Preexercise Aminoacidemia and Muscle Protein Synthesis after Resistance Exercise
Bibliographic record
Abstract
PURPOSE: We have previously shown that the aminoacidemia caused by the consumption of a rapidly digested protein after resistance exercise enhances muscle protein synthesis (MPS) more than the amino acid (AA) profile associated with a slowly digested protein. Here, we investigated whether differential feeding patterns of a whey protein mixture commencing before exercise affect postexercise intracellular signaling and MPS. METHODS: Twelve resistance-trained males performed leg resistance exercise 45 min after commencing each of three volume-matched nutrition protocols: placebo (PLAC, artificially sweetened water), BOLUS (25 g of whey protein + 5 g of leucine dissolved in artificially sweetened water; 1 × 500 mL), or PULSE (15 × 33-mL aliquots of BOLUS drink every 15 min). RESULTS: The preexercise rise in plasma AA concentration with PULSE was attenuated compared with BOLUS (P < 0.05); this effect was reversed after exercise, with two-fold greater leucine concentrations in PULSE compared with BOLUS (P < 0.05). One-hour postexercise, phosphorylation of p70 S6K(thr389) and rpS6(ser235/6) was increased above baseline with BOLUS and PULSE, but not PLAC (P < 0.05); furthermore, PULSE > BOLUS (P < 0.05). MPS throughout 5 h of recovery was higher with protein ingestion compared with PLAC (0.037 ± 0.007), with no differences between BOLUS or PULSE (0.085 ± 0.013 vs. 0.095 ± 0.010%.h(-1), respectively, P = 0.56). CONCLUSIONS: Manipulation of aminoacidemia before resistance exercise via different patterns of intake of protein altered plasma AA profiles and postexercise intracellular signaling. However, there was no difference in the enhancement of the muscle protein synthetic response after exercise. Protein sources producing a slow AA release, when consumed before resistance exercise in sufficient amounts, are as effective as rapidly digested proteins in promoting postexercise MPS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".