Abstract

Coronary artery disease (CAD) remains the leading cause of global morbidity and mortality, necessitating more 
effective and equitable preventive strategies. Proteogenomics-guided risk-based screening integrates genomic 
variation, circulating protein biomarkers, and clinical and environmental data to enhance risk prediction beyond 
traditional factors. This approach enables identification of individuals at elevated lifetime risk and supports 
targeted preventive interventions, potentially improving population health outcomes while optimizing resource 
allocation. However, implementation across diverse populations raises important methodological, ethical, and 
health-system challenges, including limited cohort diversity, variability in assay performance, data integration 
complexities, privacy governance, workforce capacity, and affordability constraints. Ensuring equitable 
deployment requires inclusive cohort development, standardized and validated assays, transparent computational 
models, and implementation frameworks grounded in community engagement and social determinants of health. 
Continuous monitoring of analytical validity, clinical validity, clinical utility, and equity metrics is essential within 
a learning health-system model. Proteogenomics-guided CAD screening, therefore, holds promise as a 
transformative precision public health strategy, provided that implementation prioritizes fairness, accessibility, 
and context-specific policy development to avoid exacerbating existing health disparities.