Association Between the Tumor Necrosis Factor-α-308G/A Gene Polymorphism and HIV-1 Susceptibility: A Meta-Analysis

Abstract

The tumor necrosis factor-α (TNF-α)-308G/A gene polymorphism influences the pathogenesis and evolution of HIV-1 disease. Many studies have evaluated the association between this polymorphism and HIV-1 susceptibility, but the exact relationship between them remains ambiguous and contradictory. Accordingly, the present study evaluates the exact association between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility. A systematic literature search was conducted and the case-control studies that were found assessing the association between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility were analyzed. The pooled odds ratios (ORs) and 95% confidence interval (CI) were calculated by a fixed effect model. Heterogeneity was analyzed by Cochran's Q and the I² statistics. Publication bias was assessed using the Begg's funnel plot and Egger's test. A total of 679 cases and 873 controls from five studies were included. Overall, no significant relationship was found between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility in this meta-analysis study (A versus G genotype model: OR=0.89, 95% CI=0.59–1.32, p=0.553; GG versus AA+AG model: OR=1.23, 95% CI=0.75–2.02, p=0.407; GG+AG versus AA model: OR=1.40, 95% CI=0.70–2.82, p=0.345; GG versus AA model: OR=1.39, 95% CI=0.69–2.80, p=0.362; AG versus AA model: OR=1.43, 95% CI=0.70–2.96, p=0.329; GG+AA versus AG model: OR=0.76, 95% CI=0.44–1.29, p=0.304). The meta-analysis found no marked association between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility.

Introduction

HIV/AIDS has been increasing with the passage of time since it was found by the U.S. Centers for Disease Control and Prevention (CDC) in 1984.¹ Despite reasonable progress made in the diagnosis of HIV/AIDS, prevention and treatment of HIV/AIDS are still among the most important public health issues globally.² As per evidence reported in a 2014 World Health Organization (WHO) worldwide study, nearly 35 million people were living with HIV in 2013, while about 2.1 million people were newly infected with HIV and there were nearly 1.5 million deaths from HIV/AIDS that year.³ Nevertheless numerous basic queries about the pathogenesis of HIV-1 infection have not been answered. A remarkable variation in susceptibility to HIV-1 infection and disease progression has been shown in several studies.^4
–6 In addition, many reports have documented that HIV infection is linked to the augmented production of proinflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin (IL)-1, IL-6, and IL-8.⁷

TNF-α in particular has been reported to be associated with the pathogenesis of many autoimmune and inflammatory diseases⁸ due to the location of its gene in the major histocompatibility complex (MHC) class III region on chromosome 6 (band p21.3).⁹ Meanwhile, several reported studies suggest that TNF-α is associated with the pathogenesis of HIV-1 infection, which can lead to the secretion of TNF-α.^10,11 However, although many population studies on the relationship between susceptibility to HIV infection and TNF-α-308G/A gene polymorphism have been conducted, the results are conflicting. Indeed, several studies also elaborate that the CAG high-producer haplotype of TNF-α gene polymorphism is associated with enhanced apoptosis of lymphocytes in HIV-1-infected individuals, thus resulting in the rapid and faster development of AIDS.

The frequency of the GA genotype was significantly higher in fast progressors (FPs) compared to the slow progressors (SPs) group (OR=3.43).⁹ On the other hand, it has also been reported that TNF-α genetic variants were irrelevant to the development of the disease in infected subjects, and many researchers have diverse conclusions and results.^12
–14 Hence, for this reason, a meta-analysis was carried out to determine if there is any relationship between TNF-α-308G/A gene polymorphism and susceptibility to HIV-1 in the population.

Materials and Methods

Strategy for literature search

An extensive search of the available literature on the association between the susceptibility to HIV-1 and TNF-α-308G/A gene polymorphism was conducted by searching the PubMed, Web of Knowledge, Embase, Chinese Web of Knowledge, Wanfang, and Chongqing VIP databases. The databases were searched up to December 2014 with the inclusion of articles in English and/or Chinese with the keywords of “Human Immunodeficiency Virus” or “HIV” or “AIDS” or “Acquired Immune Deficiency Syndrome,” polymorphism or variant or mutation, and “tumor necrosis factor” or “TNF” or “cytokine.”

Criteria for inclusion and exclusion

Strict criteria for inclusion and exclusion were used in this meta-analysis and all selected studies met the following inclusion criteria: the aim of the study (1) must be to assess the association between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility, (2) must be a relevant case-control study, and (3) must have selected published and printed data for estimating the odds ratio (OR) with a 95% confidence interval (CI) (Supplementary Table S1; Supplementary Data are available online at www-liebertpub-com.web.bisu.edu.cn/aid).

On the other hand, studies were excluded if (1) they were animal studies, (2) they were a repeated study, (3) they were review studies or abstracts, and (4) the genotype frequencies were not reported.

Data extraction

Data were extracted by two investigators using a standardized data extraction method form from all elected studies individually, and in case of disagreement between the two researchers, discussion would ensue to reach consensus. The following information was extracted from each study: name of the first author, publication year, country, background, sample size, selection criteria of controls, and genotype and allele/number of patients and controls.

Quality assessment

The Newcastle-Ottawa Scale for assessing quality of observational and nonrandomized studies was adopted for quality assessment.

Statistical analysis

The association between TNF-α 308G/A gene polymorphism and susceptibility to HIV-1 was anticipated by OR with the corresponding 95% confidence interval (CI) under an allele model (G vs. A), a dominant model (GG vs. AA+AG), a recessive model (GG+AG vs. AA), and a codominant model (GG vs. AA) and an overdominant model (GG+AA vs. AG). The implication of the pooled OR was determined by Z test. The fixed-effects model or random-effects model was utilized depending on whether heterogeneity existed among studies or not. Heterogeneity among studies was analyzed with Cochran's Q statistic and the I² statistic. p<0.10 in the Q test indicated no heterogeneity among studies. p<0.1 rather than 0.05 was considered noteworthy heterogeneity for the χ²-based Q testing and a value of 0% for I² indicated no heterogeneity and an increasing percentage implied increased heterogeneity. That is, I² will be utilized to guess the total variations across studies that are due to heterogeneity rather than chance (<25% is considered low heterogeneity, 25–50% moderate heterogeneity, and >50% high-level heterogeneity).^15,16

The numerical significance of OR was estimated using the Z test, and p<0.05 was measured as statistically important. To assess the stability of the results sensitivity analysis was done by eliminating one single study each time.¹⁷ Similarly, Hardy–Weinberg equilibrium was estimated by the χ² test in the controls.¹⁸ To find publication bias Begg's funnel plot and Egger's test were examined.^19,20 In addition, Egger's linear regression test was conducted to estimate funnel plot asymmetry (p<0.05 was estimated as significant publication bias), and other similar study analyses were done by using STATA version 12.0 software (Stata Corporation, College Station, TX). All tests were two-sided and the significance levels were found to be 0.05.

Results

Search results and characteristics of the included studies

A database was used to shortlist 236 potentially relevant articles, from which a total of 231 papers were identified for further evaluation. Afterward, a more exhaustive reading and analysis helped to remove 219 potentially relevant articles that were rejected because of their obvious irrelevance to the purpose of this study. Ultimately, five studies including 679 cases and 873 controls were incorporated in the meta-analysis. The flow chart of the search method is shown in Fig. 1, while the individuality and characteristics of these articles are listed in Table 1. All five studies provide the numbers of alleles A and G in both cases and controls. To test all the polymorphisms in the control group, the Hardy–Weinberg equilibrium model was used (Table 2).

FIG. 1.

Flow diagram of included/excluded studies.

Table 1.

Characteristics of Included Studies

First author	Country	Year	Case	Control
Singh¹¹	India	2014	200	310
Veloso¹⁰	Spain	2010	184	239
Gounden⁹	South African	2012	75	76
Konenkov¹³	Russia	2002	120	52
Knuchel¹⁴	America	1998	100	196

Table 2.

Genotype and Allele Distributions in Cases and Controls

	Cases					Controls
First author	GG	GA	AA	G	A	GG	GA	AA	G	A	HWE (p)
Singh¹¹	176	23	1	375	25	282	25	3	589	31	0.01
Veloso¹⁰	148	31	5	327	41	146	54	8	346	70	0.14
Gounden⁹	45	28	2	118	32	50	21	5	121	31	0.19
Konenkov¹³	91	29	0	211	29	47	4	1	98	6	0.14
Knuchel¹⁴	69	27	4	165	35	149	43	4	341	51	0.67

HWE, Hardy–Weinberg equilibrium.

Risk of bias assessment

The quality of the studies included in the meta-analysis was assessed with the Newcastle-Ottawa Scale, and higher scores reflect better quality of the study methodology. The average score of all studies was above 6 (these results are not shown).

Pooled analyses

The analysis of the five studies included revealed that no statistically significant heterogeneity existed under the recessive model (GG+AG vs. AA: I² =0.0%, p=0.461) or the codominant model (GG vs. AA: I² =0.0%, p=0.475; AG vs. AA: I² =24.6%, p=0.258). Consequently, a fixed-effects model was used for the recessive and codominant models. On the other hand, significant heterogeneity was observed under the allele model (G vs. A: I² =63.4%, p=0.027), the dominant model (GG vs. AA+AG: I² =68.8%, p=0.012), and the overdominant model (GG+AA vs. AG: I² =70.8%, p=0.008). For that reason, a random-effects model was used for the allele model, dominant model, and overdominant model.

In general, no significant relationship was found between TNF-α-308G/A gene polymorphism and susceptibility to HIV-1 infection (A versus G genotype model: OR=0.89, 95% CI=0.59–1.32, p=0.553; GG versus AA+AG model: OR=1.23, 95% CI=0.75–2.02, p=0.407; GG+AG versus AA model: OR=1.40, 95% CI=0.70–2.82, p=0.345; GG versus AA model: OR=1.39, 95% CI=0.69–2.80, p=0.362; AG versus AA model: OR=1.43, 95% CI=0.70–2.96, p=0.329; GG+AA versus AG model: OR=0.76, 95% CI=0.44–1.29, p=0.304) (Fig. 2).

FIG. 2.

Forest plot of the susceptibility of HIV-1 associated with tumor necrosis factor (TNF)-α 308G/A (GG vs. AA/AG).

Sensitivity analysis

Sensitivity analyses were done successively by exclusion of individual studies, and the summary ORs were not significantly changed by leaving out any of these studies, showing that the results were statistically robust (Fig. 3, the rest of results are not shown).

FIG. 3.

The results of sensitivity analysis from fixed-effects estimates (A allele vs. G allele).

Publication bias

The Begg's funnel plot and the Egger's test were used to evaluate the publication bias. The customized Begg's linear regression test and the Egger's test detected no major publication bias in the studies included (p=0.221; p=0.315 for GG vs. AA+AG, Fig. 4).

FIG. 4.

Begg's funnel plot for publication bias test (GG vs. AA/AG).

Discussion

Numerous population-based studies have been conducted to authenticate the hypothesis that TNF gene polymorphisms may boost the susceptibility to HIV-1.^9
–11 Nevertheless, at present, the available published data on the relationship between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility have provided only conflicting results (Table 3).^14,21,22 Indeed, it has been found that the positive relationship could be false and the negative results could be the result of insufficient statistical power.⁸ Accordingly, to address this issue this study was undertaken. In this meta-analysis, five studies were included. The present study is evocative of the absence of a relationship between TNF-α-308G/A gene polymorphism and susceptibility to HIV-1. Thus, the results of this meta-analysis indicate that the TNF-α-308G/A gene polymorphism is not involved in susceptibility to HIV-1.

Table 3.

Main Results of the Pooled Odds Ratios in the Meta-Analysis for the Association Between the Tumor Necrosis Factor-α 308G/A Gene Polymorphism and HIV-1

Genetic model	OR (95% CI)	Z	p value	I² %	p_het ^a	Effect model
G vs. A	0.89 (0.59, 1.32)	0.59	0.553	63.4	0.027	Random
GG vs. AA+AG	1.23 (0.75, 2.02)	0.83	0.407	68.8	0.012	Random
GG+AG vs. AA	1.40 (0.70, 2.82)	0.95	0.345	0.0	0.461	Fixed
GG vs. AA	1.39 (0.69, 2.80)	0.91	0.362	0.0	0.475	Fixed
AG vs. AA	1.43 (0.70, 2.96)	0.98	0.329	24.6	0.258	Fixed
GG+AA vs. AG	0.76 (0.44, 1.29)	1.03	0.304	70.8	0.008	Random

P _het=p value for heterogeneity.

Cytokines play a vital role in regulating the homeostasis of the immune system and alterations in their relative levels play critical roles in the immune response against HIV-1 infection and the progression of HIV-1 infection to clinical AIDS.²³ Meanwhile, infection with HIV results in deregulation of the cytokine profile, in vivo and in vitro.⁷ As cytokines and chemokines can mediate virological and immunological features in patients with HIV-1 infection, considerable interindividual differences evidently entail the participation of host genes in the mediation of HIV-1 infection and pathogenesis.²² It is also known that TNF-α is a dominant pleiotropic proinflammatory cytokine in the HIV-1 infection process, as it plays an equally crucial function in innate and adaptive immunity.²⁴ Indeed, it is widely recognized that the degree of the inflammatory reaction generated by a host is affected by inflammatory stimulus, stimulus dose, and genetic properties.²⁵

In addition, a variety of scientists have noted that the serum concentration of TNF-α increases with the progression of illness, which underlines the correlation of its increase with the decrease in the number of CD4⁺ lymphocytes and the severity of the clinical characteristics of disease.²⁶ In the development of AIDS, TNF-α increases the replication of the virus of HIV-1^27
–29 and induces apoptosis of lymphocytes.^30,31 TNF-α is a vital mediator of inflammation in patients with poor viral control and early HIV-1 disease development.³² In addition, the TNF locus is located in the HLA complex, which is linked to susceptibility to HIV infection and its progression to AIDS.³³ Moreover, TNF-α enhances viral replication in monocytes via the activation of the nuclear transcription factor NF-κB pathway.³³ Furthermore, the TNFa2 and TNFc2 alleles have been related to elevated TNF production, while the TNFa6 and TNFc1 alleles have been linked to a low serum level of TNF.³⁴

The current study presents a detailed analysis of the association between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility. The results revealed no statistically significant relationship between the overall effects of TNF-α-308G/A gene polymorphisms and HIV-1 susceptibility in any of the genetic models. The validity of the failure to detect any significant effect of this polymorphism on HIV-1 infection was endorsed by many critical factors. First, other TNF gene polymorphisms such as TNF-α-238, −307, and −1030 can affect the ability to produce TNF-α and afterward affect the susceptibility to HIV-1.^22,35
–37 In the meantime low AA genotype frequency can be an important element.⁷ Additionally, it could be due to the antagonistic relation of the opposite effect, which can be seen between TNF and other genes.³⁸ Consequently, the effect must be analyzed with vigilance, and further investigations with additional extensive numbers of case-control studies are necessary to authenticate these result.

We should all be aware of the heterogeneity that was revealed by our analyses. The main heterogeneity was found in the dominant, overdominant, and allele contrast genetic models. Sensitivity analyses were performed by successively omitting individual studies, and showed that the summary ORs were not significantly altered by leaving out these studies, indicating that our results are statistically acceptable.

This study has several possible limitations, which could influence the results. Initially, we confined our studies to published studies in English and Chinese, and thus did not include unpublished research; as a result, related articles published in other languages or unpublished studies were left out, which could lead to overlooking some related studies concerning the relationship between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility. Additionally, important heterogeneity was sensed in the dominant, overdominant, and allele contrast genetic models. It is important to note that population-based studies may not have representativeness similar to hospital-based studies. Lastly, elevated heterogeneity was observed in this study, which is a point of concern as we lacked the essential information to determine the reasons for such heterogeneity.

Apart from these drawbacks, this analysis has some key advantages. First, this is the first meta-analysis that has been performed to examine the relationship between TNF-α-308G/A gene polymorphism and HIV-1. Additionally, the relationship between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility is statistically more persuasive than any single study. Moreover, Begg's and Egger's tests could not detect any publication bias. Furthermore, the sensitivity analyses found that the summary ORs were not significantly distorted by excluding one study each time, which showed that our results were stable. However, this issue must be further investigated in order to ascertain the relationship between other related variables and the susceptibility to HIV-1.

Conclusions

This study evaluated the association between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility. However, no significant association was observed between TNF-α-308G/A gene polymorphism and HIV-1 susceptibility.

Footnotes

Acknowledgments

We thank all our colleagues working in the Department of Epidemiology and Health Statistics, School of Public Health of Central South University.

L.C. and C.J. designed the experiments; C.J. and Z.L. performed the experiments; C.J. and P.C. analyzed the data; L.C. and C.J. contributed reagents/materials/analysis tools, and C.J. and L.C. wrote the article.

Author Disclosure Statement

No competing financial interests exist.

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