Front. Genet. Frontiers in Genetics Front. Genet. 1664-8021 Frontiers Media S.A. 1382957 10.3389/fgene.2024.1382957 Genetics Systematic Review Association between vitamin D receptor gene polymorphisms and susceptibility to tuberculosis: a systematic review and meta-analysis Tao et al. 10.3389/fgene.2024.1382957 Tao Rongshan 1 † Xiao Shujuan 2 † Wang Lianping 3 4 † Hu Chunjie 5 Suo Huiqin 6 Long Ruiyu 1 Liu Hangyu 6 Luo Wei 7 Hong Feng 1 * Zhao Jingming 8 * Li Qingjie 9 * 1 The Key Laboratory of Environmental Pollution Monitoring and Disease Control, School of Public Health, Ministry of Education, Guizhou Medical University, Guiyang, China 2 Xiangya School of Public Health, Central South University, Changsha, China 3 School of Traditional Chinese Medicine Jilin Agricultural Science and Technology University, Changchun, China 4 School of Life Sciences, Jilin University, Jilin, China 5 Anorectal Center, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China 6 School of Pharmacy, Changchun University of Traditional Chinese Medicine, Changchun, China 7 Office of Infection Control, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China 8 Proctology Department, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China 9 Research Center of Traditional Chinese Medicine, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China

Edited by: John S. Spencer, Colorado State University, United States

Reviewed by: Machao Li, National Institute for Communicable Disease Control and Prevention (China CDC), China

Qing Li, Vanderbilt University Medical Center, United States

 *Correspondence: Qingjie Li, lqj19811005@163.com; Jingming Zhao, zjm-7883312@163.com; Feng Hong, fhong@gmc.edu.cn

These authors have contributed equally to this work

 20 08 2024 2024 15 1382957 29 03 2024 29 07 2024 Copyright © 2024 Tao, Xiao, Wang, Hu, Suo, Long, Liu, Luo, Hong, Zhao and Li. 2024 Tao, Xiao, Wang, Hu, Suo, Long, Liu, Luo, Hong, Zhao and Li

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

 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.

 tuberculosis gene polymorphisms VDR vitamin D receptor meta-analysis section-at-acceptance Genetics of Common and Rare Diseases Introduction

Tuberculosis (TB) is a communicable disease caused by the Mycobacterium tuberculosis complex (MTB). It is considered a major determinant of poor health and one of the leading causes of mortality, responsible for 1.6 million deaths worldwide in 2021 (Möller and Hoal, 2010). According to “Global Tuberculosis Report 2022” from the World Health Organization, approximately 10.6 million people worldwide were infected with TB in 2021, representing a 4.5% increase from the 10.1 million cases recorded in 2020. Similarly, the report cited an estimated 3.6% increase in TB incidence, to approximately 134 cases per 100,000 population, between 2021 and 2020 (WORLD HEALTH ORGANIZATION, 2022-10). These challenges highlight the serious issue associated with preventing and controlling TB epidemics.

The persisting association between MTB and its host implies that this pathogen has evolved extensive mechanisms to evade elimination by the immune system. Accordingly, it causes no substantial harm and is not transmitted until immune system responses decline due to co-infections or other factors. As a result, despite approximately one-quarter of the global population being infected with MTB, only 5%–10% of these individuals develop TB (Delgado et al., 2002; Dye et al., 1999). Moreover, the process of TB might also be associated with other factors, such as lifestyle, environment, and genetics (Hillerdal, 2000; Newport and Nejentsev, 2004; Nava-Aguilera et al., 2009). Among these, genetic factors of the host play a vital role in susceptibility or resistance to TB.

In recent decades, vitamin D has been shown to play an essential role in bone health (Ganmaa et al., 2020). Moreover, recent studies based on different populations have indicated that vitamin D deficiency increases the risk of developing TB. Vitamin D also plays a role in the biological modulation of the immune system in response to TB. Here, 1,25-dihydroxyvitamin D3 (the active form of vitamin D) is activated by 1 α -hydroxylase, which can be expressed by macrophages and other immune cells. Further, evidence suggests that the cellular functions of 1,25-dihydroxyvitamin D3 can be affected by variations in the vitamin D receptor (VDR) gene (L Bishop et al., 2020).

VDR is located on the long arm of chromosome 12q13 (Miyamoto et al., 1997). Polymorphisms of this gene are observed across various population groups, although the prevalence of specific VDR genotypes varies among populations. Several polymorphisms, including BsmI (rs1544410), ApaI (rs7975232), and TaqI (rs731236), at the 3′end of VDR with strong linkage disequilibrium have been examined. Despite having no impact on the structure of the expressed VDR protein, these three single nucleotide polymorphisms potentially have a role in regulating the expression of the VDR gene. Another gene polymorphism, FokI (rs2228570), is located in exon 2, at a translation initiation site, and is anticipated to alter the structure of the encoded protein (Figure 1) (Shaikh et al., 2016).

Genomic region and exon-intron structure of the vitamin D receptor (VDR) gene. (The VDR gene is placed on human chromosome 12q13.11, contains nine exons and encompasses various single nucleotide polymorphisms (SNPs) including FokI (F/f), BsmI (B/b), ApaI (A/a), TaqI (T/t). The presence of a T/C transition polymorphism (ATG to ACG) at the first of two potential translation initiation sites in exon II).

Multiple studies have investigated the potential effect of VDR gene polymorphisms on susceptibility to TB; however, the results of these studies have been inconsistent. This inconsistency could be due to various factors, such as small sample sizes, insufficient power to detect associations between VDR gene polymorphisms and susceptibility to TB, the study design, the ethnicity of the study population, and the genetic context. A meta-analysis, a statistical technique that combines multiple results from previous studies to increase the statistical power and improve the precision of the estimation of pooled data (Blettner et al., 1999), could thus be a good option for analyzing inconsistent results.

Several meta-analyses have been conducted to identify the potential association between VDR gene polymorphisms and TB susceptibility over the past decades; however, larger pooled datasets are required to improve the power of effect estimates. Furthermore, few studies have been performed to uncover the impact of VDR gene polymorphisms based on different ethnic backgrounds. Therefore, we performed a comprehensive meta-analysis to (1) systematically evaluate the relationship between VDR gene polymorphisms, including FokI, BsmI, ApaI, TaqI, and TB susceptibility, (2) explore the potential effect of VDR gene polymorphisms on TB susceptibility in various ethnic groups.

 Materials and methods Study selection

The PubMed, Embase, and MEDLINE databases were searched for studies to include in this meta-analysis. The keyword used were: “VDR”, “Vitamin D receptor”, “tuberculosis”, “gene”, and “polymorphism.” The reference lists of the review articles were also manually searched for additional pertinent publications. The article search was conducted for articles published until 30 December 2022.

 Inclusion and exclusion criteria

The literature was included based on the following criteria: i) case-control studies assessing the association between VDR gene polymorphisms and TB risk; ii) all participants in the studies confirmed to be negative for human immunodeficiency virus (HIV-negative), which could be examined in accordance with a certain diagnostic criterion of laboratory or antibody tests; iii) sufficient data on alleles and genotypes for the case and control groups provided to calculate the odds ratios (ORs) and 95% confidence intervals (CIs). The exclusion criteria were as follows: i) studies of control groups with gene distributions that deviated from the Hardy–Weinberg equilibrium (HWE) (Mayo, 2008); ii) low-quality studies (i.e., Newcastle–Ottawa Scale (NOS) scores (Stang, 2010) below 6); iii) review articles, abstracts, animal experiments, letters, editorials, case reports, and non-English publications.

 Data extraction and quality assessment

According to the predetermined data extraction sheet, the following data were extracted independently by two researchers (R.S. Tao and S.J. Xiao): first authors’ names, year of publication, country of origin, ethnicity, the total number of participants in the case and control groups, genotype and allele frequencies in the case and/or control groups, mean or range of age, genotyping method, and TB type. In case of discrepancies, a third reviewer (L.P. Wang) concluded on the extracted data. For quality assessments, the NOS was used. Studies were stratified into two categories, specifically low quality (scores 0–5) and high quality (scores ≥6).

 Statistical analysis

A chi-square test was used to assess the deviation from the HWE in terms of allele and genotype frequencies in the control groups. The strength of the association between VDR polymorphisms and TB susceptibility was evaluated by calculating the pooled OR and its 95% CI. Data were extracted to build different comparison genotype models for the polymorphisms (i.e., FokI, BsmI, ApaI, TaqI) of the VDR gene, as follows: i) FokI, allele model (f vs. F), dominant model (ff + Ff vs. FF), recessive model (ff vs. Ff + FF), homozygote model (ff vs. FF); ii) BsmI, allele model (b vs. B), dominant model (bb + Bb vs. BB), recessive model (bb vs. Bb + BB), homozygote model (bb vs. BB); iii) ApaI, allele model (a vs. A), dominant model (aa + Aa vs. AA), recessive model (aa vs. Aa + AA), homozygote model (aa vs. AA); iv) TaqI, allele model (t vs. T), dominant model (tt + Tt vs. TT), recessive model (tt vs. Tt + TT), homozygote model (tt vs. TT). Heterogeneity among studies was measured based on the Q statistic (a p-value with a significance level of 0.05) and the I 2 statistic, which was used to quantify the inconsistency between study results. Commonly, a fixed-effects model for the pooled OR is used for a Q statistic with p > 0.05 and I 2 < 50%. Otherwise, a random-effects model is used to combine the data if p ≤ 0.05 and I 2 ≥ 50%. Subgroup analysis was performed to evaluate the source of heterogeneity from the perspective of ethnicity, and meta-regression analysis was performed to explore the potential sources of heterogeneity based on the publication year and ethnicity. The stability of our results was assessed using a sensitivity analysis, and potential publication bias was evaluated using funnel plots and Egger’s test (Egger et al., 1997). The data for this study were analyzed using R language programming software (version 4.2.3).

 Results Characteristics of the eligible studies

In total, 788 articles were identified via a systematic literature search of the PubMed, Embase, and MEDLINE databases. After screening using our inclusion and exclusion criteria, 25 eligible articles (Delgado et al., 2002; Liu et al., 2004; Roth et al., 2004; Merza et al., 2009; Banoei et al., 2010; Marashian et al., 2010; Zhang et al., 2010; Ates et al., 2011; Kang et al., 2011; Wu et al., 2013b; Joshi et al., 2014; Sinaga et al., 2014; Fernández-Mestre et al., 2015; Salimi et al., 2015; Jafari et al., 2016; Lee et al., 2016; Rong et al., 2017; Wang et al., 2017; Devi et al., 2018; Zhang et al., 2018; Panda et al., 2019; Silva-Ramírez et al., 2019; Hidayah et al., 2021; Varzari et al., 2021; Wani et al., 2021) were included (Figure 2: PRISMA flow diagram). Of these, 20 studies (Delgado et al., 2002; Liu et al., 2004; Merza et al., 2009; Banoei et al., 2010; Marashian et al., 2010; Zhang et al., 2010; Kang et al., 2011; Wu et al., 2013a; Joshi et al., 2014; Sinaga et al., 2014; Fernández-Mestre et al., 2015; Salimi et al., 2015; Lee et al., 2016; Rong et al., 2017; Wang et al., 2017; Devi et al., 2018; Zhang et al., 2018; Panda et al., 2019; Hidayah et al., 2021; Wani et al., 2021) were from Asia and the remaining five were from Europe (Varzari et al., 2021; Ates et al., 2011), North America (Silva-Ramírez et al., 2019), and South America (Jafari et al., 2016; Roth et al., 2004). The case and control groups consisted of 3,768 and 3,742 patients, respectively. Among these articles, 22 and eight studies provided information on the FokI and ApaI polymorphisms, respectively, and 15 studies provided information on the BsmI and TaqI polymorphisms. The NOS scores of the included studies ranged from 6 to 9. Tables 1, 2 summarize the basic characteristics of genotype and allele frequencies in the included studies, respectively.

PRISMA flflow chart.

Characteristics of individual studies included in the meta-analysis.

Author (year) Country Ethnicity Case/Control Age a Type of TB b Genotyping Polymorphism(s) c NOS d (score)
Hidayah 2021 Indonesia Asian 83/118 41.0/NA PTB PCR 1,2,3,4 8
Varzari 2021 Moldova European 272/251 40.7/47.6 PTB PCR-RFLP 1,3,4 6
Wani 2021 India Asian 100/102 45.9/NA EPTB PCR-RFLP 2 6
Panda 2019 India Asian 150/150 39.4/32.1 PTB PCR-RFLP 1 7
Devi 2018 India Asian 169/227 32.6/33.4 PTB PCR-RFLP 1,2,3,4 6
Silva 2019 Mexico North American 257/457 45.3/36.5 PTB TaqMan 1,2,3,4 7
Zhang 2018 China Asian 108/59 38.0/49.3 TB PCR-RFLP 1,2,3,4 7
Rong 2017 China Asian 258/291 56.3/29.6 TB iMLDR 3 6
Wang 2017 China Asian 150/149 46.3/45.8 STB PCR-RFLP 1 6
Jafari 2016 Iran Asian 96/122 51.0/48.0 PTB ARMS-PCR 1,2,3,4 7
Lee 2016 Taiwan Asian 198/170 55.8/55.8 PTB TaqMan 1,2,3,4 7
Fernandez 2015 Venezuela South American 93/102 17-70/20-67 PTB PCR-RFLP 1,2,4 7
Salimi 2015 Iran Asian 120/131 51.5/48.1 PTB PCR-RFLP 1,2,3 9
Sinaga 2014 Indonesia Asian 76/76 NA PTB PCR-RFLP 1,3 9
Joshi 2014 India Asian 110/115 25.0/21.6 PTB PCR-RFLP 1,3 7
Wu 2013 China Asian 213/211 18-72/NA PTB PCR-RFLP 1,2 8
Ates 2011 Turkey European 128/80 47.8/54.1 PTB, EPTB PCR-RFLP 1,2,3 6
Kang 2011 South Korea Asian 155/105 17-69/21-52 PTB PCR-RFLP 1,2,3 6
Banoei2010 Iran Asian 60/62 45.8/41.0 PTB PCR 1,2,3 6
Marashian2010 Iran Asian 164/50 NA TB PCR-RFLP 1 6
Zhang 2010 China Asian 110/102 33.8/32.2 STB PCR-RFLP 1 9
Merza 2009 Iran Asian 117/60 NA PTB PCR-RFLP 1,3 6
Roth 2004 Peru South American 103/206 25.4/25.4 PTB PCR-RFLP 1,2 7
Liu 2003 China Asian 120/240 27.7/27.3 PTB PCR-RFLP 1 9
Delgado 2002 Cambodia Asian 358/106 42.2/37.5 PTB PCR-RFLP 2 6

“NA” means that the data were not available.

Age was shown as the mean or range age of cases and controls.

PTB, pulmonary tuberculosis; EPTB, extrapulmonary tuberculosis; STB, spinal tuberculosis.

1:FokI; 2:TaqI; 3:BsmI; 4:ApaI.

Newcastle-Ottawa scale.

Distributions of VDR genotype and allele among TB patients and controls.

A VDR FokI (rs2228570)
First author reference Case Control Case Control
FF Ff Ff FF Ff Ff F f F f HWE
Ates 58 60 10 35 37 8 176 80 107 53 0.926
Devi 59 106 4 119 90 18 224 114 328 126 0.986
Hidayah 20 46 17 46 62 10 86 80 154 82 0.412
Joshi 51 46 13 63 41 11 148 72 167 63 0.539
Lee 44 104 50 51 87 32 192 204 189 151 0.893
Panda 55 58 37 86 51 13 168 132 223 77 0.410
Salimi 65 44 11 93 31 7 174 66 217 45 0.157
Silva 76 119 62 80 218 159 271 243 378 536 0.939
Sinaga 27 42 7 30 34 12 96 56 94 58 0.210
Wang 22 53 75 39 68 42 97 203 146 152 0.570
Liu 29 63 28 85 120 35 121 119 290 190 0.781
Zhang 16 43 51 26 47 29 75 145 99 105 0.576
Fernandez 34 47 12 26 60 16 115 71 112 92 0.165
Jafari 41 50 5 55 61 6 132 60 171 73 0.105
Wu 72 96 45 101 88 22 240 186 290 132 0.910
Varzari 100 117 47 74 125 44 317 211 273 213 0.784
Zhang 21 80 79 21 25 13 122 238 67 51 0.735
Banoei 30 21 9 29 27 6 81 39 85 39 0.997
Kang 30 58 15 41 43 21 118 88 125 85 0.306
Marashain 97 57 10 15 30 5 251 77 60 40 0.210
Merza 67 46 4 35 25 0 180 54 95 25 0.125
Roth 9 32 59 14 78 109 50 150 106 296 0.923
B VDR BsmI (rs1544410)
First author reference Case Control Case Control
BB Bb bb BB Bb bb B b B b HWE
Ates 28 68 32 5 38 37 124 132 48 112 0.504
Devi 24 100 45 56 113 58 148 190 225 229 0.998
Hidayah 63 18 2 83 35 0 144 22 201 35 0.317
Lee 183 14 1 146 24 0 380 16 316 24 0.612
Salimi 31 66 23 39 70 22 128 112 148 114 0.609
Silva 153 96 8 273 168 16 402 112 714 200 0.274
Sinaga 0 52 24 2 18 56 52 100 22 130 0.140
Rong 137 101 20 188 86 17 375 141 462 120 0.253
Jafari 11 42 43 15 52 55 64 128 82 162 0.884
Varzari 108 121 40 94 119 34 337 201 307 187 0.931
Zhang 2 19 159 1 4 54 23 337 6 112 0.073
Banoei 13 27 20 31 26 5 53 67 88 36 0.990
Kang 2 13 135 0 8 75 17 283 8 158 0.899
Merza 7 67 43 13 21 26 81 153 47 73 0.121
C VDR TaqI (rs731236)
First author reference Case Control Case Control
TT Tt tt TT Tt tt T t T t HWE
Ates 49 65 14 30 39 11 163 93 99 61 0.957
Devi 86 73 10 116 86 25 245 93 318 136 0.342
Hidayah 72 11 0 97 19 2 155 11 213 23 0.831
Lee 186 12 0 149 20 1 384 12 318 22 0.935
Salimi 52 54 14 67 50 14 158 82 184 78 0.607
Silva 132 110 15 228 199 30 374 140 655 259 0.304
Wani 54 42 4 60 33 9 150 50 153 51 0.383
Delgado 325 30 3 96 10 0 680 36 202 10 0.878
Fernandez 51 33 2 58 38 1 135 37 154 40 0.153
Jafari 38 46 12 56 58 8 122 70 170 74 0.386
Zhang 160 19 1 52 7 0 339 21 111 7 0.889
Kang 134 14 1 85 8 1 282 16 178 10 0.323
Banoei 8 33 19 33 24 5 49 71 90 34 0.977
Roth 90 10 0 169 31 1 190 10 369 33 0.992
D VDR ApaI (rs7975232)
First author reference Case Control Case Control
AA Aa aa AA Aa aa A a A a HWE
Devi 36 83 50 49 103 75 155 183 201 253 0.480
Hidayah 38 31 14 32 61 25 107 59 125 111 0.910
Lee 103 78 17 89 65 16 284 112 243 97 0.718
Silva 96 125 36 159 218 80 317 197 536 378 0.718
Fernandez 27 42 20 29 54 18 96 82 112 90 0.711
Jafari 33 44 19 36 55 31 110 82 127 117 0.564
Varzari 60 142 65 61 128 52 262 272 250 232 0.613
Zhang 19 67 94 2 21 36 105 255 25 93 0.880

HWE, Hardy-Weinberg equilibrium.

 Test of heterogeneity

No or low heterogeneity was detected for the ApaI polymorphism, which included allele (a vs. A, I 2 = 23%, p = 0.25) homozygote (aa vs. AA, I 2 = 13%, p = 0.33), recessive (aa vs. Aa + AA, I 2 = 0%, p = 0.70), and dominant (aa + Aa vs. AA, I 2 = 30%, p = 0.19) genotype models. Similarly, the TaqI polymorphism exhibited low heterogeneity in the recessive model (tt vs. Tt + TT, I 2 = 33%, p = 0.12). Therefore, a fixed-effects model was applied to synthesize the OR for the ApaI polymorphism and a recessive model was used for the TaqI polymorphism. The remaining genotype models of VDR gene polymorphisms (i.e., FokI, BsmI, TaqI) were estimated to have substantial heterogeneity, indicating that a random-effects model could be applied to analyze the pooled OR.

 Quantitative synthesis

Table 2 presents the results of the analysis. For the FokI VDR gene polymorphism, a significant association was observed with TB susceptibility in the allele model (f vs. F, OR = 1.235, 95%CI: 1.035–1.475, p = 0.019). A similar result was observed in the recessive model (ff vs. Ff + FF, OR = 1.317, 95%CI: 1.005–1.727, p = 0.046). However, no significant association with TB susceptibility was observed in the homozygote (ff vs. FF) and dominant (ff + Ff vs. FF) models. Regarding the other three VDR gene polymorphisms (BsmI, ApaI, TaqI), there was no evidence supporting a significant association between the four genotype models and TB susceptibility.

A subgroup analysis of ethnicity was performed according to the four genotype models of the VDR gene polymorphisms. In the Asian population, for the FokI polymorphism, high heterogeneity was observed in all genotype models. Therefore, to detect more specific source of the heterogeneity, we classified Asian ethnicity as Orang Indonesia, Indian, Han population, and Iranian according to the district of the study populations. There was low or no heterogeneity in all genotype models for Han population. As a result, a Fixed-effects model was suggested to pool ORs, which showed a significant association of the FokI polymorphism with TB susceptibility in the Han population. Similarly, there was evidence of a significant association with TB susceptibility in the dominant model (ff + Ff vs. FF) for Orang Indonesia (OR = 1.571, 95%CI = 1.002–2.462, Pheterogeneity = 0.25, I2 = 24%) and Indian (OR = 1.939, 95%CI = 1.488–2.528, Pheterogeneity = 0.35, I2 = 6%). However, there was no significant association of the homozygote model and recessive model with TB susceptibility for Iranian although low heterogeneity was observed. Furthermore, evidence of a significant association with TB susceptibility was found within three genotype models of the BsmI polymorphism in the Asian. One was a homozygote model (bb vs. BB) adopting a fixed-effects model to obtain a pooled OR = 1.751 (95%CI: 1.319–2.324) due to low heterogeneity (Pheterogeneity = 0.24, I2 = 21%). Another was the dominant model (bb + Bb vs. BB, Pheterogeneity < 0.01, I2 = 62%), in which it was a significant association with TB susceptibility in Indian (OR = 1.972, 95%CI = 1.351–2.878, generated from a fixed-effects model) with no heterogeneity (Pheterogeneity = 0.99, I2 = 0%). Besides, The result of the pooled ORs by a fixed-effects model displayed a significant association with TB in the allele model (b vs. B, OR = 1.265, 95%CI = 1.009–1.588, Pheterogeneity = 0.97, I2 = 0%) for Indian (Figures 3, 4). For the ApaI polymorphism, a significant association was found in the allele model (a vs. A, OR = 0.835, generated from a common-effects model, 95%CI: 0.712–0.979, Pheterogeneity < 0.32, I2 = 15%) and the homozygote model (aa vs. AA, OR = 0.702, generated from a common-effects model, 95%CI = 0.504–0.978, Pheterogeneity = 0.44, I2 = 0%) (Supplementary Figure S2).

Subgroup analysis forest plot of four genotype models for the VDR gene FokI polymorphism on ethnicity.

Subgroup analysis forest plot of four genotype models for the VDR gene BsmI polymorphism on ethnicity.

However, no significant association between VDR gene polymorphisms and TB susceptibility was found in the South American or European populations (Figures 3, 4; Supplementary Figures S1–4). The details of the pooled ORs, heterogeneity tests, and Egger’s test for publication bias are shown in Table 3.

The results of pooled ORs, test of heterogeneity and Egger’s test for publication bias in the four genotype models of VDR gene polymorphisms in the meta-analysis.

Polymorphism No. of studies Case/Control Test of association Test of heterogeneity Egger’s test for publication bias
OR 95%CI p-value I 2 (%) p-value T p-value
FokI f vs. F 22 3052/3243 1.235 (1.035–1.475) 0.019 82.5 <0.001 0.98 0.337
ff + Ff vs. FF 22 3052/3243 1.275 (0.997–1.632) 0.053 79.0 <0.001 0.47 0.644
ff vs. Ff + FF 22 3052/3243 1.317 (1.005–1.727) 0.046 71.1 <0.001 0.31 0.756
ff vs. FF 22 3052/3243 1.427 (0.994–2.048) 0.054 79.2 <0.001 0.64 0.527
BsmI b vs. B 15 2097/2209 0.976 (0.755–1.262) 0.853 77.2 <0.001 −1.04 0.317
bb + Bb vs. BB 15 2097/2209 1.233 (0.851–1.787) 0.269 70.3 <0.001 0.18 0.864
bb vs. Bb + BB 15 2097/2209 0.902 (0.613–1.326) 0.599 70.8 <0.001 0.76 0.463
bb vs. BB 15 2097/2209 1.364 (0.838–2.221) 0.212 59.3 0.002 0.39 0.704
TaqI t vs. T 15 2028/2047 1.005 (0.801–1.260) 0.968 62.3 0.001 −0.27 0.789
tt + Tt vs. TT 15 2028/2047 1.029 (0.821–1.299) 0.803 53.3 0.008 0.32 0.758
tt vs. Tt + TT 15 2028/2047 0.986 (0.742–1.310) 0.922 28.8 0.140 −0.13 0.897
tt vs. TT 15 2028/2047 1.085 (0.616–1.910) 0.778 50.4 0.013 0.04 0.970
ApaI a vs. A 8 1276/1506 0.913 (0.818–1.019) 0.106 23.0 0.246 NA NA
aa + Aa vs. AA 8 1276/1506 0.891 (0.753–1.054) 0.178 30.1 0.187 NA NA
aa vs. Aa + AA 8 1276/1506 0.885 (0.733–1.069) 0.206 00.0 0.705 NA NA
aa vs. AA 8 1276/1506 0.836 (0.666–1.051) 0.125 13.1 0.328 NA NA

The bold values indicate that the OR (95%CI) does not include 1, and the p-value is <0.05.

 Sensitivity analysis

Sensitivity analysis was performed to evaluate the impact of an individual article on the pooled ORs using the leave-one-out method, which involves omitting a single article each time. The results indicated an obvious decrease in the heterogeneity and significance of the pooled OR within the homozygote model of the BsmI polymorphism after the deletion of one article (Jafari et al., 2016) (Supplementary Figure S5). Besides, no other individual article had a significant impact on the pooled OR or heterogeneity when omitted. This finding suggests that our results are relatively robust.

 Publication bias

Publication bias was assessed using Egger’s test, which indicated no significant publication bias (p > 0.05) among in the included studies. Funnel plots were used to obtain the evidence of bias. No distinct asymmetry was found in the funnel plots, suggesting that there was no significant publication bias (Figure 5).

Funnel plot of the genotype models of the VDR gene ApaI polymorphism. [(A): allele (a vs A); (B): dominant (aa+Aa vs AA); (C): recessive (aa vs Aa+AA); (D): homozygote (aa vs AA)].

 Meta-regression

We finally performed a meta-regression analysis to explore the potential sources of heterogeneity among the VDR gene polymorphisms within the included articles. Our meta-regression analysis indicated that ethnicity could be a potential source of heterogeneity in the FokI and BsmI polymorphisms (i.e., within the homozygote and dominant models) of the VDR gene. However, the publication year was not the main source of heterogeneity. These details are presented in Table 4.

The results of Meta-regression.

Heterogeneity source Coefficient 95%CI Z P
FokI (rs2228570)
Ethnicity Allele (f vs. F) Asian (reference) - - - -
European −0.446 (-0.948,0.056) −1.742 0.082
North American −0.876 (-1.523,-0.229) −2.654 0.008
South American −0.408 (-0.934,0.118) −1.521 0.128
Dominant (ff + Ff vs. FF) Asian (reference) - - - -
European −0.615 (-1.234,0.003) −1.951 0.051
North American −1.236 (-2.041,-0.431) −3.010 0.003
South American −0.834 (-1.569, −0.098) −2.221 0.026
Recessive (ff vs. Ff + FF) Asian (reference) - - - -
European −0.553 (-1.400,0.295) −1.278 0.201
North American −1.011 (-2.021, −0.001) −1.961 0.049
South American −0.390 (-1.230,0.450) −0.911 0.363
Homozygote (ff vs. FF) Asian (reference) - - - -
European −0.875 (-1.875,0.124) −1.716 0.086
North American −1.619 (-2.857, −0.381) −2.563 0.010
South American −0.955 (-2.020,0.110) −1.758 0.079
Publication year f vs. F 0.011 (-0.0257,0.0485) 0.602 0.548
ff + Ff vs. FF 0.021 (-0.032,0.073) 0.761 0.447
ff vs. Ff + FF 0.001 (-0.053,0.056) 0.052 0.959
ff vs. FF 0.012 (−0.064,0.087) 0.306 0.760
BsmI (rs1544410)
Ethnicity Allele (b vs. B) Asian (reference) - - - -
European −0.480 (-1.275,0.315) −1.183 0.237
North American 0.012 (-1.033,1.057) 0.023 0.982
Dominant (bb + Bb vs. BB) Asian (reference) - - - -
European −1.075 (-2.105, −0.046) −2.048 0.041
North American −0.320 (-1.566,0.926) −0.504 0.614
Recessive (bb vs. Bb + BB) Asian (reference) - - - -
Heterogeneity source Coefficient 95%CI Z P
Homozygote (bb vs. BB) European −0.452 (-1.681,0.777) −0.720 0.472
North American −0.048 (-1.833,1.736) −0.053 0.958
Asian (reference) - - - -
European −1.407 (-2.598, −0.217) −2.317 0.021
North American −0.690 (-2.323,0.943) −0.828 0.408
Publication year b vs. B −0.010 (-0.082,0.063) −0.268 0.789
bb + Bb vs. BB −0.061 (-0.158,0.037) −1.223 0.221
bb vs. Bb + BB 0.023 (-0.087,0.134) 0.409 0.683
bb vs. BB −0.021 (-0.158,0.117) −0.293 0.770
TaqI (rs731236)
Ethnicity Allele (t vs. T) Asian (reference) - - - -
European −0.456 (-1.499,0.588) −0.856 0.392
North American −0.003 (-0.887,0.881) −0.007 0.994
South American −0.332 (-1.103,0.439) −0.844 0.399
Dominant (tt + Tt vs. TT) Asian (reference) - - - -
European −0.468 (-1.712,0.776) −0.738 0.461
North American −0.059 (-1.074,0.956) −0.113 0.910
South American −0.431 (-1.304,0.443) −0.966 0.334
Recessive (tt vs. Tt + TT) Asian (reference) - - - -
European −1.283 (-2.917,0.351) −1.539 0.124
North American 0.085 (-1.118,1.287) 0.138 0.890
South American 0.115 (-1.988,2.218) 0.107 0.915
Homozygote (tt vs. TT) Asian (reference) - - - -
European −1.715 (-3.690,0.261) −1.701 0.089
North American −0.001 (-1.502,1.500) −0.001 0.999
South American −0.148 (-2.367,2.071) −0.131 0.896
Publication year t vs. T −0.013 (-0.058,0.033) −0.549 0.583
tt + Tt vs. TT −0.005 (-0.052,0.042) −0.214 0.830
tt vs. Tt + TT −0.086 (-0.184,0.012) −1.728 0.084
tt vs. TT −0.097 (-0.215,0.021) −1.612 0.107

The bold values indicate that the OR (95%CI) does not include 1, and the p-value is <0.05.

 Discussion

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. In this meta-analysis, we pooled the results of 25 published articles to assess the association between various genotype models of VDR gene polymorphisms and TB susceptibility. We found that there was a significant association between an increased risk of developing TB and the allele (f vs. F) and recessive (ff vs. Ff + FF) models of the FokI polymorphism, whereas there was no evidence that the homozygote (ff vs. FF) and dominant (ff + Ff vs. FF) models were associated with TB risk. Further analysis based on Asian ethnicity revealed a significant association, in which all genotype models of the VDR FokI polymorphism contributed to the risk of developing TB in the Han population. It was observed there has likewise correlation for Orang Indonesia and Indian in the dominant model (ff + Ff vs. FF). However, a significant association between the ApaI polymorphism in VDR and a reduced risk of TB was found in the allele model (a vs. A) and the homozygote model (aa vs. AA). A possible reason for these inconsistent findings is that individuals are exposed to different environmental factors that could affect their genetic susceptibility to TB. However, further relevant studies are required to support this viewpoint.

Previous meta-analyses have evaluated the role of VDR gene polymorphisms in TB risk. Regarding the FokI polymorphism, some meta-analyses (Xu and Shen, 2019; Mohammadi et al., 2020) found no significant association between the FokI polymorphism and TB susceptibility. However, Cao et al. (2016) and Yadav et al. (2021) found evidence of an association in the homozygote (ff vs. Ff) and recessive (ff vs. Ff + FF) models. In addition, two meta-analyses (Chen et al., 2013; Huang et al., 2015) merely found that the f allele might contribute to the risk of TB in a recessive model (ff vs. Ff + FF), and our findings were consistent with this result. The FokI polymorphism, located in exon 2 at the translation initiation site of the VDR gene, produces two different receptor proteins. The F allele, linked to the expression of a shorter protein of 424 amino acids, displays higher transcriptional activity than another protein of 427 amino acids encoded by the f allele (Ruiz-Ballesteros et al., 2020). Therefore, the f allele of FokI could potentially decrease the activity of the VDR protein, thereby obstructing the interaction between active vitamin D and VDR, which might ultimately contribute to susceptibility to TB.

With respect to the BsmI polymorphism, no significant association was observed in this study. However, we found evidence to support an increased risk of TB in the homozygote model (bb vs. BB, OR = 1.751, 95%CI: 1.319–2.324) and the dominant model (bb + Bb vs. BB, OR = 1.492, 95%CI: 1.009–2.206) in Asian (Figure 4). More specific findings of Han population and Indian showed a significant association of the dominant model with the risk of TB. A similar meta-analysis performed by Wu et al. (2013a) demonstrated a significant association between the VDR gene BsmI polymorphism and a decreased TB risk within all four genotype models, and a similar association was found in Asians. One possible reason for the inconsistency in these findings is the lack of strict inclusion and exclusion criteria. For example, this previous study did not provide the criteria for excluding HIV-positive populations, as individuals with TB can be co-infected with this virus. Finally, the accuracy of a TB diagnosis is reduced in the HIV-positive population (Bell and Noursadeghi, 2018). Furthermore, a relevant assessment of the literature quality for case-control studies was not found in any previous study. Hence, low-quality articles could have generated biased results and might have further affected the pooled effects of the meta-analysis. Another reason could be the statistical power, which normally deviates with sample sizes; therefore, more relevant studies should be conducted in the future to examine our inconsistent results.

Regarding the association between the TaqI polymorphism in the VDR gene and the risk of TB, this association was not found within any of the four genotype models of the TaqI polymorphism Möller and Hoal, 2010. This is consistent with the results of a meta-analysis by Areeshi et al (Areeshi et al., 2017). A possible explanation for this is that the t allele of the VDR TaqI polymorphism is likely involved in the active disease process, whereas the variant does not act as a primary polymorphism with respect to TB infection. However, the TaqI polymorphism in the VDR gene was found to play a role in TB development in another meta-analysis performed by Xu and Shen (2019) in 2019; however, this meta-analysis did not exclude the HIV-positive population, which could have generated bias in terms of the pooled effect.

This meta-analysis had several strengths compared to previous studies. First, we performed the meta-analysis using relatively rigorous inclusion and exclusion criteria. Therefore, only high-quality articles including HIV-negative populations and those adhering to the HWE for gene distribution were eligible for the analysis. In other words, we avoided confounding factors that might have biased the pooled effect. Another strength is that we performed a meta-regression analysis of potential sources of heterogeneity among articles. Moreover, we anticipate performing more relevant analyses to explore other possible heterogeneity sources, such as the sample size or type of TB. However, our study has some limitations that should be acknowledged. First, despite the rigorous inclusion and exclusion criteria adhered to in this meta-analysis, our sample size was relatively small. Consequently, more studies with similar criteria are required to validate our pooled results. Second, in the present meta-analysis, we included articles published in English only, from three electronic databases (PubMed, Embase, and Medline). This could introduce a potential bias if studies in other languages or those indexed in other databases are missed. It should be noted that the impact of gene–environment interactions on the susceptibility to TB was also not considered in the present study. Furthermore, we anticipate performing Genome-wide association studies (GWAS) to identify a robust correlation between VDR gene polymorphisms and TB susceptibility in future research.

 Conclusion

In summary, this meta-analysis adhered to strict inclusion and exclusion criteria to systematically evaluate the association between VDR gene polymorphisms and TB risk in the HIV-negative population. The FokI polymorphism was found to be associated with an increased risk of TB in the overall analysis. This indicates that the f allele could contribute more to TB risk than the F allele, particularly in Asians. However, the ApaI polymorphism was determined to play a protective role against TB. Further large-scale studies are required to classify the role of ethnicity and other potential factors in the relationship between VDR gene polymorphisms and TB susceptibility.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

 Author contributions

RT: Conceptualization, Data curation, Formal Analysis, Investigation, Project administration, Writing–original draft, Writing–review and editing. SX: Conceptualization, Data curation, Formal Analysis, Investigation, Project administration, Writing–original draft, Writing–review and editing. LW: Conceptualization, Data curation, Formal Analysis, Investigation, Writing–original draft, Writing–review and editing. CH: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing. HS: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing. RL: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing. HL: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing. WL: Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing, Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology. FH: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing. JZ: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing. QL: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–review and editing.

 Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the Science and Technology Foundation of Guizhou Provincial Health Commission (grant number gzwkj2022-214); and the Education Science Planning Foundation of Guizhou Province (grant number 2022c025); and the “13th Five Year Plan” Science and Technology Project from the Education Department of Jilin Province (JJKH20200887KJ); and the Jilin Science and Technology Development Program Project: 20200404082YY; and the Guizhou Provincial Science and Technology Projects (Grant number ZK[2024]-134 general project); and the Science and Technology Fund Project of Guizhou Provincial Health Commission (Grant number gzwkj2025-435); and the Jilin Provincial Science and Technology Projects: 20220204072YY.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2024.1382957/full#supplementary-material

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