Postexercise Dietary Protein Strategies to Maximize Skeletal Muscle Repair and Remodeling in Masters Endurance Athletes: A Review
Bibliographic record
Abstract
Participation rates of masters athletes in endurance events such as long-distance triathlon and running continue to increase. Given the physical and metabolic demands of endurance training, recovery practices influence the quality of successive training sessions and, consequently, adaptations to training. Research has suggested that, after muscle-damaging endurance exercise, masters athletes experience slower recovery rates in comparison with younger, similarly trained athletes. Given that these discrepancies in recovery rates are not observed after non-muscle-damaging exercise, it is suggested that masters athletes have impairments of the protein remodeling mechanisms within skeletal muscle. The importance of postexercise protein feeding for endurance athletes is increasingly being acknowledged, and its role in creating a positive net muscle protein balance postexercise is well known. The potential benefits of postexercise protein feeding include elevating muscle protein synthesis and satellite cell activity for muscle repair and remodeling, as well as facilitating muscle glycogen resynthesis. Despite extensive investigation into age-related anabolic resistance in sedentary aging populations, little is known about how anabolic resistance affects postexercise muscle protein synthesis and thus muscle remodeling in aging athletes. Despite evidence suggesting that physical training can attenuate but not eliminate age-related anabolic resistance, masters athletes are currently recommended to consume the same postexercise dietary protein dose (approximately 20 g or 0.25 g/kg/meal) as younger athletes. Given the slower recovery rates of masters athletes after muscle-damaging exercise, which may be due to impaired muscle remodeling mechanisms, masters athletes may benefit from higher doses of postexercise dietary protein, with particular attention directed to the leucine content of the postexercise bolus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".