Emerging Biomarkers in Heart Failure: Pathophysiological Mechanisms, Clinical Utility, and Multi-Omics-Driven Risk Stratification

Heart failure is a complex clinical syndrome involving neurohormonal activation, myocardial injury, inflammation, fibrosis, metabolic dysregulation, and cardiorenal dysfunction. Natriuretic peptides and cardiac troponins are established biomarkers for diagnosis and risk assessment, but they do not fully capture the biological heterogeneity and progression of heart failure. This narrative review examined emerging biomarkers through targeted searches of PubMed, Scopus, and Web of Science for literature published between January 2010 and January 2026, with earlier landmark studies included when relevant. The review focused on biomarkers, multi-omics approaches, precision cardiology, and machine learning in heart failure, emphasizing pathophysiological mechanisms, clinical utility, and translational potential. Emerging biomarkers include growth differentiation factor-15, soluble neprilysin, adrenomedullin, microRNAs, and extracellular vesicle-derived biomarkers. Genomics, transcriptomics, proteomics, metabolomics, and epigenomics further provide opportunities for systems-level molecular characterization. Pathophysiological processes such as inflammation, oxidative stress, extracellular matrix remodelling, endothelial dysfunction, and metabolic perturbations may be captured through multidimensional biomarker strategies. Integration of high-dimensional molecular and clinical data with machine learning may improve phenotyping and risk assessment in selected populations. Despite these advances, most emerging approaches remain investigational because of challenges involving standardization, reproducibility, external validation, clinical utility, cost-effectiveness, and ethical considerations. Multidimensional biomarker profiling may support precision cardiology, but prospective validation and implementation research are required before routine clinical adoption.