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Liang L, Rasmussen MLH, Piening B, Shen X, Chen S, Röst H, Snyder JK, Tibshirani R, Skotte L, Lee NC, Contrepois K, Feenstra B, Zackriah H, Snyder M, Melbye M. Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women. Cell 2021; 181:1680-1692.e15. [PMID: 32589958 PMCID: PMC7327522 DOI: 10.1016/j.cell.2020.05.002] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 03/11/2020] [Accepted: 04/29/2020] [Indexed: 01/10/2023]
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. Weekly metabolome of maternal blood changes dynamically through healthy pregnancy A metabolic clock of five blood metabolites accurately predicts gestational age Two to three metabolites identify labor onset within two, four, and eight weeks Women with metabolic clocks that outpaced ultrasound evaluation tend to deliver earlier
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Affiliation(s)
- Liang Liang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Brian Piening
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Songjie Chen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hannes Röst
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John K Snyder
- Department of Chemistry and the Chemical Instrumentation Center, Boston University, Boston, Massachusetts 02215, USA
| | - Robert Tibshirani
- Department of Statistics and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Line Skotte
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark
| | - Norman Cy Lee
- Department of Chemistry and the Chemical Instrumentation Center, Boston University, Boston, Massachusetts 02215, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark
| | - Hanyah Zackriah
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
| | - Mads Melbye
- 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.
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