Association between Gene Polymorphisms of Vitamin D Receptor and Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
(1) Background: Studies on the association between Vitamin D receptor gene polymorphism and gestational diabetes mellitus have been inconsistent. The aim of this study was to summarize available evidence on the association between polymorphisms of Vitamin D receptor genes and susceptibility to gestational diabetes mellitus. (2) Methods: We searched databases of PubMed, Web of Science, Embase, China national knowledge infrastructure (CNKI), China science and technology journal database (VIP), and Wanfang Data for relevant articles. A systematic review and a meta-analysis were done to compare the distribution of Vitamin D receptor gene polymorphisms in gestational diabetes mellitus patients with those in controls using allelic, codominant, dominant, and recessive models. (3) Results: A total of eight eligible articles were included in the systematic review and of them, six articles were included in the meta-analysis. The vitamin D receptor gene rs7975232 polymorphism was associated with gestational diabetes mellitus under the allelic model (odds ratio = 1.28, 95% confidence interval 1.06–1.56), codominant model (CC vs. AA odds ratio = 1.97, 95% confidence interval 1.28–3.05), and recessive model (odds ratio = 1.83, 95% confidence interval 1.27–2.64) in the case of low heterogeneity. High heterogeneity existed in studies on the association of vitamin D receptor genes rs1544410, rs2228570, and rs731236 with gestational diabetes mellitus, and the most common sources of heterogeneity were the year of publication and matching. (4) Conclusion: Polymorphism of the vitamin D receptor gene rs7975232 may be associated with risk of developing gestational diabetes mellitus. Future studies should be designed to include standardized data collection and matching for important confounding factors such as body mass index, age, and race.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".