Prevention & lipidsCross-sectional study
Assessing the role of polygenic risk scores in cardiovascular risk prediction: a cross-sectional analysis from the Paracelsus 10 000 cohort
- Cross-sectional study
Authors' conclusion
Polygenic risk score enhances SCORE2-based prediction of significant CAC.
Study context
- Population
- Participants of the Paracelsus 10,000 cohort with available PGS, SCORE2 and coronary artery calcium (CAC) measurements
- Participants
- 1,420 participants
- Exposure
- SCORE2 + PGS vs SCORE2 alone
- Outcome
- Prediction of significant subclinical coronary atherosclerosis (CAC > 100)
Main finding
As reported in the abstract
Prediction of CAC > 100
SCORE2
0.662
AUC, women · AUC, men 0.659
SCORE2 + PGS
0.738
AUC, women · AUC, men 0.714
NRI
- women
- 0.649
- men
- 0.450
Observational data: associations, not effects.
Further findings
Classification accuracy for CAC > 100 by PGS quintile
- Adding PGS, particularly in the highest quintiles, significantly enhanced classification accuracy
Decision curve analysis (clinical utility)
- PGS as a continuous variable gave the highest net benefit at lower threshold probabilities
Original publication
Assessing the role of polygenic risk scores in cardiovascular risk prediction: a cross-sectional analysis from the Paracelsus 10 000 cohort. Eur J Prev Cardiol. Published 8 April 2025. doi:10.1093/eurjpc/zwaf206
Summarised from the published abstract; every number is checked against it