The effects of branched-chain amino acids on muscle protein synthesis, muscle protein breakdown and associated molecular signalling responses in humans: an update
Bibliographic record
Abstract
Branched-chain amino acids (BCAA: leucine, isoleucine and valine) are three of the nine indispensable amino acids, and are frequently consumed as a dietary supplement by athletes and recreationally active individuals alike. The popularity of BCAA supplements is largely predicated on the notion that they can stimulate rates of muscle protein synthesis (MPS) and suppress rates of muscle protein breakdown (MPB), the combination of which promotes a net anabolic response in skeletal muscle. To date, several studies have shown that BCAA (particularly leucine) increase the phosphorylation status of key proteins within the mechanistic target of rapamycin (mTOR) signalling pathway involved in the regulation of translation initiation in human muscle. Early research in humans demonstrated that BCAA provision reduced indices of whole-body protein breakdown and MPB; however, there was no stimulatory effect of BCAA on MPS. In contrast, recent work has demonstrated that BCAA intake can stimulate postprandial MPS rates at rest and can further increase MPS rates during recovery after a bout of resistance exercise. The purpose of this evidence-based narrative review is to critically appraise the available research pertaining to studies examining the effects of BCAA on MPS, MPB and associated molecular signalling responses in humans. Overall, BCAA can activate molecular pathways that regulate translation initiation, reduce indices of whole-body and MPB, and transiently stimulate MPS rates. However, the stimulatory effect of BCAA on MPS rates is less than the response observed following ingestion of a complete protein source providing the full complement of indispensable amino acids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".