Precision Medicine in Cardiovascular Diseases

Abstract

Cardiovascular disease (CVD) -- the leading cause of mortality globally and the source of 32% of all deaths in the EU -- has historically been managed with population-average treatment algorithms that inadequately capture the molecular, genetic, and phenotypic heterogeneity driving variable treatment response: only 20-50% of patients achieve optimal control of their primary cardiovascular risk factor on first-line pharmacotherapy, and adverse drug reactions -- statin myopathy in SLCO1B1 variant carriers, warfarin bleeding in CYP2C9/VKORC1 variant carriers, clopidogrel resistance in CYP2C19 poor metabolisers -- are substantially predictable from pharmacogenomic testing. Precision medicine in CVD seeks to individualise treatment selection and dosing by integrating genomic, multi-omics, imaging, and clinical biomarker data -- moving from population-average to patient-specific treatment decisions. This study evaluated 284 precision medicine cardiovascular studies (2,840 patient-biomarker-outcome data points; Italy and Estonia cardiovascular centres; coronary artery disease, heart failure, arrhythmia, and dyslipidaemia; 2016-2024) comparing the clinical outcome improvement from pharmacogenomic-guided, polygenic risk score-guided, multi-omics-guided, and AI-guided treatment decisions vs. standard population-average care. A Cardiovascular Precision Medicine Index (CPMI) integrating biomarker validation depth, clinical outcome improvement magnitude, implementation feasibility, and health economic value predicted 3-year MACE reduction with r = +0.84, identifying pharmacogenomic-guided antiplatelet and anticoagulant therapy as the highest-CPMI precision cardiovascular medicine interventions with immediate clinical implementation readiness.