Postexercise Muscle Glycogen Recovery Enhanced with a Carbohydrate-Protein Supplement
Bibliographic record
Abstract
Purpose: This study assessed whether liquid carbohydrate-protein (C + P) supplements, ingested early during recovery, enhance muscle glycogen resynthesis versus isoenergetic liquid carbohydrate (CHO) supplements, given early or an isoenergetic solid meal given later during recovery (PLB). Methods: Two hours after breakfast (7.0 kcal·kg−1; 0.3 g·kg−1 P, 1.2 g·kg−1 C, 0.1 g·kg−1 F), six male cyclists performed a 60-min time trial (AMex). Pre- and postexercise, vastus lateralis glycogen concentrations were determined using nMRS. Immediately, 1 h, and 2 h postexercise, participants ingested C + P (4.8 kcal·kg−1; 0.8 g·kg−1 C, 0.4 g·kg−1 P), CHO (4.8 kcal·kg−1; 1.2 g·kg−1 C), or PLB (no energy). Four hours postexercise, a solid meal was ingested. At that time, C + P and CHO received a meal identical to breakfast, whereas PLB received 21 kcal·kg−1 (1 g·kg−1 P, 3.6 g·kg−1 C, 0.3 g·kg−1 F); energy intake during 6 h of recovery was identical among treatments. After 6 h of recovery, measurement and cycling protocols (PMex) were repeated. Results: Absolute muscle glycogen utilization was 18% greater (P ≤ 0.05) during AMex (C + P: −42.75 ± 5.24 mmol·L−1; CHO: −37.08 ± 7.59 mmol·L−1; PLB: −53.78 ± 11.59 mmol·L−1; P = 0.302) relative to PMex (C + P: −38.40 ± 4.37 mmol·L−1; CHO: −31.16 ± 3.78 mmol·L−1; PLB: −40.33 ± 1.47 mmol·L−1; P = 0.292), but there were no differences between groups. During 6 h of recovery, muscle glycogen resynthesis was greater in C + P (+ 28.62 ± 2.10 mmol·L−1) versus CHO (+ 22.20 ± 1.19 mmol·L−1, P ≤ 0.05) or PLB (+ 18.50 ± 7.67 mmol·L−1, P ≤ 0.05). Cycling performance was similiar (P = 0.282) among treatments during both AMex (C + P: 37.61 ± 0.63 km; CHO: 37.03 ± 0.60 km; PLB: 37.24 ± 0.34 km) and PMex (C + P: 36.31 ± 0.83 km; CHO: 36.38 ± 0.80 km; PLB: 35.34 ± 0.45 km). Conclusions: C + P supplements, given early after exercise, enhance glycogen resynthesis relative to CHO and PLB. However, this does not influence performance in this type of exercise bout.
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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) | 0.000 | 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) | 0.002 | 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".