Dietary animal protein intake: association with muscle mass index in older women.
Bibliographic record
Abstract
BACKGROUND: Aging is associated with reductions in muscle mass and strength, so-called sarcopenia, and is generally characterized using muscle mass index (MMI = FFM (kg)/height (m)2). It is believed that adequate nutrition especially regarding protein intake, can delay this progression and enhance the quality of life of elders. OBJECTIVES: We examined whether the predominant source of protein consumed (animal or vegetal) by older women was associated with MMI. DESIGN: Thirty-eight healthy, normal weight, sedentary women, aged between 57-75 years (mean age: 66 +/- 5 years old), and taking no medication that could influence metabolism were recruited. Body composition was measured by dual-energy X-ray absorptiometry; muscle protein content was measured by the use of creatinine excretion. Physical activity metabolism was obtained by the use of accelerometry, and indirect calorimetry. Finally, protein intake was measured with a 3-day dietary record. RESULTS: Significant correlations were observed between MMI and body mass index, fat-free mass, muscle protein content, total protein intake, animal protein intake, fat mass, visceral fat and daily energy expenditure. However, a stepwise regression analysis showed animal protein intake to be the only independent predictor of MMI (r2=0.19; p=0.008). CONCLUSIONS: Our results suggest that protein intake, especially from animal sources, may be associated with a better preservation of MMI. However, more research is needed to confirm our results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".