The impact of protein quality on the promotion of resistance exercise-induced changes in muscle mass
Bibliographic record
Abstract
Protein supplementation during resistance exercise training augments hypertrophic gains. Protein ingestion and the resultant hyperaminoacidemia provides the building blocks (indispensable amino acids - IAA) for, and also triggers an increase in, muscle protein synthesis (MPS), suppression of muscle protein breakdown (MPB), and net positive protein balance (i.e., MPS > MPB). The key amino acid triggering the rise in MPS is leucine, which stimulates the mechanistic target of rapamycin complex-1, a key signalling protein, and triggers a rise in MPS. As such, ingested proteins with a high leucine content would be advantageous in triggering a rise in MPS. Thus, protein quality (reflected in IAA content and protein digestibility) has an impact on changes in MPS and could ultimately affect skeletal muscle mass. Protein quality has been measured by the protein digestibility-corrected amino acid score (PDCAAS); however, the digestible indispensable amino acid score (DIAAS) has been recommended as a better method for protein quality scoring. Under DIAAS there is the recognition that amino acids are individual nutrients and that protein quality is contingent on IAA content and ileal (as opposed to fecal) digestibility. Differences in protein quality may have important ramifications for exercise-induced changes in muscle mass gains made with resistance exercise as well as muscle remodelling. Thus, the purpose of this review is a critical appraisal of studies examining the effects of protein quality in supplementation on changes in muscle mass and strength as well as body composition during resistance training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".