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J Clin Med. 2015 Jul 17;4(7):1498-517. doi: 10.3390/jcm4071498.

Urinary MicroRNA Profiling Predicts the Development of Microalbuminuria in Patients with Type 1 Diabetes.

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Department of Medicine, Division of Nephrology, University of New Mexico, 901 University Blvd SE, Albuquerque, NM 87106, USA.
Institute for Systems Biology, 401 Terry Ave. North, Seattle, WA 98109, USA.
Department of Medicine, Renal and Electrolyte Division, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
Children's Hospital of Pittsburgh, One Children's Hospital Drive 4401 Penn Avenue, Pittsburgh, PA 15224, USA.
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261, USA.
Pacific Northwest Diabetes Research Institute, 720 Broadway, Seattle, WA 98103, USA.
Department of Medicine, Renal and Electrolyte Division, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA.


Microalbuminuria provides the earliest clinical marker of diabetic nephropathy among patients with Type 1 diabetes, yet it lacks sensitivity and specificity for early histological manifestations of disease. In recent years microRNAs have emerged as potential mediators in the pathogenesis of diabetes complications, suggesting a possible role in the diagnosis of early stage disease. We used quantiative polymerase chain reaction (qPCR) to evaluate the expression profile of 723 unique microRNAs in the normoalbuminuric urine of patients who did not develop nephropathy (n = 10) relative to patients who subsequently developed microalbuminuria (n = 17). Eighteen microRNAs were strongly associated with the subsequent development of microalbuminuria, while 15 microRNAs exhibited gender-related differences in expression. The predicted targets of these microRNAs map to biological pathways known to be involved in the pathogenesis and progression of diabetic renal disease. A microRNA signature (miR-105-3p, miR-1972, miR-28-3p, miR-30b-3p, miR-363-3p, miR-424-5p, miR-486-5p, miR-495, miR-548o-3p and for women miR-192-5p, miR-720) achieved high internal validity (cross-validated misclassification rate of 11.1%) for the future development of microalbuminuria in this dataset. Weighting microRNA measurements by their number of kidney-relevant targets improved the prognostic performance of the miRNA signature (cross-validated misclassification rate of 7.4%). Future studies are needed to corroborate these early observations in larger cohorts.


Type 1 diabetes; gene ontology; microRNAs; microalbuminuria; prognostic model; target analysis

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