and 161 RNA sequencing samples (79 high- and 82 low-BMI samples). LEfSe of the Kraken-TCGA dataset identified nine enriched microbe genera in the high-BMI group (Shinella, Fimbriimonas, Blastomonas, Frondihabitans, Modestobacter, Caldimicrobium, Morococcus, Sclerodarnavirus, and Bifidobacterium; Figure 6A) and 11 enriched microbe genera in the low-BMI group (Rothia, Phenylobacterium, Succinimonas, Stomatobaculum, Basilea, Megasphaera, Methylobacillus, Lentimicrobium, Plesiocystis, Rubellimicrobium, and Nitrospira; Figure 6B). ## Discussion Given the high incidence and mortality rates for CRC globally, intensive efforts are being made to discover effective prognostic factors and elucidate the molecular mechanisms of CRC to improve patient prognosis 5, 6, 49-51. Although controversial, the obesity paradox - first noted with the high survival rates of hemodialysis patients with high BMI 52 - has been reported in various chronic diseases 53-56, including CRC 57-62. However, bioinformatic studies on the characteristics of TIICs and intratumoral microbiome associated with the obesity paradox in CRC are scarce. Therefore, we investigated the differences in gene expression, TIIC occurrence, and intratumoral microbiome composition according to BMI via bioinformatic analysis of CRC TCGA data. We confirmed the obesity paradox in CRC for TCGA-COAD and TCGA-READ cohorts. As shown in Figure 1, high BMI was associated with a favorable prognosis, i.e., higher OS rates. The PPI network analysis of DEGs identified 18 and 21 genes with upregulated and downregulated expressions, respectively (Figure 2E-H). Many studies have reported the functions of histone variants in CRC 63, and various dysregulated genes related to CRC prognosis have also been reported: For example, high expression levels of CXCR2, IGF1, IL1A, OSM, and PLA2G4A, and hypermethylated MAPT were associated with poor prognosis 64-69; low expression levels of GFAP and PLA2G2A were associated with poor prognosis 70, 71. However, ALOX15B and PTGS2 expression levels were not associated with CRC prognosis 72, 73. Although a more detailed analysis on the role of these aberrantly expressed genes is required, our findings suggest that these DEGs are collectively responsible for enhanced survival in the high-BMI group. GO analysis of the DEGs revealed that glutathione transferase activity was high in the high-BMI group (Figure 3A). We also observed the existence of a correlation between GSTA1 and resting CD4 T cells (Supplementary Figure S2). However, CRC prognosis was not dependent on TIIC (Supplementary Table S1). Therefore, we believe that GST activity affects the obesity paradox of CRC patients in different ways. Indeed, a meta-analysis showed that GSTM1 and GSTT1 null genotypes contributed to an increased risk of CRC in the Caucasian population 74. Low expression of GSTM1 and GSTM2 was associated with better prognosis of CRC 75. In addition, GST-pi serves as an effective marker of survival in CRC 76. These findings provide a strong foundation for the association of glutathione s-transferase (GST) activity with prognosis in CRC. Another study reported that high levels of GST activity were associated with better survival and prognosis in ovarian cancer 77. GST is considered to lower the risk of cancer by regulating reactive oxygen species (ROS) 78. In contrast, GO analysis showed that the expression of long-chain fatty acid pathways, including the AA pathway, were lower in the high-BMI CRC patient group. In vitro studies using human cell lines derived from lung cancer and CRC have shown that AA inhibitors induce apoptosis 79, 80. Disease-free survival of cholangiocarcinoma patients with low expression of the AA pathway-associated COX-2 and 5-LOX showed better prognosis 81. Inhibition of the AA pathway enzymes of COX-2, 5-LOX, and CYP450 could inhibit cell proliferation and neoangiogenesis 82. Oral cancer patients with asymptomatic loss-of-function somatic mutations in the AA pathway showed good response to chemotherapy, which was likely because of an associated downregulation of the PI3K-Akt pathway downstream 83. In contrast, dysregulation of the eicosanoid pathway by chronic inflammation has complex implications for tumorigenesis, i.e., both cancer-promoting and anti-cancer roles 84. A previous study revealed that obesity was positively associated with AA-derived 5- and 11-hydroxyeicosatetraenoic acid levels 85. Obesity induces increased AA metabolism and activates various signaling pathways, including the PI3K-Akt pathway, and inflammatory cytokines, which have conflicting effects on CRC progression. Therefore, the downregulation of long-chain fatty acid metabolic pathways because of obesity does not necessarily improve prognosis in CRC. KEGG pathway analysis revealed that drug metabolism of cytochrome P450 and metabolism of xenobiotics by cytochrome P450 were up- and downregulated, respectively (Figure 3C and 3D). This is consistent with the PPI analysis wherein the expressions of ADLH3A1, GSTA1, GSTM1, and HPGDS were upregulated and those of CYP2C9, CYP2E1, and UGT1A6 were downregulated in the high-BMI CRC patient group with good prognosis. As a matter of fact, obesity has been reported to increase the activity of cytochrome P450 2E1 86. Although more detailed mechanistic studies are required, these results suggest that cytochrome P450-related genes are critical to the obesity paradox of CRC. TIICs are important determinants of tumor development and clinical outcomes in cancer patients 23. Increased levels of tumor-infiltrating Tfh cells were correlated with increased survival of melanoma cancer patients 87, and favorable prognosis in lung squamous cell carcinoma 88. Tfh cells are more abundant in obese than lean mice 89. However, in the present study, we identified higher levels of resting CD4+ T cells and lower levels of Tfh cells in the high-BMI group. As shown in Supplementary Figures S2 and S3, CNGA3, GSTA1, HPGDS, FRZB, and WNT4 were significantly correlated with resting CD4+ T cells, and IL1RN, OSM, and PLA2G2A were significantly correlated with Tfh cells. Wnt/β-catenin signaling plays an important role in T-cell immunity 90 and cancer immunotherapy 91. Moreover, Thf cells may be involved in the occurrence of immune-related adverse events in highly efficient immune checkpoint blockade treatment through exaggerating cytotoxic T lymphocyte response 92. The generation of robust memory T cell populations is critical for T cell-based therapies to prevent and treat cancer 93. In contrast, a previous study reported that OSM not only increases the metastatic potential of breast cancer in vitro but also promotes metastasis in vivo, and may negatively affect patient survival 94. Lin Wang et al. 95 showed that OSM is associated with CD4 T cells and high-infiltration of Tfh was associated with poor prognosis. Our finding may provide guidance for further investigations regarding the mechanism of the obesity paradox in CRC. A previous report found that high-BMI patients (>30 kg/m2) have higher levels of macrophage M1 (1.13-fold higher than the 25-18.5 kg/m2 group) and lower levels of activated natural killer cells (0.25-fold lower than the 25-18.5 kg/m2 group) 96. Even though in the aforementioned study, CIBERSORT with gene expression and clinical data corresponding to CRC patients from the TCGA database was used, their results were distinctly different from ours. However, we could not conduct an informed comparison as the description of their dataset was limited. These results suggest that a more in-depth study on the role of TIICs in the obesity paradox of CRC is required. Gut microbiota plays a vital role in regulating tumorigenesis and the progression of CRC 97-99. A recent meta-analysis of CRC showed that high levels of Fusobacterium nucleatum and Bacteroides fragilis were related with poor and improved survival, respectively 100. Although we did not identify F. nucleatum or B. fragilis, Bifidobacterium was found in CRC samples of high-BMI patients. Bifidobacterium was previously found in the fecal samples of a healthy control group 101 and occurred at a low level in a CRC patient. Kosumi et al. 102 showed that the abundance of Bifidobacterium was associated with the level of signet ring cells, suggesting that Bifidobacterium might affect the tumor microenvironment and differentiation of cancer cells. Nevertheless, there was no significant difference in survival probability related to Bifidobacterium. Although little evidence exists for Bifidobacterium improving survival in CRC patients, the existing literature and our results suggest that Bifidobacterium is a potential diagnostic and prognostic marker for CRC. Recently, it has been reported that Bifidobacterium lactis and Lactobacillus plantarum suppress glioma growth in mice by inhibiting the PI3K-Akt pathway 103. Our results revealed that the AA pathway was lower but the KEGG enrichment analysis of the 14 upregulated-hypomethylated genes was associated with mTOR signaling pathway in the high-BMI CRC patients. Therefore, further studies are needed to investigate the effect and mechanisms of Bifidobacterium on the AA/PI3K-Akt/mTOR signaling pathway, and for this, a research model using CRC organoid needs to be considered. We acknowledge that there are some limitations to our study. First, considering the aim of our study - the assessment of the molecular and prognostic differences between CRC patients of varying BMI - it is difficult to determine obesity based on BMI. As we used TCGA datasets, we had to apply the WHO standard of 30 kg/m2 to define obesity. However, considering the molecular changes caused by obesity, it may be more appropriate to use waist circumference rather than BMI; however, TCGA database does not provide waist circumference. Second, although the histological type of CRC is an important factor for prognosis, it was not stratified in TCGA data. Third, the immune cell infiltration assays and microbiome analysis were based on bioinformatic techniques. Finally, we did not examine the relationship between the molecular effects of obesity and sequential change of the microbiome and the feedback between these factors. Given these limitations, it is difficult to conclude the plausibility of the obesity paradox in CRC. Future research should consider the causal relationship or underlying mechanism of the obesity paradox in CRC. ## Conclusion CRC is a common malignancy worldwide and is the second leading cause of cancer-related deaths. Obesity paradox is a phenomenon in which the prognosis of overweight patients is superior to that of underweight and normal weight patients in several chronic diseases such as CRC. This study shows that high-BMI patients with CRC have better prognosis, higher levels of resting CD4+ T cells, lower levels of Tfh cells, and different levels of intratumoral microbiota than low-BMI patients. Our study highlights the TIICs and intratumoral microbe diversity as major features of the obesity paradox in CRC. ## Funding This study was supported by grants from the National Research Foundation of Korea (grant funded by the Korean Government Ministry of Science, ICT and Future Planning; grant no. 2021R1I1A3A04037479 and 2021M3E5D7102565). ## Availability of data and materials All available data are presented within the manuscript or are available from the corresponding authors on reasonable request. ## Author contributions Conceptualization, S.K. and S.K.; Data Curation, D.M.L, and H.L.; Formal Analysis, D.M.L., Y.H.K., and S.K.; Funding Acquisition, S.K; Investigation, D.M.L., Y.H.K., and S.K.; Methodology, D.M.L. and Y.H.K.; Project Administration, S.K.; Software, D.M.L. and Y.H.K.; Supervision, Y.H.K. and S.K.; Visualization, D.M.L. and Y.H.K.; Roles/Writing - original draft, D.M.L., H.L., and S.K.; Writing - review & editing, H.L, K.E., and S.K. ## References 1. Xi Y, Xu P. 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--- title: Platycodin D confers oxaliplatin Resistance in Colorectal Cancer by activating the LATS2/YAP1 axis of the hippo signaling pathway authors: - Chien-Hao Wang - Rathinasamy Baskaran - Shawn Shang-Chuan Ng - Tso-Fu Wang - Chi-Cheng Li - Tsung-Jung Ho - Dennis Jine-Yuan Hsieh - Chia-Hua Kuo - Ming-Cheng Chen - Chih-Yang Huang journal: Journal of Cancer year: 2023 pmcid: PMC9969589 doi: 10.7150/jca.77322 license: CC BY 4.0 --- # Platycodin D confers oxaliplatin Resistance in Colorectal Cancer by activating the LATS2/YAP1 axis of the hippo signaling pathway ## Abstract Oxaliplatin-based therapy is used as a first-line drug to treat metastatic colorectal cancer. However, long-term and repeated drug treatment resulted in drug resistance and the failure of chemotherapy. Various natural compounds were previously reported to act as chemosensitizers to reverse drug resistance. In this study, we found that platycodin D (PD), a saponin found in Platycodon grandiflorum, inhibited LoVo and OR-LoVo cells proliferation, invasion, and migration ability. Our results indicated that combined treatment of oxaliplatin with PD dramatically reduced the cellular proliferation in both LoVo and OR-LoVo cells. Furthermore, treatment with PD dose-dependently decreased LATS2/YAP1 hippo signaling and survival marker p-AKT expression, as well as increased cyclin-dependent kinase inhibitor proteins such as p21 and p27 expression. Importantly, PD activates and promotes YAP1 degradation through the ubiquitination and proteasome pathway. The nuclear transactivation of YAP was significantly reduced under PD treatment, leading to transcriptional inhibition of the downstream genes regulating cell proliferation, pro-survival, and metastasis. In conclusion, our results showed that PD is suitable as a promising agent for overcoming oxaliplatin-resistant colorectal cancer. ## Introduction Colorectal cancer (CRC) is the third prevalent malignant tumor, causing malignancies. In spite of substantial advancements in CRC therapy, it remains one of the primary causes of cancer-related mortality 1. Oxaliplatin-based chemotherapy is employed in the front lines of CRC treatment all over the world. Nearly half of CRC patients receiving oxaliplatin-based chemotherapy are cured of CRC 2, 3. Oxaliplatin is a platinum based third generation chemotherapeutic agent, which acts against the cancer cells by interacting with DNA and forms cross-links between the two strands in the S phase of cell division 4. On the other hand, chemotherapy could also develop resistance to oxaliplatin, which may allow cancer cells to survive or quiescence, contributing to the cancer reappearance. Resistance to oxaliplatin is likely related to cellular transport, detoxification, DNA repair, cell death, and epigenetic alternation 5, 6. Understanding these molecular mechanisms of oxaliplatin resistance and utilizing them for developing novel therapeutic strategies for cancer therapies have been investigated 7-9. The key serine/threonine kinase of the hippo tumor-suppressive signaling pathway is Large tumor suppressor 2 (LATS2), which is present in chromosome 13Q1.11 in humans. LATS2 controls the cell cycle by regulating Yes-associated protein 1 (YAP) and Transcriptional coactivator with PDZ-binding motif (TAZ) (orthologues of Yorkie in Drosophila) phosphorylation, which are key downstream regulators in the hippo signaling pathway 10. LATS2 is a key regulator of mitotic progression and activates its downstream proteins such as YAP, retinoblastoma protein (pRB), and p53, which altogether contribute to cell cycle arrest and cancer cell growth inhibition 11. Further, LATS2 is also known to interact with other signaling pathways like estrogen signaling, and the Ras and Akt network which plays role in regulating cell proliferation, apoptosis, and metastasis of different cancer types 12, 13. Recent studies have shown the association of hippo pathway in the development of CRC 14-16. The remarkable oncogenic characteristics of the Hippo signaling pathway proteins, such YAP and TAZ, as well as their druggability, are gaining attention in recent researches in cancer drug resistance 17. Paclitaxel and cisplatin resistance is conferred by overexpressing YAP-S127A in ovarian cancer cells with low baseline YAP activity, but YAP knockdown in ovarian cancer cells with higher YAP activity improves sensitivity to paclitaxel and cisplatin. This is due to YAP-S127A lacks a significant LATS$\frac{1}{2}$ phosphorylation site and accumulates in the nucleus 18. In nasopharyngeal cancer, epithelial-mesenchymal transition (EMT) and overexpression of TAZ have been found to be positively correlated 19. While TAZ stimulates interleukin-8 (IL-8) transcription to develop resistance to doxorubicin, YAP induces doxorubicin resistance by triggering the mitogen-activated protein kinase (MAPK) pathway 20. Natural products have been extensively studied in the realm of drug discovery because they are a rich source of molecules with a wide structural variety. In vitro and in vivo anticancer properties have been observed in a wide range of natural compounds 21-23. Platycodon grandifloras common Chinese medicinal plant belongs to the family Campanulaceae which was used in traditional folk medicines in China, Japan, and Korea. The root of Platycodon grandifloras is used to cure a variety of ailments heavy cough, sore throat, bronchitis, and asthma 24, 25. Platycodin D is one of the major saponins presented in Platycodon grandifloras which possess various pharmacological properties such as anti-oxidant, anti-inflammatory, anti-obesity, anti-atherogenic, and immunomodulatory effects 26-30. PD has remarkable antitumor effects on several cancer cell lines, reducing the proliferation of cancer cell growth by inhibiting the cell cycle and inducing apoptosis 31, 32. On the basis of these studies, we hypothesize that LoVo colorectal cancer cell line develops resistance to chemotherapeutic drugs by activating the hippo signaling pathway and activates LATS2, and YAP by phosphorylation and PD treatment in the parental and resistance LoVo cells could effectively modulate the hippo signaling pathway by inhibiting the phosphorylation of LATS2, and YAP. ## Cell culture Food Industry Research and Development Institute, Hsinchu, Taiwan, provided the LoVo colon cell line, which is human colon cell line. LoVo cells were cultured in Dulbecco's Minimum Essential Medium (DMEM, Sigma, USA) containing $10\%$ fetal bovine serum (FBS) (HyCloneTM, USA), streptomycin (100 g/mL) and penicillin (100 U/mL) at 37 °C in a humidified environment of $5\%$ CO2. To establish stable colon cancer cell lines resistant to oxaliplatin, LoVo cell lines were exposed to oxaliplatin in dose-dependent manner from 0 to 25 µM for 24 hr. This was MTT-1. LoVo cell lines exposed to 21.5 µM oxaliplatin resulted around $50\%$ cell death (IC50 of oxaliplatin). Cells treated with 21.5 µM oxaliplatin (24 hr) allowed to reach $80\%$ confluence and passaged twice in this same concentration (21.5 µM) of oxaliplatin. The same procedure was repeated at increased doses of oxaliplatin (30 and 40 µM) until a cell population was selected that demonstrated at least a threefold greater IC50 (75 µM) to oxaliplatin than the parental cell lines. OXA-R LoVo cells were developed by the previous report 3. ## MTT assay To assess the cell viability of LoVo cells, the MTT [3-(4, 5-Dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium-bromide] assay was performed. A 96-well plate was seeded with 1 x 104 parental LoVo cells and 1 x 104 OXA-R LoVo cells, and the cells were exposed to various drug doses. Oxaliplatin was treated to parental LoVo and OXA-R LoVo cells at 1, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, and 80 µM for 24 hours, After the treatment time, DMEM was removed from the cells and washed with PBS. Each well received 20 μl (5 mg/mL) of MTT, which was then left to incubate for 4 hours. A microplate reader was used to detect the absorbance at 570 nm after dissolving MTT formazan crystals in 200 μl of DMSO. Platycodin D (purity ≥$98\%$, Sigma, USA) was dissolved in DMSO. ## Western blot An equal amount (30-40 µg) of protein was separated by using an 8-$12\%$ SDS-PAGE electrophoresis gel at 90V for 45 minutes. Protein from the gel was transferred to the polyvinylidene fluoride membrane at 4 °C using blotting apparatus (Bio-Rad Laboratories, Hercules, CA, USA). PVDF membrane was then submerged in $5\%$ non-fat milk powder in TBST at room temperature for 1 hr. The membrane was then washed thrice with TBS 3 times, for 5 minutes each. The membrane was then incubated overnight with primary antibody (1:1000 dilution in TBST) at 4 °C in a mechanical rocker. Then, the membrane was washed thrice with TBS 3 times, for 5 minutes each, and incubated with HRP-conjugated secondary antibody (1:5000 dilution in TBST) at room temperature for 1 h in a mechanical rocker. After washing with TBS, the membrane was submerged in chemiluminescence ECL solution (Merck Millipore, Burlington, MA, USA), the protein bands were visualized using chemi-doc apparatus (Fuji-film LAS-3000, GE Healthcare), and densitometric analysis was performed using ImageJ software (version 1.4.3.67) (NIH, Bethesda, MD, USA). Antibodies details: LATS2 (#5888, Cell Signaling, Baltimore, MD, USA), p-LATS2 (ab111344, Abcam, Cambridge, UK), YAP (sc-101199, Santa Cruz Biotechnology, Santa Cruz, CA, USA), p-YAP (#13008, Cell Signaling, Baltimore, MD, USA), TAZ (sc-48805, Santa Cruz Biotechnology, Santa Cruz, CA, USA), p21 (sc-6246, Santa Cruz Biotechnology, Santa Cruz, CA, USA), p27 (sc-1641, Santa Cruz Biotechnology, Santa Cruz, CA, USA), p-AKT (#9275s, Cell Signaling, Baltimore, MD, USA), Ki67 (ab15580, Abcam, Cambridge, UK), β-Actin (sc-47778, Santa Cruz Biotechnology, Santa Cruz, CA, USA), HDAC1 (sc-6298, Santa Cruz Biotechnology, Santa Cruz, CA, USA), Ubiquitin (sc-8017, Santa Cruz Biotechnology, Santa Cruz, CA, USA), GAPDH (sc-25778, Santa Cruz Biotechnology, Santa Cruz, CA, USA). ## Terminal Deoxynucleotidyl Transferase-mediated Nick-End Labeling (TUNEL) assay DNA breaks due to apoptosis was determined using Terminal Deoxynucleotidyl Transferase-mediated Nick-End Labeling (TUNEL) assay. In Situ Cell Death Detection Kit (Roche) was used to assay. After the cells were treated with appropriate dose and time DMEM was removed from the cells and washed with PBS thrice. Then the cells were fixed in $4\%$ formalin for 1 hr. After fixation the cells were made permeable with permeation solution ($0.1\%$ Triton X‐100 in $0.1\%$ sodium citrate) for 10 mins. Cells were washed with PBS and incubated in TUNEL solution for 1 hr at room temperature. Cells were stained with nuclear stain DAPI for 5 mins and observed under fluorescent microscope for TUNEL positive cells. ## Transwell migration and invasion assays Serum-free medium was applied to dilute the Matrigel, and 50 μl of diluted Matrigel was inoculated into each chamber. After being treated with DMSO and PD, both parental LoVo and OXA-R LoVo cells were resuspended at a density of 1 × 105 cells/mL with DMEM medium without FBS. Then, we added 0.2 mL of the cell suspension to each upper chamber of the 24-well plate, while 0.6 mL DMEM containing $20\%$ FBS was added to the lower chambers. After 24 hr, the upper chambers were washed, fixed with $4\%$ paraformaldehyde for 20 min, stained with $0.25\%$ crystal violet for 30 min, and imaged by a microscope. ## Immunofluorescence microscopy 1 × 105 parental or OXA‐resistant LoVo colon cancer cells were seeded 24 well plates and treated with PD for 24 hrs. At the end of treatment cells were washed with PBS and fixed with $4\%$ paraformaldehyde for 1 hr in room temperature. After PBS was cells were permeabilized with permeabilization solution for 10 mins. To prevent non-specific binding, cells were incubated with $10\%$ FBS for 1 hr at room temperature. 250 μl of YAP primary antibody (1:200 dilution) was added to the cells and incubated at 37°C for 3 hrs. Cells were then washed with PBS thrice and incubated with 300 μl FITC conjugated secondary antibody (1:1000 dilution) for 1 hr at room temperature. Cells were then washed and stained with DAPI for 5 mins and washed again with PBS thrice. Cells were then visualized under fluorescent microscope. ## Statistical analysis The results shown are the means ±SD of three independent experiments. Statistical analysis was performed by one-way analysis of variants followed by a Tukey's post-hoc test SPSS 16 software (SPSS, Chicago, IL, USA). ## Characterization of chemoresistance in OXP-LoVo colorectal cancer cells OXP-LoVo colorectal cancer cells were developed based on our previous study 3. Both parental and OXP-LoVo cells were treated with various concentrations of oxaliplatin (1-80 μM) for 24 hrs. and cell viability was determined by MTT assay. Oxaliplatin from 5 μM induces significant cell death in LoVo cells, however in OXP-LoVo cells cell significant cell death was observed after 20 μM of oxaliplatin (Figure 1A). IC50 values of oxaliplatin in LoVo and OXP-LoVo was found to be 21.45 μM and 75.23 μM respectively (Figure 1B). In order to establish multidrug resistance, parental LoVo and OXP-LoVo cells were treated with different concentrations of irinotecan (CPT‐11) (1-40 μM) for 24 hrs and cell viability was quantified. 5 μM of CPT-11 induces cell death significantly in parental LoVo cells but in OXP-LoVo cells 10 μM of CPT-11 induced significant cell death. IC50 of CPT-11 parental LoVo was found to be 16.91 μM, whereas in OXP-LoVo cells it was around 25.5 μM (Figure 1C&D). This result demonstrated that OXP-LoVo cells were resistant to both OXA and CPT-11. ## PD alone and combined with oxaliplatin inhibits CRC cell proliferation in parental and OXA-resistance cells Several reports have established the anticancer effect of PD 31. In the present study, we evaluated the effect of PD on parental LoVo and OXP-LoVo cell viability. Both parental LoVo and OXP-LoVo cells were treated with different concentrations of PD (1-20 μM) for 24 hrs. In both cells, PD from 10 μM induces cell death significantly and the IC50 of PD in parental LoVo and OXP-LoVo cells was found to be 10.59 μM and 13.08 μM respectively (Figure 2A&B). In order to find the combinatorial synergistic effect both the cells were treated combined with OXA (10, 15 & 20 μM) and PD (10, 15 & 20 μM) for 24 hrs, and cell viability was estimated (Figure 2C). Combined treatment of OXA and PD induces cell death in a dose-dependent manner. A combination index (CI) was calculated for the drug pair OXA and PD. CI < 1 is believed to be better compatibility with high synergistic effects. In our present study, CI was found to be below 1 for OXA (10, 15 & 20 μM) and PD (10, 15 & 20 μM) in parental LoVo and OXP-LoVo cells (Figure 2D). ## LATS2 and YAP/TAZ hippo signaling is highly activated in OXP-LoVo cells We evaluated the hippo signaling pathway proteins and cell cycle regulating protein expression in LoVo and OXP-LoVo cells. p-LAST2 p-YAP and TAZ were upregulated in OXP-LoVo cells when compared to LoVo cells (Figure 3A). On the other hand, cy-clin-dependent kinase inhibitor proteins such as p21 and p27 and cell survival protein p-Akt and Ki 67 were increased in OXP-LoVo cells than parental LoVo cells (Figure 3B). ## PD significantly downregulated LATS2 and YAP/TAZ hippo signaling in LoVo and OXP-LoVo cells Then we evaluated the effect of PD on hippo signaling, cell cycle, and cell survival protein expression. PD dose-dependently decreased LATS2/YAP1 and Taz in hippo signaling and survival marker p-AKT expression, as well as increased cyclin-dependent kinase inhibitor proteins such as p21 and p27 expression in parental and OXP-LoVo cells (Figure 4). ## PD induces apoptosis, invasion, and migration in LoVo and OXP-LoVo cells The effect of PD on apoptosis and metastasis was analyzed in the CRC cells. The number of TUNEL positive cells was significantly higher in the PD treated parental and OXP-LoVo cells when compared to their respective control groups (Figure 5A). Effect PD on metastasis was estimated by invasion, and migration assay. PD treatment in both type of cells effectively prevents the invasion, and migration of the CRCs (Figure 5B), the anti-metastasis effect of PD. ## PD inhibited Yap nuclear translocation and activation in LoVo and OXP-LoVo cells Inhibiting YAP activation and nuclear translocation might be an approach to reduce cancer progression. Then we test the effect of PD on YAP activation and nuclear translocation. We quantified the YAP expression in the nuclear and cytoplasmic extract of parental LoVo cells and OXP-LoVo cells. YAP expression in the nuclear extract was increased in OXP-LoVo cells compared to LoVo cells, however, PD treatment reduced the YAP levels in the nuclear extract. In the cytoplasmic extract, PD treatment increased YAP levels in OXP-LoVo cells (Figure 6A). Immunofluorescence assay also confirms that PD treatment in the LoVo cells and OXP-LoVo cells effectively prevents the nuclear translocation of Yap (Figure 6B). ## PD activates and promotes YAP1 degradation through the ubiquitination and proteasome pathway Immunoprecipitation assay followed by immunoblot was performed for the YAP1 to study the effect of PD on ubiquitination and proteasome pathway. MG132, a proteasome inhibitor was used. PD treatment in LoVo cells and OXP-LoVo cells degrade YAP1 through activating ubiquitination mediated proteasome degradation (Figure 7). ## Discussion PD has been reported to have an anti-cancer effect in different in vitro and in vivo 31. PD has been reported to reduce cell viability and invasion while greatly increasing apoptosis in OSCC cells in a dose-dependent manner 33. Further, PD demonstrates the anti-cancer properties in breast cancer cells in vitro and in vivo, as evidenced by decreasing cell proliferation and survival of MDA-MB-231 cells and inhibited the tumor cell volume in MDA-MB-231 xenograft tumors in BALB/c nude mice 34. In liver cancer cells, PD exerts an anti-cancer effect by inducing apoptosis and inhibiting metastasis 35. PD increased the expression of Fas and FasL at the mRNA and protein levels in HaCaT cells, causing proteolytic cleavage of caspase-8 and -3 in a time-dependent manner, and elicits extrinsic apoptosis 36. In HepG2 and MCF-7 cancer cells, PD dramatically decreased the Bcl-2/Bax ratio and elevated the expression of cleaved caspase-9, caspase-3, and Poly (ADP-ribose) polymerase (PARP) 35, 37. Furthermore, it has been discovered that platycodin D could cause G2/M phase arrest in PC3 cells 38. It has been demonstrated that platycodin D inhibits the migration and invasion of MDA-MB-231 cells in a dose-dependent manner. While MMP-2 activity was only marginally lowered, PD significantly suppressed MMP-9 activity. MMP-9 mRNA expression shown to be down-regulated by PD 39. Dysfunction of the hippo pathway has been linked to the development and metastasis of CRC 40, 41. Upregulation of hippo pathway proteins such as YAP and TAZ along with other proteins Transcriptional enhancer factor TEF-1 also known as TEA domain family member 1 (TEAD), and Octamer-binding transcription factor 4 (OCT4) have a correlation of developing adenoma to CRC 42. YAP and TAZ upregulations were shown to be substantially linked with lymph node metastases in CRC patients 43. TAZ was sued as a prognostic marker for CRC which is also linked to downstream target genes AXL and Connective tissue growth factor (CTGF) 44. Extracellular matrix (ECM), cell adhesion, serine/threonine kinase receptor, G protein-coupled receptor (GPCR), and cellular metabolism dysfunction are the direct upstream regulators of YAP/TAZ activity 45. In a clinical study, YAP expression in a CRC patient have shown to regulate epithelial-mesenchymal transition (EMT) by activating Slug and inhibiting E-cadherin 46. Higher YAP expression is linked to CRC recurrence in human CRC hepatic metastases 40. Recently, we have shown that Irinotecan (CPT-11) resistant CRC cells have shown to increase the expression GPCR such as Gαi‐2, Gαq/11, and Gαs and metastasis 47. Nuclear translocation of YAP and upregulation of β-catenin expression could decrease the overall and progression-free survival in CRC patients 43. Another study correlates the YAP/TAZ expression with the chemoresistance CRC with liver metastases and suppressing/inhibiting YAP/TAZ expression in the CRC patients sensitize the CRC to the chemotherapeutic drug cetuximab and increase progression-free survival 48. In our present study, PD treatment dose-dependently decreased LATS2/YAP1 hippo signaling and survival marker p-AKT expression, as well as increased cyclin-dependent kinase inhibitor proteins such as p21 and p27 expression. Hippo signaling is a piece of epigenetic machinery that regulates the development and progression of cancer. According to the recent in silico analysis, promotor sites of the YAP/TAZ genes are greatly enhanced with the binding sites of many transcriptional factors including P300 which acts as a histone acetyltransferase (HAT) at the YAP1 promoter and procured chromatin remodelers of other DNA or histone-modifying components such as CTCF and HAT 16. In recent years, hippo signaling in many human cancer types has been studied which provides a better understanding of their role in cancer progression. Targeting the YAP/TAZ transcriptional network chemical inhibitors might be a significant step forward in the combat against cancer therapy resistance 49. The nuclear-cytoplasmic shuttling of YAP/TAZ has been given considerable attention for its regulatory function 50. YAP/TAZ must be translocated into the nucleus to act as a transcriptional activation factor. Thus, preventing/inhibiting YAP/TAZ activation and nuclear translocation might be an approach to reduce cancer progression. In our present study, we have shown that YAP levels in the nucleus were increased OXP-LoVo cells, whereas PD significantly reduced the nuclear translocation of YAP suggesting PD sensitize OXP-LoVo cells by inhibiting nuclear translocation of YAP. A combinatorial treatment regime is the main element of chemotherapy-resistant cancer treatment that focus to sensitize chemotherapy-resistant cancer through the sequential regime of an adjuvant drug 51. Results of our present study demonstrate that combined treatment of oxaliplatin and PD reduce the cell viability significantly in OXA-LoVo cells in a dose-dependent manner. PD dose-dependently decreased LATS2/YAP1 hippo signaling and survival marker p-AKT expression, as well as increased cyclin-dependent kinase inhibitor proteins such as p21 and p27 expression. Importantly, PD activates and promotes YAP1 degradation through the ubiquitination and proteasome pathway. The nuclear transactivation of YAP was significantly reduced under PD treatment, leading to transcriptional inhibition of the downstream gene, including proliferation gene, pro-survival gene, and EMT-related gene. In conclusion, our results showed that PD is suitable as a promising agent for overcoming oxaliplatin-resistant colorectal cancer. ## Data availability statement The data that support the findings of this study are available from the corresponding author, CYH, upon reasonable request. ## Author contributions Chien-Hao Wang - Methodology, writing-original draft, writing-review and editing. Rathinasamy Baskaran - writing-original draft, writing-review and editing. Shawn Shang-Chuan Ng - resources, formal analysis. Tso-Fu Wang - investigation, formal analysis. Chi-Cheng Li - formal analysis, data curation. Tsung-Jung Ho - validation, investigation. Dennis Jine-Yuan Hsieh - project administration. Chia-Hua Kuo - data curation, project administration. 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--- title: 'Factors associated with diabetes-related distress among Asian patients with poorly controlled type-2 diabetes mellitus: a cross-sectional study in primary care' authors: - Xiaoxuan Guo - Pang Nee Frida Wong - Yi Ling Eileen Koh - Ngiap Chuan Tan journal: BMC Primary Care year: 2023 pmcid: PMC9969642 doi: 10.1186/s12875-023-02012-w license: CC BY 4.0 --- # Factors associated with diabetes-related distress among Asian patients with poorly controlled type-2 diabetes mellitus: a cross-sectional study in primary care ## Abstract ### Background Diabetes-related distress (DRD) is a negative emotional state related to the burden of living with diabetes mellitus. It has been associated with poor self-care and glycaemic control. This cross-sectional study aimed to examine the factors associated with DRD among urban Asian patients with poorly controlled type-2 diabetes mellitus (T2DM) in primary care in Singapore. The factors included demographics, diabetes history, medical co-morbidities, mood disorders and social history. ### Methods Patients with T2DM and HbA1c of $8\%$ or more were recruited from 2 public primary care centres in Singapore. They were administered a questionnaire survey to identify DRD based on the Problem Area In Diabetes (PAID) scale. Their anxiety and depression were screened using GAD-7 and PHQ-9, and quality of life (QOL) measured using the EQ-5D-5L. Their clinical data, including HbA1c, comorbidities and medications, were extracted from the electronic medical records. ### Results Among the 356 subjects, the prevalence of DRD was $17.4\%$. DRD was significantly associated with younger age (AOR ($95\%$ CI) = 0.93 (0.89–0.97), $$p \leq 0.001$$), ex-smoker status (AOR ($95\%$ CI) = 22.30 (2.43–204.71), $$p \leq 0.006$$) and history of kidney disease (AOR ($95\%$ CI) = 3.41 (1.39–8.35), $$p \leq 0.007$$). Those who screened positive for depression (AOR ($95\%$ CI) = 4.98 (1.19–20.86), $$p \leq 0.028$$) were almost five times more likely to have DRD. Quality of life was lower among those with DRD (EQ5D index score AOR ($95\%$ CI) = 0.11 (0.01–0.97), $$p \leq 0.047$$), who also tended to feel that diabetes pharmacotherapy interfered with their normal life (AOR ($95\%$ CI) = 2.89 (1.38–6.08), $$p \leq 0.005$$). ### Conclusion About 1 in 6 patients with poorly controlled T2DM had DRD. Younger age, ex-smoker status, history of kidney disease, and those with depressive symptoms were most at risk. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12875-023-02012-w. ## Introduction Type-2 diabetes mellitus (T2DM) is a challenging disease that requires commitment and adherence to a complex set of self-management tasks in order to get the best possible outcome. As a result, persons with diabetes (PWD) often feel frustrated, burnt out or overwhelmed with worries related to the current management or future implications of their disease [1]. Diabetes-related distress (DRD) refers to the negative emotional state arising from the burden of living with the disease [1]. The likelihood of DRD is higher among those with poor glycaemic control compared to those who are well-controlled [2]. Its relationship with glycaemic control is time-concordant [3]. Its presence at baseline, in one study by Aikens [2012], has been linked to future glycaemic control [4]. DRD has been associated with poorer medication adherence and lower frequencies of self-care behaviours. Both medication adherence and self-care behaviours are well-established determinants of glycaemic control, which are in turn associated with future complications and lower quality of life. The prevalence of DRD was reported as $36\%$, in a meta-analysis of 55 studies, with gender and comorbid depressive symptoms as significant factors affecting prevalence [5]. However, a high level of heterogeneity was noted in these studies, and more than half of them were from the United States of America, limiting its global generalizability Distress levels and content are influenced by psychosocial, socio-economic, cultural and demographic variables, which vary across different populations. Even within the same country, prevalence of DRD differs between study sites [6] and level of care, with primary care having a lower prevalence compared to secondary care [7, 8]. Apart from gender, depressive symptoms and glycaemic control, several other factors have been identified to be associated with DRD, including younger age [6, 9–11], certain ethnicities [6, 10], living alone, lower education [9], greater number of diabetes-related complications [9, 12], frequency and severity of hypoglycaemic episodes, diabetes-related family arguments and diabetes support gap [9]. Conversely, lower levels of DRD were found with the older adults [13], married [10, 14], employed [10], higher family and social support and better patient-physician relationship [13]. Conflicting evidence was found for the association of DRD with duration of T2DM. Several studies showed its association with DRD [8, 15, 16], while one large, multinational study showed the reverse [9]. Understanding the associated factors is key to design appropriate interventions to mitigate DRD in primary care where most PWDs are managed. A systematic review by Sturt [2015] alluded that psycho-education, when conducted by general practitioners or practice nurses for six sessions or more and for three months or more, was effective in reducing DRD [17]. A chronic disease management programme in Sydney, Australia, reported the effectiveness of a diabetes health-coaching programme in reducing DRD for those with the highest level of baseline distress. It was also effective in improving the glycaemic control in those with a baseline HbA1c above the recommended level of $7\%$ [18]. This would suggest that intervention measures would likely benefit those with a high level of distress and suboptimal glycaemic control. Singapore has a highly urbanised multi-ethnic Asian population with the prevalence of T2DM doubling from $7.3\%$ in 1992 to $13.7\%$ in 2018. DRD had been identified in PWDs of a tertiary hospital but the majority of PWDs in Singapore are managed in primary care. Hence, this study aimed to assess the prevalence of DRD and its associated factors in PWDs with poor glycaemic control of HbA1c ≥ $8\%$ in primary care. The factors included age, duration of T2DM, hypoglycaemic frequency, financial difficulties, social support and encounters with healthcare providers. ## Subjects and study design A cross-sectional survey using self-administered questionnaires and a retrospective health record review was conducted at two polyclinics (public primary care centres) in the northeast region of Singapore. Patients were recruited on a case-encounter basis at the patient monitoring stations within the polyclinic during their routine medical reviews. Patients aged 40 to 79 years with T2DM on follow-up for more than 1 year, had a latest HbA1c of ≥ $8\%$ and who could understand and comply with written and/or verbal instructions were eligible to participate in the study. While most guidelines, including the American Diabetes Association, recommend a HbA1c treatment target of < $7\%$ for most adults, for the purpose of this study, poor glycaemic control is defined as HbA1c ≥ $8\%$ [19]. Patients were excluded if they were on treatment for psychological disorders, mentally incapacitated or pregnant. The questionnaire was printed in the English language, and was tested and revised in a pilot study. Interviewers were trained to use a common script to field questions, so as to ensure standardisation. For participants who spoke only Mandarin or Malay, the questionnaire was translated to the participants by interviewers who were native Mandarin and Malay speakers respectively. Clinic staff referred patients fulfilling the eligibility criteria to a study team member, who explained the study and obtained their consent in a quiet, closed room. Following consent, the subjects filled out the questionnaire and returned it back to the study team member to check for completeness. If clarifications or translation of the questionnaire were required, the study team member would read from the standardised script based on the study protocol. ## Sample size calculation Based on a previous study done in a local tertiary outpatient specialist clinic, the prevalence of DRD was estimated to be $32\%$ [20]. With a $5\%$ precision and a $95\%$ confidence level, the sample size required for this study was calculated to be 335. This number was multiplied by $10\%$ to allow for drop-outs and missing data, and rounded up to give a final target sample of 370. ## Study questionnaire Data on demographics was collected directly from subjects using the questionnaire. The information included gender, ethnicity, marital status, education level, employment and living arrangements. For financial status, the Community Health Assist Scheme (CHAS), a local tiered health financing support programme, was used to identify lower- to middle-income households, while receipt of Medifund assistance (another health financing support scheme targeted at people from low socioeconomic status) was used to identify needy subjects who have difficulties with their medical bills despite government subsidies. Self-reported information on smoking history, exercise frequency, hypoglycaemic frequency and history of previous consults with a dietician and/or counsellor/psychologist was obtained from the participants. Questions on other psychological and social factors such as diabetes-related family arguments and financial concerns were included to identify psychosocial issues. These questions were adapted from the second Diabetes Attitudes, Wishes and Needs (DAWN-2) study [9]. The latter was a global study to assess diabetes care and self-management of PWDs, family members and healthcare professionals, and to identify determinants of effective treatment and self-management [21]. Responses were recorded on a 5-point Likert scale, 5 representing “strongly agree” and 1 representing “strongly disagree”. ## Scales and tools (i)Problem Area In Diabetes (PAID) Scale DRD was measured using the PAID scale (Annex 1). This instrument is a 20-item questionnaire, where items are rated on a 5-point Likert scale, with 0 representing “no problem” and 4 representing “a serious problem”. The scores are summed up and multiplied by 1.25 to give a total score from 0 to 100. A PAID score of ≥40 suggests distress at a level warranting clinical attention [1]. The psychometric properties of the English version [20] as well as the Chinese translation [22] have been studied and found to be valid and reliable for use in Singapore. In this report, the terms “DRD” and “elevated distress” will be used interchangeably to refer to a PAID score of ≥40.(ii)Patient Health Questionnaire (PHQ-9) and Generalised Anxiety Disorder (GAD-7) The PHQ-9 is a 9-item self-report tool used to screen for depression. Subjects are asked to rate the frequency of symptoms over the past 2 weeks on a 4-point Likert scale, with 0 representing “not at all” and 3 representing “nearly every day”. The possible score range is from 0 to 27. Scores of 5, 10, 15 and 20 represents cut-off points for mild, moderate, moderately severe and severe depression respectively [23]. The PHQ-9 has been shown to be valid and reliable (Cronbach’s α = 0.87) for use in Singapore [24]. Similarly, the GAD-7 is a 7-item self-report tool designed to screen for anxiety, where subjects rate the frequency of symptoms over the past 2 weeks on the same 4-point scale. The total score ranges from 0 to 21. Scores of 5, 10 and 15 represents cut-off points for mild, moderate and severe anxiety respectively. The scale is valid and reliable to measuring anxiety in the general population based on overseas studies [25]. A cut-off score of 10 and above was used to define cases of clinical significance for both the PHQ-9 and GAD-7.(iii)5-Level EuroQol 5 Dimensions Scale (EQ-5D-5L) The EQ-5D-5L is a simple self-report survey developed by the EuroQoL Group to measure health-related quality of life [26]. It consists of two parts. The first measures health status in five domains (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) across five response levels. The results may be presented as a descriptive profile or a single index value. Value sets for the latter are available for each country [27]. The second part records the respondent’s self-rated health on a visual analogue scale (EQ-VAS), ranging from 0, which represents the “worst imaginable health state”, to 100, which represents the “best imaginable health state”. The original instrument utilises a three-level response, with the English, Chinese and Malay versions previously validated for use in Singapore [28–30]. The newer EQ-5D-5L, which saw the introduction of two new response levels to improve sensitivity and reduce ceiling effects, has been found to be more discriminative compared to the three-level version in PWDs in Singapore [31]. ## Electronic medical records review Clinical data were extracted from the electronic medical records. The data included birth year, latest body mass index (BMI) and HbA1c reading, co-morbidities and diabetes-related complications, number of doctors and nurses visits, duration of T2DM and the number of long term medications. Nursing encounters included diabetes counselling and education. ## Statistical analyses Data analysis was performed using SPSS Version 27.0. Statistical significance was defined as $p \leq 0.05.$ Chi-square test or Fisher’s exact test was used to analyse categorical variables. For continuous variables that are normally distributed, independent t-test and one-way ANOVA were used for two groups and three or more groups respectively. Continuous variables that are non-parametric were analysed with the Mann–Whitney U and Kruskal–Wallis tests for two groups and three or more groups respectively. Potential factors with p-values less than 0.2 were included in the multiple logistic regression to obtain the adjusted odds ratio (AOR). ## Results A total of 370 patients were recruited and consented to participate in the study. 13 participants were excluded from analysis because they did not fulfil the inclusion criteria. One person dropped out mid-way through the questionnaire because of time constraint. Eventually, completed data of 356 subjects were included in the final analysis. Patient characteristics are presented in Table 1, while disease and management data are presented in Table 2. The mean age was 58.6 years, with $50.3\%$ female. The frequency of Chinese, Malay, Indian and Others were $62.1\%$, $22.5\%$, $13.5\%$ and $2.0\%$ respectively. Mean HbA1c was $9.5\%$, while mean BMI was 28.1 kg/m2. The mean duration of diabetes was 10.8 years, while the mean PAID score was 15.4. The prevalence of DRD was $17.4\%$.Table 1Patient demographicsFrequencyNo DRD bDRD bCrude OR ($95\%$ CI)p-valueTotal (%)356 (100.0)294 (82.6)62 (17.4)--Age, mean (SDa)58.6 (9.5)59.4 (9.2)55.2 (9.8)-0.002Gender Male177 (49.7)152 (85.9)25 (14.1)1- Female179 (50.3)142 (79.3)37 (20.7)1.58 (0.91–2.76)0.105Ethnicity Chinese221 (62.1)186 (84.2)35 (15.8)1- Malay80 (22.5)63 (78.8)17 (21.3)1.33 (0.76–2.31)0.316 Indian48 (13.5)38 (79.2)10 (20.8) Others7 (2.0)7 [100]0 [0]Marital Status Married301 (84.6)249 (82.7)52 (17.3)0.94 (0.45–1.98)0.871 Not married55 (15.4)45 (81.8)10 (18.2)1-Education Primary and below115 (32.3)98 (85.2)17 (14.8)0.76 (0.41–1.39)0.367 Secondary and above241 (67.7)196 (81.3)45 (18.7)1-Employment Status Employed212 (59.6)174 (82.1)38 (17.9)1.09 (0.62–1.91)0.759 Unemployed/ Retired144 (40.4)120 (83.3)24 (16.7)1-Any Financial Assistance c Yes152 (42.7)124 (81.6)28 (18.4)1.13 (0.65–1.96)0.666 No204 (57.3)170 (83.3)34 (16.7)1-a Standard deviationb PAID score < 40 indicates no distress, ≥ 40 indicates distressc Community Health Assist Scheme, Pioneer Generation Package, MedifundTable 2Disease and management characteristicsFrequencyNo DRDDRDCrude OR ($95\%$ CI)p-valueTotal (%)356 (100.0)294 (82.6)62 (17.4)--Smoker Non-smoker280 (78.7)228 (81.4)52 (18.6)5.02 (1.18–21.36)0.029 Ex-smoker30 (8.4)22 (73.3)8 (26.7)8.00 (1.56–40.91)0.013 Current smoker46 (12.9)44 (95.7)2 (4.3)1-Exercise None/Irregular292 (82.0)240 (82.2)52 (17.8)1.17 (0.56–2.45)0.677 Regular a64 (18.0)54 (84.4)10 (15.6)1-Clinical parameters and medical conditionsBMI, mean (kg/m2)28.1 (6.1)28 (6.1)29 (5.7)-0.223HbA1c, mean (%)9.5 (1.5)9.4 (1.5)9.7 (1.4)-0.242Duration of diabetes, mean (years)10.8 (7.2)10.9 (7.4)10.3 (6.1)-0.511Experience symptoms of low blood sugar in past 12 months None266 (74.9)228 (85.7)38 (14.3)1- At least once every few months89 (25.1)65 [73]24 [27]2.18 (1.22–3.90)0.008Hypertension Yes318 (89.3)263 (82.7)55 (17.3)0.93 (0.39–2.21)0.863 No38 (10.7)31 (81.6)7 (18.4)1-Dyslipidemia Yes343 (96.3)282 (82.2)61 (17.8)2.60 (0.33–20.34)0.364 No13 (3.7)12 (92.3)1 (7.7)1-Nephropathy and/or Chronic kidney disease Yes237 (66.6)188 (79.3)49 (20.7)2.13 (1.10–4.10)0.024 No119 (33.4)106 (89.1)13 (10.9)1-Number of polyclinic doctors’ visits for DM in the past 12 months5.1 (1.7)5.1 (1.7)5.2 (1.6)-0.756Has a regular doctor for diabetes (i.e. ≥ 2 visits with the same doctor in the past 12 months Yes175 (49.2)146 (83.4)29 (16.6)1- No181 (50.8)148 (81.8)33 (18.2)1.12 (0.65–1.94)0.680Number of polyclinic nurses’ encounters related to DM within the past 12 months2 (1.7)2 (1.7)2 (1.8)-0.817Were there previous nurse encounters related to DM before the past 12 months? Yes281 (78.9)231 (82.2)50 (17.8)1- No75 (21.1)63 (84.0)12 (16.0)0.88(0.44–1.75)0.716Total number of ALL active regular medications6.5 (2.4)6.5 (2.4)6.4 (2.2)-0.752Total number of DM medications2.7 (0.9)2.7 (0.9)2.8 (0.8)-0.445Number of comorbidities3.2 (1.2)3.2 (1.2)3.3 (0.9)-0.840Consulted dietician recently Yes (within 1 year)41 (11.5)32 (78.0)9 (22.0)1.39(0.63–3.08)0.417 No315 (88.5)262 (83.2)53 (16.8)1-a ≥ 5x/week AND ≥ 30 min/day Subjects with DRD were younger than those without DRD (55.2 years old vs 59.4 years old, $$p \leq 0.02$$) (Table 1). No difference in duration of illness was noted between the two groups (Table 2). Those who experienced symptoms of hypoglycaemia were more likely to have DRD compared to those without such symptoms (OR ($95\%$ CI) = 2.18 (1.22–3.90), $$p \leq 0.008$$). Non-smokers and ex-smokers were more likely to have DRD compared to current smokers (OR ($95\%$ CI) = 5.02 (1.18–21.36), $$p \leq 0.029$$ and OR ($95\%$ CI) = 8.00 (1.56–40.91), $$p \leq 0.013$$). Subjects with kidney disease were also more likely to have DRD (OR ($95\%$ CI) = 2.13 (1.10–4.10), $$p \leq 0.024$$). No difference was associated with other co-morbidities such as hypertension, dyslipidaemia, ischemic heart disease, cerebrovascular disease, diabetic eye disease, peripheral vascular disease, neuropathy, diabetic foot disease and anaemia. Financial assistance (Table 1), or previous diabetes-related nurse encounters or previous dietician encounters were not associated with DRD (Table 2). Those screened positive for anxiety and depression using GAD-7 and PHQ-9 with scores of 10 or higher were more likely to have DRD (OR ($95\%$ CI) = 16.77 (6.62–42.46) and OR ($95\%$ CI) = 23.14 (7.36–72.72), both $p \leq 0.001$) (Table 3).Table 3Psychological screeners and quality of life measureFrequencyNo DRDDRDCrude OR ($95\%$ CI)p-valueTotal (%)356 (100.0)294 (82.6)62 (17.4)--PAID score, mean (SD)15.4 (15.4)9.6 (8.5)43 (9.3)- < 0.001GAD-7 score, mean (SD)2.5 [4]1.5 (2.6)7.3 (5.8)- < 0.001GAD-7 scorea < 10331 [93]287 (86.7)44 (13.3)1- ≥ 1025 [7]7 [28]18 [72]16.77 (6.62–42.46) < 0.001PHQ-9 score, mean (SD)2.7 (3.6)2 (2.4)6.3 (5.7)- < 0.001PHQ-9 scoreb < 10337 (94.7)290 (86.1)47 (13.9)1- ≥ 1019 (5.3)4 (21.1)15 (78.9)23.14 (7.36–72.72) < 0.001EQ VAS73.9 (17.3)75.4 (16.9)66.6 (17.3)- < 0.001EQ5D Index0.9 (0.2)0.9 (0.2)0.8 (0.2)- < 0.001a GAD-7 score ≥ 10 defines anxiety of clinical significanceb PHQ-9 score ≥ 10 defines depression of clinical significance Subjects with DRD had a lower mean EQ-VAS score compared to those without DRD (66.6 vs 75.4, $p \leq 0.001$), and a lower EQ-5D index score compared to those without DRD (0.8 vs 0,9, $p \leq 0.001$) (Table 3). Subjects who felt anxious about their weight, felt discriminated against, found difficulties paying for medications, worried about their financial future and felt that their medications interfered with their normal life were more likely to have DRD (Table 4).Table 4Other Psychological and social factorsFreqNo DRDDRDCrude OR $95\%$ CI)p-valueTotal (%)356 (100.0)294 (82.6)62 (17.4)--I feel anxious about my weight Not sure17 (4.8)12 (70.6)5 (29.4)3.27 (1.75–6.09) < 0.001 Strongly agree/ Agree165 (46.3)124 (75.2)41 (24.8)4.11 (1.29–13.17)0.017 Strongly disagree/ Disagree174 (48.9)158 (90.8)16 (9.2)1-I feel discriminated against because I have diabetes Not sure12 (3.4)7 (58.3)5 (41.7)5.00 (2.25–11.12) < 0.001 Strongly agree/ Agree29 (8.1)16 (55.2)13 (44.8)4.40 (1.34–14.48)0.015 Strongly disagree/ Disagree315 (88.5)271 (86.0)44 (14.0)1-I have difficulties paying for the medications that are needed to best treat my diabetes Not sure11 (3.1)5 (45.5)6 (54.5)2.44 (1.36–4.37)0.003 Strongly agree/ Agree112 (31.5)84 (75.0)28 (25.0)8.79 (2.52–30.69)0.001 Strongly disagree/ Disagree233 (65.4)205 (88.0)28 (12.0)1-I have difficulties getting the supply of medication(s) needed to treat my diabetes Not sure5 (1.4)3 (60.0)2 (40.0)3.24 (1.57–6.69)0.001 Strongly agree/ Agree39 (11.0)25 (64.1)14 (35.9)3.86 (0.63–23.71)0.145 Strongly disagree/ Disagree312 (87.6)266 (85.3)46 (14.7)1-I am worried about my financial future due to my diabetes Not sure17 (4.8)12 (70.6)5 (29.4)3.86 (1.96–7.60) < 0.001 Strongly agree/ Agree184 (51.7)139 (75.5)45 (24.5)4.97 (1.50–16.45)0.009 Strongly disagree/ Disagree155 (43.5)143 (92.3)12 (7.7)1-My medication(s) to treat diabetes interfere(s) with my normal life Not sure11 (3.1)6 (54.5)5 (45.5)5.07 (2.77–9.27) < 0.001 Strongly agree/ Agree114 (32.0)77 (67.5)37 (32.5)8.79 (2.46–31.38)0.001 Strongly disagree/ Disagree231 (64.9)211 (91.3)20 (8.7)1-My family argues with me about how I choose to take care of my diabetes Not sure8 (2.2)5 (62.5)3 (37.5)2.25 (1.24–4.06)0.007 Strongly agree/ Agree87 (24.4)64 (73.6)23 (26.4)3.75 (0.86–16.37)0.079 Strongly disagree/ Disagree261 (73.3)225 (86.2)36 (13.8)1-The polyclinic staff has successfully managed my diabetes-related stresses Not sure44 (12.4)33 (75.0)11 (25.0)0.74 (0.37–1.5)0.408 Strongly agree/ Agree246 (69.1)208 (84.6)38 (15.4)1.36 (0.55–3.39)0.510Strongly disagree/ Disagree66 (18.5)53 (80.3)13 (19.7)1-I have ever consulted a psychologist and/or counsellor to work through my diabetes-related stresses Not sure6 (1.7)4 (66.7)2 (33.3)4.49 (1.45–13.89)0.009 Strongly agree/ Agree13 (3.7)7 (53.8)6 (46.2)2.62 (0.47–14.67)0.273 Strongly disagree/ Disagree337 (94.7)283 (84.0)54 (16.0)1-Apart from healthcare professionals, there are other persons who I can talk to about my diabetes Not sure16 (4.5)11 (68.8)5 (31.3)0.94 (0.53–1.68)0.837 Strongly agree/ Agree201 (56.5)168 (83.6)33 (16.4)2.18 (0.69–6.84)0.183 Strongly disagree/ Disagree139 [39]115 (82.7)24 (17.3)1- After adjusting for confounding factors, younger age (AOR ($95\%$ CI) = 0.93 (0.89–0.97), $$p \leq 0.004$$), ex-smokers (AOR ($95\%$ CI) = 22.30 (2.43–204.71), $$p \leq 0.006$$), history of kidney disease (AOR ($95\%$ CI) = 3.41 (1.39–8.35), $$p \leq 0.007$$) and those who felt that medications interfered with their normal lives (AOR ($95\%$ CI) = 2.89 (1.38–6.08), $$p \leq 0.005$$) remained significantly associated with DRD. A positive PHQ-9 screen (AOR ($95\%$ CI) = 4.98 (1.19–20.86), $$p \leq 0.028$$) as well as a lower EQ5D index score (AOR ($95\%$ CI) = 0.11 (0.01–0.97), $$p \leq 0.047$$) continued to be significantly associated with DRD (Table 5).Table 5Factors affecting diabetes-related distress using logistic regressionAdjusted OR ($95\%$ CI)p-valueAge0.93 (0.89–0.97)0.001Gender Male0.73 (0.31–1.67)0.453 Female1-Smoker Non-smoker6.48 (0.84–49.79)0.072 Ex-smoker22.30 (2.43–204.71)0.006 Current smoker1-Experience symptoms of low blood sugar in past 12 months None1- At least once every few months1.20 (0.55–2.63)0.651Nephropathy and/or chronic kidney disease Yes3.41 (1.39–8.35)0.007 No1-GAD-7 < 101- ≥ 103.13 (0.91–10.74)0.069PHQ-9 < 101- ≥ 104.98 (1.19–20.86)0.028EQ VAS0.99 (0.97–1.02)0.572EQ5D Index0.11 (0.01–0.97)0.047I feel anxious about my weight Strongly agree/ Agree1.64 (0.79–3.42)0.188 Not sure/ Strongly disagree/ Disagree1-I feel discriminated against because I have diabetes Strongly agree/ Agree2.41 (0.81–7.16)0.113 Not sure/ Strongly disagree/ Disagree1-I have difficulties paying for the medications that are needed to best treat my diabetes Strongly agree/ Agree0.78 (0.32–1.90)0.585 Not sure/ Strongly disagree/ Disagree1-I have difficulties getting the supply of medication(s) needed to treat my diabetes Strongly agree/ Agree2.70 (0.89–8.24)0.080 Not sure/ Strongly disagree/ Disagree1-I am worried about my financial future due to my diabetes Strongly agree/ Agree1.69 (0.73–3.91)0.218 Not sure/ Strongly disagree/ Disagree1-My medication(s) to treat diabetes interfere(s) with my normal life Strongly agree/ Agree2.89 (1.38–6.08)0.005 Not sure/ Strongly disagree/ disagree1-My family argues with me about how I choose to take care of my diabetes Strongly agree/ Agree1.34 (0.61–2.95)0.462 Not sure/ Strongly disagree/ Disagree1-I have ever consulted a psychologist and/or counsellor to work through my diabetes-related stresses Strongly agree/ Agree1.11 (0.25–4.92)0.894 Not sure/ Strongly disagree/ Disagree1- ## Discussion The DRD prevalence of $17.4\%$ is lower than the $32\%$ previously reported when the PAID questionnaire was administered in a tertiary endocrinology clinic [20]. It is compatible with two other studies where the prevalence in primary care was lower compared to secondary care [7, 8]. PWDs in secondary and tertiary centres tend to be more complex and with more complications compared to those who are treated in the primary care setting. No difference was noted in the number of comorbidities or number of medications between those with and those without DRD in our study. Although the prevalence of DRD seems to be low in primary care setting, the absolute numbers are still significant. Effective interventions to address DRD can potentially result in marked reduction in T2DM-related disease burden. Subjects with DRD were younger compared to subjects without DRD. This result is consistent with local [32] as well as overseas [6, 9–11] studies. Younger working PWDs face financial stressors, challenges at work and family responsibilities which may increase their difficulties of living with T2DM. They may perceive their illness as a threat or a loss at a time in their lives when they expect themselves to be able-bodied to perform their role as providers or caregivers for their families, hence reacting more negatively to the stressor. Clinicians need to recognise and address the challenges unique to these younger PWDs. A good doctor-patient relationship, based on empathic communication and person-centred approaches, such as motivational interviewing, has been found to reduce diabetes distress, and also improve self-care [17, 33, 34]. Smoking is well known to increase cardiovascular morbidity and mortality among PWDs, and most, if not all, guidelines would recommend that PWDs do not smoke. Our study found that ex-smokers, defined as those who have stopped smoking for at least one year, were at risk for DRD. While most trials take six months to one year as a proxy for life-time smoking cessation, a meta-analysis estimates that annual relapse rates after the first year is approximately $10\%$ [35]. Quitting smoking and remaining smoke-free is challenging, even for those without diabetes. Many who smoke do so as a response to stress. PWDs with DRD who are ex-smokers may benefit from frequent check-ins on their smoking remission status so as to keep them in the maintenance phase of smoking cessation, and to identify and reduce stressors that may trigger a relapse. The result revealed that PWDs who experienced hypoglycaemia symptoms in the past one year were more likely to have DRD. The perception that diabetes medications interfere with normal life may have been a confounder in this relationship. Hypoglycaemia has been identified as a factor associated with DRD in other multi-centre studies [9, 12]. The DAWN-2 study examined the frequency and severity of symptomatic hypoglycaemia on DRD, and found an association not only with DRD, but also with well-being (WHO-5 Wellbeing Index) and quality of life (WHOQOL-BREF). Hypoglycaemia assessment and management is an integral part of good diabetes care [36]. Managing and educating PWDs about hypoglycaemia, with a focus on addressing issues and concerns related to the PWD’s diabetes medications, not only mitigate this life-threatening risk but may also alleviate their DRD. DRD, depression and anxiety are often viewed as overlapping concepts, or part of a continuum. However, depression is a separate entity from DRD. Any PWD may have depression or DRD or both. Co et al. reported that DRD was a mediator between poor glycaemic control and health related quality of life [32]. It has also been suggested that the higher rates of depression among PWDs may be mediated by DRD [37]. Thus, psychotherapy and counselling strategies to alleviate DRD in PWD may concurrently prevent and manage their depression and improve their quality of life. The results identified anxiety around weight and perception that diabetic medications interfered with normal life as significant factors associated with DRD. Younger PWDs could be more image conscious, with higher propensity to be more troubled by weight (and appearance) compared to older PWDs. In a study on self-perception of weight of Korean women aged 20 to 79 years old, it was reported that older women tended to underestimate their weight relative to actual body mass index, compared to younger women [36]. This misperception could reduce incidence of DRD in older PWD but would potentially dampen their activation to lose weight. The results did not show a significant association between number or type of medications and DRD. While the medications were not free to the PWDs, they were dispensed at highly subsidised prices at polyclinics due to prudent national healthcare finance policies [38], which ensure that local residents will not be deprived of healthcare services due to cost. DRD is linked to lower health-related QOL [32], and social support has been shown to moderate this relationship [39]. The results of this study support the association between DRD and lower QOL. However, after adjusting for confounders, there was no significant association found between perceived social support and DRD in this study population. In 2016, Singapore’s Ministry of Health declared War on Diabetes, a whole-nation effort to tackle diabetes through various channels, such as public awareness and health promotion campaigns, patient education, and healthcare financing strategies. At the institution level, a multidisciplinary team involving care coordinators, nurses, doctors, pharmacists and medical social workers, provide holistic care through various programmes, including health coaching and telecare services. In the community, organisations such as Diabetes Singapore, provide peer support, counselling and screening for diabetes and diabetes-related complications. While the healthcare structures and social service structures are largely in place, the interface between the two continues to be a focus in the delivery of population health in Singapore. ## Strength and limitations This is the first study to report the prevalence of DRD in primary care in a cosmopolitan urbanised multi-ethnic Asian community. It will pave the way for the development of appropriate interventions to mitigate DRD among PWDs. A multidisciplinary service has been set up in the institution to address DRD among PWDs, which will be evaluated via health service research methodology. While this cross-sectional study would not be able to establish causality, the results have identified factors associated with DRD. They include the younger age and comorbidities such as kidney disease. PWDs with such risk factors will be targeted for DRD screening and intervention. ## Implications in clinical practice In Singapore, the awareness of DRD is slowly gaining pace among primary care physicians. The 2014 Ministry of Health clinical practice guidelines on T2DM recognise DRD as one of the psychosocial issues known to affect self-management and health outcomes of PWDs [40]. Clinical psychologists are now part of the multidisciplinary team in the polyclinics to support the primary care physicians to manage the multifaceted issues faced by PWDs. The effectiveness of such psychologist-led interventions is currently being evaluated. In the holistic management of PWDs, healthcare professionals are encouraged to recognise the social aspect of living with diabetes, which affects the clinical and psychological outcomes of PWDs. Many of these social factors lie beyond the PWD and the healthcare worker’s control. A first step would be to raise awareness and engage with the larger community that PWDs interact with, to help others understand and empathise with the struggles of PWDs. Public education efforts to raise awareness of DRD and its management should aim to be sustained and far-reaching. The target audience may include immediate family members, such as spouses and children. It can also include managers at the workplace to be more sympathetic and facilitative to the needs of PWDs, for example having suitable meal breaks and hypoglycaemia first aid. Education content can aim to dispel common misconceptions and provide tips to engage friends and family to support the PWDs. Such family-centric and community led services await evaluation to determine their effectiveness in alleviating the disease burden imposed on the PWDs. The associated factors and patterns of DRD could also change with time as the healthcare system and societal pressures evolve. The tool for DRD assessment has to adapt to the changing healthcare eco-system and needs to be simplified for ease of implementation in routine clinical services. ## Conclusion One in six ($17.4\%$) PWDs with poor glycaemic control had DRD in this study. Younger age, ex-smoker status, kidney disease, depressive symptoms and the perception that medications interfere with normal life are associated with their DRD. The results highlight the need for primary care physicians to proactively screen PWDs for DRD, especially when their glycaemic control is suboptimal, and to direct them for evidence-based therapy. 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--- title: 'Risk factors for early neurologic deterioration in single small subcortical infarction without carrier artery stenosis: predictors at the early stage' authors: - Di Jin - Jing Yang - Hui Zhu - Yuexia Wu - Haichao Liu - Qi Wang - Xiaoyun Zhang - Yanhua Dong - Bin Luo - Yong Shan - Lvming Zhang - Peifu Wang - Jichen Du journal: BMC Neurology year: 2023 pmcid: PMC9969648 doi: 10.1186/s12883-023-03128-3 license: CC BY 4.0 --- # Risk factors for early neurologic deterioration in single small subcortical infarction without carrier artery stenosis: predictors at the early stage ## Abstract ### Objectives This study aimed to assess the epidemiological features and explore the potential risk factors for early neurological deterioration (END) in patients with acute single small subcortical infarction (SSSI) who underwent antiplatelet therapy without carotid artery stenosis. ### Materials & methods Patients with SSSI, as confirmed by cranial magnetic resonance imaging (MRI), who were hospitalized within 48 h after the onset of symptoms were enrolled. END was mainly defined as increment in the National Institutes of Health Stroke Scale (NIHSS) score of ≥ 2 points or any new neurological deficit. Poor functional outcome was defined as modified Rankin Scale (mRS) score of > 2 points at 3-month after the onset. The association of END with multiple indicators was assessed at the early stage of admission using multivariate logistic regression analysis, and adjusted odds ratios (aORs) were calculated. ### Results A total of 280 patients were enrolled from June 2020 to May 2021, of whom, END occurred in 44 ($15.7\%$) patients (median age, 64 years; $70.5\%$ male), while END occurred during sleep in 28 ($63.6\%$) patients. History of hypertension (aOR: 4.82, $$p \leq 0.001$$), infarction in internal capsule (aOR: 3.35, $$p \leq 0.001$$), and elevated level of low-density lipoprotein cholesterol (LDL-C; aOR: 0.036, $$p \leq 0.0016$$) were significantly associated with the risk of END. Patients with END (aOR: 5.74, $$p \leq 0.002$$), history of diabetes (aOR: 2.61, $$p \leq 0.020$$), and higher NIHSS scores at discharge (per 1-score increase, aOR: 1.29, $$p \leq 0.026$$) were associated with the poor functional outcome at 3-month after the onset. ### Conclusion Patients with a history of hypertension, infarction in internal capsule or a higher level of LDL-C were found to be at a higher risk of END. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12883-023-03128-3. ## Introduction Single small subcortical infarction (SSSI), which also called lacunar stroke, is a common manifestation in acute ischemic stroke, with an incidence of approximately 15–$25\%$ in different populations [1]. The pathogenesis of SSSI is heterogeneous. Lipohyalinosis is the most typical cause, while large parent arterial disease is another underlying etiology [2]. The majority of symptoms of SSSI are commonly mild, and the general clinical prognosis is relatively satisfactory in stroke population. However, some patients, despite receiving standard antiplatelet and statin therapy, still develop neurological deterioration, such as limb motor dysfunction, dysphagia, or progressive aggravation of consciousness within 48–72 h after the onset. This progress is difficult to prevent, and some patients may eventually develop to severe, even life-threatening, functional impairment. This phenomenon is called early neurological deterioration (END) [3]. END is generally associated with less favorable outcomes than patients without deterioration [4]. Therefore, it is essential to assess the risk factors and prevention of END. Studies have shown that subtype of large artery atherosclerosis with severe proximal or intracranial atherosclerosis is an independent risk factor for END with higher odds ratios (ORs) than other subtypes of stroke[5–8]. However, patients with small vessel disease (SVD), i.e., without obvious carotid artery stenosis on radiological imaging, are also at the risk of worsening symptoms in the acute phase of cerebral infarction [4, 9]. Because of the relatively mild symptoms at the onset and the negative findings on vascular imaging, this group of patients may miss the optimal period of treatment, which may lead to the poor prognosis. The pathogenesis and risk factors of END have still remained controversial due to the lack of reliable evidence [10]. Therefore, predicting the risk of END through clinical manifestations, laboratory tests, and radiological results in the early stage of the stroke has noticeably attracted scholars’ attention in recent years. The present study aimed to evaluate the epidemiological features, explore the potential risk factors for END in patients with SSSI as confirmed by cranial magnetic resonance imaging (MRI), and provide evidence for the clinical practice at the early stage of the stroke. ## Patients’ selection This single-center, imaging-based, cross-sectional cohort study with 3-month of clinical follow-up included SSSI patients who were consecutively admitted to the Aerospace Center Hospital in Beijing (China) between June 2020 and May 2021. If there were no contraindications, all patients were routinely treated with aspirin and/or clopidogrel for antiplatelet aggregation and statin for intensive lipid-lowering. Patients with SSSI confirmed by cranial magnetic resonance diffusion-weighted imaging (MR-DWI) and apparent diffusion coefficient (ADC) were included, and the lesion needed to be the main cause of the stroke. Subcortical infarction was diagnosed as a small infarct in the territory of perforating arteriole with maximum diameter of less than 20 mm in the axial plane [11], while without limitation of layers of axial plane. All enrolled patients were hospitalized within 48 h after the onset of symptoms. Patients with a history of ischemic stroke could be included, while they needed to meet the modified Rankin Scale (mRS) score of 0–2. Patients with moderate-to-severe carotid artery stenosis (≥ $50\%$) or chronic total occlusion of the adjacent major coronary arteries, multiple lesions or watershed cerebral infarcts, cortical infarction, cardioembolism, stroke mimics, or MR-negative strokes were excluded. Patients with other diseases that might aggravate the condition, such as severe pneumonia, septic shock, or severe cardiac insufficiency, were also excluded. ## Collection of clinical data The following baseline characteristics were collected: [1] Demographic variables: age, gender, and mRS score before onset; [2] Medical history: hypertension (previous antihypertensive medication usage, systolic blood pressure (SBP) ≥ 140 mmHg, or diastolic blood pressure (DBP) ≥ 90 mmHg at discharge), diabetes (previous use of medication or hemoglobin A1c > $7.0\%$), dyslipidemia (previous usage of lipid-lowering medication, low-density lipoprotein cholesterol (LDL-C) > 3.12 mmol/L, total cholesterol (TC) > 5.17 mmol/L, or triglycerides (TG) > 1.7 mmol/L), previous ischemic stroke, and habitual smoking (current or past regular smoking); [3] Clinical features: National Institutes of Health Stroke Scale (NIHSS) score on admission, SBP and DBP on admission, subtypes of lacunar syndrome, time interval between the onset of symptoms and time of admission, undergoing thrombolysis using recombinant tissue-type plasminogen activator (rt-PA); [4] Laboratory data on admission: leukocyte count, TC, LDL-C, blood glucose, uric acid, blood urea nitrogen (BUN), creatinine (CR), and D-dimer. ## Definition of END and poor functional outcome at 3-month END was defined as any new neurological symptoms or worsening that might occur within 48 h after the onset of the stroke and persist for at least 24 h. Specifically, END should meet the at least one of the following criteria: [1] An increment in the total NIHSS score of ≥ 2 points, [2] An increment in the consciousness score (1a-1c) of ≥ 1 point, [3] An increment in the motor score (5a-6b) of ≥ 1 point, or [4] Any new neurological deficits that would be unmeasurable by NIHSS scores [9]. The NIHSS scores of all participants were evaluated by neurologists every 6 h at the first 48 h of hospitalization and at least once a day thereafter. When the patient had END symptoms, doctors on duty evaluated the NIHSS score at the first time, recorded the time from the onset to END, and performed the cranial computed tomography to exclude intracranial hemorrhage. In addition, the poor functional outcome was defined as mRS score of 3–6 points at 3-month after the onset by telephone or face-to-face consultation. ## Assessment of neuroimaging data All participations underwent MRI on a 1.5 or 3.0 Tesla scanner (1.5 Tesla MAGNETOM Avanto; 3.0 Tesla MAGNETOM Skyra; Siemens, Erlangen, Germany) within 24 h after admission. Moreover, DWI, ADC, fluid-attenuated inversion recovery, and time-of-flight MR angiography were conducted according to the routine protocol of stroke. Two experienced vascular neurologists, who were blinded to the clinical data, reviewed all imaging data, and selected eligible participants (κ-value, 0.89). Radiological features (location of the infarction, branch atheromatous disease [BAD], and visible layers of axial slices on DWI) were recorded. BAD of the lenticulostriate arteries was defined as infarcts with the maximum diameter of 10–20 mm on axial slices and being visible for no less than three axial slices, and that of the anterior pontine arteries was defined as unilateral infarcts extending to the basal surface of the pons [12]. ## Statistical analysis Data were presented as mean with standard deviation, median with interquartile range (IQR), and percentage for continuous, ordinal, and categorical variables, respectively. To compare the baseline data between the two groups, the Student’s t-test was used for normally distributed data, as well as the Wilcoxon rank-sum test or the Kruskal–Wallis test for abnormally distributed continuous and ordinal variables, and the Pearson's χ2, the Fisher's exact test or the Cochran-Mantel–Haenszel χ2 test for categorical variables. The Cohen κ coefficient was used to measure inter-rater reliability for qualitative (categorical) items. A binomial logistic regression model was utilized to assess the association between variables and END. The variables imported into the univariate regression analysis were obtained from characteristics with between-group differences in baseline data (P ≤ 0.1) and the probable risk factors of END that were confirmed in previous studies [age, gender, location in corona radiata, infarction in internal capsule and brainstem [4, 13, 14]; BAD [12]; visible layers on DWI [15]; history of diabetes [16]; blood pressure on admission [17]; leukocyte count [18]; glucose [19]; hypertriglyceridemia [20]; D-dimer and uric acid [21]; BUN/CR ratio [22] and D-dimer [23]. A multivariate logistic regression model was used to analyze possible independent factors for END and poor function outcome at 3-month after the onset using variables with P ≤ 0.1 in the univariate analysis. The corresponding estimates for ORs with $95\%$ confidence intervals (CIs) were presented. We use area under the receiver operating characteristic (ROC) curve to evaluate the validation of the model. Moreover, EpiData 3.0 software was used to collect data and establish the database. The statistical analysis was conducted using R 4.2.0 software. Two-sided $P \leq 0.05$ was considered statistically significant. ## Results Of 1,319 cases with SSSI, 280 ($21.2\%$) cases were included in the final analysis. Figure 1 shows patients’ selection process. Men comprised $70.7\%$ ($$n = 198$$) of the total, and the median age was 65 (IQR, 57–73) years. Median NIHSS scores were 2 (IQR, 1–3) points on admission and 1 (IQR, 0–2) point at discharge. A total of 44 ($15.7\%$) patients progressed to END within 48 h after the onset, while 236 ($84.3\%$) patients were clinically stable. In the END group, 20 ($45.5\%$) and 24 ($54.5\%$) patients met the diagnostic criteria for END on the first and the second days after the onset of symptoms, respectively. No END patient developed with intracranial hemorrhage or death during hospitalization. Furthermore, END occurred in 28 ($63.6\%$) patients during sleep, and END occurred in 16 ($36.4\%$) patients during wakefulness or activity. In addition, $43.2\%$ ($\frac{19}{44}$) of patients were deteriorated prior to admission, while $56.8\%$ ($\frac{25}{44}$) of patients were exacerbated during hospitalization. Comparison of the NIHSS scores of the pre-admission END and after-admission END revealed that there was no statistically significant difference between the two subgroups ($$P \leq 0.36$$). At the peak of the disease course, the median NIHSS score of END patients was elevated by 6 (IQR, 4–8) points. Fig. 1The Selection process of the study The baseline data of the END group and the clinically stable group were basically similar in the majority of features. There were some differences between the two groups. Patients with END were more likely to have history of hypertension ($$P \leq 0.033$$) and infarction in internal capsule ($$P \leq 0.003$$). Besides, patients in the END group had slightly higher NIHSS scores on admission ($$P \leq 0.060$$), slightly higher levels of LDL-C ($$p \leq 0.063$$), and more visible layers of axial slices on DWI ($$P \leq 0.061$$). Details of baseline characteristics in different groups are presented in Table 1.Table 1Baseline characteristics of clinically stable group, END group and in totalClinically Stable($$n = 236$$)END($$n = 44$$)Total($$n = 280$$)p valueDemographics Age, y66 (57–74)64 (55–70)65 (57–73)0.084 Male, n (%)167 (70.8)31 (70.5)198 (70.7)0.96 Pre-stroke modified Rankin Scale score0 (0–0)0 (0–0)0 (0–0)0.14Medical history, n (%) Hypertension144 (61.0)37 (84.1)181 (64.6)0.033 Diabetes89 (37.7)18 (40.9)107 (38.2)0.69 Dyslipidemia57 (24.2)9 (20.5)66 (23.6)0.56 Previous stroke61 (25.8)9 (20.5)70 (25.0)0.45 Smoke85 (36.0)17 (38.6)102 (36.4)0.74Clinical features NIH Stroke Scale score on admission2 (1–3)2 (1–3)2 (1–3)0.060 Time from onset to admission, day2 (1–3)2 (1–2)2 (1–3)0.16 Systolic blood pressure, mmHg145 (133–160)144 (132–156)145 (132–159)0.86 Diastolic blood pressure, mmHg80 (74–93)82 (78–92)81 (74–92)0.49Subtypes of lacunar syndrome, n (%) Pure motor stroke140 (59.3)25 (56.8)165 (58.9)0.76 Ataxic hemiparesis28 (11.9)8 (18.2)36 (12.9)0.25 Sensorimotor stroke43 (18.2)11 (25.0)54 (19.3)0.40 Pure sensory stroke19 (8.1)0 [0]19 (6.8)0.051 Atypical lacunar syndrome6 (2.5)0 [0]6 (2.1)0.59 Thrombolysis treatment (rt-PA), n (%)14 (5.9)6 (13.6)20 (7.1)0.068Magnetic resonance imaging features Left, n (%)134 (56.8)28 (63.6)162 (57.9)0.65Visible layers of axial slices0.061 Layers = 197 (41.1)11 (25.0)108 (38.6) Layers = 281 (34.3)21 (47.7)102 (36.4) Layers = 343 (18.2)6 (13.6)49 (17.5) Layers ≥ 415 (6.4)6 (13.6)21 (7.5) Branch atheromatous disease, n (%)58 (24.6)17 (38.6)75 (26.8)0.17Lesion location Thalamus, n (%)39 (16.5)3 (6.8)42 (15.0)0.10 Corona radiata, n (%)62 (26.3)9 (20.5)71 (25.4)0.42 Internal capsule, n (%)57 (24.2)20 (45.5)77 (27.5)0.003 Brainstem, n (%)65 (27.5)12 (27.3)77 (27.5)0.97 Subcortical, n (%)13 (5.5)0 [0]13 (4.6)0.23Laboratory values (on admission) White blood cell, 10^9/L6.69 (5.59–7.95)6.72 (5.97–8.23)6.69 (5.69–8.05)0.41 Neutrophils, 10^9/L4.43 (3.55–5.56)4.61 (3.73–5.97)4.50 (3.55–5.59)0.42 LDL cholesterol, mmol/L2.65 (2.11–3.18)3.01 (2.32–3.54)2.68 (2.12–3.22)0.063 Total cholesterol, mmol/L4.58 (3.80–5.18)4.88 (4.28–5.58)4.60 (3.87–5.31)0.11 Triglycerides, mmol/L1.35 (0.98–1.98)1.42 (1.02–2.12)1.36 (0.99–1.98)0.64 Glucose, mmol/L6.54 (5.48–9.39)7.53 (5.88–11.29)6.66 (5.53–9.55)0.15 Uric acid, μmol/L334.00 (277.82–385.18)314.25 (276.72–364.08)330.35 (277.22–383.10)0.22 BUN, mg/dL9.90 (7.92–11.52)9.63 (7.70–11.52)9.90 (7.88–14.44)0.74 Cr, mg/dl0.82 (0.69–0.97)0.77 (0.64–0.90)0.81 (0.67–0.95)0.12 BUN/ Cr12.35 (8.68–16.02)13.09 (10.36–14.57)11.94 (9.72–14.44)0.19 D-dimer, ug/L106(70–205)110 (76–146)106(71–196)0.99Evaluation at Discharge NIH Stroke Scale score0 (0–2)4 (2–6)1 (0–2) < 0.001 Modified Rankin Scale score0 (0–1)2 (0–4)0 (0–2) < 0.001END Early neurologic deterioration, rt-PA Recombinant tissue-type plasminogen activator, LDL Low-density lipoprotein, BUN Blood urea nitrogen, Cr Creatinine The results of univariate and multivariate logistic regression models related to the predictors of END are listed in Table 2. Multivariate logistic regression models adjusted for relevant confounders showed that history of hypertension (adjusted OR (aOR): 4.82 [$95\%$CI: 1.95–11.96], $$P \leq 0.001$$), infarction in internal capsule (aOR: 3.35 [$95\%$CI: 1.64–6.83], $$P \leq 0.001$$), and the highest quartile of LDL-C (aOR: 3.30 [$95\%$CI: 1.25–8.70], $$P \leq 0.016$$) were identified as independent predictors of END. In contrast, age, BAD, visible layers of axial slices on DWI, and other quartiles of LDL-C were not significantly associated with END. The area under ROC curve of the model is 0.735 [$95\%$CI, 0.680–0.786, $p \leq 0.0001$], with sensitivity of $56.8\%$ and specificity of $79.7\%$.Table 2Results of logistic regression analysis for predictors of ENDVariablesUnivariate ModelsMultivariable ModelsCrude OR[$95\%$ CIs]p valueAdjusted OR[$95\%$ CIs]p valueAge0.98 [0.95–1.00]0.0680.99 [0.96–1.02]0.35History of hypertension3.38 [1.45–7.89]0.0054.83 [1.95–11.96]0.001NIHSS score on admission1.08 [0.93–1.26]0.311.08 [0.91–1.28]0.39BAD1.80 [0.93–3.47]0.0801.58 [0.76–3.28]0.23Visible Layers of axis slices1.31 [0.99–1.73]0.0541.07 [0.65–1.75]0.79Infarction in internal capsule2.62 [1.35–5.08]0.0053.35 [1.64–6.83]0.001LDL-C quartileRefRefLDL-C quartile [1]1.38 [0.51–3.71]0.531.51 [0.54–4.24]0.43LDL-C quartile [2]1.35 [0.50–3.65]0.551.52 [0.54–4.27]0.42LDL-C quartile [3]2.69 [1.06–6.80]0.0363.30 [1.25–8.70]0.016LDL-C quartile (ref.): 0.92–2.15 mmol/L, LDL-C quartile [1]: 2.16–2.69 mmol/L, LDL-C quartile [2]: 2.70–3.24 mmol/L, LDL-C quartile [3]: ≥ 3.25 mmol/L. Effects are presented as adjusted odds ratios with $95\%$ CI. The pseudo-R2 of the model is 0.092END Early neurologic deterioration, OR Odds ratio, Cis Confidence intervals, BAD Branch atheromatous disease, LDL-C Low-density lipoprotein cholesterol Furthermore, 3-month mRS scores were obtained for 273 ($97.5\%$) of 280 patients. $15.8\%$ ($\frac{43}{273}$) of patients had an mRS score ≥ 3, with the incidence in the END group ($\frac{20}{42}$, $47.6\%$) was significantly higher than the clinical stable group ($9.96\%$, $\frac{23}{231}$). Patients in the END group had more severe neurological deficits with higher NIHSS scores and mRS scores ($P \leq 0.001$, Table 1 and Fig. 2). In the multivariate logistic regression analysis of predictors of poor function (Table 3), after adjustment for confounders, END (aOR: 5.74 [$95\%$CI: 1.89–17.45], $$P \leq 0.002$$), history of diabetes (aOR: 2.61 [$95\%$CI: 1.16–5.84], $$P \leq 0.020$$), and higher NIHSS scores at discharge (per 1-score increase, aOR: 1.29 [$95\%$CI: 1.03–1.61], $$P \leq 0.026$$) were associated with a less favorable functional outcome at 3-month after the onset. The area under ROC curve of the model is 0.865 [$95\%$CI, 0.819–0.903, $p \leq 0.0001$], with sensitivity of $81.4\%$ and specificity of $80.9\%$.Fig. 2Comparison of clinical outcome (modified Rankin scale scores at 3-month from onset) between early neurological deterioration group and clinically stable groupTable 3Results of logistic regression analysis for predictors of poor function at 3-monthVariablesUnivariate ModelsMultivariable ModelsCrude OR[$95\%$ CI]p valueAdjusted OR[$95\%$ CI]p valueEND8.22 [3.90–17.29] < 0.0015.74 [1.89–17.45]0.002Age (per 1 year)1.03 [0.99–1.05]0.0641.03 [0.99–1.07]0.071History of diabetes2.66 [1.37–5.16]0.0052.61 [1.16–5.84]0.020NIHSS score on admission1.47 [1.25–1.73] < 0.0011.24 [0.96–1.60]0.094Visible layers of axis slices1.29 [0.97–1.70]0.0771.03 [0.71–1.49]0.88NIHSS score at discharge1.64 [1.40–1.92] < 0.0011.29 [1.03–1.61]0.026OR Odds ratio, Cis Confidence intervals, END Early neurologic deterioration, NIHSS NIH stroke scaleEffects are presented as adjusted odds ratios with $95\%$ CI. The pseudo-R2 of the model is 0.232 ## Discussion The present study evaluated the incidence and risk factors of END in a cohort of SSSI patients. The main findings were summarized as follows: [1] END was commonly found in SSSI patients without carotid artery stenosis and occurred in $15.7\%$ of patients within 48 h after the onset of symptoms; [2] About two-thirds of patients experienced worsening of symptoms during sleep; [3] Patients with history of hypertension, infarction in internal capsule, and elevated LDL-C level were at the higher risk of END; [4] END, history of diabetes, and higher NIHSS scores at discharge were associated with poor functional outcome at 3-month after the onset. The incidence of END was about 11–$34\%$ in previously reported results [4, 13, 14, 20, 24–27]. However, these studies have differences in the inclusion criteria for END, and some of them did not exclude patients with carotid arteries stenosis or verified the lesion by MRI. To ensure the homogeneity of the study subjects, patients with cranial MRI findings were included, and patients with moderate-to-severe carotid artery stenosis, cardiogenic embolism, and stroke due to other etiologies were excluded. The results indicated that about one in six patients developed END, which was in line with the findings of another MR-based study [4] and indicated that END was not rare in patients with SSSI. To date, several studies have concentrated on the effects of hypertension on END during the acute phase of cerebral infarction, while their results were inconsistent. Yamamoto et al. found that history of hypertension was an independent factor of END in patients with SVD [8]. He et al. demonstrated that mean SBP within 24 h was the best predictor for END patients who received thrombolysis using rt-PA [28]. Vynckier et al. reported that END patients had mainly history of hypertension slightly, while neither SBP nor mean arterial blood pressure on admission was significantly associated with the risk of END [4]. The present study suggested that the history of hypertension, rather than hypertension on admission, was the risk factor for END. Hypertension is one of the most important risk factors for stroke. In INTERSTROKE study, stroke in $54\%$ of patients was attributed to the history of hypertension or blood pressure higher than $\frac{160}{90}$ mmHg [29]. The increase of blood pressure is associated with the increased arterial stiffness, affecting cerebral hemodynamics with microvascular rupture [30]. In SVD, abnormal cerebral pulsatile hemodynamics may cause structural changes and affect small arteries, arterioles, capillaries, and venules, which are finally presented as white matter hyperintensity, microbleeds, brain atrophy, and infarcts on MRI [11, 31]. Therefore, stroke patients with history of hypertension may further progress to END. On the other hand, up to $80\%$ of patients with acute ischemic stroke might experience acute hypertensive response within the first 24 h after the onset [32] and fall back at 4–10 days spontaneously. As a general symptom, the increased blood pressure on admission may transiently fluctuate, and patients’ progression to END mainly depends on the cerebrovascular reserve capacity and the occurrence of secondary side effects (e.g., cerebral edema, hyperfusion, and hemorrhagic transformation) during fluctuation of blood pressure [33]. The location of cerebral infarction may have a predictive value for END. Berberich et al. demonstrated that infarction in the internal capsule or basal ganglia increased the risk of END [13]. Patients with infarcts in the ventral pontine were also at the high risk of END [4, 14]. The present study showed that infarction in internal capsule could increase the risk of progression to END compared with the absence of infarction in this area. This phenomenon could be explained by the higher density of corticospinal tracts in this area, indicating that minor extension of arteriosclerotic plagues may lead to the noticeable progression of symptoms [3]. In addition, infarction in this area may be caused by BAD or hypoperfusion, which are also considered as possible risk factors for END. BAD lesions of lenticulostriate arteries mainly have more than three layers of axial slices on DWI, and those of the anterior pontine arteries are typically characterized by unilateral infarcts extending to the basal surface of the basal pons [12]. The layers of slices were defined as a variable because multiple layers represented the enlargement of the infarct volume, leading to neurological deterioration in patients with SVD [34]. Previous studies indicated that the number of slices significantly differed between patients with and without END [15, 35]. However, in the present study, neither BAD nor the visible layers of axial slices on DWI would be associated with the risk of END. This result indicates that simply considering the location, size or volume of infarcts may be one-sided, and combination with other imaging methods, such as cerebral perfusion and diffusion tensor imaging, may be more predictive [24]. Some scholars have demonstrated that dyslipidemia may be one of the risk factors for END. TC, LDL-C, and TG are the commonly reported lipid metabolic indicators [36]. In the present study, the highest quartile of LDL-C was found to be associated with the increased risk of END. LDL-C plays an important role, as a pro-inflammatory mediator, in the oxidative processes, and patients with a higher LDL-C level may be accompanied with a strong oxidative reaction, leading to the expansion of infarct volume [37]. However, the correlation between serum lipids and END has still remained controversial. A meta-analysis of 2 studies with involvement of 867 patients showed that the elevated level of TG was correlated with the risk of END in patients with acute cerebral infarction, while neither LDL-C nor high-density lipoprotein cholesterol (HDL-C) had a significant prognostic value [38]. These results suggested that the effects of serum lipids on the risk of END should be further assessed. In the present study, $67.2\%$ of patients experienced worsening of symptoms during sleep, which could be related to the occurrence of nocturnal oxygen desaturation (NOD, defined as pulse oximetry saturation (SpO2) < $90\%$). Kim et al. reported that NOD was an independent risk factor for END and it might mainly occur during nighttime [39]. Nocturnal desaturation may decrease cerebral perfusion and cause compensatory blood pressure surge [40]. Yoon et al. demonstrated that sleep apnea was commonly found in the acute phase of ischemic stroke, and the prevalence of END increased with the level of sleep apnea [41]. These studies have shown that in patients with acute cerebral infarction, assessment of the sleep apnea and nocturnal hypoxia at the early stage of hospitalization may be advantageous to predict the occurrence of END. In our study, patients with END, history of diabetes and NIHSS score at discharge are associated with poor functional outcome at 3-month follow-up. Patients suffered from END during the stroke course would have 8.22-fold increased risk of poor functional outcome, which is consistent with previous studies [4, 25, 42–44]. Diabetes is confirmed as an independent risk factor for ischemic stroke and may be associated with poor outcomes in previous studies [45, 46]. In our cohort, patients with a history of diabetes were 2.6 times more likely to develop END than those without diabetes. Diabetes influences prognosis of SSSI by several mechanisms, such as involving to the process of chronic inflammatory, atherosclerosis and the formation of plagues [45]. Furthermore, patients with diabetes have an elevated risk of recurrent ischemic event [47]. The present study had some limitations. Firstly, this was a single-center study, and the number of cases in the END group was limited, which might influence subgroup analysis. In the future research, the sample size can be further expanded to obtain more specific and in-depth results. Secondly, as the number of visible layers in the axial slices was considered as a surrogate criterion for judging the size of the lesion, the thickness and number of layers were slightly different for each subject on MRI, and there might be some metrical biases when measuring the infarct volume, which might affect the results. Finally, only routine MRI was used to analyze the radiological features of END patients. In the next research, multimodality imaging methods, e.g., high-resolution MRI of blood vessels, diffusion tensor imaging or cranial perfusion imaging, will be combined to more precisely explore the causes of END. ## Conclusions Nearly $16\%$ of patients with SSSI experienced END within 48 h after the onset of symptoms. END mainly occurred during sleep in patients. Patients with history of hypertension, infarction in internal capsule, and elevated LDL-C level were at a higher risk of END. END, history of diabetes, and NIHSS score at discharge were found to be associated with the poor functional outcome at 3-month follow-up. Further research is required to evaluate the specific mechanism of END. ## Supplementary Information Additional file 1. ## References 1. Yaghi S, Raz E, Yang D, Cutting S, Grory BM, Elkind MS. Lacunar stroke: mechanisms and therapeutic implications. J Neurology Neurosurg Psychiatry (2021.0) 92 823-830. DOI: 10.1136/jnnp-2021-326308 2. 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--- title: The role of discrimination and adverse childhood experiences in disordered eating authors: - Jillian D. Nelson - Laura N. Martin - Alyssa Izquierdo - Olga Kornienko - Alison E. Cuellar - Lawrence J. Cheskin - Sarah Fischer journal: Journal of Eating Disorders year: 2023 pmcid: PMC9969653 doi: 10.1186/s40337-023-00753-8 license: CC BY 4.0 --- # The role of discrimination and adverse childhood experiences in disordered eating ## Abstract ### Background In clinical research, there has been a call to move beyond individual psychosocial factors towards identifying cultural and social factors that inform mental health. Similar calls have been made in the eating disorders (ED) field underscoring the need to understand larger sociocultural influences on EDs. Discrimination is a social stressor that may influence mental health in similar ways to traumatic or adverse childhood experiences (ACEs). Given the high rates of EDs and discrimination among marginalized groups, it is vital to understand the role of discrimination and ACEs as predictors of ED symptoms in these populations. The aim of this study is to examine how perceived discrimination predicts ED pathology when statistically adjusting for gender, race, and ACEs. ### Methods The diverse study sample consisted of 331 undergraduate students from a longitudinal cohort study (ages 18–24; $66\%$ female; $35\%$ White/non-Hispanic). Participants completed measures of everyday discrimination, ACEs, and ED pathology. ### Results Following adjustment for multiple statistical comparisons, the frequency of daily discrimination predicted all ED symptoms above and beyond history of ACEs. In follow-up analyses, number of reasons for discrimination predicted cognitive restraint and purging. Differences in ED symptomatology were found based on the reason for discrimination, gender, and race. Specifically, those who experienced weight discrimination endorsed higher scores on all ED symptoms, and those experiencing gender discrimination endorsed higher body dissatisfaction, cognitive restraint, and restriction. People of color endorsed higher restriction, while female participants endorsed higher scores on all ED symptom with the exception of cognitive restraint. ### Conclusion Discrimination is a salient risk factor for ED symptoms even when accounting for individuals’ history of ACEs. Future research should utilize an intersectional approach to examine how perceived discrimination affects ED pathology over time. ( Word count: 234). ## Plain English summary Adverse childhood experiences (ACEs) increase risk for eating disorders (EDs). Discrimination based on race, gender, and gender and sexual identity is also linked to ED behaviors. This paper examined whether discrimination impacted ED behaviors when ACEs were considered to understand how they both might play a role in risk for EDs. Findings suggest that experiences of discrimination may have a greater impact on eating disorder symptoms in college students than a history of ACEs. More research is needed to understand the negative impacts of discrimination on eating disorders, in addition to history of trauma. Clinicians should attend to the ways discrimination may impact their clients’ eating disorder behaviors, and whether individuals experience bias or discrimination when seeking eating disorder treatment. ## Introduction Researchers and clinicians have largely focused on identifying and mitigating individual psychosocial factors contributing to the development and maintenance of eating disorders (EDs). Examining cultural and structural risk factors has been called for in psychological research [1]. Levine [2] and others have long described the impact of broader sociocultural factors on risk for the development of EDs. For example, they have described how Western culture emphasizes heteronormative ideals related to appearance, placing pressure on young women to be sexually appealing to heterosexual men [2, 3]. This theory acknowledges that aspects of gender and weight discrimination are related to the development and maintenance of EDs, while more recent research has examined the impacts of everyday experiences of discrimination [4]. Studying the impacts of discrimination at the individual level is one step towards understanding the effects of cultural and structural factors that impact individuals differently based on their identities. Experiences of discrimination are associated with negative mental and physical health outcomes [5–9], and ED symptoms specifically [4]. Ethnic/racial, sexual, and gender minority groups all have higher rates of EDs when compared to their non-minoritized counterparts [10, 11]. Studies have found that racial discrimination [12, 13], gender and sexual discrimination [4], and weight-based discrimination [14] are related to higher ED pathology among marginalized groups. The impact of discrimination on EDs may be best understood within the context of adverse childhood experiences (ACEs) due to the established impacts of trauma on EDs. ACEs include experiences of childhood maltreatment or trauma such as neglect, and emotional, physical, and sexual abuse [15]. A large body of empirical work has established a link between certain ACEs and EDs. Most individuals with EDs report a history of ACEs [16] and childhood maltreatment is a non-specific risk factor for EDs [17]. Individuals with an ED and ACE history tend to have more severe ED symptoms, including higher food restriction and greater concern about weight and shape, greater comorbid psychopathology, and worse treatment outcomes [18–21]. Adults with a history of ACEs are also more likely to report instances of lifetime discrimination [5]. A history of ACEs may amplify how one is impacted by discrimination later in life. Previous research found that individuals who experienced four or more ACEs, experienced greater mental health symptoms in the presence of discrimination than those who experienced two or fewer ACEs [22]. To fully understand the impacts of discrimination, another potentially traumatic interpersonal stressor, on ED pathology, it should be studied within the context of ACEs. There is growing recognition that experiences of discrimination can impact individuals in a similar manner to traumatic experiences or ACEs [e.g., 9] via shared mechanisms. ACEs may lead to emotion regulation deficits that put individuals at risk of psychological distress [23] and disordered eating can serve as a coping mechanism [23–25]. Similarly, discrimination may elicit a psychological stress response that can result in negative emotions (e.g. anxiety [13, 26]). ACEs and discrimination may also be linked to ED pathology through maladaptive beliefs about oneself or one’s body. Internalized views of oneself, self-criticism, and low self-esteem have been shown to mediate the relationship between ACEs and eating pathology [27–31]. In line with these studies, discrimination has been shown to influence negative internalized beliefs toward oneself [32–34]. Because ACEs and discrimination may engender ED symptoms via similar pathways, it is likely that experiencing a greater number of ACEs and greater frequency of daily discrimination would lead to greater cumulative ED pathology. Certain characteristics of discrimination may make it a more salient interpersonal stressor than ACEs, uniquely impacting ED behaviors. Discrimination can occur throughout the lifetime, while ACEs, by definition, occur during childhood. Discrimination may be unavoidable as it is often a chronic, daily stressor that affects members of marginalized groups who are embedded in societies characterized by bias, prejudice, and inequality [5, 35]. Individuals who experience this pervasive stressor may perceive these events as an inevitable threat to their well-being, which can result in trauma-related stress symptoms and poor health outcomes [35–37]. While ACEs are commonly assessed in healthcare settings, historically, clinicians and researchers have not gathered information on recently experienced discrimination when assessing potentially traumatic events. For these reasons, more attention must be paid to discrimination, and it is important to understand whether discrimination is associated with ED behaviors above and beyond the documented effects of ACEs. The current study investigates the impact of ACEs and perceived discrimination on ED symptoms in an ethnically and racially diverse undergraduate sample. We sought to examine the association of both factors in the same analysis for several reasons. First, few studies examine the impact of discrimination on EDs, thus more research is needed on this issue. Second, there is a lack of research on the cumulative impact of ACEs and discrimination on EDs despite associations between ACEs and discrimination, and a high prevalence of ACEs in individuals with EDs. Finally, there are important differences in the way discrimination is experienced that may make it a salient risk factor for ED symptoms (i.e., recency, chronicity, uncontrollability, social identity threat). Therefore, we sought to understand whether total discrimination (a measure of the frequency of everyday experiences of discrimination) is associated with ED symptoms above and beyond the effects of ACEs. We also examine whether ACEs moderate the relationship between discrimination and ED symptoms. ED behaviors and cognitions were examined separately (e.g. restriction, binge eating, body dissatisfaction) to determine whether associations were unique to specific ED symptoms. We hypothesized that total discrimination would account for unique variance in all ED symptoms over and above ACEs. We also hypothesize a synergistic interaction between discrimination and ACEs on ED symptoms. Further, we conducted exploratory analyses to understand the role of different identities (i.e., gender, race) and perceived reasons for discrimination in the prevalence of ED symptoms. We examined differences in ED pathology and total discrimination across gender and race and differences in ED symptoms based on which aspect of one’s identity individuals perceived to be the target of discrimination, including their race, gender, and weight. A meta-analysis by Mason and colleagues [4] found significant associations between ED symptoms and race, gender, and weight discrimination, so we hypothesized that individuals endorsing these forms of discrimination would endorse greater ED pathology. Finally, to explore how holding intersecting identities that may be targets of discrimination [38] could be disproportionately linked to ED pathology, we conducted additional regression analyses to investigate the association between the number of reasons for discrimination and ED pathology. We hypothesized individuals who endorsed more reasons for discrimination would endorse greater ED pathology. ## Participants Participants were first-time freshman undergraduate students at a large Mid-Atlantic university who enrolled in a longitudinal cohort study [39]. The initial sample consisted of 381 participants. Due to incomplete data, 50 participants were excluded from the current analyses, leaving a final sample of 331. Participants ranged in age from 18 to 23 years ($M = 18.54$, SD = 1.17). Sixty-six percent of the sample identified as female and 3 participants identified as a gender other than male or female. The sample was a majority non-white. Thirty-five percent of the sample identified as White, $25.7\%$ Asian American/Pacific Islander, $12.7\%$ Latinx, $12.1\%$ Black or African American/non-Hispanic or Latinx, $9.1\%$ two or more races, $1.5\%$ Black or African American/Hispanic or Latinx, and $3.9\%$ identified as another race/ethnicity. Regarding sexual identity, $78.9\%$ of the sample self-identified as straight, $12.4\%$ as bisexual, $3.6\%$ as gay or lesbian, $3.9\%$ as unsure, and $0.6\%$ of as “something else”. We chose to use the baseline assessment of the cohort data as it represented the first semester of college for the emerging adult participants. We wanted to examine the nature of the relationship between ACEs, discrimination, and ED symptoms because participants were transitioning into young adulthood, and often out of their childhood environments. Thus, by definition, they were not currently experiencing adverse childhood events, but were potentially experiencing discrimination as they transitioned to new settings and roles. ## Adverse childhood events (ACEs) ACEs were assessed using the ACEs-10, a 10-item measure adapted from the CDC-Kaiser Permanente Ace Study [15], assessing maltreatment and other adverse experiences before the age of 18. To remain consistent with the typical ACEs measure, dichotomous variables were created with a score of 0 (‘No, has not happened to me’) or 1 (‘Yes, has happened to me’). Due to experimenter error, one item was entered incorrectly, capturing personal experience of physical violence rather than witnessing intimate partner violence. Since another item already assesses experiencing physical abuse, this item was removed for a total of 9 items. We expect minimal effect of the missing item because research has demostrated that witnessing intimate partner violence is only weakly associated with EDs versus other ACEs items (e.g., emotional negelct, physical abuse, sexual abuse). A study examining ACEs in adults with EDs found individuals with EDs actually were significantly less likely to report witnessing intimate partner violence on the ACEs checklist than a nationally representative sample of adults [40]. In a clinical ED sample, witnessing intimate partner violence was one of the least likely ACEs to be endorsed, with only physical neglect and having a family member in prison being less likely [41]. Yet another study found that emotional neglect, physical abuse, and sexual abuse, but not witnessing intimate partner violence, predicted binge eating disorder [42]. In our sample, total scores ranged from 0 to 8 ($M = 1.57$, SD = 1.77), with higher total scores indicating greater number of ACEs. In order to be consistent with our reporting of other measures, we have calculated internal consistency for the nine included items of the ACEs checklist. The Cronbach's alpha was α =.671. Exposure to traumatic events is not considered a latent psychological construct [43]. It is instead measured as a checklist of events, which may or may not be related to each other. Therefore, we would not necessarily expect high internal consistency between items of the ACEs checklist. ## Everyday discrimination scale (EDS) The EDS [44] was used to measure lifetime experiences of and perceived reasons for discrimination. The 5-item scale asks participants to indicate the frequency that unfair treatment in interpersonal experiences occur (e.g., “You receive poorer service than other people at restaurants or stores”). Responses are rated on a scale from 0 (‘Never’) to 5 (‘Almost every day’). Participants are then prompted to attribute the reason for these experiences and are allowed to select as many options as apply, including gender, race, and religion, among others. In the current study, total discrimination was scored by creating a sum score of the 5-items assessing frequency of everyday discrimination. Number of reasons for discrimination was calculated by summing the total number of reasons endorsed as a target of discrimination. The measure had acceptable internal consistency (α =.75). ## Eating pathology symptoms inventory (EPSI) The EPSI [45] is a multidimensional 45-item self-report measure of eating pathology assessing eight factors: Body Dissatisfaction, Binge Eating, Cognitive Restraint, Excessive Exercise, Restricting, Purging, Muscle Building, and Negative Attitudes Toward Obesity. Participants are prompted to rate items based on frequency over the past four weeks on a scale from 0 (‘Never’) to 4 (‘Very Often’); higher scores indicate greater ED severity. Due to space constraints in the parent study, the 31 items measuring Body Dissatisfaction, Binge Eating, Cognitive Restraint (“I tried to exclude “unhealthy” foods from my diet”), Restricting (“People would be surprised if they knew how little I ate”), and Purging were included. Since the EPSI was developed to measure dimensions of ED behavior, the five factor scores were used separately. Internal consistency of scales in the current study ranged from αs =.75–.89. ## Procedure The current study is a secondary data analysis from a larger parent study examining health, health behaviors, and mental health as predictors of college completion and the influence of individual factors on student mental health, physical health, and wellbeing [39]. Participants were recruited through flyers on campus, brief in-class presentations, online video, postcards distributed in class, and email. Participants completed an online survey measuring physical and emotional health, nutrition, sleep, civic engagement, and academic success. They also completed an in-person visit at the University public health clinic where they were asked their medical history and underwent a physical exam and blood tests. Participants are asked to participate in the study for four years, completing the online survey once per semester and the in-person visit each fall semester. Study procedures were approved by the George Mason University Institutional Review Board. ## Data analysis Participants who did not complete an entire measure in the current analysis were excluded from the study sample. Participants missing less than $5\%$ of data from study measures were retained and missing data were replaced per standard procedures [46]. Notably, for all item-level missing data replaced, less than $1\%$ of data were missing (1–3 participants per item). Data on the EPSI and EDS were replaced using median imputation, and missing data on the ACEs and EDS dichotomous yes/no variables were replaced with 0. All statistical analyses were conducted using IBM SPSS Statistics (Version 27). Descriptive statistics were calculated to characterize the sample on demographics and key variables. Bivariate correlations of ACEs, discrimination, and ED scales were also calculated. To test primary study aims, five hierarchical multiple regressions were run with each EPSI subscale as the dependent variable. In step 1, gender and race were entered to control for the impact of demographic factors known to be associated with ED symptoms [47]. In step 2, the total ACEs score was entered. In step 3, total discrimination was entered to examine whether discrimination played a role in ED symptoms above and beyond total number of ACEs experienced. The final step of the model included the interaction term between centered total discrimination and ACEs (to avoid multi-collinearity and improve interpretability). A significance level of α =.05 was applied for testing all study hypotheses. Since five multiple hierarchical regressions were run on the same sample, results were also examined after adjusting for multiple comparisons using Bonferonni’s correction (α =.01). For follow-up analyses, we ran independent samples t-tests to understand differences in study variables based on aspects of identity (i.e., gender, race) and forms of discrimination (i.e., gender, race, weight). We ran additional regression analyses including the number of reasons for discrimination on ED symptoms. In step 1 of the regressions we controlled for gender, race, and ACEs. In step 2, we entered total discrimination and number of reasons for discrimination. ## Sample characteristics Mean scores of the five ED subscales are listed in Table 1. Approximately $62\%$ of participants reported having experienced at least one ACE before age 18, and $16\%$ reported experiencing 4 or more ACEs ($M = 1.602$, SD = 1.78). The most reported ACEs were emotional abuse and emotional neglect (see Table 2). Eighty-three percent of participants reported experiencing discrimination ($M = 4.77$, SD = 4.00). The most frequently reported reasons for experiences of discrimination were gender, race, and age (see Table 2). Weight as a reason for discrimination was endorsed by 37 participants and discrimination due to sexual orientation/identity was endorsed by 18 participants. Bivariate correlations between key variables were calculated and are reported in Table 3.Table 1Sample scores for key variablesVariableM (SD)Body dissatisfaction10.56 (6.94)Cognitive restraint4.05 (2.86)Binge eating8.05 (6.34)Restriction6.04 (5.59)Purging1.02 (2.50)ACEs1.57 (1.77)Discrimination4.77 (4.01)Eating disorder subscale scores measured by the Eating Pathology Symptom Inventory (EPSI); ACEs = total number of adverse events endorsed on the Adverse Childhood Events scale (ACEs); Discrimination = total frequency score as measured by Everyday Discrimination Scale (EDS)Table 2Endorsement of Adverse Childhood Events and discriminationn (%)Total # of ACEs 0126 (38.1) 166 (19.9) 253 (16.0) 333 (10.0) 4 or more53 (16.0)ACEs type Emotional abuse109 (34.7) Emotional neglect104 (31.9) Family member mental illness87 (26.4) Loss of biological parent69 (21.0) Family member substance use56 (17.0) Physical abuse42 (13.4) Family member in prison26 (7.9) Sexual abuse18 (5.5) Physical neglect17 (5.2)Discrimination typeGender152 (45.9)Race140 (42.3)Age120 (36.3)Physical appearance81 (24.5)Ancestry or national origins51 (15.4)Height45 (13.6)Religion41 (12.4)Weight37 (11.2)Your education or income level32 (9.7)Other35 (10.6) Political beliefs19 (5.7) Sexuality18 (5.4) Illness17 (5.1)Percentage reported is the Valid Percent. Discrimination type measured by Everyday Discrimination ScaleTable 3Bivariate Pearson Correlations between Key VariablesVariables12345671. Body dissatisfaction–2. Cognitive restraint.426–3. Binge eating.497.208–4. Restriction.291.203.169–5. Purging.548.382.379.274–6. ACEs.115.009.187.041.044–7. Total discrimination.211.187.223.196.129.242–8. Discrim reasons.232.234.127.151.194.117.462Eating disorder subscale scores measured by the Eating Pathology Symptom Inventory (EPSI); ACEs = total number of adverse events endorsed on the Adverse Childhood Events scale (ACEs); Total Discrimination = total frequency score as measured by Everyday Discrimination Scale (EDS); Discrim Reasons = total number of reasons for discrimination endorsed on Everyday Discrimination Scale (EDS). EPSI Purging, ACES, and Discrim Reasons were ln-transformed for the purpose of reporting Pearson correlation coefficients$p \leq .05.$ $p \leq .01$ ## Hierarchical multiple regression analyses Results of the five hierarchical moderated regressions are presented in Table 4. First, we examined how much variance was accounted for by total discrimination after statistically adjusting for race, gender, and ACEs in body dissatisfaction, binge eating, restriction, cognitive restraint, and purging. Then we examined whether there was an interaction between ACEs and total discrimination on each ED subscale. Gender accounted for a significant amount of variance in the final step of the models predicting body dissatisfaction, restriction, and purging. Women were more likely to have higher scores on these three subscales. Discrimination accounted for significant variance in the final step of the model predicting body dissatisfaction, restricting, and purging, such that higher scores on discrimination and identifying as a woman were associated with more symptoms after statistically adjusting for race, and ACES. Both ACES and discrimination accounted for significant variance in the final model of binge eating. Only discrimination remained significant in the final step of the model predicting cognitive restraint, where greater discrimination was associated with higher scores on cognitive restraint. Table 4Summary of Hierarchical Regression Analysis for Variables Predicting Eating Pathology Symptoms Inventory (EPSI) FactorsBody dissatisfactionBinge eatingCognitive restraintRestrictionPurgingVariablesB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βStep 1Race−.08 (.14)−.03−.21 (.14)−.09.03 (.06).03.21 (.12).10.05 (.05).05Gender4.75 (.73).341.12 (.70).09.46 (.32).081.59 (.62).14.88 (.27).18Step 2Race−.06 (.14)−.02−.18 (.14)−.07.03 (.06).03.22 (.12).10.05 (.05).05Gender4.88 (.72).351.28 (.70).10.46 (.32).081.63 (.62).15.90 (.28).18ACEs.52 (.20).13.58 (.20).16−.00 (.09)−.00.16 (.17).05.08 (.08).06Step 3Race−.07 (.14)−.03−.20 (.13)−.08.03 (.06).02.21 (.12).09.05 (.05).05Gender4.91 (.71).351.30 (.68).10.47 (.31).081.66 (.61).15.91 (.27).18*ACEs.33 (.20).09.41 (.20).12−.08 (.09)−.05.01 (.18).00.05 (.08).03Total discrim.35 (.09).20*.32 (.09).20.14 (.04).20.28 (.08).20.07 (.04).11Step 4Race−.06 (.14)−.02−.19 (.14)−.08.04 (.06).03.21 (.12).10.05 (.05).05Gender4.88 (.71).35*1.27 (.69).10.43 (.31).071.62 (.61).14.90 (.28).18*ACEs.36 (.21).09.44 (.20).12−.05 (.09)−.03.03 (.18).01.05 (.08).04Total discrim.37 (.09).21*.33 (.09).21.17 (.04).23.30 (.08).21.07 (.04).11ACEs x total discrim−.034 (.05)−.036−.03 (.05)−.04−.05 (.02)−.12−.04 (.04)−.05−.00 (.02)−.01Model fit statisticsAdjusted R2.16.07.04.06.03\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta$$\end{document}Δ R2 for Step 2.02.03.00.00.00\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta$$\end{document}Δ R2 for step 3.04.04.04.04.01\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta$$\end{document}Δ R2 for step 4.00.00.01.00.00ACEs = total number of adverse events endorsed on the Adverse Childhood Events scale (ACEs); Total Discrim = total frequency score as measured by Everyday Discrimination Scale (EDS)$p \leq .05.$ $p \leq .01.$ * $p \leq .001$ When model results (Table 4) were examined after adjusting for multiple comparisons, ACEs were no longer a significant predictor of binge eating in the final step of the model. Discrimination was no longer a significant predictor of purging. Only gender remained significant in the final step of the model, suggesting being a woman was associated with higher purging scores. All other results remained significant. Experiences of discrimination predicted greater body dissatisfaction, binge eating, cognitive restraint, and restriction above and beyond ACEs, when statistically controlling for race and gender and adjusting for multiple comparisons. The interaction of ACES and discrimination accounted for significant variance in the model predicting cognitive restraint. The interaction was visualized using interActive [48]. Results of the simple slopes significance test showed that the association between discrimination and cognitive restraint was significantly different from zero at mean levels of ACEs [β = 0.17, $95\%$ CI = (0.09, 0.25)] and 1 SD below the mean [β = 0.25, $95\%$ CI = (0.13, 0.37); see Fig. 1]. In other words, individuals with low ACEs endorsed significantly higher cognitive restraint when they reported experiencing more frequent discrimination, but significantly lower cognitive restraint with when experienced infrequent discrimination. Frequency of discrimination did not have a significant effect on cognitive restraint in those with high ACEs. No other interactions were significant. Fig. 1Simple Slopes between Total Discrimination and Cognitive Restraint at Levels of the Moderator (ACEs). Simple slopes significance test indicated association between discrimination and cognitive restraint was significantly different from zero at mean level of ACEs and − 1 SD. Black slope lines indicate significance; SD Standard deviation; PTCL Percentile, CI Confidence interval ## Follow-up analyses Follow-up analyses were conducted to compete ACEs, discrimination, and ED symptoms across demographic variables and specific reasons for discrimination. Consistent with previous discrimination research in the field of EDs, we examined gender, race, and weight discrimination [4]. Literature suggests that sexual minorities are at higher risk for ED symptoms, potentially due to minority stress and discrimination [11]. However, in this sample, there was low endorsement of sexuality as a reason for discrimination and no follow-up analyses were run. Comparisons are summarized in Tables 5 and 6.Table 5T-test Comparison of Key Variable Scores by Gender and RaceVariableGender identityRacial/Ethnic identityMale ($$n = 109$$)Female ($$n = 219$$)White ($$n = 115$$)POC ($$n = 216$$)M (SD)M (SD)M (SD)M (SD)Body dissatisfaction6.98 (5.77)12.34 (6.84)**10.89 (7.33)10.39 (6.73)Cognitive restraint3.73 (2.76)4.21 (2.91)4.05 (2.75)4.05 (2.92)Binge eating7.05 (6.08)8.58 (6.44)8.59 (7.28)7.76 (5.77)Restriction4.95 (5.15)6.57 (5.73)5.16 (5.32)6.51 (5.68)Purging0.34 (1.26)1.37 (2.88)*0.88 (2.65)1.09 (2.42)ACEs1.82 (1.85)1.48 (1.73)1.54 (1.89)1.58 (1.71)Total discrim5.01 (4.59)4.63 (3.65)3.97 (3.26)5.20 (4.30)ACEs = total number of adverse events endorsed on the Adverse Childhood Events scale (ACEs); Total Discrim = total frequency score as measured by Everyday Discrimination Scale (EDS); POC = people of color$p \leq .05.$ * $p \leq .01.$ * $p \leq .001$Table 6T-test Comparison of EPSI Factor Scores by Discrimination ReasonVariableGender discriminationRacial discriminationWeight discriminationYes ($$n = 152$$)No ($$n = 179$$)Yes ($$n = 140$$)No ($$n = 191$$)Yes ($$n = 37$$)No ($$n = 294$$)M (SD)M (SD)M (SD)M (SD)M (SD)M (SD)Body dissatisfaction12.04 (6.76)9.31 (6.85)10.80 (6.83)10.39 (7.02)15.76 (6.90)9.91 (6.68)Cognitive restraint4.49 (2.93)3.68 (2.74)4.39 (2.90)3.81 (2.81)5.41 (3.01)3.88 (2.80)Binge eating8.22 (6.46)7.90 (6.25)7.94 (5.86)8.12 (6.68)12.05 (7.33)7.54 (6.03)Restriction6.84 (5.94)5.36 (5.19)6.51 (5.30)5.70 (5.78)8.19 (6.24)5.77 (5.46)Purging1.30 (3.06)0.77 (1.88)1.21 (2.65)0.87 (2.37)2.70 (4.05).81 (2.14)$p \leq .05.$ $p \leq .01.$ * $p \leq .001$ Results of the five hierarchical regressions which included a variable indicating the number of reasons for discrimination are displayed in Table 7. In addition to a significant effect of total discrimination, the number of reasons for discrimination accounted for unique variance in cognitive restraint and purging when controlling for gender, race, and ACEs. For the other ED subscales, total discrimination, but not number of reasons, was significant. Table 7Summary of Hierarchical Regression Analysis for Variables Predicting Eating Pathology Symptoms Inventory (EPSI) FactorsVariablesBody dissatisfactionBinge eatingCognitive restraintRestrictionPurgingB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βB (SE)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}βStep 1Race−.06 (.14)−.02−.18 (.14)−.07.03 (.06).03.22 (.12).10.05 (.05).05Gender4.88 (.72).35*1.13 (.70).10.46 (.32).081.63 (.62).15.90 (.28).18*ACEs.52 (.20).13.58 (.20).16−.00 (.09)−.00.16 (.17).05.08 (.08).06Step 2Race−.09 (.14)−.03−.20 (.13)−.08.02 (.06).01.20 (.12).09.04 (.05).04Gender4.71 (.71).341.26 (.70).10.35 (.31).061.55 (.61).14.82 (.28).16*ACEs.32 (.20).08.41 (.20).11−.09 (.09)−.06−.00 (.18)−.00.04 (.08).03Total discrim.28 (.10).16.30 (.10).19.10 (.04).13.24 (.08).17.04 (.04).06Discrim reasons.47 (.25).11.19 (.24).03.31 (.11).17.26 (.22).07.22 (.10).13Model fit statisticsAdjusted R2.17.07.05.06.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta$$\end{document}Δ R2 for Step 2.05*.04.06*.04.03ACEs = total number of adverse events endorsed on the Adverse Childhood Events scale (ACEs); Total Discrim = total frequency score as measured by Everyday Discrimination Scale (EDS); Discrim Reasons = total number of reasons for discrimination endorsed on the EDS$p \leq .05.$ * $p \leq .01.$ * $p \leq .00$ ## Discussion The goal of the current study was to determine whether perceived discrimination was associated with ED symptoms above and beyond history of ACEs, in an ethnically and racially diverse sample of undergraduate students. This study expanded upon previous research by taking into account ACE history, an important consideration given the strong association between ACEs and discrimination [5], and the established relationship between childhood maltreatment and EDs [17, 20, 49]. Finally, to explore the role of identity and type of perceived discrimination, ED symptomatology, and total discrimination scores were compared across groups. Frequent discrimination was positively associated with body dissatisfaction, cognitive restraint, binge eating, and restriction above and beyond the effect of ACEs. These findings support the idea that discrimination may be a particularly salient interpersonal daily stressor with negative impacts on ED symptoms. Consistent with past research, discrimination was associated with ED behaviors that are thought to be a method of coping with negative affect (e.g., binge eating) and changing one’s appearance (e.g., restriction). It was also associated with body dissatisfaction which reflects cognitions about oneself and one’s appearance, shape or weight. Discrimination did not significantly predict purging behaviors after these statistical adjustments. This may have been due to lacking statistical power due to low endorsement of purging in the sample, and future studies should replicate these associations. Intersectionality theory suggests that individuals who hold multiple marginalized identities (e.g., Black and female, queer male) have different experiences than individuals who only hold one marginalized identity [38]. We examined the impact of multiple marginalized identities by including the number of reasons for perceived discrimination in follow-up analyses. We found that number of reasons for discrimination explained unique variance in some ED symptoms (i.e., cognitive restraint and purging) when accounting for total discrimination. These findings partially support the idea that people holding multiple marginalized identities may be at greater risk for Eds. Cognitive restraint and purging may indicate attempts to change the appearance of one’s body in response to discrimination. Because discrimination is often targeted towards visible aspects of one’s identity, people may feel compelled to change aspects of their appearance that feel within their control. Future research should consider the role of multiple marginalized identities when studying the impacts of discrimination on Eds. Inconsistent with study hypotheses, after adjusting for multiple comparisons, ACEs was not associated with any ED symptoms when discrimination was included in the model. These results are surprising given the well-documented associations between childhood maltreatment and ED symptoms [e.g., 17, 20, 49, 50]. Discrimination may have shared variance with ACEs because they contribute to ED risk via similar mechanisms. Specifically, ACEs and discrimination may both increase risk for ED through internalized negative beliefs about oneself and others [29–31, 33]. Separately, research suggests disordered eating serves as a means of coping in individuals with a history of trauma or ACEs [23–25]. ACEs and discrimination are positively associated, so it is likely that experiences of discrimination occur simultaneously with ACEs during one’s childhood, both shaping beliefs about the self or contributing to maladaptive coping behaviors. The developmental context of our study sample is noteworthy because we focus on first-year college students, many of whom had recently left their childhood environments to start college. This life transition likely changes the impact of family dynamics that have contributed to ACEs. However, when the young adults in our sample moved away to college, the daily discrimination encounters based on their marginalized identities may followed them to their new enviropnment in college. For this reason, discrimination may actually become the more salient stressor impacting ED behaviors for our sample. In adulthood, discrimination may feel more stable and pervasive than other potentially traumatic events. It is known that attributing traumatic events as internal and stable leads to poorer self-esteem and increased hopelessness [51], and these attributions may be accurately made by people experiencing discrimination because it reflects individuals’ lived experiences. The personal and chronic nature of discrimination may engender worse self-esteem and negative affect, increasing risk for ED behaviors. Future work needs to empirically examine these mediating mechanisms as well as the role of protective factors such as ethnic-racial identity development [52] and family racial socialization (e.g., [53]) to disrupt the linkages between discrimination and ED pathology. These translational and clinical efforts to prevent and intervene in harmful effects of discrimination on health need to be guided by culturally centered and culturally adaptive perspectives (e.g., [54]). Group comparisons allowed us to examine differences in perceived discrimination and ED pathology based on gender and racial identity as well as discrimination type (racial, gender, and weight discrimination). Individuals who perceived discrimination to be based on their gender or their weight reported greater ED symptoms, suggesting that gender and weight discrimination may be particularly salient risk factors for ED symptoms. These findings are not surprising given that ED symptoms are more commonly endorsed by females [55] and are often intended to change one’s shape or weight [56]. Interestingly, there were no differences in ED symptom endorsement for individuals who reported race discrimination versus those who did not. Previous research on the negative impacts of race-based stress and discrimination on mental health [9], includes evidence that experiencing racism predicts ED pathology [4]. Further, individuals who endorsed both race- and gender-based discrimination did not experience greater ED pathology than those who experienced only one of these forms of discrimination (race- or gender-discrimination). In the context of intersectionality theory, and our finding that number of reasons for discrimination predicted some ED symptoms, we may have expected that individuals who endorsed race and gender discrimination would have endorsed greater ED symptoms. The field would benefit from further examining the role of race-based discrimination in EDs in larger samples with greater representation across races and ethnicities. ## Conclusion A shift to examining cultural and systemic risk factors such as discrimination and structural racism has been called for in psychological research [1]. The unique contribution of discrimination to ED symptoms in our study highlights the importance of addressing discrimination in ED populations. Discrimination may be a more salient stressor than past traumatic or adverse experiences for some individuals but have received less attention up to this point. Therefore, the field should move towards screening for experiences of discrimination in medical, mental health, and research settings. When treating individuals with EDs, it may be beneficial to explore how stress and negative affect related to past or ongoing experiences of discrimination play into ED behaviors. It may also be important to examine the role of discrimination and bias in ED treatment. There is evidence of overweight and obesity bias among ED professionals [57], evident in the way EDs have been diagnosed until the most recent DSM-5, when atypical AN (AAN) was included as a diagnostic category [56]. Still, compared to AN, fewer individuals with AAN are being referred or admitted to treatment despite AAN being more common in many communities [58]. Additionally, men with disordered eating are less likely to be diagnosed with or seek treatment for an ED which may be due to society’s messages about who can have an ED [59–61]. If everyday experiences of discrimination have an effect on ED pathology, discriminatory practices in treatment settings may cause further harm to individuals seeking support for EDs. Clients may experience microaggressions in therapy which have been shown to negatively impact therapeutic progress, particularly if they are not addressed and processed with the clinician [62]. Clinicians should practice self-awareness and reflection to minimize client’s experience of perceived discrimination under their care. ## Limitations and future research One limitation of the current study is that the ACEs measure was missing the item referring to witnessing domestic violence. However, based on the literature reviewed, other ACEs appear to have stronger relationships with ED symptoms. Additionally, we utilized the first wave of data from a project that follows first time college freshman over four years. Thus, our analysis is cross-sectional. We chose to cross-sectional data to establish whether or not discrimination is associated with ED symtpoms at this developmental period when adjusting for the presence of ACES.. Longitudinal studies would be particularly valuable to examine whether daily experiences of discrimination predict the onset or changes in later ED symptoms as individuals move from adoelscence to young adulthood and beyond. Ecological momentary assessment studies would allow researchers to examine the association between experiences of discrimination in daily life to ED symptoms within the same day. Future research should use these methods in order to examine the temporal and functional relationship between perceived discrimination, discrimination type, and ED symptoms. The size of the current sample, although over 300 participants, led to limited representation of certain racial identities and did not allow for comparison by race. Although our sample was ethnically and racially diverse, the size of some subgroups represnting specific racial or ethnic categories was small. 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--- title: 'Predictors of outcome after endovascular treatment for tandem occlusions: a single center retrospective analysis' authors: - Brian Anthony B. Enriquez - Terje Nome - Cecilie G. Nome - Bjørn Tennøe - Christian G. Lund - Mona K. Beyer - Mona Skjelland - Anne Hege Aamodt journal: BMC Neurology year: 2023 pmcid: PMC9969668 doi: 10.1186/s12883-023-03127-4 license: CC BY 4.0 --- # Predictors of outcome after endovascular treatment for tandem occlusions: a single center retrospective analysis ## Abstract ### Background The endovascular treatment procedure in tandem occlusions (TO) is complex compared to single occlusion (SO) and optimal management remains uncertain. The aim of this study was to identify clinical and procedural factors that may be associated to efficacy and safety in the management of TO and compare functional outcome in TO and SO stroke patients. ### Methods This is a retrospective single center study of medium (MeVO) and large vessel occlusion (LVO) of the anterior circulation. Clinical, imaging, and interventional data were analyzed to identify predictive factors for symptomatic intracranial hemorrhage (sICH) and functional outcome after endovascular treatment (EVT) in TO. Functional outcome in TO and SO patients was compared. ### Results Of 662 anterior circulation stroke patients with MeVO and LVO stroke, 90 ($14\%$) had TO. Stenting was performed in 73 ($81\%$) of TO patients. Stent thromboses occurred in 8 ($11\%$) patients. Successful reperfusion with modified thrombolysis in cerebral infarction (mTICI) ≥ 2b was achieved in 82 ($91\%$). SICH occurred in seven ($8\%$). The strongest predictors for sICH were diabetes mellitus and number of stent retriever passes. Good functional clinical outcome (mRS ≤ 2) at 90-day follow up was similar in TO and SO patients ($58\%$ vs $59\%$ respectively). General anesthesia (GA) was associated with good functional outcome whereas hemorrhage in the infarcted tissue, lower mTICI score and history of smoking were associated with poor outcome. ### Conclusions The risk of sICH was increased in patients with diabetes mellitus and those with extra stent-retriever attempts. Functional clinical outcomes in patients with TO were comparable to patients with SO. ## Background Tandem occlusions (TO) are defined as intracranial vessel occlusion with concomitant high-grade stenosis or occlusion of the ipsilateral cervical internal carotid artery (cICA) and occur in around $15\%$ of patients receiving endovascular treatment (EVT) in the anterior circulation [1–3]. The EVT procedure in TO is more complex than in single occlusions (SO) as it necessitates treatment of two lesions, possible use of stent and consequently, an early initiation of antiplatelet therapy. The optimal management therefore remains uncertain [4]. To date, no randomized controlled trial addressing the management of TO have been completed. Large meta-analyses suggest an advantage of acute cICA stenting during EVT for TO patients [5, 6]. Furthermore, prioritizing treatment of the intracranial occlusion before the extracranial lesion may yield higher reperfusion and better functional outcome [7]. The current 2019 AHA/ASA guidelines on endovascular treatment of TO recommend EVT with recanalization of both extracranial and intracranial occlusions (class IIb, level B-R) [8]. However, the technical approach, timing in the treatment of cICA and medical management are still unsettled [8, 9]. More information is needed to increase the chances of better outcome and reduce serious adverse events such as symptomatic intracranial hemorrhage (sICH), embolization in new territories and stent thrombosis. The aim of this study was to compare functional outcome 90 days after treatment of TO and SO stroke patients and identify factors that may be associated to efficacy and safety in the management of TO. ## Methods This study is based on data from the Oslo Stroke Reperfusion Study (OSCAR) – a register of consecutive stroke patients treated with EVT at Oslo University Hospital, which is a highly specialized regional university hospital covering 3.1 million inhabitants during the study period. Data on patient demographics, premorbid status, and clinical parameters including, National Institutes of Health Stroke Scale (NIHSS) score upon admission and discharge, pre-interventional radiological studies, bridging therapy, procedural variables, and complications as well as functional outcome at 90-day follow-up were registered. The present study included SO and TO patients treated with EVT from January 2017 to October 2020. TO was defined as concomitant intracranial occlusion in the anterior circulation and severe stenosis or occlusion of the cICA. Baseline characteristics, comorbidity, safety measures and clinical outcome in TO and SO patients were compared. The influence of clinical and procedural factors on safety and functional outcomes in TO patients were evaluated. Detailed data acquisition of the radiologic and EVT parameters in TO patients was done retrospectively. The regional ethics committee approved the study and written consent were obtained either from the patient or their legal authorized representative. ## Imaging assessment CT scans with angiography with or without perfusion were utilized to screen patients with stroke symptoms who may be candidates for reperfusion therapies. Eligible patients for thrombolysis received treatment before transfer to our hospital for EVT or at our institution for those directly admitted and for in-hospital stroke patients. Additional imaging with brain MRI or updated CT scans were done upon arrival in cases with long transport time, altered clinical presentation, wake-up stroke, stroke of unknown onset, and when primary imaging shows poor collaterals. The recanalization procedure was performed either in general anesthesia (GA) or under conscious sedation (CS) based on the clinician’s discretion. Involvement of distal intracranial occlusions, increased likelihood of stenting or technical difficulty, and restless, agitated, or uncooperative patients were more likely to receive GA. Cerebral digital subtraction angiography and EVT was performed via femoral access. EVT of the intracranial occlusion was performed by aspiration alone, with stent retriever and distal aspiration or in combination. The cICA lesion was treated with stenting and/or percutaneous transluminal angioplasty. Method for treatment, the order of treatment for intracranial and extracranial lesions, and the type of stent used was decided by the interventional neuroradiologist. When a permanent stent was placed, all patients were given a loading dose of 300 mg Acetylsalicylic acid. Clopidogrel or Ticagrelor was administered either perioperatively or within the first 24 h, not exceeding 300 or 180 mg respectively, in total cumulative dose. When the risk of stent thrombosis was considered highly probable, monotherapy with glycoprotein 2b/3a inhibitor, Eptifibatide, was given as an alternative in bolus of 90 mcg/kg dose followed by infusion at a rate of 2 ug/kg/min. After ruling out hemorrhagic complications on follow up imaging, double antiplatelet therapy with Acetylsalicylic acid 75 mg and Clopidogrel 75 mg or Ticagrelor 60 mg twice daily was started in all patients. Roadsaver® Carotid stent (Terumo, Japan) and Precise stent (Cordis, USA) were utilized in stenting the proximal lesion. Blood pressure was adjusted based on factors such as final recanalization status, core infarction size and location, thrombolysis, and stent utilization. The modified thrombolysis in cerebral infarction (mTICI) score was rated based on the final digital subtraction angiogram [10, 11]. Scoring was evaluated by two experienced, independent interventional neuroradiologists blinded to clinical information and outcome. Disagreements were resolved by consensus. Successful reperfusion was defined as mTICI 2b, 2c or 3. MRI was obtained using Magnetom Aera 1.5 T or Magnetom Avanto Fit 1.5 T scanner (Siemens Healthcare, Erlangen, Germany). The acquisition time for the diffusion weighted imaging (DWI) protocol was 1:54 – 2:01 with the following parameters: TE 5900–6300 ms, TR 89 ms, slice thickness 5 mm and $30\%$ distant factor. Patients underwent routine follow-up MRI or CT scan within 24 h after endovascular therapy. Ischemic lesions were assessed using the Alberta stroke program early CT (ASPECT) score. Infarct volume was manually outlined based on DWI using B-value 1000 s/mm2 in combination with ADC map and calculated by multiplying the outlined slice volume with the slice thickness and interslice gap. Intracranial hemorrhage was determined using the Heidelberg bleeding classification (HBC) [12]. Worsening of NIHSS score ≥ 4 secondary to hemorrhage was considered as symptomatic intracranial hemorrhage (sICH) [13]. ## Statistical analysis Statistical analyses were performed using SPSS (V.26.0). Continuous variables were expressed as median (interquartile range, IQR) or mean (standard deviation, SD) according to data distribution. Categorical variables were expressed as frequency (percentage) and bivariate analyses were performed using the χ2 or Fisher’s exact tests. Prior to regression analyses for functional outcome as the dependent variable, multi-collinearity between possible predictor variables was assessed by the variance inflation factor (VIF), with the tolerance value set at < 2. Multiple logistic regression analyses were performed using variables having a level of significance $p \leq 0.05$ in the bivariate analyses. A univariate logistic regression was done, with sICH being the dependent variable and continuous variables as independent variables when linearity and a $p \leq 0.10$ in bivariate analysis was fulfilled. ## Patients and procedures In the study period, a total of 745 patients underwent EVT for acute stroke, comprising 662 anterior circulation medium vessel (MeVO) or large vessel occlusion (LVO) stroke who were included in the study. Direct admission and in-hospital stroke comprised of 25 patients, 23 ($4\%$) in SO and 2 ($2\%$) in TO, while the rest were transfers from other hospitals. Ninety ($14\%$) patients met the criteria for TO (Fig. 1). Seven ($8\%$) patients were primarily observed first and had a delayed EVT upon clinical deterioration. Five ($5.5\%$) patients arrived with no available imaging while 28 ($31\%$) had imaging done at the referring hospital and went straight to the angio suite. In total, 50 ($56\%$) patients had repeat imaging on arrival, of which 15 ($30\%$) had unknown stroke onset or wake-up stroke, 15 ($30\%$) patients had more than 2 h lapse since primary imaging was taken, 3 ($6\%$) had ictus > 6 h earlier and 3 ($6\%$) had NIHSS < 4. A distal migration of the intracranial thrombus on repeat imaging was observed in 10 ($11\%$) patients, 6 ($60\%$) of whom received bridging therapy. Perfusion imaging using CT or MR was done in a total of 64 ($71\%$) patients in the TO group. Fig. 1Study flowchart ## Tandem occlusion versus single occlusion Compared to the SO group, the TO group were younger (median age 68 vs 72 years, p ≤ 0.001) and predominantly male ($68\%$ vs $50\%$, $$p \leq 0.001$$). Furthermore, comorbidity with heart failure, previous transient ischemic attack TIA or stroke as well as atrial fibrillation and consequently, use of anticoagulation was less prevalent in the TO group (Table 1). Stroke severity on admission, use of intravenous thrombolysis and time from stroke onset to puncture did not differ between the groups whereas median procedure time was significantly longer in the TO group. Good functional outcome, defined as mRS ≤ 2 at 90-day follow-up, was achieved in 52 ($58\%$) of the 90 TO patients and 318 ($56\%$) of the SO patients (Fig. 2).Table 1Baseline characteristics in acute anterior circulation stroke patients with tandem (TO) and single (SO) occlusions undergoing endovascular treatmentCharacteristicTandem occlusion($$n = 90$$)Single occlusion($$n = 572$$)P valueAge, yearsa68 (56–74)72 (63–81) < 0.001Gender (men)61 [68]284 [50]0.001Arterial hypertension48 [53]277 [49]0.394Diabetes mellitus12 [13]81 [14]0.805Current or previous tobacco use48 [51]201 [65]0.091Heart failure5 [6]103 [18]0.002Atrial fibrillation10 [11]271 [48] < 0.001Previous stroke/TIA10 [11]112 [20]0.045Hyperlipidemia17 [19]88 [16]0.412Antiplatelets26 [29]175 [31]0.756Anticoagulation6 [7]144 [25] < 0.001Known onset59 [66]406 [71]0.282NIHSS score prior to EVTa15 (10–19)13 (8–18)0.089IV-tPA51 [57]320 [56]0.946MR/CT taken before thrombectomy63 [70]374 [66]0.632Time from stroke onset/recognition to puncture, mina248 (181–340)240 (184–305)0.364Time from stroke onset/recognition to recanalization, mina319 (240–400)285 (226–366)0.041Procedure time, min (IQR)a95 (75–136)58 (40–85) < 0.001TIA transient ischemic attack, NIHSS national institute of health stroke scale, EVT endovascular treatment, IV-tPA intravenous tissue-type plasminogen activatorValues are expressed as number (%) or amedian (IQR)Fig. 2Functional outcome at 90-day follow-up assessed by modified Rankin Scale (mRS) in acute anterior circulation stroke patients with tandem (TO) and single (SO) occlusions undergoing endovascular treatment ## Tandem occlusion A total of 72 ($80\%$) TO patients had a combination of extracranial carotid lesion and LVO whereas 18 ($20\%$) had MeVO based on the initial CT scan. Internal carotid artery atherosclerosis was present in 71 ($79\%$) patients while 19 ($21\%$) had carotid dissection. Stent was employed in a total of 73 ($81\%$) comprised of 39 ($53\%$) using Roadsaver alone, 32 ($44\%$) patients with Precise carotid stent, and 2 ($3\%$) using both Roadsaver and Precise. A single stent was employed in 50 ($69\%$) patients, 22 ($30\%$) patients with two stents and 1 ($1\%$) patient was treated with 3 stents. Antiplatelet therapy with Acetylsalicylic acid and Clopidogrel were given to 66 ($90\%$) while 7 ($10\%$) received Eptifibatide. Successful reperfusion of MeVO or LVO with at least TICI 2b was achieved in 82 ($91\%$) patients. Stent thrombosis occurred in 8 patients ($11\%$) on follow-up imaging. Six of which received thrombolysis though no significant association was found ($$p \leq 0.264$$). No stent thrombosis occurred in all 7 patients who received Eptifibatide, though not significant ($$p \leq 0.999$$). The median time from recanalization to stent thrombosis detection was 24 h and 38 min. Furthermore, no association was found between stent thrombosis and prior use of antiplatelet ($$p \leq 0.216$$), anticoagulation ($$p \leq 1.0$$), number of stents used ($$p \leq 0.155$$), platelet count ($$p \leq 0.972$$), cancer ($$p \leq 0.586$$), diabetes ($$p \leq 1.0$$) and smoking ($$p \leq 0.438$$). Seven of 8 stent thrombosis occurred in patients who were implanted dual layer stent (Roadsaver) which was significant ($$p \leq 0.049$$). ## Hemorrhagic complications in tandem occlusion Hemorrhage on control imaging for TO were found in 50 ($56\%$) patients, of which 8 ($8.8\%$) patients had both intra- and extra-axial hemorrhage (HBC 1–3). Distal aspiration alone was done in 2 ($4\%$) patients, 47 ($94\%$) treated using stent retriever with distal aspiration, and 1 ($2\%$) patient had proximal clot disintegration with distal migration after stenting of the internal carotid artery. Symptomatic intracranial hemorrhage in the TO group occurred in 7 ($7.8\%$) patients, including 6 ($86\%$) with intra-axial hemorrhage (HBC 1C-2) with or without extra-axial hemorrhage and 1($14\%$) had solely subarachnoid hemorrhage. All were treated using both stent retriever with distal aspiration thrombectomy. Comparison of basic characteristics, comorbidity, imaging, and treatment including procedural techniques are demonstrated in Table 2. Diabetes mellitus was associated with considerably increased sICH risk (OR 6.2; $95\%$ CI, 1.2–32.2; $$P \leq 0.047$$). The risk of sICH also increased for every extra attempt of stent retriever passes made (OR 1.7; $95\%$ CI, 1.0–2.6; $$P \leq 0.032$$). Among the patients who received stents, one treated with Eptifibatide had symptomatic bleeding ($$P \leq 0.522$$).Table 2Comparison of variables between symptomatic and no symptomatic intracranial hemorrhage in the tandem occlusion cohortCharacteristicsICH($$n = 7$$)No sICH($$n = 83$$)P valueAge, yearsa57 (52–77)68 (57–74)0.582Gender (men)6 [86]55 [66]0.421Arterial hypertension6 [86]42 [51]0.166Diabetes mellitus3 [43]9 [11]0.047Current or previous tobacco use ($$n = 73$$)4 [80]44 [65]0.066Heart failure1 [14]4 [5]0.339Atrial fibrillation1 [14]9 [11]0.575Previous stroke/TIA1 [14]9 [11]0.575Hyperlipidemia2 [29]15 [18]0.613Antiplatelets4 [57]22 [27]0.186Anticoagulation05 [6]1.00NIHSS score prior to EVT16.57 ± 5.713.86 ± 6.60.296IV-tPA4 [57]46 [57]1.00DWI ASPECTS ≥ 6 ($$n = 62$$)3 [50]37 [66]0.657DWI volume (ml) prior to EVTa ($$n = 63$$)27 (11.4–90.2)16 (8.7–35.7)0.454MRI SWI microbleeds prior to EVTa ($$n = 62$$)0 (0–2.25)0 (0–0)0.502Time from stroke onset/recognition to puncture, mina365 (212–875)240 (180–321)0.086Time from stroke onset/recognition to reperfusion, mina448 (238–745)318 (239–377)0.221Procedure time, mina129 (48–210)95 (75–135)0.772General anesthesia3 [43]58 [70]0.206Stent employed ($$n = 73$$)7[100]66 [80]0.339 Reocclusion of the carotid stent1 [20]7 [8]0.410 Intracranial lesion vs stent first0.703 Perioperative antiplatelet therapy0.522 Type of stent0.566Thrombectomy technique Stent retriever with distal aspiration7 [100]65 [78]0.791mTICI ≥ 2b6 [86]76 [92]0.491MEVO involvement1 [14]17 [21]1.00Changed occlusion location1 [14]9 [11]0.579No. of stents placeda2 (1–2)1 (1–1)0.085Stent retriever passesa3 (1–5)1 (1–2)0.032sICH symptomatic intracranial hemorrhage, TIA transient ischemic attack, NIHSS national institute of health stroke scale, EVT endovascular treatment, IV-tPA intravenous tissue-type plasminogen activator, DWI diffusion weighted imaging, ASPECTs Alberta stroke programme early CT score, mTICI modified thrombolysis in cerebral infarction, MEVO medium vessel occlusionValues are expressed as number (%), amedian (Interquartile range, IQR) or mean (standard deviation, SD) ## 90-day functional outcome in tandem occlusion Increasing NIHSS score on admission, time from stroke onset to treatment, smoking history and hemorrhagic transformation in the infarcted tissue were associated with worse outcome in TO patients (Table 3). Use of GA was associated with good outcome. Obviously, DWI volume after EVT and change in DWI volume before and after EVT as well as mTICI correlated with functional outcome. In multivariate analysis both presence of hemorrhage (OR 0.5; $95\%$ CI, 0.3–0.8; $$p \leq 0.005$$) and lower mTICI scores (OR 2.0; $95\%$ CI, 1.2–3.4; $$p \leq 0.005$$) were the strongest negative predictors of unfavorable functional outcome at 90-day follow-up. In the stented patients, neither the sequence in treating the intracranial and extracranial occlusion nor the carotid pathology showed significant association with outcome. Repeated imaging was associated with poor outcome. Further analysis of patients with or without repeated imaging showed significant difference in age, NIHSS and median door to puncture time (47 vs. 64 min, $$p \leq 0.001$$). However, there was no significant association between door to puncture time and mRS at 90 days. Table 3Comparison of variables between patients with good and poor outcome in the tandem occlusion cohortCharacteristicGood outcome($$n = 52$$)Poor outcome($$n = 38$$)P valueAge, yearsb64 ± 11.868 ± 10.90.049Gender (men)35 [67]26 [68]0.911Arterial hypertension25 [48]23 [61]0.242Diabetes mellitus5 [10]7 [18]0.225Current or previous tobacco use26 [57]22 [81]0.030Heart failure1 [2]4 [11]0.158Atrial fibrillation4 [8]6 [16]0.312Previous stroke/TIA5 [10]5 [13]0.737Hyperlipidemia8 [15]9 [24]0.320Antiplatelets12 [23]14 [37]0.155Anticoagulation2 [4]3 [8]0.646Known onset34 [65]26 [68]0.763NIHSS scoreb12.8 ± 6.815.76 ± 5.90.035IV-tPA32 [62]19 [50]0.275MR/CT retaken prior to EVT ($$n = 50$$)21 [42]29 [58]0.002Time from stroke onset/recognition to puncture, mina217 (170–298)287 (220–371)0.010Time from stroke onset/recognition to reperfusion, mina293 (224–342)364 (290–494)0.004Door to puncture time, mina55 (43–83)60 (52–81)0.153Procedure time, mina105 (78–135)95 (71–138)0.731General anesthesia40 [77]21 [55]0.030Carotid pathology Dissection14[27]5 [13] Atherosclerosis38 [73]33 [87]Stent employed ($$n = 73$$)42 [81]31 [82]0.923 Stent thrombosis4 [10]4 [13]0.711 Intracranial lesion vs stent first0.237 Perioperative antiplatelet therapy0.116 Type of stent0.847 Stented carotid pathology0.060mTICI ≥ 2b50 [96]32 [84]0.027HBC 1–221 [40]23 [60]0.004HBC 36 [12]8 [21]0.099Values are expressed as number (%) or amedian (IQR) or bmean (SD)TIA transient ischemic attack, NIHSS national institute of health stroke scale, EVT endovascular treatment, IV-tPA intravenous tissue-type plasminogen activator, mTICI modified thrombolysis in cerebral infarction, HBC Heidelberg bleeding classification score ## Discussion Tandem cervical carotid occlusion did not lower the likelihood of good functional outcome compared to single occlusions. Factors associated with favorable outcome 90 days after treatment were lower age and NIHSS score, use of general anesthesia and higher mTICI score. Hemorrhage in the infarcted tissue after EVT and history of smoking were associated with poor outcome. Moreover, a history of diabetes and multiple stent retriever attempts were associated with an increased sICH risk. The results in our study with similar outcomes in SO and TO may be related to our high-volume experience, the use of advanced imaging for patient selection, and the use of general anesthesia during stenting which can help secure good technical results. The ESCAPE-NA1 trial reported similar outcomes in TO compared to SO, showing in addition non inferiority of acute stenting in their study [14]. In earlier studies, emergent stenting of the cICA was associated with better outcomes [1, 3, 15]. Despite an increased risk of sICH in a recent meta-analysis, emergent stenting showed a trend towards lower mortality rate [5]. In our study, however, use of permanent stent was not associated with sICH. High proportion of cICA lesions treated with stent ($81\%$) and consequently early antiplatelet therapy after intravenous thrombolysis did not increase the incidence of sICH. The rate of sICH in this study was comparable to the $8\%$ rate of sICH earlier described in SO EVT [16]. Multiple attempts however, namely 3 or more stent retriever passes, have been suggested to increase the occurrence of sICH in SO EVT [17, 18]. Enomoto et al. reported that patients with more stent retriever passes were susceptible to subarachnoid hemorrhage [19]. Our study on TO demonstrate the same association between increased sICH risk and multiple retriever passes. Diabetes as a predictor for sICH in this study are in accordance with earlier studies reporting higher rates of hemorrhagic transformation or worse outcome in patients with diabetes mellitus in SO, though we did not find hyperglycemia to be associated with sICH as earlier reported [16, 19, 20]. Aside from sICH, stent thrombosis is also a main concern in treating TO. We found an $11\%$ chance of stent thrombosis in the first 24 h. The use of dual layer stent may be preferred due to its better coverage of the atherosclerotic plaque or thrombus, however, stent thrombosis was reported to be more common [21]. This is the reason antiplatelet treatment should be tailored according to the stent used and the perceived risk of sICH. Eftifibatide may be an option and seem to be safe and effective in this study in accordance with an earlier study by Jost et al. [ 22]. Further studies on the drug’s safety and optimal dose in acute stroke treatment is needed. The balance between sICH and stent thrombosis will always be a predicament in treating TO. Knowledge of possible predictors such as diabetes, number of stent passes and type of stent used, as exemplified in this study and earlier studies, may help guide clinicians and interventional neuroradiologist in their treatment strategies. There is still equipoise in the use of GA vs CS [23–26]. However, in technical challenging procedures such as in TO achieving excellent technical results may be easier in GA. Although we did not find a difference in mTICI in this study, functional outcome at 90-day follow-up was better after GA. The finding of worse outcome in the group with additional imaging compared to the patients who went directly to the angiography suite would naturally be interpreted as secondary to the extended time to recanalization. However, this is far more complex in clinical practice. Though direct to angio suite may shorten the door to groin puncture time, functional outcome did not differ in patients transferred from a local stroke unit to an EVT center [27]. Schaeffer et.al. demonstrated from a large registry that MRI before EVT in anterior SO and TO as well as posterior circulation were more likely to have better outcome. In our analysis, door to puncture time, which reflects the time used in repeat imaging, was not associated to 90-day mRS. The use of repeat imaging will always have its advantages and disadvantages which the treating physician would have to take into consideration. As demonstrated in earlier studies age was directly associated with outcome. Although elderly patients may have less favorable outcome after EVT compared to younger adults, better results can still be achieved for selected elderly patients [28, 29]. Like earlier described with all types of AIS [30], history of smoking was shown to be associated with poor functional outcome in TO. There are several limitations in our study. First, the retrospective and single center design as well as the small number of patients makes generalization of our findings limited. Pertinent variables such as actual blood pressure, post-stroke complications and management after discharge, which could affect outcome measures, was not included in the study. A full detailed comparison with SO was also not performed. ## Conclusions In this study, thrombectomy with frequent use of emergency stenting of cICA in TO was associated with acceptable safety with low prevalence of sICH. Functional outcome was comparable to the technical less complex SO patients. Diabetes mellitus and increasing number of stent retriever attempts was associated with increased risk of sICH in TO. Higher TICI score and absence of hemorrhagic transformation predicted good functional outcome. ## References 1. Feil K, Herzberg M, Dorn F, Tiedt S, Kupper C, Thunstedt DC. 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--- title: TGF-β is elevated in hyperuricemic individuals and mediates urate-induced hyperinflammatory phenotype in human mononuclear cells authors: - Viola Klück - Georgiana Cabău - Linda Mies - Femke Bukkems - Liesbeth van Emst - René Bakker - Arjan van Caam - Ioan V. Pop - Ioan V. Pop - Radu A. Popp - Simona Rednic - Cristina Pamfil - Marius Farcaş - Dragoş H. Marginean - Orsolya I. Gaal - Medeea O. Badii - Ioana Hotea - Loredana Peca - Andreea-Manuela Mirea - Valentin Nica - Doina Colcear - Mariana S. Pop - Ancuta Rus - Tania O. Crişan - Leo A. B. Joosten journal: Arthritis Research & Therapy year: 2023 pmcid: PMC9969669 doi: 10.1186/s13075-023-03001-1 license: CC BY 4.0 --- # TGF-β is elevated in hyperuricemic individuals and mediates urate-induced hyperinflammatory phenotype in human mononuclear cells ## Abstract ### Background Soluble urate leads to a pro-inflammatory phenotype in human monocytes characterized by increased production of IL-1β and downregulation of IL-1 receptor antagonist, the mechanism of which remains to be fully elucidated. Previous transcriptomic data identified differential expression of genes in the transforming growth factor (TGF)-β pathway in monocytes exposed to urate in vitro. In this study, we explore the role of TGF-β in urate-induced hyperinflammation in peripheral blood mononuclear cells (PBMCs). ### Methods TGF-β mRNA in unstimulated PBMCs and protein levels in plasma were measured in individuals with normouricemia, hyperuricemia and gout. For in vitro validation, PBMCs of healthy volunteers were isolated and treated with a dose ranging concentration of urate for assessment of mRNA and pSMAD2. Urate and TGF-β priming experiments were performed with three inhibitors of TGF-β signalling: SB-505124, 5Z-7-oxozeaenol and a blocking antibody against TGF-β receptor II. ### Results TGF-β mRNA levels were elevated in gout patients compared to healthy controls. TGF-β-LAP levels in serum were significantly higher in individuals with hyperuricemia compared to controls. In both cases, TGF-β correlated positively to serum urate levels. In vitro, urate exposure of PBMCs did not directly induce TGF-β but did enhance SMAD2 phosphorylation. The urate-induced pro-inflammatory phenotype of monocytes was partly reversed by blocking TGF-β. ### Conclusions TGF-β is elevated in individuals with hyperuricemia and correlated to serum urate concentrations. In addition, the urate-induced pro-inflammatory phenotype in human monocytes is mediated by TGF-β signalling. Future studies are warranted to explore the intracellular pathways involved and to assess the clinical significance of urate-TGF-β relation. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13075-023-03001-1. ## Background Hyperuricemia, defined as elevated serum urate levels above the saturation threshold, is the major risk factor for gout [1]. Supersaturated serum urate precipitates into monosodium urate (MSU) crystals which deposit within joints leading to recurrent inflammatory arthritis. These gout flares are initiated by interleukin (IL)-1β production by macrophages within the synovium. In these cells, stimulation of a Toll-like receptor (TLR), by free fatty acids for instance, results in the synthesis of pro-IL-1β, while MSU crystals activate the NLRP3 inflammasome leading to active caspase-1, which processes this pro-IL-1β to mature IL-1β [2]. Based on this finding, several therapies targeting IL-1β demonstrated efficacy in treating gout flares [3]. Although presenting as an intermittent flaring condition, gout is a chronic disease [4]. It affects about 2.5–$3.9\%$ of the Western population and has become more prevalent in the last decades [5]. In addition, hyperuricemia is associated with higher incidence of comorbidities such as cardiovascular disease, type 2 diabetes, metabolic syndrome, chronic kidney disease, cancer and premature ageing [6–9]. Moreover, gout patients have an increased mortality rate of 2.21 compared with the total population, and this increase is associated with high urate levels [10]. Therefore, elucidating the mechanisms responsible for this enhanced risk to develop comorbidities caused by hyperuricemia is crucial. Crişan et al. previously demonstrated that soluble urate leads to a pro-inflammatory phenotype in primary human monocytes characterized by increased production of IL-1β, a classical pro-inflammatory cytokine, and downregulation of IL-1 receptor antagonist (IL-1Ra), the natural inhibitor of IL-1 [11–13]. Features of proinflammatory reprogramming after urate exposure persisted for up to 6 days in PBMCs in vitro and were associated with epigenetic changes [14]. Previously published transcriptomic assessment revealed several differentially enriched pathways in primary monocytes treated with urate for 20 hours, including the TGF-β signalling pathway [15]. In line with this, several genetic variants in genes encoding activin receptors and inhibins belonging to the TGF-β superfamily were found to be associated with serum urate concentrations [16]. In addition, IL-37, an anti-inflammatory cytokine with an important role in gout, also functions via an interaction with SMAD3, a major intracellular signalling effector of TGF-β, further enforcing the importance of this signalling pathway in gout [17, 18]. TGF-β is generally considered an anti-inflammatory cytokine with pro-fibrotic properties which can be secreted by most immune cells [19]. It consists of three paralogs of which paralog 1 is expressed in monocytes. When TGF-β is secreted, it is in inactive form bound to its latency-associated peptide (LAP) of which it can get separated via ROS, metalloproteinases and integrins [20]. The active form of TGF-β can subsequently bind to TGF-β receptor II (TGF-βRII). Upon binding of TGF-β, two TGFβRII receptors form a heterotetramer with TGFβRI, forming a signalling-competent complex able to induce the C-terminal phosphorylation of receptor-activated SMADs [19]. The activated SMADs form a complex with SMAD4 and translocate to the nucleus, where they regulate the expression of a large number of genes involved in e.g. fibrosis and immune signalling. Independently of SMADs, TGF-β can also signal via, e.g. TAK1, ERK and the PI3K-Akt pathway, of which the latter was also shown to be involved in urate priming [15]. In myeloid cells, the function of TGF-β depends on the specific nature of the activating conditions. Generally, TGF-β stimulates cells at the resting state, whereas activated cells are inhibited [21]: in activated monocytes, TGF-β inhibits MyD88-dependent TLR- and IL-1R signalling pathways by promoting MyD88 degradation [22]. However, TGF-β alone can induce gene expression of IL-1 in peripheral blood monocytes [23–25]. Taken together, hyperuricemia has pro-inflammatory effects in human monocytes and is a risk factor for gout and its associated comorbidities. Previous findings suggest TGF-β pathway might be a relevant target to assess in relationship to the inflammation elicited by urate. Therefore, in this study, we explore the role of TGF-β in the context of hyperuricemia and urate induced reprogramming of myeloid cells. ## Volunteers For the discovery cohort, blood from 9 gout patients (8 male, 1 female, mean age 66.2 years old) was used for identification experiments. Blood from 7 healthy volunteers was used as a control (6 male, 1 female, mean age 60.4 years old). All volunteers gave informed consent to use leftover blood for research purposes. Blood draw from healthy volunteers were approved by the Ethical Committee of the Radboud University Medical Center (no. NL32357.091.10 and no. NL42561.091.12) Our validation cohort consisted of 197 individuals with normouricemia, 179 individuals with hyperuricemia without gout, and 195 patients with gout. All study participants in the gout group were included if they corresponded to the ACR/EULAR 2015 classification criteria with a score of 8 or higher. Thirty-six patients presented to the rheumatologist with acute flares. For gout management, allopurinol was used either alone or in combination with NSAIDs and/or colchicine. Volunteers in the hyperuricemia group were included based on serum urate levels of 7 mg/dl or higher and negative history of gout flares. None of the individuals with hyperuricemia were treated with urate-lowering therapies. All participants were included in Cluj-Napoca, Romania, as part of the HINT project (supported by the Romanian Ministry of European Funds; P_37_762, MySMIS 103587), and both clinical characteristics and blood were collected for analysis. For in vitro experiments, buffy coats from healthy donors were obtained after written informed consent (Sanquin blood bank, Nijmegen, the Netherlands). ## Cell isolation Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll-gradient from whole blood of volunteers and were resuspended in RPMI 1640 supplemented with 50 μg/mL gentamycin, 2 mM L-glutamine and 1 mM pyruvate medium. Monocytes were further enriched by either adherence for 1 hour followed by washout of non-adherent lymphocytes or using Percoll gradient. ## Ex vivo mRNA expression experiments The PBMCs from patients and matched healthy controls were seeded on flat-bottom 96-well plates at a density of 0.5 × 106 cells per well and incubated at 37 °C with $5\%$ CO2 for an hour. Subsequently, non-adhering cells were washed away using pre-warmed PBS and the adherent monocytes were incubated with RPMI for 4 h before cells were stored in TRIzol reagent. RNA purification was performed according to manufacturer’s instructions. Subsequently, RNA concentrations were determined using NanoDrop software and cDNA was synthesized using iScript cDNA Synthesis Kit. The SYBR Green method was used to determine the mRNA expression of TGFB1, TGFBR1, TGFBR2, MMP9, ITGAV and SMAD7 relative to reference gene B2M (primer sequences Table S1) ## In vitro TGF-β1 signalling experiments For mRNA expression, adherent monocytes from healthy volunteers were treated with dose-ranging concentrations of urate for 24 h. Subsequently, cells were stored in TRIzol and RNA isolation and qPCR were performed as described above. For the pSMAD2 assessment, Percoll monocytes isolated from healthy volunteers were seeded into a 12-wells plate (1 × 106 cells/well) and treated overnight with urate and, subsequently, TGF-β1 was added for the last hour. For collection of cell lysates, cells were kept on ice and lysed with lysis buffer (Cell signalling; Cat#9803) containing 1x Complete Protease Inhibitor Cocktail (PIC; Roche Diagnostics, #11697498001). Lysates were centrifuged at 25.000×g for 15 min at 4 °C, and supernatants were taken for Western blotting. Protein concentrations were determined using Pierce BCA Protein Assay Kit (ThermoFischer; Cat#23227) following manufacturer’s instructions, and equal amounts of protein were loaded in Laemmli sample buffer and separated on a $10\%$ SDS/PAGE gel for 2 h at 120V. After running the gel, the proteins transferred to a 0.45-μM nitrocellulose membrane (GE Healthcare; Cat#10600002) using wet transfer in Towbin buffer on ice. The membrane was blocked for unspecific binding with $5\%$ BSA-TBST followed by incubation with the primary antibody (Table S2). After overnight at 4 °C, incubation blots were washed and incubated with the secondary antibody for 30 min at RT (Table S3). After another washing step, the blots were developed using the Odyssey CLX Infrared imaging system (Licor). Quantitative assessment of band intensity was performed by Image Lab software (Bio-Rad). ## Urate priming experiments For urate priming experiments, adherent monocytes were primed for 24 h in RPMI supplemented with $10\%$ human pool serum with or without urate (Sigma, 69-93-2) and recombinant TGF-β1 (R&D Systems, Catalogue number 7754-BH-005). After 24 h, cells were restimulated with 10 ng/mL ultra-pure E. coli LPS (InVivogen, Catalogue number tlrl-pelps). Subsequently, cell-free supernatants were collected. Secretion of cytokines was measured in supernatants using ELISA kits for IL-1β, IL-6, IL-1Ra and TGF-β (R&D Systems, Catalogue number DY201, DY206, DY280 and DY240 respectively). To inhibit TGF-β receptor signalling, three inhibitors were used. The ALK$\frac{4}{5}$/7-kinase inhibitor SB-505124 (Sigma) in a concentration of 5 μM with DMSO as solvent control, 5Z-7-oxozeaenol (100 nM) dissolved in DMSO (Tocris Bioscience) and a blocking antibody against TGF-β receptor II (AF-241-NA, R&D systems) with mouse IgG1 as the isotype control (10 μg/mL). Cells were pre-incubated with the inhibitor for 0.5 h before adding urate. ## Proteomics Serum samples from controls ($$n = 196$$), hyperuricemic ($$n = 173$$) and gout patients ($$n = 213$$) collected and stored at – 80 °C were used for commercial targeted serum proteomics analysis (Olink, Uppsala, Sweden). Olink Target 96 Inflammation panel measures 92 protein biomarkers and four internal control samples, using 1 μl serum sample, by multiplex proximity extension assays [26]. The method uses two specific DNA-labelled antibodies for each protein that upon target binding come in close proximity to each other and allow the formation of a PCR reporter sequence that is quantified by real-time PCR (qPCR). Results are generated from cycle threshold (Ct) values. The normalized protein expression (NPX) values are arbitrary (log2 scale) units in which 1 NPX difference equals a two-fold change in protein abundance. Data pre-processing to minimize any technical intra- and inter-assay variation is performed using internal plate controls. Quality control was performed at both sample and protein levels and samples that did not pass QC were excluded. All proteomic data was corrected for age and gender before targeted analysis. In case of LIF where the majority of data were below the lower limit of detection, we chose to use the actual data as was recommended by Olink. ## Transcriptomics Peripheral blood mononuclear cells were isolated using whole blood from normouricemic or hyperuricemic controls and from patients with gout by density gradient centrifugation using Ficoll-Paque PLUS (Sigma Aldrich). Freshly isolated cells were kept in TRIzol reagent (Invitrogen), stored at – 80 °C and were later used for commercial RNA-Seq analysis (Beijing Genomics Institute, BGI, Beijing, China). The integrity of extracted RNA was assessed using Agilent 2100 Bio. Oligo dT magnetic beads were used to capture mRNA from total RNA. Fragmented target RNA was reverse transcribed to cDNA using random N6 primers followed by end-repair and A tailing for adaptor ligation. Purified ligation products were enriched using PCR amplification followed by denaturation and cyclization of ssDNA by splint oligos and DNA ligase generating DNA nanoballs (DNBs). Sequencing of DNBs was performed on DNBseq platform. Raw data was generated by removing reads mapped to rRNAs. Clean reads were generated using SOAPnuke software (version:v1.5.2) by removing reads with adaptors, reads with unknown bases > $10\%$ and low-quality reads, defined as reads with a quality score less than 15 in over $50\%$ bases. Clean reads were mapped to human transcriptome assembly GRCh37 (hg19) using bowtie2. Read counts were normalized using DESeq2 (Version: DESeq2_1.34.0) median of ratios method using R package (Version: R4.0.4.) and were used for downstream targeted gene expression statistical analysis. ## Statistical analysis In ex vivo experiments, Mann-Whitney U test and Welch ANOVA were performed to compare means between groups. For correction of multiple comparisons, Games-Howell correction was employed for proteomic data (n > 50 within each group) and Tamhane T2 for transcriptomics. Spearman analyses were used for correlations. For in in vitro experiments, Wilcoxon signed rank tests were employed to compare means. Differences with adjusted p-value < 0.05 were considered statistically significant. All analyses were done in GraphPad Prism 5. ## TGF-β is elevated in hyperuricemic individuals and correlates positively to serum urate levels As a first exploration, mRNA levels of TGFB1, TGFBRI, TGFBRII and three TGF-β target genes ITGAV, MMP9 and SMAD7 were compared between untreated adherent monocytes of patients with gout and age and sex matched healthy controls. Expression levels of TGFB1 were increased in individuals with gout compared to controls, and ITGAV mirrored this expression of TGFB1 (Fig. 1A, B). We identified no change for TGFBRI, TGFBRII, MMP9 and SMAD7. Moreover, serum urate levels correlated positively to TGFB1 mRNA expression in patients with gout (Fig. 1C).Fig. 1TGF-β mRNA is upregulated in gout patients and correlates to serum urate levels in the discovery cohort. PBMCs from patients with gout and matched healthy controls (HC) were isolated and adhered to a flat-bottom plate, mRNA was isolated and compared to the mean dCT of healthy controls by Mann-Whitney U tests $p \leq 0.05$ (A, B). Serum urate levels were correlated to relative mRNA expression levels and analysed by Spearman’s correlation (C, D) To validate these findings in a larger cohort, mRNA expression of the same genes (TGFB1, TGFBRI, TGFBRII, ITGAV, MMP9 and SMAD7) was assessed in unstimulated PBMCs from individuals with normouricemia, hyperuricemia and patients with gout (HINT study). Within the group of gout patients we observed no differences between intercritical and flaring patients. We observed no significant difference in receptor expression or TGFB1, ITGAV and SMAD7, but the downstream target MMP9 was increased in patients with gout compared to controls (Fig. 2A-D).Fig. 2mRNA expression of TGF-β and downstream targets in PBMCs from individuals with hyperuricemia or gout. PBMCs from individuals with normoruricemia ($$n = 110$$), hyperuricemia ($$n = 22$$) and gout ($$n = 72$$) of which 15 flaring (marked in red) were isolated and transcriptomics were analysed. Relative mRNA expression of TGFB1 (A), MMP9 (B), ITGAV (C) and SMAD7 (D) are shown. Lines represent means with SD. Means were compared by Welch ANOVA with Tamhane’s T2 multiple comparisons test. * $p \leq 0.01$ For further assessment on a protein level, serum TGF-β-LAP and two downstream targets LIF [27] and VEGFA [28] levels were determined in the same individuals with normouricemia, hyperuricemia and gout. All were significantly higher in hyperuricemic individuals compared to controls (Fig. 3A–C). Serum LIF was significantly higher in gout patients during gout flare compared to intercritical gout. However, for TGF-β-LAP no differences were observed between controls and patients with gout, suggesting they are more related to high urate levels than to gout. To test for this hypothesis, TGF-β-LAP was correlated with serum urate levels. In line with the results observed in the discovery experiments, serum urate levels were positively correlated with serum TGF-β-LAP in all cohorts combined (Pearson’s correlation = 0,19; $p \leq 0$,0001; Fig. 3D).Fig. 3Serum TGF-β-LAP, LIF and VEGF-A levels are elevated in individuals with hyperuricemia and TGF-β-LAP correlates positively to serum urate. Serum proteins were analysed by Olink proteomics panel. Flaring gout patients ($$n = 36$$) are marked in red dots. NPX was shown for TGF-β-LAP (A), LIF (B) and VEGF-A (C). Means were compared by Welch ANOVA with Games-Howell’s multiple comparisons test. * $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001.$ Spearman correlation was used to analyse the correlation between serum TGF-β-LAP to urate levels (D). Serum LIF levels were significantly higher in flaring gout patients compared to intercritical gout patients (Welch’s t-test $p \leq 0.0001$) ## Urate induces TGF-β signalling in vitro To explore whether urate may drive TGF-β expression or production, further in vitro studies were performed. Human primary monocytes isolated from healthy volunteers treated with urate showed no elevated TGFB1 mRNA or TGF-β1 protein production after 24 h as assessed by qPCR, ELISA and luciferase bioassay (Supplemental Figures 1, 2, 3). Interestingly, in monocytes treated with urate, mRNA expression of MMP9, which can activate latent TGF-β to its active form, was upregulated. Moreover, the expression of SMAD7, a negative regulator of TGF-β signalling, was significantly downregulated compared to RPMI control condition (Supplemental Figure 1). To further assess intracellular TGF-β signalling, C-terminally phosphorylated SMAD2 was measured in urate and/or TGF-β treated monocytes. Strikingly, both urate and TGF-β induced C-terminal phosphorylation of SMAD2 showing that intracellularly TGF-β signalling was more active (Fig. 4).Fig. 4Protein expression of pSMAD2 (Ser$\frac{465}{467}$) in urate primed monocytes. The monocytes were primed overnight with no, 6.25 or 12.5 mg/dL urate followed with a stimulation of 1 ng/mL TGFβ for 1 h. Cell lysates were used for western blotting. Relative pSMAD2 expression ($$n = 7$$) (A) and a representative blot (B) are shown. Wilcoxon signed-rank test was used to compare means. $p \leq 0.05$ ## Urate induced inflammation is mediated via TGF-β To assess the functional consequences of enhanced TGF-β signalling, in vitro priming experiments investigating the combined effects of urate and TGF-β on cytokine production were performed. Human monocytes from healthy volunteers were treated with urate, TGF-β or a combination of the two for 24 h, washed and subsequently stimulated with LPS. Cytokine release was measured in supernatant. Both TGF-β and urate priming increased the release of IL-1β and IL-6. Whereas urate lowered IL-1Ra release, TGF-β had no effect on the production of IL-1Ra. Interestingly, we observed a small additive effect, but no synergistic effect between TGF-β and urate (Fig. 5).Fig. 5Both TGF-β and urate demonstrate pro-inflammatory effects in a priming model without a synergistic effect. Adherent monocytes isolated from healthy volunteers ($$n = 6$$) were treated with dose-ranging concentrations of recombinant TGF-β and/or urate (50 mg/dL) for 24 h after which cells were washed and stimulated with LPS (10 ng/mL) for 24 h. IL-1β (A), IL-6 (B) and IL-Ra (C) were measured in the supernatant after 48 h culture The lack of synergy between TGF-β and urate priming led us to hypothesize that the urate-induced inflammatory phenotype of the monocytes is mediated via TGF-β. Therefore, we primed human monocytes with urate in the presence of an antibody against the TGF-β receptor II. Blocking the TGF-β receptor II partly reversed the urate induced phenotype. This was shown by the fact that IL-1β production was greatly reduced and that IL-1Ra levels were partly restored (Fig. 6A). Also, SB-505124, a kinase inhibitor of TGF-β I receptors ALK 4, 5, and 7, inhibited urate induced IL-1β and restored IL-1Ra (Fig. 6B). In addition, 5Z-7-oxozeaenol, a TGF-β-activated kinase 1 inhibitor (TAK1), reduced urate-induced IL-1β, but did not affect IL-1Ra (Fig. 6C). Together, these findings pinpoint TGF-β as a potential mediator in urate-induced pro-inflammatory phenotype of human primary monocytes. Fig. 6Blocking TGF-β signalling pathway partly reverses urate priming effects. Adherent monocytes isolated from healthy volunteers (An = 10; B-C $$n = 6$$) were treated with dose-ranging concentrations of urate (50 mg/dL) in the presence or absence of a TGF-β inhibitor for 24 h after which cells were washed and stimulated with LPS (10 ng/mL) for 24 h. IL-1β and IL-Ra were measured in the supernatant after 48 h culture. TGF-β inhibitors: a blocking antibody against TGF-β receptor II with mouse IgG1 as the isotype control (10 μg/mL), SB-505124 (5 μM) and 5Z-7-oxozeaenol (100nM) both with DMSO as solvent control. Wilcoxon signed rank test was applied to compare means. * $p \leq 0.05$ ## Discussion In this study, we assessed TGF-β in the context of hyperuricemia and gout and found that there is a role for TGF-β in urate-induced pro-inflammatory status of monocytes. In two different populations, TGF-β was elevated in subjects with hyperuricemia or gout and correlated to serum urate concentrations. In vitro, urate exposure did not directly induce TGF-β transcription or protein release in human monocytes but did induce C-terminally phosphorylated SMAD2. Moreover, urate induced elevated IL-1β production can be partly reversed by blocking TGF-β and several TGF-β receptor blockers ameliorate the urate-induced reduction of the monocyte IL-1Ra production. In this study, we explored the expression levels of TGFB1, its two receptors TGFBRI and TGFBRII and three TGF-β target genes ITGAV, MMP9 and SMAD7, the latter being a negative regulator of TGF-β signalling. In two different groups, we observed either an increased expression of TGF-β itself in gout patients or an increase in TGF-β-LAP protein in hyperuricemic individuals. Also, the expression of the downstream targets ITGAV and MMP9 were increased in gout patients. In our in vitro setting, we observed no change to TGFB1 expression, a decrease in TGFBRI and SMAD7 expression, while MMP9 expression was again increased after treatment of PBMC with soluble urate for 24 h. Although both findings point towards enhanced TGF-β signalling pathway, prolonged exposure of PBMCs to elevated urate levels in vivo results to different TGF-β signalling kinetics compared to 24 h urate priming in vitro. By showing SMAD2 phosphorylation and blocking TGF-β in vitro, we were still able to demonstrate its relevance in urate-induced inflammatory phenotype. Previously, TGF-β was studied in the context of gout. MSU crystals induce TGF-β in macrophages [29] and exogenous TGF-β inhibits MSU-crystal induced inflammation in vivo [30]. In synovial fluid, TGF-β1 is significantly elevated in acute gouty arthritis compared to osteoarthritis [31] and increases during duration of gout flare [32]. These data combined suggests an anti-inflammatory role for TGF-β in the resolution phase of gout flares. In contrast, we observed no differences in TGFB1 expression or serum TGF-β-LAP within our gout cohort between patients during gout flare and intercritical gout patients. Possibly, TGF-β has a local effect at the site of arthritis but does not result in changes in serum protein or transcripts in circulating PBMCs. However, serum LIF protein was significantly elevated in gout patients during flare. Apart from TGF-β, LIF can also be induced by IL-1β during arthritis [33, 34], which could account for these differences. In vitro, we found that co-incubation with LPS, MSU crystals and TGF-β reduces pro-inflammatory cytokines (data not shown). However, priming monocytes with TGF-β before stimulation with LPS has pro-inflammatory effects similar to urate priming. A pro-inflammatory role for TGF-β has previously been described in adaptive immunity where TGF-β is a key regulator of T helper 17 differentiation. Confirming our in vivo findings, a positive correlation between serum TGF-β and urate was also observed in patients with coronary artery disease [35]. This raises the question what the functional consequences are of elevated urate and TGF-β in humans. In mice, hyperuricemia induces TGF-β in renal tubular tissue [36, 37]. In parallel, allopurinol withdrawal in patients with chronic kidney disease leads to worsening of hypertension, acceleration of the rate of loss of kidney function and an increase in the urinary excretion of TGF-β [38]. The observed increase in fibrosis by urate is not limited to renal disorders. Serum urate has been described as being predictive of pulmonary arterial hypertension, a severe complication in patients with systemic sclerosis [39]. Moreover, Febuxostat, a urate-lowering drug, was shown to suppress angiotensin II-induced aortic fibrosis in mice [40]. We observed that the pro-inflammatory effects of urate were partly mediated by TGF-β. Treating monocytes with both urate and TGF-β had no obvious synergistic effect on cytokine production. Potentially, this could be explained by the presence of human serum in the culture medium which accounts for 3–4 ng/mL TGF-β already. Blocking the TGF-β pathway with several inhibitors reduced the production of IL-1β in human monocytes after stimulation with LPS. Combined with the observed SMAD phosphorylation, this suggests urate activates TGF-β signalling. One of the possible underlying mechanisms is that urate activates the TGF-β activated kinase 1 (TAK1), an important kinase in the TGF-β pathway. Uric acid molecules are capable of arresting TAK1 in an active-state conformation, resulting in sustained TAK1 kinase activation [41]. Both TGF-β induced SMAD$\frac{2}{3}$ and SMAD$\frac{1}{5}$ phosphorylation are mediated by TAK1 kinase activity. By using different inhibitors with each slightly different targets in the TGF-β signalling cascade, we could potentially pinpoint the pathways effected by urate. SB-505124, targeting ALK5, inhibits gene expression downstream of both pSMAD$\frac{2}{3}$ and pSMAD$\frac{1}{5}$, whereas (5Z)-7-Oxozeaenol, a TAK1 inhibitor, does so to a lesser extent and in a more limited number of genes [42]. In our in vitro urate priming models, SB-505124 both reduced IL-1β and restored IL-1Ra, whereas (5Z)-7-Oxozeaenol only inhibited IL-1β without affecting IL-1Ra. Possibly the difference in target gene expression accounts for this observed difference. Another possible intracellular mechanism involved in urate and TGF-β priming is the PI3K/Akt/mTOR pathway. Although we demonstrated pSMAD2 involvement, TGF-β can also activate PI3K resulting in phosphorylation of Akt, independently of SMADs [43]. Similarly, urate priming induces pAkt in monocytes which was reversed by a PI3K inhibitor [15]. Both urate and TGF-β activate the mammalian target of rapamycin (mTOR) via PI3K/Akt pathway, thereby presumably inhibiting autophagy. Since urate induces phosphorylation of Akt within 15 min, it is uncertain whether PI3K activation is mediated via TGF-β. Further research should explore whether these shared pathways are regulated dependent or independent of each other. Another unexplored mechanism of enhanced TGF-β signalling in urate treated monocytes is the regulation of integrin αvβ8. Human CD14+ monocytes activate TGF-β via the expression of the integrin αvβ8 and matrix metalloproteinase 14 [44]. Since TGF-β is always secreted as a latent complex, the function of TGF-β in the regulation of immune responses is controlled by mechanisms that regulate latent TGF-β activation. Finally, the cellular responses to TGF-β have been shown to be altered by pro-inflammatory cytokines such as IL-1β. In chondrocytes, TGF-β induced SMAD7 could be reversed by IL-1β treatment [45]. Likewise, we observed a significant reduction in SMAD7 after urate treatment which is known to lower IL-1Ra expression. Potentially, not urate itself, but a reduction in IL-1Ra could modulate the cellular response to TGF-β similar to IL-1β. In our priming model, blocking TGF-β signalling with SB-505124 enhanced IL-1Ra release independent of urate, further suggesting a link between IL-1Ra and TGF-β signalling. The unexplored intracellular signalling is a clear limitation of this study. Moreover, the consequence of elevated TGF-β was only evaluated in vitro. Exploring the clinical relevance of urate induced changes in TGF-β signalling is not only important for gout but also for other rheumatic diseases as osteoarthritis and systemic sclerosis. In osteoarthritis, changes in in TGF-β signalling are known to contribute to the pathogenesis [46], and serum urate was also identified as a risk factor for symptomatic knee osteoarthritis and joint space narrowing [47, 48]. Elevated serum urate is also associated with increased risk for pulmonary arterial hypertension in patients with systemic sclerosis, a complex connective tissue disease characterized by inflammation, vasculopathy and excessive fibrosis, meditated by TGF-β [39, 49]. ## Conclusions In conclusion, TGF-β is elevated in individuals with hyperuricemia correlating to serum urate levels and the urate induced pro-inflammatory phenotype in human monocytes is mediated by TGF-β signalling. Future studies are warranted to explore the intracellular pathways involved and to assess the clinical significance of the relation between serum urate and TGF-β. ## Supplementary Information Additional file 1: Table S1. Primer sequences ex vivo experiments. Table S2. Primary antibodies for western blot. Table S3. Secondary antibodies for western blot. Figure S1. mRNA expression of genes in the TGF-β signalling pathway in adherent monocytes treated with urate in vitro. PBMCs of healthy volunteers were isolated, adhered to a flat-bottom plated and cultured in medium supplemented with $10\%$ HPS with dose-ranging concentrations of urate. mRNA was isolated after 24h and compared to control condition by Wilcoxon matched-pairs signed rank test. * $p \leq 0.05$, $p \leq 0.01.$ Figure S2. Urate does not increase TGF-β release of human monocytes. 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--- title: 'Prenatal and childhood chlordecone exposure, cognitive abilities and problem behaviors in 7-year-old children: the TIMOUN mother–child cohort in Guadeloupe' authors: - Youssef Oulhote - Florence Rouget - Léah Michineau - Christine Monfort - Mireille Desrochers-Couture - Jean-Pierre Thomé - Philippe Kadhel - Luc Multigner - Sylvaine Cordier - Gina Muckle journal: Environmental Health year: 2023 pmcid: PMC9969702 doi: 10.1186/s12940-023-00970-3 license: CC BY 4.0 --- # Prenatal and childhood chlordecone exposure, cognitive abilities and problem behaviors in 7-year-old children: the TIMOUN mother–child cohort in Guadeloupe ## Abstract ### Background Chlordecone is a highly persistent organochlorine insecticide that was intensively used in banana fields in the French West Indies, resulting in a widespread contamination. Neurotoxicity of acute exposures in adults is well recognized, and empirical data suggests that prenatal exposure affects visual and fine motor developments during infancy and childhood, with greater susceptibility in boys. ### Objective To assess the associations between pre- and postnatal exposures to chlordecone and cognitive and behavioral functions in school-aged children from Guadeloupe. ### Methods We examined 576 children from the TIMOUN mother–child cohort in Guadeloupe at 7 years of age. Concentrations of chlordecone and other environmental contaminants were measured in cord- and children’s blood at age 7 years. Cognitive abilities of children were assessed with the Wechsler Intelligence Scale for Children-IV (WISC-IV), and externalizing and internalizing problem behaviors documented with the Strengths and Difficulties Questionnaire (SDQ) completed by the child’s mother. We estimated covariate-adjusted associations between cord- and 7-years chlordecone concentrations and child outcomes using structural equations modeling, and tested effect modification by sex. ### Results Geometric means of blood chlordecone concentrations were 0.13 µg/L in cord blood and 0.06 µg/L in children’s blood at age 7 years. A twofold increase in cord blood concentrations was associated with 0.05 standard deviation (SD) ($95\%$ Confidence Interval [CI]: 0.0, 0.10) higher internalizing problem scores, whereas 7-years chlordecone concentrations were associated with lower Full-Scale IQ scores (FSIQ) and greater externalized behavioral problem scores. A twofold increase in 7-year chlordecone concentrations was associated with a decrease of 0.67 point ($95\%$ CI: -1.13, -0.22) on FSIQ and an increase of 0.04 SD ($95\%$ CI: 0.0, 0.07) on externalizing problems. These associations with Cognitive abilities were driven by decreases in perceptive reasoning, working memory and verbal comprehension. Associations between 7-year exposure and perceptive reasoning, working memory, and the FSIQ were stronger in boys, whereas cord blood and child blood associations with internalizing problems were stronger in girls. ### Conclusions These results suggests that cognitive abilities and externalizing behavior problems at school age are impaired by childhood, but not in utero, exposure to chlordecone, and that prenatal exposure is related to greater internalizing behavioral problems. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12940-023-00970-3. ## Background Chlordecone (Kepone ™) is an organochlorine insecticide principally used for control of the banana root borer in Central and South America and the Caribbean, including Puerto Rico. This chemical was initially manufactured in the U.S. from 1951 to until 1976 when production and use were banned. Nonetheless, chlordecone under the brand name of Curlone ™ has been intensively used from 1976 to 1993 in banana fields in the French West Indies (FWI). This pesticide undergoes no significant biotic or abiotic degradation in the environment [2]. It has been estimated that the duration of chlordecone pollution of soil in FWI will last for decades to centuries [6]. Although chlordecone has not been used since 1993, it persists in the soil of current and former banana fields where it has been spread. Chlordecone in soil is slowly drained by rainfall towards superficial water, ground water, and marine coastal waters and contaminates the terrestrial and aquatic ecosystems, including crops, livestock, and fishing products [25]. Therefore, exposure to environmental levels to chlordecone have been documented in the FWI populations, including pregnant women, through consumption of contaminated foodstuffs [15, 24]. Chlordecone is a recognized reproductive and developmental toxicant, neurotoxic and carcinogenic in rodents [2]. Studies in humans with high occupational exposure to chlordecone demonstrate toxic effects on the nervous system, liver, and reproductive system. Chlordecone is an endocrine-disrupting chemical (EDC) with well recognized estrogenic properties both in vitro and in vivo [16, 23]. Consequently, it can perturb hormonal systems, and interfere with normal development during sensitive periods from conception to childhood. From our prospective Timoun mother–child cohort in Guadeloupe, we previously reported several pregnancy outcomes related to maternal exposure, including hypotensive effects [34], decreased length of gestation, and increased risk of preterm birth [20]. Prenatal exposure (cord-blood) to chlordecone was associated with decreased weight of newborns from mothers with excessive weight gain prenatally [18], but no risk of overall malformations in newborns were reported [31]. In infants from this cohort at 3-months of age, prenatal exposure to chlordecone was associated with increased thyroid stimulating hormone (TSH) levels in boys only [7]. When aged 7-months old, infants exhibited poorer visual recognition memory or novelty preference in relation with pre- and postnatal chlordecone exposures. Prenatal exposure to chlordecone was also related to slower visual processing speed [9]. We also reported associations between prenatal exposure and poor fine motor development at 7-months [9] and at 18-months of age, but in boys only [5]. Our group recently published the first results from the follow-up of the Timoun study at age 7 years, showing that in utero exposure and during childhood impairs visual contrast sensitivity in boys [33], whereas no associations were found with sex-typed toy preference playing time [8]. In this study, we investigated whether prenatal and childhood exposures to chlordecone are associated with cognitive abilities and behavioral problems in school aged children at 7 years. ## Study population The TIMOUN birth cohort included 1068 pregnant women during their second- or third-trimester prenatal visit recruited at public hospitals and antenatal care dispensary between 2004 and 2007 in Guadeloupe archipelago (FWI). Eligible participants resided in Guadeloupe for more than 3 years. Around $7\%$ refused to participate mainly because of refusal of the spouse, not wishing to participate in the follow-up, and not wishing to provide biological samples [20]. Mothers answered a standardized questionnaire during a face-to-face interview at enrollment. This questionnaire included sociodemographic characteristics, occupational, medical, and obstetrical information. Gestational age (in weeks of amenorrhea) was estimated by obstetricians in charge of the follow up of the pregnancy. At delivery, dietary habits and alcohol consumption during pregnancy and newborn’s health data (including birth weight) were collected and a cord blood sample was obtained to document prenatal exposure to chlordecone and other environmental contaminants. At 7 years-old, the whole cohort of liveborn singleton children ($$n = 1033$$) were invited to participate in a clinical examination, 444 families could not be contacted, refused to participate, or were excluded for other reasons (Supplementary material; Figure S1). At 7-years, 576 of the participating children underwent a neuropsychological evaluation. A maternal interview provided information concerning current health and past medical history, lifestyle, duration of breastfeeding, child behavior and other characteristics. Children’s blood samples were also obtained at age 7. The study was approved by the relevant ethical committee for studies involving human subjects (Comité de Protection des Personnes Sud-Ouest et Outremer III; n° 2011-AOOSSI–40). Each parent provided written informed consent. ## Assessment of cognitive function We assessed children’s cognitive abilities with the French validated version for France of the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV) [39]. The eight core subtests required to compute the Full-Scale IQ score (FSIQ) were performed. The administration of these core subtests provides scaled scores standardized for age, which are combined to obtain four composite scores in domains of verbal comprehension (Similarities and Vocabulary), processing speed (Coding and Symbols), working memory (Letter-Number Sequencing and Digit Span), and perceptive reasoning (Block Design and Matrix Reasoning). The sum of the 8 scaled scores provides the FSIQ score, a measure of global intellectual functioning, which was our primary outcome of cognitive abilities. ## Behavior assessment Child behavior problems were documented with the French version of the Strengths and Difficulties Questionnaire (SDQ), a 25-items screening questionnaire completed by the child’s parent. Items are representing attributes, some positives and other negatives, scored on a 3-point Likert scale: 0 (“not true”), 1 (“somewhat true”), and 2 (“certainly true”), documenting emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems and prosocial behavior. There are strong theoretical and empirical supports for classifying behavior problems according internalizing and externalizing-types of problems. The SDQ’s emotional symptoms (5 items) and peer relationship subscales (5 items) are usually combined into an internalizing problems subscale, as are the conduct (5 items) and hyperactivity/inattention (5 items) subscales into an externalizing problems subscale [13], and higher scores indicate higher difficulties. The SDQ is adapted for many different cultures and languages and has demonstrated excellent psychometric properties (http://www.sdqinfo.com/). ## Biomarkers of exposure to chlordecone and other contaminants Blood samples from the umbilical cord and the child at the 7-year visit were collected in EDTA tubes to document respectively prenatal and childhood exposure to chlordecone and other environmental contaminants. Plasma samples were stored at -30 °C in Polypropylene Nunc® tubes following centrifugation. Chlordecone, polychlorinated biphenyl congener 153 (PCB-153), dichlorodiphenyl dichloroethene (DDE) and lipids were measured in plasma. Total mercury (Hg), lead (Pb), and cadmium (Cd) were quantified in whole blood using inductively coupled plasma mass spectrometry. Determination of chlordecone and PCB-153 concentrations were done by the Center for Analytical and Research Technology at Liege University (Belgium). Contaminant concentrations were analyzed by high-resolution gas chromatography (Thermo Quest Trace 2000, Milan, Italy) equipped with a Ni63 electron capture detection. Preparation of samples and quantification method were previously described [24]. The limit of detection (LOD) was 0.06 μg/L for chlordecone, PCB-153, and DDE in cord blood, and 0.02 μg/L for child chlordecone, PCB-153, and DDE. Total cholesterol and triglyceride in plasma were determined by standard enzymatic procedures (DiaSys Diagnostoc Systems GmbH,Holzheim, Germany) and total lipid concentrations were calculated as described by Bernert et al. [ 4]. Blood Hg, Pb, and Cd concentrations were measured by inductively coupled plasma mass spectrometry (ICP-MS) at the laboratory of the Centre de toxicologie du Québec. The LOD for Hg, Pb and Cd were 0.4 μg/L, 2 μg/L and 0.1 μg/L respectively, and each run of samples included a standard. The linear calibration curve (5 to 120 pg/μl) was established with a certified chlordecone solution (Riedel-de Haën, Seelze, Germany) and a good correlation (r > 0.99) was achieved. A procedural blank, consisting of 2 ml human serum (Cambrex Bio Science, Walkersville, MD), was run with each series of 10 samples, to control the clean-up procedure. Quality control (QC) was performed by regular analyses of procedural blanks, by the injection of standard and n-hexane blanks. The QC was human serum enriched with defined concentrations of chlordecone. The chlordecone concentration in each sample and in the QC was corrected for initial sample weight, and the percentage recovery of the surrogate PCB 112. Recovery rates were always between 70 and $130\%$ [24]. ## Covariates and potential confounders We collected socio-demographic and lifestyle factors and medical history at enrollment, after delivery, and at subsequent follow-ups via administered questionnaires. We used directed acyclic graphs (Supplemental Material; Figure S2) to identify a set of covariates to include in the models from the following potential covariates: child’s age at testing (in months), sex (male; female), birth weight (grams), maternal age at pregnancy, parity (nulliparous; primipara and multiparous), duration of breastfeeding (no breastfeeding; ≤ 6 months; 7–18 months; ≥ 18 months), maternal marital status (married or in a couple; single; living with own family), socio-economic status (SES) indicators during pregnancy based on maternal education (none or some elementary school; some high school; high school diploma; college/university studies) and monthly household income (≤ 800 Euros; 800 – 2300 Euros; > 2300 Euros), maternal nonverbal cognitive abilities (Raven score) assessed with the Raven’s Progressive Matrices [29], maternal alcohol consumption (no; yes) and smoking during pregnancy (no; yes). We included in multivariable models, child’s age and sex, maternal age, parity, Raven score, education, marital status, monthly household income, and alcohol and smoking during pregnancy. ## Statistical analysis Chlordecone and other chemicals’ concentrations were log-transformed (base 2) to address skewness and limit the influence of outliers. Values below limit of detection were replaced by LOD/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\surd 2$$\end{document}√2 [17]. Initial exploratory analyses included descriptive statistics and univariate associations between exposures and measured outcomes, and potential covariates of interest. We also investigated correlations between log-transformed concentrations of environmental exposures using Pearson correlations. First, we used structural equations modeling (SEM) adjusting for the same set of covariates to investigate simultaneously the associations between cord- and 7-years exposures, with WISC-IV and SDQ scores. We ran two separate analyses for the cognitive and behavioral functions. For the WISC-IV scores, we employed a simple path analysis to incorporate all outcomes in a single model. The results are presented as the absolute change in the test score associated with a twofold increase in chlordecone concentrations. For the SDQ test, we used a two stages confirmatory factor analysis (CFA) model with first order latent functions of conduct problems, hyperactivity/inattention, emotional symptoms, and peer problems indicated by their subsequent measured SDQ item scores (Fig. 1). We added two second order latent functions for externalizing problems (indicated by conduct problems and hyperactivity/inattention first order latent functions) and internalizing problems (indicated by emotional symptoms, and peer problems first order latent functions) (Fig. 1). We adopted this two-stage CFA model since it has previously been found to show superior model fits and because it was considered theoretically meaningful [26]. All observed indicators showed significant correlations to their latent functions (Fig. 1), and the models exhibited an excellent fit to the data (Comparative fit index = 0.92, root mean square error of approximation = 0.04, Standardized Root Mean Square Residual = 0.035, and χ2 p-value: 0.06). For the SDQ SEM model, the results for behavioral functions are presented as a standard deviation (SD) change in the latent functions associated with a twofold increase in chlordecone concentrations. The SEM approach allows to reduce both multiple comparisons testing and measurement errors. Because there were missing data for some covariates and prenatal chlordecone exposures, we used the Full Information Maximum Likelihood estimation, which utilizes all available information and avoids list-wise deletion due to missing data. Fig. 1Conceptual diagram of the confirmatory factor analysis for the behavioral domain. Numbers in the arrows indicate the percent variance in the scores explained by the higher order latent functions, whereas numbers in double-headed arrows indicate covariances (All factor loadings were significant at $p \leq 0.001$). Items with an asterisk () were scored reversely We also examined potential effect modification by sex using multi-group SEM analyses. In these analyses, we constrained the loadings, variances, co-variances, and intercepts of the latent variables to be equal across the two groups of females and males. Differences in the associations in the two exposure groups were tested by comparing the value of d/SEd to the standard normal distribution, where d is the difference between the two estimates, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${SE}_{d}=\sqrt{{SE}_{1}^{2}+{SE}_{2}^{2}}$$\end{document}SEd=SE12+SE22 is the standard error of the difference [1, 28]. In additional analyses, we conducted multivariable linear regression analyses to investigate the associations between cord- and 7-years chlordecone concentrations in relation to the WISC-IV (FSIQ and four composite scores). The results are presented as the adjusted mean difference (aMD) in scores with $95\%$ confidence interval ($95\%$ CI) for a two-fold increase in chlordecone concentrations. For the SDQ internalizing and externalizing measured scores, and because scores on these tests exhibit an over-dispersed distribution, we used negative binomial regressions. Negative binomial regressions model the ratio of the mean SDQ scores among exposed and non-exposed. The results are presented as adjusted mean ratios (aMR) of the SDQ externalizing and internalizing problems with corresponding $95\%$ CIs. In all these models, the exposure estimate was reported for a two-fold increase in chlordecone concentrations—for example, an aMR of 1.1 for a two-fold increase in chlordecone suggests that the mean SDQ subscale score was $10\%$ higher for each doubling of the chlordecone concentrations. We also examined sex-specific associations by including a product interaction term (sex x chlordecone concentrations) in all analyses. We finally explored potential nonlinear relations of chlordecone concentrations (log2-transformed) with WISC-IV and SDQ scores using generalized additive models (GAMs) with penalized smoothing regression splines [40] and visually inspected plots of the smoothed data. For the behavioral function (externalizing problems and internalizing problems), we extracted the latent function scores from the factor-analytic component of the SEM and used them as outcome variables, adjusting for covariates [27]. Cord-blood and 7-years child chlordecone concentrations were introduced as spline functions. We assessed departure from linearity (at $p \leq 0.1$) by comparing the model with chlordecone concentrations introduced as a spline function with chlordecone concentrations introduced as a linear term. None of the dose–response relationships significantly deviated from linearity, all at a $p \leq 0.2$ (Supplemental Material; Figures S3 and S4). In sensitivity analyses, we adjusted simultaneously for additional exposures measured in cord blood that have been shown to exert neurotoxic effects, namely, blood Pb, Cd, and Hg, and lipid-standardized PCB-153 and pp’-DDE concentrations. All significance tests were two-sided, and the level of significance was set at a p-value < 0.05 for main estimates and interactions. However, we provide all the estimates, and base our conclusions on the effect estimates in addition to concordant trends rather than solely on hypothesis testing. All statistical analyses were conducted using R version 4.0.3 [38]. ## Results The characteristics of the study population are presented in Table 1. Outcome scores and 7-years chlordecone concentrations were available for 446 children whereas only 371 children had information on both outcome scores and cord chlordecone concentrations. About half were boys ($49\%$) and mean age at examination was 7.6 years. Mean maternal age at delivery was 32 years, most of the mothers were born in the French West Indies and more than a half achieved a high school diploma or college/university studies. About $34\%$ of mothers were nulliparous, and $40\%$ of children were breastfed for more than 6 months. Overall, children included in the prenatal and 7-years analyses did not differ with the sample of children followed up at the 7-year examination (Table 1).Table 1Description of important characteristics of the study populationCharacteristicAll ($$n = 576$$)With data* available in both child blood and outcomes at 7-years ($$n = 446$$)With data available in both cord blood and 7-years outcomes ($$n = 371$$)nMean (SD) or n (%)nMean (SD) or n (%)nMean (SD) or n (%)Child characteristics Sex576446371 Male279 ($48.4\%$)217 ($48.6\%$)170 ($45.8\%$) Female297 ($51.6\%$)229 ($51.4\%$)201 ($54.2\%$) Child age (years)5767.7 (0.2)4467.6 (0.2)3717.7 (0.2) Birth weight (g)5763117.9 (542.7)4463128.7 (530.9)3713141.5 (515.4)Maternal Characteristics Maternal age at delivery (years)57631.9 (6.6)44632.1 (6.5)37131.7 (6.7) Parity576446371 Nulliparous200 ($34.7\%$)150 ($33.6\%$)131 ($35.3\%$) Primipara191 ($33.2\%$)151 ($33.9\%$)121 ($32.6\%$) Multiparous185 ($32.1\%$)145 ($32.5\%$)119 ($32.1\%$) Breastfeeding duration (months)576446371 No breastfeeding90 ($15.6\%$)67 ($15.0\%$)60 ($16.2\%$) ≤ 6 months258 ($44.8\%$)201 ($45.1\%$)158 ($42.6\%$) 7 – 18 months119 ($20.7\%$)99 ($22.2\%$)81 ($21.8\%$) ≥ 18 months109 ($18.9\%$)79 ($17.7\%$)72 ($19.4\%$) Maternal marital status576446371 Married or in a couple314 ($56.0\%$)238 ($55.1\%$)207 ($57.7\%$) Single140 ($25.0\%$)114 ($26.4\%$)85 ($10.2\%$) Living with own family106 ($19.0\%$)80 ($18.5\%$)69 ($32.1\%$) Missing161410 Maternal education576446371 None or elementary school26 ($4.5\%$)21 ($4.7\%$)17 ($4.6\%$) Some high school275 ($47.8\%$)213 ($47.8\%$)172 ($46.4\%$) High school diploma124 ($21.5\%$)98 ($22.0\%$)91 ($24.5\%$) College/University studies151 ($26.2\%$)114 ($25.5\%$)91 ($24.5\%$) Household income (euros)566437364 ≤ 800 Euros55 ($9.7\%$)44 ($10.1\%$)33 ($9.2\%$) 800 – 2300 Euros295 ($52.1\%$)233 ($53.3\%$)181 ($50.7\%$) > 2300 Euros216 ($38.2\%$)160 ($36.6\%$)143 ($40.1\%$) Missing1097 Maternal Raven score54135.4 (12.2)42035.3 (12.1)34535.4 (12.4) Maternal smoking during pregnancy576446371 No558 ($96.9\%$)434 ($97.3\%$)359 ($96.8\%$) Yes18 ($3.1\%$)12 ($2.7\%$)12 ($3.2\%$) Alcohol during pregnancy547425361 Never535 ($97.8\%$)416 ($97.9\%$)351 ($97.2\%$) Ever12 ($2.2\%$)9 ($2.1\%$)10 ($2.8\%$) Missing292110Neurodevelopmental outcomes WISC-IV composite scores FSIQ56987.1 (16.8)44286.6 (16.6)36987.1 (17.0) Verbal comprehension56993.8 (16.2)44293.3 (16.1)36993.7 (16.0) Perceptive reasoning56983.9 (19.1)44283.4 (19.0)36984.3 (19.5) Processing speed56991.8 (15.2)44291.7 (15.1)36991.7 (15.0) Working memory56992.1 (15.1)44291.8 (15.0)36991.5 (15.2)SDQ subscales Internalizing problems5764.7 (3.3)4524.4 (3.2)3714.9 (3.3) Externalizing problems5767.0 (4.0)4527.0 (4.1)3716.9 (4.0)SD Standard deviation, SDQ Strengths and difficulties questionnaire, FSIQ Full-scale IQ, WISC-IV Wechsler intelligence scale for children, fourth edition Table 2 shows the distribution of environmental chemicals in cord and child samples. Chlordecone was detected in $88\%$ and $83\%$ of cord and child blood samples, respectively. The median chlordecone concentration in cord blood (0.21 μg/L) was higher than the one observed in child blood samples (0.05 μg/L). The percentage of detected values for prenatal and postnatal exposure to other chemicals were all greater than $80\%$ except for cord PCB-153 and cord Cd where $70\%$ and $53\%$ of the values were detected, respectively. Figure S3 shows the correlation heat map between multiple chemicals in cord and 7-years blood. The intercorrelation between cord and child chlordecone concentrations was very low ($r = 0.02$), as are the correlation coefficients between cord chlordecone and other contaminants assessed in cord blood (r’s range from -0.14 to 0.13), and between child chlordecone and other child chemicals (r’s range from -0.17 to 0.30). The correlations between and within other chemicals at different time points were weak to moderate, with the highest correlations observed between cord and 7-years pp’-DDE ($r = 0.34$).Table 2Distribution of environmental contaminants in the study population (µg/L)Time pointExposuren% < LODMin25th50thMean75th95thMaxCordChlordecone$36912\%$< LOD0.070.210.570.381.5529.8PCB-$15336830\%$< LOD< LOD0.060.120.150.481.75pp'-DDE$3687.4\%$< LOD0.100.280.670.692.7112.5Lead$3910\%$5.210.213.315.618.230.781.0Mercury$3910\%$0.84.46.67.39.315.046.1Cadmium$39147\%$< LOD< LOD0.050.100.110.350.917-yearsChlordecone$44217\%$< LOD0.020.050.120.110.377.01PCB-$1534428.3\%$< LOD0.030.070.100.120.321.29pp'-DDE$4421.9\%$< LOD0.090.190.470.411.4026.4Lead$4380\%$7.916.322.423.227.437.9213Mercury$4380\%$0.21.01.72.22.805.219Cadmium$43832\%$< LOD< LOD0.100.120.180.250.79 Table S1 shows the univariate associations of our primary measured outcomes (FSIQ, SDQ internalizing and externalizing problems scores) with important characteristics of the study population. FSIQ scores were associated with child sex, birth weight, maternal age, parity, marital status, education, household income, breastfeeding duration, and maternal Raven scores. SDQ internalizing problems scores were higher in children from mothers that are younger, single, that have a lower education, household income, and Raven score. Lower birth weight was also associated with higher internalizing problems scores. SDQ externalizing problems scores were associated with the same characteristics in addition to child sex with higher scores in boys. ## Associations between cord and 7-years chlordecone concentrations and scores of cognitive abilities Results of these analyses are presented in Fig. 2 and Supplementary material, Table S2. A twofold increase in 7-year chlordecone concentrations was associated with 0.67 ($95\%$ CI: -1.13, -0.22) lower FSIQ scores. This association was driven by 7-years chlordecone associations with working memory (β = -0.69; $95\%$ CI: -1.18, -0.19), verbal comprehension (β = -0.50; $95\%$ CI: -0.89, -0.10), and perceptive reasoning composite scores (β = -0.69; $95\%$ CI: -1.27, -0.11). No association was observed with processing speed (β = -0.17; $95\%$ CI: -0.61, 0.28). In multiple-group SEM sex-stratified analyses, a few associations showed effect modification by sex at p-effect modification (p-EM) < 0.05. Associations between 7-year chlordecone concentrations and some WISC-IV composite scores were stronger in boys (Fig. 2). For instance, a twofold increase in 7-years chlordecone concentrations was associated with 1.07 ($95\%$ CI: -1.72, -0.42) and 1.36 ($95\%$ CI: -2.04, -0.69) lower FSIQ and working memory scores in boys whereas the association was weaker or null in girls (β = -0.25; $95\%$ CI: -0.81, 0.31 and β = 0.10; $95\%$ CI: -0.54, 0.75 for FSIQ and working memory, respectively; p-EM = 0.06 and 0.002, respectively). Additionally, cord blood chlordecone concentrations were associated with 0.84 ($95\%$ CI: 0.08, 1.60) higher processing speed scores in boys whereas the association was negative in girls (β = -0.48; $95\%$ CI: -1.26, 0.29; p-EM = 0.02). No effect modification by sex was observed for other scores of cognitive abilities. Fig. 2Associations between cord-blood and 7-years chlordecone concentrations and WISC-IV composite scores obtained from the SEM path analysis, stratified by sex. Models were adjusted for child’s age and sex, maternal age, parity, Raven score, education, marital status, monthly household income, and alcohol and smoking during pregnancy ## Associations between cord and 7-years chlordecone concentrations and behavioral problems Results of these analyses are presented in Fig. 3 and Supplementary material, Table S2. Prenatal exposure to chlordecone was not associated with externalizing ($B = 0.02$ SD; $95\%$ CI: -0.04, 0.08) or internalizing ($B = 0.04$ SD; $95\%$ CI: -0.02, 0.10) problems. A twofold increase in 7-years chlordecone concentrations was associated with 0.04 SD ($95\%$ CI: 0.0, 0.08) higher (worse) externalizing problems scores. No association was observed between 7-years chlordecone concentrations and internalizing problems ($B = 0.01$ SD; $95\%$ CI: -0.05, 0.06). We also did not observe any effect modification by sex in multiple-group SEM analyses (all at p-EM > 0.10).Fig. 3Associations between cord-blood and 7-years chlordecone concentrations and standardized behavioral functions obtained using SEM analyses, stratified by sex. Models were adjusted for child’s age and sex, maternal age, parity, Raven score, education, marital status, monthly household income, and alcohol and smoking during pregnancy ## Additional analyses In additional analyses using traditional multivariable regressions, we observed similar patterns: analyses showed concordant findings for both cognitive scores using linear models (Fig. 4) and behavioral scores using negative binomial models (Fig. 5). For instance, a twofold increase in 7-years chlordecone concentrations was associated with 0.64 ($95\%$ CI: -1.09, -0.18) lower FSIQ scores. Similar results were observed for perceptive reasoning, verbal comprehension, and working memory scores. We also observed stronger associations in boys than in girls for working memory as reported in the SEM analyses. Fig. 4Associations between cord-blood and 7-years chlordecone concentrations and WISC-IV composite scores using multivariable linear analyses, stratified by sex. Models were adjusted for child’s age and sex, maternal age, parity, Raven score, education, marital status, monthly household income, and alcohol and smoking during pregnancyFig. 5Associations between cord-blood and 7-years chlordecone concentrations and measured SDQ scores using multivariable negative binomial models, stratified by sex. Models were adjusted for child’s age and sex, maternal age, parity, Raven score, education, marital status, monthly household income, and alcohol and smoking during pregnancy For the SDQ scores, a twofold increase in cord-blood chlordecone concentrations was associated with $3\%$ higher internalizing problems scores (aMR = 1.03; $95\%$ CI: 1.00, 1.06), with an effect modification by sex where associations were stronger in girls (aMR = 1.07; $95\%$ CI: 1.03, 1.12) than in boys (aMR = 0.99; $95\%$ CI: 0.95, 1.04). A twofold increase in 7-years chlordecone concentrations was also associated with $2\%$ higher externalizing problems scores (aMR = 1.02; $95\%$ CI: 1.00, 1.04; Fig. 5). Analyses investigating non-linear significant dose–response relationships showed no significant departure from linearity for any of the investigated associations (Supplemental Material; Figures S4 and S5). Additional sensitivity analyses adjusting for other co-exposures showed similar findings (Supplemental material; Figures S6 and S7). ## Discussion We previously reported effects in domains of cognitive and fine motor functions of pre- and postnatal exposures to chlordecone in 7- and 18-months old children from the TIMOUN cohort. However, there is currently no empirical data to determine the degree to which prenatal and postnatal chlordecone exposures are associated with effects in school-aged children. In the present study, we investigated both pre- and postnatal exposures to chlordecone in relation to cognitive abilities and problem behaviors at age 7 years. Our findings point to a potential detrimental effect of 7-years chlordecone concentrations, but not from in utero exposure, on cognitive abilities, with stronger effects in boys. These effects are seen with general cognitive abilities as measured by the WISC-IV, but also in specific nonverbal domains of perceptive reasoning and working memory, and verbal comprehension. Regarding behavioral problems, higher chlordecone exposure at 7 years of age was associated with higher scores of externalizing problems, while prenatal chlordecone exposure was related to higher internalizing problems scores, with stronger associations among girls. Patterns of results are very robust, with significant associations consistently repeated when outcome scores are obtained from structural equations modeling or analyzed traditionally using multivariable regressions. When aged 7-months old, infants from this cohort exhibited poorer visual recognition memory or novelty preference in relation with pre- and postnatal chlordecone exposure. Prenatal exposure was also associated with slower visual processing speed and poorer fine motor development at 7-months [9] and at 18-months in boys only [5]. In a recent investigation from the same cohort, we also reported that 7-years chlordecone concentrations were associated with poorer visual processing when copying geometric figures using the Stanford Binet copying test, while no association was observed with cord chlordecone concentrations [11]. In animal studies, chlordecone was shown to alter catecholamine activity—including dopamine—by decreasing their synaptic binding and uptake [10]. Additionally, male rats exposed to chlordecone were found to be hypersensitive to the motility increasing effects of apomorphine, a dopamine receptor agonist [37]. Chlordecone has also been shown to possess estrogen-like activity [16, 23] which may mediate the observed neurotoxic effects, especially those pertaining to the hypothalamo-pituitary axis [10]. This may also explain some sex-specific associations observed in this study. For instance, chlordecone exposure during the critical period for sexual differentiation of the brain has been shown to alter sex-dependent behaviors in adult rats [22]. Our results point to potential sexually dimorphic effects of chlordecone on both cognitive and behavioral functions. Prenatal chlordecone exposure was related to higher internalizing problems scores, with stronger associations in girls, whereas postnatal exposures exhibited stronger effects in boys, especially for working memory. In a previous investigation from this cohort, higher cord chlordecone concentrations were associated with an increase in thyroid stimulating hormone in 3-month boys, whereas postnatal exposure concentrations in breastmilk were associated with a decrease in free triiodothyronine overall, and in free thyroxine among girls [7]. Similar sexually dimorphic associations were observed in this cohort with fine motor skills at 18 months [5] and visual contrast sensitivity at 7-years [33]. Several other organochlorine insecticides have been found to have sex-specific associations, both in animal studies and in human studies [21]. For instance, perinatal exposure of mice during gestation and lactation to dieldrin altered dopaminergic neurochemistry and had greater adverse effects in the male offspring than the female [30]. Other organochlorine insecticides showed opposite effects with stronger effects on girls. For instance, prenatal exposure to pp’-DDE showed stronger associations with behavioral problems at age 7–8 years in girls [36], whereas maternal Dichlorodiphenyltrichloroethane (DDT) and pp’-DDE serum concentrations were associated with processing speed at 7-years of age, with pp’-DDE associations exhibiting stronger estimates in girls [12]. The current levels of chlordecone measured in this cohort show a significant decrease from a geometric mean of 0.13 µg/L in cord blood to 0.06 µg/L at 7-year-old children with no differences by child sex. Despite the low levels observed in this cohort, especially at 7 years, we were still able to detect associations with specific domains of development. Interestingly, prenatal Hg was also associated with poorer performance in same domains of cognitive function, including both perceptual reasoning and verbal comprehension in Faroese children [14] as in Inuit children from Northern Canada where both cohorts were exposed through maternal consumption of contaminated fish and mammals [19]. Similar domains have also been shown to be vulnerable to other neurotoxicants (Reviewed in [35] and these specific domains of cognitive function might be more sensitive to neurotoxic chemicals. Similar to other investigations, the observed effect sizes in this study are relatively modest and subtle. However, given the widespread and ubiquitous exposure to chlordecone in this population, these small effect sizes could had a considerable impact at the population level [3]. This study has several notable strengths. We use a prospective longitudinal cohort, with enrollment of mothers during their pregnancy, collection of biological samples for assessment of multiple exposures at different time points, multiple maternal interviews to document covariates, and long-term follow-up of a large number of children assessed with gold standard tests to assess cognitive function independently from maternal report. Additionally, we assessed both prenatal and postnatal exposures through state-of-the-art laboratory analyses, concurrently with assessment of other chemicals recognized or under study for their neurotoxicity such as Pb, Cd, Hg, PCB-153 and pp’-DDE. We were also able to document and measure important covariates that allowed us to thoroughly assess potential confounders, including several socioeconomic status indicators, as well as maternal non-verbal cognitive abilities. The significant associations are reported between child Full-Scale IQ and variables such as breastfeeding, family income, maternal marital status, education and Raven scores confirm the critical role of SES and family environment in promoting child development. Furthermore, obtaining these significant associations with variables consistently related to child cognitive skills and behavior in studies conducted in multiple populations, provides convergent validation for use of the WISC-IV and the SDQ in Guadeloupean children. Finally, we used a SEM approach, therefore addressing issues arising from multiple testing and missing data that may not be adequately considered by standard regression analyses. Comparison of the SEM findings with traditional approaches analyzing each test score separately yielded comparable findings. There are several limitations to this study. The reported associations could be attributable to unmeasured confounders, and although we cannot rule out possible residual or additional unmeasured confounding by other factors, we adopted a conservative approach by adjusting for several indicators of SES, i.e. marital status, income, and maternal education, even if they lay on a common causal pathway, thus reducing the potential for residual confounding. Our results are also unlikely to be attributable to other contaminants since we have adjusted for several neurotoxicants in sensitivity analysis even though their correlations with chlordecone concentrations were weak or null. The significant cross-sectional associations at 7-years limits the ability to draw strong causal inferences, although reverse causality is improbable as we do not expect cognitive and behavioral functions in the child to influence their dietary behaviors, the main source of exposure in the general population in Guadeloupe. Indeed, dietary choices at this age are mainly directed by the parent. Despite the ban of chlordecone since the 90 s, it persists in soils for decades if not centuries, constituting a long-lasting source of exposure to future generations. It has also been shown recently that chlordecone fluxes in soils drastically increased when glyphosate use began, leading to widespread ecosystem contamination [32]. The use of glyphosate in Guadeloupe may therefore lead to an increase in soil erosion and release of the stable chlordecone stored in the soils of polluted fields. ## Conclusion This is the first study to examine effects of chlordecone on cognitive and behavioral functions in school age children. The results from this study suggest that concurrent exposures to chlordecone may be associated with child development in specific domains of cognition, especially in boys, whereas prenatal exposures may be associated with behavioral function in girls. This study provides new insights on the potential neurotoxicity of chlordecone, which persists in the FWI population decades after its ban. ## Supplementary Information Additional file 1: Figure S1. Flow chart of the Timoun study (Neurodevelopment at seven years). Figure S2. Conceptual Directed Acyclic Graph representing the associations between chlordecone exposure, FSIQ, and potential confounders. Figure S3. Correlation plot of maternal and 7-years exposures. Table S1. Univariate associations between primary outcomes and characteristics of the study population. Table S2. Associations between cord- and 7-years blood chlordecone concentrations and cognitive and behavioral functions using SEM analyses, stratified by sex. Figure S4. Dose response relationship between cord- and 7-years blood chlordecone concentrations and WISC-IV scores. Figure S5. Dose response relationship between cord- and 7-years blood chlordecone concentrations and behavioral latent functions. Figure S6. Associations between cord-blood and 7-years chlordecone concentrations and WISC-IV composite scores obtained from the SEM path analysis with additional adjustment for co-exposures, stratified by sex. Figure S7. 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