The Effect of Dietary Protein on Protein Metabolism and Performance in Endurance-trained Males
Bibliographic record
Abstract
Recommendations for dietary protein are primarily based on intakes that maintain nitrogen (i.e., protein) balance rather than optimize metabolism and/or performance. PURPOSE: This study aimed to determine how varying protein intakes, including a new tracer-derived safe intake, alter whole body protein metabolism and exercise performance during training. METHODS: Using a double-blind randomized crossover design, 10 male endurance-trained runners (age, 32 ± 8 yr; V˙O2peak, 65.9 ± 7.9 mL O2·kg·min) performed three trials consisting of 4 d of controlled training (20, 5, 10, and 20 km·d, respectively) while consuming diets providing 0.94 (LOW), 1.20 (MOD), and 1.83 (HIGH) g protein·kg·d. Whole body protein synthesis, breakdown, and net balance were determined by oral [N]glycine on the first and last day of the 4-d controlled training period, whereas exercise performance was determined from maximum voluntary isometric contraction, 5-km time trial, and countermovement jump impulse (IMP) and peak force before and immediately after the 4-d intervention. RESULTS: Synthesis and breakdown were not affected by protein intake, whereas net balance showed a dose-response (HIGH > MOD > LOW, P < 0.05) with only HIGH being in positive balance (P < 0.05). There was a trend (P = 0.06) toward an interaction in 5-km Time Trial with HIGH having a moderate effect over LOW (effect size = 0.57) and small effect over MOD (effect size = 0.26). IMP decreased with time (P < 0.01) with no effect of protein (P = 0.56). There was no effect of protein intake (P ≥ 0.06) on maximum voluntary isometric contraction, IMP, or peak force performance. CONCLUSION: Our data suggest that athletes who consume dietary protein toward the upper end of the current recommendations by the American College of Sports Medicine (1.2-2 g·kg) would better maintain protein metabolism and potentially exercise performance during training.
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 | 0.000 | 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) | 0.001 | 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".