Nutrient-rich, high-quality, protein-containing dairy foods in combination with exercise in aging persons to mitigate sarcopenia
Bibliographic record
Abstract
Sarcopenic declines in muscle mass and function contribute to the risk of falls, reduced mobility, and progression to frailty in older persons. Mitigation of sarcopenia can be achieved by consumption of higher quality protein in sufficient quantities, which current research suggests are greater than the recommended intakes of approximately 0.8 g/kg bodyweight/d. In addition, higher levels of physical activity and participation in exercise to support cardiovascular fitness and musculoskeletal function work additively with protein in attenuating sarcopenia. This narrative review provides evidence to support a recommendation for per-meal protein targets in older persons that are underpinned by knowledge of muscle protein turnover. Based on work examining acute dose-responses of muscle protein synthesis (MPS) to protein, a proposed per-meal target for protein intakes is set at approximately 0.4-0.6 g protein/kg bodyweight/meal for older persons. Habitual patterns of dietary protein intake tend to emphasize a skewed protein distribution, which would not maximize muscle anabolism. Observational studies show that more even patterns of protein intake are associated with increased muscle mass and improved muscle function. A food-based approach to achieving these protein targets would be advantageous, and the nutrient density of the protein-containing foods would be particularly important for older persons. Dairy foods provide high-quality protein and contain several nutrients of concern for older persons. This brief review provides an overview of the science underpinning why dairy foods should be a point of nutritional emphasis for older persons. Practical suggestions are provided for implementation of dairy foods into dietary patterns to meet the protein and other nutrient targets for older persons.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".