The Effects of Vitamin D Supplementation on Markers Related to Endothelial Function Among Patients with Metabolic Syndrome and Related Disorders: A Systematic Review and Meta-Analysis of Clinical Trials
Bibliographic record
Abstract
Abstract This systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted to summarize the effect of vitamin D supplementation on endothelial function among people with metabolic syndrome and related disorders. Cochrane library, Embase, PubMed, and Web of Science database were searched to identify related RCTs published up 20th May 2018. To check heterogeneity a 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. Twenty-two trials of 931 potential citations were found to be eligible for current meta-analysis. The pooled findings by using random effects model indicated that vitamin D supplementation to individuals with MetS and related disorders significantly increased flow-mediated dilatation (FMD) (SMD=1.10; 95% CI, 0.38, 1.81, p=0.003). However, it did not affect pulse-wave velocity (PWV) (SMD=0.04; 95% CI, –0.25, 0.33, p=0.80) and augmentation index (AI) (SMD=0.07; 95% CI, –0.25, 0.40; p=0.65). Overall, this meta-analysis demonstrated that vitamin D supplementation to patients with metabolic syndrome and related disorders resulted in an improvement in FMD, but did not influence PWV and AI.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".