Exploring Opportunities to Better Characterize the Effects of Dietary Protein on Health across the Lifespan
Bibliographic record
Abstract
Remarkable advances have been made over the last 30 y in understanding the role of dietary protein in optimizing muscle health across the lifespan. That is, acute (<24 h) stable isotope-derived measures of muscle protein synthesis have led to established recommendations for protein quantity, quality, source, and timing of protein ingestion to support muscle health at rest, post exercise, and to overcome age-related anabolic resistance in older adults. Although muscle health is undoubtedly important, moving from muscle to other associated or disease-specific outcomes is a critical next step for the field, given the mounting evidence documenting the effects of dietary protein on measures of chronic disease and age-related decline (for example, cardiovascular disease, type 2 diabetes mellitus, obesity, frailty, and osteoporosis). In this narrative review, we posit that future studies evaluating the potential role of dietary protein build off of the existing knowledge base generated from decades of past research and focus their efforts on closing unanswered knowledge gaps pertaining to dietary protein and health across the lifespan. Throughout this review, we highlight potential methodologies and novel outcome measures that researchers may consider as starting points to facilitate the next 30 y of advances in the field of dietary protein and health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".