Year: 2026
Type: article
Abstract: Background: Chronic kidney disease (CKD) is a progressive condition characterized by largely irreversible structural and functional kidney damage, representing a major global public health burden and affecting approximately 13% of the world’s population. Its asymptomatic nature in early stages and close association with cardiovascular morbidity and mortality often lead to delayed diagnosis and poor clinical outcomes. Methods: This narrative review summarizes current evidence on emerging biomarkers for early detection and risk stratification in CKD. Peer-reviewed studies were examined focusing on biomarkers of tubular injury, inflammation, fibrosis, metabolic dysregulation, multi-omics integration, and the application of artificial intelligence and machine learning in biomarker-based risk prediction. Results: Novel biomarkers such as neutrophil gelatinase-associated lipocalin, liver-type fatty acid-binding protein, monocyte chemoattractant protein-1, soluble tumor necrosis factor receptors, fibroblast growth factor 23, and uromodulin can detect kidney damage earlier and predict disease progression more accurately than traditional tests. New proteomic and metabolomic techniques allow multiple biomarkers to be combined into panels, improving the prediction of kidney and cardiovascular outcomes. The use of artificial intelligence further supports ongoing risk assessment and personalized monitoring of disease progression. Conclusions: Biomarker-driven approaches offer significant potential to improve early diagnosis, risk stratification, and individualized management of CKD. Addressing translational barriers through large-scale validation and global collaboration is essential to improve patient outcomes.
Source: KIDNEYS
Language: English
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7127037677