IMR Press / RCM / Volume 22 / Issue 3 / DOI: 10.31083/j.rcm2203096
Open Access Original Research
Association of polymorphisms in endothelial dysfunction-related genes with susceptibility to essential hypertension in elderly Han population in Liaoning province, China
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1 Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, 110122 Shenyang, Liaoning, China
2 Department of Nursing, Shengjing Hospital of China Medical University, 110004 Shenyang, Liaoning, China
*Correspondence: wyliu@cmu.edu.cn (Wanyang Liu); liuxp@sj-hospital.org (Xiuping Liu)
These authors contributed equally.
Academic Editor: Takatoshi Kasai
Rev. Cardiovasc. Med. 2021, 22(3), 895–901; https://doi.org/10.31083/j.rcm2203096
Submitted: 8 April 2021 | Revised: 2 June 2021 | Accepted: 21 June 2021 | Published: 24 September 2021
(This article belongs to the Special Issue State-of-the-Art Cardiovascular Medicine in Asia 2021)
Copyright: © 2021 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
Abstract

Hypertension is a complex disease which is mainly influenced by genetic factors. Recently, genome-wide association study (GWAS) found three novel endothelial dysfunction-related sites: Vascular endothelial growth factor A (VEGFA) rs9472135, Faciogenital dysplasia 5 (FGD5) rs11128722, Zinc Finger C3HC-type Containing 1 (ZC3HC1) rs11556924. Endothelial dysfunction is one of the early events in pathophysiology of essential hypertension. To investigate the association of endothelial dysfunction-related genes with essential hypertension, we conducted a case-control study of 431 patients with hypertension and 345 controls. The polymorphisms were detected using Taqman Probe. The alleles and genotypes of ZC3HC1 rs11556924 and VEGFA rs9472135 were not statistically different between the two groups, while the allele of FGD5 rs11128722 was different [P = 0.045, OR = 1.265, 95% CI = (1.009–1.586)], especially in the male [P = 0.035, OR = 1.496, 95% CI = (1.037–2.158)]. Analyzing the different of genotype distribution of 3 SNPs in the two groups under different genetic models, the genotypes of FGD5 rs11128722 showed difference in male under dominant model [P = 0.049, OR = 1.610, 95% CI = (1.018–2.544)]. The polymorphism of FGD5 rs11128722 had a significant difference in Body Mass Index (BMI) among different genotypes; In the additive genetic model, BMI of GA genotype was higher than that of GG (P = 0.038); GA + AA was higher than GG in the dominant genetic model (P = 0.011). In our study, we found that the polymorphisms of VEGFA rs9472135 and ZC3HC1 rs11556924 may not significantly associated with the risk of essential hypertension, and FGD5 rs11128722 may increase the risk of it, especially in elderly men.

Keywords
Essential hypertension
Polymorphism
Vascular endothelial dysfunction
1. Introduction

Hypertension is one of the main diseases endangering human health. It is estimated that the number of untreated patients with hypertension will increase to 1.56 billion by 2025 in the world’s adult population [1, 2], more than 90% of which are primary (essential) hypertension. Hypertension is a complex multifactorial disease, which is influenced by genetic, environmental and demographic factors. The diagnosis of essential hypertension is made when no other cause for increased blood pressure is found. Previous study has shown that the influence of genetic factors on the change of blood pressure reaches 30% to 50%. Therefore, identifying the susceptibility gene loci of hypertension will help to understand the pathological and physiological characteristics of the disease [3].

In recent genome-wide association study (GWAS) studies in European population, three novel sites related to endothelial dysfunction were found [4, 5]: Vascular endothelial growth factor A (VEGFA) rs9472135, Faciogenital dysplasia 5 (FGD5) rs11128722, Zinc Finger C3HC-type Containing 1 (ZC3HC1) rs11556924. Endothelial dysfunction is one of the early events in pathophysiology of essential hypertension, which is considered to promote subclinical target organ damage and promote the progress of atherosclerosis [6]. VEGFA is produced by vascular endothelial cells (EC), which is usually called VEGF [7]. Previous studies have shown that the imbalance of VEGF signaling pathway is regarded as the key mediator of tumor angiogenesis, and hypertension is the most common adverse reaction of cancer patients based on VEGF pathway inhibitor treatment. The relationship between VEGF polymorphisms and essential hypertension has been studied in different regions and nationalities. However, the conclusions reported at home and abroad are not consistent. FGD5 is a member of the FGD family of guanine nucleotide exchange factor. The FGD family consists of FGD1-6 and Cdc42-GEF (FRG) related to FGD1. Some studies have shown that FGD5 is expressed by endothelial cells to maintain VEGFA signal transduction and endothelial chemotaxis by inhibiting the proteasome dependent degradation of VEGFR2, and activate Cdc42[8]. Abnormal activation of Cdc42 may lead to the occurrence of certain diseases, such as tumor, cardiovascular disease, diabetes mellitus and neurodegenerative disease. ZC3HC1 is a mammalian E3 ligase that regulates mitotic entry time [9, 10]. At present, only two studies have verified the relationship between ZC3HC1 and hypertension [11, 12]. Kunnas T et al. believe that ZC3HC1 rs1556924 is associated with 50-year-old patients with essential hypertension.

Due to the influence of different ethnicity and environment, it is necessary to conduct this study to confirm whether VEGFA rs9472135, FGD5 rs11128722 and ZC3HC1 rs11556924 are the susceptible sites of essential hypertension in Chinese Han population.

2. Materials and methods
2.1 Study population

A total of 431 patients with hypertension and 345 controls were enrolled from Fushun and Panjin city in Liaoning province, China. There were 302 (38.9%) male and 474 (61.1%) female in our study. In summary, participants with hypertension who met the following criteria were recruited: (1) systolic blood pressure (SBP) of at least 140 mmHg or diastolic blood pressure (DBP) of at least 90mmHg, or treatment with antihypertensive medication; (2) aged 60 years and above; (3) all patients were free from severe liver, kidney and acute or chronic infectious diseases, hyperthyroidism or hypothyroidism, systemic arteriopathy, various tumors and other cardio-cerebrovascular diseases and metabolic diseases.

2.2 Data collection and clinical evaluation

Clinical data including gender, age, height, weight, waist circumference, Body Mass Index (BMI), SBP, DBP were recorded in heath datasheet. Ten milliliters of peripheral blood of each fasting study individual was collected in EDTA vacutainers. Biochemical profiles, including fasting blood glucose (FBG), total cholesterol (TC) and triglyceride (TG) were done on automated biochemical analyzer (Murray, BS-820).

2.3 DNA isolation and genotyping

Genomic DNA was extracted from blood samples using a Blood Genetic DNA Mini Kit (CWBIO, Beijing, China). The concentration of the DNA was tested by NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA), then stored at –80 ºC for future use. Genotyping of 3 SNPs in all participants was conducted using TaqmanTM Probe (TaqmanTM SNP Genotyping Assays; Applied Biosystems, Foster City, CA, USA) and a QuantStudioTM 6 Flex Real-Time PCR (Applied Biosystems, Foster City, CA, USA). The experiment was carried out strictly according to the method of Wang’s report [13]. Total system was 5 μL, as following: 2.0 μL of purified genomic DNA, 2.5 μL of TaqPathTM ProAmpTM Master Mixes (Applied Biosystems, Foster City, CA, USA), 0.1 μL of 40 × SNP Genotyping Assay, and 0.4 μL of deoxyribonuclease-free water. The appropriate PCR thermal cycling conditions was set: maintained 5 minutes for initial denature/enzyme activation, followed by 40 cycles of 5 seconds at 95 ºC for denaturation, and 1 minutes at 60 ºC for annealing and extension. After PCR amplification, an endpoint plate read was conducted using QuantStudioTM 6 Flex Real-Time PCR System. The genotype of each sample can be determined based on the fluorescence signal. Basic information of 3 SNPs are shown in Table 1.

Table 1.Basic information of three SNPs.
Gene SNPs Chromosome position Risk allele Function
ZC3HC1 rs11556924 7 : 130023656 C>T missense mutation
FGD5 rs11128722 3 : 14916619 G>A intronic mutation
VEGFA rs9472135 6 : 43842065 T>C intronic mutation
SNPs, single nucleotide polymorphisms.
2.4 Statistical analysis

The Epidata 3.1 software package was used for database design, data entry, and data check. Statistical analysis was performed with SPSS 21.0 software (IBM, ASiaAnalytics Shanghai, Shanghai, China). Quantitative variables were expressed as mean ± standard deviation. Qualitative variables were expressed as counts and proportions. Independent-samples t-test and one-way analysis of variance (ANOVA) were used to compare the mean differences for continuous variables across levels of a categorical variable, and two-two comparison between groups using SNK-q. Univariate and multivariate logistic regression analysis was used to test the genetic susceptibility of each SNP to hypertension in genetic models. A value of P < 0.05 was considered as statistically significant.

2.5 Data availability statement

With the permission of the corresponding authors and their institutions, combined with the relevant documents, all data used for analysis will be shared after ethics approval if requested by other investigators for reasonable purposes of replicating procedures and results.

3. Results
3.1 Comparison of general clinical indicators between essential hypertension and healthy people

The clinical and demographic characteristics of 431 patients and 345 controls were reported in Table 2. The patients and controls had no significant difference in age, gender and TG. However, the patients had significantly higher levels of BMI, waist circumference, SBP, DBP, FBG, TC (P < 0.05). The proportion of overweight and obesity in the case group was higher than that in the control group.

Table 2.Clinical and epidemiological characteristics of the subjects.
Variable Patients Controls P value
(n = 431, 55.4%) (n = 345, 44.6%)
Gender [n (%)]
Male 167 (38.70) 135 (39.10) 0.941
Female 264 (61.30) 210 (60.90)
Age (year, x¯± s) 68.20 ± 9.69 66.64 ± 14.98 0.094
BMI (kg/m2) 24.05 ± 3.21 22.89 ± 2.81 <0.001
Normal (18.5–24 kg/m2) 222 (51.51) 233 (67.53)
Overweight (24–28 kg/m2) 163 (37.82) 99 (28.70) <0.001
Obesity (28 kg/m2) 46 (10.67) 13 (3.77)
Waist circumference (cm) 80.48 ± 16.89 76.95 ± 16.73 0.004
SBP (mmHg) 151.23 ± 16.40 124.27 ± 10.44 <0.001
DBP (mmHg) 91.27 ± 9.80 78.60 ± 5.98 <0.001
FBG (mmol/L, x¯± s) 5.44 ± 2.49 4.80 ± 2.33 <0.001
TG (mmol/L, x¯± s) 2.25 ± 7.03 2.17 ± 12.31 0.917
TC (mmol/L, x¯± s) 5.24 ± 3.24 4.57 ± 2.15 0.001
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; TG, triglyceride; TC, total cholesterol.
3.2 Distribution of alleles and genotypes of 3 SNPs in the two groups

It revealed that there was a strong association between alleles of FGD5 rs11128722 with hypertension risk [P = 0.045, OR = 1.265, 95% CI = (1.009–1.586)], while the genotype carrying rate did not; After stratification by gender, only the alleles distribution of male patients had a statistically significant difference [P = 0.035, OR = 1.496, 95% CI = (1.037–2.158)] (Table 3). In contrast, there was no significant difference in the alleles and genotypes of VEGFA rs9472135, ZC3HC1 rs115556924 between the two groups; Even after stratification by gender, no significant difference had been found (Table 3).

Table 3.Distribution of ZC3HC1 rs11556924, FGD5 rs11128722, VEGFA rs9472135 alleles and genotypes in two groups.
Alleles and genotypes Patients (%) Controls (%) OR (95% CI) P value
rs11556924
C 818 (94.9) 660 (95.7) 1.183 (0.735–1.903) 0.549
T 44 (5.1) 30 (4.3)
CC 388 (90.1) 315 (91.3) 0.487
CT 42 (9.7) 30 (8.7)
TT 1 (0.2) 0 (0.0)
Female
C 502 (95.1) 404 (96.2) 1.308 (0.692–2.471) 0.432
T 26 (4.9) 16 (3.8)
CC 238 (90.2) 194 (92.4) 0.421
CT 26 (9.8) 16 (7.6)
TT 0 (0.0) 0 (0.0)
Male
C 316 (94.6) 256 (94.8) 1.042 (0.508–2.135) 1
T 18 (5.4) 14 (5.2)
CC 150 (89.8) 121 (89.6) 0.54
CT 16 (9.6) 14 (10.4)
TT 1 (0.6) 0 (0.0)
rs11128722
G 607 (70.4) 518 (75.1) 1.265 (1.009–1.586) 0.045
A 255 (29.6) 172 (24.9)
GG 208 (48.3) 190 (55.1) 0.107
GA 191 (44.3) 138 (40)
AA 32 (7.4) 17 (4.9)
Female
G 376 (71.2) 310 (73.8) 1.139 (0.854–1.519) 0.381
A 152 (28.8) 110 (26.2)
GG 130 (49.2) 111 (52.9) 0.641
GA 116 (43.9) 88 (41.9)
AA 18 (6.8) 11 (5.2)
Male
G 231 (69.2) 208 (77.0) 1.496 (1.037–2.158) 0.035
A 103 (30.8) 62 (23.0)
GG 78 (46.7) 79 (58.5) 0.088
GA 75 (44.9) 50 (37.0)
AA 14 (8.4) 6 (4.4)
rs9472135
T 767 (89.0) 622 (90.1) 1.133 (0.815–1.574) 0.505
C 95 (11.0) 68 (9.9)
TT 338 (78.4) 281 (81.4) 0.247
TC 91 (2.1) 60 (17.4)
CC 2 (0.5) 4 (1.2)
Female
T 473 (89.6) 383 (91.2) 1.204 (0.777–1.865) 0.44
C 55 (10.4) 37 (8.8)
TT 209 (79.2) 175 (83.3) 0.075
TC 55 (20.8) 33 (15.7)
CC 0 (0.0) 2 (1.0)
Male
T 294 (88.0) 239 (88.5) 1.049 (0.637–1.728) 0.899
C 40 (12.0) 31 (11.5)
TT 129 (77.2) 106 (78.5) 0.929
TC 36 (21.6) 27 (20.0)
CC 2 (1.2) 2 (1.5)
3.3 Genotype distribution of 3 SNPs in the two groups under different genetic models

Besides the genotypes of FGD5 rs11128722 was different in male under dominant model [P = 0.049, OR = 1.610, 95% CI = (1.018–2.544)], there was no significant difference between genotype distribution of 3 SNPs in the two groups under different genetic models (Table 4).

Table 4.Distribution of ZC3HC1 rs11556924, FGD5 rs11128722, VEGFA rs9472135 genotypes in different gene models.
SPNs Genetic model OR (95% CI) P value
rs11556924 Additive model TT VS. CC NA NA
CT VS. CC 1.305 (0.797, 2.136) 0.321
Dominant model CT + TT VS. CC 1.336 (0.818, 2.182) 0.268
Recessive model TT VS. CT + CC NA NA
Female Additive model TT VS. CC NA NA
CT VS. CC 1.325 (0.691, 2.540) 0.421
Dominant model CT + TT VS. CC 1.325 (0.691, 2.540) 0.421
Recessive model TT VS. CT + CC NA NA
Male Additive model TT VS. CC NA NA
CT VS. CC 0.922 (0.433, 1.964) 0.849
Dominant model CT + TT VS. CC 0.980(0.464, 2.067) 1
Recessive model TT VS. CT + CC NA NA
rs11128722 Additive model AA VS. GG 1.719 (0.925, 3.197) 0.096
GA VS. GG 1.264 (0.942, 1.697) 0.134
Dominant model GA + AA VS. GG 1.314 (0.989, 1.746) 0.061
Recessive model AA VS. GA + GG 1.547 (0.844, 2.837) 0.182
Female Additive model AA VS. GG 1.397 (0.633, 3.084) 0.436
GA VS. GG 1.126 (0.773, 1.638) 0.567
Dominant model GA + AA VS. GG 1.156 (0.804, 1.661) 0.46
Recessive model AA VS. GA + GG 1.324 (0.611, 2.868) 0.565
Male Additive model AA VS. GG 2.363 (0.864, 6.464) 0.1
GA VS. GG 1.519 (0.944, 2.444) 0.093
Dominant model GA + AA VS. GG 1.610 (1.018, 2.544) 0.049
Recessive model AA VS. GA + GG 1.967 (0.735, 5.266) 0.244
rs9472135 Additive model CC VS. TT 0.416 (0.076, 2.286) 0.419
TC VS. TT 1.261 (0.878, 1.811) 0.235
Dominant model TC + CC VS. TT 1.208 (0.847, 1.724) 0.323
Recessive model CC VS. TC + TT 0.397 (0.072, 2.183) 0.415
Female Additive model CC VS. TT NA NA
TC VS. TT 1.396 (0.867, 2.246) 0.191
Dominant model TC + CC VS. TT 1.316 (0.823, 2.103) 0.289
Recessive model CC VS. TC + TT NA NA
Male Additive model CC VS. TT 0.822 (0.114, 5.932) 1
TC VS. TT 1.096 (0.625, 1.920) 0.777
Dominant model TC + CC VS. TT 1.077 (0.623, 1.861) 0.889
Recessive model CC VS. TC + TT 0.806 (0.112, 5.799) 1
SNPs, single nucleotide polymorphisms; NA, not available.
3.4 Analysis of blood pressure difference in SNP loci of 3 genes under three genetic models

There was no significant difference in SBP and DBP among different genotypes of VEGFA rs9472135, FGD5 rs11128722 and ZC3HC1 rs115556924 (Table 5).

Table 5.Comparison of blood pressure values among different genotypes of three gene polymorphisms.
SNPs Genetic model Number of cases SBP P value DBP P value
rs11556924 Additive model CC 703 139.20 ± 18.96 85.59 ± 10.28
CT 72 139.46 ± 23.66 0.624 86.11 ± 11.98 0.921
TT 1 158 85
Dominant model CT + TT 703 139.71 ± 23.60 0.857 86.10 ± 11.90 0.695
CC 73 139.20 ± 18.96 85.59 ± 10.28
Recessive model TT 1 158 0.334 85 0.951
CT + CC 775 139.22 ± 19.43 85.64 ± 10.44
rs11128722 Additive model GG 398 138.39 ± 19.98 85.03 ± 10.63
GA 329 139.68 ± 18.14 0.224 86.00 ± 9.77 0.102
AA 49 143.22 ± 22.79 88.14 ± 12.65
Dominant model GA + AA 378 140.14 ± 18.81 0.21 86.28 ± 10.20 0.097
GG 398 138.39 ± 19.98 85.03 ± 10.63
Recessive model AA 49 143.22 ± 22.79 0.139 88.14 ± 12.65 0.083
GA + GG 727 138.98 ± 19.17 85.47 ± 10.25
rs9472135 Additive model TT 619 139.00 ± 19.22 85.67 ± 10.31
TC 151 140.66 ± 20.33 0.269 85.60 ± 11.05 0.822
CC 6 128.83 ± 15.21 83.00 ± 7.10
Dominant model TC + CC 157 140.21 ± 20.25 0.486 85.50 ± 10.92 0.855
TT 619 139.00 ± 19.22 85.67 ± 10.31
Recessive model CC 6 18.83 ± 15.21 0.188 83.00 ± 7.10 0.534
TC + TT 770 139.33 ± 19.44 85.66 ± 10.46
SNPs, single nucleotide polymorphisms; SBP, systolic blood pressure; DBP, diastolic blood pressure.
3.5 Analysis of differences of BMI among SNP loci of 3 genes under three genetic models

There was no significant difference in BMI of VEGFA rs94721352 and ZC3HC1 rs111556924 among different genotypes under different genetic models; In FGD5 rs11128722, the BMI of genotype GA was higher than that of GG genotype in additive model (P = 0.038), and GA + AA genotype was higher than that of GG genotype in the dominant model (P = 0.011) (Table 6).

Table 6.Comparison of BMI among different genotypes of three gene polymorphisms.
SNPs Genetic model Number of cases BMI P value
rs11556924 Additive model CC 703 23.53 ± 3.05
CT 72 23.64 ± 3.53 0.449
TT 1 19.72
Dominant model CT + TT 703 23.53 ± 3.05 0.902
CC 73 23.58 ± 3.54
Recessive model TT 1 19.72 0.217
CT + CC 775 23.54 ± 3.09
rs11128722 Additive model GG 398 23.26 ± 3.10
GA 329 23.85 ± 3.08a 0.038
AA 49 23.68 ± 2.94a
Dominant model GA + AA 378 23.82 ± 3.06 0.011
GG 398 23.26 ± 3.10
Recessive model AA 49 23.68 ± 2.94 0.737
GA + GG 727 23.53 ± 3.10
rs9472135 Additive model TT 619 23.54 ± 3.11
TC 151 23.54 ± 3.04 0.828
CC 6 22.76 ± 3.15
Dominant model TC + CC 157 23.51 ± 3.04 0.895
TT 619 23.54 ± 3.11
Recessive model CC 6 22.76 ± 3.15 0.539
TC + TT 770 23.54 ± 3.09
SNPs, single nucleotide polymorphisms; BMI, body mass index.
aComparison with the additive model GG of rs11128722.
4. Discussion

Hypertension is a complex disease that comes about as a result of the interaction between genetic and environmental factors. Endothelial dysfunction is one of the early events in pathophysiology of essential hypertension. It’s reported that endothelial dysfunction seems to promote microvascular dysfunction and may be an early alteration in ischemic heart disease and the atherosclerosis process [14]. Genetic factors play a very important role in cardiovascular disease. Previous studies have shown that some SNPs may be protective factors for myocardial ischemia and microvascular dysfunction [15, 16]. Further research is needed to determine the relationship between endothelial dysfunction-related sites and hypertension. The present study was designed to investigate the association of three endothelial dysfunction-SNPs (VEGFA rs9472135, FGD5 rs11128722, and ZC3HC1 rs11556924) with essential hypertension in elderly Han populations in Liaoning province. In our study, we found that FGD5 rs1128722 may be a risk factor for the pathogenesis of essential hypertension in Han population in Liaoning Province, especially in the elderly male population. ZC3HC1 rs111556924 and VEGFA rs9472135 genes were not associated with hypertension.

FGD5 rs11128722 may be associated with hypertension, and allele A may be a risk factor for essential hypertension, especially in male population. It is necessary to further study the mechanism of FGD5 in regulating blood pressure, or whether FGD5 rs11128722 can change the gene and protein expression of FGD5. The results showed that the polymorphism of FGD5 rs11128722 was statistically significant in comparing BMI among different genotypes. In the additive model, the BMI of genotype GA was higher than that of GG genotype; and in the dominant model, the BMI of genotype GA + AA was higher than that of GG genotype. As obesity is a risk factor for hypertension, it is suggested that FGD5 rs11128722 may be associated with the risk of hypertension.

ZC3HC1 is a mammalian E3 ligase, which can regulate mitotic entry time [9], and may promote the development of carcinogenesis together with established active carcinogenic proteins [17]. ZC3H31 may be closely related to essential hypertension through endothelial dysfunction. The mutant ZC3HC1 rs115556924 (C>T) is located in 7q32.2 and encodes a non-synonymous substitution in ZC3HC1 gene. A recent study by López-Mejías et al. [18] showed that carotid intima-media thickness (CIMT) value of patients with rheumatoid arthritis carrying ZC3HC1 rs115556924 TT genotype was significantly higher than that of CC genotype. CIMT is a marker of subclinical atherosclerosis. Their hypothesis is that changes in the stability and functional properties of ZC3HC1 protein may cause endothelial dysfunction.

Studies have shown that FGD5 maintains VEGFA signal transduction and endothelial cell chemotaxis by inhibiting the proteasome dependent degradation of VEGFR2 [19]. VEGFA signal transduction mediated by VEGFR2 can maintain vascular homeostasis and ensure the survival of endothelial cells, and it is also necessary for vascular remodeling [20]. There are three ways for VEGFA to regulate blood pressure. The most important one is that VEGF activates VEGFR2 receptor and releases no through phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt)/eNOS pathway, thus regulating blood pressure [21]. If the content of VEGF in serum is too high, it will lead to excessive mitosis of endothelial cells in local microcirculation, and eventually the endothelial function will be damaged. The imbalance of VEGF signal transduction pathway is regarded as the key mediator of tumor angiogenesis. Therefore, a variety of drugs targeting VEGF and its receptor have been developed for the treatment of different types of tumors, and with the wide application of these drugs in clinical practice, they will lead to obvious cardiovascular side effects. Hypertension is the most common adverse reaction based on VEGF pathway inhibitors in cancer patients. The relationship between VEGF polymorphism and essential hypertension has been studied in different regions and nationalities. In Korean population, VEGF-2578C>A and -1154G>A polymorphisms have a protective effect on hypertension susceptibility [22]. VEGFA 2549 (18) I/D and VCAM1 rs3917010 were associated with SBP and DBP in Russian Tatars [23]. Observational data from a large number of participants showed that VEGF-1154G/A polymorphism can prevent hypertension [22, 24]. In another study, the -1154G/A polymorphism was described as a risk factor for hypertensive nephropathy [25].

There are some limitations in this study. (1) The sample size of this study is small, only two areas of Liaoning Province are selected for physical examination, and there is no sampling survey with larger population base, so the results of this study need to be further confirmed; (2) Hypertension is a multifactorial disease affected by both environmental and genetic factors. This study only explored the relationship between different gene loci and hypertension from the genetic aspect. In the future, the interaction between genetic factors such as ZC3HC1, VEGFA, FGD5 and other environmental factors will provide an important theoretical basis for the etiology and pathogenesis of essential hypertension; (3) Although some studies have discussed the other loci of the three genes, the loci selected in this study are the first time to be verified in Chinese population, and there are problems such as difference in ethnicity. It is expected that further studies with larger sample size will confirm the results of this study.

In a word, the polymorphisms of VEGFA rs9472135 and ZC3HC1 rs11556924 may not significantly associated with the risk of essential hypertension, and FGD5 rs11128722 may increase the risk of it, especially in elderly men.

Author contributions

NT, MWL, YW, WYL, XPL designed the research study. NT, MWL, YW, FXZ, FFN, MKS, XTW, YTY, ML, LW, LXO, ZBY, WYL, XPL collected the data. FXZ, FFN, MKS, XTW, YTY, ML, LW, LXO, ZBY provided help and advice on the data analysis. NT, MWL analyzed the data and drafted the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All subjects have written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by Ethics Committee of China Medical University.

Acknowledgment

We would like to thank patients participation and thank to all the peer reviewers for their opinions and suggestions.

Funding

Supported by the National Natural Science Foundation of China (grant no. 81573240).

Conflict of interest

The authors declare no conflict of interest.

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