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Machine learning distilled metabolite biomarkers for early stage renal injury

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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.

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Funding

This study was supported by the National Natural Science Foundation of China (Nos. 81872985, 81673578). No funding bodies had any role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA

    Yan Guo & Hui Yu

  2. 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

    Danqian Chen & Ying-Yong Zhao

Authors
  1. Yan Guo
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  2. Hui Yu
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  3. Danqian Chen
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  4. Ying-Yong Zhao
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Contributions

YG and ZYY design and wrote the manuscript, DQC and YH performed data analysis.

Corresponding authors

Correspondence to Yan Guo or Ying-Yong Zhao.

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The authors claim that they do not have any conflict of interest.

Ethical approval

The part of patient study was approved by the Ethical Committee and all patients provided informed consent prior to entering the study. The present study has complied with all relevant ethical regulations. The sample collection was approved Shaanxi Traditional Chinese Medicine Hospital (Permit Number: SXSY-235610).

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Guo, Y., Yu, H., Chen, D. et al. Machine learning distilled metabolite biomarkers for early stage renal injury. Metabolomics 16, 4 (2020). https://doi.org/10.1007/s11306-019-1624-0

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