Graves’ disease and the risk of immune-mediated diseases: Mendelian randomization and colocalization analyses in East Asian population.

doi:10.21203/rs.3.rs-3345582/v1

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Graves’ disease and the risk of immune-mediated diseases: Mendelian randomization and colocalization analyses in East Asian population.

https://doi.org/10.21203/rs.3.rs-3345582/v1

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Background

Epidemiological studies have indicated a frequent co-occurrence of Graves' disease (GD) with other immune-mediated diseases (IMDs). However, the causal relationship between them remains uncertain.

Objective

This study aimed to establish a causal relationship between GD and IMDs, as well as investigate the genetic foundation of comorbidities.

Methods

We employed Mendelian randomization (MR) to infer a causal relationship between GD and eight IMDs in the East Asian population, namely, systemic lupus erythematosus (SLE), ulcerative colitis (UC), rheumatoid arthritis (RA), atopic dermatitis (AD), Crohn's disease (CD), type 1 diabetes mellitus (T1DM), amyotrophic lateral sclerosis (ALS), and asthma. The causal association was examined using several MR methods. A sensitivity analysis was performed to identify potential heterogeneity and pleiotropy. Finally, we performed colocalization analyses to explore the possible genetic foundation.

Results

Bidirectional MR analysis indicated that GD increased the risk of SLE (odds ratio [OR]: 1.31, 95% Confidence Interval [CI]: 1.15 to 1.49, P < 0.001) and CD (OR: 1.32, 95%CI: 1.14 to 1.52, P < 0.001), and maybe a risk factor for RA, but a protective factor for AD and asthma. And we found that RA was linked to a higher risk of GD (OR: 1.26, 95%CI: 1.09 to 1.46, P = 0.002). UC and AD may be protective factors for GD, while CD was the opposite. Colocalization analyses suggested that shared causal variants between GD and other IMDs may exist.

Conclusion

This study has confirmed that GD was associated with IMDs. It may be necessary to strengthen detection to prevent the occurrence of comorbidities in clinical practice.

Graves’ disease

immune-mediated diseases

comorbidity

causal association

Mendelian randomization

colocalization

Graves' disease (GD) ranks among the most commonly occurring organ-specific autoimmune disorders and represents one of the two primary clinical phenotypes of autoimmune thyroid diseases (AITD) alongside Hashimoto's thyroiditis (HT) [1]. The annual prevalence of GD varies between 20 and 50 cases per 100,000 individuals, with no age restrictions for onset[2, 3]. However, the prevalence is higher among females[4]. Circulating thyroid-stimulating antibodies (TSAb) are pathogenic antibodies for GD, which can activate the thyrotropin receptor (TSH-R), leading to excessive production of thyroid hormone[5, 6]. Both genetic and environmental factors play a crucial role in the pathogenesis of GD, particularly in recent years where the contribution of gut microbiota has garnered significant attention[7–9]. It is crucial to understand that GD can greatly affect the immune system, which may result in the emergence of immune-mediated illnesses (IMDs) affecting other tissues beyond the thyroid gland.

Recent epidemiological studies have indicated a frequent co-occurrence of GD with other IMDs[10]. For instance, researchers have shown that individuals with systemic lupus erythematosus (SLE) may have a higher likelihood of developing thyroid autoimmunity[11, 12]. A multicenter study confirmed that rheumatoid arthritis (RA) is the most common coexisting autoimmune disease in GD and HT[13].Nevertheless, the causal relationship in this phenomenon has yet to be determined.

Mendelian Randomization (MR) is a method that uses genetic variation as an instrument to determine causal relationships between exposures and outcomes, thereby providing robust evidence for causality in observational studies[14–16]. Colocalization analysis investigates whether the co-occurrence of two traits is attributable to shared genetic variation, and when applied to two diseases, it can also determine if they have a common etiology[17, 18].

In this study, we implemented a bidirectional two-sample MR to access the causal association between GD and eight IMDs, including SLE, ulcerative colitis (UC), RA, atopic dermatitis (AD), Crohn's disease (CD), type 1 diabetes mellitus (T1DM), amyotrophic lateral sclerosis (ALS), and asthma. Upon detecting a statistically significant causal effect, we performed colocalization analyses to explore potential shared genetic mechanisms.

Study Design

We performed a bidirectional two-sample MR analysis to definitively establish a causal relationship between GD and other IMDs. The study procedure is depicted in Fig. 1.

GWAS Data Sources

The data sets used for this study were taken from publicly available genome-wide association studies (GWAS) for all the phenotypes listed in Fig. 2, and therefore ethical approval was exempted. As we know, the outcome of MR Analysis may be influenced by the differential expression of phenotype and genotype across racial groups[19]. To mitigate the impact of population stratification, we exclusively included individuals with East Asian ancestry. To be precise, the genetic instrumental variables (IVs) for GD were obtained from Biobank Japan (BBJ), comprising 2176 cases and 210277 controls[20]. Genetic IVs for CD and UC were extracted from GWAS studies by the International IBD Genetics Consortium (1690 CD and 1134 UC cases, 3719 controls) [21]. The GWAS reported by Sakaue S et al provided genetic IVs for AD (4296 cases and 163807 controls), T1DM (1219 cases and 132032 controls), and asthma (13015 cases and 162933 controls)[22]. Additionally, genetic IVs for SLE (4222 cases and 8431 controls), RA (4873 cases and 17642 controls), and ALS (1234 cases and 2850 controls) were derived from the three publicly available largest GWAS meta-analyses[23–25].

Selection of Genetic Instruments

First, we developed a stringent selection procedure to choose valid genetic IVs. Specifically, the selection of genetics IVs was based on four criteria: (1) The single nucleotide polymorphism (SNP) was considered significant at the genome-wide threshold of P = 5 × 10^− 8 to ensure a robust association with exposure, except for T1DM. To include an adequate number of SNPs, the significance threshold for T1DM was relaxed to 5 × 10^− 6. (2) We clumped IVs within a genomic region of 10000kb, and the linkage disequilibrium (LD) r2 threshold was set at 0.001. The utilization of proxy SNPs is applicable in cases where the outcome dataset does not contain a particular SNP[26]. (3) The F-statistics ([𝑏𝑒𝑡𝑎/𝑆𝐸]²) was used to assess the strength of genetic IVs[27]. SNPs with F-statistics < 10 were deemed weak IVs and excluded[28]. (4) Checked in Phenoscanner (www.phenoscanner.medschl.cam.ac.uk) and excluded SNPs associated with confounding factors (P > 1 × 10^− 5) [29]. The remaining SNPs were included in the MR analysis.

Statistical analysis

We used several MR methods to infer the causal relationship, including MR–Egger regression, weighted median, inverse variance weighted (IVW), and weighted modes. The IVW combines the Wald ratio of single SNPs, assuming that IVs exert a causal effect on the outcome solely through the exposure and not via any alternative pathway, which was employed as the primary outcome[30].

Moreover, sensitivity analysis was conducted to justify the MR results. The researchers used the Cochrane Q test to analyze heterogeneity, and the MR-Egger intercept test to test for horizontal pleiotropy. MR-PRESSO was subsequently employed to identify and remove outliers. The leave-one-out analysis was also conducted to assess whether a single SNP influenced or biased the MR results. To address the issue of multiple testing, statistical significance was attributed to association analysis results only when the p-value was < 0.006 (0.05/8). And the p-value was considered significant if it fell within the range of 0.006 to 0.05, indicating a suggestive meaning.

Colocalization Analysis

Colocalization analysis was employed to explore potential shared genetic mechanisms between GD and IMDs. In this study, we used the coloc method of the Bayesian model. The method assumes that there are several models between two traits with a given genomic region: (1) H₀: no connection to either trait; (2) H₁: connection to trait 1, but not the other; (3) H₂: connection to trait 2, but not trait 1; (4) H₃: connection to both traits, but two distinct SNPs; (5) H₄: connection to both traits and one shared SNPs. A large posterior probability of H₄ (PP. H₄ > 0.75) is considered to be strong support for colocalization[18]. And genomic regions were defined with 100kb of genetic IVs.

Associations between Graves’ Disease and Immune-mediated Diseases

The independent genetics IVs of GD and IMDs are presented in supplemental tables, respectively. As shown in Fig. 3, bidirectional MR analysis indicated that genetically predicted GD increased the risk of SLE (odds ratio [OR]: 1.31, 95% Confidence Interval [CI]: 1.15 to 1.49, P < 0.001) and CD (OR: 1.32, 95%CI: 1.14 to 1.52, P < 0.001), and maybe a risk factor for RA (OR: 1.14, 95%CI: 1.01 to 1.28, P = 0.03), but a protective factor for AD (OR: 0.86, 95%CI: 0.79 to 0.94, P = 0.014) and asthma (OR: 0.95, 95%CI: 0.92–0.99, P = 0.007). In contrast, we found that genetically predicted RA was linked to a higher risk of GD (OR: 1.26, 95%CI: 1.09 to 1.46, P = 0.002). UC (OR: 0.64, 95%CI: 0.45 to 0.93, P = 0.018) and AD (OR: 0.89, 95%CI: 0.80 to 0.99, P = 0.039) may be protective factors for GD, while CD was the opposite (OR: 1.14, 95%CI: 1.01 to 1.29, P = 0.04). There were no casual or reverse causal associations between GD and other IMDs (T1DM and ALS). No significant horizontal pleiotropy (P > 0.05) was detected.

Colocalization Analysis

We performed a colocalization analysis between GD and six IMDs (SLE, UC, RA, AD, CD, and asthma), respectively. The corresponding genomic regions were identified as 53 for SLE, 23 for UC, 27 for RA, 30 for AD, 27 for CD, and 39 for asthma. According to PP.H₄, GD and SLE shared common SNPs in three regions on chromosomes 2, 4, and 5. The highest PP.H₄ was observed on chromosomes 5:133372816–133472816 amounting to 96.9% and one casual SNP (rs244686) was revealed in gene CTB-1I21.1. Similarly, we found shared SNPs between GD and CD in seven regions on chromosomes 6 and 10. The highest PP.H₄ has reached an astonishing 100%. Unequal numbers of shared SNPs have also been found between GD and other IMDs. The results are shown in Fig. 4.

To the best of our knowledge, this is the first MR study to assess the possible causal relationship and underlying mechanisms between GD and eight IMDs. The principal outcomes provide compelling evidence supporting that genetically predicted GD increased the risk of SLE and CD, and might be a risk factor for RA, but a protective factor for AD and asthma. In contrast, we found that genetically predicted RA was linked to a higher risk of GD, whereas UC and AD might be protective factors for GD, and CD was the opposite. High PP.H₄ of colocalization analysis determined the shared SNPs between GD and IMDs.

Consistent with our findings, Kristien Boelaert and colleagues' study confirmed that RA is the most prevalent autoimmune disease that coexists with GD[13]. A systematic review conducted in 2020 provided support for a 1–3-fold increase in the prevalence of RA among patients with AITD, and conversely, a 1–6-fold increase in the prevalence of AITD among patients with RA[31]. The MR analysis indicated that patients with GD exhibit a 14% higher risk of developing RA, whereas those with RA have a 26% increased likelihood of developing GD. A cohort study of 8779 GD cases and 8779 controls in Taiwan showed that GD patients suffer an increased risk of SLE (hazard ratio [HR]: 5.45, 95%CI: 1.74-17.0) [32]. Based on our research, patients with GD have a higher chance of developing SLE as shown by a significant OR value of 1.31. And the outcomes of this study were in line with previous reports, supporting a causal relationship between GD and inflammatory bowel disease, including CD and UC[33]. In contrast to our findings, the results of observational studies indicated that AITD is the most prevalent comorbid autoimmune condition in T1DM[34]. Further, GD increases the rate of neuropathy[35]. The association between GD and AD has shown inconsistent findings in recent case-control studies[36, 37]. However, our findings indicated no association between GD and T1DM, while AD and GD were protective factors for each other. Besides, the available literature does not provide any large observational studies on asthma and ALS, however, there is evidence of co-occurrence with GD[38, 39]. And GWAS identifies shared genetic components between asthma and GD, while the MR results suggested that GD was a protective factor for asthma[40]. Confounding variables in observational studies have the potential to introduce bias into conclusions drawn from such research. Additionally, the functional implications of certain SNPs remain unclear and may impact MR results. These two factors could account for the inconsistent findings across studies.

We performed colocalization analyses to explore the potential shared genetic structure. According to the outcomes, the SNP rs12612769 of gene STAT4, located in chromosome 2, mediates the relationship between GD and SLE, RA. STAT4, activated by IL12, IFN-1, and IL23 cytokines, belongs to the STAT family[41]. It affects the differentiation of immune cells and the production of cytotoxicity and is associated with immune disorders and autoimmunity, including SLE and RA[42]. A recent study demonstrated that miR-363 could inhibit Treg cells through the target gene STAT4[43]. These findings suggest that STAT4 may play a significant role in the comorbidity of GD with RA and SLE. Similarly, the SNP rs9380340 of gene HLA-DPA1 may be a causal genetic variant between GD and CD. Previous studies indicated a strong association between HLA-DPA1 and GD[44]. And the research conducted in 2022 revealed a significant association between DPA1 and CD[45].

GD is a prevalent autoimmune disorder primarily affecting the thyroid gland, with its development influenced by both genetic and environmental factors[46]. Similarly, these factors also affect other IMDs[47–52]. Environment and shared genetic variants seem to be the potential mechanisms of the frequent co-occurrence of GD with other IMDs. As previously discussed, STAT4 is believed to be involved in the pathogenesis of GD, RA, and SLE. This correlation suggests that STAT4 may serve as an intermediary between these three conditions. Additionally, the impact of smoking on the development and progression of RA and GD, particularly in GD patients with ocular involvement, is significant. Smoking can significantly contribute to or exacerbate ocular symptoms. Therefore, smoking cessation is crucial for managing both diseases. Recently, the role of gut microbiota in the pathogenesis of these diseases has been revealed[53–55]. Variations in gut microbiota among individuals may impact the pharmacokinetics of anti-rheumatic drugs, resulting in divergent treatment outcomes at an individual level[56]. In addition, the gut microbiota and its metabolites can influence the bioavailability of intestinal microelements as well as immune regulation, which mediates the development of thyroid disease[57]. Microbial-targeted therapy may be a common strategy for the treatment of IMDs in the future.

From a clinical perspective, the presence of comorbidity can significantly impact patients' quality of life; however, it may also indicate shared etiology and pathological mechanisms among certain diseases. Consequently, drugs used to treat one condition could potentially be effective in treating another. For example, the traditional antirheumatic drugs chloroquine and hydroxychloroquine have shown great potential in the treatment of GD by inhibiting autophagy in orbital fibroblasts[58].

In summary, GD exhibits intricate associations with various IMDs through multifaceted mechanisms. Further research is imperative to elucidate the underlying mechanisms and develop effective treatments for these diseases.

Our study had several limitations. First, the limited number of cases may affect the genetic representation of the disease from this GWAS data. Second, there may be some overlap in the GWAS population, which may affect the reliability of the study conclusions. Finally, the GWAS data is derived from the East Asian population, necessitating further investigation to ascertain its generalizability across diverse ethnic groups. Therefore, a larger sample size and the involvement of different ethnic groups are needed for further research.

The MR results have confirmed that GD was associated with several IMDs. It may be necessary to strengthen detection to prevent the occurrence of comorbidities in clinical practice.

Disclosure Statement:

The authors have nothing to disclose.

Acknowledgment:

Supported by the program of Guangdong Provincial Clinical Research Center for Digestive Diseases(2020B1111170004)

Funding statement:

This study was supported by the National Natural Science Foundation of China [81900490], Project 1010 of Sixth Affiliated Hospital of Sun Yat-Sen University [1010PY(2020)-55], General Program of Natural Science Foundation of Guangdong, China [2023A1515011117], and Science and Technology Program of Guangzhou, China [202201010712].

A. Antonelli, S.M. Ferrari, A. Corrado, Di A. Domenicantonio, P. Fallahi, Autoimmune thyroid disorders. Autoimmun. Rev. 14, 174–180 (2015)
M.B. Zimmermann, K. Boelaert, Iodine deficiency and thyroid disorders. Lancet Diabetes Endocrinol. 3, 286–295 (2015)
T.J. Smith, L. Hegedüs, Graves' Disease. N Engl. J. Med. 375, 1552–1565 (2016)
Graves' disease, Nat. Reviews Disease Primers. 6, 53 (2020)
A. Antonelli et al., Graves' disease: Clinical manifestations, immune pathogenesis (cytokines and chemokines) and therapy. Best Pract. Res. Clin. Endocrinol. Metab. 34, 101388 (2020)
G.J. Kahaly, T. Diana, M. Kanitz, L. Frommer, P.D. Olivo, Prospective Trial of Functional Thyrotropin Receptor Antibodies in Graves Disease. J. Clin. Endocrinol. Metab. 105, e1006–1014 (2020)
Y. Tomer, Mechanisms of autoimmune thyroid diseases: from genetics to epigenetics. Annu. Rev. Pathol. 9, 147–156 (2014)
A. Antonelli et al., Graves' disease: Epidemiology, genetic and environmental risk factors and viruses. Best Pract. Res. Clin. Endocrinol. Metab. 34, 101387 (2020)
S. Moshkelgosha et al., Modulating gut microbiota in a mouse model of Graves' orbitopathy and its impact on induced disease. Microbiome. 9, 45 (2021)
S.M. Ferrari 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. 18, 287–292 (2019)
S.M. Ferrari et al., Systemic Lupus Erythematosus and Thyroid Autoimmunity. Front. Endocrinol. (Lausanne). 8, 138 (2017)
J.S. Yun et al., Increased risk of thyroid diseases in patients with systemic lupus erythematosus: A nationwide population-based Study in Korea. PLoS One. 12, e0179088 (2017)
K. Boelaert et al., Prevalence and relative risk of other autoimmune diseases in subjects with autoimmune thyroid disease. Am. J. Med. 123, 183e181–183e189 (2010)
J. Bowden, M.V. Holmes, Meta-analysis and Mendelian randomization: A review. Res. Synth. Methods. 10, 486–496 (2019)
E. Sanderson, G. Davey Smith, F. Windmeijer, J. Bowden, An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int. J. Epidemiol. 48, 713–727 (2019)
G. Hemani, K. Tilling, Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13, e1007081 (2017)
S. Burgess, C.N. Foley, V. Zuber, Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data. Annu. Rev. Genomics Hum. Genet. 19, 303–327 (2018)
C. Giambartolomei et al., Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014)
B. Brumpton et al., Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat. Commun. 11, 3519 (2020)
K. Ishigaki et al., Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat. Genet. 52, 669–679 (2020)
J.Z. Liu et al., Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015)
S. Sakaue et al., A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. 53, 1415–1424 (2021)
Y.F. Wang et al., Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity between ancestral groups. Nat. Commun. 12, 772 (2021)
Y. Okada et al., Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 506, 376–381 (2014)
B. Benyamin et al., Cross-ethnic meta-analysis identifies association of the GPX3-TNIP1 locus with amyotrophic lateral sclerosis. Nat. Commun. 8, 611 (2017)
G. Hemani et al., The MR-Base platform supports systematic causal inference across the human phenome. Elife 7 (2018)
B.L. Pierce, S. Burgess, Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am. J. Epidemiol. 178, 1177–1184 (2013)
B.L. Pierce, H. Ahsan, T.J. Vanderweele, Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int. J. Epidemiol. 40, 740–752 (2011)
M.A. Kamat et al., PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 35, 4851–4853 (2019)
J. Bowden, G. Davey Smith, S. Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015)
P. Conigliaro et al., Autoimmune thyroid disorders and rheumatoid arthritis: A bidirectional interplay. Autoimmun. Rev. 19, 102529 (2020)
C. Lee, S.F. Chen, Y.C. Yang, C.Y. Hsu, Y.C. Shen, Association between Graves' disease and risk of incident systemic lupus erythematosus: A nationwide population-based cohort study. Int. J. Rheum. Dis. 24, 240–245 (2021)
W. Xian et al., Graves Disease and Inflammatory Bowel Disease: A Bidirectional Mendelian Randomization. J. Clin. Endocrinol. Metab. 108, 1075–1083 (2023)
L.T. Wang et al., Graves disease is more prevalent than Hashimoto disease in children and adolescents with type 1 diabetes. Front. Endocrinol. (Lausanne). 13, 1083690 (2022)
N. Prinz et al., Characteristics of Patients with Type 1 Diabetes and Additional Autoimmune Disease in the DPV Registry. J. Clin. Endocrinol. Metab. 106, e3381–e3389 (2021)
de S. Lusignan et al., Atopic dermatitis and risk of autoimmune conditions: Population-based cohort study. J. Allergy Clin. Immunol. 150, 709–713 (2022)
T.P. Joshi et al., Association of atopic dermatitis with Graves' disease and Hashimoto's thyroiditis: A case-control study in the All of Us research program. J. Am. Acad. Dermatol. (2023)
R.A. Quax, A.J. Swaak, M.G. Baggen, Churg-Strauss Syndrome following PTU Treatment. Int J Rheumatol. 2009, 504105 (2009)
J. McMenamin, M. Croxson, Motor neurone disease and hyperthyroid Graves' disease: a chance association? J. Neurol. Neurosurg. Psychiatry. 43, 46–49 (1980)
Y. Shirai et al., Multi-trait and cross-population genome-wide association studies across autoimmune and allergic diseases identify shared and distinct genetic component. Ann. Rheum. Dis. 81, 1301–1312 (2022)
C. Yang et al., STAT4: an immunoregulator contributing to diverse human diseases. Int. J. Biol. Sci. 16, 1575–1585 (2020)
E.F. Remmers et al., STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus. N Engl. J. Med. 357, 977–986 (2007)
X. Yin et al., MiR-363-5p modulates regulatory T cells through STAT4-HSPB1-Notch1 axis and is associated with the immunological abnormality in Graves' disease. J. Cell. Mol. Med. 25, 9364–9377 (2021)
W.L. Liao et al., Analysis of HLA Variants and Graves' Disease and Its Comorbidities Using a High Resolution Imputation System to Examine Electronic Medical Health Records. Front. Endocrinol. (Lausanne). 13, 842673 (2022)
S. Sæterstad et al., Profound gene expression changes in the epithelial monolayer of active ulcerative colitis and Crohn's disease. PLoS One. 17, e0265189 (2022)
W.M. Wiersinga, K.G. Poppe, G. Effraimidis, Hyperthyroidism: aetiology, pathogenesis, diagnosis, management, complications, and prognosis. Lancet Diabetes Endocrinol. 11, 282–298 (2023)
G. Watermeyer et al., Inflammatory bowel disease in sub-Saharan Africa: epidemiology, risk factors, and challenges in diagnosis. Lancet Gastroenterol. Hepatol. 7, 952–961 (2022)
H. Li, A. Boulougoura, Y. Endo, G.C. Tsokos, Abnormalities of T cells in systemic lupus erythematosus: new insights in pathogenesis and therapeutic strategies. J. Autoimmun. 132, 102870 (2022)
M.K. Crow, Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets. Ann. Rheum. Dis. 82, 999–1014 (2023)
H.U. Scherer, T. Häupl, G.R. Burmester, The etiology of rheumatoid arthritis. J. Autoimmun. 110, 102400 (2020)
H. Li, Z. Zhang, H. Zhang, Y. Guo, Z. Yao, Update on the Pathogenesis and Therapy of Atopic Dermatitis. Clin. Rev. Allergy Immunol. 61, 324–338 (2021)
D. Thomas, V.M. McDonald, I.D. Pavord, P.G. Gibson, Asthma remission: what is it and how can it be achieved? Eur. Respir J. 60 (2022)
R. Caruso, B.C. Lo, G. Núñez, Host-microbiota interactions in inflammatory bowel disease. Nat. Rev. Immunol. 20, 411–426 (2020)
J. Cao et al., A cause-effect relationship between Graves' disease and the gut microbiome contributes to the thyroid-gut axis: A bidirectional two-sample Mendelian randomization study. Front. Immunol. 14, 977587 (2023)
E. Miyauchi, C. Shimokawa, A. Steimle, M.S. Desai, H. Ohno, The impact of the gut microbiome on extra-intestinal autoimmune diseases. Nat. Rev. Immunol. 23, 9–23 (2023)
Y. Chen et al., Gut microbiota in systemic lupus erythematosus: A fuse and a solution. J. Autoimmun. 132, 102867 (2022)
W. Jiang, G. Lu, D. Gao, Z. Lv, D. Li, The relationships between the gut microbiota and its metabolites with thyroid diseases. Front. Endocrinol. (Lausanne). 13, 943408 (2022)
Y. Guo et al., Novel Roles of Chloroquine and Hydroxychloroquine in Graves' Orbitopathy Therapy by Targeting Orbital Fibroblasts. J. Clin. Endocrinol. Metab. 105, 1906–1917 (2020)

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You are reading this latest preprint version

Graves’ disease and the risk of immune-mediated diseases: Mendelian randomization and colocalization analyses in East Asian population.

Status:

Version 1

Abstract

Background

Objective

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

Study Design

GWAS Data Sources

Selection of Genetic Instruments

Statistical analysis

Colocalization Analysis

Results

Associations between Graves’ Disease and Immune-mediated Diseases

Colocalization Analysis

Discussion

Conclusions

Declarations

References

Supplementary Files

Status:

Version 1