Beyond muscle hypertrophy: why dietary protein is important for endurance athletes
Bibliographic record
Abstract
Recovery from the demands of daily training is an essential element of a scientifically based periodized program whose twin goals are to maximize training adaptation and enhance performance. Prolonged endurance training sessions induce substantial metabolic perturbations in skeletal muscle, including the depletion of endogenous fuels and damage/disruption to muscle and body proteins. Therefore, increasing nutrient availability (i.e., carbohydrate and protein) in the post-training recovery period is important to replenish substrate stores and facilitate repair and remodelling of skeletal muscle. It is well accepted that protein ingestion following resistance-based exercise increases rates of skeletal muscle protein synthesis and potentiates gains in muscle mass and strength. To date, however, little attention has focused on the ability of dietary protein to enhance skeletal muscle remodelling and stimulate adaptations that promote an endurance phenotype. The purpose of this review is to critically discuss the results of recent studies that have examined the role of dietary protein for the endurance athlete. Our primary aim is to consider the results from contemporary investigations that have advanced our knowledge of how the manipulation of dietary protein (i.e., amount, type, and timing of ingestion) can facilitate muscle remodelling by promoting muscle protein synthesis. We focus on the role of protein in facilitating optimal recovery from, and promoting adaptations to strenuous endurance-based 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".