Type 2 diabetes and Inflammatory Bowel Disease: A Bidirectional Two-sample Mendelian Randomization Study

Type 2 diabetes and Inflammatory Bowel Disease: A Bidirectional 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 Type 2 diabetes and Inflammatory Bowel Disease: A Bidirectional Two-sample Mendelian Randomization Study Guangyi Xu, Yanhong Xu, Taohua Zheng, Ting Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3052187/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 Studies have shown that patients with inflammatory bowel diseases (IBD) coexisting with type 2 diabetes mellitus (T2DM) have higher risk of infection, increased healthcare utilization and decreased quality of life, while currently they are not treated with more effective immunosuppressive therapy. Observational studies have shown a bidirectional association between T2DM and IBD, including Crohn's disease (CD) and ulcerative colitis (UC). However, because of the difficulty in determining sequential timeliness, it is unclear whether the observed associations are causal. We investigated the association between T2DM and IBD by bidirectional two-sample Mendelian randomization (MR) to clarify the casual relationship. Methods Independent genetic variants for T2DM and IBD were selected as instruments from published genome-wide association studies (GWAS), mainly in European ancestry. Instrumental variables (IVs) associated with T2DM and IBD were extracted separately from the largest GWAS meta-analysis. MR analyses included inverse variance weighting, weighted median estimator, MR Egger regression, and sensitivity analyses with Steiger filtering and MR PRESSO. Results Genetically predicted T2DM (per log-odds ratio increase) was associated with risk for IBD. In the data samples for UC (6968 cases, 20464 controls) and CD (5956 cases, 14927 controls), the odds ratio [95% confidence interval] for T2DM on UC and CD were 0.882 (0.826,0.942), and 0.955(0.877,1.038), respectively. In contrast, among 62,892 patients with T2DM, no genetically influenced association between IBD and T2DM was observed. Conclusions The results of the bidirectional MR Study suggest that T2DM has a negative causal effect on UC, which provides implications for clinical treatment decisions in IBD patients with T2DM. The findings do not support a causal relationship between T2DM and CD, UC and T2DM, or CD and T2DM, and the impact of IBD on T2DM needs further investigation. Type 2 Diabetes Mellitus inflammatory bowel disease Mendelian randomization Figures Figure 1 Figure 2 Figure 3 1. Introduction Inflammatory bowel disease (IBD) is a type of chronic intestinal inflammation mediated by abnormal immunity. Ulcerative colitis (UC) and Crohn's disease (CD) are the two main forms of IBD, which currently affect about 0.3% of the world's population, or more than 20 million people 1 . The incidence of IBD increased dramatically in the Western world during the 20th century, but its incidence had leveled off by the 21st century 2 . The etiology of IBD is still unclear. Studies have shown that environmental factors play an important role in its occurrence and development. For example, the incidence of UC and CD is relatively low in Asia compared with North America and Europe. The prevalence of UC among European South Asian immigrants was similar to that among Europeans, whereas the prevalence of CD among European South Asian immigrants was lower than that among Europeans 3 , 4 . In addition, IBD is closely related to smoking, diet, oral contraceptives, and vaccination 5 , 6 . Like other chronic inflammatory diseases, IBD usually develops early in life and is accompanied by a host of sequelae and comorbidities, including rheumatic diseases, iron-deficiency anemia, and cancer, and is therefore a major contributor to the global burden of disease 7 . In addition, IBD is a systematic inflammatory state and is associated with significant comorbidities. Research showed that type 2 diabetes mellitus (T2DM) is similar with IBD in light of the risk factors which include genes, gut bacteria and lifestyle 8 . Research established a correlation between DM and increased severity of IBD. Specifically, IBD patients with coexistent T2DM had a higher risk of infection, increased utilization of medical resources, and decreased quality of life than those without T2DM, while currently there is no more effective immunosuppressive therapy for IBD patients 8 . Studies have shown that there is a clear clinical association between T2DM and IBD 8 , 9 . Research found that genetic susceptibility to T2DM can raise the risk of gastrointestinal diseases in patients 10 . Diabetes may negatively affect the course of IBD by adding the risk of hospitalization and infection, but does not increase IBD-related complications and mortality 11 . Similarly, IBD can also increase the risk of diabetes 12 , 13 . However, these studies were only able to demonstrate some correlation between T2DM and IBD, and the causal relationship between the two diseases remains unclear. Mendelian randomization (MR) is the use of genetic variation in nonexperimental data to estimate causality between an exposure and an outcome 14 . MR can examine the potential causal relationships from exposure to outcome using instrumental variables (IVs) 15 . The results of current MR analysis mainly rely on genome-wide association studies (GWAS) databases, usually referring to single nucleotide polymorphisms (SNPs), which are used as IVs 15 . Compared with traditional methods, MR analysis can minimize the effect of confounding factors on causal estimates because genetic variants are randomly assigned at conception, and genetic variants from parents remain unchanged after birth 16 .Recently, two-sample MR analysis has been applied to investigate relationship between IBD and many diseases (e.g., gut microbial genera, atopic dermatitis, depression, fatty acids, neurodegenerative diseases) 15 , 17 – 20 . However, previous epidemiological studies have not focused on the causal relationship between T2DM and IBD phenotypes. Bidirectional two-sample MR may help to reveal the complex causal relationship between the two diseases. Therefore, this study aimed to evaluate and analyze the causal effect between T2DM and IBD by performing a bidirectional two-sample MR analysis using pooled data from large-scale open access GWAS. The findings are expected to provide implications for taking measures to reduce the severity of IBD for patients with occurrent T2DM. 2. Materials and Methods 2.1 Study design We investigated the association of T2DM with UC and CD using a bidirectional two-sample MR approach. According to the rationale and core assumptions of MR 21 , three hypotheses were followed in this study: (1) The genetic IVs must be strongly associated with the exposure; (2) SNPs were not associated with any confounding factors of the risk-outcome association; and (3) SNPs did not affect the results through any pathway other than exposure of interest. Figure 1 details a schematic representation of the design of this study. All data used in the study were from publicly available GWAS summary statistics. Ethical approval was obtained in all original studies. Therefore, no additional ethical approval or informed consent was required for this study. 2.2 Data Sources and Instruments 2.2.1 T2DM Summary data on the association of genetic variants with doctor-diagnosed T2DM were obtained from a recent GWAS meta-analysis of 160 000 genetic variants in 62,892 patients with T2DM and 596,424 controls of European ancestry 22 . The study consisted of three contributing studies, including the Genetic Epidemiology Research on Aging (GERA), the Diabetes Genetics Replication and Meta-analysis (DIAGRAM), and the full cohort release from UK Biobank (UKB). 2.2.2 UC and CD SNPs associated with IBD including UC and CD were obtained from the International Inflammatory Bowel Disease Genetic Consortium (IIBDGC) participants. The IBD GWAS statistics provided data on IBD overall (12,882 cases, 21,770 controls) as well as on CD (5,956 cases, 14,927 controls) and UC (6 968 cases, 20,464 controls) 23 . GWAS data were provided by IEU OpenGWAS database. This database are now available as resources for a wide range of analyses, such as MR Analysis 24 . 2.2.3 Selection of instrumental variables We rigorously performed a series of quality control techniques to screen eligible genetic tools. First, the genome-wide significance level was defined as p < 5*10 − 8 to satisfy the correlation assumption so that the instrumental variables are closely related to the outcome. Second, to rule out variants in strong linkage disequilibrium (LD) and ensure the independence of each SNP, we used standard parameter SNPs (r 2 0.01) and smallest p-values were retained. SNPs with minor allele frequency (MAF < 0.010) were eliminated. Third, SNPs ( p < 5*10 − 6 ) associated with IBD were excluded by screening the GWAS catalog. Finally, ambiguous SNPs with discordant alleles (e.g., A/G vs. A/C) and palintic SNPs (e.g., A/T or G/C) were excluded from the process of reconciling exposure and outcome data sets. 2.3 Statistical Analysis Prior to MR analysis, we calculated the F-statistic (F =[beta/SE] 2 ) for T2DM IVs to quantify the strength of the instrument. The calculated results F-statistic > 10 indicate the absence of weak IVs bias 25 . We used inverse variance weighting (IVW) as the main analysis method for MR analysis. When the pleiotropic effect of IVs was not present and the sample size was large enough, the IVW estimates were consistent, valid, and close to the true value 26 . It has the most efficient instrumental variable analysis when all selected SNPs are valid IVs. The MR Egger intercept term was used to assess horizontal pleiotropy, where deviation from zero indicates directional pleiotropy. In addition, the slope of the MR Egger regression shows valid MR estimates when horizontal pleiotropy is present 27 , 28 . Complementary weighted median methods were used, which can show valid MR estimates by assuming that at least 50% of IVs are valid and ordering each IV's MR estimate as the inverse of its variance 29 . Thus, the weighted median estimator can provide reliable estimates. In addition, we performed several sensitivity analyses to examine and correct for the presence of pleiotropy in causal estimates. We performed the MR-PRESSO global test to assess horizontal pleiotropy and the presence of missing abnormal variants 30 . Cochran's Q was calculated to examine the heterogeneity of individual causal effects, with p-value < 0.05 indicating the presence of pleiotropy. Finally, we also performed an omission analysis to assess the impact of individual SNP S on MR estimates. All analyses were conducted with R (version 4.2.2), R-based package ‘TwoSampleMR’ and ‘MR-PRESSO’. 3. Results 3.1 Instrumental Variable Statistical Results Through the above series of screening processes, 110 to 114 independent SNPs were selected as IVs for T2MD. A total of 26 and 37 independent SNPs were selected as IVs for UC and CD, respectively. The F statistics of IVs ranged from 29.942 to 1578.256, indicating a small likelihood of weak IVs bias. Details regarding all IVs are provided in Supplementary Tables S1-S4. 3.2 Primary MR Analysis MR-PRESSO method was used to perform the horizontal multiple effects test, and the outliers ( p < 0.001) were removed for MR analysis. As shown in Table 1 , there was a negative causal relationship between T2DM and UC[IVW,OR/95%CI: 0.882/(0.826,0.942), p < 0.001]. However, the causal relationships between T2DM and CD, UC and T2DM, CD and T2DM were not significant, and the p value measured by the IVW method was ≥ 0.05. According to the IVW approach, each standard deviation (SD) increase in genetically measured T2DM level was associated with a 12.5% reduction in UC risk. 3.3. Supplementary and Sensitivity Analysis In addition to the main IVW analysis methods, MR-Egger and weighted median estimation methods were used to verify the accuracy of the results. These supplementary analyses were applied to confirm the negative causal relationship between T2DM and UC ( p = 0.023, 0.019, respectively). For each SD increase in T2DM measured by MR-Egger and weighted median estimation methods, the risk of UC decreased by 17.4% and 14.3%, respectively. In the mean time, the differences between T2DM and CD ( p = 0.418, 0.134, respectively), UC and T2DM ( p = 0.191, 0.190, respectively), CD and T2DM ( p = 0.964, 0.158, respectively) were not found significant by these methods, as shown in Table 1 . Heterogeneity was measured by Cochran's Q statistic. As shown in Table 1 , the results of the heterogeneity analysis indicated that significant statistical heterogeneity was detected among the genetic instrumental variables in terms of the effect of T2MD on CD (IVW, p < 0.001) and UC on T2MD (IVW, p = 0.042). Therefore, the multiplicative random effects IVW model was applied in these associations to calculate causal effects. In addition, no significant statistical heterogeneity was found between genetic instrumental variables in the effect of T2MD on UC (IVW, p = 0.153) and CD on T2MD (IVW, p = 7.381*10 − 7 ). Therefore, the fixed effects IVW model was used for the initial MR analysis. Furthermore, horizontal pleiotropy effects were tested to determine whether FA-related genetic tool variants could cause IBD through other potential pathways. As displayed in Table 1 , no significant horizontal pleiotropy was found in the MR analyses (all p values ≥ 0.05), suggesting that this MR study are virtually unlikely to be affected by potential confounding pathways and thus the results are robust and reliable. Table 1 Results of bidirectional MR Analysis of T2DM with UC and CD Effect Methods No.SNP OR(95%CI) p -Value Q-Value ( p -Value) Pleiotropy-test ( p -Value) MR-PRESSO RSSobs ( p -Value) T2DM on UC IVW MR-Egger Weighted-Median 114 0.882(0.826,0.942) 0.840(0.724,0.974) 0.867(0.769,0.977) ༜0.001 0.023 0.019 128.371 (0.153) 0.004(0.473) 221.126(0.263) T2DM on CD IVW MR-Egger Weighted-Median 110 0.955(0.877,1.038) 0.924(0.765,1.117) 0.907(0.797,1.031) 0.276 0.418 0.134 170.025 (༜0.001) 0.003(0.712) 196.280(0.946) UC on T2DM IVW MR-Egger Weighted-Median 26 1.016(0.993,1.039) 1.051(0.978,1.130) 1.019(0.991,1.047) 0.181 0.191 0.190 38.429 (0.042) -0.006 (0.340) 41.386 (0.047) CD on T2DM IVW MR-Egger Weighted-Median 37 1.017(0.996,1.039) 0.999(0.954,1.046) 1.017(0.993,1.041) 0.112 0.964 0.158 92.448 (7.381*10 − 7 ) 0.004 (0.395) 101.371 (0.499) T2DM: Type 2 Diabetes Mellitus; UC: Ulcerative colitis; CD: Crohn's Disease; No. SNP: number of SNPs included in the analysis; OR: odds ratio; CI: confidence intervals; Q-value: Cochran’s Q statistic; IVW: inverse variance weighted. Leave-one-out analyses were performed to assess the effect of individual SNP S on the final MR results. Figure 2 shows that the bidirectional residual causal effects of T2DM and CD and UC found in the omitted one analysis after sequentially omitting individual SNP S were consistent with those found in the main MR study. This evinces that no single SNP played a significant role in the final results. The analyses further demonstrate that the MR study was robust, stable, and reliable. Scatter plots were drawn to visualize the effect size of each MR method (Fig. 3 ) Forest plots were employed for the visualization of individual SNP estimates of the results (Figure S1 ). Funnel plots were used to show the balance of the distribution of the effects of individual SNPs (Figure S2 ). From these plots, it can be concluded that the effect and distribution of each SNP are balanced. 4. Discussion The present study used MR to analyze the bidirectional causal relationship between T2DM and IBD. The results showed that T2DM reduced the risk of UC, while the causal relationships between T2DM and CD, UC and T2DM, and CD and T2DM were not significant. Chen et al. 10 showed that for each unit increase in the risk of T2DM, there was a 0.93 unit decrease in the risk of ulcerative colitis. The present study confirmed that Type 2 diabetes could reduce the risk of ulcerative colitis, which may be related to the medications taken by patients with type 2 diabetes. For instance, Tseng 31 followed 340,211 metformin users and 24,478 non-metformin users in remission of IBD for 5 years and found that metformin reduced the risk for recurrence of IBD in patients with type 2 diabetes. Deng et al. found that metformin reduced tight junction (TJ) protein expression in patients with type 2 diabetes and ulcerative colitis 32 . TJ proteins mainly exist in the junctional complex between epithelial cells and endothelial cells 33 . This structure connects adjacent cell membranes closely together and closes the epithelial cell space. Its function is to allow ions and small molecular soluble substances to pass through, and to prevent toxic macromolecules and microorganisms from passing through. The intestinal mucosal cells of ulcerative colitis highly express anti-inflammatory cytokines. When induced by anti-inflammatory cytokines, the expression of TJ protein will also increase, which promotes the permeability of intestinal mucosal cells to small molecule substances, leading to the occurrence of ulcerative colitis such as diarrhea 34 . Research demonstrated that IBD and T2DM share a common pathogenetic basis, which is affected by inflammatory processes, gut microbiota imbalance, and crosstalk between various signaling pathways 9 . Liu et al. 35 found that metformin appeared to induce antiinflammatory effects that improved symptoms of ulcerative colitis. In addition to the anti-inflammatory and antioxidant properties of metformin and enhancing intestinal barrier integrity in IBD cell and animal models, Wasuwit et al. 36 revealed that metformin had the ability to restore the intestinal microbiota in mice with ulcerative colitis, thereby reducing intestinal inflammation. These evidences indicate that metformin can be used as an alternative therapy for inflammatory bowel disease 36 , but its usage and dosage are still unclear. Therefore, future studies are warranted to further clarify its mechanism of action, usage and dosage, combination of drugs, and adverse reactions. Moreover, Pioglitazone shows potential benefits in treatment of IBD in preclinical studies 37 . Tseng 38 evaluated the association between pioglitazone and major risk factors for IBD (psoriasis, arthropathy, dorsal paralysis, chronic obstructive pulmonary disease, and tobacco abuse) in 12,763 patients who had used pioglitazone and 12,763 patients with T2DM who had never used pioglitazone and found no effect. However, rosiglitazone as a thiazolidinedione antidiabetic drug effective for the treatment of T2DM has been shown to be effective in the treatment of mild-to-moderate active ulcerative colitis 39 . Therefore, some drugs such as rosiglitazone could be prioritized owing to its dual effect during treatment for patients with T2DM and UC. Studies on the safety of some biologic agents and immunosuppressive therapies in patients with IBD andT2DM have been reassuring, yet newer and safer biologic agents that could reduce the risk of infection in such patients as first-line treatment need to be advanced 40 . Despite that the present study established a causal relationship between T2DM and UC and provides implications for healthcare practice, the mechanism by which T2DM can reduce the incidence of UC rather than CD is still unclear. In future, the specific causal mechanism between T2DM and UC can be further explored by using experimental methods from the aspects of cell biological factors, physical and chemical factors, genetic factors, and immune factors. The results of this study suggest that the causal relationships between UC and T2DM, as well as CD and T2DM were not significant. Nevertheless, previous research have found that IBD can lead to an increased risk of developing diabetes in patients 12 . A study based on a Korean population showed that the increased risk of diabetes in IBD patients was more prominent in younger age groups, and the risk of diabetes was higher in CD patients than UC patients 41 . In a cohort study based on the Danish population, it was found that UC or CD increased the risk of type 2 diabetes in patients 42 . However, these studies cannot confirm a causal relationship between UC and CD and T2DM due to the cross-sectional design nature. Lai et al. 43 conducted a preliminary cohort analysis using the database of the Ministry of National Health in Taiwan while found no significant association between IBD and an increased risk of T2DM. Differences in study population and research methods may lead to disparity in these study results. A cohort study of the IBD population suggests that the increased risk of developing T2DM in IBD patients may be associated with elevated disease severity 8 . Maconi et al. 44 showed that the preferred treatment for active UC is corticosteroids, which may lead to glucose intolerance in patients, onset of diabetes, difficulty in controlling glucose levels, and complications in patients with diabetes. Therefore, the increased risk of T2DM in IBD patients may be ascribed to a variety of factors, and the conclusion regarding IBD causing the occurrence of T2DM cannot be reached. Although the current study did not find a causal relationship between IBD and T2DM, future research can start with the related factors of IBD and explore the association between IBD related factors and T2DM with large samples and multi-center methods. This study has certain strengths. The study results confirmed a causal relationship between T2DM and UC. To the best of our knowledge, this was the first study to assess the bidirectional causal effect of T2DM on the development of IBD using a bidirectional two-sample MR approach. Firstly, the design of the study was based on three main instrumental variable assumptions and conformed to the checklist of the MR Survey 45 . MR approach is less susceptible to confounding, reverse causality, and non-differentially measured exposures than observational studies 46 . Therefore, the conclusions drawn in this study are reasonable and trustworthy. Second, both large-scale GWAS were obtained from European ancestry, which allowed the bias of population stratification to be avoided. In addition, a number of sensitivity analyses were performed to ensure the consistency of causal estimates and to confirm the robustness of the current findings. Our study also has limitations. Pleiotropy is an important issue in MR studies. Our results do not appear to be affected by pleiotropy, as consistent results were obtained in sensitivity analyses, and few outliers were found using iterative IVW and MR-PRESSO methods. Second, this study only provides strong and reliable evidence for the effect of T2DM on IBD risk, whereas the effect of T2DM in patients with diagnosed IBD has not been explored. In addition, because our study was limited to pooled data, the population was not categorized by sociodemographic factors, such as age, sex, or employment, when examining casual associations. Finally, the results of the current study were based on individuals of European ancestry, and thus the generalizability of the results is limited. 5. Conclusion The results of the bidirectional MR Study suggest that T2DM has a negative causal effect on UC, which may be related to the use of metformin and pioglitazone, and thus can be considered as an alternative therapy for IBD patients. Given that the usage and dosage of metformin and pioglitazone are not clear in IBD patients, future studies are needed to further clarify its mechanism of action, usage and dosage, combination of drugs and adverse reactions. In addition, the causal relationships between T2DM and Crohn's disease (CD), UC and T2DM, and CD and T2DM were not significant. In future, the specific causal mechanism between T2DM and UC can be further explored by using experimental methods from the aspects of cell biological factors, physical and chemical factors, genetic factors, and immune factors. Abbreviations IBD: Inflammatory bowel disease; UC: Ulcerative colitis; CD: Crohn's disease; T2DM: type 2 diabetes mellitus; MR: Mendelian randomization; IVs: instrumental variables; GWAS: genome-wide association studies; SNPs: single nucleotide polymorphisms; GERA: Genetic Epidemiology Research on Aging; DIAGRAM: Diabetes Genetics Replication and Meta-analysis; UKB: UK Biobank; IIBDGC: International Inflammatory Bowel Disease Genetic Consortium; LD: linkage disequilibrium; IVW: inverse variance weighting; SD: standard deviation; TJ: tight junction Declarations Acknowledgements Not applicable Authors ’ contributions GX, YX, and TL designed the study concept; GX was used for data collection and analysis. GX, YX, and TZ interpreted the data; GX, YX, TZ, and TL drafted the manuscript; TL reviewed the manuscript. All authors read and approved the final manuscript. Funding Not applicable Availability of data and materials The datasets supporting the conclusions of this article are available in the[repository name] repository. The GWAS summary statistics for T2DM is available on the website https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST006867/ The GWAS summarystatistics for IBD (including UC and CD) is available on the websites https://gwas.mrcieu.ac.uk/datasets/ieu-a-32/ and https://gwas.mrcieu.ac.uk/datasets/ieu-a-30/.The other data generated or analyzed during this study are available in thispublished article and its supplementary information files. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. 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Jess T, Jensen BW, Andersson M, Villumsen M, Allin KH. Inflammatory Bowel Diseases Increase Risk of Type 2 Diabetes in a Nationwide Cohort Study. Clin Gastroenterol hepatology: official Clin Pract J Am Gastroenterological Association Apr. 2020;18(4):881–888e881. Lai SW, Kuo YH, Liao KF. Association Between Inflammatory Bowel Disease and Diabetes Mellitus. Clin Gastroenterol hepatology: official Clin Pract J Am Gastroenterological Association Apr. 2020;18(4):1002–3. Maconi G, Furfaro F, Sciurti R, Bezzio C, Ardizzone S, de Franchis R. Glucose intolerance and diabetes mellitus in ulcerative colitis: pathogenetic and therapeutic implications. World J Gastroenterol Apr. 2014;7(13):3507–15. Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome open research. 2019;4:186. Davey Smith G, Holmes MV, Davies NM, Ebrahim S. Mendel's laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues. Eur J Epidemiol 2020/02//. 2020;35(2):99–111. Additional Declarations No competing interests reported. Supplementary Files SupplementaryPicture.pdf TableS1.SNPsAssociatedwithT2DMandUC.csv TableS2.SNPsAssociatedwithT2DMandCD.csv TableS3.SNPsAssociatedwithUCandT2D.csv TableS4.SNPsAssociatedwithCDandT2DM.csv 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-3052187","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":210853159,"identity":"5f2fa7af-93de-4f0a-9f36-30eb9315aabb","order_by":0,"name":"Guangyi Xu","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangyi","middleName":"","lastName":"Xu","suffix":""},{"id":210853160,"identity":"c6db97d1-b079-43f8-98b7-51a0346d2c3e","order_by":1,"name":"Yanhong Xu","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanhong","middleName":"","lastName":"Xu","suffix":""},{"id":210853161,"identity":"72f5c51e-330a-4f8d-a719-0567dc594bd5","order_by":2,"name":"Taohua Zheng","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Taohua","middleName":"","lastName":"Zheng","suffix":""},{"id":210853162,"identity":"bb9ea93f-8dcf-43d6-893b-bf5498de7da7","order_by":3,"name":"Ting Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3PsWoCMRjA8S8EchRyZk0pnK9wRwZ9nMteCuLiYM/AQdauJ5Q+g5N0jHxgFx+go6cvcI7dmoqDk7lRaP5D8g3fDxKAWOwOG1B/lAAZo2LvupnMhiHCKBDjiRIJJ5tmN1aFCRHwxN96+cYpPtiZBhciCR/t289XssLUIfmQJTG0PXzffBgvjN590RwHJU7W8iUBptRzkNgtyxFyXK7llBjOnvoQfibpu9TG9SNz+Vh7lZpehE0abV0uKCs3zVaqog78RQhcnX5stbACsevmVTZM6vZ4i1zCq5mG1/+q+q3FYrHY/+wXzndKdjrInkgAAAAASUVORK5CYII=","orcid":"","institution":"Qingdao University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-06-12 08:59:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3052187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3052187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39109019,"identity":"f37f5f44-ece3-4269-a223-3dedf00508f3","added_by":"auto","created_at":"2023-06-26 18:33:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60149,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the present study design and results.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/512d9298f9d32c497f508587.png"},{"id":39106951,"identity":"af0d2c06-2bfb-45a2-b159-efe58d51ee39","added_by":"auto","created_at":"2023-06-26 18:25:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":788391,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out analysis of the bidirectional effects of type 2 diabetes mellitus (T2DM) with ulcerative colitis (UC) and Crohn's disease (CD). (\u003cstrong\u003eA\u003c/strong\u003e) Analysis of T2DM on UC; (\u003cstrong\u003eB\u003c/strong\u003e) Analysis of T2DM on CD; (\u003cstrong\u003eC\u003c/strong\u003e) Analysis of UC on 2DM; (\u003cstrong\u003eD\u003c/strong\u003e) Analysis of CD on 2DM.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/dfda55078e3636728039212a.png"},{"id":39109750,"identity":"0b56feea-470b-4e0d-8788-8cfe65bd1f11","added_by":"auto","created_at":"2023-06-26 18:41:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":825853,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of the bidirectional effects of type 2 diabetes mellitus (T2DM) with ulcerative colitis (UC) and Crohn's disease (CD). (\u003cstrong\u003eA\u003c/strong\u003e) Analysis of T2DM and UC. The X-axis shows the single nucleotide polymorphism (SNP) effect and SE (standard error) for each selected SNP from the T2DM genome-wide Pooled Association Study (GWA) dataset. The Y-axis shows SNP effects and SE on UC from the UC genome-wide Pooled Association Study (GWA) dataset. (\u003cstrong\u003eB\u003c/strong\u003e) Analysis of T2DM and CD. The X-axis shows the SNP effect and SE for each selected SNP from the GWA dataset for T2DM. The Y-axis is SNP effect and SE on CD from the GWA dataset of CD. (\u003cstrong\u003eC\u003c/strong\u003e) Analysis of UC and T2DM. The X-axis shows the SNP effect and SE for each selected SNP from the GWA dataset from UC. The Y-axis is the SNP effect and SE on T2DM from the GWA dataset for T2DM. (\u003cstrong\u003eD\u003c/strong\u003e) Analysis of CD and T2DM. The X-axis shows the SNP effect and SE for each selected SNP from the GWA dataset of CD. The Y-axis is the SNP effect and SE on T2DM from the GWA dataset for T2DM.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/0558afd4918a32d454e9337f.png"},{"id":41298122,"identity":"71e0a46a-fa73-4455-a9cd-d2af2d7a6883","added_by":"auto","created_at":"2023-08-09 11:52:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1689382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/354a90f9-0c08-4e2b-b324-0997b9f50e92.pdf"},{"id":39109751,"identity":"eb16a3dc-f1f9-44c9-b7ba-3449a65efa4d","added_by":"auto","created_at":"2023-06-26 18:41:37","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":501527,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryPicture.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/b49d38f346be39480ae5b2ff.pdf"},{"id":39106952,"identity":"51801841-d0f2-4a5a-858b-e985133844a1","added_by":"auto","created_at":"2023-06-26 18:25:37","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":40652,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.SNPsAssociatedwithT2DMandUC.csv","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/049e47eb5941471b4a677399.csv"},{"id":39109017,"identity":"0e5a957b-d693-41b3-af52-9c8299daf480","added_by":"auto","created_at":"2023-06-26 18:33:37","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":38484,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.SNPsAssociatedwithT2DMandCD.csv","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/b167c64c82f1ddba3cc96d9f.csv"},{"id":39106957,"identity":"b3db4f79-a54d-4465-8390-89f6afe614b7","added_by":"auto","created_at":"2023-06-26 18:25:37","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":9632,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.SNPsAssociatedwithUCandT2D.csv","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/1c3e82092afd16f787ea363a.csv"},{"id":39106954,"identity":"24e789ce-a1e2-4b7c-9f2f-6b7fa03f0307","added_by":"auto","created_at":"2023-06-26 18:25:37","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":13521,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.SNPsAssociatedwithCDandT2DM.csv","url":"https://assets-eu.researchsquare.com/files/rs-3052187/v1/98d99f0fc41df5cbe9ede29d.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Type 2 diabetes and Inflammatory Bowel Disease: A Bidirectional Two-sample Mendelian Randomization Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInflammatory bowel disease (IBD) is a type of chronic intestinal inflammation mediated by abnormal immunity. Ulcerative colitis (UC) and Crohn's disease (CD) are the two main forms of IBD, which currently affect about 0.3% of the world's population, or more than 20\u0026nbsp;million people \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The incidence of IBD increased dramatically in the Western world during the 20th century, but its incidence had leveled off by the 21st century \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The etiology of IBD is still unclear. Studies have shown that environmental factors play an important role in its occurrence and development. For example, the incidence of UC and CD is relatively low in Asia compared with North America and Europe. The prevalence of UC among European South Asian immigrants was similar to that among Europeans, whereas the prevalence of CD among European South Asian immigrants was lower than that among Europeans\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In addition, IBD is closely related to smoking, diet, oral contraceptives, and vaccination \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Like other chronic inflammatory diseases, IBD usually develops early in life and is accompanied by a host of sequelae and comorbidities, including rheumatic diseases, iron-deficiency anemia, and cancer, and is therefore a major contributor to the global burden of disease\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In addition, IBD is a systematic inflammatory state and is associated with significant comorbidities. Research showed that type 2 diabetes mellitus (T2DM) is similar with IBD in light of the risk factors which include genes, gut bacteria and lifestyle\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Research established a correlation between DM and increased severity of IBD. Specifically, IBD patients with coexistent T2DM had a higher risk of infection, increased utilization of medical resources, and decreased quality of life than those without T2DM, while currently there is no more effective immunosuppressive therapy for IBD patients\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies have shown that there is a clear clinical association between T2DM and IBD\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Research found that genetic susceptibility to T2DM can raise the risk of gastrointestinal diseases in patients \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Diabetes may negatively affect the course of IBD by adding the risk of hospitalization and infection, but does not increase IBD-related complications and mortality \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Similarly, IBD can also increase the risk of diabetes \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, these studies were only able to demonstrate some correlation between T2DM and IBD, and the causal relationship between the two diseases remains unclear.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is the use of genetic variation in nonexperimental data to estimate causality between an exposure and an outcome\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. MR can examine the potential causal relationships from exposure to outcome using instrumental variables (IVs) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The results of current MR analysis mainly rely on genome-wide association studies (GWAS) databases, usually referring to single nucleotide polymorphisms (SNPs), which are used as IVs\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Compared with traditional methods, MR analysis can minimize the effect of confounding factors on causal estimates because genetic variants are randomly assigned at conception, and genetic variants from parents remain unchanged after birth \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.Recently, two-sample MR analysis has been applied to investigate relationship between IBD and many diseases (e.g., gut microbial genera, atopic dermatitis, depression, fatty acids, neurodegenerative diseases) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, previous epidemiological studies have not focused on the causal relationship between T2DM and IBD phenotypes. Bidirectional two-sample MR may help to reveal the complex causal relationship between the two diseases. Therefore, this study aimed to evaluate and analyze the causal effect between T2DM and IBD by performing a bidirectional two-sample MR analysis using pooled data from large-scale open access GWAS. The findings are expected to provide implications for taking measures to reduce the severity of IBD for patients with occurrent T2DM.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eWe investigated the association of T2DM with UC and CD using a bidirectional two-sample MR approach. According to the rationale and core assumptions of MR \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, three hypotheses were followed in this study: (1) The genetic IVs must be strongly associated with the exposure; (2) SNPs were not associated with any confounding factors of the risk-outcome association; and (3) SNPs did not affect the results through any pathway other than exposure of interest. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e details a schematic representation of the design of this study. All data used in the study were from publicly available GWAS summary statistics. Ethical approval was obtained in all original studies. Therefore, no additional ethical approval or informed consent was required for this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources and Instruments\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 T2DM\u003c/h2\u003e \u003cp\u003eSummary data on the association of genetic variants with doctor-diagnosed T2DM were obtained from a recent GWAS meta-analysis of 160 000 genetic variants in 62,892 patients with T2DM and 596,424 controls of European ancestry \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The study consisted of three contributing studies, including the Genetic Epidemiology Research on Aging (GERA), the Diabetes Genetics Replication and Meta-analysis (DIAGRAM), and the full cohort release from UK Biobank (UKB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 UC and CD\u003c/h2\u003e \u003cp\u003eSNPs associated with IBD including UC and CD were obtained from the International Inflammatory Bowel Disease Genetic Consortium (IIBDGC) participants. The IBD GWAS statistics provided data on IBD overall (12,882 cases, 21,770 controls) as well as on CD (5,956 cases, 14,927 controls) and UC (6 968 cases, 20,464 controls) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. GWAS data were provided by IEU OpenGWAS database. This database are now available as resources for a wide range of analyses, such as MR Analysis \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Selection of instrumental variables\u003c/h2\u003e \u003cp\u003eWe rigorously performed a series of quality control techniques to screen eligible genetic tools. First, the genome-wide significance level was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e to satisfy the correlation assumption so that the instrumental variables are closely related to the outcome. Second, to rule out variants in strong linkage disequilibrium (LD) and ensure the independence of each SNP, we used standard parameter SNPs (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, window size\u0026thinsp;=\u0026thinsp;1000 kb). SNPs in pairs with LD r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e values greater than the specified threshold (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01) and smallest p-values were retained. SNPs with minor allele frequency (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.010) were eliminated. Third, SNPs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5*10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) associated with IBD were excluded by screening the GWAS catalog. Finally, ambiguous SNPs with discordant alleles (e.g., A/G vs. A/C) and palintic SNPs (e.g., A/T or G/C) were excluded from the process of reconciling exposure and outcome data sets.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003ePrior to MR analysis, we calculated the F-statistic (F =[beta/SE]\u003csup\u003e2\u003c/sup\u003e) for T2DM IVs to quantify the strength of the instrument. The calculated results F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 indicate the absence of weak IVs bias \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We used inverse variance weighting (IVW) as the main analysis method for MR analysis. When the pleiotropic effect of IVs was not present and the sample size was large enough, the IVW estimates were consistent, valid, and close to the true value\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. It has the most efficient instrumental variable analysis when all selected SNPs are valid IVs. The MR Egger intercept term was used to assess horizontal pleiotropy, where deviation from zero indicates directional pleiotropy. In addition, the slope of the MR Egger regression shows valid MR estimates when horizontal pleiotropy is present \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Complementary weighted median methods were used, which can show valid MR estimates by assuming that at least 50% of IVs are valid and ordering each IV's MR estimate as the inverse of its variance\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Thus, the weighted median estimator can provide reliable estimates. In addition, we performed several sensitivity analyses to examine and correct for the presence of pleiotropy in causal estimates. We performed the MR-PRESSO global test to assess horizontal pleiotropy and the presence of missing abnormal variants\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Cochran's Q was calculated to examine the heterogeneity of individual causal effects, with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating the presence of pleiotropy. Finally, we also performed an omission analysis to assess the impact of individual SNP\u003csub\u003eS\u003c/sub\u003e on MR estimates. All analyses were conducted with R (version 4.2.2), R-based package \u0026lsquo;TwoSampleMR\u0026rsquo; and \u0026lsquo;MR-PRESSO\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Instrumental Variable Statistical Results\u003c/h2\u003e \u003cp\u003eThrough the above series of screening processes, 110 to 114 independent SNPs were selected as IVs for T2MD. A total of 26 and 37 independent SNPs were selected as IVs for UC and CD, respectively. The F statistics of IVs ranged from 29.942 to 1578.256, indicating a small likelihood of weak IVs bias. Details regarding all IVs are provided in Supplementary Tables S1-S4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Primary MR Analysis\u003c/h2\u003e \u003cp\u003eMR-PRESSO method was used to perform the horizontal multiple effects test, and the outliers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were removed for MR analysis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there was a negative causal relationship between T2DM and UC[IVW,OR/95%CI: 0.882/(0.826,0.942), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. However, the causal relationships between T2DM and CD, UC and T2DM, CD and T2DM were not significant, and the p value measured by the IVW method was \u0026ge;\u0026thinsp;0.05. According to the IVW approach, each standard deviation (SD) increase in genetically measured T2DM level was associated with a 12.5% reduction in UC risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Supplementary and Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eIn addition to the main IVW analysis methods, MR-Egger and weighted median estimation methods were used to verify the accuracy of the results. These supplementary analyses were applied to confirm the negative causal relationship between T2DM and UC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, 0.019, respectively). For each SD increase in T2DM measured by MR-Egger and weighted median estimation methods, the risk of UC decreased by 17.4% and 14.3%, respectively. In the mean time, the differences between T2DM and CD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.418, 0.134, respectively), UC and T2DM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.191, 0.190, respectively), CD and T2DM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.964, 0.158, respectively) were not found significant by these methods, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eHeterogeneity was measured by Cochran's Q statistic. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the results of the heterogeneity analysis indicated that significant statistical heterogeneity was detected among the genetic instrumental variables in terms of the effect of T2MD on CD (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and UC on T2MD (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). Therefore, the multiplicative random effects IVW model was applied in these associations to calculate causal effects. In addition, no significant statistical heterogeneity was found between genetic instrumental variables in the effect of T2MD on UC (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.153) and CD on T2MD (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.381*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). Therefore, the fixed effects IVW model was used for the initial MR analysis.\u003c/p\u003e \u003cp\u003eFurthermore, horizontal pleiotropy effects were tested to determine whether FA-related genetic tool variants could cause IBD through other potential pathways. As displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, no significant horizontal pleiotropy was found in the MR analyses (all \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026ge;\u0026thinsp;0.05), suggesting that this MR study are virtually unlikely to be affected by potential confounding pathways and thus the results are robust and reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of bidirectional MR Analysis of T2DM with UC and CD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo.SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ-Value\u003c/p\u003e \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e-Value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePleiotropy-test\u003c/p\u003e \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e-Value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR-PRESSO RSSobs\u003c/p\u003e \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e-Value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM on UC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003cp\u003eWeighted-Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.882(0.826,0.942)\u003c/p\u003e \u003cp\u003e0.840(0.724,0.974)\u003c/p\u003e \u003cp\u003e0.867(0.769,0.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003cp\u003e0.023\u003c/p\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128.371\u003c/p\u003e \u003cp\u003e(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004(0.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e221.126(0.263)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM on CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003cp\u003eWeighted-Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.955(0.877,1.038)\u003c/p\u003e \u003cp\u003e0.924(0.765,1.117)\u003c/p\u003e \u003cp\u003e0.907(0.797,1.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003cp\u003e0.418\u003c/p\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170.025\u003c/p\u003e \u003cp\u003e(༜0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003(0.712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e196.280(0.946)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUC on T2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003cp\u003eWeighted-Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.016(0.993,1.039)\u003c/p\u003e \u003cp\u003e1.051(0.978,1.130)\u003c/p\u003e \u003cp\u003e1.019(0.991,1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003cp\u003e0.191\u003c/p\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.429\u003c/p\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003cp\u003e(0.340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.386\u003c/p\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD on T2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003cp\u003eWeighted-Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.017(0.996,1.039)\u003c/p\u003e \u003cp\u003e0.999(0.954,1.046)\u003c/p\u003e \u003cp\u003e1.017(0.993,1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003cp\u003e0.964\u003c/p\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.448\u003c/p\u003e \u003cp\u003e(7.381*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003cp\u003e(0.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e101.371\u003c/p\u003e \u003cp\u003e(0.499)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eT2DM: Type 2 Diabetes Mellitus; UC: Ulcerative colitis; CD: Crohn's Disease; No. SNP: number of SNPs included in the analysis; OR: odds ratio; CI: confidence intervals; Q-value: Cochran\u0026rsquo;s Q statistic; IVW: inverse variance weighted.\u003c/p\u003e \u003cp\u003eLeave-one-out analyses were performed to assess the effect of individual SNP\u003csub\u003eS\u003c/sub\u003e on the final MR results. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the bidirectional residual causal effects of T2DM and CD and UC found in the omitted one analysis after sequentially omitting individual SNP\u003csub\u003eS\u003c/sub\u003e were consistent with those found in the main MR study. This evinces that no single SNP played a significant role in the final results. The analyses further demonstrate that the MR study was robust, stable, and reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eScatter plots were drawn to visualize the effect size of each MR method (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) Forest plots were employed for the visualization of individual SNP estimates of the results (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Funnel plots were used to show the balance of the distribution of the effects of individual SNPs (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). From these plots, it can be concluded that the effect and distribution of each SNP are balanced.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study used MR to analyze the bidirectional causal relationship between T2DM and IBD. The results showed that T2DM reduced the risk of UC, while the causal relationships between T2DM and CD, UC and T2DM, and CD and T2DM were not significant.\u003c/p\u003e \u003cp\u003eChen et al. \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e showed that for each unit increase in the risk of T2DM, there was a 0.93 unit decrease in the risk of ulcerative colitis. The present study confirmed that Type 2 diabetes could reduce the risk of ulcerative colitis, which may be related to the medications taken by patients with type 2 diabetes. For instance, Tseng\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e followed 340,211 metformin users and 24,478 non-metformin users in remission of IBD for 5 years and found that metformin reduced the risk for recurrence of IBD in patients with type 2 diabetes. Deng et al. found that metformin reduced tight junction (TJ) protein expression in patients with type 2 diabetes and ulcerative colitis \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. TJ proteins mainly exist in the junctional complex between epithelial cells and endothelial cells\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This structure connects adjacent cell membranes closely together and closes the epithelial cell space. Its function is to allow ions and small molecular soluble substances to pass through, and to prevent toxic macromolecules and microorganisms from passing through. The intestinal mucosal cells of ulcerative colitis highly express anti-inflammatory cytokines. When induced by anti-inflammatory cytokines, the expression of TJ protein will also increase, which promotes the permeability of intestinal mucosal cells to small molecule substances, leading to the occurrence of ulcerative colitis such as diarrhea\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Research demonstrated that IBD and T2DM share a common pathogenetic basis, which is affected by inflammatory processes, gut microbiota imbalance, and crosstalk between various signaling pathways\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Liu et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e found that metformin appeared to induce antiinflammatory effects that improved symptoms of ulcerative colitis. In addition to the anti-inflammatory and antioxidant properties of metformin and enhancing intestinal barrier integrity in IBD cell and animal models, Wasuwit et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e revealed that metformin had the ability to restore the intestinal microbiota in mice with ulcerative colitis, thereby reducing intestinal inflammation. These evidences indicate that metformin can be used as an alternative therapy for inflammatory bowel disease\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, but its usage and dosage are still unclear. Therefore, future studies are warranted to further clarify its mechanism of action, usage and dosage, combination of drugs, and adverse reactions. Moreover, Pioglitazone shows potential benefits in treatment of IBD in preclinical studies\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Tseng\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e evaluated the association between pioglitazone and major risk factors for IBD (psoriasis, arthropathy, dorsal paralysis, chronic obstructive pulmonary disease, and tobacco abuse) in 12,763 patients who had used pioglitazone and 12,763 patients with T2DM who had never used pioglitazone and found no effect. However, rosiglitazone as a thiazolidinedione antidiabetic drug effective for the treatment of T2DM has been shown to be effective in the treatment of mild-to-moderate active ulcerative colitis\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Therefore, some drugs such as rosiglitazone could be prioritized owing to its dual effect during treatment for patients with T2DM and UC. Studies on the safety of some biologic agents and immunosuppressive therapies in patients with IBD andT2DM have been reassuring, yet newer and safer biologic agents that could reduce the risk of infection in such patients as first-line treatment need to be advanced \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite that the present study established a causal relationship between T2DM and UC and provides implications for healthcare practice, the mechanism by which T2DM can reduce the incidence of UC rather than CD is still unclear. In future, the specific causal mechanism between T2DM and UC can be further explored by using experimental methods from the aspects of cell biological factors, physical and chemical factors, genetic factors, and immune factors.\u003c/p\u003e \u003cp\u003eThe results of this study suggest that the causal relationships between UC and T2DM, as well as CD and T2DM were not significant. Nevertheless, previous research have found that IBD can lead to an increased risk of developing diabetes in patients \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A study based on a Korean population showed that the increased risk of diabetes in IBD patients was more prominent in younger age groups, and the risk of diabetes was higher in CD patients than UC patients \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In a cohort study based on the Danish population, it was found that UC or CD increased the risk of type 2 diabetes in patients \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, these studies cannot confirm a causal relationship between UC and CD and T2DM due to the cross-sectional design nature. Lai et al. \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e conducted a preliminary cohort analysis using the database of the Ministry of National Health in Taiwan while found no significant association between IBD and an increased risk of T2DM. Differences in study population and research methods may lead to disparity in these study results. A cohort study of the IBD population suggests that the increased risk of developing T2DM in IBD patients may be associated with elevated disease severity \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Maconi et al. \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e showed that the preferred treatment for active UC is corticosteroids, which may lead to glucose intolerance in patients, onset of diabetes, difficulty in controlling glucose levels, and complications in patients with diabetes. Therefore, the increased risk of T2DM in IBD patients may be ascribed to a variety of factors, and the conclusion regarding IBD causing the occurrence of T2DM cannot be reached. Although the current study did not find a causal relationship between IBD and T2DM, future research can start with the related factors of IBD and explore the association between IBD related factors and T2DM with large samples and multi-center methods.\u003c/p\u003e \u003cp\u003eThis study has certain strengths. The study results confirmed a causal relationship between T2DM and UC. To the best of our knowledge, this was the first study to assess the bidirectional causal effect of T2DM on the development of IBD using a bidirectional two-sample MR approach. Firstly, the design of the study was based on three main instrumental variable assumptions and conformed to the checklist of the MR Survey \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. MR approach is less susceptible to confounding, reverse causality, and non-differentially measured exposures than observational studies \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Therefore, the conclusions drawn in this study are reasonable and trustworthy. Second, both large-scale GWAS were obtained from European ancestry, which allowed the bias of population stratification to be avoided. In addition, a number of sensitivity analyses were performed to ensure the consistency of causal estimates and to confirm the robustness of the current findings.\u003c/p\u003e \u003cp\u003eOur study also has limitations. Pleiotropy is an important issue in MR studies. Our results do not appear to be affected by pleiotropy, as consistent results were obtained in sensitivity analyses, and few outliers were found using iterative IVW and MR-PRESSO methods. Second, this study only provides strong and reliable evidence for the effect of T2DM on IBD risk, whereas the effect of T2DM in patients with diagnosed IBD has not been explored. In addition, because our study was limited to pooled data, the population was not categorized by sociodemographic factors, such as age, sex, or employment, when examining casual associations. Finally, the results of the current study were based on individuals of European ancestry, and thus the generalizability of the results is limited.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe results of the bidirectional MR Study suggest that T2DM has a negative causal effect on UC, which may be related to the use of metformin and pioglitazone, and thus can be considered as an alternative therapy for IBD patients. Given that the usage and dosage of metformin and pioglitazone are not clear in IBD patients, future studies are needed to further clarify its mechanism of action, usage and dosage, combination of drugs and adverse reactions. In addition, the causal relationships between T2DM and Crohn's disease (CD), UC and T2DM, and CD and T2DM were not significant. In future, the specific causal mechanism between T2DM and UC can be further explored by using experimental methods from the aspects of cell biological factors, physical and chemical factors, genetic factors, and immune factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIBD: Inflammatory bowel disease; UC: Ulcerative colitis; CD: Crohn\u0026apos;s disease; T2DM: type 2 diabetes mellitus; MR: Mendelian randomization; IVs: instrumental variables; GWAS: genome-wide association studies; SNPs: single nucleotide polymorphisms; GERA: Genetic Epidemiology Research on Aging; DIAGRAM: Diabetes Genetics Replication and Meta-analysis; UKB: UK Biobank; IIBDGC: International Inflammatory Bowel Disease Genetic Consortium; LD: linkage disequilibrium; IVW: inverse variance weighting; SD: standard deviation; TJ: tight junction\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGX, YX, and TL designed the study concept; GX was used for data collection and analysis. GX, YX, and TZ interpreted the data; GX, YX, TZ, and TL drafted the manuscript; TL reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the[repository name] repository.\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics for T2DM is available on the website https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST006867/\u003c/p\u003e\n\u003cp\u003eThe GWAS summarystatistics for IBD (including UC and CD) is available on the websites https://gwas.mrcieu.ac.uk/datasets/ieu-a-32/ and https://gwas.mrcieu.ac.uk/datasets/ieu-a-30/.The other data generated or analyzed during this study are available in thispublished article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Nursing, Qingdao University,Qingdao 266071,China\u0026nbsp;\u003csup\u003e2\u003c/sup\u003eDepartment of Gastroenterology, Affiliated Hospital of Qingdao University, Qingdao266003, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg SC, Shi HY, Hamidi N, et al. 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The Effects of Fatty Acids on Inflammatory Bowel Disease: A Two-Sample Mendelian Randomization Study. Nutrients Jul 14 2022;14(14).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui G, Li S, Ye H, et al. Are neurodegenerative diseases associated with an increased risk of inflammatory bowel disease? A two-sample Mendelian randomization study. Front Immunol. 2022;13:956005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ Jul. 2018;12:362:k601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue A, Wu Y, Zhu Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun Jul. 2018;27(1):2941.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu JZ, van Sommeren S, Huang H, et al. 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The role of peroxisome proliferator-activated receptor gamma in colon cancer and inflammatory bowel disease. Archives of pathology \u0026amp; laboratory medicine Sep. 2003;127(9):1121\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTseng CH. Pioglitazone Has a Null Association with Inflammatory Bowel Disease in Patients with Type 2 Diabetes Mellitus. Pharmaceuticals (Basel Switzerland) Dec 11 2022;15(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis JD, Lichtenstein GR, Deren JJ, et al. Rosiglitazone for active ulcerative colitis: a randomized placebo-controlled trial. Gastroenterol Mar. 2008;134(3):688\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong U, Cross RK. Are Inflammatory Bowel Disease Patients With Diabetes Mellitus at Increased Risk for Poor Outcomes Including Greater Health Care Utilization? 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Clin Gastroenterol hepatology: official Clin Pract J Am Gastroenterological Association Apr. 2020;18(4):1002\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaconi G, Furfaro F, Sciurti R, Bezzio C, Ardizzone S, de Franchis R. Glucose intolerance and diabetes mellitus in ulcerative colitis: pathogenetic and therapeutic implications. World J Gastroenterol Apr. 2014;7(13):3507\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome open research. 2019;4:186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavey Smith G, Holmes MV, Davies NM, Ebrahim S. Mendel's laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues. Eur J Epidemiol 2020/02//. 2020;35(2):99\u0026ndash;111.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Type 2 Diabetes Mellitus, inflammatory bowel disease, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3052187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3052187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eStudies have shown that patients with inflammatory bowel diseases (IBD) coexisting with type 2 diabetes mellitus (T2DM) have higher risk of infection, increased healthcare utilization and decreased quality of life, while currently they are not treated with more effective immunosuppressive therapy. Observational studies have shown a bidirectional association between T2DM and IBD, including Crohn's disease (CD) and ulcerative colitis (UC). However, because of the difficulty in determining sequential timeliness, it is unclear whether the observed associations are causal. We investigated the association between T2DM and IBD by bidirectional two-sample Mendelian randomization (MR) to clarify the casual relationship.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIndependent genetic variants for T2DM and IBD were selected as instruments from published genome-wide association studies (GWAS), mainly in European ancestry. Instrumental variables (IVs) associated with T2DM and IBD were extracted separately from the largest GWAS meta-analysis. MR analyses included inverse variance weighting, weighted median estimator, MR Egger regression, and sensitivity analyses with Steiger filtering and MR PRESSO.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGenetically predicted T2DM (per log-odds ratio increase) was associated with risk for IBD. In the data samples for UC (6968 cases, 20464 controls) and CD (5956 cases, 14927 controls), the odds ratio [95% confidence interval] for T2DM on UC and CD were 0.882 (0.826,0.942), and 0.955(0.877,1.038), respectively. In contrast, among 62,892 patients with T2DM, no genetically influenced association between IBD and T2DM was observed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe results of the bidirectional MR Study suggest that T2DM has a negative causal effect on UC, which provides implications for clinical treatment decisions in IBD patients with T2DM. The findings do not support a causal relationship between T2DM and CD, UC and T2DM, or CD and T2DM, and the impact of IBD on T2DM needs further investigation.\u003c/p\u003e","manuscriptTitle":"Type 2 diabetes and Inflammatory Bowel Disease: A Bidirectional Two-sample Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-26 18:25:31","doi":"10.21203/rs.3.rs-3052187/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":"2dd28403-24a6-43ca-98c7-7d6997bd9c63","owner":[],"postedDate":"June 26th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-31T07:14:26+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-26 18:25:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3052187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3052187","identity":"rs-3052187","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}