Vitamin D Intake: Impact On Body Composition After Training In Older And Overweight Individuals
Bibliographic record
Abstract
In a previous study we have reported that, under 10 μg/d, vitamin D intake was negatively associated with fat-free mass (FFM) and muscle mass index (MMI). Because it is well recognized that resistance training is an effective method to increase FFM in sedentary individuals, we hypothesized that vitamin D intake may influence the response to resistance training. PURPOSE: To determine if vitamin D intake plays a role in the response to a resistance-training program in overweight and sedentary older individuals. METHODS: Fifty-five individuals aged 66 ± 4 years and with a mean body mass index (BMI) of 26.4 ± 2.8 kg/m2 participated in a 6-months resistance training (R; n=31) or were included in a control group (C; n=24). Dietary intakes (3-day food record) and body composition (DXA) were assessed before and after training. A repeated measure ANOVA and Pearson correlation tests were used. RESULTS: Except for vitamin D intake which was higher in C than in R (p=0.007), there were no differences between groups before training (0.08<p<0.44). Vitamin D intake was negatively associated with FFM (r=-0.31, p=0.02) and body weight (r=-0.28, p=0.04) before the intervention. After resistance training, FFM and MMI increased in R, only (p=0.04 and p=0.03, respectively). However, vitamin D intake was not related to body composition changes (0.15<p<0.18). CONCLUSION: Despite the suggested implication of vitamin D in body composition, it does not seem to influence FFM and MMI changes in response to a resistance-training program, in previously sedentary older individuals. This study was supported by the Canadian Diabetes Association.
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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 | 0.000 |
| 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".