Machine learning distilled metabolite biomarkers for early stage renal injury.
Yan Guo, Hui Yu, Danqian Chen, Ying-Yong Zhao
Author Information
Yan Guo: Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA. yanguo1978@gmail.com. ORCID
Hui Yu: Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA.
Danqian Chen: Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Ministry of Education, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China.
Ying-Yong Zhao: Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Ministry of Education, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China. zyy@nwu.edu.cn.
INTRODUCTION: With chronic kidney disease (CKD), kidney becomes damaged overtime and fails to clean blood. Around 15% of US adults have CKD and nine in ten adults with CKD do not know they have it. OBJECTIVE: Early prediction and accurate monitoring of CKD may improve care and decrease the frequent progression to end-stage renal disease. There is an urgent demand to discover specific biomarkers that allow for monitoring of early-stage CKD, and response to treatment. METHOD: To discover such biomarkers, shotgun high throughput was applied to the detection of serum metabolites biomarker discovery for early stages of CKD from 703 participants. Ultra performance liquid chromatography coupled with high-definition mass spectrometry (UPLC-HDMS)-based metabolomics was used for the determination of 703 fasting serum samples from five stages of CKD patients and age-matched healthy controls. RESULTS AND CONCLUSION: We discovered a set of metabolite biomarkers using a series of classic and neural network based machine learning techniques. This set of metabolites can separate early CKD stage patents from normal subjects with high accuracy. Our study illustrates the power of machine learning methods in metabolite biomarker study.
