Interactions between exercise and nutrition to prevent muscle waste during ageing
Bibliographic record
Abstract
The underlying cause of sarcopenia and dynapenia (age-related strength loss) are not fully elucidated, but may be the result, or combination, of alterations in lifestyle or inflammatory and endocrine profiles. What is clear is that functional ability is limited and mortality risk is elevated. Mechanistically, muscle atrophy is the result of the prolonged periods of net negative muscle protein balance, brought about by the imbalance between muscle protein synthesis (MPS) and muscle protein breakdown (MPB). Contractile loading of skeletal muscle, through resistive-type exercise and amino acid ingestion both act as a strong stimulus for MPS and, when combined, can induce a net positive protein balance and muscle hypertrophy. Given that MPS in older muscles displays a blunted response to anabolic stimuli compared with the young, the combined effect and manipulation of contractile and nutrient interventions to optimize muscle anabolism could be extremely important for counteracting sarcopenia. Specifically, the dose, absorption kinetics, leucine content, but less-so the timing of ingestion, are important determinants of the mRNA translational signalling response regulating MPS. In addition, resistance exercise-induced rates of MPS and hypertrophy appear to be dependent on exercise volume (to achieve maximal muscle fibre recruitment), as opposed to the absolute load that is lifted. A number of recent studies in young adults lend weight to this notion by showing that contraction can be manipulated; allowing low load weight lifting to effectively stimulate rates of MPS to a level comparable with traditional high loads, a finding with important implications for older adults interested in undertaking resistance exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".