Causal relationship between dermatomyositis and autoimmune d isorders: a Mendelian randomization study

Causal relationship between dermatomyositis and autoimmune d isorders: a Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal relationship between dermatomyositis and autoimmune d isorders: a Mendelian randomization study Zhongyuan Zhang, Jiajia Wang, Ping Zhu, Lingxiao Xu, Dandan Yan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5143664/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 Various autoimmune disorders have been linked to dermatomyositis (DM) based on findings from epidemiological studies. The objective of this study is to examine the causal association between autoimmune disorders and DM utilizing the methodology of Mendelian randomization (MR). Methods We employed summary statistics from the largest European genome-wide association studies (GWAS) on autoimmune disorders to assess the genetically predicted effects on DM risk in a two-sample MR framework. Single nucleotide polymorphisms (SNPs) strongly associated with 10 immune-related traits were extracted from these GWAS datasets and their effects were examined in a European DM GWAS cohort (201 cases and 172834 controls). In order to address potential bias arising from the intricate linkage disequilibrium structure observed in the human leukocyte antigen region, the analysis excluded SNPs within this specific genomic region. Subsequently, a multivariate Mendelian analysis was conducted to investigate the association between one autoimmune disease and DM. Results After applying the Bonferroni correction to account for multiple testing, our MR analyses revealed a potential heightened risk of DM associated with type 1 diabetes (T1D), one of the autoimmune diseases under investigation. We further conducted a Mendelian analysis focusing on T1D and the occurrence of DM, incorporating type 2 diabetes, viral infection, sunburns and smoking status. Our findings revealed that T1D independently increased the risk of DM, regardless of smoking and viral infection, which were previously identified as DM risk factors. Conclusion Our MR study provides evidence supporting a relationship between susceptibility to T1D and increased DM risk in the European population. Mendelian randomization Dermatomyositis Type 1 diabetes Autoimmune disorders Causal relationship Figures Figure 1 Figure 2 Background Dermatomyositis (DM) is a debilitating disorder classified among rare autoimmune diseases and characterised by polymorphous cutaneous features and variable muscle involvement [ 1 – 2 ]. While the pathogenesis of DM remains complex and inadequately understood, evidence suggests the involvement of various factors, including genetic predisposition, environmental agents and immune-mediated mechanisms [ 3 ]. Current estimates place DM incidence between 1.0 to 15 per million and prevalence between 1.2 to 21 per 100,000 individuals [ 4 ]. Despite being a rare condition, DM has a substantial impact on both the duration and quality of life. Individuals diagnosed with DM consistently exhibit lower quality of life across all domains compared to their counterparts [ 5 ]. Moreover, standardized mortality ratios and hazard ratios indicate a significantly elevated risk, ranging from 2.4 to 7.5, when compared to carefully matched control groups [ 6 – 14 ]. The increased mortality observed in individuals with DM is predominantly attributed to its associations with malignancy [ 7 , 15 – 16 ], interstitial lung disease (ILD) [ 16 – 17 ] and cardiovascular disease [ 18 ]. Thus, the identification of underlying pathologies that trigger DM may unveil avenues for novel treatments. DM is now recognised as an autoimmune disease, involving both humoral and T-cell activity [ 19 – 21 ]. T-cell-mediated myocytotoxicity and complement-mediated microangiopathy are characteristic features of DM. Endomysial capillaries are speculated to be the primary targets in DM, subjected to attack by a membranolytic complex consisting of C3b, C3bneo, C4b fragments and C5b–9 [ 21 ]. Furthermore, DM may co-occur with other autoimmune disorders, with several autoantibodies detectable in patients with DM [ 22 ]. Nonetheless, the causal relationship between DM and autoimmune disorders remains uncertain, which highlights the need for further investigation to elucidate specific biological mechanisms and provide insights for preventive interventions. The utilization of Mendelian randomization (MR) as a study design provides an opportunity to investigate the causal relationship between an exposure of interest, such as autoimmune disorders, and the outcome of interest, in this case, dermatomyositis. This approach employs instrumental variables (IVs) to facilitate a rigorous examination of causality [ 23 ]. Within MR studies, IVs play a crucial role in exploring the causal link between an exposure of interest and an outcome. These IVs consist of genetic variants that are highly associated with the exposure and meet specific assumptions, enabling the investigation of the causal association with the desired outcome. Given that these genetic variants are assigned randomly during conception, MR possesses the potential to minimize bias stemming from environmental confounders when properly conducted, similar to the design of a randomized controlled trial. This study utilized bidirectional MR to investigate the presence of a causal relationship between DM and a range of autoimmune disorders, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), asthma, celiac disease (CeD), Crohn's disease (CD), primary sclerosing cholangitis (PSC), psoriasis (PsO), irritable bowel syndrome (IBS), ulcerative colitis (UC), and type 1 diabetes (T1D). Summary statistics from the largest available genome-wide association studies (GWAS) conducted on European populations for the aforementioned traits were utilized in the analysis. Methods For a successful implementation of MR studies, it is crucial toensure that the genetic variants (SNPs) used as instrumental variables meet three essential criteria. First, the relevance assumption necessitates that the SNPs demonstrate robust associations with the exposure under investigation. Second, the independence assumption mandates that the SNPs are not influenced by common causes with the outcome. Lastly, the exclusion restriction assumption requires that the SNPs solely affect the outcome through the pathway of the exposure, excluding any other direct influences [ 24 ]. The summary of the study's overall design and the basic assumptions made in MR were presented in Fig. 1 . Study cohorts and GWAS To perform the MR analyses, we obtained summary-level data from the most extensive publicly accessible GWAS datasets for each respective trait (Table 1 ). Specifically, summary statistics for the ten autoimmune disorders GWAS were extracted from reputable sources such as the GWAS catalogue [ 25 – 26 ] and the IEU OpenGWAS project [ 27 ]. Additionally, data pertaining to DM were sourced from the FinnGen database [ 28 ]. The original publications provide comprehensive information regarding the recruitment procedures and diagnostic criteria employed in the studies. It is worth noting that all participants included in these investigations belonged to European ancestry. Furthermore, there was no substantial overlap observed between the populations examined in different GWAS cohorts. Table 1 Characteristics of the DM and autoimmune disease GWAS cohorts and results of the power analysis diseases Pubmed ID Cases Controls Sample size DM PMID: 36653562 201 172834 173035 Asthma PMID: 34103634 56,167 352,255 408,422 CD PMID: 28067908 12,194 28,072 40,266 CeD PMID: 22057235 12,041 12,228 24,269 IBS PMID: 34741163 40,548 293,220 333,768 MS PMID: 31604244 47,429 68,374 115,803 PSC PMID: 27992413 2871 12,019 14,890 PsO PMID: 23143594 10,588 22,806 33,394 RA PMID: 24390342 19,234 61,565 80,799 T1D PMID: 32005708 9266 15,584 24,840 UC PMID: 28067908 12,366 33,609 45,975 SLE PMID: 26502338 5201 9066 14,267 DM,Dermatomyositis,CD Crohn’s disease, CeD celiac disease, IBS irritable bowel syndrome, MS multiple sclerosis,PSC primary sclerosing cholangitis, PsO psoriasis, RA rheumatoid arthritis, T1D type 1 diabetes, UC ulcerative colitis, SLE systemic lupus erythematosus MR IV selection To choose genetic instruments for each of the ten exposure GWAS datasets, we employed the default settings offered by the R package TwoSampleMR. Our selection process involved extracting SNPs that reached genome-wide significance (p-value < 5.0E − 08), while excluding SNPs located within the human leukocyte antigen (HLA) region (chr6:27,477,797–34,448,354, hg19/GRCh37) [ 29 ]. In instances where an inadequate number of instrumental variables (IVs)were available in the outcome GWAS due to SNP unavailability, we utilized the LDproxyR tool to replace the IVs with proxy SNPs that demonstrated high linkage disequilibrium (LD r2 > 0.8). We applied standard clumping parameters to identify independent SNPs, utilizing a clumping window of 10,000 kb and an LD r2 cutoff of 0.001. To evaluate the strength of instrumental variables (IVs) and meet the first assumption of Mendelian randomization (MR), we calculated the R2, representing the proportion of variance in the exposures explained by the SNPs. Additionally, F-statistics were computed,providing further insights into the strength of the IVs [ 30 ]. Power calculation For each MR analysis, we utilized an online MR power calculator to establish the minimum odds ratio (OR) required, while maintaining a power of 80% (Table 1 ). MR analyses Two-sample MR analyses were conducted using the two-sample MR R package [ 31 – 32 ]. After performing IV clumping, we applied Steiger filtering to eliminate SNPs that explained a greater proportion of variance in the outcome compared to the exposure [ 31 ]. Next, we employed the inverse variance weighted (IW) method to combine the effects of various IVs. For each SNP, we computed the Wald ratio, and the individual SNP effects were meta-analyzed using IVW to obtain robust beta estimates. These beta estimates were subsequently transformed into ORs, providing conclusive effect estimates for further interpretation [ 33 – 35 ]. To evaluate the third assumption of MR, we utilized MR-Egger analysis. This approach allowed us to identify any potential violations of IV assumptions due to directional horizontal pleiotropy. By examining the intercept term in the MR-Egger regression, we could assess the presence of bias caused by pleiotropic effects and make appropriate adjustments in our analysis [ 33 ]. Furthermore, we applied the weighted median (WM) method to complement our analysis. This method calculates the median of the weighted estimates, allowing for consistent effect estimation even in scenarios where up to 50% of the IVs exhibit pleiotropic effects [ 36 ]. We assessed heterogeneity within the IVW and MR-Egger methods by conducting Cochran's Q test [ 37 ]. We generated scatter plots to visualize the outcomes of various MR methods using the TwoSampleMR package. Additionally, we employed the MR-PRESSO test to identify outlier SNPs that could potentially introduce bias in the estimates due to horizontal pleiotropy. By detecting and adjusting for these outliers, we ensured the integrity and accuracy of our results in the presence of potential confounding factors [ 38 ]. In the final step, we performed a leave-one-out analysis (LOO) to identify any individual SNP that exerted a disproportionate influence on the results of each MR study. To account for pleiotropic effects in our study, we performed multivariable MR analyses for the studied traits (type 2 diabetes, viral infection, sunburns, smoking status). We extracted SNPs that reached statistical significance, consolidated them, and performed clumping using the TwoSampleMR R package. Results Genetic instruments selection We incorporated ten exposures, namely RA, SLE, CD, UC, asthma, IBS, PsO, CeD, PSC and T1D, into our MR analysis. After implementing rigorous procedures to select IVs, the number of genetically associated variants for each exposure ranged from 9 to 19, exhibiting variability across different outcome measures. All instruments exhibited F-statistics greater than 10 (with a minimum value of 31.68), indicating that minimal bias due to weak instruments (Table 2 ). Table 2 Results of the MR analyses between liability to autoimmune disorders and the DM risk Exposure nSNP F_stat Method OR (95% CI) P Heterogeneity P MR-Egger intercept P MR-PRESSO global P Systemic lupus erythematosus IVW 1.126 (0.996, 1.274) 0.058 0.700 0.996 0.719 40 111.358 Weighted median 1.145 (0.963, 1.362) 0.126 MR Egger 1.096 (0.829, 1.448) 0.523 Crohn's disease IVW 1.053 (0.885, 1.252) 0.562 0.113 0.697 0.109 80 94.707 Weighted median 1.214 (0.948, 1.555) 0.124 MR Egger 0.965 (0.602, 1.545) 0.882 Ulcerative colitis IVW 0.988 (0.79, 1.236) 0.917 0.265 0.757 0.265 47 78.889 Weighted median 0.963 (0.701, 1.325) 0.818 MR Egger 1.094 (0.554, 2.162) 0.796 Type 1 diabetes IVW 1.153 (1.019, 1.304) 0.024 0.553 0.615 0.613 33 114.960 Weighted median 1.164 (0.986, 1.376) 0.073 MR Egger 1.113 (0.926, 1.337) 0.262 Asthma IVW 1.175 (0.763, 1.808) 0.465 0.203 0.315 0.194 76 Weighted median 1.349 (0.744, 2.446) 0.325 MR Egger 1.972 (0.662, 5.879) 0.227 Irritable bowel syndrome IVW 1.582 (0.121, 20.697) 0.727 0.708 0.397 0.730 4 31.680 Weighted median 3.462 (0.178, 67.401) 0.412 MR Egger 0 (0, 3869749.097) 0.408 Psoriasis IVW 1.027 (0.816, 1.291) 0.822 0.116 0.891 0.123 47 82.348 Weighted median 1.04 (0.764, 1.416) 0.804 MR Egger 1.065 (0.6, 1.891) 0.830 Celiac disease IVW 0.988 (0.82, 1.191) 0.902 0.156 0.507 0.095 15 333.796 Weighted median 1.056 (0.849, 1.314) 0.623 MR Egger 1.062 (0.802, 1.408) 0.680 Primary sclerosing cholangitis IVW 1.267 (0.974, 1.648) 0.077 0.779 0.449 0.802 15 79.815 Weighted median 1.346 (0.943, 1.923) 0.102 MR Egger 1.008 (0.537, 1.895) 0.980 Rheumatoid arthritis IVW 1.107 (0.878, 1.395) 0.391 0.996 0.442 0.994 50 85.279 Weighted median 0.966 (0.696, 1.341) 0.837 MR Egger 0.953 (0.611, 1.485) 0.832 IVW: Inverse variance weighted; OR: odds ratio. The ORs express efects of liability to each exposure on DM risk OR odds ratio, CI confdence interval, MR Mendelian randomization, SNP single nucleotide polymorphism,F-statistics “strength” of the instrumental variable The causal effect of autoimmune disorders on DM through univariable MR analysis When applying the Bonferroni correction threshold (p < 0.05/10 = 0.005), we observed no significant association between the genetic proxy for DM and an elevated risk of autoimmune disorders. We observed consistent MR estimates across various alternative approaches, including WM and MR-Egger regression (Table 2 , Fig. 2 A). However, our analysis revealed that T1D may increase the risk of DM (IVW OR = 1.153, 95%CI: 1.019, 1.304, p = 0.024). For the majority of MR estimates, Cochran's Q statistics revealed no substantial heterogeneity, as indicated by p-values exceeding 0.05. The results from tests assessing horizontal pleiotropy, such as the MR-Egger intercept test and MR-PRESSO, indicated minimal impact of pleiotropic effects on our findings, as evidenced by p-values exceeding 0.05 (Table 1 ). In addition, the leave-one-out (LOO) analysis provided evidence that the MR estimates were not heavily influenced by any particular SNPs, as demonstrated in Fig. 2 B. By conducting these sensitivity analyses collectively, we fortified the robustness of the estimated causal effects. Distinct causal effects of T1D on DM via multivariable MR analysis Given DM’s multifactorial aetiology involving genetic, immune and infectious factors, a multivariable MR (MVMR) analysis was conducted to assess the direct effect of T1D on DM. Consistent with the univariable MR findings, T1D was significantly associated with an increased risk of DM (IVW OR = 1.62, 95% CI: 1.299–2.021, p < 0.001) after accounting for type 2 diabetes, (IVW OR = 1.223, 95%CI: 1.04–1.438, p = 0.015) after accounting for viral infection, (IVW OR = 1.227, 95%CI: 1.09–1.381, p = 0.001) after accounting for sunburns, (IVW OR = 1.182, 95%CI: 1.027–1.36, p = 0.019) after accounting for smoking status. Collectively, MVMR analysis indicated distinct causal effects of T1D on DM (Table 3 ). Table 3 The causal effect of Type 1 diabetes on DM by multivariate MR analysis exposure Outcome Adjustment OR (95% CI) P Type 1 diabetes DM Type 2 diabetes 1.62 (1.299, 2.021) < 0.001 Viral infection 1.223 (1.04, 1.438) 0.015 Sunburns 1.227 (1.09, 1.381) 0.001 Smoking status: Current 1.182 (1.027, 1.36) 0.019 IVW: Inverse variance weighted; OR: odds ratio. Discussion In this study, we examined the causal association between DM and ten autoimmune disorders using two-sample MR approaches. To accomplish this, we utilized extensive GWAS summary data, publicly available on a large scale. This enabled us to explore the potential causal relationship between DM and various autoimmune disorders in a robust and comprehensive manner. Our findings revealed a significant causal effect of T1D on the increased risk of developing DM, which persisted even after accounting for the effects of type 2 diabetes, viral infection, sunburns and smoking status. This study represents the first MR investigation that aims to dissect the unique causal effects of autoimmune disorders on DM. By delving into these distinct causal relationships, our research provides valuable insights into various aspects of disease pathogenesis, including disease subtypes, disease management, and the development of therapeutic interventions. Autoimmune disorders often exhibit familial clustering, suggesting shared genetic and immunological factors among affected individuals. The pathophysiology of DM is multi-faceted and not fully elucidated, with contributions from genetic, environmental and immunological factors. Various genotyping investigations have identified associations between major histocompatibility complex variants and DM development, implicating specific HLA alleles in autoantibody production in both adults and children. Environmental factors such as UV light, viral infections, medications and smoking have also been proposed as potential triggers for DM. Excessive complement activation, interferons and subsequent T and B cell activation are implicated in the occurrence and development of DM [ 39 ]. As an autoimmune disease, DM may share common pathogenic mechanisms with other autoimmune diseases. Multiple studies have documented a higher prevalence of inflammatory bowel disease (IBD) among patients diagnosed with polymyositis or DM. Furthermore, these studies have shown that the presence of antinuclear antibody seropositivity can serve as a predictive factor for the development of IBD in these individuals [ 40 ]. According to the findings of Tseng et al., patients with UC exhibit a notably higher cumulative incidence of DM compared to individuals without UC. These results suggest that UC may serve as a potential independent risk factor for the concurrent development of DM, regardless of the presence of other autoimmune diseases [ 41 ]. Moreover, investigations have revealed a higher occurrence of SLE and T1D among families of children diagnosed with juvenile DM (JDM) [ 42 ]. A notable study conducted by Qu et al. employed whole exome sequencing to identify potential associations between JDM and specific genes related to T1D. The study revealed that genes such as phospholipase B1, cystic fibrosis transmembrane conductance regulator, tyrosine hydroxylase, CD6 molecule, perforin 1, and dynein axonemal heavy chain 2 exhibited potential associations with JDM [ 43 ]. A study conducted by Aikawa et al. investigated the potential association between JDM and CD, as well as the relationship between SLE and CD. The study included a cohort of 41 patients with JDM, alongside children diagnosed with SLE. Notably, among these participants, only one patient with JDM was identified as having co-occurring CD [ 44 ]. Due to the lack of further clinical investigations examining CD in children with JDM, no definitive conclusions can be drawn at this time. However, Giacomo Caio et al. conducted a study focusing on adult patients referred to a rheumatology outpatient clinic and observed a significant prevalence of CD antibodies among them. This finding underscores the significance of screening for CD in individuals presenting with rheumatological manifestations, emphasizing the importance of early detection and management of CD in this patient population [ 45 ]. Moreover, reports on whether CD increases DM risk are scarce. Our results underscore the association between T1D and an increased risk of DM, supported by multi-factor Mendelian analysis (p < 0.05). While other autoimmune diseases, including SLE and RA, did not show a significant association with DM in our study, the strength of our IVs for 10 autoimmune diseases was satisfactory, with F-numbers exceeding 10. Although few reports exist regarding the co-occurrence of T1D and DM [ 46 ], evidence suggests a shared genetic susceptibility between JDM and T1D [ 47 – 48 ], with common genetic variants implicated in both conditions. Furthermore, six T1D genes– CD6, PLB1, DNAH2, PRF1, TH , and CFTR –have been previously highlighted for their association with JDM [49]. Disorders such as T1D, multiple sclerosis, SLE, RA, Behçet's disease, polymyositis/DM and systemic scleroderma have all been associated with vitamin D deficiency to varying extents [ 50 ], suggesting abnormal vitamin D metabolism may represent a co-pathogenic pathway of T1D and DM. The examination of the causal association between autoimmune disorders and DM holds significant clinical implications. Implementing screening measures and providing early intervention for individuals with T1D could potentially mitigate the risk of developing DM in the future. Additionally, there may be potential for repurposing existing T1D treatments as a means of managing DM. These findings highlight the importance of considering preventive strategies and exploring novel therapeutic approaches for individuals at risk of developing DM, particularly among those with a history of autoimmune disorders such as T1D. This comprehensive two-sample MR study revealed that T1D increases the risk of DM. Moreover, our multi-factor Mendelian studies further support the notion that T1D is an independent risk factor for DM after correcting for common factors of type 2 diabetes, viral infection, sunburns and smoking. The robustness of our MR findings, as demonstrated across various analytical approaches, serves as compelling evidence supporting the causal link between T1D and DM. The consistency observed in our results further strengthens the validity of this association, providing a solid foundation for understanding the causality between T1D and DM. Nevertheless, it is important to acknowledge the limitations of our study. Firstly, our investigation was restricted to a study population of European descent, which may restrict the generalizability of our findings to other populations. Furthermore, certain MR analyses conducted in our study lacked sufficient statistical power to detect small effects due to the limited variability explained by the SNP instruments or the relatively small sample sizes of the GWAS for the outcomes. Additionally, the exclusion of ambiguous or palindromic SNPs from our MR instruments might have further impacted the power of our MR analyses. To address these limitations, future MR studies utilizing larger and more diverse GWAS datasets for autoimmune traits are warranted. Such studies have the potential to overcome these constraints and provide additional insights into the associations between these diseases. Declarations CONFLICT OF INTEREST STATEMENT All authors have no conflicts of interest to report. Clinical trial number Not applicable FUNDING INFORMATION This work was supported by the Development Fund of National Natural Science Foundation of China (82271844), Jiangsu Provincial Health Commission elderly health research project (LKM2022071), Jiangsu Province Chinese medicine science and technology development fund project (MS2022106) and Huai'an Key Laboratory of autoimmune diseases (HAP202302). Author Contribution JL designed study, DY, XW,YT and SL collected data, PZ and LX analysed the data,ZZ and JW wrote paper, DM and KW reviewed the article. References Didona D, Juratli, Ha. Scarsella l, eming r, Hertl m. the polymor- phous spectrum of dermatomyositis: classic features, newly described skin lesions, and rare variants. eur J Dermatol. 2020;30:229–42. Callen JP. Dermatomyositis lancet. 2000;355:53–7. DeWane. me, Waldman r, lu J. Dermatomyositis: clinical features and pathogenesis. J am acad Dermatol 2020; 82:267–81. 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Med (Kaunas). 2022;58(12):1727. 10.3390/medicina58121727 . PMID: 36556929; PMCID: PMC9781532. Tseng CC, Chang SJ, Liao WT, Chan YT, Tsai WC, Ou TT, Wu CC, Sung WY, Hsieh MC, Yen JH. Increased Cumulative Incidence of Dermatomyositis in Ulcerative Colitis: a Nationwide Cohort Study. Sci Rep. 2016;6:28175. 10.1038/srep28175 . PMID: 27325143; PMCID: PMC4914943. Niewold TB, Wu SC, Smith M et al. Familial aggregation of autoimmune disease in juvenile dermatomyositis.[J].Pediatrics, 2011, 127(5):1239–46. 10.1542/peds.2010-3022 Qu HQ, Qu J, Vaccaro C, Chang X, Mentch F, Li J, Mafra F, Nguyen K, Gonzalez M, March M, Pellegrino R, Glessner J, Sleiman P, Kao C, Hakonarson H. Genetic analysis for type 1 diabetes genes in juvenile dermatomyositis unveils genetic disease overlap. Rheumatology (Oxford). 2022;61(8):3497–3501. 10.1093/rheumatology/keac100 . PMID: 35171267. Aikawa NE, Jesus AA, Liphaus BL, Silva CA, Carneiro-Sampaio M, Viana VS, Sallum AM. Organ-specific autoanti-bodies and autoimmune diseases in juvenile systemic lupus erythematosus and juvenile dermatomyositis patients. Clin Exp Rheumatol. 2012;30:126–31. [CrossRef]. Caio G, De Giorgio R, Ursini F, Fanaro S, Volta U. Prevalence of celiac disease serological markers in a cohort of Italian rheumatological patients. Gastroenterol Hepatol Bed Bench. 2018 Summer;11(3):244–9. PMID: 30013749; PMCID: PMC6040033. Carole M, McClanahan HL, Smith SA, Garner. April, Co-occurrence of dermatomyositis and Hashimoto’s thyroiditis in a type I diabetic patient, QJM: An International Journal of Medicine, 108, Issue 4, 2015, Pages 331–3. Niewold TB, Wu SC, Smith M, Morgan GA, Pachman LM. Familial aggregation of autoimmune disease in juvenile dermatomyositis. Pediatrics. 2011;127:e1239–46. Hughes JW, Riddlesworth TD, DiMeglio LA, Qu HQ, Qu J, Vaccaro C, Chang X, Mentch F, Li J, Mafra F, Nguyen K, Gonzalez M, March M, Pellegrino R, Glessner J, Sleiman P, Kao C, Hakonarson H et al. Autoimmune diseases in children and adults with type 1diabetes from the T1D exchange clinic registry. J Clin Endocrinol Metab. Genetic analysis for type 1 diabetes genes in juvenile dermatomyositis unveils genetic disease overlap. Rheumatology (Oxford). 2022;61(8):3497–3501. Pelajo CF, Lopez-Benitez JM, Miller LC. Vitamin D and autoimmune rheumatologic disorders. Autoimmun Rev. 2010;9(7):507–10. 10.1016/j.autrev.2010.02.011 . Epub 2010 Feb 8. PMID: 20146942. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5143664","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":361982082,"identity":"852459b5-3be5-40ee-bb45-48b9397786fa","order_by":0,"name":"Zhongyuan Zhang","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":361982083,"identity":"fbdae701-2558-4df5-8d3a-56a98661172f","order_by":1,"name":"Jiajia Wang","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiajia","middleName":"","lastName":"Wang","suffix":""},{"id":361982084,"identity":"2e831b34-be12-4806-9618-afca9b1a8fef","order_by":2,"name":"Ping Zhu","email":"","orcid":"","institution":"The Affiliated Chuzhou Hospital of Traditional Chinese Medicine of Jiangsu College Of Nursing","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Zhu","suffix":""},{"id":361982085,"identity":"c0403a88-c8a6-4b47-9921-8cc15865ed6b","order_by":3,"name":"Lingxiao Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingxiao","middleName":"","lastName":"Xu","suffix":""},{"id":361982086,"identity":"c31ee539-af93-4483-9720-61adcd3d2bb3","order_by":4,"name":"Dandan Yan","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Yan","suffix":""},{"id":361982087,"identity":"e88618ed-44d9-498f-8585-8bc4b0d6873e","order_by":5,"name":"Xu Wang","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Wang","suffix":""},{"id":361982088,"identity":"13060a7d-ff9f-42c3-8563-5cac6820e886","order_by":6,"name":"Yian Tian","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yian","middleName":"","lastName":"Tian","suffix":""},{"id":361982089,"identity":"c7cdfaa2-88a8-4d83-b879-8645188bbcf0","order_by":7,"name":"Shanshan Liu","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Liu","suffix":""},{"id":361982090,"identity":"bc2fa231-e35f-4422-b4f9-efb88bd01428","order_by":8,"name":"Deqian Meng","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Deqian","middleName":"","lastName":"Meng","suffix":""},{"id":361982091,"identity":"9f5c1b57-ea88-420d-9032-5c462116f9d4","order_by":9,"name":"Kai Wang","email":"","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wang","suffix":""},{"id":361982092,"identity":"7b798743-be30-4973-85da-50e133198828","order_by":10,"name":"Ju Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACPmaGBIaEChtmfmbmAwwSYLEE/FrYQFoenEljl2xnSyBSCxAzPmw7zG/Qz2MAFSOkhZ3hmURiW5q0ATPPtweWOYcZ+NlzDBh+7sDrsDSJhHM2xubMvNsNJLcdZpDseWPA2HuGkJaytGTLZt5tEiAtBjdyDJgZ2whpYTtcv+EwzzOwFnvitLQdZjY4zMMGsUWCsJZki4QzacySzWxmQC3pPBJnnhUc7MWjhZ//TOLNH6Co5D/8TFpym7Ucf3vyxgc/8WhhYOBJgDOZgVHJA2IcwKeBgYEdIc/4Ab/SUTAKRsEoGKEAAEtkR3X8BnX7AAAAAElFTkSuQmCC","orcid":"","institution":"Huaian Clinical College of Xuzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ju","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-09-24 09:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5143664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5143664/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71477885,"identity":"61d2eaa7-a77d-4c1c-9803-5c92cfc865dc","added_by":"auto","created_at":"2024-12-16 05:27:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87728,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the study design and three MR assumptions\u003c/p\u003e\n\u003cp\u003eMR1: the genetic instruments are significantly associated with the exposure variables; MR2: the genetic variants are not associated with confounding factors; MR3: the genetic variants affect the outcome only through the exposure while not through any alternative pathways.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5143664/v1/aff976604975c3284f9b467b.jpeg"},{"id":71477884,"identity":"9f46d11a-b1e0-4d89-bf98-0a87df0a5843","added_by":"auto","created_at":"2024-12-16 05:27:01","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":668681,"visible":true,"origin":"","legend":"\u003cp\u003eA Scatter plot of the causal estimates between the T1D diabetes and DM; B: The leave-one-out (LOO) analysis for Type 1 diabetes on Dermatopolymyositis\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5143664/v1/8ef3df9da022b425287bd568.jpeg"},{"id":71478497,"identity":"a15b7091-9c9c-4d41-9c73-d7c685d8ae71","added_by":"auto","created_at":"2024-12-16 05:35:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1333033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5143664/v1/452e9b46-06a2-413d-84e6-9fc3d0e66087.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between dermatomyositis and autoimmune d isorders: a Mendelian randomization study","fulltext":[{"header":"Background","content":"\u003cp\u003eDermatomyositis (DM) is a debilitating disorder classified among rare autoimmune diseases and characterised by polymorphous cutaneous features and variable muscle involvement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the pathogenesis of DM remains complex and inadequately understood, evidence suggests the involvement of various factors, including genetic predisposition, environmental agents and immune-mediated mechanisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Current estimates place DM incidence between 1.0 to 15 per million and prevalence between 1.2 to 21 per 100,000 individuals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite being a rare condition, DM has a substantial impact on both the duration and quality of life. Individuals diagnosed with DM consistently exhibit lower quality of life across all domains compared to their counterparts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, standardized mortality ratios and hazard ratios indicate a significantly elevated risk, ranging from 2.4 to 7.5, when compared to carefully matched control groups [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The increased mortality observed in individuals with DM is predominantly attributed to its associations with malignancy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], interstitial lung disease (ILD) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and cardiovascular disease [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, the identification of underlying pathologies that trigger DM may unveil avenues for novel treatments.\u003c/p\u003e \u003cp\u003eDM is now recognised as an autoimmune disease, involving both humoral and T-cell activity [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. T-cell-mediated myocytotoxicity and complement-mediated microangiopathy are characteristic features of DM. Endomysial capillaries are speculated to be the primary targets in DM, subjected to attack by a membranolytic complex consisting of C3b, C3bneo, C4b fragments and C5b\u0026ndash;9 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, DM may co-occur with other autoimmune disorders, with several autoantibodies detectable in patients with DM [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nonetheless, the causal relationship between DM and autoimmune disorders remains uncertain, which highlights the need for further investigation to elucidate specific biological mechanisms and provide insights for preventive interventions.\u003c/p\u003e \u003cp\u003eThe utilization of Mendelian randomization (MR) as a study design provides an opportunity to investigate the causal relationship between an exposure of interest, such as autoimmune disorders, and the outcome of interest, in this case, dermatomyositis. This approach employs instrumental variables (IVs) to facilitate a rigorous examination of causality [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Within MR studies, IVs play a crucial role in exploring the causal link between an exposure of interest and an outcome. These IVs consist of genetic variants that are highly associated with the exposure and meet specific assumptions, enabling the investigation of the causal association with the desired outcome. Given that these genetic variants are assigned randomly during conception, MR possesses the potential to minimize bias stemming from environmental confounders when properly conducted, similar to the design of a randomized controlled trial. This study utilized bidirectional MR to investigate the presence of a causal relationship between DM and a range of autoimmune disorders, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), asthma, celiac disease (CeD), Crohn's disease (CD), primary sclerosing cholangitis (PSC), psoriasis (PsO), irritable bowel syndrome (IBS), ulcerative colitis (UC), and type 1 diabetes (T1D). Summary statistics from the largest available genome-wide association studies (GWAS) conducted on European populations for the aforementioned traits were utilized in the analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eFor a successful implementation of MR studies, it is crucial toensure that the genetic variants (SNPs) used as instrumental variables meet three essential criteria. First, the relevance assumption necessitates that the SNPs demonstrate robust associations with the exposure under investigation. Second, the independence assumption mandates that the SNPs are not influenced by common causes with the outcome. Lastly, the exclusion restriction assumption requires that the SNPs solely affect the outcome through the pathway of the exposure, excluding any other direct influences [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The summary of the study's overall design and the basic assumptions made in MR were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eStudy cohorts and GWAS\u003c/p\u003e \u003cp\u003eTo perform the MR analyses, we obtained summary-level data from the most extensive publicly accessible GWAS datasets for each respective trait (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, summary statistics for the ten autoimmune disorders GWAS were extracted from reputable sources such as the GWAS catalogue [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and the IEU OpenGWAS project [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, data pertaining to DM were sourced from the FinnGen database [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The original publications provide comprehensive information regarding the recruitment procedures and diagnostic criteria employed in the studies. It is worth noting that all participants included in these investigations belonged to European ancestry. Furthermore, there was no substantial overlap observed between the populations examined in different GWAS cohorts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the DM and autoimmune disease GWAS cohorts and results of the power analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediseases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePubmed ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;36653562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;34103634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56,167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352,255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e408,422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;28067908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28,072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;22057235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;34741163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40,548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e293,220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e333,768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;31604244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47,429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68,374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115,803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;27992413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;23143594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22,806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33,394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;24390342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61,565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80,799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;32005708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;28067908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33,609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45,975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID:\u0026nbsp;26502338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDM,Dermatomyositis,CD Crohn\u0026rsquo;s disease, CeD celiac disease,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIBS irritable bowel syndrome, MS multiple sclerosis,PSC primary sclerosing cholangitis, PsO psoriasis, RA rheumatoid arthritis, T1D type 1 diabetes, UC ulcerative colitis, SLE systemic lupus erythematosus\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMR IV selection\u003c/p\u003e \u003cp\u003eTo choose genetic instruments for each of the ten exposure GWAS datasets, we employed the default settings offered by the R package TwoSampleMR. Our selection process involved extracting SNPs that reached genome-wide significance (p-value\u0026thinsp;\u0026lt;\u0026thinsp;5.0E\u0026thinsp;\u0026minus;\u0026thinsp;08), while excluding SNPs located within the human leukocyte antigen (HLA) region (chr6:27,477,797\u0026ndash;34,448,354, hg19/GRCh37) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In instances where an inadequate number of instrumental variables (IVs)were available in the outcome GWAS due to SNP unavailability, we utilized the LDproxyR tool to replace the IVs with proxy SNPs that demonstrated high linkage disequilibrium (LD r2\u0026thinsp;\u0026gt;\u0026thinsp;0.8). We applied standard clumping parameters to identify independent SNPs, utilizing a clumping window of 10,000 kb and an LD r2 cutoff of 0.001. To evaluate the strength of instrumental variables (IVs) and meet the first assumption of Mendelian randomization (MR), we calculated the R2, representing the proportion of variance in the exposures explained by the SNPs. Additionally, F-statistics were computed,providing further insights into the strength of the IVs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePower calculation\u003c/p\u003e \u003cp\u003eFor each MR analysis, we utilized an online MR power calculator to establish the minimum odds ratio (OR) required, while maintaining a power of 80% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMR analyses\u003c/p\u003e \u003cp\u003eTwo-sample MR analyses were conducted using the two-sample MR R package [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. After performing IV clumping, we applied Steiger filtering to eliminate SNPs that explained a greater proportion of variance in the outcome compared to the exposure [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Next, we employed the inverse variance weighted (IW) method to combine the effects of various IVs. For each SNP, we computed the Wald ratio, and the individual SNP effects were meta-analyzed using IVW to obtain robust beta estimates. These beta estimates were subsequently transformed into ORs, providing conclusive effect estimates for further interpretation [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To evaluate the third assumption of MR, we utilized MR-Egger analysis. This approach allowed us to identify any potential violations of IV assumptions due to directional horizontal pleiotropy. By examining the intercept term in the MR-Egger regression, we could assess the presence of bias caused by pleiotropic effects and make appropriate adjustments in our analysis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, we applied the weighted median (WM) method to complement our analysis. This method calculates the median of the weighted estimates, allowing for consistent effect estimation even in scenarios where up to 50% of the IVs exhibit pleiotropic effects [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We assessed heterogeneity within the IVW and MR-Egger methods by conducting Cochran's Q test [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We generated scatter plots to visualize the outcomes of various MR methods using the TwoSampleMR package. Additionally, we employed the MR-PRESSO test to identify outlier SNPs that could potentially introduce bias in the estimates due to horizontal pleiotropy. By detecting and adjusting for these outliers, we ensured the integrity and accuracy of our results in the presence of potential confounding factors [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In the final step, we performed a leave-one-out analysis (LOO) to identify any individual SNP that exerted a disproportionate influence on the results of each MR study. To account for pleiotropic effects in our study, we performed multivariable MR analyses for the studied traits (type 2 diabetes, viral infection, sunburns, smoking status). We extracted SNPs that reached statistical significance, consolidated them, and performed clumping using the TwoSampleMR R package.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGenetic instruments selection\u003c/p\u003e \u003cp\u003eWe incorporated ten exposures, namely RA, SLE, CD, UC, asthma, IBS, PsO, CeD, PSC and T1D, into our MR analysis. After implementing rigorous procedures to select IVs, the number of genetically associated variants for each exposure ranged from 9 to 19, exhibiting variability across different outcome measures. All instruments exhibited F-statistics greater than 10 (with a minimum value of 31.68), indicating that minimal bias due to weak instruments (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the MR analyses between liability to autoimmune disorders and the DM risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF_stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeterogeneity P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR-Egger intercept P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMR-PRESSO global P\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.126 (0.996, 1.274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.145 (0.963, 1.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.096 (0.829, 1.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrohn's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.053 (0.885, 1.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.214 (0.948, 1.555)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.965 (0.602, 1.545)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUlcerative colitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988 (0.79, 1.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.963 (0.701, 1.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.094 (0.554, 2.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.153 (1.019, 1.304)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.164 (0.986, 1.376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.113 (0.926, 1.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.175 (0.763, 1.808)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.349 (0.744, 2.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.972 (0.662, 5.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrritable bowel syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.582 (0.121, 20.697)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.462 (0.178, 67.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0, 3869749.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsoriasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.027 (0.816, 1.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04 (0.764, 1.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.065 (0.6, 1.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeliac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988 (0.82, 1.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e333.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.056 (0.849, 1.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.062 (0.802, 1.408)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary sclerosing cholangitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.267 (0.974, 1.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.346 (0.943, 1.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.008 (0.537, 1.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.107 (0.878, 1.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.966 (0.696, 1.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.953 (0.611, 1.485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eIVW: Inverse variance weighted; OR: odds ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eThe ORs express efects of liability to each exposure on DM risk OR odds ratio, CI confdence interval, MR Mendelian randomization, SNP single nucleotide polymorphism,F-statistics \u0026ldquo;strength\u0026rdquo; of the instrumental variable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe causal effect of autoimmune disorders on DM through univariable MR analysis\u003c/p\u003e \u003cp\u003eWhen applying the Bonferroni correction threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05/10\u0026thinsp;=\u0026thinsp;0.005), we observed no significant association between the genetic proxy for DM and an elevated risk of autoimmune disorders. We observed consistent MR estimates across various alternative approaches, including WM and MR-Egger regression (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). However, our analysis revealed that T1D may increase the risk of DM (IVW OR\u0026thinsp;=\u0026thinsp;1.153, 95%CI: 1.019, 1.304, p\u0026thinsp;=\u0026thinsp;0.024). For the majority of MR estimates, Cochran's Q statistics revealed no substantial heterogeneity, as indicated by p-values exceeding 0.05. The results from tests assessing horizontal pleiotropy, such as the MR-Egger intercept test and MR-PRESSO, indicated minimal impact of pleiotropic effects on our findings, as evidenced by p-values exceeding 0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, the leave-one-out (LOO) analysis provided evidence that the MR estimates were not heavily influenced by any particular SNPs, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. By conducting these sensitivity analyses collectively, we fortified the robustness of the estimated causal effects.\u003c/p\u003e \u003cp\u003eDistinct causal effects of T1D on DM via multivariable MR analysis\u003c/p\u003e \u003cp\u003eGiven DM\u0026rsquo;s multifactorial aetiology involving genetic, immune and infectious factors, a multivariable MR (MVMR) analysis was conducted to assess the direct effect of T1D on DM. Consistent with the univariable MR findings, T1D was significantly associated with an increased risk of DM (IVW OR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 1.299\u0026ndash;2.021, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after accounting for type 2 diabetes, (IVW OR\u0026thinsp;=\u0026thinsp;1.223, 95%CI: 1.04\u0026ndash;1.438, p\u0026thinsp;=\u0026thinsp;0.015) after accounting for viral infection, (IVW OR\u0026thinsp;=\u0026thinsp;1.227, 95%CI: 1.09\u0026ndash;1.381, p\u0026thinsp;=\u0026thinsp;0.001) after accounting for sunburns, (IVW OR\u0026thinsp;=\u0026thinsp;1.182, 95%CI: 1.027\u0026ndash;1.36, p\u0026thinsp;=\u0026thinsp;0.019) after accounting for smoking status. Collectively, MVMR analysis indicated distinct causal effects of T1D on DM (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe causal effect of Type 1 diabetes on DM by multivariate MR analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjustment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 2 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.62 (1.299, 2.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eViral infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.223 (1.04, 1.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSunburns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.227 (1.09, 1.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmoking status: Current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.182 (1.027, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIVW: Inverse variance weighted; OR: odds ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined the causal association between DM and ten autoimmune disorders using two-sample MR approaches. To accomplish this, we utilized extensive GWAS summary data, publicly available on a large scale. This enabled us to explore the potential causal relationship between DM and various autoimmune disorders in a robust and comprehensive manner. Our findings revealed a significant causal effect of T1D on the increased risk of developing DM, which persisted even after accounting for the effects of type 2 diabetes, viral infection, sunburns and smoking status. This study represents the first MR investigation that aims to dissect the unique causal effects of autoimmune disorders on DM. By delving into these distinct causal relationships, our research provides valuable insights into various aspects of disease pathogenesis, including disease subtypes, disease management, and the development of therapeutic interventions.\u003c/p\u003e \u003cp\u003eAutoimmune disorders often exhibit familial clustering, suggesting shared genetic and immunological factors among affected individuals. The pathophysiology of DM is multi-faceted and not fully elucidated, with contributions from genetic, environmental and immunological factors. Various genotyping investigations have identified associations between major histocompatibility complex variants and DM development, implicating specific HLA alleles in autoantibody production in both adults and children. Environmental factors such as UV light, viral infections, medications and smoking have also been proposed as potential triggers for DM. Excessive complement activation, interferons and subsequent T and B cell activation are implicated in the occurrence and development of DM [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs an autoimmune disease, DM may share common pathogenic mechanisms with other autoimmune diseases. Multiple studies have documented a higher prevalence of inflammatory bowel disease (IBD) among patients diagnosed with polymyositis or DM. Furthermore, these studies have shown that the presence of antinuclear antibody seropositivity can serve as a predictive factor for the development of IBD in these individuals [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. According to the findings of Tseng et al., patients with UC exhibit a notably higher cumulative incidence of DM compared to individuals without UC. These results suggest that UC may serve as a potential independent risk factor for the concurrent development of DM, regardless of the presence of other autoimmune diseases [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, investigations have revealed a higher occurrence of SLE and T1D among families of children diagnosed with juvenile DM (JDM) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A notable study conducted by Qu et al. employed whole exome sequencing to identify potential associations between JDM and specific genes related to T1D. The study revealed that genes such as phospholipase B1, cystic fibrosis transmembrane conductance regulator, tyrosine hydroxylase, CD6 molecule, perforin 1, and dynein axonemal heavy chain 2 exhibited potential associations with JDM [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA study conducted by Aikawa et al. investigated the potential association between JDM and CD, as well as the relationship between SLE and CD. The study included a cohort of 41 patients with JDM, alongside children diagnosed with SLE. Notably, among these participants, only one patient with JDM was identified as having co-occurring CD [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Due to the lack of further clinical investigations examining CD in children with JDM, no definitive conclusions can be drawn at this time. However, Giacomo Caio et al. conducted a study focusing on adult patients referred to a rheumatology outpatient clinic and observed a significant prevalence of CD antibodies among them. This finding underscores the significance of screening for CD in individuals presenting with rheumatological manifestations, emphasizing the importance of early detection and management of CD in this patient population [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Moreover, reports on whether CD increases DM risk are scarce.\u003c/p\u003e \u003cp\u003eOur results underscore the association between T1D and an increased risk of DM, supported by multi-factor Mendelian analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While other autoimmune diseases, including SLE and RA, did not show a significant association with DM in our study, the strength of our IVs for 10 autoimmune diseases was satisfactory, with F-numbers exceeding 10. Although few reports exist regarding the co-occurrence of T1D and DM [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], evidence suggests a shared genetic susceptibility between JDM and T1D [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], with common genetic variants implicated in both conditions. Furthermore, six T1D genes\u0026ndash;\u003cem\u003eCD6, PLB1, DNAH2, PRF1, TH\u003c/em\u003e, and \u003cem\u003eCFTR\u003c/em\u003e\u0026ndash;have been previously highlighted for their association with JDM [49]. Disorders such as T1D, multiple sclerosis, SLE, RA, Beh\u0026ccedil;et's disease, polymyositis/DM and systemic scleroderma have all been associated with vitamin D deficiency to varying extents [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e50\u003c/span\u003e], suggesting abnormal vitamin D metabolism may represent a co-pathogenic pathway of T1D and DM.\u003c/p\u003e \u003cp\u003eThe examination of the causal association between autoimmune disorders and DM holds significant clinical implications. Implementing screening measures and providing early intervention for individuals with T1D could potentially mitigate the risk of developing DM in the future. Additionally, there may be potential for repurposing existing T1D treatments as a means of managing DM. These findings highlight the importance of considering preventive strategies and exploring novel therapeutic approaches for individuals at risk of developing DM, particularly among those with a history of autoimmune disorders such as T1D.\u003c/p\u003e \u003cp\u003eThis comprehensive two-sample MR study revealed that T1D increases the risk of DM. Moreover, our multi-factor Mendelian studies further support the notion that T1D is an independent risk factor for DM after correcting for common factors of type 2 diabetes, viral infection, sunburns and smoking. The robustness of our MR findings, as demonstrated across various analytical approaches, serves as compelling evidence supporting the causal link between T1D and DM. The consistency observed in our results further strengthens the validity of this association, providing a solid foundation for understanding the causality between T1D and DM.\u003c/p\u003e \u003cp\u003eNevertheless, it is important to acknowledge the limitations of our study. Firstly, our investigation was restricted to a study population of European descent, which may restrict the generalizability of our findings to other populations. Furthermore, certain MR analyses conducted in our study lacked sufficient statistical power to detect small effects due to the limited variability explained by the SNP instruments or the relatively small sample sizes of the GWAS for the outcomes. Additionally, the exclusion of ambiguous or palindromic SNPs from our MR instruments might have further impacted the power of our MR analyses. To address these limitations, future MR studies utilizing larger and more diverse GWAS datasets for autoimmune traits are warranted. Such studies have the potential to overcome these constraints and provide additional insights into the associations between these diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e\n\u003cp\u003eAll authors have no conflicts of interest to report.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eFUNDING INFORMATION\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Development Fund of National Natural Science Foundation of China (82271844), Jiangsu Provincial Health Commission elderly health research project (LKM2022071), Jiangsu Province Chinese medicine science and technology development fund project (MS2022106) and Huai\u0026apos;an Key Laboratory of autoimmune diseases (HAP202302).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJL designed study, DY, XW,YT and SL collected data, PZ and LX analysed the data,ZZ and JW wrote paper, DM and KW reviewed the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDidona D, Juratli, Ha. 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Rheumatology (Oxford). 2022;61(8):3497\u0026ndash;3501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelajo CF, Lopez-Benitez JM, Miller LC. Vitamin D and autoimmune rheumatologic disorders. Autoimmun Rev. 2010;9(7):507\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.autrev.2010.02.011\u003c/span\u003e\u003cspan address=\"10.1016/j.autrev.2010.02.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2010 Feb 8. PMID: 20146942.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, Dermatomyositis, Type 1 diabetes, Autoimmune disorders, Causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-5143664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5143664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eVarious autoimmune disorders have been linked to dermatomyositis (DM) based on findings from epidemiological studies. The objective of this study is to examine the causal association between autoimmune disorders and DM utilizing the methodology of Mendelian randomization (MR).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed summary statistics from the largest European genome-wide association studies (GWAS) on autoimmune disorders to assess the genetically predicted effects on DM risk in a two-sample MR framework. Single nucleotide polymorphisms (SNPs) strongly associated with 10 immune-related traits were extracted from these GWAS datasets and their effects were examined in a European DM GWAS cohort (201 cases and 172834 controls). In order to address potential bias arising from the intricate linkage disequilibrium structure observed in the human leukocyte antigen region, the analysis excluded SNPs within this specific genomic region. Subsequently, a multivariate Mendelian analysis was conducted to investigate the association between one autoimmune disease and DM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter applying the Bonferroni correction to account for multiple testing, our MR analyses revealed a potential heightened risk of DM associated with type 1 diabetes (T1D), one of the autoimmune diseases under investigation. We further conducted a Mendelian analysis focusing on T1D and the occurrence of DM, incorporating type 2 diabetes, viral infection, sunburns and smoking status. Our findings revealed that T1D independently increased the risk of DM, regardless of smoking and viral infection, which were previously identified as DM risk factors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur MR study provides evidence supporting a relationship between susceptibility to T1D and increased DM risk in the European population.\u003c/p\u003e","manuscriptTitle":"Causal relationship between dermatomyositis and autoimmune d isorders: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 05:26:27","doi":"10.21203/rs.3.rs-5143664/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":"c66032b1-c95b-47d1-a8f7-5f5e04e34801","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-16T05:26:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 05:26:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5143664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5143664","identity":"rs-5143664","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}