Longitudinal urine metabolic profiling and gestational age prediction in human pregnancy. Brief Bioinform 2024 Nov 22;26(1)
Date
02/16/2025Pubmed ID
39955767Pubmed Central ID
PMC11830194DOI
10.1093/bib/bbaf059Scopus ID
2-s2.0-85218972181 (requires institutional sign-in at Scopus site)Abstract
Pregnancy is a vital period affecting both maternal and fetal health, with impacts on maternal metabolism, fetal growth, and long-term development. While the maternal metabolome undergoes significant changes during pregnancy, longitudinal shifts in maternal urine have been largely unexplored. In this study, we applied liquid chromatography-mass spectrometry-based untargeted metabolomics to analyze 346 maternal urine samples collected throughout pregnancy from 36 women with diverse backgrounds and clinical profiles. Key metabolite changes included glucocorticoids, lipids, and amino acid derivatives, indicating systematic pathway alterations. We also developed a machine learning model to accurately predict gestational age using urine metabolites, offering a non-invasive pregnancy dating method. Additionally, we demonstrated the ability of the urine metabolome to predict time-to-delivery, providing a complementary tool for prenatal care and delivery planning. This study highlights the clinical potential of urine untargeted metabolomics in obstetric care.
Author List
Shen X, Chen S, Liang L, Avina M, Zackriah H, Jelliffe-Pawlowski L, Rand L, Snyder MPAuthor
Liang Liang PhD Assistant Professor in the Obstetrics and Gynecology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AdultBiomarkers
Chromatography, Liquid
Female
Gestational Age
Humans
Longitudinal Studies
Machine Learning
Metabolome
Metabolomics
Pregnancy
