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Age‐Associated Changes in Autophagy in Peripheral Blood Mononuclear Cells following Maximal Exercise: Preliminary Observations

2022· article· en· W4225411390 on OpenAlexafffund
Nicholas Goulet, James J. McCormick, Kelli E. King, Morgan K. McManus, Glen P. Kenny

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutophagyContext (archaeology)Peripheral blood mononuclear cellNeurodegenerationMedicinePeripheralBiologyCell biologyDiseaseInternal medicineApoptosisIn vitroBiochemistry

Abstract

fetched live from OpenAlex

Aging represents the progressive deterioration in the efficacy of cellular survival mechanisms, including the important mechanism of autophagy. Deficiencies in autophagic function, which occur with increasing age, may contribute to the development of many diseases, including neurodegeneration, cardiovascular diseases, and the development of various cancers. Thus, it is imperative to understand the regulation of autophagy in the context of human physiology. Of note, it is suggested that engaging in exercise represents an optimal strategy to improve autophagic function, which may help explain the many health benefits associated with regular exercise. During acute cellular stress, such as exercise, autophagy degrades misfolded or damaged proteins to provide the base constituents (i.e. amino acids) for cellular energy production. Although, until the past decade, few studies have examined the autophagic response to exercise in humans. We have recently demonstrated that exercise‐induced autophagy is intensity‐dependent, with greater intensities required to elicit an increase in autophagic flux, as indicated by elevated levels of microtubule‐associated protein 1 light chain 3 beta (LC3‐II). However, no known studies have evaluated the autophagic response to maximal exercise in humans, and it is unknown if this response is altered by aging. Therefore, we aimed to examine whether autophagy would increase in response to an acute (<15 min) incremental maximal exercise bout in peripheral blood mononuclear cells (PBMCs), and if this response would be altered in older adults. We evaluated the hypothesis that autophagy would increase in response to maximal exercise and that young adults would display greater elevations in autophagy than their older counterparts. To test this hypothesis, PBMCs were collected from 8 young (mean [SD], 20 [2.1] years; 4 women, 4 men) and 8 older (66 [5.5] years; 4 women, 4 men) adults before and immediately after a graded maximal exercise test performed on a semi‐recumbent cycle ergometer. The autophagic marker LC3‐II was assessed by Western blotting, which was normalized to β‐actin (an internal loading control) and reported as a fold change relative to its respective baseline. Data were analyzed using unpaired t‐tests with an alpha set at 0.05. Peak oxygen consumption (VO 2peak ) was significantly higher in young (41.6 [10.3] ml/kg/min) compared to older (30.3 [6.6] ml/kg/min) adults (p=0.02). Immediately following the maximal exercise bout, an increase in LC3‐II was observed in both young (1.64 [0.45], p=0.005) and older (1.21, [0.24] p=0.048) adults. When comparing between groups, the relative increase in LC3‐II was significantly greater (+27%) in young compared to older adults (p=0.03). Taken together, our preliminary findings show that autophagy is elevated in response to maximal exercise in young and older adults, albeit to a lesser extent in older adults, suggesting an impaired ability to respond to cellular stress. Further research is necessary to understand the mechanisms underlying age‐related impairments in autophagy in older adults and if these responses can be restored.

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 score0.004

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 head0.025
GPT teacher head0.256
Teacher spread0.231 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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