Yan Guo1, Hui Yu2, Danqian Chen3, Ying-Yong Zhao4. 1. Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA. yanguo1978@gmail.com. 2. Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA. 3. 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. 4. 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.
Abstract
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.
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 CKDpatients 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.
Authors: Josef Coresh; Elizabeth Selvin; Lesley A Stevens; Jane Manzi; John W Kusek; Paul Eggers; Frederick Van Lente; Andrew S Levey Journal: JAMA Date: 2007-11-07 Impact factor: 56.272
Authors: Bing Yu; Yan Zheng; Jennifer A Nettleton; Danny Alexander; Josef Coresh; Eric Boerwinkle Journal: Clin J Am Soc Nephrol Date: 2014-07-10 Impact factor: 8.237