Relationship between antioxidant intakes and class I sarcopenia in elderly men and women.
Bibliographic record
Abstract
BACKGROUND: The effect of nutritional intake on sarcopenia has been mostly examined in class II sarcopenia, i.e. when muscle mass has sufficiently decreased to induce a loss in physical capacity. Although this provides important information regarding the treatment of sarcopenia, it may not help highlight mechanisms involved at the very beginning of its development. HYPOTHESIS: We hypothesized that class I sarcopenia is associated with differences in antioxidant intakes (vitamins A, C, E and selenium) and status in healthy, older white men and women when physical activity and protein intake are taken into account. DESIGN: Fat-free mass and total appendicular skeletal muscle mass was determined by dual-energy X-ray absorptiometry in 50 healthy, older white men (n = 16) and women (n = 34) aged 60-75 yrs. Physical activity energy expenditure (PAEE) was determined using a Caltrac accelerometer over a 3-d period. Dietary protein and antioxidant intakes were estimated from a 3-d food record and serum total antioxidant activity (TAA) was measured by a ferrylmyoglobin- ABTS assay. RESULTS: The prevalence of class I sarcopenia was 23.5 % in women and 25.0 % in men; 12 participants were thus considered sarcopenic (4 men and 8 women) and 38 participants were considered nonsarcopenic (12 men and 26 women). Our results showed that PAEE, serum albumin concentrations, TAA, and the four antioxidants intake levels were similar between groups. On the other hand, our results showed that total protein intake was significantly higher (P < 0.01) in the non-sarcopenic group than in the sarcopenic group. Also, the number of Recommended Dietary Allowances (RDAs) reached for the antioxidant nutrients and protein intakes by the non-sarcopenic group was significantly higher (P < 0.01) than in the sarcopenic group. CONCLUSIONS: Although there were no significant differences between the sarcopenic and the non-sarcopenic group when antioxidant intakes were considered individually, we observed that the number of RDAs reached for antioxidant micronutrients and protein in healthy, older white men and women was lower in sarcopenic than nonsarcopenic individuals. Our results also suggest that a higher total dietary protein intake is associated with the preservation of muscle mass loss although both groups displayed values above actual RDAs. Obviously, prospective studies are needed to determine the minimum amount of protein in the diet needed to prevent class I sarcopenia and to examine the utility of antioxidant intake to combat the age-related loss in skeletal muscle mass.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".