Redefining Cardiovascular Risk: Integrative Approaches in Genomics, Inflammation, Imaging, and Artificial Intelligence

Abstract

Cardiovascular risk stratification is undergoing a major paradigm shift driven by advances in genomics, inflammatory profiling, imaging biomarkers, myocardial injury detection, and artificial intelligence. This review integrates evidence from twenty high-impact studies to outline a multidimensional framework for precision cardiovascular risk assessment. Genomic tools, particularly polygenic risk scores, consistently demonstrate strong predictive value and highlight substantial inherited susceptibility that traditional models often fail to detect. Inflammatory and immunometabolic markers emerge as equally central determinants, underscoring the biological complexity of atherosclerosis and the relevance of chronic immune activation. Imaging techniques and high-sensitivity biomarkers provide additional layers of subclinical resolution, while AI-enhanced electrocardiography introduces new opportunities for early detection. A temporal analysis reveals a rapid expansion of precision-oriented research after 2018, culminating in a robust evidence base that favors integrative models over single-domain predictors. The proposed conceptual framework illustrates how these domains converge to generate a richer, individualized understanding of cardiovascular risk. The discussion emphasizes translational challenges and the need for equitable implementation in diverse populations, particularly in Mexico, Colombia, and Ecuador, where structural barriers may limit access to emerging precision tools. Overall, the findings support a shift toward biologically informed, technologically augmented, and context-adapted cardiovascular prevention strategies capable of improving predictive accuracy and advancing global cardiovascular health.

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