Relationship between different protein intake recommendations with muscle mass and muscle strength
Bibliographic record
Abstract
OBJECTIVE: The purpose of the present study was to examine the relationship between two different levels of protein intake (0.8 vs.1.2 g/kg body weight/day) with muscle mass and muscle strength. METHOD: Seventy-two postmenopausal women were recruited. Body composition (bioelectrical impedance analysis), muscle strength (dynamometer), energy metabolism (indirect calorimetry) and dietary intake (dietary journal) were measured. We divided the women into two groups. Women with a protein intake of ≥ 1.2 g/kg body weight/day were placed in the Protein ≥ 1.2 group (n = 35), whereas women with a protein intake of 0.8-1.19 g/kg body weight/day were categorized in the Protein 0.8-1.19 group (n = 32). RESULTS: No significant difference was observed between groups for age, height, skeletal muscle mass, resting energy expenditure, total energy expenditure, carbohydrate and lipid intake. Significant differences between groups were observed for body mass index (p < 0.001), fat mass (p < 0.001) and muscle strength (hand grip and knee extensors) (p < 0.001). More specifically, the Protein ≥ 1.2 group presented a higher muscle strength as well as a lower body mass index and fat mass compared to the Protein 0.8-1.19 group. In addition, the group with a protein intake of ≥ 1.2 g/kg body weight/day presented significantly higher energy intake (p = 0.002), and essential (p < 0.001) and non-essential (p < 0.001) amino acid intake. Interestingly, when muscle strength was adjusted for essential or non-essential amino acids, differences in muscle strength persisted. CONCLUSION: The present study indicates higher levels of muscle strength in postmenopausal women with a protein intake of ≥ 1.2 g/kg body weight/day compared to 0.8-1.19 g/kg body weight/day despite no differences in muscle mass.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".