Effect of dietary protein content during recovery from high-intensity cycling on subsequent performance and markers of stress, inflammation, and muscle damage in well-trained men
Bibliographic record
Abstract
Nutrition is an important aspect of recuperation for athletes during multi-day competition or hard training. Post-exercise carbohydrate is likely to improve recovery, but the effect of protein is equivocal. The objective of this study was to determine the effect of post-exercise dietary protein content imposed over a high-carbohydrate background on subsequent performance. Using a crossover design, 12 cyclists completed 3 high-intensity rides over 4 days. Day 1 comprised 2.5 h intervals, followed by repeat-sprint performance tests on days 2 (15 h post) and 4 (60 h post), interspersed with a rest day. During 4 h recovery on days 1 and 2, cyclists ingested either 1.4 g.kg(-1).h(-1) carbohydrate, 0.7 g.kg(-1).h(-1) protein and 0.26 g.kg(-1).h(-1) fat (protein-enriched) or 2.1 g.kg(-1).h(-1) carbohydrate, 0.1 g.kg(-1).h(-1) protein, and equal fat (control). At other times, cyclists ingested a standardized high-carbohydrate diet. Anabolism was gauged indirectly by nitrogen balance, stress and inflammation via cortisol and cytokines, skeletal-muscle membrane disruption by creatine kinase, and oxidative stress by malonyl dealdehyde. Sprint mean power was not clearly different on day 2 (0.0%; 95%CL: +/-3.9%), but on day 4 it was 4.1% higher (+/-4.1%) in the protein-enriched condition relative to control. Reduced creatine kinase was possible (26%; +/-30%) but effects on oxidative stress, inflammatory markers, and cortisol were inconclusive or trivial. Overnight nitrogen balance was positive in the protein-enriched condition on day 1 (249+/-70 mg N.kg FFM(-1); mean+/-SD), but negative (-48+/-26 mg N.kg FFM(-1)) in the control condition. A nutritive effect of post-exercise protein content was not discernible short term (15 h), but a delayed performance benefit (60 h) was observed following protein-enriched high-carbohydrate ingestion.
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 | 0.001 |
| 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 | 0.001 |
| 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".