CYP3A4 and CYP11A1 Variants are Risk Factors for Ischemic Stroke: a Case Control Study

doi:10.21203/rs.2.9456/v2

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CYP3A4 and CYP11A1 Variants are Risk Factors for Ischemic Stroke: a Case Control Study

https://doi.org/10.21203/rs.2.9456/v2

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published 04 Mar, 2020

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Background This study aimed to investigate the roles of CYP3A4 and CYP11A1 variants in ischemic stroke (IS) susceptibility among the Han Chinese population.

Methods 477 patients with IS and 493 healthy controls were enrolled. Seven single-nucleotide polymorphisms (SNPs) of CYP3A4 and CYP11A1 were genotyped by Agena MassARRAY. Odds ratio (OR) and 95% confidence intervals (CI) were calculated by logistic regression adjusted for age and gender.

Results We found that CYP3A4 rs3735451 (OR = 0.81, p = 0.039) and rs4646440 (OR = 0.72, p = 0.021) polymorphisms decreased the risk of IS. CYP3A4 rs4646440 (OR = 0.74, p = 0.038) and CYP11A1 rs12912592 (OR = 1.58, p = 0.034) polymorphisms were correlated with IS risk in males. CYP3A4 rs3735451 (OR = 0.63, p = 0.031) and rs4646440 (OR = 0.57, p = 0.012) possibly weaken the IS susceptibility at age > 61 years. Besides, CYP3A4 rs4646437 (OR = 0.59, p = 0.029), CYP11A1 rs12912592 (OR = 1.84, p = 0.017) and rs28681535 (OR = 0.66, p = 0.038) were associated with IS risk at age ≤ 61 years. CYP11A1 rs28681535 TT genotype was higher high-density lipoprotein cholesterol level than the GT and GG genotype (p = 0.027).

Conclusions Our findings indicated that rs3735451, rs4646440, rs4646437 in CYP3A4 and rs28681535 in CYP11A1 might be protective factors for IS, while CYP11A1 rs12912592 polymorphism be a risk factor for IS in Chinese Han population.

Internal Medicine Specialties

Ischemic stroke

CYP3A4

CYP11A1

Polymorphism

Susceptibility

Stroke, a common multifactor neurological disease, is a common cause of death and severe disability in adults worldwide. The incidence of stroke is estimated to be more than 2 million people and more than one million people die from stroke-related causes every year in the Chinese population[1]. There are huge economic and social burdens because of stroke in China, which remains particularly high in the northern and central regions[2]. Ischemic stroke (IS) is the most common type of stroke accounting for 80%–85% of all stroke cases[3]. According to epidemiologic studies, the incidence of IS in China is significantly higher than in developed countries[4]. The pathophysiological causes of IS are unclear, but the widely accepted concept is that IS is caused by the interaction between genetic and environmental factors[5]. To date, many studies have identified that gene polymorphisms modulate the pathophysiological processes of IS and confer a small to moderate risk [6–8].

Cytochrome P450s (CYPs) is a group of complexes and structurally related enzymes with diverse metabolic and biosynthetic activities. CYP epoxygenases is metabolizing arachidonic acid (AA) to biologically active epoxyeicosatrienoic acids (EETs), which exert vascular relaxation effects and have diverse protective roles in the cardiovascular system[9]. Previous studies have shown that plasma CYP metabolite levels, including EETs are associated with IS[10, 11]. CYP3A4 gene, located on chromosome 7q21.1, is a member of the CYP3A gene family, which participates in metabolizing arachidonic acid (AA) into epoxyeicosatrienoic acids (EETs)[12]. CYP11A1 gene is located on chromosome 15q23-q24, and is involved in the metabolism of cholesterol and vitamin D, which associated with cardiovascular diseases[13, 14]. Consequently, studies concerning the possible association of CYP3A4 and CYP11A1 gene with IS may be particularly interesting for their potential biological significance.

However, few reports concerning the role of CYP3A4 and CYP11A1 polymorphisms on IS risk have been published yet. Therefore, we carried out a case-control study to explore whether polymorphisms in CYP3A4 and CYP11A1 contribute to the risk of IS in a Chinese Han population.

Study participants

A cohort of 477 IS patients and 495 control subjects were enrolled from Haikou People’s Hospital and the Affliated Hospital of Yanan University in this study. All recruited subjects were unrelated ethnic Han Chinese. All the patients were identified as having newly diagnosed IS by at least two independent neurologists, according to the clinical signs and symptoms. All patients underwent brain computed tomography (CT) scans and/or magnetic resonance imaging (MRI) as well as standardized clinical hematology, biochemistry and immunology examinations. Patients with a history of hematologic, coronary artery diseases, autoimmune diseases, systemic inflammatory diseases, blood diseases, or malignant tumors were excluded. The healthy individuals without the history of stroke, normal neurological examination results, and free from cardiovascular and cerebrovascular diseases, and immunological diseases, who received a physical examination in the same hospital, were recruited as controls. Demographic characteristics, clinical information and medications were collected with standardized questionnaires. The following clinical data were collected: age, gender, total protein, serum uric acid, blood glucose, total bilirubin, total cholesterol, triglyceride, low-density lipoprotein and high-density lipoprotein. This study protocol was approved by the Ethics Committee of Haikou People’s Hospital and was conducted according to the guidelines on the Declaration of Helsinki. Informed consent was obtained from all participants.

Sample collection and SNP genotyping

Blood samples were obtained from the peripheral veins and were stored in EDTA-coated tubes at –80C until further analysis. Genomic DNA was isolated from peripheral blood samples using the GoldMag DNA Purification Kit (GoldMag Co. Ltd, Xi′an City, China) according to the manufacturer’s instructions. The DNA concentration and purity was determined using NanoDrop 2000 (Thermo Scientifc, Waltham, MA, USA). Four CYP3A4 SNPs (rs3735451, rs4646440, rs35564277 and rs4646437) and three CYP11A1 SNPs (rs1484215, rs12912592 and rs28681535) were selected based on the NCBI SNP database and minor allele frequencies (MAFs) > 5% in the 1000 Genomes Project data (http://www.internationalgenome.org/). In order to uncover the functional effects of CYP3A4 and CYP11A1 polymorphisms, online software for HaploReg v4.1 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php) was used.

The MassARRAY platform is based on MALDI-TOF (matrix-assisted laser desorption/ionization—time of flight) mass spectrometry in a high-throughput and cost-effective manner [15, 16]. The advantage of MassARRAY platform is 1) multiplex PCR assays for up to 40 SNPs simultaneously; 2) relatively forgiving in terms of required DNA quality and quantity; 3) The primers design and the data management were implemented using Agena Design 3.0 Software and Agena Typer 4.0 software, respectively. Therefore, in our study, SNPs genotyping were performed using Agena MassARRAY system (Agena, San Diego, CA, U.S. A.) as previously described, and conducted by laboratory technicians blinded to the case–control status. The primers for PCR amplification and single base extension were present in Supplementary Table 1. Approximately 10% of samples were randomly selected to repeat genotyping for quality control, and a 100% concordant was achieved.

Data analysis

Statistical analyses were performed using SPSS version 18.0 (SPSS Inc., Chicago, IL, USA) and PLINK software. Demographic data of patients and controls were compared using student’s t-test and chi-square test. Hardy–Weinberg equilibrium (HWE) was examined via a goodness-of-fit χ² test to compare the observed genotype frequencies and the expected frequencies among the control subjects. The genotype and allele frequencies of the controls and IS patients were compared using the χ² test or Fisher’s exact test. The correlation between CYP3A4 and CYP11A1 polymorphisms and IS susceptibility was estimated by odds ratios (ORs) and 95% confidence intervals (CIs) using logistic regression analysis with adjustment for age and sex by PLINK software. Multiple inheritance models (genotype, dominant, recessive and log-additive) were estimated. Further, we calculated stratification factors using age (≤ 61 and > 61 years) and gender (male and female) to adjust for possible cofounders. Pairwise linkage disequilibrium (LD) between the selected SNPs was measured by the LD coefficient D’ using the Haploview software (version 4.2), and haplotype analyses were performed by logistic regression analysis using the PLINK software. Finally, the association between the genotypes of CYP3A4 and CYP11A1 polymorphisms and clinical parameters was tested by covariance analysis (ANCOVA). A two-tailed p-value < 0.05 was considered as significant.

In total, 477 IS patients (316 males and 161 females) and 493 control subjects (325 males and 168 females) were recruited. There were no significant differences between patients and controls in terms of gender (p = 0.898). The mean age was 64.13 ± 10.82 years for the patients with IS and 60.05 ± 6.56 years for the control subjects. Significant differences were also found in age distribution (p < 0.001), suggesting that age may have an effect on the etiology of IS. The total protein, serum uric acid, blood glucose, bilirubin, triglyceride, hemoglobin, cholesterol and low-density lipoprotein levels in the IS patients were significantly different from those noted in the healthy control subjects. The clinical characteristics of the patients were described in Table 1.

Seven SNPs in CYP3A4 and CYP11A1 were successfully genotyped, and the average variant call rate was 99.6%. Detailed information and potential function of candidate SNPs were listed in Table 2. These intronic SNPs were associated with the regulation of promoter and/or enhancer histones, changed motifs, and selected eQTL hits, suggesting they might exert biology functions in silico. MAF of all SNPs was higher than 5% of the study population. All SNPs were in HWE among the controls (p > 0.05).

The allele and genotype frequency distributions of the SNPs and their association with IS susceptibility were shown in Table 3 and Supplementary Table 2. CYP3A4 SNPs rs3735451 and rs4646440 were associated with reduced susceptibility of IS (Table 3). We found that individuals carrying rs3735451-C allele had a decreased risk of IS in allele model (OR = 0.81, 95% CI: 0.66–0.98, p = 0.039), genotype model (OR = 0.74, 95% CI: 0.57–0.97, p = 0.029), dominant model (OR = 0.73, 95% CI: 0.56–0.95, p = 0.018) and additive model (rs3735451 OR = 0.78, 95% CI: 0.63–0.96, p = 0.019), respectively. With rs4646440 GG genotype as reference, the presence of the GA genotype was associated with a significantly decreased risk of IS after adjustment for age and gender (GA vs. GG, OR = 0.72, 95% CI: 0.55–0.95, p = 0.021; GA-AA vs. GG, OR = 0.72, 95% CI: 0.55–0.94, p = 0.017, Table 3). Furthermore, rs4646440 polymorphism also might reduce the susceptibility to IS under additive model (OR = 0.77, 95% CI: 0.61–0.97, p = 0.024). Nevertheless, other polymorphisms in CYP3A4 and CYP11A1 did not relate to IS susceptibility (Supplementary Table 2).

We further analyzed whether the genotypic effects on IS risk were dependent on gender (Table 4). We found that CYP3A4 rs4646440 was associated with a decreased risk under the additive model (OR = 0.74, 95% CI: 0.56–0.98, p = 0.038), and showed a marginal p value in allele model (OR = 0.76, 95% CI: 0.58–1.00, p = 0.050) among males, which indicated insufficient evidence for claiming an association. CYP11A1 rs12912592 polymorphism also showed significant risk-increasing effects in the heterozygote model (OR = 1.58, 95% CI: 1.04–2.42, p = 0.034), and dominant model (OR = 1.56, 95% CI: 1.02–2.37, p = 0.039).

In the stratification of age, CYP3A4 SNPs rs3735451 and rs4646440 were associated with the susceptibility to IS at age 61 years (Table 5). For rs3735451, the C allele carriers had a decreased risk of IS (OR = 0.63, 95% CI: 0.41–0.96, p = 0.031 for CT vs. TT genotypes; OR = 0.65, 95% CI: 0.43–0.97, p = 0.036 for CT-CC vs. TT genotypes) after adjusting for age and gender. For rs4646440, we found that the A allele was significantly associated with a reduced risk of IS (GA vs.GG, OR = 0.57, 95% CI: 0.37–0.88, p = 0.012; and GA-AA vs.GG OR = 0.60, 95% CI: 0.40–0.91, p = 0.017). Among the population under the age of 61, we found that CYP3A4 rs4646437, CYP11A1 rs12912592 and rs28681535 were associated with IS risk. CYP3A4 rs4646437 and CYP11A1 rs28681535 polymorphisms were significantly associated with decreased risk for IS (rs4646437, OR = 0.59, 95% CI: 0.37–0.95, p = 0.029; and rs28681535, OR = 0.66, 95% CI: 0.45–0.98, p = 0.038). Additionally, the carriers of the T allele at CYP11A1 rs12912592 appeared to have a higher risk of IS (T vs G, OR = 1.64, 95% CI: 1.04–2.61, p = 0.043; GT vs GG, OR = 1.84, 95% CI: 1.11–3.05, p = 0.017 and GT-TT vs GG, OR = 1.89, 95% CI: 1.15–3.12, p = 0.013).

We next performed haplotype analyses, and the results showed that CYP3A4 rs4646440 was in strong linkage disequilibrium (LD) with rs35564277. Additionally, three CYP11A1 SNPs (rs1484215, rs12912592, and rs28681535) were in strong LD, as shown in Figure 1. However, no association of the haplotypes in CYP3A4 and CYP11A1 was found (Table 6).

Furthermore, we also assessed the association of the selected SNPs and clinical variables in patients (Table 7). Significant association was observed between the genotypes of the CYP3A4 SNPs rs3735451 and rs4646440 and the levels of total protein (p = 0.021 and p = 0.043, respectively). A significant association of CYP11A1 rs12912592 polymorphism with total bilirubin was identified (p = 0.025). Besides, the TT genotype of CYP11A1 rs28681535 was higher high-density lipoprotein cholesterol level than GT genotype and GG genotype (p = 0.027). However, there was no difference in the remaining lipid parameters among the genotypes of the selected SNPs (p > 0.05 for all).

The aim of this investigation was to discover whether there was an association between the CYP3A4 and CYP11A1 polymorphisms and IS risk in Chinese population. In this study, we found that C allele and CT genotype of rs3735451 and GA genotype of rs4646440 in CYP3A4 were significantly associated with a reduced risk of IS in the overall. We further demonstrated that CYP3A4 rs4646440 was associated with a decreased risk of IS, whereas CYP11A1 rs12912592 was associated with a higher risk of IS in males. In addition, our study found that CYP3A4 rs3735451 and rs4646440 possibly contributed to the susceptibility to IS at age > 61 years, and rs4646437 in CYP3A4 and rs12912592 and rs28681535 in CYP11A1 were associated with the risk of IS at age 61 years. Besides, the TT genotype of CYP11A1 rs28681535 was higher high-density lipoprotein cholesterol level than GT genotype and GG genotype (p = 0.027). To the best of our knowledge, this is the first study to demonstrate the association of these polymorphisms in CYP3A4 and CYP11A1 with IS risk in Chinese population.

CYP genes encode monooxygenases responsible for arachidonic acid metabolism, which is involved in cardiovascular diseases and stroke [17]. Numerous studies have suggested an association between genetic variants of CYP pathway genes and the risk of IS[18]. CYP3A4 gene encodes an enzyme, which involved in drug metabolism and synthesis of cholesterol, steroids and other lipids, and mediated the production of arachidonic acid metabolites [19, 20]. CYP11A gene, a member of CYP genes, encodes a cholesterol side chain cleavage enzyme (cytochrome P450 cholesterol side-chain cleavage, P450scc) that plays a major role in the control of steroidogenesis, by mediating the conversion of cholesterol to pregnenolone[21]. Dyslipidemia such as low concentration of high-density lipoprotein cholesterol (HDL-C), high levels of low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC) was one of the most important risk factors of IS[22]. These lines of evidence have led us to formulate the hypothesis that CYP3A4 and CYP11A1 could be of pathogenic importance in IS.

Variations in the CYP3A4 or CYP11A1 genes may influence the gene expression, which might associate with the occurrence and progression of disease. In this study, we found that CYP3A4 (rs3735451, rs4646440 and rs4646437) and CYP11A1 (rs12912592 and rs28681535) polymorphisms were significantly associated with the risk of IS. These polymorphisms are located in the intron region. Chen CH er al. reported that rs4646440 was associated with higher CYP3A4 enzyme activities [23]. Reportedly, rs4646437 influences the protein expression and enzymatic activity of hepatic CYP3A4 [24]. The functional mechanism of other variants have not been reported in previous studies. The functional mechanism of these variants have not been reported in previous studies. Based on HaploReg database, we found these polymorphisms might be associated with the regulation of promoter/enhancer histone, DNAse, proteins binding and changed motifs and/or selected eQTL hits. Several studies provided increasing evidence to support that intronic SNPs confer susceptibilities by affecting gene expression [25–27]. Therefore, we hypothesized that CYP3A4 or CYP11A1 polymorphisms may affect the expression of their genes to contribute to the risk of IS. However, further study is necessary to confirm this hypothesis.

Stroke is a sex-specific disease and the prevalence of stroke in women is lower than that in men [28, 29]. Stratified by gender, we noticed that CYP3A4 rs4646440 and CYP11A1 rs12912592 polymorphism aﬀected IS risk in males but not in females, which indicate that this risk association presented sex diﬀerence and emphasize the importance of considering heterogeneity in genetic and stroke association studies. In addition, stroke is a late-onset disease and the rate is higher in older people [30, 31]. Our study found that CYP3A4 rs3735451 and rs4646440 possibly contributed to the susceptibility to IS at age > 61 years, and CYP3A4 rs4646437and CYP11A1 rs12912592 and rs28681535 were associated with the risk of IS at age 61 years. These suggested the interactions between CYP3A4 and CYP11A1 polymorphisms and some environmental exposures (such as males, elder) contributed to the risk of IS.

Inevitably, our current study has some limitations to be considered. First, due to all participants were all enrolled in the same hospital, the inherent selecting bias and information bias could not be completely excluded for the group of patients with IS. Second, data deficiencies of some exposure factors such as obesity, smoking, and alcohol limited our ability to evaluate gene–environment interaction. Finally, explicit mechanisms of CYP3A4 and CYP11A1 polymorphism on development of IS are still bewildered and further research is needed. Despite the limitations mentioned above, the results of our present study provided scientific evidence of CYP3A4 and CYP11A1 gene with IS for the future studies.

To sum up, our study provided evidence that variants of CYP3A4 and CYP11A1 gene had a significant effect on the risk of IS in the Chinese Han population, which has not previously been reported. Our study may provide clues for the evaluation of individual susceptibility to IS and increase the understanding of the possible eﬀect of CYP3A4 and CYP11A1 gene on the development of IS. However, the replication of this research in different populations and additional functional analysis is required to completely elucidate the roles by which CYP3A4 and CYP11A1 polymorphisms predispose for IS.

IS, ischemic stroke; SNP, single-nucleotide polymorphisms; OR, odds ratio; CI, confidence intervals; CYPs, cytochrome P450s; AA, arachidonic acid; EETs, epoxyeicosatrienoic acids; MAFs, minor allele frequencies; HWE, hardy–weinberg equilibrium; LD, linkage disequilibrium; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol

Ethics approval and consent to participate

All participants were voluntary and provided written informed consent before taking part in this research. This study was approved by the Research Ethics Committee of Haikou People’s Hospital, and in compliance with the Declaration of Helsinki. The design and performance of this study involving human subjects were obviously described in a research protocol.

Consent for publication

Not applicable.

Availability of data and material

All the data regarding the findings are available within the manuscript. Anyone who is interested in the information should contact the corresponding author.

Competing interests

The authors declare that they have no conflict of interest.

Funding

This work is supported by National Natural Science Foundation of China (No.81760234).

Authors’ contributions

The work presented here was carried out in collaboration between all authors. NG carried out the molecular genetic studies and drafted the manuscript. HT and LG designed the methods and experiments, performed the statistical analyses and interpreted the results. GT designed primers and performed the SNP genotyping experiments. HL collected clinical information about patients and performed the SNP genotyping experiments. YX conceived of the study, worked on associated data collection and their interpretation, participated in the design and coordination of the study, and funded the study. All authors read and approved the final manuscript.

Acknowledgements

We are grateful to the individuals who participated in this study. We also thank the clinicians and hospital staff from the Affliated Hospital of Yanan University who contributed to the sample and data collection for this study.

1.Liu L, Wang D, Wong KS, Wang Y: Stroke and stroke care in China: huge burden, significant workload, and a national priority. Stroke 2011, 42(12):3651–3654.

2.Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, Wang L, Jiang Y, Li Y, Wang Y, Chen Z, Wu S, Zhang Y, Wang D, Wang Y, Feigin VL: Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480 687 Adults. Circulation 2017, 135(8):759–771.

3.Loo KW, Gan SH: Burden of stroke in Malaysia. International journal of stroke: official journal of the International Stroke Society 2012, 7(2):165–167.

4.Bejot Y, Daubail B, Giroud M: Epidemiology of stroke and transient ischemic attacks: Current knowledge and perspectives. Revue neurologique 2016, 172(1):59–68.

5.Bevan S, Markus HS: Genetic profiles in ischaemic stroke. Current atherosclerosis reports 2013, 15(8):342.

6.Xue Y, Shi N, Zhang B, Gao X, Gao Y, Zhou X, Du J, Chen P, Ouyang Y, Wang F: Genetic variant in ZFHX3 Geneon 16q22 associated with risk of stroke in Chinese Han population. Int J Clin Exp Patho 2016, 9:8650–8656.

7.Yu J, Luo D, Zhou F, Jin T, Liang X, Yu D: Polymorphism of TNIP1 was associated with atherosclerotic ischemic stroke in southern Han Chinese but unrelated with telomere. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2017, 10(3):3510–3516.

8.He J, Sun S, Zhang M, Ouyang Y, Zhang N, Yang M, Jin T, Xia Y: Association analysis of ALOX5 gene polymorphisms with stroke risk: a case-control study in a Chinese Han population. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2016, 9(4):4432–4437.

9.Larsen BT, Gutterman DD, Hatoum OA: Emerging role of epoxyeicosatrienoic acids in coronary vascular function. European journal of clinical investigation 2006, 36(5):293–300.

10.Ward NC, Croft KD, Blacker D, Hankey GJ, Barden A, Mori TA, Puddey IB, Beer CD: Cytochrome P450 metabolites of arachidonic acid are elevated in stroke patients compared with healthy controls. Clinical science (London, England: 1979) 2011, 121(11):501–507.

11.Yi X, Liao D, Wu L, Chen H, Li J, Wang C: CYP Genetic Variants, CYP Metabolite Levels, and Symptomatic Carotid Stenosis in Ischemic Stroke Patients. Journal of atherosclerosis and thrombosis 2016, 23(5):621–631.

12.Yang X, Zhang B, Molony C, Chudin E, Hao K, Zhu J, Gaedigk A, Suver C, Zhong H, Leeder JS, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, Ulrich RG, Slatter JG, Schadt EE, Kasarskis A, Lum PY: Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome research 2010, 20(8):1020–1036.

13.Slominski AT, Li W, Kim TK, Semak I, Wang J, Zjawiony JK, Tuckey RC: Novel activities of CYP11A1 and their potential physiological significance. The Journal of steroid biochemistry and molecular biology 2015, 151:25–37.

14.Vanga SR, Good M, Howard PA, Vacek JL: Role of vitamin D in cardiovascular health. The American journal of cardiology 2010, 106(6):798–805.

15.Oeth P, del Mistro G, Marnellos G, Shi T, van den Boom D: Qualitative and quantitative genotyping using single base primer extension coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MassARRAY). Methods in molecular biology (Clifton, NJ) 2009, 578:307–343.

16.Ellis JA, Ong B: The MassARRAY((R)) System for Targeted SNP Genotyping. Methods in molecular biology (Clifton, NJ) 2017, 1492:77–94.

17.Huang H, Al-Shabrawey M, Wang MH: Cyclooxygenase- and cytochrome P450-derived eicosanoids in stroke. Prostaglandins & other lipid mediators 2016, 122:45–53.

18.Kim SK, Yim SV, Lee BC: Association between cytochrome P450 promoter polymorphisms and ischemic stroke. Experimental and therapeutic medicine 2012, 3(2):261–268.

19.Honda A, Miyazaki T, Ikegami T, Iwamoto J, Maeda T, Hirayama T, Saito Y, Teramoto T, Matsuzaki Y: Cholesterol 25-hydroxylation activity of CYP3A. Journal of lipid research 2011, 52(8):1509–1516.

20.Ayajiki K, Fujioka H, Toda N, Okada S, Minamiyama Y, Imaoka S, Funae Y, Watanabe S, Nakamura A, Okamura T: Mediation of arachidonic acid metabolite(s) produced by endothelial cytochrome P–450 3A4 in monkey arterial relaxation. Hypertension research: official journal of the Japanese Society of Hypertension 2003, 26(3):237–243.

21.Katsumata N: Genetic defects in pregnenolone synthesis. Pediatric endocrinology reviews: PER 2012, 10 Suppl 1:98–109.

22.Wang IK, Liu CH, Yen TH, Jeng JS, Hsu SP, Chen CH, Lien LM, Lin RT, Chen AC, Lin HJ, Chi HY, Lai TC, Sun Y, Lee SP, Sung SF, Chen PL, Lee JT, Chiang TR, Lin SK, Muo CH, Ma H, Wen CP, Sung FC, Hsu CY: Cholesterol Levels Are Associated with 30-day Mortality from Ischemic Stroke in Dialysis Patients. Journal of stroke and cerebrovascular diseases: the official journal of National Stroke Association 2017, 26(6):1349–1356.

23.Chen CH, Wang SC, Tsou HH, Ho IK, Tian JN, Yu CJ, Hsiao CF, Chou SY, Lin YF, Fang KC, Huang CL, Su LW, Fang YC, Liu ML, Lin KM, Hsu YT, Liu SC, Chen A, Liu YL: Genetic polymorphisms in CYP3A4 are associated with withdrawal symptoms and adverse reactions in methadone maintenance patients. Pharmacogenomics 2011, 12(10):1397–1406.

24.Schirmer M, Rosenberger A, Klein K, Kulle B, Toliat MR, Nurnberg P, Zanger UM, Wojnowski L: Sex-dependent genetic markers of CYP3A4 expression and activity in human liver microsomes. Pharmacogenomics 2007, 8(5):443–453.

25.Seo S, Takayama K, Uno K, Ohi K, Hashimoto R, Nishizawa D, Ikeda K, Ozaki N, Nabeshima T, Miyamoto Y: Functional analysis of deep intronic SNP rs13438494 in intron 24 of PCLO gene. Plos One 2013, 8(10):e76960.

26.Wang D, Sadee W: CYP3A4 intronic SNP rs35599367 (CYP3A4*22) alters RNA splicing. Pharmacogenetics & Genomics 2016, 26(1):40–43.

27.Zhao H, Yang W, Qiu R, Li J, Xin Q, Wang X, Feng Y, Shan S, Liu Y, Gong Y: An intronic variant associated with systemic lupus erythematosus changes the binding affinity of Yinyang1 to downregulate WDFY4. Genes & Immunity 2012, 13(7):536.

28.Poorthuis MH, Algra AM, Algra A, Kappelle LJ, Klijn CJ: Female- and Male-Specific Risk Factors for Stroke: A Systematic Review and Meta-analysis. JAMA neurology 2017, 74(1):75–81.

29.Wang J, Ning X, Yang L, Tu J, Gu H, Zhan C, Zhang W, Su TC: Sex differences in trends of incidence and mortality of first-ever stroke in rural Tianjin, China, from 1992 to 2012. Stroke 2014, 45(6):1626–1631.

30.Goldstein LB, Bushnell CD, Adams RJ, Appel LJ, Braun LT, Chaturvedi S, Creager MA, Culebras A, Eckel RH, Hart RG, Hinchey JA, Howard VJ, Jauch EC, Levine SR, Meschia JF, Moore WS, Nixon JV, Pearson TA: Guidelines for the primary prevention of stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2011, 42(2):517–584.

31.O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, Rangarajan S, Islam S, Pais P, McQueen MJ, Mondo C, Damasceno A, Lopez-Jaramillo P, Hankey GJ, Dans AL, Yusoff K, Truelsen T, Diener HC, Sacco RL, Ryglewicz D, Czlonkowska A, Weimar C, Wang X, Yusuf S: Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet (London, England) 2010, 376(9735):112–123.

Table 1. Characteristics of patients with Ischemic Stroke and controls

Characteristics	Cases (n = 477)	Controls ( n = 493)	p
Age, year (mean ± SD)	64.13 ± 10.82	60.05 ± 6.56	<0.001
Gender (M/F)	316/161	325/168	0.898
TP (g/L, mean ± SD)	65.57 ± 5.80	70.88 ± 5.61	<0.001
Serum uric acid (mmol/L, mean ± SD)	284.53 ± 94.37	330 ± 80.27	<0.001
Blood glucose (mmol/L, mean ± SD)	6.33 ± 2.24	5.83 ± 1.44	0.001
TB (mmol/L, mean ± SD)	13.63 ± 6.51	17.00 ± 5.94	<0.001
TG (mmol/L, mean ± SD)	1.59 ± 1.05	4.50 ± 0.92	<0.001
Hemoglobin (g/L, mean ± SD)	136.87 ± 22.77	147.76 ± 14.31	<0.001
TC (mmol/L, mean ± SD)	3.89 ± 1.03	1.79 ± 1.16	<0.001
HDL-C(mmol/L, mean ± SD)	1.09 ± 0.26	1.09 ± 0.23	0.871
LDL-C (mmol/L, mean ± SD)	1.81 ± 0.58	2.56 ± 0.71	<0.001

SD, standard deviation; TP, total protein; TB, total bilirubin; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, lowdensity lipoprotein cholesterol.

Table 2. The information about the candidate SNPs in CYP3A4 and CYP11A1

Gene	SNP ID	Chr: Position	Alleles (minor/major)	Frequency (MAF)		HaploReg
Gene	SNP ID	Chr: Position	Alleles (minor/major)	Case	Control	HaploReg
CYP3A4	rs3735451	7:99758352	C/T	0.26	0.30	Motifs Changed, Selected eQTL hits
CYP3A4	rs4646440	7:99763247	A/G	0.19	0.23	Promoter histone marks, Enhancer histone marks, DNAse, Proteins bound, Motifs changed, Selected eQTL hits
CYP3A4	rs35564277	7:99764813	C/T	0.06	0.07	Motifs Changed
CYP3A4	rs4646437	7:99767460	A/G	0.11	0.13	Promoter histone marks, Enhancer histone marks, Motifs changed, Selected eQTL hits
CYP11A1	rs1484215	15:74347768	T/C	0.18	0.18	Enhancer histone marks, Motifs changed, Selected eQTL hits
CYP11A1	rs12912592	15:74363369	T/G	0.10	0.08	Enhancer histone marks, Motifs changed, Selected eQTL hits
CYP11A1	rs28681535	15:74367268	T/G	0.43	0.45	Promoter histone marks, Enhancer histone marks, DNAse, Motifs changed

MAF: minor allele frequency; eQTL, expression quantitative trait loci.

Table 3. Relationships between CYP3A4 and CYP11A1 polymorphism and Ischemic stroke risk

Gene SNP ID	Model	Genotype	Case	Control	Adjusted by age and gender
Gene SNP ID	Model	Genotype	Case	Control	OR (95%CI)	p
CYP3A4 rs3735451	Allele	T	705	686	1.00	0.039
	Allele	C	249	300	0.81 (0.66-0.98)	0.039
	Genotype	TT	256	228	1.00
		CT	193	230	0.74 (0.57-0.97)	0.029
		CC	28	35	0.66 (0.38-1.14)	0.135
	Dominant	TT	256	228	1.00	0.018
	Dominant	CT-CC	221	265	0.73 (0.56-0.95)	0.018
	Recessive	TT-CT	449	458	1.00	0.308
	Recessive	CC	28	35	0.76 (0.45-1.29)	0.308
	Log-additive	---	---	---	0.78 (0.63-0.96)	0.019
CYP3A4 rs4646440	Allele	G	768	754	1.00	0.046
	Allele	A	186	228	0.80 (0.64-1.00)	0.046
	Genotype	GG	307	282	1.00
		GA	154	190	0.72 (0.55-0.95)	0.021
		AA	16	19	0.72 (0.36-1.45)	0.362
	Dominant	GG	307	282	1.00	0.017
	Dominant	GA-AA	170	209	0.72 (0.55-0.94)	0.017
	Recessive	GG-GA	461	472	1.00	0.560
	Recessive	AA	16	19	0.81 (0.41-1.62)	0.560
	Log-additive	---	---	---	0.77 (0.61-0.97)	0.024

SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval.

p values were calculated by logistic regression analysis with adjustments for age and gender.

p < 0.05 means the data is statistically significant.

Table 4. Relationships between CYP3A54 and CYP11A1 polymorphism and Ischemic stroke risk according to the stratification by gender

SNP ID	Model	Genotype	Male				Female
SNP ID	Model	Genotype	Case	Control	OR (95%CI)	p	Case	Control	OR (95%CI)	p
CYP3A4 rs4646440	Allele	G	509	492	1.00	0.050	259	262	1.00	0.563
	Allele	A	123	156	0.76 (0.58-1.00)	0.050	63	72	0.89 (0.61-1.29)	0.563
	Genotype	GG	202	183	1.00	0.117	105	99	1.00	0.226
		GA	105	126	0.74 (0.53-1.05)	0.089	49	64	0.69 (0.43-1.11)	0.123
		AA	9	15	0.53 (0.22-1.27)	0.156	7	4	1.44 (0.40-5.15)	0.576
	Dominant	GG	202	183	1.00	0.052	105	99	1.00	0.188
	Dominant	GA-AA	114	141	0.72 (0.52-1.00)	0.052	56	68	0.74 (0.47-1.16)	0.188
	Recessive	GG-GA	307	309	1.00	0.237	154	163	1.00	0.438
	Recessive	AA	9	15	0.60 (0.25-1.41)	0.237	7	4	1.65 (0.47-5.81)	0.438
	Log-additive	---	---	---	0.74 (0.56-0.98)	0.038	---	---	0.83 (0.56-1.24)	0.371
CYP11A1 rs12912592	Allele	G	565	602	1.00	0.081	297	301	1.00	0.672
	Allele	T	67	50	1.43 (0.97-2.10)	0.081	25	29	0.87 (0.50-1.53)	0.672
	Genotype	GG	250	277	1.00		138	136	1.00
		GT	65	48	1.58 (1.04-2.42)	0.034	21	29	0.64 (0.34-1.19)	0.161
		TT	1	1	0.63 (0.04-10.19)	0.743	2	0	/	/
	Dominant	GG	250	277	1.00	0.039	138	136	1.00	0.224
	Dominant	GT-TT	66	49	1.56 (1.02-2.37)	0.039	23	29	0.70 (0.38-1.28)	0.224
	Recessive	GG-GT	315	325	1.00	0.704	159	165	1.00	/
	Recessive	TT	1	1	0.58 (0.04-9.45)	0.704	2	0	/	/
	Log-additive	---	---	---	1.51 (1.00-2.28)	0.051	---	---	0.78 (0.44-1.38)	0.393

SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval.

p values were calculated by logistic regression analysis with adjustments for age and gender.

p < 0.05 indicates statistical significance.

Table 5. Relationships between CYP3A4 and CYP11A1 polymorphism and Ischemic stroke risk according to the stratification by age

SNP ID	Allele/Genotype	>61				£61
SNP ID	Allele/Genotype	Case	Control	OR (95%CI)	p	Case	Control	OR (95%CI)	p
CYP3A4 rs3735451	T	403	287	1.00	0.063	302	399	1.00	0.217
	C	145	135	0.76 (0.58-1.01)	0.063	104	165	0.83 (0.62-1.11)	0.217
	TT	148	92	1.00		108	136	1.00
	CT	107	103	0.63 (0.41-0.96)	0.031	86	127	0.85 (0.58-1.24)	0.388
	CC	19	16	0.77 (0.35-1.70)	0.514	9	19	0.63 (0.27-1.47)	0.290
	CT-CC	126	119	0.65 (0.43-0.97)	0.036	95	146	0.82 (0.57-1.18)	0.288
CYP3A4 rs4646440	G	439	314	1.00	0.051	329	440	1.00	0.334
	A	109	106	0.74 (0.54-1.00)	0.051	77	122	0.84 (0.61-1.16)	0.334
	GG	176	114	1.00		131	168	1.00
	GA	87	86	0.57 (0.37-0.88)	0.012	67	104	0.82 (0.56-1.21)	0.321
	AA	11	10	0.86 (0.33-2.25)	0.764	5	9	0.79 (0.26-2.44)	0.684
	GA-AA	98	96	0.60 (0.40-0.91)	0.017	72	113	0.82 (0.56-1.20)	0.302
CYP3A4 rs4646437	G	486	368	1.00	0.487	360	485	1.00	0.284
	A	62	54	0.87 (0.59-1.28)	0.487	46	77	0.80 (0.55-1.19)	0.284
	GG	214	160	1.00		163	207	1.00
	GA	58	48	0.81 (0.49-1.32)	0.396	34	71	0.59 (0.37-0.95)	0.029
	AA	2	3	0.48 (0.06-3.65)	0.478	6	3	2.41 (0.57-10.25)	0.233
	GA-AA	60	51	0.79 (0.49-1.28)	0.335	40	74	0.67 (0.43-1.04)	0.073
CYP11A1 rs12912592	G	498	376	1.00	0.659	364	527	1.00	0.043
	T	50	42	0.90 (0.58-1.38)	0.659	42	37	1.64 (1.04-2.61)	0.043
	GG	226	168	1.00		162	245	1.00
	GT	46	40	0.93 (0.55-1.58)	0.798	40	37	1.84 (1.11-3.05)	0.017
	TT	2	1	0.53 (0.04-7.01)	0.629	1	0	/	/
	GT-TT	48	41	0.92 (0.55-1.54)	0.744	41	37	1.89 (1.15-3.12)	0.013
CYP11A1 rs28681535	G	298	237	1.00	0.603	246	309	1.00	0.076
	T	250	185	1.08 (0.83-1.39)	0.603	160	255	0.79 (0.61-1.02)	0.076
	GG	75	67	1.00		80	87	1.00
	GT	148	103	1.31 (0.82-2.09)	0.251	86	135	0.69 (0.46-1.05)	0.081
	TT	51	41	1.10 (0.61-1.99)	0.743	37	60	0.60 (0.36-1.02)	0.061
	GT-TT	199	144	1.25 (0.81-1.95)	0.318	123	195	0.66 (0.45-0.98)	0.038

SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval.

p values were calculated by logistic regression analysis with adjustments for age and gender.

p < 0.05 indicates statistical significance.

Table 6. Haplotype frequencies and their associations with Ischemic stroke risk

Gene	SNP	Haplotype	Frequency		Crude analysis		Adjusted by age and gender
Gene	SNP	Haplotype	Case	Control	OR (95% CI)	p	OR (95% CI)	p
CYP3A4	rs4646440\|rs35564277	GT	0.805	0.768	1		1
	rs4646440\|rs35564277	AT	0.138	0.159	0.82 (0.63-1.06)	0.130	0.78 (0.60-1.02)	0.070
	rs4646440\|rs35564277	AC	0.058	0.072	0.79 (0.55-1.12)	0.190	0.75 (0.52-1.08)	0.120
CYP11A1	rs1484215\|rs12912592\|rs28681535	CGT	0.430	0.446	1		1
	rs1484215\|rs12912592\|rs28681535	CGG	0.294	0.290	1.07 (0.87-1.31)	0.550	1.06 (0.85-1.31)	0.600
	rs1484215\|rs12912592\|rs28681535	TGG	0.180	0.184	1.01 (0.79-1.29)	0.940	1.06 (0.82-1.37)	0.670
	rs1484215\|rs12912592\|rs28681535	CTG	0.096	0.080	1.29 (0.92-1.81)	0.130	1.24 (0.87-1.75)	0.230

CYP3A4 block comprises the two closely linked SNPs rs4646440 and rs35564277. CYP11A14 block comprises the three closely linked SNPs rs1484215, rs12912592, and rs28681535. OR: odds ratio; 95% CI: 95% confidence interval. p values were calculated using logistic regression analysis with and without adjustment by gender and age.

Table 7: Comparisons of clinical characteristics among patients with different genotypes of selected SNPs

Characteristics	CYP3A4 rs3735451				CYP3A4 rs4646440
Characteristics	TT	TC	CC	p	AA	AG	GG	p
TP (g/L)	66.16 ± 6.02	64.65 ± 5.34	66.79 ± 6.09	0.021	66.98 ± 6.96	64.60 ± 5.33	66.02 ± 5.92	0.043
Serum uric acid (mmol/L)	290.64 ± 97.14	279.93 ± 93.17	260.83 ± 72.42	0.247	266.33 ± 61.31	280.39 ± 94.51	287.64 ± 95.95	0.583
Blood glucose (mmol/L)	6.36 ± 2.05	6.22 ± 2.33	6.77 ± 3.19	0.484	6.75 ± 2.92	6.35 ± 2.59	6.29 ± 2.02	0.734
TB (mmol/L)	13.78 ± 6.99	13.23 ± 5.32	14.95 ± 9.03	0.422	16.56 ± 11.62	13.36 ± 5.23	13.62 ± 6.75	0.237
TG (mmol/L)	1.55 ± 0.88	1.65 ± 1.24	1.53 ± 1.22	0.596	1.79 ± 1.43	1.60 ± 1.02	1.57 ± 1.06	0.771
Hemoglobin (g/L)	137.07 ± 21.57	137.51 ± 22.94	130.46 ± 31.22	0.395	133.57 ± 15.59	137.31 ± 25.98	136.82 ± 21.38	0.842
TC (mmol/L)	3.93 ± 0.99	3.89 ± 1.05	3.65 ± 1.19	0.464	3.92 ± 1.13	3.76 ± 1.10	3.96 ± 0.98	0.200
HDL-C(mmol/L)	1.10 ± 0.24	1.09 ± 0.26	1.03 ± 0.37	0.494	1.01 ± 0.31	1.07 ± 0.27	1.11 ± 0.25	0.203
LDL-C (mmol/L)	1.82 ± 0.56	1.82 ± 0.62	1.66 ± 0.56	0.446	1.80 ± 0.55	1.76 ± 0.65	1.84 ± 0.55	0.375
Characteristics	CYP11A1 rs12912592				CYP11A1 rs28681535
Characteristics	TT	GT	GG	p	TT	GT	GG	p
TP (g/L)	65.50 ± 0.56	65.32 ± 6.02	65.64 ± 5.78	0.910	65.72 ± 5.75	65.62 ± 5.82	65.46 ± 5.83	0.946
Serum uric acid (mmol/L)	245.50 ± 62.93	290.63 ± 99.32	284.76 ± 92.30	0.744	2810 ± 87.44	284.04 ± 93.26	290.48 ± 96.95	0.749
Blood glucose (mmol/L)	4.93 ± 0.21	6.57 ± 2.34	6.27 ± 2.23	0.397	6.85 ± 2.92	6.26 ± 2.12	6.11 ± 1.96	0.064
TB (mmol/L)	9.33 ± 2.82	12.02 ± 4.58	14.05 ± 6.84	0.025	13.71 ± 6.91	14.23 ± 7.35	12.75 ± 4.68	0.117
TG (mmol/L)	0.76 ± 0.39	1.65 ± 1.28	1.57 ± 0.99	0.342	1.33 ± 0.53	1.63 ± 1.05	1.63 ± 1.22	0.093
Hemoglobin (g/L)	130.67 ± 4.04	136.46 ± 27.08	136.94 ± 21.84	0.885	137.54+15.96	136.85 ± 24.84	136.31 ± 22.92	0.932
TC (mmol/L)	3.64 ± 0.33	3.97 ± 1.00	3.89 ± 1.02	0.774	3.96 ± 0.84	3.95 ± 1.10	3.81 ± 0.97	0.433
HDL-C(mmol/L)	1.21 ± 0.35	1.07 ± 0.24	1.09 ± 0.26	0.579	1.16 ± 0.27	1.08 ± 0.25	1.06 ± 0.24	0.027
LDL-C (mmol/L)	1.86 ± 0.20	1.84 ± 0.56	1.81 ± 0.59	0.871	1.88 ± 0.52	1.83 ± 0.62	1.75 ± 0.56	0.311

SNP, single nucleotide polymorphism; TP, total protein; TB, total bilirubin; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. p < 0.05 indicates statistical significance.

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CYP3A4 and CYP11A1 Variants are Risk Factors for Ischemic Stroke: a Case Control Study

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