Vitamin D Deficiency and Risk of Gestational Diabetes Mellitus in Western Countries: A Scoping Review
Bibliographic record
Abstract
Background: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication globally. Maternal vitamin D deficiency has been linked to the risk of GDM. The aim of this study was to explore and synthesise current evidence on the association between vitamin D deficiency and the development of gestational diabetes in Western countries. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodological framework. Relevant studies were identified through a comprehensive search across seven databases: ProQuest Public Health, Google Scholar, PubMed, ScienceDirect, The Lancet, BMC Public Health, the International Journal of Women’s Health, and Scopus. Studies were included based on predefined inclusion and exclusion criteria relevant to the research question. The review followed the JBI protocol, and the PRISMA flowchart was used to guide and visualise the study selection process. Results: Nineteen studies were included in the final analysis, comprising research predominantly from Australia (5), the United States (5), and Canada (4). The findings indicate a notable association between vitamin D deficiency and GDM risk, moderated by factors such as maternal age, ethnicity, seasonal variation, and body mass index (BMI). Older maternal age and higher BMI were linked with lower vitamin D levels and a higher incidence of GDM. Ethnic groups with darker skin tones showed higher rates of vitamin D deficiency, increasing vulnerability to GDM. Seasonal patterns revealed lower vitamin D levels during winter months, correlating with greater GDM risk. These patterns underscore the need for targeted preventive strategies, including the potential role of vitamin D supplementation. Conclusions: This review supports an observed association between maternal vitamin D deficiency and increased GDM risk, influenced by demographic and environmental factors. While the evidence points to a potential preventative role for vitamin D, further high-quality research, including systematic reviews and meta-analyses, is essential to establish causality and inform clinical guidelines. The review identifies knowledge gaps and suggests directions for future research and clinical practice.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".