Evidence map›Paper›PMID 34108459›Full record

SynthesisNature communications2021

Time trajectories in the transcriptomic response to exercise - a meta-analysis.

David Amar, Malene E Lindholm, Jessica Norrbom, Matthew T Wheeler, Manuel A Rivas, Euan A Ashley

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 4 pooled it
12.1field-weighted citation impact, top 1% of its field
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

69 citing papers in PubMed, 4 syntheses or guidelines pooled it, 112 citations in OpenAlex.

  1. Pooled it
  2. PROKR1-CREB-NR4A2 axis for oxidative muscle fiber specification and improvement of metabolic function.Proceedings of the National Academy of Sciences of the United States of America · 2024
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  5. Trial
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  7. Effects of exercise on whole-blood transcriptome profile in children with overweight/obesity.American journal of human biology : the official journal of the Human Biology Council · 2024
    Trial
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9 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

6 authors at 3 institutions in 2 countries.

David Amar *Center for Inherited Cardiovascular Disease, School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0002-9719-0837
Malene E Lindholm *Center for Inherited Cardiovascular Disease, School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0002-5763-7833
Jessica NorrbomDepartment of Physiology & Pharmacology, Karolinska Institutet, Stockholm, Sweden.
Matthew T WheelerCenter for Inherited Cardiovascular Disease, School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0001-8721-3022
Manuel A RivasDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0003-1457-9925
Euan A AshleyCenter for Inherited Cardiovascular Disease, School of Medicine, Stanford University, Stanford, CA, USA. euan@stanford.edu.ORCID 0000-0001-9418-9577
Stanford University · USKarolinska Institutet · SEStanford Medicine · US

Funding

Stanford MoTrPAC Bioinformatics CenterU24OD026629 · OD · STANFORD UNIVERSITY · PI ASHLEY, EUAN A, WHEELER, MATTHEW THOMAS · 2018 to 2023
$13.9M
NIH HHS U24 OD026629
6 · The paper itself

Abstract

Exercise training prevents multiple diseases, yet the molecular mechanisms that drive exercise adaptation are incompletely understood. To address this, we create a computational framework comprising data from skeletal muscle or blood from 43 studies, including 739 individuals before and after exercise or training. Using linear mixed effects meta-regression, we detect specific time patterns and regulatory modulators of the exercise response. Acute and long-term responses are transcriptionally distinct and we identify SMAD3 as a central regulator of the exercise response. Exercise induces a more pronounced inflammatory response in skeletal muscle of older individuals and our models reveal multiple sex-associated responses. We validate seven of our top genes in a separate human cohort. In this work, we provide a powerful resource ( www.extrameta.org ) that expands the transcriptional landscape of exercise adaptation by extending previously known responses and their regulatory networks, and identifying novel modality-, time-, age-, and sex-associated changes.

Indexed as

TranscriptomeAdaptation, PhysiologicalAge FactorsEndurance TrainingExerciseExtracellular Matrix ProteinsGene Regulatory NetworksHumansInflammationMuscle, SkeletalReproducibility of ResultsResistance TrainingSmad3 ProteinSystems BiologyTime FactorsExtracellular Matrix ProteinsSmad3 ProteinSMAD3 protein, human

Identifiers

PMID34108459
PMCPMC8190306
OpenAlexW3168414855

What OpenQuestion 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 OpenQuestion graph.