Muscle Metabolism during Exercise with Carbohydrate or Protein-Carbohydrate Ingestion
Bibliographic record
Abstract
Introduction: Ingesting protein (PRO) with CHO during prolonged exercise is purported to improve performance compared with CHO alone by altering the regulation of skeletal muscle energy provision. However, no study has directly investigated this issue. We tested the hypothesis that compared with CHO alone, coingestion of PRO would alter markers of metabolic control, including the magnitude of glycogen use and the net expansion of the tricarboxylic acid cycle intermediate pool, which has been linked to the capacity for oxidative energy delivery. Methods: Eight trained men (mean ± SE: age = 29 ± 2 yr; V˙O2peak = 55 ± 2 mL·kg−1·min−1) cycled at 69% ± 1% V˙O2peak for 90 min on two occasions, and biopsy samples (vastus lateralis) were obtained before and after exercise. In a randomized, double-blind manner, subjects ingested one of two drinks during exercise that contained either 6% CHO or 6% CHO + 2% PRO (CHO + PRO) at a rate of 1 L·h−1 to deliver 60 g·h−1 CHO ± 20 g·h−1 PRO. Results: CHO + PRO ingestion increased the plasma concentration of branched chain (561 ± 46 vs 301 ± 32 μmol·L−1) and essential amino acids (1071 ± 98 vs 670 ± 71 μmol·L−1) after exercise versus CHO (both P values ≤0.05). However, net muscle glycogen use (CHO + PRO = 223 ± 31 vs CHO = 185 ± 38 mmol·kg−1 dry weight) and tricarboxylic acid cycle intermediate expansion (CHO + PRO = 2.3 ± 0.7 vs CHO = 2.1 ± 0.2 mmol·kg−1 dry weight) were similar between trials. Blood creatine kinase activity and 20-km time trial performance measured approximately 24 h after the first exercise bout were not different between treatments. Conclusion: When trained men ingest CHO at a rate on the upper end of the range generally recommended to improve endurance performance, coingestion of PRO does not alter specific markers proposed to reflect an enhanced capacity for skeletal muscle energy delivery.
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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 | 0.001 | 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".