Causation Between the Gut Microbiota and Inguinal Hernia: A Two-Sample Double-Sided Mendelian Randomization Study

Causation Between the Gut Microbiota and Inguinal Hernia: A Two-Sample Double-Sided Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Causation Between the Gut Microbiota and Inguinal Hernia: A Two-Sample Double-Sided Mendelian Randomization Study Changyuan Wu, Yujin Zhu, Hongwei Xi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4073518/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 5 You are reading this latest preprint version Abstract Background Inguinal hernias are the most common type of enterocele and are frequently caused by defects in the abdominal wall muscles in the groin area. Numerous animal models and human studies have shown that the gut microbiota is associated with skeletal muscle aging and loss. However, the causation between the gut microbiota and inguinal hernia remains unclear. To reveal the causal association between the gut microbiota and inguinal hernia, we conducted a two-sample double-sided Mendelian randomization analysis. Methods We used genome-wide association analysis (GWAS) summary statistics of the gut microbiota from the MiBioGen consortium and GWAS statistics of inguinal hernia from the FinnGen R10 database. The causation between the gut microbiota and inguinal hernia was explored through the inverse variance weighted (IVW) method, MR Egger regression method, weighted median method, weighted model method, and simple model method. Sensitivity analysis was used to test whether the Mendelian randomization analysis results were reliable. Reverse Mendelian randomization was used to conduct effect analysis and sensitivity analysis using the entire gut microbiota as the outcome. Results The IVW results indicated that Verrucomicrobia, Lactobacilliales, Clostridiaceae1, Butyricococcus, Categorybacter, Hungatella, Odoribacter, and Olsenella had a direct negative causation with the gut microbiota. The reverse Mendelian Randomization results showed that Eubacterium brachygroup, Eubacterium eligensgroup, Eubacterium xylanophilumgroup, Coprococcus3, Ruminococcus1, and Senegalimassilia were directly related to inguinal hernia. The bilateral sensitivity analysis revealed no heterogeneity or horizontal pleiotropy. Conclusions The results confirmed that 8 bacterial traits had a negative causation with inguinal hernia. Reverse MR analysis revealed a positive correlation between inguinal hernia and 6 bacterial traits. Modulating the diversity and components of the gut microbiota is envisaged to contribute to improving the incidence and prognosis of inguinal hernia. Biological sciences/Computational biology and bioinformatics/Databases/Genetic databases Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Microbiology/Bacteria Health sciences/Risk factors Health sciences/Gastroenterology/Gastrointestinal system/Microbiota Health sciences/Gastroenterology/Gastrointestinal diseases/Dysbiosis Inguinal hernia Gut microbiota Mendelian randomization Causal inference Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background An enterocele is a bulging of the peritoneum, peritoneal fat, or abdominal organs through congenital or acquired orifices in the abdominal wall[ 1 – 3 ]. The probability of experiencing an inguinal hernia during one's lifetime is approximately 25% for males and 3% for females, and this probability increases in tandem with advancing years[ 4 – 6 ]. The groin hernia encompasses three distinct categories distinguished by the location of the hernia sac neck concerning the inguinal (Hesselbach) triangle: direct inguinal, indirect inguinal, and femoral [3] . Direct inguinal hernia mostly occurs in elderly men. The anatomical substance protrudes through the posterior boundary of the inguinal canal, and the neck of the hernia sac is found medially to the inferior epigastric artery. Indirect inguinal hernia commonly occurs in children and young adults. The small intestine, omentum, and other tissues protrude through the internal ring of the inguinal canal, and the hernia sac neck is located outside the inferior epigastric artery[ 7 ]. Femoral hernia mainly occurs in women over 40 years old. The tissue protrudes along the femoral canal under the inguinal ligament. The hernia sac neck is located inside the femoral blood vessel. The treatment modalities for inguinal hernias often include hernia repair as a fundamental approach; 1.6 million inguinal hernias are diagnosed in the United States each year, and more than 500,000 surgeries are performed[ 8 ]. The human intestinal environment harbors an immense population of bacteria, totaling trillions, and these microorganisms have undergone a coevolutionary journey alongside the human genome. The gut microbiota is involved in regulating the host body and is related to a variety of diseases[ 9 ]. Nevertheless, the constitution of the gut microbiota undergoes varies depending on the host species, and an array of internal and external factors, along with both biotic and abiotic influences, can induce changes in the intricate balance of the gut microbiota composition[ 10 – 15 ]. Numerous research endeavors have delved into potential mechanisms through which the gut microbiota may contribute to the depletion of skeletal muscle mass, encompassing aspects such as protein anabolism, mitochondrial dysfunction, persistent inflammation, immune responses, and imbalances in metabolic processes[ 16 ]. In a groundbreaking study conducted in 2004, Backhed et al. injected cecal contents from conventionally raised animals into the intestines of germ-free mice[ 17 ]. The results indicated that the body fat of mice in the experimental group increased by 60%, and insulin sensitivity and glucose tolerance decreased accordingly. Since skeletal muscle is one of the tissues that processes glucose, this finding suggests microbial involvement in mediating the functional regulation of muscle metabolism. AMP-activated protein kinase (AMPK) and carnitine palmitoyltransferase-1 (CPT-1) activities were significantly greater in the skeletal muscle of germ-free mice than in that of mice harboring gut microbiota, suggesting greater oxidation ability. The findings of this investigation underscore that the gut microbiota possesses the capacity to shape the biological makeup by regulating bioenergetic pathways within skeletal muscle. Alterations in the constitution of the gut microbiota have the potential to contribute to the aging of muscles and the onset of sarcopenia[ 18 – 20 ]. Within the framework of the musculoskeletal system, the gut microbiota assumes a pivotal role by intricately managing intestinal permeability, energy metabolism, hormone secretion, systemic inflammatory pathways, and immune responses[ 21 ]. Mendelian randomization (MR) is a contemporary method for determining the causal relationships between the gut microbiota and the presence of inguinal hernia. MR relies on genetic variants as instrumental variables (IVs) to evaluate the causal relationship between exposure and the disease outcomes. Because genetic variation is assigned randomly and lacks any connection to offspring, the presence of genetic variation, along with subsequent outcomes, remains unaltered by potential confounding factors. This finding substantiates the authenticity of the established causal sequence[ 22 , 23 ]. MR has been broadly employed to probe the causal connections between the gut microbiota and an array of health conditions, including metabolic diseases, irritable bowel syndrome, preeclampsia, and several other diseases [ 24 – 28 ]. Therefore, in this study, the gut microbiota and inguinal hernia were selected as the research objects, and two-sample double-sided MR analysis was used to evaluate the causation between the gut microbiota and inguinal hernia and provide new biological diagnostic markers, treatment strategies, and theoretical foundations for further research on the mechanism of inguinal hernia. Methods Design The experimental design used a two-sample double-sided MR analysis to evaluate the causation between the gut microbiota and inguinal hernia. Initially, comprehensive GWAS summary statistics about the gut microbiota and inguinal hernia were acquired. Therefore, to ensure the credibility of experimental findings, it is imperative to satisfy the three fundamental assumptions of MR. Single-nucleotide polymorphisms (SNPs) of IVs are strongly associated with exposure factors (gut microbiota or inguinal hernia); SNPs are independent of identified confounding factors; SNPs affect outcome factors (inguinal hernia or gut microbiota) only through exposure factors (gut microbiota or inguinal hernia) (Fig. 1 ). Material The GWAS summary statistics on the gut microbiota were obtained from the MiBioGen consortium and are accessible at https://mibiogen.gcc.rug.nl/ . This consortium stands as the most extensive, multiethnic collaboration, conducting a genome-wide meta-analysis specifically focused on gut microbiota[ 29 ]. During the compilation of this article, 15 bacterial species lacking distinct nomenclature, consisting of 3 families and 12 genera whose identities remain unidentified, were deliberately excluded. Subsequently, a refined selection of 196 bacterial species (comprising 119 genera, 32 families, 20 orders, 16 classes, and 9 phyla) was meticulously chosen for MR analysis. The GWAS summary statistics for inguinal hernia were obtained from the FinnGen R10 database ( https://www.finngen.fi/en ), which included 35,248 inguinal hernia cases and 352,418 controls, all of which were from European populations. The summary statistics employed in this publication originate from openly accessible databases that can be obtained without cost. All GWAS summary statistics included in the article were approved by the respective ethics agency. Instrument Variable Selection This study selected SNPs that were closely related to bacterial taxa (P < 1.0×10 − 5 ) as IVs to obtain comprehensive data. Linkage disequilibrium (LD) analysis was performed based on European population sample data, with parameters set to r 2 < 0.001 and kb = 10,000. The robustness of IVs is assessed by computing the F statistic. An F value exceeding 10 signifies the absence of a weak IV error. The IVs with F < 10 are eliminated. Finally, to prevent alleles from affecting the results, palindromic SNPs were removed through palindromic sequence detection. r 2 is the variance of exposure explained by SNPs, and its calculation formula is as follows: \({r}^{2}=2\times {\beta }^{2}\times EAF\times \left(1-EAF\right)/2\times {\beta }^{2}\times EAF\times \left(1-EAF\right)+{SE}^{2}\times 2\times N\times EAF\left(1-EAF\right)\) . Within the equation, EAF represents the frequency of the effect allele, β denotes the value of the allele's effect, and SE represents the standard error. The F statistic calculation formula is as follows: \(F=\left[\left(N-k-1\right)/k\right]\times {r}^{2}/\left(1-{r}^{2}\right)\) . N is the number of samples in the exposure statistic, and k is the number of SNPs. Sensitivity analysis The robustness of the causation of the gut microbiota to inguinal hernia was measured through a series of sensitivity analyses. Cochran Q analysis can compute distinctions among IVs, and a P value less than 0.05 indicates the presence of heterogeneity. Depending on whether there was heterogeneity, the random effects model or fixed effects model was selected for analysis. Horizontal pleiotropy testing through MR Egger regression and MR-PRESSO analysis. The stability of the statistic was tested through leave-one-out analysis, which removes individual SNPs to determine whether there are SNPs that may have a strong effect. The principal MR analysis in this research adopted the IVW method, while supplementary analyses, including MR Egger and the weighted median, were employed to enhance the depth of causal inference. If there was no horizontal pleiotropy in the data, there was no bias in the IVW results. Furthermore, this article employed reverse MR analysis to deduce the potential existence of reverse causation between the bacterial taxa identified in forward MR analysis and the occurrence of inguinal hernia. All statistical analysis procedures in this article were performed in RStudio software (version 4.3.2), using the TwoSampleMR and MR-PRESSO software packages for analysis. Results Causal effects of the gut microbiota on inguinal hernia Following the screening criteria for IVs with a significance threshold of P < 1.0 × 10 − 5 , a cumulative of 2,616 SNPs were acquired from a pool of 196 intestinal flora. These included 124 SNPs across 9 phyla, 223 SNPs spanning 16 classes, 279 SNPs in 20 orders, 444 SNPs within 32 families, and 1,546 SNPs associated with 119 genera. The F values corresponding to all SNPs were greater than 10. Therefore, this study is not susceptible to weak IV bias. MR analysis was performed on 196 bacterial taxa by the IVW method, and SNPs with P < 0.05 were screened out. The data analysis revealed that a total of 72 SNPs in 8 bacterial taxa are causally related to inguinal hernia (Fig. 2 ). These bacterial taxa included 1 phylum (12 SNPs), 1 order (15 SNPs), 1 family (10 SNPs), and 5 genera (35 SNPs), namely, Verrucomicrobia, Lactobacillales, Clostridiaceae1, Butyricococcus, Catenibacter, Hungathella, Odoribacter, and Olsenella. The results of the IVW analysis revealed that Verrucomicrobia (OR = 0.9029, 95% CI: 0.8375–0.9734, P = 0.0077), Lactobacillales (OR = 0.9087, 95% CI: 0.8378–0.9857, P = 0.0211), Clostridiaceae1 (OR = 0.9017, 95% CI: 0.8236–0.9871, P = 0.0251), Butyricococcus (OR = 0.8678, 95% CI: 0.7849–0.9594, P = 0.0056), Catenibacter (OR = 0.9211, 95% CI: 0.8562–0.9910, P = 0.0276), Hungathella (OR = 0.8748, 95% CI: 0.8133–0.9410, P = 0.0003), Odoribacter (OR = 0.8583, 95% CI: 0.7690–0.9580, P = 0.0064), Olsenella (OR = 0.9314, 95% CI: 0.8882–0.9767, P = 0.0034). MR study revealed a negative correlation between the abovementioned gut microbiota and inguinal hernia (Fig. 3 ). The Cochran Q analysis results revealed no obvious heterogeneity among the selected IVs. The MR Egger analysis results suggested a lack of horizontal pleiotropy within the bacterial taxa examined in this study (Table 1 ). The outcomes of the leave-one-out method analysis indicate the absence of evident outliers among the chosen IVs, and the MR analysis results are reliable (Fig. 4 ). Reverse MR analysis In the reverse MR analysis involving 196 intestinal flora and inguinal hernias, the results point toward a forward causation link between 6 intestinal flora and the manifestation of inguinal hernia (Fig. 5 ). A direct causative link is established between inguinal hernia and Eubacterium brachygroup, Eubacterium eligensgroup, Eubacterium xylanophilumgroup, Coprococcus3, Ruminococcus1, and Senegalimassilia (Fig. 6 ). The outcomes of sensitivity analysis in the reverse MR analysis revealed the absence of heterogeneity and horizontal pleiotropy. Table 1 Sensitivity analysis of the gut microbiota and inguinal hernia status Category Exposure factors Heterogeneity test Horizontal pleiotropy test IVW Q P value MR Egger Q P value MR Egger intercept value MR Egger intercept P value Phylum Verrucomicrobia 0.496 0.419 0.003 0.728 Order Lactobacillales 0.735 0.810 -0.011 0.195 Family Clostridiaceae1 0.971 0.964 0.006 0.568 Genus Butyricicoccus 0.608 0.503 -0.003 0.755 Genus Catenibacterium 0.459 0.403 -0.053 0.471 Genus Hungatella 0.693 0.526 -5.62E-05 0.999 Genus Odoribacter 0.450 0.671 -0.021 0.169 Genus Olsenella 0.344 0.462 -0.016 0.152 Discussion Reduced abdominal wall strength and increased intra-abdominal pressure are the two main causes of inguinal hernia[ 30 ]. Through the utilization of multiview learning approaches on fecal metagenomic data, Chen et al. established a human gut microbiota aging clock, implying that the composition of the gut microbiota changes with advancing age[ 31 ]. The insulin/insulin-like growth factor-1 (IGF-1) signaling cascade triggers the activation of the mammalian target of rapamycin (mTOR), leading to an increase in protein synthesis for the preservation of muscle mass[ 32 ]. Reduced insulin/IGF-1 signaling in older adults contributes to an increase in insulin resistance, ultimately initiating the depletion of skeletal muscle mass[ 33 ]. Cani et al. demonstrated that feeding mice a high-fat diet impaired the tight junctions of the intestinal epithelium and elevated intestinal permeability, indicating that a high-fat diet can induce leakage of lipopolysaccharide (LPS), an external membrane element found in gram-negative bacteria, from the gut into the circulation. In individuals inoculated with LPS, there was a substantial increase in the binding activity of nuclear factor-kappa B (NF-κB) and the phosphorylation of c-Jun N-terminal kinase (JNK) within skeletal muscle, demonstrating that endotoxin is involved in mediating the process of impaired skeletal muscle glucose tolerance[ 34 ]. Therefore, the interaction of myotubes with extracellular vesicles derived from gut microbes results in synergistic inhibition of insulin signaling and the induction of insulin resistance[ 35 , 36 ]. These studies indicate that as the composition of the intestinal flora changes, it may induce insulin resistance in skeletal muscles, causing skeletal muscle loss and aging. With the development of GWAS, the data volume and measurement accuracy of genetic variation have continued to expand, significantly reducing data bias in the research process and laying the foundation for research on the causation between the gut microbiota and inguinal hernia. Because genetic variation is determined before birth, measurable genetic variables are not affected by environmental factors, making causal effect studies more reliable than observational studies and equivalent to natural randomized controlled trials. Therefore, with this research method, it is more reliable to explore the causation between the gut microbiota and inguinal hernia. Our investigation employed information extracted from the most extensive GWAS meta-analysis of gut microbiota conducted by the MiBioGen consortium. Additionally, GWAS data on inguinal hernia, made available by the FinnGen R10 database, were utilized to perform a two-sample double-sided MR analysis. Using the gut microbiota as the exposure and inguinal hernia as the outcome, Verrucomicrobia, Lactobacilliales, Clostridiaceae1, Butyricococcus, Categorybacter, Hungatella, Odoribacter, and Olsenella were found to be directly negatively related to the gut microbiota. Regarding inguinal hernia as the primary exposure and gut microbiota as the corresponding outcome, Eubacterium brachygroup, Eubacterium eligensgroup, Eubacterium xylanophilumgroup, Coprococcus3, Ruminococcus1, and Senegalimassilia were directly related to inguinal hernia. Currently, there is a lack of reported studies examining the relationship between the gut microbiota and inguinal hernia. This article is the first to use MR to analyze the causation between them. Compared with individual-level experimental studies, there are differences between traditional laboratory animals and the natural environment, which cannot reflect the real living conditions of humans. Therefore, there are limitations in studying the relationship between the gut microbiota and inguinal hernia from a clinical cross-sectional perspective. Using GWAS data from the large-scale MiBioGen consortium and the FinnGen R10 database to probe genetic data on the gut microbiota and inguinal hernia can improve the statistical power of causal associations. MR studies can overcome the effects of potential confounding and inverse causation, avoid waste of resources, and evaluate the potential causative relationship between the gut microbiota and inguinal hernia considering the genetic dimension of the host. Nevertheless, it is crucial to recognize certain limitations that exist within the framework of this study. The GWAS data of inguinal hernia used in this study cannot explore potential nonlinear relationships or stratification effects caused by differences in age, sex, health status, etc., which may cause heterogeneity. Due to issues with GWAS data on the gut microbiota and inguinal hernia, this study set the threshold at P < 1×10 − 5 , and there may be some unavoidable confounding factors. Since GWAS data in public databases only include people of European ancestry, taking into account population stratification issues, this conclusion may not apply to non-European populations, and more future studies on the gut microbiota and inguinal hernia are needed for further verification. Conclusions This article presents the first extensive examination of the potential causation between the gut microbiota and inguinal hernia. In the context of the gut microbiota as the exposure and inguinal hernia as the outcome, there is a direct negative causal relationship with 8 specific intestinal flora phenotypes. With inguinal hernia as the primary exposure and gut microbiota as the corresponding outcome, reverse MR analysis revealed that 6 intestinal flora phenotypes had a direct positive association with inguinal hernia. This study further revealed the gut microbiota and metabolites may affect inguinal hernia through multiple mechanisms. Inguinal hernia is closely related to reduced abdominal wall muscle strength. The composition of the gut microbiota changes with advancing age, which may cause dangerous intestinal microbial LPS to leak from the gut into the circulation, and significantly increase NF-κβ binding activity and JNK phosphorylation in skeletal muscles. This will inhibit insulin transduction and cause insulin resistance in myotubes, which will lead to the loss and aging of skeletal muscles, reduce the strength of the abdominal wall muscles in the groin area, and increase the risk of inguinal hernia. List of abbreviations The following abbreviations are used in this manuscript: GWAS Genome-wide association analysis IVW Inverse variance weighted MR Mendelian randomization AMPK AMP-activated protein kinase CPT-1 Carnitine palmitoyltransferase-1 IVs Instrumental variables SNPs Single-nucleotide polymorphisms LD Linkage disequilibrium IGF-1 Insulin/insulin-like growth factor-1 mTOR Mammalian target of rapamycin LPS Lipopolysaccharide NF-κB Nuclear factor-kappa B JNK c-Jun N-terminal kinase Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated during and analyzed during the current study are available in the MiBioGen consortium (https://mibiogen.gcc.rug.nl/) and FinnGen R10 database (https://www.finngen.fi/en). Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions Hongwei Xi provided guidance on data analysis methods and manuscript writing. Changyuan Wu and Yujin Zhu collected and analyzed the GWAS data for the gut microbiota and inguinal hernia, and were major contributors in writing the manuscript. All authors read and approved the final manuscript. Acknowledgements We appreciated the MiBioGen consortium and FinnGen R10 database for providing open-source data. References Berndsen, M.R., T. Gudbjartsson, and F.H. Berndsen, [Inguinal hernia - review]. Laeknabladid, 2019. 105 (9): p. 385-391. Shakil, A., et al., Inguinal Hernias: Diagnosis and Management. Am Fam Physician, 2020. 102 (8): p. 487-492. Vacca, V.M., Jr., Inguinal hernia: A battle of the bulge. Nursing, 2017. 47 (8): p. 28-35. Barbaro, A., et al., Laparoscopic extraperitoneal repair versus open inguinal hernia repair: 20-year follow-up of a randomized controlled trial. Hernia, 2017. 21 (5): p. 723-727. Berger, D., Evidence-Based Hernia Treatment in Adults. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Apr, 2024 Editor assigned by journal 05 Apr, 2024 Editor invited by journal 21 Mar, 2024 Submission checks completed at journal 21 Mar, 2024 First submitted to journal 11 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4073518","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":283224643,"identity":"d61d82c1-b207-4bc8-8321-940d81838e69","order_by":0,"name":"Changyuan Wu","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changyuan","middleName":"","lastName":"Wu","suffix":""},{"id":283224644,"identity":"f74c8f21-1f45-434e-bc14-5bef54f65e79","order_by":1,"name":"Yujin Zhu","email":"","orcid":"","institution":"Shanxi Medical 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12:02:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4073518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4073518/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-71253-1","type":"published","date":"2024-09-04T16:06:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53506890,"identity":"2ce92bce-e33f-49e5-af73-bb06552096bd","added_by":"auto","created_at":"2024-03-26 20:30:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28496,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the core assumptions of Mendelian randomization\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4073518/v1/21e372a4559b8cc5a68c161b.png"},{"id":53506891,"identity":"870fd103-2e11-422b-b8da-1b6b63d43bc5","added_by":"auto","created_at":"2024-03-26 20:30:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1238962,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot to evaluate the causal effect between gut microbiota and inguinal hernia using values obtained by the IVW MR method\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4073518/v1/76f524d392bc962d4d2c6b56.png"},{"id":53506893,"identity":"15661bd2-170a-4dd5-9595-890e3ce31cf6","added_by":"auto","created_at":"2024-03-26 20:30:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418993,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the causal relationship between gut microbiotaand inguinal hernia\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4073518/v1/104aba2b232307eff4c19391.png"},{"id":53507291,"identity":"2e28f30d-faa6-4453-9564-f8d70d991b7f","added_by":"auto","created_at":"2024-03-26 20:38:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2487730,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out analysis of gut microbiota and inguinal hernia\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4073518/v1/0bf6da44601b7c17bbe6a216.png"},{"id":53506892,"identity":"c00c591c-2716-4dc9-bc73-fe46535ffccf","added_by":"auto","created_at":"2024-03-26 20:30:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96990,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot to evaluate the reverse causal effect between gut microbiota and inguinal hernia using values obtained by the IVW MR method\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4073518/v1/196ed1cefa02f3629c6d9ce1.png"},{"id":53506894,"identity":"9a5d84e4-214b-4d7d-a889-3fe0b31f1c58","added_by":"auto","created_at":"2024-03-26 20:30:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1686337,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of reverse causality between gut microbiota and inguinal hernia\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4073518/v1/7c5ba9581a04cd68fe54735a.png"},{"id":64186089,"identity":"b366bc74-57ca-475a-974f-f15a3bbf7e2e","added_by":"auto","created_at":"2024-09-09 16:24:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5315789,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4073518/v1/388c7fac-321c-42fa-8aa0-5dd77ad6c43b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causation Between the Gut Microbiota and Inguinal Hernia: A Two-Sample Double-Sided Mendelian Randomization Study","fulltext":[{"header":"Background","content":"\u003cp\u003eAn enterocele is a bulging of the peritoneum, peritoneal fat, or abdominal organs through congenital or acquired orifices in the abdominal wall[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The probability of experiencing an inguinal hernia during one's lifetime is approximately 25% for males and 3% for females, and this probability increases in tandem with advancing years[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The groin hernia encompasses three distinct categories distinguished by the location of the hernia sac neck concerning the inguinal (Hesselbach) triangle: direct inguinal, indirect inguinal, and femoral\u003csup\u003e[3]\u003c/sup\u003e. Direct inguinal hernia mostly occurs in elderly men. The anatomical substance protrudes through the posterior boundary of the inguinal canal, and the neck of the hernia sac is found medially to the inferior epigastric artery. Indirect inguinal hernia commonly occurs in children and young adults. The small intestine, omentum, and other tissues protrude through the internal ring of the inguinal canal, and the hernia sac neck is located outside the inferior epigastric artery[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Femoral hernia mainly occurs in women over 40 years old. The tissue protrudes along the femoral canal under the inguinal ligament. The hernia sac neck is located inside the femoral blood vessel. The treatment modalities for inguinal hernias often include hernia repair as a fundamental approach; 1.6\u0026nbsp;million inguinal hernias are diagnosed in the United States each year, and more than 500,000 surgeries are performed[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe human intestinal environment harbors an immense population of bacteria, totaling trillions, and these microorganisms have undergone a coevolutionary journey alongside the human genome. The gut microbiota is involved in regulating the host body and is related to a variety of diseases[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nevertheless, the constitution of the gut microbiota undergoes varies depending on the host species, and an array of internal and external factors, along with both biotic and abiotic influences, can induce changes in the intricate balance of the gut microbiota composition[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Numerous research endeavors have delved into potential mechanisms through which the gut microbiota may contribute to the depletion of skeletal muscle mass, encompassing aspects such as protein anabolism, mitochondrial dysfunction, persistent inflammation, immune responses, and imbalances in metabolic processes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In a groundbreaking study conducted in 2004, Backhed et al. injected cecal contents from conventionally raised animals into the intestines of germ-free mice[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The results indicated that the body fat of mice in the experimental group increased by 60%, and insulin sensitivity and glucose tolerance decreased accordingly. Since skeletal muscle is one of the tissues that processes glucose, this finding suggests microbial involvement in mediating the functional regulation of muscle metabolism. AMP-activated protein kinase (AMPK) and carnitine palmitoyltransferase-1 (CPT-1) activities were significantly greater in the skeletal muscle of germ-free mice than in that of mice harboring gut microbiota, suggesting greater oxidation ability. The findings of this investigation underscore that the gut microbiota possesses the capacity to shape the biological makeup by regulating bioenergetic pathways within skeletal muscle. Alterations in the constitution of the gut microbiota have the potential to contribute to the aging of muscles and the onset of sarcopenia[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Within the framework of the musculoskeletal system, the gut microbiota assumes a pivotal role by intricately managing intestinal permeability, energy metabolism, hormone secretion, systemic inflammatory pathways, and immune responses[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a contemporary method for determining the causal relationships between the gut microbiota and the presence of inguinal hernia. MR relies on genetic variants as instrumental variables (IVs) to evaluate the causal relationship between exposure and the disease outcomes. Because genetic variation is assigned randomly and lacks any connection to offspring, the presence of genetic variation, along with subsequent outcomes, remains unaltered by potential confounding factors. This finding substantiates the authenticity of the established causal sequence[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. MR has been broadly employed to probe the causal connections between the gut microbiota and an array of health conditions, including metabolic diseases, irritable bowel syndrome, preeclampsia, and several other diseases [\u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, in this study, the gut microbiota and inguinal hernia were selected as the research objects, and two-sample double-sided MR analysis was used to evaluate the causation between the gut microbiota and inguinal hernia and provide new biological diagnostic markers, treatment strategies, and theoretical foundations for further research on the mechanism of inguinal hernia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThe experimental design used a two-sample double-sided MR analysis to evaluate the causation between the gut microbiota and inguinal hernia. Initially, comprehensive GWAS summary statistics about the gut microbiota and inguinal hernia were acquired. Therefore, to ensure the credibility of experimental findings, it is imperative to satisfy the three fundamental assumptions of MR. Single-nucleotide polymorphisms (SNPs) of IVs are strongly associated with exposure factors (gut microbiota or inguinal hernia); SNPs are independent of identified confounding factors; SNPs affect outcome factors (inguinal hernia or gut microbiota) only through exposure factors (gut microbiota or inguinal hernia) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMaterial\u003c/h2\u003e \u003cp\u003eThe GWAS summary statistics on the gut microbiota were obtained from the MiBioGen consortium and are accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mibiogen.gcc.rug.nl/\u003c/span\u003e\u003cspan address=\"https://mibiogen.gcc.rug.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This consortium stands as the most extensive, multiethnic collaboration, conducting a genome-wide meta-analysis specifically focused on gut microbiota[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. During the compilation of this article, 15 bacterial species lacking distinct nomenclature, consisting of 3 families and 12 genera whose identities remain unidentified, were deliberately excluded. Subsequently, a refined selection of 196 bacterial species (comprising 119 genera, 32 families, 20 orders, 16 classes, and 9 phyla) was meticulously chosen for MR analysis. The GWAS summary statistics for inguinal hernia were obtained from the FinnGen R10 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which included 35,248 inguinal hernia cases and 352,418 controls, all of which were from European populations. The summary statistics employed in this publication originate from openly accessible databases that can be obtained without cost. All GWAS summary statistics included in the article were approved by the respective ethics agency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInstrument Variable Selection\u003c/h2\u003e \u003cp\u003eThis study selected SNPs that were closely related to bacterial taxa (P\u0026thinsp;\u0026lt;\u0026thinsp;1.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) as IVs to obtain comprehensive data. Linkage disequilibrium (LD) analysis was performed based on European population sample data, with parameters set to r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and kb\u0026thinsp;=\u0026thinsp;10,000. The robustness of IVs is assessed by computing the F statistic. An F value exceeding 10 signifies the absence of a weak IV error. The IVs with F\u0026thinsp;\u0026lt;\u0026thinsp;10 are eliminated. Finally, to prevent alleles from affecting the results, palindromic SNPs were removed through palindromic sequence detection.\u003c/p\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e is the variance of exposure explained by SNPs, and its calculation formula is as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}^{2}=2\\times {\\beta }^{2}\\times EAF\\times \\left(1-EAF\\right)/2\\times {\\beta }^{2}\\times EAF\\times \\left(1-EAF\\right)+{SE}^{2}\\times 2\\times N\\times EAF\\left(1-EAF\\right)\\)\u003c/span\u003e\u003c/span\u003e. Within the equation, EAF represents the frequency of the effect allele, β denotes the value of the allele's effect, and SE represents the standard error. The F statistic calculation formula is as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(F=\\left[\\left(N-k-1\\right)/k\\right]\\times {r}^{2}/\\left(1-{r}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e. N is the number of samples in the exposure statistic, and k is the number of SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThe robustness of the causation of the gut microbiota to inguinal hernia was measured through a series of sensitivity analyses. Cochran Q analysis can compute distinctions among IVs, and a P value less than 0.05 indicates the presence of heterogeneity. Depending on whether there was heterogeneity, the random effects model or fixed effects model was selected for analysis. Horizontal pleiotropy testing through MR Egger regression and MR-PRESSO analysis. The stability of the statistic was tested through leave-one-out analysis, which removes individual SNPs to determine whether there are SNPs that may have a strong effect.\u003c/p\u003e \u003cp\u003eThe principal MR analysis in this research adopted the IVW method, while supplementary analyses, including MR Egger and the weighted median, were employed to enhance the depth of causal inference. If there was no horizontal pleiotropy in the data, there was no bias in the IVW results. Furthermore, this article employed reverse MR analysis to deduce the potential existence of reverse causation between the bacterial taxa identified in forward MR analysis and the occurrence of inguinal hernia.\u003c/p\u003e \u003cp\u003eAll statistical analysis procedures in this article were performed in RStudio software (version 4.3.2), using the TwoSampleMR and MR-PRESSO software packages for analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eCausal effects of the gut microbiota on inguinal hernia\u003c/h2\u003e\n\u003cp\u003eFollowing the screening criteria for IVs with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;1.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, a cumulative of 2,616 SNPs were acquired from a pool of 196 intestinal flora. These included 124 SNPs across 9 phyla, 223 SNPs spanning 16 classes, 279 SNPs in 20 orders, 444 SNPs within 32 families, and 1,546 SNPs associated with 119 genera. The F values corresponding to all SNPs were greater than 10. Therefore, this study is not susceptible to weak IV bias. MR analysis was performed on 196 bacterial taxa by the IVW method, and SNPs with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were screened out. The data analysis revealed that a total of 72 SNPs in 8 bacterial taxa are causally related to inguinal hernia (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These bacterial taxa included 1 phylum (12 SNPs), 1 order (15 SNPs), 1 family (10 SNPs), and 5 genera (35 SNPs), namely, Verrucomicrobia, Lactobacillales, Clostridiaceae1, Butyricococcus, Catenibacter, Hungathella, Odoribacter, and Olsenella.\u003c/p\u003e\n\u003cp\u003eThe results of the IVW analysis revealed that Verrucomicrobia (OR\u0026thinsp;=\u0026thinsp;0.9029, 95% CI: 0.8375\u0026ndash;0.9734, P\u0026thinsp;=\u0026thinsp;0.0077), Lactobacillales (OR\u0026thinsp;=\u0026thinsp;0.9087, 95% CI: 0.8378\u0026ndash;0.9857, P\u0026thinsp;=\u0026thinsp;0.0211), Clostridiaceae1 (OR\u0026thinsp;=\u0026thinsp;0.9017, 95% CI: 0.8236\u0026ndash;0.9871, P\u0026thinsp;=\u0026thinsp;0.0251), Butyricococcus (OR\u0026thinsp;=\u0026thinsp;0.8678, 95% CI: 0.7849\u0026ndash;0.9594, P\u0026thinsp;=\u0026thinsp;0.0056), Catenibacter (OR\u0026thinsp;=\u0026thinsp;0.9211, 95% CI: 0.8562\u0026ndash;0.9910, P\u0026thinsp;=\u0026thinsp;0.0276), Hungathella (OR\u0026thinsp;=\u0026thinsp;0.8748, 95% CI: 0.8133\u0026ndash;0.9410, P\u0026thinsp;=\u0026thinsp;0.0003), Odoribacter (OR\u0026thinsp;=\u0026thinsp;0.8583, 95% CI: 0.7690\u0026ndash;0.9580, P\u0026thinsp;=\u0026thinsp;0.0064), Olsenella (OR\u0026thinsp;=\u0026thinsp;0.9314, 95% CI: 0.8882\u0026ndash;0.9767, P\u0026thinsp;=\u0026thinsp;0.0034). MR study revealed a negative correlation between the abovementioned gut microbiota and inguinal hernia (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Cochran Q analysis results revealed no obvious heterogeneity among the selected IVs. The MR Egger analysis results suggested a lack of horizontal pleiotropy within the bacterial taxa examined in this study (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The outcomes of the leave-one-out method analysis indicate the absence of evident outliers among the chosen IVs, and the MR analysis results are reliable (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eReverse MR analysis\u003c/h2\u003e\n\u003cp\u003eIn the reverse MR analysis involving 196 intestinal flora and inguinal hernias, the results point toward a forward causation link between 6 intestinal flora and the manifestation of inguinal hernia (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). A direct causative link is established between inguinal hernia and Eubacterium brachygroup, Eubacterium eligensgroup, Eubacterium xylanophilumgroup, Coprococcus3, Ruminococcus1, and Senegalimassilia (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The outcomes of sensitivity analysis in the reverse MR analysis revealed the absence of heterogeneity and horizontal pleiotropy.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSensitivity analysis of the gut microbiota and inguinal hernia status\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eExposure factors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHeterogeneity test\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHorizontal pleiotropy test\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIVW Q P value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMR Egger Q P value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMR Egger intercept value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMR Egger intercept P value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhylum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVerrucomicrobia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.728\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrder\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLactobacillales\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.735\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.195\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClostridiaceae1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.971\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.568\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eButyricicoccus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.755\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCatenibacterium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.459\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.471\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHungatella\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.693\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.62E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOdoribacter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.671\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.169\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOlsenella\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.152\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eReduced abdominal wall strength and increased intra-abdominal pressure are the two main causes of inguinal hernia[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Through the utilization of multiview learning approaches on fecal metagenomic data, Chen et al. established a human gut microbiota aging clock, implying that the composition of the gut microbiota changes with advancing age[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The insulin/insulin-like growth factor-1 (IGF-1) signaling cascade triggers the activation of the mammalian target of rapamycin (mTOR), leading to an increase in protein synthesis for the preservation of muscle mass[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Reduced insulin/IGF-1 signaling in older adults contributes to an increase in insulin resistance, ultimately initiating the depletion of skeletal muscle mass[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Cani et al. demonstrated that feeding mice a high-fat diet impaired the tight junctions of the intestinal epithelium and elevated intestinal permeability, indicating that a high-fat diet can induce leakage of lipopolysaccharide (LPS), an external membrane element found in gram-negative bacteria, from the gut into the circulation. In individuals inoculated with LPS, there was a substantial increase in the binding activity of nuclear factor-kappa B (NF-κB) and the phosphorylation of c-Jun N-terminal kinase (JNK) within skeletal muscle, demonstrating that endotoxin is involved in mediating the process of impaired skeletal muscle glucose tolerance[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, the interaction of myotubes with extracellular vesicles derived from gut microbes results in synergistic inhibition of insulin signaling and the induction of insulin resistance[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These studies indicate that as the composition of the intestinal flora changes, it may induce insulin resistance in skeletal muscles, causing skeletal muscle loss and aging.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith the development of GWAS, the data volume and measurement accuracy of genetic variation have continued to expand, significantly reducing data bias in the research process and laying the foundation for research on the causation between the gut microbiota and inguinal hernia. Because genetic variation is determined before birth, measurable genetic variables are not affected by environmental factors, making causal effect studies more reliable than observational studies and equivalent to natural randomized controlled trials. Therefore, with this research method, it is more reliable to explore the causation between the gut microbiota and inguinal hernia.\u003c/p\u003e \u003cp\u003eOur investigation employed information extracted from the most extensive GWAS meta-analysis of gut microbiota conducted by the MiBioGen consortium. Additionally, GWAS data on inguinal hernia, made available by the FinnGen R10 database, were utilized to perform a two-sample double-sided MR analysis. Using the gut microbiota as the exposure and inguinal hernia as the outcome, Verrucomicrobia, Lactobacilliales, Clostridiaceae1, Butyricococcus, Categorybacter, Hungatella, Odoribacter, and Olsenella were found to be directly negatively related to the gut microbiota. Regarding inguinal hernia as the primary exposure and gut microbiota as the corresponding outcome, Eubacterium brachygroup, Eubacterium eligensgroup, Eubacterium xylanophilumgroup, Coprococcus3, Ruminococcus1, and Senegalimassilia were directly related to inguinal hernia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCurrently, there is a lack of reported studies examining the relationship between the gut microbiota and inguinal hernia. This article is the first to use MR to analyze the causation between them. Compared with individual-level experimental studies, there are differences between traditional laboratory animals and the natural environment, which cannot reflect the real living conditions of humans. Therefore, there are limitations in studying the relationship between the gut microbiota and inguinal hernia from a clinical cross-sectional perspective. Using GWAS data from the large-scale MiBioGen consortium and the FinnGen R10 database to probe genetic data on the gut microbiota and inguinal hernia can improve the statistical power of causal associations. MR studies can overcome the effects of potential confounding and inverse causation, avoid waste of resources, and evaluate the potential causative relationship between the gut microbiota and inguinal hernia considering the genetic dimension of the host. Nevertheless, it is crucial to recognize certain limitations that exist within the framework of this study. The GWAS data of inguinal hernia used in this study cannot explore potential nonlinear relationships or stratification effects caused by differences in age, sex, health status, etc., which may cause heterogeneity. Due to issues with GWAS data on the gut microbiota and inguinal hernia, this study set the threshold at P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, and there may be some unavoidable confounding factors. Since GWAS data in public databases only include people of European ancestry, taking into account population stratification issues, this conclusion may not apply to non-European populations, and more future studies on the gut microbiota and inguinal hernia are needed for further verification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis article presents the first extensive examination of the potential causation between the gut microbiota and inguinal hernia. In the context of the gut microbiota as the exposure and inguinal hernia as the outcome, there is a direct negative causal relationship with 8 specific intestinal flora phenotypes. With inguinal hernia as the primary exposure and gut microbiota as the corresponding outcome, reverse MR analysis revealed that 6 intestinal flora phenotypes had a direct positive association with inguinal hernia. This study further revealed the gut microbiota and metabolites may affect inguinal hernia through multiple mechanisms. Inguinal hernia is closely related to reduced abdominal wall muscle strength. The composition of the gut microbiota changes with advancing age, which may cause dangerous intestinal microbial LPS to leak from the gut into the circulation, and significantly increase NF-κβ binding activity and JNK phosphorylation in skeletal muscles. This will inhibit insulin transduction and cause insulin resistance in myotubes, which will lead to the loss and aging of skeletal muscles, reduce the strength of the abdominal wall muscles in the groin area, and increase the risk of inguinal hernia.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eGWAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eGenome-wide association analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eMendelian randomization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eAMPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eAMP-activated protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eCPT-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eCarnitine palmitoyltransferase-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eIVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eSingle-nucleotide polymorphisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eLinkage disequilibrium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eIGF-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin/insulin-like growth factor-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003emTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eMammalian target of rapamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eLipopolysaccharide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eNF-\u0026kappa;B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003eNuclear factor-kappa B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.9607250755287%\" valign=\"top\"\u003e\n \u003cp\u003eJNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.0392749244713%\" valign=\"top\"\u003e\n \u003cp\u003ec-Jun N-terminal kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and analyzed during the current study are available in the MiBioGen consortium (https://mibiogen.gcc.rug.nl/) and FinnGen R10 database (https://www.finngen.fi/en).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eHongwei Xi provided guidance on data analysis methods and manuscript writing. Changyuan Wu and Yujin Zhu collected and analyzed the GWAS data for the gut microbiota and inguinal hernia, and were major contributors in writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe appreciated the MiBioGen consortium and FinnGen R10 database for providing open-source data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerndsen, M.R., T. Gudbjartsson, and F.H. Berndsen, \u003cem\u003e[Inguinal hernia - review].\u003c/em\u003e Laeknabladid, 2019. \u003cstrong\u003e105\u003c/strong\u003e(9): p. 385-391.\u003c/li\u003e\n\u003cli\u003eShakil, A., et al., \u003cem\u003eInguinal Hernias: Diagnosis and Management.\u003c/em\u003e Am Fam Physician, 2020. \u003cstrong\u003e102\u003c/strong\u003e(8): p. 487-492.\u003c/li\u003e\n\u003cli\u003eVacca, V.M., Jr., \u003cem\u003eInguinal hernia: A battle of the bulge.\u003c/em\u003e Nursing, 2017. \u003cstrong\u003e47\u003c/strong\u003e(8): p. 28-35.\u003c/li\u003e\n\u003cli\u003eBarbaro, A., et al., \u003cem\u003eLaparoscopic extraperitoneal repair versus open inguinal hernia repair: 20-year follow-up of a randomized controlled trial.\u003c/em\u003e Hernia, 2017. \u003cstrong\u003e21\u003c/strong\u003e(5): p. 723-727.\u003c/li\u003e\n\u003cli\u003eBerger, D., \u003cem\u003eEvidence-Based Hernia Treatment in Adults.\u003c/em\u003e Dtsch Arztebl Int, 2016. \u003cstrong\u003e113\u003c/strong\u003e(9): p. 150-7; quiz 158.\u003c/li\u003e\n\u003cli\u003eXu, T.Q. and R.M. 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Numerous animal models and human studies have shown that the gut microbiota is associated with skeletal muscle aging and loss. However, the causation between the gut microbiota and inguinal hernia remains unclear. To reveal the causal association between the gut microbiota and inguinal hernia, we conducted a two-sample double-sided Mendelian randomization analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used genome-wide association analysis (GWAS) summary statistics of the gut microbiota from the MiBioGen consortium and GWAS statistics of inguinal hernia from the FinnGen R10 database. The causation between the gut microbiota and inguinal hernia was explored through the inverse variance weighted (IVW) method, MR Egger regression method, weighted median method, weighted model method, and simple model method. Sensitivity analysis was used to test whether the Mendelian randomization analysis results were reliable. Reverse Mendelian randomization was used to conduct effect analysis and sensitivity analysis using the entire gut microbiota as the outcome.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe IVW results indicated that Verrucomicrobia, Lactobacilliales, Clostridiaceae1, Butyricococcus, Categorybacter, Hungatella, Odoribacter, and Olsenella had a direct negative causation with the gut microbiota. The reverse Mendelian Randomization results showed that Eubacterium brachygroup, Eubacterium eligensgroup, Eubacterium xylanophilumgroup, Coprococcus3, Ruminococcus1, and Senegalimassilia were directly related to inguinal hernia. The bilateral sensitivity analysis revealed no heterogeneity or horizontal pleiotropy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results confirmed that 8 bacterial traits had a negative causation with inguinal hernia. Reverse MR analysis revealed a positive correlation between inguinal hernia and 6 bacterial traits. Modulating the diversity and components of the gut microbiota is envisaged to contribute to improving the incidence and prognosis of inguinal hernia.\u003c/p\u003e","manuscriptTitle":"Causation Between the Gut Microbiota and Inguinal Hernia: A Two-Sample Double-Sided Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-26 20:30:07","doi":"10.21203/rs.3.rs-4073518/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-12T05:28:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-05T06:07:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-21T09:17:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-21T09:14:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-11T10:26:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"758974ba-4ffc-4edf-b454-cb1f254e08ef","owner":[],"postedDate":"March 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29817979,"name":"Biological sciences/Computational biology and bioinformatics/Databases/Genetic databases"},{"id":29817980,"name":"Biological sciences/Computational biology and bioinformatics/Data processing"},{"id":29817981,"name":"Biological sciences/Microbiology/Bacteria"},{"id":29817982,"name":"Health sciences/Risk factors"},{"id":29817983,"name":"Health sciences/Gastroenterology/Gastrointestinal system/Microbiota"},{"id":29817984,"name":"Health sciences/Gastroenterology/Gastrointestinal diseases/Dysbiosis"}],"tags":[],"updatedAt":"2024-09-09T16:16:32+00:00","versionOfRecord":{"articleIdentity":"rs-4073518","link":"https://doi.org/10.1038/s41598-024-71253-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-04 16:06:09","publishedOnDateReadable":"September 4th, 2024"},"versionCreatedAt":"2024-03-26 20:30:07","video":"","vorDoi":"10.1038/s41598-024-71253-1","vorDoiUrl":"https://doi.org/10.1038/s41598-024-71253-1","workflowStages":[]},"version":"v1","identity":"rs-4073518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4073518","identity":"rs-4073518","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}