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. 2019 Dec 20;39(12):BSR20191301. doi: 10.1042/BSR20191301

The association of TNF-α −308G/A and −238G/A polymorphisms with type 2 diabetes mellitus: a meta-analysis

Xiaoliang Guo 1,2, Chenxi Li 1,2, Jiawei Wu 1,2, Qingbu Mei 1,2, Chang Liu 1,2, Wenjing Sun 1,2, Lidan Xu 1,2,*,✉, Songbin Fu 1,2,*,✉
PMCID: PMC6923338  PMID: 31803921

Abstract

Tumor necrosis factor-α (TNF-α) is involved in insulin resistance and has long been a candidate gene implicated in type 2 diabetes mellitus (T2DM), however the association between TNF-α polymorphisms -308G/A and -238G/A and T2DM remains controversial. The present study sought to verify associations between these polymorphisms and T2DM susceptibility using a meta-analysis approach. A total of 49 case–control studies were selected up to October 2018. Statistical analyses were performed by STATA 15.0 software. The odds ratios (ORs) and 95% confidence intervals were calculated to estimate associations. Meta-analyses revealed significant associations between TNF-α −308G/A and T2DM in the allele model (P=0.000); the dominant model (P=0.000); the recessive model (P=0.001); the overdominant model (P=0.008) and the codominant model (P=0.000). Subgroup analyses also showed associations in the allele model (P=0.006); the dominant model (P=0.004) and the overdominant model (P=0.005) in the Caucasian and in the allele model (P=0.007); the dominant model (P=0.014); the recessive model (P=0.000) and the codominant model (P=0.000) in the Asian. There were no associations between TNF-α −238G/A and T2DM in the overall and subgroup populations. Meta-regression, sensitivity analysis and publication bias analysis confirmed that results and data were statistically robust. Our meta-analysis suggests that TNF-α −308G/A is a risk factor for T2DM in Caucasian and Asian populations. It also indicates that TNF-α −238G/A may not be a risk factor for T2DM. More comprehensive studies will be required to confirm these associations.

Keywords: -238G/A, -308G/A, meta analysis, single nucleotide polymorphisms, T2DM, TNF-α

Introduction

Diabetes is a global epidemic, with an estimated worldwide prevalence of 1 in 11 adults (approximately 425 million people in 2017), and is projected to increase to 629 million people by 2045 (http://www.diabetesatlas.org/). Individuals with type 2 diabetes mellitus (T2DM) accounted for 90% of this total [1]. T2DM is a complex metabolic disorder and usually involves pancreatic islet dysfunction and insulin-secreting β cell failure in the endocrine pancreas (Islets of Langerhans), allowing for the secretion of more insulin to counteract insulin resistance in peripheral tissues (adipose, skeletal muscle and liver). Ultimately, T2DM shows an uncontrolled increase in blood glucose levels [2], therefore the pathogenesis of T2DM is insulin resistance [3].

Some in vivo and in vitro studies have shown that tumor necrosis factor-α (TNF-α) induces insulin resistance to some extent, through the inhibition of intracellular signaling from the insulin receptor [4,5]. The disease has a strong genetic component, however few genes have been identified [1]. Several genome-wide association scans (GWAS) have been performed for T2DM and several candidate genes have been proposed [6–10]. Of multiple candidate genes, the TNF-α promoter polymorphisms −308G/A and −238G/A have been studied in T2DM etiology [11].

Currently, it is inconclusive whether these polymorphisms (−308G/A and −238G/A) in the TNF-α promoter lead to T2DM susceptibility. Two large-scale British association analyses found these polymorphisms were not robustly associated with T2DM [11,12] and similar results have been observed in China [13,14] and India [15]. However, studies have also suggested that −308G/A and −238G/A are risk factors for T2DM in Egypt [16] and Iran [17]. Studies from different racial backgrounds may produce conflicting results and these independent studies are confusing and controversial. Therefore, we performed a large-scale meta-analysis to investigate associations between these polymorphisms and T2DM.

Materials and methods

Literature search

This meta-analysis was conducted according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2009 (PRISMA2009). All published studies up to October 2018 were searched using the PubMed, Embase, EBSCO, OVID, and Web of science database. We used the following terms: ‘TNF-α’, ‘TNF-alpha’, ‘tumor necrosis factor-α’, ‘tumor necrosis factor-alpha’, ‘T2DM’, ‘type 2 diabetes mellitus’, ‘type 2 diabetes’, ‘type II diabetes’, ‘non-insulin dependent diabetes’, ‘NIDDM’, ‘polymorphism’, ‘variation’, ‘−308G/A’, ‘rs1800629’, ‘−238G/A’ and ‘rs361525’. Relevant references in selected articles were also included.

All articles were independently reviewed by two investigators. Studies were assessed against the following inclusion criteria: (1) the associated study of TNF-α polymorphisms (−308G/A and −238G/A) with the risk of T2DM, (2) the study was case–control designed, (3) sufficient information on genotype frequencies (GG, AA and GA) in both cases and controls to estimate an odds ratio (OR) with a 95% confidence interval (95% CI), (4) all data were original. Exclusion criteria were as follows: (1) other DM (diabetes) types were excluded, (2) non-human studies, (3) reviews, meta-analysis and non-case–control studies and (4) studies not published in English.

Quality score assessment

Study quality was assessed to guarantee the strength of results and conclusions. Quality assessment was performed according to the Newcastle–Ottawa Quality Assessment Scale (NOS), which is a validated scale for nonrandomized studies in meta-analyses [18]. This NOS uses a star system to assess the quality of a study in three domains: selection, comparability and outcome/exposure. The NOS assigns a maximum of 5 stars for selection (in the case of cross-sectional studies), 2 stars for comparability, and 3 stars for outcome/exposure. Studies achieving a score of at least 8 stars were classified as being at low risk of bias (i.e., thus reflecting the highest quality). A maximum of 9 scores, including selection, comparability and exposure items were awarded. Any score disagreements were decided by a third researcher.

Data extraction

Data were independently extracted by two investigators using a standardized form. For each study, the following information was extracted: (1) name of first author; (2) year of publication; (3) ethnicity of population; (4) sample sizes and genotype distributions; (5) allele frequency of the major variant. Ethnicity was categorized as Caucasian, Asian and African.

Statistical analysis

The Hardy–Weinberg equilibrium (HWE) test was calculated using the Chi-squared test. The distribution of allele frequencies in controls was considered to deviate from HWE when P<0.05. STATA (15.0; Stata Corporation, College Station, TX, U.S.A.) software was used to calculate meta-analysis results. Individual study heterogeneity was assessed by Cochran’s Q test and the I2 statistic (P<0.10 and I2 > 50% indicates evidence of heterogeneity) [19]. The fixed-effects model (Mantel–Haenszel method) was used to estimate the pooled OR [20], when there was no evidence of heterogeneity, otherwise the random-effects model (DerSimonian and Laird method) was used [20,21]. ORs with corresponding 95% CIs were calculated to assess associations between TNF-α promoter polymorphisms (−308G/A and −238G/A) and T2DM risks. Five genetic models were used in this meta-analysis: (1) the allele model (A allele vs. G allele); (2) the dominant model (GA+AA vs. GG); (3) the recessive model (AA vs. GA+GG); (4) the codominant model (GA vs. GG; AA vs.GG) and (5) the overdominant model (GG+AA vs. GA). A P-value <0.05 was accepted as the significant threshold for each genetic model. Three subgroups, including Caucasian, Asian and African, based on ethnicity, were analyzed to reduce influences from genetic backgrounds. A meta-regression was used to search the source of heterogeneity [22], which contained publication year, sample size, ethnicity, HWE and number of studies. The 10000 times Monte Carlo permutation test approach was used for assessing the statistical significance of meta-regression [23,24]. I2 res explained the proportion of residual variation due to heterogeneity, and adj R2 explained the proportion of between-study variation due to heterogeneity [25,26]. An I2 res close to 100% and adj R2 close to 0% further indicated no effects on heterogeneity. Pooled estimates were performed to sensitivity analysis which involved omitting one study at a time followed by recalculation to test for robustness of the summary effects [26]. To increase transparency, risk of bias ratings and meta-analyses were displayed together. Funnel plots were used to investigate the risk of publication bias [23]. Egger’s and Begg’s regression tests evaluated publication bias with quantitative analysis [27]. A P-value <0.05 was accepted as statistically significant.

Results

Study characteristics

Based on the above search strategy, 977 publications were identified in the initial search. Approximately 766 articles were excluded after scanning titles and abstracts as being non- relevant to T2DM and TNF-α −308G/A and −238G/A. Through in-depth full-text analysis of the remaining 211 publications, 49 publications were used for the final meta-analysis (Figure 1). These 49 publications contained 16246 patients and 13973 controls and were included in the −308G/A analysis, of which 14 publications, with 4935 patients and 5260 controls, were included in the −238G/A analysis. According to NOS classifications, three points or lower indicated low quality, however no publications were of low quality. The main characteristics of selected publications are shown in Table 1.

Figure 1. Study flow diagram.

Figure 1

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Table 1. Characteristics of the included studies.

Author Year Country Ethnicity Genotype in case Genotype in control P of HWE NOS
TNF-α -308G/A Total GG (%) GA (%) AA (%) Total GG (%) GA (%) AA (%)
Patel et al. [15] 2018 India Asian 388 351(90.5%) 34 (8.8%) 3 (0.8%) 493 449 (91.1%) 42 (8.5%) 2 (0.4%) 0.348 6
Umapathy et al. [43] 2018 India Asian 538 302 (56.1%) 142 (26.4%) 94 (17.5%) 218 167 (76.6%) 32 (14.7%) 19 (8.7%) 0.0001 4
Hemmed et al. [29] 2018 India Asian 862 528 (61.3%) 283 (32.8%) 51 (5.9%) 464 356 (76.7%) 96 (20.7%) 12 (2.6%) 0.080 5
Fathy et al. [44] 2018 Kuwaiti Caucasian 117 86 (73.5%) 28 (23.9%) 3 (2.6%) 42 41 (97.6%) 0 (0.0%) 1 (2.4%) 0.0001 6
Rodrigues et al. [45] 2017 Brazil Caucasian 102 78 (76.5%) 23 (22.5%) 1 (1.0%) 62 47 (75.8%) 15 (24.2%) 0 (0.0%) 0.279 6
Mortazavi et al. [46] 2017 Iran Caucasian 174 24 (13.8%) 101 (58.0%) 49 (28.2%) 185 68 (36.8%) 76 (41.1%) 41 (22.2%) 0.0291 5
Jamil et al. [47] 2017 India Asian 100 88 (88.0%) 10 (10.0%) 2 (2.0%) 100 87 (87.0%) 12 (12.0%) 1 (1.0%) 0.433 7
Doody et al. [48] 2017 India Asian 198 178 (89.9%) 18 (9.1%) 2 (1.0%) 204 189 (92.6%) 13 (6.4%) 2 (1.0%) 0.0041 7
Churnosov et al. [49] 2017 Russia Caucasian 236 176 (74.6%) 53 (22.5%) 7 (3.0%) 303 242 (79.9%) 55 (18.2%) 6 (2.0%) 0.180 5
Sesti et al. [50] 2015 Britain Caucasian 695 535 (73.7%) 176 (24.2%) 15 (2.1%) 170 129 (75.9%) 38 (22.4%) 3 (1.8%) 0.917 7
Golshani et al. [17] 2015 Iran Caucasian 1038 737 (71.0%) 269 (25.9%) 32 (3.1%) 1023 871 (85.1%) 142 (13.9%) 10 (1.0%) 0.124 6
Dabhi et al. [51] 2015 India Asian 214 185 (86.5%) 27 (12.6%) 2 (0.9%) 235 191 (81.3%) 44 (18.7%) 0 (0.0%) 0.885 4
Ghodsian et al. [52] 2015 Malaysia Asian 88 73 (83.0%) 14 (15.9%) 1 (1.1%) 232 202 (87.1%) 29 (12.5%) 1 (0.4%) 0.970 6
Dhamodharan et al. [53] 2015 India Asian 409 218 (53.3%) 117 (28.6%) 74 (18.1%) 106 77 (72.6%) 14 (13.2%) 15 (14.2%) 0.0001 5
Sikka et al. [54] 2014 India Asian 462 405 (87.7%) 55 (11.9%) 2 (0.4%) 203 176 (86.7%) 27 (13.3%) 0 (0.0%) 0.310 7
Sharma et al. [55] 2014 India Asian 51 45 (88.2%) 6 (11.8%) 0 (0.0%) 51 50 (98.0%) 1 (2.0%) 0 (0.0%) 0.944 5
Saxena et al. [56] 2013 India Asian 213 173 (81.2%) 33 (15.5%) 7 (3.3%) 140 111 (79.3%) 25 (17.9%) 4 (2.9%) 0.095 6
Garcia-Elorriaga et al. [57] 2013 Mexico Caucasian 51 41 (80.4%) 10 (19.6%) 0 (0.0%) 48 41 (85.4%) 2 (4.2%) 5 (10.4%) 0.0001 6
El Naggar et al. [16] 2013 Egypt African 30 12 (40.0%) 12 (40.0%) 6 (20.0%) 15 9 (60.0%) 1 (6.7%) 0 (0.0%) 0.868 4
Mustapic et al. [58] 2012 Croatia Caucasian 196 138 (70.4%) 55 (28.1%) 3 (15.0%) 456 336 (73.7%) 108 (23.7%) 12 (2.6%) 0.355 4
Perez-Luque et al. [30] 2012 Mexico Caucasian 95 72 (75.8%) 23 (24.2%) 0 (0.0%) 87 82 (94.3%) 5 (5.7%) 0 (0.0%) 0.783 4
Wang et al. [59] 2012 China Asian 100 74 (74.0%) 15 (15.0%) 11 (11.0%) 113 100 (88.5%) 12 (10.6%) 1 (0.9%) 0.359 5
Elsaid et al. [60] 2012 Egypt African 69 10 (14.5%) 55 (79.7%) 4 (5.8%) 106 11 (10.4%) 94 (88.7%) 1 (0.9%) 0.0001 6
Liu et al. [32] 2011 China Asian 112 67 (59.8%) 32 (28.6%) 13 (11.6%) 50 45 (90.0%) 5 (10.0%) 0 (0.0%) 0.710 5
Guzman-Flore et al. [61] 2011 Mexico Caucasian 259 225 (86.9%) 31 (12.0%) 3 (1.2%) 645 573 (88.8%) 69 (10.7%) 3 (0.5%) 0.556 5
Mukhopadhyaya et al. [62] 2010 India Asian 40 35 (87.5%) 3 (7.5%) 2 (5.0%) 40 37 (92.5%) 3 (7.5%) 0 (0.0%) 0.805 4
Boraska et al. [63] 2010 Britain Caucasian 1454 938 (64.5%) 477 (32.8%) 39 (2.7%) 2504 1633 (65.2%) 774 (30.9%) 97 (3.9%) 0.659 6
Bouhaha et al. [64] 2010 Tunis African 195 141 (72.3%) 51 (26.2%) 3 (1.5%) 299 204 (68.2%) 89 (29.8%) 6 (2.0%) 0.297 4
Liu et al. [13] 2008 China Asian 245 222 (90.6%) 21 (8.6%) 2(0.8%) 122 109 (89.3%) 13 (10.7%) 0 (0.0%) 0.534 6
Lindholm et al. [65] 2008 Scandinavia Caucasian 2927 1908 (65.2%) 906 (31.0%) 113(3.9%) 205 133 (64.9%) 66 (32.2%) 6 (2.9%) 0.520 4
Wang et al. [66] 2008 China Asian 181 157 (86.7%) 23 (12.7%) 1 (0.6%) 82 67 (81.7%) 15 (18.3%) 0 (0.0%) 0.362 5
Kim et al. [34] 2006 Korea Asian 198 174 (87.9%) 24 (12.1%) 0 (0.0%) 169 141 (83.4%) 28 (16.6%) 0 (0.0%) 0.240 4
Willer et al. [67] 2006 Finland Caucasian 761 568 (74.6%) 184 (24.1%) 9 (1.2%) 617 469 (76.0%) 134 (21.7%) 14 (2.3%) 0.235 6
Santos et al. [68] 2006 Chile Caucasian 30 27 (90.0%) 3 (10.0%) 0 (0.0%) 53 45 (84.9%) 8 (15.1%) 0 (0.0%) 0.552 4
Zeggini et al. [12] 2005 Britain Caucasian 776 484 (62.4%) 260 (33.5%) 32 (4.1%) 1213 779 (64.2%) 391 (32.2%) 43 (3.5%) 0.480 6
Tsiavou et al. [69] 2004 Greece Caucasian 32 29 (90.6%) 3 (9.4%) 0 (0.0%) 39 32 (82.1%) 7 (17.9%) 0 (0.0%) 0.538 4
Zouari et al. [70] 2004 Tunis African 280 196 (70.0%) 64 (22.9%) 20 (7.1%) 274 170 (62.0%) 93 (33.9%) 11 (4.0%) 0.698 4
Shiau et al. [14] 2003 China Asian 257 218 (84.8%) 35 (13.6%) 4 (1.6%) 187 168 (89.8%) 16 (8.6%) 3 (1.6%) 0.0021 5
Li et al. [71] 2003 Sweden Caucasian 488 333 (68.24%) 141 (28.9%) 14 (2.9%) 284 189 (66.5%) 83 (29.2%) 12 (4.2%) 0.456 6
Heijmans et al. [72] 2002 Netherlands Caucasian 79 51 (64.6%) 22 (27.8%) 6 (7.6%) 577 378 (65.5%) 189 (32.8%) 10 (1.7%) 0.0121 5
Furuta et al. [73] 2002 Japan Asian 132 129 (97.7%) 3 (2.3%) 0 (0.0%) 142 139 (97.9%) 3(2.1%) 0(0.0%) 0.899 5
Rasmussen et al. [74] 2000 Danish Caucasian 243 154 (63.4%) 79 (32.5%) 10 (4.1%) 325 214 (65.8%) 99 (30.5%) 12 (3.7%) 0.896 4
Kamizono et al. [75] 2000 Japan Asian 213 209 (98.1%) 4 (1.9%) 0 (0.0%) 259 249 (96.1%) 10 (3.9%) 0 (0.0%) 0.751 4
Pandey et al. [76] 1999 Belgium Caucasian 214 144 (67.3%) 61 (28.5%) 9 (4.2%) 200 145 (72.5%) 53 (26.5%) 2 (1.0%) 0.233 4
Hamann et al. [77] 1995 America Caucasian 138 108 (78.3%) 27 (19.6%) 3 (2.2%) 57 46 (80.7%) 10 (17.5%) 1 (1.8%) 0.604 5
Kung et al. [78] 2010 China Asian 23 0 (0.0%) 23 (100.0%) 0 (0.0%) 25 0 (0.0%) 25 (100.0%) 0 (0.0%) 0.0001 6
Ko et al. [79] 2003 China Asian 339 284 (83.8%) 50 (14.7%) 5(1.5%) 202 171 (84.7%) 31 (15.3%) 0 (0.0%) 0.238 4
Morris et al. [80] 2003 Australia Caucasian 91 53 (58.2%) 32 (35.2%) 6(6.6%) 189 126 (66.7%) 5 5(29.1%) 8 (4.2%) 0.427 4
Sobti et al. [81] 2012 India Asian 113 5 (4.4%) 100 (88.5%) 8(7.1%) 158 26 (16.5%) 116 (73.4%) 16 (10.1%) 0.0001 5
TNF-α -238G/A Total GG (%) GA (%) AA (%) Total GG (%) GA (%) AA (%)
Rasmussen et al. [82] 2000 Danish Caucasian 236 205 (86.9%) 31 (13.1%) 0 (0.0%) 309 272 (88.0%) 35 (11.3%) 2 (0.6%) 0.459 4
Kim et al. [34] 2007 Korea Asian 198 177 (89.4%) 21 (10.6%) 0 (0.0%) 169 152 (89.9%) 17 (10.1%) 0 (0.0%) 0.491 4
Sesti et al. [50] 2015 Britain Caucasian 695 624 (89.8%) 66 (9.5%) 5 (0.7%) 169 147 (87.0%) 22 (13.0%) 0 (0.0%) 0.365 7
Santos et al. [68] 2006 Chile Caucasian 30 28 (93.3%) 2 (6.7%) 0 (0.0%) 53 46 (86.8%) 7 (13.2%) 0 (0.0%) 0.607 4
Li et al. [71] 2003 Sweden Caucasian 488 460 (94.3%) 27 (9.5%) 1 (0.2%) 284 265 (93.3%) 18 (6.3%) 1 (0.4%) 0.581 6
Dhamodharan et al. [53] 2015 India Asian 133 100 (75.2%) 29 (21.8%) 4 (3.0%) 106 81 (76.4%) 23 (21.7%) 2 (1.9%) 0.806 5
Patel et al. [15] 2018 India Asian 320 292 (91.3%) 27 (8.4%) 1 (0.3%) 295 257 (87.1%) 37 (12.5%) 1 (0.3%) 0.785 7
Fathy et al. [44] 2018 Kuwaiti Caucasian 117 115 (98.3%) 2 (1.7%) 0 (0.0%) 42 41 (97.6%) 1 (2.4%) 0 (0.0%) 0.938 6
Boraska et al. [63] 2010 Britain Caucasian 1504 1331 (88.5%) 170 (11.3%) 3 (0.2%) 2518 2224 (88.3%) 288 (11.4%) 6 (0.2%) 0.296 6
Zeggini et al. [12] 2005 Britain Caucasian 560 470 (83.9%) 87 (15.5%) 3 (0.5%) 341 303 (88.9%) 37 (10.9%) 1 (0.3%) 0.908 6
Jamil et al. [47] 2017 India Asian 98 85 (86.7%) 12 (12.2%) 1 (1.0%) 102 87 (85.3%) 13 (12.7%) 2 (2.0%) 0.094 7
Shiau et al. [14] 2003 China Asian 257 218 (84.8%) 35 (13.6%) 4 (1.6%) 187 168 (89.8%) 16 (8.6%) 3 (1.6%) 0.0021 5
Guzman-Flore et al. [61] 2011 Mexico Caucasian 259 220 (84.9%) 31 (12.0%) 8 (3.1%) 645 571 (88.5%) 71 (11.0%) 3 (0.5%) 0.622 5
Mukhopadhyaya et al. [83] 2010 India Asian 40 35 (87.5%) 3 (7.5%) 2 (5.0%) 40 37 (92.5%) 3 (7.5%) 0 (0.0%) 0.805 4
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1

Deviated from HWE.

Overall population

The meta-analysis showed a significant association between TNF-α −308G/A and T2DM risk in the allele model (OR = 1.239, 95% CI = 1.108–1.385, P=0.000); the dominant model (OR = 1.280, 95% CI = 1.116–1.469, P=0.000); the recessive model (OR = 1.446, 95% CI = 1.154–1.813, P=0.001); the overdominant model (OR = 1.181, 95% CI = 1.041–1.341, P=0.008); and the codominant model (OR = 1.691, 95% CI = 1.310–2.184, P=0.000). TNF-α −238G/A was not associated (P>0.05) with T2DM in all genetic models (Table 2). After Bonferroni correction, our results were also significantly associated. The forest plot of the −308G/A polymorphism is shown in Figure 2 and −238G/A is shown in Figure 3.

Table 2. Association between TNF-α -308G/A and -238G/A and type 2 diabetes.

Genetic model Ethnicity I2 (%) P (heterogeneity) OR (95% CI) P-value P for publication bias Effects model
Begg Egger
TNF-α -308G/A A vs G
Overall 73.7 0.000‡ 1.239 (1.108–1.385) 0.000‡ 0.268 0.000 Random
Caucasian 74.6 0.000‡ 1.224 (1.060–1.413) 0.006† 0.135 0.363 Random
Asian 69.2 0.000‡ 1.324 (1.078–1.626) 0.007† 0.809 0.249 Random
African 56.2 0.077 0.960 (0.679–1.356) 0.815 0.174 0.015 Random
GA+AA vs GG
Overall 74.6 0.000‡ 1.280 (1.116–1.469) 0.000‡ 0.096 0.275 Random
Caucasian 74.6 0.000‡ 1.282 (1.085–1.514) 0.004† 0.069 0.376 Random
Asian 71.7 0.000‡ 1.367 (1.065–1.754) 0.014* 0.174 0.532 Random
African 57.6 0.070 0.844 (0.522–1.363) 0.487 0.487 0.234 Random
AA vs GG+GA
Overall 38.3 0.008† 1.446 (1.154–1.813) 0.001† 0.207 0.125 Random
Caucasian 51.3 0.005† 1.240 (0.908–1.692) 0.176 0.469 0.276 Random
Asian 0.0 0.497 1.789 (1.357–2.357) 0.000‡ 0.284 0.363 Random
African 9.4 0.346 1.809 (0.890–3.677) 0.102 0.497 0.561 Random
GA vs GG+AA
Overall 67.8 0.000‡ 1.181 (1.041–1.341) 0.008† 0.364 0.634 Random
Caucasian 66.3 0.000‡ 1.225 (1.050–1.423) 0.005† 0.243 0.594 Random
Asian 63.7 0.000‡ 1.230 (0.977–1.548) 0.079 0.846 0.619 Random
African 50.5 0.109 0.707 (0.455–1.098) 0.123 0.174 0.452 Random
AA vs GG
Overall 47.4 0.001‡ 1.691 (1.310–2.184) 0.000‡ 0.285 0.068 Random
Caucasian 62.8 0.000‡ 1.399 (0.969–2.018) 0.073 0.506 0.244 Random
Asian 0.0 0.842 2.368 (1.779–3.153) 0.000‡ 0.365 0.157 Random
African 11.6 0.335 1.605 (0.765–3.369) 0.211 1.000 0.942 Random
AA vs GA
Overall 31.8 0.029* 1.150 (0.918–1.441) 0.224 0.285 0.068 Random
Caucasian 46.8 0.013* 1.031 (0.756–1.405) 0.847 0.506 0.244 Random
Asian 0.0 0.533 1.138 (0.834–1.553) 0.414 0.365 0.157 Random
African 0.0 0.414 2.230 (1.160–4.287) 0.016* 1.000 0.942 Random
TNF-α -238G/A A vs G
Overall 23.0 0.205 1.064 (0.944–1.200) 0.309 0.524 0.821 Fixed
Caucasian 32.3 0.170 1.076 (0.938–1.234) 0.295 0.453 0.860 Fixed
Asian 22.0 0.268 1.027 (0.802–1.316) 0.832 0.881 0.639 Fixed
GA+AA vs GG
Overall 8.3 0.362 1.045 (0.921–1.187) 0.936 0.396 0.947 Fixed
Caucasian 15.8 0.306 1.056 (0.914–1.220) 0.459 0.293 0.801 Fixed
Asian 13.5 0.328 1.011 (0.774–1.320) 0.492 0.881 0.719 Fixed
AA vs GG+GA
Overall 0.0 0.497 1.554 (0.896–2.692) 0.085 0.881 0.754 Fixed
Caucasian 31.2 0.202 1.795 (0.888–4.533) 3.628 0.573 0.350 Fixed
Asian 0.0 0.810 1.243 (0.516–2.977) 0.619 0.327 0.680 Fixed
GA vs GG+AA
Overall 0.0 0.462 1.021 (0.897–1.162) 0.758 0.396 0.908 Fixed
Caucasian 4.1 0.398 1.029 (0.889–1.192) 0.698 0.453 0.689 Fixed
Asian 8.4 0.363 0.990 (0.751–1.304) 0.943 0.652 0.813 Fixed
AA vs GG
Overall 0.00 0.496 1.569 (0.905–2.721) 0.078 0.881 0.748 Fixed
Caucasian 31.6 0.198 1.807 (0.894–3.654) 0.064 0.348 0.414 Fixed
Asian 0.0 0.811 1.262 (0.523–3.046) 0.596 0.142 0.356 Fixed
AA vs GA
Overall 0.0 0.533 1.429 (0.808–2.526) 0.178 0.881 0.748 Fixed
Caucasian 24.3 0.252 1.688 (0.822–3.466) 0.117 0.348 0.414 Fixed
Asian 0.0 0.778 1.079 (0.424–2.748) 0.852 0.142 0.356 Fixed
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*

P<0.05.

†

P<0.01.

‡

P<0.001.

Figure 2. Forest plot of the association of TNF-α −308G/A and type 2 diabetes (A vs. G) in random-effects model.

Figure 2

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Each square is proportional to the study-specific weight.

Figure 3. Forest plot of the association of TNF-α −238G/A and type 2 diabetes (A vs. G) in fixed-effects model.

Figure 3

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Each square is proportional to the study-specific weight.

Subgroup by ethnicity

To derive heterogeneity and assess the genetic background, we carried out a subgroup analysis, where the overall population was divided into three subgroups, namely Caucasian, Asian and African. The subgroup analysis showed significant associations between −308G/A and T2DM risk in the Caucasian population in the allele model (OR = 1.224, 95% CI = 1.060–1.413, P=0.006); the dominant model (OR = 1.282, 95% CI = 1.085–1.514, P=0.004); the overdominant model (OR = 1.225, 95% CI = 1.050–1.423, P=0.005), and also in Asian populations in the allele model (OR = 1.324, 95% CI = 1.078–1.626, P=0.007); the dominant model (OR = 1.367, 95% CI = 1.065–1.754, P=0.014); the recessive model (OR = 1.789, 95% CI = 1.357–2.357, P=0.000); the codominant model (OR = 2.368, 95% CI = 1.779–3.153, P=0.000) and no associations between −308G/A and T2DM risk in African populations (P>0.05). For −238G/A, it was not associated (P>0.05) with T2DM in the subgroup population (Table 2).

Meta-regression and sensitivity analysis

The following covariates were considered for meta-regression: publication year, sample size, ethnicity and HWE in controls. The −308G/A results revealed no influence on the publication year (I2 res = 91.89%, adj R2 = 5.37%, P=0.084), sample size (I2 res = 94.31%, adj R2= 1.11%, P=0.215), HWE (I2 res = 92.83%, adj R2= −2.97%, P=0.882) and ethnicity, including Caucasian (P=0.106), Asian (P=0.127), using the 10000 times Monte Carlo permutation test. The −238G/A results revealed no influence from publication year (P=0.573), sample size (P=0.498) and ethnicity, including Caucasian (P=0.864) and Asian (P=0.735), using the 10000 times Monte Carlo permutation test. Sensitivity analysis revealed that some studies [17,28–32] have observed bias (Figure 4). But no significant changes in heterogeneity were observed after excluding these studies except study by Golshani et al. [17]. After its removal, the heterogeneity was greatly reduced in the Caucasian subgroup (from 74.6 to 47.4), but there was still a significant association between −308G/A and T2DM (OR = 1.148, 95% CI = 1.033–1.277, P=0.011).

Figure 4. Sensitive analysis in TNF-α −308G/A study (A) and −238G/A study (B).

Figure 4

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There is a bias and asymmetry in TNF-α−308G/A study.

Publication bias

Publication bias data for TNF-α −308G/A and −238G/A, in all genetic models are shown in Table 2. The continuity corrected results showed no existing publication bias (P>0.05). The Begg’s and Egger’s tests showed no existing publication bias in the overall population for all genetic models (Table 2). There are no bias and asymmetry found in Begg’s and Egger’s funnel plots (Figures 5 and 6).

Figure 5. Publication bias of Begg’s test (A) and Egger’s test (B) in TNF-α −308G/A study.

Figure 5

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Begg’s funnel plot shows centered at the fixed-effect summary OR, visual inspection of the funnel plot is roughly symmetrical and indicates that there is no bias (P>0.05). Egger’s funnel plot with fitted regression line, intercept represents the degree of asymmetry, close to zero, the smaller the bias. The Egger’s test indicates that there are no small-study effects (intercept = 0.514, 95% CI = −1.504–1.532) and bias (P>0.05).

Figure 6. Publication bias of Begg’s test (A) and Egger’s test (B) in TNF-α −238G/A study.

Figure 6

Open in a new tab

Begg’s funnel plot shows centered at the fixed-effect summary OR, visual inspection of the funnel plot is roughly symmetrical and indicates that there is no bias (P>0.05). Egger’s funnel plot with fitted regression line, intercept represents the degree of asymmetry, close to zero, the smaller the bias. The Egger’s test indicates that there are no small-study effects (intercept = −0.048, 95% CI = −1.405–1.309) and bias (P>0.05).

Discussion

T2DM is a complex disease where environmental and genetic factors interact. Family-based studies have found that T2DM has a strong genetic component [33] with several candidate genes identified [1]. Among these candidate genes, the TNF-α −308G/A and −238G/A polymorphisms have been widely studied. Although numerous studies have focused on these associations, their conclusions have been controversial [13,17,34,35]. A previous meta-analysis by Feng et al. [36], did not find any significant associations between the TNF-α −308 G/A polymorphism and T2DM risk in Caucasian and Asian populations. In contrast, a more recent meta-analysis by Zhao et al. [37], suggested that the TNF-α −308A variant increased by approximately 21% in T2DM incidence. Similarly, the results of two meta-analyses, of small sample sizes, showed that TNF-α −238G/A was not associated with T2DM [38,39]. Moreover, some meta-analyses were limited to specific countries and regions [40–42]. Therefore, we performed a comprehensive large-scale meta-analysis to investigate these associations.

For this meta-analysis, in order to derive reliable results, we added 12 new studies, performed quality score assessments and added multiple genetic models. Compared with previous meta-analyses [36,37], we demonstrate that TNF-α −308G/A is a risk factor for T2DM, not only in Asian but also in Caucasian populations. Additionally, we found that TNF-α −238G/A is not associated with T2DM in overall and subgroup populations. These observations illustrate the necessity for more comprehensive analyses and multiple genetic models.

To prevent possible interference from heterogeneity to our results, we sought to explain the source of heterogeneity and eliminate it. First, subgroup analysis of ethnicity and genetic models reduced between-study heterogeneity. We found that heterogeneity was reduced, but there was still high heterogeneity. Next, our meta-regression analysis attempted to reveal these heterogeneous sources. These results showed that publication year, sample size, ethnicity (Caucasian, Asian, African) and HWE were not the sources of between-study heterogeneity (P>0.05). Finally, we performed sensitivity analysis to explore the impact of a single study; our results revealed that the study by Golshani et al. [17] may have been the major contributor to this heterogeneity.

The advantages of this meta-analysis are that it expands to large-scale studies. While strictly complying with the inclusion criteria, we updated 12 studies not included in previous meta-analysis, our results are more comprehensive. To guarantee the quality of the meta-analysis, NOS and HWE analyses were conducted to assess the quality of included studies to avoid potential influences and increase the strength of the results. A strict search strategy of literature inclusion and data extraction was performed by two investigators according to inclusion and exclusion criteria. Furthermore, sensitivity analysis and meta-regression were also performed to increase the robustness of our conclusions. Subgroup analysis by ethnicity and the source of the control population were used to explain the effect of genetic background and study design.

There were some limitations to this meta-analysis. First, only studies in English were included, studies published in other languages were excluded. Second, because we excluded literature without original data, some studies were excluded. Third, other potential interactions including environmental factors, environment–gene interactions and gene–gene interactions. Additionally, some potential covariates (e.g. age, sex) were not included due to insufficient information from selected publications.

In conclusion, our meta-analysis identified that TNF-α −308G/A were associated with T2DM susceptibility. Additionally, we found that TNF-α −238G/A is not associated with T2DM in overall and subgroup populations. In the future, the influences of genetic loci, combined with environmental factors, may provide important treatment therapies for T2DM, therefore, well-conceived studies are warranted to confirm the important data presented here.

Abbreviations

GWAS

genome-wide association scans

HWE

Hardy–Weinberg equilibrium

NOS

Newcastle–Ottawa Quality Assessment Scale

OR

odds ratio

TNF-α

tumor necrosis factor-α

T2DM

type 2 diabetes mellitus

95% CI

95% confidence interval

Contributor Information

Lidan Xu, Email: xuld@ems.hrbmu.edu.cn.

Songbin Fu, Email: fusb@ems.hrbmu.edu.cn.

Author Contribution

All authors have contributed to the paper. Lidan Xu and Songbin Fu participated in the design of the study. Xiaoliang Guo and Chenxi Li drafted the article and wrote the manuscript. Jiawei Wu, Chang Liu, Qingbu Mei and Wenjing Sun assisted with analysis and interpretation of data. All authors read and approved the final manuscript.

Competing Interests

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

Funding

This work was supported by the Program for Changjiang Scholars and Innovative Research Team in University of China [grant number IRT1230]. The authors declare that they do not have any financial or personal relationships with other people or organizations that could inappropriately influence this work; there are no professional or personal interests of any nature or kind in any product, service, or company that could be construed as influencing the position presented in this manuscript.

References

  • 1.Stumvoll M., Goldstein B.J. and van Haeften T.W. (2005) Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365, 1333–1346 10.1016/S0140-6736(05)61032-X [DOI] [PubMed] [Google Scholar]
  • 2.Lawlor N., Khetan S., Ucar D. and Stitzel M.L. (2017) Genomics of Islet (Dys)function and Type 2 diabetes. Trends Genet. 33, 244–255 10.1016/j.tig.2017.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Moller D.E. and Flier J.S. (1991) Insulin resistance–mechanisms, syndromes, and implications. N. Engl. J. Med. 325, 938–948 10.1056/NEJM199109263251307 [DOI] [PubMed] [Google Scholar]
  • 4.Feinstein R., Kanety H., Papa M.Z., Lunenfeld B. and Karasik A. (1993) Tumor necrosis factor-alpha suppresses insulin-induced tyrosine phosphorylation of insulin receptor and its substrates. J. Biol. Chem. 268, 26055–26058 [PubMed] [Google Scholar]
  • 5.Hotamisligil G.S., Murray D.L., Choy L.N. and Spiegelman B.M. (1994) Tumor necrosis factor alpha inhibits signaling from the insulin receptor. Proc. Natl. Acad. Sci. U.S.A. 91, 4854–4858 10.1073/pnas.91.11.4854 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Saxena R., Voight B.F., Lyssenko V., Burtt N.P., de Bakker P.I., Chen H. et al. (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 10.1126/science.1142358 [DOI] [PubMed] [Google Scholar]
  • 7.Sladek R., Rocheleau G., Rung J., Dina C., Shen L., Serre D. et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 10.1038/nature05616 [DOI] [PubMed] [Google Scholar]
  • 9.Scott L.J., Mohlke K.L., Bonnycastle L.L., Willer C.J., Li Y., Duren W.L. et al. (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 10.1126/science.1142382 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Burton P.R., Clayton D.G., Cardon L.R., Craddock N., Deloukas P. and Duncanson A. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 10.1038/nature05911 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Zeggini E., Weedon M.N., Lindgren C.M., Frayling T.M., Elliott K.S., Lango H. et al. (2007) Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336–1341 10.1126/science.1142364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Boraska V., Rayner N.W., Groves C.J., Frayling T.M., Diakite M., Rockett K.A. et al. (2010) Large-scale association analysis of TNF/LTA gene region polymorphisms in type 2 diabetes. BMC Med. Genet. 11, 69 10.1186/1471-2350-11-69 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Zeggini E., Groves C.J., Parkinson J.R., Halford S., Owen K.R., Frayling T.M. et al. (2005) Large-scale studies of the association between variation at the TNF/LTA locus and susceptibility to type 2 diabetes. Diabetologia 48, 2013–2017 10.1007/s00125-005-1902-4 [DOI] [PubMed] [Google Scholar]
  • 13.Liu H.L., Lin Y.G., Wu J., Sun H., Gong Z.C., Hu P.C. et al. (2008) Impact of genetic polymorphisms of leptin and TNF-alpha on rosiglitazone response in Chinese patients with type 2 diabetes. Eur. J. Clin. Pharmacol. 64, 663–671 10.1007/s00228-008-0483-9 [DOI] [PubMed] [Google Scholar]
  • 14.Shiau M.Y., Wu C.Y., Huang C.N., Hu S.W., Lin S.J. and Chang Y.H. (2003) TNF-alpha polymorphisms and type 2 diabetes mellitus in Taiwanese patients. Tissue Antigens 61, 393–397 10.1034/j.1399-0039.2003.00059.x [DOI] [PubMed] [Google Scholar]
  • 15.Patel R., Palit S.P., Rathwa N., Ramachandran A.V. and Begum R. (2018) Genetic variants of tumor necrosis factor-α and its levels: a correlation with dyslipidemia and type 2 diabetes susceptibility. Clin. Nutr. 38, 1414–1422 10.1016/j.clnu.2018.06.962 [DOI] [PubMed] [Google Scholar]
  • 16.El Naggar G.F. and El-Serogy H. (2013) Is there a relation between TNF-alpha 308 promoter gene polymorphism and a risk of coronary artery disease in patients with type 2 diabetes mellitus? Life Sci. 10, 1779–1785 [Google Scholar]
  • 17.Golshani H., Haghani K., Dousti M. and Bakhtiyari S. (2015) Association of TNF-alpha 308 G/A polymorphism with type 2 diabetes: a case-control study in the Iranian Kurdish Ethnic Group. Osong Public Health Res. Perspect. 6, 94–99 10.1016/j.phrp.2015.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Stang A. (2010) Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur. J. Epidemiol. 25, 603–605 10.1007/s10654-010-9491-z [DOI] [PubMed] [Google Scholar]
  • 19.Higgins J.P., Thompson S.G., Deeks J.J. and Altman D.G. (2003) Measuring inconsistency in meta-analyses. BMJ 327, 557–560 10.1136/bmj.327.7414.557 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Mantel N. and Haenszel W. (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J. Natl. Cancer Inst. 22, 719–748 [PubMed] [Google Scholar]
  • 21.DerSimonian R. and Laird N. (1986) Meta-analysis in clinical trials. Control. Clin. Trials 7, 177–188 10.1016/0197-2456(86)90046-2 [DOI] [PubMed] [Google Scholar]
  • 22.Knapp G. and Hartung J. (2003) Improved tests for a random effects meta-regression with a single covariate. Stat. Med. 22, 2693–2710 10.1002/sim.1482 [DOI] [PubMed] [Google Scholar]
  • 23.Sterne J.A., Sutton A.J., Ioannidis J.P., Terrin N., Jones D.R., Lau J. et al. (2011) Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ 343, d4002 10.1136/bmj.d4002 [DOI] [PubMed] [Google Scholar]
  • 24.Mutze T., Konietschke F., Munk A. and Friede T. (2017) A studentized permutation test for three-arm trials in the ‘gold standard’ design. Stat. Med. 36, 883–898 10.1002/sim.7176 [DOI] [PubMed] [Google Scholar]
  • 25.Tanner-Smith E.E. and Tipton E. (2014) Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and spss. Res. Synth. Methods 5, 13–30 10.1002/jrsm.1091 [DOI] [PubMed] [Google Scholar]
  • 26.Egger M., Smith G.D. and Phillips A.N. (1997) Meta-analysis: principles and procedures. BMJ 315, 1533–1537 10.1136/bmj.315.7121.1533 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Egger M., Davey Smith G., Schneider M. and Minder C. (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629–634 10.1136/bmj.315.7109.629 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Umapathy D., Krishnamoorthy E., Mariappanadar V., Viswanathan V. and Ramkumar K.M. (2018) Increased levels of circulating (TNF-alpha) is associated with (-308G/A) promoter polymorphism of TNF-alpha gene in diabetic nephropathy. Int. J. Biol. Macromol. 107, 2113–2121 10.1016/j.ijbiomac.2017.10.078 [DOI] [PubMed] [Google Scholar]
  • 29.Hameed I., Masoodi S.R., Malik P.A., Mir S.A., Ghazanfar K. and Ganai B.A. (2018) Genetic variations in key inflammatory cytokines exacerbates the risk of diabetic nephropathy by influencing the gene expression. Gene 661, 51–59 10.1016/j.gene.2018.03.095 [DOI] [PubMed] [Google Scholar]
  • 30.Perez-Luque E., Malacara J.M., Garay-Sevilla M.E. and Fajardo M.E. (2012) Association of the TNF-alpha -308G/A polymorphism with family history of type 2 diabetes mellitus in a Mexican population. Clin. Biochem. 45, 12–15 10.1016/j.clinbiochem.2011.09.018 [DOI] [PubMed] [Google Scholar]
  • 31.Wang L.-L., Du Z.-W., Liu J.-N., Wu M., Song Y., Jiang R.-H. et al. (2012) Association of UCP3, APN, and TNF-α gene polymorphisms with type 2 diabetes in a population of northern Chinese Han patients. 28, 255–258 [Google Scholar]
  • 32.Liu B., Yu N., Tan L.S., Liu J.B., Guo Y. and Pan Y.P. (2011) A study of frequency of TNF alpha gene with type 2 diabetes mellitus with chronic periodontitis. Shanghai Kou Qiang Yi Xue 20, 169–173 [PubMed] [Google Scholar]
  • 33.Poulsen P., Kyvik K.O., Vaag A. and Beck-Nielsen H. (1999) Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study. Diabetologia 42, 139–145 10.1007/s001250051131 [DOI] [PubMed] [Google Scholar]
  • 34.Kim H.R., Lee M.K. and Park A.J. (2006) The -308 and -238 polymorphisms of the TNF-alpha promoter gene in Type 2 diabetes mellitus. Korean J. Lab. Med. 26, 58–63 10.3343/kjlm.2006.26.1.58 [DOI] [PubMed] [Google Scholar]
  • 35.Pan H.Y., Zhang P.X., Ni F.Y. and Li Q. (2006) Association between tumor necrosis factor-alpha-308 G/A polymorphism and type 2 diabetes mellitus. Chin. J. Clin. Rehabil. 10, 72–74 [Google Scholar]
  • 36.Feng R.N., Zhao C., Sun C.H. and Li Y. (2011) Meta-analysis of TNF 308 G/A polymorphism and type 2 diabetes mellitus. PLoS ONE 6, e18480 10.1371/journal.pone.0018480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Zhao Y., Li Z., Zhang L., Zhang Y., Yang Y., Tang Y. et al. (2014) The TNF-alpha -308G/A polymorphism is associated with type 2 diabetes mellitus: an updated meta-analysis. Mol. Biol. Rep. 41, 73–83 10.1007/s11033-013-2839-1 [DOI] [PubMed] [Google Scholar]
  • 38.Meng N., Zhang Y., Li H., Ma J. and Qu Y. (2014) Association of tumor necrosis factor alpha promoter polymorphism (TNF-α 238 G/A and TNF-α 308 G/A) with diabetic mellitus, diabetic retinopathy and diabetic nephropathy: a meta-analysis. Curr. Eye Res. 39, 194–203 10.3109/02713683.2013.834942 [DOI] [PubMed] [Google Scholar]
  • 39.Feng R., Li Y., Zhao D., Wang C., Niu Y. and Sun C. (2009) Lack of association between TNF 238 G/A polymorphism and type 2 diabetes: a meta-analysis. Acta Diabetol. 46, 339–343 10.1007/s00592-009-0118-3 [DOI] [PubMed] [Google Scholar]
  • 40.Yako Y.Y., Guewo-Fokeng M., Balti E.V., Bouatia-Naji N., Matsha T.E., Sobngwi E. et al. (2016) Genetic risk of type 2 diabetes in populations of the African continent: a systematic review and meta-analyses. Diabetes Res. Clin. Pract. 114, 136–150 10.1016/j.diabres.2016.01.003 [DOI] [PubMed] [Google Scholar]
  • 41.Liu Z.H., Ding Y.L., Xiu L.C., Pan H.Y., Liang Y., Zhong S.Q. et al. (2013) A meta-analysis of the association between TNF-alpha -308G>A polymorphism and type 2 diabetes mellitus in Han Chinese population. PLoS ONE 8, e59421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Sefri H., Benrahma H., Charoute H., Lakbakbi el Yaagoubi F., Rouba H., Lyoussi B. et al. (2014) TNF A -308G>A polymorphism in Moroccan patients with type 2 diabetes mellitus: a case-control study and meta-analysis. Mol. Biol. Rep. 41, 5805–5811 [DOI] [PubMed] [Google Scholar]
  • 43.Umapathy D., Krishnamoorthy E., Mariappanadar V., Viswanathan V. and Ramkumar K.M. (2018) Increased levels of circulating (TNF-α) is associated with (-308G/A) promoter polymorphism of TNF-α gene in diabetic nephropathy. Int. J. Biol. Macromol. 107, 2113–2121 10.1016/j.ijbiomac.2017.10.078 [DOI] [PubMed] [Google Scholar]
  • 44.Fathy S.A., Mohamed M.R., Ali M.A.M., EL-H A.E. and Alattar A.T. (2018) Influence of IL-6, IL-10, IFN-γ and TNF-α genetic variants on susceptibility to diabetic kidney disease in type 2 diabetes mellitus patients. Biomarkers 24, 43–55 10.1080/1354750X.2018.1501761 [DOI] [PubMed] [Google Scholar]
  • 45.Rodrigues K.F., Pietrani N.T., Bosco A.A., Campos F.M.F., Sandrim V.C. and Gomes K.B. (2017) IL-6, TNF-alpha, and IL-10 levels/polymorphisms and their association with type 2 diabetes mellitus and obesity in Brazilian individuals. Arch Endocrinol. Metab. 61, 438–446 10.1590/2359-3997000000254 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Mortazavi E., Eslami B., Aghahosseini P., Ahron F., Amininejad A., Mahmoodi S. et al. (2017) Association of mannose-binding lectin rs1800450 and tumor necrotic factor-α rs1800620 polymorphism with Helicobacter pylori in type II diabetes mellitus. Monoclon. Antib. Immunodiagn. Immunother. 36, 236–241 10.1089/mab.2017.0039 [DOI] [PubMed] [Google Scholar]
  • 47.Jamil K., Jayaraman A., Ahmad J., Joshi S. and Kumar Y.S. (2017) TNF-alpha-308G/A and-238G/A polymorphisms and its protein network associated with type 2 diabetes mellitus. Saudi J. Biol. Sci. 24, 1195–1203 10.1016/j.sjbs.2016.05.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Doody N.E., Dowejko M.M., Akam E.C., Cox N.J., Bhatti J.S., Singh P. et al. (2017) The Role of TLR4, TNF-alpha and IL-1beta in Type 2 Diabetes Mellitus Development within a North Indian Population. Ann. Hum. Genet. 81, 141–146 10.1111/ahg.12197 [DOI] [PubMed] [Google Scholar]
  • 49.Churnosov M.I., Belousova O.N. and Sirotina S.S. (2017) Study of the associations between polymorphic markers rs1800629 TNFα, rs909253 Ltα, rs767455 TNFR1, rs1061624 TNFR2 and the development of type 2 diabetes. Diabetes Mellitus 20, 166–171 10.14341/2072-0351-5845 [DOI] [Google Scholar]
  • 50.Sesti L.F., Crispim D., Canani L.H., Polina E.R., Rheinheimer J., Carvalho P.S. et al. (2015) The -308G>a polymorphism of the TNF gene is associated with proliferative diabetic retinopathy in Caucasian Brazilians with type 2 diabetes. Invest. Ophthalmol. Vis. Sci. 56, 1184–1190 [DOI] [PubMed] [Google Scholar]
  • 51.Dabhi B. and Mistry K.N. (2015) Oxidative stress and its association with TNF-alpha-308 G/C and IL-1alpha-889 C/T gene polymorphisms in patients with diabetes and diabetic nephropathy. Gene 562, 197–202 10.1016/j.gene.2015.02.069 [DOI] [PubMed] [Google Scholar]
  • 52.Ghodsian N., Akhlaghi M., Ramachandran V., Heidari F., Haghvirdizadeh P., Eshkoor S.A. et al. (2015) Association of TNF-α G308A gene polymorphism in essential hypertensive patients without type 2 diabetes mellitus. Genet. Mol. Res. 14, 18974–18979 10.4238/2015.December.29.4 [DOI] [PubMed] [Google Scholar]
  • 53.Dhamodharan U., Viswanathan V., Krishnamoorthy E., Rajaram R. and Aravindhan V. (2015) Genetic association of IL-6, TNF-α and SDF-1 polymorphisms with serum cytokine levels in diabetic foot ulcer. Gene 565, 62–67 10.1016/j.gene.2015.03.063 [DOI] [PubMed] [Google Scholar]
  • 54.Sikka R., Raina P., Matharoo K., Bandesh K., Bhatia R., Chakrabarti S. et al. (2014) TNF-alpha (g.-308 G >A) and ADIPOQ (g. + 45 T >G) gene polymorphisms in type 2 diabetes and microvascular complications in the region of Punjab (North-West India). Curr. Eye Res. 39, 1042–1051 [DOI] [PubMed] [Google Scholar]
  • 55.Sharma N., Joseph R., Arun R., Chandni R., Srinivas K.L. and Banerjee M. (2014) Cytokine gene polymorphism (interleukin-1β +3954, Interleukin-6 [-597/-174] and tumor necrosis factor-α -308) in chronic periodontitis with and without type 2 diabetes mellitus. Indian J. Dent. Res. 25, 375–380 10.4103/0970-9290.138343 [DOI] [PubMed] [Google Scholar]
  • 56.Saxena M., Srivastava N. and Banerjee M. (2013) Association of IL-6, TNF-alpha and IL-10 gene polymorphisms with type 2 diabetes mellitus. Mol. Biol. Rep. 40, 6271–6279 10.1007/s11033-013-2739-4 [DOI] [PubMed] [Google Scholar]
  • 57.Garcia-Elorriaga G., Mendoza-Aguilar M., del Rey-Pineda G. and Gonzalez-Bonilla C. (2013) Genetic polymorphisms of the tumor necrosis factor and lymphotoxin alpha in type 2 diabetes. Rev. Med. Inst. Mex. Seguro. Soc. 51, 42–49 [PubMed] [Google Scholar]
  • 58.Mustapic M., Popovic Hadzija M., Pavlovic M., Pavkovic P., Presecki P., Mrazovac D. et al. (2012) Alzheimer’s disease and type 2 diabetes: the association study of polymorphisms in tumor necrosis factor-alpha and apolipoprotein E genes. Metab. Brain Dis. 27, 507–512 10.1007/s11011-012-9310-1 [DOI] [PubMed] [Google Scholar]
  • 59.Wang L.L., Du Z.W., Liu J.N., Wu M., Song Y., Jiang R.H. et al. (2012) Association of UCP3, APN, and TNF-alpha gene polymorphisms with type 2 diabetes in a population of Northern Chinese Han patients. Chem. Res. Chin. Univ. 28, 255–258 [Google Scholar]
  • 60.Elsaid A., Helaly M.A., Hatata E.S.Z., Fouda O. and Settin A. (2012) TNF-α-308 and INF-γ+874 gene polymorphisms in relation to susceptibility and severity of type 2 diabetes mellitus among egyptian cases. Eur. J. Gen. Med. 9, 173–177 10.29333/ejgm/82455 [DOI] [Google Scholar]
  • 61.Guzman-Flores J.M., Munoz-Valle J.F., Sanchez-Corona J., Cobian J.G., Medina-Carrillo L., Garcia-Zapien A.G. et al. (2011) Tumor necrosis factor-alpha gene promoter -308G/A and -238G/A polymorphisms in Mexican patients with type 2 diabetes mellitus. Dis. Markers 30, 19–24 10.1155/2011/360312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Mukhopadhyaya P.N., Acharya A., Chavan Y., Purohit S.S. and Mutha A. (2010) Metagenomic study of single-nucleotide polymorphism within candidate genes associated with type 2 diabetes in an Indian population. Genet. Mol. Res. 9, 2060–2068 10.4238/vol9-4gmr883 [DOI] [PubMed] [Google Scholar]
  • 63.Boraska V., Rayner N.W., Groves C.J., Frayling T.M., Diakite M., Rockett K.A. et al. (2010) Large-scale association analysis of TNF/LTA gene region polymorphisms in type 2 diabetes. BMC Med. Genet. 11, 69 10.1186/1471-2350-11-69 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Bouhaha R., Baroudi T., Ennafaa H., Vaillant E., Abid H., Sassi R. et al. (2010) Study of TNF alpha-308G/A and IL6-174G/C polymorphisms in type 2 diabetes and obesity risk in the Tunisian population. Clin. Biochem. 43, 549–552 10.1016/j.clinbiochem.2010.01.008 [DOI] [PubMed] [Google Scholar]
  • 65.Lindholm E., Bakhtadze E., Cilio C., Agardh E., Groop L. and Agardh C.D. (2008) Association between LTA, TNF and AGER polymorphisms and late diabetic complications. PLoS ONE 3, e2546 10.1371/journal.pone.0002546 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Wang N., Huang K., Zou H., Shi Y., Zhu J., Tang W. et al. (2008) No association found between the promoter variants of TNF-α and diabetic retinopathy in Chinese patients with type 2 diabetes. Curr. Eye Res. 33, 377–383 10.1080/02713680802008220 [DOI] [PubMed] [Google Scholar]
  • 67.Willer C.J., Bonnycastle L.L., Conneely K.N., Duren W.L., Jackson A.U., Scott L.J. et al. (2007) Screening of 134 single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes replicates association with 12 SNPs in nine genes. Diabetes 56, 256–264 10.2337/db06-0461 [DOI] [PubMed] [Google Scholar]
  • 68.Santos M.J., Patino G.A., Angel B.B., Martinez H.J., Perez B.F., Villarroel B.A. et al. (2006) [Association between tumor necrosis factor-alpha promoter polymorphisms and type 2 diabetes and obesity in Chilean elderly women]. Rev. Med. Chil. 134, 1099–1106 [DOI] [PubMed] [Google Scholar]
  • 69.Tsiavou A., Hatziagelaki E., Chaidaroglou A., Manginas A., Koniavitou K., Degiannis D. et al. (2004) TNF-alpha, TGF-beta1, IL-10, IL-6, gene polymorphisms in latent autoimmune diabetes of adults (LADA) and type 2 diabetes mellitus. J. Clin. Immunol. 24, 591–599 10.1007/s10875-004-6239-0 [DOI] [PubMed] [Google Scholar]
  • 70.Zouari Bouassida K., Chouchane L., Jellouli K., Cherif S., Haddad S., Gabbouj S. et al. (2004) Polymorphism of stress protein HSP70-2 gene in Tunisians: susceptibility implications in type 2 diabetes and obesity. Diabetes Metab. 30, 175–180 10.1016/S1262-3636(07)70104-0 [DOI] [PubMed] [Google Scholar]
  • 71.Li H., Groop L., Nilsson A., Weng J. and Tuomi T. (2003) A combination of human leukocyte antigen DQB1*02 and the tumor necrosis factor alpha promoter G308A polymorphism predisposes to an insulin-deficient phenotype in patients with type 2 diabetes. J. Clin. Endocrinol. Metab. 88, 2767–2774 10.1210/jc.2002-020506 [DOI] [PubMed] [Google Scholar]
  • 72.Heijmans B.T., Westendorp R.G., Droog S., Kluft C., Knook D.L. and Slagboom P.E. (2002) Association of the tumour necrosis factor alpha -308G/A polymorphism with the risk of diabetes in an elderly population-based cohort. Genes Immun. 3, 225–228 10.1038/sj.gene.6363859 [DOI] [PubMed] [Google Scholar]
  • 73.Furuta M., Yano Y., Ito K., Gabazza E.C., Katsuki A., Tanaka T. et al. (2002) Relationship of the tumor necrosis factor-alpha -308 A/G promoter polymorphism with insulin sensitivity and abdominal fat distribution in Japanese patients with type 2 diabetes mellitus. Diabetes Res. Clin. Pract. 56, 141–145 10.1016/S0168-8227(01)00358-8 [DOI] [PubMed] [Google Scholar]
  • 74.Rasmussen S.K., Urhammer S.A., Jensen J.N., Hansen T., Borch-Johnsen K. and Pedersen O. (2000) The -238 and -308 G→A polymorphisms of the tumor necrosis factor α gene promotor are not associated with features of the insulin resistance syndrome or altered birth weight in Danish Caucasians. J. Clin. Endocrinol. Metab. 85, 1731–1734 [DOI] [PubMed] [Google Scholar]
  • 75.Kamizono S., Yamada K., Seki N., Higuchi T., Kimura A., Nonaka K. et al. (2000) Susceptible locus for obese type 2 diabetes mellitus in the 5′-flanking region of the tumor necrosis factor-alpha gene. Tissue Antigens 55, 449–452 10.1034/j.1399-0039.2000.550508.x [DOI] [PubMed] [Google Scholar]
  • 76.Pandey J.P., Zamani M. and Cassiman J.J. (1999) Epistatic effects of genes encoding tumor necrosis factor-alpha, immunoglobulin allotypes, and HLA antigens on susceptibility to non-insulin-dependent (type 2) diabetes mellitus. Immunogenetics 49, 860–864 10.1007/s002510050565 [DOI] [PubMed] [Google Scholar]
  • 77.Hamann A., Mantzoros C., Vidal-Puig A. and Flier J.S. (1995) Genetic variability in the TNF-α promoter is not associated with type II diabetes mellitus (NIDDM). Biochem. Biophys. Res. Commun. 211, 833–839 10.1006/bbrc.1995.1887 [DOI] [PubMed] [Google Scholar]
  • 78.Kung W.J., Lin C.C., Liu S.H. and Chaung H.C. (2010) Association of interleukin-10 polymorphisms with cytokines in type 2 diabetic nephropathy. Diabetes Technol. Ther. 12, 809–813 10.1089/dia.2010.0085 [DOI] [PubMed] [Google Scholar]
  • 79.Ko G.T., Lee S.C., Pu Y.B., Ng M.C., So W.Y., Thomas N. et al. (2003) Tumour necrosis factor-alpha promoter gene polymorphism at - 308 (genotype AA) in Chinese subjects with Type 2 diabetes. Diabet. Med. 20, 167–168 10.1046/j.1464-5491.2003.00829_1.x [DOI] [PubMed] [Google Scholar]
  • 80.Morris A.M., Heilbronn L.K., Noakes M., Kind K.L. and Clifton P.M. (2003) -308 Nco I polymorphism of tumour necrosis factor alpha in overweight Caucasians. Diabetes Res. Clin. Pract. 62, 197–201 10.1016/j.diabres.2003.08.002 [DOI] [PubMed] [Google Scholar]
  • 81.Sobti R.C., Kler R., Sharma Y.P., Talwar K.K. and Singh N. (2012) Risk of obesity and type 2 diabetes with tumor necrosis factor-alpha 308G/A gene polymorphism in metabolic syndrome and coronary artery disease subjects. Mol. Cell. Biochem. 360, 1–7 10.1007/s11010-011-0917-z [DOI] [PubMed] [Google Scholar]
  • 82.Rasmussen S.K., Urhammer S.A., Jensen J.N., Hansen T., Borch-Johnsen K. and Pedersen O. (2000) The -238 and -308 G–>A polymorphisms of the tumor necrosis factor alpha gene promoter are not associated with features of the insulin resistance syndrome or altered birth weight in Danish Caucasians. J. Clin. Endocrinol. Metab. 85, 1731–1734 [DOI] [PubMed] [Google Scholar]
  • 83.Mukhopadhyaya P.N., Acharya A., Chavan Y., Purohit S.S. and Mutha A. (2010) Metagenomic study of single-nucleotide polymorphism within candidate genes associated with type 2 diabetes in an Indian population. Genet. Mol. Res. 9, 2060–2068 10.4238/vol9-4gmr883 [DOI] [PubMed] [Google Scholar]

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