Recent Perspectives Regarding the Role of Dietary Protein for the Promotion of Muscle Hypertrophy with Resistance Exercise Training
Bibliographic record
Abstract
Skeletal muscle supports locomotion and serves as the largest site of postprandial glucose disposal; thus it is a critical organ for physical and metabolic health. Skeletal muscle mass is regulated by the processes of muscle protein synthesis (MPS) and muscle protein breakdown (MPB), both of which are sensitive to external loading and aminoacidemia. Hyperaminoacidemia results in a robust but transient increase in rates of MPS and a mild suppression of MPB. Resistance exercise potentiates the aminoacidemia-induced rise in MPS that, when repeated over time, results in gradual radial growth of skeletal muscle (i.e., hypertrophy). Factors that affect MPS include both quantity and composition of the amino acid source. Specifically, MPS is stimulated in a dose-responsive manner and the primary amino acid agonist of this process is leucine. MPB also appears to be regulated in part by protein intake, which can exert a suppressive effect on MPB. At high protein doses the suppression of MPB may interfere with skeletal muscle adaptation following resistance exercise. In this review, we examine recent advancements in our understanding of how protein ingestion impacts skeletal muscle growth following resistance exercise in young adults during energy balance and energy restriction. We also provide practical recommendations for exercisers who wish to maximize the hypertrophic response of skeletal muscle during resistance exercise 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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".