Abstract

Alcohol consumption has been established to be a major factor in the development and progress of cancer. Genetic polymorphisms of alcohol-metabolism genes result in differences between individuals in exposure to acetaldehyde, leading to possible carcinogenic effects. Arg47His (rs1229984 G > A) in ADH1B have been frequently studied for its potential effect on carcinogenesis. However, the findings are as yet inconclusive. To gain a more precise estimate of this potential association, we conducted a meta-analysis including 66 studies from 64 articles with 31999 cases and 50964 controls. The pooled results indicated that ADH1B Arg47His polymorphism is significantly associated with the decreased risk of overall cancer (homozygous model, odds ratio (OR) = 0.62, 95% confidence interval (CI) = 0.49–0.77; heterozygous model, OR = 0.71, 95% CI = 0.60–0.84; recessive model, OR = 0.83, 95% CI = 0.76–0.91; dominant model, OR = 0.62, 95% CI = 0.53–0.72; and allele comparison, OR = 0.82, 95% CI = 0.75–0.89). Stratified analysis by cancer type and ethnicity showed that a decreased risk was associated with esophageal cancer and head and neck cancer amongst Asians. In conclusion, our meta-analysis suggested that ADH1B Arg47His polymorphism was significantly associated with decreased overall cancer risk. These findings need further validation in large multicenter investigations.

Introduction

Cancer is a major public health problem worldwide. According to GLOBOCAN worldwide estimates, an estimated 14.1 million new cancer cases and 8.2 million cancer-related deaths occurred in 2012 [1]. In addition, the incidence of cancer is predicted to reach 25 million worldwide by 2032 [2]. This growing cancer burden is expected as populations expand and age. Meanwhile, certain lifestyles, such as alcohol consumption, are likely to further boost the burden [1–3].

Alcohol consumption is the third-largest risk factor for global health burden [4]. Approximately 3.3 million deaths, almost 5.9% of total deaths worldwide in 2012, were attributable to alcohol consumption [5]. As early as 2002, approximately 3.6% of all cancers and 3.5% of all cancer deaths were reported due to alcohol consumption [3]. It is well established that alcohol is first catalytically oxidized to acetaldehyde, mainly by alcohol dehydrogenases (ADH), and then to harmless acetate by aldehyde dehydrogenases (ALDH) [6,7]. Acetaldehyde may stimulate carcinogenesis by disrupting DNA synthesis and repair, inhibiting DNA methylation, and by interacting with retinoid metabolism [8,9]. Genetic polymorphisms of alcohol-metabolism genes result in differences between individuals in exposure to acetaldehyde, leading to possible carcinogenic effects [10]. Amongst them, Arg47His (rs1229984 G > A) in ADH1B have been frequently studied for its potential effect on the carcinogenesis. Compared with the Arg/Arg individuals, the His/His individuals have a 40-fold higher enzyme activity oxidized alcohol to toxic acetaldehyde [7].

Epidemiologic studies have extensively explored the association between ADH1B Arg47His polymorphism and cancer risk. However, the findings are as yet inconclusive. Several meta-analyses published before 2016 associated this polymorphism only with esophageal, head and neck, gastric, colorectal, and upper aerodigestive tract cancer [11–16]. However, no meta-analyses have ever investigated the association between ADH1B Arg47His polymorphism and overall cancer risk, including other types of cancer. In addition, several more studies with larger sample size were published since 2016 [17–24]. Therefore, we performed an updated meta-analysis including the most recent and relevant studies to clarify the association between ADH1B Arg47His polymorphism and the overall cancer risk, involving 66 studies with 31999 cases and 50964 controls [17–80].

Methods

Identification of relevant studies

A systematic literature search was conducted in the following electronic databases: Medline and Embase database up to 1 July 2018. The following search terms were used: ‘ADH1B or ADH2’ or ‘polymorphism or variant’ or ‘cancer or carcinoma or tumor’. In addition, reviews and references lists of eligible studies were manually searched to identify additional relevant articles.

Inclusion and exclusion criteria

The eligible articles must meet the following criteria. The inclusion criteria were as follows: (i) studies evaluating the association between ADH1B Arg47His polymorphism and overall cancer risk; (ii) case–control studies; (iii) studies with sufficient information to calculate the odds ratio (OR) and its 95% confidence interval (CI). The major exclusion criteria were as follows: (i) no control group; (ii) duplicate publication; (iii) reviews, meta-analyses, conference reports, or editorial articles; (iv) no available data.

Data extraction

Investigators independently extracted the relevant information from all eligible studies according to the inclusion and exclusion criteria listed above. A final consensus was achieved regarding each selected study. The following information was extracted from each study: first author’s surname, publication year, country, ethnicity, cancer type, control source, genotyping method, number of cases and controls with different genotypes, and Hardy–Weinberg equilibrium (HWE) of genotypes in controls.

Statistical analysis

The strength of the association between ADH1B Arg47His polymorphism and overall cancer risk was evaluated by calculating ORs and 95% CIs. The pooled ORs were also estimated using homozygous model (His/His vs. Arg/Arg), heterozygous model (Arg/His vs. Arg/Arg), recessive model [His/His vs. (Arg/His + Arg/Arg)], dominant model [(Arg/His + His/His) vs. Arg/Arg], as well as allele comparison (His vs. Arg). Stratification analyses were further conducted according to ethnicity, cancer type, control source, and HWE. Chi square-based Q-test was applied to assess between-study heterogeneity. If no heterogeneity (P>0.10) was found, the fixed-effect model (Mantel–Haenszel method) was performed [81]. Otherwise, the random-effect model (DerSimonian and Laird method) was used [82]. Sensitivity analysis was carried out to assess the stability of the results, and potential publication bias was assessed with Begg’s funnel plot and Egger’s linear regression test [83]. All the statistical analyses were calculated using STATA software (version 11.0, Stata Corporation, College Station, TX). A P-value less than 0.05 was considered statistically significant.

Results

Study characteristics

As listed in Figure 1, a total of 432 potential records were initially identified from Medline and Embase using the search terms listed above. After a screening of the titles and abstracts, 146 publications were subjected for further evaluation. Of them, 59 articles were excluded for irrelevant information, 13 for only meta-analysis, 12 for no sufficient data, and 1 was excluded for duplicate study. In addition, three studies were manually identified from reviews and references lists of the eligible studies. Ultimately, 64 articles investigating the association between ADH1B Arg47His polymorphism and cancer risk were included in the final meta-analysis [17–80].

Flow chart of studies included in our meta-analysis

Figure 1
Flow chart of studies included in our meta-analysis
Figure 1
Flow chart of studies included in our meta-analysis

Overall, 66 studies from 64 articles with 31999 cases and 50964 controls were finally included in our meta-analysis. As shown in Table 1, there were 48 studies conducted amongst Asians, 15 amongst Caucasians, and 3 amongst mixed ethnic group. With respect to cancer type, 23 studies addressed esophageal cancer, 16 head and neck cancer, 10 colorectal cancer, 6 gastric cancer, 4 hepatocellular, 3 upper aerodigestive tract cancer, 2 pancreatic and 1 bladder and breast cancer. Regarding control source, 34 studies were hospital-based and 32 studies were population-based. With respect to HWE, 52 met HWE, 5 departed from HWE, and 9 had not enough information.

Table 1
Main characteristics of included studies in our meta-analysis
Author Year Country Ethnicity Cancer type Control source Genotyping method Number of cases Number of controls HWE 
       GG GA AA GG GA AA  
Zhong 2016 China Asian Colorectal HB PCR-RFLP 85 125 64 152 172 34 Yes 
Masaoka 2016 Japan Asian Bladder HB TaqMan 38 33 27 265 448 Yes 
Liu 2016 China Asian Hepatocellular HB Affymetrix 48 262 283 236 1229 1748 Yes 
Kagemoto 2016 Japan Asian Esophageal PB Multiplex PCR 31 36 50 60 389 676 Yes 
Chen 2016 China Asian Gastric HB PCR-RFLP 83 117 46 104 125 45 Yes 
Ji 2015 Korea Asian Head and neck HB TaqMan 26 107 127 15 125 190 Yes 
Hidaka 2015 Japan Asian Gastric PB TaqMan 32 173 252 35 168 254 Yes 
Bediaga 2015 Spain Caucasian Head and neck PB TaqMan 78 61 61 203 391 39 1 NA 
Ye 2014 China Asian Esophageal HB PCR-RFLP 224 400 377 150 578 663 Yes 
Tsai 2014 China Asian Head and neck HB TaqMan 47 165 224 25 221 268 No 
Chung 2014 China Asian UADT HB MassARRAY 68 76 108 25 111 125 Yes 
Yuan 2013 China Asian Head and neck PB TaqMan 42 180 170 72 362 455 Yes 
Wu 2013 China Asian Esophageal PB TaqMan 138 309 355 101 410 510 Yes 
Gao 2013 China Asian Esophageal PB TaqMan 252 907 939 199 909 1155 Yes 
Dura 2013 Dutch Caucasian Esophageal PB TaqMan 326 20 406 23 Yes 
Crous-Bou 2013 Spain Caucasian Colorectal PB Illumina 457 324 79 513 360 54 Yes 
Liang 2012 Island Mixed Head and neck PB TaqMan 530 38 593 76 15 No 
Gu 2012 China Asian Esophageal HB MassArray 53 168 158 26 170 182 Yes 
Ferrari 2012 France Caucasian Colorectal PB TaqMan 1129 97 1800 176 Yes 
Duell 2012 Spain Caucasian Gastric PB Illumina 317 45 1133 132 Yes 
Chiang 2012 China Asian Colorectal HB PCR-RFLP 34 62 43 205 297 Yes 
Yin 2011 Japan Asian Colorectal PB PCR-RFLP 25 161 268 71 393 588 Yes 
Wang 2011 China Asian Esophageal HB PCR-CTPP 15 34 33 17 67 78 Yes 
McKay 2011 France Caucasian UADT PB Illumina 6776 4161 4161 7742 9071 907 1 NA 
Marichalar-Mendia 2011 Spain Caucasian Head and neck PB TaqMan 80 71 71 203 391 39 1 NA 
Ji 2011 Korea Asian Head and neck HB TaqMan 30 87 108 15 112 174 Yes 
Hakenewerth 2011 U.S.A. Mixed Head and neck PB Illumina 1192 311 311 1243 791 791 NA 
Wei 2010 U.S.A. Caucasian Head and neck HB PCR-RFLP 1059 51 1075 52 Yes 
Tanaka 2010 Japan Asian Esophageal HB Affymetrix 151 5911 5911 44 7761 776 1 NA 
Soucek 2010 Czech Caucasian Head and neck HB TaqMan 101 21 111 10 Yes 
Mohelnikova- Duchonova 2010 Czech Caucasian Pancreatic PB TaqMan 213 22 242 22 Yes 
Garcia 2010 Brazil Mixed Head and neck HB PCR-RFLP 195 12 213 29 Yes 
Cao 2010 China Asian Gastric PB DHPLC 40 148 194 29 160 193 Yes 
Yang 2009 China Asian Colorectal HB SNPLex 39 181 205 62 319 370 Yes 
Oze 2009 Japan Asian UADT HB TaqMan 71 222 292 53 408 709 Yes 
Kawase 2009 Japan Asian Breast HB TaqMan 25 162 265 47 322 539 Yes 
Kanda 2009 Japan Asian Pancreatic HB TaqMan 55 101 74 551 975 Yes 
Ding 2009 China Asian Esophageal PB DHPLC 75 108 19 96 106 Yes 
Cui 2009 Japan Asian Esophageal PB Illumina 194 363 510 151 986 1626 Yes 
Akbari 2009 Iran Asian Esophageal PB MassARRAY 21 232 490 73 471 827 Yes 
Solomon 2008 India Asian Head and neck HB PCR-RFLP 13 56 57 38 54 Yes 
Lee 2008 China Asian Esophageal HB PCR-RFLP 117 149 140 46 275 335 Yes 
Guo 2008 China Asian Esophageal HB PCR-RFLP 17 25 38 24 168 288 Yes 
Gao 2008 China Asian Colorectal PB DHPLC 15 73 102 20 109 93 Yes 
Ding 2008 China Asian Hepatocellular PB PCR-RFLP 21 132 54 26 97 84 Yes 
Zhang 2007 U.S.A. Caucasian Gastric PB TaqMan 261 31 352 48 Yes 
Yin 2007 Japan Asian Colorectal PB PCR-RFLP 40 294 345 37 289 452 Yes 
Yang 2007 China Asian Esophageal PB PCR-CTPP 33 80 78 22 76 100 Yes 
Hiraki 2007 Japan Asian Head and neck HB TaqMan 26 75 138 31 213 471 Yes 
Asakage 2007 Japan Asian Head and neck PB PCR-RFLP 31 223 388 19 28 49 No 
Sakamoto 2006 Japan Asian Hepatocellular HB PCR-CTPP 12 73 124 13 103 159 Yes 
Matsuo 2006 Japan Asian Colorectal HB PCR-CTPP 19 102 136 36 259 473 Yes 
Hashibe 2006 France Caucasian Head and neck HB TaqMan 719 471 471 877 1081 108 1 NA 
Hashibe 2006 France Caucasian Esophageal HB TaqMan 163 41 41 792 951 95 1 NA 
Chen 2006 China Asian Esophageal HB PCR-RFLP 88 117 125 39 240 313 Yes 
Yang 2005 China Asian Esophageal HB PCR-CTPP 85 74 22 168 304 Yes 
Wu 2005 China Asian Esophageal PB PCR-RFLP 39 49 46 16 191 130 No 
Landi 2005 France Caucasian Colorectal HB Millipore 292 54 263 48 Yes 
Risch 2003 Germany Caucasian Head and neck PB PCR-RFLP 227 18 227 24 Yes 
Chao 2003 China Asian Esophageal HB PCR-RFLP 19 41 28 43 55 Yes 
Yokoyama 2002 Japan Asian Esophageal PB PCR-RFLP 51 73 110 31 220 383 Yes 
Boonyaphiphat 2002 Thailand Asian Esophageal HB APLP 15 86 101 28 139 94 No 
Yokoyama 2001 Japan Asian Esophageal PB PCR-RFLP 56 561 561 145 3811 381 1 NA 
Yokoyama 2001 Japan Asian Gastric PB PCR-RFLP 28 101 101 145 3811 381 1 NA 
Takeshita 2000 Japan Asian Hepatocellular PB PCR-RFLP 36 63 43 74 Yes 
Hori 1997 Japan Asian Esophageal HB PCR-RFLP 20 31 40 20 43 Yes 
Author Year Country Ethnicity Cancer type Control source Genotyping method Number of cases Number of controls HWE 
       GG GA AA GG GA AA  
Zhong 2016 China Asian Colorectal HB PCR-RFLP 85 125 64 152 172 34 Yes 
Masaoka 2016 Japan Asian Bladder HB TaqMan 38 33 27 265 448 Yes 
Liu 2016 China Asian Hepatocellular HB Affymetrix 48 262 283 236 1229 1748 Yes 
Kagemoto 2016 Japan Asian Esophageal PB Multiplex PCR 31 36 50 60 389 676 Yes 
Chen 2016 China Asian Gastric HB PCR-RFLP 83 117 46 104 125 45 Yes 
Ji 2015 Korea Asian Head and neck HB TaqMan 26 107 127 15 125 190 Yes 
Hidaka 2015 Japan Asian Gastric PB TaqMan 32 173 252 35 168 254 Yes 
Bediaga 2015 Spain Caucasian Head and neck PB TaqMan 78 61 61 203 391 39 1 NA 
Ye 2014 China Asian Esophageal HB PCR-RFLP 224 400 377 150 578 663 Yes 
Tsai 2014 China Asian Head and neck HB TaqMan 47 165 224 25 221 268 No 
Chung 2014 China Asian UADT HB MassARRAY 68 76 108 25 111 125 Yes 
Yuan 2013 China Asian Head and neck PB TaqMan 42 180 170 72 362 455 Yes 
Wu 2013 China Asian Esophageal PB TaqMan 138 309 355 101 410 510 Yes 
Gao 2013 China Asian Esophageal PB TaqMan 252 907 939 199 909 1155 Yes 
Dura 2013 Dutch Caucasian Esophageal PB TaqMan 326 20 406 23 Yes 
Crous-Bou 2013 Spain Caucasian Colorectal PB Illumina 457 324 79 513 360 54 Yes 
Liang 2012 Island Mixed Head and neck PB TaqMan 530 38 593 76 15 No 
Gu 2012 China Asian Esophageal HB MassArray 53 168 158 26 170 182 Yes 
Ferrari 2012 France Caucasian Colorectal PB TaqMan 1129 97 1800 176 Yes 
Duell 2012 Spain Caucasian Gastric PB Illumina 317 45 1133 132 Yes 
Chiang 2012 China Asian Colorectal HB PCR-RFLP 34 62 43 205 297 Yes 
Yin 2011 Japan Asian Colorectal PB PCR-RFLP 25 161 268 71 393 588 Yes 
Wang 2011 China Asian Esophageal HB PCR-CTPP 15 34 33 17 67 78 Yes 
McKay 2011 France Caucasian UADT PB Illumina 6776 4161 4161 7742 9071 907 1 NA 
Marichalar-Mendia 2011 Spain Caucasian Head and neck PB TaqMan 80 71 71 203 391 39 1 NA 
Ji 2011 Korea Asian Head and neck HB TaqMan 30 87 108 15 112 174 Yes 
Hakenewerth 2011 U.S.A. Mixed Head and neck PB Illumina 1192 311 311 1243 791 791 NA 
Wei 2010 U.S.A. Caucasian Head and neck HB PCR-RFLP 1059 51 1075 52 Yes 
Tanaka 2010 Japan Asian Esophageal HB Affymetrix 151 5911 5911 44 7761 776 1 NA 
Soucek 2010 Czech Caucasian Head and neck HB TaqMan 101 21 111 10 Yes 
Mohelnikova- Duchonova 2010 Czech Caucasian Pancreatic PB TaqMan 213 22 242 22 Yes 
Garcia 2010 Brazil Mixed Head and neck HB PCR-RFLP 195 12 213 29 Yes 
Cao 2010 China Asian Gastric PB DHPLC 40 148 194 29 160 193 Yes 
Yang 2009 China Asian Colorectal HB SNPLex 39 181 205 62 319 370 Yes 
Oze 2009 Japan Asian UADT HB TaqMan 71 222 292 53 408 709 Yes 
Kawase 2009 Japan Asian Breast HB TaqMan 25 162 265 47 322 539 Yes 
Kanda 2009 Japan Asian Pancreatic HB TaqMan 55 101 74 551 975 Yes 
Ding 2009 China Asian Esophageal PB DHPLC 75 108 19 96 106 Yes 
Cui 2009 Japan Asian Esophageal PB Illumina 194 363 510 151 986 1626 Yes 
Akbari 2009 Iran Asian Esophageal PB MassARRAY 21 232 490 73 471 827 Yes 
Solomon 2008 India Asian Head and neck HB PCR-RFLP 13 56 57 38 54 Yes 
Lee 2008 China Asian Esophageal HB PCR-RFLP 117 149 140 46 275 335 Yes 
Guo 2008 China Asian Esophageal HB PCR-RFLP 17 25 38 24 168 288 Yes 
Gao 2008 China Asian Colorectal PB DHPLC 15 73 102 20 109 93 Yes 
Ding 2008 China Asian Hepatocellular PB PCR-RFLP 21 132 54 26 97 84 Yes 
Zhang 2007 U.S.A. Caucasian Gastric PB TaqMan 261 31 352 48 Yes 
Yin 2007 Japan Asian Colorectal PB PCR-RFLP 40 294 345 37 289 452 Yes 
Yang 2007 China Asian Esophageal PB PCR-CTPP 33 80 78 22 76 100 Yes 
Hiraki 2007 Japan Asian Head and neck HB TaqMan 26 75 138 31 213 471 Yes 
Asakage 2007 Japan Asian Head and neck PB PCR-RFLP 31 223 388 19 28 49 No 
Sakamoto 2006 Japan Asian Hepatocellular HB PCR-CTPP 12 73 124 13 103 159 Yes 
Matsuo 2006 Japan Asian Colorectal HB PCR-CTPP 19 102 136 36 259 473 Yes 
Hashibe 2006 France Caucasian Head and neck HB TaqMan 719 471 471 877 1081 108 1 NA 
Hashibe 2006 France Caucasian Esophageal HB TaqMan 163 41 41 792 951 95 1 NA 
Chen 2006 China Asian Esophageal HB PCR-RFLP 88 117 125 39 240 313 Yes 
Yang 2005 China Asian Esophageal HB PCR-CTPP 85 74 22 168 304 Yes 
Wu 2005 China Asian Esophageal PB PCR-RFLP 39 49 46 16 191 130 No 
Landi 2005 France Caucasian Colorectal HB Millipore 292 54 263 48 Yes 
Risch 2003 Germany Caucasian Head and neck PB PCR-RFLP 227 18 227 24 Yes 
Chao 2003 China Asian Esophageal HB PCR-RFLP 19 41 28 43 55 Yes 
Yokoyama 2002 Japan Asian Esophageal PB PCR-RFLP 51 73 110 31 220 383 Yes 
Boonyaphiphat 2002 Thailand Asian Esophageal HB APLP 15 86 101 28 139 94 No 
Yokoyama 2001 Japan Asian Esophageal PB PCR-RFLP 56 561 561 145 3811 381 1 NA 
Yokoyama 2001 Japan Asian Gastric PB PCR-RFLP 28 101 101 145 3811 381 1 NA 
Takeshita 2000 Japan Asian Hepatocellular PB PCR-RFLP 36 63 43 74 Yes 
Hori 1997 Japan Asian Esophageal HB PCR-RFLP 20 31 40 20 43 Yes 

Abbreviations: APLP, amplified product length polymorphism; DHPLC, denaturing high-performance liquid chromatography; HB, hospital-based, NA, not applicable; PB, population-based; PCR-CTPP, PCR with the confronting-two-pair primer; PCR-RFLP, PCR-restriction fragment length polymorphism; UADT, upper aerodigestive tract.

1

The number of GA + AA.

Meta-analysis results

The main results for the association between ADH1B Arg47His polymorphism and cancer risk are shown in Table 2 and Figure 2. We found that ADH1B Arg47His polymorphism significantly associated with the decreased risk of overall cancer under all the five genetic models: homozygous model, OR = 0.62, 95% CI = 0.49–0.77; heterozygous model, OR = 0.71, 95% CI = 0.60–0.84; recessive model, OR = 0.83, 95% CI = 0.76–0.91; dominant model, OR = 0.62, 95% CI = 0.53–0.72; and allele comparison, OR = 0.82, 95% CI = 0.75–0.89.

Forest plot of the association between ADH1B Arg47His polymorphism and the overall cancer risk under the allele comparison model

Figure 2
Forest plot of the association between ADH1B Arg47His polymorphism and the overall cancer risk under the allele comparison model
Figure 2
Forest plot of the association between ADH1B Arg47His polymorphism and the overall cancer risk under the allele comparison model
Table 2
Meta-analysis of the association between the ADH1B Arg47His and cancer risk
Variables Sample size Case/control Homozygous Heterozygous Recessive Dominant Allele comparison 
  His/His vs. Arg/Arg Arg/His vs. Arg/Arg His/His vs. (Arg/His + Arg/Arg) (Arg/His + His/His) vs. Arg/Arg His vs. Arg 
  OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet 
Total 31999/50964 0.62 (0.49–0.77) <0.001 0.71 (0.60–0.84) <0.001 0.83 (0.76–0.91) <0.001 0.62 (0.53–0.72) <0.001 0.82 (0.75–0.89) <0.001 
Ethnicity            
  Asian 17057/31885 0.60 (0.48–0.76) <0.001 0.66 (0.53–0.81) <0.001 0.82 (0.75–0.91) <0.001 0.58 (0.47–0.72) <0.001 0.80 (0.72–0.88) <0.001 
  Caucasian 12970/16908 1.45 (1.05–2.02) 0.727 1.01 (0.90–1.13) 0.570 1.45 (1.05–2.00) 0.712 0.81 (0.64–1.03) <0.001 1.06 (0.96–1.17) 0.569 
  Mixed 1972/2171 0.35 (0.13–0.93) 0.743 0.53 (0.37–0.75) 0.606 0.37 (0.14–0.98) 0.751 0.46 (0.36–0.60) 0.651 0.50 (0.36–0.68) 0.545 
Cancer type            
  Colorectal 4821/7697 1.19 (0.82–1.72) <0.001 0.99 (0.88–1.11) 0.857 1.19 (0.91–1.55) <0.001 1.03 (0.88–1.21) 0.099 1.05 (0.90–1.23) <0.001 
  Hepatocellular 1111/3820 0.84 (0.64–1.10) 0.541 1.16 (0.84–1.61) 0.328 0.81 (0.61–1.08) 0.041 0.98 (0.76–1.28) 0.452 0.88 (0.76–1.02) 0.270 
  Esophageal 9117/15930 0.39 (0.28–0.55) <0.001 0.47 (0.34–0.64) <0.001 0.72 (0.62–0.83) <0.001 0.41 (0.31–0.54) <0.001 0.67 (0.57–0.78) <0.001 
  Gastric 1770/2930 1.02 (0.76–1.36) 0.637 1.03 (0.84–1.27) 0.356 1.02 (0.86–1.22) 0.973 0.77 (0.48–1.23) <0.001 1.03 (0.92–1.16) 0.629 
  Head and neck 6646/7901 0.55 (0.31–0.97) <0.001 0.77 (0.52–1.12) <0.001 0.78 (0.66–0.93) 0.092 0.64 (0.47–0.87) <0.001 0.80 (0.66–0.96) <0.001 
  UADT 7613/9173 0.31 (0.23–0.42) 0.921 0.33 (0.21–0.53) 0.161 0.70 (0.57–0.86) 0.260 0.39 (0.26–0.58) 0.010 0.62 (0.54–0.71) 0.924 
  Pancreatic 395/1865 1.65 (0.62–4.38) 0.345 1.29 (0.76–2.20) 0.430 1.09 (0.78–1.52) 0.513 1.26 (0.75–2.13) 0.358 1.12 (0.86–1.45) 0.774 
Control source            
  HB 10560/20932 0.53 (0.40–0.71) <0.001 0.64 (0.51–0.81) <0.001 0.79 (0.69–0.90) <0.001 0.56 (0.44–0.72) <0.001 0.77 (0.68–0.87) <0.001 
  PB 21439/30032 0.75 (0.52–1.07) <0.001 0.79 (0.61–1.02) <0.001 0.89 (0.78–1.02) <0.001 0.68 (0.54–0.85) <0.001 0.87 (0.76–0.99) <0.001 
HWE            
  YES 20769/37678 0.60 (0.48–0.76) <0.001 0.71 (0.60–0.84) <0.001 0.81 (0.74–0.89) <0.001 0.67 (0.56–0.81) <0.001 0.81 (0.74–0.88) <0.001 
  NO 1987/1892 0.75 (0.22–2.65) <0.001 0.66 (0.23–1.90) <0.001 1.08 (0.76–1.55) 0.006 0.72 (0.25–2.09) <0.001 0.92 (0.59–1.45) <0.001 
Variables Sample size Case/control Homozygous Heterozygous Recessive Dominant Allele comparison 
  His/His vs. Arg/Arg Arg/His vs. Arg/Arg His/His vs. (Arg/His + Arg/Arg) (Arg/His + His/His) vs. Arg/Arg His vs. Arg 
  OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet 
Total 31999/50964 0.62 (0.49–0.77) <0.001 0.71 (0.60–0.84) <0.001 0.83 (0.76–0.91) <0.001 0.62 (0.53–0.72) <0.001 0.82 (0.75–0.89) <0.001 
Ethnicity            
  Asian 17057/31885 0.60 (0.48–0.76) <0.001 0.66 (0.53–0.81) <0.001 0.82 (0.75–0.91) <0.001 0.58 (0.47–0.72) <0.001 0.80 (0.72–0.88) <0.001 
  Caucasian 12970/16908 1.45 (1.05–2.02) 0.727 1.01 (0.90–1.13) 0.570 1.45 (1.05–2.00) 0.712 0.81 (0.64–1.03) <0.001 1.06 (0.96–1.17) 0.569 
  Mixed 1972/2171 0.35 (0.13–0.93) 0.743 0.53 (0.37–0.75) 0.606 0.37 (0.14–0.98) 0.751 0.46 (0.36–0.60) 0.651 0.50 (0.36–0.68) 0.545 
Cancer type            
  Colorectal 4821/7697 1.19 (0.82–1.72) <0.001 0.99 (0.88–1.11) 0.857 1.19 (0.91–1.55) <0.001 1.03 (0.88–1.21) 0.099 1.05 (0.90–1.23) <0.001 
  Hepatocellular 1111/3820 0.84 (0.64–1.10) 0.541 1.16 (0.84–1.61) 0.328 0.81 (0.61–1.08) 0.041 0.98 (0.76–1.28) 0.452 0.88 (0.76–1.02) 0.270 
  Esophageal 9117/15930 0.39 (0.28–0.55) <0.001 0.47 (0.34–0.64) <0.001 0.72 (0.62–0.83) <0.001 0.41 (0.31–0.54) <0.001 0.67 (0.57–0.78) <0.001 
  Gastric 1770/2930 1.02 (0.76–1.36) 0.637 1.03 (0.84–1.27) 0.356 1.02 (0.86–1.22) 0.973 0.77 (0.48–1.23) <0.001 1.03 (0.92–1.16) 0.629 
  Head and neck 6646/7901 0.55 (0.31–0.97) <0.001 0.77 (0.52–1.12) <0.001 0.78 (0.66–0.93) 0.092 0.64 (0.47–0.87) <0.001 0.80 (0.66–0.96) <0.001 
  UADT 7613/9173 0.31 (0.23–0.42) 0.921 0.33 (0.21–0.53) 0.161 0.70 (0.57–0.86) 0.260 0.39 (0.26–0.58) 0.010 0.62 (0.54–0.71) 0.924 
  Pancreatic 395/1865 1.65 (0.62–4.38) 0.345 1.29 (0.76–2.20) 0.430 1.09 (0.78–1.52) 0.513 1.26 (0.75–2.13) 0.358 1.12 (0.86–1.45) 0.774 
Control source            
  HB 10560/20932 0.53 (0.40–0.71) <0.001 0.64 (0.51–0.81) <0.001 0.79 (0.69–0.90) <0.001 0.56 (0.44–0.72) <0.001 0.77 (0.68–0.87) <0.001 
  PB 21439/30032 0.75 (0.52–1.07) <0.001 0.79 (0.61–1.02) <0.001 0.89 (0.78–1.02) <0.001 0.68 (0.54–0.85) <0.001 0.87 (0.76–0.99) <0.001 
HWE            
  YES 20769/37678 0.60 (0.48–0.76) <0.001 0.71 (0.60–0.84) <0.001 0.81 (0.74–0.89) <0.001 0.67 (0.56–0.81) <0.001 0.81 (0.74–0.88) <0.001 
  NO 1987/1892 0.75 (0.22–2.65) <0.001 0.66 (0.23–1.90) <0.001 1.08 (0.76–1.55) 0.006 0.72 (0.25–2.09) <0.001 0.92 (0.59–1.45) <0.001 

Abbreviations: HB, hospital-based; PB, population-based; UADT, upper aerodigestive tract.

Values in bold indicate P<0.05.

Regarding the stratified analysis by ethnicity, a decreased cancer risk was also detected amongst Asians under all the genetic models: homozygous model, OR = 0.60, 95% CI = 0.48–0.76; heterozygous model, OR = 0.66, 95% CI = 0.53–0.81; recessive model, OR = 0.82, 95% CI = 0.75–0.91; dominant model, OR = 0.58, 95% CI = 0.47–0.72; and allele comparison, OR = 0.80, 95% CI = 0.72–0.88, and amongst mixed ethnic group: homozygous model, OR = 0.35, 95% CI = 0.13–0.93; heterozygous model, OR = 0.53, 95% CI = 0.37–0.75; recessive model, OR = 0.37, 95% CI = 0.14–0.98; dominant model, OR = 0.46, 95% CI = 0.36–0.60; and allele comparison, OR = 0.50, 95% CI = 0.36–0.68. However, an increased risk of cancer was detected amongst Caucasians under homozygous model (OR = 1.45, 95% CI = 1.05–2.02) and recessive model (OR = 1.45, 95% CI = 1.05–2.00).

Regarding the stratified analysis by cancer type, the ADH1B Arg47His polymorphism significantly decreased the risk of esophageal cancer: homozygous model, OR = 0.39, 95% CI = 0.28–0.55; heterozygous model, OR = 0.47, 95% CI = 0.34–0.66; recessive model, OR = 0.72, 95% CI = 0.62–0.83; dominant model, OR = 0.41, 95% CI = 0.31–0.54; and allele comparison, OR = 0.67, 95% CI = 0.57–0.78; upper aerodigestive tract cancer: homozygous model, OR = 0.31, 95% CI = 0.23–0.42; heterozygous model, OR = 0.33, 95% CI = 0.21–0.53; recessive model, OR = 0.70, 95% CI = 0.57–0.86; dominant model, OR = 0.39, 95% CI = 0.26–0.58; and allele comparison, OR = 0.62, 95% CI = 0.54–0.71; and head and neck cancer: homozygous model, OR = 0.55, 95% CI = 0.31–0.97; recessive model, OR = 0.78, 95% CI = 0.66–0.93; dominant model, OR = 0.64, 95% CI = 0.47–0.87; and allele comparison, OR = 0.80, 95% CI = 0.66–0.96.

Regarding the stratified analysis by control source and HWE, a decreased cancer risk was detected in hospital-based studies: homozygous model, OR = 0.53, 95% CI = 0.40–0.71; heterozygous model, OR = 0.64, 95% CI = 0.51–0.81; recessive model, OR = 0.79, 95% CI = 0.69–0.90; dominant model, OR = 0.56, 95% CI = 0.44–0.72; and allele comparison, OR = 0.77, 95% CI = 0.68–0.87; population-based studies: dominant model, OR = 0.68, 95% CI = 0.54–0.85; and allele comparison, OR = 0.87, 95% CI = 0.76–0.99; and also the studies in agreement with HWE: homozygous model, OR = 0.60, 95% CI = 0.48–0.76; heterozygous model, OR = 0.71, 95% CI = 0.60–0.84; recessive model, OR = 0.81, 95% CI = 0.74–0.89; dominant model, OR = 0.67, 95% CI = 0.56–0.81; and allele comparison, OR = 0.81, 95% CI = 0.74–0.88.

Sensitivity analysis and publication bias

Substantial heterogeneities were found under all the five genetic models (P<0.001). Therefore, the random-effect model was adopted to assess the ORs and 95% CIs. Furthermore, the leave-one-out sensitivity analyses indicated that no single study could change the pooled ORs. The results of the Begg’s funnel plot and Egger’s linear regression test showed no evidence of publication bias (homozygous model, P=0.227; heterozygous model, P=0.697; recessive model, P=0.663; dominant model, P=0.599; and allele comparison P=0.342, see Figure 3).

Funnel plot analysis to detect publication bias for ADH1B Arg47His polymorphism under the allele comparison model

Figure 3
Funnel plot analysis to detect publication bias for ADH1B Arg47His polymorphism under the allele comparison model
Figure 3
Funnel plot analysis to detect publication bias for ADH1B Arg47His polymorphism under the allele comparison model

Discussion

Alcohol consumption has been established to be a major factor in the development and progress of cancer [13]. Alcohol is first catalytically oxidized to acetaldehyde, mainly by ADH, and then to harmless acetate by ALDH [6,7]. Acetaldehyde, a Group I human carcinogen classified by the International Agency for Research on Cancer (IARC), may stimulate carcinogenesis by disrupting DNA synthesis and repair [8,9,84]. Therefore, to reduce the risk of cancer, it is important to modulate exposure levels to acetaldehyde in the liver. ADH1B gene, also known as ADH2, is located on chromosome 4q22 and is the locus responsible for the majority of activities of ADH function [25]. Arg47His (rs1229984 G > A) in ADH1B led to a single amino acid substitution of arginine (Arg) for histidine (His) at codon 47. Compared with the Arg/Arg individuals, the His/His individuals have a 40-fold higher enzyme activity oxidized alcohol to toxic acetaldehyde, thereby inducing tumorigenesis [25,85].

To the best of our knowledge, this is the first meta-analysis investigating the association between ADH1B Arg47His polymorphism and the overall cancer risk. A total of 66 studies from 64 articles with 31999 cases and 50964 controls were included, and the large sample size provided adequate power to detect this association. Overall, ADH1B Arg47His polymorphism was associated with a decreased risk of overall cancer under all the five genetic models. Stratified analysis by ethnicity revealed that ADH1B Arg47His polymorphism reduced cancer risk amongst Asians and mixed ethnicity group but increased risk amongst Caucasians. Stratified analysis by cancer type revealed that ADH1B Arg47His polymorphism reduced risk in esophageal cancer, upper aerodigestive tract cancer, and head and neck cancer, while no effect was found on colorectal, hepatocellular, gastric and pancreatic cancer. In stratified analysis by control source and HWE, a decreased cancer risk was detected in hospital-based studies, population-based studies, and also the studies in agreement with HWE.

There were several meta-analyses focussed on ADH1B Arg47His polymorphism and only one particular type of cancer risk, such as esophageal, head and neck, gastric and colorectal cancer [11–15]. For esophageal cancer, Mao et al. [11] found that the 47His allele was significantly associated with the reduced risk of this cancer when compared with the 47Arg allele. And these findings were replicated in our meta-analysis. For head and neck cancer, the 47His allele was also found to be associated with decreased risk of head and neck cancer amongst Asians only under the dominant model [12]. However, similar results were found under the other three models in our analysis, which may be attributed to a larger sample size including eight more studies. Interestingly, Chen et al. [15] found that ADH1B Arg47His polymorphism was associated with decreased risk of colorectal cancer supported by four studies. However, this decreased risk was not present in the current one including six more studies. It was noteworthy that we found that ADH1B Arg47His polymorphism was associated with decreased cancer risk amongst Asians while increased cancer risk amongst Caucasians. In Caucasian population, the A allele was found to associate with an increased risk of colorectal cancer [32]. The opposite findings may result from the difference of ethnicity with the 47His allele occupied more than 90% amongst Asians but fewer than 20% amongst Caucasians [7]. Furthermore, we re-analyzed the ethnic groups of Asian and Caucasian people. Amongst Asians, a decreased cancer risk was also detected in esophageal cancer and head and neck cancer. While in Caucasians, we did not repeat the results, but an increased cancer risk was detected in colorectal cancer (homozygous model, OR = 1.55, 95% CI = 1.10–2.20 and recessive model, OR = 1.55, 95% CI = 1.11–2.18).

Several limitations in the current meta-analysis should be addressed. First, a number of studies adopted in our meta-analysis had relatively small sample size for each cancer type, like bladder and breast cancer. Second, because of the absence of original data, our analyses were based on unadjusted estimates of ORs without adjustment for other confounding factors. Third, there were substantial heterogeneities in all the five genetic models, hence the random-effect model was adopted and might present unstable results. Overall, due to these limitations, the findings in the current meta-analysis should be interpreted with caution.

In conclusion, our meta-analysis suggested that ADH1B Arg47His polymorphism was significantly associated with the decreased overall cancer risk, especially for esophageal cancer and head and neck cancer amongst Asians.

Funding

This work was supported by the Natural Science Foundation of Hunan Province, China [grant number 2017JJ2156].

Competing interests

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

Author contribution

All authors contributed significantly to this work. B.T. designed the research study. B.T. and N.N. performed the research study, analyzed the data, and wrote the paper. Both the authors reviewed the paper.

Abbreviations

     
  • ADH

    alcohol dehydrogenase

  •  
  • ALDH

    aldehyde dehydrogenase

  •  
  • CI

    confidence interval

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • OR

    odds ratio

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