Yehong Yang1, Shuo Zhang1, Bin Lu1, Wei Gong1, Xuehong Dong1, Xiaoyan Song1, Weiwei Zhao1, Jiefeng Cui2, Yinkun Liu2, Renming Hu3. 1. Institute of Endocrinology and Diabetology, Department of Endocrinology, Huashan Hospital, Fudan University, Shanghai, 200040, P.R. China. 2. Proteome research section, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai, 200032, P.R. China. 3. , 12 Middle Wurumuqi Road, Shanghai, 200040, P.R. China. renminghu@fudan.edu.cn.
Abstract
PURPOSE: The purpose of this work is to examine the serum proteomic profiles associated with the subsequent development of diabetic nephropathy (DN) in patients with type 2 diabetes and to develop and validate a decision tree based on the profiles to predict the risk of DN in advance by albuminuria. METHODS: Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry was used to obtain the proteomic profiles from baseline serum samples of 84 patients with type 2 diabetes with normal albuminuria, including 42 case subjects who developed DN after 4 years and 42 control subjects who remained normoalbuminuric over the same 4 years. From signatures of protein mass, a decision tree was established for predicting DN. RESULTS: At baseline, urinary albumin/creatinine ratio was similar between the case and control groups. The intensities of 5 peaks detected by CM10 chips appeared up-regulated, whereas 18 peaks were down-regulated more than twofold in the case group than compared with the control group in the training set. An optimum discriminatory decision tree for case subjects created with four nodes using four distinct masses was challenged with testing set. The positive predictive value was 77.8% (7/9), and the negative predictive value was 72.7% (8/11). CONCLUSIONS: We developed and validated a decision tree to predict DN in patients with type 2 diabetes.
PURPOSE: The purpose of this work is to examine the serum proteomic profiles associated with the subsequent development of diabetic nephropathy (DN) in patients with type 2 diabetes and to develop and validate a decision tree based on the profiles to predict the risk of DN in advance by albuminuria. METHODS: Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry was used to obtain the proteomic profiles from baseline serum samples of 84 patients with type 2 diabetes with normal albuminuria, including 42 case subjects who developed DN after 4 years and 42 control subjects who remained normoalbuminuric over the same 4 years. From signatures of protein mass, a decision tree was established for predicting DN. RESULTS: At baseline, urinary albumin/creatinine ratio was similar between the case and control groups. The intensities of 5 peaks detected by CM10 chips appeared up-regulated, whereas 18 peaks were down-regulated more than twofold in the case group than compared with the control group in the training set. An optimum discriminatory decision tree for case subjects created with four nodes using four distinct masses was challenged with testing set. The positive predictive value was 77.8% (7/9), and the negative predictive value was 72.7% (8/11). CONCLUSIONS: We developed and validated a decision tree to predict DN in patients with type 2 diabetes.
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