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Aging and Disease  2015 , 6 (6): 486-498 https://doi.org/10.14336/AD.2015.0505

Original Article

Relationship between CYP17A1 Genetic Polymorphism and Essential Hypertension in a Chinese Population

Chuan-Fang Dai, Xiang Xie*, Yi-Tong Ma*, Yi-Ning Yang, Xiao-Mei Li, Zhen-Yan Fu, Fen Liu, Bang-Dang Chen, Min-Tao Gai

Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China

Corresponding authors:  *Correspondence should be addressed to: Xiang Xie or Yi-Tong Ma. First Affiliated Hospital of Xinjiang Medical University, Urumqi, China. Email: xiangxie999@sina.com or myt_xj@sina.com.*Correspondence should be addressed to: Xiang Xie or Yi-Tong Ma. First Affiliated Hospital of Xinjiang Medical University, Urumqi, China. Email: xiangxie999@sina.com or myt_xj@sina.com.

Received: 2015-03-12

Revised:  2015-05-4

Accepted:  2015-05-5

Online:  2015-11-17

Copyright:  2015

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Abstract

The relationship between CYP17A1 genetic polymorphisms and essential hypertension (EH) remains unclear. The aim of this study was to investigate the association of CYP17A1 genetic polymorphisms with EH in Han and Uighur populations in China. A Han population including 558 people (270 EH patients and 288 controls) and a Uighur population including 473 people (181 EH patients and 292 controls) were selected. Five single-nucleotide polymorphisms (SNPs) (rs4919686, rs1004467, rs4919687, rs10786712, and rs2486758) were genotyped using real-time PCR (TaqMan). In the Uighur population, for the total and the men, rs4919686, rs4919687 and rs10786712 were found to be associated with EH (rs4919686: P≤0.02, rs4919687: P≤0.002, rs10786712: P≤0.004, respectively). The difference remained statistically significant after the multivariate adjustment (all P<0.05). The overall distributions of the haplotypes established by SNP1-SNP3, SNP1-SNP4, SNP1-SNP3-SNP5 and SNP1-SNP4-SNP5 were significantly different between the EH patients and the control subjects (for the total: P=0.013, P=0.008, P=0.032, P=0.010, for men: P<0.001, P=0.001, P=0.010, P=0.00). In the Han population, for men, rs2486758 was found to be associated with EH in a recessive model (P=0.007); the significant difference was not retained after the adjustment for the covariates (date not shown). The A allele of rs4919686 could be a susceptible genetic marker, and the T allele of rs10786712 could be a protective genetic marker of EH. The AC genotype of rs4919686, the AG genotype of rs4919687 and the TT genotype of rs10786712 could be protective genetic markers of EH.

Keywords: CYP17A1 gene ; single nucleotide polymorphism ; essential hypertension ; case-control study

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Chuan-Fang Dai, Xiang Xie*, Yi-Tong Ma*, Yi-Ning Yang, Xiao-Mei Li, Zhen-Yan Fu, Fen Liu, Bang-Dang Chen, Min-Tao Gai. Relationship between CYP17A1 Genetic Polymorphism and Essential Hypertension in a Chinese Population[J]. Aging and Disease, 2015, 6(6): 486-498 https://doi.org/10.14336/AD.2015.0505

Essential hypertension (EH) affects one-fourth of adults worldwide, and this proportion is expected to increase to one-third by 2025 [1]. Hypertension is one of the most important risk factors for cardiovascular diseases, stroke, and end-stage renal disease and is the most important risk factor for morbidity and mortality [2-5]. Each year, approximately one-half of all cases of stroke and myocardial ischemia worldwide are caused by hypertension [6]. The etiology and pathogenesis of EH are likely to comprise a multifactorial disorder resulting from environmental and genetic factors and their interaction. Over the last decade, scientists have found many gene variants associated with EH [7-9], and twin studies have shown that variations in blood pressure have a heritability factor of approximately 50% [10].

The CYP17A1 gene encodes a member of the cytochrome P450 superfamily of enzymes. The cytochrome P450 proteins are monooxygenases that catalyze many reactions involved in drug metabolism as well as the synthesis of cholesterol, steroids and other lipids, and they are responsible for the metabolism of xenobiotics and many endogenous substances whose metabolites have critical roles in the maintenance of cardiovascular health [11, 12]. Recently, several studies have indicated that CYP17A1 is associated with hypertension [13-16]. Genome-wide association studies (GWAS) could screen for the gene polymorphism loci associated with hypertension [17]. Tabara et al [18] performed a multiple regression analysis with possible covariates and showed that CYP17A1 was independently associated with blood pressure (BP) traits and hypertension. They confirmed that CYP17A1 independently determined BP traits and hypertension after adjusting for age, sex, body mass index (BMI), and drinking habits. In 2010, Liu et al [19] found that CYP17A1 gene rs1004467 was significantly associated with increased systolic blood pressure (SBP: P=0.005), diastolic blood pressure (DBP: P=0.01) and risk of hypertension (P=0.0009).

In humans, the CYP17A1 gene is located on chromosome 10q24.3, consisting of eight exons and seven introns, and is primarily expressed in the adrenal glands and gonads. The CYP17A1 gene produces the P450c17 protein, which is a key enzyme in the steroidogenic pathway that produces sex hormones. Some evidence has indicated that the levels of sex hormones could affect the development of cardiovascular and cerebrovascular diseases [20]. Sex hormones including estrogens protect against oxidative stress and are known to be vaso protective [21-23].

In this case-control study, we aimed to assess the association between the polymorphism of CYP17A1 and essential hypertension in a Chinese population.

MATERIAL AND METHODS

Ethical approval of the study protocol

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

Study population

We randomly recruited 270 Han patients (145 men, 125 women) and 181 Uighur patients (103 men, 78 women) with EH and 288 and 292 ethnically and geographically matched control group subjects. All the subjects attended the First Affiliated Hospital of Xinjiang Medical University from 2007 to 2013 as inpatients. All the patients presented with hypertension defined as having an SBP/DBP ≥140/90 mmHg [24], and the participants with hypertension had parents, siblings, or both with hypertension, were undergoing antihypertensive medication therapy or had been previously diagnosed with hypertension. In addition, we excluded any subjects with secondary hypertension, such as primary aldosteronism or kidney disease. Patients with multiple organ failure, a mental disorder, or chronic inflammatory disease were excluded from this study. The normotensive controls had no family history of hypertension, had never been treated with antihypertensive medications, and presented with SBP/DBP <120/80 mmHg; additionally, participants with coronary artery disease, multiple organ failure, or a mental disorder were excluded from this study.

Biochemical analyses

For the biochemical analyses, 5 ml of fasting venous blood was drawn by venipuncture from all the participants. The blood samples were collected and centrifuged at 4000 ×g for 5 min to separate the plasma content (including the plasma and blood cells). The genomic DNA was extracted using the standard phenol-chloroform method [25]. The DNA samples were stored at -80 °C for future analysis. For the analyses, the DNA was diluted to a 50-ng/μL concentration. The plasma concentrations of glucose, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), creatinine (Cr) and uric acid (UA) were measured using standard methods in the Central Laboratory of First Affiliated Hospital of Xinjiang Medical University, as described previously [26-28].

Genotyping of the CYP17A1 gene

Using Haploview 4.2 software and the HapMap phase II database, five tag SNPs (SNP1: rs4919686, SNP2: rs1004467, SNP3: rs4919687, SNP4: rs10786712, SNP5: rs2486758) were obtained using a minor allele frequency (MAF) ≥0.05 and linkage disequilibrium patterns, with r2≥ 0.8 as the cut off. The genotyping was confirmed by the TaqMan SNP Genotyping Assay (Applied Biosystems, Foster City, CA). The TaqMan SNP Genotyping Assays were performed using Taq amplification.

Statistical analysis

The statistical analyses were performed using the SPSS 17.0 for Windows (SPSS Institute, Chicago, USA). Statistical significance was established as a P-value < 0.05. All the continuous variables (e.g., age, TC, TG, HDL-C, LDL-C, BMI) are presented as the means ± standard deviation (SD), and the difference between the EH and control groups was analyzed using an independent-sample T-test. All the classification variables (e.g., the frequencies of smoking, drinking, diabetes mellitus, and CYP17A1 genotypes) and the Hardy-Weinberg equilibrium were analyzed using the χ2 test or Fisher’s exact test, as appropriate. Logistic regression analyses with effect ratios (odds ratio [OR] and 95% CI) were used to assess the contribution of the major risk factors. The linkage disequilibrium (LD) analysis and haplotype-based case-control analysis were performed using the expectation maximization (EM) algorithm [29] and SHEsis software (www.analysis.bio-x.cn/ SHEsisMain.htm). The pairwise linkage disequilibrium analysis was performed using five SNP pairs, and the frequency distribution of the haplotypes was calculated by performing a permutation test using the bootstrap method.

RESULTS

Characteristics of the study participants

As shown in Table 1, for the Han and Uighur populations, there was no significant difference in age between the EH patients and the control subjects, which indicated that the study was an age-matched case-control study. In the Han population, for the total subjects and women participants, the incidence of diabetes and the plasma concentration of uric acid (UA) were significantly higher in the EH subjects than in the controls; for the total subjects, the following values were significantly higher for the EH patients than for the control subjects: the incidence of drinking and the BMI; for the male subjects, the incidence of drinking and smoking were significantly higher in the EH subjects than in the controls; for the female subjects, the BMI and the plasma concentration of Cr were significant higher for the EH patients than for the control participants. In the Uighur population, for the total subjects, the incidence of diabetes, smoking, and drinking were significantly higher in the EH subjects than in the controls; for the male subjects, the incidence of smoking and drinking were significantly higher for the EH patients compared to the control subjects; for the female subjects, the plasma concentration of Cr was significantly higher for the EH patients than for the control participants.

Table 1   Demographic and clinical characteristics of study participants

Han
TotalMenWomen
EHcontrolsPEHcontrolsPEHcontrolsP
Number (n)270288145157125131
Age, mean (SD)62.47(9.88)61.52(10.03)0.26460.15(11.08)60.19(11.19)0.77964.75(7.71)63.12(8.2)0.103
Diabetes (%)35(13.0)19(6.6)0.01117(11.7)15(9.6)0.54018(14.4)4(3.1)0.001
Smoking (%)41(15.2)29(10.1)0.06841(28.3)28(17.8)0.03101(0.9)0.328
Drinking (%)36(13.3)23(8.0)0.04036(25.1)23(14.6)0.018001
BMI, mean (SD)26.35(3.66)25.44(3.31)0.00226.94(3.85)26.04(3.15)0.10625.69(3.33)24.73(3.37)0.023
Glu(mmol/L)5.79(2.17)5.49(1.56)0.0625.97(2.21)5.53(1.59)0.0775.61(1.48)5.44(1.53)0.525
TG(mmol/L)2.05(1.96)1.90(1.44)0.3262.19(2.17)2.09(1.68)0.6511.89(1.69)1.69(1.06)0.250
TC(mmol/L)4.30(1.36)4.30(0.997)0.9694.14(1.11)4.16(0.97)0.8554.51(0.95)4.46(1.00)0.703
HDL(mmol/L)1.11(0.32)1.12(0.32)0.6051.03(0.28)1.04(0.30)0.6511.20(0.33)1.21(0.32)0.771
LDL(mmol/L)2.53(0.94)2.55(0.83)0.8262.48(1.02)2.53(0.82)0.6152.85(2.25)2.57(0.84)0.303
UA(umol/L)330.82(91.34)312.89(75.23)0.012355.18(79.27)340.57(73.76)0.101303.59(96.77)279.32(62.43)0.019
Cr(umol/L)73.60(17.45)71.15(17.80)0.10479.49(15.27)78.31(17.83)0.54266.63(17.34)62.54(13.46)0.038
BUN(mmol/L)5.36(1.93)5.23(1.76)0.3055.55(1.52)5.52(1.85)0.8685.22(2.31)4.88(1.59)0.183

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Table 1(Continue)   Demographic and clinical characteristics of study participants

Uighur
TotalMenWomen
EHcontrolsPEHcontrolsPEHcontrolsP
Number (n)1812921032107882
Age, mean (SD)58.78(9.08)58.30(9.36)0.58158.55(9.11)58.51(9.43)0.96959.09(9.09)59.33(8.98)0.867
Diabetes (%)26(14.4)23(7.9)0.02413(12.6)15(7.1)0.13912(15.4)8(9.8)0.282
Smoking (%)26(14.36)16(5.48)0.00126(25.2)16(7.6)<0.001001
Drinking (%)17(9.39)12(4.11)0.02017(16.5)11(5.2)0.00101(1.2)0.328
BMI, mean (SD)26.97(3.73)26.54(4.69)0.25127.17(3.46)26.69(3.96)0.27226.68(4.25)26.17(4.33)0.443
Glu(mmol/L)5.89(2.56)5.53(2.08)0.1015.82(2.60)5.44(1.89)0.1565.99(2.51)5.76(2.48)0.561
TG(mmol/L)1.84(1.16)1.78(1.07)0.6291.71(1.00)1.80(1.06)0.4842.00(1.33)1.73(1.09)0.179
TC(mmol/L)4.31(1.34)4.27(1.19)0.6974.22(0.98)4.29(1.27)0.6354.44(1.31)4.21(0.99)0.235
HDL(mmol/L)1.02(0.34)1.03(0.36)0.7450.99(0.34)1.02(0.36)0.4091.07(0.33)1.06(0.36)0.924
LDL(mmol/L)2.77(0.99)2.62(0.87)0.1152.74(0.93)2.62(0.87)0.2912.80(1.07)2.63(0.90)0.300
UA(umol/L)297.57(94.43)293.25(94.41)0.639319.29(79.93)312.23(94.76)0.527268.70(104.55)244.73(76.66)0.107
Cr(umol/L)74.65(31.63)72.33(30.79)0.44380.10(21.40)78.08(33.35)0.58167.40(40.55)57.69(15.34)0.049
BUN(mmol/L)5.51(2.02)5.35(1.50)0.3275.75(1.91)5.39(1.54)0.0865.19(2.12)5.23(1.39)0.913

Continuous variables are expressed as mean ± SD. Categorical variables are expressed as percentages. BMI, body mass index; BUN, blood urea nitrogen; Cr, creatinine; DM, diabetes mellitus; Glu, glucose; TG, triglyceride; TC, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein; UA, uric acid. The P value of the continuous variables was calculated by the Independent t-test. The P value of the categorical variables was calculated by Fisher’s exact test.

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Distributions of CYP17A1 genotypes

As shown in Table 2, in the Han population, the distributions of the genotypes and alleles for each SNP were in good agreement with the predicted Hardy-Weinberg equilibrium values (data not shown). For the total, male, and female participants, the distribution of SNP1 (rs4919686), SNP2 (rs1004467), SNP3 (rs4919687) and SNP4 (rs10786712) genotypes did not show a significant difference between the EH patients and the control subjects (P>0.05) in the dominant, recessive, and additive models. For the total and female subjects, the distribution of the SNP5 (rs2486758) genotypes did not show a significant difference between the EH patients and the control subjects. For the male subjects, the distribution of SNP5 (rs2486758) showed a difference between the EH patients and the control subjects in a recessive model (TT+CT vs. CC; P=0.007).

As shown in Table 3, in the Uighur population, the distributions of the genotypes and alleles for each SNP were in good agreement with the predicted Hardy-Weinberg equilibrium values (data not shown). For the total, male, and female participants, the distribution of the SNP2 (rs1004467) genotypes did not show a significant difference between the EH and control subjects (P>0.05). For the total, the distribution of the SNP1 (rs4919686) genotypes, the dominant model (AC + CC vs AA), and the additive model (AA+AC vs CC) frequency showed significant difference between the EH and control subjects (P=0.020, P=0.025, P=0.007, respectively), and the dominant model and additive model were significantly lower in the subjects with EH than in the controls (26.5% vs. 36.7%, 21.8% vs. 33.8%). The distribution of the SNP3 (rs4919687) genotypes, the recessive model (AG+GG vs AA), and the additive model (AA+GG vs. AG) showed a significant difference between the EH and control subjects (P<0.002, P=0.046, P=0.001, respectively), and the recessive model and additive model were significantly higher in the controls than in the EH subjects (88.2% vs 84.1%, 42.2% vs 26.7%). The distribution of the SNP4 (rs10786712) genotypes, the dominant model (CT + TT vs CC) and the allele frequency showed significant differences between the EH patients and the control subjects (P=0.004, P=0.001, P=0.001, respectively), and the dominant model and allele frequency were significantly higher in the EH subjects than in the controls (40.5% vs 26.0%, 62.7% vs 51.8%).

For men, the distribution of the SNP1(rs4919686) genotypes, dominant model (AC + CC vs AA), additive model (AA+AC vs CC) and allele frequency showed significant differences between the EH patients and the control subjects (P=0.004, P=0.002, P=0.001, P=0.014, respectively), and the dominant model, additive model, and allele frequency were significantly lower in the subjects with EH than in the controls (21.2% vs 38.7%, 17.2% vs 35.7%,12.6% vs 20.9%). The distribution of the SNP3 (rs4919687) genotypes, dominant model (AA + AG vs GG), recessive model (AG+GG vs AA), and additive model (AA+GG vs AG) showed significant differences between the EH and control subjects (P<0.001, P=0.044, P=0.026, P<0.001, respectively), and the dominant model, recessive model and additive model were significantly higher in the controls than in the EH subjects (59.2% vs 47.0%, 85.4% vs 75.0%, 44.7% vs 22.0%). The distribution of the SNP4 (rs10786712) genotypes, dominant model (CT + TT vs CC), recessive model (CT + CC vs TT), and allele frequency showed significant difference between the EH patients and the control subjects (P=0.002, P=0.001, P=0.013, P<0.001, respectively), and the dominant model, recessive model, and allele frequency were significantly higher in the EH subjects compared with the controls (44.0% vs 25.9%, 87% vs 74.6%,65.5% vs 50.2%).

For women, the distribution of the SNP5 (rs2486758) genotypes and recessive model (CT+TT vs CC) showed significant differences between the EH patients and control subjects (P=0.025, P=0.008, respectively), and the genotypes and recessive model frequency were significantly lower in the EH subjects than in the controls (54.0% vs 54.9%, 90.5% vs 100%).

Table 2   Genotype and Allele distributions in Han patients with EH and control participants

VariantsTotalMenWomen
EH
n(%)
Control
n(%)
PEH
n(%)
Control
n(%)
PEH
n(%)
Control
n(%)
P
Rs4919686 (SNP1)
GenotypingAA183(74.4)203(78.4)0.56794(71.8)111(78.2)0.46589(77.4)92(78.6)0.974
AC60(24.4)53(20.5)35(26.7)29(20.4)25(21.7)24(20.5)
CC3(1.2)3(1.2)2(1.5)2(1.4)1(0.9)1(0.9)
Recessive modelCC3(1.2)3(1.2)0.9492(1.5)2(1.4)0.9351(0.9)1(0.9)0.990
AA+AC243(98.8)256(98.8)129(98.5)140(98.6)114(99.0)116(99.1)
Dominant modelAA183(74.4)203(78.4)0.29137(28.2)111(78.2)0.22189(77.4)92(78.6)0.819
AC+CC63(25.6)56(21.6)57(27.0)31(21.8)26(22.6)25(21.4)
Additive modelAC60(24.4)53(20.5)0.29035(26.7)29(20.4)0.22025(21.7)24(20.5)0.819
AA+CC186(75.6)206(79.5)96(73.3)113(79.6)90(78.3)93(79.5)
AlleleA426(86.6)459(88.6)0.329223(85.1)251(88.4)0.260203(88.3)208(88.9)0.832
C66(13.4)59(11.4)39(14.9)33(11.6)27(11.7)26(11.1)
Rs1004467 (SNP2)
GenotypingCC52(20.1)46(15.9)0.24030(20.7)24(15.3)0.36423(19.3)22(16.5)0.592
CT124(47.9)158(54.7)71(49.0)88(56.1)55(46.2)70(52.6)
TT83(32.0)85(29.4)44(30.3)45(28.7)41(34.5)41(30.8)
Recessive modelCC52(20.1)46(15.9)0.20530(20.7)24(15.3)0.22123(19.3)22(16.5)0.564
CT+TT207(79.9)243(84.1)115(79.3)133(84.7096(80.7)111(83.5)
Dominant modelTT83(32.0)85(29.4)0.50444(30.3)45(28.7)0.47941(34.5)41(30.8)0.540
CC+CT176(68.0)204(70.6)101(69.7)112(71.3)78(65.5)92(69.2)
Additive modelCT124(47.9)158(54.7)0.11271(49.0)88(56.1)0.21855(46.2)70(52.6)0.309
CC+TT135(52.1)131(45.3)74(51.0)69(43.9)64(53.8)64(47.4)
AlleleC228(44.0)250(43.3)0.799131(45.2)136(43.3)0.646101(42.4)114(42.9)0.924
T290(56.0)328(56.7)159(54.8)178(56.7)137(57.6)152(57.1)
Rs4919687 (SNP3)
GenotypingAA9(3.4)15(5.2)0.5905(3.5)8(5.1)0.7244(3.4)7(5.3)0.641
AG90(34.4)96(33.4)49(34.3)56(35.9)41(34.5)40(30.5)
GG163(62.2)176(61.3)89(62.2)92(59.0)74(62.2)84(64.1)
Recessive modelAA9(3.4)15(5.2)0.3055(3.5)8(5.1)0.4894(3.4)7(5.3)0.445
AG+GG253(96.6)272(94.8)138(96.5)148(94.9)115(96.6)124(94.7)
Dominant modelGG163(62.2)176(61.3)0.83089(62.2)92(59.0)0.56474(62.2)84(64.1)0.751
AA+AG99(37.8)111(38.7)54(37.8)64(41.0)45(37.8)47(35.9)
Additive modelAG90(34.4)96(33.4)0.82449(34.3)56(35.9)0.76841(34.5)40(30.5)0.508
AA+GG172(65.6)191(66.6)94(65.7)100(64.1)78(65.5)91(69.5)
AlleleA108(20.6)126(22.0)0.58859(20.6)72(23.1)0.47049(20.6)54(20.6)0.995
G416(79.4)448(78.0)227(79.4)240(76.9)189(79.4)208(79.4)
Rs10786712 (SNP4)
GenotypingCC50(20.2)56(21.6)0.91529(21.8)30(21.1)0.98521(18.3)26(22.2)0.754
CT126(50.8)128(49.4)62(46.6)66(46.5)64(55.7)62(53.0)
TT72(29.0)75(29.0)42(31.6)46(32.4)30(26.1)29(24.8)
Recessive modelTT72(29.0)75(29.0)0.98542(31.6)46(32.4)0.88530(26.1)29(24.8)0.820
CC+CT176(71.0)184(71.0)91(68.4)96(67.6)85(73.9)88(75.2)
Dominant modelCC50(20.2)56(21.6)0.68629(21.8)30(21.1)0.89121(18.3)26(22.2)0.453
CT+TT198(79.8)203(78.4)104(78.2)112(78.9)94(81.7)91(77.8)
Additive modelCT126(50.8)128(49.4)0.75562(46.6)66(46.5)0.98264(55.7)62(53.0)0.684
CC+TT122(49.2)131(50.6071(53.4)76(53.5)51(44.3)55(47.0)
AlleleC226(45.6)240(46.3)0.806120(45.1)126(44.4)0.860106(46.1)114(48.7)0.570
T270(54.4)278(53.7)146(54.9)158(55.6)124(53.9)120(51.3)
Rs2486758 (SNP5)
GenotypingCC14(5.2)6(2.1)0.07611(7.7)2(1.3)0.0233(2.4)4(3.1)0.423
CT82(30.7)103(36.4)46(32.2)57(36.5)36(29.0)46(36.2)
TT171(64.0)174(61.5)86(60.1)97(62.2)85(68.5)77(60.6)
Recessive modelCC14(5.2)6(2.1)0.05111(7.7)2(1.3)0.0073(2.4)4(3.1)0.725
CT+TT253(94.8)277(97.9)132(92.3)154(98.7)121(97.6)123(96.9)
Dominant modelTT171(64.0)174(61.5)0.53586(60.1)97(62.2)0.71885(68.5)77(60.6)0.190
CC+CT96(36.0)109(38.5)57(39.9)59(37.8)39(31.5)50(39.4)
Additive modelCT82(30.7)103(36.4)0.15846(32.2)57(36.5)0.42736(29.0)46(36.2)0.225
CC+TT185(69.3)180(63.6)97(67.8)99(63.5)88(71.0)81(63.8)
AlleleC110(20.6)115(20.3)0.90868(23.8)61(19.6)0.21042(16.9)54(21.3)0.218
T424(79.4)451(79.7)218(76.2)251(80.4)206(83.1)200(78.7)

EH, essential hypertension; N, number of participants; SNP, single-nucleotide polymorphism.

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Logistic regression analyses

Table 4, Uighurshows, in the Uighur population for the total subjects and the men, the multivariable logistic regression analysis combining the genotypes with the following variables: the incidence of diabetes, smoking, and drinking as well as the BMI; after the multivariate adjustment, SNP1 (rs4919686) remained significantly associated with EH in the additive model (total: OR=0.559, 95%CI:0.356-0.879, P=0.012, men: OR=0.386, 95%CI: 0.211-0.706, P=0.002) and in the dominant model (total: OR=1.568, 95%CI: 0.324-0.934, P=0.027, men: OR=2.262, 95%CI: 1.285-3.980, P=0.005) (data not shown). After the multivariate adjustment, SNP3 (rs4919687) remained significantly associated with EH in the additive model (total: OR=0.520, 95%CI: 0.341-0.792, P=0.002, men: OR=0.371, 95%CI: 0.213-0.644, P<0.001) and in the recessive model (total: OR=1.840, 95%CI: 1.080-3.136, P=0.025, men: OR=1.954, 95%CI: 1.061-3.565, P=0.031) (data not shown).

The significant difference of SNP4 (rs10786712) (total: OR=1.968, 95%CI: 1.294-2.993, P=0.002, men: OR=2.189, 95%CI: 1.306-3.667, P=0.003) was retained after adjustment of the major confounding factors for EH in the dominant model.

Table 3   Genotype and Allele distributions in Uighur patients with EH and control participants

VariantsTotalMenWomen
EH
n(%)
Control n(%)PEH
n(%)
Control n(%)PEH
n(%)
Control n(%)P
Rs4919686 (SNP1)
GenotypingAA125(73.5)176(63.3)0.02078(78.8)122(61.3)0.00447(66.2)54(68.4)0.626
AC37 (21.8)94 (33.8)17(17.2)71(35.7)20(28.2)23(29.1)
CC8 (4.7)8 (2.9)4(4.0)6(3.0)4(5.6)2(2.5)
Recessive modelCC8 (4.7)8 (2.9)0.3124(4.0)6(3.0)0.6434(5.6)2(2.5)0.333
AA+AC162(95.3)270(97.1)95(96.0)193(97.0)67(94.4)77(97.5)
Dominant modelAA125(73.5)176(63.3)0.02578(78.8)122(61.3)0.00247(66.2)54(68.4)0.779
AC+CC45(26.5)102(36.7)21(21.2)77(38.7)24(33.8)25(31.6)
Additive modelAC37 (21.8)94 (33.8)0.00717(17.2)71(35.7)0.00120(28.2)23(29.1)0.898
AA+CC133(78.2)184(66.2)82(82.8)128(64.3)51(71.8)56(70.9)
AlleleA287(84.4)446(80.2)0.114173(87.4)315(79.1)0.014114(80.3)131(82.9)0.557
C53(15.6)110(19.8)25(12.6)83(20.9)28(19.7027(17.1)
Rs1004467 (SNP2)
GenotypingCC42(24.6)72(25.2)0.81216(16.2)37(17.9)0.82126(36.1)35(43.8)0.425
CT85(49.7)148(51.7)53(53.5)114(55.1)32(44.4)35(43.8)
TT44(25.7)66(23.1)30(30.3)56(27.1)14(19.4)10(12.5)
Recessive modelCC42(24.6)72(25.2)0.88316(16.2)37(17.9)0.71126(36.1)35(43.8)0.337
CT+TT129(75.4)214(74.8)83(83.8)170(82.1)46(63.9)45(56.3)
Dominant modelTT44(25.7)66(23.1)0.52130(30.3)56(27.1)0.55414(19.4)10(12.5)0.241
CC+CT127(74.3)220(76.9)69(69.7)151(72.9)58(80.6)70(87.5)
Additive modelCT85(49.7)148(51.7)0.67353(53.5)114(55.1)0.80132(44.4)35(43.8)0.931
CC+TT86(50.3)138(48.3)46(46.5)93(44.9)40(55.6)45(56.3)
AlleleC169(49.4)292(51.0)0.63385(42.9)188(45.4)0.56384(58.3)105(65.6)0.191
T173(50.6)280(49.0)113(57.1)226(54.6)60(41.7)55(34.4)
Rs4919687 (SNP3)
GenotypingAA32(18.6)34(11.8)0.00225(25.0)30(14.6)<0.0017(9.7)4(4.9)0.518
AG46(26.7)121(42.2)22(22.0)92(44.7)24(33.3)29(35.8)
GG94(54.7)132(46.0)53(53.0)84(40.8)41(56.9)48(59.3)
Recessive modelAA32(18.6)34(11.8)0.04625(25.0)30(14.6)0.0267(9.7)4(4.9)0.253
AG+GG140(81.4)253(88.2)75(75.0)176(85.4)65(90.3)77(95.1)
Dominant modelGG94(54.7)132(46.0)0.07253(53.0)84(40.8)0.04441(56.9)48(59.3)0.772
AA+AG78(45.3)155(54.0)47(47.0)122(59.2)31(43.1)33(40.7)
Additive modelAG46(26.7)121(42.2)0.00122(22.0)92(44.7)<0.00124(33.3)29(35.8)0.749
AA+GG126(73.3)166(57.8)78(78.0)114(55.3)48(66.7)52(64.2)
AlleleA110(32.0)189(32.9)0.76672(36.0)152(36.9)0.83038(26.4)37(22.8)0.471
G234(68.0)385(67.1)128(64.0)260(63.1)106(73.6)125(77.2)
Rs10786712 (SNP4)
GenotypingCC70(40.5)73(26.0)0.00444(44.0)52(25.9)0.00226(35.6)21(26.3)0.310
CT77(44.5)145(51.6)43(43.0)98(48.8)34(46.6)47(58.8)
TT26(15.0)63(22.4)13(13.0)51(25.4)13(17.8)12(15.0)
Recessive modelTT26(15.0)63(22.4)0.05413(13.0)51(25.4)0.01313(17.8)12(15.0)0.639
CC+CT147(85.0)218(77.6)87(87.0)150(74.6)60(82.2)68(85.0)
Dominant modelCC70(40.5)73(26.0)0.00144(44.0)52(25.9)0.00126(35.6)21(26.3)0.210
CT+TT103(59.5)208(74.0)56(56.0)149(74.1)47(64.4)59(73.8)
Additive modelCT77(44.5)145(51.6)0.14243(43.0)98(48.8)0.34634(46.6)47(58.8)0.132
CC+TT96(55.5)136(48.4)57(57.0)103(51.2)39(53.4)33(41.3)
AlleleC217(62.7)291(51.8)0.001131(65.5)202(50.2)<0.00186(58.9)89(55.6)0.563
T129(37.3)271(42.2)69(34.5)200(49.8)60(41.1)71(44.4)
Rs2486758 (SNP5)
GenotypingCC11(6.6)9(3.3)0.1515(4.9)9(4.5)0.2896(9.5)00.025
CT67(40.4)100(36.6)44(42.7)68(33.8)23(36.5)32(45.1)
TT88(53.0)164(60.1)54(52.4)124(61.7)34(54.0)39(54.9)
Recessive modelCC11(6.6)9(3.3)0.1055(4.9)9(4.5)0.8826(9.5)00.008
CT+TT155(93.4)264(96.7)98(95.1)192(95.5)57(90.5)71(100)
Dominant modelTT88(53.0)164(60.1)0.14754(52.4)124(61.7)0.12134(54.0)39(54.9)0.911
CC+CT78(47.0)109(39.9)49(47.6)77(38.3)29(46.0)32(45.1)
Additive modelCT67(40.4)100(36.6)0.43544(42.7)68(33.8)0.12823(36.5)32(45.1)0.315
CC+TT99(59.6)173(63.4)59(57.3)133(66.2)40(63.5)39(54.9)
AlleleC89(26.8)118(21.6)0.07954(26.2)86(21.4)0.18135(27.8)32(22.5)0.323
T243(73.2)428(78.4)152(73.8)316(78.6)91(72.2)110(77.5)

EH, essential hypertension; N, number of participants; SNP, single-nucleotide polymorphism.

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Table 4   Multiple logistic regression analysis for EH patients and control subjects

TotalMenWomen
OR95% CIPOR95% CIPOR95% CIP
rs4919686
Additive model (CC+CT vs TT)0.5590.356-0.8790.0120.3860.211-0.7060.0020.9980.487-2.0440.995
Diabetes1.5380.808-2.9290.1901.6060.691-3.7370.2711.4300.527-3.8770.482
Smoking3.1431.272-7.7630.0131.1670.445-3.0590.754___
Drinking0.9790.313-3.0630.9710.8560.263-2.7920.797___
BMI1.0130.694-1.0650.6151.0160.951-1.0860.6301.0210.946-1.1020.592
rs4919687
Additive
model (CC+CT vs TT)
0.5200.341-0.7920.0020.3710.213-0.644<0.0010.9390.478-1.8440.854
Diabetes1.4570.769-2.7610.2491.5910.703-3.5990.2651.2680.460-3.4990.646
Smoking3.7801.396-10.240.0091.2040.439-3.3040.718___
Drinking0.6880.202-2.3460.5500.7390.209-2.6050.638-_-
BMI1.0200.970-1.0730.4371.0290.963-1.0990.3961.0240.948-1.1060.551
rs10786712
Dominant model (CC+CT vs TT)1.9681.294-2.9930.0022.1891.306-3.6670.0031.4540.717-2.9490.299
Diabetes1.3660.709-2.6300.3511.4380.613-3.3700.4041.2840.470-3.5090.626
Smoking3.4791.335-9.0660.0111.2250470-3.1950.678___
Drinking0.8860.277-2.8330.8390.8860.273-2.8730.841-_-
BMI1.0120.962-1.0640.5641.0210.956-1.0900.5321.0180.943-1.0990.654

OR, odds ratios; 95%CI, 95% confidence intervals

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For women, after the multivariate adjustment, SNP5 (rs2486758) did not remain significantly associated with EH in the recessive model (data not shown). Similarly, in the Han population, for men, the recessive model showed that for SNP5 (rs2486758), a significant difference was not retained after adjustment for the covariates.

LD analysis

In the Han population, all five of the SNPs are located in one haplotype block because the∣D'∣values were beyond 0.5, and all of the r2 values were below 0.5; therefore, the five SNPs were used to construct the haplotypes. In the Uighur population, because the ∣D'∣for SNP1-SNP2, SNP2-SNP3, SNP2-SNP4, and SNP2-SNP5 were < 0.5, SNP2 could not be used to construct the haplotypes with another SNP; therefore, we constructed the haplotypes using SNP1, SNP3, SNP4, and SNP5.

Haplotype analyses

In the haplotype-based case-control analysis of the Han population, the haplotypes were established for males through the use of different combinations of the five SNPs (Table 5). The frequencies of the A-A-T, A-A-T, A-T-T-T and A-A-T-T haplotypes established by SNP1-SNP3-SNP4, SNP1-SNP3-SNP5, SNP1-SNP2-SNP4-SNP5, and SNP1-SNP3-SNP4-SNP5, respectively, were significantly higher for the control subjects than for the EH patients (P=0.049, P=0.030, P=0.044, and P=0.046, respectively). The distribution of the A-A and A-C-A haplotypes, established by SNP1-SNP3 and SNP1-SNP2-SNP3, respectively, showed a significant difference between the EH patients and the control subjects (P=0.041, P=0.032, respectively) as well. For the total and female subjects, the overall distribution of the haplotypes was not significantly different between the EH patients and the control subjects.

UighurIn the haplotype-based case-control analysis of the Uighur population, the haplotypes were established through the use of different combinations of the four SNPs (Table 6). For the total and males, the overall distribution of the haplotypes established by SNP1-SNP3, SNP1-SNP4, SNP1-SNP3-SNP5 and SNP1-SNP4-SNP5 were significantly different between the EH patients and the control subjects (for the total: P=0.013, P=0.008, P=0.032, and P=0.010, respectively; for the males:P<0.001, P=0.001, P=0.010, and P=0.002, respectively); the frequencies of the A-A, A-C, A-A-T and A-C-T haplotypes established by SNP1-SNP3,SNP1-SNP4,SNP1-SNP3-SNP5, and SNP1-SNP4-SNP5, respectively, were significantly higher for the EH patients than for the control subjects (for the total: P=0.032, P=0.002, P=0.039, P=0.039, respectively; for the males: P=0.012, P<0.001, P=0.033, P=0.009, respectively). The frequencies of the C-A-T, C-A-T, C-T-T and C-A-T-T haplotypes established by SNP1-SNP3-SNP4, SNP1-SNP3-SNP5, SNP1-SNP4-SNP5, and SNP1-SNP3-SNP4-SNP5, respectively, were lower for the EH patients than for the control subjects (for the total: P=0.015, P=0.009, P=0.021, P=0.005, respectively; for the males:P<0.001, P=0.001, P=0.003, P=0.001, respectively). For the males, the frequency of the A-A-C haplotype established by SNP1-SNP3-SNP4 and the A-A-C-T haplotype established by SNP1-SNP3-SNP4-SNP5 were significantly lower for the control subjects than for the EH patients (P=0.014 and P=0.006, respectively). For the females, the overall distribution of the haplotypes was not significantly different between the EH patients and the control subjects.

Table 5   Haplotype analysis in Han men patients with EH and in control subjects

HaplotypesHaplotype FrequenciesX2POR95%CI
SNP1SNP2SNP3SNP4SNP5EHControl
AA0.0770.1284.1560.0410.5620.321-0.984
ACA0.0090.0354.6160.0320.2440.061-0.980
AAT0.0710.1183.8760.0490.5630.316-1.103
AAT0.0670.1204.6860.0300.5250.290-0.948
ATTT0.0710.1214.0420.0440.5550.311-0.991
AATT0.0700.1193.9910.0460.5540.309-0.995

EH, essential hypertension; haplotype with frequencies > 0.03 were estimated using SHEsis software; SNP, single-nucleotide polymorphism; OR, odds ratio.

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Table 6   Haplotype analysis in patients with EH and in control subjects (Uygur)

1234Overall P valueFrequency in totalFrequency in manFrequency in woman
TotalManWomanEHControlP valueEHControlP valueEHControlP value
SNP1SNP30.013<
0.001
0.268
H1AA0.2070.1500.0320.2770.1860.0120.1080.0690.127
H2CA0.1170.1790.0140.0810.187<0.0010.1690.2590.833
SNP1SNP40.0080.0010.570
H1AC0.6250.5180.0020.6600.505<0.0010.5700.5570.814
H3CT0.1550.1980.1030.1240.2160.0140.2040.1710.459
SNP3SNP40.008<0.0010.154
H1AC0.1250.0830.0440.1990.1050.0020.0170.026_
H2AT0.1990.2460.1050.1650.2680.0060.2510.1950.262
H4GT0.1770.2410.0240.1840.2320.1800.1690.2540.067
SNP1SNP3SNP40.002<0.0010.053
H2CAT0.1180.1790.0150.0810.187<0.0010.1690.1590.860
H3AAC0.1220.0830.0650.1940.1060.0030.0170.027_
SNP1SNP3SNP50.0320.0100.378
H1AAT0.1650.1170.0390.2050.1370.0330.1010.0660.265
H2CAT0.1070.1760.0090.0810.1840.0010.1510.1510.880
SNP1SNP4SNP50.0100.0020.771
H1ACT0.3720.3070.0390.4120.3040.0090.2960.319
H2CTT0.1340.1980.0210.1090.2070.0030.1870.167
SNP1SNP3SNP4SNP50.0030.0030.093
H1AGTT0.1380.2200.0050.1380.2070.0460.1350.251
H2CATT0.1030.1750.0050.0820.1830.0010.1490.151
H3AACT0.0840.0550.0990.1330.0660.0070.0090.028

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

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DISCUSSION

In our study, we found that variations in the CYP17A1 gene were associated with EH in a Uighur population of China.

Numerous CYP subfamilies, such as CYP2C9 (EET synthesis) [30], CYP4A11 (20-HETE synthesis) [31], CYP8A1 (prostacyclin synthesis) [32], and CYP11B2 (aldosterone synthesis) [33], have been shown to be associated with EH. The P450c17 proteins have two types of enzyme activity, and P450c17 is an important enzyme that catalyzes the formation of all endogenous androgens. Therefore, CYP17A1 genetic mutations could cause the loss of the enzyme activity of P450c17 and potentially reduce androgen biosynthesis. Androgens serve as precursors to estrogens; normal estrogen signaling is dependent on CYP17A1 as well. The mechanism by which the CYP17A1 gene leads to hypertension is unclear. Recently, animal experiments and clinical observations have demonstrated that the occurrence of hypertension is related to the levels of sex hormones in the body [34]. A clinical study [35] showed that testosterone levels play an important role in the progression of hypertension in elderly men, whereas lower testosterone levels promote hypertension. The incidence of hypertension among premenopausal women was significantly lower than that among men [36]. Therefore, estrogen likely plays a protective role in the cardiovascular system.

We found that polymorphisms of CYP17A1 were associated with EH in the Uighur population. In total and in the men, for SNP1 (rs4919686), the frequency of AC genotypes is higher in the control subjects than in the EH subjects, and there were significant differences in the genotypes, dominant model, and additive model, after multivariate adjustment of the confounding factors for EH. The significant difference was retained, which indicated that the AC genotypes might be protective against EH; the frequency of the A allele is higher in the EH patients than in the control subjects, which indicated that the A allele is a risk factor for EH. For SNP3 (rs4919686), the frequency of the AG genotypes is higher in the control subjects than in the EH subjects, and there were significant differences in the genotypes, recessive model, and additive model; the significant difference was retained after the multivariate adjustment of the confounding factors for EH, which indicated that the AG genotypes might be protective against EH. For SNP4 (rs10786712), the frequency of the TT genotypes and the T allele are higher in the control subjects than in the EH subjects, and there were significant differences in the genotypes, dominant model, and allele frequency; after the multivariate adjustment of the confounding factors for EH, the significant difference was retained, which indicated that the TT genotypes and T allele might be protective against EH. In addition, based on such findings, we hypothesized that a haplotype analysis would be useful for the assessment of the associations between haplotypes and EH. For the total and the men, we succeeded identifying four susceptible haplotypes (A-A of SNP1-SNP3, A-C of SNP1-SNP4, A-A-T of SNP1-SNP3-SNP5, and A-C-T of SNP1-SNP4-SNP5), and these haplotypic analysis results are consistent with the genotypic analysis results for SNP1 (rs4919686), which showed that the A allele confers risk. Additionally, we identified four protective haplotypes (C-A-T of SNP1-SNP3-SNP4, C-A-T of SNP1-SNP3-SNP5, C-T-T of SNP1-SNP4-SNP5, and C-A-T-T of SNP1-SNP3-SNP4-SNP5), and these haplotypic analysis results are consistent with the genotypic analysis results of SNP4 (rs10786712), which showed that the T allele is protective. For women, the overall distribution of this haplotype was not significantly different between the EH patients and the control subjects (all P>0.05).

In the Han population, for men, the distribution of the SNP5 (rs2486758) recessive model (TT+CT vs. CC) showed a difference between EH and the control subjects (P=0.007); however, in the recessive model of SNP5 (rs2486758), a significant difference was not retained after adjustment for the covariates (date not shown). In addition, based on these findings, we hypothesized that haplotype analysis would be useful for the assessment of associations between haplotypes and EH. In men, we identified six protective haplotypes (A-A of SNP1-SNP3, A-C-A of SNP1-SNP2-SNP3, A-A-T of SNP1-SNP3-SNP4, A-A-T of SNP1-SNP3-SNP5, A-T-T-T of SNP1-SNP2-SNP4-SNP5, and A-A-T-T of SNP1-SNP3-SNP4-SNP5), which indicated that the A allele of SNP1 (rs4919686), the A allele of SNP3 (rs4919687), the T allele of SNP4 (rs10786712), and the T allele of SNP5 (rs2486758) could be protective genetic markers of EH. Four types of alleles could decrease the risk of hypertension.

This study is the first to investigate the differences between human CYP17A1 and EH in the Chinese population and is the first haplotype-based case-control study of the correlations of CYP17A1 with EH. In the Uighur population, for the total and the men, the A allele of rs4919686 could be a susceptible genetic marker, the AC genotype could be a protective genetic marker of EH, and the AG genotype of rs4919687 and the TT genotype of rs10786712 might be protective against EH. This study was limited by the relatively small sample size, and a large number of clinical samples and investigation of other SNPs of CYP17A1 would be required for future studies. Additional studies are necessary to isolate the functional mutations that associate the polymorphism of the CYP17A1 gene with EH.

Limitations of this Study

This study was limited by the relatively small sample size, which might have led to weak statistical significance and wide CIs in the estimation of the OR.

Acknowledgements

This study was supported by Special Financial Grant from the China Postdoctoral Science Foundation (2014T70956) and Xinjiang Science and Technology Projects (201491181).


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