Maximizing muscle protein anabolism: the role of protein quality
Bibliographic record
Abstract
PURPOSE OF REVIEW: Muscle protein synthesis (MPS) and muscle protein breakdown are simultaneous ongoing processes. Here, we examine evidence for how protein quality can affect exercise-induced muscle protein anabolism or protein balance (MPS minus muscle protein breakdown). Evidence is highlighted showing differences in the responses of MPS, and muscle protein accretion, with ingestion of milk-based and soy-based proteins in young and elderly persons. RECENT FINDINGS: Protein consumption, and the accompanying hyperaminoacidemia, stimulates an increase in MPS and a small suppression of muscle protein breakdown. Beyond the feeding-induced rise in MPS, small incremental addition of new muscle protein mass occurs following intense resistance exercise which over time (i.e. resistance training) leads to muscle hypertrophy. Athletes make use of the paradigm of resistance training and eating to maximize the gains in their skeletal muscle mass. Importantly, however, metabolically active skeletal muscle can offset the morbidities associated with the sarcopenia of aging such as type II diabetes, decline in aerobic fitness and the reduction in metabolic rate that can lead to fat mass accumulation. SUMMARY: Recent evidence suggests that consumption of different proteins can affect the amplitude and possibly duration of MPS increases after feeding and this effect interacts and is possibly accentuated with resistance 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".