Vitamin D Receptor Gene Polymorphisms Differentiated Between Tuberculosis Disease and Infection: Causal Association Study

Jaina Razbek,1 Yanggui Chen,2 Jiandong Yang,2 Yaying Zhang,1 Baofeng Wen,1 Junan Wang,1 Xiaomin Wang,1 Guliziba Kuerbanjiang,1 Abulikemu Aili,1 Mingqin Cao1 1Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, 830011, People’s Republic of China; 2Department of Prevention and Control of Tuberculosis, Urumqi Centre for Disease Control and Prevention, Urumqi, 830011, People’s Republic of ChinaCorrespondence: Mingqin Cao, Email [email protected]: Latent tuberculosis infection (LTBI) is a critical stage in tuberculosis (TB)control, and few studies have addressed the role of vitamin D receptor(VDR) gene polymorphisms in differentiating between TB and late-onset TB from an immunogenetic perspective.Patients and Methods: Recruitment of tuberculosis patients and latently infected population in Urumqi, Xinjiang, and use of propensity score matching(PSM) to match the two groups and control confounding to further construct a Bayesian network to analyze causal associations between VDR polymorphisms and tuberculosis disease status.Results: 137 LTBI and 237 TB were obtained through PSM. Logistic regression showed that the VDR gene BsmI locus, TaqI locus, and ApaI locus were associated with a higher risk of TB in a codominant model (P< 0.05). Further Bayesian network construction showed that occupation and being a VDR gene BsmI locus were direct influences on TB disease status, and the VDR gene TaqI locus played an indirect role through the BsmI locus, and the probability of TB risk was highest in individuals with manual labour and BsmI locus of the C/T type, which was 84.15%.Conclusion: Bayesian network modelling intuitively revealed that individuals with a C/T type of BsmI locus and physical labour are at high risk of TB compared with TB infection, and they are key factors between with TB disease, providing reference evidence for controlling TB progression.Keywords: tuberculosis, disease state, vitamin D receptor, propensity score matching, Bayesian network