重庆医学2026，Vol.55Issue(5)：1037-1043,1049,8.DOI:10.3969/j.issn.1671-8348.2026.05.012
两样本孟德尔随机化分析静息态网络与危险饮酒行为的因果关系
Two-sample Mendelian randomization analysis of the causal association between resting-state networks and hazardous alcohol use
摘要
Abstract
Objective The two-sample Mendelian randomization(MR)method was employed to inves-tigate the causal relationship between resting-state network structural connectivity(SC)and functional con-nectivity(FC)and hazardous alcohol use,with the aim of providing a new perspective for the study of the neu-robiological mechanism of hazardous alcohol use and offering scientific basis for the formulation of interven-tion strategies.Methods Based on the publicly available genome-wide association analysis(GWAS)database,16 phenotypes of resting-state network SC and FC were selected,along with 2 phenotypes of hazardous alcohol use.The inverse variance weighted(IVW)method was used as the main analysis method,supplemented by MR-Egger regression and weighted median(WME)for MR analysis.The stability of the results was evaluated through heterogeneity analysis,gene-level pleiotropy test,and sensitivity analysis.Results The IVW analysis results showed that the visual network SC was negatively correlated with the AUDIT-Total score(OR=0.73,95%CI:0.54-0.98,P=0.04)and alcohol dependence(OR=1.53×10-3,95%CI:4.38×10-6-5.33×10-1,P=0.03).The results of different MR analysis methods had consistent Beta values.The leave-one-out method analysis showed stable results,and there was no heterogeneity or multiplicity interference.Conclusion Reduced SC in the visual network may increase the risk of hazardous alcohol use.No reverse causal relationship between hazardous alcohol use and SC/FC in the resting-state network was found.关键词
静息态网络/饮酒/功能连接/结构连接/孟德尔随机化Key words
resting state network/drinking alcohol/functional connection/structural connection/Men-delian randomization分类
医药卫生引用本文复制引用
余洁,陈巧一,马进..两样本孟德尔随机化分析静息态网络与危险饮酒行为的因果关系[J].重庆医学,2026,55(5):1037-1043,1049,8.基金项目
重庆市自然科学基金项目(CSTB2024NSCQ-MSX0526). （CSTB2024NSCQ-MSX0526）
