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Associations between three common single nucleotide polymorphisms (rs266729, rs2241766, and rs1501299) of ADIPOQ and cardiovascular disease: a meta-analysis

Contributed equally
Lipids in Health and Disease201817:126

https://doi.org/10.1186/s12944-018-0767-8

  • Received: 22 February 2018
  • Accepted: 4 May 2018
  • Published:

Abstract

Background

Inconsistencies have existed in research findings on the association between cardiovascular disease (CVD) and single nucleotide polymorphisms (SNPs) of ADIPOQ, triggering this up-to-date meta-analysis.

Methods

We searched for relevant studies in PubMed, EMBASE, Cochrane Library, CNKI, CBM, VIP, and WanFang databases up to 1st July 2017. We included 19,106 cases and 31,629 controls from 65 published articles in this meta-analysis. STATA 12.0 software was used for all statistical analyses.

Results

Our results showed that rs266729 polymorphism was associated with the increased risk of CVD in dominant model or in heterozygote model; rs2241766 polymorphism was associated with the increased risk of CVD in the genetic models (allelic, dominant, recessive, heterozygote, and homozygote). In subgroup analysis, significant associations were found in different subgroups with the three SNPs. Meta-regression and subgroup analysis showed that heterogeneity might be explained by other confounding factors. Sensitivity analysis revealed that the results of our meta-analysis were stable and robust. In addition, the results of trial sequential analysis showed that evidences of our results are sufficient to reach concrete conclusions.

Conclusions

In conclusion, our meta-analysis found significant increased CVD risk is associated with rs266729 and rs2241766, but not associated with rs1501299.

Keywords

  • ADIPOQ
  • Single nucleotide polymorphisms
  • Cardiovascular disease
  • Association
  • Meta-analysis

Background

Cardiovascular disease (CVD) is the primary cause of death worldwide, leading to 32% of all deaths worldwide in 2013 [1]. Epidemiological and biological evidences demonstrate that multiple environmental and genetic factors are implicated in CVD, although the etiology of CVD has not been fully elucidated [25]. Identifying CVD-relative risk factors is critical in control of the development and progress of CVD.

Adiponectin is involved in CVD: low levels of adiponectin (hypoadipoectinemia) positively correlate with the risk of CVD, and higher levels of adiponectin protect against this disease [611]. Adiponectin is synthesized and secreted by adipose tissue [12], osteoblasts [13], skeletal muscle [14], and cardiomyocytes [15]. This protein, as one of the most abundant adipocytokines in blood, has anti-atherogenic, cardioprotective, anti-inflammatory, and antithrombotic properties [1620].

Adiponectin is encoded by ADIPOQ which is located in chromosome 3q27 [21], and adiponectin levels are influenced by single-nucleotide polymorphisms (SNPs) in ADIPOQ [22]. SNPs in ADIPOQ have been found to be associated with CVD [23, 24], diabetes [25, 26], stroke [27, 28], myocardial infarction [29, 30], cancer [31, 32], kidney disease [33, 34], and even gynecological conditions [35, 36]. Previous studies have shown the association between SNPs in ADIPOQ (rs3774261, rs1063537, rs2082940, rs2241766, rs266729, and rs1501299) and CVD/subclinical CVD [30, 37, 38]. The three common SNPs of ADIPOQ (rs266729, rs2241766, and rs1501299) were most widely studied. However, findings from previous studies on the three SNPs in relation to CVD risk are inconsistent and inconclusive.

For rs266729 (− 11,377 C/G) in ADIPOQ, Du et al. [39] and Zhang et al. [40] found that the SNP is associated with CVD risk; Stenvinkel et al. [41] revealed that rs266729 is associated with the decreased risk of CVD; Zhang et al. [40], Cheong et al. [27], and Chiodini et al. [29] found that there is no significant association between rs266729 and CVD. For rs2241766 (+ 45 T/G), Pischon et al. [42] and Jung et al. [43] identified no association between rs2241766 and the risk of coronary artery disease (CAD) in patients with type 2 diabetic mellitus (T2DM); Du et al. [39], Oliveira et al. [44], and Mofarrah et al. [45] found that there is a significant association between rs2241766 polymorphism and CAD risk; Chang et al. [46] revealed that rs2241766 is associated with the decreased risk of CVD. Moreover, for rs1501299 (+ 276 G / T), Bacci et al. [47] and Esteghamati et al. [48] revealed that rs1501299 is associated with the decreased risk of CAD; Mohammadzadeh et al. [38], however, reported that there is an association between rs1501299 and CAD risk; Foucan et al. [49] found that there is no significant association between rs1501299 and CAD in patients with T2DM. Thus, those results are inconsistent.

Meta-analysis performed by Zhang et al. in 2012 revealed that associations between the SNPs (rs2241766, rs1501299, and rs266729) in ADIPOQ and CVD were significant but weak [50]. Since that data, several more studies have emerged to investigate the association between SNPs in ADIPOQ and susceptibility to CVD [37, 38, 45]. In this study, we further collected references and updated meta-analysis of association between SNPs (rs2241766, rs1501299, and rs266729) in ADIPOQ and CVD in order to get a more precise and reliable assessment of the association.

Methods

Search strategy

We performed an extensive literature search in PubMed, EMBASE, Cochrane Library, CNKI, CBM, VIP, and WanFang databases for published articles on the association between ADIPOQ polymorphisms and CVD risk up to July 1st, 2017. The literature search was done without any language or population restrictions imposed. During the literature search, we used various combinations of keywords, such as ‘coronary heart disease (CHD)’ or ‘coronary artery disease’ or ‘cardiovascular disease’ or ‘ischemic heart disease’ or ‘angina’ or ‘myocardial infarction (MI)’ or ‘stroke’ or ‘atherosclerosis’ or ‘arteriosclerosis’ or ‘coronary stenosis’ combined with ‘ADIPOQ’ or ‘APM1’ or ‘ACDC’ or ‘adiponectin gene’ and ‘polymorphisms’ or ‘variants’ or ‘variations’. Joseph Sam Kanu and Shuang Qiu independently performed the literature search for potential articles included in this meta-analysis. All articles retrieved were first organized in reference manager software (Endnote 6).

Inclusion and exclusion criteria

A study included in this meta-analysis was based on the following criteria: 1) the study has sufficient data to allow association between CVD risk and ADIPOQ SNP to be assessed; 2) the study included original data (independence among studies); 3) evaluation of the ADIPOQ polymorphisms (rs266729, rs2241766, and rs1501299) and CVD risk; 4) the language of the study was English or Chinese; and 5) observed genotype frequencies in controls must be consistent with Hardy–Weinberg equilibrium (HWE). We excluded a study based on: 1) the study contained overlapping data; 2) the study with missing information (particularly genotype distributions), after corresponding author, who was contacted by us with email, failed to provide the required information; and 3) genome scans investigating linkages with no detailed genotype distributions between cases and controls. Where there was a disagreement on the selection of a study, the issue was resolved by discussion or consensus with the third investigator (Ri Li). For articles with missing data, we emailed the corresponding authors for the required data.

Assessment of study quality

We used the NATURE-published guidelines proposed by the NCI-NHGRI Working Group on Replication in Association Studies for assessing the quality of each study included in this meta-analysis [51]. These guidelines have a checklist of 53 conditions for authors, journal editors, and referees to interpret data and results of genome-wide or other genotype–phenotype association studies clearly and unambiguously. We used the first set of 34 conditions in assessing the quality of each study. We allocated a score of 1 point for each condition a study met, and no point (0 score) if the condition or requirement is lacking. Each study was given a total Quality Score – the sum of all points each study obtained. Study quality assessment was independently carried out by Joseph Sam Kanu and Shuang Qiu.

Data extraction

Joseph Sam Kanu and Shuang Qiu extracted data from each study independently. We summarized the information extracted from each article in Table 1. The characteristics of articles included first author, year of publication, country in which the study was done, study population (ethnicity), numbers of cases and controls, genotyping method, SNPs investigated, genotype frequency of cases and controls, and outcome (Table 1; Additional file 1: Tables S1, S2, and S3).
Table 1

Characteristics of included studies

Study

ID

Year

Country

Population

Outcome

Sample size

Genotyping

Method

Quality

Score

Cases

Controls

Lacquemant Swiss70

1

2004

Switzerland

European

CAD

107

181

Other

9

Lacquemant French70

2

2004

France

European

CAD

55

134

Other

9

Bacci47

3

2004

Italy

European

CAD

142

234

Other

8

Ohashi71

4

2004

Japan

East Asian

CAD

383

368

TaqMan

7

Stenvinkel41

5

2004

America

European

CVD

63

141

Other

6

Filippi72

6

2005

Italy

European

CAD

580

466

Other

9

Ru Y73

7

2005

China

East Asian

CHD

131

136

TaqMan

6

Qi174

8

2005

America

European

CVD

239

640

TaqMan

10

Qi224

9

2006

America

European

CVD

285

704

TaqMan

10

Wang JN75

10

2006

China

East Asian

CHD

120

131

PCR-RFLP

7

Hegener 176

11

2006

America

European

MI

341

341

TaqMan

11

Hegener 276

12

2006

America

European

Stroke

259

259

TaqMan

11

Jung43

13

2006

Korea

East Asian

CAD

88

 68

TaqMan

8

Gable 177

14

2007

UK

European

CVD

266

2,727

PCR-RFLP

11

Gable 277

15

2007

UK

European

MI

530

564

PCR-RFLP

12

Pischon42

16

2007

America

European

CHD

1,036

2,071

TaqMan

11

Lu F78

17

2007

China

East Asian

CHD

135

131

PCR-RFLP

7

Hoefle79

18

2007

Austria

European

CHD

277

125

TaqMan

7

Yamada80

19

2008

Japan

East Asian

ACI

313

971

Other

9

Oguri81

20

2009

Japan

East Asian

MI

773

1,114

Other

10

Chang46

21

2009

China

East Asian

CAD

600

718

PCR-RFLP

9

Zhang XL82

22

2009

China

East Asian

CHD

205

135

PCR-RFLP

8

Zhong C83

23

2010

China

East Asian

CAD

198

237

TaqMan

10

Foucan 184

24

2010

France

African

CAD

57

159

TaqMan

7

Xu L85

25

2010

China

East Asian

CHD

153

73

PCR-RFLP

8

Chiodini29

26

2010

Italy

European

MI

503

503

TaqMan

10

Persson86

27

2010

Sweden

European

MI

244

244

TaqMan

9

Chen XL87

28

2010

China

East Asian

Stroke

357

345

TaqMan

8

Luo SX88

29

2010

China

East Asian

CHD

221

100

PCR-RFLP

8

Caterina89

30

2011

Italy

European

MI

1,864

1,864

Other

13

Al-Daghri90

31

2011

Saudi A.

West Asian

CAD

123

295

PCR-RFLP

8

Prior91

32

2011

UK

European

CHD

 85

298

PCR-RFLP

7

Leu92

33

2011

China

East Asian

Stroke

 80

3,330

Other

10

Liu F28

34

2011

China

East Asian

Stroke

302

338

PCR-RFLP

9

Rodriguez93

35

2011

Spain

European

CVD

119

555

TaqMan

9

Chen F94

36

2011

China

East Asian

CHD

93

102

PCR-RFLP

8

Maimaitiyiming95

37

2011

China

East Asian

CHD

196

124

PCR-RFLP

8

Hu HH96

38

2011

China

East Asian

CHD

150

152

Other

8

Zhang YM97

39

2011

China

East Asian

CHD

149

167

PCR-RFLP

8

Zhou NN98

40

2011

China

East Asian

CAD

358

65

PCR-RFLP

8

Sabouri99

41

2011

UK

European

CAD

329

106

PCR-RFLP

8

Boumaiza100

42

2011

Tunisia

African

CAD

212

104

PCR-RFLP

10

Chengang101

43

2012

China

East Asian

CAD

267

250

PCR-RFLP

8

Esteghamati48

44

2012

Iran

West Asia

CAD

114

127

PCR-RFLP

10

Gui102

45

2012

China

East Asian

CAD

438

443

TaqMan

10

Katakami23

46

2012

Japan

East Asian

CVD

213

2,424

Other

12

Oliveira44

47

2012

Brazil

European

CAD

450

153

Other

10

Shi KL103

48

2012

China

East Asian

CAD

396

292

Other

8

Zhang HF104

49

2012

China

East Asian

ATHERO

394

118

PCR-RFLP

8

Nannan105

50

2012

China

East Asian

CAD

213

467

Other

10

Antonopoulos106

51

2013

Greece

European

CAD/MI

462

132

Other

11

Rizk107

52

2013

Qatar

West Asian

ACS/MI

142

122

Other

12

Wang CH108

53

2013

China

East Asian

CAD

101

116

TaqMan

9

Wu/276109

54

2013

China

East Asian

CHD

188

200

PCR-RFLP

9

Cheung110

55

2014

China

East Asian

CHD

184

2,012

Other

11

Foucan 249

56

2014

France

African

CAD

54

146

TaqMan

8

Shaker30

57

2014

Egypt

African

MI

 60

60

PCR-RFLP

8

Li Yang111

58

2014

China

East Asian

CAD

234

365

PCR-RFLP

8

Alehagen112

59

2015

Sweden

European

ATHERO

105

371

TaqMan

6

Torres113

60

2015

Portugal

European

ATHERO

43

263

Other

7

Zhang M114

61

2015

China

East Asian

CAD

563

412

Other

11

Liu Yun115

62

2015

China

East Asian

CAD

200

200

PCR-RFLP

7

Du SX39

63

2016

China

East Asian

CAD

493

304

PCR-RFLP

9

Mofarrah45

64

2016

Iran

West Asia

CAD

152

72

Other

8

Mohammadzadeh38

65

2016

Iran

West Asia

CAD

100

100

PCR-RFLP

9

Suo SZ116

66

2016

China

East Asian

CAD

128

130

PCR-RFLP

9

Zhang Min40

67

2016

China

East Asian

MI

306

412

Other

9

Li SS117

68

2017

China

East Asian

Stroke

385

418

PCR-RFLP

10

ACI atherothrombotic cerebral infarction, ACS Acute Coronary Syndrome, ATHERO Atherosclerosis, CAD coronary artery disease, CHD coronary heart disease, CVD cardiovascular disease, IHD ischemic heart disease, MI myocardial infarction

The 70-117 references are listed in Additional file 4

Statistical analysis

HWE was evaluated for each study using Goodness of fit Chi-square test in control groups, and P < 0.05 was considered as a significant deviation from HWE. The strength of association between the three ADIPOQ polymorphisms and CVD susceptibility was assessed using odds ratios (OR) and 95% confidence intervals (95% CI). The associations were measured based on five different genetic models: allelic model (rs266729: G versus C; rs2241766: G versus T; rs1501299: T versus G), dominant model (rs266729: GG + GC versus CC; rs2241766: GG + GT versus TT; rs1501299: TT + TG versus GG), recessive model (rs266729: GG versus GC + CC; rs2241766: GG versus GT + TT; rs1501299: TT versus TG + GG), heterozygote model (rs266729: GC versus CC; rs2241766: GT versus TT; rs1501299: TG versus GG), and homozygote model (rs266729: GG versus CC; rs2241766: GG versus TT; rs1501299: TT versus GG). Heterogeneity were evaluated by the Chi-square test based Q-statistic, and quantified by I 2 -statistic [52]. If there was no substantial statistical heterogeneity (P > 0.10, I 2  ≤ 50%), data were pooled by fixed-effect model (Mantel and Haenszel methods); otherwise, the heterogeneity was evaluated by random-effect model (DerSimonian and Laird methods). Meta-regression analysis was performed to detect main sources of heterogeneity. In addition, subgroup analyses were stratified by population (European, East Asian, West Asian, and African), genotyping method (PCR-RFLP, TaqMan, and Others), sample size (< 1000 and ≥ 1000), and quality score (< 10 and ≥ 10). Sensitivity analysis was performed to examine stability of our results by omitting each study in each turn. Publication bias was measured by funnel plots [53], and quantified by the Begg’s and Egger’s tests [54] (P < 0.05 considered statistically significant publication bias). STATA 12.0 software (StataCorp. 2011. Stata Statistical Software: Release 12. College Station, TX: StataCorp LP) was used for all statistical analyses. P-value < 0.05 was considered statistically significant, except where other-wise specified. A separate analysis was performed for each SNPs included in the meta-analysis.

Trial sequential analysis (TSA)

Traditional meta-analysis may result in type I and type II errors owing to dispersed data and repeated significance testing [55, 56]. To reduce the risk of type I error, TSA was used to estimate required information size (RIS) and confirm statistical reliability with an adjusted threshold for statistical significance [57]. In present meta-analysis, we used trial sequential analysis software (TSA, version 0.9; Copenhagen Trial Unit, Copenhagen, Denmark, 2011) by setting an overall type I error of 5%, a statistical test power of 80%, and a relative risk reduction of 20% [58, 59].

If the Z-curve crosses trial sequential monitoring boundary or RIS has been reached, a sufficient level of evidence has been reached and further studies are unneeded; otherwise, additional studies are needed to reach a sufficient conclusion.

Results

Overall results

This meta-analysis included 68 studies from 65 articles after literature search and critical screening, as described in methods (Fig. 1). Meta-analysis of the rs266729 (− 11,377 C > G), rs2241766 (+ 45 T > G), and rs1501299 (+ 276 G > T) variants included 29, 40, and 44 studies, respectively. We summarize the characteristics of each primary study in Table 1. Detailed characteristics of those studies are further presented in Additional file 1: Tables S1, S2, and S3, respectively. Overall, this meta-analysis included a total of 50,735 subjects (19,106 cases and 31,629 controls).
Fig. 1
Fig. 1

Flow diagram showing details of results of databases searched exclusion and inclusion of studies/articles in the meta-analysis. CNKI: Chinese National Knowledge Infrastructure; CBM: Chinese BioMedical Literature on Disc

Meta-analysis results

Association between rs266729 (− 11,377 C > G) polymorphism and CVD

The meta-analysis of the association between rs266729 (− 11,377 C > G) polymorphism and CVD included 29 studies with 29,021 subjects (10,506 cases and 18,515 controls). Significant heterogeneity among studies was observed (P h  < 0.10 or I 2 ≥ 50%). Thus, we selected random-effect model, and found that rs266729 polymorphism was associated with the increased risk of CVD in dominant model (GG + GC VS CC: OR = 1.129, 95% CI = 1.028–1.239, P = 0.011) and in heterozygote model (GC VS CC: OR = 1.141, 95% CI = 1.041–1.250, P = 0.005) (Table 2, Fig. 2).
Table 2

Overall and subgroup meta-analysis of the association between ADIPOQ rs266729, −11,377 C > G polymorphisms and CVD

Categories

n

Sample size

G VS C

GG + GC VS CC

GG VS GC + CC

GC VS CC

GG VS CC

Case/Control

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

Overall

29

10,506/18,515

1.079 (1.000, 1.165)

0.051

65.8/0.000

1.129 (1.028, 1.239)

0.011

64.5/0.000

0.989 (0.838, 1.168)

0.898

48.5/0.002

1.141 (1.041, 1.250)

0.005

59.9/0.000

1.037 (0.867, 1.239)

0.692

53.4/0.000

Population

 European

17

6,355/11,666

1.022 (0.948, 1.102)

0.564

37.6/0.060

1.071 (0.974, 1.178)

0.158

40.8/0.041

0.879 (0.714, 1.082)

0.224

40.0/0.045

1.102 (0.995, 1.220)

0.062

43.5/0.029

0.908 (0.739, 1.116)

0.360

36.9/0.064

 East Asian

12

4,151/6,849

1.154 (1.000, 1.332)

0.051

76.8/0.000

1.198 (1.006, 1.427)

0.043

75.7/0.000

1.149 (0.887, 1.487)

0.293

52.5/0.017

1.184 (1.002, 1.398)

0.048

70.7/0.000

1.231 (0.919, 1.650)

0.164

61.4/0.003

Genotyping

 PCR-RFLP

  8

2,382/4,976

1.186 (0.978, 1.438)

0.083

77.5/0.000

1.276 (1.014, 1.607)

0.038

75.4/0.000

1.162 (0.813, 1.661)

0.411

53.5/0.035

1.282 (1.032, 1.592)

0.025

69.7/0.002

1.285 (0.859, 1.922)

0.223

61.3/0.011

 TaqMan

12

3,910/6,312

1.031 (0.935, 1.137)

0.544

45.3/0.044

1.054 (0.948, 1.173)

0.331

30.7/0.146

0.951 (0.720, 1.256)

0.721

53.5/0.014

1.064 (0.960, 1.180)

0.236

20.6/0.242

0.973 (0.735, 1.288)

0.849

52.2/0.018

 Others

  9

4,214/7,227

1.045 (0.921, 1.186)

0.493

63.6/0.005

1.095 (0.926, 1.296)

0.289

68.7/0.001

0.923 (0.711, 1.197)

0.545

39.6/0.103

1.121 (0.941, 1.336)

0.201

68.7/0.001

0.949 (0.717, 1.255)

0.713

44.9/0.069

Sample size

  < 1000

21

5,048/6,708

1.065 (0.952, 1.192)

0.270

67.9/0.000

1.114 (0.973, 1.276)

0.119

65.9/0.000

0.955 (0.744, 1.228)

0.722

53.4/0.002

1.128 (0.988, 1.287)

0.075

60.9/0.000

0.992 (0.759, 1.298)

0.956

57.6/0.001

  ≥ 1000

  8

5,458/11,807

1.108 (1.004, 1.222)

0.042

63.4/0.008

1.162 (1.026, 1.315)

0.018

64.6/0.006

1.017 (0.835, 1.240)

0.864

38.6/0.122

1.172 (1.035, 1.326)

0.012

61.5/0.011

1.087 (0.877, 1.347)

0.445

45.4/0.077

Quality score

  < 10

16

3,489/5,128

1.152 (1.007, 1.318)

0.040

68.0/0.000

1.211 (1.032, 1.420)

0.019

64.6/0.000

1.147 (0.861, 1.528)

0.348

49.6/0.013

1.207 (1.036, 1.406)

0.016

57.8/0.002

1.215 (0.888, 1.664)

0.224

55.9/0.003

  ≥ 10

13

7,017/13,387

1.019 (0.940, 1.105)

0.646

55.5/0.008

1.062 (0.954, 1.182)

0.271

61.3/0.002

0.883 (0.744, 1.048)

0.155

31.5/0.131

1.089 (0.974, 1.219)

0.135

61.8/0.002

0.915 (0.767, 1.093)

0.327

33.5/0.115

n study numbers, Bold values represent statistically significant findings

Fig. 2
Fig. 2

Forest plots of the association between rs266729 polymorphism and CVD risk. (a) dominant model; (b) heterozygote model

Based on population, genotyping method, sample size, and quality score, we performed subgroup analyses. On the basis of population, rs266729 polymorphism was associated with the increased risk of CVD under dominant model (GG + GC VS CC: OR = 1.198, 95% CI = 1.006–1.427, P = 0.043) and under heterozygote model (GC VS CC: OR = 1.184, 95% CI = 1.002–1.398, P = 0.048) in East Asian. On the basis of genotyping methods, a significant risk association between rs266729 polymorphism and CVD was found when genotyping was performed using PCR-RFLP method under dominant model (GG + GC VS CC: OR = 1.276, 95% CI = 1.014–1.607, P = 0.038) and under heterozygote model (GC VS CC: OR = 1.282, 95% CI = 1.032–1.592, P = 0.025). On the basis of sample size or quality score, we found that rs266729 polymorphism was associated with the increased risk of CVD under allelic, dominant, and heterozygote models (all OR > 1 and P < 0.05), after pooled the ORs by the subgroups of sample size ≥ 1000 or quality score ≤ 10 (Table 2).

Association between rs2241766 (+ 45 T > G) polymorphism and CVD

The meta-analysis of the association between rs2241766 (+ 45 T > G) polymorphism and CVD included 40 studies with 25,548 subjects (10,746 cases and 14,802 controls). Using inverse-variance weighted random effect model (P h  < 0.10 or I 2 ≥ 50%), we found that rs2241766 polymorphism was associated with the increased risk of CVD in the five genetic models (allelic, dominant, recessive, heterozygote, and homozygote) (all OR > 1 and P < 0.05) (Table 3, Fig. 3).
Table 3

Overall and subgroup meta-analysis of the association between ADIPOQ rs2241766, +45 T > G polymorphisms and CVD

Categories

n

Sample size

G VS T

GG + GT VS TT

GG VS GT + TT

GT VS TT

GG VS TT

Case/Control

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

OR (95% CI)

P

I2(%)/Ph

Overall

40

10,746/14,802

1.216 (1.102, 1.343)

< 0.001

72.4/0.000

1.229 (1.103, 1.369)

< 0.001

65.6/0.000

1.286 (1.061, 1.560)

0.011

49.7/0.000

1.172 (1.063, 1.292)

0.001

53.3/0.000

1.361 (1.095, 1.690)

0.005

57.7/0.000

Population

 European

12

4,452/7,255

1.067 (0.918, 1.242)

0.398

60.4/0.003

1.105 (0.937, 1.303)

0.238

58.3/0.006

0.779 (0.576, 1.055)

0.106

0.0/0.663

1.123 (0.956, 1.319)

0.157

53.0/0.015

0.792 (0.584, 1.073)

0.132

0.0/0.585

 East Asian

20

5,305/6,505

1.194 (1.057, 1.348)

0.004

70.5/0.000

1.225 (1.057, 1.420)

0.007

67.3/0.000

1.315 (1.068, 1.618)

0.010

43.1/0.024

1.180 (1.029, 1.353)

0.018

58.1/0.001

1.431 (1.112, 1.842)

0.005

58.6/0.001

 West Asian

  5

660/719

1.550 (1.002, 2.396)

0.049

80.8/0.000

1.392 (0.893, 2.170)

0.145

71.3/0.007

2.715 (1.452, 5.079)

0.002

50.2/0.091

1.099 (0.779, 1.549)

0.591

43.2/0.134

2.767 (1.347, 5.683)

0.006

59.2/0.044

 African

  3

329/323

2.200 (0.890, 5.437)

0.088

75.3/0.017

2.148 (0.952, 4.844)

0.066

65.2/0.056

2.010 (0.251, 16.080)

0.511

50.8/0.154

1.919 (0.998, 3.688)

0.051

45.1/0.162

2.295 (0.250, 21.058)

0.463

55.2/0.135

Genotyping

 PCR-RFLP

20

4,814/6,319

1.242 (1.055, 1.462)

0.009

77.1/0.000

1.279 (1.057, 1.548)

0.012

74.4/0.000

1.335 (1.034, 1.722)

0.027

40.5/0.035

1.221 (1.023, 1.458)

0.027

67.0/0.000

1.442 (1.054, 1.975)

0.022

57.1/0.001

 TaqMan

  7

2,616/3,715

1.087 (0.895, 1.320)

0.400

64.8/0.009

1.118 (0.920, 1.357)

0.262

53.9/0.043

0.872 (0.513, 1.482)

0.614

56.9/0.041

1.123 (0.951, 1.326)

0.172

34.9/0.162

0.896 (0.506, 1.588)

0.708

62.2/0.021

 Other

13

3,316/4,768

1.263 (1.075, 1.485)

0.005

68.7/0.000

1.238 (1.056, 1.452)

0.009

51.4/0.016

1.453 (1.021, 2.066)

0.038

56.7/0.006

1.150 (1.004, 1.317)

0.044

27.6/0.166

1.522 (1.056, 2.193)

0.024

57.7/0.005

Sample size

  < 1000

34

7,651/6,381

1.298 (1.164, 1.448)

< 0.001

66.6/0.000

1.317 (1.163, 1.492)

< 0.001

61.1/0.000

1.512 (1.264, 1.809)

< 0.001

25.5/0.096

1.239 (1.102, 1.393)

< 0.001

51.4/0.000

1.620 (1.324, 1.981)

< 0.001

35.9/0.024

  ≥ 1000

  6

3,095/8,421

0.920 (0.834, 1.015)

0.097

23.9/0.255

0.945 (0.841, 1.062)

0.344

25.5/0.243

0.690 (0.539, 0.885)

0.003

0.0/0.758

0.981 (0.879, 1.094)

0.728

11.3/0.343

0.669 (0.519, 0.862)

0.002

0.0/0.661

Quality score

  < 10

26

5,467/4,951

1.366 (1.176, 1.586)

< 0.001

77.0/0.000

1.404 (1.183, 1.667)

< 0.001

72.8/0.000

1.529 (1.202, 1.944)

0.001

46.0/0.008

1.314 (1.121, 1.539)

0.001

64.4/0.000

1.692 (1.274, 2.248)

< 0.001

58.4/0.000

  ≥ 10

14

5,279/9,851

1.036 (0.944, 1.139)

0.455

37.3/0.079

1.038 (0.955, 1.128)

0.376

0.0/0.575

0.978 (0.719, 1.331)

0.887

49.9/0.017

1.043 (0.956, 1.137)

0.343

0.0/0.818

0.985 (0.725, 1.340)

0.925

47.5/0.025

n study numbers; Bold values represent statistically significant findings

Fig. 3
Fig. 3

Forest plots of the association between rs2241766 polymorphism and CVD risk. (a) allelic model; (b) dominant model; (c) recessive model; (d) heterozygote model; (e) homozygote model

Subgroup analyses were stratified by population, genotyping method, sample size, and quality score. Firstly, on the basis of population, rs2241766 polymorphism was associated with the increased risk of CVD under the five dominant models in East Asian and under allelic, recessive, and homozygote models in West Asian (all OR > 1 and P < 0.05). Secondly, on the basis of genotyping method, the results that genotyping was done by PCR-RFLP or other methods showed that rs2241766 polymorphism was associated with the increased risk of CVD under five genetic models (all OR > 1 and P < 0.05). Thirdly, on the basis of sample size, rs2241766 polymorphism was associated with the increased risk of CVD under the five genetic models in the subgroup of sample size ≤1000 (all OR > 1 and P < 0.05), but was associated with the decreased risk of CVD in the subgroup of sample size ≥1000 under recessive model (GG VS GT + TT: OR = 0.696, 95% CI = 0.539–0.885, P = 0.003) and under homozygote model (GG VS TT: OR = 0.669, 95% CI = 0.519–0.862, P = 0.002). Finally, on the basis of quality score, when we pooled the ORs by the subgroups of quality score ≤ 10, we found that rs2241766 polymorphism was associated with the increased risk of CVD under the five genetic models (all OR > 1 and P < 0.05) (Table 3).

Association between rs1501299 (+ 276 G > T) polymorphism and CVD

The meta-analysis of the association between rs1501299 (+ 276 G > T) polymorphism and CVD included 44 studies with 37,371 subjects (12,852 cases and 24,519 controls). Using the inverse-variance weighted random effect model (P h  < 0.10 or I 2 ≥ 50%), we found that there was no association between rs1501299 polymorphism and CVD in the five genetic models (all P > 0.05) (Table 4).
Table 4

Overall and subgroup meta-analysis of the association between ADIPOQ rs1501299, +276 G > T polymorphism and CVD

Categories

n

Sample size

T VS G

TT + TG VS GG

TT VS TG + GG

TG VS GG

TT VS GG

Case/Control

OR (95%CI)

P

I2(%)/Ph

OR (95%CI)

P

I2(%)/Ph

OR (95%CI)

P

I2(%)/Ph

OR (95%CI)

P

I2(%)/Ph

OR (95%CI)

P

I2(%)/Ph

Overall

44

12,852/24,519

0.956 (0.893, 1.023)

0.189

64.7/0.000

0.967 (0.890, 1.051)

0.431

60.6/0.000

0.899 (0.797, 1.015)

0.086

42.0/0.002

0.987 (0.913, 1.066)

0.737

49.2/0.000

0.886 (0.766, 1.025)

0.104

55.5/0.000

Population

 European

18

7,002/11,337

0.957 (0.901, 1.016)

0.146

16.2/0.260

0.967 (0.896, 1.043)

0.380

16.1/0.262

0.851 (0.717, 1.011)

0.066

35.2/0.070

0.988 (0.909, 1.073)

0.773

21.3/0.201

0.854 (0.722, 1.012)

0.068

30.2/0.110

 East Asian

20

5,107/12,291

0.966 (0.849, 1.098)

0.594

77.5/0.000

0.977 (0.834, 1.145)

0.776

74.4/0.000

0.945 (0.778, 1.149)

0.572

52.2/0.004

0.988 (0.858, 1.138)

0.867

64.0/0.000

0.940 (0.726, 1.217)

0.638

69.0/0.000

 West Asian

  4

479/645

0.973 (0.643, 1.473)

0.897

79.6/0.002

0.960 (0.564, 1.635)

0.880

77.8/0.004

0.999 (0.578, 1.727)

0.997

41.9/0.160

0.952 (0.587, 1.546)

0.843

70.2/0.018

0.986 (0.477, 2.040)

0.970

62.1/0.048

 African

  2

264/246

0.848 (0.629, 1.143)

0.278

11.8/0.287

0.856 (0.583, 1.257)

0.428

0.0/0.490

0.724 (0.415, 1.266)

0.258

7.8/0.298

0.927 (0.614, 1.400)

0.719

0.0/0.725

0.700 (0.374, 1.312)

0.266

14.7/0.279

Genotyping

 PCR-RFLP

14

3,359/5,817

0.970 (0.833, 1.128)

0.688

74.2/0.000

0.997 (0.825, 1.206)

0.978

70.6/0.000

0.881 (0.684, 1.136)

0.329

55.3/0.006

1.051 (0.858, 1.202)

0.861

58.4/0.003

0.901 (0.648, 1.253)

0.535

68.4/0.000

 TaqMan

13

3,666/6,001

0.977 (0.869, 1.099)

0.701

61.3/0.002

0.987 (0.854, 1.140)

0.859

58.0/0.005

0.970 (0.791, 1.189)

0.771

30.1/0.144

1.001 (0.874, 1.146)

0.994

47.8/0.028

0.956 (0.749, 1.221)

0.718

46.8/0.032

 Others

17

5,827/12,701

0.930 (0.841, 1.029)

0.159

59.7/0.001

0.935 (0.827, 1.058)

0.287

55.1/0.003

0.866 (0.715, 1.048)

0.140

40.9/0.041

0.959 (0.852, 1.079)

0.484

46.3/0.019

0.841 (0.678, 1.044)

0.117

49.6/0.011

Sample size

  < 1000

36

8,167/9,201

0.945 (0.868, 1.029)

0.191

64.8/0.000

0.959 (0.864, 1.065)

0.438

60.2/0.000

0.876 (0.756, 1.016)

0.079

41.8/0.005

0.985 (0.895, 1.085)

0.758

47.8/0.001

0.865 (0.722, 1.036)

0.116

56.0/0.000

  ≥ 1000

  8

4,685/15,318

0.984 (0.877, 1.104)

0.784

68.6/0.002

0.985 (0.855, 1.134)

0.831

66.7/0.004

0.968 (0.784, 1.195)

0.762

44.8/0.080

0.987 (0.863, 1.129)

0.853

59.7/0.015

0.955 (0.748, 1.219)

0.711

56.3/0.025

Quality score

  < 10

24

4,690/5,424

0.954 (0.848, 1.074)

0.438

69.1/0.000

0.976 (0.842, 1.132)

0.752

65.3/0.000

0.879 (0.725, 1.065)

0.189

41.7/0.018

1.002 (0.876, 1.145)

0.981

52.8/0.001

0.876 (0.683, 1.122)

0.294

59.3/0.000

  ≥ 10

20

8,162/19,095

0.959 (0.886, 1.038/)

0.298

60.0/0.000

0.963 (0.875, 1.060)

0.442

55.3/0.002

0.915 (0.782, 1.072)

0.273

44.7/0.017

0.976 (0.890, 1.070)

0.599

46.3/0.013

0.902 (0.756, 1.075)

0.250

52.2/0.004

In the subgroup analysis, no significant association was found between rs1501299 polymorphism and CVD risk under the five genetic models in any subgroup (all P > 0.05) (Table 4).

Heterogeneity analysis

In this meta-analysis, meta-regression was used to investigate the source of heterogeneity by year, population, genotyping method, sample size, and quality score. We found that sample size (allelic model: P = 0.019; dominant model: P = 0.032; recessive model: P < 0.001; and homozygote model: P < 0.001) and quality score (allelic model: P = 0.035; dominant model: P = 0.032; recessive model: P < 0.001; and homozygote model: P < 0.001) contributed to the observed heterogeneity across all the studies of the association between rs2241766 polymorphisms and CVD risk. However, in the meta-analysis of the associations between rs266729/rs1501299 polymorphisms and CVD risk, we did not identify the source of heterogeneity (all P > 0.05) (Additional file 2: Table S4).

Publication bias and sensitivity analysis

Publication bias was measured by funnel plots and quantified by Begg’s and Egger’s tests. No publication bias was found among the studies regarding the association between rs266729 polymorphisms and CVD risk (all P > 0.05). Publication biases were found in analyses of the associations between rs2241766 polymorphisms and CVD risk (allelic model: PEgger = 0.001, PBegg = 0.031; dominant model: PEgger = 0.001, PBegg = 0.003; and heterozygote mode: PEgger = 0.003, PBegg = 0.003), and between rs1501299 polymorphisms and CVD risk (recessive model: PEgger = 0.031, PBegg = 0.035) (Table 5 and Additional file 3: Figures S1, S2, and S3). Sensitivity analyses showed that this meta-analysis was relatively stable and credible (Figs. 4, 5, and 6).
Table 5

Publication bias assessment of this meta-analysis

SNPs

Genetic model

Egger’s test

Begg’s test

t-value

P

z-value

P

rs266729

Allelic model

  0.60

0.552

0.47

0.639

Dominant model

  0.77

0.451

0.62

0.536

Recessive model

−0.67

0.507

0.92

0.358

Heterozygote model

  0.79

0.435

0.81

0.420

Homozygote model

−0.45

0.658

0.73

0.464

rs2241766

Allelic model

  3.52

0.001

2.16

0.031

Dominant model

  3.63

0.001

2.99

0.003

Recessive model

  0.72

0.476

0.40

0.687

Heterozygote model

  3.17

0.003

2.97

0.003

Homozygote model

  0.88

0.383

0.33

0.744

rs1501299

Allelic model

−0.80

0.427

0.96

0.337

Dominant model

  0.09

0.930

0.13

0.895

Recessive model

−2.24

0.031

2.11

0.035

Heterozygote model

  0.60

0.549

0.11

0.911

Homozygote model

−1.45

0.155

1.49

0.137

Fig. 4
Fig. 4

Sensitivity analyses of the association between rs266729 polymorphism and CVD risk. (a) allelic model; (b) dominant model; (c) recessive model; (d) heterozygote model; (e) homozygote model

Fig. 5
Fig. 5

Sensitivity analyses of the association between rs2241766 polymorphism and CVD risk. (a) allelic model; (b) dominant model; (c) recessive model; (d) heterozygote model; (e) homozygote model

Fig. 6
Fig. 6

Sensitivity analyses of the association between rs1501299 polymorphism and CVD risk. (a) allelic model; (b) dominant model; (c) recessive model; (d) heterozygote model; (e) homozygote model

TSA

In the TSA of rs266729 and CVD, the Z-curve crossed trial sequential monitoring boundary and the sample size reached RIS in dominant and heterozygote models (Fig. 7). In allelic, recessive, and homozygote models, the sample size also reached RIS, although the Z-curve did not cross trial sequential monitoring boundary (Fig. 7). In the TSA of rs2241766/rs1501299 and CVD, the sample size reached RIS in the five genetic models (Figs. 8 and 9). Thus, concrete conclusions were reached and further studies were not required.
Fig. 7
Fig. 7

Trial sequential analysis of the association between rs266729 and CVD risk. (a) allelic model; (b) dominant model; (c) recessive model; (d) heterozygote model; (e) homozygote model

Fig. 8
Fig. 8

Trial sequential analysis of the association between rs2241766 and CVD risk. (a) allelic model; (b) dominant model; (c) recessive model; (d) heterozygote model; (e) homozygote model

Fig. 9
Fig. 9

Trial sequential analysis of the association between rs1501299 and CVD risk. (a) allelic model; (b) dominant model; (c) recessive model; (d) heterozygote model; (e) homozygote model

Discussion

In this meta-analysis, we collected up-to-date information (July 1st, 2017) to investigate the association between ADIPOQ SNPs and the risk of CVD. Our results demonstrate that rs266729 and rs2241766 variants of ADIPOQ are associated with the increased risk of CVD, but rs1501299 is not associated with CVD risk.

In view of the association between rs266729 and CVD risk, Yang et al. (2012) [60], Zhou et al. (2012) [61], and Zhang et al. (2012) [50] performed meta-analyses. Yang et al. reported that rs266729 is associated with the increased risk of CAD in allelic and dominant models [60]. Zhou et al. found the same association in overall population, Europeans, and East Asian in allelic, dominant and heterozygote models [61]. Zhang et al. also revealed that rs266729 is associated with the increased risk of CAD in overall population and East Asian in allelic model [50]. Our results further identified that rs266729 is associated with the increased risk of CVD in overall population and East Asian in dominant and heterozygote models. In addition, our results revealed that the significant association in studies on the basis of PCR-RFLP method, indicating that different genotyping method may result in different statistical results.

The association between rs2241766 and CVD risk also has been the subject of meta-analysis [6064]. These studies are inconsistent. Yang et al. found no significant association between rs2241766 and CAD risk [60]. Zhang et al. found no overall significant risk association between CHD and rs2241766 in Han Chinese population [62]. Zhou et al. reported that rs2241766 is associated with the decreased risk of CVD in recessive and homozygote models, and the decreased risk of CVD in East Asian in allelic, dominant, recessive, and homozygote models [61]. Zhou et al. performed a meta-analysis of the association between rs2241766 and CVD risk in allelic model, and they found that rs2241766 is associated with the increased risk of CVD [63]. In our meta-analysis, we found that rs2241766 is associated with the increased risk of CVD in overall population and East Asian in all the five genetic models, and in West Asian in allelic, recessive, and homozygote models. Our findings is in agreement with the results of Zhou et al., but is in disagreement with the results of Yang et al., Zhang et al., and Zhou et al.

With regard to the association between 1,501,299 and CVD, the results are also conflicting [50, 60, 61]. Zhou et al. revealed no significant association between rs1501299 polymorphism with CAD susceptibility [61]. Qi et al. reported the extremely large decrease in CVD risk associated with rs1501299 polymorphism in diabetic patients [24]. Zhang et al. reported only the weak protective effect of the rs1501299 variant against CVD in general study subjects [50]. The meta-analysis by Zhao et al. revealed that rs1501299 polymorphism may play a protective role for CAD among patients with T2DM [22]. In comparison, our results revealed no significant association.

Different genetic admixture and environmental factors among human populations, which tend to explain ethnic background, strongly modulate the effects of ADIPOQ polymorphisms on adiponectin levels [65, 66]. Studies have reported that low levels of adiponectin (hypoadipoectinemia) correlate with the risk of CVD, and high levels of adiponectin protect against this disease [611]. These conflicting results of associations between the ADIPOQ polymorphism and CVD risk may be due to differences in publication bias, sample size, or insufficient statistical power. In addition, evidences have showed that studies which deviate from HWE in controls may reflect the presence of genotyping errors, population stratification, and selection bias in the controls (or without representation of studied sample). Thus, including those studies may decrease the quality of a meta-analysis or generate inconsistent results [67].

Heterogeneity across all the studies of the associations should be noted because it may potentially affect the strengths of the present meta-analysis. We, thus, used random effect model. Our results showed that sample size and quality score are the factors of heterogeneity across all studies of association between rs2241766 polymorphisms and CVD, but no factors contribute the heterogeneity across all studies of association between rs266729/rs1501299 polymorphisms and CVD. However, heterogeneity was still high in the subgroup analysis of the two factors. For these reasons, heterogeneity might be explained by other confounding factors, such as gene-gene interaction and gene-environment interaction.

Our meta-analysis has some limitations. Firstly, significant publication bias was found in the analysis of rs2241766 (under allelic, dominant, and heterozygote models) and rs1501299 (under recessive model). Secondly, our meta-analysis mainly included Europeans and Asians with only few other races, thus limiting our power to generalize our findings in other races. Finally, our results might be affected by the potential weaknesses of genetic association studies, such as phenotype misclassifications, genotyping error, population stratification, gene-environment or gene-gene interactions, and selective reporting biases [68, 69].

Despite the limitations highlighted above, our meta-analysis also had some strength. Firstly, we searched extensively and investigated more studies and more participants than any other meta-analyses performed on the association between ADIPOQ variant and CVD, which give our study more statistical power to draw valid conclusion on this issue. Secondly, sensitivity analysis showed that the results of our meta-analysis are stable and robust. Thirdly, the evidence of our results are sufficient to reach concrete conclusions, which were proved by TSA for the first time. We strongly believe our findings will help settle some of the controversies surrounding the ADIPOQ-CVD association research.

Conclusions

Our meta-analysis found significant increased CVD risk is associated with rs266729 and rs2241766, but not associated with rs1501299. Investigating gene–gene and gene–environment interactions is needed to give more insight into the genetic association between ADIPOQ variants and CVD.

Notes

Abbreviations

CAD: 

Coronary artery disease

CHD: 

Coronary heart disease

CVD: 

Cardiovascular disease

HWE: 

Hardy–Weinberg equilibrium

MI: 

Myocardial infarction

RIS: 

Required information size

SNPs: 

Single-nucleotide polymorphisms

T2DM: 

Type 2 diabetic mellitus

TSA: 

Trial sequential analysis

Declarations

Acknowledgements

The authors would like to thank staff and students of the Meta-analysis Group of the Department of Epidemiology and Biostatistics, School of Public Health of Jilin University for their contributions to this work.

Funding

This work was supported by The National Natural Science Foundation of China (Grant 81573230), the Ministry of Science and Technology of the People’s Republic of China (grant number: 2015DFA31580), and the Science and Technology Commission of Jilin Province (grant number: 20150101130JC).

Availability of data and materials

Please contact author for data requests.

Authors’ contributions

Conception and design: JSK, SQ, YC, and YL. Provision of study materials: JSK, SQ, RL, and CK; Collection and assembly of data: JSK, SQ, and RL. Data analysis and interpretation: JSK, SQ, and RL. Manuscript writing: JSK and SQ. Revised the language/article: All authors. Final approval of manuscript: All authors.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declared that they have no competing interest.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Epidemiology and Biostatistics, School of Public Health of Jilin University, 1163 Xinmin Street, Changchun, 130021, China
(2)
The Cardiovascular Center, the First Hospital of Jilin University, Changchun, 130021, China

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