Dietary Protein Quantity, Quality, and Exercise Are Key to Healthy Living: A Muscle-Centric Perspective Across the Lifespan
Bibliographic record
Abstract
A healthy eating pattern, regardless of age, should consist of ingesting high quality protein preferably in adequate amounts across all meals throughout the day. Of particular relevance to overall health is the growth, development, and maintenance of skeletal muscle tissue. Skeletal muscle not only contributes to physical strength and performance, but also contributes to efficient macronutrient utilization and storage. Achieving an optimal amount of muscle mass begins early in life with transitions to "steady-state" maintenance as an adult, and then safeguarding against ultimate decline of muscle mass with age, all of which are influenced by physical activity and dietary (e.g., protein) factors. Current protein recommendations, as defined by recommended dietary allowances (RDA) for the US population or the population reference intakes (PRI) in Europe, are set to cover basic needs; however, it is thought that a higher protein intake might be necessary for optimizing muscle mass, especially for adults and individuals with an active lifestyle. It is necessary to balance the accurate assessment of protein quality (e.g., digestible indispensable amino acid score; DIAAS) with methods that provide a physiological correlate (e.g., established measures of protein synthesis, substrate oxidation, lean mass retention, or accrual, etc.) in order to accurately define protein requirements for these physiological outcomes. Moreover, current recommendations need to shift from single nutrient guidelines to whole food based guidelines in order to practically acknowledge food matrix interactions and other required nutrients for potentially optimizing the health effects of food. The aim of this paper is to discuss protein quality and amount that should be consumed with consideration to the presence of non-protein constituents within a food matrix and potential interactions with physical activity to maximize muscle mass throughout life.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".