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Research Papers: Gerotarget (Focus on Aging):

CDKN2BAS polymorphisms are associated with coronary heart disease risk a Han Chinese population

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Oncotarget. 2016; 7:82046-82054. https://doi.org/10.18632/oncotarget.12575

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Qingbin Zhao _, Shudan Liao, Huiyi Wei, Dandan Liu, Jingjie Li, Xiyang Zhang, Mengdan Yan and Tianbo Jin

Abstract

Qingbin Zhao1, Shudan Liao2, Huiyi Wei1, Dandan Liu3, Jingjie Li4,5, Xiyang Zhang5, Mengdan Yan4,5 and Tianbo Jin4,5

1 Department of Geratology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China

2 Department of Cardiology, Xi’an Center Hospital, Xi’an, Shaanxi, China

3 Department of Endocrinology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China

4 Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, Xi’an, Shaanxi, China

5 Xi’an Tiangen Precision Medical Institute, Xi’an, Shaanxi, China

Correspondence to:

Qingbin Zhao, email:

Keywords: coronary heart disease; single nucleotide polymorphisms; CDKN2BAS; case-control,Gerotarget

Received: September 01, 2016 Accepted: October 05, 2016 Published: October 11, 2016

Abstract

The goal of our study was to determine whether CDKN2BAS polymorphisms are associated with coronary heart disease (CHD) risk in a Han Chinese population. Eight SNPs were genotyped in 676 men and 465 women. We used χ2 tests and genetic model analyses to evaluate associations between the SNPs and CHD risk. We found that rs10757274 was associated with an increased risk of CHD in both men (allele G: Odds ratio [OR] = 1.30, 95% confidence interval [CI]: 1.05-1.61, P = 0.018; codominant model: P = 0.042; recessive model: OR = 1.70, 95% CI: 1.10-2.62, P = 0.016; log-additive model: OR = 1.34, 95% CI: 1.05-1.71, P = 0.019) and women (dominant model: OR = 2.26, 95% CI: 1.28-3.99, P = 0.004). In addition, rs7865618 was associated with an 8.10-fold increased risk of CHD in women under a recessive model (OR = 8.10, 95% CI: 1.74-37.68, P = 0.006). Interestingly, the haplotype AA (rs10757274 and rs1333042) of CDKN2BAS was associated with decreased the risk of CHD in men (OR = 0.72, 95% CI: 0.55 - 0.95, P = 0.022).


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Introduction

Coronary heart disease (CHD), is a major cause of morbidity and mortality worldwide [1] and one of the most common chronic inflammatory diseases, characterized by remodeling and narrowing of the blood vessels (coronary arteries) that supply oxygen and blood to the heart [2]. CHD is a complex disease influenced by both environmental and genetic factors [3, 4]. Epidemiological studies have identified many risk factors for CHD, including age, gender, smoking, obesity, diabetes, hyperlipidemia, hypertension, lack of exercise, and dietary factors. Twin and family studies have demonstrated that a significant proportion (40-50%) of susceptibility to CHD is inherited [5]. Although atherosclerosis is one of the main pathophysiological mechanisms of CHD [2], the responsible molecular and genetic determinants remain largely unidentified. Recently, both genome-wide association studies (GWAS) and candidate gene studies have reported that CDKN2BAS (cyclin-dependent kinase inhibitor 2B antisense RNA) is a risk gene for CHD susceptibility [6-8].

CDKN2BAS encodes an antisense non-coding RNA, and is located near the CDKN2A-CDKN2B gene. The precise function of CDKN2BAS is unclear, but it regulates the expression of neighboring protein-coding genes, like CDKN2A, CDKN2B, and MTAP, that enhance the progression of atherosclerosis by influencing vascular remodeling, thrombogenesis, and plaque stability [9, 10]. Therefore, CDKN2BAS expression plays a pivotal role in the development of CHD by altering the dynamics of vascular cell proliferation. In addition, single nucleotide polymorphisms (SNPs) in CDKN2BAS are associated with the risk of multiple diseases, including comprising CHD [7, 9, 11], myocardial infarction [7], type 2 diabetes [12], ischemic stroke [13], and periodontitis [14].

While many studies have demonstrated that polymorphisms in CDKN2BASare associated with the risk of CHD, few studies have focused on the effects of these alterations on susceptibility to CHD in the Han Chinese population. Therefore, we performed a case-control study to investigate the associations between these SNPs and the risk of CHD in the Han Chinese men and women.

Results

Basic information on the eight SNPs in CDKN2BAS, including chromosomal position, role, allele, minor allele frequency (MAF), and Hardy-Weinberg Equilibrium (HWE) test results are shown in Table 1. All SNPs were in HWE in the control groups (P > 0.05). We then compared the differences in frequency distributions of alleles between cases and controls using Chi-squared tests and found that the frequency of allele “G” of rs10757274 in CDKN2BAS was higher in cases, and it was associated with a 1.30-fold increased risk of CHD in men at a 5% level (OR = 1.30, 95% CI: 1.05-1.61, P = 0.018).

Table 1: Allele frequencies in cases and controls and odds ratio estimates for CHD

Chr: Chromosome; MAF: Minor allele frequency; HWE: Hardy-Weinberg equilibrium; OR: Odd ratio; 95% CI: 95% Confidence interval.

P values were calculated from Chi-square /Fisher's exact.

P <0.05 indicates statistical significance.

 

We further assessed the association between each SNP and CHD risk using unconditional logistic regression analysis after adjusting for age. The results of genetic model analyses (codominant, dominant, recessive, log-additive) are presented in Table 2. We identified that rs10757274 was associated with increased CHD risk in men under a codominant model (P = 0.042); a recessive model (GG vs AA+AG: OR = 1.70, 95% CI: 1.10-2.62, P = 0.016) and a log-additive model (OR = 1.34, 95% CI: 1.05-1.71, P = 0.019). Interestingly, rs10757274 was also associated with increased CHD risk in women under both a codominant model (P = 0.004) and a dominant model (AG+GG vs AA: OR = 2.26, 95% CI: 1.28-3.99, P = 0.004). Allele “G” of rs7865618 was also associated with increased CHD risk under a codominant model (P = 0.021). A similar trend was observed for rs7865618, which was associated with a 6.79-fold increased risk of CHD in women under a recessive model (GG vs AA+GA: OR = 8.10, 95% CI: 1.74-37.68, P = 0.006).

Table 2: Genetic models analyses of the association between CDKN2BAS SNPs and CHD susceptibility in men and women

SNP-ID

Model

Genotype

Men

Women

Case

Control

OR (95% CI)

P-value

Case

Control

OR (95% CI)

P-value

rs7865618

Codominant

A/A

227 (78.3%)

290 (75.3%)

1

127 (77%)

240 (80%)

1

0.021

G/A

59 (20.3%)

88 (22.9%)

0.80 (0.52-1.23)

0.430

30 (18.2%)

57 (19%)

1.07 (0.57-1.98)

G/G

4 (1.4%)

7 (1.8%)

0.54 (0.13-2.18)

8 (4.8%)

3 (1%)

8.20 (1.76-38.35)

Dominant

A/A

227 (78.3%)

290 (75.3%)

1

127 (77%)

240 (80%)

1

G/A-G/G

63 (21.7%)

95 (24.7%)

0.78 (0.52-1.18)

0.240

38 (23%)

60 (20%)

1.37 (0.77-2.45)

0.290

Recessive

A/A-G/A

286 (98.6%)

378 (98.2%)

1

157 (95.2%)

297 (99%)

1

G/G

4 (1.4%)

7 (1.8%)

0.57 (0.14-2.28)

0.420

8 (4.8%)

3 (1%)

8.10 (1.74-37.68)

0.006

Log-additive

---

---

---

0.78 (0.54-1.14)

0.200

---

---

1.58 (0.97-2.58)

0.068

rs11790231

Codominant

G/G

217 (74.6%)

281 (73.6%)

1

121 (73.3%)

208 (69.3%)

1

0.520

A/G

62 (21.3%)

95 (24.9%)

0.99 (0.65-1.50)

0.380

39 (23.6%)

86 (28.7%)

0.74 (0.42-1.29)

A/A

12 (4.1%)

6 (1.6%)

2.15 (0.71-6.52)

5 (3%)

6 (2%)

1.23 (0.28-5.36)

Dominant

G/G

217 (74.6%)

281 (73.6%)

1

121 (73.3%)

208 (69.3%)

1

A/G-A/A

74 (25.4%)

101 (26.4%)

1.07 (0.72-1.60)

0.720

44 (26.7%)

92 (30.7%)

0.78 (0.45-1.33)

0.350

Recessive

G/G-A/G

279 (95.9%)

376 (98.4%)

1

160 (97%)

294 (98%)

1

A/A

12 (4.1%)

6 (1.6%)

2.15 (0.71-6.51)

0.160

5 (3%)

6 (2%)

1.33 (0.31-5.76)

0.700

Log-additive

---

---

---

1.14 (0.81-1.60)

0.450

---

---

0.85 (0.53-1.35)

0.490

rs1412832

Codominant

T/T

161 (55.5%)

194 (50.5%)

1

84 (50.9%)

153 (51%)

1

0.760

T/C

108 (37.2%)

161 (41.9%)

0.76 (0.52-1.09)

0.250

72 (43.6%)

123 (41%)

1.02 (0.62-1.69)

C/C

21 (7.2%)

29 (7.5%)

0.70 (0.36-1.37)

9 (5.5%)

24 (8%)

0.71 (0.27-1.90)

Dominant

T/T

161 (55.5%)

194 (50.5%)

1

84 (50.9%)

153 (51%)

1

T/C-C/C

129 (44.5%)

190 (49.5%)

0.75 (0.53-1.06)

0.100

81 (49.1%)

147 (49%)

0.97 (0.60-1.57)

0.890

Recessive

T/T-T/C

269 (92.8%)

355 (92.5%)

1

156 (94.5%)

276 (92%)

1

C/C

21 (7.2%)

29 (7.5%)

0.79 (0.42-1.51)

0.480

9 (5.5%)

24 (8%)

0.70 (0.27-1.83)

0.470

Log-additive

---

---

---

0.80 (0.61-1.05)

0.110

---

---

0.92 (0.63-1.36)

0.680

rs6475606

Codominant

T/T

156 (53.6%)

183 (47.7%)

1

77 (46.7%)

146 (48.7%)

1

0.920

C/T

110 (37.8%)

169 (44%)

0.73 (0.50-1.05)

0.210

76 (46.1%)

130 (43.3%)

0.91 (0.55-1.50)

C/C

25 (8.6%)

32 (8.3%)

0.76 (0.40-1.43)

12 (7.3%)

24 (8%)

0.89 (0.36-2.23)

Dominant

T/T

156 (53.6%)

183 (47.7%)

1

77 (46.7%)

146 (48.7%)

1

C/T-C/C

135 (46.4%)

201 (52.3%)

0.73 (0.52-1.04)

0.077

88 (53.3%)

154 (51.3%)

0.90 (0.56-1.47)

0.680

Recessive

T/T-C/T

266 (91.4%)

352 (91.7%)

1

153 (92.7%)

276 (92%)

1

C/C

25 (8.6%)

32 (8.3%)

0.88 (0.48-1.61)

0.670

12 (7.3%)

24 (8%)

0.94 (0.39-2.27)

0.880

Log-additive

---

---

---

0.81 (0.62-1.06)

0.120

---

---

0.93 (0.63-1.36)

0.700

rs1333040

Codominant

T/T

156 (53.6%)

188 (49.1%)

1

80 (48.5%)

148 (49.3%)

1

0.930

T/C

111 (38.1%)

165 (43.1%)

0.78 (0.54-1.12)

0.380

73 (44.2%)

128 (42.7%)

0.91 (0.55-1.52)

C/C

24 (8.2%)

30 (7.8%)

0.81 (0.43-1.55)

12 (7.3%)

24 (8%)

0.90 (0.36-2.24)

Dominant

T/T

156 (53.6%)

188 (49.1%)

1

80 (48.5%)

148 (49.3%)

1

T/C-C/C

135 (46.4%)

195 (50.9%)

0.78 (0.55-1.11)

0.170

85 (51.5%)

152 (50.7%)

0.91 (0.56-1.48)

0.710

Recessive

T/T-T/C

267 (91.8%)

353 (92.2%)

1

153 (92.7%)

276 (92%)

1

C/C

24 (8.2%)

30 (7.8%)

0.91 (0.49-1.69)

0.760

12 (7.3%)

24 (8%)

0.94 (0.39-2.27)

0.880

Log-additive

---

---

---

0.85 (0.65-1.11)

0.230

---

---

0.93 (0.64-1.37)

0.720

rs1537370

Codominant

T/T

156 (53.6%)

185 (48%)

1

77 (46.7%)

146 (48.7%)

1

0.920

C/T

111 (38.1%)

170 (44.2%)

0.73 (0.51-1.06)

0.240

76 (46.1%)

130 (43.3%)

0.91 (0.55-1.50)

C/C

24 (8.2%)

30 (7.8%)

0.79 (0.41-1.51)

12 (7.3%)

24 (8%)

0.89 (0.36-2.23)

Dominant

T/T

156 (53.6%)

185 (48%)

1

77 (46.7%)

146 (48.7%)

1

C/T-C/C

135 (46.4%)

200 (52%)

0.74 (0.53-1.05)

0.093

88 (53.3%)

154 (51.3%)

0.90 (0.56-1.47)

0.680

Recessive

T/T-C/T

267 (91.8%)

355 (92.2%)

1

153 (92.7%)

276 (92%)

1

C/C

24 (8.2%)

30 (7.8%)

0.91 (0.49-1.70)

0.760

12 (7.3%)

24 (8%)

0.94 (0.39-2.27)

0.880

Log-additive

---

---

---

0.82 (0.62-1.07)

0.150

---

---

0.93 (0.63-1.36)

0.700

rs10757274

Codominant

A/A

82 (28.2%)

130 (33.8%)

1

39 (23.6%)

102 (34%)

1

0.004

A/G

137 (47.1%)

188 (48.8%)

1.16 (0.78-1.72)

0.042

101 (61.2%)

141 (47%)

2.60 (1.44-4.71)

G/G

72 (24.7%)

67 (17.4%)

1.86 (1.13-3.05)

25 (15.2%)

57 (19%)

1.52 (0.72-3.23)

Dominant

A/A

82 (28.2%)

130 (33.8%)

1

39 (23.6%)

102 (34%)

1

A/G-G/G

209 (71.8%)

255 (66.2%)

1.33 (0.91-1.93)

0.130

126 (76.4%)

198 (66%)

2.26 (1.28-3.99)

0.004

Recessive

A/A-A/G

219 (75.3%)

318 (82.6%)

1

140 (84.8%)

243 (81%)

1

G/G

72 (24.7%)

67 (17.4%)

1.70 (1.10-2.62)

0.016

25 (15.2%)

57 (19%)

0.81 (0.43-1.51)

0.500

Log-additive

---

---

---

1.34 (1.05-1.71)

0.019

---

---

1.31 (0.92-1.87)

0.130

rs1333042

Codominant

G/G

139 (47.9%)

162 (42.6%)

1

74 (44.9%)

130 (43.3%)

1

0.810

G/A

122 (42.1%)

168 (44.2%)

0.88 (0.61-1.27)

0.120

75 (45.5%)

136 (45.3%)

0.87 (0.52-1.45)

A/A

29 (10%)

50 (13.2%)

0.55 (0.31-0.97)

16 (9.7%)

34 (11.3%)

0.81 (0.36-1.84)

Dominant

G/G

139 (47.9%)

162 (42.6%)

1

74 (44.9%)

130 (43.3%)

1

G/A-A/A

151 (52.1%)

218 (57.4%)

0.79 (0.56-1.12)

0.190

91 (55.1%)

170 (56.7%)

0.86 (0.53-1.39)

0.530

Recessive

G/G-G/A

261 (90%)

330 (86.8%)

1

149 (90.3%)

266 (88.7%)

1

A/A

29 (10%)

50 (13.2%)

0.59 (0.34-1.01)

0.050

16 (9.7%)

34 (11.3%)

0.87 (0.40-1.90)

0.730

Log-additive

---

---

---

0.78 (0.61-1.01)

0.058

---

---

0.89 (0.62-1.28)

0.530

OR: Odd ratio; 95% CI: 95% Confidence.

P values were calculated from the Wald test.

P < 0.05 indicates statistical significance.

Linkage Disequilibrium (LD) analysis demonstrated that two SNPs (rs10757274 and rs1333042) in CDKN2BAS showed strong linkage (Figure 1). Furthermore, the haplotype AA (rs10757274 and rs1333042) was significantly associated with a decreased risk of CHD in men (OR = 0.72, 95% CI: 0.55 - 0.95, P = 0.022; Table 3).

Table 3: Haplotype frequencies and association with the risk of CHD in men

Chr

Gene

SNPs

Haplotype

F_A

F_U

OR (95% CI)

P-value

9

CDKN2BAS

rs10757274|rs1333042

GG

0.481

0.415

1

---

AA

0.309

0.350

0.72 (0.55 - 0.95)

0.022

AG

0.208

0.232

0.75 (0.54 - 1.04)

0.083

F_A: Frequency in cases; F_U: Frequency in controls; OR: Odd ratio; 95% CI: 95% Confidence.

P values were calculated using the Wald test.

P < 0.05 indicates statistical significance.

Haplotype block map for the eight SNPs in

Figure 1: Haplotype block map for the eight SNPs in CDKN2BAS in men. Block 1 includes rs1412832, rs6475606, rs1333040 and rs1537370; Block 2 includes rs10757274 and rs1333042. The LD between two SNPs is standardized D’.

Additionally, we also investigated the associations between CDKN2BAS polymorphisms and CHD-related biochemical indicators, including triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), creatinine (CRE), neutrophil (NEU), platelet distribution width (PDW) as shown in Table 4. The levels of HLD-C with rs6475606 (P = 0.022) and rs1333040 (P = 0.045) in men, and rs1412832 (P = 0.017) in women, were different between groups. Levels of CRE with rs7865618 (P = 0.035) and rs1412832 (P = 0.015) in men, and rs11790231 and rs6475606 (P = 0.033) in women, were also different between groups. In addition, the levels of NEU with three SNPs (rs7865618: P = 0.037, rs11790231: P = 0.018 and rs6475606: P = 0.027) and the levels PDW with four SNPs (rs7865618: P = 0.029, rs1333040: P = 0.047, rs1537370: P = 0.020 and rs10757274: P = 0.047) were different between groups, but only in women.

Table 4: Lipid levels according to genotype in men and women

Sex

SNP-ID

Genotype (N)

TG

TC

HDL-C

LDL-C

CRE

NEU

PDW

Mean ± SD

P

Mean ± SD

P

Mean ± SD

P

Mean ± SD

P

Mean ± SD

P

Mean ± SD

P

Mean ± SD

P

Men

rs7865618

CC(137)

1.70±0.11

3.90±0.09

1.20±0.02

1.84±0.08

86.82±1.75

5.24±0.27

14.54±0.25

TT(19)

1.74±0.27

0.524

3.95±0.43

0.607

1.15±0.07

0.549

1.87±0.24

0.927

85.63±3.18

0.035

5.18±0.69

0.699

14.16±0.59

0.496

CT(119)

1.93±0.19

4.04±0.11

1.09±0.02

1.88±0.07

80.58±1.72

5.56±0.29

14.11±0.27

rs11790231

GG(4)

1.54±0.46

3.54±0.77

1.00±0.01

1.75±0.52

74.63±9.56

4.67±0.63

13.03±1.11

AA(217)

1.77±0.12

0.752

3.96±0.08

0.745

1.10±0.02

0.644

1.87±0.06

0.953

84.50±1.38

0.546

5.22±0.20

0.210

14.22±0.20

0.277

GA(53)

1.95±0.20

4.00±0.16

1.08±0.04

1.84±0.09

82.90±2.22

6.02±0.52

14.82±0.39

rs1412832

GG(203)

1.79±0.12

3.95±0.08

1.10±0.02

1.86±0.06

83.94±1.33

5.57±0.23

14.41±0.20

AA(12)

1.46±0.15

0.659

3.80±0.26

0.803

0.99±0.06

0.281

1.88±0.20

0.994

99.43±11.89

0.015

5.42±0.81

0.161

12.67±0.74

0.186

AG(60)

1.92±0.17

4.03±0.17

1.10±0.03

1.86±0.11

81.30±1.59

4.68±0.33

14.28±0.36

rs6475606

CC(20)

1.76±0.24

3.64±0.25

0.95±0.04

1.72±0.13

79.66±4.68

6.02±1.05

14.00±0.61

TC(101)

1.78±0.15

0.979

4.05±0.13

0.371

1.12±0.03

0.022

1.91±0.08

0.651

84.30±2.00

0.613

5.76±0.30

0.131

14.11±0.28

0.519

TT(153)

1.82±0.15

3.95±0.09

1.10±0.02

1.85±0.08

84.36±1.52

5.05±0.24

14.50±0.24

rs1333040

CC(24)

1.77±0.20

3.78±0.22

0.98±0.05

1.79±0.13

86.96±6.80

5.97±0.90

13.51±0.59

TT(147)

1.79±0.15

0.992

3.94±0.09

0.638

1.11±0.02

0.045

1.86±0.08

0.907

84.74±1.55

0.501

5.12±0.25

0.320

14.61±0.25

0.148

CT(104)

1.81±0.15

4.02±0.13

1.11±0.03

1.88±0.08

82.46±1.63

5.59±0.29

14.09±0.26

rs1537370

CC(23)

1.64±0.16

3.68±0.21

0.98±0.05

1.79±0.13

86.34±7.09

6.05±0.93

13.43±0.62

TC(105)

1.84±0.15

0.867

4.04±0.13

0.410

1.11±0.03

0.055

1.88±0.08

0.915

82.62±1.62

0.592

5.57±0.29

0.299

14.10±0.25

0.134

TT(147)

1.79±0.15

3.94±0.09

1.11±0.02

1.86±0.08

84.74±1.55

5.12±0.25

14.61±0.25

rs10757274

CC(23)

1.64±0.16

3.68±0.21

0.98±0.05

1.79±0.13

86.34±7.09

6.05±0.93

13.43±0.62

TT(147)

1.79±0.15

0.867

3.94±0.09

0.410

1.11±0.02

0.055

1.86±0.08

0.915

84.74±1.55

0.592

5.12±0.25

0.299

14.61±0.25

0.134

CT(105)

1.84±0.15

4.04±0.13

1.11±0.03

1.88±0.08

82.62±1.62

5.57±0.29

14.10±0.25

rs1333042

GG(69)

1.74±0.15

3.95±0.13

1.15±0.03

1.94±0.15

84.05±1.95

4.85±0.36

14.49±0.35

AA(78)

1.69±0.16

0.650

3.88±0.15

0.766

1.07±0.03

0.098

1.81±0.09

0.644

82.23±2.22

0.591

5.71±0.40

0.258

14.24±0.31

0.856

AG(128)

1.90±0.17

4.01±0.10

1.08±0.02

1.84±0.06

85.10±1.84

5.43±0.26

14.28±0.26

Women

rs7865618

CC(78)

1.88±0.17

4.33±0.11

1.16±0.03

1.96±0.07

72.77±1.89

4.01±0.24

13.77±0.36

TT(14)

2.09±0.42

0.320

4.78±0.30

0.213

1.24±0.07

0.264

2.24±0.23

0.383

70.62±4.72

0.849

5.94±1.02

0.037

16.28±0.82

0.029

CT(62)

1.62±0.09

4.23±0.14

1.22±0.03

2.05±0.10

70.82±3.47

4.32±0.34

13.97±0.41

rs11790231

GG(7)

1.41±0.13

4.49±0.28

1.12±0.10

2.15±0.12

89.85±25.84

6.83±0.87

14.08±1.01

AA(120)

1.72±0.12

0.119

4.24±0.10

0.163

1.19±0.02

0.759

1.99±0.07

0.615

71.83±1.53

0.033

4.14±0.24

0.018

14.09±0.31

0.986

GA(28)

2.22±0.26

4.66±0.22

1.20±0.05

2.12±0.14

66.94±1.65

4.28±0.38

13.98±0.57

rs1412832

GG(113)

1.84±0.13

4.35±0.11

1.16±0.02

2.02±0.07

71.43±2.13

4.44±0.25

13.69±0.31

AA(5)

1.59±0.13

0.769

5.26±0.42

0.082

1.39±0.12

0.017

2.70±0.35

0.079

67.38±4.81

0.788

4.27±0.99

0.490

14.90±1.40

0.051

AG(37)

1.69±0.15

4.15±0.13

1.26±0.04

1.94±0.11

73.57±3.07

3.86±0.35

15.13±0.49

rs6475606

CC(8)

2.22±0.44

5.00±0.21

1.27±0.08

2.45±0.21

89.42±22.61

6.44±0.98

14.23±0.92

TC(68)

1.91±0.20

0.291

4.33±0.13

0.179

1.19±0.03

0.681

1.99±0.08

0.212

69.15±1.62

0.033

3.97±0.28

0.027

13.38±0.37

0.065

TT(79)

1.65±0.09

4.26±0.12

1.19±0.03

2.01±0.08

72.22±1.96

4.35±0.30

14.63±0.38

rs1333040

CC(11)

1.99±0.34

4.82±0.19

1.23±0.07

2.33±0.17

82.56±17.23

5.93±0.82

14.18±0.80

TT(73)

1.62±0.09

0.276

4.25±0.13

0.248

1.19±0.03

0.838

2.00±0.09

0.326

72.20±2.13

0.172

4.28±0.31

0.068

14.73±0.40

0.047

CT(71)

1.94±0.20

4.34±0.12

1.18±0.03

1.19±0.08

69.74±1.50

4.05±0.28

13.39±0.36

rs1537370

CC(11)

1.99±0.34

4.82±0.19

1.23±0.07

2.33±0.17

82.56±17.23

5.93±0.82

14.18±0.80

TC(69)

1.95±0.20

0.263

4.36±0.12

0.221

1.19±0.03

0.850

2.00±0.08

0.327

69.40±1.55

0.152

3.96±0.27

0.050

13.27±0.36

0.020

TT(75)

1.62±0.09

4.23±0.13

1.19±0.03

2.00±0.09

72.42±2.06

4.36±0.31

14.78±0.39

rs10757274

CC(11)

1.99±0.34

4.82±0.19

1.23±0.07

2.33±0.17

82.56±17.23

5.93±0.82

14.18±0.80

TT(73)

1.62±0.09

0.276

4.25±0.13

0.248

1.19±0.03

0.838

2.00±0.09

0.326

72.20±2.13

0.172

4.28±0.31

0.068

14.73±0.40

0.047

CT(71)

1.94±0.20

4.34±0.12

1.18±0.03

1.19±0.08

69.74±1.50

4.05±0.28

13.39±0.36

rs1333042

GG(24)

1.62±0.18

3.95±0.23

1.24±0.06

1.83±0.16

70.55±3.08

4.13±0.55

15.27±0.67

AA(36)

1.99±0.21

0.528

4.52±0.14

0.116

1.17±0.03

0.554

2.16±0.11

0.223

74.91±5.50

0.608

4.68±0.44

0.584

13.45±0.60

0.113

AG(95)

1.77±0.14

4.36±0.11

1.19±0.03

2.02±0.07

70.93±1.70

4.20±0.26

14.03±0.31

TG: Triglyceride; TC: Total cholesterol; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low density lipoprotein cholesterol; CRE: Creatinine; NEU: Neutrophil;

PDW: Platelet distribution width

DISCUSSION

The goal of our case-control study was to explore the associations of eight polymorphisms in CDKN2BAS with the risk of CHD in a Han Chinese population. We found that rs10757274 was associated with an increased risk of CHD both in men and women. However, rs7865618 was correlated with an increased risk of CHD only in women. In addition, we also found that the haplotype “AA” (rs10757274 and rs1333042) of CDKN2BAS was associated with a decreased CHD risk in men.

Previous studies have demonstrated that CDKN2BAS transcript levels show a strong correlation with the severity of atherosclerosis. In addition, the modulation of CDKN2BAS expression influences CHD susceptibility. The function of CDKN2BAS is unknown, but it regulates the expression of CDKN2A and CDKN2B, which encode cyclin-dependent kinase inhibitors 2A and 2B, indicating a regulatory role for CDKN2BAS in cellular proliferation. Polymorphisms at the 9p21 region may induce higher expression of the CDKN2BAS transcript, thereby inhibiting the expression of CDKN2A and CDKN2B.

Several studies have demonstrated a strong association of rs10757274 with CHD in Pakistani [9], Caucasian [15], and South-West Iranian [16] population. Our results are consistent with these previous findings d. However, one previous study found no association of rs10757274 with CHD in a Han Chinese population (Shenzhen) [17].The differences between our study and this study are likely due to differences in the two study populations. How rs10757274 affects the risk of CHD is unclear, but this SNP may regulate the expression of EU741058 and p16INK4a, which modulate the risk of developing CHD [18]. Some studies have also suggested that rs10757274impairs the mechanical properties of the arterial wall and thus influences vascular diseases [19].

It was previously shown that rs1333042 is associated with the risk of CHD in the Han Chinese [20, 21] and Saudi populations [7], which is inconsistent with our findings. The differences in these studies may be explained by ethnic differences, environment, or lifestyle that also affected the development of CHD. We also observed that rs7865618 was associated with CHD risk in women. However, this SNP has previously only been found to interact with other SNPs to affect the development of CHD [6]. In future studies, we will verify our results using a larger sample size.

We didn’t observe any association between rs1333040 and the risk of CHD, which is consistent with the studies by Cao et al. [21] and Golabgir Khademi et al. [22]. This SNP was significantly associated with the risk of CHD in a Northern Indian population [23] and in African American women [24]. Genetic variation and differences in life styles among populations probably explain the population disparities in the association of this SNP and susceptibility to CHD. Furthermore, recent studies have reported that the levels of TG, TC, HDL-C, LDL-C, CRE, NEU and PDW effectively predict the risk of CHD [25-28]. Interestingly, we also demonstrated that SNPs in CDKN2BAS were correlated with the levels of these biochemical indicators and differed between cases and controls.

Several limitations should be acknowledged in the present study. First, the sample size was relatively small and the participants were limited to Chinese ethnicity. Second, there were differences in some clinical characteristics between the patients and controls. Although several confounders have been adjusted for the statistical analyses, we could not completely eliminate the potential influences of these factors on the results. Finally, the biological mechanism of genetic variants in CDKN2BAS was not investigated in this study. It will be important to follow up and validate our findings with larger sample sizes.

In conclusion, our results suggest that, in a Chinese Han population, rs10757274 in CDKN2BAS is associated with the risk of CHD both in men and women, rs7865618 is correlated with an increased risk of CHD only in women, and the haplotype AA (rs10757274 and rs1333042) of CDKN2BAS is associated with a decreased CHD risk in men. Thus, these SNPs could have clinical importance as pre-diagnostic markers. Further study is required to determine the functional effects of these SNPs and validate these findings in larger populations.

Materials and Methods

Ethics statement

Written informed consent was obtained from all study participants before the interview. This study protocol was approved by the Ethical Committee of the Yanan University Affiliated Hospital and the First Affiliated Hospital of Xi’an Jiaotong University, and complied with the World Medical Association Declaration of Helsinki.

Study participants

The study included 676 men (291 CHD cases with a mean age of 60 years and 385 healthy controls with a mean age of 48 years) and 465 women (165 CHD cases with a mean age of 64 years and 300 healthy controls with a mean age of 50 years). All CHD cases were recruited from the Cardiovascular Internal Medicine Department of Yanan University Affiliated Hospital between February 2014 and April 2015. The 685 healthy controls were randomly selected from physical examination center of the same hospital during the same period. The inclusion and exclusion criteria for participants were as follows: First, all subjects were of the ethnic Han origin and not related to each other. Second, all participants diagnosis we based on standardized electrocardiogram, echocardiography, blood tests and coronary angiography and judged by two or three independent cardiologists. Third, all individuals were excluded from the study if they had other cardiac diseases (congenital heart disease, cardiomyopathy, or rheumatic heart disease), diabetes, hypertension, or severe liver or kidney disease.s, Patients who had previously received angioplasty, intravenous thrombolysis, coronary artery stents, or coronary artery bypass surgery were also excluded. Basic characteristics of all enrolled controls were collected with a standard epidemiological questionnaires conducted by well-trained interviewers. The cases information was collected through consultation with treating physicians or from medical chart review. Peripheral venous blood (5 ml) was collected from each participant using vacutainer tubes containing ethylene diamine tetra-acetic acid (EDTA) and then stored at -80°C. A clinical examination at which a blood sample was drawn for routine analysis of blood levels, biochemical tests, coagulation function, and genetic analyses.

SNP selection and genotyping

The eight SNPs in CDKN2BAS (rs7865618, rs1179023, rs1412832, rs6475606, rs1333040, rs1537370, rs10757274 and rs1333042) were selected from previous reports for their association with CHD [9, 21, 23, 29, 30]. The minor allele frequency of each SNP was > 5% in the HapMap of the Chinese Han Beijing (CHB) population. Genomic DNA was extracted from whole blood using the GoldMag-Mini Whole Blood Genomic DNA Purification Kit according to the manufacturer’s protocol (GoldMag. Co. Ltd., Xi’an, China). DNA concentration and purity were evaluated using a spectrophotometer (NanoDrop 2000; Thermo Fisher Scientific, Waltham, MA, USA). Polymerase chain reaction (PCR) and extension primers for the SNPs were designed using the Sequenom MassARRAY Assay Design 3.0 software (Sequenom, San Diego, CaliforniaCA, USA). Genotyping was performed using the Sequenom MassARRAY platform (Sequenom, San Diego, CA, USA) according to the standard instructions. Sequenom Typer 4.0 software was used for data management and analyses.

Statistical analysis

SPSS 19.0 (SPSS Inc., Chicago, IL, USA) and Microsoft Excel (Microsoft Corp., Redmond, WA, USA) were used for statistical analyses. Genotypic frequencies in controls (men and women) were tested for departure from HWE using a Fisher’s exact test. The allelic frequencies were compared between cases and controls by Chi-squared test/Fisher’s exact test, and the relative risk was estimated by odd ratios (ORs) and 95% confidence intervals (CIs). The genetic model analyses (codominant, dominant, recessive, log-additive) were applied using PLINK software to assess the significance of SNPs. ORs and 95% CIs were calculated using unconditional logistic regression analysis with adjustment for age. The p values were calculated with the Wald test. Haploview software (version 4.2) was used for analyses of the pairwise linkage disequilibrium (LD), haplotype structure and genetic association at polymorphism loci. All p values were two-sided, and p < 0.05 is considered statistically significant.

Acknowledgments

We thank all authors for their contributions and support. We are grateful to all participants in the study who provided blood samples. We would also like to thank the hospital staff who contributed to data collection for this study.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Grant support

This work is supported by the National Natural Science Foundations (No. 81200098) and the Key Science and Technology Program of Shaanxi Province (No. 2014K11-03-04-06).

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