Epigenetic Mechanisms Linking Chronic Obstructive Pulmonary Disease and Atrial Fibrillation: A Multi-Omics Mendelian Randomization Study

慢性阻塞性肺疾病与心房颤动之间的表观遗传机制:一项多组学孟德尔随机化研究

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Abstract

BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) and atrial fibrillation (AF) frequently co-occur, yet the underlying molecular mechanisms remain unclear. We assessed whether genetic liability to COPD increases AF risk and mapped epigenetic mediators using multi-omics data. METHODS: We integrated genome-wide association data for COPD and AF with epigenome-wide DNA methylation profiles and gene expression datasets. Summary-data-based Mendelian randomization served as the primary approach to link disease-associated variants with methylation, gene expression, and AF, complemented by two-sample Mendelian randomization, colocalization, and checks for pleiotropy. We prioritized COPD-related CpG sites and genes, examined functional context using protein-protein interaction networks and pathway enrichment, and nominated candidate therapeutics with drug-prediction algorithms followed by molecular docking. Analyses were conducted primarily in cohorts of European ancestry. RESULTS: Genetic liability to COPD was associated with a higher risk of AF (odds ratio [OR] = 1.156, 95% confidence interval [CI] 1.025-1.304; p = 0.018). We identified 109 COPD-related CpG sites associated with AF risk. Cross-omics triangulation highlighted seven candidate genes, with FES showing consistent evidence across genomic, epigenetic, and transcriptomic layers. Functional analyses indicated enrichment in immune signaling and signal transduction pathways. Docking analyses suggested favorable binding between predicted drugs and their targets. CONCLUSION: These findings support a pathway by which COPD may influence AF risk through epigenetic regulation and prioritize CpG sites, genes, and potentially druggable targets for further study. Because the underlying datasets are predominantly from individuals of European ancestry, replication in more diverse populations is needed to assess generalizability.

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