Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women

Research output: Contribution to journalJournal articlepeer-review

  • Liang Liang
  • Marie Louise Hee Rasmussen
  • Brian Piening
  • Xiaotao Shen
  • Songjie Chen
  • Hannes Röst
  • John K. Snyder
  • Robert Tibshirani
  • Line Skotte
  • Norman CY Lee
  • Kévin Contrepois
  • Bjarke Feenstra
  • Hanyah Zackriah
  • Michael Snyder
  • Melbye, Mads

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.

Original languageEnglish
Issue number7
Pages (from-to)1680-1692.e15
Number of pages29
Publication statusPublished - 2020

    Research areas

  • delivery prediction, gestational age, human pregnancy, longitudinal profiling, machine learning, metabolic clock, metabolic pathways, metabolomics

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