Causal relationship between gut microbiota and constipation: a two-sample Mendelian randomization study

Causal relationship between gut microbiota and constipation: a two-sample 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 Research Article Causal relationship between gut microbiota and constipation: a two-sample Mendelian randomization study Nan He, Guangzhao Li, Shenghuan Zhang, kai sheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3713020/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background As one of the most common gastrointestinal disorders, recent study has shown that dysfunction in the gut microbiota might lead to constipation. However, the causality between the gut microbiota and constipation is unclear. Methods Genome-Wide Association Studies (GWAS) used to obtain the summary-level data of constipation through IEU OpenGWAS database, and constipation was used as outcome. In addition, MiBioGen was used to gain the summary data of gut microbiota, including Ruminiclostridium 9, Intestinibacter , Anaerotruncus , Butyricimonas and Hungatella , which were used as exposure factors. Univariable Mendelian randomization (MR) analysis was used to evaluate the causality of gut microbiota on constipation. In briefly, Inverse-variance weighted (IVW) method was regarded as a main method to estimate the causality of gut microbiota and constipation, and supplemented by other four methods, including MR-egger, Weighted median, Simple mode and Weighted mode. Finally, funnel plot, heterogeneity test, horizontal pleiotropy test and leave-one-out test were used to evaluate the reliability of MR results. Results Ruminiclostridium 9 and Intestinibacter were causally associated with constipation and were the protective factors for constipation based on the MR analysis of IVW. The causal odds ratio (OR) values of Ruminiclostridium 9 and Intestinibacter were 0.75 (95% confidence interval (CI) 0.73–0.78; P < 0.001) and 0.89 (95% CI 0.86–0.93; P < 0.001) for constipation. Moreover, Anaerotruncus , Butyricimonas and Hungatella were also causally associated with constipation but were the risk factors for constipation. The OR values of Anaerotruncus , Butyricimonas and Hungatella were 1.08 (95% CI 1.02–1.13; P = 0.007), 1.07 (95% CI 1.01–1.13; P = 0.015), 1.03 (95% CI 1.00-1.06; P = 0.037) respectively. Furthermore, validation by funnel plot, heterogeneity test and horizontal pleiotropy test showed that MR results were reliable. Conclusion Ruminiclostridium 9, Intestinibacter , Anaerotruncus , Butyricimonas and Hungatella were identified as causalities of constipation, which provided a basis for understanding pathology of constipation and new insights into prevention and treatment. Constipation Gut microbiota Mendelian randomization Causal relationship GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Constipation, one of the common gastrointestinal disorders, manifests as hard tool and extended bowl movement cycles. The prevalence is estimated from 12–19% [1]. Constipation affects a broad spectrum of ages, with reports indicating that around 9% of children, 16% of adults and 33.5% of the elderly are afflicted [2, 3]. Severe constipation can lead to significant complications such as rectal bleeding, nausea, vomiting, weight loss, bowel obstruction, fecal impaction, hemorrhoids, anal fissures, and rectal prolapse. These physical ailments can greatly diminish an individual's quality of life and often contribute to mental stress [4]. In addition, recent epidemiological studies showed that constipation is independently associated with adverse clinical outcomes, such as end-stage renal disease (ESRD), cardiovascular (CV) disease, and mortality, possibly mediated by alterations in gut microbiota and increased production of fecal metabolites [5]. Furthermore, the 15-year survival rate of patients with chronic constipation is 20% worse than that of patients without chronic constipation[6]. Currently, consitipation is treated normally by pharmacotherapy such as lactulose, osmotic laxatives, stimulant laxatives and intestinal secretagogues [7]. However, the drug effect and adverse reactions were different in different patients. According to the reserch [8], nearly half patients with constipation who take medication were dissatisfied with traditional treatment options, primarily because of lack of efficacy. Therefore, identifying the causes of constipation and providing targeted treatments is the most effective approach to eliminate constipation. Its pathophysiology is complex, with primary constipation stemming from intrinsic issue in colonic or anorectal function, while secondary constipation causes are associated with systemic diseases or medications [9–11]. No matter which group the patient was, the composition and function of gut microbiota were imbalanced during constipation [12]. Compared with healthy subjects, irritable bowel syndrome with constipation (IBS-C) patients had an inconsistent composition of gut microbiota [13]. Additionally, constipation could also be effectively treated by regulating the composition of gut microbiota [14]. Therefore, a comprehensive understanding of the correlation between gut microbiota and constipation may hold the key to effectively addressing the issue of constipation. Mendelian randomization (MR) is an increasingly used statistical method, which utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to establish a causal relationship between an exposure and a desired outcome [15]. Instrumental variable analysis employed in MR helps simulate the randomization process of causal reasoning in randomized controlled trials (RCTs), making the design less susceptible to confounding and reverse causal bias [16]. There are several studys on MR of constipation, and it has been reported that cardiovascular disease (CVD) [17], Parkinson's disease (PD) [18], growth factor (GF) [19] have direct causal relationships with constipation. However, MR study of gut microbiota and constipation was not investigated yet. Therefore, we designated constipation as the outcome and considered the exposure to be the gut microbiota for the MR method. This provided a new reference for exploring the causal relationship between the gut microbiota and constipation. 2. Materials and methods 2.1 Data source IEU Open Genome-Wide Association Studies (IEU OpenGWAS) summary ( https://gwas.mrcieu.ac.uk/ ) had been used to obtain the summary-level data of constipation, and constipation was used as the outcome. Through search “constipation” in IEU OpenGWAS database, finn-b-K11_CONSTIPATION (sample: 218792; number of single nucleotide polymorphisms (nSNP): 16,380,466) was enrolled in this study. In addition, summary data of gut microbiota including Ruminiclostridium 9 (genus.Ruminiclostridium 9.id.11357; sample: 16725; nSNP: 978), Intestinibacter (genus.Intestinibacter.id.11345; sample: 12303; nSNP: 614), Anaerotruncus (genus.Anaerotruncus.id.2054; sample: 16566; nSNP: 518), Butyricimonas (genus.Butyricimonas.id.945; sample: 10737; nSNP: 644) and Hungatella (genus.Hungatella.id.11306; sample: 4209; nSNP: 383), was sourced from MiBioGen ( https://mibiogen.gcc.rug.nl/menu/main/home ), which contained the largest study in the human microbiome, and were used as the exposure factors. 2.2 Selection of IVs To explore the causality of gut microbiota on constipation, MR used genome-wide significant variants as IVs. The propose of MR analysis was based on three assumptions: first, IVs should be highly related to the exposure factor only; second, IVs were not associated with any other factors that might be related to both exposure and outcome; third, IVs only influenced the outcome through exposure. In order to satisfy these three assumptions, SNPs with P value < 1 × 10 − 5 and strong correlation to exposure were selected, and the SNPs with linkage disequilibrium (r 2 < 0.001, within a 10,000 kb window) were removed by using ‘extract outcome data’ function of ‘TwoSample MR’. 2.3 Study design On the basis of univariable MR analysis, the causality of gut microbiota on constipation was evaluated. MR analysis was performed through R package ‘TwoSample MR’ (version 0.5.6) [20]. Inverse-variance weighted (IVW) method was performed to estimate the causality of gut microbiota and constipation. Moreover, other four methods (MR-egger, Weighted median, Simple mode and Weighted mode) of MR were used to verify the result of MR. 2.4 Estimation of causal effects between gut microbiota and constipation Finally, funnel plot, heterogeneity test, horizontal pleiotropy test and leave-one-out test were used to evaluate whether the MR analysis satisfied these three assumptions through ‘mr heterogeneity’, ‘Horizontal pleiotropy’ and ‘mr leaveoneout’ function of ‘TwoSample MR’. 3. Results 3.1 Ruminiclostridium 9 and Intestinibacter were the protective factors of constipation Based on the results of univariable MR analysis in IVW method, Ruminiclostridium 9 and Intestinibacter were causally related to constipation ( P < 0.05, Table 1 ). As shown in Tables 1, 169 and 77 SNPs were regarded as the IVs of Ruminiclostridium 9 and Intestinibacter respectively. Although the other four methods (MR-egger, Weighted median, Simple mode and Weighted mode) of MR were displayed in the tables, we focused on the results of IVW only. The causal odds ratio (OR) suggested that Ruminiclostridium 9 (OR = 0.75; 95% confidence interval (CI) 0.73–0.78; P < 0.001) and Intestinibacter (OR = 0.89; 95% CI 0.86–0.93; P < 0.001) were the protective factors of constipation. The result was verified through scatter plot (Fig. 1A-B, slopes < 0) as well. Besides that, scatter plots also indicated that there were no other factors influencing the outcome, since the interceptions of all those five MR methods were approached to 0. Table.1 Mendelian Randomization results of protective factors of constipation Figure 1 Scatter plots for the causal association between gut microbiota and constipation (A.) Ruminiclostridium9 (B). Intestinibacter 3.2 Anaerotruncus , Butyricimonas and Hungatella were the risk factors for constipation According to the results of univariable MR analysis, Anaerotruncus , Butyricimonas and Hungatella were causally related to constipation respectively ( Table 2 ). The OR indicated that Anaerotruncus (OR = 1.08; 95% CI 1.02–1.13; P = 0.007), Butyricimonas (OR = 1.07; 95% CI 1.01–1.13; P = 0.015), and Hungatella (OR = 1.03; 95% CI 1.00-1.06; P = 0.037) were the risk factors of constipation. The result was also verified through scatter plot (Fig. 2A-C, slopes > 0). Besides that, scatter plots also indicated that there were no other factors influencing the outcome, since the interceptions of all those five MR methods were approached to 0. Table. 2 Mendelian Randomization results of risk factors of constipation Figure 2 Scatter plots for the causal association between gut microbiota and constipation (A.) Anaerotruncus , (B.) Butyricimonas and (C.) Hungatella 3.3 Validation of MR results The most IVs of Ruminiclostridium 9 were symmetrically distributed in a funnel plot (Fig. 3A), without outlying SNPs ( Supplementary Fig. 1A ), suggesting the effectiveness of IVs, as well as most IVs of Intestinibacter (Fig. 3B, Supplementary Fig. 1B ), Anaerotruncus (Fig. 4A, Supplementary Fig. 2A ), Butyricimonas (Fig. 4B, Supplementary Fig. 2B ) and Hungatella (Fig. 4C, Supplementary Fig. 2C ). As shown in Table 3 , the heterogeneity test and horizontal pleiotropy test results of Ruminiclostridium 9 (Q-pvalue = 0.99; P = 0.90), Intestinibacter (Q-pvalue = 0.56; P = 0.55), Anaerotruncus (Q-pvalue = 0.99; P = 0.49), Butyricimonas (Q-pvalue = 0.96; P = 0.23) and Hungatella (Q-pvalue = 1.00; P = 0.98) suggested that no heterogeneity and horizontal pleiotropy existed. Namely, MR results were reliable. Figure 3: Funnel plot assessing SNP bias in Mendelian Randomization analysis (A.) Ruminiclostridium9 (B). Intestinibacter Figure 3: Funnel plot assessing SNP bias in Mendelian Randomization analysis (A.) Anaerotruncus , (B.) Butyricimonas and (C.) Hungatella Table.4 Heterogeneity test and horizontal pleiotropy test results 4. Discussion Constipation is a syndrome that indicates the combination of various complex diseases. The factors leading to the constipation are very complex, including unbalanced diet, reduced activity, medication intake, and other underlying diseases [21]. Interestingly, series studies have proposed a potential link between constipation and gut microbiota. In the state of constipation, disorders in bowel movement and secretion can be caused by changes in the composition of the gut microbiota, which in turn can be attributed to the abnormality of the intestine and its metabolism [22]. Gut microbiota may contribute to the development of constipation through microbial metabolic activities including bile acids, short-chain fatty acids, 5-hydroxytryptamine, and methane [23]. Hypothesizing that the presence of methanogenic gut microbiota may contribute to the development of constipation by reducing bowel movement [24]. The results from clinical studies on probiotics and fecal microbiota transplantation (FMT) suggest that constipation is caused by microbiota dysbiosis [25]. Meanwhile, the effect of probiotics on constipation shows promising results, which further proved that improving the gut microbiota structure is beneficial to the management of constipation [26]. However, the results from previous works of literature are limited to observational correlations, and reverse causality may be inevitable. So, we attempted to identify the specific bacterial species composition associated with constipation in gut microbiota through MR analysis. In this study, Ruminiclostridium 9 and Intestinibacter were the protective factors of constipation. It suggested that the presence of these two bacteria might be beneficial to relieve constipation. Ruminiclostridium was a kind of mesophilic anaerobic cellulolytic bacteria, which could be able to utilize xylan either [27, 28]. It was proved that long-term intake of pork meat proteins could increase Ruminiclostridium 9 in the gut content of mice [29]. Meanwhile, abundance of Ruminiclostridium 9 altered when probiotic (PROB) or probiotic/prebiotic/essential oil supplement (PPEO) had been taken during a subclinical NE challenge in broiler chickens. Consistently, abundance of Intestinibacter in gut was closely related to diet such as arabinoxylan and mixed linkage glucans [30] as well as flaxseed oil [31]. These nutrients were generally believed to help relieve constipation. The presence of Intestinibacter was beneficial for the production of long-chain fatty acids, coumaric acid, and other metabolites in cruciferous plants [32]. But the abundance of Ruminiclostridium 9 and Intestinibacter was not always beneficial for gut health. It was indicated that the risk of obesity, fasting plasma insulin (FPI) and colorectal cancer (CRC) were positively associated with these two group bacterials [34, 35]. On the contrary, Anaerotruncus , Butyricimonas and Hungatella were identify as the risk factors for constipation. A previous study revealed, as non-motor symptoms of Parkinson’s disease (PD), constipation was related to gut microbiota and the progression of PD through the “gut-microbiota-brain” axis. Compared to the non-constipated PD patients, the relative abundance of Anaerotruncus and Hungatella were enriched in the constipated PD group [36]. Similarly, another research indicated that the abundance of Butyricimonas was remarkably increased in FC patients [37]. After daug treatment like lactulose, Anaerotruncus declined in the healthy control group [38]. These studies provided the evidences that these genera might have a negative association with constipation. Nevertheless, different conclusions were put forward. The abundance of Anaerotruncus was significant lower in constipation patients in Japan [39]. This might be attributed to the presence of additional factors that contribute to the relationship between the gut microbiota and constipation such as gastrointestinal active peptides [40], depression [41] or dietary infammatory index [42]. In this study, MR was used to analyze the causal relationship between gut microbiota and constipation for the first time. We identifed specifc types of microbes associated with constipation, which affords a fresh constipation into the pathogenesis of this disease and the possibility of developing treatment strategies. However, this analysis only utilized GWAS data from European populations. More investigations in diverse populations are demanded. In addition, MR is an effectively causal analysis method. Further experiments besides basic and clinical are needed to confirm these findings and to understand the underlying mechanisms involved. Declarations Funding Chengdu University of Traditional Chinese Medicine (QJRC2023034). Conflict of Interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Nan He: Conceptualization(equal); Data curation; Formal analysis(equal); Investigation(equal); Methodology(equal); Writing-original draft(equal); Writing-review & editing(equal). Guangzhao Li: Data curation; Formal analysis; Investigation; Methodology. Shenghuan Zhang: Data curation; Formal analysis. Kai Sheng: Conceptualization(equal); Data curation(equal); Formal analysis; Investigation; Methodology; Writing-original draft(equal); Writing-review & editing(equal). Data Availability statement The data analyzed in this study are sourced exclusively from publicly available datasets. Specifically, we utilized data from the Integrative Epidemiology Unit (IEU) Open GWAS database, accessible at [ https://gwas.mrcieu.ac.uk ], and the MiBioGen database, available at [ www.mibiogen.org ]. References Jakub Wlodarczyk A. W. s., Jakub Fichna, Adam Dziki, Lukasz Dziki, Wlodarczyk a. M.,Current Overview on Clinical Management of, journal of clinical medicine, 10 (2021) 1738. Soh A. Y. S., Kang J. Y., Siah K. T. H., Scarpignato C., Gwee K. A.,Searching for a definition for pharmacologically refractory constipation: A systematic review, Journal of Gastroenterology and Hepatology, 33 (3) (2018) 564–575. Rajindrajith S., Devanarayana N. M., Thapar N., Benninga M. 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C.,Gut microbiome signatures reflect different subtypes of irritable bowel syndrome, Gut Microbes, 15 (1) (2022). Costa L. M., Mendes M. M., Oliveira A. C., Magalhães K. G., Shivappa N., Hebert J. R., da Costa T. H. M., Botelho P. B.,Dietary inflammatory index and its relationship with gut microbiota in individuals with intestinal constipation: a cross-sectional study, European Journal of Nutrition, 61 (1) (2021) 341–355. Tables Table 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table.1 Mendelian Randomization results of protective factors of constipation Table2.xlsx Table. 2 Mendelian Randomization results of risk factors of constipation Table3.xlsx Table 3, The heterogeneity test and horizontal pleiotropy test results Table4.xlsx Table.4 Heterogeneity test and horizontal pleiotropy test results SupplementaryFigure1A.pdf SupplementaryFigure1B.pdf SupplementaryFigure2A.pdf SupplementaryFigure2B.pdf SupplementaryFigure2C.pdf Cite Share Download PDF Status: Posted Version 1 posted 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-3713020","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":256981697,"identity":"d8449e38-e35b-4651-888a-2efa78d345cd","order_by":0,"name":"Nan He","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"He","suffix":""},{"id":256981701,"identity":"73db1fa4-4675-45f6-ba83-86b956487e63","order_by":1,"name":"Guangzhao Li","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangzhao","middleName":"","lastName":"Li","suffix":""},{"id":256981705,"identity":"8fa22483-7b7b-4709-9d96-ec6c9ecf3518","order_by":2,"name":"Shenghuan Zhang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shenghuan","middleName":"","lastName":"Zhang","suffix":""},{"id":256981706,"identity":"ea18d368-ec4f-4060-9635-f72921f9f6a9","order_by":3,"name":"kai sheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACCQbGBhBiYGAHEgkFIDHGxgMPiNLCcwCoxQCspeFAAl4tYDUgFkiZAUQUrxbJ9sMNjD932OTJR75O/PDAgCGav38xyBY7eVxapHkSG5h5z6QVG97O3SwBdFjujBsPQVqSDRtwaJFjAGphbDucuHF27gawlg0SB0FaDjDi1ML/EOiwtv+JG2ee3fwDWYs9Li3SEokNDLxtBxLnS/Bug9jC3wjWkohLi+SMhw2HeduSEzfw5G6zSDCQAPoFFMgGycm4tEicT3/48GebXeL89rObb/6osMnt7z/+8MGHCjtbXFpA4ACIMDgAMQI1gvACebih/AeIUD4KRsEoGAUjCQAAQ+9keU4eMU8AAAAASUVORK5CYII=","orcid":"","institution":"Shriners Hospital for Children","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"kai","middleName":"","lastName":"sheng","suffix":""}],"badges":[],"createdAt":"2023-12-06 04:59:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3713020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3713020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47874369,"identity":"51b6ca7e-4225-41ef-8b1a-5cb3a595431c","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171371,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots for the causal association between gut microbiota and constipation (A.) \u003cem\u003eRuminiclostridium9\u003c/em\u003e(B). \u003cem\u003eIntestinibacter\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/ee72ec18a38b9fb6f6ecd00b.png"},{"id":47874374,"identity":"f27c0ccb-e179-414a-80a2-35a338db3cb8","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219135,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots for the causal association between gut microbiota and constipation (A.) \u003cem\u003eAnaerotruncus, \u003c/em\u003e(B.) \u003cem\u003eButyricimonas and \u003c/em\u003e(C.) \u003cem\u003eHungatella\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/1ca6016a13c013c6450f8797.png"},{"id":47874865,"identity":"a88e69ec-2bd3-4c5e-b936-3ed93ca887f7","added_by":"auto","created_at":"2023-12-08 18:38:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96098,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot assessing SNP bias in Mendelian Randomization analysis (A.) \u003cem\u003eRuminiclostridium9\u003c/em\u003e (B). \u003cem\u003eIntestinibacter\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/5c9b34be2107940b90d7f825.png"},{"id":47874864,"identity":"b7dd3625-47ba-435b-8f1b-38a84019f912","added_by":"auto","created_at":"2023-12-08 18:38:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115352,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3: Funnel plot assessing SNP bias in Mendelian Randomization analysis (A.) \u003cem\u003eAnaerotruncus, \u003c/em\u003e(B.) \u003cem\u003eButyricimonas and \u003c/em\u003e(C.) \u003cem\u003eHungatella\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/a978cd858f956f5c816c735f.png"},{"id":47878265,"identity":"b41ac19e-21d5-4049-a452-bdeb7750d327","added_by":"auto","created_at":"2023-12-08 19:02:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":580566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/051dfc62-c09a-41c5-aec5-8aabd28ce2a3.pdf"},{"id":47874370,"identity":"b76aa2d5-d995-47cd-bf38-34f0a6a96023","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9841,"visible":true,"origin":"","legend":"\u003cp\u003eTable.1 Mendelian Randomization results of protective factors of constipation\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/bc0774c9533fbe26483b8b36.xlsx"},{"id":47874372,"identity":"19bdb6ad-b180-401f-ab41-26872075ba65","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10644,"visible":true,"origin":"","legend":"\u003cp\u003eTable. 2 Mendelian Randomization results of risk factors of constipation\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/52719259372a8c1cb1959c5e.xlsx"},{"id":47874868,"identity":"b9ba378d-8080-4f9f-b122-d9e870e563ff","added_by":"auto","created_at":"2023-12-08 18:38:38","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e, The heterogeneity test and horizontal pleiotropy test results\u003c/p\u003e","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/21149af3aef7a8e42521de0a.xlsx"},{"id":47874371,"identity":"a98b557d-864e-47b3-a460-a88578826703","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":9834,"visible":true,"origin":"","legend":"\u003cp\u003eTable.4 Heterogeneity test and horizontal pleiotropy test results\u003c/p\u003e","description":"","filename":"Table4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/8e27df8561080743bb8a39b8.xlsx"},{"id":47874382,"identity":"34fa57ae-52df-455a-8f0a-8daaefabdd0a","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21645,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/2c1cc368a21f3a32b32990f8.pdf"},{"id":47875325,"identity":"22388b14-dea0-4384-8dea-28930b33db5e","added_by":"auto","created_at":"2023-12-08 18:46:38","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":12708,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1B.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/a30686b9dce3e53af8469827.pdf"},{"id":47877041,"identity":"d95827cf-4716-44c2-95b8-5f34283d6042","added_by":"auto","created_at":"2023-12-08 18:54:38","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13673,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/0a48983774d84a929a583a7f.pdf"},{"id":47874381,"identity":"c16404ba-71ec-42f7-b797-d488263269f4","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10199,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2B.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/64ff9be1b8cb16890380921d.pdf"},{"id":47874380,"identity":"17d36c49-d3cc-4440-a98a-6c94c892f3de","added_by":"auto","created_at":"2023-12-08 18:30:38","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":10143,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2C.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3713020/v1/aaf5072a46035e01fa530ab3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between gut microbiota and constipation: a two-sample Mendelian randomization study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eConstipation, one of the common gastrointestinal disorders, manifests as hard tool and extended bowl movement cycles. The prevalence is estimated from 12\u0026ndash;19% [1]. Constipation affects a broad spectrum of ages, with reports indicating that around 9% of children, 16% of adults and 33.5% of the elderly are afflicted [2, 3]. Severe constipation can lead to significant complications such as rectal bleeding, nausea, vomiting, weight loss, bowel obstruction, fecal impaction, hemorrhoids, anal fissures, and rectal prolapse. These physical ailments can greatly diminish an individual's quality of life and often contribute to mental stress [4]. In addition, recent epidemiological studies showed that constipation is independently associated with adverse clinical outcomes, such as end-stage renal disease (ESRD), cardiovascular (CV) disease, and mortality, possibly mediated by alterations in gut microbiota and increased production of fecal metabolites [5]. Furthermore, the 15-year survival rate of patients with chronic constipation is 20% worse than that of patients without chronic constipation[6]. Currently, consitipation is treated normally by pharmacotherapy such as lactulose, osmotic laxatives, stimulant laxatives and intestinal secretagogues [7]. However, the drug effect and adverse reactions were different in different patients. According to the reserch [8], nearly half patients with constipation who take medication were dissatisfied with traditional treatment options, primarily because of lack of efficacy. Therefore, identifying the causes of constipation and providing targeted treatments is the most effective approach to eliminate constipation.\u003c/p\u003e \u003cp\u003eIts pathophysiology is complex, with primary constipation stemming from intrinsic issue in colonic or anorectal function, while secondary constipation causes are associated with systemic diseases or medications [9\u0026ndash;11]. No matter which group the patient was, the composition and function of gut microbiota were imbalanced during constipation [12]. Compared with healthy subjects, irritable bowel syndrome with constipation (IBS-C) patients had an inconsistent composition of gut microbiota [13]. Additionally, constipation could also be effectively treated by regulating the composition of gut microbiota [14]. Therefore, a comprehensive understanding of the correlation between gut microbiota and constipation may hold the key to effectively addressing the issue of constipation.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is an increasingly used statistical method, which utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to establish a causal relationship between an exposure and a desired outcome [15]. Instrumental variable analysis employed in MR helps simulate the randomization process of causal reasoning in randomized controlled trials (RCTs), making the design less susceptible to confounding and reverse causal bias [16]. There are several studys on MR of constipation, and it has been reported that cardiovascular disease (CVD) [17], Parkinson's disease (PD) [18], growth factor (GF) [19] have direct causal relationships with constipation. However, MR study of gut microbiota and constipation was not investigated yet. Therefore, we designated constipation as the outcome and considered the exposure to be the gut microbiota for the MR method. This provided a new reference for exploring the causal relationship between the gut microbiota and constipation.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eIEU Open Genome-Wide Association Studies (IEU OpenGWAS) summary (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) had been used to obtain the summary-level data of constipation, and constipation was used as the outcome. Through search \u0026ldquo;constipation\u0026rdquo; in IEU OpenGWAS database, finn-b-K11_CONSTIPATION (sample: 218792; number of single nucleotide polymorphisms (nSNP): 16,380,466) was enrolled in this study. In addition, summary data of gut microbiota including \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 (genus.Ruminiclostridium 9.id.11357; sample: 16725; nSNP: 978), \u003cem\u003eIntestinibacter\u003c/em\u003e (genus.Intestinibacter.id.11345; sample: 12303; nSNP: 614), \u003cem\u003eAnaerotruncus\u003c/em\u003e (genus.Anaerotruncus.id.2054; sample: 16566; nSNP: 518), \u003cem\u003eButyricimonas\u003c/em\u003e (genus.Butyricimonas.id.945; sample: 10737; nSNP: 644) and \u003cem\u003eHungatella\u003c/em\u003e (genus.Hungatella.id.11306; sample: 4209; nSNP: 383), was sourced from MiBioGen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mibiogen.gcc.rug.nl/menu/main/home\u003c/span\u003e\u003cspan address=\"https://mibiogen.gcc.rug.nl/menu/main/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contained the largest study in the human microbiome, and were used as the exposure factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection of IVs\u003c/h2\u003e \u003cp\u003eTo explore the causality of gut microbiota on constipation, MR used genome-wide significant variants as IVs. The propose of MR analysis was based on three assumptions: first, IVs should be highly related to the exposure factor only; second, IVs were not associated with any other factors that might be related to both exposure and outcome; third, IVs only influenced the outcome through exposure. In order to satisfy these three assumptions, SNPs with \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e and strong correlation to exposure were selected, and the SNPs with linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, within a 10,000 kb window) were removed by using \u0026lsquo;extract outcome data\u0026rsquo; function of \u0026lsquo;TwoSample MR\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study design\u003c/h2\u003e \u003cp\u003eOn the basis of univariable MR analysis, the causality of gut microbiota on constipation was evaluated. MR analysis was performed through R package \u0026lsquo;TwoSample MR\u0026rsquo; (version 0.5.6) [20]. Inverse-variance weighted (IVW) method was performed to estimate the causality of gut microbiota and constipation. Moreover, other four methods (MR-egger, Weighted median, Simple mode and Weighted mode) of MR were used to verify the result of MR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Estimation of causal effects between gut microbiota and constipation\u003c/h2\u003e \u003cp\u003eFinally, funnel plot, heterogeneity test, horizontal pleiotropy test and leave-one-out test were used to evaluate whether the MR analysis satisfied these three assumptions through \u0026lsquo;mr heterogeneity\u0026rsquo;, \u0026lsquo;Horizontal pleiotropy\u0026rsquo; and \u0026lsquo;mr leaveoneout\u0026rsquo; function of \u0026lsquo;TwoSample MR\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 and \u003cem\u003eIntestinibacter\u003c/em\u003e were the protective factors of constipation\u003c/h2\u003e \u003cp\u003eBased on the results of univariable MR analysis in IVW method, \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 and \u003cem\u003eIntestinibacter\u003c/em\u003e were causally related to constipation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). As shown in \u003cb\u003eTables\u0026nbsp;1, 169 and 77\u003c/b\u003e SNPs were regarded as the IVs of \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 and \u003cem\u003eIntestinibacter\u003c/em\u003e respectively. Although the other four methods (MR-egger, Weighted median, Simple mode and Weighted mode) of MR were displayed in the tables, we focused on the results of IVW only. The causal odds ratio (OR) suggested that \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 (OR\u0026thinsp;=\u0026thinsp;0.75; 95% confidence interval (CI) 0.73\u0026ndash;0.78; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eIntestinibacter\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.89; 95% CI 0.86\u0026ndash;0.93; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the protective factors of constipation. The result was verified through scatter plot (Fig.\u0026nbsp;1A-B, slopes\u0026thinsp;\u0026lt;\u0026thinsp;0) as well. Besides that, scatter plots also indicated that there were no other factors influencing the outcome, since the interceptions of all those five MR methods were approached to 0.\u003c/p\u003e \u003cp\u003eTable.1 Mendelian Randomization results of protective factors of constipation\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;1 Scatter plots for the causal association between gut microbiota and constipation (A.) \u003cem\u003eRuminiclostridium9\u003c/em\u003e (B). \u003cem\u003eIntestinibacter\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e were the risk factors for constipation\u003c/h2\u003e \u003cp\u003eAccording to the results of univariable MR analysis, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e were causally related to constipation respectively (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). The OR indicated that \u003cem\u003eAnaerotruncus\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.08; 95% CI 1.02\u0026ndash;1.13; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), \u003cem\u003eButyricimonas\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.07; 95% CI 1.01\u0026ndash;1.13; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), and \u003cem\u003eHungatella\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.03; 95% CI 1.00-1.06; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) were the risk factors of constipation. The result was also verified through scatter plot (Fig.\u0026nbsp;2A-C, slopes\u0026thinsp;\u0026gt;\u0026thinsp;0). Besides that, scatter plots also indicated that there were no other factors influencing the outcome, since the interceptions of all those five MR methods were approached to 0.\u003c/p\u003e \u003cp\u003eTable. 2 Mendelian Randomization results of risk factors of constipation\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;2 Scatter plots for the causal association between gut microbiota and constipation (A.) \u003cem\u003eAnaerotruncus\u003c/em\u003e, (B.) \u003cem\u003eButyricimonas and\u003c/em\u003e (C.) \u003cem\u003eHungatella\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation of MR results\u003c/h2\u003e \u003cp\u003eThe most IVs of \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 were symmetrically distributed in a funnel plot (Fig.\u0026nbsp;3A), without outlying SNPs (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e), suggesting the effectiveness of IVs, as well as most IVs of \u003cem\u003eIntestinibacter\u003c/em\u003e (Fig.\u0026nbsp;3B, \u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e), \u003cem\u003eAnaerotruncus\u003c/em\u003e (Fig.\u0026nbsp;4A, \u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e), \u003cem\u003eButyricimonas\u003c/em\u003e (Fig.\u0026nbsp;4B, \u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e) and \u003cem\u003eHungatella\u003c/em\u003e (Fig.\u0026nbsp;4C, \u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e). As shown in \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e, the heterogeneity test and horizontal pleiotropy test results of \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 (Q-pvalue\u0026thinsp;=\u0026thinsp;0.99; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90), \u003cem\u003eIntestinibacter\u003c/em\u003e (Q-pvalue\u0026thinsp;=\u0026thinsp;0.56; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55), \u003cem\u003eAnaerotruncus\u003c/em\u003e (Q-pvalue\u0026thinsp;=\u0026thinsp;0.99; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49), \u003cem\u003eButyricimonas\u003c/em\u003e (Q-pvalue\u0026thinsp;=\u0026thinsp;0.96; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23) and \u003cem\u003eHungatella\u003c/em\u003e (Q-pvalue\u0026thinsp;=\u0026thinsp;1.00; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98) suggested that no heterogeneity and horizontal pleiotropy existed. Namely, MR results were reliable.\u003c/p\u003e \u003cp\u003eFigure 3: Funnel plot assessing SNP bias in Mendelian Randomization analysis (A.) \u003cem\u003eRuminiclostridium9\u003c/em\u003e (B). \u003cem\u003eIntestinibacter\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFigure 3: Funnel plot assessing SNP bias in Mendelian Randomization analysis (A.) \u003cem\u003eAnaerotruncus\u003c/em\u003e, (B.) \u003cem\u003eButyricimonas and\u003c/em\u003e (C.) \u003cem\u003eHungatella\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTable.4 Heterogeneity test and horizontal pleiotropy test results\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eConstipation is a syndrome that indicates the combination of various complex diseases. The factors leading to the constipation are very complex, including unbalanced diet, reduced activity, medication intake, and other underlying diseases [21]. Interestingly, series studies have proposed a potential link between constipation and gut microbiota. In the state of constipation, disorders in bowel movement and secretion can be caused by changes in the composition of the gut microbiota, which in turn can be attributed to the abnormality of the intestine and its metabolism [22]. Gut microbiota may contribute to the development of constipation through microbial metabolic activities including bile acids, short-chain fatty acids, 5-hydroxytryptamine, and methane [23]. Hypothesizing that the presence of methanogenic gut microbiota may contribute to the development of constipation by reducing bowel movement [24]. The results from clinical studies on probiotics and fecal microbiota transplantation (FMT) suggest that constipation is caused by microbiota dysbiosis [25]. Meanwhile, the effect of probiotics on constipation shows promising results, which further proved that improving the gut microbiota structure is beneficial to the management of constipation [26]. However, the results from previous works of literature are limited to observational correlations, and reverse causality may be inevitable. So, we attempted to identify the specific bacterial species composition associated with constipation in gut microbiota through MR analysis.\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 and \u003cem\u003eIntestinibacter\u003c/em\u003e were the protective factors of constipation. It suggested that the presence of these two bacteria might be beneficial to relieve constipation. \u003cem\u003eRuminiclostridium\u003c/em\u003e was a kind of mesophilic anaerobic cellulolytic bacteria, which could be able to utilize xylan either [27, 28]. It was proved that long-term intake of pork meat proteins could increase \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 in the gut content of mice [29]. Meanwhile, abundance of \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 altered when probiotic (PROB) or probiotic/prebiotic/essential oil supplement (PPEO) had been taken during a subclinical NE challenge in broiler chickens. Consistently, abundance of \u003cem\u003eIntestinibacter\u003c/em\u003e in gut was closely related to diet such as arabinoxylan and mixed linkage glucans [30] as well as flaxseed oil [31]. These nutrients were generally believed to help relieve constipation. The presence of \u003cem\u003eIntestinibacter\u003c/em\u003e was beneficial for the production of long-chain fatty acids, coumaric acid, and other metabolites in cruciferous plants [32]. But the abundance of \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 and \u003cem\u003eIntestinibacter\u003c/em\u003e was not always beneficial for gut health. It was indicated that the risk of obesity, fasting plasma insulin (FPI) and colorectal cancer (CRC) were positively associated with these two group bacterials [34, 35].\u003c/p\u003e \u003cp\u003eOn the contrary, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e were identify as the risk factors for constipation. A previous study revealed, as non-motor symptoms of Parkinson\u0026rsquo;s disease (PD), constipation was related to gut microbiota and the progression of PD through the \u0026ldquo;gut-microbiota-brain\u0026rdquo; axis. Compared to the non-constipated PD patients, the relative abundance of \u003cem\u003eAnaerotruncus\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e were enriched in the constipated PD group [36]. Similarly, another research indicated that the abundance of \u003cem\u003eButyricimonas\u003c/em\u003e was remarkably increased in FC patients [37]. After daug treatment like lactulose, \u003cem\u003eAnaerotruncus\u003c/em\u003e declined in the healthy control group [38]. These studies provided the evidences that these genera might have a negative association with constipation. Nevertheless, different conclusions were put forward. The abundance of \u003cem\u003eAnaerotruncus\u003c/em\u003e was significant lower in constipation patients in Japan [39]. This might be attributed to the presence of additional factors that contribute to the relationship between the gut microbiota and constipation such as gastrointestinal active peptides [40], depression [41] or dietary infammatory index [42].\u003c/p\u003e \u003cp\u003eIn this study, MR was used to analyze the causal relationship between gut microbiota and constipation for the first time. We identifed specifc types of microbes associated with constipation, which affords a fresh constipation into the pathogenesis of this disease and the possibility of developing treatment strategies. However, this analysis only utilized GWAS data from European populations. More investigations in diverse populations are demanded. In addition, MR is an effectively causal analysis method. Further experiments besides basic and clinical are needed to confirm these findings and to understand the underlying mechanisms involved.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eChengdu University of Traditional Chinese Medicine (QJRC2023034).\u003c/p\u003e \u003ch2\u003eConflict of Interest statement\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNan He: Conceptualization(equal); Data curation; Formal analysis(equal); Investigation(equal); Methodology(equal); Writing-original draft(equal); Writing-review \u0026amp; editing(equal). Guangzhao Li: Data curation; Formal analysis; Investigation; Methodology. Shenghuan Zhang: Data curation; Formal analysis. Kai Sheng: Conceptualization(equal); Data curation(equal); Formal analysis; Investigation; Methodology; Writing-original draft(equal); Writing-review \u0026amp; editing(equal).\u003c/p\u003e\u003ch2\u003eData Availability statement\u003c/h2\u003e \u003cp\u003eThe data analyzed in this study are sourced exclusively from publicly available datasets. Specifically, we utilized data from the Integrative Epidemiology Unit (IEU) Open GWAS database, accessible at [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], and the MiBioGen database, available at [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.mibiogen.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.mibiogen.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJakub Wlodarczyk A. W. s., Jakub Fichna, Adam Dziki, Lukasz Dziki, Wlodarczyk a. M.,Current Overview on Clinical Management of, journal of clinical medicine, \u003cstrong\u003e10\u003c/strong\u003e(2021) 1738.\u003c/li\u003e\n \u003cli\u003eSoh A. Y. S., Kang J. 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S., Yoshihiko) ; Inoue, R (Inoue, Ryo) ; Inatomi, O (Inatomi, Osamu) ; Nishida, A (Nishida, Atsushi) ; Morishima, S (Morishima, So) ; Imai, T (Imai, Takayuki) ; Kawahara, M (Kawahara, Masahiro) ; Naito, Y (Naito, Yuji) ; Andoh, A (Andoh, Akira,Mucosa-associated gut microbiome in Japanese patients with functional constipation, JOURNAL OF CLINICAL BIOCHEMISTRY AND NUTRITION, \u003cstrong\u003e68\u003c/strong\u003e(2) (2021) 187\u0026ndash;192.\u003c/li\u003e\n \u003cli\u003eChai M., Wang L., Li X., Zhao J., Zhang H., Wang G., Chen W.,Different Bifidobacterium bifidum strains change the intestinal flora composition of mice via different mechanisms to alleviate loperamide-induced constipation, Food \u0026amp; Function, \u003cstrong\u003e12\u003c/strong\u003e(13) (2021) 6058\u0026ndash;6069.\u003c/li\u003e\n \u003cli\u003eSu Q., Tun H. M., Liu Q., Yeoh Y. K., Mak J. W. Y., Chan F. K. L., Ng S. C.,Gut microbiome signatures reflect different subtypes of irritable bowel syndrome, Gut Microbes, \u003cstrong\u003e15\u003c/strong\u003e(1) (2022).\u003c/li\u003e\n \u003cli\u003eCosta L. M., Mendes M. M., Oliveira A. C., Magalh\u0026atilde;es K. G., Shivappa N., Hebert J. R., da Costa T. H. M., Botelho P. B.,Dietary inflammatory index and its relationship with gut microbiota in individuals with intestinal constipation: a cross-sectional study, European Journal of Nutrition, \u003cstrong\u003e61\u003c/strong\u003e(1) (2021) 341\u0026ndash;355.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Constipation, Gut microbiota, Mendelian randomization, Causal relationship, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-3713020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3713020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs one of the most common gastrointestinal disorders, recent study has shown that dysfunction in the gut microbiota might lead to constipation. However, the causality between the gut microbiota and constipation is unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGenome-Wide Association Studies (GWAS) used to obtain the summary-level data of constipation through IEU OpenGWAS database, and constipation was used as outcome. In addition, MiBioGen was used to gain the summary data of gut microbiota, including \u003cem\u003eRuminiclostridium\u003c/em\u003e 9, \u003cem\u003eIntestinibacter\u003c/em\u003e, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e, which were used as exposure factors. Univariable Mendelian randomization (MR) analysis was used to evaluate the causality of gut microbiota on constipation. In briefly, Inverse-variance weighted (IVW) method was regarded as a main method to estimate the causality of gut microbiota and constipation, and supplemented by other four methods, including MR-egger, Weighted median, Simple mode and Weighted mode. Finally, funnel plot, heterogeneity test, horizontal pleiotropy test and leave-one-out test were used to evaluate the reliability of MR results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 and \u003cem\u003eIntestinibacter\u003c/em\u003e were causally associated with constipation and were the protective factors for constipation based on the MR analysis of IVW. The causal odds ratio (OR) values of \u003cem\u003eRuminiclostridium\u003c/em\u003e 9 and \u003cem\u003eIntestinibacter\u003c/em\u003e were 0.75 (95% confidence interval (CI) 0.73\u0026ndash;0.78; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.89 (95% CI 0.86\u0026ndash;0.93; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for constipation. Moreover, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e were also causally associated with constipation but were the risk factors for constipation. The OR values of \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e were 1.08 (95% CI 1.02\u0026ndash;1.13; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), 1.07 (95% CI 1.01\u0026ndash;1.13; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), 1.03 (95% CI 1.00-1.06; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) respectively. Furthermore, validation by funnel plot, heterogeneity test and horizontal pleiotropy test showed that MR results were reliable.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRuminiclostridium\u003c/em\u003e 9, \u003cem\u003eIntestinibacter\u003c/em\u003e, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eHungatella\u003c/em\u003e were identified as causalities of constipation, which provided a basis for understanding pathology of constipation and new insights into prevention and treatment.\u003c/p\u003e","manuscriptTitle":"Causal relationship between gut microbiota and constipation: a two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-08 18:30:33","doi":"10.21203/rs.3.rs-3713020/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36c084c3-c5d6-47e1-9515-929729d06817","owner":[],"postedDate":"December 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-08T18:30:35+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-08 18:30:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3713020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3713020","identity":"rs-3713020","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}