The impact of exercise and nutrition on the regulation of skeletal muscle mass
Bibliographic record
Abstract
The maintenance of skeletal muscle mass and strength throughout life is a key determinant of human health and well-being. There is a gradual loss of both skeletal muscle mass and strength with ageing (a process termed sarcopenia) that increases the risk of functional dependence, morbidity and mortality. Understanding the factors that regulate the size of human muscle mass, particularly during the later years of life, has therefore become an area of intense scientific inquiry. The amount of muscle mass is determined by coordinated changes in muscle protein synthesis (MPS) and muscle protein breakdown (MPB). In this review, we assess both classical and contemporary work that has examined how resistance exercise and nutrition impact on MPS and MPB. Special consideration is given to the role of different sources of dietary protein (food vs. supplements) and non-protein nutrients such as omega-3 fatty acids in regulating MPS. We also critically evaluate recent studies that have employed novel 'omic' technologies such as dynamic protein profiling to probe for changes in rates of MPS and MPB at the individual protein level following exercise. Finally, we provide suggestions for future research that we hope will yield important information for the development of exercise and nutritional strategies to counteract muscle loss in a variety of clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".