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Record W2117562576 · doi:10.1113/jp270699

Age‐related differences in lean mass, protein synthesis and skeletal muscle markers of proteolysis after bed rest and exercise rehabilitation

2015· article· en· W2117562576 on OpenAlexfundno aff
Ruth Tanner, Lucille Brunker, Jakob Agergaard, Katherine M. Barrows, Robert A. Briggs, Oh Sung Kwon, Laura Young, Paul N. Hopkins, Elena Volpi, Robin L. Marcus, Paul C. LaStayo, Micah J. Drummond

Bibliographic record

VenueThe Journal of Physiology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesBC Cancer AgencyNational Institute on AgingAmerican Federation for Aging Research
KeywordsAnabolismBed restLean body massmTORC1SarcopeniaEndocrinologyInternal medicineMuscle atrophySkeletal muscleProtein degradationCatabolismAgeingChemistryMedicineBiochemistryMetabolismApoptosisPI3K/AKT/mTOR pathwayBody weight

Abstract

fetched live from OpenAlex

Key points Five days of bed rest resulted in a reduction in leg lean mass and strength in older adults. After bed rest, older (but not younger) adults had reduced amino acid‐induced anabolic sensitivity (blunted muscle protein synthesis; MPS) and enhanced markers associated with the ubiquitin proteasome and autophagy–lysosomal systems (increase in molecular markers related to muscle proteolysis). Younger adults did not lose leg lean mass (via DXA) after 5 days of bed rest despite blunted amino acid‐induced mTORC1 signalling and increased skeletal muscle REDD1, REDD2 and MURF1 mRNA expression. Exercise rehabilitation restored bed rest‐induced deficits in lean mass, strength, nutrient‐induced protein anabolism (protein synthesis and mTORC1 signalling) and select muscle proteolytic markers in older adults. Abstract Bed rest‐induced muscle loss and impaired muscle recovery may contribute to age‐related sarcopenia. It is unknown if there are age‐related differences in muscle mass and muscle anabolic and catabolic responses to bed rest. A secondary objective was to determine if rehabilitation could reverse bed rest responses. Nine older and fourteen young adults participated in a 5‐day bed rest challenge (BED REST). This was followed by 8 weeks of high intensity resistance exercise (REHAB). Leg lean mass (via dual‐energy X‐ray absorptiometry; DXA) and strength were determined. Muscle biopsies were collected during a constant stable isotope infusion in the postabsorptive state and after essential amino acid (EAA) ingestion on three occasions: before (PRE), after bed rest and after rehabilitation. Samples were assessed for protein synthesis, mTORC1 signalling, REDD1/2 expression and molecular markers related to muscle proteolysis (MURF1, MAFBX, AMPKα, LC3II/I, Beclin1). We found that leg lean mass and strength decreased in older but not younger adults after bedrest (P < 0.05) and was restored after rehabilitation. EAA‐induced mTORC1 signalling and protein synthesis increased before bed rest in both age groups (P < 0.05). Although both groups had blunted mTORC1 signalling, increased REDD2 and MURF1 mRNA after bedrest, only older adults had reduced EAA‐induced protein synthesis rates and increased MAFBX mRNA, p‐AMPKα and the LC3II/I ratio (P < 0.05). We conclude that older adults are more susceptible than young persons to muscle loss after short‐term bed rest. This may be partially explained by a combined suppression of protein synthesis and a marginal increase in proteolytic markers. Finally, rehabilitation restored bed rest‐induced deficits in lean mass and strength in older adults.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.215
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations223
Published2015
Admission routes1
Has abstractyes

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