Abstract
BACKGROUND
An animal study reported that TGF-β1 maturation was linked to the homeostasis of blood pressure and elastogenesis of essential hypertension (EH). Recent advances require further research of TGF-β1 receptor in EH.
METHODS
A case–control study comprised of 2,012 adult hypertension case patients and 2,210 adult control subjects was conducted, and the association with blood pressure was further tested in children. Logistic regression and calculated genetic risk score were used to evaluate the effects of one single nucleotide polymorphism (SNP) and multiple SNPs on EH, respectively.
RESULTS
The genetic risk score of 10 SNPs showed a significant association with hypertension; the odds ratio of the upper quartile vs. the lower quartile was 1.282 (P = 4.67×10−3). rs7256241 in miR-518 was significantly associated with diastolic blood pressure (DBP) change in control subjects (P = 0.002), and this association was also observed in children (P = 0.04). The systolic blood pressure (SBP) and DBP of female patients taking reserpine were higher with the C and G alleles of rs3773661 (P = 0.004) and rs7256241 (P = 0.002), respectively. In patients taking Zhen Ju Jiang Ya tablets, SBP and DBP decreased linearly with rs749794 (P = 0.004 and P = 0.048, respectively). SBP decreased linearly with rs1155705 (P = 0.007) and rs11709624 (P = 0.04), but increased with rs1036096 (P = 0.03) in male patients. In male patients taking Jiang Ya tablets, SBP increased linearly with rs11709624 (P = 0.007), DBP increased linearly with rs1155705 (P = 0.03) whereas decreased with rs7256241 (P = 0.04).
CONCLUSIONS
Our results suggest that TGFBR2 and miR-518 harbor variants that increase the risk of EH and affect blood pressure homeostasis as well as efficacy of antihypertensive agents.
Keywords: association studies, blood pressure, hypertension, miR-518 gene, transforming growth factor β1, TGF-β1 receptor 2 gene.
Cardiovascular disease has become a leading cause of mortality worldwide over the last few decades.1,2 Essential hypertension (EH) was reported as one of the most important modifiable risk factors for cardiovascular disease.3 EH and the efficacy of an antihypertensive agent are influenced by varying combinations of genetic and environmental factors,4 and achieving blood pressure (BP) control requires an integrated approach with multiple medications. Recently, genome-wide association studies have been conducted to identify susceptibility genes for hypertension.5,6
Large artery stiffening and increased peripheral resistance caused by artery remodeling have been shown to be the primary causes of BP increases and organ impairment.7 Transforming growth factor betas (TGF-βs) are potent regulatory cytokines with diverse functions of cell proliferation, differentiation, embryogenesis, and angiogenesis.8 Animal studies have reported that certain genetic defects of Emilin1 and Firillin1 in the TGF-β1 signaling pathway are accompanied by abnormal vessel structure and hypertension.9,10 We first reported that Emilin1 and Fibrillin1 were associated with hypertension in the human population,11,12 and this finding was subsequently replicated in Mongolian and Japanese populations.13,14 Additionally, the TGF-β1 pathway regulates vascular development and remodeling through TGF-β1 receptor1 (TGFBR1) and receptor2 (TGFBR2),15 and defects in these 2 receptors have been reported to be associated with thoracic aortic aneurysms in humans16–19 and pulmonary hypertension in mice.20,21 Therefore, we sought to determine whether these TGF-β1 receptor genes affect genetic susceptibility to EH or BP in humans.
MicroRNAs (miRNAs) are encoded in the chromosomal DNA and transcribed as longer stem-loop-like precursors.22,23 The recent identification of miRNAs expressed in specific cardiac and vascular cell types suggested important regulatory roles during cardiomyocyte differentiation, vessel formation, and cardiac hypertrophy. Evidence has showed that miR-126 is a key positive regulator of angiogenic signaling in vascular development of zebrafish and mice.24–26 Aldosterone inhibits miR-208a in hypertension and increases cardiac hypertrophy in hypertensive mice.27 Furthermore, by repressing the mineralocorticoid receptor gene NR3C2, miR-124 and miR-135a were found to participate in the renin–angiotensin–aldosterone system and BP regulation.28 Therefore, further study is warranted to assess the genetic effects of miRNA on BP, hypertension, and the modulation of antihypertensive response.
In this study, we tested the association of 7 tagging single nucleotide polymorphism (tagSNPs) of TGFBR2 and the variation of rs7256241 in miR-518 with hypertension and BP in a case–control study of adult populations and further replicated the positive association of rs7256241 and BP in a population of children.
METHODS
Subjects
The adult subjects of the case–control study were recruited by the epidemiological cluster sampling approach in 2 townships approximately 20 kilometers apart in Yixing county, Jiangsu province, China. In this study, 2,012 participants with systolic BP (SBP) ≥140mm Hg and/or diastolic BP (DBP) ≥90mm Hg or who were currently being treated with antihypertensive medication were selected. A total of 2,210 age group– (within 5 years) and sex-matched subjects with free hypertension were selected as control subjects from the same resource population.
Trained research staff administered a standard questionnaire to obtain the demographic characteristics of the participants and a history of taking antihypertensive medication. All participants received physical examinations, and the weight, height and 3 BP measurements were obtained from each participant by trained and certified observers according to a standard protocol. A mercury sphygmomanometer was used to measure the participants’ BP after a rest of at least 5 minutes in the sitting position. The participants were advised to avoid alcohol, cigarette smoking, coffee, tea, and exercise for at least 30 minutes before their BP measurements. Blood samples were drawn after 10 hours of overnight fasting to measure total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and glucose (GLU).
Additionally, a child population was recruited from Suqian, in the northern area of Jiangsu province, by the epidemiological cluster sampling approach. Six primary and junior high schools in urban and rural areas were surveyed, and 2,045 children aged 5–15 years were investigated. An epidemiological questionnaire was used to obtain demographic characteristics. The BP, body height, and weight of the children were measured. Blood samples were collected from a total 1,805 children, including 962 boys and 843 girls. The positive correlation between the polymorphism and BP in the adult population was verified by assessing the Z scores of SBP and DBP in children, which were calculated for children according to World Health Organization reference29 using specific ages, sexes, and heights.
All of the adult subjects and the parents or guardians of children provided written informed consent, and the study protocol was approved by the Research Ethics Committee of Nanjing Medical University.
A summary of the characteristics of all subjects included in this study are presented in Table 1.
Table 1.
Comparison of demographic and clinical characteristics between case patients and control subjects
| Characteristics | Group | Case–control study | Children | |||
|---|---|---|---|---|---|---|
| Hypertension (n = 2,012) | Normotension (n = 2,210) | t/χ2 | P value | |||
| Sex | Male | 845 (42.0%) | 870 (39.4%) | 3.024 | 0.082 | 964 (53.3%) |
| Female | 1,167 (58.0%) | 1,340 (60.6%) | 844 (46.7%) | |||
| Age, y | 61.72±10.79 | 59.40±10.64 | 7.022 | <0.001 | 10.08±2.92 | |
| Blood pressure, mm Hg | SBP | 143.32±13.75 | 123.83±11.28 | 50.075 | <0.001 | 98.62±12.05 |
| DBP | 88.29±8.11 | 78.38±6.02 | 44.739 | <0.001 | 63.95±9.44 | |
| TC, mmol/L | 4.92±1.05 | 4.80±1.00 | 3.686 | <0.001 | 3.46±0.77 | |
| TG, mmol/L | 1.86±1.58 | 1.54±1.21 | 7.272 | <0.001 | 0.77±0.39 | |
| HDL-C, mmol/L | 1.37±0.33 | 1.36±0.33 | 0.838 | 0.40 | — | |
| LDL-C, mmol/L | 2.79±0.88 | 2.66±0.74 | 5.177 | <0.001 | — | |
| GLU, mmol/L | 5.80±1.99 | 5.49±1.69 | 5.381 | <0.001 | 4.31±0.91 | |
| BMI, kg/m2 | 24.66±3.52 | 23.74±3.21 | 8.816 | <0.001 | 17.49±3.15 | |
| Smoking | Yes | 503 (25%) | 510 (23.2%) | 1.834 | 0.18 | — |
| No | 1,509 (75%) | 1,687 (76.8%) | ||||
| Drinking | Yes | 451 (22.4%) | 448 (20.4%) | 2.513 | 0.11 | — |
| No | 1,561 (77.6%) | 1,747 (79.6%) | ||||
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides.
Categories of antihypertensive medications
In this study, a single antihypertensive treatment of at least 2 weeks was included to analyze the correlation between current BP and genetic variants. Compound reserpine, Zhen Ju Jiang Ya tablets, and Jiang Ya tablets were the 3 main drugs used in our study population. The main components of compound reserpine are hydrochlorothiazide, promethazine hydrochloride, and dihydralazine sulfate. Zhen Ju Jiang Ya tablets contain traditional Chinese medicine consisting of nacre powder, wild chrysanthemum cream powder, rutin, hydrochlorothiazide, and clonidine hydrochloride. Jiang Ya tablets are also a traditional Chinese medicine.
SNP selection and genotyping
The TGFBR2 gene (Gene ID: 7048) is mapped to chromosome 3p22 and spans 87,637 base pairs. The SNPs were identified using the database of Han Chinese in Beijing, China, population of the International HapMAP Project (HapMap Data Rel 24/phase II Nov08, dbSNP b126). In a previous study,30 we evaluated the association between 2 SNPs (rs6785358 and rs764522) covering 5kb of the TGFBR2 promoter and hypertension. In this study, we selected SNPs with a minor allele frequency ≥5% covering the TGFBR2 coding sequence and a 2-kb downstream region to establish tagSNPs using Haploview software (version 4.1; Broad Institute of MIT and Harvard, MA), and rs6785358 and rs764522 were also included. The threshold of pairwise linkage disequilibrium was set as r 2 = 0.80. Finally, 7 tagSNPs (rs9850060, rs3773645, rs749794, rs3773661, rs11709624, rs1155705, and rs1036096) in TGFBR2 were predicted to have potential function in FastSNP (http://fastsnp.ibms.sinica.edu.tw/pages/input_CandidateGeneSearch.jsp).
In addition, we performed a bioinformatics prediction analysis of the miRNA targets of TGFBR2 gene using the Microsm Targets web application (http://www.ebi.ac.uk/enright-srv/microcosm/cgi-bin/targets/v5/search.pl); 4 miRNAs were predicted to bind to the TGFBR2 gene, including hsa-miR-518, hsa-miR-515, hsa-miR-34, and hsa-miR-219. Only miR-518 was found to have a G/T polymorphism of rs7256241 with a minor allele frequency >0.05. Therefore, a total of 8 SNPs were selected to test for EH in this study (bioinformatics analysis are shown in Supplementary Table S1).
DNA sampling and genotyping
The blood samples were drawn into ethylenediamine tetraacetic acid–containing receptacles. Genomic DNA was isolated using proteinase K digestion and phenol-chloroform extraction. 5′-Nuclease TaqMan assays (Life Technologies, Carlsbad, CA) were used to genotype the polymorphisms on an ABI PRISM 7900HT Sequence Detection system (Applied BioSystems, Foster City, CA). The primers and probes for the TaqMan assays were designed using Primer Express Oligo Design software version 2.0 and are available upon request as TaqMan Pre-Designed SNP Genotyping Assays. Each plate contained blank samples as negative controls for the confirmation of genotyping quality. There was 100% consistency in a 5% sample of duplicate testing, and the genotyping success rates were >99.9%.
Statistical analyses
The allele frequencies and genotype distributions of the case patients and control subjects were compared using 2-sided χ2 tests. Among the control subjects, the genotype frequencies were tested using Fisher exact χ2 test for Hardy–Weinberg equilibrium.31 The association between the genotypes and hypertension was evaluated by computing the odds ratios (ORs) and 95% confidence intervals (CIs). A multiple logistic regression model was used to adjust for covariables. Additionally, a general linear model was applied to compare the BP levels (means ± SD) between the genotypes. All of the statistical analyses were performed using SPSS for Windows version 13.0 (SPSS, Chicago, IL).
The Haplo.score values generated using R software (http://cran.r-project.org/) as outlined by Schaid et al.32 were used to test the associations between the statistically inferred haplotypes and EH. The Haplo.glm approach was also used to obtain the ORs of risk haplotypes.33 Furthermore, genetic risk scores (GRS) were calculated to summarize the joint effects of multiple SNPs on hypertension and BP.34 Statistical significance was set at P < 0.05 (2 tails). The p.adjust (pairwise) false discovery rate (FDR) function in R software was used to adjust P values for multiple comparisons.
The power of this case–control study was calculated using the Power and Sample Size Calculation software (Dupont WD, Plummer WD: http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/Power Sample Size). At the 5% significance level, we had 94.1% power to detect an OR of 1.3 with a lower minor allele frequency of 0.212 of rs9850060 in the control subjects.
RESULTS
Characteristics of the participants
The demographic and clinical characteristics of adult and children populations are summarized in Table 1. Although an age group–matching (5 years) approach was conducted, the hypertension case patients were an average 2.32 years older than the control subjects (P < 0.001). The case patients generally had higher SBP, DBP, body mass index, TC, TG, low-density lipoprotein cholesterol, and GLU levels than the control subjects. The mean of high-density lipoprotein cholesterol and the proportions of men, smoking status, and drinking status were not significantly different between the adult case patients and control subjects.
Single locus association analysis
The observed genotype distributions of all 8 SNPs in the control subjects did not deviate significantly from Hardy–Weinberg equilibrium (P > 0.05). There were no significant differences in the genotype and allele frequencies of any of the 8 SNPs observed between case patients and control subjects even after adjustment for sex, age, BMI, TC, TG, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, GLU, drinking, and smoking (Table 2). The results of the dominant and recessive model association analyses are provided in Supplementary Table S2.
Table 2.
Distributions of genotype and allelic frequency between case and control groups
| SNPs | Group | Additive model | Allele gene | P valueb | ||
|---|---|---|---|---|---|---|
| WT/ HT/MT | OR (95% CI)a P value | Major/minor | OR (95% CI) P value | |||
| rs9850060 | AA/AG/GG | A/G | ||||
| Case | 1,295/631/85 | 0.923 (0.828–1.028) | 3,221/801 | 0.924 (0.831–1.027) | 0.22 | |
| Control | 1,379/718/109 | 0.15 | 3,476/936 | 0.14 | ||
| rs3773645 | CC /CG/GG | C/G | ||||
| Case | 912/883/215 | 1.052 (0.957–1.156) | 2,707/1,313 | 1.043 (0.952–1.143) | 0.73 | |
| Control | 1,026/964/219 | 0.29 | 3,016/1,402 | 0.36 | ||
| rs749794 | CC /CT/TT | C/T | ||||
| Case | 905/888/219 | 0.957 (0.871–1.051) | 2,698/1,326 | 0.968 (0.885–1.060) | 0.74 | |
| Control | 969/994/247 | 0.36 | 2,932/1,488 | 0.49 | ||
| rs3773661 | GG/GC/CC | G/C | ||||
| Case | 996/867/179 | 0.938 (0.852–1.033) | 2,789/1,225 | 0.952 (0.868–1.045) | 0.27 | |
| Control | 1,024/977/209 | 0.19 | 3,025/1,395 | 0.30 | ||
| rs11709624 | GG/GC/CC | G/C | ||||
| Case | 1,008/843/156 | 0.933 (0.846–1.028) | 2,859/1,155 | 0.925 (0.843–1.016) | 0.76 | |
| Control | 1,073/928/207 | 0.16 | 3,074/1,342 | 0.11 | ||
| rs1155705 | GG/GA/AA | G/A | ||||
| Case | 959/864/188 | 0.918 (0.834–1.009) | 2,782/1,240 | 0.928 (0.847–1.018) | 0.73 | |
| Control | 1,012/961/236 | 0.08 | 2,985/1,433 | 0.11 | ||
| rs1036096 | CC/CT/TT | C/T | ||||
| Case | 694/968/350 | 1.036 (0.948–1.132) | 2,356/1,668 | 1.014 (0.930–1.106) | 0.24 | |
| Control | 780/1,043/387 | 0.43 | 2,603/1,817 | 0.75 | ||
| rs7256241 | TT/TG/GG | T/G | ||||
| Case | 770/956/286 | 1.013 (0.925–1.110) | 2,496/1,528 | 1.008 (0.923–1.101) | 0.60 | |
| Control | 861/1,027/321 | 0.74 | 2,749/1,669 | 0.85 | ||
Abbreviations: CI, confidence interval; HT, heterozygote; MT, mutant type; OR, odds ratio; SNP, single nucleotide polymorphism; WT, wild-type.
aAdjusted for age, sex, body mass index, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, glucose, drinking, and smoking.
b P value of χ2 test for comparison of allele frequencies between case and control groups.
Haplotype analyses
Further haplotype analyses of rs9850060 and rs11709624 (r 2 < 0.2) showed that compared with the common haplotype Hap1 (A-G) in the control group, there were no significant associations between haplotype Hap2 (A-C) and Hap3 (G-C) and hypertension after adjusting for covariables (Supplementary Table S3).
Stratification analyses
Furthermore, stratification analyses by sex, age, drinking, and smoking were conducted, and results with statistical correlation are listed in Table 3. The adjusted ORs of the additive and dominant model were 0.779 (95% CI = 0.615–0.987) and 0.739 (95% CI = 0.558–0.980) for rs9850060, respectively, and 0.844 (95% CI = 0.726–0.981) and 0.807 (95% CI = 0.664–0.98) for rs3773661, respectively. The recessive model of rs11709624 and the additive model of rs1155705 showed statistical significance in the population aged <55 years and the nonsmoking group, with adjusted ORs of 0.673 (95% CI = 0.462–0.981) and 0.896 (95% CI = 0.803–1.000), respectively. However, none of the FDR-adjusted P values reached statistical significance.
Table 3.
Stratified analysis by sex, age, smoking and drinking
| SNPs | Stratum | Group | WT/HT/MT | Genotype OR (95% CI)a P value | ||
|---|---|---|---|---|---|---|
| Additive | Dominant | Recessive | ||||
| rs9850060 | AA/AG/GG | |||||
| Drinking | Case | 303/130/18 | 0.779 (0.615–0.987) | 0.739 (0.558–0.980) | 0.743 (0.383–1.440) | |
| Control | 269/154/22 | 0.04 | 0.04 | 0.38 | ||
| No drinking | Case | 992/501/67 | 0.967 (0.856–1.092) | 0.971 (0.839–1.123) | 0.905 (0.646–1.267) | |
| Control | 1.100/560/86 | 0.59 | 0.69 | 0.56 | ||
| rs3773661 | GG/GC/CC | |||||
| Male | Case | 421/355/69 | 0.844 (0.726–0.981) | 0.807 (0.664–0.98) | 0.810 (0.577–1.137) | |
| Control | 389/396/85 | 0.027 | 0.031 | 0.22 | ||
| Female | Case | 545/512/110 | 1.007 (0.889–1.142) | 1.013 (0.860–1.192) | 1.000 (0.756–1.321) | |
| Control | 635/581/124 | 0.91 | 0.88 | 0.998 | ||
| rs11709624 | GG/GC/CC | |||||
| <55 years | Case | 289/235/47 | 0.879 (0.744–1.037) | 0.917 (0.736–1.142) | 0.673 (0.462–0.981) | |
| Control | 394/330/95 | 0.13 | 0.44 | 0.04 | ||
| ≥55 years | Case | 719/608/109 | 0.964 (0.854–1.087) | 0.959 (0.823–1.116) | 0.944 (0.711–1.253) | |
| Control | 679/598/112 | 0.55 | 0.59 | 0.69 | ||
| rs1155705 | GG/GA/AA | |||||
| Smoking | Case | 237/215/51 | 0.983 (0.810–1.193) | 0.989 (0.766–1.277) | 0.951 (0.623–1.451) | |
| Control | 239/218/52 | 0.86 | 0.93 | 0.82 | ||
| No smoking | Case | 722/649/137 | 0.896 (0.803–1.000) | 0.885 (0.766–1.021) | 0.829 (0.651–1.056) | |
| Control | 768/737/182 | 0.049 | 0.09 | 0.13 | ||
Abbreviations: CI, confidence interval; HT, heterozygote; MT, mutant type; OR, odds ratio; SNP, single nucleotide polymorphism; WT, wild-type.
aAdjusted for age, sex, body mass index, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, glucose, drinking, and smoking.
GRS analysis of the joint effects of multiple SNPs on hypertension
GRS values were calculated for a total of 10 SNPs in miR-518 and TGFBR2, including rs6785358 and rs764522. The SNPs were recoded into same direction risk score and then summed to calculate the GRS. The joint genetic effect of these SNPs on hypertension was then estimated (Supplementary Table S4). An association analysis indicated that the P value of the trend test for the association of GRS quartiles and hypertension was 0.04. The frequency ratio of hypertension case patient to control subject in the GRS upper quartile of the hypertension group (33.1% vs. 30.8%) was significantly different from that of the GRS lower quartile group (21.29% vs. 25.0%), and the adjusted OR was 1.282 (95% CI = 0.1.079–0.522; P = 4.67×10−3 (Table 4). The FDR-adjusted P value was 0.01.
Table 4.
Association analysis of genetic risk scores and hypertension
| Quartile | No. | Hypertension | P value | OR (95% CI) a | |
|---|---|---|---|---|---|
| Case patient | Control subject | ||||
| Q1, ≤17 | 965 | 439 (21.29%) | 526 (25.0%) | — | — |
| Q2, 18–19 | 902 | 446 (22.3%) | 456 (21.6%) | 0.158 | 1.143 (0.949–1.377) |
| Q3, 20–21 | 932 | 456 (22.8%) | 476 (22.6%) | 0.078 | 1.184 (0.981–1.429) |
| Q4, ≥22 | 1,312 | 663 (33.1%) | 649 (30.8%) | 4.67×10−3 | 1.282 (1.079–1.522) |
Abbreviations: CI, confidence interval; OR, odds ratio.
aAdjusted for age, sex, body mass index, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, glucose, drinking, and smoking.
Quantitative trait analysis of BP
Because antihypertensive treatment may affect the measured SBP and DBP, we divided the subjects into an antihypertensive-treated group, an untreated hypertension group, and a control group (Supplementary Table S5). The results of the linear regression analysis are presented in Supplementary Figure S1. DBP showed a linear decrease with the rs749794 and rs7256241 variations in the antihypertensive-treated group and control group, respectively (P = 0.03 and P = 0.002, respectively), after adjustment for all covariables. In contrast, SBP showed a linear increase with the rs3773661 variations in the antihypertensive-treated group, with a P value of 0.01 (Supplementary Figure S2). In addition, a GRS for BP consisting of 10 SNPs was calculated because all single SNPs were encoded into the same direction of effect on SBP and DBP in the control population (Supplementary Table S6). No statistical correlation was detected between the GRS for BP and SBP or DBP, with standardized coefficients of 0.035 and 0.038, respectively (P = 0.10 and PI = 0.08, respectively). No significant difference in BP was observed between the GRS for BP quartiles (P > 0.10).
Further stratification analysis showed that in the female hypertensive participants taking compound reserpine, SBP showed a linear increase with the rs3773661 variation G to C (P = 0.004), and the TT and GG genotype carriers of rs7256241 had higher DBP than TG genotype (P = 0.002) (Supplementary Figure S3). In hypertensive participants taking Zhen Ju Jiang Ya Tablets, both the SBP and DBP decreased linearly with the rs749794 variation C to T (P = 0.004 and P = 0.048, respectively), and this correlation trend was replicated in male hypertensive participants (P = 0.002). The SBP decreased linearly with the rs11709624 variation G to C (P = 0.04) but increased with the rs1036096 variation C to T (P=0.027) in male hypertensive participants. The SBP decreased linearly with the rs1155705 variation G to A in the male hypertensive subgroup (P = 0.01). However, in male hypertensive subgroup, the rs7256241 TT and GG genotype carriers had a lower SBP than the male TG genotype carrier (P = 0.04) (Supplementary Figure S4a,b). In male hypertensive participants taking Jiang Ya tablets, the SBP linearly increased with the rs11709624 variation G to C (P = 0.007), and DBP increased linearly with the rs1155705 variation G to A (P = 0.03) but decreased with the rs7256241 variation T to G (P = 0.04) (Supplementary Figure S5a,b). All of these data are provided in Supplementary Table S7.
Association study of rs7256241 and BP in children
The Z scores of SBP and DBP were compared between the different genotypes of rs7256241 in children (Table 5), but the result did not reach statistical significance (P = 0.09 for DBP). Further stratification analysis by area showed that the Z score of DBP increased linearly with the rs7256241 variation in rural children, with a P value of 0.04 after adjustment for covariables, including age, sex, BMI, TC, TG, and GLU.
Table 5.
Comparison of Z scores of blood pressure among genotypes of rs7256241 in children
| Genotype | Total (Z score) | Rural (Z score) | Urban (Z score) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No. | SBP | DBP | No. | SBP | DBP | No. | SBP | DBP | |
| TT | 744 | −0.46±1.13 | 0.23±0.83 | 401 | −0.94±0.97 | 0.0±0.83 | 343 | 0.11±1.05 | 0.48±0.75 |
| TG | 797 | −0.44±1.08 | 0.32±0.82 | 456 | −0.77±0.99 | 0.17±0.83 | 341 | 0.00±1.04 | 0.52±0.76 |
| GG | 229 | −0.48±0.97 | 0.24±0.70 | 128 | −0.89±0.79 | 0.11±0.74 | 101 | 0.05±0.91 | 0.40±0.63 |
| F | 0.194 | 2.425 | 2.831 | 3.355 | 0.446 | 1.007 | |||
| P valuea | 0.824 | 0.089 | 0.059 | 0.035 | 0.640 | 0.366 | |||
Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure.
aAdjusted for age, sex, body mass index, total cholesterol, triglycerides, and glucose.
DISCUSSION
Recent series studies have shown that the TGF-β1 pathway regulates the development and remodeling of blood vessels and the pathogenesis of hypertension.9,26,35 The downstream TGF-β1 receptors, TGFBR1 and TGFBR2, have recently been associated with hereditary connective tissue disorders with widespread vascular involvement.8,35,36 Therefore, we were interested in determining whether the TGF-β1 receptors affect genetic susceptibility to hypertension.
In this study, stratification analysis indicated that rs9850060, rs3773661, rs11709624, and rs1155705 were associated with EH in the drinking, male, aged <55 years, and nonsmoking subpopulations, respectively. The results suggest that TGFBR2 harbors multiple variants with weak or small effects on EH. The upper quartile of the GRS of 10 SNPs showed a significant association with the risk of hypertension (P = 4.67×10−3), and the association remained statistically significant after FDR correction. The results indicated that genetic polymorphisms of TGFBR2 most likely contribute to the risk of EH. This study therefore further promotes the utility of GRS to evaluate multiple common variations in complex traits such as EH.
In contrast with the association between SNPs and the risk of hypertension, the results also indicated that rs7256241 was significantly associated with DBP in the control population (P = 0.002), and this finding was further verified in a population of rural children (P = 0.04). Although the direction of the association is opposite in these 2 groups, this finding suggests that the rs7256241 variant may affect DBP in relatively earlier stage and may have different genetic effects at different age. Additionally, a functional prediction of the rs7256241 variant (http://www.cbrc.jp/research/db/TFSEARCH.html) showed that the G allele variation was predicted to produce a transcription factor binding site for c-Myb. Recently, there has been substantial evidence regarding the roles of miRNAs in the development, modeling, and function of various cardiovascular tissues.37–39 The loss of miR-14 in vascular smooth muscle cells causes significant reductions in BP due to decreased vascular contractility.40 An improved understanding of the interplay between miRNA and genetic variation in the miRNA binding sites may reveal part of this missing BP heritability.41 Therefore, further functional research on the regulation of c-Myb by miR-518 is warranted to evaluate the relationship between changes in TGFBR2 expression and DBP homeostasis.
Compound reserpine is one of the most commonly used medications in rural Chinese hypertension patients. Previously, it was reported that reserpine attenuates the increase in TGF-β1 expression in rats.42 The results of our study indicated that patients with variants of rs3773661, rs749794, rs7256241, rs11709624, and rs1155705 had differential response to treatment with compound reserpine, Zhen Ju Jiang Ya tablets, and Jiang Ya tablets. Although strict randomized grouping was absent in the subgroup analyses of the antihypertensive medications, the significant association between several SNPs and BP with obvious numeric trends warrants further prospective follow-up studies on the genetic effects of TGFBR2 and miR-518 on the efficacy of current popular antihypertensive drugs.
Our study may have the following limitations. First, the FDR-adjusted P values of the stratification analysis failed to reach significance. Second, we did not detect serum miR-518; whether the rs7256241 variant affects miR-518 expression requires further functional research. Third, the positive effects of the TGFBR2 SNPs and rs7256241 in miR-518 on the efficacy of antihypertensive agents may have affected the baseline BP measurements. Last, potential biases, including information bias, selective bias, and confounding bias often distort the results of epidemiological association studies. Regardless of the limitations, this study is the first to report a positive association between miR-518 and DBP and an association between miR-518 and TGFBR2 with the efficacy of antihypertensive drugs. This finding may provide candidate pharmacogenetic markers for antihypertensive efficacy, although potential confounding factors may be involved.
In conclusion, the findings of this study suggest that the TGFBR2 gene and miR-518 gene impart a cumulative effect on the risk of hypertension and the efficacy of reserpine, Zhen Ju Jiang Ya tablets, and Jiang Ya tablets. These findings provide new insights into the molecular mechanism of BP homeostasis and the role of TGFBR2 and miR-518 in TGF-β1 signaling pathway in the pharmacogenetics of antihypertensive medications.
SUPPLEMENTARY MATERIALS
Supplementary materials are available at American Journal of Hypertension (http://ajh.oxfordjournals.org).
DISCLOSURE
The authors declared no conflict of interest.
Supplementary Material
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (No. 81273165, No. 30800947, and grant No.81072367); the Natural Science Foundation of Jiangsu Province (No. BK2011776); Science & Technology Program of Wuxi (No. ZD1011 and CSEW1N1112); and the Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Jinfeng Chen, Xianghai Zhao, and Hairu Wang equally contributed to this work.
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