Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women.
delivery prediction
gestational age
human pregnancy
longitudinal profiling
machine learning
metabolic clock
metabolic pathways
metabolomics
Journal
Cell
ISSN: 1097-4172
Titre abrégé: Cell
Pays: United States
ID NLM: 0413066
Informations de publication
Date de publication:
25 06 2020
25 06 2020
Historique:
received:
04
09
2018
revised:
11
03
2020
accepted:
29
04
2020
entrez:
27
6
2020
pubmed:
27
6
2020
medline:
22
1
2021
Statut:
ppublish
Résumé
Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities.
Identifiants
pubmed: 32589958
pii: S0092-8674(20)30564-X
doi: 10.1016/j.cell.2020.05.002
pmc: PMC7327522
pii:
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1680-1692.e15Subventions
Organisme : NIDDK NIH HHS
ID : P30 DK116074
Pays : United States
Informations de copyright
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Interests M.S. is a co-founder and member of the scientific advisory boards of the following: Personalis, SensOmics, Filtricine, Qbio, January, Mirvie, and Oralome. He is a member of the scientific advisory board of Jungla. M.M. is a co-founder of Mirvie. L.L., M.S., and M.M. are inventors on the patent application PCT/US2019/052515 related to this work.
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