Vitamin D receptor gene polymorphisms and the risk of the type 1 diabetes: a meta-analysis based on 40 case–control studies
Bibliographic record
Abstract
Abstract Objective The association between the vitamin D receptor ( VDR ) gene polymorphisms and the risk of Type 1 diabetes mellitus (T1DM) has been evaluated in several studies. However, the findings were inconclusive. Thus, we conducted a meta-analysis to comprehensively evaluate the effect of VDR gene polymorphisms on the risk of T1DM.Methods All relevant studies reporting the association between VDR gene polymorphisms and susceptibility to T1DM published up to July 2019 were identified by comprehensive systematic database search in web of science, Scopus, and PubMed. Strength of association were assessed by calculating of pooled odds ratios (ORs) and 95% confidence intervals (CIs). The methodological quality of each study was assessed according to the Newcastle–Ottawa Scale. Subgroup analysis stratified by ethnicity was also conducted.Results A total of 40 case–control studies were included in this meta-analysis. The results of overall population rejected any significant association between VDR gene polymorphisms and T1DM risk. However, the pooled results of subgroup analysis revealed significant negative and positive associations between FokI and BsmI genes polymorphism and T1DM in African and American populations, respectively.Conclusions This meta-analysis suggested a significant association between VDR gene polymorphism and T1DM susceptibility in ethnic-specific analysis.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.008 | 0.011 |
| 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".