The causal effect of autoimmune diseases on myelodysplastic syndrome:a Mendelian randomization study

The causal effect of autoimmune diseases on myelodysplastic syndrome:a 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 The causal effect of autoimmune diseases on myelodysplastic syndrome:a Mendelian randomization study Zhengyang Miao, Wenwei Zhu, Yongming Zhou, Hailin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4504312/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 The relationship between different types of autoimmune diseases and myelodysplastic syndrome (MDS) are inconclusive. Therefore,we employed Mendelian randomization (MR) to explore the causal associations between autoimmune diseases and MDS. Methods Single nucleotide polymorphisms (SNPs) significantly associated with 10 autoimmune diseases were extracted from the summary statistics of European genome-wide association studies (GWAS). A two-sample MR analysis was performed using summary-level statistics sourced from GWAS datasets. Inverse-variance weighting (IVW),MR‒Egger,and weighted median (WM) were further supported by several sensitivity analyses. Results Four autoimmune diseases showed genetical predisposition to MDS: rheumatoid arthritis(OR = 1.186,95%CI = 1.028–1.367, P = 0.019), multiple sclerosis(OR = 1.247,95%CI = 1.013–1.534, P = 0.037), myasthenia gravis(OR = 1.326,95%CI = 1.010–1.742, P = 0.042), and hashimoto thyroiditis(OR = 1.519,95%CI = 1.008–2.290, P = 0.046).Nevertheless,no similar causal relationship was found between the remaining seven autoimmune diseases and MDS.The accuracy and robustness of these findings were confirmed by sensitivity tests. Conclusions We are the first to use MR analysis to explore the causal relationships between autoimmune diseases and MDS.The mechanism of this causal link needs to be further explored. Mendelian randomization (MR) autoimmune diseases(ADs) genome-wide association studies (GWAS) myelodysplastic syndrome (MDS) single-nucleotide polymorphisms (SNPs) Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Autoimmune diseases are pathologic disorders caused by dysfunctions of the immune system that affect 3–5% of the population. Patients with autoimmune diseases produce high-affinity autoantibodies that target "self" molecules anywhere in the body. To date, nearly 100 different autoimmune diseases have been identified [ 1 ] . Autoimmune diseases are more common in women than in men.The age of onset varies from one autoimmune disease to another. Autoimmune diseases are associated with an increased risk of lymphoproliferative disorders and myeloid malignancies. Myelodysplastic syndrome (MDS) are a heterogeneous group of disorders characterized by abnormal development of myeloid hematopoietic stem progenitor cells and clonal hematopoiesis, which associated with cytogenetic abnormalities and genetic abnormalities.MDS is prevalent in older males, with a median age of onset of approximately 70 years. Epidemiologic studies have shown that 10–30% of patients with MDS have co-morbid autoimmune disorders.Immune dysregulation is a common pathogenic driver of both diseases [ 2 ] . Study shows increased risk of myeloid tumors in patients with autoimmune diseases compared to patients without autoimmune diseases. A population-based case-control study in Sweden analyzed patients with various autoimmune diseases individually and found that some diseases were associated with particularly high ORs for MDS, such as 7.9 for myasthenia gravis (MG), 3.9 for systemic lupus erythematosus (SLE), and 1.7 for rheumatoid arthritis (RA) [ 3 ] .Another study included 1,408 patients with MDS, of whom 391 ( 28%) had comorbid autoimmune diseases, including 171 patients with hypothyroidism, 41 with RA, 28 with psoriasis (PsO).And 63 patients have more than one autoimmune disease [ 4 ] . Randomized controlled trials(RCTs) are the gold standard of clinical randomized evidence,but in reality there are still limitations,such as difficulties in implementation and ethical restrictions.Mendelian randomization(MR) studies make up for its shortcomings by allowing the use of single nucleotide polymorphisms (SNPs) as a surrogate for exposure in order to assess the causality between exposure and the outcome of interest,independent of confounding factors.MR studies are uninterrupted by ethical issues and difficulties in the collection of cases, and can be implemented directly. There have been no MR studies about the causal relationship between autoimmune diseases and MDS. The aim of this study was to investigate the incidental effect of autoimmune diseases on the risk of developing MDS through Mendelian randomization (MR) analysis of pooled statistics from genome-wide association study (GWAS) datasets,in order to identify specific risk groups for early intervention and prevention. 2 Materials and methods 2.1 Data sources for autoimmune diseases and MDS GWAS The data of autoimmune diseases were sourced from the OPEN GWAS website ( https://gwas.mrcieu.ac.uk/ ). The GWAS databases for MDS were available from the FinnGen website ( https://www.finngen.fi/en ). Autoimmune disorders in this study include type 1 diabetes (T1D), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriasis (PsO), multiple sclerosis (MS), myasthenia gravis(MG),celiac disease (CeD), inflammatory bowel disease (IBD), graves' disease (GD),and hashimoto thyroiditis(HT). More detailed information for the cohorts of cases and controls can be found in Supplementary Table S1 . 2.2 Genetic instrumental variant selection for autoimmune diseases MR study designs have to satisfy three core assumptions: 1) there is a strong association between instrumental variants (IVs) and exposure factors; 2) the IVs are independent of confounding factors of the exposure–outcome relationship; and 3) genetic variants can only affect the outcome through the exposure and not through other pathways. The above three assumptions were visualized in Fig. 1 . To fulfill the mentioned core assumptions, first, the IVs with genome-wide significance (P < 5 × 10 − 8 ) were extracted from the exposure GWAS.The linkage disequilibrium (LD) of r 2 = 0.001 and clumping distance = 10,000.Then,palindromic SNPs were excluded when harmonizing the GWAS data.Next, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test was employed to discern potential outlier SNPs for correcting potential horizontal pleiotropy [ 5 – 6 ] . Finally, the remaining SNPs were utilized for MR analysis. And the F-statistic was calculated for each leftover SNP to measure the strength of genetic IVs. IVs with F-statistic > 10 indicate not a weak genetic instrument. Three fundamental principles of MR analysis are as follows:1) Correlation assumption: there is a strong association between instrumental variants (IVs) and exposure factors; 2) Independence assumption: the IVs are independent of confounding factors of the exposure–outcome relationship; and 3) Exclusivity assumption: genetic variants can only affect the outcome through the exposure and not through other pathways. 2.3 MR analysis Three MR methods including MR-Egger, inverse variance-weighted (IVW), and weighted median (WM) were applied for the estimation of the causality of autoimmune diseases and MDS. The IVW model assumes that all enrolled SNPs are valid genetic IVs and no pleiotropic effects exist [ 7 ] . If the causal effect estimates of the three models are inconsistent, the results of IVW methods are considered as the main outcome. The odds ratio (OR) was treated as the effect size for the determination of the direction of causality, with OR > 1 indicating that the exposure was a risk factor for the outcome. P-values < 0.05 indicated a statistically significant finding of causality. Cochran’s Q test was used to examine the heterogeneity among genetic IVs. The sensitivity analysis was mainly performed by the leave-one-out method. The MR-Egger intercept test was conducted to detect directional pleiotropy, with P-value < 0.05 demonstrating the presence of directional pleiotropy. Funnel plots were plotted to assess directional pleiotropy, similar as its role in meta-analysis to assess publication bias. The MR analysis is implemented in the R software (version 4.3.3) using the R packages “TwoSampleMR” and “MRPRESSO”. The flow schematic of this study was shown in Fig. 2 . MDS:myelodysplastic syndrome;LD: linkage disequilibrium;IVs: instrumental variants;WM: weighted Median; IVW: inverse variance weighted. 3 Results 3.1 IVs selection This study included 39 SNPs for T1D, 85 SNPs for RA, 40 SNPs for SLE, 49 SNPs for PsO, 48 SNPs for MS, 6 SNPs for MG, 38 SNPs for CeD,127 SNPs for IBD, 23 SNPs for GD and 10 SNPs for HT, as they were strongly and independently associated with the respective autoimmune diseases. The F-statistics of these SNPs ranged from 29 to 1487, which indicates that the bias caused by weak instruments is likely negligible.Following this, we harmonised data of autoimmune diseases with those of MDS. The data are presented in Supplementary Table S2 . 3.2 Effects of ten autoimmune diseases on MDS The results of IVW analysis show the relationship between an elevated risk of MDS and an increased risk of RA (OR = 1.186,95%CI = 1.028–1.367, P = 0.019), MS (OR = 1.247,95%CI = 1.013–1.534, P = 0.037),MG(OR = 1.326,95%CI = 1.010–1.742, P = 0.042), and HT (OR = 1.519,95%CI = 1.008–2.290, P = 0.046).T1D, SLE, and GD showed positive correlations with MDS, but none of them reached statistical significance.A negative correlation was observed between PsO,CeD,IBD and MDS, but it did not reach statistical significance.The Cochran’s Q test results suggested that heterogeneity was not observed in the MR study.The MR-Egger intercept test indicating that there was no horizontal pleiotropy. Theses results are displayed in Fig. 3 . For the relationship between RA,MS,MG,HT and MDS,the scatterplot (Fig. 4 ) showed an elevated risk of MDS in patients with autoimmune diseases. The forest plot showed the effect sizes and their 95% CIs for each of the independent SNPs in the exposure GWAS, as well as the overall causal estimates from the MR-Egger and IVW models. Sensitivity analyses were consistent with no evidence of bias due to genetic pleiotropy. Visual inspection of leave-one-out plots and funnel plots did not reveal any obvious directional pleiotropy( Supplemental Fig. 1,Fig. 2 ,Fig. 3 ). Due to the lack of effective SNPs for MDS in reverse MR studies, reverse MR analysis was not performed. 4 Discussion MDS is a heterogeneous hematological malignancy characterized by thrombocytopenia with a high risk of transformation to acute myeloid leukemia. Therapeutic measures for this disease include transfusion to stimulate hematopoiesis and iron chelation therapy, demethylation therapy, immunosuppressive therapy,and hematopoietic stem cell transplantation.Patients with MDS frequently present with immune abnormalities, which include asymptomatic isolated abnormalities of serum immunogenic parameters (e.g., antinuclear antibody positivity, rheumatoid factor positivity), nonspecific autoimmune inflammation (e.g., noninfectious fever, polyarthralgia), and autoimmune diseases (e.g., RA, hypothyroidism, vasculitis) [ 8 ] . Autoimmune diseases and MDS can occur sequentially or simultaneously. Abnormal inflammation in patients with autoimmune diseases can cause clonal evolution, disturbances in bone marrow growth, and lead to the development of myeloid tumors. In contrast, abnormal inflammation in the bone marrow microenvironment of patients with MDS can activate the adaptive immune response and trigger autoimmune disease. Most patients have autoimmune diseases before they are diagnosed with MDS [ 9 – 10 ] . Patients can have autoimmune disease even before they have progressed to a state of MDS. The prevalence of clonal hematopoiesis of undetermined significance (CHIP), a precursor disease to MDS, is higher in patients with autoimmune diseases.A German study recruited 200 patients with non-hematologic diseases and found a significant correlation between CHIP and autoimmune diseases [ 11 ] . The prevalence of clonal hematopoiesis in patients with RA and anti-neutrophil cytoplasmic antibody-associated vasculitis was 17% and 30.4%, respectively [ 12 – 13 ] . In addition, patients with ulcerative colitis have been found to have slightly higher levels of CHIP than the general population.The inflammatory environment of the intestinal tract in patients promotes CHIP with a unique mutational profile, particularly clones with DNMT3A and PPM1D mutations [ 14 ] . Patients with autoimmune diseases combined with MDS may be associated with shared genetic susceptibility, chronic immune stimulation and immune dysregulation,and immunosuppressive drug use [ 15 ] . Autoimmune diseases and hematologic malignancies share common signaling pathway abnormalities, such as the tumor suppressor gene TP53 and the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin cellular pathway signaling. The role of inflammation in cancer development and progression is complex. The family of transcription factors NF-κB plays a crucial role in inflammation and innate immunity.Constitutive activation of NF-κB in patients with chronic inflammatory diseases promotes tumor development [ 16 ] . In addition, neutrophils release reactive oxygen species during inflammation to kill pathogens.This innate immune response may lead to DNA damage and produce genetic mutations that can trigger tumorigenesis [ 17 ] . The role of the immune system in MDS has been highly investigated. In low-risk MDS, dysfunctional T-cell responses and innate immune activation induce apoptosis of hematopoietic precursor cells [ 18 ] . The age-dependent accumulation of somatic mutations is thought to underlie the age-related nature of MDS [ 19 ] .The increase in systemic inflammation associated with aging has been termed " inflammageing". Elevated levels of the pro-inflammatory cytokines TNFα, IL-1β, and IL-6 activate chronic inflammation and cause damage to hematopoietic stem cells [ 20 – 21 ] . Patients with autoimmune diseases combined with MDS are more common in young women [ 9 , 22 ] . Most studies have shown no difference in the distribution of MDS histologic subtypes between patients with and without autoimmune disease. However, some studies suggest that different MDS subtypes may have different etiologies.Patients with autoimmune diseases may be predisposed to develop specific subtypes of MDS. The main MDS subtypes associated with autoimmune diseases are MDS with multilineage dysplasia (MDS-MLD), MDS with single-lineage dysplasia (MDS-SLD), and MDS with excess blasts (MDS-EB) [ 9 , 23 – 24 ] . In addition, MDS with trisomy 8 is often associated with behcet’s disease. Connective tissue disease is more common in patients with low-risk MDS [ 9 , 25 ] . There is controversy about the impact of autoimmune diseases on the survival prognosis of MDS patients, which may be related to the specific type of autoimmune disease. Three multicenter case-control studies in France showed that MDS patients with a combination of systemic inflammatory and autoimmune diseases, giant cell arteritis, and vasculitis did not have a significant difference in overall survival (OS) compared with MDS patients without immune abnormalities.But patients with MDS-associated vasculitis had a lower rate of progression to acute leukemia compared with control subjects [ 9 , 26 – 27 ] . A Spanish study found shorter survival in this subset of patients with MDS combined with autoimmune disease, especially in low-risk MDS patients [ 10 ] . This can be explained by the fact that patients with MDS combined with autoimmune diseases have more severe immune dysregulation, which leads to more severe bone marrow failure and disease progression [ 28 ] .Conversely, it has been shown that MDS patients combined with autoimmune disease appears to have a better survival benefit. Recently,a US study retrospectively analyzed 15,277 patients with MDS from the SEER Medicare database, of whom 2,442 (16%) had pre-existing autoimmune disease, which reduced the risk of death by 11%.In low-risk MDS, the presence of pre-existing autoimmune disease was associated with an increased risk of leukemic transformation [ 29 ] . Another case-control study showed that this group of patients with MDS combined with autoimmune disease had better OS and lower leukemic transformation rates [ 4 ] . It has been hypothesized that the reason for this may be that autoimmune-driven tumor clones have unique mutational or cytogenetic risk profiles and are less aggressive. Observational studies and RCTs have limitations.They require the collection of a certain number of cases and long-term follow-up.MR studies can elucidate causality through the genetic variant susceptibility loci of the disease. This is the first two-sample MR study on the causal effect of autoimmune diseases on MDS. The MR results showed that patients with RA, MS, MG, and HT had an elevated risk of developing MDS of 18.6%, 24.7%, 32.6%, and 51.9%, respectively. Our study did not find a causal relationship between other types of autoimmune diseases and MDS. The present study also has some limitations. First, the population participating in this study was predominantly European.So we should be cautious in applying this finding to other populations. Secondly,despite the fact that the genetic tools we chose were strongly predictive of exposure,it is still possible that the hypotheses about correlation and exclusion limits may not have been fully validated. It has also been reported that long term use of Immunosuppressive and biological agents also increases the risk of MDS.Due to the lack of GWAS associated with specific treatments of autoimmune diseases, it is not possible to validate a causal relationship through MR studies.Relevant studies can be carried out in the future. 5 Conclusions In conclusion, our MR study provides evidential support for a possible causal relationship between autoimmune diseases and the development of MDS from a genetic perspective.The mechanism of this causal relationship needs to be further investigated.Further analyses in the patients would be more beneficial in providing evidential support. This reminds physicians of the need to focus on follow-up of patients with autoimmune diseases in order to screen for MDS. Declarations Supplementary information is available for this article. Acknowledgments The referenced studies or consortiums are gratefully acknowledged by the authors for contributing open-access datasets for the analysis. Author contributions Zhengyang Miao,Wenwei Zhu,Yongming Zhou,and Hailin Chen performed the literature review, drafted and revised the manuscript; Zhengyang Miao,Hailin Chen and Yongming Zhou contributed to the critical revision of the manuscript;Zhengyang Miao,Wenwei Zhu,and Hailin Chen analyzed data. All authors read and approved the final manuscript. Funding This study was funded by National Natural Science Foundation of China(82205003),National Administration of Traditional Chinese Medicine National Studio for Heritage of Famous Traditional Chinese Medicine Experts under grant number [2022]75, Shanghai Clinical Key Specialty Construction Project under Grant number SHSLCZDZK 05201,State Administration of Traditional Chinese Medicine High-level Chinese Medicine Key Discipline Construction Project (zyyzdxk-202365),Shanghai University of Traditional Chinese Medicine Summit Plateau Team Project (30304114341),Shanghai Famous Elderly Chinese Medicine Doctor Academic Experience Research Studio Construction Project (SHGZS-2017019). Data availability All data generated or analyzed during this study are included in this published article [and its supplementary information files] Conflict of interest The authors declare that there is no conflict of interests regarding the publication of this paper. 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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-4504312","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312224363,"identity":"59ad7448-3899-4380-aeb0-c66dd43efdd7","order_by":0,"name":"Zhengyang Miao","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhengyang","middleName":"","lastName":"Miao","suffix":""},{"id":312224365,"identity":"24ceb8b4-7645-41cc-b027-d53acc40d8af","order_by":1,"name":"Wenwei Zhu","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenwei","middleName":"","lastName":"Zhu","suffix":""},{"id":312224367,"identity":"32fc2a40-155f-42a5-8c80-cff759c5cf7c","order_by":2,"name":"Yongming Zhou","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongming","middleName":"","lastName":"Zhou","suffix":""},{"id":312224370,"identity":"ac8fbc30-c22f-4b00-a14f-b1a84ec2bc36","order_by":3,"name":"Hailin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACCQYGZiApwwbh2vDw8zcQp4UHqiVNRnLGAaK0MPBAuYdtDBoS8Ovgn9187HFBjQUPn3SPmTRv23keA4YDjB8+5uCx5M6xdOMZx4AOkzkD0nKbx5y5gVly5jbcWgwkcsykediAWkAMkBbLhgNszLx4teR/k+b5B9dyjsfgQAIhLTlsQJVwLQcIa5G4kQZU2QfSklZsOedcMo/kjIPNeP3CPyP5mTTPtzo5+RnJG2+8KbOz5+dvPvjhIx4tSIDDRIoXHKGMDUSpBwL2xx9//CFW8SgYBaNgFIwkAABpuETQ3E5ruQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hailin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-30 16:27:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4504312/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4504312/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58648483,"identity":"4aad9742-e2c8-4399-8e1f-ac37acdeea42","added_by":"auto","created_at":"2024-06-19 09:29:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80704,"visible":true,"origin":"","legend":"\u003cp\u003eThree core assumptions of Mendelian randomization (MR) analysis.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/87ca5ba5bb0b3ad3b392f950.png"},{"id":58648484,"identity":"45f93366-1df8-4bc4-aeac-a3e8600d1647","added_by":"auto","created_at":"2024-06-19 09:29:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241621,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of the Mendelian randomization (MR) study.\u003c/p\u003e\n\u003cp\u003eMDS:myelodysplastic syndrome;LD: linkage disequilibrium;IVs: instrumental variants;WM: weighted Median; IVW: inverse variance weighted.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/51113d53517b37f311b304d7.png"},{"id":58648485,"identity":"93babe82-a16e-4f45-980a-f4517337fa39","added_by":"auto","created_at":"2024-06-19 09:29:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431142,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plots revealed the causal association of autoimmune diseases with myelodysplastic syndrome(MDS).\u003c/p\u003e\n\u003cp\u003eSNP:single-nucleotide polymorphism;OR: odds ratio; 95%CI: 95% confidence interval of OR;T1D:type 1 diabetes;RA:rheumatoid arthritis;SLE: systemic lupus erythematosus;PsO: psoriasis;MS:multiple sclerosis;MG:myasthenia gravis;CeD:celiac disease;IBD:inflammatory bowel disease;GD:graves' disease;HT:hashimoto thyroiditis.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/5f01f32d543989f97604f5ce.png"},{"id":58649275,"identity":"731bd6e1-4cbb-4ba2-8358-b77d04de9d4f","added_by":"auto","created_at":"2024-06-19 09:37:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":990548,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eThe scatter plot from genetically predicted rheumatoid arthritis(RA) on myelodysplastic syndrome(MDS). \u003cstrong\u003e(b)\u003c/strong\u003e The scatter plot from genetically predicted multiple sclerosis(MS) on MDS. \u003cstrong\u003e(c)\u003c/strong\u003e The scatter plot from genetically predicted myasthenia gravis(MG) on MDS. \u003cstrong\u003e(d)\u003c/strong\u003eThe scatter plot from genetically predicted hashimoto thyroiditis(HT) on MDS.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/aca644f9f908a7b4f6238c53.png"},{"id":67578081,"identity":"050736b9-72f0-4aab-8bf2-914d16b7f8f6","added_by":"auto","created_at":"2024-10-27 13:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1893347,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/43582ec9-c520-4d78-b535-760cd5f2e92b.pdf"},{"id":58648490,"identity":"93c3d99e-3deb-4b11-a6f6-917c566157fe","added_by":"auto","created_at":"2024-06-19 09:29:11","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":237078092,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/1c7d6bc7b5d00df65731784a.tif"},{"id":58648488,"identity":"5cc4d1cd-2033-4a83-bb11-40e25fbee449","added_by":"auto","created_at":"2024-06-19 09:29:10","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":190108168,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/2538777d8973076363a074a4.tif"},{"id":58649277,"identity":"472f0fea-857a-4195-9d27-3d1ad61b933d","added_by":"auto","created_at":"2024-06-19 09:37:09","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":203927164,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/44c93d919e753272ba9e154c.tif"},{"id":58648487,"identity":"6219fb56-72d8-4787-9eb4-b0a758b57585","added_by":"auto","created_at":"2024-06-19 09:29:05","extension":"xls","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":154112,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable.xls","url":"https://assets-eu.researchsquare.com/files/rs-4504312/v1/53641d4e3df0fbe5f64f804f.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"The causal effect of autoimmune diseases on myelodysplastic syndrome:a Mendelian randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAutoimmune diseases are pathologic disorders caused by dysfunctions of the immune system that affect 3\u0026ndash;5% of the population. Patients with autoimmune diseases produce high-affinity autoantibodies that target \"self\" molecules anywhere in the body. To date, nearly 100 different autoimmune diseases have been identified\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Autoimmune diseases are more common in women than in men.The age of onset varies from one autoimmune disease to another. Autoimmune diseases are associated with an increased risk of lymphoproliferative disorders and myeloid malignancies. Myelodysplastic syndrome (MDS) are a heterogeneous group of disorders characterized by abnormal development of myeloid hematopoietic stem progenitor cells and clonal hematopoiesis, which associated with cytogenetic abnormalities and genetic abnormalities.MDS is prevalent in older males, with a median age of onset of approximately 70 years. Epidemiologic studies have shown that 10\u0026ndash;30% of patients with MDS have co-morbid autoimmune disorders.Immune dysregulation is a common pathogenic driver of both diseases\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudy shows increased risk of myeloid tumors in patients with autoimmune diseases compared to patients without autoimmune diseases. A population-based case-control study in Sweden analyzed patients with various autoimmune diseases individually and found that some diseases were associated with particularly high ORs for MDS, such as 7.9 for myasthenia gravis (MG), 3.9 for systemic lupus erythematosus (SLE), and 1.7 for rheumatoid arthritis (RA)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.Another study included 1,408 patients with MDS, of whom 391 ( 28%) had comorbid autoimmune diseases, including 171 patients with hypothyroidism, 41 with RA, 28 with psoriasis (PsO).And 63 patients have more than one autoimmune disease\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRandomized controlled trials(RCTs) are the gold standard of clinical randomized evidence,but in reality there are still limitations,such as difficulties in implementation and ethical restrictions.Mendelian randomization(MR) studies make up for its shortcomings by allowing the use of single nucleotide polymorphisms (SNPs) as a surrogate for exposure in order to assess the causality between exposure and the outcome of interest,independent of confounding factors.MR studies are uninterrupted by ethical issues and difficulties in the collection of cases, and can be implemented directly. There have been no MR studies about the causal relationship between autoimmune diseases and MDS. The aim of this study was to investigate the incidental effect of autoimmune diseases on the risk of developing MDS through Mendelian randomization (MR) analysis of pooled statistics from genome-wide association study (GWAS) datasets,in order to identify specific risk groups for early intervention and prevention.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data sources for autoimmune diseases and MDS GWAS\u003c/h2\u003e\n \u003cp\u003eThe data of autoimmune diseases were sourced from the OPEN GWAS website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003c/span\u003e). The GWAS databases for MDS were available from the FinnGen website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003c/span\u003e). Autoimmune disorders in this study include type 1 diabetes (T1D), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriasis (PsO), multiple sclerosis (MS), myasthenia gravis(MG),celiac disease (CeD), inflammatory bowel disease (IBD), graves\u0026apos; disease (GD),and hashimoto thyroiditis(HT). More detailed information for the cohorts of cases and controls can be found in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Genetic instrumental variant selection for autoimmune diseases\u003c/h2\u003eMR study designs have to satisfy three core assumptions: 1) there is a strong association between instrumental variants (IVs) and exposure factors; 2) the IVs are independent of confounding factors of the exposure\u0026ndash;outcome relationship; and 3) genetic variants can only affect the outcome through the exposure and not through other pathways. The above three assumptions were visualized in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. To fulfill the mentioned core assumptions, first, the IVs with genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were extracted from the exposure GWAS.The linkage disequilibrium (LD) of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001 and clumping distance\u0026thinsp;=\u0026thinsp;10,000.Then,palindromic SNPs were excluded when harmonizing the GWAS data.Next, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test was employed to discern potential outlier SNPs for correcting potential horizontal pleiotropy\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Finally, the remaining SNPs were utilized for MR analysis. And the F-statistic was calculated for each leftover SNP to measure the strength of genetic IVs. IVs with F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 indicate not a weak genetic instrument.\u003cbr\u003eThree fundamental principles of MR analysis are as follows:1) Correlation assumption: there is a strong association between instrumental variants (IVs) and exposure factors; 2) Independence assumption: the IVs are independent of confounding factors of the exposure\u0026ndash;outcome relationship; and 3) Exclusivity assumption: genetic variants can only affect the outcome through the exposure and not through other pathways.\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 MR analysis\u003c/h2\u003eThree MR methods including MR-Egger, inverse variance-weighted (IVW), and weighted median (WM) were applied for the estimation of the causality of autoimmune diseases and MDS. The IVW model assumes that all enrolled SNPs are valid genetic IVs and no pleiotropic effects exist\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. If the causal effect estimates of the three models are inconsistent, the results of IVW methods are considered as the main outcome. The odds ratio (OR) was treated as the effect size for the determination of the direction of causality, with OR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicating that the exposure was a risk factor for the outcome. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a statistically significant finding of causality. Cochran\u0026rsquo;s Q test was used to examine the heterogeneity among genetic IVs. The sensitivity analysis was mainly performed by the leave-one-out method. The MR-Egger intercept test was conducted to detect directional pleiotropy, with P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 demonstrating the presence of directional pleiotropy. Funnel plots were plotted to assess directional pleiotropy, similar as its role in meta-analysis to assess publication bias. The MR analysis is implemented in the R software (version 4.3.3) using the R packages \u0026ldquo;TwoSampleMR\u0026rdquo; and \u0026ldquo;MRPRESSO\u0026rdquo;. The flow schematic of this study was shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cbr\u003eMDS:myelodysplastic syndrome;LD: linkage disequilibrium;IVs: instrumental variants;WM: weighted Median; IVW: inverse variance weighted.\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 IVs selection\u003c/h2\u003e\n \u003cp\u003eThis study included 39 SNPs for T1D, 85 SNPs for RA, 40 SNPs for SLE, 49 SNPs for PsO, 48 SNPs for MS, 6 SNPs for MG, 38 SNPs for CeD,127 SNPs for IBD, 23 SNPs for GD and 10 SNPs for HT, as they were strongly and independently associated with the respective autoimmune diseases. The F-statistics of these SNPs ranged from 29 to 1487, which indicates that the bias caused by weak instruments is likely negligible.Following this, we harmonised data of autoimmune diseases with those of MDS. The data are presented in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Effects of ten autoimmune diseases on MDS\u003c/h2\u003e\n \u003cp\u003eThe results of IVW analysis show the relationship between an elevated risk of MDS and an increased risk of RA (OR\u0026thinsp;=\u0026thinsp;1.186,95%CI\u0026thinsp;=\u0026thinsp;1.028\u0026ndash;1.367, P\u0026thinsp;=\u0026thinsp;0.019), MS (OR\u0026thinsp;=\u0026thinsp;1.247,95%CI\u0026thinsp;=\u0026thinsp;1.013\u0026ndash;1.534, P\u0026thinsp;=\u0026thinsp;0.037),MG(OR\u0026thinsp;=\u0026thinsp;1.326,95%CI\u0026thinsp;=\u0026thinsp;1.010\u0026ndash;1.742, P\u0026thinsp;=\u0026thinsp;0.042), and HT (OR\u0026thinsp;=\u0026thinsp;1.519,95%CI\u0026thinsp;=\u0026thinsp;1.008\u0026ndash;2.290, P\u0026thinsp;=\u0026thinsp;0.046).T1D, SLE, and GD showed positive correlations with MDS, but none of them reached statistical significance.A negative correlation was observed between PsO,CeD,IBD and MDS, but it did not reach statistical significance.The Cochran\u0026rsquo;s Q test results suggested that heterogeneity was not observed in the MR study.The MR-Egger intercept test indicating that there was no horizontal pleiotropy. Theses results are displayed in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFor the relationship between RA,MS,MG,HT and MDS,the scatterplot (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) showed an elevated risk of MDS in patients with autoimmune diseases. The forest plot showed the effect sizes and their 95% CIs for each of the independent SNPs in the exposure GWAS, as well as the overall causal estimates from the MR-Egger and IVW models. Sensitivity analyses were consistent with no evidence of bias due to genetic pleiotropy. Visual inspection of leave-one-out plots and funnel plots did not reveal any obvious directional pleiotropy( Supplemental Fig. 1,Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e,Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Due to the lack of effective SNPs for MDS in reverse MR studies, reverse MR analysis was not performed.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eMDS is a heterogeneous hematological malignancy characterized by thrombocytopenia with a high risk of transformation to acute myeloid leukemia. Therapeutic measures for this disease include transfusion to stimulate hematopoiesis and iron chelation therapy, demethylation therapy, immunosuppressive therapy,and hematopoietic stem cell transplantation.Patients with MDS frequently present with immune abnormalities, which include asymptomatic isolated abnormalities of serum immunogenic parameters (e.g., antinuclear antibody positivity, rheumatoid factor positivity), nonspecific autoimmune inflammation (e.g., noninfectious fever, polyarthralgia), and autoimmune diseases (e.g., RA, hypothyroidism, vasculitis)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAutoimmune diseases and MDS can occur sequentially or simultaneously. Abnormal inflammation in patients with autoimmune diseases can cause clonal evolution, disturbances in bone marrow growth, and lead to the development of myeloid tumors. In contrast, abnormal inflammation in the bone marrow microenvironment of patients with MDS can activate the adaptive immune response and trigger autoimmune disease. Most patients have autoimmune diseases before they are diagnosed with MDS\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Patients can have autoimmune disease even before they have progressed to a state of MDS. The prevalence of clonal hematopoiesis of undetermined significance (CHIP), a precursor disease to MDS, is higher in patients with autoimmune diseases.A German study recruited 200 patients with non-hematologic diseases and found a significant correlation between CHIP and autoimmune diseases\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The prevalence of clonal hematopoiesis in patients with RA and anti-neutrophil cytoplasmic antibody-associated vasculitis was 17% and 30.4%, respectively\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In addition, patients with ulcerative colitis have been found to have slightly higher levels of CHIP than the general population.The inflammatory environment of the intestinal tract in patients promotes CHIP with a unique mutational profile, particularly clones with DNMT3A and PPM1D mutations\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with autoimmune diseases combined with MDS may be associated with shared genetic susceptibility, chronic immune stimulation and immune dysregulation,and immunosuppressive drug use\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Autoimmune diseases and hematologic malignancies share common signaling pathway abnormalities, such as the tumor suppressor gene TP53 and the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin cellular pathway signaling. The role of inflammation in cancer development and progression is complex. The family of transcription factors NF-κB plays a crucial role in inflammation and innate immunity.Constitutive activation of NF-κB in patients with chronic inflammatory diseases promotes tumor development\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In addition, neutrophils release reactive oxygen species during inflammation to kill pathogens.This innate immune response may lead to DNA damage and produce genetic mutations that can trigger tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The role of the immune system in MDS has been highly investigated. In low-risk MDS, dysfunctional T-cell responses and innate immune activation induce apoptosis of hematopoietic precursor cells\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The age-dependent accumulation of somatic mutations is thought to underlie the age-related nature of MDS\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.The increase in systemic inflammation associated with aging has been termed \" inflammageing\". Elevated levels of the pro-inflammatory cytokines TNFα, IL-1β, and IL-6 activate chronic inflammation and cause damage to hematopoietic stem cells\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with autoimmune diseases combined with MDS are more common in young women\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Most studies have shown no difference in the distribution of MDS histologic subtypes between patients with and without autoimmune disease. However, some studies suggest that different MDS subtypes may have different etiologies.Patients with autoimmune diseases may be predisposed to develop specific subtypes of MDS. The main MDS subtypes associated with autoimmune diseases are MDS with multilineage dysplasia (MDS-MLD), MDS with single-lineage dysplasia (MDS-SLD), and MDS with excess blasts (MDS-EB)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In addition, MDS with trisomy 8 is often associated with behcet\u0026rsquo;s disease. Connective tissue disease is more common in patients with low-risk MDS\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere is controversy about the impact of autoimmune diseases on the survival prognosis of MDS patients, which may be related to the specific type of autoimmune disease. Three multicenter case-control studies in France showed that MDS patients with a combination of systemic inflammatory and autoimmune diseases, giant cell arteritis, and vasculitis did not have a significant difference in overall survival (OS) compared with MDS patients without immune abnormalities.But patients with MDS-associated vasculitis had a lower rate of progression to acute leukemia compared with control subjects\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. A Spanish study found shorter survival in this subset of patients with MDS combined with autoimmune disease, especially in low-risk MDS patients\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. This can be explained by the fact that patients with MDS combined with autoimmune diseases have more severe immune dysregulation, which leads to more severe bone marrow failure and disease progression\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.Conversely, it has been shown that MDS patients combined with autoimmune disease appears to have a better survival benefit. Recently,a US study retrospectively analyzed 15,277 patients with MDS from the SEER Medicare database, of whom 2,442 (16%) had pre-existing autoimmune disease, which reduced the risk of death by 11%.In low-risk MDS, the presence of pre-existing autoimmune disease was associated with an increased risk of leukemic transformation\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Another case-control study showed that this group of patients with MDS combined with autoimmune disease had better OS and lower leukemic transformation rates\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. It has been hypothesized that the reason for this may be that autoimmune-driven tumor clones have unique mutational or cytogenetic risk profiles and are less aggressive.\u003c/p\u003e \u003cp\u003eObservational studies and RCTs have limitations.They require the collection of a certain number of cases and long-term follow-up.MR studies can elucidate causality through the genetic variant susceptibility loci of the disease. This is the first two-sample MR study on the causal effect of autoimmune diseases on MDS. The MR results showed that patients with RA, MS, MG, and HT had an elevated risk of developing MDS of 18.6%, 24.7%, 32.6%, and 51.9%, respectively. Our study did not find a causal relationship between other types of autoimmune diseases and MDS. The present study also has some limitations. First, the population participating in this study was predominantly European.So we should be cautious in applying this finding to other populations. Secondly,despite the fact that the genetic tools we chose were strongly predictive of exposure,it is still possible that the hypotheses about correlation and exclusion limits may not have been fully validated. It has also been reported that long term use of Immunosuppressive and biological agents also increases the risk of MDS.Due to the lack of GWAS associated with specific treatments of autoimmune diseases, it is not possible to validate a causal relationship through MR studies.Relevant studies can be carried out in the future.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn conclusion, our MR study provides evidential support for a possible causal relationship between autoimmune diseases and the development of MDS from a genetic perspective.The mechanism of this causal relationship needs to be further investigated.Further analyses in the patients would be more beneficial in providing evidential support. This reminds physicians of the need to focus on follow-up of patients with autoimmune diseases in order to screen for MDS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e is available for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe referenced studies or consortiums are gratefully acknowledged by the authors for contributing open-access datasets for the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhengyang Miao,Wenwei Zhu,Yongming Zhou,and\u0026nbsp;Hailin Chen\u0026nbsp;performed the literature review, drafted and revised the manuscript;\u0026nbsp;Zhengyang Miao,Hailin Chen\u0026nbsp;and\u0026nbsp;Yongming Zhou\u0026nbsp;contributed to the critical revision of the manuscript;Zhengyang Miao,Wenwei Zhu,and\u0026nbsp;Hailin Chen\u0026nbsp;analyzed data. All authors 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 funded by\u0026nbsp;National Natural Science Foundation of China(82205003),National Administration of Traditional Chinese Medicine National Studio for Heritage of Famous Traditional Chinese Medicine Experts under grant number [2022]75, Shanghai Clinical Key Specialty Construction Project under Grant number SHSLCZDZK 05201,State Administration of Traditional Chinese Medicine High-level Chinese Medicine Key Discipline Construction Project (zyyzdxk-202365),Shanghai University of Traditional Chinese Medicine Summit Plateau Team Project (30304114341),Shanghai Famous Elderly Chinese Medicine Doctor Academic Experience Research Studio Construction Project (SHGZS-2017019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interests regarding the publication of this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach study included was approved by their institutional ethics review committee, and all participants provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to reproduce material from other sources\u003c/strong\u003e N/A.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration (including trial number)\u003c/strong\u003e N/A.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang L, Wang FS, Gershwin ME. 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The Journal of experimental medicine, 218(7), e20201544. https://doi.org/10.1084/jem.20201544\u003c/li\u003e\n\u003cli\u003eGiannouli S, Voulgarelis M, Zintzaras E, Tzioufas AG, Moutsopoulos HM.(2004). Autoimmune phenomena in myelodysplastic syndromes: a 4-yr prospective study. Rheumatology (Oxford, England), 43(5), 626\u0026ndash;632. https://doi.org/10.1093/rheumatology/keh136\u003c/li\u003e\n\u003cli\u003eKim YE, Ahn SM, Oh JS, et al.(2024). Incidence of and risk factors for myelodysplastic syndrome in patients with rheumatologic diseases. Rheumatology (Oxford, England), 63(5), 1305\u0026ndash;1312. https://doi.org/10.1093/rheumatology/kead374\u003c/li\u003e\n\u003cli\u003eGrignano E, Jachiet V, Fenaux P, Ades L, Fain O, Mekinian A. (2018). Autoimmune manifestations associated with myelodysplastic syndromes. Annals of hematology, 97(11), 2015\u0026ndash;2023. https://doi.org/10.1007/s00277-018-3472-9\u003c/li\u003e\n\u003cli\u003eToyonaga T, Nakase H, Matsuura M, et al.(2013). 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Blood advances, 7(22), 6913\u0026ndash;6922. https://doi.org/10.1182/bloodadvances.2023011050\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":"Mendelian randomization (MR), autoimmune diseases(ADs), genome-wide association studies (GWAS), myelodysplastic syndrome (MDS), single-nucleotide polymorphisms (SNPs)","lastPublishedDoi":"10.21203/rs.3.rs-4504312/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4504312/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between different types of autoimmune diseases and myelodysplastic syndrome (MDS) are inconclusive. Therefore,we employed Mendelian randomization (MR) to explore the causal associations between autoimmune diseases and MDS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSingle nucleotide polymorphisms (SNPs) significantly associated with 10 autoimmune diseases were extracted from the summary statistics of European genome-wide association studies (GWAS). A two-sample MR analysis was performed using summary-level statistics sourced from GWAS datasets. Inverse-variance weighting (IVW),MR‒Egger,and weighted median (WM) were further supported by several sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour autoimmune diseases showed genetical predisposition to MDS: rheumatoid arthritis(OR\u0026thinsp;=\u0026thinsp;1.186,95%CI\u0026thinsp;=\u0026thinsp;1.028\u0026ndash;1.367, P\u0026thinsp;=\u0026thinsp;0.019), multiple sclerosis(OR\u0026thinsp;=\u0026thinsp;1.247,95%CI\u0026thinsp;=\u0026thinsp;1.013\u0026ndash;1.534, P\u0026thinsp;=\u0026thinsp;0.037), myasthenia gravis(OR\u0026thinsp;=\u0026thinsp;1.326,95%CI\u0026thinsp;=\u0026thinsp;1.010\u0026ndash;1.742, P\u0026thinsp;=\u0026thinsp;0.042), and hashimoto thyroiditis(OR\u0026thinsp;=\u0026thinsp;1.519,95%CI\u0026thinsp;=\u0026thinsp;1.008\u0026ndash;2.290, P\u0026thinsp;=\u0026thinsp;0.046).Nevertheless,no similar causal relationship was found between the remaining seven autoimmune diseases and MDS.The accuracy and robustness of these findings were confirmed by sensitivity tests.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe are the first to use MR analysis to explore the causal relationships between autoimmune diseases and MDS.The mechanism of this causal link needs to be further explored.\u003c/p\u003e","manuscriptTitle":"The causal effect of autoimmune diseases on myelodysplastic syndrome:a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 09:28:59","doi":"10.21203/rs.3.rs-4504312/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":"4de37693-c2ce-4a77-abc7-b75f4a24f587","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-27T13:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-19 09:28:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4504312","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4504312","identity":"rs-4504312","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}