Editorial
What’s the Ultimate Risk Score for CAD Patients?
2025 Volume 32 Issue 8 Pages 924-925
Details
2025 Volume 32 Issue 8 Pages 924-925
See article vol. 32: 929-961
Coronary artery disease (CAD) is a leading cause of cardiovascular mortality, making primary and secondary prevention critical. Traditionally, clinical risk factors for cardiovascular diseases, such as diabetes, hypertension, dyslipidemia, and smoking, have been identified, and risk scores such as the Framingham Risk Score and ASCVD Risk Score have been developed based on these factors. However, existing clinical risk scores are insufficient for predicting individuals at risk of premature CAD events, as age predominantly drives long-term risk assessments1). Consequently, these conventional risk scores fail to provide adequate prognostic stratification, underscoring the need for individualized risk assessment and management. Incorporating genetic information into risk scores has emerged as a promising approach to enhance precision.
In recent years, the concept of polygenic inheritance, in which numerous genetic variants with small effect sizes collectively contribute to disease onset, has gained wide recognition in various diseases. Polygenic factors have been shown to play a more significant role in the context of coronary artery disease and myocardial infarction than monogenic factors2). Polygenic risk scores (PRS) represent quantitative measures of polygenic effects. PRS is calculated using the effect sizes of specific single-nucleotide variants (SNVs) identified through large-scale genome-wide association studies (GWAS) targeting specific diseases. It quantifies the cumulative impact of tens of thousands to millions of SNVs on the onset of cardiovascular disease as a unidimensional risk score. Previous research has demonstrated that a higher CAD PRS is associated with an elevated risk of CAD3). Moreover, the CAD PRS provides a level of risk discrimination comparable to that of individual traditional clinical risk factors4). Incorporating PRS into existing clinical risk scores has been shown to enhance the accuracy of prognostic prediction.
This study introduced a novel approach focused on PDGFs/PDGFRB, which are implicated in atherosclerosis5). By scoring 13 genetic variants related to these factors, the study precisely calculated genetic risk scores (GRS) associated with major adverse cardiovascular events (MACEs), cardiovascular disease (CVD), stroke, CAD recurrence, CVD-related mortality, and all-cause mortality. The results demonstrated that this approach improved prognostic stratification relative to conventional clinical risk scores, such as the GRACE score, and added predictive value to existing clinical risk scores and comorbidities. However, this single-center study utilized a Chinese gene database, necessitating validation studies to evaluate the generalizability of this score. Genetic risk assessment is essential for achieving more precise personalized medicine. A key advantage of genetic testing is its ability to identify biological risk factors early in life, even before clinical risk factors emerge. Importantly, even among individuals with high genetic risk, environmental and lifestyle factors may mitigate this predisposition to cardiovascular disease6). Therefore, individuals with a high degree of genetic risk represent a critical target for preventive medicine. Ultimately, clinical practice may require the development of a highly accurate risk score that integrates multiple pathophysiologically relevant genes with traditional clinical risk factors. Targeting a particular gene in this study (PDGFs/PDGFRB) may be a promising way to combat the residual risk of CAD.
The authors declare that they have no conflicts of interest to disclose.
