Longitudinal Urine Metabolic Profiling and Gestational Age Prediction in Pregnancy

Abstract

Pregnancy is a critical time that has long-term impacts on both maternal and fetal health. During pregnancy, the maternal metabolome undergoes dramatic systemic changes, although correlating longitudinal changes in maternal urine remain largely unexplored. We applied an LCMS-based untargeted metabolomics profiling approach to analyze 346 longitudinal maternal urine samples collected throughout pregnancy for 36 women from diverse ethnic backgrounds with differing clinical characteristics. We detected 20,314 metabolic peaks and annotated 875 metabolites. Altered metabolites include a broad panel of glucocorticoids, lipids, and amino acid derivatives, which revealed systematic pathway alterations during pregnancy. We also developed a machine-learning model to precisely predict gestational age (GA) at time of sampling using urine metabolites that provides a non-invasive method for pregnancy dating. This longitudinal maternal urine study demonstrates the clinical utility of using untargeted metabolomics in obstetric settings.

Publication
bioRxiv
Dr. Songjie Chen
Dr. Songjie Chen
Research Scientist
Xiaotao SHEN/申小涛
Xiaotao SHEN/申小涛
Nanyang Assistant Professor

Metabolomics, Multi-omics, Bioinformatics, Systems Biology.

Dr. Liang Liang
Dr. Liang Liang
Research Scientist