Assessing the Role of Polygenic Risk Scores in Cardiovascular Risk Prediction: A Cross-Sectional Analysis from the Paracelsus 10,000 Cohort.

Abstract

Introduction: Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality. SCORE2 may underestimate risk in those classified as low-to-moderate risk. Polygenic risk scores (PGS) capture genetic predisposition to CVD and could enhance traditional models. This study examines whether integrating PGS with SCORE2 improves the prediction of significant subclinical coronary atherosclerosis, defined as coronary artery calcium (CAC) >100.

Methods: We analyzed data from 1,420 participants in the Paracelsus 10,000 cohort with available PGS, SCORE2, and CAC measurements. Predictive performance was compared across SCORE2 alone, PGS alone, and their combination, assessed using the Akaike Information Criterion (AIC) and area under the receiver operating characteristic curve (AUC). Decision Curve Analysis (DCA) was performed to evaluate clinical utility.

Results: PGS improved the prediction of CAC >100 beyond SCORE2 alone, increasing the AUC from 0.662 to 0.738 in women and from 0.659 to 0.714 in men, with substantial Net Reclassification Improvement (NRI: women 0.649, men 0.450). The addition of PGS, particularly in the highest quintiles, significantly enhanced classification accuracy for CAC >100. Decision curve analysis demonstrated that using PGS as a continuous variable provided the highest net benefit at lower threshold probabilities, supporting its role in refining risk stratification, especially in low-to-moderate risk populations.

Conclusion: PGS enhances SCORE2-based prediction of significant CAC. These findings highlight the potential of PGS to refine cardiovascular risk stratification, supporting targeted screening and prevention. Prospective validation, assessment of long-term cardiovascular outcomes, and cost-effectiveness analysis are warranted to guide clinical implementation.