Effect of statins on serum vitamin D concentrations: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background We conducted a systematic review and meta‐analysis to assess the effects of statin therapy on serum vitamin D concentrations. Materials and methods We searched multiple databases including PubMed, MEDLINE, Web of Science and Google Scholar from inception to May 2016, for studies on the effects of statin treatment on serum vitamin D concentration. Quantitative data synthesis used random‐effects models meta‐analysis, with sensitivity analysis conducted using the leave‐one‐out method. Heterogeneity was quantitatively assessed using the I2 index. The systematic review's registration number was CRD42016035974. Results In all, seven of 644 studies met our selection criteria including three randomized controlled trials (RCT), three observational cohort studies and one case–control study. Across RCTs, treatment with statins was associated a significant increase in serum vitamin D concentrations [weighted mean difference (WMD) 2·71 ng/mL, 95% CI 0·19–5·24, I2 62·1%). Across studies of non‐RCT design, statins treatment was associated with a decrease in vitamin D concentrations (WMD −0·70 ng/mL, 95% CI −1·20 to −0·20, I2 56·3%). These findings were robust in sensitivity analyses. Conclusions This meta‐analysis was inconclusive on the effects of statins on vitamin D, with conflicting directions of the effects from interventional and observational studies. The suggested favourable effects from RCTs need to be confirmed in larger studies with extended follow‐up in order to determine the possible health benefits.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".