Urine proteomics identifies biomarkers for diabetic kidney disease at different stages

Clinical Proteomics

Xing Yang¹  Tongqing Gong¹  Lifeng Liu¹  Haibo Liu¹  Xinliang Li¹  Yi Wang²  Jun Qin²  Lan Song²  Xiaotian Ni²  Jianping Wang³  Mingze Bai³  Yuan Zhang⁴  Anxiang Li⁴  Xianyu Tang⁴  Jinzhu Huang⁴  Lu Sun⁴  Dongyin Zou⁴  Liu He⁴  Yaqing Xia⁴  Lulu Wu⁴  Haoyue Huang⁴  Guanjie Fan⁴  Hua Wei⁴  Liyan Wu⁴  Yuping Lin⁴  Lulu Luo⁴  Jianxuan Wen⁴  Qingshun Liang⁴  Guowei Chen⁴  Xiuming Li⁴  Qiyun Lu⁴  Ling Zhao⁴  Qubo Chen⁴  Wenwen Xie⁴  Zhenjie Liu⁴  Jiali He⁴ 

[1] Beijing Pineal Health Management Co., Ltd, 102206, Beijing, China;State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Proteome Research Center, Institute of Lifeomics, 102206, Beijing, China;State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Proteome Research Center, Institute of Lifeomics, 102206, Beijing, China;Chongqing Key Laboratory of Big Data for Bio Intelligence, School of Bioinformation, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China;The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 510120, Guangzhou, China;The Second Clinical College of Guangzhou, University of Chinese Medicine, 510120, Guangzhou, China;Guangdong Provincial Hospital of Chinese Medicine, 510120, Guangzhou, China;Guangdong Provincial Academy of Chinese Medical Sciences, 510120, Guangzhou, China;

关键词: Urine;    Proteomics;    DKD;    Progression monitoring;   

DOI  :  10.1186/s12014-021-09338-6

来源: Springer

【 摘 要 】

BackgroundType 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the ‘gold standard’ for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress.MethodsIn this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs.ResultsWe quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3.ConclusionsOur study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.

【 授权许可】

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