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Association between KIF6 rs20455 polymorphism and the risk of coronary heart disease (CHD): a pooled analysis of 50 individual studies including 40,059 cases and 64,032 controls

Contributed equally
Lipids in Health and Disease201817:4

https://doi.org/10.1186/s12944-017-0651-y

  • Received: 14 February 2017
  • Accepted: 25 December 2017
  • Published:

Abstract

Background

The KIF6 rs20455 polymorphism has been verified as an important genetic factor of coronary heart disease (CHD), but with controversial results. The aim of this study was to explore the association between KIF6 rs20455 polymorphism and susceptibility to CHD.

Methods

All eligible studies were identified by searching Medline (mainly PubMed), EMBASE, the Web of Science, Cochrane Collaboration Database, Chinese National Knowledge Infrastructure, Wanfang Database and China Biological Medicine up to October 5, 2016.Odds ratios (ORs) with 95% confidence interval (CI) were used to explore the association between KIF6 rs20455 polymorphism and CHD risk. Begg’s and Egger’s tests were used to examine the publication bias. Subgroup analysis and sensitivity analysis were performed to test the reliability and stability of the results. All the analyses were carried out by Stata 12.0 software.

Results

A total of 28 publications including 50 individual studies were analyzed in this present work. There are no significant association found between KIF6 rs20455 polymorphism and CHD risk (Homozygote model: OR = 1.007, 95% CI =0.952–1.066, P = 0.801; Heterozygote model: OR = 1.009, 95% CI = 0.968–1.052, P = 0.636; Dominant model: OR = 1.007, 95% CI = 0.966–1.048, P = 0.753; Recessive model: OR = 0.989, 95% CI = 0.943–1.037, P = 0.655; Allele comparison model: OR = 1.00, 95% CI = 0.971–1.030, P = 0.988). Furthermore, subgroup analyses were performed by ethnicity, source of control.

Conclusions

Our result suggests that KIF6 rs20455 polymorphism may not be associated with CHD susceptibility. However, additional very well-designed large-scale studies are warranted to confirm our results.

Keywords

  • Coronary heart disease
  • KIF6 rs20455
  • Polymorphism
  • Meta-analysis

Background

Coronary heart disease (CHD), a multifactorial heart disorder resulting from both environmental and genetic factors [1], is one of the leading causes of disability and death around the world [2]. Epidemiology studies have suggested that hypertension, hyperlipidemia, diabetes mellitus, obesity and smoking are major risk factors for CHD [3]. In recent years, more and more studies reveled that several loci and variants are strongly associated with CHD [4, 5]. It has been estimated that approximately 50% of the variability of the major risk factors for CHD is determined by genetics [6].

The KIF6 protein is one of several molecular components that mediate intracellular transport of organelles, protein complexes, and mRNAs. A common Trp719Arg (rs20455) SNP in exon 19 of the KIF6 gene has been identified as a potential risk factor for CHD [7, 8]. The KIF6 protein belongs to the kinesin superfamily, which is involved in the intracellular transport in a microtubule and ATP-dependent manner [9]. The rs20455 polymorphism replaces the nonpolar ‘Trp’ residue in codon 719 with a basic ‘Arg’ amino acid. This SNP lies near the putative cargo binding taildomain and may alter the cargo activity of KIF6 [10]. Carriers of the 719Arg allele exhibit a 50% increased risk of events compared with non-carriers [8, 11]. Up to now, multiple large prospective and case–control studies have reported the association between KIF6 rs20455 polymorphism and the risk of CHD. However, somestudies have not verified inconsistent results. Published studies have generally been restricted in terms of sample size and ethnic diversity, and individual studies may have in-sufficient power to achieve a comprehensive and reliable conclusion. In view of the discrepancies in the findings of previous published studies, we aimed to perform a meta-analysis of the published studies to clarify the association between KIF6 rs20455 polymorphism and CHD to get a better under-standing of this relationship.

Methods

Literature search

A comprehensive search for all related studies from both electric databases, such as, Medline (mainly PubMed), Embase, Web of science, China National Knowledge Infrastructure (CNKI) et al., and hand search from references of all eligible literatures. Single or combinations of the following keywords were used: “kinesin like protein 6” or “KIF6” or “rs20455” or “719Arg”, “single nucleotide polymorphism, SNP or variation, mutation”, “genetic association” and “coronary heart disease” or “CHD”. No language and sample size were set. When more than one studies of the same population were included in several publications, only the most recent or complete studies were included in this meta-analysis.

Selection criteria

Articles included should meet following criteria: an appropriate description of KIF6 rs20455 polymorphism in CHD cases and healthy controls; results expressed as odds ratio (OR); and studies with a 95% confidence interval (CI) for OR with sufficient data to calculate these numbers. While for the exclusion criteria provided as follows: studies without raw data; case-only studies, family-based studies, case reports, editorials, and review articles (including meta-analyses). In studies with overlapping cases/controls, the study with the higher quality score or the study with more information on the origin of the cases/controls was included in the meta-analysis.

Data extraction

Two researchers extracted important information independently and carefully from all eligible studies according to the criteria listed above. Any disagreement will be resolved by the two authors through discussion or the third author. The following data were extracted from each included study: first author’s surname, year of publication, country, ethnicity, genotyping method, source of control, total number of cases and controls, distributions of KIF6 rs20455 genotypes. Different ethnicity descents were categorized as Caucasian, Asian, and Mixed populations (the original studies didn’t clarify the race of the subjects or mixed races).

Statistical analysis

We adopted poled ORs and corresponding 95% confidence interval (CIs) to detect the association between KIF6 rs20455 polymorphism and CHD risk. Heterogeneity was explored by Q statistic [12], and the P value was <0.05 will be considered statistically significant. Heterogeneity was also assessed using the I 2 statistic, which takes values between 0% and 100% with higher values denoting greater degree of heterogeneity (I 2  = 0–25%: noheterogeneity; I 2  = 25–50%: moderate heterogeneity; I 2  = 50–75%: large heterogeneity; I 2  = 75–100%: extreme heterogeneity) [13]. Different statistical models will be selected according to the result of heterogeneity. Random (Der Simonian-Laird method) [14] will be used to calculate the precise results when the P value of heterogeneity was <0.05, or the I 2  > 50%. Otherwise, fixed effects model (Mantel-Haenszel method) will be adopted [15]. Five genetic comparison model were carried out and calculated as follows: homozygote model (GG vs. AA), heterozygote model (AG vs. AA), recessive model (GG vs. AG + AA), and dominant model (GG + AG vs. AA), and allele comparison model (G-allele vs. A-allele). Hardy–Weinberg equilibrium in the control group was tested by the chi-square test for goodness of fit, and a P value of <0.05 was considered significant. Subgroup analyses were performed by ethnicity, source of control, to confirm if our results were stable and robust [16]. Begg’s funnel plots [17] and Egger’s test [18] were explored to examine if potential publication bias were existed in this study. Sensitivity analysis was carried out by sequentially omitting each study and finding the influence on the overall summary estimate [19]. All the statistical analyses were finished by STATA software (version 12.0; Stata Corporation, College Station, TX). All the P values were two-sided.

Results

Characteristics of all included studies

Totally, 209 potential relevant studies were searched through several databases. Based on the including criteria listed above, only 28 articles including 50 separate studies were included finally [8, 2046]. A flow diagram summarizing the process of study selection was present in Fig. 1. The baseline characteristics ofall included studies were listed in Table 1. Helgadottir et al. contained two individual studies [25], Samani et al. contained two individual studies [26], Assimes et al. contained 20 individual studies [31], and Wu et al. contained two separate studies [41]. Moreover, there were 37 studies from Caucasian decedent, 9 studies from Asian populations and the rest 14 studies from mixed populations. There were 20 population-based (PB) studies, 21 hospital-based (HB) studies and four family based (FB) study, three community based (CB) study, two hospital and community based (H-CB) study. Different ethnicity descents were categorized as Caucasian, Asian and Mix (the original studies didn’t clarify the race of the subjects or mixed races).
Fig. 1
Fig. 1

The process of literature research

Table 1

Characteristics of all studies included in this meta-analysis

Author

Year

Country

Ethnicity

Control source

Case

Control

Case

Control

P HWE

AA

AG

GG

AA

AG

GG

Berglund et al.

1993

Sweden

Caucasian

PB

86

99

35

38

13

33

54

12

Yes

Vartiainen et al.

2000

Finland

Caucasian

PB

167

172

64

81

22

73

76

23

Yes

Senti et al.

2001

Spain

Caucasian

PB

312

317

134

139

39

141

137

39

Yes

Yusuf et al.

2004

Several

Asian

PB

1092

1187

351

498

243

389

531

267

Yes

Low et al.

2005

USA

Caucasian

HB

204

260

89

86

29

114

111

35

Yes

Helgadottir et al.1

2007

USA

Caucasian

PB

875

447

370

399

106

174

221

52

Yes

Helgadottir et al.2

2007

USA

Caucasian

PB

933

468

359

441

133

194

213

61

Yes

Samani et al.1

2007

Germany

Caucasian

PB

1126

1277

447

529

150

522

593

162

Yes

Samani et al.2

2007

Germany

Caucasian

PB

722

1643

293

328

101

662

753

228

Yes

Meng et al.

2008

Ireland

Caucasian

FB

482

622

203

226

53

261

292

69

Yes

Meiner et al.

2008

USA

Caucasian

PB

505

559

187

228

90

216

260

83

Yes

Serre et al.

2008

Several

Mixed

PB

789

859

335

337

117

354

402

103

Yes

Morgan et al.

2008

USA

Caucasian

HB

807

637

322

377

108

256

304

77

Yes

Assimes et al.

2008

USA

Caucasian

PB

505

514

162

187

83

144

183

130

Yes

Vennemann et al.

2008

Germany

Caucasian

PB

793

1121

311

379

103

430

528

163

Yes

Sutton et al.

2008

USA

Caucasian

FB

1575

970

545

570

183

297

347

86

Yes

Martinelli et al.

2008

Italy

Caucasian

PB

1106

383

437

501

168

145

191

47

Yes

Iakoubova et al.

2008

Scottland

Caucasian

PB

481

1080

104

137

35

256

204

59

Yes

Stewart et al.

2009

Canada

Caucasian

HB

1540

1455

183

695

662

205

616

634

Yes

Luke et al.

2009

Austria

Caucasian

HB

505

782

73

254

178

102

373

307

Yes

Bare et al.

2010

Costa Rican

Caucasian

PB

1987

2147

785

952

250

896

966

285

Yes

Assimes et al.1

2010

U.S.A

Mixed

PB

505

514

192

220

93

161

213

140

Yes

Assimes et al.2

2010

Germany

Caucasian

HB

793

1121

311

379

103

430

528

163

Yes

Assimes et al.3

2010

U.S.A

Mixed

HB

1575

970

561

670

344

306

433

231

Yes

Assimes et al.4

2010

Iceland

Caucasian

PB

4313

24,952

2131

1779

403

11,813

10,689

2450

Yes

Assimes et al.5

2010

Finland

Caucasian

PB

167

172

64

81

22

73

76

23

Yes

Assimes et al.6

2010

U.S.A

Mixed

FB

378

2652

108

182

88

679

1105

868

Yes

Assimes et al.7

2010

Germany

Caucasian

PB

722

1643

293

328

101

662

753

228

Yes

Assimes et al.8

2010

Germany

Caucasian

HB

1126

1277

447

529

150

522

593

162

Yes

Assimes et al.9

2010

U.S.A

Caucasian

CB

505

559

187

228

90

216

260

83

Yes

Assimes et al.10

2010

Mixed

Caucasian

H-CB

789

859

335

337

117

354

402

103

Yes

Assimes et al.11

2010

Mixed

Asian

H-CB

1092

1187

351

498

243

389

531

267

Yes

Assimes et al.12

2010

Ireland

Caucasian

FB

482

622

203

226

53

261

292

69

Yes

Assimes et al.13

2010

Sweden

Caucasian

PB

86

99

35

38

13

33

54

12

Yes

Assimes et al.14

2010

U.S.A

Caucasian

HB

875

447

370

399

103

174

221

52

Yes

Assimes et al.15

2010

U.S.A

Caucasian

HB

204

260

89

86

29

114

111

35

Yes

Assimes et al.16

2010

U.S.A

Caucasian

HB

807

637

322

377

108

256

304

77

Yes

Assimes et al.17

2010

U.S.A

Caucasian

HB

933

468

359

441

133

194

213

61

Yes

Assimes et al.18

2010

Spain

Caucasian

CB

312

317

134

139

39

141

137

39

Yes

Assimes et al.19

2010

Italy

Caucasian

HB

1106

383

437

501

168

145

191

47

Yes

Assimes et al.20

2010

U.K.

Caucasian

CB

1922

2933

792

890

240

1242

1299

392

Yes

Bhanushali et al.

2011

India

Asian

HB

227

150

70

111

46

33

80

37

Yes

Peng et al.

2012

China

Asian

HB

289

522

69

149

71

139

262

121

Yes

Wu et al.1

2012

China

Asian

HB

356

568

104

164

88

168

268

132

Yes

Wu et al.2

2012

China

Asian

HB

114

568

16

68

30

168

268

132

Yes

Wu et al.

2014

China

Asian

HB

288

346

74

141

73

101

166

79

Yes

Hamidizadeh et al.

2015

Iran

Caucasian

HB

100

100

35

48

17

63

27

10

No

Vishnuprabu et al.

2015

India

Asian

HB

510

532

107

252

151

121

251

160

Yes

Hubacek et al.

2016

Czech

Caucasian

HB

1889

1191

691

856

302

440

543

195

Yes

Vatte et al.

2016

Saudi Arabia

Asian

HB

1002

984

277

513

212

286

464

234

Yes

1–20: represents different studies in one publication; HB hospital based study, PB population based study, FB family based study, CB community based study, H-CB hospital and community based study, HWE Hardy-Weinberg equilibrium. Mix: the original studies didn’t clarify the race of the subjects or mixed races

Quantitative synthesis

All the eligible data were calculated and significant heterogeneity was detected under homozygote (I 2  = 33.9%; Pheterogeneity = 0.012), heterozygote (I 2  = 35.5%; Pheterogeneity = 0.008), dominant (I 2  = 39.8; Pheterogeneity = 0.002), recessive (I 2  = 26.5%; Pheterogeneity = 0.047) and allele comparison model (I 2  = 44.2%; Pheterogeneity = 0.001) between this gene variation and the risk of CHD. So, random-effect model was used to calculate the statistical parameters. Overall, there were no significant association existed between KIF6 rs20455 polymorphism and the risk of CHD (Homozygote model: OR = 1.007, 95% CI =0.952–1.066, P = 0.801, Fig. 2; Heterozygote model: OR = 1.009, 95% CI = 0.968–1.052, P = 0.636, Fig. 3; Dominant model: OR = 1.007, 95% CI = 0.966–1.048, P = 0.753, Fig. 4; Recessive model: OR = 0.989, 95% CI = 0.943–1.037, P = 0.655, Fig. 5; Allele comparison model: OR = 1.00, 95% CI = 0.971–1.030, P = 0.988, Fig. 6). Furthermore, we explored the subgroup analyses by ethnicity and source of control. All the results were listed in Table 2.
Fig. 2
Fig. 2

Forest plot of the association between KIF6 rs20455 gene polymorphism and CHD risk (under homozygote model)

Fig. 3
Fig. 3

Forest plot of the association between KIF6 rs20455 gene polymorphism and CHD risk (under heterozygote model)

Fig. 4
Fig. 4

Forest plot of the association between KIF6 rs20455 gene polymorphism and CHD risk (under dominant model)

Fig. 5
Fig. 5

Forest plot of the association between KIF6 rs20455 gene polymorphism and CHD risk (under recessive model)

Fig. 6
Fig. 6

Forest plot of the association between KIF6 rs20455 gene polymorphism and CHD risk (under allele comparison model)

Table 2

Main results of pooled ORs with 95% CI in the meta-analysis

Variables

No.

Pheterogneity

Analysis model

OR (95% CI)

P

PBegg’s

PEgger’s

Homozygote model

Total

50

0.012

Random model

1.007 (0.952–1.066)

0.801

0.106

0.108

Ethnicity

 Caucasian

37

0.45

Fixed model

1.012 (0.964–1.063)

0.622

  

 Asian

9

0.158

Fixed model

1.038 (0.933–1.154)

0.494

  

 Mixed

4

0.004

Random model

0.771 (0.57–1.043)

0.0731

  

Source of control

 PB

20

0.038

Random model

0.981 (0.895–1.076)

0.691

  

 HB

21

0.096

Fixed model

1.027 (0.956–1.103)

0.891

  

 FB

4

0.038

Fixed model

0.907 (0.767–1.072)

0.016

  

 CB

3

0.427

Fixed model

1.019 (0.872–1.189)

0.816

  

 H-CB

2

0.368

Fixed model

1.073 (0.895–1.286)

0.446

  

Heterozygote model

Total

50

0.008

Random model

1.009 (0.968–1.052)

0.636

0.089

0.070

Ethnicity

       

 Caucasian

37

0.035

Random model

0.955 (0.963–1.029)

0.790

  

 Asian

9

0.071

Fixed model

1.089 (0.995–1.191)

0.065

  

 Mixed

4

0.639

Fixed model

0.893 (0.799–0.999)

0.047

  

Source of control

 PB

20

0.067

Random model

0.979 (0.938–1.021)

0.316

  

 HB

21

0.004

Random model

1.040 (0.956–1.132)

0.356

  

 FB

4

0.807

Fixed model

0.966 (0.859–1.085)

0.558

  

 CB

3

0.924

Fixed model

1.064 (0.957–1.183)

0.254

  

 H-CB

2

0.265

Fixed model

0.967 (0.841–1.112)

0.637

  

Dominant model

Total

50

0.002

Random model

1.007 (0.966–1.048)

0.753

0.061

0.058

Ethnicity

       

 Caucasian

37

0.034

Random model

1.013 (0.970–1.057)

0.568

  

 Asian

9

0.054

Fixed model

1.071 (0.984–1.165)

0.112

  

 Mixed

4

0.508

Fixed model

0.854 (0.770–0.947)

0.003

  

Source of control

 PB

20

0.026

Random model

0.991 (0.932–1.055)

0.786

  

 HB

21

0.002

Random model

1.040 (0.958–1.129)

0.346

  

 FB

4

0.820

Fixed model

0.948 (0.848–1.059)

0.342

  

 CB

3

0.986

Fixed model

1.053 (0.953–1.164)

0.310

  

 H-CB

2

0.551

Fixed model

0.993 (0.871–1.132)

0.917

  

Recessive model

Total

50

0.047

Random model

0.989 (0.943–1.037)

0.655

0.025

0.040

Ethnicity

       

 Caucasian

37

0.541

Fixed model

1.002 (0.959–1.048)

0.919

  

 Asian

9

0.819

Fixed model

0.983 (0.898–1.075)

0.705

  

 Mixed

4

<0.001

Random model

0.811 (0.592–1.111)

0.191

  

Source of control

 PB

20

0.040

Random model

0.982 (0.902–1.069)

0.668

  

 HB

21

0.796

Fixed model

0.989 (0.919–1.064)

0.715

  

 FB

4

0.004

Random model

0.924 (0.661–1.291)

0.643

  

 CB

3

0.287

Fixed model

1.009 (0.843–1.209)

0.883

  

 H-CB

2

0.142

Fixed model

1.099 (0.856–1.412)

0.395

  

Allele comparison model

Total

50

0.001

Random model

1.00 (0.971–1.030)

0.988

0.052

0.066

Ethnicity

       

 Caucasian

37

0.067

Fixed model

0.999 (0.977–1.022)

0.950

  

 Asian

9

0.186

Fixed model

1.022 (0.968–1.079)

0.428

  

 Mixed

4

0.009

Random model

0.855 (0.742–0.985)

<0.001

  

Source of control

 PB

20

0.004

Random model

0.990 (0.943–1.040)

0.690

  

 HB

21

0.017

Random model

1.015 (0.967–1.066)

0.547

  

 FB

4

0.045

Random model

0.877 (0.691–1.113)

0.361

  

 CB

3

0.653

Fixed model

1.025 (0.953–1.102)

0.507

  

 H-CB

2

0.776

Fixed model

1.019 (0.931–1.115)

0.687

  

No. number of studies, OR odds ratio, 95% CI 95% confidence interval, HB hospital based study, PB population based study, FB family based study, CB community based study, H-CB hospital and community based study

Sensitivity analysis

The sensitivity analysis was performed to evaluate the influence of each individual study on the pooled OR by omitting every single study. The analysis results reflected that our results were statistically stable and reliable.

Publication bias

There was no significant publication bias found in the meta-analysis, reflected by P values from Begg’s correlation (Heterozygote model: P = 0.089; Dominant model: P = 0.061; Allele comparison model: P = 0.052, Fig. 7) and Egger’s regression (Heterozygote model: P = 0.070; Dominant model: P = 0.058; Allele comparison model: P = 0.066, Fig. 8). However, significant publication bias found in the meta-analysis, reflected by P values from Begg’s correlation (Homozygote model: P = 0.046; Recessive model: P = 0.025) and Egger’s regression (Homozygote model: P = 0.041; Recessive model: P = 0.040). All the results are listed in Table 2.
Fig. 7
Fig. 7

Begg’s test of the association between KIF6 rs20455 gene polymorphism and CHD risk (under allele comparison model)

Fig. 8
Fig. 8

Egger’s test the association between KIF6 rs20455 gene polymorphism and CHD risk (under allele comparison model)

Discussion

Large sample and unbiased epidemiological studies of predisposition genes polymorphisms could provide insight into the in vivo relationship between candidate genes and complex diseases. Many epidemiological studies have investigated the relationship between the KIF6 rs20455 polymorphism and the risk of CHD, but because of small sample size and the low statistical power of individual studies, results have been contradictory. In this present study, we searched all eligible studies to date and got the precise result if KIF6 rs20455 polymorphism could contribute to the risk of CHD. To the best of our knowledge, our present work was the most comprehensive one through enrolling all eligible studies.

Herein, we included 50 individual studies, including 40,059 cases and 64,032 controls. Overall, there was no association between KIF6 rs20455 polymorphism and CHD risk. Hamidizadeh et al. found that significant association was found between this gene polymorphism and CHD risk among Caucasian populations [43], and the result was verified in another study through enrolling 143,000 subjects [40]. However, no association was found in a meta-analysis, among South Asians, African-Americans, Hispanics, East Asians, and mixed decedent populations [39]. Furthermore, other recent studies were also found no association existed between this gene polymorphism and CHD risk [25, 26, 4749]. When we got the subgroup analyses by ethnicity, there was also no association found among Caucasian and Asian populations. While decreased risk of this gene polymorphism and CHD risk was found among mixed populations. Of note, mixed populations means the original studies didn’t clarify the race of the subjects or mixed races. This result may be not provided some useful information for clinical deeds. So, further studies should be performed with clearly race or ethnicity stated in their work.

Publication bias was found in some genetic models. The explanations might arise from some aspects. First, our meta-analysis took into consideration only fully published data, and the abstract and conference papers were excluded. Second, this meta-analysis only focused on papers published in Chinese and English language, and some eligible studies which were reported in other languages might be missed. Third, positive results tend to be accepted by journals while negative results are often rejected or not even submitted. We should point out that the publication bias might partly account for the results, but which were not affected deeply. When we adjusted the results using the trim and fill method, the adjusted risk estimate was attenuated but remained significant, indicating the stability of our results.

Some limitations of this meta-analysis should be addressed. Firstly, heterogeneity is a potential problem when interpreting all the results of meta-analysis. Although we minimized the likelihood by performing a careful search for published studies, using the explicit criteria for study inclusion, the significant between-study heterogeneity still existed in most of comparison. The presence of heterogeneity can result from differences in the age distribution, selection of controls, prevalence lifestyle factors and so on. Secondly, only published studies were included in this meta-analysis. Therefore, potential publication bias was existed in some genetic models. Despite the limitations, our meta-analysis significantly increased the statistical power based on substantial data from different studies. The sensitivity analyses outcomes reflected that our results were statistically stable and reliable.

In conclusion, this present meta-analysis suggests that carriers of KIF6 rs20455 polymorphism may irrelative to the risk of CHD. We also observed no compelling evidence of an association between the KIF6 rs20455 SNP and CHD in multiple race/ethnic groups. These findings do not support the clinical utility of testing for the KIF6 rs20455 polymorphism in the primary prevention of CHD and indirectly question whether genotype information at this locus is able to identify subjects most likely to benefit from the use of statins.

Abbreviations

CHD: 

Coronary heart disease

CHS: 

Cardiovascular Health Study

CI: 

Confidence interval

KIF6: 

Kinesin-like protein 6

OR: 

Odds ratio

PROSPER: 

Prospective Study of Pravastatin in the Elderly at Risk

SNPs: 

Single nucleotide polymorphisms

WHS: 

the Women’s Health Study

Declarations

Acknowledgements

We thank all our colleagues of this present work.

Funding

Not applicable.

Availability of data and materials

Please contact author for data requests.

Authors’ contributions

YL, ZC, HS participated in the design of the study. YL, ZC, HS carried out the literature search and data extraction. YL, ZC, HS participated in the analysis of eligible data. YL, ZC, HS wrote the manuscript All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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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)
Heart Function Examination Room, the First People’s Hospital of Lianyungang, Affiliated Hospital of Xuzhou Medical University, Lianyungang, Jiangsu, 222002, China
(2)
Department of Neurosurgery, the first People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
(3)
Department of Cardiology, the First People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China

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