Open Access

Relationship between CYP17A1 genetic polymorphism and coronary artery disease in a Chinese Han population

  • Chuan-Fang Dai1,
  • Xiang Xie1Email author,
  • Yi-Ning Yang1,
  • Xiao-Mei Li1,
  • Ying-Ying Zheng1,
  • Zhen-Yan Fu1,
  • Fen Liu1,
  • Bang-Dang Chen1,
  • Min-Tao Gai1 and
  • Yi-Tong Ma1Email author
Lipids in Health and Disease201514:16

https://doi.org/10.1186/s12944-015-0007-4

Received: 4 October 2014

Accepted: 29 January 2015

Published: 7 March 2015

Abstract

Background

CYP17A1 gene encodes P450c17 proteins, which is a key enzyme that catalyzes the formation of sex hormones. Many clinical studies showed that sex hormones levels play an important role in the pathogenesis of coronary artery disease (CAD). However, the relationship between CYP17A1 genetic polymorphisms and CAD remains unclear. The aim of this study was to investigate the association of CYP17A1 genetic polymorphisms with CAD in a Han population of China.

Methods

A total of 997 people include 490 patients and 507 controls were selected for the present study. Five single-nucleotide polymorphisms (SNPs) (rs4919686, rs1004467, rs4919687, rs10786712, and rs2486758) were genotyped by using the real-time PCR (TaqMan) method.

Results

For men, the rs10786712 was found to be associated with CAD in a recessive model (P = 0.016), after adjustment of the major confounding factors, the significant difference was retained (OR = 1.644, 95% confidence interval [CI]: 1.087-2.488, P = 0.019). For women, the rs1004467 was also found to be associated with CAD in a dominant model (P = 0.038), the difference remained statistically significant after multivariate adjustment (OR = 1.623, 95% CI: 1.023-2.576, P = 0.040). The distribution of rs4919687 genotypes showed a significant difference between CAD and control participants in a recessive model (P = 0.019), the significant difference was retained after adjustment for covariates (OR = 0.417, 95% CI: 0.188-0.926, P = 0.032).

Conclusion

Rs1004467, rs4919687, rs10786712 of CYP17A1 gene are associated with CAD in Han population of China. The TT genotype of rs10786712 could be a protective genetic marker of CAD in men. The CC genotype of rs1004467 and the AA genotype of rs4919687 could be risk genetic markers of CAD in women. However, large sample size study including other SNPs of CYP17A1 should be performed in future studies.

Keywords

CYP17A1 Single nucleotide polymorphism Coronary artery disease Case control study

Introduction

Coronary artery disease (CAD) is a complex multifactorial disorder resulting from several susceptibility genes and multiple environmental determinants [1],[2]. Recently, genetic basis of CAD has gained considerable interest [3], heritable factors accounted for 40%-60% in occurrence and development of CAD [4]. Various genes have been shown to be associated with CAD [5],[6]. Some large-scale association studies have identified many common, uncommon and functional variants for CAD [7],[8]. The CYP17A1 gene, located on chromosome 10q24.3, is mainly expressed in the adrenal glands and gonads. This gene encodes a member of enzymes of the cytochrome P450 superfamily. The cytochrome P450 proteins are monooxygenases and responsible for not only the metabolism of xenobiotics but also a host of endogenous substance whose metabolites have critical roles in the maintenance of cardiovascular health [9],[10]. Mounting evidences have demonstrated that CYP enzymes are involved in the pathogenesis of CAD. For example, the CYP8A1 predominantly in vascular endothelial and smooth muscle cells, and acts mainly as an enzyme that converts prostaglandin H2 (PGH2) into prostacyclin (PGI2), some studies suggested that gene polymorphisms of CYP8A1 were associated with cardiovascular risk [11]. In addition, CYP1A1, CYP1A2 (metabolize tobacco polycyclic aromatic hydrocarbons and aromatic amines during smoking) [12],[13], CYP2C8, CYP2J2 ([EET] synthesis) [14],[15], CYP11B2 (aldosterone synthesis) [16], CYP17, and CYP19 (synthesis of sex hormones) [17], have been demonstrated to be associated with CAD.

In humans, CYP17A1 gene is responsible for the synthesis of P450c17 proteins, which is a key enzyme in the steroidogenic pathway. CYP17A1 genetic mutations affect the synthesis of steroids, which are the precursors of sex hormones. Some evidences have indicated that the levels of sex hormones can affect the development of cardiovascular and cerebrovascular diseases [18].

Recently, two large-scale association analyses identified 13 new susceptibility loci for CAD including CYP17A1 gene [19],[20]. Adam S Butterworth et al. [21] selected 15596 patients with CAD and 34992 controls to examine 2100 genes including 49094 genetic variations, and suggested that CYP17A1 gene is one of the susceptibility genes for CAD. However, the relation between CYP17A1 gene and CAD in Chinese population remains unclear. Therefore, in the present study, we aimed to assess the association between the polymorphisms of CYP17A1 and CAD in Chinese Han population.

Methods

Ethical approval of the study protocol

This study was conducted according to the standards of the Declaration of Helsinki, and was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Xinjiang, China). Written informed consent was obtained from each participant, and explicitly provided permission for DNA analyses as well as collection of relevant clinical data.

Subjects

Study population was from a Han population who lived in the Xinjiang Uygur Autonomous Region of China, all subjects attended as inpatients and had a differential diagnosis for chest pain encountered in the Cardiac Catheterization Laboratory of First Affiliated Hospital of Xinjiang Medical University from 2007 to 2013. Approximately 3000 patients undergo coronary angiography every year and we selected almost 1000 Han CAD patients and 1000 healthy persons diagnosed by angiography from 2007 to 2013. Highly skilled physicians were undertaken the coronary angiography using the Judkins approach. Least two experienced imaging specialists were interpreted the findings of coronary angiography, finally, the diagnosis of CAD was made according to the angiography report. All CAD groups defined as the presence of at least one significant coronary artery stenosis of > 50% luminal diameter. Patients with congenital heart disease, multiple organ failure syndrome, malignancy or chronic inflammatory disease were excluded. According to the exclusion criteria, 124 people were excluded from the 1000 CAD patients. Each control subject also underwent a coronary angiogram and did not show coronary artery stenosis. These individuals had no electrocardiographic signs of CAD, regional wall motion abnormalities, and no relevant valvular abnormalities in echocardiograms. Control subjects with CAD and any neoplasm, cardiomyopathy or severe illness limiting life expectance or refusing consent were excluded, according to the exclusion criteria, 82 people were excluded from the 1000 healthy persons. Finally, to ensure matching for age and gender, we selected 490 patients and 507 healthy persons from the 876 CAD patients and 918 healthy persons. Some of the controls have hypertension, and diabetes mellitus, which means control group expose to the same risk factors of CAD while the results of coronary angiogram are normal. The diagnosis of hypertension was established if patients were on antihypertensive medication or if the mean of 3 measurements of systolic blood pressure (SBP) at least 140 mmHg, and/or diastolic blood pressure (DBP) at least 90 mmHg, or a previous diagnosis of hypertension and the use of antihypertensive medication; Diabetes mellitus was defined on the basis of the American Diabetes Association [22]. In addition, individuals with fasting plasma glucose > 7.0 mmol/L or with a history of diabetes or treatment with hypoglycemic agent were considered diabetic. Smoking was classified as smokers (including current or ex-smokers) or non-smokers.

Blood collection and DNA extraction

Before cardiac catheterization, 5 ml of fasting venous blood drawn by venipuncture in the Cardiac Catheterization Laboratory were taken from all participants. The blood samples were collected into tubes containing ethylene diamine tetraacetic acid (EDTA), and centrifuged at 4000 × g for 5 min to separate the plasma content (including plasma, serum and blood cells). Genomic DNA was extracted from the peripheral leukocytes using standard phenol-chloroform method [23]. The DNA samples were stored at −80°C for future analysis. Before genetic analysis, the final concentration of the DNA was diluted to 50 ng/μL.

Genotyping

There are 662 SNPs for the human CYP17A1 gene listed in the National Center for Biotechnology Information SNP database (http://www.ncbi.nlm.nih.gov/SNP). Using Haploview 4.2 software and International HapMap Project website phase I &II database (http://www.hapmap.org), we obtained five tag SNPs (SNP1: rs4919686, SNP2: rs1004467, SNP3: rs4919687, SNP4: rs10786712, SNP5: rs2486758) by using minor allele frequency (MAF) ≥ 0.05 and linkage disequilibrium patterns with r2 ≥ 0.8 as a cut off. Genotyping in the present case–control study was confirmed by the TaqMan SNP Genotyping Assay (Applied Biosystems, Foster City, CA, USA). The primers and probes used in the TaqMan SNP Genotyping Assays were chosen based on information available at the ABI Web site (http://myscience.appliedbiosystems.com). Thermal cycling was done using the Applied Biosystems 7900HT Fast Real-Time PCR System. Plates were read on the sequence detection systems (SDS) automation controller software v2.3 (ABI). PCR amplification was performed using 2.5 μL of TaqMan Universal Master Mix, 0.15 μL probes and 1.85 ddH2O in a 6-μL final reaction volume containing 1 μL DNA. The thermal cycling conditions were as follows: 50°C for 2 min; 95°C for 10 min; 50 cycles of 95°C for 15 s; and 60°C for 1 min. Thermal cycling was performed using the Sequence Detection Systems (SDS) automation controller software v2.3 (ABI).

Biochemical analysis

Serum concentrations of glucose (Glu), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), creatinine (Cr) and uric acid (UA) were measured using standard methods in the Central Laboratory of First Affiliated Hospital of Xinjiang Medical University as described previously [24]-[26].

Statistical analysis

Data analysis was carried out using the computer software Statistical Package for Social Sciences SPSS 17.0 for Windows (SPSS Institute, Chicago, USA). Hardy-Weinberg equilibrium was assessed by χ2 analysis. Differences in measurement variables (e.g. age, BMI, TC, TG, HDL-C, LDL-C) were analyzed using means ± standard deviation (SD). The difference between the CAD and control groups was analyzed using an independent-sample t-test. Differences in frequencies of smoking, drinking, hypertension, diabetes mellitus, and CYP17A1 genotypes were analyzed using χ2 test or Fisher’s exact test while appropriate. P-value < 0.05 was considered statistically significant. Logistic regression analyses with effect ratios (odds ratio [OR] and 95% CI) were used to assess contribution of major risk factors. Statistical significance was established at P < 0.05. Based on the genotype data of the genetic variations, we performed linkage disequilibrium (LD) analysis and haplotype-based case–control analysis, using the expectation maximization (EM) algorithm [27] and the software SHEsis (http://analysis.bio-x.cn/SHEsisMain.htm). The pairwise linkage disequilibrium analysis was performed using five SNP pairs. We usedD'values of > 0.5 to assign SNP locations to one haplotype block. Single nucleotide polymorphisms with an r2 value of < 0.5 were selected as tagged. In the haplotype-based case–control analysis, haplotypes with a frequency of < 0.03 were excluded. The frequency distribution of the haplotypes was calculated by performing a permutation test using the bootstrap method.

Results

Table 1 shows demographic and clinical characteristics of the study participants. There was no significant difference in age between CAD patients and control subjects. It means the study was an age-matched case–control study. For total subjects, men, and women participants, there was no significant difference in the following variables between the CAD patients and the control participants: hypertension, smoking, drinking, diastolic blood pressure (DBP), and serum concentration of total cholesterol, low-density lipoprotein cholesterol, and uric acid. The incidence of diabetes, and the plasma concentration of glucose, creatinine was significantly higher in subjects with CAD than in the controls. For total participants, the following values were significantly higher for the CAD patients as compared to the control subjects: systolic blood pressure (SBP), the plasma concentration of triglyceride, high-density lipoprotein cholesterol. And the plasma concentration of HDL was significantly lower for patients with CAD than for control participants. For women, the systolic blood pressure (SBP), triglyceride, were significantly higher for patients with CAD than for control participants.
Table 1

Demographic and clinical characteristics of study participants

 

Total

Men

Women

CAD

Control

P value

CAD

Control

P value

CAD

Control

P value

Number(n)

490

507

 

278

263

 

212

244

 

Age, mean (SD)

61.94 (9.97)

61.20 (10.07)

0.242

60.65 (11.11)

59.70 (11.32)

0.326

63.63 (7.95)

62.81 (8.23)

0.280

EH, n (%)

236 (48.2)

219 (43.2)

0.115

119 (42.8)

106 (40.3)

0.555

117 (55.2)

113 (46.3)

0.059

Diabetes, n (%)

102 (20.8)

40 (7.9)

<0.001

55 (19.8)

23 (8.7)

<0.001

47 (22.2)

17 (7.0)

<0.001

Smoking, n (%)

63 (12.9)

50 (9.9)

0.136

60 (21.6)

49 (18.6)

0.392

3 (1.4)

1 (4)

0.519

Drinking, n (%)

55 (11.2)

43 (8.5)

0.146

54 (19.4)

43 (16.3)

0.371

1 (5)

0

0.994

BMI, mean (SD)

25.85 (3.41)

25.48 (3.26)

0.076

26.41 (3.35)

25.72 (3.37)

0.018

25.26 (3.39)

25.16 (3.10)

0.753

SBP, mean (SD)

138.74 (26.35)

135.49 (24.09)

0.045

136.44 (25.77)

134.82 (23.11)

0.448

141.84 (26.87)

136.20 (25.13)

0.024

DBP, mean (SD)

85.78 (18.10)

84.18 (15.66)

0.143

85.48 (18.42)

84.52 (15.94)

0.521

86.17 (17.71)

83.81 (15.38)

0.136

Glu, mean (SD)

6.28 (2.69)

5.55 (1.73)

<0.001

6.21 (2.77)

5.62 (1.92)

0.004

6.37 (2.57)

5.48 (1.50)

< 0.001

TG, mean (SD)

2.24 (2.13)

1.90 (1.48)

0.005

2.30 (2.10)

2.04 (1.57)

0.110

2.15 (2.17)

1.75 (1.37)

0.026

TC, mean (SD)

4.25 (1.16)

4.33 (1.02)

0.306

4.06 (0.99)

4.18 (1.04)

0.160

4.51 (1.30)

4.48 (0.97)

0.777

HDL, mean (SD)

1.08 (0.33)

1.13 (0.32)

0.013

1.01 (0.28)

1.05 (0.29)

0.148

1.16 (0.38)

1.22 (0.33)

0.104

LDL, mean (SD)

2.51 (0.94)

2.55 (0.86)

0.457

2.43 (0.91)

2.52 (0.89)

0.220

2.80 (2.46)

2.57 (0.82)

0.212

UA, mean (SD)

320.94 (94.17)

321.07 (82.94)

0.982

344.51 (91.79)

348.25 (75.07)

0.609

289.55 (88.13)

291.72 (81.14)

0.788

Cr, mean (SD)

77.79 (33.00)

72.21 (17.82)

0.001

84.09 (33.96)

79.15 (16.84)

0.033

69.39 (29.73)

64.71 (15.71)

0.045

BUN, mean (SD)

5.38 (1.96)

5.29 (1.86)

0.446

5.54 (1.68)

5.51 (1.68)

0.822

5.17 (2.27)

5.05 (2.01)

0.562

Data are presented as mean ± SD or n (%). Continuous variables are expressed as mean ± SD. Categorical variables are expressed as percentages. MI, body mass index; BUN, blood urea nitrogen; Cr, creatinine; DBP, diastolic blood pressure; DM, diabetes mellitus; Glu, glucose; TG, triglyceride; TC, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein; EH, essential hypertension; SBP, systolic blood pressure; UA, uric acid.

The P value of the continuous variables was calculated by the Independent t-test. The P value of the categorical variables was calculated by Fisher’s exact test.

Table 2 shows the distribution of genotypes and alleles of SNP1, SNP2, SNP3, SNP4 and SNP5 for the CYP17A1 gene. The genotype distributions for each SNP were in good agreement with the predicted Hardy–Weinberg equilibrium values (data not shown). For total, men, and women subjects, the distribution of SNP1 (rs4919686) and SNP5 (rs2486758) genotypes, dominant model, recessive model, and additive model did not show a significant difference between CAD and control participants (P > 0.05, respectively). For total participants, the other three SNPs (SNP2: rs1004467, SNP3: rs4919687, SNP4: rs10786712) genotypes, dominant model, recessive model, additive model, and allele frequency also did not show a significant difference between the CAD patients and the control subjects (P > 0.05 respectively). For men subjects, distribution of rs10786712 recessive model (CC + CT vs. TT) showed difference between CAD and control subjects (P = 0.016), and the recessive model was significantly lower in subjects with CAD than in controls (75.7% vs. 66.1%). For women participants, the dominant model (CC + CT vs. TT) of rs1004467 showed difference between CAD and control subjects (P = 0.038), and the dominant model was significantly higher in CAD patients than in control participants (76.6% vs. 67.8%). The distribution of the recessive model (AG + GG vs. AA) of rs4919687 was significantly higher in patients with CAD than in control participants (89.8% vs. 95.5%), and showed a significant difference between CAD and control subjects (P = 0.019).
Table 2

Genotype and Allele distributions in patients with CAD and control participants

Variants

Total

Man

Woman

CAD, n (%)

Control, n (%)

P value

CAD n (%)

Control n (%)

P value

CAD n (%)

Control n (%)

P value

Rs4919686 (SNP1)

Genotyping

AA

365 (76.2)

356 (77.1)

0.710

205 (75.6)

181 (76.1)

0.836

160 (76.9)

175 (78.1)

0.811

AC

110 (23.0)

100 (21.6)

63 (23.2)

53 (22.3)

47 (22.6)

47 (21.0)

CC

4 (0.8)

6 (1.3)

3 (1.1)

4 (1.7)

1 (0.5)

2 (0.9)

Recessive model

CC

4 (0.8)

6 (1.3)

0.707

3 (1.1)

4 (1.7)

0.863

1 (0.5)

2 (0.9)

1

AA + AC

475 (99.2)

456 (98.7)

268 (98.9)

234 (98.3)

207 (99.5)

222 (99.1)

Dominant model

AA

365 (76.2)

356 (77.1)

0.756

205 (75.6)

181 (76.1)

0.915

160 (76.9)

175 (78.1)

0.765

AC + CC

114 (23.8)

106 (22.9)

66 (24.4)

57 (23.9)

48 (23.1)

49 (21.9)

Additive model

AC

110 (23.0)

100 (21.6)

0.627

63 (23.2)

53 (22.3)

0.793

47 (22.6)

47 (21.0)

0.685

AA + CC

369 (77.0)

362 (78.4)

208 (76.8)

185 (77.7)

161 (77.4)

177 (79.0)

Allele

A

840 (87.7)

812 (87.9)

0.897

473 (87.3)

415 (87.2)

0.968

367 (88.2)

397 (88.6)

0.856

C

118 (12.3)

112 (12.1)

69 (12.7)

61 (12.8)

49 (11.8)

51 (11.4)

Rs1004467 (SNP2)

Genotyping

CC

86 (17.8)

89 (17.5)

0.993

41 (14.7)

45 (17.1)

0.240

45 (22.0)

44 (18.0)

0.106

CT

250 (51.8)

264 (52.0)

138 (49.6)

142 (54.0)

112 (54.6)

122 (49.8)

TT

147 (30.4)

155 (30.5)

99 (35.6)

76 (28.9)

48 (23.4)

79 (32.2)

Recessive model

CC

86 (17.8)

89 (17.5)

0.906

41 (14.7)

45 (17.1)

0.453

45 (22.0)

44 (18.0)

0.290

CT + TT

397 (82.2)

419 (82.5)

237 (85.3)

218 (82.9)

160 (78.0)

201 (82.0)

Dominant model

TT

147 (30.4)

155 (30.5)

0.979

99 (35.6)

76 (28.9)

0.095

48 (23.4)

79 (32.2)

0.038

CC + CT

336 (69.6)

353 (69.5)

179 (64.4)

187 (71.1)

157 (76.6)

166 (67.8)

Additive model

CT

250 (51.8)

264 (52.0)

0.948

138 (49.6)

142 (54.0)

0.311

112 (54.6)

122 (49.8)

0.306

CC + TT

233 (48.2)

244 (48.0)

140 (50.4)

121 (46.0)

93 (45.4)

123 (50.2)

Allele

C

422 (43.7)

442 (43.5)

0.935

220 (39.6)

232 (44.1)

0.130

202 (49.3)

210 (42.9)

0.055

T

544 (56.3)

574 (56.5)

336 (60.4)

294 (55.9)

208 (50.7)

280 (57.1)

Rs4919687 (SNP3)

Genotyping

AA

30 (6.3)

24 (4.7)

0.551

9 (3.3)

13 (5.0)

0.538

21 (10.2)

11 (4.5)

0.063

AG

155 (32.4)

171 (33.8)

93 (33.9)

93 (35.5)

62 (30.2)

78 (32.0)

GG

294 (61.4)

311 (61.5)

172 (62.8)

156 (59.5)

122 (59.5)

155 (63.5)

Recessive model

AA

30 (6.3)

24 (4.7)

0.295

9 (3.3)

13 (5.0)

0.328

21 (10.2)

11 (4.5)

0.019

AG + GG

449 (93.7)

482 (95.3)

265 (96.7)

249 (95.0)

184 (89.8)

233 (95.5)

Dominant model

GG

294 (61.4)

311 (61.5)

0.978

172 (62.8)

156 (59.5)

0.443

122 (59.5)

155 (63.5)

0.384

AA + AG

185(38.6)

195 (38.5)

102 (37.2)

106 (40.5)

83 (40.5)

89 (36.5)

Additive model

AG

155 (32.4)

171 (33.8)

0.632

93 (33.9)

93 (35.5)

0.705

62 (30.2)

78 (32.0)

0.695

AA + GG

324 (67.6)

335 (66.2)

181 (66.1)

169 (64.5)

143 (69.8)

166 (68.0)

Allele

A

215 (22.4)

219 (21.6)

0.668

111 (20.3)

119 (22.7)

0.328

104 (25.4)

100 (20.5)

0.083

G

743 (77.6)

793 (78.4)

437 (79.7)

405 (77.3)

306 (74.6)

388 (79.5)

Rs10786712 (SNP4)

Genotyping

CC

114 (23.7)

96 (20.8)

0.407

64 (23.5)

51 (21.3)

0.055

50 (23.9)

45 (20.2)

0.264

CT

239 (49.7)

228 (49.4)

142 (52.2)

107 (44.8)

97 (46.4)

121 (54.3)

TT

128 (26.6)

138 (29.9)

66 (24.3)

81 (33.9)

62 (29.7)

57 (25.6)

Recessive model

TT

128 (26.6)

138 (29.9)

0.266

66 (24.3)

81 (33.9)

0.016

62 (29.7)

57 (25.6)

0.340

CC + CT

353 (73.4)

324 (70.1)

206 (75.7)

158 (66.1)

147 (70.3)

166 (74.4)

Dominant model

CC

114 (23.7)

96 (20.8)

0.281

64 (23.5)

51 (21.3)

0.554

50 (23.9)

45 (20.2)

0.348

CT + TT

367 (76.3)

366 (79.2)

208 (76.5)

188 (78.7)

159 (76.1)

178 (79.8)

Additive model

CT

239 (49.7)

228 (49.4)

0.917

142 (52.2)

107 (44.8)

0.093

97 (46.4)

121 (54.3)

0.103

CC + TT

242 (50.3)

234 (50.6)

130 (47.8)

132 (55.2)

112 (53.6)

102 (45.7)

Allele

C

467(48.5)

420 (45.5)

0.179

270 (49.6)

209 (43.7)

0.059

197 (47.1)

211 (47.3)

0.958

T

495 (51.5)

504 (54.5)

274 (50.4)

269 (56.3)

221 (52.9)

235 (52.7)

Rs2486758 (SNP5)

Genotyping

CC

14 (2.9)

16 (3.2)

0.950

9 (3.3)

10 (3.8)

0.912

5 (2.4)

6 (2.5)

0.983

CT

162 (33.3)

164 (32.8)

95 (34.5)

87 (33.3)

67 (31.6)

77 (32.2)

TT

311 (63.9)

320 (64.0)

171 (62.2)

164 (62.8)

140 (66.0)

156 (65.3)

Recessive model

CC

14 (2.9)

16 (3.2)

0.766

9 (3.3)

10 (3.8)

0.727

5 (2.4)

6 (2.5)

0.917

CT + TT

473 (97.1)

484 (96.8)

266 (96.7)

251 (96.2)

207 (97.6)

233 (97.5)

Dominant model

TT

311 (63.9)

320 (64.0)

0.964

171 (62.2)

164 (62.8)

0.876

140 (66.0)

156 (65.3)

0.864

CC + CT

176 (36.1)

180 (36.0)

104 (37.8)

97 (37.2)

72 (34.0)

83 (34.7)

Additive model

CT

162 (33.3)

164 (32.8)

0.877

95 (34.5)

87 (33.3)

0.767

67 (31.6)

77 (32.2)

0.889

CC + TT

325 (66.7)

336 (67.2)

180 (65.5)

174 (66.7)

145 (68.4)

162 (67.8)

Allele

C

190 (19.5)

196 (19.6)

0.959

113 (20.5)

107 (20.1%

0.985

77 (18.2)

89 (18.6)

0.859

T

784 (80.5)

804 (80.4)

437 (79.5)

415 (79.5)

347 (81.8)

389 (81.4)

CAD, Coronary artery disease; N, number of participants; SNP, single-nucleotide polymorphism.

Tables 3, 4, and 5 showed the multivariable logistic regression analyses done with the following variables: prevalence of conventional risk factors for CAD including hypertension, diabetes, smoking and drinking, and plasma concentration of blood glucose, TG, Cr, and SBP. For women (Table 3), after multivariate adjustment, rs1004467 remains significantly association with CAD in dominant model (OR = 1.623, 95%CI: 1.023-2.576, P = 0.040); rs4919687 (Table 4) remains significantly association with CAD (OR = 0.417, 95%CI: 0.188-0.926, P = 0.032) in recessive model. For men (Table 5), the significant difference of rs10786712 (OR = 1.644, 95% confidence interval [CI]:1.087-2.488, P = 0.019) was retained after adjustment of the major confounding factors for CAD in recessive model.
Table 3

Multiple logistic regression analysis for CAD patients and control subjects (rs1004467)

 

Total

Men

Woman

 

OR

95% CI

P

OR

95% CI

P

OR

95% CI

P

Dominant model (CC + CT vs TT)

1.024

0.766- 1.370

0.872

0.731

0.498- 1.075

0.112

1.623

1.023- 2.576

0.040

Hypertension

0.988

0.729- 1.340

0.939

0.955

0.635- 1.436

0.825

1.082

0.670- 1.748

0.747

Diabetes

2.485

1.619- 3.812

< 0.001

2.246

1.248- 4.044

0.007

2.957

1.556- 5.619

0.001

Smoking

1.490

0.827- 2.676

0.184

1.381

0.748- 2.549

0.303

2.291

0.180- 29.077

0.523

Drinking,

0.908

0.485- 1.703

0.764

0.941

0.493- 1.796

0.845

0

0

1

Glucose

1.147

1.058- 1.243

0.001

1.107

0.991- 1.235

0.071

1.198

1.065- 1.347

0.003

triglyceride

1.088

1.055- 1.177

0.038

1.060

0.960- 1.171

0.249

1.138

0.988- 1.310

0.072

Creatinine

1.099

1.002- 1.005

0.011

1.009

1.000- 1.018

0.046

1.004

0.991- 1.016

0.567

SBP

1.003

0.997- 1.009

0.271

1.001

0.993- 1.009

0.851

1.006

0.997- 1.015

0.191

CAD, Coronary artery disease; OR, odds ratios; 95%CI, 95% confidence intervals; SBP, systolic blood pressure.

Table 4

Multiple logistic regression analysis for CAD patients and control subjects (rs4919687)

 

Total

Men

Woman

 

OR

95% CI

P

OR

95% CI

P

OR

95% CI

P

Recessive model (AG + GG vs AA)

0.673

0.372- 1.217

0.190

1.324

0.512- 3.428

0.563

0.417

0.188- 0.926

0.032

Hypertension

1.001

0.738- 1.358

0.996

0.958

0.637- 1.442

0.838

1.155

0.717- 1.862

0.55

Diabetes

2.484

1.618- 3.814

< 0.001

2.370

1.310- 4.289

0.004

2.857

1.508- 5.411

0.001

Smoking

1.546

0.853- 2.803

0.151

1.455

0.783- 2.701

0.235

2.959

2.33- 37.579

0.403

Drinking,

0.861

0.455- 1.629

0.645

0.864

0.449- 1.662

0.662

0

0

1

Glucose

1.154

1.063- 1.253

0.001

1.094

0.979- 1.223

0.114

1.218

1.080- 1.373

0.001

triglyceride

1.096

1.012- 1.187

0.024

1.064

0.963- 1.175

0.224

1.154

1.003- 1.327

0.045

Creatinine

1.009

1.002- 1.015

0.013

1.009

1.000- 1.018

0.054

1.003

0.991- 1.016

0.606

SBP

1.003

0.997- 1.009

0.272

1.001

0.993- 1.009

0.853

1.005

0.996- 1.014

0.246

CAD, Coronary artery disease; OR, odds ratios; 95%CI, 95% confidence intervals; SBP, systolic blood pressure.

Table 5

Multiple logistic regression analysis for CAD patients and control subjects (rs10786712)

 

Total

Men

Woman

OR

95% CI

P

OR

95% CI

P

OR

95% CI

P

Recessive model (CC + CT vs TT)

1.193

0.879- 1.619

0.259

1.644

1.087- 2.488

0.019

0.786

0.495- 1.248

0.308

Hypertension

1.010

0.742- 1.377

0.947

0.996

0.655- 1.513

0.985

1.117

0.691- 1.805

0.651

Diabetes

2.305

1.481- 3.587

< 0.001

2.458

1.313- 4.600

0.005

2.433

1.278- 4.630

0.007

Smoking

1.523

0.833-2.785

0.172

1.421

0.756- 2.669

0.275

2.637

0.210- 33.171

0.453

Drinking,

0.825

0.434- 1.567

0.556

0.835

0.431- 1.618

0.593

0

0

1

Glucose

1.171

1.075- 1.276

< 0.001

1.111

0.988- 1.250

0.079

1.227

1.084- 1.388

0.001

triglyceride

1.082

0.999- 1.571

0.052

1.062

0.960- 1.175

0.243

1.134

0.986- 1.304

0.077

Creatinine

1.009

1.001- 1.016

0.010

1.010

1.000-1.019

0.046

1.005

0.993- 1.017

0.433

SBP

1.002

0.996- 1.008

0.492

0.999

0.990- 1.007

0.795

1.005

0.996- 1.014

0.281

CAD, Coronary artery disease; OR, odds ratios; 95%CI, 95% confidence intervals; SBP, systolic blood pressure.

Table 6 shows patterns of linkage disequilibrium (LD) analysis in the CYP17A1. All 5 SNPs are located in one haplotype block forD'values were beyond 0.5, and all of the r2 values were below 0.5. Because theD'for SNP2-SNP3 was < 0.5, this meant that SNP2 and SNP3 could not be used to simultaneously construct haplotypes. As the minor allele frequency of SNP3 was larger than that of SNP2, we constructed haplotypes using SNP1, SNP3, SNP4, and SNP5.
Table 6

Pairwise linkage disequilibrium for five SNPs

D' values

r2 values

 

SNP1

SNP2

SNP3

SNP4

SNP5

SNP1

 

0.575

0.911

1.000

0.913

SNP2

0.035

 

0.470

0.567

0.630

SNP3

0.407

0.049

 

0.874

0.723

SNP4

0.123

0.219

0.194

 

0.926

SNP5

0.028

0.074

0.036

0.236

 

D' above the diagonal and r2 below the diagonal.

Table 7 shows the result of haplotype analysis. In the haplotype-based case–control analysis, haplotypes were established through different combinations of the 4 SNPs. For total, including men and women, the overall distribution of haplotypes were no significantly different between the CAD patients and the control subjects. For men, the frequencies of the C–T, G–C–T, A–C–T, and A–G–C–T haplotypes respectively established by SNP4–SNP5, SNP3–SNP4–SNP5, SNP1–SNP4–SNP5, and SNP1–SNP3–SNP4–SNP5 were significantly higher for the CAD patients as compared to the control subjects (P = 0.047, P = 0.048, P = 0.040, and P = 0.039, respectively). The frequencies of the A–A–T, A–T–T, and A–A–T–T haplotypes respectively established by SNP1–SNP3–SNP5, SNP1–SNP4–SNP5, and SNP1–SNP3–SNP4–SNP5 were lower for CAD patients than for control participants (P = 0.040, P = 0.031, P = 0.033, respectively). For women, the frequency of the A–T, A–A–T, A–A–T, and A–A–T–T haplotypes established by SNP3–SNP4, SNP1–SNP3–SNP4, SNP1–SNP3–SNP5, and SNP1–SNP3–SNP4–SNP5 respectively were significantly higher for the CAD patients as compared to the control subjects (P = 0.041, P = 0.034, P = 0.035, and P = 0.045, respectively).
Table 7

Haplotype analysis in patients with CAD and in control subjects

Haplotype

No. 1

No. 2

No. 3

No. 4

Overall P value

Frequency in total

Frequency in man

Frequency in woman

Total

Man

Woman

CAD

Control

P value

CAD

Control

P value

CAD

Control

P value

  

SNP3

 

SNP5

0.877

0.340

0.105

         

H1

 

A

 

T

   

0.217

0.206

0.617

0.188

0.222

0.190

0.249

0.189

0.041

H2

 

G

 

C

   

0.183

0.182

0.985

0.188

0.196

0.754

0.170

0.167

0.951

H3

 

G

 

T

   

0.591

0.597

0.686

0.609

0.570

0.182

0.573

0.627

0.074

   

SNP4

SNP5

0.399

0.099

0.964

         

H1

  

C

C

   

0.192

0.186

0.793

0.201

0.195

0.857

0.180

0.177

0.985

H2

  

C

T

   

0.294

0.268

0.235

0.299

0.241

0.047

0.288

0.296

0.787

H3

  

T

T

   

0.509

0.535

0.201

0.494

0.550

0.055

0.528

0.520

0.854

 

SNP1

SNP3

SNP4

 

0.612

0.167

0.155

         

H1

A

A

T

    

0.094

0.094

0.981

0.070

0.104

0.053

0.127

0.082

0.034

H2

A

G

C

    

0.471

0.446

0.280

0.487

0.436

0.102

0.449

0.456

0.834

H3

A

G

T

    

0.299

0.327

0.192

0.307

0.324

0.552

0.290

0.331

0.189

 

SNP1

SNP3

 

SNP5

0.961

0.225

0.154

         

H1

A

A

 

T

   

0.098

0.096

0.933

0.067

0.103

0.040

0.135

0.088

0.035

H2

A

G

 

C

   

0.186

0.181

0.795

0.192

0.194

0.972

0.173

0.167

0.853

H3

C

A

 

T

   

0.116

0.109

0.701

0.119

0.117

0.893

0.110

0.102

0.742

 

SNP1

 

SNP4

SNP5

0.415

0.117

0.986

         

H1

A

 

C

T

   

0.295

0.268

0.225

0.302

0.242

0.040

0.286

0.296

0.733

H2

A

 

T

T

   

0.385

0.419

0.104

0.364

0.427

0.031

0.415

0.411

0.953

H3

C

 

T

T

   

0.122

0.115

0.668

0.127

0.121

0.830

0.114

0.109

0.830

  

SNP3

SNP4

SNP5

0.463

0.211

0.282

         

H1

 

A

T

T

   

0.211

0.205

0.776

0.190

0.221

0.219

0.239

0.187

0.071

H2

 

G

C

C

   

0.180

0.177

0.905

0.190

0.189

0.968

0.165

0.164

0.976

H3

 

G

C

T

   

0.291

0.266

0.239

0.297

0.241

0.048

0.284

0.292

0.761

 

SNP1

SNP3

SNP4

SNP5

0.577

0.098

0.302

         

H1

A

A

T

T

   

0.092

0.094

0.878

0.066

0.103

0.033

0.127

0.084

0.045

H2

A

G

C

C

   

0.181

0.177

0.851

0.191

0.189

0.949

0.169

0.165

0.913

H3

A

G

C

T

   

0.292

0.226

0.221

0.300

0.243

0.039

0.280

0.290

0.693

H4

A

G

T

T

   

0.294

0.326

0.124

0.297

0.324

0.360

0.291

0.329

0.213

H5

C

A

T

T

   

0.117

0.110

0.673

0.121

0.116

0.817

0.110

0.103

0.753

CAD, Coronary artery disease; haplotype with frequencies >0.03 were estimated using SHEsis software; P value was calculated by permutation test using the bootstrap method; SNP, single-nucleotide polymorphism.

Discussion

Relationship between CYP genetic polymorphism and cardiovascular disease (CVD) has been established [10]. In our study, we found the variations in CYP17A1 gene was associated with CAD in a Han population of China, even after multivariate adjustment, the association still maintained This is the first study to reveal the relation between CAD and CYP17A1 gene in Chinese population.

The pathogenesis of CAD includes the disorders of lipoprotein metabolism [28],[29], disturbance of blood coagulation and fibrinolytic system [30],[31], insulin resistance or diabetes [32],[33], hypertension [34],[35], and the impairment and inflammation of vascular endothelium [36]-[38]. As previously mentioned, P450c17proteins is an important enzyme that catalyzes the formation of all endogenous androgens. Therefore, CYP17A1 genetic mutations can loss of the enzyme activity of P450c17 and potentially reduce androgen biosynthesis.

In recent years, many clinical studies showed that testosterone levels play an important role in the progress of CAD among elderly men [39], whereas lower testosterone levels promote CAD [40]. In addition, some evidences have indicated that testosterone also was related to the risk factors of CAD. For example, numerous studies have confirmed that the dysfunction of vascular endothelial as a key mechanism of occurrence and development of CAD [36]-[38], the levels of physiological testosterone can promote the endothelial cells release of nitric oxide (NO) through improve the vascular endothelial function, and low levels of testosterone can decrease the vascular endothelial function and promote the occurrence of CAD [41]. The disorders of lipoprotein metabolism also one of primary mechanisms of CAD [28],[29], research shows there was a positive correlation between the levels of testosterone and HDL-C, and a negatively correlated between the levels of testosterone and the TG, TC, LDL-C and VLDL. Malkin et al. also confirmed that the low levels of testosterone can lead to lipid disorders, and supplement testosterone can correct dyslipidemia [42]. Insulin resistance and diabetes are the significant independent risk factors for CAD [32],[33]. Selvin et al. indicated that there is a relationship between diabetes and the low free or low bioactive testosterone levels [43]. Blood coagulation and fibrinolytic system is the important mechanism of the CAD [30],[31], physiological levels of testosterone can improve the function of endothelial cells [44], low levels of testosterone can increase the proteins related to clotting factor VIII, causing endothelial dysfunction and vascular inflammatory reaction, finally, platelet adhesion in the damaged blood vessels, resulting in the incidence of CAD. Androgens serve as precursors to estrogens, so normal estrogen signaling is also dependent on CYP17A1. Estrogen plays a very important role in many other physiological and pathological process, such as mediation vasoconstriction, vascular endothelium repair and lipid metabolism, involves in glucose metabolism and insulin related signal transduction pathways, etc., which are directly or indirectly affect the function of cardiovascular system. Wellons et al. reported that early menopause is associated with an increased risk of CAD [45], as well as high levels of endogenous estrogen explain the low prevalence of CAD in premenopausal women [46]. It is worth noting that the protective value of sex hormones appears to be sex-specific, high levels of estrogen and oestrone in men are associated with an increased risk of and CAD [39].

In our study, we found that polymorphisms of CYP17A1 were associated with risk of CAD in a Han population. For rs10786712, in men, the recessive model (CC + CT vs TT) was significantly higher in control subjects than in CAD patients, after multivariate adjustment of confounding factors such as plasma concentration of TG, Glu, Cr, incidence of hypertension, diabetes, drinking, and smoking for CAD, the significant difference was retained. This indicated that the TT genotype might be protecting against for CAD in men. For rs1004467 and rs4919687, in women, the dominant model (rs1004467) and recessive model (rs4919687) were significantly higher in CAD subjects than in control participants, after multivariate adjustment of confounding factors, the significant difference was retained. This result indicated that CC genotype of rs1004467 and AA genotype of rs4919687 are risk factor for CAD in women.

In addition, we hypothesized that haplotype analysis would be useful for the assessment of association between haplotypes and CAD. For men, we found a susceptible haplotype [A-C-T (SNP1-SNP4-SNP5)], and a protective haplotype [A-T-T (SNP1-SNP4-SNP5)]. And these haplotypic analysis results were consistent with the genotypic analysis results of SNP4 (rs10786712) that the CC genotype confers risk and the TT genotype is protective. For women, significant differences were found for the frequency of occurrence of the haplotype (A-T of SNP3-SNP5, A-A-T of SNP1-SNP3-SNP4, A-A-T of SNP1-SNP3-SNP5, and A-A-T-T of SNP1-SNP3-SNP4-SNP5, respectively).

There are still several limitations must be mentioned in the present study. Firstly, we did not perform enzymatic LDL, which would decrease the mean and SD of the LDL results. Secondly, we did not collect the data on lipid-lowering drug levels and drug compliance. Finally, this study was limited by the relatively small sample size, it needs a large number of clinical samples and investigation of other SNPs of CYP17A1 in future studies.

Conclusion

In conclusion, this is the first study to investigate the differences between the human CYP17A1 and CAD in Han population of China, and is the first haplotype-based case–control study and to correlations its association with CAD. Rs1004467, rs4919687, rs10786712 of CYP17A1gene are associated with CAD in Han population of China. The TT genotype of rs10786712 could be a protective genetic marker of CAD in men. The CC genotype of rs1004467 and the AA genotype of rs4919687 could be two risk genetic marker of CAD in women. However, large sample size study including other SNPs of CYP17A1 should be performed in future studies.

Declarations

Acknowledgements

This study was supported by National Natural Science Foundation of China (81160017 and 81470014) and Xinjiang Science and Technology Projects (201491181).

Authors’ Affiliations

(1)
Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University

References

  1. Manace LC, Godiwala TN, Babyatsky MW: Genomics of cardiovascular disease. Mt Sinai J Med. 2009, 76: 613-623. 10.1002/msj.20151View ArticlePubMedGoogle Scholar
  2. Zee RY1, Cheng S, Hegener HH, Erlich HA, Ridker PM: Genetic variants of arachidonate 5-lipoxygenase-acti-vating protein, and risk of incident myocardial infarction and ischemic stroke: a nested case–control approach. Stroke. 2006, 37: 2007-2011. 10.1161/01.STR.0000229905.25080.01View ArticlePubMedGoogle Scholar
  3. Maouche S, Schunkert H: Strategies beyond genome-wide association studies for atherosclerosis. Arterioscler Thromb Vasc. 2012, 32: 170-181. 10.1161/ATVBAHA.111.232652.View ArticleGoogle Scholar
  4. Roberts R, Stewart AF: Genes and coronary artery disease: where are we?. J Am Coll Cardiol. 2012, 60: 1715-1721. 10.1016/j.jacc.2011.12.062View ArticlePubMedGoogle Scholar
  5. Nordlie MA, Wold LE, Kloner RA: Genetic contributors toward increased risk for 301 ischemic heart disease. J Mol Cell Cardiol. 2005, 39: 667-679. 10.1016/j.yjmcc.2005.06.006View ArticlePubMedGoogle Scholar
  6. Damani SB, Topol EJ: Emerging genomic applications in coronary artery disease. J ACC Cardiovasc. 2011, 4: 473-482.Google Scholar
  7. O'Donnell CJ, Kavousi M, Smith AV, Kardia SL, Feitosa MF, Hwang SJ: Genome-wide association study for coronary artery calcification with follow-Up in myocardial infarction. J Circulation. 2012, 124: 2855-2864. 10.1161/CIRCULATIONAHA.110.974899.View ArticleGoogle Scholar
  8. Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H: Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. J Nat Genet. 2011, 43: 333-338. 10.1038/ng.784.View ArticleGoogle Scholar
  9. Zordoky BN, El-Kadi AO: Effect of cytochrome P450 polymorphism on arachidonic acid metabolism and their impact on cardiovascular diseases. Pharmacol Ther. 2010, 125: 446-463. 10.1016/j.pharmthera.2009.12.002View ArticlePubMedGoogle Scholar
  10. Elbekai RH, El-Kadi AO: Cytochrome P450 enzymes: central players in cardiovascular health and disease. Pharmacol Ther. 2006, 112: 564-587. 10.1016/j.pharmthera.2005.05.011View ArticlePubMedGoogle Scholar
  11. Nakayama T, Soma M, Rehemudula D, Takahashi Y, Tobe H, Satoh M: Association of 5’ upstream promoter region of prostacyclin synthase gene variant with cerebral infarction. Am J Hypertens. 2000, 13: 1263-1267. 10.1016/S0895-7061(00)01216-4View ArticlePubMedGoogle Scholar
  12. Wang XL, Greco M, Sim AS, Duarte N, Wang J, Wilcken DE: Effect of CYP1A1 MspI polymorphism on cigarette smoking related coronary artery disease and diabetes. Atherosclerosis. 2002, 162: 391-397. 10.1016/S0021-9150(01)00723-7View ArticlePubMedGoogle Scholar
  13. Cornelis MC, El-Sohemy A, Campos H: Genetic polymorphism of CYP1A2 increases the risk of myocardial infarction. J Med Genet. 2004, 41: 758-762. 10.1136/jmg.2004.022012PubMed CentralView ArticlePubMedGoogle Scholar
  14. Yasar U1, Bennet AM, Eliasson E, Lundgren S, Wiman B, De Faire U, Rane A: Allelic variants of cytochromes P450 2C modify the risk for acute myocardial infarction. Pharmacogenetics. 2003, 13: 715-720. 10.1097/00008571-200312000-00002View ArticlePubMedGoogle Scholar
  15. Spiecker M, Darius H, Hankeln T, Soufi M, Sattler AM, Schaefer JR, Node K, Börgel J, Mügge A, Lindpaintner K, Huesing A, Maisch B, Zeldin DC, Liao JK: Risk of coronary artery disease associated with polymorphism of the cytochrome P450 epoxygenase CYP2J2. Circulation. 2004, 110: 2132-2136. 10.1161/01.CIR.0000143832.91812.60PubMed CentralView ArticlePubMedGoogle Scholar
  16. Hengstenberg C1, Holmer SR, Mayer B, Löwel H, Engel S, Hense HW, Riegger GA, Schunkert H: Evaluation of the aldosterone synthase (CYP11B2) gene polymorphism in patients with myocardial infarction. Hypertension. 2000, 35: 704-709. 10.1161/01.HYP.35.3.704View ArticlePubMedGoogle Scholar
  17. Letonja M, Peterlin B, Bregar D, Petrovic D: Are the T/C polymorphism of the CYP17 gene and the tetranucleotide repeat (TTTA) polymorphism of the CYP19 gene genetic markers for premature coronary artery disease in Caucasians?. Folia Biol (Praha). 2005, 51: 76-81.Google Scholar
  18. Fan YM1, Raitakari OT, Kähönen M, Hutri-Kähönen N, Juonala M, Marniemi J, Viikari J, Lehtimäki T: Hepatic lipase promoter C-480 T polymorphism is associated with serum lipids levels, but not subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study. Clin Genet. 2009, 76: 46-53. 10.1111/j.1399-0004.2009.01180.xView ArticlePubMedGoogle Scholar
  19. Nehal N, Mehta MD: MSCE: Large-Scale Association Analysis Identifies 13 New Susceptibility Loci for Coronary Artery Disease. Circ Cardiovasc Genet. 2011, 4: 327-329. 10.1161/CIRCGENETICS.111.960443.View ArticleGoogle Scholar
  20. Schunkert H1, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H: Large-scale association analyses identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011, 43: 333-338. 10.1038/ng.784PubMed CentralView ArticlePubMedGoogle Scholar
  21. Butterworth AS, Braund PS, Farrall M: Large-scale gene-centric analysis identifies novel variants for coronary artery disease. PLoS Genet. 2011, 7: e1002260-10.1371/journal.pgen.1002260.View ArticleGoogle Scholar
  22. American Diabetes Association, Clinical practice recommendations. Diabetes Care. 1997, 20: S1-S70. 10.2337/diacare.20.1.S1.View ArticleGoogle Scholar
  23. Nakayama T, Soma M, Rahmutula D, Ozawa Y, Kanmatsuse K: Isolation of the 5-flanking region of genes by thermal asymmetric interlaced polymerase chain reaction. Med Sci Monit. 2001, 7: 345-349.PubMedGoogle Scholar
  24. Xiang X, Ma YT, Fu ZY, Yang YN, Xiang M, Chen BD, Wang YH, Fen L: Haplotype Analysis of the CYP8A1 gene associated with myocardial infarction. Clin Appl Thromb-Hem. 2009, 15: 574-580. 10.1177/1076029608329581.View ArticleGoogle Scholar
  25. Xie X, Ma YT, Fu ZY, Yang YN, Ma X, Chen BD, Wang YH, Liu F: Association of polymorphisms of PTGS2 and CYP8A1 with myocardial infarction. Clin Chem Lab Med. 2009, 47: 347-352. 10.1515/CCLM.2009.078View ArticlePubMedGoogle Scholar
  26. Yang YN, Wang XL, Ma YT, Xie X, Fu ZY, Li XM, Chen BD, Liu F: Association of interaction between smoking and CYP 2C19*3 polymorphism with coronary artery disease in a Uighur population. Clin Appl Thromb Hemost. 2010, 16: 579-583. 10.1177/1076029610364522View ArticlePubMedGoogle Scholar
  27. Dempster AP, Laird NM, Rubin DB: Maximum likelihood from in complete data via the EM algorithm. J R Stat Soc. 1977, 39: 1-22.Google Scholar
  28. Arsenault BJ, Lemieux I, Després JP, Wareham NJ, Kastelein JJ, Khaw KT, Boekholdt SM: The hypertriglyceridemic-waist phenotype and the risk of coronary artery disease: results from the EPIC-Norfolk prospective population study. CMAJ. 2010, 182: 1427-1432. 10.1503/cmaj.091276PubMed CentralView ArticlePubMedGoogle Scholar
  29. Goswami B1, Rajappa M, Singh B, Ray PC, Kumar S, Mallika V: Inflammation and dyslipidaemia: a possible interplay between established risk factors in North Indian males with coronary artery disease. Cardiovasc J Afr. 2010, 21: 103-108.PubMed CentralPubMedGoogle Scholar
  30. Maruyama I: Coagulation/fibrinolytic system and platelet in acute coronary syndrome. Nihon Rinsho. 1998, 56: 2488-2492.PubMedGoogle Scholar
  31. Folsom AR1, Aleksic N, Park E, Salomaa V, Juneja H, Wu KK: Prospective study of fibrinolytic factors and incident coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study. Arterioscler Thromb Vasc Biol. 2001, 21: 611-617. 10.1161/01.ATV.21.4.611View ArticlePubMedGoogle Scholar
  32. Raz I: Relationship between blood glucose control and improved cardiovascular outcome after stent implantation in diabetic patients. Cardiology. 2010, 116: 48-50. 10.1159/000314330View ArticlePubMedGoogle Scholar
  33. Reaven GM: Insulin resistance, the insulin resistance syndrome, and cardiovascular disease. Panmineva Med. 2005, 47: 201-210.Google Scholar
  34. Srikanthan VS, Dunn FG: Hypertension and coronary artery disease. Med Clin North Am. 1997, 81: 1147-1163. 10.1016/S0025-7125(05)70572-1View ArticlePubMedGoogle Scholar
  35. Nakamura Y, Saitoh S, Takagi S, Ohnishi H, Chiba Y, Kato N, Akasaka H, Miura T, Tsuchihashi K, Shimamoto K: Impact of abnormal glucose tolerance, hypertension and other risk factors on coronary artery disease. Circ J. 2007, 71: 20-25. 10.1253/circj.71.20View ArticlePubMedGoogle Scholar
  36. Mehta JL, Saldeen TG, Rand K: Interactive role of infection, inflammation and traditional risk factors in atherosclerosis and coronary artery disease. J Am Coll Cardiol. 1998, 31: 1217-1225. 10.1016/S0735-1097(98)00093-XView ArticlePubMedGoogle Scholar
  37. Farmer JA, Torre-Amione G: Atherosclerosis and inflammation. Curr Atheroscler Rep. 2002, 4: 92-98. 10.1007/s11883-002-0031-5View ArticlePubMedGoogle Scholar
  38. Kitta Y, Obata JE, Nakamura T, Hirano M, Kodama Y, Fujioka D, Saito Y, Kawabata K, Sano K, Kobayashi T, Yano T, Nakamura K, Kugiyama K: Persistent impairment of endothelial vasomotor function has a negative impact on outcome in patients with coronary artery disease. J Am Coll Cardiol. 2009, 53: 323-330. 10.1016/j.jacc.2008.08.074View ArticlePubMedGoogle Scholar
  39. Naessen T1, Sjogren U, Bergquist J, Larsson M, Lind L, Kushnir MM: Endogenous steroids measured by high-specificity liquid chromatography-tandem mass spectrometry and prevalent cardiovascular disease in 70-year-old men and women. J Clin Endocrinol Metab. 2010, 95: 1889-1897. 10.1210/jc.2009-1722View ArticlePubMedGoogle Scholar
  40. Malkin CJ, Pugh PJ, Morris PD, Asif S, Jones TH, Channer KS: Low serum testosterone and increased mortality in men with coronary heart disease. Heart. 2010, 96: 1821-1825. 10.1136/hrt.2010.195412View ArticlePubMedGoogle Scholar
  41. Maturana MA1, Breda V, Lhullier F, Spritzer PM: Relationship between endogenous testosterone and cardiovascular risk in early postmenopausal women. Metabolism. 2008, 57: 961-965. 10.1016/j.metabol.2008.02.012View ArticlePubMedGoogle Scholar
  42. Malkin CJ1, Pugh PJ, Jones RD, Kapoor D, Channer KS, Jones TH: The effect of testosterone replacement on endogenous inflammatory eytokines and lipid profiles in hypogonadal men. J Clin Endocrinol Metab. 2004, 89: 3313-3318. 10.1210/jc.2003-031069View ArticlePubMedGoogle Scholar
  43. Selvin E, Feinleib M, Zhang L: Androgens and diabetes in men results from the Third National Health and Nutrition Examination Survey (NHANES III). Diabetes Care. 2007, 30: 234-238. 10.2337/dc06-1579View ArticlePubMedGoogle Scholar
  44. Jin H, Lin J, Fu L: Physiological testosterone stimulates tissue plasminogen activator and tissue factor pathway inhibitor and inhibits plasminogen activator inhibitor type l release in endothelial cells. Biochem Cell Bid. 2007, 85: 246-251. 10.1139/O07-011.View ArticleGoogle Scholar
  45. Wellons M, Ouyang P, Schreiner PJ: Early menopause predicts future coronary heart disease and stroke: the Multi-Ethnic Study of Atherosclerosis. Menopause. 2012, 19: 1081-1087. 10.1097/gme.0b013e3182517bd0PubMed CentralView ArticlePubMedGoogle Scholar
  46. Mendelsohn ME, Karas RH: The protective effects of estrogen on the cardiovascular system. N EngI J Med. 1999, 340: 1801-1811. 10.1056/NEJM199906103402306.View ArticleGoogle Scholar

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