Exploring the bidirectional relationships between MRI resting-state functional connectivity networks and cardiovascular diseases: a Mendelian randomization study.

OBJECTIVE: Brain functional connectivity alterations have been observed in cardiovascular diseases (CVDs), but the causality between brain resting-state functional connectivity networks and CVDs remains undetermined. We aimed to investigate the bidirectional causality between brain network connectivity and CVDs using Mendelian randomization (MR) analysis. METHODS: Using genome-wide association study (GWAS) data from the UK Biobank (n = 34,691), we conducted bidirectional two-sample MR analyses between 191 resting-state functional MRI phenotypes and four major CVDs: hypertension, atrial fibrillation (AF), heart failure (HF), and coronary artery disease (CAD). Sensitivity analyses, including MR-Egger regression and weighted median methods, were conducted to ensure the robustness of causal estimates and to test for potential pleiotropy. RESULTS: For hypertension, four networks showed negative causal associations (ORs 0.882-0.904), primarily involving motor, subcortical-cerebellar, default mode, and visual networks. In AF, we observed both increased connectivity in salience and default mode networks (ORs 1.157-1.288) and decreased connectivity in visual-motor networks (OR 0.790). For HF, three networks showed significant associations: decreased connectivity in visual and temporal networks (ORs 0.791-0.804) and increased connectivity in motor networks (OR 1.352). CAD was associated with increased connectivity in both default mode and central executive networks (ORs 1.145-1.147). These relationships remained robust after multiple sensitivity analyses. CONCLUSION: Our findings reveal novel bidirectional causal relationships between specific brain functional networks and CVDs, with distinct patterns of network involvement for different CVDs suggesting disease-specific mechanisms in the cardio-cerebral axis. These findings identify potential neuroimaging biomarkers for early detection and monitoring of cardiovascular diseases.