Cardiovascular (CV) disease continues to be the primary cause of mortality globally and represents a significant health and socioeconomic problem of modern society. Predicting the CV risk is a fundamental component of primary prevention strategies. 

Over the past five decades, a wide range of CV disease risk scores have been created and updated (1). The widely used SCORE2 model estimates an individual’s absolute 10-year risk of major cardiovascular events based on traditional risk factors and regional risk stratification; however, it may underestimate risk in specific subgroups, including younger adults, women, and individuals classified as low to moderate risk (2). Coronary artery calcium (CAC) is a strong marker of subclinical atherosclerosis. A score > 100 is linked to a higher risk of CV events and helps improve risk classification in intermediate-risk individuals (3). But widespread CAC screening is limited by cost and logistics, making targeted imaging essential for the efficient use of resources.  Over the last few years, polygenic risk scores (PRSs) have gained interest as a promising tool with the potential for more personalized cardiovascular risk prediction (4). There is still limited evidence on whether genetic information, together with the standard risk model, can improve the prediction of subclinical atherosclerosis.

In the recent paper published in the European Journal of Preventive Cardiology, Wernly and colleagues (5) investigated whether combining polygenic risk scores (PGS) with SCORE2, a traditional CV risk assessment tool, improves the prediction of significant coronary artery calcium (CAC > 100), an indicator of early heart disease. This study analyzed 1,420 participants from the Paracelsus 10,000 cohort who had available data on PGSs, SCORE2 clinical risk model, and CAC measurements, to compare the predictive accuracy of SCORE2 alone versus its integration with PGS.  Findings revealed that adding PGS substantially enhances risk stratification, especially in women and younger individuals, groups often underestimated by conventional CV risk assessment methods. This study showed that PGS holds significant potential to refine CV risk assessment, supporting more targeted screening and preventive strategies in clinical practice (early lifestyle intervention and pharmacotherapy). 
    
Although PRSs have demonstrated potential in enhancing CV risk prediction and despite growing interest from clinicians for commercially available PRSs, they are not yet formally incorporated into the major current cardiovascular prevention guidelines.  The ESC Council on Cardiovascular Genomics/ESC Cardiovascular Risk Collaboration/European Association of Preventive Cardiology recently published a clinical consensus statement (6). This document helps in understanding PRS in the context of CV disease and presents the significant challenges of clinical implementation of PRSs for CV risk prediction (need for broader validation in diverse ancestries,  long-term prospective studies, cost-effectiveness analyses, and regulatory, ethical, and educational hurdles for both clinicians and patients). Addressing these obstacles is essential for facilitating the adoption of PRSs as valuable instruments for personalized CV risk assessment.