OBJECTIVE— Chronic renal insufficiency and/or proteinuria in type 2 diabetes may stem from chronic renal diseases (CKD) other than classic diabetic nephropathy in more than onethird of patients. We interrogated urine proteomic profiles generated by surface-enhanced laser desorption/ionization-time of flight/mass spectrometry with the aim of isolating a set of biomarkers able to reliably identify biopsy-proven diabetic nephropathy and to establish a stringent correlation with the different patterns of renal injury. RESEARCH DESIGN AND METHODS— Ten micrograms of urine proteins from 190 subjects (20 healthy subjects, 20 normoalbuminuric, and 18 microalbuminuric diabetic patients and 132 patients with biopsy-proven nephropathy: 65 diabetic nephropathy, 10 diabetic with nondiabetic CKD [nd-CKD], and 57 nondiabetic with CKD) were run using a CM10 ProteinChip array and analyzed by supervised learning methods (Classification and Regression Tree analysis). RESULTS— The classification model correctly identified 75% of patients with normoalbuminuria, 87.5% of those with microalbuminuria, and 87.5% of those with diabetic nephropathy when applied to a blinded testing set. Most importantly, it was able to reliably differentiate diabetic nephropathy from nd-CKD in both diabetic and nondiabetic patients. Among the best predictors of the classification model, we identified and validated two proteins, ubiquitin and β2-microglobulin. CONCLUSIONS— Our data suggest the presence of a specific urine proteomic signature able to reliably identify type 2 diabetic patients with diabetic glomerulosclerosis.
Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy
LAMACCHIA, OLGA;Rocchetti M.T.;CIGNARELLI, MAURO;
2010-01-01
Abstract
OBJECTIVE— Chronic renal insufficiency and/or proteinuria in type 2 diabetes may stem from chronic renal diseases (CKD) other than classic diabetic nephropathy in more than onethird of patients. We interrogated urine proteomic profiles generated by surface-enhanced laser desorption/ionization-time of flight/mass spectrometry with the aim of isolating a set of biomarkers able to reliably identify biopsy-proven diabetic nephropathy and to establish a stringent correlation with the different patterns of renal injury. RESEARCH DESIGN AND METHODS— Ten micrograms of urine proteins from 190 subjects (20 healthy subjects, 20 normoalbuminuric, and 18 microalbuminuric diabetic patients and 132 patients with biopsy-proven nephropathy: 65 diabetic nephropathy, 10 diabetic with nondiabetic CKD [nd-CKD], and 57 nondiabetic with CKD) were run using a CM10 ProteinChip array and analyzed by supervised learning methods (Classification and Regression Tree analysis). RESULTS— The classification model correctly identified 75% of patients with normoalbuminuria, 87.5% of those with microalbuminuria, and 87.5% of those with diabetic nephropathy when applied to a blinded testing set. Most importantly, it was able to reliably differentiate diabetic nephropathy from nd-CKD in both diabetic and nondiabetic patients. Among the best predictors of the classification model, we identified and validated two proteins, ubiquitin and β2-microglobulin. CONCLUSIONS— Our data suggest the presence of a specific urine proteomic signature able to reliably identify type 2 diabetic patients with diabetic glomerulosclerosis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
