Leucine Supplementation Has No Further Effect on Training-induced Muscle Adaptations
Bibliographic record
Abstract
INTRODUCTION: Several acute studies have suggested that leucine is a key amino acid to drive muscle protein synthesis. However, there are very few studies on the long-term effects of leucine supplementation on resistance training (RT)-induced gains in muscle mass and strength. We sought to determine the impact of 10 g of leucine on muscle mass and strength in response to RT in healthy young men. METHODS: Twenty-five, resistance-trained men (27 ± 5 yr; 78.4 ± 11.6 kg; 24.8 ± 3.0 kg·m) consuming 1.8 ± 0.4 g protein·kg·d, were randomly assigned to receive 2 × 5 g·d supplementation of either free leucine (LEU n = 12) or alanine (PLA n = 13) while undergoing a supervised 12-wk, twice-weekly lower-limb RT program. One-repetition maximum (leg-press 1RM) and muscle cross-sectional area (mCSA) of the vastus lateralis were determined before (PRE) and after (POST) the intervention. Additionally, three 24-h dietary recalls were also performed at PRE and POST. RESULTS: Protein intake was roughly double that of the RDA in both groups and remained unchanged across time with no differences detected between groups. Similar increases were observed between groups in leg-press 1RM (LEU, 19.0% ± 9.4% and PLA, 21.0% ± 10.4%, P = 0.31) and mCSA (LEU, 8.0% ± 5.6% and PLA, 8.4% ± 5.1%, P = 0.77). CONCLUSIONS: High-dose leucine supplementation did not enhance gains in muscle strength and mass after a 12-wk RT program in young resistance-trained males consuming adequate amounts of dietary protein.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".