Association between vitamin D receptor gene polymorphisms and susceptibility to tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective Tuberculosis (TB) is the leading cause of mortality worldwide. Previous studies have reported that TB susceptibility can be caused by vitamin D deficiency, which is affected by polymorphisms in the vitamin D receptor (VDR) gene. However, these results have been inconsistent. Therefore, we performed a meta-analysis to investigate the association between VDR polymorphisms and TB susceptibility. Methods We systematically searched for relevant literature in PubMed, Embase, and Medline databases through December 31st, 2022. Inclusion and exclusion criteria were made to ensure that HIV-negative population is the targeted subjects. The pooled odds ratio (OR) and 95% confidence interval (CI) were then used to assess the strength of the association, and the quality of the included articles was evaluated using the Newcastle–Ottawa Scale. Potential sources of heterogeneity were evaluated based on subgroup and meta-regression analyses. Results In our meta-analysis, we found that the FokI polymorphism in the VDR gene was associated with increased TB susceptibility in the allele and recessive genotype models (OR f vs. F = 1.235, 95%CI: 1.035–1.475; OR ff vs. Ff + FF = 1.317, 95%CI: 1.005–1.727. Further subgroup analysis based on ethnicity demonstrated the association with the risk of TB in all genotype models of the FokI polymorphism for Han population. Meta-regression analysis also indicated that ethnicity could be a potential source of heterogeneity in the FokI and BsmI polymorphisms in the VDR gene. However, publication year was another source of heterogeneity for the TaqI polymorphism. Conclusion In summary, the FokI polymorphism in the VDR gene was found to increase the risk of TB in the HIV-negative population, both overall and in Asian populations. The findings presented in this paper could provide clues for preventing TB from the perspective of vitamin D supplementation, which is a controversial topic in the field of medicine and health.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".