Effect of vitamin D supplementation on type 2 diabetes biomarkers: an umbrella of interventional meta-analyses
Bibliographic record
Abstract
BACKGROUND: Vitamin D supplementation exerts several supporting effects on improving glycemic status, however, results are inconclusive. Thus, in the present study, we aimed to conduct an umbrella of meta-analysis regarding the impact of vitamin D on type 2 diabetes (T2DM) biomarkers. METHODS: The Scopus, PubMed, Web of Science, Embase, and Google Scholar online databases were searched up to March 2022. All meta-analyses evaluating the impact of vitamin D supplementation on T2DM biomarkers were considered eligible. Overall, 37 meta-analyses were included in this umbrella meta-analysis. RESULTS: Our findings indicated that vitamin D supplementation significantly decreased fasting blood sugar (FBS) (WMD = - 3.08; 95% CI: - 3.97, - 2.19, p < 0.001, and SMD = - 0.26; 95% CI: - 0.38, - 0.14, p < 0.001), hemoglobin A1c (HbA1c) (WMD = - 0.05; 95% CI: - 0.10, - 0.01, p = 0.016, and SMD = - 0.16; 95% CI: - 0.27, - 0.05, p = 0.004), insulin concentrations (WMD = - 2.62; 95% CI: - 4.11, - 1.13; p < 0.001, and SMD = - 0.33; 95% CI: - 0.56, - 0.11, p = 0.004), and homeostatic model assessment for insulin resistance (HOMA-IR) (WMD = - 0.67; 95% CI: - 1.01, - 0.32, p < 0.001, and SMD = - 0.31; 95% CI: - 0.46, - 0.16, p < 0.001). CONCLUSION: This umbrella meta-analysis proposed that vitamin D supplementation may improve T2DM biomarkers.
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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 | 0.060 | 0.116 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.049 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".