MEALTIME DISTRIBUTION OF PROTEIN INTAKE AND LEAN MASS AND MUSCLE STRENGTH IN NUAGE PARTICIPANTS
Bibliographic record
Abstract
In addition to total intake, protein distribution across meals may affect sarcopenia. An even distribution further increased muscle protein synthesis compared to a skewed intake, in young adults. We studied whether this short-term result translates into long-term preservation of lean mass (LM) and muscle strength in healthy older adults of the NuAge study (827 men, 914 women). Outcomes were measured at baseline and 2-3-year follow-up. Protein intake was calculated from 6x24-h food recalls. Results: In men and women, LM declined by 2.5% and 2.0%, muscle strength by 20.0% and 18.2%, and mobility score by 6.5% and 7.8 % (P<0.05). Rates of decline were not independently affected by the quantity and distribution of protein intake. Yet, participants with more evenly distributed protein intake had higher LM and muscle strength throughout follow-up, even after controlling for confounders (P<0.05). This could translate in delaying reaching a sarcopenic threshold, affecting functionality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".