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Open AccessArticle

Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction

1
Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
2
Department of Internal Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Korea
3
Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul 05505, Korea
4
Convergence Medicine Research Center, Asan Institute for Life Sciences, Seoul 05505, Korea
5
Clinical Proteomics Core Laboratory, Convergence Medicine Research Center, Asan Medical Center, Seoul 05505, Korea
6
Bio-Medical Institute of Technology, Asan Medical Center, Seoul 05505, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2020, 21(12), 4236; https://doi.org/10.3390/ijms21124236
Received: 3 April 2020 / Revised: 5 June 2020 / Accepted: 12 June 2020 / Published: 14 June 2020
(This article belongs to the Special Issue Biomarkers of Renal Diseases)
Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): p-value < 0.05), but none showed significantly better performance than albumin. To improve the predictive power by multivariate analysis, five proteins (ACP2, CTSA, GM2A, MUC1, and SPARCL1) were selected as significant by an AUC-based random forest method. The application of two classifiers—support vector machine and random forest—showed that the multivariate model performed better than univariate analysis of mucin-1 (AUC: 0.935 vs. 0.791) and albumin (AUC: 1.0 vs. 0.722). The urinary proteome can reflect kidney function directly and can predict the prognosis of patients with chronic kidney dysfunction. Classification based on five urinary proteins may better predict the prognosis of DKD patients than urinary albumin concentration or eGFR. View Full-Text
Keywords: urine; diabetic kidney disease; kidney function; proteomics; mass spectrometry; statistical clinical model; machine learning urine; diabetic kidney disease; kidney function; proteomics; mass spectrometry; statistical clinical model; machine learning
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Ahn, H.-S.; Kim, J.H.; Jeong, H.; Yu, J.; Yeom, J.; Song, S.H.; Kim, S.S.; Kim, I.J.; Kim, K. Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction. Int. J. Mol. Sci. 2020, 21, 4236.

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