Prevalence of Vitamin D Deficiency in Children and Adolescents With Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Our systematic review and meta-analysis examined the proportion of vitamin D deficiency in children and adolescents with type 1 diabetes mellitus (T1DM). We conducted a comprehensive literature search across multiple databases, including PubMed, Cochrane Library, Web of Science, Ovid Medline, and Embase, for studies published between January 2016 and March 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 29 studies comprising 2516 participants were included in the final analysis. Quality assessment was performed using the Newcastle-Ottawa Scale. The pooled analysis revealed that 46% (95% CI: 34-58%) of children and adolescents with T1DM had vitamin D deficiency, with significant heterogeneity observed across studies (I² = 97.98%, p<0.01). Subgroup analysis showed geographical variations with the highest deficiency rates in Africa (74%). The definition of vitamin D deficiency also impacted results, with cutoffs of <25 ng/mL yielding the highest proportion (80%) and <12 ng/mL the lowest (14%). Despite methodological limitations, including clinical setting bias, varied study designs, and inconsistent deficiency thresholds, our findings highlight the substantial burden of vitamin D deficiency in pediatric T1DM patients. This suggests the need for routine screening and potential supplementation strategies, though further research is required to establish optimal vitamin D levels for T1DM management and determine whether supplementation could play a preventive or therapeutic role in this population.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.053 |
| Bibliometrics | 0.010 | 0.011 |
| 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) | 0.003 | 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".