Genetic Polymorphisms of ALOX5AP and CYP3A5 Increase Susceptibility to Ischemic Stroke and are Associated with Atherothrombotic Events in Stroke Patients Xingyang Yi, MD,* Biao Zhang, MD,* Chun Wang, MSc,* Duanxiu Liao, MSc,* Jing Lin, MD,† and Lifen Chi, MD†

Background: The contributions of gene–gene interactions to pathogenesis of stroke remain largely elusive. The present study was designed to investigate the associations between genetic variations and ischemic stroke risk, the roles of gene–gene interactions in ischemic stroke, and their associations with atherothrombotic events. Methods: Among 396 patients with ischemic stroke and 378 controls, we examined 8 variants from 5 genes, including ALOX5AP-SG13S32 (rs9551963), SG13S42 (rs4769060), SG13S89 (rs4769874), SG13S114 (rs10507391), EPHX2 G860A (rs751141), CYP2C9*2 C430T (rs1799853), CYP2C9*3 A1075C (rs1057910), and CYP3A5 A6986G (rs776746), using matrix-assisted laser desorption/ionization time of flight mass spectrometry. Gene–gene interactions were determined by the generalized multifactor dimensionality reduction (GMDR) method. All ischemic stroke patients were followed up 12 months for atherothrombotic events, including recurrent ischemic stroke and other vascular events. Results: Single-gene variant analysis showed no significant differences in the genotype distributions of the 8 variants between the 2 groups. However, the GMDR analysis showed a significant interaction between rs10507391 and rs776746, in those cases carrying rs10507391 AA and rs776746 GG, the risk of ischemic stroke increased by 2.014 times (95% confidence interval [CI], 1.896-6.299; P 5 .006), and the atherothrombotic events occurred more frequently in those patients during follow-up period (P , .001). Multiple Cox regression analysis showed that the interaction between rs10507391 AA and rs776746 GG was an independent risk factor for atherothrombotic events (relative risk 5 2.921; 95% CI, 1.118-7.012; P 5 .008). Conclusions: The interaction between rs10507391 and rs776746 increases the susceptibility to ischemic stroke and is associated with atherothrombotic events in stroke patients. Key Words: Stroke— GMDR—genetics—variants—5-lipoxygenase activating protein—cytochrome P450. Ó 2014 by National Stroke Association

Stroke is a leading cause of death and disability worldwide.1,2 In China, there are approximately 3 million new stroke cases every year with ischemic stroke accounting for 43.7-78.9% of all strokes.3 The development of stroke

is influenced by a variety of risk factors, including hypertension, smoking, diabetes mellitus, genetic predispositions, and other chronic and inflammatory diseases. For instance, atherosclerosis, a chronic inflammatory disease,

From the *Department of Neurology, People’s Hospital of Deyang City, Deyang; and †Department of Neurology, Third Affiliated Hospital of Wenzhou Medical College, Wenzhou, China. Received September 5, 2014; revision received September 24, 2014; accepted September 26, 2014. The authors declare no conflict of interests. This study was supported by Scientific Research Foundation of Sichuan Provincial Health Department (Grant No.140025) and

Zhejiang Province Science and Technology Research Foundation (Grant No. 2012c33106). Address correspondence to Xingyang Yi, MD, Department of Neurology, People’s Hospital of Deyang City, Deyang 618000, Sichuan, China. E-mail: [email protected]. 1052-3057/$ - see front matter Ó 2014 by National Stroke Association http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2014.09.035

Journal of Stroke and Cerebrovascular Diseases, Vol. -, No. - (---), 2014: pp 1-9

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X. YI ET AL.

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has been linked to the development of stroke. In the development of the chronic inflammatory disease, the main inflammatory mediators are the metabolic products of arachidonic acid (AA).5 It is well documented that AA is readily metabolized by the cyclooxygenase, lipoxygenase (LOX), and cytochrome P450 enzymes (CYP), generating prostanoids, leukotrienes, and epoxyeicosatrienoic acids (EETs), respectively.6,7 Genetic variations are major factors modulating the inflammatory response, thus resulting in changes in pathogenesis of related diseases and their response to therapy. For example, several common allelic variants of the ALOX5AP gene have been reported to increase the risk of myocardial infarction (MI) and stroke in a Scottish population.8-10 ALOX5AP encodes the 5-lipoxygenase– activating protein, a key enzyme involved in leukotriene biosynthesis within the LOX pathway. However, other subsequent studies in several non-Icelandic populations have shown that there was no association between variants of ALOX5AP and ischemic stroke or MI.11,12 The possible reason for the conflicting results may be that these loci are participating in a large biologic pathway and that there are multiple contributors to genetic risk for stroke in various populations. For example, ALOX5AP may contribute substantially to the stroke risk in Iceland populations but may play a smaller role in other non-Icelandic populations. EETs have diverse cardiovascular protective effects.13 AA is metabolized by CYP epoxygenases, resulting in at least 4 EETs.14 There is an increasing interest in investigating the possible role of genetic variations in CYP genes in stroke, but the results reported are not conclusive. For example, the variations in the CYP2C8 and CYP2C9 genes, encoding 2 major CYP epoxygenases, are reported not to be associated with the risk of MI or ischemic stroke.15 Although CYP3A5, a key metabolizing enzyme for clopidogrel, is thought to have minimal epoxygenase activity,16 its polymorphism has been suggested to be a predictor of atherothrombotic events in patients treated with clopidogrel.17 In addition, other enzymes are responsible for further metabolism of EETs and could have a role in stroke as well. For instance, soluble epoxide hydrolase, encoded by the EPHX2 gene, metabolizes EETs to less biologically active dihydroxyeicosatrienoic acids.18 Therefore, the possible effects of these metabolizing enzymes on stroke should also be considered. It is generally believed that stroke is a common complex trait that does not follow the Mendelian pattern of inheritance.19 Considering that gene–gene and gene– environment interactions may also be responsible for the complex trait, investigations with single-locus methods may not be appropriate for the study of genetic polymorphisms of stroke. It is possible that particular genes may contribute to the complex diseases only by their interactions with other genes, and therefore, the

effects of individual loci may be too small to be detected.20 Considering that both LOX and CYP pathways are involved in AA metabolism, it would be interesting to determine their interactions and the role of such gene– gene interaction in stroke. To facilitate the analysis of complex genetic polymorphisms, Lou et al21 have proposed the generalized multifactor dimensionality reduction (GMDR) framework, which is based on the score of a generalized linear model, to determine the gene–gene interactions. In the present study, we tested the hypothesis that interactions of multigenetic variants of the genes in the LOX and CYP pathways would confer a higher stroke risk than variants in single genes, which will be related to recurrent ischemic stroke (RIS) and other vascular events after stroke. Using matrix-assisted laser desorption/ ionization time of flight mass spectrometry (MALDITOF MS), we evaluated 8 variants on 5 genes in the LOX and CYP pathways in patients with ischemic stroke and controls. We also explored the roles of the gene–gene interactions in the susceptibility to ischemic stroke and the development of atherothrombotic events after stroke. It is hoped that the results can be useful in developing predictive models in the diagnosis and treatment of stroke.

Materials and Methods Ethics Statement This clinical study protocol was reviewed and approved by the Ethics Committee of the People’s Hospital of Deyang City and the Ethics Committee of the Third Affiliated Hospital of Wenzhou Medical College. Each of the participants provided an informed consent (in Chinese language) before participating in this study. In most cases, the participants provided their written informed consent. For patients who could not read or write standard Chinese, the consent was verbal. In that case, the research nurse documented the participant consent in writing, including the contents and methods of information provided to the participant and the date and time of the verbal consent, which was then witnessed and signed by another research nurse who was not on the research team. The informed consent record, either written or verbal, was kept in the participant’s hospital chart. The aforementioned ethics committees reviewed and approved this consent procedure.

Study Populations The study population comprised 396 ischemic stroke patients and 378 controls. Patients who had suffered their first ischemic stroke and were admitted into our hospitals were consecutively recruited into this study between August 1, 2010, and March 31, 2013. The inclusion criteria were as follows: (1) 18 years of age or older; (2) diagnosis

GENETIC POLYMORPHISMS AND STROKE RISK

of ischemic stroke based on both clinical findings and the results of a neurologic examination using computerized tomography (CT) or magnetic resonance imaging; (3) ischemic stroke related to atherothrombotic (AT; n 5 260) and small artery disease (SAD, n 5 136) according to the TOAST (Trial of ORG 10172 in the Acute Stroke Treatment) classification system22; and (4) consent to participate in this study. The exclusion criteria were as follows: (1) cardiogenic cerebral embolisms or cerebral infarction caused by other factors or unknown causes; (2) family history of apoplexy; (3) history of stroke; (4) cerebral hemorrhage; and (5) unwilling to participate in this study. The control subjects were selected from outpatients with no history of stroke as confirmed by medical history, physical, and laboratory examinations at our center. All control subjects underwent a brain CT or magnetic resonance scan to exclude possible asymptomatic ischemic or hemorrhagic lesions. The control patients were not genetically related to the cerebral infarction patients. The participants enrolled in the present study as controls were free of arteritis, infection, tumor, blood disease, severe heart, lung, liver, kidney and thyroid diseases, and autoimmune diseases. The vascular risk factors for each of the participants (patients and controls) were collected, including age, gender, hypertension, diabetes, cigarette smoking, alcohol intake, total plasma cholesterol, triglycerides, and low-density lipoprotein cholesterol.

Genotyping A 3-mL blood sample from each participant was drawn from an arm vein into a sterile tube containing ethylenediaminetetraacetic acid and stored at 280 C until anal-

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ysis. Genomic DNA was extracted from the whole blood sample using a modified phenol/chloroform method23 and purified using the UNIQ-10 kit (Sangon Biotech Co, Ltd, Shanghai, China). The selection of genotyping markers was based on previous studies that have demonstrated significant associations between the single nucleotide polymorphisms (SNPs) listed as follows and stroke.8,9,15,24,25 Additional consideration was based on each SNP’s minor allele frequency. The 8 SNPs tested in the present study included the 4 newly added ones in the ALOX5AP gene in the LOX pathway and one each from the EPHX2, CYP2C9*2, CYP2C9*3, and CYP3A5 genes in the CYP pathway, based on the NCBI database (http://www. ncbi.nlm.nih.gov/SNP). The 8 TagSNPs found in the human HapMap project database (http://www.hap map.org) with minor allele frequency.10 or more were rs9551963, rs4769060, rs4769874, rs10507391, rs751141, rs1799853, rs1057910, and rs776746. Genotyping of the 8 variants was accomplished using MALDI-TOF MS.24,26 In brief, Each SNP gene possessed a specific genotype, with 2 amplification primers and 1 extension primer (Table 1). The reaction mixture was desalted by adding 6 mg of cation exchange resin (Sequenom Inc, San Diego, CA), mixed well, and then, resuspended in 25 mL of water. Once the primer extension reaction was complete, the samples were spotted onto a 384 well spectroCHIP (Sequenom Inc) using MassARRAY Nanodispenser (Sequenom Inc) and genotyped using the MALDI-TOF MS spectrometer (Sequenom Inc). Genotype calling was performed in real time with MassARRAY RT software, version 3.0.0.4, and analyzed using the MassARRAY Typer software, version 3.4 (Sequenom Inc).

Table 1. Amplification and extension primers used in this study SNPs SG13S32 (rs9551963) SG13S42 (rs4769060) SG13S89 (rs4769874) SG13S114 (rs10507391) C430T (rs1799853) A1075C (rs1057910) A6986G (rs776746) G860A (rs751141)

Forward primer and reverse primer (50 / 30 ) F: ACGTTGGATGGGGTTCAAGAGAGAAATTCC R: CGTTGGATGAGTTCTTGACCTCACCAACC F: ACGTTGGATGGAAGGGTAGAAGTGTCTCAG R: ACGTTGGATGCGTGGTAATGGGTTTTGAGG F: ACGTTGGATGCACCAGGGAGCAAGCATTAG R: ACGTTGGATGTTTCAGGCATGCTCTGCACC F: ACGTTGGATGTCCAGATGTATGTCCAAGCC R: ACGTTGGATGCTCTTAAGGTAGGTCTATGG F: ACGTTGGATGCTGCGGAATTTTGGGATGGG R: ACGTTGGATGACCCACCCTTGGTTTTTCTC F: ACGTTGGATGCTACACAGATGCTGTGGTGC R: ACGTTGGATGTGTCACAGGTCACTGCATGG F: ACGTTGGATGGATGAAGGGTAATGTGGTCC R: ACGTTGGATGCCATAATCTCTTTAAAGAGC F: ACGTTGGATGTTTTCTAGATCCCTGCTCTG R: ACGTTGGATGAGCAGATGACTCTCCATAGC

Abbreviations: F, forward primer; R, reverse primer; SNPs, single nucleotide polymorphisms.

Extension primer (50 / 30 ) ACTGGGGAAGGATCTCATC TGAACTTATTTCAAACCCAAG GGATTAGCAATGCATTATCACA GCCTCTCTTTGCAATTCTA GAGGAGCATTGAGGAC CACGAGGTCCAGAGATAC CCAAACAGGGAAGAGATA CCCAGGCAGGTTACC

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Table 2. Clinical characteristics of the patients and controls Characteristics

Stroke (n 5 396)

Control (n 5 378)

P value

Age, mean 6 SD, y Male, n (%) Body mass index, mean 6 SD, kg/M2 Hypertension, n (%) Diabetes mellitus, n (%) Previous MI, n (%) Cigarette smoking, n (%) Alcohol drinking, n (%) TC, mean 6 SD, mM LDL, mean 6 SD, mM TG, mean 6 SD, mM rs9551963 AA, n (%) AC, n (%) CC, n (%) rs4769060 AA, n (%) AG, n (%) GG, n (%) rs4769874 AA, n (%) AG, n (%) GG, n (%) rs10507391 AA, n (%) AT, n (%) TT, n (%) rs1799853 CC, n (%) rs1057910 AA, n (%) AC, n (%) CC, n (%) rs776746 AA, n (%) AG, n (%) GG, n (%) rs751141 GG, n (%) AG, n (%) AA, n (%)

68.79 6 11.11 235 (59.34) 24.10 6 2.33 302 (76.26) 138 (34.85) 0 165 (41.67) 184 (46.46) 5.54 6 1.36 3.15 6 1.27 1.96 6 1.12

64.98 6 10.29 222 (58.73) 23.90 6 2.62 99 (26.19) 97 (25.66) 0 159 (42.06) 170 (44.97) 5.36 6 1.21 2.99 6 1.19 1.83 6 1.02

,.001 .924 .221 ,.001 .032 – .942 .694 .061 .376 .182 .582

140 (35.35) 192 (48.48) 64 (16.16)

147 (38.89) 178 (47.09) 53 (14.02)

151 (38.13) 192 (48.48) 53 (13.38)

150 (39.68) 178 (47.09) 50 (13.23)

0 (0) 28 (7.07) 368 (92.93)

0 (0) 15 (3.97) 363 (96.03)

68 (17.17) 201 (50.76) 127 (32.07)

52 (13.76) 195 (51.59) 131 (34.65)

396 (100)

378 (100)

360 (90.91) 35 (8.84) 1 (.25)

349 (92.33) 28 (7.41) 1 (.26)

45 (11.36) 151 (38.13) 200 (50.51)

40 (10.58) 135 (35.71) 203 (53.70)

247 (62.37) 132 (33.3) 17 (4.29)

240 (63.49) 118 (31.22) 20 (5.29)

.412

.072

.396

– .824

.682

.717

Abbreviations: LDL, low-density lipoprotein; MI, myocardial infarction; SD, standard deviation; TC, total cholesterol; TG, triglycerides.

Study End Points All of the stroke patients were followed up at our outpatient clinics at 1 month after discharge and every 2 or 3 months thereafter. Clinical events were assessed on the basis of the information provided by hospital records and the referring physician or a phone interview with the patient by the investigators. The investigators who evaluated the clinical end points were blinded to the results of the DNA analysis. The primary end point was a composite of atherothrombotic events, including RIS, MI, deep venous

thrombosis (DVT), and death within 12 months after first stroke. RIS was defined as a new focal neurologic deficit of vascular origin lasting at least 24 hours that was proven to be nonhemorrhagic by either CT or magnetic resonance imaging scanning. MI was defined as the presence of at least 2 of the following findings: (1) ischemic symptoms; (2) cardiac enzyme (creatine kinase isoenzyme) concentration at least twice the upper limit of normal; or (3) new electrocardiographic changes compatible with MI. Death was defined as vascular mortality due to MI, ischemic stroke, and other vascular causes.

GENETIC POLYMORPHISMS AND STROKE RISK

Statistical Analysis All statistical analyses were performed using SPSS 16.0 (SPSS Inc, Chicago, IL). The c2 test was used to analyze the deviation of Hardy–Weinberg equilibrium for genotype frequencies and to compare genotype frequencies. Continuous variables were compared using the Student t test. Discrete variables were compared using the c2 tests or Fisher exact test. The gene–gene interaction was analyzed using the GMDR Beta program, version .7 software (www.healthsystem.virginia. edu/internet/addiction-genomics/) and multiple logistic regression. The relative risk (RR) of genotype and prevalence of cerebral infarction was expressed with odds ratios and its 95% confidence intervals (CIs). The c2 test was used to compare the incidences of clinical end points among the genotypes. Cox proportional hazards model was used to describe the risks for the composite end point of RIS, DVT, MI, and death during the 12-month period after first stroke. The values of RR with 95% CI were reported. All tests were 2-sided, and a P value of .05 was considered statistically significant.

Results Demographic Characteristics and Genotyping Results Baseline characteristics of cases and controls are listed in Table 2. There was a significant difference in the mean ages between the 2 groups (P , .001). As expected, stroke patients had a higher prevalence of risk factors for vascular diseases, including a history of hypertension (P , .001) and diabetes (P 5 .032). The genotype distributions of the 8 variants examined in this study were consistent with the Hardy–Weinberg Equilibrium model (P . .05). There were no significant differences in the frequencies of the genotypes of the 8 variants between the stroke patients and the controls (P . .05).Moreover, there were no significant difference in genotype frequencies between AT and SAD patients (P . .05).

Gene–Gene Interactions and Stroke Risk High order interactions were investigated for ischemic stroke using the GMDR method. With covariate adjustments, the best model for ischemic stroke included rs10507391 and rs776746 was scored 10/10 for Cross Validation Consistency and 9/10 by the Sign Test (P 5 .0108; Table 3). The RR of the 9 combinations of genotypes of rs10507391 and rs776746 was considered as an interactive variable. Logistic regression analysis showed that the gene–gene interactions of rs10507391 AA and rs776746 GG predicted a significantly higher risk of cerebral infarction (adjusted for age, hypertension, diabetes mellitus; odds ratio 5 2.014; 95% CI, 1.896-6.299; P 5 .006; Table 4). The other combinations of genotypes analyzed did not reach the statistical significance level.

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Table 3. Comparison of the best models for prediction accuracy, cross-validation consistency, and P values identified by GMDR Training Testing Crossbalanced balanced validation Sign Best model* accuracy accuracy consistency test (P) 1 1,2 1,2,3 1,2,4,5 1,2,3,4,5 1,2,3,4,5,6 1,2,3,4,5,6,7 1,2,3,4,5,6,7,8

.5476 .5923 .5565 .6523 .7156 .7238 .7354 .7386

.4632 .5646 .4353 .4231 .4765 .4822 .4856 .4921

7/10 10/10 5/10 4/10 10/10 10/10 10/10 10/10

3 (.9435) 9 (.0108) 2 (.9763) 2 (.9796) 5 (.9693) 5 (.7233) 4 (.8325) 4 (.8426)

*The numbers 1-8 represent rs10507391, rs776746, rs751141, rs9551963, rs4769060, rs1057910, rs4769874, and rs1799853, respectively.

Gene–Gene Interactions and Outcomes after Stroke Among the 396 stroke patients, 4 (1.01%) were lost in follow-up, resulting in a complete rate of follow-up being 98.99% (392 of 396). During the 12-month period after first stroke, atherothrombotic events occurred in 54 patients (37 RIS, 5 died, 6 MI, and 6 DVT). Atherothrombotic events that occurred during the 12 months following the stroke are listed in Table 5. There were no significant differences in the frequencies of atherothrombotic events among the genotypes of the 8 variants (P ..05). However, compared with the patients not carrying rs10507391 AA and rs776746 GG, the patients carrying rs10507391 AA and rs776746 GG had significantly higher frequencies of atherothrombotic events (P , .001). Old age (.68 yr), AT stroke, hypertension, diabetes, and high low-density lipoprotein cholesterol levels were associated with higher numbers of atherothrombotic events after stroke (P ,.05). The results from multiple Cox regression analyses are listed in Table 6. Hypertension (RR 5 1.412; 95% CI, 1.001-3.232; P 5 .042), diabetes (RR 5 1.522; 95% CI, 1.011-3.722; P 5 .016), and carrying rs10507391 AA and rs776746 GG (RR 5 2.921; 95% CI, 1.118-7.012; P 5 .008)

Table 4. Multiple logistic regression analysis of the major risk factors for stroke Risk factor

OR

95% CI

P value

Age Hypertension Diabetes mellitus rs10507391 AA and rs776746 GG

1.895 11.127 1.124 2.014

1.116-3.986 7.474-19.046 1.001-2.457 1.896-6.299

.012 .000 .035 .006

Abbreviations: CI, confidence interval; OR, odds ratio.

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Table 5. Atherothrombotic events during 12-month follow-up in stroke patients (n 5 396) Genotype rs9551963 AA (n 5 140) AC (n 5 192) CC (n 5 64) P value rs4769060 AA (n 5 151) AG (n 5 192) GG (n 5 53) P value rs4769874 AA (n 5 0) AG (n 5 28) GG (n 5 368) P value rs10507391 AA (n 5 68) AT (n 5 201) TT (n 5 127) P value rs1799853 CC (n 5 396) rs1057910 AA (n 5 360) AC (n 5 35) CC (n 5 1) P value rs776746 AA (n 5 45) AG (n 5 151) GG (n 5 200) P value rs751141 GG (n 5 247) AG (n 5 132) AA (n 5 17) P value Patients carrying rs10507391 AA and rs776746 GG (n 5 35) Patients not carrying rs10507391 AA and rs776746 GG (n 5 361) P value Age .68 years, n 5 246 #68 years, n 5 150 P value Gender Male, n 5 235 Female, n 5 161 P value Stroke subtype AT, n 5 260 SAD, n 5 136 P value Hypertension Yes, n 5 302

RIS, n (%)

Death, n (%)

MI, n (%)

DVT, n (%)

Total, n (%)

12 (9.57) 19 (9.89) 6 (9.38) .936

2 (1.43) 2 (1.04) 1 (1.56) .942

2 (1.43) 3 (1.56) 1 (1.56) .912

2 (1.43) 4 (2.08) 0 (.00) .923

18 (12.86) 28 (14.58) 8 (12.50) .864

15 (10.00) 17 (8.85) 5 (9.43) .938

2 (1.32) 3 (1.56) 0 (.00) .632

2 (1.32) 3 (1.56) 1 (1.87) .643

3 (1.99) 2 (1.04) 1 (1.87) .611

22 (14.57) 25 (13.02) 7 (13.21) .412

– 3 (10.71) 34 (9.24) .835

– 1 (3.57) 4 (1.09) .278

– 0 (.00) 6 (1.63) .282

– 1 (3.57) 5 (1.36) .278

– 5 (17.86) 49 (13.32) .488

8 (11.76) 19 (9.45) 10 (7.87) .701

1 (1.47) 3 (1.49) 1 (.78) .601

2 (2.94) 3 (1.49) 1 (.78) .622

1 (1.47) 4 (1.99) 1 (.78) .654

12 (17.65) 29 (14.43) 13 (10.24) .314

37 (9.34)

5 (1.26)

6 (1.52)

6 (1.52)

54 (13.64)

33 (9.17) 4 (11.43) 0 (.00) .611

4 (1.11) 1 (2.86) 0 (.00) .324

5 (1.39) 1 (2.86) 0 (.00) .352

5 (1.39) 1 (2.86) 0 (.00) .348

47 (13.06) 7 (20.00) 0 (.00) .225

6 (13.33) 14 (9.27) 17 (8.50) .301

1 (2.22) 2 (1.32) 2 (1.00) .592

0 (.00) 2 (1.32) 4 (2.00) .601

1 (2.22) 2 (1.32) 3 (1.50) .595

8 (17.78) 20 (13.25) 26 (13.00) .411

23 (9.31) 13 (9.85) 1 (5.88) .625 8 (22.86)

4 (1.62) 1 (.76) 0 (.00) .410 (2.86)

4 (1.62) 2 (1.52) 0 (.00) .402 2 (5.71)

3 (1.21) 2 (1.52) 1 (5.88) .423 0 (.00)

34 (13.77) 18 (13.64) 2 (11.76) .739 11 (31.43)

29 (8.03)

4 (1.11)

4 (1.11)

6 (1.66)

43 (11.91)

,.001

.274

.375

.402

,.001

29 (12.4) 8 (6.0) .032

4 (1.6) 1 (.7) .124

4 (1.6) 2 (1.3) .098

4 (1.6) 2 (1.3) .325

41 (16.7) 13 (8.7) .023

22 (9.4) 15 (9.3) .997

3 (1.3) 2 (1.2) .994

4 (1.2) 2 (1.2) .999

3 (1.3) 3 (1.9) .998

32 (13.6) 22 (13.7) .998

30 (11.5) 7 (5.1) .031

4 (1.5) 1 (.7) .089

4 (1.5) 2 (1.5) .992

4 (1.5) 2 (1.5) .992

42 (16.2) 12 (8.8) .045

34 (11.3)

4 (1.3)

5 (1.7)

5 (1.7)

48 (15.9) (Continued )

GENETIC POLYMORPHISMS AND STROKE RISK

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Table 5. (Continued ) Genotype No, n 5 94 P value Diabetes Yes, n 5 138 No, n 5 258 P value High LDL-C Yes, n 5 213 No, n 5 183 P value High TC Yes, n 5 200 No, n 5 196 P value Smoking Yes, n 5 165 No, n 5 231 P value Alcohol intake Yes, n 5 184 No, n 5 212 P value

RIS, n (%)

Death, n (%)

MI, n (%)

DVT, n (%)

Total, n (%)

3 (3.2) .023

1 (1.1) .142

1 (1.1) .213

1 (1.1) .213

6 (6.4) .024

19 (13.8) 18 (7.0) .033

3 (2.2) 2 (.8) .214

3 (2.2) 3 (1.2) .366

2 (1.4) 4 (1.6) .332

27 (19.6) 27 (10.5) .016

27 (12.7) 10 (5.5) .014

3 (1.4) 2 (1.1) .284

5 (2.2) 1 (.5) .362

3 (1.4) 3 (1.6) .182

38 (17.8) 16 (8.7) .009

20 (10.0) 17 (8.7) .623

3 (1.5) 2 (1.0) .688

4 (2.0) 2 (1.0) .762

3 (1.5) 3 (1.5) .731

30 (15.0) 24 (12.2) .371

16 (9.7) 21 (9.1) .812

2 (1.2) 3 (1.3) .897

4 (2.4) 2 (.9) .768

2 (1.2) 4 (1.7) .812

24 (14.5) 30 (13.0) .683

17 (9.2) 20 (9.4) .962

3 (1.6) 2 (.9) .877

4 (2.2) 2 (.9) .799

2 (1.1) 4 (1.9) .916

26 (14.1) 28 (13.2) .792

Abbreviations: AT, atherothrombosis; DVT, deep venous thrombosis; LDL-C, low-density lipoprotein cholesterol; MI, myocardial infarction; RIS, recurrent ischemic stroke; SAD, small artery disease; TC, total cholesterol. Data are presented as n (%).

were shown to be independent risk factors for atherothrombotic events in the stroke patients (Table 6).

Discussion The present study demonstrated that there were no significant differences in the genotype frequency distributions of the 8 variants between the cases and the controls; this finding was significantly different from previous Table 6. Cox regression analysis of the risk factors for atherothrombotic events within 12 months after first stroke Factor

P value

RR

95% CI

Gender (female) Age, y Hypertension Diabetes Smoking High LDL-C High TC Interactive variable AT stroke

.152 .085 .042 .016 .274 .087 .263 .008 1.06

.936 1.124 1.412 1.522 .921 1.323 .954 2.921 .93-2.86

.962-1.884 .956-1.252 1.001-3.232 1.011-3.722 .856-1.312 .985-2.965 .825-1.536 1.118-7.012 .079

Abbreviations: AT, atherothrombosis; CI, confidence interval; LDL-C, low-density lipoprotein cholesterol; RR, relative risk; TC, total cholesterol.

studies.8,9,27 Helgadottir et al8,9 reported that the SNP haplotype of the gene ALOX5AP is associated with twice the risk of MI and cardiovascular diseases in Icelandic and Scottish populations. L~ ohmussaar et al27 suggest that sequence variants in the ALOX5AP gene are significantly associated with stroke, particularly in males. The possible role of genetic variations in CYP genes in the development and prognosis of stroke remains unclear. It has been suggested that genetic variations of the EPHX2 gene contribute to an increased risk of ischemic stroke.28,29 The reasons for differences between our results and reported results are not known, but may be associated with race difference, as seen with other pharmacogenomic studies.12 In addition, stroke is a common and complex disorder caused by multiple genes and multiple risk factors, and the development of the disease may be determined by several variations with minor genetic effects, without major gene effects being observed.30 As a result of gene–gene and gene– environment interactions, linkage analysis, which is often used to investigate single-gene disorders, may not be suitable for the genetic study of stroke. In the present study, we employed GMDR analysis31,32 to determine gene–gene interactions. Although single loci among the 8 variants were not associated with stroke, based on the linkage analyses, the GMDR analyses revealed that the risk for stroke significantly increased

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in the patients carrying both rs10507391 AA and rs776746 GG genotypes, indicating that the interaction between the 2 gene variants increased the susceptibility to stroke. Our findings add to the evidence that interactions between genes with marginal effects can increase the risk of common and complex diseases, such as stroke.33 To date, study design of gene variations in stroke often employs a case–control approach, with few being prospective follow-up study. In the present study, there were no significant differences in the frequencies of subsequent atherothrombotic events among patients with the various genotypes of the 8 variants. However, the patients carrying rs10507391 AA and rs776746 GG had significantly higher frequencies of atherothrombotic events than their counterparts without these gene types. Multiple Cox regression analyses also indicated that the gene–gene interactions were an independent indicator of higher risk of atherothrombotic events. The nature of the interactions between ALOX5AP and CYP3A5 and biologic consequence remains unknown. One possible explanation is that the 2 genes are all involved in AA metabolism and the impaired equilibrium for EETs may account for biologic consequence of the interaction between the 2 genes. ALOX5AP encodes 5-lipoxygenase–activating protein, which activates arachidonate 5-lypoxygenase, leading to leukotriene synthesis.34 When the ALOX5AP gene is mutated, the coding proteins can increase 5-lipoxygenase activity, promoting leukotriene-mediated damage to vascular walls, and forming vulnerable plaques, and leading to ischemic stroke.35 AA is metabolized by CYP epoxygenases into 4 EETs, which have diverse cardiovascular protective effects.36,37 The CYP3A5 enzyme is one of the epoxygenase enzymes, and CYP3A5 gene mutations can reduce EET generation38 and increase the risk for high blood pressure.39 As hypertension is one of the most important risk factors for stroke, CYP3A5 gene polymorphism may also be associated with atherosclerosis and cerebral infarction. Therefore, the interactions between these 2 genes may lead to the progression of atherosclerosis, increasing the cerebral infarction susceptibility and frequency of subsequent atherothrombotic events after stroke.

Limitations Of note, the present study had some limitations. First, because of the limited sample size in this 2-center study, these results may not represent the full disease status in China’s populations. Our results should be confirmed in future large-scale, multi-center studies. Second, because the selection of target SNPs was based on literature that was the result from other countries, we might have missed the opportunity to identify novel genetic variants in Chinese stroke patients. Third, this study represented

an association study. The molecular mechanisms for the gene–gene interaction and its biologic consequence should be investigated in future laboratory and clinical studies.

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9 29. Gschwendtner A, Ripke S, Freilinger T, et al. Genetic variation in soluble epoxide hydrolase (EPHX2) is associated with an increased risk of ischemic stroke in white Europeans. Stroke 2008;39:1593-1596. 30. Schork NJ, Murray SS, Frazer KA, et al. Common vs. rare allele hypotheses for complex diseases. Curr Opin Genet Dev 2009;19:212-219. 31. Liu J, Sun K, Bai Y, et al. Association of three-gene interaction among MTHFR, ALOX5AP and NOTCH3 with thrombotic stroke: a multicenter case-control study. Hum Genet 2009;125:649-656. 32. Yi XY, Zhou Q, Lin J, et al. Interaction between ALOX5AP-SG13S114A/T and COX-2-765G/C increases susceptibility to cerebral infarction in a Chinese population. Genet Mol Res 2013;12:1660-1669. 33. Cook NR, Zee RY, Ridker PM. Tree and spline based association analysis of gene-gene interaction models for ischemic stroke. Stat Med 2004;23:1439-1453. 34. Pergola C, Gerstmeier J, Monch B, et al. The novel benzimidazole derivative BRP-7 inhibits leukotriene biosynthesis in vitro and in vivo by targeting 5lipoxygenase-activating protein (FLAP). Br J Pharmacol 2014;171:3051-3064. 35. Dichgans M. Genetics of ischaemic stroke. Lancet Neurol 2007;6:149-161. 36. Node K, Huo Y, Ruan X, et al. Anti-inflammatory properties of cytochrome P450 epoxygenase-derived eicosanoids. Science 1999;285:1276-1279. 37. Wang Y, Wei X, Xiao X, et al. Arachidonic acid epoxygenase metabolites stimulate endothelial cell growth and angiogenesis via mitogen-activated protein kinase and phosphatidylinositol 3-kinase/Akt signaling pathways. J Pharmacol Exp Ther 2005;314:522-532. 38. Burk O, Wojnowski L. Cytochrome P450 3A and their regulation. Naunyn Schmiedebergs Arch Pharmacol 2004;369:105-124. 39. Bochud M, Eap CB, Elston RC, et al. Association of CYP3A5 genotypes with blood pressure and renal function in African families. J Hypertens 2006;24:923-929.

Genetic polymorphisms of ALOX5AP and CYP3A5 increase susceptibility to ischemic stroke and are associated with atherothrombotic events in stroke patients.

The contributions of gene-gene interactions to pathogenesis of stroke remain largely elusive. The present study was designed to investigate the associ...
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