Evidence map›Paper›PMID 40593273›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2025

Plasma and ovarian metabolomic responses to chronic stress in female mice.

Nan Lin, Tianyi Huang, Chirag J Patel, Elizabeth M Poole, Clary B Clish, Guillermo N Armaiz-Pena, Archana S Nagaraja, A Heather Eliassen, Katherine H Shutta, Raji Balasubramanian and 5 more

Abstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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.

Nan Lin *Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. nlin6@bwh.harvard.edu.
Tianyi Huang *Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Elizabeth M PooleJazz Pharmaceuticals, Philadelphia, PA, USA.
Clary B ClishBroad Institute of the Massachusetts Institute of Technology, Harvard University, Cambridge, MA, USA.
Guillermo N Armaiz-PenaDivision of Pharmacology, Department of Basic Sciences, School of Medicine, Ponce Health Sciences University, Ponce, PR, USA.
Archana S NagarajaDepartment of Gynecologic Oncology & Reproductive Medicine, UT MD Anderson Cancer Center, Houston, TX, USA.
A Heather EliassenChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Katherine H ShuttaChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Raji BalasubramanianDepartment of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA.
Laura D KubzanskyDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Susan E HankinsonDepartment of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA.
Oana A Zeleznik *Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Anil K Sood *Department of Gynecologic Oncology & Reproductive Medicine, UT MD Anderson Cancer Center, Houston, TX, USA.
Shelley S Tworoger *Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
SYSTEMS APPROACHES TO THE EPIDEMIOLOGY, GENETICS AND GENOMICS OF LUNG DISEASEST32HL007427 · NHLBI · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI DAWN L DEMEO, Edwin K Silverman · 1985 to 2026
$13.6M
U.T. M. D. Anderson Cancer Center SPORE in Ovarian CancerP50CA217685 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI SOOD, ANIL K · 2017 to 2021
$7.9M
Harnessing the power of exosomes for non-coding RNA deliveryR35CA209904 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI SOOD, ANIL K · 2017 to 2023
$5.0M
Metabolomic profile of chronic distress in relation to diseases of aging across diverse populationsR01AG051600 · NIA · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Susan E Hankinson, LAURA D KUBZANSKY · 2017 to 2026
$4.9M
Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5M
Psychological stress, associate biologic mediators, and ovarian cancer riskR01CA163451 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI TWOROGER, SHELLEY S · 2012 to 2015
$1.7M
Elucidating Inflammatory and Metabolic Pathways in Obstructive Sleep Apnea DevelopmentK01HL143034 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI HUANG, TIANYI · 2018 to 2022
$885k
NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA217685NCI NIH HHS R01 CA163451NCI NIH HHS R35 CA209904NHLBI NIH HHS K01 HL143034NHLBI NIH HHS K01HL143034NHLBI NIH HHS T32 HL007427NIA NIH HHS R01 AG051600NIA NIH HHS R01AG051600NIEHS NIH HHS R01 ES032470NIEHS NIH HHS R01ES032470NIH HHS CA163451NIH HHS P50CA217685
6 · The paper itself

Abstract

introductionChronic stress has been linked with higher risk of ovarian cancer and one posited pathway is through altered metabolism of amino acids, lipids, and other small molecule metabolites. However, the types of alterations that occur may not be uniform across tissue types.

objectivesWe aim to examine and compare the impacts of chronic stress on metabolomic changes in circulation and ovarian tissue.

methodsTwelve-week-old, healthy, female, C57 black mice were randomly assigned to three-week of chronic stress using daily restraint (2-hours/day; n = 9) or normal care (n = 10). Metabolomic profiling was conducted on plasma and ovarian tissues via mass spectrometry. We utilized Wilcoxon Rank Tests, Metabolite Set Enrichment Analysis, Differential Network Analysis and a previously derived metabolite-based distress score to identify metabolomic alterations under restraint stress. We used the false discovery rate to account for testing multiple correlated comparisons.

resultsIn plasma, individual lysophosphatidylcholines and the metabolite class carnitines were positively associated while diacylglycerols and triacylglycerols were inversely associated with restraint stress (adjusted-p < 0.2). In contrast, in ovarian tissue, diacylglycerols and triacylglycerols were positively associated while carnitines were inversely associated with restraint stress (adjusted-p < 0.2). Other metabolites (cholesteryl esters, phosphatidylcholines/ phosphatidylethanolamines plasmalogens and multiple amino acids) were inversely associated with restraint stress in both plasma and ovarian tissue (adjusted-p < 0.2). A previously developed human metabolite-based distress score was higher in restraint stress mice compared to controls, with a larger difference observed in ovarian tissue than in plasma.

conclusionThese findings suggest research to understand the metabolic impact of chronic stress needs to consider both systemic and tissue-specific alterations.

Indexed as

MetabolomeMetabolomicsOvaryStress, PhysiologicalStress, PsychologicalAnimalsFemaleMiceMice, Inbred C57BLChronic stressFemale miceMetabolitesOvarian tissuePlasma

Identifiers

PMID40593273
PMCPMC12593224

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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.