Genetic Associations Between Autoimmune Diseases and Anemia: A Mendelian Randomization Analysis to Inform Clinical Practice.

Genetic Associations Between Autoimmune Diseases and Anemia: A Mendelian Randomization Analysis to Inform Clinical Practice. | 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 Genetic Associations Between Autoimmune Diseases and Anemia: A Mendelian Randomization Analysis to Inform Clinical Practice. yifen shi, xin zhuang, peng chen, rong yang, xiaoying man, ruochen wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3484652/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: Autoimmune diseases and anemia are clinically distinct yet recent studies suggest a potential association. The causal link is unclear, prompting this study's utilization of univariate and multivariate Mendelian randomization analyses to probe a possible causal connection. Method: A thorough literature review and analysis of summary statistics from genome-wide association studies (GWAS) data, sourced from public databases, were conducted. Ten autoimmune diseases and anemia were selected for scrutiny. Single Nucleotide Polymorphisms (SNPs) significantly associated with these diseases were identified, serving as instrumental variables with anemia as the outcome variable. Both univariable and multivariable Mendelian randomization analyses were performed to assess the causal link. Results: Ten autoimmune diseases were analyzed concerning their relationship with anemia. Univariate analysis revealed that Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis genetically contribute to anemia risk. Multivariate analysis sustained a significant association between the genetic predisposition toward Type 1 Diabetes, Multiple Sclerosis and anemia risk. Conclusion: This study supports the notion that autoimmune diseases negatively influence anemia risk, suggesting that targeting autoimmune diseases may be key to mitigating anemia risk. The relationship between autoimmune diseases and anemia warrants further investigation for potential preventive and treatment strategies. Biological sciences/Immunology/Immunological disorders/Autoimmune diseases Biological sciences/Immunology/Autoimmunity autoimmune diseases anemia causal relationship multivariable Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Autoimmune Diseases (ADs) epitomize a class of ailments engendered by immune system dysregulation, culminating in the body mounting aggressive responses against its own tissues. Depending upon clinical manifestations and the affected organ systems 1 2 , autoimmune diseases bifurcate into systemic and organ-specific types. Systemic autoimmune diseases, such as Systemic Lupus Erythematosus (SLE), Rheumatoid Arthritis (RA), and Multiple Sclerosis, have the propensity to affect multiple organ systems; conversely, organ-specific autoimmune diseases like Type 1 Diabetes 3 and Thyroiditis predominantly afflict particular organs or tissues 4 5 . The pathogenesis of autoimmune diseases is intricate, encompassing myriad factors like genetics, environment, and immune regulation 6 7 . Presently, despite the clinical application of various immunosuppressive and immunoregulatory therapies, the enhancement of their efficacy remains imperative, and their prolonged usage might precipitate severe side effects 8 9 10 . Anemia, a prevalent hematologic disorder, denotes a pathological condition characterized by a diminished quantity of red blood cells or a reduction in the hemoglobin content within the blood, culminating in a compromised blood oxygen-carrying capacity 11 12 . Data from the World Health Organization (WHO) delineates that over 1.5 billion individuals globally are afflicted by anemia, underscoring the substantial public health concern it poses worldwide. Etiologically, anemia can be stratified into diverse types such as iron-deficiency anemia, hemolytic anemia 13 , and aplastic anemia 14 15 . The principal risk factors for anemia encompass malnutrition, iron deficiency, chronic ailments, parasitic infections, and genetic determinants. The therapeutic approach to anemia predominantly hinges on its etiology. Within the scope of autoimmune diseases, anemia's manifestation may be allied with immune-mediated destruction of red blood cells and impaired bone marrow hematopoietic functionality 16 . The interplay between autoimmune diseases and anemia predominantly manifests as a potential reciprocal augmentation in the incidence and progression of both disease categories. At present, the impact of anemia on specific types of autoimmune diseases remains ambiguous, including whether a closer association with particular autoimmune diseases exists; the evidence to elucidate the causality of this relationship is insufficient. Mendelian Randomization (MR) analysis is a potent instrument in genetic epidemiology, extensively employed to explore causal associations between diseases 17 18 . It leverages naturally occurring genetic variations as instrumental variables, furnishing robust statistical evidence for potential causal relationships between diseases 18 19 20 . MR analysis effectively mitigates bias risks arising from unmeasured confounders, reverse causality, and measurement errors 21 22 , thereby presenting a viable alternative to randomized controlled trials 23 . For a valid MR analysis, three critical assumptions are requisite: (1) a strong association between the genetic variation and the exposure; (2) the absence of influence on the genetic variation by any other potential confounders; and (3) the impact of the genetic instrumental variable on the outcome solely through the exposure 24 25 26 . The latter two assumptions are collectively denoted as the independence of horizontal pleiotropy and can be empirically validated 27 . Multivariable Mendelian Randomization (MVMR) analysis broadens MR analysis's scope, facilitating the examination of relationships between multiple exposures and diseases within a consolidated analytical framework. This study leveraged Genome-Wide Association Study (GWAS) summary statistics to execute both two-sample Mendelian Randomization (MR) and multivariable MR analyses, investigating the causal link between 10 autoimmune diseases and anemia risk. Through a systematic review of extensive literature and the analysis of publicly available large-scale genomic data, we aspire to unveil novel insights into the interlinking pathophysiological mechanisms between autoimmune diseases and anemia, thereby providing a genetic foundation for forthcoming clinical interventions and treatment strategies. 2. Research Design and Data Sources 2.1 Ethical Approval While this study does not encompass human or animal subjects, we uphold a commitment to safeguarding the rights and confidentiality pertaining to historical literature. All documents and data will be securely housed, with no disclosure of sensitive information concerning individuals or institutions. Transparency will be maintained throughout the research process, encompassing methodologies, data processing, and result analysis. All research findings will be disseminated in the public domain to foster the propagation of scientific knowledge. Rigorous measures will be employed to manage and safeguard research data, ensuring its confidentiality and integrity. Data will reside in a secure environment, accessible solely to members of the research team. Given that this study entails the review and analysis of historical literature without involving living individuals, informed consent is not necessitated. 2.2 Research Design and Data Sources In this study, ten autoimmune diseases were considered as exposure variables, namely, Systemic Lupus Erythematosus 28 , Inflammatory Bowel Disease 29 , Celiac Disease, Sjögren's Syndrome, Systemic Sclerosis, Ankylosing Spondylitis, Hyperthyroidism 28 , Type 1 Diabetes 30 , Multiple Sclerosis 31 , and Rheumatoid Arthritis 32 . Extensive literature and the FinnGen database were scrutinized to ascertain genetic variations associated with these autoimmune diseases within European populations. The count of Single Nucleotide Polymorphisms (SNPs) in the GWAS data for each autoimmune disease exhibited significant variation; thus, for precise analysis, genetic variations associated with Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis, meeting the genome-wide significance levels (p<5×10^−8) and not in linkage disequilibrium (LD r^2 < 0.1, kb = 10,000), were extracted. To mitigate bias from weak instrumental variables, the proportion of phenotypic variance explained by each instrumental variable was computed using R^2:R^2= [2×EAF×(1-EAF)×(β)^2]/[(2×EAF×(1-EAF)×(β)^2)+(2×EAF×(1-EAF)×N×se(β)^2)], where EAF denotes the effect allele frequency, β represents the effect size, N is the sample size, and se(β) is the standard error of the genetic effect. Subsequently, the F-statistic was calculated as F=[R^2×(N-k-1)]/[(1-R^2)×k], to evaluate the robustness of the statistic, with k being the number of instrumental variables 26 33 34 . SNPs with F<10 were excluded, being categorized as weak instrumental variables 20 . Data concerning anemia were acquired from both literature and the FinnGen database 29 , encompassing 5,259 cases and 479,339 controls, with a total of 9,587,836 SNPs. All cases were confirmed as anemic, all participants were of European descent, and informed consent was procured from all participants. Table 1 delineates the conditions of the exposure and outcome variables. 2.3 MR Analysis The MR analysis was executed utilizing the TwoSampleMR package within the R computational environment. The primary methodology employed was the random-effects inverse-variance weighted (IVW) method, amalgamating the causal effect estimates derived from each SNP's Wald ratio, under the presumption that all these SNPs are valid. Three ancillary methods—weighted median, weighted mode, and MR-Egger—were harnessed to augment the IVW estimates, as they have the capacity to furnish more reliable estimates under a broader array of conditions albeit at the cost of efficiency (manifested in wider confidence intervals). Both univariate and multivariate MR analyses were undertaken to discern potential risk factors for anemia. In the univariate MR analysis, the causal nexus between each risk factor and anemia was evaluated. Conversely, the multivariate MR analysis encompassed all risk factors delineated in the univariate analysis, endeavoring to identify independent risk factors. As illustrated in Figure 1, the solid lines and arrows stemming from Assumption1 are permissible, whilst the dashed lines and arrows originating from Assumption2 and Assumption3 are not. 2.4 Heterogeneity and Sensitivity Analysis Heterogeneity tests are initiated when the Cochran Q statistic suggests that the disparities in individual effect sizes are attributed to actual variations among SNPs rather than sampling errors; a p-value < 0.05 signifies the existence of heterogeneity. MR-Egger regression is utilized to discern potential horizontal pleiotropy, should the intercept deviate from zero 35 . In MR-Egger regression, the intercept denotes the mean pleiotropic effect across all instrumental variables. Consequently, a significantly non-zero intercept in the MR-Egger test implies the presence of pleiotropy. An asymmetrical distribution in the funnel plot may also serve as an indication of horizontal pleiotropy 36 . The MR-PRESSO test endeavors to identify and rectify outliers in IVW linear regression, encompassing the MR-PRESSO global test, outlier detection, and distortion test 37 . To evaluate the robustness and consistency of the findings, analyses were performed by singularly omitting each SNP. All statistical analyses and data visualization tasks were executed in R 4.2.2 software. 3. Results 3.1 Univariate MR The two-sample MR analysis employing the IVW method illustrated that genetic predisposition towards Type 1 Diabetes (IVW: Odds Ratio OR = 1.0009, 95% Confidence Interval CI = 1.0005-1.0016, p < 0.001) (Figures 2A-B), Multiple Sclerosis (IVW: OR = 1.0005, CI = 1.0000-1.0011, p < 0.001) (Figures 2C-D), and Rheumatoid Arthritis (IVW: OR = 1.0013, CI = 1.0008-1.0019, p < 0.001) (Figures 2E-F) exert deleterious pathogenic effects on anemia. Similarly, the MR-Egger regression revealed analogous risk estimates for Type 1 Diabetes (OR = 1.0009, CI = 1.0004-1.0014, p<0.001) and Rheumatoid Arthritis (OR =1.0015, CI = 1.0007-1.0024, p < 0.001), while establishing no causal relationship between genetically determined Multiple Sclerosis and anemia (OR = 1.0000, CI = 0.9990-1.0011, p = 0.960). The weighted median method aligned with the aforementioned findings for Type 1 Diabetes (OR = 1.0009, CI = 1.0006-1.0013, p < 0.001) and Rheumatoid Arthritis (OR = 1.0013, CI = 1.0007-1.0020, p < 0.001), yet reiterated the absence of a causal relationship for genetically determined Multiple Sclerosis with anemia (OR = 1.0002, CI = 0.9994-1.0009, p = 0.649). Upon the amalgamation of exposure and outcome datasets, a judiciously curated assortment of Single Nucleotide Polymorphisms (SNPs) within Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis were designated for advanced Mendelian Randomization (MR) analysis (Supplementary 1).Other disorders, namely, Systemic Lupus Erythematosus, Inflammatory Bowel Disease, Celiac Disease, Sjögren's Syndrome, Systemic Sclerosis, Ankylosing Spondylitis, and Hyperthyroidism, exhibited no causal linkage with anemia (Table 2, Figure 4A-C and Figure S).Cochrane's Q test is utilized to quantify the heterogeneity of individual causal effects, with a p-value < 0.05 signifying the presence of heterogeneity. Consequently, the employment of random-effects IVW MR analysis is warranted, and the evidence substantiates the heterogeneity among Type 1 Diabetes, Multiple Sclerosis, Rheumatoid Arthritis, and anemia (p < 0.001). The funnel plot further corroborates the symmetry of the SNPs (Figures 3A-C). The MR-Intercept and MR-PRESSO global tests reveal the absence of horizontal pleiotropy in the associations with Type 1 Diabetes (P = 0.832), Multiple Sclerosis (P = 0.225), and Rheumatoid Arthritis (P = 0.490). The MR-PRESSO results indicate no outliers within the MR analysis. Moreover, the leave-one-out tests affirm that the MR analysis outcomes for Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis remain uninfluenced by individual SNPs (Figures 3D-F), thereby showcasing the stability and robustness of these results. Table 1. Description of datasets used for analysis. Phenotype Data Sources No. of Cases No. of Controls Population Exposures Systemic lupus erythematosus Literature 647 482,264 European Inflammatory bowel disease Literature 4,101 480,497 NA Coeliac disease FinnGen 1,973 210,964 European Sicca syndrome FinnGen 1,290 213,145 European Systemic sclerosis FinnGen 302 213,145 European Ankylosing spondylitis FinnGen 599 217,431 European Hyperthyroidism Literature 3,557 456,942 European Type 1 diabetes Literature 9,266 15,574 European Multiple sclerosis Literature 14,498 24,091 European Rheumatoid arthritis Literature 14,361 43,923 European Outcomes Anemia Literature 5,259 479,339 European Table 2 Mendelian randomization association of genetically predicted autoimmune diseases with anemia. Exposures No. of SNP method OR Lower Limit of OR Upper Limit of OR P Systemic lupus erythematosus 5 MR Egger 1.0018 0.9972 1.0065 0.502 Weighted median 1.0010 1.0002 1.0018 0.011 IVW 1.0012 1.0002 1.0023 0.024 Simple mode 1.0009 0.9999 1.0020 0.145 Weighted mode 1.0009 0.9998 1.0021 0.187 Inflammatory bowel disease 21 MR Egger 0.8300 0.7220 0.9542 0.017 Weighted median 0.9871 0.8883 1.0969 0.810 IVW 0.9814 0.9047 1.0644 0.650 Simple mode 1.0144 0.8101 1.2703 0.902 Weighted mode 0.8461 0.7282 0.9830 0.041 Coeliac disease 11 MR Egger 1.0007 1.0001 1.0013 0.051 Weighted median 1.0008 1.0005 1.0011 <0.001 IVW 1.0006 1.0003 1.0011 <0.001 Simple mode 1.0006 0.9998 1.0013 0.162 Weighted mode 1.0008 1.0004 1.0011 <0.001 Sicca syndrome 3 MR Egger 1.0025 1.0011 1.0039 0.174 Weighted median 1.0016 1.0008 1.0024 <0.001 IVW 1.0015 1.0008 1.0023 <0.001 Simple mode 1.0011 0.9995 1.0028 0.302 Weighted mode 1.0017 1.0009 1.0025 0.051 Systemic sclerosis 1 Wald ratio 1.0017 1.0000 1.0034 0.053 Ankylosing spondylitis 7 MR Egger 0.9999 0.9996 1.0002 0.516 Weighted median 0.9999 0.9997 1.0001 0.160 IVW 0.9999 0.9997 1.0001 0.312 Simple mode 0.9999 0.9996 1.0002 0.610 Weighted mode 0.9999 0.9997 1.0001 0.278 Hyperthyroidism 12 MR Egger 1.0032 1.0006 1.0059 0.039 Weighted median 1.0017) 1.0006 1.0027 0.002 IVW 1.0019 1.0008 1.0030 0.001 Simple mode 1.0009 0.9992 1.0026 0.335 Weighted mode 1.0019 1.0007 1.0032 0.012 Type 1 diabetes* 41 MR Egger 1.0009 1.0004 1.0014 <0.001 Weighted median 1.0009 1.0006 1.0013 <0.001 IVW 1.0009 1.0005 1.0016 <0.001 Simple mode 1.0006 0.9998 1.0014 0.178 Weighted mode 1.0008 1.0005 1.0011 <0.001 Multiple sclerosis* 49 MR Egger 1.0000 0.9990 1.0011 0.960 Weighted median 1.0002 0.9994 1.0009 0.649 IVW 1.0005 1.0000 1.0011 <0.001 Simple mode 1.0010 0.9994 1.0027 0.231 Weighted mode 1.0001 0.9993 1.0009 0.792 Rheumatoid arthritis* 40 MR Egger 1.0015 1.0007 1.0024 <0.001 Weighted median 1.0013 1.0007 1.0020 <0.001 IVW 1.0013 1.0008 1.0019 <0.001 Simple mode 1.0012 1.0000 1.0024 0.06 Weighted mode 1.0012 1.0006 1.0018 <0.001 3.2 Multivariable MR Within the multivariable MR analysis, the MVMR IVW findings suggest a direct causal impact of Type 1 Diabetes and Multiple Sclerosis on the risk of anemia onset, with robust associations noted between Type 1 Diabetes, Multiple Sclerosis, and anemia (Type 1 Diabetes-IVW: p=0.012; Multiple Sclerosis-IVW: p=0.001). In comparison to MVMR IVW, the MVMR Egger analysis elucidates comparable pathogenic effects of Type 1 Diabetes (p=0.028) and Multiple Sclerosis (p=0.002) on anemia, albeit with a relatively diminished efficiency. Conversely, the weighted median method identifies only Multiple Sclerosis as exerting a pathogenic influence on anemia (p=0.016). Additionally, scant evidence hints at the potential distortion of the estimated effects of Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis in the multivariable MR model due to directional pleiotropy, as reflected by a p-value of 0.974 in the MVMR-Egger intercept test (Table 3). Table 3 Multivariate MR results of Type 1 diabetes, Multiple sclerosis and Rheumatoid arthritis on risk of anemia. Exposure OR (95% CI) β Pval MVMR_IVW Pval MVMR_Egger Pval P inter MVMR_median Pval F-statistic Type 1 diabetes 1.0007 (1.0002,1.0013) 0.0007 0.012 0.012 0.028 0.974 0.182 18 Multiple sclerosis 1.0012 (1.0005,1.0020) 0.0012 0.001 0.001 0.002 0.016 37 Rheumatoid arthritis 1.0008 (0.9999,1.0017) 0.0008 0.074 0.074 0.079 0.149 24 4. Discussion In the present study, a two-sample Mendelian Randomization (MR) approach was utilized to evaluate the potential causal linkage between 10 autoimmune diseases and anemia. Although several observational studies have investigated the association between autoimmune diseases and anemia, the underlying causal dynamics remain intricate and elusive. This MR study elucidates, for the first time, a causal relationship between pertinent autoimmune diseases and an elevated risk of anemia. The association between autoimmune diseases and anemia is progressively corroborated by evidence, potentially bearing significant ramifications for the early appraisal and prevention of anemia's incidence and severity. This may favorably influence the alleviation of anemia's repercussions and potential adverse clinical outcomes. Numerous studies have unequivocally delineated such associations. For instance, a Swedish population study discerned a notable correlation between malignant anemia and the presence of 14 disparate autoimmune diseases (AIDs) within the Swedish populace 38 . Of 34 patients who underwent esophagogastroduodenoscopy (EGD) or gastrectomy due to malignant anemia, 32 were diagnosed with autoimmune or immunological diseases 39 . A dual-center, retrospective analysis of 188 patients with malignant anemia disclosed a prevalent association with autoimmune thyroiditis 40 . A retrospective examination spanning two decades revealed that as many as 29.4% of 34 neonatal lupus erythematosus cases were accompanied by anemia 41 . Similarly, another retrospective investigation ascertained that the anemia incidence rate soared to 51.24% among 1057 patients afflicted with rheumatoid arthritis 42 . The mechanistic association between autoimmune diseases and anemia is mediated through multiple intricate biological processes 43 . Initially, immune dysregulation can instigate enduring inflammatory responses, known to disrupt iron metabolism and utilization, consequently precipitating anemia. Inflammatory cells and cytokines, namely tumor necrosis factor-αand interferon-γ,may restrain erythropoiesis, thereby contributing to anemia 44 . Secondly, specific immune disorders such as systemic lupus erythematosus (SLE) 45 46 may exhibit autoimmunity that directly targets red blood cells or their progenitors, culminating in hemolytic anemias like autoimmune hemolytic anemia (AIHA) 47 . Within AIHA, autoantibodies mark an individual's red blood cells for enhanced clearance via Fc receptor (FcR)-mediated phagocytosis, underscoring the immunological mediation of hemolytic anemia in autoimmune scenarios 48 .Thirdly,certain pharmacological interventions for immune disorders may impair bone marrow functionality or precipitate other anemia-inducing side effects. Fourthly, autoimmune reactions can devastate or impair cells within the stomach essential for vitamin B12 absorption, causing pernicious anemia. Lastly, specific forms of immune disorders may induce bone marrow suppression, impacting erythropoiesis. These mechanisms elucidate the complex interplay between autoimmune reactions and anemia, laying a groundwork for further examination in Mendelian randomization discussions. Our bi-variate Mendelian randomization analysis intimates that Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis exert deleterious effects on anemia, with multivariable MR substantiating the detrimental interrelation among Type 1 Diabetes, Multiple Sclerosis, and anemia. These adverse associations may be mediated through mechanisms pertinent to immune responses 49 , chronic inflammation, and drug interactions. In Type 1 Diabetes, the autoimmune apparatus erroneously targets pancreatic β-cells, a dysregulation similarly manifested in certain anemic conditions 50 . Multiple Sclerosis, being an immune-mediated malady, may harbor shared immunoregulatory aberrations or pathways with autoimmune responses manifest in anemia 51 . Type 1 Diabetes may incite chronic inflammation, adversely impacting erythropoiesis and iron metabolism, thereby precipitating anemia. Microvascular complications and renal maladies are prevalent in individuals with Type 1 Diabetes, potentially further impeding erythropoiesis and blood circulation 52 . Chronic inflammation, a recurrent comorbidity in Multiple Sclerosis and an inherent characteristic of the chronic inflammatory disorder Rheumatoid Arthritis, may detrimentally affect bone marrow function and iron metabolism, exacerbating anemia 53 . Lastly, pharmaceuticals employed to ameliorate Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis may engage in interactions with drugs modulating anemia, consequently altering red blood cell counts. In framing the Mendelian randomization discourse, an examination of the shared biological and physiological mechanisms, alongside potential genetic and environmental determinants, may elucidate the complexities and intersections among Type 1 diabetes, multiple sclerosis, and rheumatoid arthritis in relation to anemia. This endeavor could unveil novel perspectives and avenues for ensuing clinical research and therapeutic interventions. In summation, our investigation boasts several merits. Initially, a comprehensive strategy was deployed to ascertain the causal ramifications of 10 autoimmune diseases on anemia susceptibility. In contrast to conventional epidemiological inquiries, the two-sample Mendelian Randomization (MR) approach furnishes a superior tier of evidence for causal associations, given its diminished vulnerability to potential biases and confounders, contingent upon the satisfaction of primary assumptions. Secondly, we procured the most recent Genome-Wide Association Study (GWAS) summary statistics for Type 1 diabetes, multiple sclerosis, and rheumatoid arthritis, in addition to anemia outcomes, to orchestrate the principal multivariable MR analysis, thereby bolstering the credibility of our conclusions. Thirdly, the effect estimates harvested from diverse data sources exhibited consistency, as evidenced by the robust methodologies employed in the sensitivity analysis to negate multiple pleiotropy. Collectively, we amalgamated assorted data resources, numerous statistical techniques, and multivariable MR analysis to scrutinize the causal nexus between autoimmune diseases and anemia. Our findings corroborate the presence of independent causal linkages between the augmented risk of anemia and Type 1 diabetes, multiple sclerosis, and rheumatoid arthritis. Nonetheless, several limitations warrant consideration. Firstly, despite diligent endeavors to amass the most current Genome-Wide Association Study (GWAS) summary statistics for autoimmune diseases and anemia for the principal Mendelian Randomization (MR) analysis, the data predominantly emanate from European ancestry, mandating a judicious interpretation of our study findings. We advocate for ensuing research in diverse ethnic populations, given the potential variability in genetic structures across ethnicities. Secondly, the absence of unmeasured confounding factors between genetic variables and anemia risk was noted. Independent GWAS data were garnered for two-sample MR analysis to the greatest extent feasible. Yet, theoretically, confounding between genetic variables and outcomes may persist. Consequently, our study findings ought to be construed with circumspection. Future prospective cohort studies, boasting larger sample sizes, would be more propitious and may engender more compelling causal inferences. 5. Conclusion In summation, our findings furnish additional evidence regarding the detrimental impacts of autoimmune diseases (namely Type 1 diabetes and multiple sclerosis) on anemia susceptibility, accentuating the significance of social and psychological facets in comprehending anemia and refining prevailing treatment approaches. Declarations Acknowledgments The investigation was executed utilizing the FinnGen website complemented by a thorough literature review. The authors express their profound gratitude towards the participants and coordinators for their invaluable contributions to this distinctive database. Funding The project was supported by the Natural Science Foundation of Zhejiang Province (No. LQ19H080002), and the Public Welfare Science and Technology Project of Wenzhou (No. Y20190119). Public Welfare Science and Technology Project of Wenzhou (No. Y20220028). Author Contributions Conceptualization, XZ; Methodology, XZ and PC; Software, XZ; Formal analysis, XZ and RY;Data curation,RCW and XYM; Supervision,YFS; Visualization,XZ; Project administration, YFS; Writing – original draft, XZ; Writing–review & editing, XZ and YFS. All authors have read and agreed to the published version of the manuscript. Declarations of interest None 10、Data Availability Statement The original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding author. References Lehman HK. Autoimmunity and Immune Dysregulation in Primary Immune Deficiency Disorders. Curr Allergy Asthma Rep. 2015;15(9):53. Chi X, Huang M, Tu H, et al. Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections. Sci China Life Sci. 2023;66(7):1482-1517. Popoviciu MS, Kaka N, Sethi Y, et al. 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J Int Med Res. 2022;50(3):3000605221088560. Karakus V, Atas U, Uzuntas S, et al. A Rare Nephrotic Syndrome Related to Chronic Lymphocytic Leukemia: Focal Segmental Glomerulosclerosis. Cureus. 2022;14(11):e31545. Chang J, Debreli Coskun M, Kim J. Inflammation alters iron distribution in bone and spleen in mice. Metallomics. 2023;15(10). Santacruz JC, Mantilla MJ, Rueda I, et al. A Practical Perspective of the Hematologic Manifestations of Systemic Lupus Erythematosus. Cureus. 2022;14(3):e22938. Gamal-Eldeen AM, Fahmy CA, Raafat BM, et al. Circulating Levels of Hypoxia-regulating MicroRNAs in Systemic Lupus Erythematosus Patients with Hemolytic Anemia. Curr Med Sci. 2022;42(6):1231-1239. Fattizzo B, Barcellini W. Autoimmune hemolytic anemia: causes and consequences. Expert Rev Clin Immunol. 2022;18(7):731-745. Barcellini W, Giannotta J, Fattizzo B. Autoimmune hemolytic anemia in adults: primary risk factors and diagnostic procedures. Expert Rev Hematol. 2020;13(6):585-597. Amendt T, Yu P. TLR7 and IgM: Dangerous Partners in Autoimmunity. Antibodies (Basel). 2023;12(1). Witek PR, Witek J, Pańkowska E. [Type 1 diabetes-associated autoimmune diseases: screening, diagnostic principles and management]. Med Wieku Rozwoj. 2012;16(1):23-34. Najdaghi S, Davani DN, Ghajarzadeh M, et al. Autoimmune hemolytic anemia after treatment with fingolimod in a patient with multiple sclerosis (MS): A case report and review of the literature. Autoimmun Rev. 2022;21(12):103203. McGill JB, Bell DS. Anemia and the role of erythropoietin in diabetes. J Diabetes Complications. 2006;20(4):262-272. Chen YF, Xu SQ, Xu YC, et al. Inflammatory anemia may be an indicator for predicting disease activity and structural damage in Chinese patients with rheumatoid arthritis. Clin Rheumatol. 2020;39(6):1737-1745. Additional Declarations There is NO conflict of interest to disclose. Having read the above statement, there is NO conflict of interest to disclose. This is noted in the cover letter and manuscript. Supplementary Files FIgureS.tif FIgureS FigureS1.png FigureS2.png FigureS3.png FigureS4.png FigureS5.png FigureS6.png FigureS7.png Supplementary1.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3484652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":269022720,"identity":"49dafcfc-af7c-45c4-8a04-d83ffca248cb","order_by":0,"name":"yifen shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACfvnDhx98+GEjx9jMfIA4LZIz2NIMZ/akGTO3tyUQp8XgBo+CNAfb4UT2njMGRLrsdg+DMQPP4QTeGTkfb7xhsJPTbSCgg3HO2QOPCyzS8yRn5G62nMOQbGx2gIAWZoa8BOMZPNbFhjNyt0nzMBxI3EZICxtDjoE0Dxtz4v4bOc+I08IjAdbinNjYc4aNOC0SPMcggczY3mZsOceACL/YH2+GR+XDG28q7OQIakGzktioQdJCqo5RMApGwSgYEQAAoJNE8+Wn+F4AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"yifen","middleName":"","lastName":"shi","suffix":""},{"id":269022721,"identity":"19f104d6-65f4-4e31-ab3f-dc1b543decd7","order_by":1,"name":"xin zhuang","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"xin","middleName":"","lastName":"zhuang","suffix":""},{"id":269022722,"identity":"799267b1-fba3-4aa6-ba5e-813ea8e9d41b","order_by":2,"name":"peng chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"peng","middleName":"","lastName":"chen","suffix":""},{"id":269022723,"identity":"bf2981fe-034a-490a-89ba-df8bf633b1d6","order_by":3,"name":"rong yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"rong","middleName":"","lastName":"yang","suffix":""},{"id":269022724,"identity":"ed875788-ecc1-4286-81c3-52cb30d45c91","order_by":4,"name":"xiaoying man","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"xiaoying","middleName":"","lastName":"man","suffix":""},{"id":269022725,"identity":"9970d2c5-1b8f-4406-8b7b-e2dced49868d","order_by":5,"name":"ruochen wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"ruochen","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2023-10-24 06:30:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3484652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3484652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50330752,"identity":"2c01961d-178c-4944-8e3c-6e5267f59a52","added_by":"auto","created_at":"2024-01-29 21:43:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":220085,"visible":true,"origin":"","legend":"\u003cp\u003emechanism diagram\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3484652/v1/acb4b3bfa743818c7659ff37.png"},{"id":50331964,"identity":"747055b2-b46e-4c37-a3e1-a347232562b5","added_by":"auto","created_at":"2024-01-29 21:51:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1171893,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between Type 1 diabetes,multiple sclerosis,rheumatoid arthritis and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 2A: Forest plot of causal effects of Type 1 diabetes and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 2B: Scatter plot of causal effects of Type 1 diabetes and anemia. The slope of the line represents the causal effect of each method.\u003c/p\u003e\n\u003cp\u003eFigure 2C: Forest plot of causal effects of multiple sclerosis and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 2D: Scatter plot of causal effects of multiple sclerosis and anemia. The slope of the line represents the causal effect of each method.\u003c/p\u003e\n\u003cp\u003eFigure 2E: Forest plot of causal effects of rheumatoid arthritis and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 2F: Scatter plot of causal effects of rheumatoid arthritis and anemia. The slope of the line represents the causal effect of each method.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3484652/v1/848212d8a0c6ac666daeb258.png"},{"id":50330754,"identity":"0e0bb9a0-924f-4dbb-8d27-f387dd0835e6","added_by":"auto","created_at":"2024-01-29 21:43:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":876239,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect size for Type 1 diabetes,multiple sclerosis,rheumatoid arthritis and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 3A: The funnel plot showed that the SNPs were symmetric, indicating that there was no heterogeneity in the association with Type 1 diabetes and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 3B: The funnel plot showed that the SNPs were symmetric, indicating that there was no heterogeneity in the association with multiple sclerosis and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 3C: The funnel plot showed that the SNPs were symmetric, indicating that there was no heterogeneity in the association with rheumatoid arthritis and anemia.\u003c/p\u003e\n\u003cp\u003eFigure 3D: The leave-one-out test showed that the result was not affected by single\u003c/p\u003e\n\u003cp\u003einfluential SNP, so this association with Type 1 diabetes and anemia was stable.\u003c/p\u003e\n\u003cp\u003eFigure 3E: The leave-one-out test showed that the result was not affected by single\u003c/p\u003e\n\u003cp\u003einfluential SNP, so this association with multiple sclerosis and anemia was stable.\u003c/p\u003e\n\u003cp\u003eFigure 3F: The leave-one-out test showed that the result was not affected by single\u003c/p\u003e\n\u003cp\u003einfluential SNP, so this association with rheumatoid arthritis and anemia was stable.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3484652/v1/1a03281a5681f65f9c98bd2c.png"},{"id":50330753,"identity":"8824cf6e-ccb9-4712-b1ed-39edbd962600","added_by":"auto","created_at":"2024-01-29 21:43:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":286819,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization association of genetically predicted Type 1 diabetes,multiple sclerosis and rheumatoid arthritis with anemia.\u003c/p\u003e\n\u003cp\u003eFigure 4A：Mendelian randomization association of genetically predicted Type 1 diabetes with anemia.\u003c/p\u003e\n\u003cp\u003eFigure 4B：Mendelian randomization association of genetically predicted multiple sclerosis with anemia.\u003c/p\u003e\n\u003cp\u003eFigure 4C：Mendelian randomization association of genetically predicted rheumatoid arthritis with anemia.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3484652/v1/0d9037a8e354450ef2fea5dd.png"},{"id":57011635,"identity":"ad69b2a7-375d-4c2c-be20-6874f0d36710","added_by":"auto","created_at":"2024-05-23 11:34:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3192523,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3484652/v1/f13d87b2-9ab5-40e9-9334-220750199adc.pdf"},{"id":50331969,"identity":"815247f6-680f-4526-bcb4-4cf35d538101","added_by":"auto","created_at":"2024-01-29 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21:59:33","extension":"xls","order_by":25,"title":"","display":"","copyAsset":false,"role":"supplement","size":61440,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.xls","url":"https://assets-eu.researchsquare.com/files/rs-3484652/v1/e8ff80c91b0e8647d9a1a643.xls"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.\nHaving read the above statement, there is NO conflict of interest to disclose. This is noted in the cover letter and manuscript.","formattedTitle":"Genetic Associations Between Autoimmune Diseases and Anemia: A Mendelian Randomization Analysis to Inform Clinical Practice.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAutoimmune Diseases (ADs) epitomize a class of ailments engendered by immune system dysregulation, culminating in the body mounting aggressive responses against its own tissues. Depending upon clinical manifestations and the affected organ systems\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e, autoimmune diseases bifurcate into systemic and organ-specific types. Systemic autoimmune diseases, such as Systemic Lupus Erythematosus (SLE), Rheumatoid Arthritis (RA), and Multiple Sclerosis, have the propensity to affect multiple organ systems; conversely, organ-specific autoimmune diseases like Type 1 Diabetes\u003csup\u003e3\u003c/sup\u003e and Thyroiditis predominantly afflict particular organs or tissues\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e. The pathogenesis of autoimmune diseases is intricate, encompassing myriad factors like genetics, environment, and immune regulation\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e. Presently, despite the clinical application of various immunosuppressive and immunoregulatory therapies, the enhancement of their efficacy remains imperative, and their prolonged usage might precipitate severe side effects\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAnemia, a prevalent hematologic disorder, denotes a pathological condition characterized by a diminished quantity of red blood cells or a reduction in the hemoglobin content within the blood, culminating in a compromised blood oxygen-carrying capacity\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e. Data from the World Health Organization (WHO) delineates that over 1.5 billion individuals globally are afflicted by anemia, underscoring the substantial public health concern it poses worldwide. Etiologically, anemia can be stratified into diverse types such as iron-deficiency anemia, hemolytic anemia\u003csup\u003e13\u003c/sup\u003e, and aplastic anemia\u003csup\u003e14\u003c/sup\u003e\u003csup\u003e15\u003c/sup\u003e. The principal risk factors for anemia encompass malnutrition, iron deficiency, chronic ailments, parasitic infections, and genetic determinants. The therapeutic approach to anemia predominantly hinges on its etiology. Within the scope of autoimmune diseases, anemia\u0026apos;s manifestation may be allied with immune-mediated destruction of red blood cells and impaired bone marrow hematopoietic functionality\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe interplay between autoimmune diseases and anemia predominantly manifests as a potential reciprocal augmentation in the incidence and progression of both disease categories. At present, the impact of anemia on specific types of autoimmune diseases remains ambiguous, including whether a closer association with particular autoimmune diseases exists; the evidence to elucidate the causality of this relationship is insufficient.\u003c/p\u003e\n\u003cp\u003eMendelian Randomization (MR) analysis is a potent instrument in genetic epidemiology, extensively employed to explore causal associations between diseases\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e. It leverages naturally occurring genetic variations as instrumental variables, furnishing robust statistical evidence for potential causal relationships between diseases\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e. MR analysis effectively mitigates bias risks arising from unmeasured confounders, reverse causality, and measurement errors\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e, thereby presenting a viable alternative to randomized controlled trials\u003csup\u003e23\u003c/sup\u003e. For a valid MR analysis, three critical assumptions are requisite: (1) a strong association between the genetic variation and the exposure; (2) the absence of influence on the genetic variation by any other potential confounders; and (3) the impact of the genetic instrumental variable on the outcome solely through the exposure\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e. The latter two assumptions are collectively denoted as the independence of horizontal pleiotropy and can be empirically validated\u003csup\u003e27\u003c/sup\u003e. Multivariable Mendelian Randomization (MVMR) analysis broadens MR analysis\u0026apos;s scope, facilitating the examination of relationships between multiple exposures and diseases within a consolidated analytical framework.\u003c/p\u003e\n\u003cp\u003eThis study leveraged Genome-Wide Association Study (GWAS) summary statistics to execute both two-sample Mendelian Randomization (MR) and multivariable MR analyses, investigating the causal link between 10 autoimmune diseases and anemia risk. Through a systematic review of extensive literature and the analysis of publicly available large-scale genomic data, we aspire to unveil novel insights into the interlinking pathophysiological mechanisms between autoimmune diseases and anemia, thereby providing a genetic foundation for forthcoming clinical interventions and treatment strategies.\u003c/p\u003e"},{"header":"2. Research Design and Data Sources","content":"\u003cp\u003e2.1 Ethical Approval\u003c/p\u003e\n\u003cp\u003eWhile this study does not encompass human or animal subjects, we uphold a commitment to safeguarding the rights and confidentiality pertaining to historical literature. All documents and data will be securely housed, with no disclosure of sensitive information concerning individuals or institutions. Transparency will be maintained throughout the research process, encompassing methodologies, data processing, and result analysis. All research findings will be disseminated in the public domain to foster the propagation of scientific knowledge. Rigorous measures will be employed to manage and safeguard research data, ensuring its confidentiality and integrity. Data will reside in a secure environment, accessible solely to members of the research team. Given that this study entails the review and analysis of historical literature without involving living individuals, informed consent is not necessitated.\u003c/p\u003e\n\u003cp\u003e2.2 Research Design and Data Sources\u003c/p\u003e\n\u003cp\u003eIn this study, ten autoimmune diseases were considered as exposure variables, namely, Systemic Lupus Erythematosus\u003csup\u003e28\u003c/sup\u003e, Inflammatory Bowel Disease\u003csup\u003e29\u003c/sup\u003e, Celiac Disease, Sj\u0026ouml;gren\u0026apos;s Syndrome, Systemic Sclerosis, Ankylosing Spondylitis, Hyperthyroidism\u003csup\u003e28\u003c/sup\u003e, Type 1 Diabetes\u003csup\u003e30\u003c/sup\u003e, Multiple Sclerosis\u003csup\u003e31\u003c/sup\u003e, and Rheumatoid Arthritis\u003csup\u003e32\u003c/sup\u003e. Extensive literature and the FinnGen database were scrutinized to ascertain genetic variations associated with these autoimmune diseases within European populations. The count of Single Nucleotide Polymorphisms (SNPs) in the GWAS data for each autoimmune disease exhibited significant variation; thus, for precise analysis, genetic variations associated with Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis, meeting the genome-wide significance levels (p\u0026lt;5\u0026times;10^\u0026minus;8) and not in linkage disequilibrium (LD r^2 \u0026lt; 0.1, kb = 10,000), were extracted. To mitigate bias from weak instrumental variables, the proportion of phenotypic variance explained by each instrumental variable was computed using R^2:R^2= [2\u0026times;EAF\u0026times;(1-EAF)\u0026times;(\u0026beta;)^2]/[(2\u0026times;EAF\u0026times;(1-EAF)\u0026times;(\u0026beta;)^2)+(2\u0026times;EAF\u0026times;(1-EAF)\u0026times;N\u0026times;se(\u0026beta;)^2)], where EAF denotes the effect allele frequency, \u0026beta; represents the effect size, N is the sample size, and se(\u0026beta;) is the standard error of the genetic effect. Subsequently, the F-statistic was calculated as F=[R^2\u0026times;(N-k-1)]/[(1-R^2)\u0026times;k], to evaluate the robustness of the statistic, with k being the number of instrumental variables\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e. SNPs with F\u0026lt;10 were excluded, being categorized as weak instrumental variables\u003csup\u003e20\u003c/sup\u003e. Data concerning anemia were acquired from both literature and the FinnGen database\u003csup\u003e29\u003c/sup\u003e, encompassing 5,259 cases and 479,339 controls, with a total of 9,587,836 SNPs. All cases were confirmed as anemic, all participants were of European descent, and informed consent was procured from all participants. Table 1 delineates the conditions of the exposure and outcome variables.\u003c/p\u003e\n\u003cp\u003e2.3 MR Analysis\u003c/p\u003e\n\u003cp\u003eThe MR analysis was executed utilizing the TwoSampleMR package within the R computational environment. The primary methodology employed was the random-effects inverse-variance weighted (IVW) method, amalgamating the causal effect estimates derived from each SNP\u0026apos;s Wald ratio, under the presumption that all these SNPs are valid. Three ancillary methods\u0026mdash;weighted median, weighted mode, and MR-Egger\u0026mdash;were harnessed to augment the IVW estimates, as they have the capacity to furnish more reliable estimates under a broader array of conditions albeit at the cost of efficiency (manifested in wider confidence intervals). Both univariate and multivariate MR analyses were undertaken to discern potential risk factors for anemia. In the univariate MR analysis, the causal nexus between each risk factor and anemia was evaluated. Conversely, the multivariate MR analysis encompassed all risk factors delineated in the univariate analysis, endeavoring to identify independent risk factors. As illustrated in Figure 1, the solid lines and arrows stemming from Assumption1 are permissible, whilst the dashed lines and arrows originating from Assumption2 and Assumption3 are not.\u003c/p\u003e\n\u003cp\u003e2.4 Heterogeneity and Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003eHeterogeneity tests are initiated when the Cochran Q statistic suggests that the disparities in individual effect sizes are attributed to actual variations among SNPs rather than sampling errors; a p-value \u0026lt; 0.05 signifies the existence of heterogeneity. MR-Egger regression is utilized to discern potential horizontal pleiotropy, should the intercept deviate from zero\u003csup\u003e35\u003c/sup\u003e. In MR-Egger regression, the intercept denotes the mean pleiotropic effect across all instrumental variables. Consequently, a significantly non-zero intercept in the MR-Egger test implies the presence of pleiotropy. An asymmetrical distribution in the funnel plot may also serve as an indication of horizontal pleiotropy\u003csup\u003e36\u003c/sup\u003e. The MR-PRESSO test endeavors to identify and rectify outliers in IVW linear regression, encompassing the MR-PRESSO global test, outlier detection, and distortion test\u003csup\u003e37\u003c/sup\u003e. To evaluate the robustness and consistency of the findings, analyses were performed by singularly omitting each SNP. All statistical analyses and data visualization tasks were executed in R 4.2.2 software.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Univariate MR\u003c/p\u003e\n\u003cp\u003eThe two-sample MR analysis employing the IVW method illustrated that genetic predisposition towards Type 1 Diabetes (IVW: Odds Ratio OR = 1.0009, 95% Confidence Interval CI = 1.0005-1.0016, p \u0026lt; 0.001) (Figures 2A-B), Multiple Sclerosis (IVW: OR = 1.0005, CI = 1.0000-1.0011, p \u0026lt; 0.001) (Figures 2C-D), and Rheumatoid Arthritis (IVW: OR = 1.0013, CI = 1.0008-1.0019, p \u0026lt; 0.001) (Figures 2E-F) exert deleterious pathogenic effects on anemia. Similarly, the MR-Egger regression revealed analogous risk estimates for Type 1 Diabetes (OR = 1.0009, CI = 1.0004-1.0014, p\u0026lt;0.001) and Rheumatoid Arthritis (OR =1.0015, CI = 1.0007-1.0024, p \u0026lt; 0.001), while establishing no causal relationship between genetically determined Multiple Sclerosis and anemia (OR = 1.0000, CI = 0.9990-1.0011, p = 0.960). The weighted median method aligned with the aforementioned findings for Type 1 Diabetes (OR = 1.0009, CI = 1.0006-1.0013, p \u0026lt; 0.001) and Rheumatoid Arthritis (OR = 1.0013, CI = 1.0007-1.0020, p \u0026lt; 0.001), yet reiterated the absence of a causal relationship for genetically determined Multiple Sclerosis with anemia (OR = 1.0002, CI = 0.9994-1.0009, p = 0.649). Upon the amalgamation of exposure and outcome datasets, a judiciously curated assortment of Single Nucleotide Polymorphisms (SNPs) within Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis were designated for advanced Mendelian Randomization (MR) analysis (Supplementary 1).Other disorders, namely, Systemic Lupus Erythematosus, Inflammatory Bowel Disease, Celiac Disease, Sj\u0026ouml;gren\u0026apos;s Syndrome, Systemic Sclerosis, Ankylosing Spondylitis, and Hyperthyroidism, exhibited no causal linkage with anemia (Table 2, Figure 4A-C and Figure S).Cochrane\u0026apos;s Q test is utilized to quantify the heterogeneity of individual causal effects, with a p-value \u0026lt; 0.05 signifying the presence of heterogeneity. Consequently, the employment of random-effects IVW MR analysis is warranted, and the evidence substantiates the heterogeneity among Type 1 Diabetes, Multiple Sclerosis, Rheumatoid Arthritis, and anemia (p \u0026lt; 0.001). The funnel plot further corroborates the symmetry of the SNPs (Figures 3A-C). The MR-Intercept and MR-PRESSO global tests reveal the absence of horizontal pleiotropy in the associations with Type 1 Diabetes (P = 0.832), Multiple Sclerosis (P = 0.225), and Rheumatoid Arthritis (P = 0.490). The MR-PRESSO results indicate no outliers within the MR analysis. Moreover, the leave-one-out tests affirm that the MR analysis outcomes for Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis remain uninfluenced by individual SNPs (Figures 3D-F), thereby showcasing the stability and robustness of these results.\u003c/p\u003e\n\u003cp\u003eTable 1. Description of datasets used for analysis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eData Sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003eNo. of Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003eNo. of Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eExposures\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e482,264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eInflammatory bowel disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e4,101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e480,497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eCoeliac disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e1,973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e210,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eSicca syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e1,290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e213,145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eSystemic sclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e213,145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eAnkylosing spondylitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e217,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eHyperthyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e3,557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e456,942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eType 1 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e9,266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e15,574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple sclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e14,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e24,091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eRheumatoid arthritis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e14,361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e43,923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.76106194690266%\" valign=\"top\"\u003e\n \u003cp\u003eAnemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.58407079646018%\" valign=\"top\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79646017699115%\" valign=\"top\"\u003e\n \u003cp\u003e5,259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56637168141593%\" valign=\"top\"\u003e\n \u003cp\u003e479,339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.292035398230087%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Mendelian randomization association of genetically predicted autoimmune diseases with anemia.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" valign=\"top\"\u003e\n \u003cp\u003eExposures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" valign=\"top\"\u003e\n \u003cp\u003eNo. of SNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003eLower Limit of OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003eUpper Limit of OR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e0.9972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eInflammatory bowel disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e0.8300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e0.7220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e0.9542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e0.9871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.8883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e0.9814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.8101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.2703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e0.8461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.7282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e0.9830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eCoeliac disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e1.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eSicca syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" valign=\"top\"\u003e\n \u003cp\u003eSystemic sclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eWald ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eAnkylosing spondylitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e0.9996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eHyperthyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e1.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eType 1 diabetes*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e1.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eMultiple sclerosis*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e0.9990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e0.9993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.908127208480565%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eRheumatoid arthritis*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.021201413427562%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.371024734982333%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.250883392226148%\" valign=\"top\"\u003e\n \u003cp\u003e1.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130742049469964%\" valign=\"top\"\u003e\n \u003cp\u003e1.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.307420494699647%\" valign=\"top\"\u003e\n \u003cp\u003e1.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.010600706713781%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.58823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eWeighted mode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e1.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.529411764705884%\" valign=\"top\"\u003e\n \u003cp\u003e1.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2 Multivariable MR\u003c/p\u003e\n\u003cp\u003eWithin the multivariable MR analysis, the MVMR IVW findings suggest a direct causal impact of Type 1 Diabetes and Multiple Sclerosis on the risk of anemia onset, with robust associations noted between Type 1 Diabetes, Multiple Sclerosis, and anemia (Type 1 Diabetes-IVW: p=0.012; Multiple Sclerosis-IVW: p=0.001). In comparison to MVMR IVW, the MVMR Egger analysis elucidates comparable pathogenic effects of Type 1 Diabetes (p=0.028) and Multiple Sclerosis (p=0.002) on anemia, albeit with a relatively diminished efficiency. Conversely, the weighted median method identifies only Multiple Sclerosis as exerting a pathogenic influence on anemia (p=0.016). Additionally, scant evidence hints at the potential distortion of the estimated effects of Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis in the multivariable MR model due to directional pleiotropy, as reflected by a p-value of 0.974 in the MVMR-Egger intercept test (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3 Multivariate MR results of Type 1 diabetes, Multiple sclerosis and Rheumatoid arthritis on risk of anemia.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.85575364667747%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.155591572123177%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.048622366288493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26580226904376%\" valign=\"top\"\u003e\n \u003cp\u003ePval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.85899513776337%\" valign=\"top\"\u003e\n \u003cp\u003eMVMR_IVW Pval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.021069692058347%\" valign=\"top\"\u003e\n \u003cp\u003eMVMR_Egger Pval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.752025931928687%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003einter\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.831442463533225%\" valign=\"top\"\u003e\n \u003cp\u003eMVMR_median\u0026nbsp;Pval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.210696920583468%\" valign=\"top\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.85575364667747%\" valign=\"top\"\u003e\n \u003cp\u003eType 1 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.155591572123177%\" valign=\"top\"\u003e\n \u003cp\u003e1.0007\u003c/p\u003e\n \u003cp\u003e(1.0002,1.0013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.048622366288493%\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26580226904376%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.85899513776337%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.021069692058347%\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.752025931928687%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.831442463533225%\" valign=\"top\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.210696920583468%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47246891651865%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple sclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.321492007104796%\" valign=\"top\"\u003e\n \u003cp\u003e1.0012\u003c/p\u003e\n \u003cp\u003e(1.0005,1.0020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.012433392539965%\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.058614564831261%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.900532859680284%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.078152753108348%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.190053285968029%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47246891651865%\" valign=\"top\"\u003e\n \u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.321492007104796%\" valign=\"top\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003cp\u003e(0.9999,1.0017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.012433392539965%\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.058614564831261%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.900532859680284%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.078152753108348%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"top\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.190053285968029%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present study, a two-sample Mendelian Randomization (MR) approach was utilized to evaluate the potential causal linkage between 10 autoimmune diseases and anemia. Although several observational studies have investigated the association between autoimmune diseases and anemia, the underlying causal dynamics remain intricate and elusive. This MR study elucidates, for the first time, a causal relationship between pertinent autoimmune diseases and an elevated risk of anemia.\u003c/p\u003e\n\u003cp\u003eThe association between autoimmune diseases and anemia is progressively corroborated by evidence, potentially bearing significant ramifications for the early appraisal and prevention of anemia\u0026apos;s incidence and severity. This may favorably influence the alleviation of anemia\u0026apos;s repercussions and potential adverse clinical outcomes. Numerous studies have unequivocally delineated such associations. For instance, a Swedish population study discerned a notable correlation between malignant anemia and the presence of 14 disparate autoimmune diseases (AIDs) within the Swedish populace\u003csup\u003e38\u003c/sup\u003e. Of 34 patients who underwent esophagogastroduodenoscopy (EGD) or gastrectomy due to malignant anemia, 32 were diagnosed with autoimmune or immunological diseases\u003csup\u003e39\u003c/sup\u003e. A dual-center, retrospective analysis of 188 patients with malignant anemia disclosed a prevalent association with autoimmune thyroiditis\u003csup\u003e40\u003c/sup\u003e. A retrospective examination spanning two decades revealed that as many as 29.4% of 34 neonatal lupus erythematosus cases were accompanied by anemia\u003csup\u003e41\u003c/sup\u003e. Similarly, another retrospective investigation ascertained that the anemia incidence rate soared to 51.24% among 1057 patients afflicted with rheumatoid arthritis\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe mechanistic association between autoimmune diseases and anemia is mediated through multiple intricate biological processes\u003csup\u003e43\u003c/sup\u003e. Initially, immune dysregulation can instigate enduring inflammatory responses, known to disrupt iron metabolism and utilization, consequently precipitating anemia. Inflammatory cells and cytokines, namely tumor necrosis factor-\u0026alpha;and interferon-\u0026gamma;,may restrain erythropoiesis, thereby contributing to anemia\u003csup\u003e44\u003c/sup\u003e. Secondly, specific immune disorders such as systemic lupus erythematosus (SLE)\u003csup\u003e45\u003c/sup\u003e\u003csup\u003e46\u003c/sup\u003e may exhibit autoimmunity that directly targets red blood cells or their progenitors, culminating in hemolytic anemias like autoimmune hemolytic anemia (AIHA)\u003csup\u003e47\u003c/sup\u003e. Within AIHA, autoantibodies mark an individual\u0026apos;s red blood cells for enhanced clearance via Fc receptor (FcR)-mediated phagocytosis, underscoring the immunological mediation of hemolytic anemia in autoimmune scenarios\u003csup\u003e48\u003c/sup\u003e.Thirdly,certain pharmacological interventions for immune disorders may impair bone marrow functionality or precipitate other anemia-inducing side effects. Fourthly, autoimmune reactions can devastate or impair cells within the stomach essential for vitamin B12 absorption, causing pernicious anemia. Lastly, specific forms of immune disorders may induce bone marrow suppression, impacting erythropoiesis. These mechanisms elucidate the complex interplay between autoimmune reactions and anemia, laying a groundwork for further examination in Mendelian randomization discussions.\u003c/p\u003e\n\u003cp\u003eOur bi-variate Mendelian randomization analysis intimates that Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis exert deleterious effects on anemia, with multivariable MR substantiating the detrimental interrelation among Type 1 Diabetes, Multiple Sclerosis, and anemia. These adverse associations may be mediated through mechanisms pertinent to immune responses\u003csup\u003e49\u003c/sup\u003e, chronic inflammation, and drug interactions. In Type 1 Diabetes, the autoimmune apparatus erroneously targets pancreatic \u0026beta;-cells, a dysregulation similarly manifested in certain anemic conditions\u003csup\u003e50\u003c/sup\u003e. Multiple Sclerosis, being an immune-mediated malady, may harbor shared immunoregulatory aberrations or pathways with autoimmune responses manifest in anemia\u003csup\u003e51\u003c/sup\u003e. Type 1 Diabetes may incite chronic inflammation, adversely impacting erythropoiesis and iron metabolism, thereby precipitating anemia. Microvascular complications and renal maladies are prevalent in individuals with Type 1 Diabetes, potentially further impeding erythropoiesis and blood circulation\u003csup\u003e52\u003c/sup\u003e. Chronic inflammation, a recurrent comorbidity in Multiple Sclerosis and an inherent characteristic of the chronic inflammatory disorder Rheumatoid Arthritis, may detrimentally affect bone marrow function and iron metabolism, exacerbating anemia\u003csup\u003e53\u003c/sup\u003e. Lastly, pharmaceuticals employed to ameliorate Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis may engage in interactions with drugs modulating anemia, consequently altering red blood cell counts.\u003c/p\u003e\n\u003cp\u003eIn framing the Mendelian randomization discourse, an examination of the shared biological and physiological mechanisms, alongside potential genetic and environmental determinants, may elucidate the complexities and intersections among Type 1 diabetes, multiple sclerosis, and rheumatoid arthritis in relation to anemia. This endeavor could unveil novel perspectives and avenues for ensuing clinical research and therapeutic interventions.\u003c/p\u003e\n\u003cp\u003eIn summation, our investigation boasts several merits. Initially, a comprehensive strategy was deployed to ascertain the causal ramifications of 10 autoimmune diseases on anemia susceptibility. In contrast to conventional epidemiological inquiries, the two-sample Mendelian Randomization (MR) approach furnishes a superior tier of evidence for causal associations, given its diminished vulnerability to potential biases and confounders, contingent upon the satisfaction of primary assumptions. Secondly, we procured the most recent Genome-Wide Association Study (GWAS) summary statistics for Type 1 diabetes, multiple sclerosis, and rheumatoid arthritis, in addition to anemia outcomes, to orchestrate the principal multivariable MR analysis, thereby bolstering the credibility of our conclusions. Thirdly, the effect estimates harvested from diverse data sources exhibited consistency, as evidenced by the robust methodologies employed in the sensitivity analysis to negate multiple pleiotropy. Collectively, we amalgamated assorted data resources, numerous statistical techniques, and multivariable MR analysis to scrutinize the causal nexus between autoimmune diseases and anemia. Our findings corroborate the presence of independent causal linkages between the augmented risk of anemia and Type 1 diabetes, multiple sclerosis, and rheumatoid arthritis.\u003c/p\u003e\n\u003cp\u003eNonetheless, several limitations warrant consideration. Firstly, despite diligent endeavors to amass the most current Genome-Wide Association Study (GWAS) summary statistics for autoimmune diseases and anemia for the principal Mendelian Randomization (MR) analysis, the data predominantly emanate from European ancestry, mandating a judicious interpretation of our study findings. We advocate for ensuing research in diverse ethnic populations, given the potential variability in genetic structures across ethnicities. Secondly, the absence of unmeasured confounding factors between genetic variables and anemia risk was noted. Independent GWAS data were garnered for two-sample MR analysis to the greatest extent feasible. Yet, theoretically, confounding between genetic variables and outcomes may persist. Consequently, our study findings ought to be construed with circumspection. Future prospective cohort studies, boasting larger sample sizes, would be more propitious and may engender more compelling causal inferences.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summation, our findings furnish additional evidence regarding the detrimental impacts of autoimmune diseases (namely Type 1 diabetes and multiple sclerosis) on anemia susceptibility, accentuating the significance of social and psychological facets in comprehending anemia and refining prevailing treatment approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe investigation was executed utilizing the FinnGen website complemented by a thorough literature review. The authors express their profound gratitude towards the participants and coordinators for their invaluable contributions to this distinctive database.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe project was supported by the Natural Science Foundation of Zhejiang Province (No. LQ19H080002), and the Public Welfare Science and Technology Project of Wenzhou (No. Y20190119). Public Welfare Science and Technology Project of Wenzhou (No. Y20220028).\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, XZ; Methodology, XZ and PC; Software, XZ; Formal analysis, XZ and RY;Data curation,RCW and XYM; Supervision,YFS; Visualization,XZ; Project administration, YFS; Writing \u0026ndash; original draft, XZ; Writing\u0026ndash;review \u0026amp; editing, XZ and YFS. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eDeclarations of interest\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e10、Data Availability Statement\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLehman HK. Autoimmunity and Immune Dysregulation in Primary Immune Deficiency Disorders. Curr Allergy Asthma Rep. 2015;15(9):53.\u003c/li\u003e\n\u003cli\u003eChi X, Huang M, Tu H, et al. Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections. Sci China Life Sci. 2023;66(7):1482-1517.\u003c/li\u003e\n\u003cli\u003ePopoviciu MS, Kaka N, Sethi Y, et al. 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[Type 1 diabetes-associated autoimmune diseases: screening, diagnostic principles and management]. Med Wieku Rozwoj. 2012;16(1):23-34.\u003c/li\u003e\n\u003cli\u003eNajdaghi S, Davani DN, Ghajarzadeh M, et al. Autoimmune hemolytic anemia after treatment with fingolimod in a patient with multiple sclerosis (MS): A case report and review of the literature. Autoimmun Rev. 2022;21(12):103203.\u003c/li\u003e\n\u003cli\u003eMcGill JB, Bell DS. Anemia and the role of erythropoietin in diabetes. J Diabetes Complications. 2006;20(4):262-272.\u003c/li\u003e\n\u003cli\u003eChen YF, Xu SQ, Xu YC, et al. Inflammatory anemia may be an indicator for predicting disease activity and structural damage in Chinese patients with rheumatoid arthritis. Clin Rheumatol. 2020;39(6):1737-1745.\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":"autoimmune diseases, anemia, causal relationship, multivariable Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3484652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3484652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Autoimmune diseases and anemia are clinically distinct yet recent studies suggest a potential association. The causal link is unclear, prompting this study's utilization of univariate and multivariate Mendelian randomization analyses to probe a possible causal connection.\u003c/p\u003e\n\u003cp\u003eMethod: A thorough literature review and analysis of summary statistics from genome-wide association studies (GWAS) data, sourced from public databases, were conducted. Ten autoimmune diseases and anemia were selected for scrutiny. Single Nucleotide Polymorphisms (SNPs) significantly associated with these diseases were identified, serving as instrumental variables with anemia as the outcome variable. Both univariable and multivariable Mendelian randomization analyses were performed to assess the causal link.\u003c/p\u003e\n\u003cp\u003eResults: Ten autoimmune diseases were analyzed concerning their relationship with anemia. Univariate analysis revealed that Type 1 Diabetes, Multiple Sclerosis, and Rheumatoid Arthritis genetically contribute to anemia risk. Multivariate analysis sustained a significant association between the genetic predisposition toward Type 1 Diabetes, Multiple Sclerosis and anemia risk.\u003c/p\u003e\n\u003cp\u003eConclusion: This study supports the notion that autoimmune diseases negatively influence anemia risk, suggesting that targeting autoimmune diseases may be key to mitigating anemia risk. The relationship between autoimmune diseases and anemia warrants further investigation for potential preventive and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Genetic Associations Between Autoimmune Diseases and Anemia: A Mendelian Randomization Analysis to Inform Clinical Practice.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 21:43:28","doi":"10.21203/rs.3.rs-3484652/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":"7f60a91b-b877-4e94-9378-9ea6c59edf23","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28344741,"name":"Biological sciences/Immunology/Immunological disorders/Autoimmune diseases"},{"id":28344742,"name":"Biological sciences/Immunology/Autoimmunity"}],"tags":[],"updatedAt":"2024-05-23T11:26:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 21:43:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3484652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3484652","identity":"rs-3484652","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}