http://informahealthcare.com/ahb ISSN: 0301-4460 (print), 1464-5033 (electronic) Ann Hum Biol, 2015; 42(2): 184–194 ! 2014 Informa UK Ltd. DOI: 10.3109/03014460.2014.911958

RESEARCH PAPER

The association between endothelial nitric oxide synthase gene G894T polymorphism and hypertension in Han Chinese: a case-control study and an updated meta-analysis Jielin Liu1, Lijuan Wang1, Ya Liu1, Zuoguang Wang1, Mei Li1, Bei Zhang1, Hao Wang1, Kuo Liu2, and Shaojun Wen1

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1

Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, PR China and 2Emergency Department, China MeiTan General Hospital, National Mining Medical Center, Beijing, PR China Abstract

Keywords

Background: The G894T (rs1799983) polymorphism in endothelial nitric oxide synthase (eNOS/NOS3) gene has been implicated in susceptibility to essential hypertension (EH) in some studies, but no clear consensus has been reached in the Chinese population. Aims: This study aimed to investigate the association of the G894T polymorphism and EH in Han Chinese. Subjects and methods: First, a case-control study was performed involving 1525 subjects in northern Han Chinese to study the association between G894T variants and EH and then a meta-analysis was conducted of all available studies in Han Chinese. A total of 25 studies comprising 13 443 subjects were finally included in this meta-analysis. Results: The present case-control study failed to show significant association of G894T variant with EH in northern Han Chinese. The subsequent meta-analysis showed that this polymorphism might be associated with EH in Han Chinese (p50.001, OR ¼ 1.32), especially in southern Han Chinese (p50.001, OR ¼ 1.59), but not in northern Han Chinese (p ¼ 0.12, OR ¼ 1.16). The meta-regression analysis suggested that the geographic difference of subjects was related to heterogeneity (p ¼ 0.029). Conclusions: The relationship between the G894T polymorphism and hypertension in Han Chinese may be attributed to the difference in geographic background of subjects. It is necessary to carry out further research with a large sample size and focusing on gene–environment interactions.

Chinese, eNOS, essential hypertension, endothelial nitric oxide synthase, hypertension, polymorphism, single nucleotide polymorphism

Introduction Essential hypertension (EH) is considered to be a complex disease of multi-factorial origin, to which genetic and environmental factors might contribute interactively. The physiological mechanism regulating blood pressure is considered the key component in the genetic basis of EH. As a consequence, a considerable number of gene variants have been assessed as candidate determinants of the risk of hypertension. As a potent vasodilator and multi-potential molecule that maintains vascular integrity, circulating nitric oxide (NO) plays a crucial role in the regulation of endothelial function, the control of blood pressure and cardiovascular homeostasis (Moncada & Higgs, 1993). Endothelial nitric oxide synthase

Correspondence: Dr. Shaojun Wen, Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, 2 Anzhen Road, Beijing 100029, PR China. Tel: +86 10 64456463. Fax: +86 10 64416527. E-mail: [email protected]

History Received 14 January 2014 Revised 17 March 2014 Accepted 27 March 2014 Published online 19 May 2014

(eNOS/NOS3) is a protein which synthesizes NO constitutively via a reaction including the conversion of L-arginine to L -citrulline. Because endothelial nitric oxide availability is regulated at the level of synthesis, the gene that encodes eNOS is thought to be a candidate gene for hypertension (Hingorani, 2001). The eNOS gene has been localized to chromosome 7q35-36 (Marsden et al., 1993). Spanning 4.4 kb of genomic DNA, the gene comprises 26 exons that encode a 135-kD protein containing 1203 amino acids. The eNOS gene has been extensively screened for variation. As the genomic sequence of eNOS is highly polymorphic, it was of added interest to these findings to confirm which variant(s) at eNOS might have a functional potential to affect the final bioavailability of eNOS and, thus, affect the development of hypertension. The common variation identified that leads to an amino acid substitution in the mature protein is the G894T or Glu298Asp (rs1799983) variant, in which a guanine/ thymine substitution at exon 7 leads to a glutamate/aspartate substitution at position 298 (Hingorani et al., 1999). Genetic association studies regarding the relationship between G894T variant and hypertension have yielded inconsistent results, including studies conducted in China.

DOI: 10.3109/03014460.2014.911958

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Some studies reported that the T-allele was associated with increased risk of hypertension in Chinese (Dong et al., 2006; Jia et al., 2003; Li et al., 2004; Wang & Ni, 2010), while other studies reported a lack of association (Ma et al., 2006; Tang et al., 2007; Zhao et al., 2006b; Zhou et al., 2010). We thought the ambiguous claims about the association between G894T and EH require further investigation, as the T-allele frequencies in the controls were considerably different among the Chinese studies previously reported (from 2.7–40.5%). Also, data from different populations with various genetic backgrounds could lead to conflicting results. To address this issue, we conducted a case-control study to evaluate the association of eNOS G894T polymorphism with the EH among Han Chinese and then made a meta-analysis in Han Chinese.

Methods Case-control study Study population On the basis of origin, family history and migration status, all subjects in this study were collected from one geographical area which is located in northern China. The study complied with the Declaration of Helsinki, the local ethics committee of Beijing Anzhen Hospital of the Capital University of Medical Sciences had approved the research protocol. Written informed consent was obtained from each participant. Blood pressure was measured by trained and certified observers according to a common protocol adapted from procedures recommended by the European Society of Hypertension (Mancia et al., 2007). After sitting for 30 minutes in a quiet room, three measurements with a standardized mercury sphygmomanometer were performed with at least 5-minute intervals. One of the four bladders (standard, larger, smaller, paediatric) was chosen and all readings were obtained from the right arm. Hypertension was defined as an average SBP 4140 mmHg and/or an average DBP 490 mmHg and/or self-reported current treatment for hypertension with anti-hypertensive medication. The control subjects had systolic and diastolic blood pressures 5140 mmHg and 590 mmHg, respectively, and should never have been treated for hypertension (Mancia et al., 2007). They underwent a detailed interview, physical examination and laboratory analysis to exclude individuals with evidence of coronary artery disease, vascular disease, stroke, secondary hypertension, diabetes mellitus, hepatic disorders, cancer, renal diseases and endocrine diseases such as hyperthyroidism. Information on smoking and drinking habits was obtained by interview. A smoker was defined as a cigarette consumer who has smoked 100 cigarettes and a drinker was defined as an alcohol consumer who drank 12 times during the year (Gu et al., 2006). Genotyping Genomic DNA was extracted from leukocytes using the standard phenol-chloroform methods. We genotyped SNPs using the TaqMan assay. The eNOS SNP Taqman probes and primers were obtained from Applied Biosystems Assayby-Design Service for SNP genotyping. The sample DNA was

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amplified by PCR following the recommendations of the manufacturer. Thermal cycling was done on a GeneAmp PCR System 9700 thermal cycler (Applied Biosystems, Foster City, CA). Genotypes were differentiated by analysing the fluorescence levels of PCR products using an ABI PRISM 7900HT Sequence Detector (Applied Biosystems). Genotyping was performed blindly to all other data. Meta-analysis Literature search The PubMed, EMBASE, CBM (China Biological Medicine Database, http://cbm.imicams.ac.cn), CNKI (China National Knowledge Infrastructure platform, http://www.cnki.net), Wanfang (http://www.wanfangdata.com.cn/) and VIP (http:// www.cqvip.com.cn) databases were used to search for publications with the last update on December 2013. The following search criterion was used: ‘‘eNOS’’ or ‘‘endothelial nitric oxide synthase’’ or ‘‘NOS3’’, ‘‘polymorphism’’ and ‘‘hypertension’’ or ‘‘blood pressure’’, in combination with ‘‘Chinese’’ or ‘‘China’’. The search was restricted to studies on human subjects and articles in English or Chinese. The full texts of the retrieved studies were read carefully to assess their appropriateness for inclusion in the meta-analysis. References cited in the articles were also screened to identify potentially relevant studies. In studies with overlapping cases/controls, the most complete and recent results were extracted. Inclusion/exclusion criteria Studies included in the current meta-analysis had to meet the following criteria: (a) studies investigating the relationship between eNOS G894T polymorphism and hypertension in Han Chinese individuals, (b) using an unrelated case-control design (family-based studies design with linkage considerations were excluded), (c) had available genotype frequencies in both cases and controls and (d) evaluated EH only, excluding secondary forms of hypertension. Hypertension was defined as systolic blood pressure (SBP) 4140 mmHg and/or diastolic blood pressure (DBP) 490 mmHg and/or treatment with anti-hypertensive medication. Case reports, editorials and review articles were excluded. If a paper did not provide relevant data on genotype frequencies or the data provided were not sufficient, we contacted corresponding or original authors by e-mail in order to obtain the raw data. Data extraction For each study, information was collected concerning the following characters of subjects: first author, journal, year of publication, ancestry background, demographics, clinical characteristics, source of subjects, diagnostic standard for hypertension, matching, validity of the genotyping method, the number of cases and controls and the distribution of genotypes and alleles. All data were reviewed and extracted by two investigators independently (JL and KL). Discrepancies between the two investigators were discussed until consensus was achieved. If they did not come to an agreement, the third investigator (YL) adjudicated the disagreement.

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Statistical analysis Case-control study

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SPSS (Version 17.0; SPSS, Chicago, IL) was used for database management and statistical analysis. The t-test for continuous variables and the chi-square test for categorical ones were used to test difference between cases and controls. The association between the polymorphism and EH was analysed by logistic regression adjusted for covariates. Twotailed p50.05 was accepted as statistically significant. Expectation maximization (EM) algorithm was applied to estimate the missing data in the present case-control study (Little & Rubin, 1987). Hardy–Weinberg equilibrium (HWE) was tested by the chi-square test based on a web program (http:// www.ihg.gsf.de/cgi-bin/hw/hwa1.pl). Meta-analysis Odds ratios (OR) corresponding to 95% confidence interval (CI) were applied to assess the strength of association of G894T with the risk of EH. We calculated the OR and respective 95% CI for each included study according to the method of Woolf (1995). Based on the individual OR, a pooled OR was estimated only for the dominant genetic model (GT+TT vs GG), due to the facts that: (a) the effect of G894T polymorphism on plasma NO levels had previously been shown to be dominant (Yoon et al., 2000); (b) the low frequency of homozygosity for high-risk alleles would yield a considerable number of studies with zero cell counts, generating unreliable OR estimates; and (c) multiple comparisons would inflate type I error. The significance of the pooled OR was determined by the Z-test; a p value of50.05 was considered significant. In sub-group analysis, we classified all Han Chinese into Northern Han Chinese (N-Han) and Southern Han Chinese (S-Han), with the Yangtze River serving as a geographical boundary (Wen et al., 2004; Zhang et al., 2010). A 2-based Q-statistic test was performed to assess the heterogeneity between studies (Lau et al., 1997). Heterogeneity was considered significant for p50.10. Heterogeneity was quantified with the I2 metric [I2 ¼ (Qdf)/Q], which was independent of the number of studies in the meta-analysis. I2 takes values between 0–100%, with higher values denoting greater degrees of heterogeneity (I2 ¼ 0–25%, no heterogeneity; I2 ¼ 25–50%: moderate heterogeneity; I2 ¼ 50–75%: large heterogeneity; I2 ¼ 75–100%: extreme heterogeneity) (Higgins et al., 2003). The pooled OR was estimated using the fixed-effects model (MantelHaensezel) when homogeneity was observed. Otherwise, the random-effects model (DerSimonian Laird) was used (Petitti, 1994). To examine putative sources of heterogeneity, a random-effects meta-regression of the log OR was also fitted, in which the between-study variance was estimated using the restricted maximum likelihood approach (Van Houwelingen et al., 2002). The year of publication, the total sample size, the geographic distribution of subjects (S-Han and N-Han), the age of cases and controls and the percentage of men were assigned as study-level covariates. Publication bias was investigated by funnel plot, in which the standard error of log (OR) of each study was plotted

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against its OR. An asymmetric plot suggested possible publication bias. Furthermore, funnel-plot asymmetry was assessed by the method of Egger’s linear regression test (Egger et al., 1997). The significance of the intercept was determined by the t-test suggested by Egger and p50.05 was considered representative of statistically significant publication bias. Sensitivity analyses were conducted by sequentially deleting a single study each time in an attempt to identify the potential influence of the individual data set to the pooled ORs. The distribution of the genotypes in the control group was tested for Hardy–Weinberg equilibrium (HWE; p40.05). Studies with controls not in HWE were subjected to a sensitivity analysis. In addition, we also made a further sensitivity analysis considering the total sample size of each study. All statistical analyses were performed using the software Review-Manager 5.0 (Oxford, UK) and Stata version 10.0 (Stata Corporation, College Station, TX). All statistical tests were two-sided.

Results Results of the present case-control study Characteristics of the participants in the case-control study A total of 1525 unrelated participants comprising 637 normotensive controls from the general public and 888 patients with EH were recruited in this study. The baseline characteristics of our study population were presented in Table 1. Patients with EH and normotensive controls showed a similar age (p ¼ 0.070) and gender (p ¼ 0.430) distribution. Aside from blood pressure measurements, significant differences in body mass index (BMI), total cholesterol, triglyceride, glucose, blood urea nitrogen (BUN), the ratio of drinkers and the number of subjects with family history of EH were Table 1. The baseline characteristics of our study population.

Gender (male/female) Age (years) SBP (mmHg) DBP (mmHg) BMI (kg/m2) TC (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L) TG (mmol/L) Glu (mmol/L) Bun (mmol/L) CRE ALT (U/L) HR (bmp) Smoking (n) Drinking (n) Positive family history of EH

EH (n ¼ 888)

NT (n ¼ 637)

p

527/361 52.29 ± 10.26 141.40 ± 16.80 91.36 ± 11.72 27.09 ± 9.98 5.53 ± 2.91 1.63 ± 1.14 2.96 ± 1.15 2.16 ± 1.49 5.17 ± 0.46 5.74 ± 2.75 76.98 ± 19.98 26.26 ± 15.57 71.51 ± 9.16 216 230 529

365/272 51.41 ± 8.60 117.26 ± 12.32 76.45 ± 8.53 24.89 ± 3.19 4.99 ± 0.89 1.76 ± 0.99 2.93 ± 1.10 1.63 ± 1.12 4.94 ± 0.53 5.24 ± 3.26 76.43 ± 14.40 24.74 ± 13.43 71.57 ± 9.60 132 90 155

0.430 0.070 50.001 50.001 50.001 50.001 0.079 0.700 50.001 50.001 0.006 0.579 0.056 0.915 0.108 50.001 50.001

Data are presented as mean ± deviation and were compared by one-way ANOVA. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; BUN, blood urea nitrogen; CRE, creatinine; TC, total cholesterol; TG, triglyceride; HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; GLU, glucose; HR, heart rate.

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observed between the hypertensives and the normotensives. As some current-treated hypertensive patients were included in the case group, the levels of SBP and DBP in the case group were not much higher than 140/90 mmHg.

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(p ¼ 0.531, OR ¼ 1.25, 95% CI ¼ 0.62–2.49) or in subjects without family history (p ¼ 0.692, OR ¼ 1.07, 95% CI ¼ 0.75–1.53). Results of meta-analysis

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Genetic information on our case-control study

Description of studies identified in meta-analysis

The genotype distributions for G894T polymorphism were in conformity with the HWE in both cases (p ¼ 0.763) and controls (p ¼ 0.826). The allele and genotype distribution for this polymorphism are shown in the last line of Table 2. Univariate analysis indicated that no statistical significance was observed for the genotype (2 ¼ 1.336, p ¼ 0.513) and allele (2 ¼ 1.323, p ¼ 0.250) association of G894T polymorphism with EH, even under assumption of the dominant genetic modes of inheritance (p ¼ 0.258, OR ¼ 1.16, 95% CI ¼ 0.90–1.51). Logistic regression analysis was performed after adjusting for gender, age, family history, BMI, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, serum triglyceride levels, plasma glucose level, blood urea nitrogen level, smoking habits and drinking habits. We did not even observe a significant association between G894T polymorphism and EH (p ¼ 0.238, OR ¼ 0.75, 95% CI ¼ 0.47–1.21). Sub-group analyes were performed by gender and showed that no significant association was found either in male (p ¼ 0.935, OR ¼ 0.96, 95% CI ¼ 0.41–2.26) or in female (p ¼ 0.207, OR ¼ 0.53, 95% CI ¼ 0.20–1.42) participants. Similarly, in the sub-group analysis by family history of EH, the risk for hypertension was not shown to be associated with the G894T polymorphism either in subjects with family history

Through a comprehensive search, a total of 37 potentially relevant articles were initially identified. After further application of our inclusion/exclusion criteria, 24 papers were considered in this meta-analysis. Among the 13 excluded studies, six studies did not focus on the Han Chinese population (Tang et al., 2008; Wang et al., 2006a; Xu et al., 2004; Zhang et al., 2006, 2009; Zhao et al., 2008), seven papers shared the same or overlapping data. Along with the present study, there were 25 studies containing 7088 hypertensive patients and 6355 normal controls included in the present meta-analysis. Of the 25 studies, excepting the present study, there was another unpublished thesis obtained from a medical doctorate dissertation database which was a sub-database of the CNKI and Wanfang databases (Chao, 2006). The detailed characteristics of all eligible studies in the present meta-analysis are described in Table 3. Sample sizes, genotype numbers, allele frequency in both cases and controls and p values of HWE in controls are listed in Table 2. Main meta-analysis results In this meta-analysis, we observed a wide variation of T-allele frequencies across different studies, ranging from 7.2–37.2% for cases and from 2.7–40.5% for controls, respectively.

Table 2. Sample size of each study, the distribution of G894T genotypes and T-allele frequencies of cases and controls and p values of HWE in controls. Sample size

Cases

Controls

T-allele frequency (%)

Reference

Cases

Controls

GG

GT

TT

GG

GT

TT

Cases

Controls

HWE p* value

Di et al. (2002) Liu & Ha (2002) Jia et al. (2003) Tan et al. (2003) Li et al. (2004) Wang et al. (2006b) Liang et al. (2006) Zhao et al. (2006b) Zhao et al. (2006a) Dong et al. (2006) Chao (2006) Dang & Chen (2006) Ma et al. (2006) Tang et al. (2007) Li et al. (2009b) Li et al. (2009a) Niu et al. (2009) Wang & Mao (2009) Zhou et al. (2010) Wang & Ni (2010) Men et al. (2011) Li et al. (2011) Zou et al. (2011) Chen et al. (2011) Current study Total

95 103 116 112 310 277 124 501 150 97 150 184 192 184 299 235 1305 230 176 154 190 510 346 160 888 7088

95 74 136 112 151 547 100 489 70 87 150 97 122 196 266 240 1154 186 131 150 94 510 385 176 637 6355

70 54 83 73 226 233 108 404 103 41 103 117 76 91 225 156 1071 175 137 98 164 320 280 138 710 5256

25 44 29 25 81 40 11 93 40 50 39 63 89 80 71 69 192 46 38 40 19 129 62 21 169 1565

0 5 4 14 3 4 5 4 7 6 8 4 27 13 3 10 42 9 1 16 7 61 4 1 9 267

83 55 114 78 126 468 85 387 56 62 119 76 46 95 205 186 954 110 98 116 89 367 312 154 524 4968

12 19 20 26 24 74 14 97 13 23 29 21 53 83 59 47 182 64 32 30 5 89 70 20 108 1214

0 0 2 8 1 5 1 5 1 2 2 0 23 18 2 7 18 12 1 4 0 54 3 2 5 176

25 (13.2) 54 (26.2) 37 (15.9) 53 (23.7) 87 (14.0) 48 (8.7) 21 (8.5) 101 (10.1) 54 (18.0) 62 (32.0) 55 (18.3) 71 (19.3) 143 (37.2) 106 (28.8) 77 (12.9) 89 (18.9) 276 (10.6) 64 (13.9) 40 (11.4) 72 (23.4) 33 (8.7) 251 (24.6) 70 (10.1) 23 (7.2) 187 (10.5) 2099

12 (6.3) 19 (12.8) 24 (8.8) 42 (18.8) 26 (8.6) 84 (7.7) 16 (8.0) 107 (10.9) 15 (10.8) 27 (15.5) 33 (11.0) 21 (10.8) 99 (40.5) 119 (30.3) 63 (11.8) 61 (12.7) 218 (9.4) 88 (20.9) 34 (13.0) 38 (12.7) 5 (2.7) 197 (19.3) 76 (9.9) 24 (6.8) 118 (9.3) 1566

0.51 0.21 0.32 0.01 0.90 0.28 0.62 0.69 0.81 0.93 0.88 0.23 0.27 0.98 0.31 0.07 0.008 0.518 0.35 0.24 0.791 5 0.0001 0.667 0.161 0.826

HWE, Hardy–Weinberg equilibrium. *The p value of HWE determined by the 2 test.

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Table 3. Detailed characteristics of eligible studies considered in the meta-analysis.

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Authors, Year

Region

Source

Method

Diagnostic standard

Matching HWE a

Di et al., 2002

Jiangsu

H-B

PCR-RELP

SBP 160, DBP 95

Yes

Yes

Liu & Ha, 2002

Hubei

H-B

PCR-SSCP

Yesa

Yes

Jia et al. 2003

Shandong

P-B

PCR-RELP

Yesa

Yes

Tan et al., 2003

Chongqing

H-B

PCR-RFLP

SBP 140, DBP 90 SBP 140, DBP 90 SBP 140, DBP 90

Yesa

No

Li et al., 2004

Beijing

H-B

PCR-RELP

Yesa

Yes

Wang et al., 2006b

Liaoning

H-B

PCR-RELP

No

Yes

Liang et al., 2006

Guangdong

H-B

Gene chip

SBP 140, DBP 90 SBP 140, DBP 90 SBP 140, DBP 90

Yesa

Yes

Zhao et al., 2006b

PCR-RFLP

SBP 160, Yesa DBP 100

Yes

Zhao et al., 2006a

P-B Beijing, Shandong, Jilin Guangdong H-B

PCR-RFLP

Yesa

Yes

Dong et al., 2006

Guangdong

PCR-RFLP

SBP 140, DBP 90 SBP 140, DBP 90 SBP 140, DBP 90 SBP 140, DBP 90

Yesa

Yes

a

Yes

Yes

Yesb

Yes

Chao, 2006

Shandong

H-B H-B

PCR-RFLP

Dang & Chen, 2006 Hubei

H-B

PCR-RFLP

Ma et al., 2006

P-B

NASP-PCR SBP 140, DBP 90 PCR-RFLP SBP 140, DBP 90 PCR-RFLP SBP 140, DBP 90 PCR-RFLP SBP 140, DBP 90 PCR-RFLP SBP 140, DBP 90

Yesb

Yes

a

Yes

Yes

Yesb

Yes

Yesa

Yes

No

No

SBP 140, DBP 90 SBP 140, DBP 90

Yesa

Yes

Yesb

Yes

SBP 140, DBP 90 SBP 140, DBP 9 SBP 140, DBP 90

Yesa

Yes

Yesb

Yes

Yes

No

SBP 140, DBP 90 SBP 140, DBP 90

Yesa

Yes

Yesa

Yes

Chongqing

Tang et al., 2007

Hubei

H-B

Li et al., 2009b

Shandong

P-B

Li et al., 2009a

Liaoning

H-B

Niu et al., 2009

Beijing

P-B

Wang & Mao, 2009

Shandong

H-B

Genechip

Zhou et al., 2010

Xinjiang

H-B

PCR-RFLP

Wang & Ni, 2010

Yunnan

H-B

PCR-RFLP

Men et al., 2011

Jiangsu

H-B

Genechip

Li et al., 2011

Yunnan

H-B

PCR-RFLP

Zou et al., 2011

Xinjiang

P-B

PCR-RFLP

Chen et al., 2011

Ningxia

H-B

PCR-RFLP

a

Characteristics Cases were hypertensive patients with age more than 60 years old and untreated. Controls were age- and sex-matched healthy individuals. Cases and controls were age- and sex-matched. Both of the two groups were free of DM. The age of cases and controls was identified from 30–50 years. Controls were free of DM. Cases were hypertensive patients free of DM and renal diseases. Controls were matched with cases by sex and age. DM and any other chronic disease were excluded. Cases and controls were age- and sex-matched. Both of the two groups were free of DM. Cases and controls were all free of DM and other cardiovascular diseases. Cases were hypertensive patients and controls were age- and sex-matched healthy individuals with no history of cardiovascular diseases. All subjects with a clinical history of secondary hypertension, coronary heart disease and diabetes were excluded from either cases or controls. Cases and controls were all free of DM and any other chronic diseases. Cases were hypertensive patients without DM. Controls had no familial hypertension history. Cases and controls were all free of CAD and other chronic diseases. Cases were hypertensive patients with family history of hypertension. Controls were healthy individuals with no history of CAD and other chronic diseases. Cases were hypertensive individuals. Controls had no DM or any other chronic disease. Subjects suffering from renal diseases and other chronic disease were excluded. Cases and controls were all free of DM and other cardiovascular diseases. Cases were hypertensive individuals. Subjects suffering from chronic disease were excluded. Cases were hypertensive patients aged 560 years and without any anti-hypertensive treatment. Controls were subjects without familial history of hypertension. The hypertension patients with age more than 60 years were included in cases. Cases were hypertensive patients with no history of CAD and secondary hypertension. The controls were free of hypertension family history and DM. Subjects suffering from chronic disease, CAD and DM were excluded. Cases and controls were all free of DM. Controls had a negative family history of EH. Cases were all free of DM, AMI and cerebrovascular diseases. Controls were matched with cases by sex and age. Cases were free of DM. Controls were free of family history of EH. Cases and controls were all free of DM.

H-B, hospital-based study; P-B, population-based study; SBP, systolic blood pressure (mmHg); DBP, diastolic blood pressure (mmHg); PCR-RFLP, polymerase chain reaction and restriction fragment length polymorphism; PCR-SSCP, polymerase chain reaction and single strand conformation polymorphism; PCR-MS, polymerase chain reaction and mutagenically separated; NASP-PCR, nested allele-specific primer and polymerase chain reaction; CAD, coronary artery diseases; DM, diabetes mellitus; BMI, body mass index. a Age- and sex-matched; bAge-matched.

As the heterogeneity between studies was significant, the random-effects model was used to pool the results in dominant genetic models. When all the eligible studies were pooled together, the results revealed that subjects

harbouring the T-allele had a significantly higher odds of hypertension compared with subjects with the GG genotype (TT + GT vs GG: p50.001, ORRE ¼ 1.32, 95% CI ¼ 1.13– 1.55, pheterogeneity50.001, I2 ¼ 69%) (Figure 1). Taking the

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Figure 1. Meta-analysis for the association between eNOS G894T polymorphism and hypertension in Han Chinese population. ‘‘Events’’ indicates the number of (TT + GT) and ‘‘total’’ indicates the total number of GG genotype plus (GT + TT) genotype. ‘‘S-Han’’ indicates the southern Han Chinese population and ‘‘N-Han’’ indicates the northern Han Chinese population.

significant heterogeneity into consideration, the metaregression was conducted with the dependent variable logarithm OR. In meta-regression, geographic difference was significantly associated with the magnitude of OR among studies (coefficient ¼ 0.427, p ¼ 0.029), while publication year, total sample size, age of cases and controls and percentage of men in cases and controls were not correlated with heterogeneity. Sub-group analysis Considering the fact that geographic difference might bias the overall association, we furthermore divided the Han Chinese into Northern Han Chinese (N-Han) and Southern Han Chinese (S-Han) by the Yangtze River (Wen et al., 2004; Zhang et al., 2010). In S-Han Chinese, T-allele carriers had a significantly higher risk of EH than GG genotype carriers (p50.001, OR ¼ 1.59, 95% CI ¼ 1.23–2.05,

pheterogeneity ¼ 0.005, I2 ¼ 61%). No significant association was found in N-Han Chinese (p ¼ 0.12, OR ¼ 1.16, 95% CI ¼ 0.96–1.41, pheterogeneity50.001, I2 ¼ 67%) (Figure 1). Sensitivity analysis To investigate the influence of the individual dataset on the pooled ORs, we sequentially deleted a single study involved in the meta-analysis each time. The removal of any one study did not result in a movement of the point estimate outside the 95% CIs, which suggested that there was no single study influencing the pooled OR qualitatively of total subjects. Then we removed three studies due to the genotype distribution in controls deviating from HWE and found the corresponding pooled OR was not substantially altered (p ¼ 0.002, OR ¼ 1.35, 95% CI ¼ 1.11–1.63, pheterogeneity50.001, I2 ¼ 71%) (Li et al., 2011; Niu et al., 2009; Tan et al., 2003). Additional sensitivity analysis taking into account the

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sample size showed that the significant association between G894T polymorphism and EH only existed in studies with small sample sizes (total sample size less than 200) and moderate sample sizes (total sample size between 200–500). In large studies (total sample size more than 500), no significant results were found (p ¼ 0.08, OR ¼ 1.12, 95% CI ¼ 0.99–1.28) (Figure 2). In this set of sub-groups from nine large studies, no heterogeneity was observed with nearly no between-study variance (pheterogeneity ¼ 0.25, I2 ¼ 23%).

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Publication bias An important source of bias in every meta-analysis was publication bias. The Begg’s funnel plot and Egger’s test were performed for the dominant comparison (TT + GT vs GG) to evaluate the publication bias of literatures. The shape of the funnel plot did not reveal any evidence of obvious asymmetry (Figure 3) and the Egger’s test suggested the absence of publication bias among all the studies included (t ¼ 1.71, p ¼ 0.102).

Discussion We performed a case-control study and a meta-analysis on the association between eNOS gene G894T polymorphism and EH in Han Chinese. The present case-control study suggested that there was no significant association between G894T variant and EH in northern Han Chinese. The meta results showed a significant association overall and in S-Han Chinese, but not in northern Han Chinese, which was consistent with our current study. Lacolley et al. (1998) first reported that the G894T allele of eNOS was associated with essential hypertension in human subjects. Then, a number of studies were conducted to investigate the association between this polymorphism and EH in Chinese, but the results were inconsistent. The reasons for these inconsistent results were complicated, of which the ethnic specificity, environmental heterogeneity and sampling strategies might be the most important potential confounding factors. It was necessary to reconcile the conflicting results in a well-defined population. It is well known that China is a huge multi-ethnic country with 56 identified ethnic groups. Among these groups, Han Chinese is the largest ethnic group, making up over 93% of the total population (Cavalli-Sforza, 1998). In addition, in the Han population, it has been verified that there were differences in genetic background between N-Han and S-Han. Therefore, the present case-control study, involving 888 cases and 637 controls, focused only on Han Chinese residents in northern China and failed to show significant association between G894T polymorphism and the risk of hypertension, which was similar to the results of two other studies involving more than 1000 northern Han Chinese subjects (Zhao et al., 2006b; Niu et al., 2009). In contrast, most of the studies with moderate sample sizes and/or in S-Han Chinese showed the T-allele associating a higher risk of EH. It appeared likely from these studies that sample sizes and population structure may partly explain the inconsistent results. To avoid a premature conclusion, we thereby conducted a meta-analysis for all available information regarding the association of G894T polymorphism with hypertension in Han Chinese.

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The meta results showed overall significant association of G894T polymorphism with EH in Han Chinese. As the result of meta-regression suggested that heterogeneity was closely related to geographic factors (p ¼ 0.029), a sub-group analysis on stratification by geography was conducted, which showed that T-allele carriers had a higher risk of EH than those subjects with GG genotype in the S-Han sub-group, while no significant associations were observed in the N-Han sub-group. This result was consistent with the current study. The reasons for the different results between S-Han and N-Han might include the following aspects: (1) Difference in allele frequency. After taking a fresh look at individual studies in the meta-analysis, it was surprising to notice the wide distributions of G894T allele. Also the mean frequency of T-allele in S-Han (22.69%) was more than double than that in N-Han (11.28%). As suggested by a recent data-mining study, differences in allele frequency could result in a reversal of allelic effects, where a protective allele became a risk factor or an unrelated factor in replication studies (Greene et al., 2009). Historical human migration affected Southern and Northern Han Chinese differently, with the northern Han Chinese population being under strong genetic influence from other populations (Chu et al., 1998). This might be the reason for the difference in allele frequency between N-Han and S-Han. (2) Environmental factors. Hypertension is a multifactorial disease involving both environmental and genetic components. The environmental factors in the north of China, such as geographic location, climate condition, eating habits and lifestyle, were significantly different from those in the south of China. The gene–environment interaction might partly influence the findings of polymorphism-hypertension associated analysis. (3) Difference in sample size. In the present meta-analysis, there were 14 studies on northern Han Chinese involving 9915 subjects, with only 11 studies involving 3518 subjects on southern Han Chinese. This might be another reason causing the discrepancy results between S-Han and N-Han. Li et al. (2011) conducted a meta-analysis on the relationship of G894T polymorphism and risk of EH in Chinese and found that the T-allele was related to increased risk of EH. These results were similar to the present study in overall Han Chinese, but there were still some differences between the two studies. First, they did not consider geographic factors in sub-group analysis of Han Chinese. In fact, some phylogenetic studies have reported that there was a moderate spectrum of genetic variation among people living in different parts of the country (Horai et al., 1996; Omoto & Saitoh, 1997), which suggested that geographic difference should not been ignored in genetic studies. Second, contrary to previous meta-analyses, besides searching for published articles in six electronic databases, we also put equal emphasis on negative unpublished reports and brought our own data into the meta-analysis. Therefore, biases of selection/inclusion were less likely to have occurred. Finally, there were 8529 subjects of Han Chinese from 17 studies in Li et al.’s analysis, while a larger number of participants of Han nationality were included in the present analysis (13 43

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Figure 2. Association between G894T polymorphism and hypertension in Han Chinese among studies with different sample sizes. ‘‘Events’’ indicates the number of (GT + TT) genotype and ‘‘total’’ indicates the total number of GG genotype plus (GT + TT) genotype.

subjects from 25 studies). A lower frequency of TT genotype among Asians has been reported previously and is confirmed in the present meta-analysis (Miyamoto et al., 1998), which means that an analysis including a larger sample size would possibly obtain more reliable estimates of the effect of this polymorphism in these populations. After stratification by geographic difference, significant heterogeneity was still observed in the N-Han sub-group and S-Han sub-group respectively. In addition, we observed that the early studies published before 2006 on the relationship between G894T variants and hypertension in Chinese were almost statistically significant (Figure 1) and had very strong OR estimates varying from 1.23–2.63. Importantly, these

results, derived from small case-control studies, were not supported by subsequent large case-control replication studies performed in Chinese populations, with modest OR estimates varying from 0.91–1.12. Therefore, we made a further sensitivity analysis considering sample sizes and failed to show significant associations in the sub-group with large sample sizes. As a result, we could not rule out the possibility that the statistically significant findings obtained in the present meta-analyses might be the consequence of preferential publication of statistically significant studies. Further studies with large sample size and well-designed genetic-environmental interaction are warranted to lessen the potential study bias and publication bias.

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Conclusions

Figure 3. Begg’s funnel plot of the Egger’s test of G894T polymorphism for publication bias. No asymmetry was found, as indicated by the p-value of Egger’s test.

Some mechanistic studies had been published investigating the functional effect of the eNOS gene polymorphism. This variant was found to be not located in any functional consensus sequence, but computer analysis revealed that the G894T mutation resulted in a conformational change in the eNOS protein from helix to tight turn (Miyamoto et al., 1998). Also it has been reported that eNOS with aspartate but not glutamate at position 298 was cleaved, resulting in the generation of N-terminal 35-kDa and C-terminal 100-kDa fragments (Tesauro et al., 2000), which might be associated with the activity of eNOS. However, in the following studies, these results were verified to be an artifact of sample preparation and unlikely to occur intracellularly, which demonstrated that the Asp substitution at 298 did not have a major effect in modulating eNOS activity (Fairchild et al., 2001; McDonald et al., 2004). In addition, the results of the association between G894T polymorphism and NO level were also conflicting (Nejatizadeh et al., 2008; Persu et al., 2002; Zdoukopoulos et al., 2011). As the present evidence on the relationship between G894T polymorphism and the biological activity of eNOS were not sufficient, further studies would be needed to investigate the molecular biology mechanism of G894T variant. Strengths and limitations Despite the clear strength of our study including large sample sizes, some limitations require discussion. First, in the present case-control study as well as the following meta-analysis, we only focused on hypertension as a categorical trait rather than continuous blood pressure, which might lose some information. Second, in the meta-analysis, because of data limitations, we were unable to retrieve data on various potential confounders (age, sex, BMI, smoking, drinking, etc.) from the original publications, which have been regarded as effective modulators for the development of hypertension. Third, gene–gene and gene–environment interactions were not analysed in the present meta-analysis, due to the lack of information from the original studies. It was possible that the potential role of eNOS G894T polymorphism was diluted or masked by gene–gene or gene–environment interactions.

In summary, the present study and the meta-analysis had brought up the argument that the G894T variants might be less pre-disposing to hypertension in the Han Chinese than previously estimated. The further meta-analysis shows a significant association between this polymorphism and EH in southern Han Chinese, but not in northern Han Chinese. The results of the meta-regression analysis indicate that a probable gene–environment interaction occurred, which suggested that the G894T variant might be only prominent in specific environmental conditions. In addition, a significant association was not observed in studies with large sample sizes. Therefore, further family-based tests for association, together with large case-control studies that investigate combinations of polymorphisms and gene–environment interaction, should be performed to make conclusive claims about the genetics of hypertension.

Acknowledgements We are very grateful and thank Dr Zhou Jiapeng for scientific editing. We also thank all participants in this study.

Declaration of interest The authors report no conflict of interest. This work was supported by the Beijing Natural Science Foundation in People’s Republic of China [7120001, 7102045].

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