Molecular regulation of human skeletal muscle protein synthesis in response to exercise and nutrients: a compass for overcoming age-related anabolic resistance
Bibliographic record
Abstract
Skeletal muscle mass, a strong predictor of longevity and health in humans, is determined by the balance of two cellular processes, muscle protein synthesis (MPS) and muscle protein breakdown. MPS seems to be particularly sensitive to changes in mechanical load and/or nutritional status; therefore, much research has focused on understanding the molecular mechanisms that underpin this cellular process. Furthermore, older individuals display an attenuated MPS response to anabolic stimuli, termed anabolic resistance, which has a negative impact on muscle mass and function, as well as quality of life. Therefore, an understanding of which, if any, molecular mechanisms contribute to anabolic resistance of MPS is of vital importance in formulation of therapeutic interventions for such populations. This review summarizes the current knowledge of the mechanisms that underpin MPS, which are broadly divided into mechanistic target of rapamycin complex 1 (mTORC1)-dependent, mTORC1-independent, and ribosomal biogenesis-related, and describes the evidence that shows how they are regulated by anabolic stimuli (exercise and/or nutrition) in healthy human skeletal muscle. This review also summarizes evidence regarding which of these mechanisms may be implicated in age-related skeletal muscle anabolic resistance and provides recommendations for future avenues of research that can expand our knowledge of this area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".