Precision Medicine Breakthrough: Multi-Omics Integration Elevates CAD Risk Prediction

Coronary artery disease (CAD) remains the leading cause of global mortality. Multi-omics integration offers unprecedented opportunities for precision risk stratification beyond conventional approaches. However, comprehensive integration of genomic, proteomic, and metabolomic data for CAD prediction remains underexplored.

We integrated genomic (n=442,574), proteomic (n=54,219 with 1,472 proteins), and metabolomic (n=121,249 with 249 metabolites) data from UK Biobank. Multi-omics integration employed similarity network fusion combined with elastic net regularization in the 23,776 participants with complete multi-omics data, with a late integration (stacking) strategy applied for prediction modeling across the full cohort. Internal validation encompassed temporal split (2006-2008 vs. 2009-2010), geographical split (England vs. Scotland/Wales), 10-fold cross-validation, and bootstrap resampling (1,000 iterations).

Among 442,574 participants (34,241 CAD cases, 7.74%), we identified 241 independent genomic loci and integrated 1,472 proteins with 249 metabolites. Network analysis revealed four major biological modules: vascular dysfunction (37.8% variance), lipid metabolism (25.4%), inflammatory response (19.2%), and cardiac remodeling (13.6%). The integrated multi-omics model achieved superior discrimination (C-statistic: 0.798, 95% CI: 0.793-0.803) compared to clinical factors alone (0.741) and genomics-only approaches (0.704, P<0.001), with net reclassification improvement of 10.34% (95% CI: 8.92-11.77%). Temporal validation demonstrated robust transportability (C-statistic: 0.791) with consistent calibration (Hosmer-Lemeshow χ²=8.73, P=0.366). Individuals in the highest risk quintile exhibited 4.52-fold increased CAD odds (95% CI: 4.28-4.78) with 10-year incidence of 11.24% compared to 2.51% in the lowest quintile. Drug target analysis identified 8 proteins targeted by approved cardiovascular drugs and 5 additional targets in Phase II/III clinical trials, supporting translational potential.

Multi-omics integration substantially enhances CAD risk prediction with robust internal validation, enabling targeted precision medicine strategies through successful intermediate-risk reclassification. These findings establish a framework for clinical translation of multi-omics risk assessment.