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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Jun 15;8(6):10187–10203.

Association of Vitamin D receptor gene TaqI polymorphisms with tuberculosis susceptibility: a meta-analysis

Yan Cao 1,*, Xinjing Wang 1,*, Zhihong Cao 1, Xiaoxing Cheng 1
PMCID: PMC4538126  PMID: 26309718

Abstract

Vitamin D receptor (VDR) is a receptor of vitamin D3, which plays a pivotal role in regulating cell proliferation and differentiation, lymphocyte activation and cytokine production, and is associated with TB susceptibility. Growing studies explored the association of TaqI polymorphism of VDR with tuberculosis (TB) susceptibility. However, the results were inconsistent and conflicting. To assess the relationship between the VDR TaqI gene polymorphism and the risk of TB, a meta-analysis was performed. Databases including PubMed and EMbase were systematically searched for genetic association studies of TaqI polymorphism of VDR and tuberculosis until February 15, 2015. Data were extracted by two independent authors and pooled odds ratio (OR) with 95% confidence interval (CI) was calculated to assess the strength of the association between VDR TaqI gene polymorphism and TB risk, meta-regression and subgroup analyses were performed to identify the source of heterogeneity. Thirty-eight studies with a total of 6881 cases and 7511 controls were reviewed in the present meta-analysis. A statistically significant correlations were observed between VDR TaqI gene polymorphism and TB risk in South and West Asians (t vs. T: OR=1.27, 95% CI=1.07-1.51, P=0.007; tt vs. TT: OR=1.59, 95% CI=1.11-2.26, P=0.011; tt vs. Tt + TT: OR=1.43, 95% CI=1.17-1.73, P=0.000; tt + Tt vs. TT: OR=1.32, 95% CI=1.05-1.67, P=0.019). Heterogeneity between studies was not pronounced, and meta-regression found no source contributed to heterogeneity. However, after stratified analysis with respect to genotyping methods and sample size, significant association was found in “small” studies (<500 participants) and studies with “PCR-RFLP” methods. Synthesis of the available studies suggests that t allele of the VDR TaqI polymorphism is significantly associated with an increased TB risk in South and West Asians.

Keywords: Tuberculosis, Vitamin D receptor, TaqI polymorphisms

Introduction

Tuberculosis (TB) is one of the most common infectious diseases and the leading cause of mortality worldwide, with an estimated 9 million new cases and 1.5 million deaths occurred in 2013. According to World Health Organization (WHO) report, approximately 56% of new cases occurred in the South-East Asia and Western Pacific Regions [1]. It is well known that tuberculosis susceptibility may be influenced by multiple genetic, socio-economic and environmental factors [2,3], which contains single nucleotide polymorphisms (SNP) as a major factor.

Vitamin D (VitD) is well known to play a critical role in modulating monocyte and macrophage activity and influencing human innate immunity to certain infectious agents including M. tuberculosis [4]. Vitamin D receptor (VDR) is a nuclear hormone receptor, which upon binding to vitamin D3, interacts with Vitamin D response elements and signals other target genes. It is highly expressed on dendritic cells, activated T lymphocytes and macrophages. After binding with Vitamin D, VDR could modulate cytokine responses by T cells [5,6], and thus represents antibacterial responses in innate immunity [7]. Many recent studies have demonstrated the critical role of VDR in the inflammatory-related immune response to active TB disease [8-10]. Human VDR gene is located on chromosome 12q13.11 and contains 14 exons [11]. The VDR polymorphisms located in coding region and 3’ untranslated region are FokI, TaqI, ApaI and BsmI. Accumulated evidence has suggested that polymorphisms in the VDR gene may influence the expression and function of VDR and subsequent downstream vitamin D-mediated effect [12]. To date, several meta-analyses focused on the association of VDR TaqI polymorphisms with tuberculosis risk across different ethnicities [13-15], however, due to the limitations of sample size and broadly statistical analysis, the results were varied and inconsistent among different employed genetic models. In addition, the previous studies did not cover all eligible publications related to tuberculosis and thus resulted in biased effect sizes. To ascertain the authentic effect of VDR TaqI polymorphisms on susceptibility to tuberculosis, we conducted a meta-analysis including all eligible case-control studies focused on the relationship between the VDR TaqI polymorphism and tuberculosis risk.

Materials and methods

Literature Search

A systematic search was conducted using the databases of the US National Institutes of Health (PubMed), Web of Science and Embase databases (last search was updated on February 15 2015), with the combination of terms like: ‘VDR’OR ‘Vitamin D receptor’ OR ‘FokI’ OR ‘rs10735810’ AND ‘polymorphism’ OR ‘mutation’ OR ‘SNP’ OR ‘Single Nucleotide Polymorphism’ AND ‘tuberculosis’. To identify the extra eligible studies, the relevant published studies and review articles were manually examined. The search in these databases was limited to articles relating to humans. No language restrictions were applied.

The identified studies in our meta-analysis met all of the following criteria: (1) studies had to assess the association between Vitamin D receptor TaqI polymorphisms and tuberculosis risk; (2) case-control studies or cohort design, and studies included available genotype frequencies to calculate odds ratio (OR) and 95% confidence interval (CI); (3) independent studies using original data. Studies were excluded for the following criteria: (1) the studies not providing genotype distribution or allele frequency data; (2) reviews or case reports, case studies without control subjects; (3) duplicated previous publications. At last, 38 case-control published studies from PubMed, Web of Science and Embase were available. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist was available as supplementary material, as displayed in Checklist S1.

Data extraction

Two investigators (CY and WXJ) independently performed the required data extraction, and then conducted group discussion to resolve the disagreements. The following data were extracted from each study: publication year, the name of first author, country, ethnicity, study design, genotyping method, diagnosis method of cases, the tuberculosis type, number of cases and controls, genotype and allele frequencies for cases and controls, HIV status of cases and controls, and source of genotyping. According to the source of controls, all eligible studies were defined as hospital-based (HB) and population-based (PB). If data concerning the genotype distributions were not displayed regarding the included studies, the primary author was contacted via electronic mail to obtain the missing data.

Statistical analysis

Minor allele frequency was calculated manually based on genotypic distribution among cases and controls. We assessed Hardy-Weinberg Equilibrium (HWE) among control population for each study using Hardy-Weinberg Equilibrium Online Calculator (http://www.changbioscience.com/genetics/hardy.html). And a P value of >0.05 was considered to meet HWE.

All statistical analyses in this meta-analysis were carried out using the software Stata 12.0 (Stata Corporation, College Station, TX, USA), with two-sided p values. The extracted data from all publications were tested using five genetic models i.e. allele model (t vs. T), homozygote model (tt vs. TT), heterozygote model (Tt vs. TT), dominant model (tt + Tt vs. TT) and recessive model (tt vs. Tt + TT). Odds ratios (ORs) with a corresponding 95% confidence interval (CI) were calculated (for all five genetic models) to assess the strength of association between VDR TaqI polymorphism and the TB risk. The significance of pooled OR was measured by the Z-test (P<0.05 was considered statistically significant). Heterogeneity assumption was assessed by the x2-based Q-statistics and Higgins I2 test. Those resulting with I2>50% were identified as a heterogeneous group, then ORs were pooled according to random effect model (Mantel-Haenszel method) [16]. Otherwise the fixed effect model was adopted (DerSimonian-Laird method) [17].

Subgroup analyses were conducted based on these genetic models to define the sources of heterogeneity, according to ethnicities, sample size, tuberculosis type, HWE, the source of controls as well as for the genotyping methods. A meta-regression was used to illustrate the potential reasons for heterogeneity between the studies. We classified the studies that were conducted in Asia into two groups: East and Southeast Asia (China, South Korean, South Sumatera, Indonesia and Cambodia) and South and West Asia (India and Iran). As a result, the enrolled studies were classified into five subgroups based on ethnicity: East and Southeast Asians, South and West Asians, Africans, Europeans and Americans. Studies with more than 500 participants were defined as “large”, and studies with less 500 participants were defined as “small”.

Sensitivity analysis was conducted to evaluate stability of the results by deleting of a single study at a time, the pooled ORs were recalculated to determine whether individual study could influence the overall results. Furthermore, publication bias was identified by examining the Begg’s funnel plots [18] and Egger’s regression test [19].

Results

Characteristics of eligible studies

A total of 413 potential studies were identified by preliminary searching PubMed, Embase and Web of Science, among which 38 case-control studies were selected according to the inclusion and exclusion criteria, involving a total of 6881 tuberculosis patients and 7511 control subjects in this meta-analysis [20-57]. The detailed literature search strategy and included or excluded studies were explained in Figure 1. The baseline characteristics, such as author name, publication year, region, ethnicity, design, genotyping method, numbers about cases and controls were depicted in Table 1. The publication year of eligible studies ranged from 1999 to 2014. The study participants were broadly classified according to the predominant ancestry, including East and Southeast Asians, South and West Asians, Africans, Europeans and Americans. Three studies adopted hospital-based control [37,45,46], while the other thirty-five studies adopted population-based control. Thirty were pulmonary TB studies, two were extra-pulmonary TB studies [48,53] and the other six studies were pulmonary and extra-pulmonary merged studies [23,30,35,36,45,56]. HIV status of the studied population was considered in twenty-six studies. All of them adopted blood samples for genotyping. Genotyping for Vitamin D receptor TaqI polymorphism across all studies, twenty-nine were conducted using PCR-RFLP assay [20,21,23,25-38,40,42,44,46-53,56], and the other nine studies were merged into the “other methods” group. The results of HWE test in the control population and genotype frequencies of TaqI polymorphisms were recalculated and extracted from all eligible publications, and were shown in Table 2. Twenty-nine of the eligible studies met the HWE (P>0.05), except for nine study [21,23,25,26,28,29,31,34,40].

Figure 1.

Figure 1

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Flow diagram of search strategy and study selection process (TIF).

Table 1.

Main characteristics of included studies summarized for the meta-analysis

Year First Author Country Ethnicity Study design Tuberculosis controls HIV status Source of genotyping
Part of the body Sample size Diagnosis method Genotyping method Sample size
2014 Arji N Morocco African PB Pulmonary tuberculosis 274 AFB smear and culture PCR-RFLP Healthy persons 203 Negative blood
2013 Wu China ES Asian PB Pulmonary tuberculosis 213 Clinical symptoms bacteriology X-ray PCR-RFLP Healthy persons 211 Negative blood
2013 Alexandra Romania European PB Pulmonary tuberculosis 68 Not available ARMS-PCR Healthy persons 110 Negative blood
2012 Rathored India SW Asian PB MDR tuberculosis and drug sensitive pulmonary tuberculosis 692 AFB smear and culture PCR-RFLP Healthy persons 205 Negative blood
2011 J Kim South Korean ES Asian PB Pulmonary (98) and extra pulmonary tuberculosis (62) 160 AFB smear and culture Pyro sequencing Healthy persons 156 Not available blood
2011 T Kang South Korean ES Asian PB Pulmonary tuberculosis 103 AFB smear and culture PCR-RFLP Healthy persons 105 Not available blood
2011 Sudarto South Sumatera, Indonesia ES Asian PB Pulmonary tuberculosis 40 positive acid-fast bacilli sputum examination PCR-RFLP Healthy persons 40 Not available blood
2011 A Singh India SW Asian PB Pulmonary tuberculosis 101 AFB smear or culture PCR-RFLP Healthy persons 225 Negative blood
2011 Sharma India SW Asian PB Pulmonary tuberculosis 474 AFB smear or culture PCR-RFLP Healthy persons 607 Not available blood
2011 Wang X China ES Asian PB Pulmonary tuberculosis 213 AFB smear or culture PCR-RFLP Healthy persons 211 Not available blood
2011 Ates Turkey European PB Pulmonary (98) and extra pulmonary tuberculosis (30) 128 AFB smear or culture PCR-RFLP Healthy persons 80 Not available blood
2010 Marashian Iran SW Asian PB Pulmonary tuberculosis 164 AFB smear and X-ray PCR-RFLP contacts 50 Not available blood
2009 Banoei Iran SW Asian PB Pulmonary tuberculosis 60 Confirmed in Massih Danes hvari PCR-RFLP Healthy subjects 62 Negative blood
2009 Vidyarani India SW Asian PB Pulmonary tuberculosis 40 AFB smear and culture PCR-RFLP Healthy subject 49 Not available blood
2009 Selvaraj India SW Asian PB Pulmonary tuberculosis 65 Clinical symptom, AFB smear and culture PCR-RFLP Healthy subjects 60 Negative blood
2009 Alagarasu India SW Asian PB Pulmonary (187) and extra pulmonary tuberculosis (30) 217 AFB smear, clinical criteria and X-ray PCR-RFLP Healthy controls 144 Cases (51%), Controls (0) blood
2009 Meng XJ China ES Asian PB Pulmonary (185 and extra pulmonary tuberculosis (39) 224 AFB smear or culture PCR-RFLP Healthy controls 225 Negative blood
2009 Jiao WW China ES Asian HB Pulmonary tuberculosis 125 Clinical symptom, AFB smear and X-ray PCR-RFLP Healthy controls 446 Negative blood
2008 Selvaraj India SW Asian PB Pulmonary tuberculosis 51 AFB smear and culture PCR-RFLP Normal health subjects 60 Negative blood
2008 Liu Y.-D China ES Asian PB Pulmonary tuberculosis 60 AFB smear and culture SNaPshot Normal health subjects 30 Negative blood
2007 Wilbur Paraguay American PB Pulmonary tuberculosis 54 Clinical symptoms, PPD test PCR-RFLP No symptoms 124 Not available blood
2007 Olesen Guinea-Bissau African PB Pulmonary tuberculosis 320 AFB smear and clinical criteria TaqMan Healthy controls 344 HIV positive in 33% case sand negative in controls blood
2007 Babb South Africa African PB Pulmonary tuberculosis 249 AFB smear and XRay PCR-RFLP No clinical history or symptoms of TB 352 Negative blood
2007 Soborg Tanzanian African PB Pulmonary tuberculosis 435 Culture PCR-SSP Culture negative 416 HIV positive in 44% cases and 18% controls blood
2006 Chen XR China ES Asian PB Pulmonary tuberculosis 140 Clinical symptoms, AFB smear and XRay PCR-RFLP household contacts 139 Negative blood
2006 Lombard Venda African HB Pulmonary and meningeal tuberculosis 66 AFB smear ARMS-PCR Healthy controls with no history of TB 86 Negative blood
2004 Bornman Gambia, Guinea- Bissau, Guinea African HB Pulmonary tuberculosis 416 AFB or culture PCR-RFLP Healthy community control subjects 718 Cases (12.5%), controls (6.8%) blood
2004 Fitness Malawi African PB Pulmonary tuberculosis 386 AFB smear, culture and histology PCR-RFLP Healthy controls 624 Cases (67.6%), Controls (13.1%) blood
2004 Selvaraja India SW Asian PB Spinal tuberculosis patients 64 X-ray and Clinical criteria PCR-RFLP 77 were contacts and 26 were normal healthy subjects. 103 Not available blood
2004 Selvarajb India SW Asian PB Pulmonary tuberculosis 46 AFB smear, culture and and radiographic abnormalities PCR-RFLP clinically normal 64 Negative blood
2004 Roth Peru American PB Pulmonary tuberculosis 100 AFB smear PCR-RFLP Two healthy controls, 1PPD+ and 1PPD- 201 Negative blood
2004 Liu China ES Asian PB Pulmonary tuberculosis 120 AFB smear, culture and X-ray PCR-RFLP normal controls 240 Negative blood
2002 Delgado Cambodia ES Asian PB Pulmonary tuberculosis 358 AFB smear PCR-RFLP contacts with no TB 106 Negative Blood
2000 Selvaraja India SW Asian PB Spinal tuberculosis 66 Culture and X-ray PCR-RFLP contacts with no TB 80 Not available Blood
2000 Selvarajb India SW Asian PB Pulmonary tuberculosis 44 AFB smear and culture PCR-SSOP contacts with no TB 66 Not available Blood
2000 Selvarajc India SW Asian PB Pulmonary tuberculosis 200 X-ray and Clinical criteria PCR-SSOP patient contacts 108 Not available Blood
2000 Wilkinson India SW Asian PB Pulmonary tuberculosis (27) and military tuberculosis (64) 91 Biopsy or culture Tuberculosis PCR-RFLP contacts with no TB 116 Negative Blood
1999 Bellamy Gambia African PB Pulmonary tuberculosis 408 AFB smear PCR-SSCP Male donors 414 Negative Blood
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a,b,c: To differentiate the different articles by the same author (Selvaraj) in the same year (2004, 2000). PB: population-based; HB: hospital-based; AFB, Acid-fast bacilli; HIV, human immunodeficiency virus; MDR, multi-drug resistance for isoniazide and rifampicin; PPD, purified protein derivative; SNPs, single nucleotide polymorphism; TB, tuberculosis; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism.

Table 2.

Distribution of gene polymorphism of studies included in the meta-analysis

Year First Author Case Control
genotype Minor allele genotype Minor allele HWE
TT Tt tt MAF TT Tt tt MAF p-value
2014 Arji N 137 79 58 0.36 109 48 46 0.34 0.9185
2013 Fang Wu 191 19 3 0.06 183 23 5 0.08 0.0004
2013 Alexandra 16 52 0 0.38 43 48 19 0.39 0.3802
2012 J. Rathored 290 285 117 0.38 97 79 29 0.33 0.0002
2011 J Kim 143 16 1 0.06 137 18 1 0.06 0.6319
2011 T Kang 134 14 1 0.05 148 85 8 0.05 0.0022
2011 Sudarto 14 12 14 0.50 17 9 14 0.46 0.0120
2011 A. Singh 61 30 10 0.25 132 60 33 0.28 0.0563
2011 Sharma 138 95 42 0.33 258 275 47 0.32 0.0002
2011 Wang X 191 19 3 0.06 183 23 5 0.08 0.0004
2011 Ates 49 65 14 0.36 30 39 11 0.38 0.7659
2010 Marashian 63 93 8 0.33 26 24 0 0.24 0.0256
2009 Banoei 8 33 19 0.59 33 24 5 0.27 0.8288
2009 Vidyarani 15 18 7 0.40 27 18 4 0.27 0.6863
2009 Selvaraj 24 33 8 0.38 27 21 12 0.38 0.0497
2009 Alagarasu 82 95 38 0.40 70 62 14 0.31 0.9597
2009 Meng XJ 154 66 4 0.17 170 50 5 0.13 0.5640
2009 Jiao WW 113 12 0 0.05 387 58 1 0.07 0.4423
2008 Selvaraj 18 23 10 0.42 34 22 4 0.25 0.8633
2008 Liu Y.-D 54 5 1 0.06 24 6 0 0.10 0.5428
2007 Wilbur 22 28 4 0.33 59 58 5 0.28 0.0438
2007 Olesen 150 145 25 0.30 161 150 34 0.32 0.9132
2007 Babb 136 94 19 0.27 190 140 22 0.26 0.5723
2007 Soborg 247 172 19 0.24 233 162 30 0.26 0.7997
2006 Chen XR 137 3 0 0.01 134 5 0 0.02 0.8290
2006 Lombard 51 30 5 0.23 47 34 1 0.22 0.0571
2004 Bornman 174 132 37 0.30 331 253 50 0.28 0.8644
2004 Fitness 261 154 22 0.23 384 241 47 0.25 0.2791
2004 Selvaraja 27 28 9 0.36 40 48 14 0.37 0.9470
2004 Selvarajb 13 23 10 0.47 27 27 10 0.37 0.8388
2004 Roth 90 10 0 0.05 169 31 1 0.08 0.9928
2004 Liu 105 12 3 222/18 203 32 5 438/42 0.4821
2002 Delgado 325 30 3 680/36 96 10 0 202/10 0.6103
2000 Selvaraja 27 30 9 84/48 32 38 10 102/58 0.8042
2000 Selvarajb 15 21 8 51/37 22 37 7 81/51 0.1386
2000 Selvarajc 79 90 31 248/152 51 42 15 144/72 0.1939
2000 Wilkinson 39 46 6 124/58 45 58 13 148/84 0.3750
1999 Bellamy 204 177 27 585/231 188 177 49 553/275 0.4603
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a,b,c: To differentiate the different articles by the same author (Selvaraj) in the same year (2004, 2000). HWE, Hardy-Weinberg equilibrium; MAF, minor allele frequency.

Quantitative data synthesis

Pooled analysis. Table 2 shows the genotype distribution and allele frequencies in the original 38 studies. The overall frequency of t allele in TaqI polymorphism was 25.3% in cases and 23.9% in controls. This analysis, using five different genetic models showed low heterogeneity (I2 range =37-47.5% for all comparisons). The significant association has not been detected in all the five genetic models between TaqI polymorphism and risk of tuberculosis (Table 3).

Table 3.

Meta-analysis results

t vs. T tt vs. TT tt vs. Tt + TT Tt vs. TT tt + Tt vs. TT
N OR (95% CI) Heterogeneity (I2, PQ) OR (95% CI) Heterogeneity (I2, PQ) OR (95% CI) Heterogeneity (I2, PQ) OR (95% CI) Heterogeneity (I2, PQ) OR (95% CI) Heterogeneity (I2,PQ)
Total 38 1.03 (0.97, 1.09)F 47.5%; 0.001 1.09 (0.96, 1.25)F 46.9%, 0.001 1.05 (0.92, 1.19)F 42.2%, 0.004 1.03 (0.96, 1.12)F 37%, 0.013 1.04 (0.96, 1.12)F 40.3%, 0.006
Ethnicities
    ES Asians 11 0.93 (0.77, 1.11)F 0%; 0.707 0.93 (0.55, 1.57)F 0%, 0.996 0.89 (0.53, 1.48)F 0%, 0.998 0.94 (0.76, 1.16)F 0%, 0.494 0.93 (0.76, 1.14)F 0%, 0.587
    SW Asians 15 1.27 (1.07, 1.51)* 63.0%; 0.001 1.59 (1.11, 2.26)* 56.5%, 0.004 1.43 (1.17, 1.73)*,F 46%, 0.026 1.25 (0.99, 1.59) 57.8%, 0.003 1.32 (1.05, 1.67)* 61.6%, 0.001
    Africans 8 0.95 (0.87, 1.03)F 0%; 0.432 0.86 (0.64, 1.16) 51.2%, 0.045 0.86 (0.64, 1.15) 51.9%, 0.042 0.98 (0.88, 1.10)F 0%, 0.927 0.96 (0.86, 1.07)F 0%, 0.884
    Americans 2 0.92 (0.43, 1.97) 67.9%; 0.078 1.69 (0.48, 5.96)F 0%, 0.488 1.56 (0.46, 5.33)F 0%, 0.559 0.91 (0.43, 1.91) 54.2%, 0.14 0.92 (0.40, 2.09) 63.7%, 0.097
    Europeans 2 0.94 (0.70, 1.27)F 0%; 0.894 0.32( 0.03,3.85) 66.2%, 0.086 0.20 (0.01, 6.66) 82.5%, 0.017 1.70 (0.61, 4.74) 79.9%, 0.026 1.39 (0.66, 2.95) 65.1%, 0.091
Sample size
    Largea 9 0.97 (0.89, 1.04)F 29.0%; 0.187 0.96 (0.70, 1.31) 61.7%, 0.008 0.98 (0.70, 1.37) 68.7%, 0.001 0.94 (0.85, 1.04)F 12.1%, 0.334 0.94 (0.85, 1.04)F 0%, 0.530
    Smallb 29 1.12 (1.03, 1.23)*,F 47.2%; 0.003 1.24 (1.02, 1.52)*,F 39.1%, 0.019 1.09 (0.91, 1.31)F 25.6%, 0.109 1.18 (1.05, 1.33)*,F 33.4%, 0.043 1.17 (1.05, 1.31)*,F 41%, 0.012
Genotyping method
    PCR-RFLP 29 1.10 (0.98, 1.23) 51.8%; 0.001 1.26 (1.08, 1.46)*,F 40.6%, 0.015 1.20 (1.04, 1.39)*,F 29.5%, 0.073 1.04 0.94, 1.14)F 37.3%, 0.024 1.06 (0.97, 1.16)F 45.1%, 0.005
    Other methods 9 0.92 (0.83, 1.03)F 0%; 0.594 0.73 (0.56, 0.95)*,F 19.4%, 0.27 0.69 (0.54, 0.90)*,F 45.6%, 0.065 1.03 (0.89, 1.19)F 42.8%, 0.082 0.97 (0.85, 1.12)F 19.4%, 0.270
Source of controls
    Contactsc 11 1.06 (0.93, 1.21)F 0.0%; 0.526 1.15 (0.83, 1.60)F 0%, 0.857 1.13 (0.82, 1.55)F 0%, 0.875 1.05 (0.88, 1.25)F 0%, 0.510 1.06 (0.90, 1.25)F 0%, 0.478
    Healthyd 27 1.06 (0.95, 1.18) 57.5%; 0.000 1.16 (0.89, 1.53) 58.8%, 0.000 1.07 (0.84, 1.38) 55%, 0.000 1.03 (0.94, 1.12)F 47.4%, 0.004 1.08 (0.94, 1.23) 50.2%, 0.002
HWE
    PHWE>0.05 29 1.05 (0.94, 1.17) 54.9%; 0.000 1.14 (0.87, 1.48) 53.9%, 0.000 0.97 (0.84, 1.12)F 43.3%, 0.008 1.05 (0.96, 1.15)F 34.9%, 0.034 1.04 (0.95, 1.13)F 45.1%, 0.005
    PHWE<0.05 9 1.09 (0.96, 1.23)F 0%; 0.476 1.35 (1.02, 1.78)*,F 0%, 0.569 1.33 (1.02, 1.73)*,F 27.3%, 0.201 0.97 (0.82, 1.16)F 47.2%, 0.056 1.03 (0.87, 1.21)F 27.5%, 0.200
Tuberculosis type
    pulmonary 29 1.05 (0.95, 1.17) 53.2%; 0.000 1.15 (0.90, 1.48) 52.6%, 0.001 1.03 (0.90, 1.18)F 48.5%, 0.002 1.03 (0.94, 1.12)F 46%, 0.003 1.02 (0.94, 1.11)F 47.9%, 0.002
    Extra and pulmonary 7 1.13 (0.95, 1.33)F 30%; 0.21 1.29 (0.84, 1.96)F 39.6%, 0.141 1.22 (0.81, 1.82)F 35.7%, 0.169 1.12 (0.90, 1.39)F 0%, 0.545 1.14 (0.93, 1.41)F 0.9%, 0.410
    extra 2 0.97 (0.70, 1.36)F 0%; 0.855 1.00 (0.49, 2.04)F 0%, 0.876 1.06 (0.55, 2.06)F 0%, 0.915 0.90 (0.55, 1.46)F 0%, 0.873 0.92 (0.58, 1.46)F 0%, 0.855
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Abbreviations: N: number of studies included; OR: odds ratio; Ph: p value for heterogeneity; PQ: Cochran’s Q statistics; I2: Higgin’s I2 statistics.

F

Results derived using Fixed effects for analysis. Random effects were used for all other calculations.

*

OR with statistical significance;

a

studies with more than 500 participants;

b

studies with less than 5000 participants;

c

studies with controls from patient contacts;

d

studies with controls from healthy person.

Subgroup analysis. Subgroup meta-analyses according to different races (Africans, East and Southeast Asians, South and West Asians, Americans and Europeans) have been conducted based on the five genetic models. 15 studies belonged to the South and West Asians [23,27,28,31-35,38,48,49,53-56], 11 to the East and Southeast Asians [21,24-26,36,37,39,44,51,52], 8 to the Africans [20,41-43,45-47,57], 2 to the Americans [40,50] and 2 to the Europeans [22,30] group. Appropriate effects were used for further analysis according to the heterogeneity. We found significant positive correlations between the t allele polymorphisms and increased risks of tuberculosis in South and West Asians (t vs. T: OR=1.27, 95% CI=1.07-1.51, P=0.007; tt vs. TT: OR=1.59, 95% CI=1.11-2.26, P=0.011; tt vs. Tt + TT: OR=1.43, 95% CI=1.17-1.73, P=0.000; tt + Tt vs. TT: OR=1.32, 95% CI=1.05-1.67, P=0.019) (Table 3; Figure 2A-D). However, the Africans, East and Southeast Asians, Americans and Europeans groups showed no significant difference in all five genetic models (Table 3).

Figure 2.

Figure 2

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Forest plots showing the association of the TaqI polymorphisms with risk of tuberculosis for five ancestral subgroups. A. t vs. T; B. tt vs. TT; C. tt vs. Tt + TT; D. tt + Tt vs. TT (TIF).

In a further stratified analysis by genotyping methods, significant associations were observed in the studies using PCR-RLFP for homozygote model (tt vs. TT: OR=1.26, 95% CI=1.08-1.46, P=0.004) and recessive model (tt vs. Tt + TT: OR=1.20, 95% CI=1.04-1.39, P=0.014). In contrast, decreased risks of tuberculosis was found in studies using other methods for homozygote model (tt vs. TT: OR=0.73, 95% CI=0.56-0.95, P=0.019) and recessive model (tt vs. Tt + TT: OR=0.69, 95% CI=0.54-0.90, P=0.005) (Table 3; Figure 3A, 3B). In the stratified analysis by sample size, statistically significant associations were found in the “small” studies for allele model (t vs. T: OR=1.12, 95% CI=1.03-1.23, P= 0.010), homozygote model (tt vs. TT: OR=1.24, 95% CI=1.02-1.52, P=0.030), heterozygote model (Tt vs. TT: OR=1.18, 95% CI=1.05-1.33, P=0.007) and dominant model (tt + Tt vs. TT: OR=1.17, 95% CI=1.05-1.31, P=0.006), respectively (Table 3; Figure 4A-D). However, there was no significant difference in “big” studies [23,28,37,41-43,46,47,57] for all five models. In subgroup analyses according to HWE in controls, significant associations were observed in the studies not in HWE for homozygote model (tt vs. TT: OR=1.35, 95% CI=1.02-1.78, P=0.034) and recessive model (tt vs. Tt + TT: OR=1.33, 95% CI=1.02-1.73, P=0.035) (Table 3; Figure 5A, 5B). However, when stratified by source of control and tuberculosis type, statistical significant association was not detected in all subgroups.

Figure 3.

Figure 3

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Forest plots showing the association of the TaqI polymorphisms with risk of tuberculosis for genotyping methods. A. tt vs. TT; B. tt vs. Tt + TT (TIF).

Figure 4.

Figure 4

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Forest plots showing the association of the TaqI polymorphisms with risk of tuberculosis for sample sizes. A. t vs. T; B. tt vs. TT; C. Tt vs. TT; D. tt + Tt vs. TT (TIF).

Figure 5.

Figure 5

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Forest plots showing the association of the TaqI polymorphisms with risk of tuberculosis for HWE. A. tt vs. TT; B. tt vs. Tt + TT (TIF).

Sensitivity analysis

Sensitivity analysis was conducted to evaluate the root of heterogeneity in every genetic model. The pooled OR in none of the studied genetic models affected by excluding studies one after another (Figure 6). This indicates that this meta-analysis is reliable in nature.

Figure 6.

Figure 6

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Sensitivity analysis for heterogeneity. A. t vs. T; B. tt vs. TT; C. Tt vs. TT; D. tt vs. Tt + TT; E. tt + Tt vs. TT (TIF).

Publication bias

The Begg’s funnel plot and the Egger’s linear regression test were performed to evaluate the publication bias of all included studies. The funnel plots seemed symmetrical under all the five genetic models (Figure 7A. t vs. T: z=0.16, P=0.870; Figure 7B. tt vs. TT: z=0.55, P=0.583; Figure 7C. Tt vs. TT: z=0.49, P=0.624; Figure 7D. tt + Tt vs. TT: z=1.29, P=0.195; Figure 7E. tt vs. Tt + TT: z=0.58, P=0.565). Egger’s test also suggested that there was no significant publication bias under all the genetic models (Figure 8A. t vs. T: t=0.85, P=0.402; Figure 8B. tt vs. TT: t=0.83, P=0.413; Figure 8C. Tt vs. TT: t=1.41, P=0.168; Figure 8D. tt + Tt vs. TT: t=1.58, P=0.124; Figure 8E. tt vs. Tt + TT: t=0.89, P=0.377)

Figure 7.

Figure 7

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Begg’s funnel plots for publication bias test. A. t vs. T; B. tt vs. TT; C. Tt vs. TT; D. tt vs. Tt + TT; E. tt + Tt vs. TT (TIF).

Figure 8.

Figure 8

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Egger’s linear regression plots for publication bias test. A. t vs. T; B. tt vs. TT; C. Tt vs. TT; D. tt vs. Tt + TT; E. tt + Tt vs. TT (TIF).

Discussion

Extensive evidence regarding the potential association between VDR TaqI polymorphism and TB risk, however, the results from published studies remain controversial. The discrepancies may be partly attributed to differing genetic backgrounds and environment among various populations. Up till now, there were three meta-analyses investigating the correlation between VDR TaqI polymorphism and TB risk. Nevertheless, due to the limitations of relatively smaller sample size, it failed detect any genetic associations either in the overall analysis or in subgroup analyses stratified by ethnicity [13-15]. To adjust for potential confounding factors, we examined the data in this meta-analysis, adding more recently published studies, by regional stratification, sample size and genotyping methods, to evaluate the association between VDR TaqI polymorphisms and TB risk more comprehensively and rigorously. Our pooled meta-analysis found there to be no significant evidence on the association between the VDR TaqI polymorphism and human tuberculosis risk, but subgroup results suggested an ethnic-specific increased risk in genotypes carrying the minor (t) allele in the SW Asian population, whereas the tuberculosis risk was positively related to the “small” sample size and negatively related to the “others” genotyping methods.

TB is a serious public health problem in worldwide which prevention and control dependent on many factors such as early diagnosis, drug resistance, vaccine and HIV co-infection. In the past decades, the association between genetic factors and host susceptibility to TB has been widely studied. VDR is known as an intracellular hormone receptor. This receptor exerts immune modulatory effects in regulating cell proliferation and differentiation, lymphocyte activation and cytokine production, and is associated with TB susceptibility [4]. Underlying mechanisms have been proposed based on its function as vitamin D receptor. Vitamin D is an immunomodulator hormone that enhances macrophage phagocytosis of live M. tuberculosis and induces the expression of antimicrobial peptide cathelicidin which restricts the growth of M. tuberculosis in monocytes [7]. Vitamin D exerts its actions through vitamin D receptor (VDR), which variants may influence VDR activity and subsequent downstream vitamin D-mediated effect [12]. One of the most widely studied polymorphism in human VDR gene is TaqI, which is located in exon 9. This gene polymorphism is located within the 3’ untranslated region which is known to be involved in regulation of gene expression, especially through regulation of VDR mRNA stability, thus affects the circulating 25-hydroxyvitamin D levels. Positive association between VDR TaqI polymorphisms and the tuberculosis infections has provided strong evidence for this hypothesis. However, this polymorphism might have diverse roles in different ethnic populations: significantly associations with TB were observed in SW Asian population, but not among ES Asians, Africans, Europeans and Americans. It might be owing to pertinent environmental factors which were able to influence serum vitamin D concentrations on different populations, including dietary factors, intensity and hours of sunlight. Additionally, the different genotype frequencies of VDR TaqI polymorphisms between populations may contribute to inconsistent associations with tuberculosis risk. The other stratified analyses suggested sample size might partly affect the association between VDR TaqI gene polymorphism and TB risk. There was a significantly increased TB risk of four genetic models of VDR TaqI gene polymorphism in “small” studies, but insignificant association was found in “large” studies. It was worth noting that seven out of nine of the “large” studies were based on Africans in which a trend of reduced TB risk was observed in t allele genotype. Additionally, small sample size studies tend to overestimate the influence of genetic factors [58]. In this meta-analysis, we also found an increased TB risk of two genetic models in studies with PHWE<0.05. It is probable that studies without HWE in controls hint a non-random inclusion or genotyping error, which may led to misleading results. Interestingly, we observed that significant association was reversed in “other methods” studies relative to in “PCR-RFLP” studies. It may be due to high detection rate of PCR-RFLP methods. Thus, more “large” studies in agreement with HWE based on SW Asians which used PCR-RFLP methods are required to quantify this effect size reliably.

In the present study, the subgroup analyses suggested ethnicities, source of controls and tuberculosis type might partly explain the moderate heterogeneity between studies observed under some genetic models. Moreover, the heterogeneity was not remarkably decreased upon exclusion of the studies that deviated from the HWE (Table S2). Further meta-regression analyses were performed as well to identify potential sources of the heterogeneity (Table S1). However, we could not find the source among publication years, ethnicity, sample sizes, HWE, genotype methods and source of controls. We could not further explore the source of heterogeneity because not all necessary information could be obtained from all the studies included. However, eligible studies were conducted in 19 countries in this meta-analysis, thus the cause of heterogeneity may partly due to the environmental factors quite different in these countries which may influence VDR gene expression and modulate genotype-related risk, gene-environment interaction. Additionally, the different experimental designs, diagnosis standards, ages of participants and HIV status also may contribute to the heterogeneity.

Current systematic review has several limitations that require careful consideration. First, the interactions of environmental risk factors, other co-variables and host cells might elucidate the mechanism by which TaqI polymorphism increase TB risk. More original data need to be obtained to interpret the gene environment interactions. Second, only articles in English or Chinese were included, which may impeded the completeness of evidence and deviate the results. Third, relevant stratifications could not be made for many studies due to incomplete information (e.g., by diagnosis standards, ages of participants or HIV status). In addition, in the subgroup analysis according to regional geography, only 2 studies concerning the relationship between the TaqI polymorphism and the Americans and Europeans were included; such a small sample size makes the analyses be prone to bias. Thus, further studies on the association of the TaqI polymorphism with TB risk are warranted to verify current findings. Fourth, some of the included studies did not mention whether their study populations were in HWE. Based on the data supplied by the articles and own calculations, significant deviations from HWE (P<0.05) in controls were observed for nine studies on TaqI polymorphisms. Their results should be interpreted with greater caution. We therefore repeated the meta-analyses after exclusion of these studies. However, this exclusion did not materially affect the results (Table S2).

In conclusion, results from this meta-analysis demonstrate that VDR TaqI polymorphism is associated with increased TB risk in SW Asians, while the relationship between tuberculosis risk and Americans and Europeans need to be proved in future large scale studies. However, due to the moderate strength of the associations, their values to be used for risk prediction should be considered cautiously and future large scale case-control studies are required to validate these findings.

Acknowledgements

The study was supported by key project of 309th Hospital to Y.C. (2014ZD-004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure of conflict of interest

None.

Supporting Information

ijcem0008-10187-f9.pdf (296.3KB, pdf)

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