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Association between paraoxonase gene and stroke in the Han Chinese population

Contributed equally
BMC Medical Genetics201314:16

DOI: 10.1186/1471-2350-14-16

Received: 3 April 2012

Accepted: 22 January 2013

Published: 28 January 2013

Abstract

Background

The human paraoxonase (PON) gene family has three isoforms: PON1, PON2 and PON3. These genes are implicated as potential risk factors of cerebrovascular disease and can prevent oxidative modification of low-density lipoproteins and atherosclerosis. This study evaluated the association between the genetic variants of all three PON genes and the risks of total stroke, ischemic stroke and hemorrhagic stroke in the Han Chinese population.

Methods

A total of 1016 subjects were recruited, including 508 healthy controls and 498 patients (328 with ischemic stroke and 170 with hemorrhagic stroke). A total of 11 single nucleotide polymorphisms (SNPs) covering the PON genes were genotyped for statistical analysis. Two of the 11 SNPs (rs662 and rs854560) were contextualized in a meta-analysis of ischemic stroke.

Results

The presence of rs705381 (−162) in the promoter region of PON1 was significantly associated with total stroke (P adjusted  = 0.0007, OR = 0.57 [95% CI = 0.41-0.79]) and ischemic stroke (P adjusted  = 0.0017, OR = 0.54 [95% CI = 0.37-0.79]) when analyzed using a dominant model, but was not associated with hemorrhagic stroke. There was also a nominal association between rs854571 (−824) and total stroke. Meta-analysis demonstrated a significant nominal association between rs662 and ischemic stroke, but there was no evidence of an association between rs662 and ischemic stroke risk in a single site association study.

Conclusions

These findings indicate that polymorphisms of PON1 gene may be a risk factor of stroke.

Keywords

Polymorphisms Paraoxanase gene Hemorrhagic stroke Ischemic stroke Association

Background

Stroke is recognized as one of the leading causes of death and severe neurological disability worldwide. Ischemic and hemorrhagic stroke are the two primary subtypes [1]. Data from family-based studies [2], twin studies [3, 4], and animal experiments [5, 6] indicate that genetic factors play a major role in stroke. A small isolated group of strokes have previously been ascribed to single-gene disorders [7].

Intermediate phenotypes of stroke are seen clinically. Atherosclerosis, as an intermediate phenotype of stroke, has been extensively investigated as a major underlying cause of cardio- and cerebrovascular disease [810]. There is also a strong inverse association between high-density lipoprotein (HDL) levels and the development of atherosclerosis, and similar results have been found between low-density lipoprotein (LDL) peroxidation and the development of atherosclerosis [11, 12].

The paraoxonase (PON) gene family comprises three isoforms, PON1, PON2 and PON3, located in 7q21.3-22.1 [13]. The 60 to 80% structural similarity among these three members accounts for their functional similarity [13, 14]. All three isoforms have been implicated as candidate genes for atherosclerosis and cardiovascular diseases due to their ability to attenuate lipid peroxidation, and due to their antioxidant and antiatherogenic effects [1517]. Low levels of PON activity are thought to increase the risk of atherosclerosis [18], and thereby contribute to a predisposition towards stroke, coronary artery disease (CAD) and vascular disorders in diabetes [1921]. Other studies have demonstrated a positive association between single nucleotide polymorphisms (SNPs) in PON genes and stroke susceptibility [2225], although conflicting results have been seen in different ethnic groups [2628]. However, there are limited number of prospective studies validating the association between PON genes and the risk of stroke in the Han Chinese population [26, 2830].

A negative association has previously been demonstrated between SNPs in the coding region of PON1 and PON2, and the development of stroke. In this study we wanted to evaluate the levels of ischemic and hemorrhagic risk conferred by SNPs in the whole PON family in a large Chinese population. With this aim in mind, we conducted a case–control study in the Han Chinese population to evaluate the possible association of PON family genes with total stroke and its subtypes.

Methods

Subjects

The study sample included 508 healthy controls and 498 patients, including 328 with ischemic stroke and 170 with hemorrhagic stroke who presented consecutively to the Department of Neurology, Beijing Tiantan Hospital, between December 2010 and March 2011. The subjects were unrelated to one another and were recruited from the Han Chinese population.

Hemorrhagic stroke included hypertensive cerebral hemorrhage and subarachnoid hemorrhage. Patients with hemorrhage due to trauma, tumor, vascular malformation and coagulopathy were excluded. Ischemic stroke was defined as a sudden onset of focal or global neurologic deficit with signs and symptoms persisting for more than 24 h. Patients with a history or occurrence of transient ischemic attack, cerebral embolism, cerebral trauma, cerebrovascular malformations, coagulation disorders, autoimmune diseases, tumors, peripheral vascular disease, or chronic infection diseases were excluded from the study.

All diagnoses were confirmed by brain computed tomography and/or magnetic resonance imaging. The brain images were independently assessed by a technologist and a physician.

Control subjects were recruited from the health examination department of the Beijing Tiantan Hospital. These subjects had no clinical or radiological evidence of stroke and other neurological diseases. They were also free from autoimmune disease, liver disease, nephrosis, and hematological disorders

Sex, age, total plasma cholesterol (TC), triglycerides (TG), HDL, and LDL cholesterol were documented on entry into the study. Potential vascular risk factors were evaluated, including hypertension, diabetes mellitus, atrial fibrillation, and ischemic heart disease. Hypertension was defined according to WHO/ISH criteria [31] as systolic blood pressure ≥140 mmHg and/or diastolic pressure ≥ 90 mmHg with concomitant use of antihypertensive medications Diabetes mellitus was defined as fasting plasma glucose ≥7.0 mmol/L or current treatment with anti-diabetic drugs.

The experimental protocol was approved by the Ethics Committee of the Beijing Tiantan Hospital. Written informed consent was obtained from all participants prior to entering the study.

Genotyping

Eleven single nucleotide polymorphisms (SNPs) were genotyped. These included: rs662 (Gln192Arg), rs13306698 (Arg160Gly), rs854560 (Leu55Met) in coding region of PON1; rs705379 (−107/-108), rs705381 (−160/-162), rs854571 (−824/-832), rs854572 (−907/-909) in the promoter of PON1; rs12026 (Ala148Gly) and rs7493 (Ser311Cys) of PON2, together with rs2074353 (located in intron) and rs1053275 (Ala99Ala) for PON3.

The SNPs were genotyped using the Sequenom Mass ARRAY platform (Sequenom, San Diego, CA) according to the iPLEX Gold Application Guide available at (http://www.sequenom.com/sites/genetic-analysis/applications/snp-genotyping). The genotyping analysis was undertaken according to the manufacturer’s protocol, using recommended reagents in the iPLEX Gold SNP genotyping kit. Briefly, specific assays were designed using the Mass ARRAY Assay Design software package (v3.1). The process involved a locus-specific PCR reaction based on a locus-specific primer extension reaction. Residual nucleotides were dephosphorylated with SAP enzymes before undertaking the iPLEX GOLD primer extension reactions.

Following the single-base extension reactions the products were desalinated with Spectro CLEAN resin (Sequenom). A 10 nL aliquot of the desalinated product was spotted onto a 384-format Spectro CHIP with the Mass ARRAY Nanodispenser. Mass determination was carried out with the MALDI-TOF mass spectrometer and Mass ARRAY Type 4.0 software was used for data acquisition.

SNP genotypes were named using cluster analysis with a default parameter setting. Genotypes were further reviewed manually to correct classification errors caused by clustering artifacts.

Statistical analysis

Statistical analysis was undertaken using PLINK software (http://pngu.mgh.harvard.edu/~purcell/plink/) [32]. Hardy-Weinberg equilibrium tests (HWE) were performed for each SNP, and association tests were undertaken using additive, dominant, or recessive genetic models.

Logistic regression was used for risk stratification with or without covariate adjustments determined by significant differences between total stroke patients and controls (i.e. age, HDL, and hypertension). The model with the highest likelihood was considered to provide the best-fit genetic model for each SNP. Haplotype-based association analysis was performed using logistic regression with or without adjustment for covariates. A single site association test between rs662 and rs854560 and ischemic stroke was conducted using an allele-based model. Bonferroni correction was undertaken for the 10 SNPs that were adopted into the single site association analysis.

Linkage disequilibrium analysis and haplotype selection were performed using Haploview software with parameter settings for pairwise tagging with D’ >0.95 [33]. The Omnibus ANOVA test was conducted using R software [34].

Inverse variance meta-analysis (RevMan 4.0 software) was used to contextualize our studies with two meta-analyses, using the data from PMID: 20856122 [35] and PMID: 18511872 [30], which also studied the association between rs662 and rs854560 loci and ischemic stroke.

Values of P <0.005 were considered to represent the threshold for statistical significance.

Results

Clinical characteristics of total stroke patients and controls

Table 1 shows demographic characteristics and clinical vascular variables in the control and total stroke patients. There were no significant differences in levels of TC, TG and LDL between the controls and total stroke cases. However, HDL levels were significantly lower in stroke cases than in controls and mean age and incidence of hypertension were significantly higher.
Table 1

Comparison of clinical variables between total strokes and control subjects

Variables

Stroke cases (n = 498)

Control cases (n = 498)

Ischemic stroke, n

328

 

Hemorrhagic stroke, n

170

Age, years

60.45 ± 14.27*

56.48 ± 4.55

Male, n (%)

142 (28)

140 (28)

TC, mmol/L

4.41 ± 1.31

4.36 ± 1.33

TG, mmol/L

1.54 ± 0.95

1.56 ± 1.26

HDL, mmol/L

1.10 ± 0.28*

1.28 ± 0.27

LDL, mmol/L

2.54 ± 0.89

2.52 ± 0.56

Hypertension, n (%)

413 (83)*

310 (62)

Diabetes, n (%)

130 (26)

122 (24)

Data are shown as mean ± standard deviation (SD) or as n (%). Abbreviations: TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein.*Significant differences between cases and controls.

Linkage disequilibrium

A total of eleven gene polymorphisms were genotyped in the cases and controls. For PON1 these included three coding-region polymorphisms (rs662/Q192R, rs13306698/Arg160Gly, and rs854560/Leu55Met) and four regulatory-region polymorphisms (rs705379/-107/-108, rs705381/-160/-162, rs854571/-824/-832, and rs854572/-907/-909). There were also two coding-region polymorphisms of PON2 (rs12026/Ala148Gly, and rs7493/Ser311Cys), and two coding-region polymorphisms of PON3 (rs2074353 located in intron and rs1053275/Ala99Ala). The total rate of successful genotyping was 98.6%. All genotype distributions within the studied polymorphisms were in Hardy-Weinberg equilibrium (P >0.05), in both cases and controls, except for rs705379 (−107/-108) (P <0.001), which was located in the promoter of PON1.

The results of linkage disequilibrium evaluation analyses are shown in Figure 1A. In this analysis, SNPs with a pairwise r2 >0.9 were considered to be in the same block. Based on this approach, four haplotype blocks (Block1: rs854560-rs13306698-rs662; Block2: rs854572-rs854571-rs705381; Block3: rs1053275-rs2074353; Block4: rs12026-rs7493) were identified (Figure 1B).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-14-16/MediaObjects/12881_2012_Article_1028_Fig1_HTML.jpg
Figure 1

Linkage disequilibrium analysis of the ten SNPs investigated in healthy controls (a). Four blocks were identified using Haploview software: Block1 (rs854560-rs13306698-rs662); Block2 (rs854572-rs854571-rs705381); Block3 (rs1053275-rs2074353); Block4 (rs12026-rs7493) (b).

Single site association

The association between the ten SNPs included in the four blocks and total stroke occurrence was analyzed using additive, dominant, genotype, and recessive models. As shown in Table 2, two polymorphisms, rs705381 and rs854571 were significantly associated with total stoke using additive and dominant models. The allele A of rs705381 and the allele T of rs854571 were both less frequent in patients with total stroke than in controls. The association remained significant after logistic regression analysis adjusting for age, HDL and hypertension using the additive model (rs705381, P adjusted  = 0.0058, OR = 0.67 [95% CI = 0.50-0.89]; and rs854571, P adjusted  = 0.0330, OR = 0.80 [95% CI = 0.65-0.98]). However, both P-values failed to reach significance after the Bonferroni adjustment for multiple comparisons. Analysis using the dominant model, showed that the differences in rs705381 remained significant after Bonferroni correction (P adjusted  = 0.0007, OR = 0.57 [95% CI = 0.41-0.79]), but the differences in rs854571 did not. There was no significant association between any of the SNPs of PON genes and total strokes when analyzed using the recessive model.
Table 2

Association between SNPs and total stroke using the additive, dominant, genotype, and the recessive models

SNP

Model

Allele or geno

F_Stroke

F_Control

T-Statistic

Logistic Regression

OR(95% CI)

P unadjusted

OR(95% CI)

P adjusted

rs854571

Additive

C > T

298/992

344/982

0.79(0.65-0.96)

1.71E-02

0.80(0.65-0.98)

3.30E-02

 

Dominant

CC + CT/TT

253/496

289/491

0.73(0.57-0.94)

1.33E-02

0.75(0.57-0.99)

3.96E-02

 

Recessive

CC/CT + TT

45/496

55/491

0.79(0.52-1.20)

2.69E-01

0.75(0.48-1.17)

2.06E-01

rs13306698

Additive

A > G

98/1016

98/988

0.97(0.71-1.31)

8.31E-01

1.00(0.71-1.40)

9.99E-01

 

Dominant

AA + AG/GG

97/508

96/494

0.98(0.71-1.34)

8.92E-01

1.02(0.72-1.44)

9.20E-01

 

Recessive

AA/AG + GG

1/508

2/494

0.49(0.04-5.37)

5.55E-01

0.39(0.03-5.06)

4.74E-01

rs854572

Additive

C > G

443/1004

413/964

1.05(0.88-1.26)

5.69E-01

1.09(0.89-1.32)

4.08E-01

 

Dominant

CC + CG/GG

343/502

324/482

1.05(0.81-1.38)

7.10E-01

1.11(0.82-1.48)

5.04E-01

 

Recessive

CC/CG + GG

100/502

89/482

1.10(0.80-1.51)

5.62E-01

1.13(0.80-1.61)

4.93E-01

rs7493

Additive

C > G

192/1016

176/974

1.06(0.84-1.33)

6.32E-01

1.00(0.78-1.29)

9.89E-01

 

Dominant

CC + CG/GG

173/508

163/487

1.03(0.79-1.34)

8.45E-01

1.00(0.75-1.34)

9.85E-01

 

Recessive

CC/CG + GG

19/508

13/487

1.42(0.69-2.90)

3.41E-01

1.00(0.46-2.18)

9.93E-01

rs662

Additive

G > A

389/1014

356/978

1.08(0.91-1.29)

3.86E-01

1.05(0.87-1.28)

5.93E-01

 

Dominant

GG + GA/AA

303/507

282/489

1.09(0.85-1.40)

5.02E-01

1.05(0.80-1.39)

7.31E-01

 

Recessive

GG/GA + AA

86/507

74/489

1.15(0.82-1.61)

4.32E-01

1.12(0.77-1.62)

5.65E-01

rs12026

Additive

C > G

192/1010

174/978

1.09(0.86-1.37)

4.80E-01

1.05(0.81-1.35)

7.17E-01

 

Dominant

CC + CG/GG

173/505

162/489

1.05(0.81-1.37)

7.07E-01

1.05(0.78-1.40)

7.56E-01

 

Recessive

CC/CG + GG

19/505

12/489

1.55(0.75-3.24)

2.39E-01

1.13(0.51-2.50)

7.72E-01

rs1053275

Additive

A > G

203/1000

186/994

1.10(0.89-1.37)

3.82E-01

1.10(0.86-1.39)

4.53E-01

 

Dominant

AA + AG/GG

179/500

165/497

1.12(0.86-1.46)

3.88E-01

1.13(0.85-1.51)

3.90E-01

 

Recessive

AA/AG + GG

24/500

21/497

1.14(0.63-2.08)

6.62E-01

1.04(0.54-1.99)

9.17E-01

rs705381

Additive

G > A

106/988

151/990

0.67(0.51-0.87)

3.13E-03*

0.67(0.50-0.89)

5.80E-03*

 

Dominant

GG + GA/AA

95/494

144/495

0.58(0.43-0.78)

3.20E-04*

0.57(0.41-0.79)

7.10E-04*

 

Recessive

GG/GA + AA

11/494

7/495

1.59(0.61-4.13)

3.43E-01

1.64(0.58-4.61)

3.52E-01

rs2074353

Additive

A > G

253/996

230/982

1.11(0.91-1.36)

3.16E-01

1.12(0.90-1.40)

3.09E-01

 

Dominant

AA + AG/GG

219/498

197/491

1.17(0.91-1.51)

2.20E-01

1.23(0.93-1.62)

1.46E-01

 

Recessive

AA/AG + GG

34/498

33/491

1.02(0.62-1.67)

9.47E-01

0.91(0.53-1.57)

7.31E-01

rs854560

Additive

A > T

41/1014

39/996

1.03(0.66-1.61)

8.84E-01

0.95(0.58-1.56)

8.37E-01

 

Dominant

AA + AT/TT

40/507

38/498

1.04(0.65-1.65)

8.78E-01

0.97(0.58-1.62)

9.12E-01

 

Recessive

AA/AT + TT

1/507

1/498

0.98(0.06-15.75)

9.90E-01

0.40(0.02-7.40)

5.39E-01

Variants are described as minor allele or geno; the contrast allele refers to the minor allele; OR: odds ratio; CI: confidence interval; P unjusted : unadjusted P-value from t-test; P adjusted : P value adjusted using logistic regression analysis with age, HD and hypertension as covariates. F_Stroke and F_Control represent the frequency of minor allele or geno in total stroke patients and controls respectively. Significant P values (P <0.05) are in bold and P* < 0.005 (Bonferroni multiple correction threshold).

As shown in Table 3, rs705381 was significantly associated with ischemic stroke after adjustment of confounders in both additive and dominant models (P adjusted  = 0.0017, OR = 0.54 [95% CI = 0.37-0.79]). However, no significant association with ischemic stroke was found using the recessive model.
Table 3

Association between SNPs with ischemic stroke using the additive, dominant, genotype, and the recessive models

SNP

Model

Allele or geno

F_IS

Control

T-Statistic

Logistic Regression

OR(95% CI)

P unadjusted

OR(95% CI)

P adjusted

rs854571

Additive

C > T

200/660

344/982

0.80(0.65-0.99)

4.34E-02

0.84(0.66-1.07)

1.62E-01

 

Dominant

CC + CT/TT

170/330

289/491

0.74(0.56-0.98)

3.79E-02

0.80(0.58-1.10)

1.63E-01

 

Recessive

CC/CT + TT

30/330

55/491

0.79(0.50-1.27)

3.31E-01

0.82(0.49-1.37)

4.45E-01

rs13306698

Additive

A > G

62/676

98/988

0.91(0.65-1.29)

6.00E-01

1.07(0.72-1.59)

7.43E-01

 

Dominant

AA + AG/GG

61/338

96/494

0.91(0.64-1.30)

6.16E-01

1.09(0.72-1.64)

6.83E-01

 

Recessive

AA/AG + GG

1/338

2/494

0.73(0.07-8.08)

7.98E-01

0.55(0.04-7.85)

6.63E-01

rs854572

Additive

C > G

285/668

413/964

0.99(0.82-1.21)

9.44E-01

0.98(0.78-1.23)

8.29E-01

 

Dominant

CC + CG/GG

220/334

324/482

0.94(0.70-1.27)

6.87E-01

0.94(0.67-1.32)

7.23E-01

 

Recessive

CC/CG + GG

65/334

89/482

1.07(0.75-1.52)

7.21E-01

1.01(0.67-1.53)

9.70E-01

rs7493

Additive

C > G

124/676

176/974

1.02(0.79-1.32)

8.85E-01

0.98(0.73-1.31)

8.82E-01

 

Dominant

CC + CG/GG

114/338

163/487

1.01(0.75-1.36)

9.39E-01

0.99(0.71-1.39)

9.68E-01

 

Recessive

CC/CG + GG

10/338

13/487

1.11(0.48-2.57)

8.04E-01

0.85(0.34-2.12)

7.23E-01

rs662

Additive

G > A

276/674

356/978

1.19(0.98-1.45)

7.34E-02

1.18(0.94-1.47)

1.46E-01

 

Dominant

GG + GA/AA

212/337

282/489

1.25(0.94-1.66)

1.31E-01

1.20(0.86-1.66)

2.84E-01

 

Recessive

GG/GA + AA

64/337

74/489

1.32(0.91-1.90)

1.45E-01

1.35(0.88-2.06)

1.67E-01

rs12026

Additive

C > G

124/672

174/978

1.05(0.81-1.36)

7.26E-01

1.02(0.76-1.37)

8.78E-01

 

Dominant

CC + CG/GG

114/336

162/389

1.04(0.77-1.39)

8.11E-01

1.04(0.74-1.45)

8.33E-01

 

Recessive

CC/CG + GG

10/336

12/489

1.22(0.52-2.86)

6.48E-01

0.95(0.38-2.42)

9.21E-01

rs1053275

Additive

A > G

144/666

186/994

1.19(0.94-1.52)

1.51E-01

1.17(0.89-1.54)

2.70E-01

 

Dominant

AA + AG/GG

129/333

165/497

1.27(0.95-1.70)

1.02E-01

1.24(0.89-1.73)

2.10E-01

 

Recessive

AA/AG + GG

15/333

21/497

1.07(0.54-2.11)

8.47E-01

1.07(0.49-2.31)

8.68E-01

rs705381

Additive

G > A

74/660

151/990

0.70(0.52-0.95)

2.02E-02

0.65(0.47-0.92)

1.35E-02

 

Dominant

GG + GA/AA

65/330

144/395

0.60(0.43-0.83)

2.50E-03*

0.54(0.37-0.79)

1.67E-03*

 

Recessive

GG/GA + AA

9/330

7/495

1.96(0.72-5.30)

1.88E-01

1.85(0.60-5.64)

2.83E-01

rs2074353

Additive

A > G

176/662

230/982

1.18(0.94-1.47)

1.53E-01

1.23(0.95-1.60)

1.09E-01

 

Dominant

AA + AG/GG

153/331

197/491

1.28(0.97-1.70)

8.30E-02

1.38(1.00-1.91)

5.29E-02

 

Recessive

AA/AG + GG

23/331

33/491

1.04(0.60-1.80)

8.99E-01

1.06(0.56-1.99)

8.69E-01

rs854560

Additive

A > T

30/674

39/996

1.14(0.70-1.85)

5.93E-01

1.19(0.69-2.07)

5.36E-01

 

Dominant

AA + AT/TT

29/337

38/498

1.14(0.69-1.89)

6.11E-01

1.24(0.70-2.21)

4.57E-01

 

Recessive

AA/AT + TT

1/337

1/498

1.48(0.09-23.73)

7.82E-01

0.43(0.02-9.62)

5.92E-01

Variants are described as minor allele or geno and the contrast allele refers to the minor allele; OR: odds ratio; CI: confidence interval; P unjusted : unadjusted P-value from t-test; P adjusted : P value adjusted using logistic regression analysis with age, HD and hypertension as covariates F_IS and F_Control represent the frequency of minor allele in ischemic stroke patients and controls respectively. Significant P values (P < 0.05) are in bold and P* < 0.005 (Bonferroni multiple correction threshold).

Rs854571 was associated with hemorrhagic stroke, with marginal significance (P unadjusted  = 0.0500, OR = 0.76 [95% CI = 0.57-1.00]) using the additive model, and rs705381 showed a significant association in both additive (P adjusted  = 0.0290, OR = 0.62 [95% CI = 0.40-0.95]) and dominant models (P adjusted  = 0.0165, OR = 0.57 [95% CI = 0.36-0.90]) (Table 4). However, neither of the two SNPs was significantly associated with hemorrhagic stroke after the Bonferroni correction. Thus, there was no significant finding for hemorrhagic stroke with any of the three models.
Table 4

Association between SNPs and hemorrhagic stroke using the additive, dominant, genotype, and the recessive models

SNP

Model

Allele or geno

F_HS

F_Control

T-Statistic

Logistic Regression

OR(95% CI)

P unadjusted

OR(95% CI)

P adjusted

rs854571

Additive

C > T

92/316

344/982

0.76(0.57-1.00)

5.00E-02

0.76(0.57-1.01)

5.54E-02

 

Dominant

CC + CT/TT

78/158

289/491

0.68(0.48-0.98)

3.68E-02

0.70(0.48-1.01)

5.57E-02

 

Recessive

CC/CT + TT

14/158

55/491

0.77(0.42-1.43)

4.08E-01

0.71(0.38-1.34)

2.95E-01

rs13306698

Additive

A > G

33/324

98/988

1.03(0.67-1.59)

8.85E-01

1.06(0.68-1.66)

7.93E-01

 

Dominant

AA + AG/GG

33/162

96/494

1.06(0.68-1.65)

7.95E-01

1.09(0.69-1.72)

7.06E-01

 

Recessive

AA/AG + GG

0/162

2/494

0.00(0.00-inf)

9.99E-01

0.00(0.00-inf)

9.99E-01

rs854572

Additive

C > G

151/320

413/964

1.20(0.93-1.54)

1.73E-01

1.24(0.95-1.61)

1.20E-01

 

Dominant

CC + CG/GG

118/160

324/482

1.37(0.92-2.04)

1.23E-01

1.38(0.91-2.08)

1.27E-01

 

Recessive

CC/CG + GG

33/160

89/482

1.15(0.73-1.79)

5.46E-01

1.25(0.79-1.99)

3.38E-01

rs7493

Additive

C > G

64/324

176/974

1.12(0.81-1.55)

4.94E-01

1.05(0.75-1.46)

7.77E-01

 

Dominant

CC + CG/GG

57/162

163/487

1.08(0.74-1.57)

6.90E-01

1.03(0.70-1.51)

8.94E-01

 

Recessive

CC/CG + GG

7/162

13/487

1.65(0.65-4.20)

2.97E-01

1.28(0.49-3.36)

6.13E-01

rs662

Additive

G > A

108/324

356/978

0.88(0.68-1.14)

3.36E-01

0.85(0.65-1.12)

2.48E-01

 

Dominant

GG + GA/AA

88/162

282/489

0.87(0.61-1.25)

4.56E-01

0.82(0.56-1.18)

2.85E-01

 

Recessive

GG/GA + AA

20/162

74/489

0.79(0.47-1.34)

3.83E-01

0.80(0.46-1.38)

4.26E-01

rs12026

Additive

C > G

64/322

174/978

1.15(0.83-1.59)

3.95E-01

1.10(0.78-1.53)

5.92E-01

 

Dominant

CC + CG/GG

57/161

162/389

1.11(0.76-1.61)

5.96E-01

1.07(0.72-1.57)

7.44E-01

 

Recessive

CC/CG + GG

7/161

12/489

1.81(0.70-4.67)

2.22E-01

1.47(0.55-3.92)

4.39E-01

rs1053275

Additive

A > G

58/318

186/994

0.97(0.71-1.33)

8.55E-01

0.96(0.70-1.33)

8.07E-01

 

Dominant

AA + AG/GG

49/159

165/497

0.90(0.61-1.32)

5.77E-01

0.91(0.61-1.35)

6.36E-01

 

Recessive

AA/AG + GG

9/159

21/497

1.36(0.61-3.03)

4.53E-01

1.17(0.51-2.69)

7.09E-01

rs705381

Additive

G > A

32/312

151/990

0.62(0.41-0.94)

2.42E-02

0.62(0.40-0.95)

2.90E-02

 

Dominant

GG + GA/AA

30/156

144/395

0.58(0.37-0.90)

1.61E-02

0.57(0.36-0.90)

1.65E-02

 

Recessive

GG/GA + AA

2/156

7/495

0.91(0.19-4.40)

9.02E-01

1.22(0.24-6.22)

8.13E-01

rs2074353

Additive

A > G

74/340

230/982

0.99(0.74-1.32)

9.57E-01

0.96(0.72-1.29)

8.05E-01

 

Dominant

AA + AG/GG

63/159

197/491

0.98(0.68-1.41)

9.11E-01

0.98(0.67-1.42)

8.98E-01

 

Recessive

AA/AG + GG

11/159

33/491

1.03(0.51-2.09)

9.31E-01

0.87(0.42-1.82)

7.21E-01

rs854560

Additive

A > T

9/324

39/996

0.70(0.34-1.46)

3.46E-01

0.57(0.26-1.25)

1.58E-01

 

Dominant

AA + AT/TT

9/162

38/498

0.71(0.34-1.51)

3.74E-01

0.57(0.26-1.28)

1.74E-01

 

Recessive

AA/AT + TT

0/162

NA

NA

NA

NA

NA

Variants are described as minor allele or geno and the contrast allele refers to the minor allele; P unjusted : unadjusted P-value from t-test; P adjusted : P value adjusted using logistic regression analysis with age, HD and hypertension as covariates. F_HS and F_Control represent the frequency of minor allele in hemorrhagic stroke patients and controls respectively. NA means not applicable. Significant P values (P < 0.05) are in bold.

Haplotype analysis

Haplotype analysis conducted in the four blocks, with or without adjustment for age, HDL and hypertension as covariates is shown in Table 5. Block 2 consisting of rs854572, rs854571 and rs705381 was associated with total stroke (P = 0.0129 Omnibus test), and included one protective haplotype C-T-C (OR = 0.64; P unadjusted  = 0.0013,) and one nominal risk haplotype C-C-C (OR = 1.24; P unadjusted  = 0.0442,). The association for haplotype C-T-C remained significant after adjustment for age, HDL and hypertension as covariates (OR = 0.65; P = 0.0037). No other significant haplotype associations were found.
Table 5

Haplotypes of the four blocks between total strokes and control subjects

Haplotype

Logistic Regression

OR

P unadjusted

OR

P adjusted

Block1: rs854560-rs13306698-rs662

OMNIBUS

NA

0.9371

NA

0.9569

TAA

1.03

0.8820

0.95

0.8390

AAA

1.08

0.4170

1.06

0.5790

AGG

0.95

0.7640

0.98

0.8840

AAG

0.94

0.4810

0.96

0.6580

Block2: rs854572-rs854571-rs705381

OMNIBUS

NA

0.0129

NA

0.0394

CTT

1.05

0.6170

1.08

0.4200

CTC

0.64

0.0013

0.65

0.0037

GCC

0.99

0.9110

0.99

0.9280

CCC

1.24

0.0442

1.19

0.1420

Block3: rs1053275-rs2074353

OMNIBUS

NA

0.4970

NA

0.5757

GG

1.10

0.3970

1.10

0.4210

AG

1.09

0.6630

1.13

0.5880

AA

0.90

0.2920

0.89

0.3010

Block4: rs12026-rs7493

OMNIBUS

NA

0.2479

NA

0.5467

GG

1.08

0.5390

1.03

0.8430

CC

0.92

0.5020

0.96

0.7660

Haplotypes observed in <1% of the control subjects are not listed in the table. OR: odds ratio; P unjusted : unadjusted P-value from t-test; P adjusted : P value adjusted using logistic regression analysis with age, HD and hypertension as covariates. OMIBUS value was calculated by an ANOVA analysis for including or not including the haplotype information in a likelihood ration test of nested model. The OR in one block for each haplotype was calculated by using all the other haplotypes in the same block as the reference haplotype. Significant P values (P < 0.05) are in bold.

Meta-analysis

Two meta-analyses, PMID: 20856122 [35] and PMID: 18511872 [30], which studied the association between rs662 and rs854560 loci and ischemic stroke were contextualized with our study using the random effects model. Forests plot for rs662 from 25 studies including our own are shown in Figure 2. There was a nominal significant association between rs662 and ischemic stroke (P = 0.0100, OR = 1.08 [95% CI = 1.02-1.15]) yielding 1.08 per G allele copy, with no statistical evidence for statistical heterogeneity (P = 0.0400, I 2  = 36%) between studies.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-14-16/MediaObjects/12881_2012_Article_1028_Fig2_HTML.jpg
Figure 2

Meta-analysis of studies investigating the association of PON1 rs662 with ischemic stroke using a random effects model. The point estimate of the OR (square proportional to the weight of each study) and 95% CI for the OR (extending lines) for each study. The summary OR and 95% CIs by random effects calculations are depicted as a diamond. Values higher than 1 indicate that the G allele is associated with increased risk of ischemic stroke.

There was no evidence of an association between rs854560 and ischemic stroke risk (P = 0.3700, OR = 0.97 [95% CI = 0.91-1.04]) and no evidence of heterogeneity (P = 0.2700, I 2  = 16%) between studies (Figure 3).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-14-16/MediaObjects/12881_2012_Article_1028_Fig3_HTML.jpg
Figure 3

Meta-analysis of studies investigating the association of PON1 rs854560 with ischemic stroke using a random effects model. Values higher than 1 indicate that the A allele is associated with increased risk of ischemic stroke risk. The layout is the same as that in Figure 2.

Discussion

The present study investigated the association of 11 polymorphisms in 3 PON genes with the risk of stroke. Using a dominant model, we demonstrated that rs705381 (−162) was significantly associated with total stroke and ischemic stroke but not with hemorrhagic stroke. There was also a nominal association between rs854571 (−824) and stroke with the allele T as a protective factor.

Both rs705381 and rs854571 polymorphisms located in the promoter region of PON1 were associated with stroke, which was consistent with previous findings [19, 3639]. The protective effect of -162 T polymorphism on total stroke and ischemic stroke was also consistent with previous observations [40] which suggested that NF-1, a ubiquitous nuclear factor and a transcriptional activator, has a binding site on PON1 if allele A appears at −162. Other studies have shown that -162 T polymorphism results in higher expression levels of PON1[40, 41] There is also evidence to suggest a correlation between AA (−162) and high PON activities in Caucasians [42].

Our results support the hypothesis that the protective effect of -162 T polymorphism might be attributable to high PON activity [42]. We also found weak evidence to suggest that -824 T was associated with a reduced propensity to suffer stroke. However, the evidence was no longer apparent after Bonferroni correction for multiple comparisons. It has been previously reported that -824 T (824A in their finding) was associated with low serum PON levels [43]. Negative associations between −162 and −824 have been reported in studies in American populations [23, 40]. These findings highlight the potential influence of ethnic differences in terms of the founder effect and identical-by-descent principles [44, 45].

Patients with coronary heart disease (CHD) have been shown to have a higher frequency of -162 T allele than the controls, suggesting allele A may be associated with risk of CHD in the Han Chinese population [46]. However, in our study, we found a protective effect of the -162 T polymorphism on stroke.

Haplotype analysis further confirmed our positive results and identified a positive association between the protective haplotype C-T-C and the risk haplotype C-C-C of rs854572-rs854571-rs705381 (Block 2) with total stroke. No significant associations were observed for stroke susceptibility with the two coding region polymorphisms in PON2, which was consistent with previous findings in the Han Chinese population and in North Americans [24, 29], although a positive association of Ser311Cys was found in a Polish population [22].

The absence of any positive correlations between stroke risk and the two PON3 polymorphisms in our study was also consistent with reported findings in Caucasian and North American patients [24, 27].

Our study was conducted in a relatively large Chinese sample pool and included careful assessment of two stroke subtypes. We also selected common variants in all three members of the PON gene family. However, functional detection of PON activities was not undertaken in the present study and investigation of the association between SNPs and large or small vessel strokes was not possible as a complete classification of the subtype of ischemic stroke subjects was not available in our study. In our study, results from both adjusted and unadjusted analyses were in line with each other. However, in other settings, authorities have discouraged the use of data adjustments for the determination of the total genetic effect [47]. It, therefore, remains uncertain as to whether adjusted or unadjusted data should be used to interpret our results in clinical context.

Conclusion

The study identified rs705381 (−162) as being significantly associated with total stroke and ischemic stroke, and demonstrated a weak association for rs854571 (−824) in the Han Chinese population. These findings support the involvement of PON polymorphisms in the development of stroke. Further studies with larger sample sizes are required to validate these findings and to elucidate the underlying biological mechanisms.

Notes

Declarations

Acknowledgements

The project was supported by National Key Technology R&D Program in the 11th Five year Plan of China (2008BAI52B03), the Natural Science Foundation of China (81130022, 81272302, 31000553), the National 863 project (2012AA02A515), the Foundation for the Author of National Excellent Doctoral Dissertation of China (201026), and Shanghai Science and Technology Development Funds (12QA1401900).

Authors’ Affiliations

(1)
Laboratory Diagnosis Center, Beijing Tiantan Hospital Affiliated to Capital Medical University
(2)
Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University
(3)
CapitalBio Corporation
(4)
National Engineering Research Center for Beijing Biochip Technology

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  48. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/14/16/prepub

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© Zhang et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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