孟德尔随机化分析乳糜泻与自身免疫性甲状腺疾病的因果关系

doi:10.3969/j.issn.1674-8115.2025.06.012

摘要/Abstract

摘要：

目的·通过两样本孟德尔随机化（Mendelian randomization，MR）方法探讨乳糜泻（celiac disease，CeD）与桥本甲状腺炎（Hashimoto thyroiditis，HT）及格雷夫斯病（Graves disease，GD）之间的双向因果关系。方法·从公开的全基因组关联研究（Genome-Wide Association Studies，GWAS）数据库中提取与CeD、HT和GD相关的单核苷酸多态性（single nucleotide polymorphism，SNP）数据，作为工具变量。以逆方差加权法（inverse variance weighted，IVW）作为主要分析方法，同时将MR-Egger法、加权中位数法（weighted median，WME）、加权模式法（weighted mode，WMO）3种方法的结果作为参考，评估CeD与HT及GD之间的因果关联。使用不同的GWAS数据进行重复分析，验证结果的稳健性。通过Cochran's Q检验判断异质性，通过MR-Egger截距检验评估多效性，采用留一法（leave-one-out）分析单个SNP对结果的影响程度。结果·IVW分析结果表明，遗传预测的CeD显著增加HT［试验组：OR=1.186 （95%CI 1.114~1.262），P<0.001；验证组：OR=1.218（95%CI 1.090~1.361），P<0.001］和GD［试验组：OR=1.214 （95%CI 1.155~1.276），P<0.001；验证组：OR=1.273（95%CI 1.161~1.396），P<0.001］的风险。反向MR分析中，HT对CeD不存在因果关系，而遗传预测的GD则显著增加CeD［试验组：OR=1.259（95%CI 1.006~1.576），P=0.044；验证组：OR=1.387（95%CI 1.233~1.560），P<0.001］的风险。敏感性分析表明，结果未受水平多效性的影响。结论·CeD可能增加HT和GD的发病风险，而GD可能增加CeD的发病风险，但HT对CeD不存在影响。

关键词: 乳糜泻, 桥本甲状腺炎, 格雷夫斯病, 孟德尔随机化

Abstract:

Objective ·To investigate the bidirectional causal relationships between celiac disease (CeD) and Hashimoto thyroiditis (HT) as well as Graves disease (GD), using a two-sample Mendelian randomization (MR) approach. Methods ·Single nucleotide polymorphisms (SNPs) related to CeD, HT and GD were extracted from publicly available Genome-Wide Association Studies (GWAS) databases and used as instrumental variables. The inverse-variance weighted (IVW) method served as the primary analytical approach, supplemented by MR-Egger, weighted median (WME) and weighted mode (WMO) methods, to evaluate the causal associations between CeD and both HT and GD. Replication analyses using alternative GWAS datasets were conducted to validate the robustness of the results. Heterogeneity was assessed using Cochran's Q test, and pleiotropy was evaluated via MR-Egger intercept test. Leave-one-out analyses were performed to assess the impact of individual SNPs on the results. Results ·The IVW analysis results indicated that genetically predicted CeD significantly increased the risk of HT [discovery group: OR=1.186 (95%CI 1.114‒1.262), P<0.001; replication group: OR=1.218 (95%CI 1.090‒1.361), P<0.001] and GD [discovery group: OR=1.214 (95%CI 1.155‒1.276), P<0.001; replication group: OR=1.273 (95%CI 1.161‒1.396), P<0.001]. However, reverse MR analyses did not provide evidence for a causal relationship between HT and CeD, while genetically predicted GD significantly increased the risk of CeD [discovery group: OR=1.259 (95%CI 1.006‒1.576), P=0.044; replication group: OR=1.387 (95%CI 1.233‒1.560), P<0.001]. Sensitivity analyses suggested that the results were not influenced by horizontal pleiotropy. Conclusion ·CeD may be causally associated with a higher risk of HT and GD, while GD may increase the risk of developing CeD. HT does not appear to have an impact on CeD.

Key words: celiac disease (CeD), Hashimoto thyroiditis (HT), Graves disease (GD), Mendelian randomization (MR)

中图分类号:

R574.5

闫军浩, 郭晓磊, 罗昭锋, 唐坚, 王争. 孟德尔随机化分析乳糜泻与自身免疫性甲状腺疾病的因果关系[J]. 上海交通大学学报（医学版）, 2025, 45(6): 766-773.

YAN Junhao, GUO Xiaolei, LUO Zhaofeng, TANG Jian, WANG Zheng. Mendelian randomization analysis of causal relationship between celiac disease and autoimmune thyroid disease[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(6): 766-773.

图/表 4

表1 GWAS数据集来源及特征

Tab 1 Sources and characteristics of GWAS datasets

Trait	Population	Case/n	Control/n	Sample size/n	SNP number/n	Year	Data source
CeD(ebi-a-GCST005523)	European	11 812	11 837	23 649	97 422	2011	IEU Open GWAS
CeD(GCST90436342)	European	1 855	334 783	336 638	26 043 299	2018	GWAS Catalog
HT(E4_THYROIDITAUTOIM)	European	612	385 630	386 242	20 975 030	2024	Finngen database
GD(E4_GRAVES_STRICT)	European	3 437	450 296	453 733	20 973 994	2024	Finngen database

图1 CeD对AITD影响的MR分析

Fig 1 MR analysis of the impact of CeD on AITD

图2 AITD对CeD影响的MR分析

Fig 2 MR analysis of the impact of AITD on CeD

图3 留一法敏感性分析结果Note： A/B. Effect of CeD on HT. C/D. Effect of CeD on GD. E/F. Effect of GD on CeD.

Fig 3 Results of the leave-one-out sensitivity analysis

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