Global metabolic profiling to model biological processes of aging in twins
File(s)
Author(s)
Bunning, Bryan J
Contrepois, Kevin
Lee-McMullen, Brittany
Dhondalay, Gopal Krishna R
Zhang, Wenming
Type
Journal Article
Abstract
Aging is intimately linked to system-wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross-sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long-chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R2 = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out-of-bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age-related variability in metabolic profiles in response to environmental exposure.
Date Issued
2020-01
Date Acceptance
2019-10-29
Citation
Aging Cell, 2020, 19 (1)
ISSN
1474-9718
Publisher
Wiley
Journal / Book Title
Aging Cell
Volume
19
Issue
1
Copyright Statement
© 2019 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000509956800004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
ADULTS
AGE
aging
Cell Biology
CREATININE
Geriatrics & Gerontology
LC-MS
Life Sciences & Biomedicine
machine learning
MARKERS
MEN
metabolomics
OMICS
PATHWAYS
random forest
Science & Technology
TESTOSTERONE
TRENDS
twins
Publication Status
Published
Article Number
e13073
Date Publish Online
2019-11-19