The Association Between Vitamin D Deficiency and Insulin Resistance in Patients with Type 2 Diabetes: A Meta-Analysis
Bibliographic record
Abstract
Background: Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by insulin resistance and impaired glucose regulation. Emerging evidence suggests a potential link between vitamin D deficiency and insulin resistance, although findings remain inconsistent. Objective: This meta-analysis investigates the association between serum vitamin D levels and insulin resistance, measured primarily by HOMA-IR, among patients with T2DM or prediabetes. Methods: Studies were identified through a systematic search of electronic databases and included randomized controlled trials and observational studies. The pooled correlation coefficients, odds ratios, and mean differences were computed. Quality assessment was conducted using Cochrane RoB 2 and the Newcastle-Ottawa Scale. Results: A total of 15 studies involving over 12,000 participants were analyzed. The meta-analysis revealed a modest but significant inverse correlation between serum vitamin D and insulin resistance (r = -0.18, 95% CI: -0.29 to -0.08). Heterogeneity was moderate to high (I² = 67–97%), attributed to variability in vitamin D thresholds and population characteristics. Conclusion: Low vitamin D levels are modestly associated with increased insulin resistance in T2DM. While supplementation shows potential, particularly in combination therapies, further high-quality trials are necessary to establish causality and optimal therapeutic strategies.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.062 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".