Evidence map›Paper›PMID 40649926›Full record

ArticleInternational journal of molecular sciences2025

Metabolomic Profiles During and After a Hypertensive Disorder of Pregnancy: The EPOCH Study.

Mark A Hlatky, Chi-Hung Shu, Nasim Bararpour, Brenna M Murphy, Sabina M Sorondo, Nicholas J Leeper, Frank Wong, David K Stevenson, Gary M Shaw, Marcia L Stefanick and 7 more

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

17 authors.

Mark A HlatkyDepartment of Health Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-4686-9441
Chi-Hung ShuDepartment of Anesthesia, Stanford University School of Medicine, Stanford, CA 94305, USA.
Nasim BararpourDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Brenna M MurphyDepartment of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Sabina M SorondoDepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
Nicholas J LeeperDepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-0905-2806
Frank WongDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0009-0001-7640-2693
David K StevensonDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Gary M ShawDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Marcia L StefanickDepartment of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Heather A BoydDepartment of Epidemiology Research, Statens Serum Institut, Copenhagen 2300, Denmark.ORCID 0000-0001-6849-9985
Mads MelbyeDanish Cancer Institute, and Department of Clinical Medicine, University of Copenhagen, Copenhagen 2200, Denmark.ORCID 0000-0001-8264-6785
Oshra SedanDepartment of Health Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.
Ronald J WongDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-1205-6936
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-0784-7987
Nima AghaeepourDepartment of Anesthesia, Stanford University School of Medicine, Stanford, CA 94305, USA.
Virginia D WinnDepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-1136-2907

Funding

NIH HHS 1R01HL139844-04
6 · The paper itself

Abstract

Hypertensive disorders of pregnancy are associated with a higher risk of later cardiovascular disease, but the mechanistic links are unknown. We recruited two groups of women, one during pregnancy and another at least two years after delivery, including both cases (with a hypertensive disorder of pregnancy) and controls (with a normotensive pregnancy). We measured metabolites using liquid chromatography-mass spectroscopy and applied machine learning to identify metabolomic signatures at three time points: antepartum, postpartum, and mid-life. The mean ages of the pregnancy cohort (58 cases, 46 controls) and the mid-life group (71 cases, 74 controls) were 33.8 and 40.8 years, respectively. The levels of 157 metabolites differed significantly between the cases and the controls antepartum, including 19 acylcarnitines, 12 gonadal steroids, 11 glycerophospholipids, nine fatty acids, six vitamin D metabolites, and four corticosteroids. The machine learning model developed using all antepartum metabolite levels discriminated well between the cases and the controls antepartum (c-index = 0.96), postpartum (c-index = 0.63), and in mid-life (c-index = 0.60). Levels of 10,20-dihydroxyeicosanoic acid best distinguished the cases from the controls both antepartum and postpartum. These data suggest that the pattern of differences in metabolites found antepartum continues to distinguish women who had a hypertensive disorder of pregnancy from women with a normotensive pregnancy for years after delivery.

Indexed as

Hypertension, Pregnancy-InducedMetabolomeMetabolomicsAdultCase-Control StudiesFemaleHumansMachine LearningPostpartum PeriodPregnancycase–control studygestational hypertensionmachine learningmetabolomicspreeclampsia

Identifiers

PMID40649926
PMCPMC12250391

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.