Evaluating the causal effects between Grave’s disease and diabetes mellitus: a bidirectional Mendelian randomization study

Evaluating the causal effects between Grave’s disease and diabetes mellitus: a bidirectional Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the causal effects between Grave’s disease and diabetes mellitus: a bidirectional Mendelian randomization study Yuhan Zhang, Liuxiang Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4229672/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Graves’ disease (GD) is an autoimmune disease associated with an increased incidence of other autoimmune diseases. To investigate the causality between GD and Diabetes mellitus (DM), we designed bidirectional two-sample Mendelian randomization (MR) and multivariable MR (MVMR) studies. Methods Single-nucleotide polymorphisms (SNPs) associated with GD, thyroid peroxidase (TPO), thyroglobulin (Tg), thyroid-stimulating hormone (TSH), type 1 diabetes (T1D), and type 2 diabetes (T2D) were obtained from the IEU Open GWAS and FinnGen biobank databases. For the forward MR study, we used GD (sample size = 458,620) as the exposure and T1D (sample size = 520,580) and T2D (sample size = 211,766) as the outcomes. Next, T1D and T2D were used as exposure variables, and GD was used as the outcome variable for the reverse MR analysis. Finally, MVMR analysis was conducted to investigate the probable relationship between DM and indicators for thyroid function like TPO, Tg, and TSH. The inverse variance weighting (IVW) was used as the main method. Finally, the heterogeneity and sensitivity were assessed. Results There were 27, 88, and 55 SNPs associated with GD, T1D, and T2D, respectively. A significant causal connection between GD and T1D (odds ratio [OR] [95% confidence interval, CI] = 1.411 [1.077–1.848], P = 0.012) and T2D (OR [95% CI] = 1.059 [1.025–1.095], P = 5.53e-04) was found in the forward MR analysis. However, reverse MR suggested that there was a genetic susceptibility to T1D that increased the likelihood of developing GD (OR [95% CI] = 1.059 [1.025–1.095], P = 5.53e-04), while T2D did not (OR [95% CI] = 0.963 [0.870–1.066], P = 0.468). Furthermore, there was inadequate evidence to suggest that abnormal TSH, TPO, and Tg levels increase the risk of incident T1D or T2D in individuals with GD. MVMR revealed no causal relationship among Tg, TSH, TPO, T1D, or T2D. Conclusion Evidence of a bidirectional causative relationship between GD and T1D and a unidirectional causal relationship between GD and T2D was discovered using MR analyses. MVMR analysis showed no statistically relevant causality between TSH, TPO, or Tg and either T1D or T2D. Graves’ disease Mendelian randomization Type 1 diabetes Type 2 diabetes Autoimmune disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Diabetes mellitus (DM), a metabolic disorder characterized by high blood glucose levels, is one of the fastest-growing global health problems of the 21st century. Globally, 537 million people were estimated to have diabetes by 2021. By 2030, this figure is likely to rise to 643 million, and by 2045, it is predicted to reach 783 million 1 . Associated complications of DM affect multiple organs and include cardiovascular disease, diabetic nephropathy, retina-related eye diseases, neuropathy, and diabetic foot ulcers 2,3 . There are three broad forms of DM: type 1 (T1D), type 2 (T2D), and gestational diabetes. T1D is an autoimmune illness mostly caused by the immune system attacking the β-cells in the pancreas that produce insulin 4–6 . T1D mainly occurs in pediatric patients; however, some patients with latent autoimmune diabetes are diagnosed during adulthood 7 . T2D is mostly caused by insulin resistance 8 and is distinguished by the absence of pathological alterations in the pancreas and insensitivity of other body parts and tissues to insulin. Obesity is believed to be the primary cause of T2D 9 . However, some studies have demonstrated that immunology also contributes to its pathophysiology 10–13 . Owing to its high incidence and numerous complications, DM has a considerable influence on the quality of life of patients and is a heavy strain on society. Investigating the risk factors for DM development and implementing early preventive interventions are crucial. Diffuse toxic goiter, also known as Graves' disease (GD), is an autoimmune condition that affects the thyroid glands. Hyperthyroidism is mainly caused by GD 14 . Many patients with GD exhibit abnormal levels of indicators of thyroid function, thyroid-stimulating hormone (TSH), thyroid peroxidase (TPO), and thyroglobulin (Tg). Patients with GD typically present with a range of hypermetabolic symptoms due to elevated thyroid hormone levels, including hyperthermia, hyperhidrosis, irritability, and cardiac arrhythmias, such as atrial fibrillation and flutter 15 . In addition to thyroid function abnormalities, patients diagnosed with GD may have abnormal thyroid antigens and antibodies 16,17 . Moreover, individuals with GD have a significantly higher risk for other autoimmune diseases, including T1D 18 , rheumatoid arthritis 19 , and systemic lupus erythematosus 20 , than the general population. Previous studies have reported a correlation between GD and T1D 18,21,22 ; however, few studies have examined bidirectional causality. Thus, it is crucial to demonstrate a causal link between GD and T2D. Mendelian randomization (MR) analysis is a novel research method that has emerged over the last decade. MR analysis is superior to randomized controlled trials in several respects. First, MR analysis uses genetic variation (single-nucleotide polymorphisms [SNPs]) as a random assignment to remove the effects of confounding factors and reverse causality, enabling the examination of the cause–effect relationship between exposure and outcome 23–26 . Additionally, compared with traditional randomized group experiments, MR can save time and reduce economic costs while obtaining a larger sample size. To identify any potential bidirectional causal relationship between GD, T1D, and T2D, this study used genome-wide association study (GWAS) data from publicly accessible databases in a bidirectional two-sample analysis. Furthermore, we clarified the causal relationship between TSH, TPO, and Tg and T1D and T2D through multifactorial MR analysis. This study offers a novel approach to the prevention of GD and DM. 2 Materials and Methods 2.1 Study design A bidirectional two-sample MR study was performed to determine the cause–effect relationship between GD and T1D. We also performed multivariate MR (MVMR) analysis to reduce the interference of confounding variables, such as TPO, TSH, and Tg, on the results. The research design process is illustrated in Fig. 1. 2.2 GWAS data sources and selection of instrumental variables (IVs) Utilizing genetic IVs associated with GD obtained from a study by Sakaue et al. 27 , this study investigated 220 human phenotypes, including thyroid dysfunction, in a European population with a large sample size of 458,620. Genetic data associated with TPO, Tg, and TSH were acquired from a study carried out by Benjamin et al. with a sample size of 3301 28 . Genetic data associated with GD can be found in the IEU Open GWAS database ( https://gwas.mrcieu.ac.uk/ ). To render the selected IVs for MR analysis comprehensive, three assumptions must be made: ( 1 ) The SNPs utilized as IVs should have a high correlation with exposure. Therefore, maintaining a clear logical structure when presenting these points is essential. ( 2 ) Genetic variation has no bearing on the possible genetic or environmental factors that could influence the outcome. ( 3 ) IVs should only affect the outcomes through exposure 29,30 . Subsequently, we determined the following selection criteria to reduce the likelihood of missing SNPs while maintaining a high correlation with exposure: P-value < 5×10 − 8 , linkage disequilibrium (LD) r 2 < 0.001, and window kb = 10,000. The SNPs used as IVs are listed in Supplementary Tables 2–4. F-statistics for every SNP were also produced to evaluate the usefulness of the IVs according to this formula: $$F = \left(N-2\right)\times {R}^{2}/(1-{R}^{2})$$ More precisely, a powerful instrument is typically indicated by an F-statistic greater than 10, which implies that IVs are predictive of the exposure variable 31 . Supplementary Table 1 provides detailed phenotypic information. 2.3 Genetic variants with a risk of T1D and T2D To perform reverse MR analysis, it was necessary to extract SNPs related to T1D and T2D. GWAS data for T1D originated from a study conducted by Chiou et al. that encompassed a cohort of 520,580 individuals from the European population, including 18,942 cases and 501,638 controls 32 . The associated data were obtained from https://gwas.mrcieu.ac.uk/ . The GWAS information for T2D was gathered from the FinnGen biobank database (mailto:@online{finngen), containing 29,193 cases and 182,573 controls 33 . The assumptions and selection criteria mentioned previously are also applicable to the reverse MR analysis. 2.4 Two-sample MR analysis and sensitivity analysis Three methods were used: inverse variance weighting (IVW), weighted median, and MR-Egger regression. Giving more weight to variants with smaller standard errors makes IVW a highly efficient method that provides precise and powerful estimates of causal effects 34 . Therefore, IVW was regarded as the primary method. Scatter plots were constructed to visualize the MR results. The results were subjected to statistical tests and sensitivity studies to evaluate reliability. In a two-sample MR analysis, heterogeneity generally refers to discrepancies in effect estimates across different loci or studies. Heterogeneity among the SNPs in IVW computation was assessed using Cochran's Q test 35 . If the P -value was greater than 0.05, the assumption of existing heterogeneity among SNPs was rejected; otherwise, a random-effects model was used. Subsequently, the MR-Egger intercept test was performed to identify horizontal pleiotropic effects 36 . If the P -value obtained by this test was < 0.05, the selected SNPs influenced multiple phenotypes. This means that a single genetic variant may affect more than one phenotype, and that these effects are not mediated by the primary exposure of interest. The MR-PRESSO test was used for global and distortion testing. The presence or absence of heterogeneity was determined using a global test. Heterogeneity was considered present if the results were statistically significant. Conversely, if the P -values for the global testing were greater than 0.05, heterogeneity was absent. The distortion test is used to identify outliers and determine whether the MR analysis results are affected 37 ; therefore, this test was used in the present study. Random-effect models were utilized to estimate the MR effect sizes and assess a meaningful cause–effect link between exposure and outcome. Finally, the total effect of each remaining SNP was estimated using the leave-one-out methodology 38 . Funnel and forest plots were constructed to display the results more clearly and concisely. 2.5 Reverse MR analysis and sensitivity analysis To determine whether there was a reverse causal relationship between T1D, T2D, and GD, we performed reverse MR analysis. The same genotypes and SNPs were used for consistency. We examined GD, T1D, and T2D as outcomes. Similar to the forward MR analysis, the SNPs for T1D and T2D were extracted using the criteria mentioned previously and are shown in Supplementary Tables 3 and 4. 2.6 MVMR analysis As most patients with GD have comorbidities, such as abnormal serum TPO, Tg, and TSH levels, we conducted an MVMR analysis to identify whether these factors contribute to the heightened risk of developing T1D and T2D. 3 Results 3.1 IV selection We selected the following SNPs associated with GD, T1D, and T2D according to previous criteria. There were 27, 88, and 55 SNPs associated with GD, T1D, and T2D, respectively. In addition, 15 outliers were found for GD, 8 for T1D, and 1 for T2D through the MR-PRESSO test. Supplementary all selected SNPs and MR-PRESSO results are displayed in supplementary material. The F-statistic for each SNP was > 10, suggesting a significant association between the SNPs and exposure. 3.2 Two-sample MR analysis The IVW analysis results demonstrated a substantial causal link between GD and T1D (odds ratio [OR] (95% confidence interval [CI]) = 1.411 (1.077–1.848), P = 0.012). This suggests that the presence of GD is associated with a 41.1% increased risk of T1D. The causal relationship between GD and T2D was also significant (OR (95% CI) = 1.059 (1.025–1.095), P = 5.53e-04). Table 1 and Fig. 2 present the results of the MR analysis. Table 1 Result of forward and reverse mendelian randomization analysis Bidirectional MR analysis Exposure-outcome method P-value OR [95%CI] GD—T1D MR Egger 0.046 2.295[1.060,4.970] Weighted median 0.949 0.998[0.927,1.074] IVW 0.012 1.411[1.077,1.848] GD—T2D MR Egger 0.118 1.077[0.984, 1.179 Weighted median 0.012 1.028[1.006,1.050] IVW 5.53e-04 1.059[1.025,1.095] T2D—GD MR Egger 0.798 0.968[0.757,1.238] Weighted median 0.933 0.994[0.874,1.131] IVW 0.468 0.963[0.869,1.066] T1D—GD MR Egger 2.479e-04 1.124[1.059,1.194] Weighted median 9.794e-37 1.192[1.160,1.224] IVW 1.913e-10 1.173[1.117,1.231] GD: Grave’s Disease; T1D: type 1 diabetes; T2D: type 2 diabetes. 3.3 Sensitivity analyses Sensitivity analyses included the Cochran's Q test and horizontal pleiotropy testing (Table 2 ). Heterogeneity was present in all analyses, as indicated by a P-value < 0.05. The MR-PRESSO test results showed that heterogeneity and outliers were present in all datasets. The MR effect magnitude was re-estimated using random-effects models, and causality was verified. The MR-Egger intercept tests of GD had P > 0.05, which suggests that horizontal pleiotropy was not significant (Table 2 ). The funnel plots for these analyses are shown in Fig. 3. Ultimately, the leave-one-out analysis and visualization supported the reliability of our findings (Fig. 4). Table 2 Results of heterogeneity and horizontal pleiotropy in the bidirectional MR analysis. Heterogeneity and pleiotropy test Exposure-outcome Cochran’s Q Test MR Egger intercept Egger-Q Value Pval-Egger IVW-Q value Pval-IVW Intercept value pval GD-T1D 1621.986 0 1738.855 0 -0.107 0.201 GD-T2D 163.088 1.181e-22 164.114 2.013e-22 -0.004 0.701 T1D-GD 422.197 6.886e-47 432.656 2.269e-48 0.032 0.012 T2D-GD 98.391 1.524e-4 98.396 2.125e-4 -6.310e-4 0.962 GD: Grave’s Disease; T1D: type 1 diabetes; T2D: type 2 diabetes. 3.4 Reverse MR analysis and sensitivity analysis The reverse MR analysis and sensitivity analysis results shown in Table 1 and Fig. 5 revealed the presence of a causal connection between T1D and GD (IVW, OR (95% CI) = 1.059 (1.025–1.095), P = 5.53e-04). In contrast, genetic susceptibility to T2D did not increase the risk of developing GD (OR (95% CI) = 0.963 (0.870–1.066), P = 0.468). Table 2 displays the sensitivity analysis results of the reverse MR analysis. The funnel and leave-one-out plots of the reverse MR are shown in Figs. 6 and 7, respectively. 3.5 MVMR analysis Table 3 demonstrates the causal links between GD, T1D, and T2D. In comparison, no causal relationship was observed between Tg, TSH, or TPO and T1D or T2D. Table 3 results of MVMR analysis between DM and indicators for thyroid function. Multivariable MR analysis Exposure-outcome pval OR [95%CI] GD-T1D 0.0093 1.471[1.099,1.969] Tg -T1D 0.392 2.584[0.293,22.770] GD-T1D 0.013 1.429[1.078 ,1.895] TSH-T1D 0.747 1.257[0.314,5.022] GD-T1D 0.010 1.441[1.093,1.901] TPO-T1D 0.498 0.732[0.297 1.806] GD-T2D 0.364 1.015[0.982,1.050] Tg -T2D 0.241 0.854 0.656 1.112 GD-T2D 0.240 1.020[0.987,1.054] TPO-T2D 0.783 0.987[0.899,1.083] GD-T2D 0.222 1.021[0.987,1.056] TSH-T2D 0.313 0.917[0.775,1.085] GD: Grave’s Disease; T1D: type 1 diabetes; T2D: type 2 diabetes; Tg: thyroglobulin; TSH: thyroid stimulating hormone; TPO: thyroid peroxidase. 4 Discussion GD and both T1D and T2D were causally linked in this study, indicating that a genetic predisposition to GD increases the risk of both T1D and T2D. However, reverse MR suggested that genetic susceptibility to T1D increases the risk of developing GD, while T2D does not. Furthermore, there is inadequate evidence to suggest that abnormal TSH, TPO, and Tg levels increase the risk of developing T1D or T2D in patients with GD. We believe that the increased risk of developing DM due to GD can be explained by certain aspects, such as the commonality of autoimmune mechanisms. GD is a chronic autoimmune thyroid disorder characterized by an abnormal immune response to thyroid antigens. Several animal studies have described a correlation between GD and regulatory T cells (Tregs) 39,40 . Some studies have reported varying degrees of functional impairment, decreased Treg cell efficiency, and enrichment of T helper 17 cells (Th17) in patients with GD 41–43 . Th17 cells play a role in promoting inflammation and immune responses, whereas Tregs act as immunosuppressors. There may be a link between the onset and progression of DM (types 1 and 2) and an imbalance between Tregs and Th17 cells 43–45 . A population-based study conducted in Finland revealed a significant correlation between T1D and other autoimmune diseases, including GD 46 . The study found a significant association between hyperthyroidism and an increased risk of developing T1D (OR 2.98 [2.27–3.90]). In addition, GD has been identified as a primary cause of hyperthyroidism 47 . In a separate study, it was found that out of 491 patients diagnosed with T1D, 122 tested positive for the TPO antibody, including 15 with autoimmune thyroid disease 48 . In addition, a study of 500 individuals reported that patients with autoimmune thyroid disorders were more likely to develop other autoimmune diseases 49 . Among them, researchers found T1D in 3.1% of patients with GD, confirming our finding that genetic susceptibility to GD increases the risk of developing T1D. The onset and progression of T2D is a complex process involving multiple mechanisms. Several studies have reported on the involvement of immune factors in the pathogenesis of this disease. Obesity-associated chronic inflammation is an important factor in the predisposition to T2D 44,50 . Multiple studies have shown that an imbalance between Treg and Th17 cells, specifically a deficiency of Tregs and an excess of Th17 cells, is linked to obesity, insulin resistance, and T2D 51–53 . Some studies have suggested that systemic Treg defects are associated with diabetic nephropathy, retinopathy, and diabetic foot ulcers 54,55 . An increasing number of Tregs in the body has been shown to improve the severity of diabetes in some animal studies. Fengjie et al. discovered that Tregs can promote the mobilization of endothelial progenitor cells, anti-inflammatory effects, and cytoprotection 56 . This can inhibit vascular endothelial hyperplasia and alleviate the vascular degeneration caused by elevated blood glucose levels in diabetic pigs. One study found that CD4 + Tregs decreased in the visceral tissues of patients with T2D 57 . This suggests that an imbalance between the quality and capability of Treg and Th17 cells is involved in the development of GD, T1D, and T2D. This finding is consistent with the bidirectional causal relationship between GD and T1D. In contrast, the etiology of T2D is multifactorial, with immunological factors being one of the many contributors to its onset or progression. Consequently, patients with GD may have an increased risk of T2D due to the disruption of immune system homeostasis, while T2D does not necessarily increase the risk of GD. Chronic inflammation should also be taken into consideration. It is common for autoimmune diseases to be accompanied by a chronic inflammatory response 58–61 . Researchers have revealed that some cytokines, including transforming growth factor beta (TGF-β), interleukin-6 (IL-6), IL-4, IL-5, IL-10, IL-13, IL-1β, and interferon-gamma (IFN-γ), are associated with GD. T1D is associated with chronic inflammation 62 , and several studies have reported that cytokines, such as IFN-γ, tumor necrosis factor (TNF)-α, and IL-1β, contribute to the pathogenesis of T1D 63–65 . We speculate that chronic inflammation triggered by GD may contribute to the development of T1D, and vice versa. This finding suggests a bidirectional causal relationship between GD and T1D. In recent research, T2D has been increasingly associated with chronic inflammation 66 . Chronic inflammation with increased levels of various cytokines contributes to the development of T2D. It has been reported that conventional type 1 dendritic cells can promote insulin resistance by increasing IFN-γ production 67 . IL1-β, IL-6, IL-8, TNF-α, NF-κB, and MAPK were also significantly increased in the T2D and prediabetes groups compared to non-diabetic groups, indicating their role as pro-inflammatory factors 68 . Chronic inflammation resulting from GD-induced increases in the levels of inflammatory factors, such as IFN-γ, IL-1β, and TNF-α in vivo, may contribute to insulin resistance and increase the risk of developing T2D by affecting insulin sensitivity. It is essential to note that this suggestion is based on objective evidence rather than subjective evaluation. Chronic inflammation is not the sole factor in the development of T2D. Therefore, the complex etiology of T2D indicates that GD development is not necessarily increased by T2D. Similar to other studies, this study has certain limitations. First, database limitations prevented the inclusion of indicators related to thyroid function, such as thyroid hormone, TSH receptor, anti-Tg, and anti-TPO. Additional research is required to examine the relationships between other indicators and DM. Second, our study did not investigate the causal relationship between Hashimoto's thyroiditis and T1D or T2D. Future studies should explore autoimmune thyroiditis. Third, the scope of this study was restricted to the European population. Therefore, additional research is necessary to verify whether our findings are applicable to other research groups. 5 Conclusion In our study, a two-sample MR analysis was used to find evidence of bidirectional causality between GD and T1D and unidirectional causality between GD and T2D. No significant causal relationship between TSH, TPO, or Tg and T1D or T2D was found in the MVMR analysis. These findings provide new insights into the control of DM. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contribution Yuhan Zhang wrote the main manuscript text and Liuxiang Fu prepared figures 1-3. All authors reviewed the manuscript. Acknowledgement We want to acknowledge the participants and investigators of the FinnGen study.The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organisations and biobanks within Finland and international industry partners.We are also very grateful to the IEU open GWAS project for helping us with our research. Data Availability Data is provided within the manuscript or supplementary information files Funding None References Magliano DJ, Boyko EJ, committee IDFDAtes. IDF Diabetes Atlas. In: Idf diabetes atlas. Brussels: International Diabetes Federation © International Diabetes Federation, 2021.; 2021. Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843. Lim JZ, Ng NS, Thomas C. Prevention and treatment of diabetic foot ulcers. J R Soc Med. 2017;110(3):104–109. Wasserfall C, Nick HS, Campbell-Thompson M, et al. Persistence of Pancreatic Insulin mRNA Expression and Proinsulin Protein in Type 1 Diabetes Pancreata. Cell Metab. 2017;26(3):568–575.e563. Donath MY. Type 1 diabetes: what is the role of autoimmunity in β cell death? J Clin Invest. 2022;132(20). Quattrin T, Mastrandrea LD, Walker LSK. Type 1 diabetes. Lancet. 2023;401(10394):2149–2162. Ravikumar V, Ahmed A, Anjankar A. A Review on Latent Autoimmune Diabetes in Adults. Cureus. 2023;15(10):e47915. Galicia-Garcia U, Benito-Vicente A, Jebari S, et al. Pathophysiology of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020;21(17). Chen Y, Lundeen EA, Koyama AK, et al. Prevalence of Testing for Diabetes Among US Adults With Overweight or Obesity, 2016–2019. Prev Chronic Dis. 2023;20:E116. Jensen AH, Winther-Sørensen M, Burisch J, et al. Autoimmune liver diseases and diabetes: A propensity score matched analysis and a proportional meta-analysis. Liver Int. 2023;43(11):2479–2491. Nițulescu IM, Ciulei G, Cozma A, Procopciuc LM, Orășan OH. From Innate Immunity to Metabolic Disorder: A Review of the NLRP3 Inflammasome in Diabetes Mellitus. J Clin Med. 2023;12(18). Lampousi AM, Carlsson S, Löfvenborg JE, et al. Interaction between plasma phospholipid odd-chain fatty acids and GAD65 autoantibodies on the incidence of adult-onset diabetes: the EPIC-InterAct case-cohort study. Diabetologia. 2023;66(8):1460–1471. Fan B, Lim CKP, Poon EWM, et al. Differential Associations of GAD Antibodies (GADA) and C-Peptide With Insulin Initiation, Glycemic Responses, and Severe Hypoglycemia in Patients Diagnosed With Type 2 Diabetes. Diabetes Care. 2023;46(6):1282–1291. Burch HB, Cooper DS. Management of Graves Disease: A Review. Jama. 2015;314(23):2544–2554. Subekti I, Pramono LA. Current Diagnosis and Management of Graves' Disease. Acta Med Indones. 2018;50(2):177–182. Petranović Ovčariček P, Görges R, Giovanella L. Autoimmune Thyroid Diseases. Semin Nucl Med. 2023. Kravchenko V, Zakharchenko T. Thyroid hormones and minerals in immunocorrection of disorders in autoimmune thyroid diseases. Front Endocrinol (Lausanne). 2023;14:1225494. Wang LT, Huang CY, Lin CH, et al. Graves disease is more prevalent than Hashimoto disease in children and adolescents with type 1 diabetes. Front Endocrinol (Lausanne). 2022;13:1083690. Gu P, Pu B, Ma Y, et al. Appraising the causal relationship between thyroid function and rheumatoid arthritis: a two-sample bidirectional Mendelian randomization study. Front Immunol. 2023;14:1238757. Chu YC, Yu KH, Lin WT, Wang WT, Chen DP. Finding the Common Single-Nucleotide Polymorphisms in Three Autoimmune Diseases and Exploring Their Bio-Function by Using a Reporter Assay. Biomedicines. 2023;11(9). Ferrari SM, Fallahi P, Ruffilli I, et al. The association of other autoimmune diseases in patients with Graves' disease (with or without ophthalmopathy): Review of the literature and report of a large series. Autoimmun Rev. 2019;18(3):287–292. Zala A, Thomas R. Antigen-specific immunotherapy to restore antigen-specific tolerance in Type 1 diabetes and Graves' disease. Clin Exp Immunol. 2023;211(2):164–175. Ebrahim S, Davey Smith G. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet. 2008;123(1):15–33. Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization. Jama. 2017;318(19):1925–1926. Li P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20(1):443. Zhu G, Zhou S, Xu Y, et al. Chickenpox and multiple sclerosis: A Mendelian randomization study. J Med Virol. 2023;95(1):e28315. Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53(10):1415–1424. Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–79. Gagliano Taliun SA, Evans DM. Ten simple rules for conducting a mendelian randomization study. PLoS Comput Biol. 2021;17(8):e1009238. Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–764. Chen L, Yang H, Li H, He C, Yang L, Lv G. Insights into modifiable risk factors of cholelithiasis: A Mendelian randomization study. Hepatology. 2022;75(4):785–796. Chiou J, Geusz RJ, Okino ML, et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature. 2021;594(7863):398–402. Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508–518. Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186. Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34(21):2926–2940. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–698. Meng H, Jiang L, Song Z, Wang F. Causal Associations of Circulating Lipids with Osteoarthritis: A Bidirectional Mendelian Randomization Study. Nutrients. 2022;14(7). Dong R, Zhang Q, Peng H. Gastroesophageal reflux disease and the risk of respiratory diseases: a Mendelian randomization study. J Transl Med. 2024;22(1):60. Zhou J, Bi M, Fan C, et al. Regulatory T cells but not T helper 17 cells are modulated in an animal model of Graves' hyperthyroidism. Clin Exp Med. 2012;12(1):39–46. Yuan Q, Zhao Y, Zhu X, Liu X. Low regulatory T cell and high IL-17 mRNA expression in a mouse Graves' disease model. J Endocrinol Invest. 2017;40(4):397–407. Tan Y, Chen W, Liu C, Zheng X, Guo A, Long J. Effect of IL-21 on the Balance of Th17 Cells/Treg Cells in the Pathogenesis of Graves' Disease. Endocr Res. 2019;44(4):138–147. Su X, Yin X, Liu Y, et al. Gut Dysbiosis Contributes to the Imbalance of Treg and Th17 Cells in Graves' Disease Patients by Propionic Acid. J Clin Endocrinol Metab. 2020;105(11). Fan Q, Liu Y, Rao J, et al. Anti-Atherosclerosis Effect of Angong Niuhuang Pill via Regulating Th17/Treg Immune Balance and Inhibiting Chronic Inflammatory on ApoE(-/-) Mice Model of Early and Mid-Term Atherosclerosis. Front Pharmacol. 2019;10:1584. Barbosa P, Pinho A, Lázaro A, et al. CD8(+) Treg cells play a role in the obesity-associated insulin resistance. Life Sci. 2024;336:122306. Xu Q, Zhang X, Li T, Shao S. Exenatide regulates Th17/Treg balance via PI3K/Akt/FoxO1 pathway in db/db mice. Mol Med. 2022;28(1):144. Mäkimattila S, Harjutsalo V, Forsblom C, Groop PH. Every Fifth Individual With Type 1 Diabetes Suffers From an Additional Autoimmune Disease: A Finnish Nationwide Study. Diabetes Care. 2020;43(5):1041–1047. Grixti L, Lane LC, Pearce SH. The genetics of Graves' disease. Rev Endocr Metab Disord. 2023. Triolo TM, Armstrong TK, McFann K, et al. Additional autoimmune disease found in 33% of patients at type 1 diabetes onset. Diabetes Care. 2011;34(5):1211–1213. Sharma H, Sahlot R, Purwar N, et al. Co-existence of type 1 diabetes and other autoimmune ailments in subjects with autoimmune thyroid disorders. Diabetes Metab Syndr. 2022;16(2):102405. Coope A, Torsoni AS, Velloso LA. MECHANISMS IN ENDOCRINOLOGY: Metabolic and inflammatory pathways on the pathogenesis of type 2 diabetes. Eur J Endocrinol. 2016;174(5):R175-187. Ou Q, Power R, Griffin MD. Revisiting regulatory T cells as modulators of innate immune response and inflammatory diseases. Front Immunol. 2023;14:1287465. Fabbrini E, Cella M, McCartney SA, et al. Association between specific adipose tissue CD4 + T-cell populations and insulin resistance in obese individuals. Gastroenterology. 2013;145(2):366–374.e361-363. Gu QW, Sun Q, Wang J, Gu WS, Wang W, Mao XM. Effects of Glycemic Variability on Regulatory T Cells in Patients with Type 2 Diabetes and Kidney Disease. Diabetes Metab Syndr Obes. 2023;16:2365–2375. Gonzalez LL, Garrie K, Turner MD. Type 2 diabetes - An autoinflammatory disease driven by metabolic stress. Biochim Biophys Acta Mol Basis Dis. 2018;1864(11):3805–3823. Zhang J, Chen J, Gao C, et al. Maggot treatment promotes healing of diabetic foot ulcer wounds possibly by upregulating Treg levels. Diabetes Res Clin Pract. 2022;184:109187. Guo F, Ren Z, Liu D, Wang L, Hou X, Chen W. The Inhibitory Effect of Regulatory T Cells on the Intimal Hyperplasia of Tissue-Engineered Blood Vessels in Diabetic Pigs. Front Bioeng Biotechnol. 2022;10:929867. Wu D, Han JM, Yu X, et al. Characterization of regulatory T cells in obese omental adipose tissue in humans. Eur J Immunol. 2019;49(2):336–347. Widder J, Dorfinger K, Wilfing A, et al. The immunoregulatory influence of transforming growth factor beta in thyroid autoimmunity: TGF beta inhibits autoreactivity in Graves' disease. J Autoimmun. 1991;4(4):689–701. Gianoukakis AG, Khadavi N, Smith TJ. Cytokines, Graves' disease, and thyroid-associated ophthalmopathy. Thyroid. 2008;18(9):953–958. Kutluturk F, Yarman S, Sarvan FO, Kekik C. Association of cytokine gene polymorphisms (IL6, IL10, TNF-α, TGF-β and IFN-γ) and Graves' disease in Turkish population. Endocr Metab Immune Disord Drug Targets. 2013;13(2):163–167. Ueland HO, Ulvik A, Løvås K, et al. Systemic Activation of the Kynurenine Pathway in Graves Disease With and Without Ophthalmopathy. J Clin Endocrinol Metab. 2023;108(6):1290–1297. Ermis E, Nargis T, Webster K, Tersey SA, Anderson RM, Mirmira RG. Leukotriene B4 receptor 2 governs macrophage migration during tissue inflammation. J Biol Chem. 2023;300(1):105561. De George DJ, Ge T, Krishnamurthy B, Kay TWH, Thomas HE. Inflammation versus regulation: how interferon-gamma contributes to type 1 diabetes pathogenesis. Front Cell Dev Biol. 2023;11:1205590. Sun W, Liu J, Zhao R, et al. Knockdown of IFNAR2 reduces the inflammatory response in mouse model of type 1 diabetes. Biochem Biophys Res Commun. 2022;619:9–14. Gonuguntla S, Humphrey RK, Gorantla A, Hao E, Jhala US. Stress-induced pseudokinase TRB3 augments IL1β signaling by interacting with Flightless homolog 1. J Biol Chem. 2023;299(8):104803. Zhang M, Gao Y, Tian L, et al. Association of serum 25-hydroxyvitamin D(3) with adipokines and inflammatory marker in persons with prediabetes mellitus. Clin Chim Acta. 2017;468:152–158. Hildreth AD, Padilla ET, Gupta M, et al. Adipose cDC1s contribute to obesity-associated inflammation through STING-dependent IL-12 production. Nat Metab. 2023;5(12):2237–2252. Fenercioglu AK, Gonen MS, Uzun H, et al. The Association between Serum 25-Hydroxyvitamin D3 Levels and Pro-Inflammatory Markers in New-Onset Type 2 Diabetes Mellitus and Prediabetes. Biomolecules. 2023;13(12). Additional Declarations No competing interests reported. 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(DM), a metabolic disorder characterized by high blood glucose levels, is one of the fastest-growing global health problems of the 21st century. Globally, 537\u0026nbsp;million people were estimated to have diabetes by 2021. By 2030, this figure is likely to rise to 643\u0026nbsp;million, and by 2045, it is predicted to reach 783\u0026nbsp;million \u003csup\u003e1\u003c/sup\u003e. Associated complications of DM affect multiple organs and include cardiovascular disease, diabetic nephropathy, retina-related eye diseases, neuropathy, and diabetic foot ulcers \u003csup\u003e2,3\u003c/sup\u003e. There are three broad forms of DM: type 1 (T1D), type 2 (T2D), and gestational diabetes. T1D is an autoimmune illness mostly caused by the immune system attacking the β-cells in the pancreas that produce insulin \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e. T1D mainly occurs in pediatric patients; however, some patients with latent autoimmune diabetes are diagnosed during adulthood \u003csup\u003e7\u003c/sup\u003e. T2D is mostly caused by insulin resistance \u003csup\u003e8\u003c/sup\u003e and is distinguished by the absence of pathological alterations in the pancreas and insensitivity of other body parts and tissues to insulin. Obesity is believed to be the primary cause of T2D \u003csup\u003e9\u003c/sup\u003e. However, some studies have demonstrated that immunology also contributes to its pathophysiology \u003csup\u003e10\u0026ndash;13\u003c/sup\u003e. Owing to its high incidence and numerous complications, DM has a considerable influence on the quality of life of patients and is a heavy strain on society. Investigating the risk factors for DM development and implementing early preventive interventions are crucial.\u003c/p\u003e \u003cp\u003eDiffuse toxic goiter, also known as Graves' disease (GD), is an autoimmune condition that affects the thyroid glands. Hyperthyroidism is mainly caused by GD \u003csup\u003e14\u003c/sup\u003e. Many patients with GD exhibit abnormal levels of indicators of thyroid function, thyroid-stimulating hormone (TSH), thyroid peroxidase (TPO), and thyroglobulin (Tg). Patients with GD typically present with a range of hypermetabolic symptoms due to elevated thyroid hormone levels, including hyperthermia, hyperhidrosis, irritability, and cardiac arrhythmias, such as atrial fibrillation and flutter \u003csup\u003e15\u003c/sup\u003e. In addition to thyroid function abnormalities, patients diagnosed with GD may have abnormal thyroid antigens and antibodies \u003csup\u003e16,17\u003c/sup\u003e. Moreover, individuals with GD have a significantly higher risk for other autoimmune diseases, including T1D \u003csup\u003e18\u003c/sup\u003e, rheumatoid arthritis \u003csup\u003e19\u003c/sup\u003e, and systemic lupus erythematosus \u003csup\u003e20\u003c/sup\u003e, than the general population. Previous studies have reported a correlation between GD and T1D \u003csup\u003e18,21,22\u003c/sup\u003e; however, few studies have examined bidirectional causality. Thus, it is crucial to demonstrate a causal link between GD and T2D.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis is a novel research method that has emerged over the last decade. MR analysis is superior to randomized controlled trials in several respects. First, MR analysis uses genetic variation (single-nucleotide polymorphisms [SNPs]) as a random assignment to remove the effects of confounding factors and reverse causality, enabling the examination of the cause\u0026ndash;effect relationship between exposure and outcome \u003csup\u003e23\u0026ndash;26\u003c/sup\u003e. Additionally, compared with traditional randomized group experiments, MR can save time and reduce economic costs while obtaining a larger sample size.\u003c/p\u003e \u003cp\u003eTo identify any potential bidirectional causal relationship between GD, T1D, and T2D, this study used genome-wide association study (GWAS) data from publicly accessible databases in a bidirectional two-sample analysis. Furthermore, we clarified the causal relationship between TSH, TPO, and Tg and T1D and T2D through multifactorial MR analysis. This study offers a novel approach to the prevention of GD and DM.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e2.1 Study design\u003c/p\u003e \u003cp\u003eA bidirectional two-sample MR study was performed to determine the cause\u0026ndash;effect relationship between GD and T1D. We also performed multivariate MR (MVMR) analysis to reduce the interference of confounding variables, such as TPO, TSH, and Tg, on the results. The research design process is illustrated in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e2.2 GWAS data sources and selection of instrumental variables (IVs)\u003c/p\u003e \u003cp\u003eUtilizing genetic IVs associated with GD obtained from a study by Sakaue et al. \u003csup\u003e27\u003c/sup\u003e, this study investigated 220 human phenotypes, including thyroid dysfunction, in a European population with a large sample size of 458,620. Genetic data associated with TPO, Tg, and TSH were acquired from a study carried out by Benjamin et al. with a sample size of 3301 \u003csup\u003e28\u003c/sup\u003e. Genetic data associated with GD can be found in the IEU Open GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To render the selected IVs for MR analysis comprehensive, three assumptions must be made: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The SNPs utilized as IVs should have a high correlation with exposure. Therefore, maintaining a clear logical structure when presenting these points is essential. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Genetic variation has no bearing on the possible genetic or environmental factors that could influence the outcome. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) IVs should only affect the outcomes through exposure \u003csup\u003e29,30\u003c/sup\u003e. Subsequently, we determined the following selection criteria to reduce the likelihood of missing SNPs while maintaining a high correlation with exposure: P-value\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, linkage disequilibrium (LD) r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and window kb\u0026thinsp;=\u0026thinsp;10,000. The SNPs used as IVs are listed in Supplementary Tables\u0026nbsp;2\u0026ndash;4. F-statistics for every SNP were also produced to evaluate the usefulness of the IVs according to this formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$F = \\left(N-2\\right)\\times {R}^{2}/(1-{R}^{2})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMore precisely, a powerful instrument is typically indicated by an F-statistic greater than 10, which implies that IVs are predictive of the exposure variable \u003csup\u003e31\u003c/sup\u003e. Supplementary Table\u0026nbsp;1 provides detailed phenotypic information.\u003c/p\u003e \u003cp\u003e2.3 Genetic variants with a risk of T1D and T2D\u003c/p\u003e \u003cp\u003eTo perform reverse MR analysis, it was necessary to extract SNPs related to T1D and T2D. GWAS data for T1D originated from a study conducted by Chiou et al. that encompassed a cohort of 520,580 individuals from the European population, including 18,942 cases and 501,638 controls \u003csup\u003e32\u003c/sup\u003e. The associated data were obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The GWAS information for T2D was gathered from the FinnGen biobank database (mailto:@online{finngen), containing 29,193 cases and 182,573 controls \u003csup\u003e33\u003c/sup\u003e. The assumptions and selection criteria mentioned previously are also applicable to the reverse MR analysis.\u003c/p\u003e \u003cp\u003e2.4 Two-sample MR analysis and sensitivity analysis\u003c/p\u003e \u003cp\u003eThree methods were used: inverse variance weighting (IVW), weighted median, and MR-Egger regression. Giving more weight to variants with smaller standard errors makes IVW a highly efficient method that provides precise and powerful estimates of causal effects \u003csup\u003e34\u003c/sup\u003e. Therefore, IVW was regarded as the primary method. Scatter plots were constructed to visualize the MR results. The results were subjected to statistical tests and sensitivity studies to evaluate reliability. In a two-sample MR analysis, heterogeneity generally refers to discrepancies in effect estimates across different loci or studies. Heterogeneity among the SNPs in IVW computation was assessed using Cochran's Q test \u003csup\u003e35\u003c/sup\u003e. If the \u003cem\u003eP\u003c/em\u003e-value was greater than 0.05, the assumption of existing heterogeneity among SNPs was rejected; otherwise, a random-effects model was used. Subsequently, the MR-Egger intercept test was performed to identify horizontal pleiotropic effects \u003csup\u003e36\u003c/sup\u003e. If the \u003cem\u003eP\u003c/em\u003e-value obtained by this test was \u0026lt;\u0026thinsp;0.05, the selected SNPs influenced multiple phenotypes. This means that a single genetic variant may affect more than one phenotype, and that these effects are not mediated by the primary exposure of interest. The MR-PRESSO test was used for global and distortion testing. The presence or absence of heterogeneity was determined using a global test. Heterogeneity was considered present if the results were statistically significant. Conversely, if the \u003cem\u003eP\u003c/em\u003e-values for the global testing were greater than 0.05, heterogeneity was absent. The distortion test is used to identify outliers and determine whether the MR analysis results are affected \u003csup\u003e37\u003c/sup\u003e; therefore, this test was used in the present study. Random-effect models were utilized to estimate the MR effect sizes and assess a meaningful cause\u0026ndash;effect link between exposure and outcome. Finally, the total effect of each remaining SNP was estimated using the leave-one-out methodology \u003csup\u003e38\u003c/sup\u003e. Funnel and forest plots were constructed to display the results more clearly and concisely.\u003c/p\u003e \u003cp\u003e2.5 Reverse MR analysis and sensitivity analysis\u003c/p\u003e \u003cp\u003eTo determine whether there was a reverse causal relationship between T1D, T2D, and GD, we performed reverse MR analysis. The same genotypes and SNPs were used for consistency. We examined GD, T1D, and T2D as outcomes. Similar to the forward MR analysis, the SNPs for T1D and T2D were extracted using the criteria mentioned previously and are shown in Supplementary Tables\u0026nbsp;3 and 4.\u003c/p\u003e \u003cp\u003e2.6 MVMR analysis\u003c/p\u003e \u003cp\u003eAs most patients with GD have comorbidities, such as abnormal serum TPO, Tg, and TSH levels, we conducted an MVMR analysis to identify whether these factors contribute to the heightened risk of developing T1D and T2D.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 IV selection\u003c/p\u003e \u003cp\u003eWe selected the following SNPs associated with GD, T1D, and T2D according to previous criteria. There were 27, 88, and 55 SNPs associated with GD, T1D, and T2D, respectively. In addition, 15 outliers were found for GD, 8 for T1D, and 1 for T2D through the MR-PRESSO test. Supplementary all selected SNPs and MR-PRESSO results are displayed in supplementary material. The F-statistic for each SNP was \u0026gt;\u0026thinsp;10, suggesting a significant association between the SNPs and exposure.\u003c/p\u003e \u003cp\u003e3.2 Two-sample MR analysis\u003c/p\u003e \u003cp\u003eThe IVW analysis results demonstrated a substantial causal link between GD and T1D (odds ratio [OR] (95% confidence interval [CI])\u0026thinsp;=\u0026thinsp;1.411 (1.077\u0026ndash;1.848), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). This suggests that the presence of GD is associated with a 41.1% increased risk of T1D. The causal relationship between GD and T2D was also significant (OR (95% CI)\u0026thinsp;=\u0026thinsp;1.059 (1.025\u0026ndash;1.095), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.53e-04). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;2 present the results of the MR analysis.\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\u003eResult of forward and reverse mendelian randomization analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBidirectional MR analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure-outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR [95%CI]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGD\u0026mdash;T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.295[1.060,4.970]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998[0.927,1.074]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.411[1.077,1.848]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGD\u0026mdash;T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.077[0.984, 1.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.028[1.006,1.050]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.53e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.059[1.025,1.095]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eT2D\u0026mdash;GD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.968[0.757,1.238]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994[0.874,1.131]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.963[0.869,1.066]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eT1D\u0026mdash;GD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.479e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.124[1.059,1.194]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.794e-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.192[1.160,1.224]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.913e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.173[1.117,1.231]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGD: Grave\u0026rsquo;s Disease; T1D: type 1 diabetes; T2D: type 2 diabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.3 Sensitivity analyses\u003c/p\u003e \u003cp\u003eSensitivity analyses included the Cochran's Q test and horizontal pleiotropy testing (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Heterogeneity was present in all analyses, as indicated by a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The MR-PRESSO test results showed that heterogeneity and outliers were present in all datasets. The MR effect magnitude was re-estimated using random-effects models, and causality was verified. The MR-Egger intercept tests of GD had \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, which suggests that horizontal pleiotropy was not significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The funnel plots for these analyses are shown in Fig.\u0026nbsp;3. Ultimately, the leave-one-out analysis and visualization supported the reliability of our findings (Fig.\u0026nbsp;4).\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\u003eResults of heterogeneity and horizontal pleiotropy in the bidirectional MR analysis.\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eHeterogeneity and pleiotropy test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure-outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCochran\u0026rsquo;s Q Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMR Egger intercept\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgger-Q Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePval-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW-Q value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePval-IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1621.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1738.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.181e-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.013e-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1D-GD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e422.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.886e-47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e432.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.269e-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D-GD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.524e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.125e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.310e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eGD: Grave\u0026rsquo;s Disease; T1D: type 1 diabetes; T2D: type 2 diabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.4 Reverse MR analysis and sensitivity analysis\u003c/p\u003e \u003cp\u003eThe reverse MR analysis and sensitivity analysis results shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;5 revealed the presence of a causal connection between T1D and GD (IVW, OR (95% CI)\u0026thinsp;=\u0026thinsp;1.059 (1.025\u0026ndash;1.095), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.53e-04). In contrast, genetic susceptibility to T2D did not increase the risk of developing GD (OR (95% CI)\u0026thinsp;=\u0026thinsp;0.963 (0.870\u0026ndash;1.066), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.468). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the sensitivity analysis results of the reverse MR analysis. The funnel and leave-one-out plots of the reverse MR are shown in Figs.\u0026nbsp;6 and 7, respectively.\u003c/p\u003e \u003cp\u003e3.5 MVMR analysis\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the causal links between GD, T1D, and T2D. In comparison, no causal relationship was observed between Tg, TSH, or TPO and T1D or T2D.\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\u003eresults of MVMR analysis between DM and indicators for thyroid function.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMultivariable MR analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure-outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR [95%CI]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.471[1.099,1.969]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTg -T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.584[0.293,22.770]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.429[1.078 ,1.895]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH-T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.257[0.314,5.022]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.441[1.093,1.901]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPO-T1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.732[0.297 1.806]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.015[0.982,1.050]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTg -T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.854 0.656 1.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.020[0.987,1.054]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPO-T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.987[0.899,1.083]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD-T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.021[0.987,1.056]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH-T2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.917[0.775,1.085]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eGD: Grave\u0026rsquo;s Disease; T1D: type 1 diabetes; T2D: type 2 diabetes; Tg: thyroglobulin; TSH: thyroid stimulating hormone; TPO: thyroid peroxidase.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eGD and both T1D and T2D were causally linked in this study, indicating that a genetic predisposition to GD increases the risk of both T1D and T2D. However, reverse MR suggested that genetic susceptibility to T1D increases the risk of developing GD, while T2D does not. Furthermore, there is inadequate evidence to suggest that abnormal TSH, TPO, and Tg levels increase the risk of developing T1D or T2D in patients with GD.\u003c/p\u003e \u003cp\u003eWe believe that the increased risk of developing DM due to GD can be explained by certain aspects, such as the commonality of autoimmune mechanisms. GD is a chronic autoimmune thyroid disorder characterized by an abnormal immune response to thyroid antigens. Several animal studies have described a correlation between GD and regulatory T cells (Tregs) \u003csup\u003e39,40\u003c/sup\u003e. Some studies have reported varying degrees of functional impairment, decreased Treg cell efficiency, and enrichment of T helper 17 cells (Th17) in patients with GD \u003csup\u003e41\u0026ndash;43\u003c/sup\u003e. Th17 cells play a role in promoting inflammation and immune responses, whereas Tregs act as immunosuppressors. There may be a link between the onset and progression of DM (types 1 and 2) and an imbalance between Tregs and Th17 cells \u003csup\u003e43\u0026ndash;45\u003c/sup\u003e. A population-based study conducted in Finland revealed a significant correlation between T1D and other autoimmune diseases, including GD \u003csup\u003e46\u003c/sup\u003e. The study found a significant association between hyperthyroidism and an increased risk of developing T1D (OR 2.98 [2.27\u0026ndash;3.90]). In addition, GD has been identified as a primary cause of hyperthyroidism \u003csup\u003e47\u003c/sup\u003e. In a separate study, it was found that out of 491 patients diagnosed with T1D, 122 tested positive for the TPO antibody, including 15 with autoimmune thyroid disease \u003csup\u003e48\u003c/sup\u003e. In addition, a study of 500 individuals reported that patients with autoimmune thyroid disorders were more likely to develop other autoimmune diseases \u003csup\u003e49\u003c/sup\u003e. Among them, researchers found T1D in 3.1% of patients with GD, confirming our finding that genetic susceptibility to GD increases the risk of developing T1D. The onset and progression of T2D is a complex process involving multiple mechanisms. Several studies have reported on the involvement of immune factors in the pathogenesis of this disease. Obesity-associated chronic inflammation is an important factor in the predisposition to T2D \u003csup\u003e44,50\u003c/sup\u003e. Multiple studies have shown that an imbalance between Treg and Th17 cells, specifically a deficiency of Tregs and an excess of Th17 cells, is linked to obesity, insulin resistance, and T2D \u003csup\u003e51\u0026ndash;53\u003c/sup\u003e. Some studies have suggested that systemic Treg defects are associated with diabetic nephropathy, retinopathy, and diabetic foot ulcers \u003csup\u003e54,55\u003c/sup\u003e. An increasing number of Tregs in the body has been shown to improve the severity of diabetes in some animal studies. Fengjie et al. discovered that Tregs can promote the mobilization of endothelial progenitor cells, anti-inflammatory effects, and cytoprotection \u003csup\u003e56\u003c/sup\u003e. This can inhibit vascular endothelial hyperplasia and alleviate the vascular degeneration caused by elevated blood glucose levels in diabetic pigs. One study found that CD4\u0026thinsp;+\u0026thinsp;Tregs decreased in the visceral tissues of patients with T2D \u003csup\u003e57\u003c/sup\u003e. This suggests that an imbalance between the quality and capability of Treg and Th17 cells is involved in the development of GD, T1D, and T2D. This finding is consistent with the bidirectional causal relationship between GD and T1D. In contrast, the etiology of T2D is multifactorial, with immunological factors being one of the many contributors to its onset or progression. Consequently, patients with GD may have an increased risk of T2D due to the disruption of immune system homeostasis, while T2D does not necessarily increase the risk of GD.\u003c/p\u003e \u003cp\u003eChronic inflammation should also be taken into consideration. It is common for autoimmune diseases to be accompanied by a chronic inflammatory response \u003csup\u003e58\u0026ndash;61\u003c/sup\u003e. Researchers have revealed that some cytokines, including transforming growth factor beta (TGF-β), interleukin-6 (IL-6), IL-4, IL-5, IL-10, IL-13, IL-1β, and interferon-gamma (IFN-γ), are associated with GD. T1D is associated with chronic inflammation \u003csup\u003e62\u003c/sup\u003e, and several studies have reported that cytokines, such as IFN-γ, tumor necrosis factor (TNF)-α, and IL-1β, contribute to the pathogenesis of T1D \u003csup\u003e63\u0026ndash;65\u003c/sup\u003e. We speculate that chronic inflammation triggered by GD may contribute to the development of T1D, and vice versa. This finding suggests a bidirectional causal relationship between GD and T1D. In recent research, T2D has been increasingly associated with chronic inflammation \u003csup\u003e66\u003c/sup\u003e. Chronic inflammation with increased levels of various cytokines contributes to the development of T2D. It has been reported that conventional type 1 dendritic cells can promote insulin resistance by increasing IFN-γ production \u003csup\u003e67\u003c/sup\u003e. IL1-β, IL-6, IL-8, TNF-α, NF-κB, and MAPK were also significantly increased in the T2D and prediabetes groups compared to non-diabetic groups, indicating their role as pro-inflammatory factors \u003csup\u003e68\u003c/sup\u003e. Chronic inflammation resulting from GD-induced increases in the levels of inflammatory factors, such as IFN-γ, IL-1β, and TNF-α in vivo, may contribute to insulin resistance and increase the risk of developing T2D by affecting insulin sensitivity. It is essential to note that this suggestion is based on objective evidence rather than subjective evaluation. Chronic inflammation is not the sole factor in the development of T2D. Therefore, the complex etiology of T2D indicates that GD development is not necessarily increased by T2D.\u003c/p\u003e \u003cp\u003eSimilar to other studies, this study has certain limitations. First, database limitations prevented the inclusion of indicators related to thyroid function, such as thyroid hormone, TSH receptor, anti-Tg, and anti-TPO. Additional research is required to examine the relationships between other indicators and DM. Second, our study did not investigate the causal relationship between Hashimoto's thyroiditis and T1D or T2D. Future studies should explore autoimmune thyroiditis. Third, the scope of this study was restricted to the European population. Therefore, additional research is necessary to verify whether our findings are applicable to other research groups.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn our study, a two-sample MR analysis was used to find evidence of bidirectional causality between GD and T1D and unidirectional causality between GD and T2D. No significant causal relationship between TSH, TPO, or Tg and T1D or T2D was found in the MVMR analysis. These findings provide new insights into the control of DM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYuhan Zhang wrote the main manuscript text and Liuxiang Fu prepared figures 1-3. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe want to acknowledge the participants and investigators of the FinnGen study.The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organisations and biobanks within Finland and international industry partners.We are also very grateful to the IEU open GWAS project for helping us with our research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMagliano DJ, Boyko EJ, committee IDFDAtes. IDF Diabetes Atlas. In: \u003cem\u003eIdf diabetes atlas.\u003c/em\u003e Brussels: International Diabetes Federation \u0026copy; International Diabetes Federation, 2021.; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim JZ, Ng NS, Thomas C. Prevention and treatment of diabetic foot ulcers. J R Soc Med. 2017;110(3):104\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWasserfall C, Nick HS, Campbell-Thompson M, et al. Persistence of Pancreatic Insulin mRNA Expression and Proinsulin Protein in Type 1 Diabetes Pancreata. Cell Metab. 2017;26(3):568\u0026ndash;575.e563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonath MY. Type 1 diabetes: what is the role of autoimmunity in β cell death? J Clin Invest. 2022;132(20).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuattrin T, Mastrandrea LD, Walker LSK. Type 1 diabetes. Lancet. 2023;401(10394):2149\u0026ndash;2162.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavikumar V, Ahmed A, Anjankar A. A Review on Latent Autoimmune Diabetes in Adults. Cureus. 2023;15(10):e47915.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalicia-Garcia U, Benito-Vicente A, Jebari S, et al. Pathophysiology of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020;21(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Lundeen EA, Koyama AK, et al. Prevalence of Testing for Diabetes Among US Adults With Overweight or Obesity, 2016\u0026ndash;2019. Prev Chronic Dis. 2023;20:E116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJensen AH, Winther-S\u0026oslash;rensen M, Burisch J, et al. Autoimmune liver diseases and diabetes: A propensity score matched analysis and a proportional meta-analysis. Liver Int. 2023;43(11):2479\u0026ndash;2491.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNițulescu IM, Ciulei G, Cozma A, Procopciuc LM, Orășan OH. From Innate Immunity to Metabolic Disorder: A Review of the NLRP3 Inflammasome in Diabetes Mellitus. J Clin Med. 2023;12(18).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLampousi AM, Carlsson S, L\u0026ouml;fvenborg JE, et al. Interaction between plasma phospholipid odd-chain fatty acids and GAD65 autoantibodies on the incidence of adult-onset diabetes: the EPIC-InterAct case-cohort study. Diabetologia. 2023;66(8):1460\u0026ndash;1471.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan B, Lim CKP, Poon EWM, et al. Differential Associations of GAD Antibodies (GADA) and C-Peptide With Insulin Initiation, Glycemic Responses, and Severe Hypoglycemia in Patients Diagnosed With Type 2 Diabetes. Diabetes Care. 2023;46(6):1282\u0026ndash;1291.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurch HB, Cooper DS. Management of Graves Disease: A Review. Jama. 2015;314(23):2544\u0026ndash;2554.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubekti I, Pramono LA. Current Diagnosis and Management of Graves' Disease. Acta Med Indones. 2018;50(2):177\u0026ndash;182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetranović Ovčariček P, G\u0026ouml;rges R, Giovanella L. Autoimmune Thyroid Diseases. Semin Nucl Med. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKravchenko V, Zakharchenko T. Thyroid hormones and minerals in immunocorrection of disorders in autoimmune thyroid diseases. Front Endocrinol (Lausanne). 2023;14:1225494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang LT, Huang CY, Lin CH, et al. Graves disease is more prevalent than Hashimoto disease in children and adolescents with type 1 diabetes. Front Endocrinol (Lausanne). 2022;13:1083690.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu P, Pu B, Ma Y, et al. Appraising the causal relationship between thyroid function and rheumatoid arthritis: a two-sample bidirectional Mendelian randomization study. Front Immunol. 2023;14:1238757.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu YC, Yu KH, Lin WT, Wang WT, Chen DP. Finding the Common Single-Nucleotide Polymorphisms in Three Autoimmune Diseases and Exploring Their Bio-Function by Using a Reporter Assay. Biomedicines. 2023;11(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrari SM, Fallahi P, Ruffilli I, et al. The association of other autoimmune diseases in patients with Graves' disease (with or without ophthalmopathy): Review of the literature and report of a large series. Autoimmun Rev. 2019;18(3):287\u0026ndash;292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZala A, Thomas R. Antigen-specific immunotherapy to restore antigen-specific tolerance in Type 1 diabetes and Graves' disease. Clin Exp Immunol. 2023;211(2):164\u0026ndash;175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbrahim S, Davey Smith G. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet. 2008;123(1):15\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmdin CA, Khera AV, Kathiresan S. Mendelian Randomization. Jama. 2017;318(19):1925\u0026ndash;1926.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20(1):443.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu G, Zhou S, Xu Y, et al. Chickenpox and multiple sclerosis: A Mendelian randomization study. J Med Virol. 2023;95(1):e28315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53(10):1415\u0026ndash;1424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGagliano Taliun SA, Evans DM. Ten simple rules for conducting a mendelian randomization study. PLoS Comput Biol. 2021;17(8):e1009238.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755\u0026ndash;764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Yang H, Li H, He C, Yang L, Lv G. Insights into modifiable risk factors of cholelithiasis: A Mendelian randomization study. Hepatology. 2022;75(4):785\u0026ndash;796.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiou J, Geusz RJ, Okino ML, et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature. 2021;594(7863):398\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508\u0026ndash;518.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34(21):2926\u0026ndash;2940.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693\u0026ndash;698.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng H, Jiang L, Song Z, Wang F. Causal Associations of Circulating Lipids with Osteoarthritis: A Bidirectional Mendelian Randomization Study. Nutrients. 2022;14(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong R, Zhang Q, Peng H. Gastroesophageal reflux disease and the risk of respiratory diseases: a Mendelian randomization study. J Transl Med. 2024;22(1):60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Bi M, Fan C, et al. Regulatory T cells but not T helper 17 cells are modulated in an animal model of Graves' hyperthyroidism. Clin Exp Med. 2012;12(1):39\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Q, Zhao Y, Zhu X, Liu X. Low regulatory T cell and high IL-17 mRNA expression in a mouse Graves' disease model. J Endocrinol Invest. 2017;40(4):397\u0026ndash;407.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan Y, Chen W, Liu C, Zheng X, Guo A, Long J. Effect of IL-21 on the Balance of Th17 Cells/Treg Cells in the Pathogenesis of Graves' Disease. Endocr Res. 2019;44(4):138\u0026ndash;147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu X, Yin X, Liu Y, et al. Gut Dysbiosis Contributes to the Imbalance of Treg and Th17 Cells in Graves' Disease Patients by Propionic Acid. J Clin Endocrinol Metab. 2020;105(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Q, Liu Y, Rao J, et al. Anti-Atherosclerosis Effect of Angong Niuhuang Pill via Regulating Th17/Treg Immune Balance and Inhibiting Chronic Inflammatory on ApoE(-/-) Mice Model of Early and Mid-Term Atherosclerosis. Front Pharmacol. 2019;10:1584.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbosa P, Pinho A, L\u0026aacute;zaro A, et al. CD8(+) Treg cells play a role in the obesity-associated insulin resistance. Life Sci. 2024;336:122306.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Q, Zhang X, Li T, Shao S. Exenatide regulates Th17/Treg balance via PI3K/Akt/FoxO1 pathway in db/db mice. Mol Med. 2022;28(1):144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026auml;kimattila S, Harjutsalo V, Forsblom C, Groop PH. Every Fifth Individual With Type 1 Diabetes Suffers From an Additional Autoimmune Disease: A Finnish Nationwide Study. Diabetes Care. 2020;43(5):1041\u0026ndash;1047.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrixti L, Lane LC, Pearce SH. The genetics of Graves' disease. Rev Endocr Metab Disord. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTriolo TM, Armstrong TK, McFann K, et al. Additional autoimmune disease found in 33% of patients at type 1 diabetes onset. Diabetes Care. 2011;34(5):1211\u0026ndash;1213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma H, Sahlot R, Purwar N, et al. Co-existence of type 1 diabetes and other autoimmune ailments in subjects with autoimmune thyroid disorders. Diabetes Metab Syndr. 2022;16(2):102405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoope A, Torsoni AS, Velloso LA. MECHANISMS IN ENDOCRINOLOGY: Metabolic and inflammatory pathways on the pathogenesis of type 2 diabetes. Eur J Endocrinol. 2016;174(5):R175-187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOu Q, Power R, Griffin MD. Revisiting regulatory T cells as modulators of innate immune response and inflammatory diseases. Front Immunol. 2023;14:1287465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFabbrini E, Cella M, McCartney SA, et al. Association between specific adipose tissue CD4\u0026thinsp;+\u0026thinsp;T-cell populations and insulin resistance in obese individuals. Gastroenterology. 2013;145(2):366\u0026ndash;374.e361-363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu QW, Sun Q, Wang J, Gu WS, Wang W, Mao XM. Effects of Glycemic Variability on Regulatory T Cells in Patients with Type 2 Diabetes and Kidney Disease. Diabetes Metab Syndr Obes. 2023;16:2365\u0026ndash;2375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzalez LL, Garrie K, Turner MD. Type 2 diabetes - An autoinflammatory disease driven by metabolic stress. Biochim Biophys Acta Mol Basis Dis. 2018;1864(11):3805\u0026ndash;3823.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Chen J, Gao C, et al. Maggot treatment promotes healing of diabetic foot ulcer wounds possibly by upregulating Treg levels. Diabetes Res Clin Pract. 2022;184:109187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo F, Ren Z, Liu D, Wang L, Hou X, Chen W. The Inhibitory Effect of Regulatory T Cells on the Intimal Hyperplasia of Tissue-Engineered Blood Vessels in Diabetic Pigs. Front Bioeng Biotechnol. 2022;10:929867.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu D, Han JM, Yu X, et al. Characterization of regulatory T cells in obese omental adipose tissue in humans. Eur J Immunol. 2019;49(2):336\u0026ndash;347.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWidder J, Dorfinger K, Wilfing A, et al. The immunoregulatory influence of transforming growth factor beta in thyroid autoimmunity: TGF beta inhibits autoreactivity in Graves' disease. J Autoimmun. 1991;4(4):689\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGianoukakis AG, Khadavi N, Smith TJ. Cytokines, Graves' disease, and thyroid-associated ophthalmopathy. Thyroid. 2008;18(9):953\u0026ndash;958.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKutluturk F, Yarman S, Sarvan FO, Kekik C. Association of cytokine gene polymorphisms (IL6, IL10, TNF-α, TGF-β and IFN-γ) and Graves' disease in Turkish population. Endocr Metab Immune Disord Drug Targets. 2013;13(2):163\u0026ndash;167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUeland HO, Ulvik A, L\u0026oslash;v\u0026aring;s K, et al. Systemic Activation of the Kynurenine Pathway in Graves Disease With and Without Ophthalmopathy. J Clin Endocrinol Metab. 2023;108(6):1290\u0026ndash;1297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErmis E, Nargis T, Webster K, Tersey SA, Anderson RM, Mirmira RG. Leukotriene B4 receptor 2 governs macrophage migration during tissue inflammation. J Biol Chem. 2023;300(1):105561.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe George DJ, Ge T, Krishnamurthy B, Kay TWH, Thomas HE. Inflammation versus regulation: how interferon-gamma contributes to type 1 diabetes pathogenesis. Front Cell Dev Biol. 2023;11:1205590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, Liu J, Zhao R, et al. Knockdown of IFNAR2 reduces the inflammatory response in mouse model of type 1 diabetes. Biochem Biophys Res Commun. 2022;619:9\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonuguntla S, Humphrey RK, Gorantla A, Hao E, Jhala US. Stress-induced pseudokinase TRB3 augments IL1β signaling by interacting with Flightless homolog 1. J Biol Chem. 2023;299(8):104803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, Gao Y, Tian L, et al. Association of serum 25-hydroxyvitamin D(3) with adipokines and inflammatory marker in persons with prediabetes mellitus. Clin Chim Acta. 2017;468:152\u0026ndash;158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHildreth AD, Padilla ET, Gupta M, et al. Adipose cDC1s contribute to obesity-associated inflammation through STING-dependent IL-12 production. Nat Metab. 2023;5(12):2237\u0026ndash;2252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFenercioglu AK, Gonen MS, Uzun H, et al. The Association between Serum 25-Hydroxyvitamin D3 Levels and Pro-Inflammatory Markers in New-Onset Type 2 Diabetes Mellitus and Prediabetes. Biomolecules. 2023;13(12).\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":"Graves’ disease, Mendelian randomization, Type 1 diabetes, Type 2 diabetes, Autoimmune disease","lastPublishedDoi":"10.21203/rs.3.rs-4229672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4229672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGraves\u0026rsquo; disease (GD) is an autoimmune disease associated with an increased incidence of other autoimmune diseases. To investigate the causality between GD and Diabetes mellitus (DM), we designed bidirectional two-sample Mendelian randomization (MR) and multivariable MR (MVMR) studies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSingle-nucleotide polymorphisms (SNPs) associated with GD, thyroid peroxidase (TPO), thyroglobulin (Tg), thyroid-stimulating hormone (TSH), type 1 diabetes (T1D), and type 2 diabetes (T2D) were obtained from the IEU Open GWAS and FinnGen biobank databases. For the forward MR study, we used GD (sample size\u0026thinsp;=\u0026thinsp;458,620) as the exposure and T1D (sample size\u0026thinsp;=\u0026thinsp;520,580) and T2D (sample size\u0026thinsp;=\u0026thinsp;211,766) as the outcomes. Next, T1D and T2D were used as exposure variables, and GD was used as the outcome variable for the reverse MR analysis. Finally, MVMR analysis was conducted to investigate the probable relationship between DM and indicators for thyroid function like TPO, Tg, and TSH. The inverse variance weighting (IVW) was used as the main method. Finally, the heterogeneity and sensitivity were assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were 27, 88, and 55 SNPs associated with GD, T1D, and T2D, respectively. A significant causal connection between GD and T1D (odds ratio [OR] [95% confidence interval, CI]\u0026thinsp;=\u0026thinsp;1.411 [1.077\u0026ndash;1.848], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and T2D (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.059 [1.025\u0026ndash;1.095], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.53e-04) was found in the forward MR analysis. However, reverse MR suggested that there was a genetic susceptibility to T1D that increased the likelihood of developing GD (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.059 [1.025\u0026ndash;1.095], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.53e-04), while T2D did not (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.963 [0.870\u0026ndash;1.066], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.468). Furthermore, there was inadequate evidence to suggest that abnormal TSH, TPO, and Tg levels increase the risk of incident T1D or T2D in individuals with GD. MVMR revealed no causal relationship among Tg, TSH, TPO, T1D, or T2D.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEvidence of a bidirectional causative relationship between GD and T1D and a unidirectional causal relationship between GD and T2D was discovered using MR analyses. MVMR analysis showed no statistically relevant causality between TSH, TPO, or Tg and either T1D or T2D.\u003c/p\u003e","manuscriptTitle":"Evaluating the causal effects between Grave’s disease and diabetes mellitus: a bidirectional Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-11 18:00:58","doi":"10.21203/rs.3.rs-4229672/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":"56b98583-486e-43b9-a11c-d4179fdc5128","owner":[],"postedDate":"April 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-15T16:52:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-11 18:00:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4229672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4229672","identity":"rs-4229672","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}