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Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women.
Liang, Liang; Rasmussen, Marie-Louise Hee; Piening, Brian; Shen, Xiaotao; Chen, Songjie; Röst, Hannes; Snyder, John K; Tibshirani, Robert; Skotte, Line; Lee, Norman Cy; Contrepois, Kévin; Feenstra, Bjarke; Zackriah, Hanyah; Snyder, Michael; Melbye, Mads.
Affiliation
  • Liang L; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Rasmussen MH; Department of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark.
  • Piening B; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Shen X; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Chen S; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Röst H; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Snyder JK; Department of Chemistry and the Chemical Instrumentation Center, Boston University, Boston, Massachusetts 02215, USA.
  • Tibshirani R; Department of Statistics and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
  • Skotte L; Department of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark.
  • Lee NC; Department of Chemistry and the Chemical Instrumentation Center, Boston University, Boston, Massachusetts 02215, USA.
  • Contrepois K; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Feenstra B; Department of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark.
  • Zackriah H; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
  • Snyder M; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: mpsnyder@stanford.edu.
  • Melbye M; Department of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, 2200, Denmark; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: mmelbye@stanford.
Cell ; 181(7): 1680-1692.e15, 2020 06 25.
Article in En | MEDLINE | ID: mdl-32589958
ABSTRACT
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pregnancy / Gestational Age / Metabolomics Type of study: Prognostic_studies / Risk_factors_studies Limits: Adult / Female / Humans Language: En Journal: Cell Year: 2020 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pregnancy / Gestational Age / Metabolomics Type of study: Prognostic_studies / Risk_factors_studies Limits: Adult / Female / Humans Language: En Journal: Cell Year: 2020 Type: Article Affiliation country: United States