Promoter Polymorphism (−308G/A) of Tumor Necrosis Factor-Alpha ( TNF-α ) Gene and Asthma Risk: An Updated Meta-Analysis

Abstract

Background and Aims:

The relationship between the promoter polymorphism (−308G/A) of the tumor necrosis factor-alpha (TNF-α) gene and the susceptibility to asthma has been tested in several studies. However, the results have been inconsistent. Therefore, we performed an updated meta-analysis to evaluate the relationship between this promoter polymorphism of the TNF-α gene and the risk of asthma.

Methods:

Fifty case-control studies were included in this meta-analysis which provided 17,937 controls and 9961 asthma patients. The pooled p-value, odds ratio (OR), and 95% confidence interval (95% CI) were used to investigate the strength of the association of this polymorphism of the TNF-α gene with the risk of asthma. The meta-analysis was carried out by Comprehensive Meta-Analysis software.

Results:

The results of our meta-analysis revealed that the TNF-α polymorphism (−308, G/A) was strongly associated with the risk of asthma (p < 0.05 in the allelic, dominant, and recessive models, respectively). In further analyses, based on age group and ethnicity, we observed this association for all subpopulations examined (p < 0.05 in allelic, dominant, and recessive models, respectively).

Conclusion:

This large-scale meta-analysis supports a strong association between the TNF-α gene promoter polymorphism (−308G/A) and the development to asthma in both children and adults.

Introduction

Asthma is a common chronic lower respiratory disease. It is characterized by chronic airway inflammation associated with variable airflow obstruction and airway hyperresponsiveness. Previous studies revealed that tumor necrosis factor-α (TNF-α) is implicated in pathogenesis of asthma. TNF-α gene encodes a multifunctional proinflammatory cytokine that is versatile and very popular (Kim et al., 2017a). TNF was derived from the ability to control endotoxin-induced tumor necrosis gene (Carswell et al., 1975). It is mainly produced in macrophage cell but it is also widely expressed in granulocytes, fibroblasts, epithelial cells, and so on (Spriggs et al., 1992; Li et al., 2018). It is also involved in inflammation, even “master regulator” of proinflammatory cytokine production (Maini et al., 1995; Michlewska et al., 2009), regulation of apoptosis in macrophage (Takada et al., 2007), differentiation (Witsell and Schook, 1992), and so on. Like this, TNF has a variety of functions so that it is associated with various diseases related to these functions. As TNF-α mediates survival of macrophage, it plays an important role in sepsis (Conte et al., 2006). A macrophage-derived TNF-α downregulates macrophage scavenger receptor gene expression and is related to the development of atherosclerosis (Hsu et al., 1996). Because TNF-α is associated with and involved in a variety of pathways, mutations in the gene are also associated with the development of various diseases. TNF-α polymorphism is associated with dengue (Santos et al., 2017), cancer including gastric cancer and hepatocellular cancer (Wang et al., 2016a), systemic lupus erythematosus (Yang et al., 2017), immune thrombocytopenia (Zhang et al., 2017), ischemic stroke (Song and Cheng, 2017), and others. Thus, it is natural that there are many studies on this.

Asthma cannot escape the touch of TNF-α. Asthma is a complex disease and has diverse causes. Not only environmental factors such as indoor allergen (Ahluwalia and Matsui, 2011), socioeconomic and ethnic disparities (Gold and Wright, 2005), and drug use including acetaminophen (Henderson and Shaheen, 2013) or antibiotics (Murk et al., 2011a) but also genetic factors such as promoter polymorphisms in the chromosome 5 gene cluster (Rosenwasser et al., 1995), polymorphism of ADAM33 (Van Eerdewegh et al., 2002), KCNS3 (Hao et al., 2005), HNMT gene (Preuss et al., 1998), and so on could be the cause of asthma. Some genetic variants could cause asthma when they interact with other environmental factors, gene polymorphisms, or specific diseases (Martinez, 2007). Among them, quantitative locus analysis of airway hyperresponsiveness in mice revealed that one of the locus contained TNF-α (De Sanctis et al., 1995). In addition, there have been many studies on the polymorphism of TNF-α and the occurrence of asthma. TNF-α polymorphism has been reported to be a genetic risk factor for the development of asthma in the UK/Irish child (Winchester et al., 2000).

The aim of this study is to evaluate the relationship between promoter polymorphism (−308G/A) of TNF-α gene and the development of asthma in updated meta-analysis.

Materials and Methods

Search strategy

We performed a comprehensive electronic search including PUBMED, EMBASE, scholar of google, and Korean databases was performed until April 1, 2016 to search eligible studies. We used the following keywords to find eligible studies: “tumor necrosis factor,” “TNF-alpha”, or “TNF-A,” AND “polymorphism,” “polymorphisms,” or “variant” AND “rs1800629,” or”−308” AND “acute rejection,” “asthma,” or “meta analysis.” The previous meta-analysis studies about TNF-α (−308G/A) and asthma were considered as reference. Additional studies were identified by a hand search of the references of original studies.

Study selection

Published articles were recruited in our updated meta-analysis if they met the inclusion criteria: (i) evaluation of the association between the TNF-α polymorphism (−308G/A) and asthma; (ii) study was designed using the case-control study or cohort studies; (iii) contained sufficient distribution of TNF-α polymorphism (−308G/A) in the asthma group and the control group for the estimation of an odds ratio (OR), 95% confidence interval (CI), and p-value. Studies that deviated from Hardy-Weinberg equilibrium (HWE) in the control group were excluded.

Data extraction

The investigators extracted data and reached consensus on all of the data. We extracted data from the selected articles including the first author's name, year of publication, population of subject, number of cases and controls, and genotype frequency of TNF-α polymorphism (−308G/A).

Statistical analysis

HWE in control subjects was assessed by chi-squared test. The pooled p-value, OR, and 95% CI were used to assess association between susceptibility to asthma and TNF-α polymorphism (−308G/A). And we calculated the heterogeneity among studies. The heterogeneity test was referred for the null hypothesis that all studies evaluated the same effect. The chi-square-based Q test and I² test were applied. When p-value of the Q test was less than 0.05 or I² statistic was >50%, we considered significant heterogeneity. If there was significant heterogeneity, the random-effects Mantel-Haenszel method was adopted to evaluate the point estimates and 95% CI. Otherwise, the fixed-effects Mantel-Haenszel method was adopted.

For meta-analysis of TNF-α polymorphism (−308G/A), the pooled ORs, 95% CI, and p-value were calculated using combination of genotype. We first estimated the risk of the A/A genotype and A/G genotype for asthma compared with the G/G genotype as dominant model, respectively, and then evaluated the risk of “A allele versus G allele,” “A/A genotype+A/G genotype versus G/G genotype,” and “A/A genotype versus A/G genotype+G/G genotype” on risk of asthma, assuming dominant and recessive effects of the variant A allele, respectively (Seok et al., 2014; Park and Kim, 2015; Kang et al., 2016, 2017; Kim et al., 2017b). The p < 0.05 was regarded as statistically significant association with asthma. Meta-analysis was performed using the comprehensive meta-analysis software (Biostat, Englewood, NJ).

Results

Table 1 shows the main characteristics of selected studies included in our updated meta-analysis. Briefly, we performed updated meta-analysis with fifty articles (Moffatt and Cookson, 1997; Albuquerque et al., 1998; Chagani et al., 1999; Tan et al., 1999; Louis et al., 2000; Winchester et al., 2000; Hakonarson et al., 2001; Witte et al., 2002; Buckova et al., 2002; El Bahlawan et al., 2003; Gao et al., 2003; Zhifang et al., 2003; Guo and Zhou, 2004; Beghe et al., 2004; Liu et al., 2004; Zhai and Li, 2004; Shin et al., 2004; Sandford et al., 2004; Wang et al., 2004; Bilolikar et al., 2005; Zhao et al., 2005; Gupta et al., 2005; Kim et al., 2006, 2008; Schubert et al., 2006; Sharma et al., 2006; Tolgyesi et al., 2006; Hong et al., 2007; Kamali-Sarvestani et al., 2007; Mak et al., 2007; Kumar et al., 2008; Kurotani et al., 2008; Trajkov et al., 2008; Wang et al., 2009; Mahdaviani et al., 2009; Aytekin et al., 2009; Castro-Giner et al., 2009; Daley et al., 2009; Jimenez-Morales et al., 2009; Michel et al., 2010; Cui and Wang, 2010; Jiffri and Elhawary, 2011; Murk et al., 2011b; Jia et al., 2012; Chiang et al., 2013; Shaker et al., 2013; Ying et al., 2013; Dhaouadia et al., 2011; Despotovic et al., 2015; Yang et al., 2015; Zheng et al. 2012). After pooling all data, these 50 articles included 17,937 asthma patients and 9961 control subjects.

Table 1.

Characteristics of Eligible Studies Included in the Meta-Analysis

First author	Year	Ethnicity	Asthma/control	Asthma			Controls			Diagnostic method
First author	Year	Ethnicity	Asthma/control	A/A	A/G	G/G	A/A	A/G	G/G	Diagnostic method
Moffatt	1997	Caucasian	88/312	8	36	44	12	89	211	Questionnaire & physician diagnosed asthma
Albuquerque	1998	Caucasian	124/50	9	40	75	7	19	24	Questionnaire & physician diagnosed asthma
Chagani	1999	Caucasian	251/43	101		150	9		34	Physician diagnosed asthma
Tan	1999	Asian	105/187	0	22	83	0	37	150
Louis	2000	Caucasian	95/98	0	31	64	2	27	69	Physician diagnosed asthma
Winchester1	2000	Caucasian	20/416	2	9	9	17	116	283	Questionnaire & physician diagnosed asthma
Winchester2	2000	Asian	6/275	0	2	4	3	33	239	Questionnaire & physician diagnosed asthma
Hakonarson	2001	Caucasian	94/94	3	27	64	2	25	67	Physician diagnosed asthma
Buckova	2002	Caucasian	151/155	3	46	102	1	38	116
Witte	2002	Mixed	235/273	4	67	164	6	55	212	Questionnaire & physician diagnosed asthma
El Bahlawan	2003	Caucasian	38/231	2	5	31	4	45	182
Gao	2003	Asian	125/96	26	52	47	11	41	44	Physician diagnosed asthma
Li	2003	Asian	30/26	5	16	9	2	10	14
Beghe	2004	Caucasian	142/45	1	33	108	1	8	36	Methacholine challenge test and wheeze in the last
Sandford	2004	Asian	106/118	0	26	80	0	16	102	International Study of Asthma and Allergies in Childhood Phase II
Shin	2004	Asian	534/170	2	50	482	2	37	131	Questionnaire & physician diagnosed asthma
Wang	2004	Asian	191/129	2	49	140	0	18	111	Questionnaire
Guo	2004	Asian	48/21	4	28	16	3	11	7
Liu	2004	Asian	113/126	0	15	98	0	22	104
Zhai	2004	Asian	64/80	6	14	44	1	12	67
Bilolikar	2005	Mixed	100/100	7	47	46	6	31	63	Physician diagnosed asthma
Gupta	2005	Asian	155/211	3	36	116	1	32	178	Questionnaire & physician diagnosed asthma
Zhao	2005	Asian	50/80	0	5	45	0	9	71
Kim	2006	Asian	360/257	1	43	316	0	40	217	Physician diagnosed asthma
Schubert	2006	Caucasian	231/270	6	65	160	6	67	197	Physician diagnosed asthma
Sharma	2006	Asian	488/390	2	111	375	4	65	321	Physician diagnosed asthma (National Asthma Education and Prevention Program)
Tolgyesi	2006	Caucasian	144/174	4	41	99	5	47	122	Physician diagnosed asthma (Global Initiative for Asthma)
Hong	2007	Asian	635/153	5	82	548	0	14	139	Physician diagnosed asthma (American Thoracic Society guidelines)
Kamali-Sarvestani	2007	Caucasian	203/113	0	28	175	1	9	103	Physician diagnosed asthma (Global Initiative for Asthma)
Mak	2007	Asian	292/292	1	47	244	2	40	250	Questionnaire & physician diagnosed asthma
Kim	2008	Asian	715/240	6	95	614	0	21	219	Physician diagnosed asthma (American Thoracic Society guidelines)
Kumar	2008	Asian	123/100	2	35	86	0	18	82	Questionnaire & physician diagnosed asthma
Trajkov	2008	Caucasian	74/281	1	9	64	0	30	251	Questionnaire & physician diagnosed asthma
Aytenkin	2009	Caucasian	46/67	0	11	35	0	16	51
Castro-Giner	2009	Caucasian	529/8259	19	163	347	213	1991	6055	Questionnaire
Daley	2009	Caucasian	644/751	25	203	416	23	216	512	Questionnaire & physician diagnosed asthma
Jimenez-Morales	2009	Mexican	226/400	1	25	200	0	23	377	Physician diagnosed asthma (American Thoracic Society and the Global Initiative for Asthma)
Mahdaviani	2009	Caucasian	27/137	0	17	10	0	39	98	Physician diagnosed asthma (National Asthma Education and Prevention Program)
Wang	2009	Asian	448/510	3	100	345	7	94	409	Physician diagnosed asthma (Global Initiative for Asthma)
Michel	2010	Caucasian	703/658	23	207	473	13	158	487	Questionnaire & physician diagnosed asthma
Cui	2010	Asian	100/104	2	6	92	2	13	89
Dhaouadi	2011	Caucasian	107/168	6	31	70	4	42	122
Jiffri	2011	Caucasian	120/120	0	33	87	0	15	105	Physician diagnosed asthma (Global Initiative for Asthma)
Murk	2011b	Mixed	100/487	2	20	78	15	113	359	Perinatal Risk of Asthma in Infants of Asthmatic Mothers study
Jia	2012	Asian	91/89	1	14	76	0	7	82
Zheng	2012	Asian	198/110	5	25	168	0	17	93	Physician diagnosed asthma (Global Initiative for Asthma)
Shaker	2013	Caucasian	100/100	8	60	32	4	30	66	Physician diagnosed asthma (American Thoracic Society and the Global Initiative for Asthma)
Li	2013	Asian	65/50	4	16	45	1	6	43
Yang	2015	Asian	248/226	7	76	165	2	37	187	Physician diagnosed asthma (Global Initiative for Asthma)
Despotovic	2015	Caucasian	79/95	0	22	57	1	24	70	Physician diagnosed asthma (Global Initiative for Asthma)

Among these 50 studies, any studies did not show an error in the HWE in the control group (p > 0.05, data not shown). To estimate risk evaluation in all asthma cases and control, we tested the heterogeneity in each model, respectively (dominant, recessive, and allele model). The results of the heterogeneity test for meta-analysis are shown in Tables 2 and 3, Figures 1 -3. If the result of the Q test was p < 0.05 or the I² statistic was >50%, the random-effects method was adopted. Otherwise, the fixed-effects method was applied. In allele analysis (A allele vs. G allele) between TNF-α polymorphism (−308G/A) and susceptibility to asthma, the minor A allele showed strong association with asthma (all population, OR = 1.344, 95% CI = 1.207-1.497, p < 0.0001; Asian population, OR = 1.342, 95% CI = 1.099-1.639, p = 0.004; Caucasian population, OR = 1.350, 95% CI = 1.187-1.536, p < 0.0001 in random model, respectively) (Table 2 and Fig. 1). In dominant model (A/A genotype+A/G genotype vs. G/G genotype), A/A genotype+A/G genotype presented association with asthma (all population, OR = 1.407, 95% CI = 1.246-1.590, p < 0.0001; Asian population, OR = 1.351, 95% CI = 1.088-1.677, p = 0.007; Caucasian population, OR = 1.452, 95% CI = 1.242-1.698, p < 0.0001 in random model, respectively) (Table 2 and Fig. 2). In recessive model (A/A genotype vs. A/G genotype+G/G genotype), A/A genotype also showed the association with asthma (all population, OR = 1.420, 95% CI = 1.157-1.743, p = 0.0008; Asian population, OR = 1.596, 95% CI = 1.058-2.408, p = 0.026; Caucasian population, OR = 1.425, 95% CI = 1.109-1.830, p = 0.0006 in fixed model, respectively) (Table 2 and Fig. 3).

FIG. 1.

Odds ratio and 95% CI of individual and pooled data for TNF-α polymorphism (−308G/A) and susceptibility to asthma in allele model. CI, confidence interval; TNF-α, tumor necrosis factor-alpha.

FIG. 2.

Odds ratio and 95% CI of individual and pooled data for TNF-α polymorphism (−308G/A) and susceptibility to asthma in dominant model.

FIG. 3.

Odds ratio and 95% CI of individual and pooled data for TNF-α polymorphism (−308G/A) and susceptibility to asthma in recessive model.

Table 2.

Overall Analysis Between Tumor Necrosis Factor-Alpha Polymorphism (−308G/A) and Susceptibility to Asthma

Genetic comparison	Ethnicity	No. of studies	OR (95% CI)	p	Heterogeneity		Model
Genetic comparison	Ethnicity	No. of studies	OR (95% CI)	p	p	I ²	Model
A vs. G	All	49	1.344 (1.207-1.497)	<0.0001	<0.001	59.683	Random
	Asian	24	1.342 (1.099-1.639)	0.004	<0.001	68.588	Random
	Caucasian	21	1.350 (1.187-1.536)	<0.0001	0.01	46.522	Random
A/A+A/G vs. G/G	All	49	1.407 (1.246-1.590)	<0.0001	<0.001	60.35	Random
	Asian	24	1.351 (1.088-1.677)	0.007	<0.001	67.424	Random
	Caucasian	21	1.452 (1.242-1.698)	<0.0001	0.003	51.755	Random
A/A vs. A/G+G/G	All	50	1.420 (1.157-1.743)	0.0008	0.634	<0.001	Fixed
	Asian	24	1.596 (1.058-2.408)	0.026	0.485	<0.001	Fixed
	Caucasian	22	1.425 (1.109-1.830)	0.006	0.534	<0.001	Fixed

Bold numbers are indicants of significant association with risk of asthma.

95% CI, 95% confidence interval; OR, odds ratio.

Table 3.

Overall Analysis Between Tumor Necrosis Factor-Alpha Polymorphism (−308G/A) and Susceptibility to Asthma in Age Group

Genetic comparison	Ethnicity	Age group	No. of studies	OR (95% CI)	p	Heterogeneity		Model
Genetic comparison	Ethnicity	Age group	No. of studies	OR (95% CI)	p	p	I²	Model
A vs. G	Asian	Children	10	1.425 (1.198-1.694)	<0.0001	0.167	30.255	Fixed
		Adults	8	1.386 (1.019-1.885)	0.037	0.002	69.119	Random
	Caucasian	Children	9	1.340 (1.017-1.766)	0.037	0.001	68.119	Random
		Adults	8	1.335 (1.174-1.517)	<0.0001	0.954	<0.001	Fixed
A/A+A/G vs. G/G	Asian	Children	10	1.451 (1.205-1.748)	<0.0001	0.156	31.509	Fixed
		Adults	8	1.378 (0.983-1.932)	0.062	0.003	67.591	Random
	Caucasian	Children	9	1.432 (1.017-2.016)	0.04	<0.001	71.562	Random
		Adults	8	1.395 (1.205-1.615)	<0.0001	0.988	<0.001	Fixed
A/A vs. A/G+G/G	Asian	Children	10	1.724 (0.771-3.855)	0.184	0.576	<0.001	Fixed
		Adults	8	1.812 (1.055-3.115)	0.031	0.405	3.227	Fixed
	Caucasian	Children	9	1.367 (0.901-2.073)	0.142	0.369	7.838	Fixed
		Adults	8	1.399 (0.920-2.126)	0.116	0.367	8.154	Fixed

Bold numbers are indicants of significant association with risk of asthma.

According to age group in asthma, we performed the updated meta-analysis. Table 3 presents the overall analysis with TNF-α polymorphism (−308G/A) and susceptibility to asthma in children/adults. The A allele in allele model significantly increased susceptibility to asthma both in children and adults in Asian and Caucasian population (p < 0.05 in each group) (Table 3). These results obtained from these genetic models suggested that TNF-α polymorphism (−308G/A) was significantly associated with a strong risk of asthma both in children and adults in Asian and Caucasian population.

Discussion

This meta-analysis was carried out to comprehensively interpret the previous studies. Previously there were two meta-analysis articles (Huang et al., 2014; Yang et al., 2014). However, they not only missed several previous existing published articles but also included articles out of the HWE. In addition, three more reports have been published since 2014. Thus, this meta-analysis removed all incorrect articles, added nonincluded articles, and reevaluated overall.

This meta-analysis included a total of 50 articles and showed statistical significance between TNF-α polymorphism and the development of asthma. Table 2 shows a clear relationship between TNF-α polymorphism and the development of asthma in allele and dominant, recessive model, and all races. The symptoms of childhood asthma and adult asthma are similar, and similar treatments are used. But there is a difference between the two. Adult asthma is caused by many causes such as mainly allergies, infection, hormonal factors, and so on and has worse prognosis and poorer response to treatment (de Nijs et al., 2013). Therefore, additional meta-analysis was carried out by dividing each of them. Table 3 shows that allele model was significant in all races in both childhood asthma and adult asthma. In the dominant model, there was no statistical significance in Asian adult asthma. Also, there was no statistical significance in recessive model except for Asian adult asthma. Childhood asthma was not significant in the recessive model. These results may be due to differences of mechanism in childhood asthma and adult asthma.

We found the relationship between asthma and TNF-α polymorphism and confirmed the difference between childhood asthma and adult asthma in this article. Currently, socioeconomic costs associated with asthma are increasing. Asthma has a significant impact on medical resource use and related costs in the Asia-Pacific region (Wang et al., 2016b). Early diagnosis, prevention, and timely and effective treatment of asthma could reduce illness and health care costs and improve quality of life and be clinically and economically beneficial.

In this meta-analysis, a statistically significant relationship between asthma and TNF-α polymorphism was confirmed. However, there is an unsatisfactory point. First of all, there are only Asian and Caucasian ethnic race. There was a lack of research on various other races and could not be examined. And the interaction with other factors also plays an important role in the onset of asthma. Simple one gene could not be an asthma gene and environment elements cannot be ignored. But it is regrettable that environmental factors or other genetic factors could not be considered. If environmental factors are considered and reenforced in more races and more samples, more accurate results will be obtained.

Footnotes

Acknowledgment

This study was supported by a grant from the Traditional Korean Medicine R&D Project, Ministry of Health & Welfare, Republic of Korea (HI15C0171).

Authors' Contributions

J.Y.B confirmed the topic of this article. Y.R.H., D.H., J.C., and J.-H.C. performed the statistical analysis and analyzed the results. S.-J.H. and M.-S. P. contributed to revised article. S.K.K and S.W.K. wrote the article. All authors approved the final version of the article.

Author Disclosure Statement

No competing financial interests exist.

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