Prioritizing acquired risk factors of atherosclerotic cardiovascular disease according to genetic predisposition: implications for precise prevention.

BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death worldwide. This study aimed to investigate the contributions of acquired risk factors to ASCVD across different genetic risk groups.

METHODS: This study included 430,191 participants aged 40 - 69 from the UK Biobank cohort. Population-attributable fractions (PAFs) of 25 acquired risk factors (including four socioeconomic determinants, three psychosocial factors, five lifestyles, nine cardiometabolic factors, and four clinical comorbidities) for coronary artery disease (CAD), ischemic stroke (IS), and peripheral artery disease (PAD) were assessed across three genetic risk groups (low, moderate, and high). The contributions of 25 risk factors to ASCVD were ranked by PAFs within each genetic risk group.

RESULTS: A total of 32,908 CAD events, 6,819 IS events, and 5,560 PAD events occurred during a median follow-up of 13.8 years. The associations between acquired risk factors and ASCVD varied across the three genetic risk groups, with the most significant diversity observed in the associations between cardiometabolic factors and CAD (PInteraction < 0.001). In addition, the associations of psychosocial factors with CAD and IS decreased with increasing genetic risk (both PInteraction =0.018). Cardiometabolic factors were the leading contributors to ASCVD incidence across all genetic risk groups (PAF for CAD: 48.5%-56.5%; IS: 35.5%-45.5%; PAD: 48.1%-52.0%), with hypertension being the predominant factor in all groups. Still, the contributions of certain cardiometabolic factors (e.g., overweight/obesity, high low-density lipoprotein cholesterol, and elevated C-reactive protein) to ASCVD varied by genetic risk. Socioeconomic determinants ranked second in contribution to ASCVD across different genetic risk levels (CAD: 6.0%-11.9%; IS: 4.8%-12.8%; PAD: 23.6%-33.4%), which was primarily driven by less education. Socioeconomic determinants (CAD: 9.0% vs. 6.0%; IS: 12.8% vs. 4.8%; PAD: 33.4% vs. 23.6%) and psychosocial factors (CAD: 3.5% vs. 2.5%; IS: 7.6% vs. 0.0%; PAD: 4.9% vs. 4.8%) contributed more to ASCVD in the low genetic risk groups than in the high genetic risk groups. The PAFs of lifestyles were relatively consistent across different genetic risk groups. Although the associations between clinical comorbidities and ASCVD were the strongest, their contributions to ASCVD were relatively small (CAD: 2.7%%-4.9%; IS: 1.7%-4.9%; PAD: 9.3%-10.4%).

CONCLUSIONS: This study provides comprehensive information regarding the genetic risk-specific contributions of acquired risk factors to ASCVD. Prioritizing risk factors based on genetic predisposition may help to promote precise and efficient prevention of ASCVD.