Analysis of urinary proteomic patterns for diabetic nephropathy by ProteinChip

Abstract

AbstractDiabetic nephropathy (DN) is the main cause of mortality for diabetic patients. The objective of this work was to develop a proteomic approach to detect proteins or peptides in urine for identifying individuals in the early stage of DN. We obtained urine samples from 106 diabetic patients and 50 healthy subjects. Early stage of DN was defined as urine albumin‐to‐creatinine ratio between 30 to 299 mg/g. Mass spectra were generated using surface‐enhanced laser desorption/ionization time‐of‐flight mass spectrometry. Peaks were detected by Ciphergen SELDI software version 3.1. Over 1000 proteins or peptides were obtained using ProteinChip. About 200 of them, the m/z values were in the range from 1008.5 to 79 942.3 Da. These values were significantly differentiated between diabetic patients and contro1 subjects. A mathematical analysis revealed that a cluster of 8 up‐regulated proteins and 16 down‐regulated proteins was in the diabetic patients, with m/z values from 2197.3 to 79 613 Da. Four top‐ranked proteins, with m/z values of 4139.0, 4453.5, 5281.1, and 5898.5 Da, were selected as the potential fingerprints for detection of early stage DN with a sensitivity of 75% and a specificity of 80%. ProteinChip technology may be a novel non‐invasive method for detecting early stage DN.