Gembillo G, Soraci L, Messina R, Lo Cicero L, Spadaro G, Cuzzola F, Calderone M, Ricca MF, Di Piazza S, Sudano F, Peritore L, Santoro D. Urinary biomarkers of diabetic kidney disease. World J Diabetes 2026; 17(1): 110502 [DOI: 10.4239/wjd.v17.i1.110502]
Corresponding Author of This Article
Guido Gembillo, MD, Consultant, Unit of Nephrology and Dialysis, AOU “G. Martino”, University of Messina, Via Consolare Valeria n 1, Messina 98125, Sicilia, Italy. ggembillo@gmail.com
Research Domain of This Article
Urology & Nephrology
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Diabetes. Jan 15, 2026; 17(1): 110502 Published online Jan 15, 2026. doi: 10.4239/wjd.v17.i1.110502
Urinary biomarkers of diabetic kidney disease
Guido Gembillo, Luca Soraci, Rossella Messina, Lorenzo Lo Cicero, Giuseppe Spadaro, Felicia Cuzzola, Michela Calderone, Maria Federica Ricca, Simona Di Piazza, Flavia Sudano, Luigi Peritore, Domenico Santoro
Guido Gembillo, Rossella Messina, Lorenzo Lo Cicero, Giuseppe Spadaro, Felicia Cuzzola, Michela Calderone, Maria Federica Ricca, Simona Di Piazza, Flavia Sudano, Luigi Peritore, Domenico Santoro, Unit of Nephrology and Dialysis, AOU “G. Martino”, University of Messina, Messina 98125, Sicilia, Italy
Luca Soraci, Unit of Geriatric Medicine, Italian National Research Center on Aging (IRCCS INRCA), Cosenza 87100, Calabria, Italy
Author contributions: Gembillo G, Soraci L, Soraci D, Lo Cicero L and Peritore L contributed to conceptualization; Gembillo G, Messina R contributed to methodology; Gembillo G, Messina R, Spadaro G, Lo Cicero L, Santoro D contributed to writing original draft preparation; Di Piazza S, Calderone M, Cuzzola F, Ricca MF contributed to writing review and editing; Gembillo G, Soraci L, Santoro D contributed to supervision; Gembillo G, Soraci L, Messina R, Lo Cicero L, Spadaro G, Cuzzola F, Calderone M, Ricca MF, Di Piazza S, Sudano F, Peritore L, Santoro D have read and agreed the version of the manuscript to be published.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Guido Gembillo, MD, Consultant, Unit of Nephrology and Dialysis, AOU “G. Martino”, University of Messina, Via Consolare Valeria n 1, Messina 98125, Sicilia, Italy. ggembillo@gmail.com
Received: June 9, 2025 Revised: July 6, 2025 Accepted: November 18, 2025 Published online: January 15, 2026 Processing time: 220 Days and 14 Hours
Core Tip
Core Tip: Diabetic kidney disease is a major complication of diabetes and a leading cause of chronic kidney disease. Traditional markers such as albuminuria and estimated glomerular filtration rate often detect damage at a late stage, limiting early intervention. Emerging urinary biomarkers including kidney injury molecule 1, neutrophil gelatinase associated lipocalin, monocyte chemoattractant protein 1, and urinary microRNAs offer earlier and more specific detection of tubular injury, inflammation, and oxidative stress. These biomarkers enhance risk assessment, disease monitoring, and evaluation of treatment response. Incorporating them into clinical practice could enable earlier diagnosis, personalized therapy, and improved outcomes in diabetic kidney disease management.
