Gestational Dating by Urine Metabolic Profile at High Resolution Weekly Sampling Timepoints: Discovery and Validation

Background: Pregnancy triggers longitudinal metabolic alterations in women to allow precisely-programmed fetal growth. Comprehensive characterization of such a “metabolic clock” of pregnancy may provide a molecular reference in relation to studies of adverse pregnancy outcomes. However, a high-resol...

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Vydáno v:Frontiers in Molecular Medicine Ročník 2; s. 844280
Hlavní autoři: Sylvester, Karl G., Hao, Shiying, Li, Zhen, Han, Zhi, Tian, Lu, Ladella, Subhashini, Wong, Ronald J., Shaw, Gary M., Stevenson, David K., Cohen, Harvey J., Whitin, John C., McElhinney, Doff B., Ling, Xuefeng B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 27.04.2022
Témata:
Molecular Medicine
date of delivery
metabolite
gestational age
urine
normal pregnancy
ISSN:2674-0095, 2674-0095
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Shrnutí:Background: Pregnancy triggers longitudinal metabolic alterations in women to allow precisely-programmed fetal growth. Comprehensive characterization of such a “metabolic clock” of pregnancy may provide a molecular reference in relation to studies of adverse pregnancy outcomes. However, a high-resolution temporal profile of metabolites along a healthy pregnancy remains to be defined. Methods: Two independent, normal pregnancy cohorts with high-density weekly urine sampling (discovery: 478 samples from 19 subjects at California; validation: 171 samples from 10 subjects at Alabama) were studied. Urine samples were profiled by liquid chromatography-mass spectrometry (LC-MS) for untargeted metabolomics, which was applied for gestational age dating and prediction of time to delivery. Results: 5,473 urinary metabolic features were identified. Partial least-squares discriminant analysis on features with robust signals ( n = 1,716) revealed that the samples were distributed on the basis of the first two principal components according to their gestational age. Pathways of bile secretion, steroid hormone biosynthesis, pantohenate, and CoA biosynthesis, benzoate degradation, and phenylpropanoid biosynthesis were significantly regulated, which was collectively applied to discover and validate a predictive model that accurately captures the chronology of pregnancy. With six urine metabolites (acetylcholine, estriol-3-glucuronide, dehydroepiandrosterone sulfate, α-lactose, hydroxyexanoy-carnitine, and l -carnitine), models were constructed based on gradient-boosting decision trees to date gestational age in high accordance with ultrasound results, and to accurately predict time to delivery. Conclusion: Our study characterizes the weekly baseline profile of the human pregnancy metabolome, which provides a high-resolution molecular reference for future studies of adverse pregnancy outcomes.
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This article was submitted to Bioinformatics and Artificial Intelligence for Molecular Medicine, a section of the journal Frontiers in Molecular Medicine
Sotiris Kotsiantis, University of Patras, Greece
Reviewed by: Fidele Tugizimana, Omnia, South Africa
Edited by: Alessandra Luchini, George Mason University, United States
ISSN:2674-0095
2674-0095
DOI:10.3389/fmmed.2022.844280