The effect of branched-chain amino acid on muscle damage markers and performance following strenuous exercise: a systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis determined whether the ergogenic effects of branched-chain amino acids (BCAA) ameliorated markers of muscle damage and performance following strenuous exercise. In total, 25 studies were included, consisting of 479 participants (age 24.3 ± 8.3 years, height 1.73 ± 0.06 m, body mass 70.8 ± 9.5 kg, females 26.3%). These studies were rated as fair to excellent following the PEDro scale. The outcome measures were compared between the BCAA and placebo conditions at 24 and 48 hours following muscle-damaging exercises, using standardised mean differences and associated p-values via forest plots. Our meta-analysis demonstrated significantly lower levels of indirect muscle damage markers (creatine kinase, lactate dehydrogenase and myoglobin) at 48 hours post-exercise (standardised mean difference [SMD] = −0.41; p < 0.05) for the BCAA than placebo conditions, whilst muscle soreness was significant at 24 hours post-exercise (SMD = −0.28 ≤ d ≤ −0.61; p < 0.05) and 48 hours post-exercise (SMD = −0.41 ≤ d≤ −0.92; p < 0.01). However, no significant differences were identified between the BCAA and placebo conditions for muscle performance at 24 or 48 hours post-exercise (SMD = 0.08 ≤ d ≤ 0.21; p > 0.05). Overall, BCAA reduced the level of muscle damage biomarkers and muscle soreness following muscle-damaging exercises. However, the potential benefits of BCAA for muscle performance recovery is questionable and warrants further investigation to determine the practicality of BCAA for ameliorating muscle damage symptoms in diverse populations. PROSPERO registration number: CRD42020191248. Novelty: BCAA reduces the level of creatine kinase and muscle soreness following strenuous exercise with a dose–response relationship. BCAA does not accelerate recovery for muscle performance.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".