Vitamin D Supplementation, Glycemic Control, and Insulin Resistance in Prediabetics: A Meta-Analysis
Bibliographic record
Abstract
Diabetes prevention is a public health priority. Vitamin D supplementation may help prevent the development of diabetes in persons at increased risk. We performed a meta-analysis of controlled clinical trials that assessed glycemic outcome measures among adults at risk for type 2 diabetes, including prediabetes, overweight, or obesity. We searched PUBMED/ MEDLINE, CINAHL, and Google Scholar databases for trials published prior to April 2017. Placebo-controlled clinical trials with random allocation to vitamin D with or without calcium supplementation were selected. Data collection included country, study design, inclusion criteria, sample size, form, and dose of vitamin D, supplementation interval, control group, duration, participant characteristics, comorbidities, baseline and follow-up serum 25-hydroxyvitamin D [25(OH)D] concentration, and available outcome measures [glycosylated hemoglobin (HbA1c), fasting plasma glucose, plasma glucose after 2-hour oral glucose tolerance test, and homeostatic model assessment of insulin resistance (HOMA-IR)]. Data synthesis was conducted using random-effect models (PROSPERO registration no. CRD42017055326). Twenty-eight trials, representing 3848 participants, met the eligibility criteria. Compared with the control group, vitamin D supplementation significantly reduced HbA1c level by -0.48% (95% CI, -0.79 to -0.18), fasting plasma glucose level by -0.46 mmol/L (95% CI, -0.74 to -0.19), and HOMA-IR level by -0.39 (95% CI, -0.68 to -0.11). Subgroup analysis revealed that the effects of vitamin D supplementation on different glycemic measures were influenced by age, calcium coadministration, vitamin D deficiency, serum 25(OH)D level after supplementation, and duration of supplementation. Vitamin D supplementation and improved vitamin D status improved glycemic measures and insulin sensitivity and may be useful as part of a preventive strategy for type 2 diabetes.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.050 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".