Autoimmune Thyroid Disease and Myasthenia Gravis: A study bidirectional Mendelian randomization

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Objective Therefore, we employed a Mendelian randomization (MR) analysis to investigate the causal relationship between AITD and MG. Methods To explore the interplay between AITD and MG, We conducted MR studies utilizing GWAS-based summary statistics in the European ancestry.Several techniques were used to ensure the stability of the causal effect, such as random-effect inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was evaluated by calculating Cochran's Q value. Moreover, the presence of horizontal pleiotropy was investigated through MR-Egger regression and MR-PRESSO Results The IVW method indicates a causal relationship between both GD(OR 1.31,95%CI 1.08 to 1.60,P = 0.005) and autoimmune hypothyroidism (OR: 1.26, 95% CI: 1.08 to 1.47, P = 0.002) with MG. However, there is no association found between FT4(OR 0.88,95%CI 0.65 to 1.18,P = 0.406), TPOAb(OR: 1.34, 95% CI: 0.86 to 2.07, P = 0.186), TSH(OR: 0.97, 95% CI: 0.77 to 1.23, P = 0.846), and MG. The reverse MR analysis reveals a causal relationship between MG and GD(OR: 1.50, 95% CI: 1.14 to 1.98, P = 3.57e-3), with stable results. On the other hand, there is a significant association with autoimmune hypothyroidism(OR: 1.29, 95% CI: 1.04 to 1.59, P = 0.019), but it is considered unstable due to the influence of horizontal pleiotropy (MR PRESSO Distortion Test P < 0.001). MG has a higher prevalence of TPOAb(OR: 1.84, 95% CI: 1.39 to 2.42, P = 1.47e-5) positivity and may be linked to elevated TSH levels(Beta:0.08,95% CI:0.01 to 0.14,P = 0.011), while there is no correlation between MG and FT4(Beta:-9.03e-3,95% CI:-0.07 to 0.05,P = 0.796). Conclusion AITD patients are more susceptible to developing MG, and MG patients also have a higher incidence of GD. Autoimmune Thyroid Disease Graves disease hypothyroidism Mendelian randomization GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.Introduction Autoimmune thyroid disorders (AITD) emerge from an immune system malfunction, giving rise to an immune onslaught against the thyroid gland( 1 ).AITD stand as the most prevalent autoimmune disorders and hold the position of being the most frequently observed pathological conditions of the thyroid gland( 2 ). This category encompasses two major clinical manifestations: Graves' disease (GD) and Hashimoto's thyroiditis (HT), both of which share a common characteristic—lymphocytic infiltration of the thyroid parenchyma( 3 ). The defining clinical traits of GD and HT involve thyrotoxicosis and hypothyroidism, correspondingly( 4 ).The infiltration of the thyroid by autoreactive lymphocytes and the generation of antibodies against three primary thyroid antigens, namely thyroid peroxidase (TPO), thyroglobulin (TG), and thyroid-stimulating hormone receptor (TSHR), are instigated by the activation of T- and B cell pathways( 5 ).AITD's etiology is presently comprehended as multifactorial, resulting from the intricate interplay between particular susceptibility genes and environmental exposures,with genetic differences and susceptibility playing an important role in the etiology of GD and HT( 6 ). Myasthenia gravis (MG) exemplifies a classic autoimmune disorder mediated by antibodies, primarily affecting the neuromuscular junction( 7 ).Antibody-mediated processes underlie MG, where antibodies are generated against key components such as the acetylcholine receptor (AchR), the muscle-specific kinase antibody (MuSK), and the agrin receptor low-density lipoprotein receptor-related protein-4 antibody (LRP4)( 8 ).The precise triggering of the autoimmune response in MG remains undisclosed, however, it is evident that deviations within the thymus gland (hyperplasia and neoplasia) have a substantial role, particularly in patients with anti-AChR antibodies( 9 , 10 ) and the development of the disorder is plausibly subject to genetic predisposition( 11 ). MG and AITD exhibit certain similarities, such as both being organ-specific, antibody-mediated, and contributing to ocular myopathy and exophthalmos( 12 ).Some studies have documented the rising incidence of thyroid disorders in MG, with a higher propensity for MG patients to develop HT and other autoimmune thyroid disorders( 13 , 14 ).Patients diagnosed with HT and GD exhibited a heightened subsequent risk of developing MG( 15 ).However, there is a lack of consistency among the reported results, with some studies indicating no clinical association between myasthenia symptomatology and thyroid dysfunction, as well as no significant impact on myasthenic symptoms when the endocrine disorders improve( 16 ).The relationship between AITD and MG is still a topic of ongoing debate, and observational studies are susceptible to the influence of reverse causality and confounding effects.To explore the causal association between AITD and MG, we employed a bidirectional Mendelian randomization (MR) approach in this study. This method utilized genetic variants obtained from genome-wide association studies as instrumental variables (IVs) to mitigate biases commonly found in observational epidemiological studies, such as reverse causation. 2.Materials and methods 2.1 Study design and the assumption of MR Employing a two-sample Mendelian randomization (MR) design, we ascertained the overall effects, with the primary objective of assessing the connection between AITD and MG. In a separate two-step MR investigation, we explored whether thyroid function characteristics acted as intermediaries in the impact of AITD on MG. A reverse MR analysis was carried out to assess the reciprocal influence of MG on AITD( Figure1 ).The MR analysis was conducted under the following assumptions: (i) the single nucleotide polymorphisms (SNPs) used as IVs were obtained from GWAS and displayed associations with the exposures; (ii) the IVs were not associated with confounding factors; (iii) the IVs had an exclusive influence on the risk of outcomes solely through the exposures(17). 2.2 Data sources FinnGen constitutes a substantial collaboration between the public and private sectors, with the objective of gathering and scrutinizing genomic and health information from 500,000 individuals enrolled in FinnGen biobanks(https://www.finngen.fi/en). Within this framework, the FinnGen Biobank of European descent has furnished the Genome-Wide Association Study (GWAS) data associated with ATID, encompassing GD with 4,462 cases and 320,703 controls, as well as autoimmune hypothyroidism with 40,926 cases and 274,069 controls(18). We obtained the summary data for thyroid function GWAS from the ThyroidOmics Consortium, an initiative established to investigate the factors influencing thyroid disorders and thyroid function(19). In a meta-analysis, the analysis of thyroid-stimulating hormone (TSH) included data from 22 distinct cohorts, encompassing a total of 54,288 individuals, while analyses of free thyroxine (FT4) were based on data from 19 cohorts involving 49,269 individuals(19).The GWAS information for thyroid peroxidase antibodies (TPOAb) was extracted from a separate meta-analysis conducted on a general population of 18,297 individuals across 11 different populations. Among these individuals, there were 1,769 cases with TPOAb positivity(20).Samples of individuals with MG were gathered from collaborative sources in both the United States and Italy, constituting a total of 1,873 cases and 36,370 controls(21). The diagnosis of MG relied on established clinical criteria, specifically the presence of characteristic, fatigue-induced muscle weakness, alongside electrophysiological and/or pharmacological anomalies, and further confirmed by the presence of anti-acetylcholine receptor antibodies(22).The complete information is in Table1 . Table1 Details of GWAS included in MR analyses. Traits Consortia Ethnicity Cases Control Sample size PMID Graves FinnGen Biobank European 4462 320703 325165 36653562 Autoimmune hypothyroidism FinnGen Biobank European 40926 274069 314995 36653562 TPOAb The ThyroidOmics Consortium European 1769 16528 18297 24586183 FT4 The ThyroidOmics Consortium European / / 49269 30367059 TSH The ThyroidOmics Consortium European / / 54288 30367059 Myasthenia gravis HumanOmniExpress arrays European 1873 36370 38243 35074870 2.3 Selection of genetic instrumental variables To obtain IVs while satisfying the assumption of strong correlation between the exposure and SNPs, we applied a genome-wide significance threshold of P-value (P<5×10-8). Additionally, the datasets were harmonized through the removal of variants in potential linkage disequilibrium (r2 =0.001, 10,000 kb).Subsequently,we standardized the effect estimates for both exposure and outcome variants and eliminated any potential SNPs with incompatible alleles or palindromic SNPs(23).To assess the strength of genetically determined IVs and avoid any bias towards weak IVs, we used F statistics (beta2/se2)(24) and ensured that F>10 in line with the first MR assumption(25, 26). 2.4 Mendelian randomization analyses The main analysis utilized the inverse-variance weighted (IVW) approach under a random-effects model, which accounts for heterogeneity across SNPs(27). We conducted several sensitivity analyses to ensure the robustness of the primary analysis. The weighted median (WM) method, requiring over 50% of the weight corresponding to valid IVs, was also employed to estimate the causal effects(28). Additionally, we evaluated possible horizontal pleiotropy using MR-Egger intercepts(28, 29). To detect and correct for any potential horizontal pleiotropic outliers, we utilized the MR-PRESSO framework, adjusting the IVW estimate through outlier removal(30). Furthermore, we conducted a leave-one-out analysis to investigate whether the effect estimates were impacted by any singular outlier variant.The analyses were conducted using the R software (version 4.2.3) Two-Sample MR package. Result Forward MR Analysis between AITD and MG After data screening, 24 SNPS were extracted from GD data, 117 SNPS were extracted from the Autoimmune hypothyroidism data, 8 SNPS were extracted from TPOAb data, 41 SNPS were extracted from TSH data, and 19 SNPS were extracted from FT4 data ( Supplementary1 Tables 1-5 ).The assessment of the effects of these 24 valid IVs on MG consistently revealed a causal association between GD and MG (OR 1.31,95%CI 1.08 to 1.60,P=0.005), and this direction of effect remained consistent when employing both the MR-Egger and Weighted Median methods.Subsequent testing revealed the presence of heterogeneity (Q-pval =1.180e-05), leading to the adoption of a random-effects model to estimate the MR effect size. Neither evidence of horizontal pleiotropy was found through the MR Egger intercept(egger intercept P=0.996), nor was there any significant difference in results after removing two outlier identified by the MR Presso test (MR PRESSO Distortion Test P=0.929).The results remained stable before and after the correction( Supplementary2 Table1 ).The IVW method, upon analysis, indicated a significant association between autoimmune hypothyroidism and an increased risk of MG (OR: 1.26, 95% CI: 1.08 to 1.47, P =0.002, Figure 1). This direction of effect was consistent with the Weighted Median method, and the MR Egger intercept (egger intercept P=0.127) did not reveal any evidence of pleiotropy. After removing six outliers with the MR Presso approach, the results remained unchanged (MR PRESSO Distortion Test P = 0.620). Furthermore, the IVW method found no significant associations between TSH, FT4, and TPOAb with the risk of MG (refer to Figure 1). These consistent findings were replicated using alternative methodologies and through replicative analyses(Supplementary2 Table1). Reverse MR Analysis between MG and AITD During the reverse MR analysis, six SNPs were extracted from the MG dataset and utilized as IVs. The IVW method demonstrated a significant association between MG and an increased risk of GD (OR: 1.50, 95% CI: 1.14 to 1.98, P =3.57e-3, Figure 2 ). This association was corroborated by the Weighted Median method (OR: 1.21, 95% CI: 1.10 to 1.33, P =6.04e-5, Figure 2 ) and the Weighted Mode method (OR: 1.20, 95% CI: 1.10 to 1.31, P =9.30e-3, Figure 2 ). Moreover, the MR Egger intercept (Egger intercept P = 0.310) did not indicate the presence of pleiotropy. After the removal of four outliers with the MR Presso technique, the results remained stable (MR PRESSO Distortion Test P=1). Causal associations were also observed between MG and autoimmune hypothyroidism, supported by the IVW method (OR: 1.29, 95% CI: 1.04 to 1.59, P =0.019, Figure 2), the Weighted Median method (OR: 1.12, 95% CI: 1.07 to 1.17, P =7.43e-8, Figure 2), and the Weighted Mode method (OR: 1.12, 95% CI: 1.08 to 1.17, P =2.02e-3, Figure 2). However, further MR Presso testing revealed the presence of pleiotropy (MR PRESSO Distortion Test P < 0.001), indicating instability in the results. Following harmonization, a combined total of 2 valid IVs were identified for the association between MG and TPOAb, while 1 valid IV was found for the relationship between MG and FT4/TSH. The IVW method revealed a causal relationship between MG and TPOAb (OR: 1.84, 95% CI: 1.39 to 2.42, P =1.47e-5, Figure 3 ), and the Wald ratio method indicated an association between MG and elevated TSH (Beta:0.08,95% CI:0.01 to 0.14,P =0.011, Figure 4 ), whereas there was no observed correlation with FT4. F-statistics and Visualization of MR F-statistics were employed to calculate the values for each valid IV, with none of them falling below 10 (Supplementary1 Tables9–14). The arrangement of figures from left to right includes forest plots, scatter plots, funnel plots, and leave-one-out plots showcasing the MR Effect ( Figure 5 ). The scatter plots exhibit a positive correlation trend between ATID and MG, which is also evident in the reverse MR analysis. The symmetrical funnel plots indicate result stability. The forest plots allow for the observation of the effects of each SNP, while the leave-one-out analysis validates the significance of the results( Figure 5 ). Discussion This is the first MR analysis conducted on AITD and MG. Previous case reports have described the co-occurrence of GD and MG(31-34). However, the occurrence of thyroid-associated ophthalmopathy(TAO) and MG together is extremely rare. A retrospective study of 1482 MG cases revealed that only 20 cases (1.3%) were identified with TAO(35). The sequence of onset between AITD and MG remains unclear. Studies have reported the TNF-α -863 polymorphism is likely to be associated with MG combined with TAO(33).Both AITD and MG demonstrate a noticeable genetic predisposition(7, 36, 37). Thymoma or thymus hyperplasia is commonly linked to MG(7), and the amelioration of neuromuscular symptoms following thymectomy suggests the involvement of a dysfunctional thymus in the development of MG(38). The presence of thymus hyperplasia in GD was initially described in 1912 and is a prevalent finding (approximately 40% in histology) in patients with thyrotoxicosis(39-41). Multiple lines of evidence indicate that thyroid hormones themselves induce thymus hyperplasia(42-44). In this context, the promiscuous expression of the TSH receptor in thymocytes may be responsible for the autoimmune-mediated expansion of the thymus in GD, facilitated by TSH receptor-stimulating autoantibodies(45). Conversely, it has also been observed that the size of the thymus decreases after thyroidectomy, reflecting the correction of thyrotoxicosis as well as the reduction of the autoimmune response against the TSH receptor(46).Reduction of TPOAb following Thymectomy in Patients with MG(47). The existence of a correlation between AITD and MG remains a subject of debate. This study utilized MR analysis to provide evidence supporting a causal relationship between AITD and MG based on genetic variation. The findings complement the conclusions drawn from previous observational studies. Our results indicate a higher susceptibility of AITD patients to MG and a greater likelihood of MG patients developing GD. However, the reliability of the results for Autoimmune hypothyroidism is considered questionable due to the influence of horizontal pleiotropy. Furthermore, MG patients exhibit a higher prevalence of TPOAb positivity. Additionally, a positive correlation between MG and TSH is observed, although further validation is required as only one SNP was analyzed. Our study possessed evident advantages.Firstly, it stood as the inaugural research endeavor to analyze the causal association between AITD and MG using bidirectional two-sample Mendelian randomization.Moreover, the exposure and outcome datasets were sourced from different databases, thereby mitigating the potential interference caused by sample overlap (33). The instrumental variables (IVs) employed in our study were SNPs exhibiting strong associations (P＜5e-8) and high intensity (F-statistics > 10). Consequently, the exposure and outcome samples in this study were more comparable, lending greater credibility to our conclusions.Furthermore, our study incorporated a comprehensive sensitivity analysis. Nonetheless, it is crucial to acknowledge the limitations of our research. Firstly, the available GWAS data for MG and TPOAb is currently restricted, comprising a small number of cases and a limited set of extractable SNPs. To ensure further validation, larger sample sizes of GWAS data are required. Secondly, we did not stratify the causal effects of GD and MG based on gender and age, which may introduce potential heterogeneity due to variations in health status, age, or gender. Moreover, it is worth noting that our study population consisted of Europeans, and therefore, the generalizability of our conclusions to a global population may be limited. Conclusion In summary, our bidirectional two-sample MR analysis explores the relationship between AITD and MG, elucidating the causal associations that retrospective studies fail to address from a perspective of genetic variation. It reveals the bidirectional causal relationship between GD and MG, as well as the causal relationship between hypothyroidism and MG. Furthermore, it indicates a higher prevalence of TPOAb positivity in MG patients, potentially linked to elevated TSH levels. These findings supplement the evidence from previous observational studies. Declarations Contributor Information ： Suijian Wang,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China, [email protected] Shaoda Lin,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China, [email protected] Xiaohong Chen,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China, [email protected] Daiyun Chen,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China, [email protected] Authorcontributions Research design and conceptualization,S.W.,data management,S.W.,S.L.,Investigation and analysis,S.W.,D.C.,Verification,S.W.,X.C.,visualization,D.C.,writing and review,S.L.,project integration and editor,S.W.,X.C,the manuscript has been reviewed and approved by all authors prior to its publication. Funding This work was supported by the Guangdong Provincial Science and Technology Special Funds (Project No. 20211231071-33) and the Clinical Research Enhancement Program (Project No. 2014110108). Availability of data and material For access to the data utilized in this research, interested parties can contact the corresponding author directly. Conflicts of interest Not applicable References Antonelli A, Ferrari SM, Corrado A, Di Domenicantonio A, Fallahi P. Autoimmune thyroid disorders. Autoimmun Rev. 2015 Feb;14(2):174-80. Jacobson DL, Gange SJ, Rose NR, Graham NMH. Epidemiology and estimated population burden of selected autoimmune diseases in the United States. Clin Immunol Immunopathol. 1997 Sep;84(3):223-43. Tomer Y. Mechanisms of Autoimmune Thyroid Diseases: From Genetics to Epigenetics. In: Abbas AK, Galli SJ, Howley PM, editors. Annual Review of Pathology: Mechanisms of Disease, Vol 9. Palo Alto: Annual Reviews; 2014. p. 147-56. Kyritsi EM, Kanaka-Gantenbein C. 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Thyroid function modulates thymic endocrine activity. The Journal of clinical endocrinology and metabolism. 1986 Mar;62(3):474-8. van der Weerd K, van Hagen PM, Schrijver B, et al. Thyrotropin acts as a T-cell developmental factor in mice and humans. Thyroid. 2014 Jun;24(6):1051-61. Giménez-Barcons M, Colobran R, Gómez-Pau A, et al. Graves' disease TSHR-stimulating antibodies (TSAbs) induce the activation of immature thymocytes: a clue to the riddle of TSAbs generation? Journal of immunology (Baltimore, Md : 1950). 2015 May 1;194(9):4199-206. Rotondo Dottore G, Leo M, Ricciardi R, et al. Disappearance of Anti-Thyroid Autoantibodies following Thymectomy in Patients with Myasthenia Gravis. European thyroid journal. 2021 Jun;10(3):237-47. Additional Declarations No competing interests reported. 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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-3427396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":239237245,"identity":"22c91c6b-fbf0-4e01-829a-dba76b181d54","order_by":0,"name":"suijian Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"suijian","middleName":"","lastName":"Wang","suffix":""},{"id":239237246,"identity":"0adc4950-a97b-4ea3-b4fe-ba964a9aee8f","order_by":1,"name":"Shaoda Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBAC+RlAgoeBQY6Nvf3gAyBbhoGBDb8WgxsQLcb8PGeSDYB8HsJaJCBaEmfOSDCTIE6LdPPDB29qGBg3HEhIq/jY9oeHn70tgeFHxTbcfplzzNhwzjEGZoMDB4/dnNlmwCPZc+wAY8+Z27ituZFgJs3DxsBmcLAh7TYvUIvBjfQGZsY2fFrSv0nz/GPgMTjMYFZMpJYcM2neNgYJyTYGM2aIlrQDeLUY3MgpNpzbJ2HAz8OTLDnjnDHILwkH8flFfkb6xgdvvtnUt8k/P/jhQ5mcHDDEDB/8qMDjMAiQQOUeIKR+FIyCUTAKRgF+AAAobFRPEo4/HwAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shaoda","middleName":"","lastName":"Lin","suffix":""},{"id":239237247,"identity":"e02d0ceb-1fe1-4c7a-ae7a-bee68170afe2","order_by":2,"name":"Xiaohong Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Chen","suffix":""},{"id":239237248,"identity":"8df29f08-d297-473e-8a31-555fd2d40409","order_by":3,"name":"Daiyun Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daiyun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2023-10-10 12:14:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3427396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3427396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44610361,"identity":"c0a73cdd-2d22-4553-8ca5-a76789ece3fa","added_by":"auto","created_at":"2023-10-14 00:20:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174770,"visible":true,"origin":"","legend":"\u003cp\u003eA flow chart outlining the study design and the steps involved in MR analysis.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/53ff69da76569d67139a51d8.jpg"},{"id":44609421,"identity":"158079ad-3efb-42d5-9254-e294a9d0bf34","added_by":"auto","created_at":"2023-10-14 00:12:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":207202,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of causal effect estimates in forward MR. GD, Graves disease; SNP, single-nucleotide polymorphism;IVW,inverse variance weighted.MG,Myasthenia gravis;TPOAb,thyroid peroxidase antibody;FT4,free thyroxine4;TSH,thyroid stimulating hormone.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/d5deaacc1bdf1317c28c8012.jpg"},{"id":44609424,"identity":"6e62e11d-9e08-4eee-a712-2a47b87b1a0b","added_by":"auto","created_at":"2023-10-14 00:12:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142988,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of causal effect estimates in reverse MR,GD, Graves disease; SNP, single-nucleotide polymorphism;IVW,inverse variance weighted.MG,Myasthenia gravis;TPOAb,thyroid peroxidase antibody.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/064a240f7fa27183f3245f16.jpg"},{"id":44609420,"identity":"7c0e163e-b251-4f88-a526-1cbe7365f106","added_by":"auto","created_at":"2023-10-14 00:12:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10464,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of causal effect estimates in reverse MR,IVW,inverse variance weighted.MG,Myasthenia gravis;TPOAb,thyroid peroxidase antibody;FT4,free thyroxine4;TSH,thyroid stimulating hormone.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/733398d9ca7247f402533ebb.jpg"},{"id":44609426,"identity":"463ccfa0-1f90-4cb8-aec0-56c87e7927b3","added_by":"auto","created_at":"2023-10-14 00:12:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2121671,"visible":true,"origin":"","legend":"\u003cp\u003eFrom left to right includes forest plots, scatter plots, funnel plots, and leave-one-out plots showcasing the MR Effect.A,the MR Effect of GD on MG;B,the MR Effect of hypothyroidism on MG;C,the MR Effect of MG on GD;D,the MR Effect of MG on hypothyroidism.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/bb3b0211ee291db501b93e1e.jpg"},{"id":44955585,"identity":"68cbec6b-9de9-4100-ac61-790ba748d631","added_by":"auto","created_at":"2023-10-20 01:07:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1357551,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/bccbfa01-7ac0-49b5-acc8-37a0351ac988.pdf"},{"id":44609425,"identity":"b9d6ab48-db5b-48cc-a516-06794359fb25","added_by":"auto","created_at":"2023-10-14 00:12:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":199553,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/40223bfa34b426062a5a7caa.pdf"},{"id":44609427,"identity":"80de64cf-3d61-477c-ad2f-29276072cdb6","added_by":"auto","created_at":"2023-10-14 00:12:49","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":162015,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3427396/v1/3134ec074b992264daffb35a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autoimmune Thyroid Disease and Myasthenia Gravis: A study bidirectional Mendelian randomization","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eAutoimmune thyroid disorders (AITD) emerge from an immune system malfunction, giving rise to an immune onslaught against the thyroid gland(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).AITD stand as the most prevalent autoimmune disorders and hold the position of being the most frequently observed pathological conditions of the thyroid gland(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This category encompasses two major clinical manifestations: Graves' disease (GD) and Hashimoto's thyroiditis (HT), both of which share a common characteristic\u0026mdash;lymphocytic infiltration of the thyroid parenchyma(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The defining clinical traits of GD and HT involve thyrotoxicosis and hypothyroidism, correspondingly(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).The infiltration of the thyroid by autoreactive lymphocytes and the generation of antibodies against three primary thyroid antigens, namely thyroid peroxidase (TPO), thyroglobulin (TG), and thyroid-stimulating hormone receptor (TSHR), are instigated by the activation of T- and B cell pathways(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).AITD's etiology is presently comprehended as multifactorial, resulting from the intricate interplay between particular susceptibility genes and environmental exposures,with genetic differences and susceptibility playing an important role in the etiology of GD and HT(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMyasthenia gravis (MG) exemplifies a classic autoimmune disorder mediated by antibodies, primarily affecting the neuromuscular junction(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).Antibody-mediated processes underlie MG, where antibodies are generated against key components such as the acetylcholine receptor (AchR), the muscle-specific kinase antibody (MuSK), and the agrin receptor low-density lipoprotein receptor-related protein-4 antibody (LRP4)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).The precise triggering of the autoimmune response in MG remains undisclosed, however, it is evident that deviations within the thymus gland (hyperplasia and neoplasia) have a substantial role, particularly in patients with anti-AChR antibodies(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and the development of the disorder is plausibly subject to genetic predisposition(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMG and AITD exhibit certain similarities, such as both being organ-specific, antibody-mediated, and contributing to ocular myopathy and exophthalmos(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).Some studies have documented the rising incidence of thyroid disorders in MG, with a higher propensity for MG patients to develop HT and other autoimmune thyroid disorders(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).Patients diagnosed with HT and GD exhibited a heightened subsequent risk of developing MG(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).However, there is a lack of consistency among the reported results, with some studies indicating no clinical association between myasthenia symptomatology and thyroid dysfunction, as well as no significant impact on myasthenic symptoms when the endocrine disorders improve(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).The relationship between AITD and MG is still a topic of ongoing debate, and observational studies are susceptible to the influence of reverse causality and confounding effects.To explore the causal association between AITD and MG, we employed a bidirectional Mendelian randomization (MR) approach in this study. This method utilized genetic variants obtained from genome-wide association studies as instrumental variables (IVs) to mitigate biases commonly found in observational epidemiological studies, such as reverse causation.\u003c/p\u003e"},{"header":"2.Materials and methods","content":"\u003cp\u003e\u003cem\u003e2.1 Study design and the assumption of MR\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEmploying a two-sample Mendelian randomization (MR) design, we ascertained the overall effects, with the primary objective of assessing the connection between AITD and MG. In a separate two-step MR investigation, we explored whether thyroid function characteristics acted as intermediaries in the impact of AITD on MG. A reverse MR analysis was carried out to assess the reciprocal influence of MG on AITD(\u003cstrong\u003eFigure1\u003c/strong\u003e).The MR analysis was conducted under the following assumptions: (i) the single nucleotide polymorphisms (SNPs) used as IVs were obtained from GWAS and displayed associations with the exposures; (ii) the IVs were not associated with confounding factors; (iii) the IVs had an exclusive influence on the risk of outcomes solely through the exposures(17).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Data sources\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFinnGen constitutes a substantial collaboration between the public and private sectors, with the objective of gathering and scrutinizing genomic and health information from 500,000 individuals enrolled in FinnGen biobanks(https://www.finngen.fi/en). Within this framework, the FinnGen Biobank of European descent has furnished the Genome-Wide Association Study (GWAS) data associated with ATID, encompassing GD with 4,462 cases and 320,703 controls, as well as autoimmune hypothyroidism with 40,926 cases and 274,069 controls(18). We obtained the summary data for thyroid function GWAS from the ThyroidOmics Consortium, an initiative established to investigate the factors influencing thyroid disorders and thyroid function(19). In a meta-analysis, the analysis of thyroid-stimulating hormone (TSH) included data from 22 distinct cohorts, encompassing a total of 54,288 individuals, while analyses of free thyroxine (FT4) were based on data from 19 cohorts involving 49,269 individuals(19).The GWAS information for thyroid peroxidase antibodies (TPOAb) was extracted from a separate meta-analysis conducted on a general population of 18,297 individuals across 11 different populations. Among these individuals, there were 1,769 cases with TPOAb positivity(20).Samples of individuals with MG were gathered from collaborative sources in both the United States and Italy, constituting a total of 1,873 cases and 36,370 controls(21). The diagnosis of MG relied on established clinical criteria, specifically the presence of characteristic, fatigue-induced muscle weakness, alongside electrophysiological and/or pharmacological anomalies, and further confirmed by the presence of anti-acetylcholine receptor antibodies(22).The complete information is in \u003cstrong\u003eTable1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetails of GWAS included in MR analyses.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"746\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.541554959785522%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.41823056300268%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsortia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.857908847184987%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.908847184986596%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.785522788203753%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePMID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.541554959785522%\"\u003e\n \u003cp\u003eGraves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.41823056300268%\"\u003e\n \u003cp\u003eFinnGen Biobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.857908847184987%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\"\u003e\n \u003cp\u003e4462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\"\u003e\n \u003cp\u003e320703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.908847184986596%\"\u003e\n \u003cp\u003e325165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.785522788203753%\"\u003e\n \u003cp\u003e36653562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.541554959785522%\"\u003e\n \u003cp\u003eAutoimmune hypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.41823056300268%\"\u003e\n \u003cp\u003eFinnGen Biobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.857908847184987%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\"\u003e\n \u003cp\u003e40926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\"\u003e\n \u003cp\u003e274069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.908847184986596%\"\u003e\n \u003cp\u003e314995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.785522788203753%\"\u003e\n \u003cp\u003e36653562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.541554959785522%\"\u003e\n \u003cp\u003eTPOAb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.41823056300268%\"\u003e\n \u003cp\u003eThe ThyroidOmics Consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.857908847184987%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\"\u003e\n \u003cp\u003e1769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\"\u003e\n \u003cp\u003e16528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.908847184986596%\"\u003e\n \u003cp\u003e18297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.785522788203753%\"\u003e\n \u003cp\u003e24586183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.541554959785522%\"\u003e\n \u003cp\u003eFT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.41823056300268%\"\u003e\n \u003cp\u003eThe ThyroidOmics Consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.857908847184987%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.908847184986596%\"\u003e\n \u003cp\u003e49269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.785522788203753%\"\u003e\n \u003cp\u003e30367059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.541554959785522%\"\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.41823056300268%\"\u003e\n \u003cp\u003eThe ThyroidOmics Consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.857908847184987%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.908847184986596%\"\u003e\n \u003cp\u003e54288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.785522788203753%\"\u003e\n \u003cp\u003e30367059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.541554959785522%\"\u003e\n \u003cp\u003eMyasthenia gravis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.41823056300268%\"\u003e\n \u003cp\u003eHumanOmniExpress arrays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.857908847184987%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\"\u003e\n \u003cp\u003e1873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.847184986595174%\"\u003e\n \u003cp\u003e36370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.908847184986596%\"\u003e\n \u003cp\u003e38243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.785522788203753%\"\u003e\n \u003cp\u003e35074870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e2.3 Selection of genetic instrumental variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain IVs while satisfying the assumption of strong correlation between the exposure and SNPs, we applied a genome-wide significance threshold of P-value (P\u0026lt;5\u0026times;10-8). Additionally, the datasets were harmonized through the removal of variants in potential linkage disequilibrium (r2 =0.001, 10,000 kb).Subsequently,we standardized the effect estimates for both exposure and outcome variants and eliminated any potential SNPs with incompatible alleles or palindromic SNPs(23).To assess the strength of genetically determined IVs and avoid any bias towards weak IVs, we used F statistics (beta2/se2)(24)\u0026nbsp;and ensured that F\u0026gt;10 in line with the first MR assumption(25, 26).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4 Mendelian randomization analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe main analysis utilized the inverse-variance weighted (IVW) approach under a random-effects model, which accounts for heterogeneity across SNPs(27). We conducted several sensitivity analyses to ensure the robustness of the primary analysis. The weighted median (WM) method, requiring over 50% of the weight corresponding to valid IVs, was also employed to estimate the causal effects(28). Additionally, we evaluated possible horizontal pleiotropy using MR-Egger intercepts(28, 29). To detect and correct for any potential horizontal pleiotropic outliers, we utilized the MR-PRESSO framework, adjusting the IVW estimate through outlier removal(30). Furthermore, we conducted a leave-one-out analysis to investigate whether the effect estimates were impacted by any singular outlier variant.The analyses were conducted using the R software (version 4.2.3) Two-Sample MR package.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eForward MR Analysis between AITD and MG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter data screening, 24 SNPS were extracted from GD data, 117 SNPS were extracted from the Autoimmune hypothyroidism data, 8 SNPS were extracted from TPOAb data, 41 SNPS were extracted from TSH data, and 19 SNPS were extracted from FT4 data ( \u003cstrong\u003eSupplementary1 Tables 1-5\u003c/strong\u003e ).The assessment of the effects of these 24 valid IVs on MG consistently revealed a causal association between GD and MG (OR 1.31,95%CI 1.08 to 1.60,P=0.005), and this direction of effect remained consistent when employing both the MR-Egger and Weighted Median methods.Subsequent testing revealed the presence of heterogeneity (Q-pval =1.180e-05), leading to the adoption of a random-effects model to estimate the MR effect size. Neither evidence of horizontal pleiotropy was found through the MR Egger intercept(egger intercept P=0.996), nor was there any significant difference in results after removing two outlier identified by the MR Presso test (MR PRESSO Distortion Test P=0.929).The results remained stable before and after the correction(\u003cstrong\u003eSupplementary2 Table1\u003c/strong\u003e).The IVW method, upon analysis, indicated a significant association between autoimmune hypothyroidism and an increased risk of MG (OR: 1.26, 95% CI: 1.08 to 1.47, P =0.002, Figure 1). This direction of effect was consistent with the Weighted Median method, and the MR Egger intercept (egger intercept P=0.127) did not reveal any evidence of pleiotropy. After removing six outliers with the MR Presso approach, the results remained unchanged (MR PRESSO Distortion Test P = 0.620). Furthermore, the IVW method found no significant associations between TSH, FT4, and TPOAb with the risk of MG (refer to Figure 1). These consistent findings were replicated using alternative methodologies and through replicative analyses(Supplementary2 Table1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse MR Analysis between MG and AITD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the reverse MR analysis, six SNPs were extracted from the MG dataset and utilized as IVs. The IVW method demonstrated a significant association between MG and an increased risk of GD\u0026nbsp;(OR: 1.50, 95% CI: 1.14\u0026nbsp;to\u0026nbsp;1.98, P =3.57e-3, \u003cstrong\u003eFigure 2\u003c/strong\u003e). This association was corroborated by the Weighted Median method (OR: 1.21, 95% CI: 1.10\u0026nbsp;to\u0026nbsp;1.33, P =6.04e-5, \u003cstrong\u003eFigure 2\u003c/strong\u003e) and the Weighted Mode method (OR: 1.20, 95% CI: 1.10\u0026nbsp;to\u0026nbsp;1.31, P =9.30e-3, \u003cstrong\u003eFigure 2\u003c/strong\u003e). Moreover, the MR Egger intercept (Egger intercept P = 0.310) did not indicate the presence of pleiotropy. After the removal of four outliers with the MR Presso technique, the results remained stable (MR PRESSO Distortion Test P=1). Causal associations were also observed between MG and autoimmune hypothyroidism, supported by the IVW method (OR: 1.29, 95% CI: 1.04\u0026nbsp;to\u0026nbsp;1.59, P =0.019, Figure 2), the Weighted Median method (OR: 1.12, 95% CI: 1.07\u0026nbsp;to\u0026nbsp;1.17, P =7.43e-8, Figure 2), and the Weighted Mode method (OR: 1.12, 95% CI: 1.08\u0026nbsp;to\u0026nbsp;1.17, P =2.02e-3, Figure 2). However, further MR Presso testing revealed the presence of pleiotropy (MR PRESSO Distortion Test P \u0026lt; 0.001), indicating instability in the results.\u003c/p\u003e\n\u003cp\u003eFollowing harmonization, a combined total of 2 valid IVs were identified for the association between MG and TPOAb, while 1 valid IV was found for the relationship between MG and FT4/TSH. The IVW method revealed a causal relationship between MG and TPOAb (OR: 1.84, 95% CI: 1.39\u0026nbsp;to\u0026nbsp;2.42, P =1.47e-5, \u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e), and the Wald ratio method indicated an association between MG and elevated TSH (Beta:0.08,95% CI:0.01\u0026nbsp;to 0.14,P =0.011,\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e), whereas there was no observed correlation with FT4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF-statistics and Visualization of MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF-statistics were employed to calculate the values for each valid IV, with none of them falling below 10 (Supplementary1 Tables9\u0026ndash;14). The arrangement of figures from left to right includes forest plots, scatter plots, funnel plots, and leave-one-out plots showcasing the MR Effect (\u003cstrong\u003eFigure 5\u003c/strong\u003e). The scatter plots exhibit a positive correlation trend between ATID and MG, which is also evident in the reverse MR analysis. The symmetrical funnel plots indicate result stability. The forest plots allow for the observation of the effects of each SNP, while the leave-one-out analysis validates the significance of the results(\u003cstrong\u003eFigure 5\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first MR analysis conducted on AITD and MG. Previous case reports have described the co-occurrence of GD and MG(31-34). However, the occurrence of thyroid-associated ophthalmopathy(TAO)\u0026nbsp;and MG together is extremely rare. A retrospective study of 1482 MG cases revealed that only 20 cases (1.3%) were identified with TAO(35). The sequence of onset between AITD and MG remains unclear. Studies have reported the TNF-\u0026alpha; -863 polymorphism is likely to be associated with MG combined with TAO(33).Both AITD and MG demonstrate a noticeable genetic predisposition(7, 36, 37). Thymoma or thymus hyperplasia is commonly linked to MG(7), and the amelioration of neuromuscular symptoms following thymectomy suggests the involvement of a dysfunctional thymus in the development of MG(38). The presence of thymus hyperplasia in GD was initially described in 1912 and is a prevalent finding (approximately 40% in histology) in patients with thyrotoxicosis(39-41). Multiple lines of evidence indicate that thyroid hormones themselves induce thymus hyperplasia(42-44). In this context, the promiscuous expression of the TSH receptor in thymocytes may be responsible for the autoimmune-mediated expansion of the thymus in GD, facilitated by TSH receptor-stimulating autoantibodies(45). Conversely, it has also been observed that the size of the thymus decreases after thyroidectomy, reflecting the correction of thyrotoxicosis as well as the reduction of the autoimmune response against the TSH receptor(46).Reduction of\u0026nbsp;TPOAb\u0026nbsp;following Thymectomy in Patients with\u0026nbsp;MG(47).\u003c/p\u003e\n\u003cp\u003eThe existence of a correlation between AITD and MG remains a subject of debate. This study utilized MR analysis to provide evidence supporting a causal relationship between AITD and MG based on genetic variation. The findings complement the conclusions drawn from previous observational studies. Our results indicate a higher susceptibility of AITD patients to MG and a greater likelihood of MG patients developing GD. However, the reliability of the results for Autoimmune hypothyroidism is considered questionable due to the influence of horizontal pleiotropy. Furthermore, MG patients exhibit a higher prevalence of TPOAb positivity. Additionally, a positive correlation between MG and TSH is observed, although further validation is required as only one SNP was analyzed.\u003c/p\u003e\n\u003cp\u003eOur study possessed evident advantages.Firstly, it stood as the inaugural research endeavor to analyze the causal association between AITD and MG using bidirectional two-sample \u0026nbsp; Mendelian randomization.Moreover, the exposure and outcome datasets were sourced from different databases, thereby mitigating the potential interference caused by sample overlap (33). \u0026nbsp;The instrumental variables (IVs) employed in our study were SNPs exhibiting strong associations (P＜5e-8) and high intensity (F-statistics \u0026gt; 10). Consequently, the exposure and outcome samples in this study were more comparable, lending greater credibility to our conclusions.Furthermore, our study incorporated a comprehensive sensitivity analysis.\u003c/p\u003e\n\u003cp\u003eNonetheless, it is crucial to acknowledge the limitations of our research. Firstly, the available GWAS data for MG and TPOAb is currently restricted, comprising a small number of cases and a limited set of extractable SNPs. To ensure further validation, larger sample sizes of GWAS data are required. Secondly, we did not stratify the causal effects of GD and MG based on gender and age, which may introduce potential heterogeneity due to variations in health status, age, or gender. Moreover, it is worth noting that our study population consisted of Europeans, and therefore, the generalizability of our conclusions to a global population may be limited.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our bidirectional two-sample MR analysis explores the relationship between AITD and MG, elucidating the causal associations that retrospective studies fail to address from a perspective of genetic variation. It reveals the bidirectional causal relationship between GD and MG, as well as the causal relationship between hypothyroidism and MG. Furthermore, it indicates a higher prevalence of TPOAb positivity in MG patients, potentially linked to elevated TSH levels. These findings supplement the evidence from previous observational studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributor Information\u003c/strong\u003e：\u003c/p\u003e\n\u003cp\u003eSuijian Wang,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China,[email protected] \u003c/p\u003e\n\u003cp\u003eShaoda Lin,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China,[email protected] \u003c/p\u003e\n\u003cp\u003eXiaohong Chen,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China,[email protected] \u003c/p\u003e\n\u003cp\u003eDaiyun Chen,Department of Endocrinology, The First Affiliated Hospital, School of Medicine,59 Changping Road,Shantou University,Shantou515041,China,[email protected] \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorcontributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch design and conceptualization,S.W.,data management,S.W.,S.L.,Investigation and analysis,S.W.,D.C.,Verification,S.W.,X.C.,visualization,D.C.,writing and review,S.L.,project integration and editor,S.W.,X.C,the manuscript has been reviewed and approved by all authors prior to its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Guangdong Provincial Science and Technology Special Funds (Project No. 20211231071-33) and the Clinical Research Enhancement Program (Project No. 2014110108).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor access to the data utilized in this research, interested parties can contact the corresponding author directly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAntonelli A, Ferrari SM, Corrado A, Di Domenicantonio A, Fallahi P. 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Journal of the National Cancer Institute. 1951 Jun;11(6):1153-61.\u003c/li\u003e\n\u003cli\u003eFabris N, Mocchegiani E, Mariotti S, Pacini F, Pinchera A. Thyroid function modulates thymic endocrine activity. The Journal of clinical endocrinology and metabolism. 1986 Mar;62(3):474-8.\u003c/li\u003e\n\u003cli\u003evan der Weerd K, van Hagen PM, Schrijver B, et al. Thyrotropin acts as a T-cell developmental factor in mice and humans. Thyroid. 2014 Jun;24(6):1051-61.\u003c/li\u003e\n\u003cli\u003eGim\u0026eacute;nez-Barcons M, Colobran R, G\u0026oacute;mez-Pau A, et al. Graves\u0026apos; disease TSHR-stimulating antibodies (TSAbs) induce the activation of immature thymocytes: a clue to the riddle of TSAbs generation? Journal of immunology (Baltimore, Md : 1950). 2015 May 1;194(9):4199-206.\u003c/li\u003e\n\u003cli\u003eRotondo Dottore G, Leo M, Ricciardi R, et al. Disappearance of Anti-Thyroid Autoantibodies following Thymectomy in Patients with Myasthenia Gravis. European thyroid journal. 2021 Jun;10(3):237-47.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autoimmune Thyroid Disease, Graves disease, hypothyroidism, Mendelian randomization, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-3427396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3427396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious studies have suggested a potential association between AITD and MG, but the evidence is limited and controversial, and the exact causal relationship remains uncertain.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTherefore, we employed a Mendelian randomization (MR) analysis to investigate the causal relationship between AITD and MG.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo explore the interplay between AITD and MG, We conducted MR studies utilizing GWAS-based summary statistics in the European ancestry.Several techniques were used to ensure the stability of the causal effect, such as random-effect inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was evaluated by calculating Cochran's Q value. Moreover, the presence of horizontal pleiotropy was investigated through MR-Egger regression and MR-PRESSO\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe IVW method indicates a causal relationship between both GD(OR 1.31,95%CI 1.08 to 1.60,P\u0026thinsp;=\u0026thinsp;0.005) and autoimmune hypothyroidism (OR: 1.26, 95% CI: 1.08 to 1.47, P\u0026thinsp;=\u0026thinsp;0.002) with MG. However, there is no association found between FT4(OR 0.88,95%CI 0.65 to 1.18,P\u0026thinsp;=\u0026thinsp;0.406), TPOAb(OR: 1.34, 95% CI: 0.86 to 2.07, P\u0026thinsp;=\u0026thinsp;0.186), TSH(OR: 0.97, 95% CI: 0.77 to 1.23, P\u0026thinsp;=\u0026thinsp;0.846), and MG. The reverse MR analysis reveals a causal relationship between MG and GD(OR: 1.50, 95% CI: 1.14 to 1.98, P\u0026thinsp;=\u0026thinsp;3.57e-3), with stable results. On the other hand, there is a significant association with autoimmune hypothyroidism(OR: 1.29, 95% CI: 1.04 to 1.59, P\u0026thinsp;=\u0026thinsp;0.019), but it is considered unstable due to the influence of horizontal pleiotropy (MR PRESSO Distortion Test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). MG has a higher prevalence of TPOAb(OR: 1.84, 95% CI: 1.39 to 2.42, P\u0026thinsp;=\u0026thinsp;1.47e-5) positivity and may be linked to elevated TSH levels(Beta:0.08,95% CI:0.01 to 0.14,P\u0026thinsp;=\u0026thinsp;0.011), while there is no correlation between MG and FT4(Beta:-9.03e-3,95% CI:-0.07 to 0.05,P\u0026thinsp;=\u0026thinsp;0.796).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAITD patients are more susceptible to developing MG, and MG patients also have a higher incidence of GD.\u003c/p\u003e","manuscriptTitle":"Autoimmune Thyroid Disease and Myasthenia Gravis: A study bidirectional Mendelian randomization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-14 00:12:44","doi":"10.21203/rs.3.rs-3427396/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":"8808cca7-06f7-4bab-b094-fd071e7e14a9","owner":[],"postedDate":"October 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-20T00:59:15+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-14 00:12:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3427396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3427396","identity":"rs-3427396","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}