Serum Biomarkers and Endothelial Dysfunction as Early Predictors of Cardiovascular Risk in Chronic Kidney Disease: A Multi-Biomarker Meta-Analysis and Clinical Translation Framework

Abstract

Background: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality among individuals with chronic kidney disease (CKD), often manifesting before progression to end-stage renal disease. Traditional risk markers such as glomerular filtration rate (GFR) and albuminuria offer limited predictive capacity for early cardiovascular events in CKD patients. Objective: This study investigates the predictive value of emerging serum biomarkers namely asymmetric dimethylarginine (ADMA), cystatin C, fetuin-A, and matrix Gla protein (MGP) in improving cardiovascular risk stratifycation in CKD populations. Methods: A PRISMA-compliant systematic review and meta-analysis was conducted, pooling hazard ratios for biomarker associations with cardiovascular outcomes. Additionally, machine learning models (Random Forest and Logistic Regression) were developed using biomarker and clinical data to classify patients into cardiovascular risk categories. The best-performing model informed the creation of the CKD-CV Early Risk Score (CERS). Results: All four biomarkers showed significant associations with cardiovascular risk, with ADMA and cystatin C emerging as the strongest predictors. The Random Forest model achieved an AUC-ROC of 0.88, outperforming logistic regression. The resulting CERS effectively stratified patients into low-, moderate-, and high-risk groups, offering a practical tool for early clinical intervention. Conclusion: Integrating novel serum biomarkers into clinical models enhances early cardiovascular risk prediction in CKD. The proposed CERS framework supports a shift from reactive to preventive care, enabling timely, personalized interventions. Future directions include incorporation of genomics, real-time biosensing, and prospective trials to validate clinical utility.