What is the impact of vitamin D supplementation on glycemic control in people with type-2 diabetes: a systematic review and meta-analysis of randomized controlled trails
Bibliographic record
Abstract
BACKGROUND: There is conflicting evidence on the effect of vitamin D on glycemic control. Therefore, in the current meta-analyses, we aimed to assess the effect of vitamin D supplementation on the glycemic control of type 2 diabetes (T2D) patients. METHODS: We conducted a comprehensive search in electronic databases including; PubMed/Medline, Web of Science, Scopus, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and NIH's Clinical Trials Registry, from the inception of each database up to January first, 2021. RESULTS: A total of 46 randomized controlled trials (RCTs) consisting of 2164 intervention subjects and 2149 placebo controls were included in this meta-analysis. Pooled analyses for HbA1c showed a significant change between the intervention and placebo group, the weighted mean difference (WMD)(95% confidence interval(CI)) was -0.20%(-0.29, -0.11) with P < 0.001. Analyses for assessing changes in FPG found a significant reduction in the intervention group after vitamin D supplementation, the WMD (95%CI) was -5.02 mg/dl (-6.75,-3.28) with P < 0.001. The result of pooled analyses for HOMA-IR revealed a significant change between the intervention and control group, the WMD (95%CI) was -0.42(-0.76, -0.07) with P = 0.019. The subgroup analyses showed the most efficacy in a higher dose and short intervention period and in subjects with deficient vitamin D status. CONCLUSION: Vitamin D supplementation might be beneficial for the reduction of FPG, HbA1c, and HOMA-IR in type 2 diabetes patients with deficient vitamin D status. This effect was especially prominent when vitamin D was given in large doses and for a short period of time albeit with substantial heterogeneity between studies and a probability of publication bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.037 | 0.046 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".