Prediction of diabetic nephropathy using urine proteomic profiling 10 years prior to development of nephropathy.

OBJECTIVE: We examined whether proteomic technologies identify novel urine proteins associated with subsequent development of diabetic nephropathy in subjects with type 2 diabetes before evidence of microalbuminuria. RESEARCH DESIGN AND METHODS: In a nested case-control study of Pima Indians with type 2 diabetes, baseline (serum creatinine <1.2 mg/dl and urine albumin excretion <30 mg/g) and 10-year urine samples were examined. Case subjects (n = 31) developed diabetic nephropathy (urinary albumin-to-creatinine ratio >300 mg/g) over 10 years. Control subjects (n = 31) were matched to case subjects (1:1) according to diabetes duration, age, sex, and BMI but remained normoalbuminuric (albumin-to-creatinine ratio <30 mg/g) over the same 10 years. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was performed on baseline urine samples, and training (14 cases:14 controls) and validation (17:17) sets were tested.
RESULTS: At baseline, A1C levels differed between case and control subjects. SELDI-TOF MS detected 714 unique urine protein peaks. Of these, a 12-peak proteomic signature correctly predicted 89% of cases of diabetic nephropathy (93% sensitivity, 86% specificity) in the training set. Applying this same signature to the independent validation set yielded an accuracy rate of 74% (71% sensitivity, 76% specificity). In multivariate analyses, the 12-peak signature was independently associated with subsequent diabetic nephropathy when applied to the validation set (odds ratio [OR] 7.9 [95% CI 1.5-43.5], P = 0.017) and the entire dataset (14.5 [3.7-55.6], P = 0.001), and A1C levels were no longer significant.
CONCLUSIONS: Urine proteomic profiling identifies normoalbuminuric subjects with type 2 diabetes who subsequently develop diabetic nephropathy. Further studies are needed to characterize the specific proteins involved in this early prediction.