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.

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.

Study flow diagram

Figure 1
Study flow diagram
Figure 1
Study flow diagram
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. [152018 India Asian 388 351(90.5%) 34 (8.8%) 3 (0.8%) 493 449 (91.1%) 42 (8.5%) 2 (0.4%) 0.348 
Umapathy et al. [432018 India Asian 538 302 (56.1%) 142 (26.4%) 94 (17.5%) 218 167 (76.6%) 32 (14.7%) 19 (8.7%) 0.0001 
Hemmed et al. [292018 India Asian 862 528 (61.3%) 283 (32.8%) 51 (5.9%) 464 356 (76.7%) 96 (20.7%) 12 (2.6%) 0.080 
Fathy et al. [442018 Kuwaiti Caucasian 117 86 (73.5%) 28 (23.9%) 3 (2.6%) 42 41 (97.6%) 0 (0.0%) 1 (2.4%) 0.0001 
Rodrigues et al. [452017 Brazil Caucasian 102 78 (76.5%) 23 (22.5%) 1 (1.0%) 62 47 (75.8%) 15 (24.2%) 0 (0.0%) 0.279 
Mortazavi et al. [462017 Iran Caucasian 174 24 (13.8%) 101 (58.0%) 49 (28.2%) 185 68 (36.8%) 76 (41.1%) 41 (22.2%) 0.0291 
Jamil et al. [472017 India Asian 100 88 (88.0%) 10 (10.0%) 2 (2.0%) 100 87 (87.0%) 12 (12.0%) 1 (1.0%) 0.433 
Doody et al. [482017 India Asian 198 178 (89.9%) 18 (9.1%) 2 (1.0%) 204 189 (92.6%) 13 (6.4%) 2 (1.0%) 0.0041 
Churnosov et al. [492017 Russia Caucasian 236 176 (74.6%) 53 (22.5%) 7 (3.0%) 303 242 (79.9%) 55 (18.2%) 6 (2.0%) 0.180 
Sesti et al. [502015 Britain Caucasian 695 535 (73.7%) 176 (24.2%) 15 (2.1%) 170 129 (75.9%) 38 (22.4%) 3 (1.8%) 0.917 
Golshani et al. [172015 Iran Caucasian 1038 737 (71.0%) 269 (25.9%) 32 (3.1%) 1023 871 (85.1%) 142 (13.9%) 10 (1.0%) 0.124 
Dabhi et al. [512015 India Asian 214 185 (86.5%) 27 (12.6%) 2 (0.9%) 235 191 (81.3%) 44 (18.7%) 0 (0.0%) 0.885 
Ghodsian et al. [522015 Malaysia Asian 88 73 (83.0%) 14 (15.9%) 1 (1.1%) 232 202 (87.1%) 29 (12.5%) 1 (0.4%) 0.970 
Dhamodharan et al. [532015 India Asian 409 218 (53.3%) 117 (28.6%) 74 (18.1%) 106 77 (72.6%) 14 (13.2%) 15 (14.2%) 0.0001 
Sikka et al. [542014 India Asian 462 405 (87.7%) 55 (11.9%) 2 (0.4%) 203 176 (86.7%) 27 (13.3%) 0 (0.0%) 0.310 
Sharma et al. [552014 India Asian 51 45 (88.2%) 6 (11.8%) 0 (0.0%) 51 50 (98.0%) 1 (2.0%) 0 (0.0%) 0.944 
Saxena et al. [562013 India Asian 213 173 (81.2%) 33 (15.5%) 7 (3.3%) 140 111 (79.3%) 25 (17.9%) 4 (2.9%) 0.095 
Garcia-Elorriaga et al. [572013 Mexico Caucasian 51 41 (80.4%) 10 (19.6%) 0 (0.0%) 48 41 (85.4%) 2 (4.2%) 5 (10.4%) 0.0001 
El Naggar et al. [162013 Egypt African 30 12 (40.0%) 12 (40.0%) 6 (20.0%) 15 9 (60.0%) 1 (6.7%) 0 (0.0%) 0.868 
Mustapic et al. [582012 Croatia Caucasian 196 138 (70.4%) 55 (28.1%) 3 (15.0%) 456 336 (73.7%) 108 (23.7%) 12 (2.6%) 0.355 
Perez-Luque et al. [302012 Mexico Caucasian 95 72 (75.8%) 23 (24.2%) 0 (0.0%) 87 82 (94.3%) 5 (5.7%) 0 (0.0%) 0.783 
Wang et al. [592012 China Asian 100 74 (74.0%) 15 (15.0%) 11 (11.0%) 113 100 (88.5%) 12 (10.6%) 1 (0.9%) 0.359 
Elsaid et al. [602012 Egypt African 69 10 (14.5%) 55 (79.7%) 4 (5.8%) 106 11 (10.4%) 94 (88.7%) 1 (0.9%) 0.0001 
Liu et al. [322011 China Asian 112 67 (59.8%) 32 (28.6%) 13 (11.6%) 50 45 (90.0%) 5 (10.0%) 0 (0.0%) 0.710 
Guzman-Flore et al. [612011 Mexico Caucasian 259 225 (86.9%) 31 (12.0%) 3 (1.2%) 645 573 (88.8%) 69 (10.7%) 3 (0.5%) 0.556 
Mukhopadhyaya et al. [622010 India Asian 40 35 (87.5%) 3 (7.5%) 2 (5.0%) 40 37 (92.5%) 3 (7.5%) 0 (0.0%) 0.805 
Boraska et al. [632010 Britain Caucasian 1454 938 (64.5%) 477 (32.8%) 39 (2.7%) 2504 1633 (65.2%) 774 (30.9%) 97 (3.9%) 0.659 
Bouhaha et al. [642010 Tunis African 195 141 (72.3%) 51 (26.2%) 3 (1.5%) 299 204 (68.2%) 89 (29.8%) 6 (2.0%) 0.297 
Liu et al. [132008 China Asian 245 222 (90.6%) 21 (8.6%) 2(0.8%) 122 109 (89.3%) 13 (10.7%) 0 (0.0%) 0.534 
Lindholm et al. [652008 Scandinavia Caucasian 2927 1908 (65.2%) 906 (31.0%) 113(3.9%) 205 133 (64.9%) 66 (32.2%) 6 (2.9%) 0.520 
Wang et al. [662008 China Asian 181 157 (86.7%) 23 (12.7%) 1 (0.6%) 82 67 (81.7%) 15 (18.3%) 0 (0.0%) 0.362 
Kim et al. [342006 Korea Asian 198 174 (87.9%) 24 (12.1%) 0 (0.0%) 169 141 (83.4%) 28 (16.6%) 0 (0.0%) 0.240 
Willer et al. [672006 Finland Caucasian 761 568 (74.6%) 184 (24.1%) 9 (1.2%) 617 469 (76.0%) 134 (21.7%) 14 (2.3%) 0.235 
Santos et al. [682006 Chile Caucasian 30 27 (90.0%) 3 (10.0%) 0 (0.0%) 53 45 (84.9%) 8 (15.1%) 0 (0.0%) 0.552 
Zeggini et al. [122005 Britain Caucasian 776 484 (62.4%) 260 (33.5%) 32 (4.1%) 1213 779 (64.2%) 391 (32.2%) 43 (3.5%) 0.480 
Tsiavou et al. [692004 Greece Caucasian 32 29 (90.6%) 3 (9.4%) 0 (0.0%) 39 32 (82.1%) 7 (17.9%) 0 (0.0%) 0.538 
Zouari et al. [702004 Tunis African 280 196 (70.0%) 64 (22.9%) 20 (7.1%) 274 170 (62.0%) 93 (33.9%) 11 (4.0%) 0.698 
Shiau et al. [142003 China Asian 257 218 (84.8%) 35 (13.6%) 4 (1.6%) 187 168 (89.8%) 16 (8.6%) 3 (1.6%) 0.0021 
Li et al. [712003 Sweden Caucasian 488 333 (68.24%) 141 (28.9%) 14 (2.9%) 284 189 (66.5%) 83 (29.2%) 12 (4.2%) 0.456 
Heijmans et al. [722002 Netherlands Caucasian 79 51 (64.6%) 22 (27.8%) 6 (7.6%) 577 378 (65.5%) 189 (32.8%) 10 (1.7%) 0.0121 
Furuta et al. [732002 Japan Asian 132 129 (97.7%) 3 (2.3%) 0 (0.0%) 142 139 (97.9%) 3(2.1%) 0(0.0%) 0.899 
Rasmussen et al. [742000 Danish Caucasian 243 154 (63.4%) 79 (32.5%) 10 (4.1%) 325 214 (65.8%) 99 (30.5%) 12 (3.7%) 0.896 
Kamizono et al. [752000 Japan Asian 213 209 (98.1%) 4 (1.9%) 0 (0.0%) 259 249 (96.1%) 10 (3.9%) 0 (0.0%) 0.751 
Pandey et al. [761999 Belgium Caucasian 214 144 (67.3%) 61 (28.5%) 9 (4.2%) 200 145 (72.5%) 53 (26.5%) 2 (1.0%) 0.233 
Hamann et al. [771995 America Caucasian 138 108 (78.3%) 27 (19.6%) 3 (2.2%) 57 46 (80.7%) 10 (17.5%) 1 (1.8%) 0.604 
Kung et al. [782010 China Asian 23 0 (0.0%) 23 (100.0%) 0 (0.0%) 25 0 (0.0%) 25 (100.0%) 0 (0.0%) 0.0001 
Ko et al. [792003 China Asian 339 284 (83.8%) 50 (14.7%) 5(1.5%) 202 171 (84.7%) 31 (15.3%) 0 (0.0%) 0.238 
Morris et al. [802003 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 
Sobti et al. [812012 India Asian 113 5 (4.4%) 100 (88.5%) 8(7.1%) 158 26 (16.5%) 116 (73.4%) 16 (10.1%) 0.0001 
TNF-α -238G/A    Total GG (%) GA (%) AA (%) Total GG (%) GA (%) AA (%)   
Rasmussen et al. [822000 Danish Caucasian 236 205 (86.9%) 31 (13.1%) 0 (0.0%) 309 272 (88.0%) 35 (11.3%) 2 (0.6%) 0.459 
Kim et al. [342007 Korea Asian 198 177 (89.4%) 21 (10.6%) 0 (0.0%) 169 152 (89.9%) 17 (10.1%) 0 (0.0%) 0.491 
Sesti et al. [502015 Britain Caucasian 695 624 (89.8%) 66 (9.5%) 5 (0.7%) 169 147 (87.0%) 22 (13.0%) 0 (0.0%) 0.365 
Santos et al. [682006 Chile Caucasian 30 28 (93.3%) 2 (6.7%) 0 (0.0%) 53 46 (86.8%) 7 (13.2%) 0 (0.0%) 0.607 
Li et al. [712003 Sweden Caucasian 488 460 (94.3%) 27 (9.5%) 1 (0.2%) 284 265 (93.3%) 18 (6.3%) 1 (0.4%) 0.581 
Dhamodharan et al. [532015 India Asian 133 100 (75.2%) 29 (21.8%) 4 (3.0%) 106 81 (76.4%) 23 (21.7%) 2 (1.9%) 0.806 
Patel et al. [152018 India Asian 320 292 (91.3%) 27 (8.4%) 1 (0.3%) 295 257 (87.1%) 37 (12.5%) 1 (0.3%) 0.785 
Fathy et al. [442018 Kuwaiti Caucasian 117 115 (98.3%) 2 (1.7%) 0 (0.0%) 42 41 (97.6%) 1 (2.4%) 0 (0.0%) 0.938 
Boraska et al. [632010 Britain Caucasian 1504 1331 (88.5%) 170 (11.3%) 3 (0.2%) 2518 2224 (88.3%) 288 (11.4%) 6 (0.2%) 0.296 
Zeggini et al. [122005 Britain Caucasian 560 470 (83.9%) 87 (15.5%) 3 (0.5%) 341 303 (88.9%) 37 (10.9%) 1 (0.3%) 0.908 
Jamil et al. [472017 India Asian 98 85 (86.7%) 12 (12.2%) 1 (1.0%) 102 87 (85.3%) 13 (12.7%) 2 (2.0%) 0.094 
Shiau et al. [142003 China Asian 257 218 (84.8%) 35 (13.6%) 4 (1.6%) 187 168 (89.8%) 16 (8.6%) 3 (1.6%) 0.0021 
Guzman-Flore et al. [612011 Mexico Caucasian 259 220 (84.9%) 31 (12.0%) 8 (3.1%) 645 571 (88.5%) 71 (11.0%) 3 (0.5%) 0.622 
Mukhopadhyaya et al. [832010 India Asian 40 35 (87.5%) 3 (7.5%) 2 (5.0%) 40 37 (92.5%) 3 (7.5%) 0 (0.0%) 0.805 
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. [152018 India Asian 388 351(90.5%) 34 (8.8%) 3 (0.8%) 493 449 (91.1%) 42 (8.5%) 2 (0.4%) 0.348 
Umapathy et al. [432018 India Asian 538 302 (56.1%) 142 (26.4%) 94 (17.5%) 218 167 (76.6%) 32 (14.7%) 19 (8.7%) 0.0001 
Hemmed et al. [292018 India Asian 862 528 (61.3%) 283 (32.8%) 51 (5.9%) 464 356 (76.7%) 96 (20.7%) 12 (2.6%) 0.080 
Fathy et al. [442018 Kuwaiti Caucasian 117 86 (73.5%) 28 (23.9%) 3 (2.6%) 42 41 (97.6%) 0 (0.0%) 1 (2.4%) 0.0001 
Rodrigues et al. [452017 Brazil Caucasian 102 78 (76.5%) 23 (22.5%) 1 (1.0%) 62 47 (75.8%) 15 (24.2%) 0 (0.0%) 0.279 
Mortazavi et al. [462017 Iran Caucasian 174 24 (13.8%) 101 (58.0%) 49 (28.2%) 185 68 (36.8%) 76 (41.1%) 41 (22.2%) 0.0291 
Jamil et al. [472017 India Asian 100 88 (88.0%) 10 (10.0%) 2 (2.0%) 100 87 (87.0%) 12 (12.0%) 1 (1.0%) 0.433 
Doody et al. [482017 India Asian 198 178 (89.9%) 18 (9.1%) 2 (1.0%) 204 189 (92.6%) 13 (6.4%) 2 (1.0%) 0.0041 
Churnosov et al. [492017 Russia Caucasian 236 176 (74.6%) 53 (22.5%) 7 (3.0%) 303 242 (79.9%) 55 (18.2%) 6 (2.0%) 0.180 
Sesti et al. [502015 Britain Caucasian 695 535 (73.7%) 176 (24.2%) 15 (2.1%) 170 129 (75.9%) 38 (22.4%) 3 (1.8%) 0.917 
Golshani et al. [172015 Iran Caucasian 1038 737 (71.0%) 269 (25.9%) 32 (3.1%) 1023 871 (85.1%) 142 (13.9%) 10 (1.0%) 0.124 
Dabhi et al. [512015 India Asian 214 185 (86.5%) 27 (12.6%) 2 (0.9%) 235 191 (81.3%) 44 (18.7%) 0 (0.0%) 0.885 
Ghodsian et al. [522015 Malaysia Asian 88 73 (83.0%) 14 (15.9%) 1 (1.1%) 232 202 (87.1%) 29 (12.5%) 1 (0.4%) 0.970 
Dhamodharan et al. [532015 India Asian 409 218 (53.3%) 117 (28.6%) 74 (18.1%) 106 77 (72.6%) 14 (13.2%) 15 (14.2%) 0.0001 
Sikka et al. [542014 India Asian 462 405 (87.7%) 55 (11.9%) 2 (0.4%) 203 176 (86.7%) 27 (13.3%) 0 (0.0%) 0.310 
Sharma et al. [552014 India Asian 51 45 (88.2%) 6 (11.8%) 0 (0.0%) 51 50 (98.0%) 1 (2.0%) 0 (0.0%) 0.944 
Saxena et al. [562013 India Asian 213 173 (81.2%) 33 (15.5%) 7 (3.3%) 140 111 (79.3%) 25 (17.9%) 4 (2.9%) 0.095 
Garcia-Elorriaga et al. [572013 Mexico Caucasian 51 41 (80.4%) 10 (19.6%) 0 (0.0%) 48 41 (85.4%) 2 (4.2%) 5 (10.4%) 0.0001 
El Naggar et al. [162013 Egypt African 30 12 (40.0%) 12 (40.0%) 6 (20.0%) 15 9 (60.0%) 1 (6.7%) 0 (0.0%) 0.868 
Mustapic et al. [582012 Croatia Caucasian 196 138 (70.4%) 55 (28.1%) 3 (15.0%) 456 336 (73.7%) 108 (23.7%) 12 (2.6%) 0.355 
Perez-Luque et al. [302012 Mexico Caucasian 95 72 (75.8%) 23 (24.2%) 0 (0.0%) 87 82 (94.3%) 5 (5.7%) 0 (0.0%) 0.783 
Wang et al. [592012 China Asian 100 74 (74.0%) 15 (15.0%) 11 (11.0%) 113 100 (88.5%) 12 (10.6%) 1 (0.9%) 0.359 
Elsaid et al. [602012 Egypt African 69 10 (14.5%) 55 (79.7%) 4 (5.8%) 106 11 (10.4%) 94 (88.7%) 1 (0.9%) 0.0001 
Liu et al. [322011 China Asian 112 67 (59.8%) 32 (28.6%) 13 (11.6%) 50 45 (90.0%) 5 (10.0%) 0 (0.0%) 0.710 
Guzman-Flore et al. [612011 Mexico Caucasian 259 225 (86.9%) 31 (12.0%) 3 (1.2%) 645 573 (88.8%) 69 (10.7%) 3 (0.5%) 0.556 
Mukhopadhyaya et al. [622010 India Asian 40 35 (87.5%) 3 (7.5%) 2 (5.0%) 40 37 (92.5%) 3 (7.5%) 0 (0.0%) 0.805 
Boraska et al. [632010 Britain Caucasian 1454 938 (64.5%) 477 (32.8%) 39 (2.7%) 2504 1633 (65.2%) 774 (30.9%) 97 (3.9%) 0.659 
Bouhaha et al. [642010 Tunis African 195 141 (72.3%) 51 (26.2%) 3 (1.5%) 299 204 (68.2%) 89 (29.8%) 6 (2.0%) 0.297 
Liu et al. [132008 China Asian 245 222 (90.6%) 21 (8.6%) 2(0.8%) 122 109 (89.3%) 13 (10.7%) 0 (0.0%) 0.534 
Lindholm et al. [652008 Scandinavia Caucasian 2927 1908 (65.2%) 906 (31.0%) 113(3.9%) 205 133 (64.9%) 66 (32.2%) 6 (2.9%) 0.520 
Wang et al. [662008 China Asian 181 157 (86.7%) 23 (12.7%) 1 (0.6%) 82 67 (81.7%) 15 (18.3%) 0 (0.0%) 0.362 
Kim et al. [342006 Korea Asian 198 174 (87.9%) 24 (12.1%) 0 (0.0%) 169 141 (83.4%) 28 (16.6%) 0 (0.0%) 0.240 
Willer et al. [672006 Finland Caucasian 761 568 (74.6%) 184 (24.1%) 9 (1.2%) 617 469 (76.0%) 134 (21.7%) 14 (2.3%) 0.235 
Santos et al. [682006 Chile Caucasian 30 27 (90.0%) 3 (10.0%) 0 (0.0%) 53 45 (84.9%) 8 (15.1%) 0 (0.0%) 0.552 
Zeggini et al. [122005 Britain Caucasian 776 484 (62.4%) 260 (33.5%) 32 (4.1%) 1213 779 (64.2%) 391 (32.2%) 43 (3.5%) 0.480 
Tsiavou et al. [692004 Greece Caucasian 32 29 (90.6%) 3 (9.4%) 0 (0.0%) 39 32 (82.1%) 7 (17.9%) 0 (0.0%) 0.538 
Zouari et al. [702004 Tunis African 280 196 (70.0%) 64 (22.9%) 20 (7.1%) 274 170 (62.0%) 93 (33.9%) 11 (4.0%) 0.698 
Shiau et al. [142003 China Asian 257 218 (84.8%) 35 (13.6%) 4 (1.6%) 187 168 (89.8%) 16 (8.6%) 3 (1.6%) 0.0021 
Li et al. [712003 Sweden Caucasian 488 333 (68.24%) 141 (28.9%) 14 (2.9%) 284 189 (66.5%) 83 (29.2%) 12 (4.2%) 0.456 
Heijmans et al. [722002 Netherlands Caucasian 79 51 (64.6%) 22 (27.8%) 6 (7.6%) 577 378 (65.5%) 189 (32.8%) 10 (1.7%) 0.0121 
Furuta et al. [732002 Japan Asian 132 129 (97.7%) 3 (2.3%) 0 (0.0%) 142 139 (97.9%) 3(2.1%) 0(0.0%) 0.899 
Rasmussen et al. [742000 Danish Caucasian 243 154 (63.4%) 79 (32.5%) 10 (4.1%) 325 214 (65.8%) 99 (30.5%) 12 (3.7%) 0.896 
Kamizono et al. [752000 Japan Asian 213 209 (98.1%) 4 (1.9%) 0 (0.0%) 259 249 (96.1%) 10 (3.9%) 0 (0.0%) 0.751 
Pandey et al. [761999 Belgium Caucasian 214 144 (67.3%) 61 (28.5%) 9 (4.2%) 200 145 (72.5%) 53 (26.5%) 2 (1.0%) 0.233 
Hamann et al. [771995 America Caucasian 138 108 (78.3%) 27 (19.6%) 3 (2.2%) 57 46 (80.7%) 10 (17.5%) 1 (1.8%) 0.604 
Kung et al. [782010 China Asian 23 0 (0.0%) 23 (100.0%) 0 (0.0%) 25 0 (0.0%) 25 (100.0%) 0 (0.0%) 0.0001 
Ko et al. [792003 China Asian 339 284 (83.8%) 50 (14.7%) 5(1.5%) 202 171 (84.7%) 31 (15.3%) 0 (0.0%) 0.238 
Morris et al. [802003 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 
Sobti et al. [812012 India Asian 113 5 (4.4%) 100 (88.5%) 8(7.1%) 158 26 (16.5%) 116 (73.4%) 16 (10.1%) 0.0001 
TNF-α -238G/A    Total GG (%) GA (%) AA (%) Total GG (%) GA (%) AA (%)   
Rasmussen et al. [822000 Danish Caucasian 236 205 (86.9%) 31 (13.1%) 0 (0.0%) 309 272 (88.0%) 35 (11.3%) 2 (0.6%) 0.459 
Kim et al. [342007 Korea Asian 198 177 (89.4%) 21 (10.6%) 0 (0.0%) 169 152 (89.9%) 17 (10.1%) 0 (0.0%) 0.491 
Sesti et al. [502015 Britain Caucasian 695 624 (89.8%) 66 (9.5%) 5 (0.7%) 169 147 (87.0%) 22 (13.0%) 0 (0.0%) 0.365 
Santos et al. [682006 Chile Caucasian 30 28 (93.3%) 2 (6.7%) 0 (0.0%) 53 46 (86.8%) 7 (13.2%) 0 (0.0%) 0.607 
Li et al. [712003 Sweden Caucasian 488 460 (94.3%) 27 (9.5%) 1 (0.2%) 284 265 (93.3%) 18 (6.3%) 1 (0.4%) 0.581 
Dhamodharan et al. [532015 India Asian 133 100 (75.2%) 29 (21.8%) 4 (3.0%) 106 81 (76.4%) 23 (21.7%) 2 (1.9%) 0.806 
Patel et al. [152018 India Asian 320 292 (91.3%) 27 (8.4%) 1 (0.3%) 295 257 (87.1%) 37 (12.5%) 1 (0.3%) 0.785 
Fathy et al. [442018 Kuwaiti Caucasian 117 115 (98.3%) 2 (1.7%) 0 (0.0%) 42 41 (97.6%) 1 (2.4%) 0 (0.0%) 0.938 
Boraska et al. [632010 Britain Caucasian 1504 1331 (88.5%) 170 (11.3%) 3 (0.2%) 2518 2224 (88.3%) 288 (11.4%) 6 (0.2%) 0.296 
Zeggini et al. [122005 Britain Caucasian 560 470 (83.9%) 87 (15.5%) 3 (0.5%) 341 303 (88.9%) 37 (10.9%) 1 (0.3%) 0.908 
Jamil et al. [472017 India Asian 98 85 (86.7%) 12 (12.2%) 1 (1.0%) 102 87 (85.3%) 13 (12.7%) 2 (2.0%) 0.094 
Shiau et al. [142003 China Asian 257 218 (84.8%) 35 (13.6%) 4 (1.6%) 187 168 (89.8%) 16 (8.6%) 3 (1.6%) 0.0021 
Guzman-Flore et al. [612011 Mexico Caucasian 259 220 (84.9%) 31 (12.0%) 8 (3.1%) 645 571 (88.5%) 71 (11.0%) 3 (0.5%) 0.622 
Mukhopadhyaya et al. [832010 India Asian 40 35 (87.5%) 3 (7.5%) 2 (5.0%) 40 37 (92.5%) 3 (7.5%) 0 (0.0%) 0.805 
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.

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

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

Each square is proportional to the study-specific weight.

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

Each square is proportional to the study-specific weight.

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

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

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

Each square is proportional to the study-specific weight.

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 
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 
*

P<0.05.

P<0.01.

P<0.001.

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).

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

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

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

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

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).

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

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

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

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).

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

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

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).

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

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.

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.

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

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These authors are co-corresponding authors

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