The Evolution of Cardiovascular Risk Assessment: From Framingham to Multi-omics

Emmanuel Auchi Edafe* Val-Ugboma Demi Somtoolisa, Nweke Chidera Colette, Christian Kenneth Nweke

Abstract

Cardiovascular disease (CVD) remains the leading cause of global morbidity and mortality, making early and accurate risk assessment a critical public health priority. This narrative review explores the profound evolution of cardiovascular risk stratification through three major developmental leaps.

The first leap encompasses the establishment of traditional, population-based risk algorithms, such as the Framingham Risk Score, Pooled Cohort Equations, and QRISK, which utilize standard clinical parameters to estimate short-to-long-term incidence of cardiovascular events. While foundational, these static models often miscalculate risk in diverse, contemporary, or global populations. The second leap highlights the integration of novel biomarkers and non-invasive cardiovascular imaging, notably coronary artery calcium (CAC) scoring, which allows clinicians to detect subclinical atherosclerosis and refine individualized residual risk. The third and current leap is driven by the advent of artificial intelligence (AI), continuous wearable monitoring, and multi-omics—including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Together, these technologies are shifting risk prediction from episodic evaluations to dynamic, highly personalized profiling.

Furthermore, this paper addresses the specific challenges of cardiovascular risk screening in low-resource environments, such as Nigeria, emphasizing the necessity for non-laboratory-based charts and mobile health technologies to bridge the screening gap. Ultimately, the future of cardiovascular prevention lies in the convergence of multimodal data fusion, federated learning, and digital cardiovascular twins, which will enable truly proactive and precise cardiovascular care.