Protein consumption following aerobic exercise increases whole-body protein turnover in older adults
Bibliographic record
Abstract
Research measuring whole-body protein turnover (WBPT) after both exercise and nutrition has generally focused on resistance exercise; however, there is a paucity of data regarding the effect of postaerobic exercise nutrition, especially in older adults. It is not known if postexercise protein feeding has a beneficial effect on protein turnover after low- to moderate-intensity exercise. We investigated whether consuming protein plus carbohydrate (PRO) immediately after an acute bout of aerobic exercise has an additive effect over carbohydrate alone (CHO) on WBPT in older individuals. Twelve healthy older adults (age, 59 ± 4 years) were studied on 2 separate occasions after 1 h of exercise at approximately 50% of maximal rate of oxygen uptake, followed by 4 h of recovery. Immediately following exercise, subjects ingested a CHO (60 g) or an isocaloric PRO beverage (40 g carbohydrate, 20 g whey protein). Whole-body protein metabolism was determined using [1-13C]leucine infusion (60 mg prime; 75 mgh(-1) continuous), and sampling blood and expired breath. Rates of whole-body leucine appearance and oxidation, and nonoxidative leucine disposal during the third and fourth hours of postexercise recovery were higher in the PRO group (2.51 ± 0.55, 0.78 ± 0.37, and 1.71 ± 0.44 micromol kg(-1)·min(-1), respectively) than in the CHO group (1.81 ± 0.27, 0.33 ± 0.14, and 1.47 ± 0.25 micromol kg(-1)·min-1, respectively; p = 0.001). Our results indicate that consumption of a PRO beverage after aerobic exercise increased WBPT to a greater extent than a CHO beverage.
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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 | 0.001 |
| 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".