Independent and Combined Effects of Calcium and Vitamin D Supplementation on Blood Lipids in Overweight or Obese Premenopausal Women: A Triple-Blind Randomized Controlled Clinical Trial
Bibliographic record
Abstract
Background: Dyslipidemia is often associated with obesity and contributes to the increased risk of atherosclerosis, heart disease, and stroke. This study was designed to evaluate the independent or combined effect of calcium and vitamin D (Ca + Vit D) supplementation on blood lipid profile in overweight or obese premenopausal women. Methods: This study is a triple-blind, randomized, parallel, placebo-controlled trial. About 100 overweight or obese (body mass index (BMI) of 25–40 kg/m 2 ) premenopausal (aged 30–50 years) women, recruited from Shiraz University of Medical Sciences (SUMS) clinics, were allocated into 4 groups: (1) calcium (Ca) supplementation (2 tablets per day; each containing 500 mg calcium carbonate), (2) vitamin D (Vit D) supplementation (2 tablets per day; each containing 200 IU vitamin D3), (3) Ca + Vit D supplementation (2 tablets per day; each containing 500 mg calcium carbonate plus 200 IU vitamin D3), (4) placebo supplementation (2 tablets per day, containing micro-cellulose). All participants received a 500 kcal energy-restricted diet. Blood lipids, serum vitamin D, and anthropometric indices were measured at baseline and after 8 weeks. Physical activity and 3-day dietary records were taken at baseline and every 4 weeks during the intervention. Results: At 8 weeks, triglyceride levels were significantly decreased in the Ca group ( P = 0.002). Low-density lipoprotein (LDL) levels were decreased in the Ca + Vit D group ( P = 0.04) and high-density lipoprotein (HDL) levels decreased in both the Ca and Ca + Vit D groups ( P = 0.006, P = 0.004, respectively). The results of one-way ANOVA indicated that changes in the serum lipid profile levels were not significantly different among the four groups ( P = 0.90, P = 0.86, P = 0.61, P = 0.27, and P = 0.19, respectively for TG, TC, LDL, HDL, and LDL/HDL). The results were not significant even after adjusting for potential covariates. Conclusions: Although the results were not significantly different among the four treated groups at 8 weeks, within-group changes like the reduction in triglyceride and LDL levels, respectively in the Ca group and Ca + Vit D group, and HDL levels in both the Ca and Ca + Vit D groups were significant. These changes may have potentially significant public health implications.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".