Isolated vitamin D supplementation improves the immune-inflammatory biomarkers in younger postmenopausal women: a randomized, double-blind, placebo-controlled trial
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the effect of vitamin D (VitD) supplementation on immune-inflammatory biomarkers in younger postmenopausal women. METHODS: In this double-blind, placebo-controlled trial, 160 postmenopausal women aged 50 to 65 years with amenorrhea ≥12 months were randomized into two groups: VitD group, oral supplementation with 1000 IU VitD3/day (n = 80) or placebo group (n = 80). The intervention time was 9 months, and the women were assessed at baseline and endpoint. Serum levels of interleukins (ILs)-1β, IL-5, IL-6, IL-10, IL-12ρ70, IL-17α, tumor necrosis factor-alpha, and interferon-gamma were determined by immunoassay. Plasma concentrations of 25-hydroxyvitamin D [25(OH)D] were measured by high-performance liquid chromatography. Per-protocol analysis was adopted as the statistical method using a gamma distribution and repeated measures design, followed by Wald's multiple comparisons test. RESULTS: The two groups were similar at baseline in terms of clinical and laboratory parameters. After 9 months, there was a significant increase of 25(OH)D levels in the VitD group (+45.4%, P < 0.001) and a decrease (-18.5%, P = 0.049) in the placebo group. A significant decrease in IL-5, IL-12p70, IL-17α, tumor necrosis factor-alpha, and interferon-gamma levels was observed in the VitD group (P < 0.05). IL-5 and IL-6 levels were significantly lower in the VitD group compared to the placebo group (P < 0.05). There were no significant intervention effects on serum IL-1β or IL-10 levels in either group (P > 0.05). CONCLUSIONS: In younger postmenopausal women, isolated supplementation with 1000 IU of VitD3 for 9 months was associated with a reduction in proinflammatory biomarkers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".