Skeletal muscle protein metabolism in the elderly: Interventions to counteract the 'anabolic resistance' of ageing
Bibliographic record
Abstract
Age-related muscle wasting (sarcopenia) is accompanied by a loss of strength which can compromise the functional abilities of the elderly. Muscle proteins are in a dynamic equilibrium between their respective rates of synthesis and breakdown. It has been suggested that age-related sarcopenia is due to: i) elevated basal-fasted rates of muscle protein breakdown, ii) a reduction in basal muscle protein synthesis (MPS), or iii) a combination of the two factors. However, basal rates of muscle protein synthesis and breakdown are unchanged with advancing healthy age. Instead, it appears that the muscles of the elderly are resistant to normally robust anabolic stimuli such as amino acids and resistance exercise. Ageing muscle is less sensitive to lower doses of amino acids than the young and may require higher quantities of protein to acutely stimulate equivalent muscle protein synthesis above rest and accrue muscle proteins. With regard to dietary protein recommendations, emerging evidence suggests that the elderly may need to distribute protein intake evenly throughout the day, so as to promote an optimal per meal stimulation of MPS. The branched-chain amino acid leucine is thought to play a central role in mediating mRNA translation for MPS, and the elderly should ensure sufficient leucine is provided with dietary protein intake. With regards to physical activity, lower, than previously realized, intensity high-volume resistance exercise can stimulate a robust muscle protein synthetic response similar to traditional high-intensity low volume training, which may be beneficial for older adults. Resistance exercise combined with amino acid ingestion elicits the greatest anabolic response and may assist elderly in producing a 'youthful' muscle protein synthetic response provided sufficient protein is ingested following 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".