Impact of vitamin D supplementation on C-reactive protein; a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
To evaluate the effect of vitamin D supplementation on C-reactive protein (CRP) through a systematic review and meta-analysis of randomized control trials (RCTs). PubMed-Medline, SCOPUS, Google Scholar and Web of Science databases were searched (up until April 2016) to identify RCTs evaluating the impact of vitamin D supplementation on CRP. We used random effects models (using DerSimonian-Laird method) as well as the generic inverse variance methods for quantitative data synthesis. For sensitivity analysis, we applied leave-one-out approach. To examine the heterogeneity we used I2 index. Registration code: CRD42016036932. Among 1274 search items , 24 studies met the inclusion criteria in the final evaluation. Pooling the data together indicated a non-significant decrease in CRP level following administration of vitamin D (weighted mean difference [WMD] -0.26(mg/l), (95% CI -0.75 to 0.22, N = 26 arms, heterogeneity p = 0.042; I 2 54.2%). The WMDs for IL6 was 0.67 pg/ml, (95% CI 0.29 to 1.06, N = 16 arms, heterogeneity p = 0.234; I 2 19.1%), 0.43 pg/ml, (95% CI 0.08 to 1.05, N = 26 arms, heterogeneity p = 0.120; I 2 42.1%), for IL10, and −0.11 pg/ml, (95% CI -0.53 to 0.30, N = 12 arms, heterogeneity p = 0.423; I 2 9.2%) for TNF-α, 4.03 pg/ml, (95% CI 3.50 to 4.57, N = 3 arms, heterogeneity p = 0.752; I 2 8.1%) for adiponectin. Sensitivity analyses confirmed the robustness of the findings. This study provided evidence that vitamin D supplementation had no impact on serum CRP, IL10, and TNF-α, while significantly increased serum IL6. We recommend RCTs with longer period of follow-up time (12 months) for future studies to provide explicit results.
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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 | 0.038 | 0.100 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.028 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".