Machine Learning‐Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics

Journal of the American Heart Association, Volume 15, Issue 12 , June 16, 2026. BackgroundCoronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited.

Here, we integrate conventional risk factors, polygenic risk scores, and large‐scale proteomics to develop a unified model for enhanced CAD risk prediction.MethodsUsing data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32 330) and internal validation (n=13 857) sets, and Scotland/Wales participants formed an external validation set (n=5775).

Incident CAD was ascertained from linked health records. A 202‐protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations‐guided forward selection identified a compact protein panel.ResultsAcross cohorts, the median age was 58 years and ∼45% were men.

Protein proteomic risk score was dose‐dependently associated with CAD risk.