Mitigating disuse‐induced skeletal muscle atrophy in ageing: Resistance exercise as a critical countermeasure
Bibliographic record
Abstract
The gradual deterioration of physiological systems with ageing makes it difficult to maintain skeletal muscle mass (sarcopenia), at least partly due to the presence of 'anabolic resistance', resulting in muscle loss. Sarcopenia can be transiently but markedly accelerated through periods of muscle disuse-induced (i.e., unloading) atrophy due to reduced physical activity, sickness, immobilisation or hospitalisation. Periods of disuse are detrimental to older adults' overall quality of life and substantially increase their risk of falls, physical and social dependence, and early mortality. Disuse events induce skeletal muscle atrophy through various mechanisms, including anabolic resistance, inflammation, disturbed proteostasis and mitochondrial dysfunction, all of which tip the scales in favour of a negative net protein balance and subsequent muscle loss. Concerningly, recovery from disuse atrophy is more difficult for older adults than their younger counterparts. Resistance training (RT) is a potent anabolic stimulus that can robustly stimulate muscle protein synthesis and mitigate muscle losses in older adults when implemented before, during and following unloading. RT may take the form of traditional weightlifting-focused RT, bodyweight training and lower- and higher-load RT. When combined with sufficient dietary protein, RT can accelerate older adults' recovery from a disuse event, mitigate frailty and improve mobility; however, few older adults regularly participate in RT. A feasible and practical approach to improving the accessibility and acceptability of RT is through the use of resistance bands. Moving forward, RT must be prescribed to older adults to mitigate the negative consequences of disuse atrophy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".