Abstract

Primary open angle glaucoma (POAG) and normal tension glaucoma (NTG) cause irreversible blindness while current medications cannot completely inhibit disease progression. An understanding of immunopathogenesis is thus a keystone to develop novel drug targets and genetic markers are still required for early diagnosis. Toll-like receptor 4 (TLR4) is an essential player in inflammation in various diseases. However, the TLR4 polymorphisms have not been completely elucidated in both types of glaucoma. The aim of the present study was to identify the association between TLR4 polymorphism and glaucoma (POAG and NTG) via the use of a comprehensive review and meta-analysis. The relevant studies were collected from PubMed, Excerpta Medica Database (EMBASE), and Web of Science to identify eight included articles, assessed for quality by a modified Newcastle-Ottawa Scale (NOS) for gene association study. A meta-analysis was applied to calculate the pooled odds-ratio and 95% confidence intervals (CIs) to evaluate the association between TLR4 polymorphism and glaucoma. The results revealed that TLR4 rs1927911 A/G, rs12377632 C/T, and rs2149356 G/T significantly decrease the risk of POAG and NTG in allele contrast models 0.71-, 0.71-, and 0.67-fold, respectively. Moreover, rs4986790 A/G and rs4986791 C/T showed a stringent association with POAG in allele contrast, heterozygous, recessive, and overdominant models. In conclusion, this meta-analysis represented a significant correlation between TLR4 polymorphisms and both types of glaucoma suggesting that TLR4 might be involved in the pathogenesis of glaucoma and may be applied as a genetic marker for disease screening.

Introduction

Glaucoma, characterized by retinal ganglion cell (RGCs) death and optic nerve fiber loss, is a common optic neuropathy which is the second-highest cause of blindness worldwide [1,2]. It was estimated that there will be approximately 79.6 million people living with glaucoma by 2020, increasing to 111 million by 2040. The vast majority of glaucoma types are POAG, which is associated with elevated intraocular pressure (IOP) without a recognizable secondary cause such as ocular surgery, ocular trauma, or laser iridotomy [3,4]. On the other hand, some POAG patients are able to appear normal in terms of IOP of <22 mmHg. This type is classified as NTG. POAG and NTG share similarities in phenotypes composed of having normal anterior chamber angles, peripapillary retinal nerve fiber layer (RNFL) thinning, glaucomatous optic neuropathy (GON), and corresponding visual field (VF) defects. Therefore, it is believed that POAG and NTG illustrate a continuum of open-angle glaucoma in which there is a difference in some causative factors and IOP [5,6]. Although the precise and accurate molecular mechanisms of POAG and NTG have not been investigated, it is possible that patients with POAG and NTG have some overlapping factors in both multiple genetic and environmental interactions.

The emerging roles of inflammation and immunity were suggested as a paradigm shift in glaucoma pathogenesis. A gain-of-function mutation of pro-inflammatory gene TBK1-tumor necrosis factor (TNF) receptor associated factor NF-κB activator (TANK) binding kinase1 associated with NTG [7,8]. In a mouse model of inherited glaucoma, DBA/J2 mice showed a correlation between microglia activation and axon loss [9]. Moreover, early stages of the glaucoma model showed that neuroinflammatory response and inhibition of pro-inflammatory pathways play a role in the neuroprotective effect [10]. Amongst inflammatory molecules, Toll-like receptors (TLRs), referred to as transmembrane pattern recognition receptors (PRRs), play a significant role in innate immunity. The TLR activation initiates the inflammatory process via recognition with pathogen-associated molecular patterns (PAMPs), derived structure from microorganisms known as exogenous ligands, damage-associated molecular patterns (DAMPs), and derived cellular motifs from tissue damage known as endogenous ligands [11]. Currently, there are approximately ten TLRs which are identified in humans. TLR4 was the first to be discovered and is well known through study to bind to lipopolysaccharide (LPS) in Gram-negative bacteria, a lipoprotein of the RNA virus, and several heat shock proteins (HSPs) of host components [12–15]. In recent years, evidence suggests that the TLR4 plays multiple roles which are related to POAG. Activation of TLR4 alters the trabecular meshwork fibrosis during TGF-β stimulation and subsequently causes elevated IOP [16]. TLR4/HMGB1 binding activates RGC apoptosis in the acute glaucoma model [17–19]. The HSPs and LPS, being ligands of TLR4, were previously implicated as candidate antigens of NTG [20]. Moreover, Tenascin-C, another DAMP of TLR4, has been increased in astrocytes of the glaucomatous optic nerve head [21]. Therefore, the TLR4 may play a role in glaucoma pathogenesis.

In this decade, advanced sequencing technology has resulted in personal medicine and biomarker development. To achieve this aim, gene association studies are required as personalized information to predict the disease outcomes. Single nucleotide polymorphisms (SNPs) in the TLR4 gene were associated with both infectious diseases and non-communicable diseases [22–24]. To date, several positions of TLR4 polymorphisms have been examined for proposal as risk or protective factors of glaucoma. However, the results are still controversial, both for POAG and NTG. To solve this, we applied a comprehensive review and meta-analysis between genetic models of TLR4 polymorphism and glaucoma. Our results will provide supporting evidence relating to glaucoma as an autoimmune disease.

Materials and methods

Searching strategy

To identify the related article, the searching, dependent on electronic literature databases, was performed in PubMed, EMBASE, and Web of Science. The search terms were updated until 4 December 2018 and used the following keywords: (variants OR genetic polymorphisms OR genetic polymorphism OR genotyping OR SNP OR SNPs OR SNP OR SNPs OR polymorphisms OR polymorphism OR nucleotide polymorphism OR gene variation OR haplotype) AND (TLR-4 OR TLR4 OR receptor, TLR4 OR TLR4 receptor OR toll 4 receptor OR toll-4 receptor OR toll-like receptor 4) AND (POAG OR POAG OR NTG OR NTG). Two independent investiators searched and subsequently screened the titles and abstracts to identify eligible articles based on inclusion and exclusion criteria. The investigator entered into a discussion to resolve disagreements to achieve the final consensus. An additional publication was considered via related article screening.

Inclusion and exclusion criteria

Selected publications included in our study were: (1) case-control or cohort study on the association of TLR4 polymorphisms and POAG; (2) odds-ratio (OR) or relative risk with a 95% CI; (3) human study, and (4) providing information for all genetic models. The exclusion criteria were: (1) abstract, case-report review, and systematic review; (2) other TLRs study; (3) non-relevant study, and (4) insufficient data.

Quality assessment

The modified NOS for genetic association study was applied for quality assessment of included articles which were evaluated by two independent investigators [25]. The NOS criteria are separated into three outlines: (1) subject selection; (2) the comparability of subjects; and (3) exposure. The total score is 9, with 0–3 classified as a low-quality study, 4–6 classified as a moderate-quality study, and 7–9 classified as a high-quality study. The moderate-quality and high-quality studies were considered for inclusion in the meta-analysis. Any disagreement was resolved by discussion for any final consensus.

Data extraction and synthesis

Briefly, the name of the first author, the publication year, the number of cases and controls, genotype distribution of cases and controls, genotyping methods, and case definition were extracted by two investigators independently extracted relevant information from the eligible studies. An external participant was invited as an expert to resolve some disagreements.

Statistical analysis

To decrease the selection bias, the Hardy-Weinberg equilibrium (HWE) was evaluated by using chi-square testing in control groups with P<0.05 showing a deviation from HWE. The strength of association between TLR4 polymorphisms and glaucoma was represented as OR with 95% CIs. All allelic models (allele contrast, homozygous comparison, heterozygous comparison, dominant model, recessive model, and overdominant model) were examined for association by using an adjusted P-value for multiple testing via the Bonferroni method. The I2 value was considered to assess the heterogeneity amongst different studies. I2 <50% and P>0.05 was considered a homogeneous population. Consequently, the pool OR was combined using the fixed-effect model otherwise the random-effect models were performed when I2 >50% and P<0.05. Moreover, publication bias was tested by funnel plot and Egger’s regression test. Sensitivity analysis was considered to evaluate the stability of the meta-analysis result and root of heterogeneity by all studies removing one by one. The statistical analysis and meta-analysis were carried out using MetaGenyo [26].

Results

Characteristics of included studies

By using a systematic searching strategy, we identified a total of 48 records relating to POAG from three electronic databases. After removing duplicate articles, 26 studies were enrolled by title and abstract screening and 13 studies were excluded in this step. This resulted in 13 articles being downloaded and the full text being considered. Five articles were excluded. Eventually, eight articles (Figure 1) [27–34], containing ten polymorphisms of TLR4 (rs4986790, rs4986791, rs10759930, rs1927914, rs1927911, rs12377632, rs2149356, rs11536889, rs7037117, and rs7045953), were applied in our quality assessment (Supplementary Table S1) and meta-analysis. Amongst these studies were six articles which studied POAG with IOP >22 mmHg (two articles on rs4986790, two articles on rs4986791, two articles on rs1927911, rs12377632, rs2149356, rs11536889, and one article on rs7037117: Table 1). All the studies of POAG with IOP >22 were based on Mexican, Saudi Arabian, Japanese, and Han Chinese populations. On the other hand, there were three studies conducted on TLR4 polymorphisms (rs10759930, rs1927914, rs1927911, rs12377632, rs2149356, rs11536889, rs7037117, and rs7045953) with NTG of which two studies were carried out with Japanese populations and one study was carried out with a South Korean population.

Flow diagram showing review protocol for eligible studies

Figure 1
Flow diagram showing review protocol for eligible studies
Figure 1
Flow diagram showing review protocol for eligible studies
Table 1
General demographics of TLR4 gene polymorphisms and POAG which were included in our study
AuthorsStudy/ disease settingCriteria caseMean case age (years)Mean control age (years)Genotyping methodsPositionsNOS
Navarro-Partida et al. 2017 [27Mexican population/ POAG IOP > 22 mmHg in each eye, cup-to-disc ratio >0.7, VF defects determined by Humphrey 24-2 standard automated perimetry and open anterior chamber angle 66.49 ± 14.3 63.28 ± 7.93 Real-time PCR rs4986790 rs4986791 
Navarro-Partida et al. 2017 [28Mexican population/ POAG IOP > 22 mmHg in each eye, cup-to-disc ratio >0.7, VF defects determined by Humphrey 24-2 standard automated perimetry and open anterior chamber angle 66.49 ± 14.4 63.28 ± 7.94 Real-time PCR rs11536889 rs1927911 rs12377632 rs2149356 
Mousa et al. 2016 [29Saudi Arab population/ POAG Not stated. The author stated that the case participants were clinically confirmed diagnosed 60.90 ± 12.7 57.7 ± 10.4 Real-time PCR rs4986791 
Abu-Amero et al. 2017 [30Saudi Arab population/ POAG (1) Appearance of the disc and retina nerve fiber layer, (2) VF abnormalities, and (3) open anterior chamber angles bilaterally on gonioscopy 60.90 ± 12.8 69.7 ± 11.3 Real-time PCR rs4986790 
Takano et al. 2012 [31Japanese population/ POAG and NTG (POAG): IOP >22 mmHg in each eye, cup-to-disc ratio >0.7, VF defects determined by Goldmann perimetry and/or Humphrey VF analysis and open anterior chamber angle (NTG): IOP < 22 mmHg, and the same characteristic as that of POAG group. The patients with glaucoma secondary causes were excluded POAG: 64.60 ± 14.3 NTG: 58.60 ± 13.1 57.7 ± 10.6 PCR-sequencing 
Chen et al. 2012 [32Chinese population/ POAG (1) Shaffer grade III or IV open iridocorneal angle on gonioscopy, (2) Humphrey VF analysis, and (3) IOP ≥22 mmHg was measured by applanation tonometry 48.57 ± 17.5 72.2 ± 6.7 Real-time PCR rs7037117 
Suh et al. 2011 [33South Korean population/ NTG IOP <24 mmHg in each eye was measured by Goldmann tonometer, VF defects determined by Humphrey 30-2 standard automated perimetry in association with an open angle on gonioscopy. These are not history of angle closure, ocular trauma, corneal opacity, laser iridotomy, inflammatory eye disease, or ocular surgery NA NA Real-time PCR 
Shibuya et al. 2008 [34Japanese population/ NTG NTG: (1) the presence of GON with corresponding VF loss, (2) normal open angle with angle width of Shaffer grade 2 or higher and (3) IOP <22 mmHg on repeat measurement with Goldmann applanation tonometry NA NA Real-time PCR 
AuthorsStudy/ disease settingCriteria caseMean case age (years)Mean control age (years)Genotyping methodsPositionsNOS
Navarro-Partida et al. 2017 [27Mexican population/ POAG IOP > 22 mmHg in each eye, cup-to-disc ratio >0.7, VF defects determined by Humphrey 24-2 standard automated perimetry and open anterior chamber angle 66.49 ± 14.3 63.28 ± 7.93 Real-time PCR rs4986790 rs4986791 
Navarro-Partida et al. 2017 [28Mexican population/ POAG IOP > 22 mmHg in each eye, cup-to-disc ratio >0.7, VF defects determined by Humphrey 24-2 standard automated perimetry and open anterior chamber angle 66.49 ± 14.4 63.28 ± 7.94 Real-time PCR rs11536889 rs1927911 rs12377632 rs2149356 
Mousa et al. 2016 [29Saudi Arab population/ POAG Not stated. The author stated that the case participants were clinically confirmed diagnosed 60.90 ± 12.7 57.7 ± 10.4 Real-time PCR rs4986791 
Abu-Amero et al. 2017 [30Saudi Arab population/ POAG (1) Appearance of the disc and retina nerve fiber layer, (2) VF abnormalities, and (3) open anterior chamber angles bilaterally on gonioscopy 60.90 ± 12.8 69.7 ± 11.3 Real-time PCR rs4986790 
Takano et al. 2012 [31Japanese population/ POAG and NTG (POAG): IOP >22 mmHg in each eye, cup-to-disc ratio >0.7, VF defects determined by Goldmann perimetry and/or Humphrey VF analysis and open anterior chamber angle (NTG): IOP < 22 mmHg, and the same characteristic as that of POAG group. The patients with glaucoma secondary causes were excluded POAG: 64.60 ± 14.3 NTG: 58.60 ± 13.1 57.7 ± 10.6 PCR-sequencing 
Chen et al. 2012 [32Chinese population/ POAG (1) Shaffer grade III or IV open iridocorneal angle on gonioscopy, (2) Humphrey VF analysis, and (3) IOP ≥22 mmHg was measured by applanation tonometry 48.57 ± 17.5 72.2 ± 6.7 Real-time PCR rs7037117 
Suh et al. 2011 [33South Korean population/ NTG IOP <24 mmHg in each eye was measured by Goldmann tonometer, VF defects determined by Humphrey 30-2 standard automated perimetry in association with an open angle on gonioscopy. These are not history of angle closure, ocular trauma, corneal opacity, laser iridotomy, inflammatory eye disease, or ocular surgery NA NA Real-time PCR 
Shibuya et al. 2008 [34Japanese population/ NTG NTG: (1) the presence of GON with corresponding VF loss, (2) normal open angle with angle width of Shaffer grade 2 or higher and (3) IOP <22 mmHg on repeat measurement with Goldmann applanation tonometry NA NA Real-time PCR 

A represents TLR4 polymorphism at rs10759930, rs1927914, rs1927911, rs12377632, rs2149356, rs11536889, rs7037117, and rs7045953

All studies were of case-control design and published between 2008 and 2017. The case definition of POAG was based on clinical manifestation. Briefly, the patient has VF defects or abnormalities, no history of angle closure or secondary causes. Only one study in NTG applied IOP <24 mmHg which has a difference from two studies (IOP < 22 mmHg) (Table 1).

One study performed genotyping using PCR-sequencing and eight studies assessed using real-time PCR. The genotype details are presented in Table 2. The genotype distribution of control in all studies was in accordance with the Hardy-Weinberg equilibrium. Moreover, we also showed the minor allele frequency (MAF) for which nine polymorphisms represented the same minor allele except for rs12377632 for which there was one study which showed a different result (Table 2).

Table 2
The genotype distribution of TLR4 polymorphisms in cases and controls
AuthorsAllele (1/2)CasesControlsHWE P-valueMAF
111222111222CasesControls
rs4986790 A/G          
Navarro-Partida et al. 2017 [27 166 20 106 0.88 0.06 0.01 
Abu-Amero et al. 2017 [30 70 14 86 0.13 0.09 0.05 
rs4986791 C/T          
Navarro-Partida et al. 2017 [27 165 21 105 0.85 0.06 0.02 
Mousa et al. 2016 [29 73 11 87 0.67 0.08 0.04 
rs10759930 T/C          
Takano et al. 2012 [31 190 262 97 103 85 28 0.12 0.42 0.33 
Suh et al. 2011 [33 52 72 23 126 191 63 0.51 0.40 0.42 
Shibuya et al. 2008 [34 81 127 42 137 141 40 0.69 0.42 0.35 
rs1927914 A/G          
Takano et al. 2012 [31 184 270 95 105 82 29 0.05 0.42 0.32 
Suh et al. 2011 [33 52 72 23 126 192 62 0.44 0.4 0.42 
Shibuya et al. 2008 [34 82 126 42 137 141 40 0.69 0.42 0.35 
rs1927911 G/A          
Navarro-Partida et al. 2017 [28 83 87 17 64 34 11 0.06 0.32 0.26 
Takano et al. 2012 [31 190 267 92 106 85 25 0.22 0.41 0.31 
Suh et al. 2011 [33 53 71 23 129 190 61 0.52 0.40 0.41 
Shibuya et al. 2008 [34 87 122 41 141 135 42 0.29 0.41 0.34 
rs12377632 C/T          
Navarro-Partida et al. 2017 [28 27 89 71 27 51 31 0.51 0.62 0.52 
Takano et al. 2012 [31 190 280 79 104 87 25 0.30 0.40 0.32 
Suh et al. 2011 [33 54 70 23 127 191 62 0.49 0.39 0.41 
Shibuya et al. 2008 [34 86 122 42 140 138 40 0.51 0.41 0.34 
rs2149356 G/T          
Navarro-Partida et al. 2017 [28 61 100 26 61 36 12 0.07 0.41 0.28 
Takano et al. 2012 [31 192 262 95 107 85 24 0.26 0.41 0.31 
Suh et al. 2011 [33 54 70 23 128 191 61 0.46 0.39 0.41 
Shibuya et al. 2008 [34 87 122 41 140 138 40 0.51 0.41 0.34 
rs11536889 G/C          
Navarro-Partida et al. 2017 [28 146 40 84 22 0.30 0.11 0.13 
Takano et al. 2012 [31 291 228 30 127 76 13 0.72 0.26 0.24 
Suh et al. 2011 [33 77 62 221 139 20 0.76 0.27 0.24 
Shibuya et al. 2008 [34 146 93 11 177 119 22 0.74 0.23 0.26 
rs7037117 A/G          
Takano et al. 2012 [31 333 189 27 153 54 0.14 0.22 0.17 
Chen et al. 2012 [32 124 46 14 145 77 0.57 0.20 0.20 
Suh et al. 2011 [33 85 51 11 211 143 26 0.79 0.25 0.26 
Shibuya et al. 2008 [34 138 98 14 213 94 11 0.87 0.25 0.18 
rs7045953 A/G          
Takano et al. 2012 [31 465 81 191 24 0.79 0.08 0.06 
Suh et al. 2011 [33 126 19 314 60 0.12 0.08 0.09 
Shibuya et al. 2008 [34 203 45 269 49 0.14 0.10 0.08 
AuthorsAllele (1/2)CasesControlsHWE P-valueMAF
111222111222CasesControls
rs4986790 A/G          
Navarro-Partida et al. 2017 [27 166 20 106 0.88 0.06 0.01 
Abu-Amero et al. 2017 [30 70 14 86 0.13 0.09 0.05 
rs4986791 C/T          
Navarro-Partida et al. 2017 [27 165 21 105 0.85 0.06 0.02 
Mousa et al. 2016 [29 73 11 87 0.67 0.08 0.04 
rs10759930 T/C          
Takano et al. 2012 [31 190 262 97 103 85 28 0.12 0.42 0.33 
Suh et al. 2011 [33 52 72 23 126 191 63 0.51 0.40 0.42 
Shibuya et al. 2008 [34 81 127 42 137 141 40 0.69 0.42 0.35 
rs1927914 A/G          
Takano et al. 2012 [31 184 270 95 105 82 29 0.05 0.42 0.32 
Suh et al. 2011 [33 52 72 23 126 192 62 0.44 0.4 0.42 
Shibuya et al. 2008 [34 82 126 42 137 141 40 0.69 0.42 0.35 
rs1927911 G/A          
Navarro-Partida et al. 2017 [28 83 87 17 64 34 11 0.06 0.32 0.26 
Takano et al. 2012 [31 190 267 92 106 85 25 0.22 0.41 0.31 
Suh et al. 2011 [33 53 71 23 129 190 61 0.52 0.40 0.41 
Shibuya et al. 2008 [34 87 122 41 141 135 42 0.29 0.41 0.34 
rs12377632 C/T          
Navarro-Partida et al. 2017 [28 27 89 71 27 51 31 0.51 0.62 0.52 
Takano et al. 2012 [31 190 280 79 104 87 25 0.30 0.40 0.32 
Suh et al. 2011 [33 54 70 23 127 191 62 0.49 0.39 0.41 
Shibuya et al. 2008 [34 86 122 42 140 138 40 0.51 0.41 0.34 
rs2149356 G/T          
Navarro-Partida et al. 2017 [28 61 100 26 61 36 12 0.07 0.41 0.28 
Takano et al. 2012 [31 192 262 95 107 85 24 0.26 0.41 0.31 
Suh et al. 2011 [33 54 70 23 128 191 61 0.46 0.39 0.41 
Shibuya et al. 2008 [34 87 122 41 140 138 40 0.51 0.41 0.34 
rs11536889 G/C          
Navarro-Partida et al. 2017 [28 146 40 84 22 0.30 0.11 0.13 
Takano et al. 2012 [31 291 228 30 127 76 13 0.72 0.26 0.24 
Suh et al. 2011 [33 77 62 221 139 20 0.76 0.27 0.24 
Shibuya et al. 2008 [34 146 93 11 177 119 22 0.74 0.23 0.26 
rs7037117 A/G          
Takano et al. 2012 [31 333 189 27 153 54 0.14 0.22 0.17 
Chen et al. 2012 [32 124 46 14 145 77 0.57 0.20 0.20 
Suh et al. 2011 [33 85 51 11 211 143 26 0.79 0.25 0.26 
Shibuya et al. 2008 [34 138 98 14 213 94 11 0.87 0.25 0.18 
rs7045953 A/G          
Takano et al. 2012 [31 465 81 191 24 0.79 0.08 0.06 
Suh et al. 2011 [33 126 19 314 60 0.12 0.08 0.09 
Shibuya et al. 2008 [34 203 45 269 49 0.14 0.10 0.08 

Bold text indicates minor allele.

Meta-analysis results and publication bias

To investigate the potential association of other TLR4 polymorphisms with glaucoma (POAG and NTG), three studies about rs10759930 polymorphism (946 cases and 914 controls), three studies about rs1927914 polymorphism (946 cases and 914 controls), four studies about rs1927911 polymorphism (1133 cases and 1023 controls), four studies about rs12377632 polymorphism (1133 cases and 1023 controls), four studies about rs2149356 polymorphism (1133 cases and 1023 controls), four studies about rs11536889 polymorphism (1133 cases and 1023 controls), four studies about rs7037117 polymorphism (1130 cases and 1144 controls), and three studies about rs7045953 polymorphism (946 cases and 914 controls) were enrolled for meta-analysis resulting in three positions (rs1927911, rs12377632, and rs2149356) showing significant association with glaucoma (Table 3) in five genetic models, three genetic models, and five genetic models, respectively. The allele contrast model, homozygous model, and recessive model were associated with glaucoma in all positions (allele contrast model, rs1927911; OR = 0.78, P=0.02, rs12377632; OR = 0.78, P=0.02, rs2149356; OR = 0.71, P=0.02, homozygous model, rs1927911; OR = 0.68, P=0.01, rs12377632; OR = 0.65, P=0.00, rs2149356; OR = 0.62, P=0.02, recessive model, rs1927911; OR = 0.69, P=0.02, rs12377632; OR = 0.70, P=0.04, rs2149356; OR = 0.64, P=0.03). An Egger’s test and funnel plot suggested that no publication bias exists in these genetic models (Supplementary Table S1 and Supplementary Figure S1). Altogether, the results suggest that the TLR4 polymorphism (rs1927911, rs12377632, and rs2149356) is related to glaucoma in both POAG and NTG.

Table 3
The meta-analysis of TLR4 polymorphisms and glaucoma (POAG and NTG)
SNPs 1/2Genetic models
ABCDEFG
rs10759930 T/C        
OR (95% CI) 0.80 (0.62;1.04) 0.68 (0.43;1.07) 0.91 (0.68;1.22) 0.74 (0.52;1.04) 0.79 (0.60;1.04) 0.73 (0.50;1.05) 1.23 (1.00;1.50) 
p-valuea 0.099 0.0969 0.5602 0.0917 0.0972 0.0946 0.0395 
I2(%) 69.49 55.13 0.00 61.23 0.00 69.73 21.27 
p-valueb 0.03 0.10 0.87 0.07 0.40 0.04 0.28 
rs1927914 A/G        
OR (95% CI) 0.80 (0.61;1.04) 0.68 (0.44;1.05) 0.95 (0.71;1.27) 0.72 (0.48;1.07) 0.80 (0.61;1.06) 0.71 (0.47;1.07) 1.25 (0.94;1.67) 
p-valuea 0.0957 0.0845 0.7305 0.1117 0.1248 0.0002 0.1201 
I2 (%) 70.85 52.25 0.00 71.07 0.00 74.80 52.19 
p-valueb 0.03 0.12 0.86 0.03 0.50 0.00 0.12 
rs1927911 G/A        
OR (95% CI) 0.78 (0.63;0.97) 0.68 (0.52;0.91) 0.98 (0.74;1.30) 0.69 (0.50;0.94) 0.82 (0.63;1.09) 0.69 (0.51;0.94) 1.32 (1.10;1.59) 
p-valuea 0.0227 0.0095 0.8788 0.0186 0.147 0.0169 0.0033 
I2 (%) 57.17 34.69 0.00 58.89 0.00 61.98 47.70 
p-valueb 0.07 0.20 0.62 0.06 0.51 0.04 0.13 
rs12377632 C/T        
OR (95% CI) 0.78 (0.63;0.97) 0.65 (0.49;0.85) 0.90 (0.69;1.17) 0.72 (0.52;1.00) 0.78 (0.61;1.00) 0.70 (0.50;0.98) 1.20 (1.00;1.45) 
p-valuea 0.0245 0.0024 0.4159 0.3675 0.0471 0.0397 0.0442 
I2 (%) 59.28 44.68 0.00 59.31 0.00 66.11 40.13 
p-valueb 0.06 0.14 0.86 0.06 0.60 0.03 0.17 
rs2149356 G/T        
OR (95% CI) 0.74 (0.57;0.96) 0.62 (0.41;0.92) 0.91 (0.69;1.20) 0.65 (0.43;0.99) 0.76 (0.58;0.98) 0.64 (0.43;0.96) 1.35 (0.97;1.88) 
p-valuea 0.0241 0.0229 0.4955 0.0425 0.0371 0.0309 0.0747 
I2 (%) 72.99 50.68 0.00 76.15 0.00 77.88 67.73 
p-valueb 0.01 0.10 0.75 0.00 0.51 0.00 0.02 
rs11536889 G/C        
OR (95% CI) 0.97 (0.83;1.13) 1.20 (0.78;1.84) 1.40 (0.90;2.16) 0.87 (0.72;1.06) 1.27 (0.84;1.94) 0.91 (0.75;1.10) 1.17 (0.96;1.41) 
p-valuea 0.7054 0.4045 0.1351 0.1599 0.594 0.3168 0.1153 
I2 (%) 11.33 3.39 0.00 0.00 0.00 0.00 0.00 
p-valueb 0.33 0.38 0.64 0.54 0.48 0.39 0.66 
rs7037117 A/G        
OR (95% CI) 0.83 (0.65;1.04) 0.67 (0.44;1.00) 0.76 (0.50;1.16) 0.88 (0.58;1.32) 0.70 (0.47;1.04) 0.84 (0.59;1.18) 1.11 (0.74;1.66) 
p-valuea 0.1116 0.0476 0.2056 0.5233 0.0752 0.311 0.6291 
I2 (%) 56.82 0.00 23.83 77.33 0.00 72.32 78.02 
p-valueb 0.07 0.62 0.27 0.00 0.59 0.01 0.00 
rs7045953 A/G        
OR (95% CI) 0.87 (0.67;1.13) 0.78 (0.23;2.60) 0.76 (0.22;2.64) 0.88 (0.66;1.17) 0.78 (0.23;2.59) 0.87 (0.66;1.15) 1.13 (0.85;1.50) 
p-valuea 0.2853 0.6812 0.6672 0.3715 0.6789 0.3194 0.3852 
I2 (%) 27.21 0.00 0.00 16.26 0.00 23.98 14.22 
p-valueb 0.25 0.50 0.60 0.30 0.52 0.27 0.31 
SNPs 1/2Genetic models
ABCDEFG
rs10759930 T/C        
OR (95% CI) 0.80 (0.62;1.04) 0.68 (0.43;1.07) 0.91 (0.68;1.22) 0.74 (0.52;1.04) 0.79 (0.60;1.04) 0.73 (0.50;1.05) 1.23 (1.00;1.50) 
p-valuea 0.099 0.0969 0.5602 0.0917 0.0972 0.0946 0.0395 
I2(%) 69.49 55.13 0.00 61.23 0.00 69.73 21.27 
p-valueb 0.03 0.10 0.87 0.07 0.40 0.04 0.28 
rs1927914 A/G        
OR (95% CI) 0.80 (0.61;1.04) 0.68 (0.44;1.05) 0.95 (0.71;1.27) 0.72 (0.48;1.07) 0.80 (0.61;1.06) 0.71 (0.47;1.07) 1.25 (0.94;1.67) 
p-valuea 0.0957 0.0845 0.7305 0.1117 0.1248 0.0002 0.1201 
I2 (%) 70.85 52.25 0.00 71.07 0.00 74.80 52.19 
p-valueb 0.03 0.12 0.86 0.03 0.50 0.00 0.12 
rs1927911 G/A        
OR (95% CI) 0.78 (0.63;0.97) 0.68 (0.52;0.91) 0.98 (0.74;1.30) 0.69 (0.50;0.94) 0.82 (0.63;1.09) 0.69 (0.51;0.94) 1.32 (1.10;1.59) 
p-valuea 0.0227 0.0095 0.8788 0.0186 0.147 0.0169 0.0033 
I2 (%) 57.17 34.69 0.00 58.89 0.00 61.98 47.70 
p-valueb 0.07 0.20 0.62 0.06 0.51 0.04 0.13 
rs12377632 C/T        
OR (95% CI) 0.78 (0.63;0.97) 0.65 (0.49;0.85) 0.90 (0.69;1.17) 0.72 (0.52;1.00) 0.78 (0.61;1.00) 0.70 (0.50;0.98) 1.20 (1.00;1.45) 
p-valuea 0.0245 0.0024 0.4159 0.3675 0.0471 0.0397 0.0442 
I2 (%) 59.28 44.68 0.00 59.31 0.00 66.11 40.13 
p-valueb 0.06 0.14 0.86 0.06 0.60 0.03 0.17 
rs2149356 G/T        
OR (95% CI) 0.74 (0.57;0.96) 0.62 (0.41;0.92) 0.91 (0.69;1.20) 0.65 (0.43;0.99) 0.76 (0.58;0.98) 0.64 (0.43;0.96) 1.35 (0.97;1.88) 
p-valuea 0.0241 0.0229 0.4955 0.0425 0.0371 0.0309 0.0747 
I2 (%) 72.99 50.68 0.00 76.15 0.00 77.88 67.73 
p-valueb 0.01 0.10 0.75 0.00 0.51 0.00 0.02 
rs11536889 G/C        
OR (95% CI) 0.97 (0.83;1.13) 1.20 (0.78;1.84) 1.40 (0.90;2.16) 0.87 (0.72;1.06) 1.27 (0.84;1.94) 0.91 (0.75;1.10) 1.17 (0.96;1.41) 
p-valuea 0.7054 0.4045 0.1351 0.1599 0.594 0.3168 0.1153 
I2 (%) 11.33 3.39 0.00 0.00 0.00 0.00 0.00 
p-valueb 0.33 0.38 0.64 0.54 0.48 0.39 0.66 
rs7037117 A/G        
OR (95% CI) 0.83 (0.65;1.04) 0.67 (0.44;1.00) 0.76 (0.50;1.16) 0.88 (0.58;1.32) 0.70 (0.47;1.04) 0.84 (0.59;1.18) 1.11 (0.74;1.66) 
p-valuea 0.1116 0.0476 0.2056 0.5233 0.0752 0.311 0.6291 
I2 (%) 56.82 0.00 23.83 77.33 0.00 72.32 78.02 
p-valueb 0.07 0.62 0.27 0.00 0.59 0.01 0.00 
rs7045953 A/G        
OR (95% CI) 0.87 (0.67;1.13) 0.78 (0.23;2.60) 0.76 (0.22;2.64) 0.88 (0.66;1.17) 0.78 (0.23;2.59) 0.87 (0.66;1.15) 1.13 (0.85;1.50) 
p-valuea 0.2853 0.6812 0.6672 0.3715 0.6789 0.3194 0.3852 
I2 (%) 27.21 0.00 0.00 16.26 0.00 23.98 14.22 
p-valueb 0.25 0.50 0.60 0.30 0.52 0.27 0.31 
a

OR p-value, b heterogeneous p-value

A: Allele contrast model, B: Homozygous model, C: Heterozygous (12 vs. 22), D: Heterozygous (11 vs. 12), E: Dominant model, F: Recessive model, and G: Overdominant model.

Bold text showed statistical significance in meta-analysis model. Italic text represented Egger’s test P-value < 0.05.

Subgroup analysis of POAG and NTG

Due to limitations of materials of NTG in TLR4 rs4986790 and rs4986791 studies, there were two studies in POAG which were combined by a meta-analysis that included 272 cases and 204 healthy controls (Table 4). The meta-analysis results showed that four genetic models were statistically related between these positions and the susceptibility of POAG: allele contrast model (rs4986790; OR = 0.40, P =0.01, rs4986791; OR = 0.41, P=0.01) (Figure 2), heterozygous model (rs4986790; OR = 0.36, P=0.01, rs4986791; OR = 0.44, P=0.03), recessive model (rs4986790; OR = 0.37, P=0.01, rs4986791; OR = 0.42, P=0.02), and overdominant model (rs4986790; OR = 2.73, P =0.01, rs4986791; OR = 2.21, P =0.03) (Table 4).

Forest plot of association between two TLR4 polymorphisms and POAG

Figure 2
Forest plot of association between two TLR4 polymorphisms and POAG

The area of the square was proportional to the study’s weight. The horizontal line represents 95% CI. The overall effect was illustrated as diamonds with the lateral points showing the CI. The forest plots of rs4986790 and rs4986791 associated with POAG were shown as an allele contrast model.

Figure 2
Forest plot of association between two TLR4 polymorphisms and POAG

The area of the square was proportional to the study’s weight. The horizontal line represents 95% CI. The overall effect was illustrated as diamonds with the lateral points showing the CI. The forest plots of rs4986790 and rs4986791 associated with POAG were shown as an allele contrast model.

Table 4
Meta-analysis of rs4986791 and rs4986790 associated with POAG
Genetic modelsOR (95% CI)p-valueaI2(%)p-valueb
rs4986791     
Allele contrast C vs. T 0.41 (0.20;0.82) 0.0117 0.00 0.38 
Homozygote compairison CC vs. TT 0.38 (0.04;3.71) 0.4074 0.00 0.79 
Heterozygote comparison CT vs. TT 1.84 (0.08;8.92) 0.8861 0.00 0.6 
Heterozygote comparison CC vs. CT 0.44 (0.21;0.92) 0.0294 0.00 0.34 
Dominant model CC+CT vs. TT 0.41 (0.04;3.97) 0.4408 0.00 0.78 
Recessive model CC vs. CT + TT 0.42 (0.20;0.86) 0.0173 0.00 0.36 
Overdominant model CT vs. CC+TT 2.21 (1.07;4.56) 0.0315 0.00 0.33 
rs4986790     
Allele contrast A vs. G 0.40 (0.20;0.80) 0.0096 26.49 0.24 
Homozygote comparison AA vs. GG 0.67 (0.08;0.51) 0.7112 0.00 0.84 
Heterozygote comparison AG vs. GG 1.83 (0.20;16.67) 0.5905 0.00 0.96 
Heterozygote comparison AA vs. AG 0.36 (0.17;0.76) 0.0075 0.00 0.38 
Dominant model AA+AG vs. GG 0.73 (0.08;6.03) 0.7745 0.00 0.83 
Recessive model AA vs. AG + GG 0.37 (0.18;0.76) 0.0074 1.54 0.31 
Overdominant model AG vs. AA+GG 2.73 (1.30;5.73) 0.0077 0.00 0.38 
Genetic modelsOR (95% CI)p-valueaI2(%)p-valueb
rs4986791     
Allele contrast C vs. T 0.41 (0.20;0.82) 0.0117 0.00 0.38 
Homozygote compairison CC vs. TT 0.38 (0.04;3.71) 0.4074 0.00 0.79 
Heterozygote comparison CT vs. TT 1.84 (0.08;8.92) 0.8861 0.00 0.6 
Heterozygote comparison CC vs. CT 0.44 (0.21;0.92) 0.0294 0.00 0.34 
Dominant model CC+CT vs. TT 0.41 (0.04;3.97) 0.4408 0.00 0.78 
Recessive model CC vs. CT + TT 0.42 (0.20;0.86) 0.0173 0.00 0.36 
Overdominant model CT vs. CC+TT 2.21 (1.07;4.56) 0.0315 0.00 0.33 
rs4986790     
Allele contrast A vs. G 0.40 (0.20;0.80) 0.0096 26.49 0.24 
Homozygote comparison AA vs. GG 0.67 (0.08;0.51) 0.7112 0.00 0.84 
Heterozygote comparison AG vs. GG 1.83 (0.20;16.67) 0.5905 0.00 0.96 
Heterozygote comparison AA vs. AG 0.36 (0.17;0.76) 0.0075 0.00 0.38 
Dominant model AA+AG vs. GG 0.73 (0.08;6.03) 0.7745 0.00 0.83 
Recessive model AA vs. AG + GG 0.37 (0.18;0.76) 0.0074 1.54 0.31 
Overdominant model AG vs. AA+GG 2.73 (1.30;5.73) 0.0077 0.00 0.38 
a

OR P-value, b heterogeneous P-value.

Previous results in combined glaucoma types showed great heterogeneity amongst studies relating to rs1927911, rs12377632, and rs2149356 (I2 > 50%, P<0.05). Therefore, we employed stratification by using the glaucoma types (POAG and NTG). All positions were associated with POAG in the recessive model (rs1927911; OR = 0.60, P=0.00 (Table 5), rs12377632; OR = 0.60, P=0.01 (Table 6), rs2149356; OR = 0.51, P=0.03 (Table 7). However, the rs1927911 correlated with NTG in the homozygous model (OR = 0.70, P=0.025 (Table 5)).

Table 5
Meta-analysis result of rs1927911 based on subgroup analysis
SNPsOR (95% CI)P-valueHeterogeneityEffect modelsEgger’s P-value
I2 (%)P-value
Allele contrast 
POAG 0.58 (0.61–1.00) 0.0538 0.58 
NTG 0.82 (0.65–1.04) 0.0969 59.27 0.09 0.329 
Homozygous model 
POAG 0.79 (0.46–1.35) 0.3891 0.85 
NTG 0.70 (0.51–0.95) 0.0258 38.59 0.19 F 0.37 
Heterozygous model (GA vs. AA) 
POAG 1.35 (0.80–2.28) 0.2574 0.56 
NTG 0.91 (0.67–1.23) 0.5256 0.88 0.95 
Heterozygous model (GG vs. GA) 
POAG 0.56 (0.39–0.81) 0.0023 0 0.5618 F 
NTG 0.77 (0.62–0.96) 0.0208 46.34 0.15 0.03 
Dominant model 
POAG 1.05 (0.64–1.71) 0.8558 0.8274 
NTG 0.80 (0.60–1.07) 0.1329 0.49 0.68 
Recessive model 
POAG 0.60 (0.43–0.86) 0.0047 0 0.64 F - 
NTG 0.77 (0.56–1.05) 0.1024 58.24 0.09 0.0437 
Overdominant model 
POAG 1.64 (1.17–2.33) 0.0042 0 0.40 F - 
NTG 1.18 (0.96–1.44) 0.1139 0.37 0.11 
SNPsOR (95% CI)P-valueHeterogeneityEffect modelsEgger’s P-value
I2 (%)P-value
Allele contrast 
POAG 0.58 (0.61–1.00) 0.0538 0.58 
NTG 0.82 (0.65–1.04) 0.0969 59.27 0.09 0.329 
Homozygous model 
POAG 0.79 (0.46–1.35) 0.3891 0.85 
NTG 0.70 (0.51–0.95) 0.0258 38.59 0.19 F 0.37 
Heterozygous model (GA vs. AA) 
POAG 1.35 (0.80–2.28) 0.2574 0.56 
NTG 0.91 (0.67–1.23) 0.5256 0.88 0.95 
Heterozygous model (GG vs. GA) 
POAG 0.56 (0.39–0.81) 0.0023 0 0.5618 F 
NTG 0.77 (0.62–0.96) 0.0208 46.34 0.15 0.03 
Dominant model 
POAG 1.05 (0.64–1.71) 0.8558 0.8274 
NTG 0.80 (0.60–1.07) 0.1329 0.49 0.68 
Recessive model 
POAG 0.60 (0.43–0.86) 0.0047 0 0.64 F - 
NTG 0.77 (0.56–1.05) 0.1024 58.24 0.09 0.0437 
Overdominant model 
POAG 1.64 (1.17–2.33) 0.0042 0 0.40 F - 
NTG 1.18 (0.96–1.44) 0.1139 0.37 0.11 

Bold text showed statistical significance in meta-analysis model.

Table 6
Meta-analysis result of rs12377632 based on subgroup analysis
SNPsOR (95% CI)P-valueHeterogeneityEffect modelsEgger’s P-value
I2 (%)P-value
Allele contrast 
POAG 0.71 (0.56–0.90) 0.0050 0 0.60 F 
NTG 0.86 (0.70–1.07) 0.1807 54.03 0.11 0.27 
Homozygous model 
POAG 0.51 (0.32–0.83) 0.0062 0 0.52 F 
NTG 0.81 (0.60–1.11) 0.1932 33.83 0.22 0.18 
Heterozygous model (CT vs. TT) 
POAG 0.80 (0.53–1.21) 0.2875 0.80 
NTG 1.03 (0.76–1.41) 0.8091 0.37 0.35 
Heterozygous model (CC vs. CT) 
POAG 0.65 (0.43–0.97) 0.0365 0 0.63 F 
NTG 0.77 (0.53–1.12) 0.1787 65.02 0.05 0.12 
Dominant model 
POAG 0.68 (0.46–1.01) 0.0564 0.10 
NTG 0.92 (0.69–1.23) 0.589 0.38 0.03 
Recessive model 
POAG 0.60 (0.41–0.88) 0.0091 0 0.5 F 
NTG 0.78 (0.55–1.11) 0.1701 64.95 0.06 0.01 
Overdominant model 
POAG 1.10 (0.79–1.54) 0.5772 0.71 
NTG 1.23 (0.90–1.69) 0.2082 59.83 0.08 0.43 
SNPsOR (95% CI)P-valueHeterogeneityEffect modelsEgger’s P-value
I2 (%)P-value
Allele contrast 
POAG 0.71 (0.56–0.90) 0.0050 0 0.60 F 
NTG 0.86 (0.70–1.07) 0.1807 54.03 0.11 0.27 
Homozygous model 
POAG 0.51 (0.32–0.83) 0.0062 0 0.52 F 
NTG 0.81 (0.60–1.11) 0.1932 33.83 0.22 0.18 
Heterozygous model (CT vs. TT) 
POAG 0.80 (0.53–1.21) 0.2875 0.80 
NTG 1.03 (0.76–1.41) 0.8091 0.37 0.35 
Heterozygous model (CC vs. CT) 
POAG 0.65 (0.43–0.97) 0.0365 0 0.63 F 
NTG 0.77 (0.53–1.12) 0.1787 65.02 0.05 0.12 
Dominant model 
POAG 0.68 (0.46–1.01) 0.0564 0.10 
NTG 0.92 (0.69–1.23) 0.589 0.38 0.03 
Recessive model 
POAG 0.60 (0.41–0.88) 0.0091 0 0.5 F 
NTG 0.78 (0.55–1.11) 0.1701 64.95 0.06 0.01 
Overdominant model 
POAG 1.10 (0.79–1.54) 0.5772 0.71 
NTG 1.23 (0.90–1.69) 0.2082 59.83 0.08 0.43 

Bold text showed statistical significance in meta-analysis model.

Table 7
Meta-analysis result of rs2149356 based on subgroup analysis
SNPsOR (95% CI)P-valueHeterogeneityEffect modelsEgges’s P-value
I2 (%)P-value
Allele contrast 
POAG 0.69 (0.46–1.03) 0.0716 63.21 0.10 
NTG 0.82 (0.63–1.06) 0.1260 67.34 0.05 0.36 
Homozygous model 
POAG 0.61 (0.36–1.02) 0.0596 0.34 
NTG 0.69 (0.44–1.09) 0.111 53.44 0.12 0.40 
Heterozygous model (GT vs. TT) 
POAG 1.18 (0.72–1.97) 0.4993 0.80 
NTG 0.85 (0.63–1.16) 0.3116 0.8 0.9 
Heterozygous model (GG vs. GT) 
POAG 0.49 (0.34–0.71) 0.0243 63.57 0.1 R - 
NTG 0.80 (0.58–1.11) 0.1811 52.76 0.12 0.07 
Dominant model 
POAG 0.88 (0.55–1.41) 0.5936 0.64 
NTG 0.77 (0.57–1.02) 0.0695 2.26 0.36 0.69 
Recessive model 
POAG 0.51 (0.28–0.93) 0.0277 65.20 0.09 R - 
NTG 0.78 (0.55–1.10) 0.1552 65.02 0.06 0.07 
Overdominant model 
POAG 1.76 (1.03–3.02) 0.0391 59.76 0.11 R - 
NTG 1.14 (0.93–1.39) 0.2054 2.94 0.36 0.15 
SNPsOR (95% CI)P-valueHeterogeneityEffect modelsEgges’s P-value
I2 (%)P-value
Allele contrast 
POAG 0.69 (0.46–1.03) 0.0716 63.21 0.10 
NTG 0.82 (0.63–1.06) 0.1260 67.34 0.05 0.36 
Homozygous model 
POAG 0.61 (0.36–1.02) 0.0596 0.34 
NTG 0.69 (0.44–1.09) 0.111 53.44 0.12 0.40 
Heterozygous model (GT vs. TT) 
POAG 1.18 (0.72–1.97) 0.4993 0.80 
NTG 0.85 (0.63–1.16) 0.3116 0.8 0.9 
Heterozygous model (GG vs. GT) 
POAG 0.49 (0.34–0.71) 0.0243 63.57 0.1 R - 
NTG 0.80 (0.58–1.11) 0.1811 52.76 0.12 0.07 
Dominant model 
POAG 0.88 (0.55–1.41) 0.5936 0.64 
NTG 0.77 (0.57–1.02) 0.0695 2.26 0.36 0.69 
Recessive model 
POAG 0.51 (0.28–0.93) 0.0277 65.20 0.09 R - 
NTG 0.78 (0.55–1.10) 0.1552 65.02 0.06 0.07 
Overdominant model 
POAG 1.76 (1.03–3.02) 0.0391 59.76 0.11 R - 
NTG 1.14 (0.93–1.39) 0.2054 2.94 0.36 0.15 

Bold text showed statistical significance in meta-analysis model.

Since the stratification by using the glaucoma types still showed heterogeneity, we conducted a sensitivity analysis to reveal the influence of each study on the pooled OR of glaucoma. The sensitivity analysis of the association between SNPs (rs1927911, rs12377632, and rs2149356) and glaucoma was performed in the allele contrast model and shown in Table 8. The result suggested that there is one study which is the root of heterogeneity which affected the pooled OR. Finally, the source of heterogeneity was excluded from the meta-analysis resulting in TLR4 polymorphism association with glaucoma (Figure 3).

Forest plot of association between two TLR4 polymorphisms and NTG

Figure 3
Forest plot of association between two TLR4 polymorphisms and NTG

The area of the square was proportional to the study’s weight. The horizontal line represents 95% CI. The overall effect was illustrated as diamonds with the lateral points showing the CI. The forest plots of rs1927911, rs12377632, and rs214356 associated with NTG were shown as an allele contrast model.

Figure 3
Forest plot of association between two TLR4 polymorphisms and NTG

The area of the square was proportional to the study’s weight. The horizontal line represents 95% CI. The overall effect was illustrated as diamonds with the lateral points showing the CI. The forest plots of rs1927911, rs12377632, and rs214356 associated with NTG were shown as an allele contrast model.

Table 8
Sensitivity analysis for the allele contrast model in the meta-analysis
Omitting studyOR95% CII2P-value
rs1927911     
Navarro-Partida et al. 2017a 0.80 (0.61;1.04) 70.74 0.03 
Takano et al. 2012 0.84 (0.67;1.06) 48.09 0.14 
Suh et al. 2011 0.71 (0.61;0.82) 0.00 0.66 
Shibuya et al. 2008 0.79 (0.58;1.08) 71.29 0.03 
overall effect 0.78 (0.64;0.97) 57.17 0.07 
rs12377632     
Navarro-Partida et al. 2017a 0.82 (0.63;1.06) 68.36 0.04 
Takano et al. 2012 0.82 (0.62;1.09) 67.12 0.04 
Suh et al. 2011 0.71 (0.61;0.83) 0.00 0.86 
Shibuya et al. 2008 0.80 (0.59;1.09) 71.92 0.03 
overall effect 0.79 (0.64;0.97) 72.99 0.01 
rs2149356     
Navarro-Partida et al. 2017a 0.80 (0.60;1.07) 75.42 0.02 
Takano et al. 2012 0.78 (0.55;1.10) 76.64 0.01 
Suh et al. 2011 0.66 (0.56;0.78) 9.06 0.33 
Shibuya et al. 2008 0.73 (0.49;1.08) 81.96 0.00 
overall effect 0.74 (0.57;0.96) 72.99 0.01 
Omitting studyOR95% CII2P-value
rs1927911     
Navarro-Partida et al. 2017a 0.80 (0.61;1.04) 70.74 0.03 
Takano et al. 2012 0.84 (0.67;1.06) 48.09 0.14 
Suh et al. 2011 0.71 (0.61;0.82) 0.00 0.66 
Shibuya et al. 2008 0.79 (0.58;1.08) 71.29 0.03 
overall effect 0.78 (0.64;0.97) 57.17 0.07 
rs12377632     
Navarro-Partida et al. 2017a 0.82 (0.63;1.06) 68.36 0.04 
Takano et al. 2012 0.82 (0.62;1.09) 67.12 0.04 
Suh et al. 2011 0.71 (0.61;0.83) 0.00 0.86 
Shibuya et al. 2008 0.80 (0.59;1.09) 71.92 0.03 
overall effect 0.79 (0.64;0.97) 72.99 0.01 
rs2149356     
Navarro-Partida et al. 2017a 0.80 (0.60;1.07) 75.42 0.02 
Takano et al. 2012 0.78 (0.55;1.10) 76.64 0.01 
Suh et al. 2011 0.66 (0.56;0.78) 9.06 0.33 
Shibuya et al. 2008 0.73 (0.49;1.08) 81.96 0.00 
overall effect 0.74 (0.57;0.96) 72.99 0.01 

Discussion

Our meta-analysis was based on systematic collected studies relating to TLR4 gene polymorphisms and glaucoma. To our knowledge, this is the first study that attempted to combine the POAG and NTG studies because these two types of glaucoma are overlapping in some factors [5,6]. Interestingly, combined analysis showed rs1927911, rs12377632, and rs2149356 in the allele contrast model were strongly associated with glaucoma (POAG and NTG) suggesting that the POAG and NTG share some pathogenesis factors that may relate to TLR4. Additionally, the different MAS in rs12377632 did not disturb the meta-analysis result [28] suggesting that there is a different population structure in this position [35]. This was supported by stringent heterogeneity. To eliminate this limitation, the sensitivity analysis was applied and illustrated a similar result after removing one publication in NTG, which is a source of heterogeneity. It is possible that Suh et al. [33] performed in a different setting and the Korean ethnic group may have some factors which interfere with the result.

Besides the combined analysis, we conducted a subgroup analysis by using types of diseases (POAG or NTG). Surprisingly, rs1927911, rs12377632, and rs2149356, which exist in an intron, were still associated with POAG. However, the functions of polymorphisms have not been examined. Only rs1927911 was associated with NTG. It is possible that there are functions relating to RNA stability and regulation, resulting in alteration of protein expression because several translated regions or a part of intron might relate to miRNA [36]. Moreover, previous in silico analysis showed that rs2149356G is able to form miRNA which targets autophagy genes [37]. Therefore, rs1927911 and rs12377632 should be under this circumstance. Definitely, all positions should be validated by in vitro functional assay to prove this hypothesis.

Additionally, coding polymorphisms of TLR4 rs4986790 A/G (Asp299Gly) and rs4986791 C/T (Thr399Ile), for which both mutations exist in a co-segregation, were also robustly associated with POAG. Taken together, this implied that the TLR4 might play an essential role in POAG pathogenesis. Although the exact functions of these mutations are underinvestigated, there is evidence indicating that these mutations enhance cell death in hepatic stellate cells [38].

Moreover, Asp299Gly and Thr399Ile impair the ability to respond to TLR4 ligands in colorectal cancer cell lines [39]. On the contrary, crystallography studies show that it did not intrude upon LPS binding [40]. Therefore, these mutations may cause RGC apoptosis in POAG, by which several endogenous ligands of TLR4 were up-regulated in the glaucomatous eye [41].

TLR4 activation plays a role in innate immunity and subsequently the adaptive immune response [42]. Currently, several lines of evidence showed that TLR4 relates to the pathophysiology of various diseases as well as autoimmunity [43]. For instance, TLR4 knockout mice reduce autoantibody production and vasculature inflammation in systemic lupus erythematosus (SLE) and atherosclerosis, respectively [44,45]. The activated TLR4 by using endogenous ligands induces pro-inflammatory cytokine and chemokine production in both human synovial fibroblasts and peripheral blood mononuclear cells (PBMC) from rheumatoid arthritis patients leading to cartilage inflammation and degeneration [46–48]. In multiple sclerosis patients and experimental autoimmune encephalomyelitis models, the TLR4 was up-regulated and HMGB-1, a TLR4 ligand, plays a role in the disease progression [49–51]. Additionally, TLR4 polymorphisms are associated with various inflammatory diseases such as aortic aneurysmal disease, periodontitis, psoriasis arthritis, and Crohn’s disease [52–55].

In the recent year, there is new evidence suggesting that CD4+ T cells enter into the retina and cause neurodegeneration in the glaucoma model. The CD4+ T cells are primed by HSP of normal flora and subsequently crossreacted with mouse or human HSP, TLR4 ligands [56]. Moreover, increasing oral microflora is correlated with microglial activation and neuronal loss via TLR4 signaling, while inhibition of TLR4 causes the neuroprotective effect [57]. In addition, several previous studies indicated that glaucoma (POAG and NTG) has a paradigm shift into inflammatory disease and autoimmunity [21,58–60]. However, to support this paradigm, further evidence is still required. Our study reported that the TLR4 polymorphisms are associated with POAG and NTG which serve as supporting material for glaucoma relating to inflammatory disease and autoimmunity.

There are limitations which appeared in this meta-analysis and these should not be ignored to improve validity and reliability. First, the sample size should be expanded. Second, a relative descent is biased in the Japanese population. The other population descents are still required, especially for the African, Caucasian, and Han Chinese populations. Moreover, additional factors may affect the results, such as co-segregation events whereby our meta-analysis cannot assess the haplotype between TLR4 polymorphisms and glaucoma. At last, the natural history of IOP is not simplified by a single measurement. However, several included articles, which were enrolled in our meta-analysis, did not mention in this regard. It is well known that the IOP is fluctuation over 24 h [61]. Therefore, this may decrease the power of this meta-analysis.

In conclusion, the present study demonstrated that there are associations between TLR4 rs1927911, rs12377632, and rs2149356 and glaucoma (POAG and NTG), while rs4986790 A/G and rs4986791 C/T strongly decrease the risk of POAG suggesting that TLR4 may play a role in glaucoma pathogenesis, which should be classified as a neuroinflammatory and autoimmune disease, and should be considered as a genetic marker for POAG. Previously, the role of TLR4 in POAG was exploited and suggested that it may save as a potential therapeutic strategy [62]. However, the functions of TLR4 polymorphisms in the glaucoma model are unidentified. Therefore, to confirm these associations, the laboratory experiment and a well-designed case-control study are still required for the authentication of the results.

Data availability

The data for meta-analysis are available in Table 2. The other data are available from the corresponding author upon request.

Acknowledgements

The authors would like to thank Dr. Danai Tonkerdmongkol, for acting as a third party for resolving disagreements to achieve final consensus.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

T.P. and N.C. were provided support by the Faculty of Optometry, Ramkhamhaeng University. The publication charge was covered by Faculty of Optometry, Ramkhamhaeng University.

Author contribution

T.P. designed the topic of the study. The searching and extracted data were independently performed by T.P. and N.C. T.P. drafted the manuscript. N.C. checked, commented, and approved the final manuscript.

Abbreviations

     
  • DAMPs

    damage-associated molecular patterns

  •  
  • EMBASE

    Excerpta Medica Database

  •  
  • GON

    glaucomatous optic neuropathy

  •  
  • HSP

    heat shock protein

  •  
  • HWE

    Hardy-Weinberg equilibrium

  •  
  • IOP

    intraocular pressure

  •  
  • LPS

    lipopolysaccharide

  •  
  • MAF

    minor allele frequency

  •  
  • NOS

    Newcastle-Ottawa scale

  •  
  • NTG

    normal tension glaucoma

  •  
  • POAG

    primary open-angle glaucoma

  •  
  • RGC

    retinal ganglion cell

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • TLR

    toll-like receptor

  •  
  • TNF

    tumor necrosis factor

  •  
  • VF

    visual field

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