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Combining Genomics With Lipid and Inflammatory Biomarkers to Predict Coronary Artery Disease Risk: UK Biobank Study
Raysha Farah1, Min Seo Kim2, Buu Truong2
1Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Journal of the American College of Cardiology
|March 18, 2026
Summary
Integrating coronary artery disease (CAD) polygenic risk score (PRS), LDL-C, Lp(a), and hsCRP improves risk prediction. This combined biomarker approach enhances identification of individuals at higher risk for CAD.
Area of Science:
- Cardiovascular Disease Epidemiology
- Genomic Risk Prediction
- Biomarker Discovery
Background:
- Coronary artery disease (CAD) risk is influenced by polygenic risk score (PRS), low-density-lipoprotein cholesterol (LDL-C), lipoprotein(a) (Lp(a)), and high-sensitivity C-reactive protein (hsCRP).
- The synergistic predictive value of combining these genomic, lipid, and inflammatory biomarkers with traditional risk scores for CAD remains under investigation.
Purpose of the Study:
- To evaluate the independent predictive capabilities of CAD PRS, LDL-C, Lp(a), and hsCRP for incident CAD.
- To assess these biomarkers' predictive performance across diverse age and sex demographics.
- To determine if integrating these four biomarkers enhances CAD risk stratification compared to existing models.
Main Methods:
- Utilized UK Biobank data from 215,695 participants (aged 40-69) with baseline biomarker measurements.
- Employed a 12-year follow-up period to track incident CAD events.
- Applied multivariable-adjusted Cox models, net reclassification index, and C-statistics to analyze biomarker associations and model performance.
Main Results:
- Each biomarker (CAD PRS, LDL-C, Lp(a), hsCRP) demonstrated independent associations with incident CAD, with hazard ratios ranging from 1.20 to 1.79.
- CAD PRS showed a stronger association in men than women (P-interaction ≤ 0.001), and all biomarkers were more predictive at younger ages (P < 0.0001).
- A combined 4-biomarker model achieved a higher C-statistic (0.753) than pooled cohort equations (0.740), significantly improving risk reclassification (continuous NRI of 32.0%).
Conclusions:
- Coronary artery disease polygenic risk score, LDL-C, hsCRP, and Lp(a) are independently associated with CAD risk in an age- and sex-specific manner.
- Measuring these four biomarkers collectively offers improved risk prediction for midlife individuals.
- This integrated biomarker approach holds potential for enhancing clinical identification of individuals at elevated risk for coronary artery disease.
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