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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.
Insights
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.
Background:
Coronary artery disease (CAD) polygenic risk score (PRS), low-density-lipoprotein cholesterol (LDL-C), lipoprotein(a) (Lp(a)), and high-sensitivity C-reactive protein (hsCRP) are biomarkers that predict CAD. It is unclear whether integrating genomics with lipid and inflammatory biomarkers could complement traditional risk scores in identifying people at risk of CAD.
Objectives:
This study assesses the predictive value of CAD PRS, LDL-C, Lp(a), and hsCRP for incident CAD across different age and sex groups.
Methods:
Participants (n = 215,695) from the UK Biobank aged 40 to 69 years with baseline CAD PRS, LDL-C, Lp(a), and hsCRP values were followed for 12 years to assess the incidence of CAD. We evaluated a multivariable-adjusted Cox model that included all 4 biomarkers, net reclassification index, C-statistics, and population attributable risk across different age and sex groups.
Results:
Over a 12-year follow-up, 4,721 men and 2,425 women developed CAD. The HRs for incident CAD associated with each biomarker elevation were 1.79 (95% CI: 1.70-1.89) for CAD PRS, 1.60 (95% CI: 1.48-1.66) for LDL-C, 1.20 (95% CI: 1.12-1.29) for Lp(a), and 1.64 (95% CI: 1.57-1.72) for hsCRP. CAD PRS demonstrated a stronger association in men (HR per 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 conferred greater HRs at younger ages (P < 0.0001). Individuals with all biomarkers elevated had a 4.65-fold increased risk of CAD compared with those with no elevated biomarkers. A combined 4-biomarker model had a higher C-statistic of 0.753 compared with the pooled cohort equations (C-statistic of 0.740). The C-statistic of the combined 4-biomarker model was also higher in younger individuals in both sexes and yielded a 32.0% continuous net reclassification index when compared with the pooled cohort equations.
Conclusions:
CAD PRS, LDL-C, hsCRP, and Lp(a) show independent age- and sex-specific associations with CAD. Measuring all 4 biomarkers may improve midlife CAD risk prediction for both male and female patients.
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