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

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; Mads Melbye

doi:10.1016/j.cell.2020.05.002

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

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^*, Mads Melbye

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

206 Citationer (Scopus)

51 Downloads (Pure)

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.

Originalsprog	Engelsk
Tidsskrift	Cell
Vol/bind	181
Udgave nummer	7
Sider (fra-til)	1680-1692.e15
Antal sider	29
ISSN	0092-8674
DOI	https://doi.org/10.1016/j.cell.2020.05.002
Status	Udgivet - 2020

Adgang til dokumentet

10.1016/j.cell.2020.05.002Licens: CC BY

Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant WomenForlagets udgivne version, 3,06 MBLicens: CC BY

Citationsformater

Liang, L., Rasmussen, M. L. H., Piening, B., Shen, X., Chen, S., Röst, H., Snyder, J. K., Tibshirani, R., Skotte, L., Lee, N. CY., Contrepois, K., Feenstra, B., Zackriah, H., Snyder, M., & Melbye, M. (2020). Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women. Cell, 181(7), 1680-1692.e15. https://doi.org/10.1016/j.cell.2020.05.002

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title = "Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women",

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.",

keywords = "delivery prediction, gestational age, human pregnancy, longitudinal profiling, machine learning, metabolic clock, metabolic pathways, metabolomics",

author = "Liang Liang and Rasmussen, {Marie Louise Hee} and Brian Piening and Xiaotao Shen and Songjie Chen and Hannes R{\"o}st and Snyder, {John K.} and Robert Tibshirani and Line Skotte and Lee, {Norman CY} and K{\'e}vin Contrepois and Bjarke Feenstra and Hanyah Zackriah and Michael Snyder and Mads Melbye",

year = "2020",

doi = "10.1016/j.cell.2020.05.002",

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T1 - Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women

AU - Liang, Liang

AU - Rasmussen, Marie Louise Hee

AU - Piening, Brian

AU - Shen, Xiaotao

AU - Chen, Songjie

AU - Röst, Hannes

AU - Snyder, John K.

AU - Tibshirani, Robert

AU - Skotte, Line

AU - Lee, Norman CY

AU - Contrepois, Kévin

AU - Feenstra, Bjarke

AU - Zackriah, Hanyah

AU - Snyder, Michael

AU - Melbye, Mads

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - delivery prediction

KW - gestational age

KW - human pregnancy

KW - longitudinal profiling

KW - machine learning

KW - metabolic clock

KW - metabolic pathways

KW - metabolomics

U2 - 10.1016/j.cell.2020.05.002

DO - 10.1016/j.cell.2020.05.002

M3 - Journal article

C2 - 32589958

AN - SCOPUS:85086798738

SN - 0092-8674

VL - 181

SP - 1680-1692.e15

JO - Cell

JF - Cell

IS - 7

ER -