Accurate risk prediction remains central to coronary artery disease (CAD) prevention. This study published in the Journal of American College of Cardiology evaluated whether combining a CAD polygenic risk score (PRS) with lipid and inflammatory biomarkers enhances prediction beyond conventional approaches. Data were derived from 215,695 participants aged 40–69 years in the UK Biobank, all with baseline measurements of CAD PRS, low-density lipoprotein cholesterol (LDL-C), lipoprotein(a) (Lp[a]), and high-sensitivity C-reactive protein (hsCRP).

Participants were followed for 12 years to assess incident CAD. Multivariable-adjusted Cox proportional hazards models, C-statistics, net reclassification index (NRI), and population attributable risk were analyzed across age and sex groups.

During follow-up, CAD developed in 4,721 men and 2,425 women. Each biomarker independently predicted CAD risk: PRS (hazard ratio [HR] 1.79; 95% CI 1.70–1.89), LDL-C (HR 1.60; 95% CI 1.48–1.66), Lp(a) (HR 1.20; 95% CI 1.12–1.29), and hsCRP (HR 1.64; 95% CI 1.57–1.72). The association for PRS was stronger in men (HR per standard deviation [SD] 1.49; 95% CI 1.45–1.54) than women (HR per SD 1.37; 95% CI 1.31–1.44; P-interaction ≤0.001). All biomarkers showed greater relative risk at younger ages (P<0.0001). Individuals with all four biomarkers elevated had a 4.65-fold higher CAD risk compared with those without elevations.

The combined four-biomarker model demonstrated improved discrimination (C-statistic 0.753) compared with pooled cohort equations (0.740), with a 32.0% continuous NRI. These findings indicate incremental predictive value across demographic subgroups.
Simultaneous assessment of genomic, lipid, and inflammatory markers improved CAD risk stratification. Enhanced prediction was observed particularly in younger individuals and across both sexes.