DECODING THE CARDIO-RENAL-METABOLIC AXIS: A SYSTEMATIC REVIEW OF BIOMARKERS PREDICTING HEART FAILURE IN CHRONIC KIDNEY DISEASE

Abstract

Background: Chronic kidney disease (CKD) and heart failure (HF) frequently coexist, reflecting the pathophysiological interplay of the cardio-renal-metabolic (CRM) axis. Biomarkers are increasingly recognized as critical tools for early risk stratification, diagnosis, and therapeutic monitoring. Objective: To systematically review the evidence on circulating and urinary biomarkers predicting HF among CKD patients. Methods: Following PRISMA 2020 guidelines, we searched PubMed, Embase, Scopus, and Cochrane up to July 2025. Eligible studies included observational cohorts, randomized trials, and meta-analyses evaluating biomarkers in CKD populations with HF outcomes. Data were extracted on study design, population, biomarker type, predictive performance, and clinical utility. Results: From 4,326 records screened, 71 studies met inclusion criteria. Biomarkers clustered into five categories: (1) Cardiac stretch markers (BNP, NT-proBNP) — consistently predictive of incident HF and adverse outcomes in CKD, though thresholds require renal adjustment; (2) Cardiac injury markers (troponins, H-FABP) — strong prognostic indicators of hospitalization and mortality; (3) Inflammatory & fibrotic markers (hsCRP, IL-6, galectin-3, ST2) — associated with ventricular remodeling and progressive decline; (4) Renal and tubular injury markers (NGAL, KIM-1, cystatin C) — predicted cardiorenal events independent of eGFR; (5) Metabolic markers (FGF-23, adiponectin, leptin, uric acid) — linked to LV hypertrophy and HFpEF phenotypes. Multi-marker panels and machine-learning approaches improved risk prediction over single markers. Conclusion: Biomarker-guided risk stratification holds promise for early HF detection in CKD, but clinical translation requires standardized cutoffs, external validation, and integration with imaging and risk scores. Future trials should evaluate biomarker-driven treatment algorithms.