Causal Relationships between Common Autoimmune Disease and Carpal Tunnel Syndrome: Study of Univariable and Multivariable Mendelian Randomization.

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Xin Tan, Shirong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3456971/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Whether Autoimmune diseases (AIDs) are a risk factor for carpal tunnel syndrome (CTS) is questionable and has been reported in observational studies, but the quality of the evidence is low and inconclusive. Our study intends to assess the causal association between common AIDs and CTS through univariable and multivariable mendelian randomization (MR). Methods we mainly utilized univariable MR analysis through IVW. Weighted median, MR-Egger analysis to assess the association of AIDs and CTS. Then, we extended the limits of univariable MR analysis through multivariable mendelian randomization in IVW, Egger method, Lasso and median method. The mediating effect was calculated by mediating MR. Finally, the MR-PRESSO, Cochran's Q test and F-values are calculated to assess the levels of pleiotropy, heterogeneity, and intensity of selected IVs and exposures through mediated MR. Results Univariable MR results showed a positive correlation from RA, GD, T1D to ILD with an increased relative risk. While, adjusting by other two AIDs through multivariable mendelian randomization, only T1D is robustly correlated with CTS. T1D can also produce effects on CTS through RA, GD as mediators. Furthermore, the outcome of MR-Egger intercept did not provide evidence of horizontal pleiotropy. The F-value results were all greater than 10, indicating that the selected IV and exposure intensities were appropriate. Conclusion Based on the results of univariable mendelian randomization analysis, the study found genetic evidence supporting a positive causal relationship from RA, T1D, GD to CTS, but only T1D maintained consistent results after multivariable MR analysis. In addition, 24.3% and 25.1% of the effects of T1D on CTS were mediated by RA and GD, respectively. Therefore, appropriate intervention of T1D can reduce the incidence of CTS. Treatment of T1D should be considered a primary preventive measure for CTS. Biological sciences/Genetics Biological sciences/Genetics/Genetic association study Biological sciences/Genetics/Genetic association study/Genome wide association studies Autoimmune Disease Carpal Tunnel Syndrome Type 1 Diabetes Multivariable Mendelian Randomization Study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Carpal tunnel syndrome (CTS), A common nerve entrapment syndromes occurring in women, is a loss of palmar sensation, motor function and even disability caused by compression of the median nerve by the narrow osteofibrous canal of the wrist joint, which severely affects the quality of life of patients and has imposed an estimated $ 270- $ 480 million annual economic burden [ 1 , 2 ]. The disease has mostly been previously reported as a peripheral neurological manifestation in various autoimmune diseases (AIDs), especially rheumatoid arthritis (RA), type I diabetes (T1D), Graves' disease (GD), etc.[ 3 – 5 ]. At the same time, dysregulation of the immune system manifested by changes in the levels of immune cells and cytokines can also be observed in patients with CTS [ 6 ]. But the quality of evidence that is relatively low because previous studies have been mostly observational with poor sample size. Meanwhile, the interconnectedness among AIDs makes it difficult to rule out confounding factors. Therefore, whether AIDs are risk factor for CTS needs further confirmation. Mendelian randomization (MR) is an analysis that utilizes genetic variation instrumental variates (IVs) to explore the causality between exposures and outcomes[ 7 ]. In MR analysis, causal direction is specific and not easily confused and avoiding reverse causal bias in observational studies[ 8 ]. Besides, compared with randomized controlled trials, MR owns the advantages of high feasibility, cost, and time efficient. Traditional univariate MR analysis assesses the impact of individual predictor variables on the outcome. Three hypotheses are used to determine the validity of IV - it must be (I) related to the exposure; (II) Not associated with outcomes of exposure; and (III) independent of all confounding factors [ 9 ]. If IV is associated with confounding of exposures and outcomes, it conflicts with these assumptions above and may lead to potential bias and errors. To avoid such bias, multivariate MR analysis is introduced to jointly assesses the effect of these valid IVs on the outcomes. Multivariate MR analysis, extension of univariate MR, considers pleiotropy among multiple traits [ 10 ]. Compared to traditional univariate one, multivariate MR has broader assumptions: Genetic variation affects exposure to multiple measures, as do exclusion restrictions and exchangeability assumptions. By analyzing several risk factors at the same time, it is possible to estimate the causal impact of one risk factor on the outcome independently of the other risk factors. [ 11 ]. In this study, we performed both types of mendelian randomization analyses and calculate the mediating effect [ 12 , 13 ], The schematic diagram is shown in Fig. 1. We selected meaningful AIDs through univariate and multivariate MR analyses. We verified whether the effects of the selected AIDs on CTS were mediated through other AIDs. That is, the direct effect of AID on CTS is an effect formed through mediation and is equal to α*. The total effect of AID on the CTS is the impact of AID on the CTS alone via univariate Mendelian randomization and is equal to α., the mediating effect is βγ (product of exposure on mediator and mediator on outcome) and mediation proportion is βγ/α. Finally, the robustness of the results is verified by sensitivity and heterogeneity tests. 2. Materials and methods 2.1 Design of study The study analyzed the causal effects of common AIDs on CTS using univariable and multivariable MR analysis, with RA, SLE, GD, type 1 diabetes (T1M), Crohn's disease (CD) and ulcerative colitis (UC)as exposure factors, and CTS as an outcome factor. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)-MR statement[ 14 ]. 2.2 Data sources We screened for genetic variants associated with the above AIDS from a sea of large genome-wide association studies (GWAS)[ 15 – 19 ]. To diminish bias of population stratification, all included pooled data were from European descent in European Bioinformatics Institute. 2.3 Instrumental variables selection Single nucleotide polymorphisms (SNPs) were included under the assumption that SNPs were strongly correlated with AIDs. SNPs are not associated with CTS. SNPs were then extracted as instrumental variables (IV) that were corelated with AIDs at the genome-wide significant level. Threshold are set for linkage disequilibrium at R 2 < 0.001 for excluding linkage disequilibrium effect. Besides, SNP associated with outcome was eliminated and harmonize to exclude palindromic and incompatible SNPs through Catalog, Phenoscanner and R. F-value of the selected IVs ≥ 10 was the confirmation of their strength. 2.4. univariable MR analysis 2.4.1. Effect size estimates: Univariable MR analysis were conducted by the standard method – IVW method which assumes each SNP is valid for pooling data from MR. IVW fixed-effect model was credible without heterogeneity[ 20 ]. If there exists heterogeneity, we choose the random-effect model. Whereafter, we carried out a battery of sensitivity analysis. MR-Egger regression was performed to estimate the reliability of the IVW method while exhibited relatively low precision. Besides, we used the weighted median method as a supplementary method to further validate the dependability of the results [ 21 ]. 2.4.2. Assess of horizontal pleiotropy and heterogeneity: To make our results more plausible, we conducted the MR-Egger intercept test for cross-sectional pleiotropy. P value < 0.05 indicates the presence of horizontal pleiotropy and vice versa. [ 22 ]. We executed Cochran’s Q test in IVW and MR-Egger methods to quantitatively assess the heterogeneity of causal effect sizes among genetic instruments. 2.5. multivariable MR analysis Selected multivariable MR analysis: We first performed multivariable MR analysis for selected exposure factors and used the following four methods: IVW, Egger, median and Lasso. The selection criteria are: (1) univariable MR results in all three methods betas are isotropic and at least IVW method p is less than 0.05; (2) No horizontal pleiotropy (P>0.05). F-values were calculated for each exposure because there may be a decrease after adjusting for other exposures. MVMR-Lasso is another method aimed at identifying and reducing weight outliers, with an overall respectable performance in terms of mean square error [ 23 ]. The exposures meaningful both in univariate and multivariate MR analyses were the target exposure. Then, the presence of mediating effects was tested by the method from Fig. 1 according to the MVMR hypotheses. All above analyses were followed by measuring their heterogeneity using MR-PRESSO tests. 2.6. Reverse MR Finally, we also conducted a reversed MR analysis to determine if there is a causal impact from CTS to AIDs. All results were exhibited as odds ratios (OR) and 95% confidence intervals (CI). The TwoSampleMR, MR-PRESSO, MVMR, forestploter packages in R software is applied for statistical analysis and graphing. 3. Result 3.1. exposure selection From our criteria, 7 datasets were selected as exposure, All F statistics value of chosen IVs were larger than 10, suggesting that the strength of genetic instruments.. Table 1 shows the characteristics of the selected 7 datasets. Table 1 Data of seven used sample phenotypes Trait GWAS ID Sample size case control SNPs Population SLE ebi-a-GCST003156 14,267 5,201 9,066 7,071,163 European RA ebi-a-GCST90013534 58,284 14,361 43,923 13,108,512 European T1D ebi-a-GCST90014023 520,580 18,942 501,638 59,999,551 European GD ebi-a-GCST90018847 458,620 1,678 456,942 24,189,816 European CD ebi-a-GCST003044 20,883 5,956 14,927 110,583 European UC ebi-a-GCST003045 27,432 6,968 20,464 110,944 European CTS ebi-a-GCST90018813 480,201 18,855 461,346 24,181,062 European GWAS: genome-wide association studies; SNP: single nucleotide polymorphisms; SLE: systemic lupus erythematosus; RA: rheumatoid arthritis; T1D: type I diabetes; GD: Graves' disease; CD: Crohn's disease; UC: ulcerative colitis; CTS: carpal tunnel syndrome 3.2. univariable MR analysis Based on the absence of heterogeneity after population selection, we used a fixed-effects model for IVW. Results from MR suggest that RA, T1D and GD are risk factors for CTS (P<0.05). Sensitivity analysis of them was performed by the weighted median method (P<0.05) and MR-Egger regression (P<0.05) while the P value of weighted median method in RA was over 0.05. Figure 2 performed the cursory analyses results of three selected exposures. Figure 3 shows the specific number of valid SNPs, pleiotropy (MR-Egger regression), heterogeneity tests and results in forest plot from all exposures univariable MR. All MR-Egger intercept (P > 0.05), excluding ulcerative colitis (UC), was not statistically significant which demonstrates no genetic pleiotropic bias in the results. P value of Cochran's Q test from almost all exposures below 0.05 generally showing some heterogeneity. 3.3 multivariable MR analysis First, the F-values of each chosen exposure were larger than 10 after adjusting. Besides, the results of three selected exposure multivariable MR analysis showed that after adjusting for the effects of other AIDs, only T1D showed a strong association with CTS (Fig. 4 and Table 2 ). Afterwards, the results of the mediation effect are shown in Table 3 , and it can be seen that there is a mediating effect of RA, GD in the causal relationship of T1D for CTS, and the percentages of mediating effects were 24.3% and 25.1% respectively. This also demonstrates the need for MVMR in MR analysis. Finally, MR-PRESSO tests exceeded 0.05, validating the strength of the results in our study. Table 2 The outcome of four methods in the three selected multivariate MR analyses. – OR (95%CI) Exposure IVW MV-Egger Lasso Median RA 1.01 (0.98–1.03) 1.02 (0.99–1.05) 1.01 (0.98–1.03) 1.00 (0.96–1.03) T1D 1.02 (1.01,1.04) * 1.02 (1.00-1.04) * 1.02 (1.01–1.04) * 1.02 (1.00-1.04) GD 1.00 (0.97–1.03) 1.01 (0.97–1.04) 1.00 (0.97–1.03) 1.01 (0.97–1.05) OR: odds ratios; 95%CI: 95% confidence interval; IVW: inverse variant weighted; T1D: type I diabetes; RA: rheumatoid arthritis; GD: Graves' disease; *:p<0.05 was considered statistically significant. Table 3 Mediation effect and MVMR result of T1D from other selected exposures separately: Mediator Total effect (α) Effect β Effect γ Mediation effect (α*) Mediated proportion Effect size (95% CI) Effect size (95% CI) Effect size (95% CI) Effect size (95% CI) P-value Effect size (95% CI) RA 1.03 (1.02–1.04) 1.25 (1.19–1.31) 1.03 (1.01–1.05) 1.02 (1.01–1.04) 0.005 24.3% (10.2%-35.8%) GD 1.17 (1.12–1.23) 1.04 (1.02–10.7) 1.03 (1.02–1.04) 0.000 25.1% (12.6%-36.9%) Total effect (α): the effect of T1D on carpal tunnel syndrome; Effect β: the effect of T1D on mediators; Effect γ: the effect of mediators on carpal tunnel syndrome; Mediation effect (α): the effect of T1D on carpal tunnel syndrome via mediators. OR: odds ratios; 95%CI: 95% confidence interval; IVW: inverse variant weighted; RA: rheumatoid arthritis; GD: Graves' disease. 3.4. Reverse MR The results showed that genetic predisposition to CTS had no potential causal effect on AIDs(P 0.05). 4. Discussion Autoimmune diseases (AIDs) consist of two main pathological processes: on the one hand, an immunodeficiency in which one or more components of the immune system fail to respond protectively to pathogens, and on the other hand, a tolerance defect that fails to distinguish between self and nonself [ 24 ]. AIDs often present with a variety of extra-disease manifestations, including pulmonary manifestations (ILD, etc.) and, as mentioned in this article, peripheral nerve manifestations (CTS, etc.)[ 25 ]. However, conclusions may be biased by the existed limitations of previous research, including confounders and reverse causality. In this study, based on the results of univariable MR analysis, we found that some AIDs: RA, T1D and GD increase the risk of CTS. After adjusting for other AIDs for multivariable MR analysis, we have even stronger reasons to believe that T1D can not only increase the risk of CTS directly, but also affect CTS through mediator like SLE, GD. Therefore, our study considering that controlling T1D reduces the risk of CTS and emphasizes the validity and necessity of multivariable MR analysis in MR analysis. Diabetes has long been recognized as a high-risk factor for CTS, but most studies have focused more on diabetes itself, ignoring its typology. And because of the rapid changes in blood glucose in T1D, the development of chronic diseases such as CTS is often overlooked. A Rochester Diabetic Neuropathy study found that although no significant difference was found in the rate of diagnosis by neurophysiology, the incidence of symptoms of CTS was twice as high in patients with T1D as in those with T2D. [ 26 ]. Singh et al. found that T1D patients owned a high lifetime risk of developing symptomatic CTS and a strong association with age and duration of diabetes[ 27 ]. Pathologically, long-term patients with T1D accumulate excess advanced glycosylation end-products (AGEs). Within collagen fibers, covalent cross-links are formed, and AGE cross-links are commonly considered causing degradation of the biological and mechanical functions of tendons and ligaments [ 28 ]. AGE-binding receptor ligands generate reactive oxygen species and NF-κβ signaling, accelerating AGE cross-linking in collagen fibers, and leading to sustained up-regulation and dysfunction of pro-inflammatory mediators [ 29 , 30 ]. In addition, microvascular disease brought on by T1D leads to tissue hypoxia, reduced levels of VEGF, excessive production of oxygen radicals and the creation of an apoptotic milieu causing tendon damage and reduced neovascularization within the degenerative tendon [ 31 , 32 ]. In addition, patients with diabetic carpal tunnel syndrome have a higher incidence of vascular proliferation, synovial edema, and increased vessel wall thickness. [ 33 ]. Combined with our results, it is reasonable to assume that T1D is a high-risk factor for CTS. However, the differences in the effects of different types of diabetes on CTS and the specific mechanisms needs further exploration. Our study also analyzed the association between other common AIDs and the risk of carpal tunnel syndrome. These include SLE, RA, GD, CD and UC. We found that RA and GD were also positively associated with carpal tunnel syndrome risk. However, after subjecting selected exposures to multivariable mendelian randomization analyses for all and between the two and adjusting for them, only the association between T1D and CTS was retained, demonstrating that the effect of T1D on CTS is independent of the other AIDs, and that T1D can exert a mediating effect through RA and GD to influence CTS. The association of RA, GD and CTS has been previously reported. RA-induced synovitis and peritendinous fibrosis of the flexor tendons were previously thought to lead to median nerve entrapment and be a risk factor for CTS. [ 34 , 35 ]. Karada and his team found that CTS was more common in the RA patients than that in the normal population (17.0% versus 4.4%, P = 0.038), but there was no significant correlation with RA disease activity [ 36 ]. A study based on 15,802 patients with CTS and 31,604 controls selected from a national insurance dataset of 1 million participants found that rheumatoid was more strongly associated with CTS than other metabolic disorders (e.g., gout, hypertension, and diabetic arthritis), and that this association was more pronounced in the ≤ 39 year old group. [ 37 ]. However, a retrospective study over 12 years did not find a significant difference in CTS incidence between RA and control populations (4.18/1000 yrs. vs 0.3-5.0/1000 yrs.)[ 38 ]. As for GD, its association with CTS has been rarely reported, and the mechanism is not clear. [ 39 , 40 ]. And subsequent reports seem to deny the connection. [ 41 ]. Combined with our results, we are conservative about the association of RA, GD, and CTS. Looking forward to more in-depth studies and strong evidence to support this in the future. In this study, both types of MR were applied to analyze the causal relationship between common AIDs and CTS, and it was convincingly proved that T1D increases susceptibility to CTS. It is also possible to generate effects on CTS through RA and GD as mediators. Compared to other general observational studies, The advantages of this study are distinct: First, we minimize confounding especially among AIDs and reverse causality bias as possible; Then, the reliability of the conclusions is improved by IVW, following MR-Egger regression and weighted median; Finally, no potential horizontal pleiotropism was shown, confirming the robustness of our conclusion. Of course, this study also has certain limitations༚1.The study population is limited to the European ancestry, so the applicability of the result to other populations needs to be further validated;2.Unobserved horizontal pleiotropism may affect the reliability of the results༛3. The large number of SNPs used for some of the exposures may increase the risk of biasing our results due to invalid SNPs, but the F-values of selected IVs and exposure and consistent observations from other sensitivity analyses verified our summary. 5. Conclusion This MR study shows with solid genetic evidence and methods that T1D may increase susceptibility to CTS independently. and can mediate the effects of CTS through RA and GD. Controlling T1D is effective in reducing the incidence of CTS. Treatment of T1D should be considered a primary preventive measure for CTS. Declarations Funding: There is no financial support for this study. 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A histological and immunohistochemical study of the subsynovial connective tissue in idiopathic carpal tunnel syndrome. The Journal of bone and joint surgery American volume. 2004;86(7):1458–66. Epub 2004/07/15. doi: 10.2106/00004623-200407000-00014 . PubMed PMID: 15252093. Karadag O, Kalyoncu U, Akdogan A, Karadag YS, Bilgen SA, Ozbakır S, et al. Sonographic assessment of carpal tunnel syndrome in rheumatoid arthritis: prevalence and correlation with disease activity. Rheumatology international. 2012;32(8):2313–9. Epub 2011/05/25. doi: 10.1007/s00296-011-1957-0 . PubMed PMID: 21607558. Tseng CH, Liao CC, Kuo CM, Sung FC, Hsieh DP, Tsai CH. Medical and non-medical correlates of carpal tunnel syndrome in a Taiwan cohort of one million. European journal of neurology. 2012;19(1):91 – 7. Epub 2011/06/03. doi: 10.1111/j.1468-1331.2011.03440.x . PubMed PMID: 21631646. Lee KH, Lee CH, Lee BG, Park JS, Choi WS. The incidence of carpal tunnel syndrome in patients with rheumatoid arthritis. International journal of rheumatic diseases. 2015;18(1):52–7. Epub 2014/09/10. doi: 10.1111/1756-185x.12445 . PubMed PMID: 25196946. Manganelli P, Pavesi G, Salaffi F. Bilateral carpal tunnel syndrome in Graves' disease. Z Rheumatol. 1987;46(1):34–5. Epub 1987/01/01. PubMed PMID: 3591021. Cakir M, Samanci N, Balci N, Balci MK. Musculoskeletal manifestations in patients with thyroid disease. Clinical endocrinology. 2003;59(2):162–7. Epub 2003/07/17. doi: 10.1046/j.1365-2265.2003.01786 .x. PubMed PMID: 12864792. Shiri R. Hypothyroidism and carpal tunnel syndrome: a meta-analysis. Muscle & nerve. 2014;50(6):879 – 83. Epub 2014/09/11. doi: 10.1002/mus.24453 . PubMed PMID: 25204641. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3456971","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":243964344,"identity":"f57b8937-96e6-4428-8d7b-64dbe7936308","order_by":0,"name":"Xin Tan","email":"","orcid":"","institution":"The Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Tan","suffix":""},{"id":243964345,"identity":"452f0976-602a-461f-9660-9a74d7f4d150","order_by":1,"name":"Shirong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCQgpx8/ewMBMihYLY8meA6RpqUjccCOBSC38s5uPPfy5R4Jx5sznDx8XtjHI84sdIGDJnWPpxjzPJJj5pXOMjWe2MRjOnJ2AX4uBRI6ZNMMBCTbJ2Tls0rxtDAkGtwlqyf8m+eOABI/BzePPiNWSwybBc0BCwuAGgxlxWiRupJlJA7UYSPYA/cJzToKwX/hnJD8DOqyuvp/9+MPHPGU28vzSBLRg2Eqa8lEwCkbBKBgF2AEAER47iQF2geMAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shirong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2023-10-17 10:23:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3456971/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3456971/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45597437,"identity":"1e270ff8-02b2-4bdb-833b-03b8ea42d3f9","added_by":"auto","created_at":"2023-10-31 21:32:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43621,"visible":true,"origin":"","legend":"\u003cp\u003eResearch flow and validation diagram.\u003c/p\u003e\n\u003cp\u003eThe target AID has a direct effect of α* and a total effect of α. Effect β: the effect of T1D on mediators; Effect γ: the effect of mediators on carpal tunnel syndrome; Mediation effect (α): the effect of T1D on carpal tunnel syndrome via mediators.. AID: autoimmune disease; CTS: carpal tunnel syndrome.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3456971/v1/b1c2a9d615acb849ffd78d76.png"},{"id":45596610,"identity":"3e0c09c1-5fc9-457f-8d04-126b5d31d414","added_by":"auto","created_at":"2023-10-31 21:24:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142168,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of three selected univariable MR analysis result in three methods\u003c/p\u003e\n\u003cp\u003eIt can be roughly seen from the figure that the intercept of each exposure is very close to 0. This proves that there is roughly no horizontal pleiotropy. IVW: inverse variant weighted.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3456971/v1/1558505ab310fdecb19f1822.png"},{"id":45596641,"identity":"983f817e-ac54-4c45-ad80-d73d79b86162","added_by":"auto","created_at":"2023-10-31 21:24:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":300895,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot summarizing results of univariate MR analysis.\u003c/p\u003e\n\u003cp\u003eThe exposures that can be selected by the IVW method alone are RA, T1D and GD. OR: odds ratios; 95%CI: 95% confidence interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3456971/v1/09bfb1b3c82dfdaaa210c650.png"},{"id":45596590,"identity":"f84b61a7-7de3-4368-957c-e8309e4f05a1","added_by":"auto","created_at":"2023-10-31 21:24:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":218350,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot summarizing results of multivariable MR analysis.\u003c/p\u003e\n\u003cp\u003eThe only exposure that can be enrolled through the four methods is only T1D. OR: odds ratios; 95%CI: 95% confidence interval.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3456971/v1/863b74bbf894ea87045d9620.png"},{"id":48840252,"identity":"45b9c335-96d6-4b8b-82d9-debc2d905486","added_by":"auto","created_at":"2023-12-27 06:52:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":903745,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3456971/v1/00be25a4-bbfc-4bf0-979c-26ad64356cc0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Relationships between Common Autoimmune Disease and Carpal Tunnel Syndrome: Study of Univariable and Multivariable Mendelian Randomization.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCarpal tunnel syndrome (CTS), A common nerve entrapment syndromes occurring in women, is a loss of palmar sensation, motor function and even disability caused by compression of the median nerve by the narrow osteofibrous canal of the wrist joint, which severely affects the quality of life of patients and has imposed an estimated \u003cspan\u003e$\u003c/span\u003e270-\u003cspan\u003e$\u003c/span\u003e480\u0026nbsp;million annual economic burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The disease has mostly been previously reported as a peripheral neurological manifestation in various autoimmune diseases (AIDs), especially rheumatoid arthritis (RA), type I diabetes (T1D), Graves' disease (GD), etc.[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. At the same time, dysregulation of the immune system manifested by changes in the levels of immune cells and cytokines can also be observed in patients with CTS [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. But the quality of evidence that is relatively low because previous studies have been mostly observational with poor sample size. Meanwhile, the interconnectedness among AIDs makes it difficult to rule out confounding factors. Therefore, whether AIDs are risk factor for CTS needs further confirmation.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is an analysis that utilizes genetic variation instrumental variates (IVs) to explore the causality between exposures and outcomes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In MR analysis, causal direction is specific and not easily confused and avoiding reverse causal bias in observational studies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Besides, compared with randomized controlled trials, MR owns the advantages of high feasibility, cost, and time efficient. Traditional univariate MR analysis assesses the impact of individual predictor variables on the outcome. Three hypotheses are used to determine the validity of IV - it must be (I) related to the exposure; (II) Not associated with outcomes of exposure; and (III) independent of all confounding factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. If IV is associated with confounding of exposures and outcomes, it conflicts with these assumptions above and may lead to potential bias and errors.\u003c/p\u003e \u003cp\u003eTo avoid such bias, multivariate MR analysis is introduced to jointly assesses the effect of these valid IVs on the outcomes. Multivariate MR analysis, extension of univariate MR, considers pleiotropy among multiple traits [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Compared to traditional univariate one, multivariate MR has broader assumptions: Genetic variation affects exposure to multiple measures, as do exclusion restrictions and exchangeability assumptions. By analyzing several risk factors at the same time, it is possible to estimate the causal impact of one risk factor on the outcome independently of the other risk factors. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we performed both types of mendelian randomization analyses and calculate the mediating effect [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], The schematic diagram is shown in Fig.\u0026nbsp;1. We selected meaningful AIDs through univariate and multivariate MR analyses. We verified whether the effects of the selected AIDs on CTS were mediated through other AIDs. That is, the direct effect of AID on CTS is an effect formed through mediation and is equal to α*. The total effect of AID on the CTS is the impact of AID on the CTS alone via univariate Mendelian randomization and is equal to α., the mediating effect is βγ (product of exposure on mediator and mediator on outcome) and mediation proportion is βγ/α. Finally, the robustness of the results is verified by sensitivity and heterogeneity tests.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Design of study\u003c/h2\u003e\n \u003cp\u003eThe study analyzed the causal effects of common AIDs on CTS using univariable and multivariable MR analysis, with RA, SLE, GD, type 1 diabetes (T1M), Crohn\u0026apos;s disease (CD) and ulcerative colitis (UC)as exposure factors, and CTS as an outcome factor. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)-MR statement[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data sources\u003c/h2\u003e\n \u003cp\u003eWe screened for genetic variants associated with the above AIDS from a sea of large genome-wide association studies (GWAS)[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. To diminish bias of population stratification, all included pooled data were from European descent in European Bioinformatics Institute.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Instrumental variables selection\u003c/h2\u003e\n \u003cp\u003eSingle nucleotide polymorphisms (SNPs) were included under the assumption that SNPs were strongly correlated with AIDs. SNPs are not associated with CTS.\u003c/p\u003e\n \u003cp\u003eSNPs were then extracted as instrumental variables (IV) that were corelated with AIDs at the genome-wide significant level. Threshold are set for linkage disequilibrium at \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for excluding linkage disequilibrium effect. Besides, SNP associated with outcome was eliminated and harmonize to exclude palindromic and incompatible SNPs through Catalog, Phenoscanner and R. F-value of the selected IVs\u0026thinsp;\u0026ge;\u0026thinsp;10 was the confirmation of their strength.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. univariable MR analysis\u003c/h2\u003e\u003cspan\u003e\u003cstrong\u003e2.4.1. Effect size estimates:\u003c/strong\u003e Univariable MR analysis were conducted by the standard method \u0026ndash; IVW method which assumes each SNP is valid for pooling data from MR. IVW fixed-effect model was credible without heterogeneity[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. If there exists heterogeneity, we choose the random-effect model. Whereafter, we carried out a battery of sensitivity analysis. MR-Egger regression was performed to estimate the reliability of the IVW method while exhibited relatively low precision. Besides, we used the weighted median method as a supplementary method to further validate the dependability of the results [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.2. Assess of horizontal pleiotropy and heterogeneity:\u003c/strong\u003e To make our results more plausible, we conducted the MR-Egger intercept test for cross-sectional pleiotropy. P value \u003c 0.05 indicates the presence of horizontal pleiotropy and vice versa. [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. We executed Cochran\u0026rsquo;s Q test in IVW and MR-Egger methods to quantitatively assess the heterogeneity of causal effect sizes among genetic instruments.\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. multivariable MR analysis\u003c/h2\u003e\n \u003cp\u003eSelected multivariable MR analysis: We first performed multivariable MR analysis for selected exposure factors and used the following four methods: IVW, Egger, median and Lasso. The selection criteria are: (1) univariable MR results in all three methods betas are isotropic and at least IVW method p is less than 0.05; (2) No horizontal pleiotropy (P\u003e0.05). F-values were calculated for each exposure because there may be a decrease after adjusting for other exposures. MVMR-Lasso is another method aimed at identifying and reducing weight outliers, with an overall respectable performance in terms of mean square error [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The exposures meaningful both in univariate and multivariate MR analyses were the target exposure. Then, the presence of mediating effects was tested by the method from Fig.\u0026nbsp;1 according to the MVMR hypotheses. All above analyses were followed by measuring their heterogeneity using MR-PRESSO tests.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Reverse MR\u003c/h2\u003e\n \u003cp\u003eFinally, we also conducted a reversed MR analysis to determine if there is a causal impact from CTS to AIDs.\u003c/p\u003e\n \u003cp\u003eAll results were exhibited as odds ratios (OR) and 95% confidence intervals (CI). The TwoSampleMR, MR-PRESSO, MVMR, forestploter packages in R software is applied for statistical analysis and graphing.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. exposure selection\u003c/h2\u003e \u003cp\u003eFrom our criteria, 7 datasets were selected as exposure, All F statistics value of chosen IVs were larger than 10, suggesting that the strength of genetic instruments.. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characteristics of the selected 7 datasets.\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\u003eData of seven used sample phenotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST003156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,071,163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\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\u003eebi-a-GCST90013534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58,284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43,923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,108,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\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\u003eebi-a-GCST90014023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e501,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59,999,551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90018847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e458,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e456,942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24,189,816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\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\u003eebi-a-GCST003044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\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\u003eebi-a-GCST003045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110,944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90018813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e480,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e461,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24,181,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eGWAS: genome-wide association studies; SNP: single nucleotide polymorphisms; SLE: systemic lupus erythematosus; RA: rheumatoid arthritis; T1D: type I diabetes; GD: Graves' disease; CD: Crohn's disease; UC: ulcerative colitis; CTS: carpal tunnel syndrome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. univariable MR analysis\u003c/h2\u003e \u003cp\u003eBased on the absence of heterogeneity after population selection, we used a fixed-effects model for IVW. Results from MR suggest that RA, T1D and GD are risk factors for CTS (P\u003c0.05). Sensitivity analysis of them was performed by the weighted median method (P\u003c0.05) and MR-Egger regression (P\u003c0.05) while the P value of weighted median method in RA was over 0.05. Figure\u0026nbsp;2 performed the cursory analyses results of three selected exposures. Figure\u0026nbsp;3 shows the specific number of valid SNPs, pleiotropy (MR-Egger regression), heterogeneity tests and results in forest plot from all exposures univariable MR. All MR-Egger intercept (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), excluding ulcerative colitis (UC), was not statistically significant which demonstrates no genetic pleiotropic bias in the results. P value of Cochran's Q test from almost all exposures below 0.05 generally showing some heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 multivariable MR analysis\u003c/h2\u003e \u003cp\u003eFirst, the F-values of each chosen exposure were larger than 10 after adjusting. Besides, the results of three selected exposure multivariable MR analysis showed that after adjusting for the effects of other AIDs, only T1D showed a strong association with CTS (Fig.\u0026nbsp;4 and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Afterwards, the results of the mediation effect are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and it can be seen that there is a mediating effect of RA, GD in the causal relationship of T1D for CTS, and the percentages of mediating effects were 24.3% and 25.1% respectively. This also demonstrates the need for MVMR in MR analysis. Finally, MR-PRESSO tests exceeded 0.05, validating the strength of the results in our study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe outcome of four methods in the three selected multivariate MR analyses. \u0026ndash; OR (95%CI)\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=\"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=\"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\u003eIVW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMV-Egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLasso\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.99\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.96\u0026ndash;1.03)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01,1.04) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02 (1.00-1.04) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02 (1.00-1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.97\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.97\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR: odds ratios; 95%CI: 95% confidence interval; IVW: inverse variant weighted; T1D: type I diabetes; RA: rheumatoid arthritis; GD: Graves' disease; *:p\u003c0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation effect and MVMR result of T1D from other selected exposures separately:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal effect (α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect γ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMediation effect (α*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMediated proportion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect size (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect size (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect size (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect size (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect size (95% CI)\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.19\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.3% (10.2%-35.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (1.12\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.02\u0026ndash;10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.1% (12.6%-36.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eTotal effect (α): the effect of T1D on carpal tunnel syndrome; Effect β: the effect of T1D on mediators; Effect γ: the effect of mediators on carpal tunnel syndrome; Mediation effect (α): the effect of T1D on carpal tunnel syndrome via mediators. OR: odds ratios; 95%CI: 95% confidence interval; IVW: inverse variant weighted; RA: rheumatoid arthritis; GD: Graves' disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Reverse MR\u003c/h2\u003e \u003cp\u003eThe results showed that genetic predisposition to CTS had no potential causal effect on AIDs(P\u003c0.05). MR-Egger and weighted median did not exhibit a causality as well (both P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAutoimmune diseases (AIDs) consist of two main pathological processes: on the one hand, an immunodeficiency in which one or more components of the immune system fail to respond protectively to pathogens, and on the other hand, a tolerance defect that fails to distinguish between self and nonself [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. AIDs often present with a variety of extra-disease manifestations, including pulmonary manifestations (ILD, etc.) and, as mentioned in this article, peripheral nerve manifestations (CTS, etc.)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, conclusions may be biased by the existed limitations of previous research, including confounders and reverse causality. In this study, based on the results of univariable MR analysis, we found that some AIDs: RA, T1D and GD increase the risk of CTS. After adjusting for other AIDs for multivariable MR analysis, we have even stronger reasons to believe that T1D can not only increase the risk of CTS directly, but also affect CTS through mediator like SLE, GD. Therefore, our study considering that controlling T1D reduces the risk of CTS and emphasizes the validity and necessity of multivariable MR analysis in MR analysis.\u003c/p\u003e \u003cp\u003eDiabetes has long been recognized as a high-risk factor for CTS, but most studies have focused more on diabetes itself, ignoring its typology. And because of the rapid changes in blood glucose in T1D, the development of chronic diseases such as CTS is often overlooked. A Rochester Diabetic Neuropathy study found that although no significant difference was found in the rate of diagnosis by neurophysiology, the incidence of symptoms of CTS was twice as high in patients with T1D as in those with T2D. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Singh et al. found that T1D patients owned a high lifetime risk of developing symptomatic CTS and a strong association with age and duration of diabetes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Pathologically, long-term patients with T1D accumulate excess advanced glycosylation end-products (AGEs). Within collagen fibers, covalent cross-links are formed, and AGE cross-links are commonly considered causing degradation of the biological and mechanical functions of tendons and ligaments [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. AGE-binding receptor ligands generate reactive oxygen species and NF-κβ signaling, accelerating AGE cross-linking in collagen fibers, and leading to sustained up-regulation and dysfunction of pro-inflammatory mediators [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, microvascular disease brought on by T1D leads to tissue hypoxia, reduced levels of VEGF, excessive production of oxygen radicals and the creation of an apoptotic milieu causing tendon damage and reduced neovascularization within the degenerative tendon [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, patients with diabetic carpal tunnel syndrome have a higher incidence of vascular proliferation, synovial edema, and increased vessel wall thickness. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Combined with our results, it is reasonable to assume that T1D is a high-risk factor for CTS. However, the differences in the effects of different types of diabetes on CTS and the specific mechanisms needs further exploration.\u003c/p\u003e \u003cp\u003eOur study also analyzed the association between other common AIDs and the risk of carpal tunnel syndrome. These include SLE, RA, GD, CD and UC. We found that RA and GD were also positively associated with carpal tunnel syndrome risk. However, after subjecting selected exposures to multivariable mendelian randomization analyses for all and between the two and adjusting for them, only the association between T1D and CTS was retained, demonstrating that the effect of T1D on CTS is independent of the other AIDs, and that T1D can exert a mediating effect through RA and GD to influence CTS. The association of RA, GD and CTS has been previously reported. RA-induced synovitis and peritendinous fibrosis of the flexor tendons were previously thought to lead to median nerve entrapment and be a risk factor for CTS. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Karada and his team found that CTS was more common in the RA patients than that in the normal population (17.0% versus 4.4%, P\u0026thinsp;=\u0026thinsp;0.038), but there was no significant correlation with RA disease activity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A study based on 15,802 patients with CTS and 31,604 controls selected from a national insurance dataset of 1\u0026nbsp;million participants found that rheumatoid was more strongly associated with CTS than other metabolic disorders (e.g., gout, hypertension, and diabetic arthritis), and that this association was more pronounced in the \u0026le;\u0026thinsp;39 year old group. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, a retrospective study over 12 years did not find a significant difference in CTS incidence between RA and control populations (4.18/1000 yrs. vs 0.3-5.0/1000 yrs.)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As for GD, its association with CTS has been rarely reported, and the mechanism is not clear. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. And subsequent reports seem to deny the connection. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Combined with our results, we are conservative about the association of RA, GD, and CTS. Looking forward to more in-depth studies and strong evidence to support this in the future.\u003c/p\u003e \u003cp\u003eIn this study, both types of MR were applied to analyze the causal relationship between common AIDs and CTS, and it was convincingly proved that T1D increases susceptibility to CTS. It is also possible to generate effects on CTS through RA and GD as mediators. Compared to other general observational studies, The advantages of this study are distinct: First, we minimize confounding especially among AIDs and reverse causality bias as possible; Then, the reliability of the conclusions is improved by IVW, following MR-Egger regression and weighted median; Finally, no potential horizontal pleiotropism was shown, confirming the robustness of our conclusion. Of course, this study also has certain limitations༚1.The study population is limited to the European ancestry, so the applicability of the result to other populations needs to be further validated;2.Unobserved horizontal pleiotropism may affect the reliability of the results༛3. The large number of SNPs used for some of the exposures may increase the risk of biasing our results due to invalid SNPs, but the F-values of selected IVs and exposure and consistent observations from other sensitivity analyses verified our summary.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis MR study shows with solid genetic evidence and methods that T1D may increase susceptibility to CTS independently. and can mediate the effects of CTS through RA and GD. Controlling T1D is effective in reducing the incidence of CTS. Treatment of T1D should be considered a primary preventive measure for CTS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThere is no financial support for this study.\u003c/p\u003e\n\u003cp\u003eData availability:\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the IEU repository, https://gwas.mrcieu.ac.uk/\u003c/p\u003e\n\u003cp\u003eInformed Consent Statement:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest:\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOlney RK. Carpal tunnel syndrome: complex issues with a \"simple\" condition. Neurology. 2001;56(11):1431\u0026ndash;2. 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PubMed PMID: 25204641.\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":"Autoimmune Disease, Carpal Tunnel Syndrome, Type 1 Diabetes; Multivariable Mendelian Randomization Study","lastPublishedDoi":"10.21203/rs.3.rs-3456971/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3456971/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eWhether Autoimmune diseases (AIDs) are a risk factor for carpal tunnel syndrome (CTS) is questionable and has been reported in observational studies, but the quality of the evidence is low and inconclusive. Our study intends to assess the causal association between common AIDs and CTS through univariable and multivariable mendelian randomization (MR).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ewe mainly utilized univariable MR analysis through IVW. Weighted median, MR-Egger analysis to assess the association of AIDs and CTS. Then, we extended the limits of univariable MR analysis through multivariable mendelian randomization in IVW, Egger method, Lasso and median method. The mediating effect was calculated by mediating MR. Finally, the MR-PRESSO, Cochran's Q test and F-values are calculated to assess the levels of pleiotropy, heterogeneity, and intensity of selected IVs and exposures through mediated MR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariable MR results showed a positive correlation from RA, GD, T1D to ILD with an increased relative risk. While, adjusting by other two AIDs through multivariable mendelian randomization, only T1D is robustly correlated with CTS. T1D can also produce effects on CTS through RA, GD as mediators. Furthermore, the outcome of MR-Egger intercept did not provide evidence of horizontal pleiotropy. The F-value results were all greater than 10, indicating that the selected IV and exposure intensities were appropriate.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBased on the results of univariable mendelian randomization analysis, the study found genetic evidence supporting a positive causal relationship from RA, T1D, GD to CTS, but only T1D maintained consistent results after multivariable MR analysis. In addition, 24.3% and 25.1% of the effects of T1D on CTS were mediated by RA and GD, respectively. Therefore, appropriate intervention of T1D can reduce the incidence of CTS. Treatment of T1D should be considered a primary preventive measure for CTS.\u003c/p\u003e","manuscriptTitle":"Causal Relationships between Common Autoimmune Disease and Carpal Tunnel Syndrome: Study of Univariable and Multivariable Mendelian Randomization.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-31 21:24:41","doi":"10.21203/rs.3.rs-3456971/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":"d0657cdd-fbad-4e19-bbcb-39c7bceb535e","owner":[],"postedDate":"October 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25750318,"name":"Biological sciences/Genetics"},{"id":25750319,"name":"Biological sciences/Genetics/Genetic association study"},{"id":25750320,"name":"Biological sciences/Genetics/Genetic association study/Genome wide association studies"}],"tags":[],"updatedAt":"2023-12-27T06:44:22+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-31 21:24:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3456971","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3456971","identity":"rs-3456971","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}