Evidence map›Paper›PMID 33921590›Full record

ArticleInternational journal of molecular sciences2021

Muscle Proteomic and Transcriptomic Profiling of Healthy Aging and Metabolic Syndrome in Men.

Marine Gueugneau, Cécile Coudy-Gandilhon, Christophe Chambon, Julien Verney, Daniel Taillandier, Lydie Combaret, Cécile Polge, Stéphane Walrand, Frédéric Roche, Jean-Claude Barthélémy and 2 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 15% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.

  1. Pooled it
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  10. Chaperone Proteins: The Rising Players in Muscle Atrophy.Journal of cachexia, sarcopenia and muscle · 2025
    Review
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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

12 authors at 3 institutions in 1 country.

Marine GueugneauUniversité Clermont Auvergne, INRAE, UNH, Unité de Nutrition Humaine, CRNH Auvergne, 63000 Clermont-Ferrand, France.
Cécile Coudy-GandilhonUniversité Clermont Auvergne, INRAE, UNH, Unité de Nutrition Humaine, CRNH Auvergne, 63000 Clermont-Ferrand, France.
Christophe ChambonMetabolomic and Proteomic Exploration Facility, Université Clermont Auvergne, INRAE, 63000 Clermont-Ferrand, France.ORCID 0000-0003-0357-9807
Julien VerneyLaboratoire AME2P, Université Clermont Auvergne, 3533 Clermont-Ferrand, France.
Daniel TaillandierUniversité Clermont Auvergne, INRAE, UNH, Unité de Nutrition Humaine, CRNH Auvergne, 63000 Clermont-Ferrand, France.
Lydie CombaretUniversité Clermont Auvergne, INRAE, UNH, Unité de Nutrition Humaine, CRNH Auvergne, 63000 Clermont-Ferrand, France.ORCID 0000-0001-6550-8700
Cécile PolgeUniversité Clermont Auvergne, INRAE, UNH, Unité de Nutrition Humaine, CRNH Auvergne, 63000 Clermont-Ferrand, France.ORCID 0000-0002-8730-5776
Stéphane WalrandUniversité Clermont Auvergne, INRAE, UNH, Unité de Nutrition Humaine, CRNH Auvergne, 63000 Clermont-Ferrand, France.
Frédéric RocheService de Physiologie Clinique et de l'Exercice, CHU Saint Etienne, 42055 Saint Etienne, France.
Jean-Claude BarthélémyService de Physiologie Clinique et de l'Exercice, CHU Saint Etienne, 42055 Saint Etienne, France.
Léonard FéassonUnité de Myologie, Service de Physiologie Clinique et de l'Exercice, Centre Référent Maladies Neuromusculaires Euro-NmD, 42000 CHU de Saint-Etienne, France.
Daniel BéchetUniversité Clermont Auvergne, INRAE, UNH, Unité de Nutrition Humaine, CRNH Auvergne, 63000 Clermont-Ferrand, France.ORCID 0000-0002-3812-8099
Université Clermont Auvergne · FRInserm · FRUniversité Claude Bernard Lyon 1 · FR

Funding

Caisse d'Epargne Rhone Alpes (CERA Sarcopenie n°30)European Commission MyoAge (EC Fp7 CT-223756)Fonds Européens de Développement Régional (FEDER)French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25)
6 · The paper itself

Abstract

(1) Background: Aging is associated with a progressive decline in muscle mass and function. Aging is also a primary risk factor for metabolic syndrome, which further alters muscle metabolism. However, the molecular mechanisms involved remain to be clarified. Herein we performed omic profiling to decipher in muscle which dominating processes are associated with healthy aging and metabolic syndrome in old men. (2) Methods: This study included 15 healthy young, 15 healthy old, and 9 old men with metabolic syndrome. Old men were selected from a well-characterized cohort, and each vastus lateralis biopsy was used to combine global transcriptomic and proteomic analyses. (3) Results: Over-representation analysis of differentially expressed genes (ORA) and functional class scoring of pathways (FCS) indicated that healthy aging was mainly associated with upregulations of apoptosis and immune function and downregulations of glycolysis and protein catabolism. ORA and FCS indicated that with metabolic syndrome the dominating biological processes were upregulation of proteolysis and downregulation of oxidative phosphorylation. Proteomic profiling matched 586 muscle proteins between individuals. The proteome of healthy aging revealed modifications consistent with a fast-to-slow transition and downregulation of glycolysis. These transitions were reduced with metabolic syndrome, which was more associated with alterations in NADH/NAD

Indexed as

AnimalsGlycolysisHumansMetabolic SyndromeMuscle, SkeletalProteomicsSarcopeniaTranscriptomeagingmetabolic syndromeproteomesarcopeniaskeletal muscletranscriptome

Identifiers

PMID33921590
PMCPMC8074053
OpenAlexW3153813549

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.