Protein Requirements for Maximal Muscle Mass and Athletic Performance Are Achieved with Completely Plant-Based Diets Scaled to Meet Energy Needs: A Modeling Study in Professional American Football Players
Bibliographic record
Abstract
American football players consume large quantities of animal-sourced protein in adherence with traditional recommendations to maximize muscle development and athletic performance. This contrasts with dietary guidelines, which recommend reducing meat intake and increasing consumption of plant-based foods to promote health and reduce the risk of chronic disease. The capacity of completely plant-based diets to meet the nutritional needs of American football players has not been studied. This modeling study scaled dietary data from a large cohort following completely plant-based diets to meet the energy requirements of professional American football players to determine whether protein, leucine, and micronutrient needs for physical performance and health were met. The Cunningham equation was used to estimate calorie requirements. Nutrient intakes from the Adventist Health Study 2 were then scaled to this calorie level. Protein values ranged from 1.6-2.2 g/kg/day and leucine values ranged from 3.8-4.1 g/meal at each of four daily meals, therefore meeting and exceeding levels theorized to maximize muscle mass, muscle strength, and muscle protein synthesis, respectively. Plant-based diets scaled to meet the energy needs of professional American football players satisfied protein, leucine, and micronutrient requirements for muscle development and athletic performance. These findings suggest that completely plant-based diets could bridge the gap between dietary recommendations for chronic disease prevention and athletic performance in American football players.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".