Early diagnosis of diabetic nephropathy using protein pattern based on urinary biomarkers
10.3760/cma.j.issn.1009-9158.2009.10.005
- VernacularTitle:尿蛋白标志物模型早期诊断糖尿病肾病的临床应用
- Author:
Wei JIANG
;
Yongchang YANG
;
Daiwen XIAO
;
Bo HUANG
;
Qi HU
;
Wenfang HUANG
- Publication Type:Journal Article
- Keywords:
Diabetic nephropathies;
Proteinuria;
Spectrometry;
mass;
matrix-assisted laser desorption-ionization;
tau Proteins;
Protein array analysis;
Early diagnosis
- From:
Chinese Journal of Laboratory Medicine
2009;32(10):1101-1107
- CountryChina
- Language:Chinese
-
Abstract:
Objective To search for protein markers in urine from patients with diabetic nephropathy by proteomic method and discuss its clinical significance in laboratory diagnosis of diabetic nephropathy. Methods This study included 129 patients with diabetic nephropathy, 61 diabetes mellitus patients, and 102 healthy volunteers. The urinary protein profiles were obtained using surface-enhanced laser desorption-ionization time of flight mass spectrometry (SELDI-TOF-MS) and Au Chip (ProteinChip Gold Array). The differential peaks were screened by Biomaker Wizard software and the decision tree pattern was developed by Biomarker Patterns Software (BPS). The model was blindly tested to validate diagnostic efficiency. Some differentially expressed protein was preliminarily identified according to the molecular weight as compared with mass spectrometry data of standard proteins. Results Totally 40 distinguished protein peaks(t value: - 9.81-24.52, P < 0.05) were obtained after comparing the samples between diabetic nephropathy and the control groups. The peak with m/z 66 916 was automatically screened by BPS to develop decision tree pattern. The pattern was blindly tested and yielded a sensitivity of 98.7% (78/79) and a specificity of 98.2% (111/113). After we compared results from diabetic nephropathy with those from diabetes mellitus, twenty-four differential peaks were obtained in diabetic nephropathy (t value: -6.95-14.45,P < 0.05). The peaks with m/z 4 008, 11 619 and 66 916 were automatically screened by BPS to establish decision tree pattern. The model was blindly tested and yielded the sensitivity(129/129) and specificity(61/61) of 100%. After we compared our results with mass spectrometry data of standard proteins, the four differentially expressed proteins with m/z 11 619, 23 529, 66 916 and 79 378 were supposed to be β_2-microglobulin, α1-microglobulin, albumin and transfcrrin. Conclusion The preliminary results suggest that these SELDI-TOF and Au chip have the potential application value in identification of protein source and early diagnosis of diabetic nephropathy, and evaluation of renal injury.
