Characterization of LC-MS based urine metabolomics in healthy children and adults

PeerJ. 2022 Jun 22:10:e13545. doi: 10.7717/peerj.13545. eCollection 2022.

Abstract

Previous studies reported that sex and age could influence urine metabolomics, which should be considered in biomarker discovery. As a consequence, for the baseline of urine metabolomics characteristics, it becomes critical to avoid confounding effects in clinical cohort studies. In this study, we provided a comprehensive lifespan characterization of urine metabolomics in a cohort of 348 healthy children and 315 adults, aged 1 to 78 years, using liquid chromatography coupled with high resolution mass spectrometry. Our results suggest that sex-dependent urine metabolites are much greater in adults than in children. The pantothenate and CoA biosynthesis and alanine metabolism pathways were enriched in early life. Androgen and estrogen metabolism showed high activity during adolescence and youth stages. Pyrimidine metabolism was enriched in the geriatric stage. Based on the above analysis, metabolomic characteristics of each age stage were provided. This work could help us understand the baseline of urine metabolism characteristics and contribute to further studies of clinical disease biomarker discovery.

Keywords: Adults; Characterization; Children; Metabolomics; Urine.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Biomarkers / metabolism
  • Body Fluids* / metabolism
  • Child
  • Chromatography, Liquid / methods
  • Humans
  • Metabolomics / methods
  • Tandem Mass Spectrometry*

Substances

  • Biomarkers

Grants and funding

This work was funded by the National Natural Science Foundation of China (No. 82170524, 31901039), the Beijing Medical Research (No. 2018-7), the CAMS Innovation Fund for Medical Sciences (2021-1-I2M-016), the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority (XTCX201815), the Beihang University and Capital Medical University Advanced Innovation Centre for Big Data-Based Precision Medicine Plan (BHME-201910) and Beijing Talents Fund (2018000021469G278). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.