The Effects of Vitamin D Supplementation on Biomarkers of Inflammation and Oxidative Stress Among Women with Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Abstract The current systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to summarize the effect of vitamin D supplementation on biomarkers of inflammation and oxidative stress among women with polycystic ovary syndrome (PCOS). Cochrane library, Embase, PubMed, and Web of Science database were searched to identify related randomized-controlled articles (RCTs) published up to November 2017. Two researchers assessed study eligibility, extracted data, and evaluated risk of bias of included RCTs, independently. To check heterogeneity Q-test and I2 statistics were used. Data were pooled by using the random-effect model and standardized mean difference (SMD) was considered as summary effect size. Seven RCTs were included into our meta-analysis. The findings showed that vitamin D supplementation in women with PCOS significantly decreased high-sensitivity C-reactive protein (hs-CRP) (SMD −1.03; 95% CI, −1.58, −0.49; p <0.001) and malondialdehyde (MDA) (SMD −1.64, 95% CI −2.26 to −1.02, p <0.001), and significantly increased total antioxidant capacity (TAC) levels (SMD 0.86, 95% CI 0.08 to 1.64, p=0.03). Vitamin D supplementation had no significant effect on nitric oxide (NO) (SMD 0.11, 95% CI −0.44 to 0.66, p=0.69) and total glutathione (GSH) levels (SMD 0.54, 95% CI −0.20 to 1.28, p=0.15). Overall, the current meta-analysis demonstrated that vitamin D supplementation to women with PCOS resulted in an improvement in hs-CRP, MDA and TAC, but did not affect NO and GSH levels.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".