
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-04882
00044
10.1097/MD.0000000000039709
3
4400
Research Article
Observational Study
Causal relationship between thyroid function and multiple sclerosis: A bidirectional Mendelian randomization study
Cui Wenhui BA cwh19850088966@163.com
a
Wang Bin MA dzhwangqiang@163.com
b
Shi Keqing BA Shifuyan@126.com
a
Wang Xin BA dzhwangqiang@163.com
a
Chen Shuyu BA jjxjchenshuyu@163.com
a
Xu Aolong BA xuaolong223@163.com
a
Shi Fuyan PhD Shifuyan@126.com
c
Wang Suzhen PhD dzhwangqiang@163.com
c
Zhang Xueli PhD xueli214@126.com
d
Yang Xiaorong PhD yangxiaorong@sdu.edu.cn
e
https://orcid.org/0000-0001-7336-3329
Wang Qiang PhD f*
a College of Public Health, Shandong Second Medical University, Shandong, China
b Dezhou Hospital of Traditional Chinese Medicine, Shandong, China
c Department of Health Statistics, Shandong Second Medical University, Shandong, China
d Department of Histology and Embryology, Shandong Second Medical University, Shandong, China
e Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Shandong, China
f Department of Epidemiology, Shandong Second Medical University, Shandong, China.
* Correspondence: Qiang Wang, Department of Epidemiology, Shandong Second Medical University, No. 7166 Baotong West Street, Weifang, 261053, Shandong, China (e-mail: dzhwangqiang@163.com).
13 9 2024
13 9 2024
103 37 e3970904 5 2024
01 7 2024
23 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

An association between thyroid function and multiple sclerosis (MS) has been reported in several observational studies, but the causal relationship between them is still unclear. Thus, this study used a bidirectional Mendelian randomization (MR) to investigate the associations between thyroid function and MS. Bidirectional MR was used to explore the causal relationship between thyroid function (thyroid-stimulating hormone [TSH], free thyroxine [FT4], hyperthyroidism, and hypothyroidism) and MS. Genome-wide association study (GWAS) data of thyroid function and MS were obtained from the ThyroidOmics Consortium and the FinnGen Consortium, respectively. Inverse-variance weighted method (IVW) was the primary analysis method to assess causality with Weighted median, MR-Egger regression, weighted mode, and simple mode as auxiliary methods. Sensitivity analyses were performed using heterogeneity tests, horizontal pleiotropy tests and leave-one-out method. There was a positive causal relationship between TSH and MS (IVW: OR = 1.202, 95% CI: 1.040–1.389, P = .013), and no strong evidence was found for an effect of FT4 (IVW: OR = 1.286, 95% CI: 0.990–1.671, P = .059), hypothyroidism (IVW: OR = 1.247, 95% CI: 0.961–1.617, P = .096), and hyperthyroidism (IVW: OR = 0.966, 95% CI: 0.907–1.030, P = .291) on the risk of MS. In the reverse MR results, there was no causal relationship between MS and TSH (IVW: β = −0.009, P = .184), FT4 (IVW: β = −0.011, P = .286), hypothyroidism (IVW: OR = 0.992, 95% CI: 0.944–1.042, P = .745), and hyperthyroidism (IVW: OR = 1.026, 95% CI: 0.943–1.117, P = .549). Cochran’s Q test, MR-Egger intercept test, MR-PRESSO global test, and Leave-one-out did not observe horizontal pleiotropy and heterogeneity. In conclusion, MR analysis supported a positive causal relationship between TSH and MS.

causal relationship
Mendelian randomization
multiple sclerosis
thyroid function
Social Science Planning Research Program of Shandong Province(CN)21CRK03 Wang QiangTaishan Scholars Program of Shandong Provincetsqn202312328 Xiaorong YangOPEN-ACCESSTRUE
SDCT
==== Body
pmc 1. Introduction

Multiple sclerosis (MS) is a chronic autoimmune disease (AD) characterized by inflammatory demyelinating lesions in the white matter of the central nervous system (CNS).[1] Following its development, MS can lead to a decline in the quality of life of MS patients and irreversible disability.[2,3] Approximately 2.8 million people worldwide suffer from MS, which mainly affects women aged 20 to 40.[4,5] MS poses new challenges for patients, significantly increasing their socioeconomic burden and affecting their relationships with life partners.[6] In 2019, the number of global deaths from MS was approximately 22,439.[7] Compared to 13,356 deaths in 1990, the number of deaths resulted from MS in 2019 increased by 68%.[7]

Typical clinical features commonly seen in MS patients include fatigue, decreased visual acuity, cognitive impairment, limb ataxia and impaired rectal function, etc[8] Many studies have identified risk factors for MS, such as smoking, vitamin D deficiency, EB virus infection, and so on.[9–11] The etiology of MS is multifactorial, potentially involving genetic factors, environmental factors, autoimmune responses and viral infections.[8] Nonetheless, the exact etiology and pathogenesis of MS are still unclear, and in-depth exploration and research are needed.[12] So far, there is no way to cure MS. In clinical practice, initiating disease-modifying medications or therapies is generally the preferred method for the treatment and progression of MS.[13] Due to the limitations of disease-modifying medications or therapies, rehabilitation interventions and lifestyle changes have been considered as new treatment methods.[14]

The thyroid is the largest endocrine gland in the human body.[15] Thyroid hormones are regulated by thyroid-stimulating hormone (TSH), and free thyroxine (FT4) is the active form of thyroid hormone in the human body.[16] Hypothyroidism and hyperthyroidism are 2 forms of thyroid dysfunction caused by insufficient or excessive hormone secretion.[17] It is not surprising that thyroid dysfunction affects various organs and systems, including the immune system.[18] Thyroid hormones are crucial for systemic metabolism, neural function metabolism, and autoimmune response, which have gradually attracted attention in MS research.[19] As an autoimmune disease, MS is characterized by immune-mediated attacks on the CNS.[20] The main target of MS attacks is the myelinated axons on the CNS, leading to demyelination and recurrent T-cell responses.[20,21] The components of myelin induce the activation of T cells in the peripheral immune system of MS patients.[22] Thyroid hormone is crucial for CNS myelination and re-myelination.[23] Meanwhile, MS and thyroid diseases may also present with common symptoms such as fatigue, muscle weakness and so on.[8,24] Therefore, thyroid hormones have a clinical interrelationship with MS. Previous research findings have indicated an association between thyroid function or thyroid disease and MS.[25,26] MS patients had a higher risk of thyroid disease.[25,26] However, some scholars came to the opposite conclusion, suggesting that there is no difference in the incidence of thyroid disease between MS patients and the general population.[27–29] The conclusions regarding the relationship between thyroid function and MS are controversial. Moreover, the results of these observational studies may be affected by unknown confounding factors. Furthermore, further exploration of the correlation between thyroid function and MS has important clinical significance and value for the prevention and diagnosis of MS, which is beneficial for improving the quality of life of MS patients. Thus, the causal relationship between thyroid function and MS needs to be further explored.

Mendelian randomization (MR) is a genetic epidemiological method for studying causal relationships between exposures and relevant clinical outcomes. The MR method uses genetic variation as instrumental variables (IVs) to reveal causal relationships, based on whole genome sequencing data. Single nucleotide polymorphisms (SNPs), as the primary IVs, follow the Mendelian laws of inheritance. And it effectively avoids potential reverse causality and controls confounding factors, with low cost and advantages over traditional observational research.[30] Here, this study used the bidirectional MR method to explore the causal relationship between thyroid function and MS to address the weak evidence obtained from previous studies. The objectives of this article were to: investigate the causal relationship of thyroid function and MS; explore the etiology of MS in depth, provide new insights for clinical prevention and treatment of MS patients and reduce their disease burden.

2. Materials and methods

2.1. Study design

Figure 1 indicated the bidirectional MR flow chart of thyroid function and MS. This study followed Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines.[31] The Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization research report specification included 20 checklist (Supplementary file 1, Supplemental Digital Content, http://links.lww.com/MD/N572).

Figure 1. Bidirectional MR flow chart of thyroid function and MS. FT4 = free thyroxine, IVs = instrumental variables, MR = Mendelian randomization, MS = multiple sclerosis, SNP = single nucleotide polymorphism, TSH = thyroid-stimulating hormone.

Based on the GWAS data of thyroid function indicators (TSH, FT4, hyperthyroidism, and hypothyroidism) and MS from public databases, 2 MR analyses were performed. The IVs associated with thyroid function were screened for positive MR analysis to explore the causal effect of thyroid function on MS. Then, the causal effect of MS on thyroid function was obtained by reverse MR analysis using MS-related IVs. The MR design must satisfy 3 core assumptions.[32] Firstly, SNPs must be strongly associated with the exposure of interest (correlation assumption). Secondly, SNPs should not be directly associated with the outcome, but should only influence the outcome results through the exposure (exclusion assumption). Thirdly, SNPs should not be associated with confounding factors in the exposure-endpoints associations (independence assumption). These 3 core assumptions were satisfied in this study, which would be discussed in detail below.

2.2. Genome-wide association study data sources

The summary statistics of GWAS on thyroid function (TSH, FT4, hyperthyroidism, and hypothyroidism) were obtained from a comprehensive GWAS Meta-analysis conducted by the ThyroidOmics Consortium (https://transfer.sysepi.medizin.uni-greifswald.de/thyroidomics/datasets/). The consortium presented comprehensive GWAS statistics on thyroid function, including 271,040 individuals of European ancestry with normal thyroid function.[33] This study used the recently updated GWAS summary data for thyroid function. The ThyroidOmics Consortium is a platform dedicated to improving understanding of the genetic basis underlying thyroid function and diseases. Participants aged < 18 years, of non-European ancestry, using thyroid medication (defined as ATC [Anatomical Therapeutic Chemical] code H03), or with a history of thyroid surgery were excluded from all analyses.[33]

For the measurement of multiple sclerosis, this study used the latest R10 release from the FinnGen Consortium (https://r10.finngen.fi/), a database that includes 2409 cases of multiple sclerosis diagnosed according to ICD-10 (International Classification of Diseases) diagnostic criteria and 408,561 controls. The FinnGen consortium is a constantly evolving project. In the sample quality control steps of the FinnGen consortium, individuals with ambiguous gender, high genotype missingness (>5%), excess heterozygosity (±4SD), and non-Finnish ancestry were excluded.[34]

The data utilized in this study were publicly available. Therefore, it did not require ethics committee approval and informed consent. Data sources for exposure and outcome are listed in Supplementary Table S1, Supplemental Digital Content, http://links.lww.com/MD/N574.

2.3. Instrumental variable selection

IVs were screened by satisfying 3 major assumptions of MR analysis to ensure the stability of MR results.

Firstly, SNPs related to exposure were screened to meet the correlation assumption. For the sake of including more SNPs associated with thyroid function, this study adopted a more lenient threshold of P < 5 × 10−7.[35,36] The strength of the instrument was assessed by calculating the F value using the formula F = β2/SE2, where β and SE refered to the effect value of SNP on the exposure and the standard error of β, respectively.[37] If the F value is greater than or equal to 10, it means that there is no weak instrumental variable, ensuring the strength of the instrumental variable.[38] Moreover, this study applied the PLINK clustering method for linkage disequilibrium (LD) analysis to ensure the independence of SNPs with the European Thousand Genomes data. The influence of confounding factors on MR results was eliminated by removing SNPs with r2 >0.001 to the most significant SNP in the 10,000 kb range.[39] Subsequently, SNPs directly associated with common potential confounders (BMI, smoking, alcohol consumption) were excluded to satisfy independence.[40] Finally, SNPs significantly associated with the outcome variables were excluded in order to satisfy the exclusion assumption.[41]

In addition, the information of IV in the outcome was extracted so that the effect values of exposure and outcome corresponded to the same effect allele. Subsequently, palindromic SNPs with intermediate allele frequencies were discarded.[42]

After the above series of rigorous screening, the screened SNPs were subjected to the final causal analysis.

2.4. Statistical analyses

The bidirectional causal association between thyroid function and MS was analyzed by inverse variance weighting (IVW) method as the main MR analysis method. The weighted median, MR-Egger regression method, weighted mode and simple mode were used to assist in the analysis and assessment. The IVW method was calculated by utilizing the ratio method for each SNP to obtain its estimates. This method has the highest reliability and accuracy of results when there is no horizontal pleiotropy in the instrumental variables.[43] The IVW method is the standard method of MR.[44] The MR-Egger regression method was used to test whether there was potential pleiotropy in instrumental variables. In this method, the existence of the intercept term is considered and the intercept term is used to express the effect estimate of potential pleiotropy.[45] The weighted median method can handle up to 50% of unbiased instrumental variables, which is insensitive to outliers.[46] In addition, when thyroid function was the outcome belonged to the continuous variable, this study used β value and 95% confidence interval to express the outcome.[47] When MS was the outcome belonged to binary variables, odds ratio (OR) and 95% confidence interval were used to express the outcome.[47] Cochrane’s Q test showed the presence of heterogeneity and P > .05 indicated no heterogeneity. If P was extremely less than 0.05, it indicated the presence of strong heterogeneity among the instrumental variables and the IVW’s random-effects model was used to estimate the amount of the MR effect.[48] The MR-Egger intercept test was used to demonstrate the existence of horizontal pleiotropy. P > .05 indicated that horizontal pleiotropy did not exist. MR-PRESSO global test checked horizontal pleiotropy by excluding outlier SNPs (outliers).[49] In order to further test the stability of results, leave-one-out method was used to evaluate the impact of each SNP on the overall results.[50]

Statistical analyses were performed by R 4.3.1 software with “TwoSampleMR” and “MR-PRESSO” packages. All reported probabilities (P values) were 2-sided. P < .05 was considered statistically significant.

3. Results

3.1. Positive MR analysis of thyroid function and MS

The specific information of SNPs related to TSH, FT4, hyperthyroidism or hypothyroidism can be found in the Supplementary Table S2, Supplemental Digital Content, http://links.lww.com/MD/N574. The confounders-related SNPs, SNPs potentially associated with the outcome and palindromic SNPs were removed from this study by strictly screening. The final instrumental variables used to analyze the causal association among TSH, FT4, hypothyroidism, hyperthyroidism and MS were 153, 45, 9 and 25 SNPs, respectively (Supplementary Table S3, Supplemental Digital Content, http://links.lww.com/MD/N574). Each F value was greater than 10, ensuring the strength of instrumental variables (Supplementary Table S3, Supplemental Digital Content, http://links.lww.com/MD/N574).

The results of the IVW method indicated a causal association between TSH and MS (OR = 1.202, 95% CI: 1.040–1.389, P = .013). The results of the other 4 methods are consistent with those of the IVW method (Fig. 2). Heterogeneity and horizontal pleiotropy were not found (Table 1). In addition, a forest plot of the causal effect of each SNP on MS risk was provided (Fig. 3). The scatter plot demonstrated the relationship between TSH and MS (Fig. 3). The funnel plot visualized heterogeneity (Fig. 3). Leave-one-out showed that the effect of TSH on MS was not affected by a single SNP (Fig. 3).

Table 1 Results of heterogeneity test and horizontal pleiotropy test (causal effect of thyroid function on MS).

Exposure	Outcome	Heterogeneity test	Pleiotropy test	
IVW	MR-Egger	MR-Egger intercept test	MR-PRESSO global test	
Q	P	Q	P	Intercept	P	RSS obs	P	
TSH	MS	113.826	.991	113.822	.989	−0.000	.957	136.206	.984	
FT4	MS	39.501	.665	35.856	.772	0.023	.063	43.561	.909	
Hypothyroidism	MS	10.521	.230	10.296	.172	−0.010	.707	18.028	.202	
Hyperthyroidism	MS	23.073	.516	22.644	.482	−0.010	.519	31.463	.487	
FT4 = free thyroxine, IVW = inverse variance weighting, MR = Mendelian randomization, MR-PRESSO = Mendelian Randomization Pleiotropy RESidual Sum and Outlier, MS = multiple sclerosis, RSS = residual sum of squares, TSH = thyroid-stimulating hormone.

Figure 2. The effect of thyroid function on multiple sclerosis by 5 MR methods. FT4 = free thyroxine, MR = Mendelian randomization, MS = multiple sclerosis, TSH = thyroid-stimulating hormone.

Figure 3. Visualization of Mendelian randomization results (causal effect of TSH on multiple sclerosis). TSH = thyroid-stimulating hormone.

Furthermore, the results from 5 different MR methods did not reveal any statistically significant associations between FT4, hypothyroidism, hyperthyroidism and MS (all P values > .05; Fig. 2). There was no heterogeneity or horizontal pleiotropy (Table 1). Leave-one-out showed that the outcome was stable (Supplementary Fig. S1, Supplemental Digital Content, http://links.lww.com/MD/N573).

3.2. Inverse MR analysis of MS and thyroid function

The specific information of SNPs related to multiple sclerosis can be found in the Supplementary Table S4, Supplemental Digital Content, http://links.lww.com/MD/N574. Similar to the positive MR analysis, 8, 9, 9 and 10 SNPs were finally screened to analyze the causal association of MS with TSH, FT4, hypothyroidism, and hyperthyroidism (Supplementary Table S5, Supplemental Digital Content, http://links.lww.com/MD/N574). Each F value was greater than 10 by calculating, which indicated that the instrumental variables are strong (Supplementary Table S5, Supplemental Digital Content, http://links.lww.com/MD/N574).

The results from 5 different MR methods did not reveal any statistically significant associations between MS and TSH, FT4, hypothyroidism, hyperthyroidism (all P values > .05; Fig. 4). There was no heterogeneity or horizontal pleiotropy (Table 2). Leave-one-out showed that the outcome was stable (Supplementary Fig. S2, Supplemental Digital Content, http://links.lww.com/MD/N573).

Table 2 Results of heterogeneity test and horizontal pleiotropy test (causal effect of MS on thyroid function).

Exposure	Outcome	Heterogeneity test	Pleiotropy test	
IVW	MR-Egger	MR-Egger intercept test	MR-PRESSO global test	
Q	P	Q	P	Intercept	P	RSS obs	P	
MS	TSH	6.796	.450	6.430	.377	0.005	.580	9.002	.436	
MS	FT4	11.224	.189	11.073	.135	0.003	.766	16.566	.279	
MS	Hypothyroidism	5.033	.754	4.939	.667	−0.007	.768	6.281	.775	
MS	Hyperthyroidism	4.780	.853	4.741	.785	−0.007	.848	5.972	.866	
FT4 = free thyroxine, IVW = inverse variance weighting, MR = Mendelian randomization, MR-PRESSO = Mendelian Randomization Pleiotropy RESidual Sum and Outlier, MS = multiple sclerosis, RSS = residual sum of squares, TSH = thyroid-stimulating hormone.

Figure 4. The effect of multiple sclerosis on thyroid function by 5 MR methods. FT4 = free thyroxine, MR = Mendelian randomization, MS = multiple sclerosis, TSH = thyroid-stimulating hormone.

4. Discussion

The present study conducted a comprehensive bidirectional MR study to examine the potential causal relationship between thyroid function and MS, using genetic variants as instrumental variables for thyroid function. The results of this study indicated a significant positive causal relationship between TSH and MS.

The current study revealed a significant positive causal relationship between TSH and MS, which was consistent with the findings of previous epidemiological studies.[25,26,51] A prospective study showed a strong association between thyroid disease and MS.[25] Thyroid hormones may played an important role in determining the clinical type and progression prognosis of MS patients.[52] Compared with the general population, the incidence rate of autoimmune thyroid disease in MS patients was significantly increased.[53] TSH levels may changed in MS, and TSH deter mination was recommended in all MS patients.[51] MS patients had an increased risk of autoimmune thyroid disease and the prevalence of autoimmune thyroid disease in MS patients was higher than that in general population.[26,51] Previous studies reported that autoimmune comorbidities were common in patients with MS, autoimmune thyroid disease (AITD), rheumatoid arthritis, etc.[54] A large proportion of MS and AITD patients were observed to share some clinical manifestations and serological characteristics.[55] There was a strong correlation between AITD and MS.[56] The mechanism of comorbidity between autoimmune diseases and MS was not fully understood. Some studies suggested that there may be a common genetic susceptibility between autoimmune diseases and MS.[57] The rs763361 of non-synonymous SNP in CD226 on chromosome 18q22 was highly correlated with various types of AD, including MS, AITD and rheumatoid arthritis.[58] The rs763361 may affect the signaling function of T cells.[59] The cellular mechanism of rs763361 was the main cause of the occurrence of various AD.[59] AITD was the result of the patient’s autoimmune system dysfunction.[60] The increased TSH levels were a manifestation of AITD.[61] Meanwhile, an increased TSH levels are also an important diagnostic signal for AITD. It can be seen that elevated TSH levels are closely related to AITD. In summary, this study inferred a possible correlation between elevated TSH levels and MS through analysis of biological mechanisms. Therefore, the causal relationship between TSH levels and MS seemed reasonable, although the exact physiological and pathological mechanisms were unclear. This study found that the aforementioned biological mechanisms can partially explain this causal relationship.

This study had several significant advantages. Firstly, the innovation of this study was the use of bidirectional MR methods to explore the causal relationship between thyroid function and MS. The MR method can overcome the shortcomings of randomized controlled trial methods, such as complex design and implementation, high cost and difficult follow-up. Secondly, previous observational studies had yielded results that only suggest a potential association not a causal relationship. MR method can overcome this shortcoming of observational studies. Besides, the MR design can avoid the effect of reverse causality. Thirdly, MR studies adhere to Mendelian’s second law, which states that the principle of random allocation during gamete formation. This principle helps avoid the impact of confounding factors on individuals and overcome various interferences such as social environment, lifestyle, etc. Therefore, this advantage of MR research can control for confounding bias. Finally, the data used in this study were all derived from the European populations, with a large sample size and relatively low heterogeneity, and high statistical power.

However, there were some limitations to this study. Firstly, the study was conducted only on the European populations. Therefore, the race is relatively simple. Thus, the conclusions of this article should be used with caution when extrapolating them to other ethnic groups. Secondly, this study did not stratify GWAS data by sex. So, it is not yet possible to confirm whether the association between thyroid function and MS is influenced by sex. Finally, although known confounders were excluded as much as possible by MR analysis, there are still some unknown potential confounders that were not taken into account.

5. Conclusion

In conclusion, this study provided new evidence of the causal relationship between thyroid function and MS. The results supported a positive causal relationship between TSH and MS. This finding may be helpful to diagnose MS and explore new treatment methods for MS. Besides, this finding increased the cognition of basic knowledge about the pathogenesis of MS. However, the causal relationship among FT4, hyperthyroidism, hypothyroidism, and MS was not discovered. These findings are needed to be further confirmed by future researches.

Acknowledgments

We express gratitude to the ThyroidOmics Consortium and the FinnGen Consortium database for providing the datasets.

Author contributions

Data curation: Wenhui Cui, Bin Wang.

Formal analysis: Keqing Shi, Xin Wang, Shuyu Chen, Aolong Xu, Fuyan Shi, Suzhen Wang.

Funding acquisition: Xueli Zhang, Xiaorong Yang, Qiang Wang.

Investigation: Wenhui Cui, Bin Wang.

Methodology: Wenhui Cui, Bin Wang.

Project administration: Xueli Zhang, Xiaorong Yang, Qiang Wang.

Resources: Wenhui Cui, Bin Wang.

Software: Wenhui Cui, Bin Wang.

Supervision: Xueli Zhang, Xiaorong Yang, Qiang Wang.

Validation: Xueli Zhang, Xiaorong Yang, Qiang Wang.

Visualization: Keqing Shi, Xin Wang, Shuyu Chen, Aolong Xu, Fuyan Shi, Suzhen Wang.

Writing – original draft: Wenhui Cui, Bin Wang.

Supplementary Material

Abbreviations:

AD autoimmune disease

AITD autoimmune thyroid disease

CI confidence interval

CNS central nervous system

FT4 free thyroxine

GWAS genome-wide association study

IVs instrumental variables

IVW inverse variance weighted

MR mendelian randomization

MS multiple sclerosis

OR odds ratio

RA rheumatoid arthritis

RCT randomized controlled trial

SNP single nucleotide polymorphism

TSH thyroid-stimulating hormone

This study was sponsored by Social Science Planning Research Program of Shandong Province, China (Grant Number 21CRK03), and Taishan Scholars Program of Shandong Province (tsqn202312328).

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Cui W, Wang B, Shi K, Wang X, Chen S, Xu A, Shi F, Wang S, Zhang X, Yang X, Wang Q. Causal relationship between thyroid function and multiple sclerosis: A bidirectional Mendelian randomization study. Medicine 2024;103:37(e39709).

WC, BW, XZ, XY, and QW contributed to this article equally.
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