Protein to Maximize Whole-Body Anabolism in Resistance-trained Females after Exercise
Bibliographic record
Abstract
INTRODUCTION: Current athlete-specific protein recommendations are based almost exclusively on research in males. PURPOSE: Using the minimally invasive indicator amino acid oxidation technique, we determined the daily protein intake that maximizes whole-body protein synthesis (PS) and net protein balance (NB) after exercise in strength-trained females. METHODS: Eight resistance-trained females (23 ± 3.5 yr, 67.0 ± 7.7 kg, 163.3 ± 3.7 cm, 24.4% ± 6.9% body fat; mean ± SD) completed a 2-d controlled diet during the luteal phase before performing an acute bout of whole-body resistance exercise. During recovery, participants consumed eight hourly meals providing a randomized test protein intake (0.2-2.9 g·kg·d) as crystalline amino acids modeled after egg protein, with constant phenylalanine (30.5 mg·kg·d) and excess tyrosine (40.0 mg·kg·d) intakes. Steady-state whole-body phenylalanine rate of appearance (Ra), oxidation (Ox; the reciprocal of PS), and NB (PS - Ra) were determined from oral [C] phenylalanine ingestion. Total protein oxidation was estimated from the urinary urea-creatinine ratio (U/Cr). RESULTS: A mixed model biphase linear regression revealed a break point (i.e., estimated average requirement) of 1.49 ± 0.44 g·kg·d (mean ± 95% confidence interval) in Ox (r = 0.64) and 1.53 ± 0.32 g·kg·d in NB (r = 0.65), indicating a saturation in whole-body anabolism. U/Cr increased linearly with protein intake (r = 0.56, P < 0.01). CONCLUSIONS: Findings from this investigation indicate that the safe protein intake (upper 95% confidence interval) to maximize anabolism and minimize protein oxidation for strength-trained females during the early ~8-h postexercise recovery period is at the upper end of the recommendations of the American College of Sports Medicine for athletes (i.e., 1.2-2.0 g·kg·d).
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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) | 0.001 | 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".