An Evidence based, Population tailored, Economical Risk Prediction Tool for Coronary Artery Disease: A Need of the Hour

Dear Editor, Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, with an alarming rise in prevalence globally.[1] India, often referred to as the “heart disease capital of the world,” faces a significant burden of CAD, with cases emerging at a younger age.[2] Therefore, early risk prediction is the need of the hour to overcome the burden, but at the same time, the interplay of genetic predisposition, environmental factors, ethnicity, and lifestyle has made risk stratification complex. Efforts have been made by researchers in this direction for ages with CAD risk scoring choices. To the best of our knowledge, most of the conventional risk assessment tools such as Atherosclerotic Cardiovascular Disease Risk Calculator by the American College of Cardiology,[3] Framingham Risk Score,[4] and Atherosclerosis Burden Score[5] are based on the facts related to Standard Modifiable Cardiovascular Risk Factors (SMuRFs)[6] including subjective and clinical parameters. They are not population-tailored, though each population has distinct genetic and metabolic profiles. Moreover, only around 25% of patients with acute coronary syndrome have been reported to show correlations with SMuRFs.[7] Further, the present era being inclined toward precision medicine, there remains a critical gap in incorporating genomic insights for a more personalized and effective approach to CAD, particularly for risk prediction in the Indian population. In the present era, there is an increasing focus on obtaining polygenic risk score (PRS), which is a measure of an individual’s genetic predisposition to a specific condition or disease. This is calculated by considering the collective effect of multiple genetic variations. Currently, PRS is being integrated and tested in breast and ovarian cancers. For instance, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm model is implemented as a web interface in the CanRisk Tool, a publicly available tool for medical practitioners and researchers for breast cancer screening and risk stratification.[8] Beyond oncology, PRS is also being actively explored for cardiovascular disease (CVD) risk assessment. A recent report outlined a potential framework for integrating PRS into health checks, aiming to enhance early detection and prevention strategies. Similarly, the Electronic Medical Records and Genomics Network has assessed CVD-related PRS across various populations and healthcare settings. These efforts highlight the growing role of PRS in personalized medicine, with ongoing research focused on optimizing its implementation in routine clinical practice.[9] Further, recent advancements in genomic research have identified several single-nucleotide polymorphisms (SNPs) associated with CAD susceptibility globally,[10] with many studies highlighting unique genetic risk factors in Indian cohorts.[11] Genome-wide association studies (GWAS) have pinpointed loci related to lipid metabolism, inflammation, and vascular integrity that contribute to CAD risk. Despite this growing evidence, genetic markers have not yet been widely integrated into clinical practice for CAD screening in India. A cost-effective, targeted SNP panel tailored for the Indian population could enhance early risk stratification and preventive strategies. The development of a genomic-biochemical CAD risk assessment tool specifically designed for early screening in the Indian population is a primary requirement in the current scenario. Such a tool would integrate key CAD-associated SNPs identified in Indian cohorts with established biochemical markers such as low-density lipoprotein, glycated hemoglobin, homocysteine, NT-proBNP, PCSK9, and inflammatory cytokines. The feasibility of this approach is supported by existing GWAS data, which indicate that a select panel of high-impact SNPs can effectively differentiate high-risk individuals from healthy controls. Given the cost constraints in India, whole-genome sequencing may not be feasible for large-scale screening, but targeted genotyping of key SNPs using cost-effective techniques such as Sanger sequencing could make this tool affordable and scalable for widespread use. To develop and validate this screening tool for the Indian population, the following steps are required: Selection of Clinically Relevant SNPs for Indians: Curating a panel of SNPs from established GWAS studies that demonstrate a strong association with CAD risk in Indian cohorts. Furthermore, validation in a small cohort with smaller patient and control groups can be performed Integration with Biochemical and Clinical Markers: Assessing how genomic data can complement the existing clinical risk factors specific to the Indian population, such as higher prevalence of metabolic syndrome and early-onset CAD Development of a CAD Risk Prediction Model: Utilizing logistic regression, PRS, or machine learning approaches to create a robust risk stratification algorithm validated for Indian patients Validation in Indian Cohorts: Conducting population-based validation studies across diverse Indian ethnic groups to assess the tool’s predictive power, sensitivity, and specificity in real-world settings Translation into a Digital Tool: Implementing a user-friendly mobile or web-based application tailored for Indian healthcare settings, allowing physicians and individuals to assess CAD risk based on genetic and biochemical parameters. The incorporation of genomic data into CAD risk prediction [Figure 1] represents a shift toward precision medicine in cardiovascular health care, particularly for India, where early identification of high-risk individuals can significantly impact disease prevention and management. By leveraging genetic insights alongside biochemical markers, we can enhance early detection, improve preventive strategies, and reduce the burden of CAD-related morbidity. This will be an initiative to bridge the gap between genomics and clinical practice in CAD management.Figure 1: Summary.Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.