Evidence map›Paper›PMID 32589958›Full record

ArticleCell2020

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 and 5 more

Abstract read
In one paragraph

Article in Cell, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
181citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

181 citing papers in PubMed, 1 synthesis or guideline pooled it.

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121 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Liang LiangDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Marie-Louise Hee RasmussenDepartment of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark.
Brian PieningDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Xiaotao ShenDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Songjie ChenDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Hannes RöstDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
John K SnyderDepartment of Chemistry and the Chemical Instrumentation Center, Boston University, Boston, Massachusetts 02215, USA.
Robert TibshiraniDepartment of Statistics and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Line SkotteDepartment of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark.
Norman Cy LeeDepartment of Chemistry and the Chemical Instrumentation Center, Boston University, Boston, Massachusetts 02215, USA.
Kévin ContrepoisDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Bjarke FeenstraDepartment of Epidemiology Research, Statens Serum Institut, Copenhagen, 2300, Denmark.
Hanyah ZackriahDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
Michael SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: mpsnyder@stanford.edu.
Mads MelbyeDepartment 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.edu.

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
NIDDK NIH HHS P30 DK116074
6 · The paper itself

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.

Indexed as

Gestational AgeAdultBiomarkersFemaleFetusHumansMetabolic Networks and PathwaysMetabolomeMetabolomicsPregnancyPregnant PeopleBiomarkersdelivery predictiongestational agehuman pregnancylongitudinal profilingmachine learningmetabolic clockmetabolic pathwaysmetabolomics

Identifiers

PMID32589958
PMCPMC7327522

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.