Cardiovascular Disease in the Context of Systems Immunology and Metabolomics

Background: Cardiovascular disease (CVD) is increasingly recognized as a complex, immunometabolic disorder involving dynamic interactions between immune cells, metabolic pathways, and vascular tissues. Traditional risk models based on cholesterol levels and blood pressure fail to capture the molecular heterogeneity and systemic nature of CVD. Systems immunology and metabolomics have emerged as integrative frameworks capable of unraveling the immune-metabolic networks that drive atherosclerosis, myocardial remodeling, and heart failure progression. By combining high-throughput data with computational modeling, these approaches offer a holistic view of disease pathogenesis and open new avenues for biomarker discovery and therapeutic targeting. Methods and Results: This review synthesizes findings from multi-omics studies incorporating single-cell immune profiling, mass spectrometry–based metabolomics, and systems-level network analysis in both human cohorts and experimental models of CVD. Immune cell mapping reveals expansion of pro-inflammatory myeloid subsets, dysregulated T-cell polarization, and altered cytokine networks in atherosclerotic plaques and failing myocardium. Metabolomic profiling identifies perturbations in fatty acid oxidation, amino acid metabolism, and TCA cycle intermediates, many of which correlate with immune activation and adverse clinical outcomes. Integration of immunologic and metabolic signatures using machine learning algorithms enables stratification of CVD patients into mechanistically distinct subgroups with differential risk profiles and therapeutic responsiveness. Key mediators such as itaconate, succinate, kynurenine, and sphingosine-1-phosphate emerge as central hubs linking metabolism to inflammation. Conclusion: Systems immunology and metabolomics provide powerful, complementary insights into the pathobiology of cardiovascular disease, revealing new mechanistic pathways and potential intervention points. Their integration into clinical research and practice may facilitate precision phenotyping, improve risk prediction, and enable the development of targeted immunometabolic therapies for CVD.