Urine Proteome Analysis May Allow Non-Invasive Differential Diagnosis of Diabetic Nephropathy

AbstractObjective: Chronic renal insufficiency and/or proteinuria in type 2 diabetes may stem from chronic renal diseases (CKD) other than classic diabetic nephropathy (DN) in over one third of cases. We interrogated urine proteomic profiles generated by SELDI-TOF/MS with the aim to isolate a set of biomarkers able to reliably identify biopsy-proven DN and to establish a stringent correlation with the different patterns of renal injury. Research design and methods: Ten mug urine proteins from 190 subjects [20 healthy subjects (HS), 20 normoalbuminuric (NAD) and 18 microalbuminuric (MICRO) diabetic patients, and 132 patients with biopsy-proven nephropathy (65 DN, 10 diabetics with non-diabetic CKD (nd-CKD) and 57 non-diabetic patients with CKD)] were run by CM10 ProteinChip array and analysed by supervised learning methods (CART analysis). Results: The classification model correctly identified 75% NAD, 87.5% MICRO and 87.5% DN when applied to a blinded testing set. Most importantly, it was able to reliably differentiate DN from nd-CKD in both diabetic and non-diabetic patients. Among the best predictors of the classification model, we identified and validated 2 proteins, ubiquitin and ss2-microglobulin. Conclusions: Our data suggest the presence of a specific urine proteomic signature able to reliably identify type 2 diabetic patients with diabetic glomerulosclerosis.