Targeting Metabolic Drivers of Cardiovascular Disease: Insights for Therapeutic Innovation.

Cardiovascular disease (CVD) remains the leading cause of death worldwide, and metabolic dysfunction is increasingly recognized as an important component of its pathophysiology. Clinical evidence supports a role for dysregulated lipid and glucose metabolism in atherosclerosis, heart failure, and cardiometabolic disease, while alterations in amino acid metabolism, mitochondrial energetics, redox signaling, and immunometabolic pathways are supported by growing experimental and observational evidence. Advances in metabolomics, systems biology, and multi-omics approaches have further identified metabolic signatures associated with cardiovascular phenotypes and have provided new insights into pathways regulating endothelial function, vascular inflammation, oxidative stress, and cardiac remodeling. This review synthesizes established clinical evidence and emerging experimental findings on metabolic mechanisms in CVD, with particular emphasis on their potential therapeutic and biomarker implications. We discuss current and investigational strategies targeting lipid and glucose metabolism, mitochondrial dysfunction, oxidative stress, and immunometabolic signaling, while distinguishing clinically validated approaches from those that remain primarily preclinical or hypothesis-generating. Key translational challenges include the complexity and tissue specificity of metabolic networks, limitations of existing experimental models, potential safety liabilities, and the need for longitudinal studies integrating metabolic and clinical phenotypes. Finally, we examine how metabolomics, AI, organ-on-chip models, and systems-level approaches could improve target identification, patient stratification, and therapeutic development. These advances provide a framework for translating mechanistic insights into metabolism-informed cardiovascular therapies and biomarkers, while highlighting the evidence gaps that must be addressed before broader clinical implementation.