Considerations for protein intake in managing weight loss in athletes
Bibliographic record
Abstract
A large body of evidence now shows that higher protein intakes (2-3 times the protein Recommended Dietary Allowance (RDA) of 0.8 g/kg/d) during periods of energy restriction can enhance fat-free mass (FFM) preservation, particularly when combined with exercise. The mechanisms underpinning the FFM-sparing effect of higher protein diets remain to be fully elucidated but may relate to the maintenance of the anabolic sensitivity of skeletal muscle to protein ingestion. From a practical point of view, athletes aiming to reduce fat mass and preserve FFM should be advised to consume protein intakes in the range of ∼1.8-2.7 g kg(-1) d(-1) (or ∼2.3-3.1 g kg(-1) FFM) in combination with a moderate energy deficit (-500 kcal) and the performance of some form of resistance exercise. The target level of protein intake within this recommended range requires consideration of a number of case-specific factors including the athlete's body composition, habitual protein intake and broader nutrition goals. Athletes should focus on consuming high-quality protein sources, aiming to consume protein feedings evenly spaced throughout the day. Post-exercise consumption of 0.25-0.3 g protein meal(-1) from protein sources with high leucine content and rapid digestion kinetics (i.e. whey protein) is recommended to optimise exercise-induced muscle protein synthesis. When protein is consumed as part of a mixed macronutrient meal and/or before bed slightly higher protein doses may be optimal.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".