Evidence map›Paper›PMID 39621510›Full record

ArticleJournal of cachexia, sarcopenia and muscle2025

Skeletal Muscle mRNA Splicing Variants Association With Four Different Fitness and Energetic Measures in the GESTALT Study.

Stefano Donega, Nirad Banskota, Esha Gupta, Marta Gonzalez-Freire, Ann Zenobia Moore, Ceereena Ubaida-Mohien, Rachel Munk, Linda Zukley, Yulan Piao, Chris Bergeron and 14 more

Abstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  4. Review
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  8. Review
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

24 authors.

Stefano DonegaLongitudinal Studies Section (LSS), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.ORCID 0000-0003-0669-1618
Nirad BanskotaLaboratory of Genetics and Genomics (LGG), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Esha GuptaLaboratory of Genetics and Genomics (LGG), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Marta Gonzalez-FreireTranslational Research in Aging and Longevity Group (TRIAL group), Fundació Institut d'Investigació Sanitària Illes Balears (IdISBa), Palma de Mallorca, Spain.
Ann Zenobia MooreLongitudinal Studies Section (LSS), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Ceereena Ubaida-MohienLongitudinal Studies Section (LSS), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Rachel MunkLaboratory of Genetics and Genomics (LGG), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Linda ZukleyClinical Research Core (CRC), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Yulan PiaoLaboratory of Genetics and Genomics (LGG), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Chris BergeronClinical Research Core (CRC), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Jan BergeronClinical Research Core (CRC), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Arsun BektasClinical Research Core (CRC), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Marta ZampinoLongitudinal Studies Section (LSS), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Carole StaggConfocal Imaging Facility, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Fred IndigConfocal Imaging Facility, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Lisa M HartnellLongitudinal Studies Section (LSS), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Mary KailehClinical Research Core (CRC), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Kenneth FishbeinClinical Research Core (CRC), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Richard G SpencerLaboratory of Clinical Investigation, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Myriam GorospeLaboratory of Genetics and Genomics (LGG), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Supriyo DeLaboratory of Genetics and Genomics (LGG), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Josephine M EganClinical Research Core (CRC), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Ranjan SenLaboratory of Molecular Biology and Immunology (LMBI), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.
Luigi FerrucciLongitudinal Studies Section (LSS), National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA.ORCID 0000-0002-6273-1613

Funding

Intramural Research Program of the National Institute on Aging, NIH
6 · The paper itself

Abstract

backgroundPhysical activity is essential for maintaining muscle mitochondrial function and aerobic capacity. The molecular mechanisms underlying such protective effects are incompletely understood, in part because it is difficult to separate the effects of disease status and physical activity. We explored the association of human skeletal muscle transcriptomic with four measures of energetics and mitochondria oxidative capacity in healthy individuals.

methodsUsing RNA sequencing of vastus lateralis muscle biopsies from 82 GESTALT participants (52 males, aged 22-89 years), we explored gene and splicing variant expression profiles associated with self-reported physical activity, peak oxygen consumption (VO

resultsThe four measures of energetics were negative correlated with age and generally intercorrelated. We identified protein-coding genes associated with four energetic measures adjusting for age, muscle fiber-ratio, sex and batch effect. Mitochondrial pathways were overrepresented across all energetic variables, albeit with little overlap at the gene level. Alternative spliced transcript isoforms associated with energetics were primarily enriched for cytoplasmic ribonucleoprotein granules. The splicing pathway was up-regulated with aging in low but not in high fitness participants, and transcript isoforms detected in the low fitness group pertain to processes such as cell cycle regulation, RNA/protein localization, nuclear transport and catabolism.

conclusionsA consistent mitochondrial signature emerged across all energetic measures. Alternative splicing was enhanced in older, low fitness participants supporting the energy-splicing axis hypothesis. The identified splicing variants were enriched in pathways involving the accumulation of ribonucleoproteins in cytoplasmic granules, whose function remains unclear. Further research is needed to understand the function of these proteoforms in promoting adaptation to low energy availability.

Indexed as

Muscle, SkeletalRNA, MessengerAdultAgedAged, 80 and overEnergy MetabolismExerciseFemaleHumansMaleMiddle AgedRNA SplicingTranscriptomeYoung AdultRNA, Messengeragingalternative splicingenergyexercisekPCrmitochondria respirometrymusclephysical activityVO2

Identifiers

PMID39621510
PMCPMC11695105

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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.