Beyond eGFR and Albuminuria: Biological Pathways and Multiomics in Cardiovascular-Kidney-Metabolic Disease.

Cardiovascular-kidney-metabolic disease (CKM) represents a growing public health challenge driven by the convergence of obesity, diabetes, and cardiovascular risk within aging populations. Despite major therapeutic advances, risk stratification remains anchored to conventional markers such as creatinine, urine albumin-to-creatinine ratio, natriuretic peptides, troponins, lipids, and glycated hemoglobin. Although prognostically useful, these parameters largely reflect established organ dysfunction rather than the upstream biological processes responsible for disease initiation and progression. Recent advances in proteomics, metabolomics, transcriptomics, and genomics offer promise in refining CKM from an organ-based construct towards a more integrated biological network characterized by immune activation, fibrosis, endothelial dysfunction, mitochondrial impairment, and endocrine crosstalk. Lipotoxic pathways underpinning metabolic dysfunction-associated steatotic liver disease are increasingly recognized within this network but are not yet formally incorporated into CKM staging. Emerging biomarkers may quantify these processes before overt organ dysfunction becomes apparent, identifying individuals who are biologically active but clinically silent. Integrating pathway-oriented biomarkers alongside established clinical measures may enable earlier, mechanism-informed risk stratification. As therapeutic options expand, including sodium-glucose cotransporter-2 (SGLT2) inhibitors, incretin-based therapies, and nonsteroidal mineralocorticoid receptor antagonists, aligning therapy with the appropriate biological phenotype presents a further challenge. Biomarker innovation may therefore bridge disease mechanisms and targeted treatment strategies. This review synthesizes the current understanding of molecular pathways underpinning CKM and examines novel biomarkers spanning inflammatory, fibrotic, metabolic, and microvascular biology. We discuss integrated multiomics strategies and artificial intelligence in refining risk prediction, identifying molecular endotypes, and guiding personalized treatment.