Advances in Precision Medicine for Chronic Kidney Disease: Biomarkers, Risk Stratification, and Personalized Therapeutic Approaches

Abstract

Chronic kidney disease (CKD) is a biologically heterogeneous condition with great variations in the progression, complications and treatment responses in people with the same clinical syndromes. The concept of precision medicine is to address this variability by leveraging molecular, clinical and digital data to inform individual diagnosis and treatment. This review discusses the current understanding of precision nephrology, including the role of genetic susceptibility, metabolic dysfunction, inflammation, fibrosis, and multi-omics-defined disease subtypes. It also assesses newly introduced biomarkers such as KIM-1, NGAL, suPAR, DKK3, cystatin C, extracellular vesicles and multi-omics panels to detect, diagnose, and follow-up patients early. Dynamic prediction models, interpretable machine learning, artificial intelligence, and digital health platforms are discussed as tools for improving risk stratification and identifying patients at greatest risk of kidney function decline. Personalized therapeutic approaches include biomarker-guided treatment, RAAS inhibition, SGLT2 inhibitors, GLP-1 receptor agonists, pathway-specific therapies, gene and RNA therapeutics, and precision nutrition. Despite rapid progress, clinical implementation remains limited by inadequate validation, data fragmentation, cost, regulatory uncertainty, infrastructure demands, and inequitable access. Future progress will depend on standardized assays, interoperable data systems, representative cohorts, transparent algorithms, and prospective evidence of clinical utility. Finally, the integration of multi-omics, computational medicine, and personalized treatment could help facilitate mechanism-based classification and personalized treatment of CKD.