Supplementation of a suboptimal protein dose with leucine or essential amino acids: effects on myofibrillar protein synthesis at rest and following resistance exercise in men
Bibliographic record
Abstract
Leucine is a nutrient regulator of muscle protein synthesis by activating mTOR and possibly other proteins in this pathway. The purpose of this study was to examine the role of leucine in the regulation of human myofibrillar protein synthesis (MPS). Twenty-four males completed an acute bout of unilateral resistance exercise prior to consuming either: a dose (25 g) of whey protein (WHEY); 6.25 g whey protein with total leucine equivalent to WHEY (LEU); or 6.25 g whey protein with total essential amino acids (EAAs) equivalent to WHEY for all EAAs except leucine (EAA-LEU). Measures of MPS, signalling through mTOR, and amino acid transporter (AAT) mRNA abundance were made while fasted (FAST), and following feeding under rested (FED) and post-exercise (EX-FED) conditions. Leucinaemia was equivalent between WHEY and LEU and elevated compared to EAA-LEU (P=0.001). MPS was increased above FAST at 1–3 h post-exercise in both FED (P <0.001) and EX-FED (P <0.001) conditions with no treatment effect.At 3–5 h, only WHEY remained significantly elevated above FAST in EX-FED(WHEY 184% vs. LEU 55% and EAA-LEU 35%; P =0.036). AAT mRNA abundance was increased above FAST after feeding and exercise with no effect of leucinaemia. In summary, a low dose of whey protein supplemented with leucine or all other essential amino acids was as effective as a complete protein (WHEY) in stimulating postprandial MPS; however only WHEY was able to sustain increased rates of MPS post-exercise and may therefore be most suited to increase exercise-induced muscle protein accretion.
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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 | 0.000 |
| 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".