GENETIC RISK IN CARDIOVASCULAR DISEASE: HOW CLOSE ARE WE TO CLINICAL TRANSLATION?

Abstract

Cardiovascular disease (CVD) remains the leading global cause of morbidity and mortality, influenced by a complex interplay of genetic and environmental factors. By many, medicine is entering the era of personalized management approach, in one direction because of the advances in genomics, particularly genome-wide association studies (GWAS). Monogenic disorders such as familial hypercholesterolemia, driven by mutations in LDLR, APOB, and PCSK9, illustrate the profound impact of single-gene defects on lipid metabolism and coronary artery disease (CAD) risk. In contrast, polygenic risk scores aggregate multiple variants to refine individual risk prediction for multifactorial diseases such as CAD, though their predictive utility remains modest when added to conventional clinical models. Beyond protein-coding genes, non-coding RNAs (miRNAs, lncRNAs) and endothelial nitric oxide synthase (eNOS) polymorphisms have emerged as key regulators of vascular function and inflammation, offering novel insights into disease mechanisms. However, the clinical translation of genetic testing is hindered by limited predictive accuracy, ethnic bias in genomic research, and challenges in interpreting variants of uncertain significance. Ethical considerations, including psychological impact and data privacy, further complicate its application. Future directions emphasize integrating multi-omics data, diversifying genetic studies, and advancing gene-based therapies such as CRISPR-mediated PCSK9 editing and RNA silencing approaches. Ultimately, while genetic testing holds promise for precision medicine in cardiovascular care, its implementation must be accompanied by improved risk modeling, equitable population representation, and rigorous clinical validation.