Effect of vitamin D supplementation or fortification on bone turnover markers in women: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Vitamin D is a vital indicator of musculoskeletal health, as it plays an important role through the regulation of bone and mineral metabolism. This meta-analysis was performed to investigate the effects of vitamin D supplementation/fortification on bone turnover markers in women. All human randomised clinical trials reported changes in bone resorption markers (serum C-terminal telopeptide of type-I collagen (sCTX) and urinary type I collagen cross-linked N-telopeptide (uNTX)) or bone formation factors (osteocalcin (OC), bone alkaline phosphatase (BALP) and procollagen type-1 intact N-terminal propeptide (P1NP)) following vitamin D administration in women (aged ≥ 18 years) were considered. Mean differences (MD) and their respective 95 % CI were calculated based on fixed or random effects models according to the heterogeneity status. Subgroup analyses, meta-regression models, sensitivity analysis, risk of bias, publication bias and the quality of the included studies were also evaluated. We found that vitamin D supplementation had considerable effect on sCTX (MD: −0·038, n 22) and OC (MD: −0·610, n 24) with high heterogeneity and uNTX (MD: −8·188, n 6) without heterogeneity. Our results showed that age, sample size, dose, duration, baseline vitamin D level, study region and quality of studies might be sources of heterogeneity in this meta-analysis. Subgroup analysis also revealed significant reductions in P1NP level in dose less than 600 μg/d and larger study sample size (>100 participants). Moreover, no significant change was found in BALP level. Vitamin D supplementation/fortification significantly reduced bone resorption markers in women. However, results were inconsistent for bone formation markers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".