Bidirectional relationship between vitamin D deficiency and type 1/type 2 diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Diabetes mellitus and vitamin D deficiency (VDD) are widespread global health concerns with overlapping metabolic risks. Emerging evidence suggests a bidirectional relationship: VDD exacerbates insulin resistance, whereas diabetes mellitus disrupts vitamin D metabolism. Methods: This meta-analysis was registered prospectively (PROSPERO CRD42025639951). We conducted a comprehensive search of PubMed, Embase, Web of Science, and the Cochrane Library from their inception to January 2025 for observational studies examining the bidirectional associations between VDD and diabetes mellitus. Studies were eligible if they (1) employed cohort or case-control designs, (2) defined VDD as serum 25-hydroxyvitamin D [25(OH)D] < 20 ng/mL, and (3) diagnosed diabetes mellitus according to the American Diabetes Association (ADA) criteria. Two reviewers independently extracted data and assessed study quality using the Newcastle-Ottawa scale. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using random-effects models (STATA 15.1 and RevMan 5.4). Results: Among 53 studies (n = 552,032), individuals with VDD had a 53% increased risk of developing type 2 diabetes mellitus (T2DM) (OR = 1.53, 95% CI: 1.38–1.70). Conversely, individuals with type 1 diabetes mellitus (T1DM) and T2DM had a 2.02-fold and 2.62-fold increased risk of VDD, respectively. Subgroup analyses demonstrated stronger associations in Asian populations (T1DM: OR = 2.21; Europe: OR = 1.65; P < 0.05 for regional difference) and among normal-weight T2DM patients (OR = 7.68, compared to obese: OR = 5.21). Discussion: This meta-analysis reveals a bidirectional link between VDD and diabetes mellitus, emphasizing subtype- and phenotype-specific risk profiles. Clinically, routine monitoring of serum 25(OH)D levels is recommended for diabetic patients, particularly in high-risk subgroups such as individuals with T1DM or lean T2DM phenotypes, and suggests targeted vitamin D supplementation for high-risk groups. On a public health scale, fortifying staple foods with vitamin D in regions with high deficiency rates, such as Asia, could alleviate the dual burden of VDD and diabetes mellitus.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".