Oral Supplementation Using Gamma-Aminobutyric Acid and Whey Protein Improves Whole Body Fat-Free Mass in Men After Resistance Training
Bibliographic record
Abstract
BACKGROUND: Oral gamma-aminobutyric acid (GABA) supplementation increases growth hormone (GH) serum levels and protein synthesis. Therefore, post-exercise supplementation using GABA and protein may help enhance training-induced muscle hypertrophy. We evaluated whether GABA with whey protein enhanced muscular hypertrophy in men after progressive resistance training. METHODS: Twenty-one healthy men (26 - 48 years) were randomized to receive whey protein (WP; 10 g) or whey protein + GABA (WP + GABA; 10 g + 100 mg) daily for 12 weeks. Both groups performed resistance training twice per week (three sets of 12 repetitions at 60% of maximal strength; leg press, leg extension, leg curl, chest press, and pull down). Body composition was assessed using dual-energy X-ray absorptiometry. RESULTS: In the WP + GABA group, resting plasma GH concentrations were significantly elevated at 4 and 8 weeks, compared to baseline. However, resting plasma GH concentrations in the WP group were only significantly elevated at 8 weeks. After 12 weeks, the WP + GABA group exhibited significantly greater increase in whole body fat-free mass than the WP group. CONCLUSIONS: The GABA and whey protein combination was more effective for increasing whole body fat-free mass; daily GABA supplementation may help enhance exercise-induced muscle hypertrophy.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".