Causal association between type 2 diabetes mellitus, inflammatory bowel disease and iron deficiency anemia: A multivariable Mendelian randomization Study

Causal association between type 2 diabetes mellitus, inflammatory bowel disease and iron deficiency anemia: A multivariable Mendelian randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Causal association between type 2 diabetes mellitus, inflammatory bowel disease and iron deficiency anemia: A multivariable Mendelian randomization Study Qingluo Yang Yang, Xue Gao, Juping Wang, Shuqin WU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3859699/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 Purpose To investigate the casual role of type 2 diabetes mellitus(T2DM) and inflammatory bowel disease (IBD) in iron deficiency anemia (IDA). Methods Univariable and multivariable Mendelian randomization (MR) analyses were conducted to evaluate the associations of T2DM, ulcerative colitis (UC) and Crohn's disease (CD) with risks for IDA. Results CD and T2DM were found to be associated with IDA in all three diseases. The ORs were 1.035(95% CI 1.006–1.064; p = 0.049) for CD and 1.086(95% CI 1.004–1.168; p = 0.022) for T2DM, respectively. Furthermore, when assessing CD and T2DM simultaneously using multivariable MR, both were found to be associated with an increased risk of IDA (OR 1.039, 95% CI 1.001–1.069, p = 0.012; OR 1.100, 95% CI 1.034–1.166, p = 0.005). But considering the effects of UC and CD in multivariable MR, only T2DM was causally associated with IDA (OR 1.104, 95% CI 1.037–1.171, p = 0.004). Conclusion Associations were found in the incidence of IDA and an increased risk of T2DM and CD, highlighting the importance of IDA prevention in patients with T2DM and CD. type 2 diabetes mellitus inflammatory bowel disease iron deficiency anemia mendelian randomization Figures Figure 1 Figure 2 Figure 3 Introduction Iron deficiency (ID) is the most common cause of anemia worldwide. The etiology of iron deficiency anemia (IDA) varies by age, gender, and socioeconomic status 1 . The most common cause of IDA is blood loss, especially among elderly patients. Additionally, IDA can occur when dietary iron intake is low and there is an increased demand for iron in the body, such as during pregnancy. Reduced iron absorption, as seen in celiac disease, can also lead to IDA 2 . ID and IDA are significant contributors to many diseases and disabilities globally 3 . In 2016, there were over 1.2 billion cases of IDA worldwide, with 41.7% occurring in children under 5 years of age, 40.1% in pregnant women, and 32.5% in non-pregnant women, making it a major global public health concern. Controlling anemia is a priority for the World Health Organization (WHO), with the goal of reducing the prevalence of anemia in women by 50% by 2025 4 . IDA poses a significant burden among patients hospitalized for Crohn's disease, as shown by a large cohort study conducted in the United States 5 ; another observational study focusing on patients with inflammatory bowel disease (IBD) revealed that approximately one-fifth of the cohort experienced anemia primarily due to ID 6 ; recent guidelines from the European Crohn's Disease and Colitis Organization underline the importance of regular screening and prompt treatment for IDA in individuals with IBD 7 ; a recent study reported varying prevalence rates of ID in hospitalized IBD patients, ranging from 13–90%, with an average of 23% 8 . In the context of type 2 diabetes mellitus (T2DM), a retrospective study involving 1,137 patients found that 39.3% had ID, with ID being most prevalent (61.7%) among those with a duration of diabetes less than 5 years 9 ; another study demonstrated an association between IDA and elevated glycosylated hemoglobin concentrations, which significantly declined following iron treatment 10 . Furthermore, a large retrospective study identified higher HbA1c concentrations in patients with IDA compared to those without, emphasizing the need for clinicians to consider the presence of IDA when interpreting HbA1c levels for diabetes diagnosis and treatment decisions 11 . However, it should be noted that traditional observational studies are susceptible to confounding factors and reverse causality bias, making it challenging to establish a causative relationship between T2DM and the etiology of IDA. Most randomized controlled trials are both costly and time-consuming, so an alternative method of reinforcing causal reasoning can help guide the potential plausibility of these trials. Mendelian randomization (MR) is an alternative method that uses genetic variation as an instrumental variable to infer the potential causal relationship of exposure to an outcome 12 . Because genetic variants are randomly assigned at meiosis and fertilization, they are relatively independent of the act of self-selection and develop well before disease onset, minimizing the problems of confounding and reverse causality 13 . However, if multiple exposures are present, the results of univariable MR reflect the total effect of each exposure on the results, including direct effects and effects that interact with other exposures. Multivariable MR is an extension to standard (univariable) MR that allows genetic variants to be associated with multiple exposures and estimates the direct causal effect of each exposure in a single analytical model. The instrumental variable assumptions in multivariable MR require that each variant to be associated with at least one exposure, not associated with the outcome via confounding, and not influence the outcome except possibly by being associated with one or more exposures included in the analytical model. For identification, it is also required that there is no complete collinearity between genetic associations; that is, there are variants that can account for independent variation in each exposure 14 . In multivariable MR analysis, instrumental variables (IV) can be extracted from the summary statistics of large-scale, non-overlapping genome-wide association studies (GWAS). Here, we will apply multivariable MR analysis to explore the potential causal relationship between T2DM, IBD, and IDA. Materials and methods Study Design To investigate the causal relationship between different diseases and IDA, we conducted a comprehensive study utilizing MR analysis. The study design is visually presented in Fig. 1 . Our study consisted of three main MR analyses. In the first analysis, called univariable MR, we assessed the causal effect of three distinct diseases—T2DM, UC, and CD—on the development of IDA. For a more comprehensive understanding, we developed four multivariable MR models in the second analysis. Model 1 focused on distinguishing whether UC or CD had a primary causal relationship with IDA. Models 2 and 3 examined the association between T2DM and IDA, separately considering its relationship with UC and CD. Lastly, Model 4 aimed to evaluate the combined effects of T2DM, CD, and UC on IDA, analyzing their individual impacts and potential causal connections. In conducting this study, we utilized publicly available GWAS data, which had previously undergone rigorous ethical approval and obtained informed consent from participants 15 . GWAS Data Source We obtained data on IBD from a GWAS meta-analysis 16 of the International Society for IBD Genetics, which comprised 20,883 individuals with CD [5,956 cases and 14,927 controls] and 27,432 individuals with UC [6,969 cases and 20,464 controls)]. T2DM data were derived from a meta-analysis of 72,127 subjects of European ancestry, including 12,391 T2DM and 57,196 controls 17 . To access the study protocol and data, you can visit the online source here: https://gwas.mrcieu.ac.uk/ . For IDA data, we utilized the Finland database, which consisted of 217,202 subjects [6,087 IDA cases and 211,115 controls]. You can find more information about the IDA dataset at this link: https://www.finngen.fi/fi . . Instrumental Variable Selection In univariable MR analysis, we first identified independent (linkage disequilibrium, LD clumping r 2 threshold = 0.01, window size = 10 Mb), genome-wide significant (p < 5 x 10 − 8 ) single nucleotide polymorphisms (SNPs) associated with each trait. For the multivariable MR analyses, in Model 1, we pooled all genome-wide significant SNPs associated with any of the traits, including UC and CD. In Models 2 and 3, the traits included UC, T2DM and CD, T2DM. In Model 4, traits included UC, CD, and T2DM. Then we compared clumped these SNPs with respect to the lowest p value to any of the exposures in a model using a 1-Mb window and pairwise LD R2 10 was considered valid and reliable IV. We also calculated the conditional F-statistic to characterize instrument strengths in multivariable MR 19 . MR Analyses In the univariable MR analysis, we employed the inverse-variance weighted (IVW) method as the primary approach to estimate the causal effect of each of the three diseases on IDA individually 14 . To ensure the robustness of our findings, we also conducted sensitivity analyses using four additional methods: the weighted median method, the weighted mode method, the simple mode method, and the MR-Egger 20 . The weighted median method is particularly useful in scenarios where there is high pleiotropy, meaning that a single genetic variant influences multiple traits. This method provides reliable estimates even when more than half of the genetic variants used as IVs are valid 21 . Similarly, the weighted mode method yields consistent results even if most of the IVs involved are invalid 22 . To account for potential horizontal pleiotropy, which refers to situations where the IVs affect the outcome through pathways other than the exposure of interest, we employed MR-Egger regression methods 20 . By examining the MR-Egger intercept and homogeneity, we were able to detect and correct for directional pleiotropy. Furthermore, we utilized Cochran's Q statistic 23 and the MR-Egger test (intercept) 24 to assess heterogeneity and pleiotropy. These sensitivity analyses and statistical tests enhance the reliability and validity of our findings, allowing us to account for potential biases and ensure the robustness of our conclusions. In next step, we conducted multivariable MR using the multivariable IVW method as the primary approach. This method allows us to simultaneously estimate the causal effects of multiple diseases on IDA. To address both measured and unmeasured pleiotropy, which occur when IVs affect both the exposure and outcome through pathways other than the intended causal relationship, we utilized two additional methods: the multivariable MR-Egger 20 and MR-Lasso methods 25 . Similar to the univariable MR analysis, we assessed heterogeneity and pleiotropy in the multivariable MR analysis using Cochran's Q statistic 23 and multivariable MR-Egger test (intercept) 24 to test for heterogeneity and pleiotropy. All the aforementioned statistical analyses were performed using the "TwoSampleMR"(version 0.5.6) and "Mendelian Randomization"(version 0.5.1) packages in the statistical program R(version 4.1.1) 26 . Statistical significance was defined as p < 0.05. Results Instrumental Variables The number of instrumental variables and the phenotypic variances they accounted for by the instrumental variables are shown in Table 1 . We ensured the quality of these instruments by examining the F-statistic values. In the univariable MR analysis (Table SI), all SNPs had F-statistics greater than 10, indicating that they are strong instruments. Similarly, in the multivariable MR analysis (Table S2), we calculated the conditional F-statistic, which also exceeded 10. These results indicate that there is no potential weak instrument bias present in our study. Table 1 Number of Instrumental Variables and Associated Phenotypic Variance Outcome UC a CD a T2DM b IDA SNP(n) 39 56 17 R2(%) 92.9 50.9 15.8 R2 stands for phenotypic variance accounted for by the single-nucleotide polymorphisms (SNPs), and it is determined through “get_r_pn” in the “TwoSampleMR” R package. a The summary-level genome-wide association study data for ulcerative colitis (UC) and Crohn's disease (CD) were downloaded from Reference 27 . b The summary-level genome-wide association study data for type 2 diabetes(T2DM) were downloaded from Reference 17 . Causal Effect of UC, CD and T2DM on IDA On UC and the causal relationship between IDA is analyzed, the instrument heterogeneity was observed under the significance level of 0.05 rendering (Cochran 's Q test, p 0.05), and the fixed-effects IVW method was used. The results showed that CD and T2DM were both causally associated with IDA (p < 0.05; Fig. 2 ). The ORs for CD were 1.035 (95% CI 1.006–1.064; p = 0.049), and for T2DM, the ORs were 1.086 (95% CI 1.004–1.168; p = 0.022). After Bonferroni correction, both associations remained statistically significant (p = 0.05/3). In contrast, no causal effect was found between UC and IDA (OR 1.022, 95% CI 0.986–1.059; p = 0.230). Multivariable MR In the multivariable IVW analysis of Models 1 and 2, which assessed the association between UC, CD, T2DM, and IDA, we observed that elevated CD and T2DM were independently associated with an increased risk of IDA (OR 1.048, 95% CI 1.001–1.088, P = 0.022; OR 1.121,95%CI 1.052–1.189, p = 0.001). When conducting a multivariable analysis (Model 3) including both CD and T2DM, we found that both conditions were significantly associated with an elevated risk of IDA (OR 1.039, 95% CI 1.001–1.069, p = 0.012; OR 1.100, 95% CI 1.034–1.166, P = 0.005). These findings were consistent with the results obtained from the MR-Egger and MR-Lasso methods. Furthermore, in the multivariable MR analysis considering the effects of UC and CD, only T2DM was causally linked to IDA compared to univariable MR (OR 1.104, 95% CI 1.037–1.171, p = 0.004; Fig. 3 ). No other causal relationship was identified. These effect estimates were in agreement with the IVW estimates derived using the MR-Egger method (Table S4). However, when employing the MR-Lasso method, both CD and T2DM were associated with an increased risk of IDA after adjusting for the influence of UC (OR 1.038, 95% CI 1.007–1.070, P = 0.020; OR 1.109, 95% CI 1.043–1.175, p = 0.002), which corroborated the findings of Model 3. No heterogeneity or horizontal pleiotropy was detected (Table S5). Discussion In the univariable and multivariable MR study, we systematically assessed the iron deficiency anemia with type 2 diabetes, ulcerative colitis and Crohn's disease risk. Consistent with previous research results 5 , 9 – 11 , 28 , our findings demonstrated a significant positive association between IDA and an increased risk of both T2DM and CD. Furthermore, our multivariable MR analysis substantiated a direct causal relationship between T2DM and IDA, while accounting for the confounding effects of other related diseases. However, when considering the joint influence of T2DM, CD, and UC on IDA, it was evident that CD was subject to the mediating effects of the other two diseases, thereby precluding the existence of a causal association between CD and IDA. These results underscore the complex interplay among various diseases and highlight the necessity of incorporating multiple factors when investigating causal relationships. According to the World Health Organization (WHO), ID is the most common nutritional disorder in the world 29 . Although there is limited epidemiological evidence linking gastrointestinal diseases to T2DM, cross-sectional studies suggest an increased prevalence of peptic ulcer, severe acute gastritis, and irritable bowel syndrome characterized by symptoms such as abdominal pain, constipation, and diarrhea in T2DM 30 , 31 . The authors of a cross-sectional study observed a reduction in colitis in patients with metabolic syndrome 32 , which may be attributable to altered local inflammatory responses facilitating the involvement of immunosuppressive cells and mediators, thus providing a plausible explanation for the inverse causal association between T2DM and UC. It is worth noting that individuals with T2DM frequently exhibit disturbed glucose and insulin metabolism, accelerated vascular endothelial injury, and chronic inflammation 33 , which may be associated with observed gastrointestinal disorders. A previous study showed that T2DM was significantly associated with a lower risk of CD 34 . In one study, iron deficiency in IBD was caused by inadequate intake, malabsorption (including duodenal involvement and surgical resection), and chronic blood loss due to mucosal ulceration 35 . However, studies have shown that anemia of chronic disease predominates in CD, while IDA is prevalent in UC 36 . Consequently, when all three factors are concurrently present, T2DM, acting as an active driver, initially inhibits UC, resulting in a diminished risk of UC and masking its causality with IDA. Simultaneously, T2DM is significantly linked to a lower risk of CD, thereby diminishing the causal association between CD and IDA. Conversely, CD exacerbated by T2DM becomes the primary disease associated with IDA. The advantages of the present study include the following aspects. Firstly, the use of MR design helps to reduce confounding and reverse causation bias, thereby providing more reliable estimates of causal effects. Additionally, the study draws on a large dataset of European populations, which reduces the likelihood of demographic bias influencing the results. Furthermore, these associations were estimated in independent data sources and pooled through meta-analysis, which ensured sufficient statistical power and robustness of the findings. However, some limitations should be acknowledged. Firstly, the data used in this study is not the most recent, which may have limited the statistical power of our MR analysis. Secondly, our study only relied on genetic data to infer the possible causal relationships between T2DM, IBD, and IDA, and the underlying mechanisms are not fully understood and require further investigation. Thirdly, the GWAS data used in this study were derived from individuals of European ancestry, which may limit the generalizability of our findings to other ethnic groups. Conclusion Our study suggests that iron deficiency anemia is causally associated with an increased risk of T2DM and CD. These findings underscore the importance of IDA prevention in patients with T2DM and CD. Future studies are warranted to explore the biological mechanisms underlying these associations and to further validate our findings in diverse populations. Abbreviations T2DM: type 2 diabetes IBD: inflammatory bowel disease UC: ulcerative colitis CD: Crohn's disease IDA: iron deficiency anemia MR: Mendelian randomization GWAS: genome-wide association studies Declarations Ethical Standards Disclosure This study is based on publicly available summarized data. The protocol and data collection were approved by the ethics committee of each genome-wide association study requires no additional ethical statement or informed consent. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are extracted from the public GWAS summary data available. The GWASs for ulcerative colitis and Crohn's disease can be obtained through the IEU open GWAS project (https://gwas.mrcieu.ac.uk/).The GWASs for iron deficiency anemia were provided by the Finland database (https://www.finngen.fi/fi). Author contributions Qingluo Yang designed the study, reviewed and analysed the data and wrote the paper. Xiaoxi Zhang contributed to data collection; Yuanhan Wang interpreted the data; Xue Huang and Jiarui Jing prepared all the tables; Xue Gao and Juping Wang reviewed and edited the manuscript.; and supervision was carried out by Shuqin Wu and Juping Wang. All authors have read and approved the final manuscript. Funding This study was supported by the funds of the National Natural Science Foundation of China (Grant numbers: 82103949). This work was also supported in part by the Shanxi Science and Technology Research of China (202103021223234). We are grateful to all the studies that have made the public GWAS summary data available, and to all the investigators and participants who contributed to these studies. Conflict of interest statement The authors declare no competing financial interests. 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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-3859699","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267058492,"identity":"20967f31-cafa-40d4-9c03-6fd74a2e339d","order_by":0,"name":"Qingluo Yang Yang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingluo","middleName":"Yang","lastName":"Yang","suffix":""},{"id":267058493,"identity":"f5688cbf-87f9-4f34-abcf-dc8e5ccb111d","order_by":1,"name":"Xue Gao","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Gao","suffix":""},{"id":267058494,"identity":"17969cbf-ae91-4526-9ada-3973a03422ef","order_by":2,"name":"Juping Wang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juping","middleName":"","lastName":"Wang","suffix":""},{"id":267058495,"identity":"c314a476-40bb-4886-82dd-d115bac63e8c","order_by":3,"name":"Shuqin WU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYJCCgx+ABD8z8+EHxOpgPCwBJCXb2dIMiNXCfIAHSBqc51GQIEq9wfnDGw5IMBxO3HyYh8GAocYmmrCWG2kFBwqAWrYd5j3wgOFYWm4DIS1mN3gMgLbcBmrhSzBgbDhMhJbzZwyAfrmduLmZx0CCOC0HciBaNjATq8Ue6JfDEgb/jWccBgZyAjF+kew/vPnjh4o02f7+w4cffKixIawFCAzACAwSiFAO1TIKRsEoGAWjAB8AALhdQxGTNuZdAAAAAElFTkSuQmCC","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shuqin","middleName":"","lastName":"WU","suffix":""}],"badges":[],"createdAt":"2024-01-13 09:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3859699/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3859699/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49716490,"identity":"c57540d3-82aa-47f8-b7ba-9a044113e39c","added_by":"auto","created_at":"2024-01-16 21:51:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138328,"visible":true,"origin":"","legend":"\u003cp\u003eOverall study design. CD = Crohn's disease, UC = Ulcerative colitis, T2DM = type 2 diabetes, IVW= inverse-variance weighted.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3859699/v1/f07f56c232cf9336414cc49b.png"},{"id":49717162,"identity":"31c3e709-5617-4640-933d-1c745cf6e524","added_by":"auto","created_at":"2024-01-16 21:59:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156724,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariable Mendelian randomization results using different methods. SNP N = number of single nucleotide polymorphisms, OR = odds ratio, CI = confidence interval, CD = Crohn's disease, UC = Ulcerative colitis, T2DM = type 2 diabetes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3859699/v1/fec3af60d206d6c6aa8264b5.png"},{"id":49716488,"identity":"da405e31-e231-4457-8261-5beb2d3c7653","added_by":"auto","created_at":"2024-01-16 21:51:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":170749,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Mendelian randomization using the inverse-variance weighted method. Model 1 included ulcerative colitis and Crohn's disease. Model 2 included type 2 diabetes and ulcerative colitis. Model 3 included type 2 diabetes and Crohn's disease. Model 4 included ulcerative, type 2 diabetes and Crohn's disease. SNP N = number of single-nucleotide polymorphisms, OR = odds ratio, CI = confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3859699/v1/931b56af9ec9045c3410b661.png"},{"id":50392640,"identity":"8867ebdb-53f6-4752-9590-09ba18058370","added_by":"auto","created_at":"2024-01-30 19:39:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":753612,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3859699/v1/24a82e1f-3b99-4a3f-a1e3-ddf3cd68de7b.pdf"},{"id":49716491,"identity":"4dde8b4a-fdcb-4174-8fe7-c4534bf9d90e","added_by":"auto","created_at":"2024-01-16 21:51:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":132749,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryContent.docx","url":"https://assets-eu.researchsquare.com/files/rs-3859699/v1/420eaaefefecd64e628d1998.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal association between type 2 diabetes mellitus, inflammatory bowel disease and iron deficiency anemia: A multivariable Mendelian randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIron deficiency (ID) is the most common cause of anemia worldwide. The etiology of iron deficiency anemia (IDA) varies by age, gender, and socioeconomic status \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The most common cause of IDA is blood loss, especially among elderly patients. Additionally, IDA can occur when dietary iron intake is low and there is an increased demand for iron in the body, such as during pregnancy. Reduced iron absorption, as seen in celiac disease, can also lead to IDA\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. ID and IDA are significant contributors to many diseases and disabilities globally\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In 2016, there were over 1.2\u0026nbsp;billion cases of IDA worldwide, with 41.7% occurring in children under 5 years of age, 40.1% in pregnant women, and 32.5% in non-pregnant women, making it a major global public health concern. Controlling anemia is a priority for the World Health Organization (WHO), with the goal of reducing the prevalence of anemia in women by 50% by 2025\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIDA poses a significant burden among patients hospitalized for Crohn's disease, as shown by a large cohort study conducted in the United States\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e; another observational study focusing on patients with inflammatory bowel disease (IBD) revealed that approximately one-fifth of the cohort experienced anemia primarily due to ID\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e; recent guidelines from the European Crohn's Disease and Colitis Organization underline the importance of regular screening and prompt treatment for IDA in individuals with IBD\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e; a recent study reported varying prevalence rates of ID in hospitalized IBD patients, ranging from 13\u0026ndash;90%, with an average of 23%\u003csup\u003e8\u003c/sup\u003e. In the context of type 2 diabetes mellitus (T2DM), a retrospective study involving 1,137 patients found that 39.3% had ID, with ID being most prevalent (61.7%) among those with a duration of diabetes less than 5 years\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e; another study demonstrated an association between IDA and elevated glycosylated hemoglobin concentrations, which significantly declined following iron treatment\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, a large retrospective study identified higher HbA1c concentrations in patients with IDA compared to those without, emphasizing the need for clinicians to consider the presence of IDA when interpreting HbA1c levels for diabetes diagnosis and treatment decisions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, it should be noted that traditional observational studies are susceptible to confounding factors and reverse causality bias, making it challenging to establish a causative relationship between T2DM and the etiology of IDA.\u003c/p\u003e \u003cp\u003eMost randomized controlled trials are both costly and time-consuming, so an alternative method of reinforcing causal reasoning can help guide the potential plausibility of these trials. Mendelian randomization (MR) is an alternative method that uses genetic variation as an instrumental variable to infer the potential causal relationship of exposure to an outcome\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Because genetic variants are randomly assigned at meiosis and fertilization, they are relatively independent of the act of self-selection and develop well before disease onset, minimizing the problems of confounding and reverse causality\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, if multiple exposures are present, the results of univariable MR reflect the total effect of each exposure on the results, including direct effects and effects that interact with other exposures. Multivariable MR is an extension to standard (univariable) MR that allows genetic variants to be associated with multiple exposures and estimates the direct causal effect of each exposure in a single analytical model. The instrumental variable assumptions in multivariable MR require that each variant to be associated with at least one exposure, not associated with the outcome via confounding, and not influence the outcome except possibly by being associated with one or more exposures included in the analytical model. For identification, it is also required that there is no complete collinearity between genetic associations; that is, there are variants that can account for independent variation in each exposure\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In multivariable MR analysis, instrumental variables (IV) can be extracted from the summary statistics of large-scale, non-overlapping genome-wide association studies (GWAS). Here, we will apply multivariable MR analysis to explore the potential causal relationship between T2DM, IBD, and IDA.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eTo investigate the causal relationship between different diseases and IDA, we conducted a comprehensive study utilizing MR analysis. The study design is visually presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Our study consisted of three main MR analyses. In the first analysis, called univariable MR, we assessed the causal effect of three distinct diseases\u0026mdash;T2DM, UC, and CD\u0026mdash;on the development of IDA. For a more comprehensive understanding, we developed four multivariable MR models in the second analysis. Model 1 focused on distinguishing whether UC or CD had a primary causal relationship with IDA. Models 2 and 3 examined the association between T2DM and IDA, separately considering its relationship with UC and CD. Lastly, Model 4 aimed to evaluate the combined effects of T2DM, CD, and UC on IDA, analyzing their individual impacts and potential causal connections. In conducting this study, we utilized publicly available GWAS data, which had previously undergone rigorous ethical approval and obtained informed consent from participants\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGWAS Data Source\u003c/h2\u003e \u003cp\u003eWe obtained data on IBD from a GWAS meta-analysis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e of the International Society for IBD Genetics, which comprised 20,883 individuals with CD [5,956 cases and 14,927 controls] and 27,432 individuals with UC [6,969 cases and 20,464 controls)]. T2DM data were derived from a meta-analysis of 72,127 subjects of European ancestry, including 12,391 T2DM and 57,196 controls\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. To access the study protocol and data, you can visit the online source here: \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. For IDA data, we utilized the Finland database, which consisted of 217,202 subjects [6,087 IDA cases and 211,115 controls]. You can find more information about the IDA dataset at this link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/fi\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e .\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental Variable Selection\u003c/h2\u003e \u003cp\u003eIn univariable MR analysis, we first identified independent (linkage disequilibrium, LD clumping r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e threshold\u0026thinsp;=\u0026thinsp;0.01, window size\u0026thinsp;=\u0026thinsp;10 Mb), genome-wide significant (p\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) single nucleotide polymorphisms (SNPs) associated with each trait. For the multivariable MR analyses, in Model 1, we pooled all genome-wide significant SNPs associated with any of the traits, including UC and CD. In Models 2 and 3, the traits included UC, T2DM and CD, T2DM. In Model 4, traits included UC, CD, and T2DM. Then we compared clumped these SNPs with respect to the lowest p value to any of the exposures in a model using a 1-Mb window and pairwise LD R2\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Next, to verify the strength of the selected instruments, we calculated the F-statistic of the remaining SNPs of the three exposures\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and only SNP with F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 was considered valid and reliable IV. We also calculated the conditional F-statistic to characterize instrument strengths in multivariable MR\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMR Analyses\u003c/h2\u003e \u003cp\u003eIn the univariable MR analysis, we employed the inverse-variance weighted (IVW) method as the primary approach to estimate the causal effect of each of the three diseases on IDA individually\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. To ensure the robustness of our findings, we also conducted sensitivity analyses using four additional methods: the weighted median method, the weighted mode method, the simple mode method, and the MR-Egger\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The weighted median method is particularly useful in scenarios where there is high pleiotropy, meaning that a single genetic variant influences multiple traits. This method provides reliable estimates even when more than half of the genetic variants used as IVs are valid\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Similarly, the weighted mode method yields consistent results even if most of the IVs involved are invalid\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. To account for potential horizontal pleiotropy, which refers to situations where the IVs affect the outcome through pathways other than the exposure of interest, we employed MR-Egger regression methods\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. By examining the MR-Egger intercept and homogeneity, we were able to detect and correct for directional pleiotropy. Furthermore, we utilized Cochran's Q statistic\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and the MR-Egger test (intercept) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e to assess heterogeneity and pleiotropy. These sensitivity analyses and statistical tests enhance the reliability and validity of our findings, allowing us to account for potential biases and ensure the robustness of our conclusions.\u003c/p\u003e \u003cp\u003eIn next step, we conducted multivariable MR using the multivariable IVW method as the primary approach. This method allows us to simultaneously estimate the causal effects of multiple diseases on IDA. To address both measured and unmeasured pleiotropy, which occur when IVs affect both the exposure and outcome through pathways other than the intended causal relationship, we utilized two additional methods: the multivariable MR-Egger\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and MR-Lasso methods\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Similar to the univariable MR analysis, we assessed heterogeneity and pleiotropy in the multivariable MR analysis using Cochran's Q statistic\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and multivariable MR-Egger test (intercept)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e to test for heterogeneity and pleiotropy.\u003c/p\u003e \u003cp\u003eAll the aforementioned statistical analyses were performed using the \"TwoSampleMR\"(version 0.5.6) and \"Mendelian Randomization\"(version 0.5.1) packages in the statistical program R(version 4.1.1)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental Variables\u003c/h2\u003e \u003cp\u003eThe number of instrumental variables and the phenotypic variances they accounted for by the instrumental variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We ensured the quality of these instruments by examining the F-statistic values. In the univariable MR analysis (Table SI), all SNPs had F-statistics greater than 10, indicating that they are strong instruments. Similarly, in the multivariable MR analysis (Table S2), we calculated the conditional F-statistic, which also exceeded 10. These results indicate that there is no potential weak instrument bias present in our study.\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\u003eNumber of Instrumental Variables and Associated Phenotypic Variance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUC \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT2DM\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8\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\u003eR2 stands for phenotypic variance accounted for by the single-nucleotide polymorphisms (SNPs), and it is determined through \u0026ldquo;get_r_pn\u0026rdquo; in the \u0026ldquo;TwoSampleMR\u0026rdquo; R package.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e The summary-level genome-wide association study data for ulcerative colitis (UC) and Crohn's disease (CD) were downloaded from Reference\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e The summary-level genome-wide association study data for type 2 diabetes(T2DM) were downloaded from Reference\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCausal Effect of UC, CD and T2DM on IDA\u003c/h2\u003e \u003cp\u003eOn UC and the causal relationship between IDA is analyzed, the instrument heterogeneity was observed under the significance level of 0.05 rendering (Cochran 's Q test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table S3), so the effect of adopted random IVW method. For other analyses, no evidence of instrumental heterogeneity was found (Cochran's Q test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the fixed-effects IVW method was used. The results showed that CD and T2DM were both causally associated with IDA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ORs for CD were 1.035 (95% CI 1.006\u0026ndash;1.064; p\u0026thinsp;=\u0026thinsp;0.049), and for T2DM, the ORs were 1.086 (95% CI 1.004\u0026ndash;1.168; p\u0026thinsp;=\u0026thinsp;0.022). After Bonferroni correction, both associations remained statistically significant (p\u0026thinsp;=\u0026thinsp;0.05/3). In contrast, no causal effect was found between UC and IDA (OR 1.022, 95% CI 0.986\u0026ndash;1.059; p\u0026thinsp;=\u0026thinsp;0.230).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable MR\u003c/h2\u003e \u003cp\u003eIn the multivariable IVW analysis of Models 1 and 2, which assessed the association between UC, CD, T2DM, and IDA, we observed that elevated CD and T2DM were independently associated with an increased risk of IDA (OR 1.048, 95% CI 1.001\u0026ndash;1.088, P\u0026thinsp;=\u0026thinsp;0.022; OR 1.121,95%CI 1.052\u0026ndash;1.189, p\u0026thinsp;=\u0026thinsp;0.001). When conducting a multivariable analysis (Model 3) including both CD and T2DM, we found that both conditions were significantly associated with an elevated risk of IDA (OR 1.039, 95% CI 1.001\u0026ndash;1.069, p\u0026thinsp;=\u0026thinsp;0.012; OR 1.100, 95% CI 1.034\u0026ndash;1.166, P\u0026thinsp;=\u0026thinsp;0.005). These findings were consistent with the results obtained from the MR-Egger and MR-Lasso methods. Furthermore, in the multivariable MR analysis considering the effects of UC and CD, only T2DM was causally linked to IDA compared to univariable MR (OR 1.104, 95% CI 1.037\u0026ndash;1.171, p\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No other causal relationship was identified. These effect estimates were in agreement with the IVW estimates derived using the MR-Egger method (Table S4). However, when employing the MR-Lasso method, both CD and T2DM were associated with an increased risk of IDA after adjusting for the influence of UC (OR 1.038, 95% CI 1.007\u0026ndash;1.070, P\u0026thinsp;=\u0026thinsp;0.020; OR 1.109, 95% CI 1.043\u0026ndash;1.175, p\u0026thinsp;=\u0026thinsp;0.002), which corroborated the findings of Model 3. No heterogeneity or horizontal pleiotropy was detected (Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the univariable and multivariable MR study, we systematically assessed the iron deficiency anemia with type 2 diabetes, ulcerative colitis and Crohn's disease risk. Consistent with previous research results\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, our findings demonstrated a significant positive association between IDA and an increased risk of both T2DM and CD. Furthermore, our multivariable MR analysis substantiated a direct causal relationship between T2DM and IDA, while accounting for the confounding effects of other related diseases. However, when considering the joint influence of T2DM, CD, and UC on IDA, it was evident that CD was subject to the mediating effects of the other two diseases, thereby precluding the existence of a causal association between CD and IDA. These results underscore the complex interplay among various diseases and highlight the necessity of incorporating multiple factors when investigating causal relationships.\u003c/p\u003e \u003cp\u003eAccording to the World Health Organization (WHO), ID is the most common nutritional disorder in the world\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Although there is limited epidemiological evidence linking gastrointestinal diseases to T2DM, cross-sectional studies suggest an increased prevalence of peptic ulcer, severe acute gastritis, and irritable bowel syndrome characterized by symptoms such as abdominal pain, constipation, and diarrhea in T2DM\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The authors of a cross-sectional study observed a reduction in colitis in patients with metabolic syndrome\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, which may be attributable to altered local inflammatory responses facilitating the involvement of immunosuppressive cells and mediators, thus providing a plausible explanation for the inverse causal association between T2DM and UC. It is worth noting that individuals with T2DM frequently exhibit disturbed glucose and insulin metabolism, accelerated vascular endothelial injury, and chronic inflammation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which may be associated with observed gastrointestinal disorders. A previous study showed that T2DM was significantly associated with a lower risk of CD\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In one study, iron deficiency in IBD was caused by inadequate intake, malabsorption (including duodenal involvement and surgical resection), and chronic blood loss due to mucosal ulceration\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. However, studies have shown that anemia of chronic disease predominates in CD, while IDA is prevalent in UC\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Consequently, when all three factors are concurrently present, T2DM, acting as an active driver, initially inhibits UC, resulting in a diminished risk of UC and masking its causality with IDA. Simultaneously, T2DM is significantly linked to a lower risk of CD, thereby diminishing the causal association between CD and IDA. Conversely, CD exacerbated by T2DM becomes the primary disease associated with IDA.\u003c/p\u003e \u003cp\u003eThe advantages of the present study include the following aspects. Firstly, the use of MR design helps to reduce confounding and reverse causation bias, thereby providing more reliable estimates of causal effects. Additionally, the study draws on a large dataset of European populations, which reduces the likelihood of demographic bias influencing the results. Furthermore, these associations were estimated in independent data sources and pooled through meta-analysis, which ensured sufficient statistical power and robustness of the findings.\u003c/p\u003e \u003cp\u003eHowever, some limitations should be acknowledged. Firstly, the data used in this study is not the most recent, which may have limited the statistical power of our MR analysis. Secondly, our study only relied on genetic data to infer the possible causal relationships between T2DM, IBD, and IDA, and the underlying mechanisms are not fully understood and require further investigation. Thirdly, the GWAS data used in this study were derived from individuals of European ancestry, which may limit the generalizability of our findings to other ethnic groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study suggests that iron deficiency anemia is causally associated with an increased risk of T2DM and CD. These findings underscore the importance of IDA prevention in patients with T2DM and CD. Future studies are warranted to explore the biological mechanisms underlying these associations and to further validate our findings in diverse populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eT2DM: type 2 diabetes\u003c/p\u003e\n\u003cp\u003eIBD:\u0026nbsp;inflammatory bowel disease\u003c/p\u003e\n\u003cp\u003eUC:\u0026nbsp;ulcerative colitis\u003c/p\u003e\n\u003cp\u003eCD: Crohn\u0026apos;s disease\u003c/p\u003e\n\u003cp\u003eIDA: iron deficiency anemia\u003c/p\u003e\n\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eGWAS: genome-wide association studies\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Standards Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based on publicly available summarized data. The protocol and data collection were approved by the ethics committee of each genome-wide association study requires no additional ethical statement or informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\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 data that support the findings of this study are extracted from the public GWAS summary data available. The GWASs for ulcerative colitis and Crohn\u0026apos;s disease can be obtained through the IEU open GWAS project (https://gwas.mrcieu.ac.uk/).The GWASs for iron deficiency anemia were provided by the Finland database (https://www.finngen.fi/fi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQingluo Yang designed the study, reviewed and analysed the data and wrote the paper. Xiaoxi Zhang contributed to data collection; Yuanhan Wang interpreted the data; Xue Huang and Jiarui Jing prepared all the tables; Xue Gao and Juping Wang reviewed and edited the manuscript.; and supervision was carried out by Shuqin Wu and Juping Wang. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the funds of the National Natural Science Foundation of China (Grant numbers: 82103949). This work was also supported in part by the Shanxi Science and Technology Research of China (202103021223234). We are grateful to all the studies that have made the public GWAS summary data available, and to all the investigators and participants who contributed to these studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConflict of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the studies that have made the public GWAS summary data available, and to all the investigators and participants who contributed to these studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWarner, M. 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The prevalence, characteristics, and determinants of anaemia in newly diagnosed patients with inflammatory bowel disease. \u003cem\u003ePrz Gastroenterol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 39-47, doi:10.5114/pg.2019.83424 (2019).\u003c/li\u003e\n\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, iron deficiency anemia, mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3859699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3859699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo investigate the casual role of type 2 diabetes mellitus(T2DM) and inflammatory bowel disease (IBD) in iron deficiency anemia (IDA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUnivariable and multivariable Mendelian randomization (MR) analyses were conducted to evaluate the associations of T2DM, ulcerative colitis (UC) and Crohn's disease (CD) with risks for IDA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCD and T2DM were found to be associated with IDA in all three diseases. The ORs were 1.035(95% CI 1.006\u0026ndash;1.064; p\u0026thinsp;=\u0026thinsp;0.049) for CD and 1.086(95% CI 1.004\u0026ndash;1.168; p\u0026thinsp;=\u0026thinsp;0.022) for T2DM, respectively. Furthermore, when assessing CD and T2DM simultaneously using multivariable MR, both were found to be associated with an increased risk of IDA (OR 1.039, 95% CI 1.001\u0026ndash;1.069, p\u0026thinsp;=\u0026thinsp;0.012; OR 1.100, 95% CI 1.034\u0026ndash;1.166, p\u0026thinsp;=\u0026thinsp;0.005). But considering the effects of UC and CD in multivariable MR, only T2DM was causally associated with IDA (OR 1.104, 95% CI 1.037\u0026ndash;1.171, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAssociations were found in the incidence of IDA and an increased risk of T2DM and CD, highlighting the importance of IDA prevention in patients with T2DM and CD.\u003c/p\u003e","manuscriptTitle":"Causal association between type 2 diabetes mellitus, inflammatory bowel disease and iron deficiency anemia: A multivariable Mendelian randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 21:51:24","doi":"10.21203/rs.3.rs-3859699/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":"c609bb1e-ee59-4f1d-8e70-df07f9d11223","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-30T19:31:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 21:51:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3859699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3859699","identity":"rs-3859699","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}