Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy

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 one-third 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.

Figure 1

Classification and regression tree analysis of diabetic nephropathy and nd-CKD. A: Histological picture of one patient with diabetic nephropathy and one patient with nd-CKD and their respective SELDI urine protein profiles. B: Prediction success of CART analysis on the training set (upper table) and on the testing set with nondiabetic (intermediate table) and diabetic (lower table) patients with nd-CKD. C: ROC analysis of the ability of the proteomic signature to identify diabetic nephropathy. DN, biopsy-proven diabetic nephropathy; nd-CKD1, nondiabetic patients with nondiabetic chronic kidney disease; nd-CKD2, diabetic patients with nondiabetic chronic kidney disease. (A high-quality digital representation of this figure is available in the online issue.)

Figure 2

CART analysis of diabetic patients with normoalbuminuria, microalbuminuria, and diabetic nephropathy. A: Prediction success of the CART analysis for the training set (upper table), after 10-fold cross-validation and the independent testing set (lower table). B: ROC analysis of the ability of the proteomic signature to identify diabetic nephropathy. DN, biopsy-proven diabetic nephropathy; MICRO, microalbuminuric diabetic patients; NAD, normoalbuminuric diabetic patients.

Figure 3

Validation of β2MG and ubiquitin differential excretion. A, top: Representative SELDI spectra (gel view) showing β2MG excretion in patients with diabetic nephropathy compared with that in healthy subjects and patients with normoalbuminuria, microalbuminuria (left), and nd-CKD (right). Bottom: β2-MG urine (U) excretion as measured by ELISA (mean ± SEM) in patients with diabetic nephropathy compared with nd-CKD. B, top: Ubiquitin urine excretion as measured by SELDI analysis on the whole urine profile (mean ± SEM) in patients with diabetic nephropathy compared with nd-CKD. Bottom: SELDI profiling of urine ubiquitin immunoprecipitated by a specific monoclonal antibody (ubiquitin IP) and run on a CM10 ProteinChip array. Representative SELDI spectra (gel view) from six patients with diabetic nephropathy and eight patients with nd-CKD are shown. *P < 0.05. DN, biopsy-proven diabetic nephropathy; MICRO, microalbuminuric diabetic patients; HS, healthy subjects; NAD, normoalbuminuric diabetic patients.