Journal of Human Hypertension (2015), 1–5 © 2015 Macmillan Publishers Limited All rights reserved 0950-9240/15 www.nature.com/jhh
ORIGINAL ARTICLE
Association of gene polymorphisms in RANKL/RANK/OPG system with hypertension and blood pressure in Chinese women P Duan, Z-M Wang, J Liu, L-N Wang, Z Yang and P Tu Recent studies have revealed that the receptor activator of nuclear factor-kappa B ligand/RANK/osteoprotegerin (RANKL/RANK/ OPG) system has an important role in vascular calcification, which is contributory to various cardiovascular diseases and intimately linked to the regulation of blood pressure. Therefore, we performed a case–control study to investigate the associations of 21 single-nucleotide polymorphisms (SNPs) in the TNFSF11, TNFRSF11A and TNFRSF11B genes in the RANKL/RANK/OPG system with hypertension and blood pressure in post-menopausal Chinese women. In this study, 503 hypertensive patients and 509 normal controls were recruited. Genotyping was performed using the high-throughput Sequenom genotyping platform. The results showed that two SNPs (rs6567270 and rs4603673) in the TNFRSF11A were associated with hypertension (P = 0.010 and P = 0.013, respectively) and systolic blood pressure (P = 0.024 and P = 0.023, respectively). One SNP (rs9646629) in the TNFRSF11A showed significant association with diastolic blood pressure (P = 0.031). The results of this study suggest that TNFRSF11A but not TNFSF11 and TNFRSF11B genetic variation is associated with hypertension and blood pressure in Chinese women. The findings provide additional support for the genetic role of RANKL/RANK/OPG system in hypertension and blood pressure regulation. Journal of Human Hypertension advance online publication, 26 March 2015; doi:10.1038/jhh.2015.13
INTRODUCTION Hypertension is a serious public health problem in the world because of its high prevalence and consequently increased risk of various cardiovascular disease.1 It is estimated that the prevalence of hypertension in Chinese adults was 18% in 2002 according to the National Survey,2 and this prevalence increased to 29.6% in 2010.3 Hypertension and blood pressure are complex genetic traits, which are determined by genetic and environmental factors. Twin studies have indicated that blood pressure is highly heritable, ranging from 48% to 60% for systolic blood pressure (SBP), and from 34% to 67% for diastolic blood pressure (DBP).4 The heritabilities of SBP and DBP are 46% and 30% in Han Chinese, respectively.5 Over recent years, extensive work has been done in the area of genetics of hypertension. An increasing number of hypertension-associated genetic variants have been reported6–8 but the specific genes involved in it have not yet been fully defined. Receptor activator of nuclear factor-kappa B ligand (RANKL), its cognate receptor RANK and the decoy receptor osteoprotegerin (OPG) are members of tumor necrosis factor superfamily, and they are encoded by gene TNFSF11 (gene map locus 13q14), TNFRSF11A (gene map locus 18q22.1) and TNFRSF11B (gene map locus 8q24), respectively. The RANKL/RANK/OPG signaling system has a key role in the regulation of activation, differentiation and survival of osteoclasts and it is necessary for pathophysiology of bone remodeling.9 Recent data have shown that the RANKL/ RANK/OPG system is also involved in vascular systems.10,11 OPG is considered to be a marker and risk factor for cardiovascular disease,12 and circulating OPG levels are associated with blood
pressure,13 peripheral arterial disease,14 coronary artery disease and cardiovascular mortality.15 Recently, further studies have showed that the RANKL/RANK/ OPG system has an important role in vascular calcification and atherosclerosis.16 Moreover, vascular calcification is considered to be a major cause of isolated systolic hypertension and contributory to various cardiovascular diseases.17 Therefore, the RANKL/RANK/OPG system may be involved in hypertension and blood pressure regulation, and genetic variations in the genes encoding RANKL, RANK and OPG may have roles in hypertension. In the present study, we performed a case–control study to investigate the associations of single-nucleotide polymorphisms (SNPs) and haplotypes in the TNFSF11, TNFRSF11A and TNFRSF11B genes in the RANKL/RANK/OPG system with blood pressure and hypertension in Chinese females. MATERIALS AND METHODS Participants We recruited 1082 unrelated post-menopausal Chinese women (aged 42– 89 years) of Han ethnicity from 10 community centers within Nanchang during the years 2011 and 2012. All of the participants were collected by the department of endocrinology and metabolism from a local population. Demographic information was collected by interview. Age at enrollment, income, educational level, smoking history, alcohol intake, and a detailed medical history, especially the history of hypertension, were collected through a questionnaire. Daily physical activity were assessed by homemade semi-quantitative questionnaire according to the following scale: high physical activity was defined as regular exercise, such as playing ball games or running at least 30 min at least three times a week; moderate
Department of Endocrinology and Metabolism, Nanchang Key Laboratory of Diabetes, The Third Hospital of Nanchang, Nanchang city, People’s Republic of China. Correspondence: Professor P Tu, Department of Endocrinology and Metabolism, Nanchang Key Laboratory of Diabetes, The Third Hospital of Nanchang, NO.2, Xiangshan road, Xihu District, Nanchang city, Jiangxi province 330009, People’s Republic of China. E-mail: [email protected] Received 29 August 2014; revised 9 December 2014; accepted 20 January 2015
TNFRSF11A gene polymorphisms and hypertension P Duan et al
2 physical activity was defined as mild exercise, such as long walks, dancing or house cleaning; low physical activity was assigned to women who did not participate in any of above-mentioned activities. Participants were excluded from the study if they suffered from diseases deemed to affect blood pressure, such as adrenal diseases and chronic kidney disease. Finally, a total of 503 unrelated hypertensive patients and 509 controls for the case–control study were selected. Written informed consent was obtained from each participant and the protocol was approved by the Ethics Committee of The Third Hospital of Nanchang.
Anthropometric and blood pressure measurement Blood pressure was measured in the sitting position using a mercury sphygmomanometer. The reported SBP and DBP readings were the average of the three readings measured at 10 min intervals. Hypertension status was defined as SBP ⩾ 140 mm Hg and/or DBP ⩾ 90 mm Hg and/or treatment with antihypertensive medication. Height and weight were measured and body mass index was calculated as weight (in kilograms) divided by height (in meters) squared.
Blood collection and measurements Blood samples were collected from each participant in the morning after a minimum of 10 h fasting. Measurements of serum biochemical parameters, including fasting plasma glucose, total cholesterol, triglycerides, highdensity lipoprotein cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, blood uric acid, blood urea nitrogen and creatinine were performed on the day of the collection. All the serum biochemical profiles were measured using a automatic biochemistry analyzer (ADVIA 2400, Siemens Inc., Munich, Germany).
SNP selection and genotyping Tagging SNPs with r2 ⩾ 0.80 and minor allele frequency (MAF) ⩾ 0.10 were selected using a haplotype tagging strategy and obtained from the database on Han Chinese in HapMap. A total of 21 SNPs within three candidate genes (six in TNFSF11, nine in TNFRSF11A and six in TNFRSF11B) were included in the study. Genomic DNA was extracted from whole blood samples using the QIAamp DNA Mini Kit (Qiagen Inc., Hilden, Germany). Genotyping was performed using the high-throughput Sequenom genotyping platform (MassARRAY MALDI-TOF MS system, Sequenom Inc., San Diego, CA, USA).
Statistical analyses The differences in basic characteristics between the normotensive and hypertensive groups were tested by two-sample t-test for quantitative variables and χ2-test for categorical ones. The concordance of the tagging SNPs to the Hardy–Weinberg equilibrium was checked using χ2-test. Linear regression was used to analyze SBP and DBP as quantitative traits. Hypertension was analyzed as a binary trait using the logistic regression. All association tests were based on the additive model, age, body mass index, smoking history, alcohol intake, daily physical activity and other risk factors were considered to be covariates in all the models and adjusted in process of correlation analysis. All P-values reported were two-sided, and those o0.05 were considered statistically significant. Bonferroni correction was used in multiple comparisons. Data were analyzed using SPSS version 13.0 for Windows (SPSS Inc., Chicago, IL, USA). Linkage disequilibrium structure was visualized using Haploview 4.2.18 The significance of each haplotype was analyzed by PLINK software.19
RESULTS The basic characteristics of the hypertensive (n = 503) and normotensive (n = 509) individuals are shown in Table 1. The mean SBP and DBP were significantly higher in the hypertensive group (153.0 ± 15.1 mm Hg and 84.8 ± 11.8 mm Hg) than in the normotensive group (122.0 ± 11.4 mm Hg and 74.4 ± 8.5 mm Hg). Significant differences were found in the basic characteristics including weight, body mass index, alanine aminotransferase, blood uric acid, triglycerides, high-density lipoprotein cholesterol, and fasting plasma glucose between the hypertensive and normotensive subjects. Journal of Human Hypertension (2015), 1 – 5
Table 1.
Basic characteristics of the subjects
Characteristics
Case–control analysis Normotensive Hypertensive P-value
n Age (years) Educational level Primary (%) Secondary (%) University (%) Income ⫹1000 (yuan per month) 1000–2000 (yuan per month) ⩾ 2000 (yuan per month)
509 57.4 ± 9.2
503 58.4 ± 9.0
0.082 0.28
163 (32.0%) 185 (36.8%) 296 (58.2%) 274 (54.5%) 50 (9.8%) 44 (8.7%) 0.056 63 (12.4%) 61 (12.1%) 308 (60.5%) 337 (67.0%) 138 (27.1%) 105 (20.9%)
Height (m) Weight (kg) BMI (kg m−2) Smoking history Never (%) Past (%) Current (%)
154.9 ± 5.9 57.0 ± 8.0 23.8 ± 2.9
154.2 ± 6.1 0.073 59.9 ± 9.8 o0.001 25.2 ± 3.5 o0.001 0.94 495 (97.2%) 488 (97.0%) 2 (0.4%) 2 (0.4%) 12 (2.4%) 13 (2.6%)
Alcohol intake (%) Never (%) Past (%) Current (%)
472 (92.7%) 464 (92.2%) 3 (0.6%) 4 (0.8%) 34 (6.7%) 35 (7.0%)
0.92
Physical activity High physical activity (%) 11 (2.2%) 12 (2.4%) Moderate physical activity (%) 384 (75.4%) 392 (77.9%) Low physical activity (%) 114 (22.4%) 99 (19.7%) ALT (U l − 1) AST (U l − 1) BUN (mmol l − 1) Cr (μmol l − 1) URCA (μmol l − 1) TC (mmol l − 1) TG (mmol l − 1) HDL-C (mmol l − 1) LDL-C (mmol l − 1) FPG (mmol l − 1) Serum calcium (mmol l − 1) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg)
26.3 ± 21.2 29.3 ± 15.6 5.08 ± 1.15 69.0 ± 13.4 285.2 ± 72.3 5.45 ± 1.24 1.25 ± 0.90 1.65 ± 0.37 3.22 ± 0.82 4.88 ± 1.63 2.38 ± 0.11 122.0 ± 11.4 74.4 ± 8.5
30.8 ± 25.6 30.1 ± 13.7 4.84 ± 1.22 69.2 ± 14.0 310.6 ± 73.5 5.58 ± 1.19 1.46 ± 0.91 1.58 ± 0.35 3.30 ± 0.84 5.28 ± 1.51 2.38 ± 0.12 153.0 ± 15.1
0.58
0.003 0.38 0.21 0.74 o0.001 0.088 o0.001 0.002 0.12 o0.001 0.76 o0.001
84.8 ± 11.8 o0.001
Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; Cr, creatinine; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; URCA, blood uric acid. The data are presented as the means ± s.d. or n (%). Two-sample t-test was used to compare the means among groups. χ2-test was used for testing categorical data among groups.
Association results for blood pressure and hypertension in relation to each of the SNPs are presented in Table 2. Two SNPs (rs6567270 and rs4603673) in TNFRSF11A were associated with hypertension after adjusting for other risk factors (P = 0.010 and P = 0.013, respectively). When SBP and DBP were analyzed as the phenotype, the two SNPs (rs6567270 and rs4603673) were still significantly associated with SBP (P = 0.024 and P = 0.023, respectively). One SNP (rs9646629) in TNFRSF11A showed significant association with DBP (P = 0.031). However, after the Bonferroni correction, the associations of the SNPs with blood pressure and hypertension were no longer statistically significant. © 2015 Macmillan Publishers Limited
TNFRSF11A gene polymorphisms and hypertension P Duan et al
3 Table 2.
Associations of tagging SNPs of TNFSF11, TNFRSF11A and TNFRSF11B with blood pressure and hypertension
Gene
TNFRSF11B TNFRSF11B TNFRSF11B TNFRSF11B TNFRSF11B TNFRSF11B TNFSF11 TNFSF11 TNFSF11 TNFSF11 TNFSF11 TNFSF11 TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A
SNP
rs1485286 rs11573869 rs3102728 rs11573819 rs2073618 rs2073617 rs9525641 rs2277439 rs2324851 rs2875459 rs2200287 rs9533166 rs9962159 rs4603673 rs7239261 rs4500848 rs6567270 rs1805034 rs4303637 rs4941131 rs9646629
Allele
C/T A/G T/C G/A C/G A/G T/C A/G G/A C/T G/A T/C A/G C/G C/A C/T T/A T/C C/T T/C G/C
Function
Intron Intron Intron Intron Asn by Lys UTR-5 Intron Intron Intron Intron Intron Intron Intron Intron Intron Intron Intron Ala by Val Intron Intron Intron
HWE
0.52 0.60 0.49 0.54 0.63 0.63 0.16 0.34 0.41 0.50 0.55 0.13 0.75 0.93 0.22 0.44 0.19 0.42 0.97 0.54 0.25
MAF
0.40 0.17 0.14 0.16 0.25 0.37 0.47 0.30 0.30 0.22 0.22 0.13 0.43 0.17 0.23 0.26 0.40 0.30 0.47 0.33 0.46
SBP
DBP
Hypertension
Beta
P-value
P-Bonf
Beta
P-value
P-Bonf
OR
P-value
P-Bonf
− 1.4 1.9 − 0.11 − 1.0 − 0.67 0.15 − 0.97 -0.90 − 0.92 2.0 2.0 0.70 0.36 − 2.7 0.33 − 1.17 2.0 − 1.0 − 0.42 − 0.32 1.5
0.14 0.12 0.94 0.43 0.53 0.88 0.30 0.36 0.35 0.064 0.074 0.60 0.69 0.023 0.76 0.25 0.024 0.32 0.65 0.74 0.10
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.50 1.0 1. 0.51 1.0 1.0 1.0 1.0
− 0.51 0.52 − 0.44 − 0.079 − 0.27 0.21 0.59 − 0.53 − 0.58 0.11 0.10 0.051 − 0.21 − 1.2 0.63 − 0.28 0.33 − 0.68 0.61 0.50 1.1
0.34 0.44 0.56 0.91 0.65 0.69 0.26 0.34 0.29 0.86 0.87 0.95 0.68 0.086 0.29 0.63 0.52 0.24 0.23 0.37 0.031
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.64
0.87 1.2 1.1 0.86 0.88 0.97 1.0 0.91 0.91 1.0 1.0 0.95 1.1 0.74 1.1 0.91 1.3 0.91 0.96 0.99 1.1
0.12 0.10 0.57 0.22 0.22 0.73 0.83 0.33 0.33 0.73 0.81 0.73 0.50 0.013 0.65 0.35 0.010 0.31 0.64 0.88 0.26
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.27 1.0 1.0 0.21 1.0 1.0 1.0 1.0
Abbreviations: Beta, the regression coefficient; DBP, diastolic blood pressure; HWE, P-values for Hardy–Weinberg equilibrium; MAF, minor allele frequency; OR, odds ratio; P-Bonf, P-value by Bonferroni correction; SBP, systolic blood pressure; SNP, single-nucleotide polymorphism. Bold indicates significant (P o0.05).
Figure 1. Patterns of linkage disequilibrium in the TNFSF11, TNFRSF11A and TNFRSF11B genes. D’ values multiplied by 100 are shown as numbers in the diamonds. R2 values are indicated by the degree of darkness.
The linkage disequilibrium blocks of the genes were generated by the Haploview software and linkage disequilibrium structure of the genes are shown in Figure 1. Four candidate haplotype blocks, two in TNFRSF11A, one in TNFSF11 and one in TNFRSF11B were found in our study. Haplotype CG of block rs7239261-rs4603673 and AT of block rs6567270-rs1805034 of TNFRSF11A showed significant association with hypertension (P = 0.014 and P = 0.010, respectively) and SBP (P = 0.026 and P = 0.024, respectively) (Table 3), but no haplotype in the three genes was found to be significantly associated with DBP. Notably, in our study, all the significantly associated SNPs and haplotypes were observed in the TNFRSF11A, genetic variation in TNFSF11 and TNFRSF11B did not show significant association with blood pressure or hypertension. © 2015 Macmillan Publishers Limited
DISCUSSION In the present study, we demonstrated that two SNPs (rs6567270 and rs4603673) of TNFRSF11A were significantly associated with SBP and hypertension in post-menopausal Chinese women. The associations became nonsignificant after the Bonferroni correction, which is a common outcome for the Bonferroni method owing to its conservative nature, which can lead to a true association result being nullified unnecessarily. Furthermore, there was little evidence of an association between TNFSF11 or TNFRSF11B and blood pressure or hypertension in our cohort. Further haplotype analyses were in agreement with our individual SNP results. These findings are inconsistent with a previous report by Golledge et al.20 The authors found one SNP (rs11573901) of Journal of Human Hypertension (2015), 1 – 5
TNFRSF11A gene polymorphisms and hypertension P Duan et al
4 Table 3. Gene
Associations of haplotypes of TNFSF11, TNFRSF11A and TNFRSF11B with blood pressure and hypertension Haplotype
Frequency
SBP
DBP
Hypertension
P-value
Beta
P-value
χ2
TNFSF11: rs9525641-rs2277439-rs2324851-rs2875459-rs2200287-rs9533166 TAGTAC 0.13 0.84 TAGTAT 0.086 3.1 TGACGT 0.30 − 0.65 CAGCGT 0.46 − 0.73 TAGCGT 0.018 2.5
0.52 0.054 0.51 0.43 0.46
0.088 0.018 − 0.49 0.67 − 2.9
0.91 0.98 0.38 0.20 0.13
0.16 0.57 0.78 0.11 1.4
0.69 0.45 0.38 0.74 0.24
TNFRSF11A: rs4603673-rs7239261 CA GC CC
0.23 0.17 0.60
0.39 − 2.7 1.2
0.72 0.026 0.17
0.67 − 1.1 0.17
0.27 0.095 0.73
0.25 6.0 2.1
0.61 0.014 0.15
TNFRSF11A: rs6567270-rs1805034 TC AT TT
0.30 0.40 0.30
− 1.0 2.0 − 1.4
0.32 0.024 0.15
− 0.67 0.33 0.25
0.24 0.52 0.65
0.99 6.6 3.1
0.32 0.010 0.077
0.49 0.54 0.99 0.10 0.26 0.30
− 0.28 − 0.083 − 0.49 0.53 −0.52 0.98
0.64 0.91 0.52 0.43 0.47 0.21
1.7 1.3 0.27 3.0 0.23 0.99
0.19 0.25 0.60 0.085 0.63 0.32
Beta
TNFRSF11B: rs1485286-rs11573869-rs3102728-rs11573819-rs2073618 TATGG 0.25 − 0.74 CATAC 0.16 − 0.77 CACGC 0.14 − 0.016 CGTGC 0.17 1.9 TATGC 0.16 − 1.4 CATGC 0.13 1.4
P-value
Abbreviations: Beta, regression coefficient; DBP, diastolic blood pressure; SBP, systolic blood pressure; χ2, χ2-value. Bold indicates significant (Po0.05).
TNFRSF11B was significantly associated with diastolic blood pressure in Australian men. The inconsistency between the results of the present study and the other may be due to the proportion variation of genotypes among different race groups. Two recent studies reported that one SNP (rs9594782) of TNFSF11 showed significant association with acute coronary syndrome21 and aortic calcification.22 Brändström et al.23,24 found that polymorphisms in TNFRSF11B were associated with vascular morphology and function. In addition, a large amount of data had confirmed that vascular morphology and function were intimately linked to the regulation of blood pressure.25,26 The associations of genetic variations in TNFRSF11A with blood pressure and hypertension found in the present study can be explained by the possible roles of the encoded protein in vascular function. First of all, RANK, encoded by TNFRSF11A, is a key protein in the RANKL/RANK/OPG system, and previous studies have indicated that the RANKL/RANK/OPG system has an important role in vascular calcification.27 Vascular calcification is an active and regulated process, which is indispensable to cardiovascular diseases and may contribute to the mechanisms of hypertension by decreasing the elasticity of the artery.28 Observational studies have suggested that serum OPG level is associated with arterial stiffness29 and aortic calcification.30 On the other hand, evidence from animal models suggests that OPG may be protective against vascular calcification because OPG-deficient mice developed vascular calcification.31 The possible mechanism of vascular calcification regulated by the RANKL/RANK/OPG system is that RANK can modulate vascular smooth muscle cell calcification by regulating bone morphogenetic protein 4 production through activation of the nuclear factor-kappa B signaling pathway.32 In addition, studies have indicated that the RANKL/RANK/OPG system is closely related to endothelial cell dysfunction,33 which has an important role in hypertension by regulating the release of nitric oxide.34 In brief, the RANKL/RANK/OPG system may be involved in the hypertension through endothelial dysfunction and vascular calcification. Consequently, genetic variation in genes in Journal of Human Hypertension (2015), 1 – 5
the RANKL/RANK/OPG system may have a role in hypertension and blood pressure regulation. This study has some potential limitations. First, it is a case– control study. Therefore, recruitment and selection bias cannot be excluded. Second, as the associations between genes and complex traits may be race dependent, and different results can be obtained in different races, the results need to be confirmed in other racial populations. Furthermore, the exact mechanisms underlying the association observed and the specific functions of the gene polymorphisms in the RANKL/RANK/OPG system remain to be determined. In conclusion, the present study finds a significant association of genetic polymorphisms in TNFRSF11A with blood pressure and hypertension. The findings provide additional support for the genetic role of the RANKL/RANK/OPG system in hypertension and blood pressure regulation. Further studies with larger sample sizes and different races are needed to replicate and confirm our results. What is known about topic ● The RANKL/RANK/OPG system has an important role in vascular calcification. Vascular calcification is intimately linked to the regulation of blood pressure and contributory to various cardiovascular diseases. What this study adds ● Polymorphisms in TNFRSF11A gene in RANKL/RANK/OPG system are associated with hypertension and blood pressure. ● TNFRSF11A genotype status may provide additional support for the genetic role of RANKL/RANK/OPG system in hypertension and blood pressure regulation.
CONFLICT OF INTEREST The authors declare no conflict of interest.
© 2015 Macmillan Publishers Limited
TNFRSF11A gene polymorphisms and hypertension P Duan et al
ACKNOWLEDGEMENTS This study was supported by grants from the National Natural Science Foundation of China (no. 81260133) and the Key Projects of Health Department of Jiangxi province, China (no. 20114030).
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5 18 Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 263–265. 19 Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575. 20 Golledge J, Biros E, Clancy P, Cooper M, Palmer LJ, Norman PE. A single-nucleotide polymorphism in the gene encoding osteoprotegerin is associated with diastolic blood pressure in older men. Am J Hypertens 2009; 22: 1167–1170. 21 Choe WS, Kim HL, Han JK, Choi YE, Seo B, Cho HJ et al. Association between OPG, RANK and RANKL gene polymorphisms and susceptibility to acute coronary syndrome in Korean population. J Genet 2012; 91: 87–89. 22 Rhee EJ, Yun EJ, Oh KW, Park SE, Park CY, Lee WY et al. The relationship between Receptor Activator of Nuclear Factor-kappaB Ligand (RANKL) gene polymorphism and aortic calcification in Korean women. Endocr J 2010; 57: 541–549. 23 Brändström H, Stiger F, Lind L, Kahan T, Melhus H, Kindmark A. A single nucleotide polymorphism in the promoter region of the human gene for osteoprotegerin is related to vascular morphology and function. Biochem Biophys Res Commun 2002; 293: 13–17. 24 Brändström H, Stiger F, Kahan T, Melhus H, Nyström F, öHman KP et al. A single nucleotide polymorphism in the promoter region of the osteoprotegerin gene is related to intima-media thickness of the carotid artery in hypertensive patients. The Swedish Irbesartan Left Ventricular Hypertrophy Investigation vs Atenolol (SILVHIA). Blood Press 2004; 13: 152–157. 25 Kawai T, Ohishi M, Ito N, Onishi M, Takeya Y, Yamamoto K et al. Alteration of vascular function is an important factor in the correlation between visit-to-visit blood pressure variability and cardiovascular disease. J Hypertens 2013; 31: 1387–1395. 26 Thanassoulis G, Lyass A, Benjamin EJ, Larson MG, Vita JA, Levy D et al. Relations of exercise blood pressure response to cardiovascular risk factors and vascular function in the Framingham Heart Study. Circulation 2012; 125: 2836–2843. 27 Collin-Osdoby P. Regulation of vascular calcification by osteoclast regulatory factors RANKL and osteoprotegerin. Circ Res 2004; 95: 1046–1057. 28 Kalra SS, Shanahan CM. Vascular calcification and hypertension: cause and effect. Ann Med 2012; 44: S85–S92. 29 Tousoulis D, Siasos G, Maniatis K, Oikonomou E, Kioufis S, Zaromitidou M et al. Serum osteoprotegerin and osteopontin levels are associated with arterial stiffness and the presence and severity of coronary artery disease. Int J Cardiol 2013; 167: 1924–1928. 30 Huang JW, Lien YC, Yang CY, Liu KL, Wu CF, Yen CJ et al. Osteoprotegerin, inflammation and dyslipidemia are associated with abdominal aortic calcification in non-diabetic patients on peritoneal dialysis. Nutr Metab Cardiovasc Dis 2014; 24: 236–242. 31 Bucay N, Sarosi I, Dunstan CR, Morony S, Tarpley J, Capparelli C et al. osteoprotegerin-deficient mice develop early onset osteoporosis and arterial calcification. Genes Dev 1998; 12: 1260–1268. 32 Panizo S, Cardus A, Encinas M, Parisi E, Valcheva P, López-Ongil S et al. RANKL increases vascular smooth muscle cell calcification through a RANK-BMP4dependent pathway. Circ Res 2009; 104: 1041–1048. 33 Shin JY, Shin YG, Chung CH. Elevated serum osteoprotegerin levels are associated with vascular endothelial dysfunction in type 2 diabetes. Diabetes Care 2006; 29: 1664–1666. 34 Zhang W, Wang Q, Wu Y, Moriasi C, Liu Z, Dai X et al. Endothelial cell-specific liver kinase B1 deletion causes endothelial dysfunction and hypertension in mice in vivo. Circulation 2014; 129: 1428–1439.
Journal of Human Hypertension (2015), 1 – 5
ORIGINAL ARTICLE
Association of gene polymorphisms in RANKL/RANK/OPG system with hypertension and blood pressure in Chinese women P Duan, Z-M Wang, J Liu, L-N Wang, Z Yang and P Tu Recent studies have revealed that the receptor activator of nuclear factor-kappa B ligand/RANK/osteoprotegerin (RANKL/RANK/ OPG) system has an important role in vascular calcification, which is contributory to various cardiovascular diseases and intimately linked to the regulation of blood pressure. Therefore, we performed a case–control study to investigate the associations of 21 single-nucleotide polymorphisms (SNPs) in the TNFSF11, TNFRSF11A and TNFRSF11B genes in the RANKL/RANK/OPG system with hypertension and blood pressure in post-menopausal Chinese women. In this study, 503 hypertensive patients and 509 normal controls were recruited. Genotyping was performed using the high-throughput Sequenom genotyping platform. The results showed that two SNPs (rs6567270 and rs4603673) in the TNFRSF11A were associated with hypertension (P = 0.010 and P = 0.013, respectively) and systolic blood pressure (P = 0.024 and P = 0.023, respectively). One SNP (rs9646629) in the TNFRSF11A showed significant association with diastolic blood pressure (P = 0.031). The results of this study suggest that TNFRSF11A but not TNFSF11 and TNFRSF11B genetic variation is associated with hypertension and blood pressure in Chinese women. The findings provide additional support for the genetic role of RANKL/RANK/OPG system in hypertension and blood pressure regulation. Journal of Human Hypertension advance online publication, 26 March 2015; doi:10.1038/jhh.2015.13
INTRODUCTION Hypertension is a serious public health problem in the world because of its high prevalence and consequently increased risk of various cardiovascular disease.1 It is estimated that the prevalence of hypertension in Chinese adults was 18% in 2002 according to the National Survey,2 and this prevalence increased to 29.6% in 2010.3 Hypertension and blood pressure are complex genetic traits, which are determined by genetic and environmental factors. Twin studies have indicated that blood pressure is highly heritable, ranging from 48% to 60% for systolic blood pressure (SBP), and from 34% to 67% for diastolic blood pressure (DBP).4 The heritabilities of SBP and DBP are 46% and 30% in Han Chinese, respectively.5 Over recent years, extensive work has been done in the area of genetics of hypertension. An increasing number of hypertension-associated genetic variants have been reported6–8 but the specific genes involved in it have not yet been fully defined. Receptor activator of nuclear factor-kappa B ligand (RANKL), its cognate receptor RANK and the decoy receptor osteoprotegerin (OPG) are members of tumor necrosis factor superfamily, and they are encoded by gene TNFSF11 (gene map locus 13q14), TNFRSF11A (gene map locus 18q22.1) and TNFRSF11B (gene map locus 8q24), respectively. The RANKL/RANK/OPG signaling system has a key role in the regulation of activation, differentiation and survival of osteoclasts and it is necessary for pathophysiology of bone remodeling.9 Recent data have shown that the RANKL/ RANK/OPG system is also involved in vascular systems.10,11 OPG is considered to be a marker and risk factor for cardiovascular disease,12 and circulating OPG levels are associated with blood
pressure,13 peripheral arterial disease,14 coronary artery disease and cardiovascular mortality.15 Recently, further studies have showed that the RANKL/RANK/ OPG system has an important role in vascular calcification and atherosclerosis.16 Moreover, vascular calcification is considered to be a major cause of isolated systolic hypertension and contributory to various cardiovascular diseases.17 Therefore, the RANKL/RANK/OPG system may be involved in hypertension and blood pressure regulation, and genetic variations in the genes encoding RANKL, RANK and OPG may have roles in hypertension. In the present study, we performed a case–control study to investigate the associations of single-nucleotide polymorphisms (SNPs) and haplotypes in the TNFSF11, TNFRSF11A and TNFRSF11B genes in the RANKL/RANK/OPG system with blood pressure and hypertension in Chinese females. MATERIALS AND METHODS Participants We recruited 1082 unrelated post-menopausal Chinese women (aged 42– 89 years) of Han ethnicity from 10 community centers within Nanchang during the years 2011 and 2012. All of the participants were collected by the department of endocrinology and metabolism from a local population. Demographic information was collected by interview. Age at enrollment, income, educational level, smoking history, alcohol intake, and a detailed medical history, especially the history of hypertension, were collected through a questionnaire. Daily physical activity were assessed by homemade semi-quantitative questionnaire according to the following scale: high physical activity was defined as regular exercise, such as playing ball games or running at least 30 min at least three times a week; moderate
Department of Endocrinology and Metabolism, Nanchang Key Laboratory of Diabetes, The Third Hospital of Nanchang, Nanchang city, People’s Republic of China. Correspondence: Professor P Tu, Department of Endocrinology and Metabolism, Nanchang Key Laboratory of Diabetes, The Third Hospital of Nanchang, NO.2, Xiangshan road, Xihu District, Nanchang city, Jiangxi province 330009, People’s Republic of China. E-mail: [email protected] Received 29 August 2014; revised 9 December 2014; accepted 20 January 2015
TNFRSF11A gene polymorphisms and hypertension P Duan et al
2 physical activity was defined as mild exercise, such as long walks, dancing or house cleaning; low physical activity was assigned to women who did not participate in any of above-mentioned activities. Participants were excluded from the study if they suffered from diseases deemed to affect blood pressure, such as adrenal diseases and chronic kidney disease. Finally, a total of 503 unrelated hypertensive patients and 509 controls for the case–control study were selected. Written informed consent was obtained from each participant and the protocol was approved by the Ethics Committee of The Third Hospital of Nanchang.
Anthropometric and blood pressure measurement Blood pressure was measured in the sitting position using a mercury sphygmomanometer. The reported SBP and DBP readings were the average of the three readings measured at 10 min intervals. Hypertension status was defined as SBP ⩾ 140 mm Hg and/or DBP ⩾ 90 mm Hg and/or treatment with antihypertensive medication. Height and weight were measured and body mass index was calculated as weight (in kilograms) divided by height (in meters) squared.
Blood collection and measurements Blood samples were collected from each participant in the morning after a minimum of 10 h fasting. Measurements of serum biochemical parameters, including fasting plasma glucose, total cholesterol, triglycerides, highdensity lipoprotein cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, blood uric acid, blood urea nitrogen and creatinine were performed on the day of the collection. All the serum biochemical profiles were measured using a automatic biochemistry analyzer (ADVIA 2400, Siemens Inc., Munich, Germany).
SNP selection and genotyping Tagging SNPs with r2 ⩾ 0.80 and minor allele frequency (MAF) ⩾ 0.10 were selected using a haplotype tagging strategy and obtained from the database on Han Chinese in HapMap. A total of 21 SNPs within three candidate genes (six in TNFSF11, nine in TNFRSF11A and six in TNFRSF11B) were included in the study. Genomic DNA was extracted from whole blood samples using the QIAamp DNA Mini Kit (Qiagen Inc., Hilden, Germany). Genotyping was performed using the high-throughput Sequenom genotyping platform (MassARRAY MALDI-TOF MS system, Sequenom Inc., San Diego, CA, USA).
Statistical analyses The differences in basic characteristics between the normotensive and hypertensive groups were tested by two-sample t-test for quantitative variables and χ2-test for categorical ones. The concordance of the tagging SNPs to the Hardy–Weinberg equilibrium was checked using χ2-test. Linear regression was used to analyze SBP and DBP as quantitative traits. Hypertension was analyzed as a binary trait using the logistic regression. All association tests were based on the additive model, age, body mass index, smoking history, alcohol intake, daily physical activity and other risk factors were considered to be covariates in all the models and adjusted in process of correlation analysis. All P-values reported were two-sided, and those o0.05 were considered statistically significant. Bonferroni correction was used in multiple comparisons. Data were analyzed using SPSS version 13.0 for Windows (SPSS Inc., Chicago, IL, USA). Linkage disequilibrium structure was visualized using Haploview 4.2.18 The significance of each haplotype was analyzed by PLINK software.19
RESULTS The basic characteristics of the hypertensive (n = 503) and normotensive (n = 509) individuals are shown in Table 1. The mean SBP and DBP were significantly higher in the hypertensive group (153.0 ± 15.1 mm Hg and 84.8 ± 11.8 mm Hg) than in the normotensive group (122.0 ± 11.4 mm Hg and 74.4 ± 8.5 mm Hg). Significant differences were found in the basic characteristics including weight, body mass index, alanine aminotransferase, blood uric acid, triglycerides, high-density lipoprotein cholesterol, and fasting plasma glucose between the hypertensive and normotensive subjects. Journal of Human Hypertension (2015), 1 – 5
Table 1.
Basic characteristics of the subjects
Characteristics
Case–control analysis Normotensive Hypertensive P-value
n Age (years) Educational level Primary (%) Secondary (%) University (%) Income ⫹1000 (yuan per month) 1000–2000 (yuan per month) ⩾ 2000 (yuan per month)
509 57.4 ± 9.2
503 58.4 ± 9.0
0.082 0.28
163 (32.0%) 185 (36.8%) 296 (58.2%) 274 (54.5%) 50 (9.8%) 44 (8.7%) 0.056 63 (12.4%) 61 (12.1%) 308 (60.5%) 337 (67.0%) 138 (27.1%) 105 (20.9%)
Height (m) Weight (kg) BMI (kg m−2) Smoking history Never (%) Past (%) Current (%)
154.9 ± 5.9 57.0 ± 8.0 23.8 ± 2.9
154.2 ± 6.1 0.073 59.9 ± 9.8 o0.001 25.2 ± 3.5 o0.001 0.94 495 (97.2%) 488 (97.0%) 2 (0.4%) 2 (0.4%) 12 (2.4%) 13 (2.6%)
Alcohol intake (%) Never (%) Past (%) Current (%)
472 (92.7%) 464 (92.2%) 3 (0.6%) 4 (0.8%) 34 (6.7%) 35 (7.0%)
0.92
Physical activity High physical activity (%) 11 (2.2%) 12 (2.4%) Moderate physical activity (%) 384 (75.4%) 392 (77.9%) Low physical activity (%) 114 (22.4%) 99 (19.7%) ALT (U l − 1) AST (U l − 1) BUN (mmol l − 1) Cr (μmol l − 1) URCA (μmol l − 1) TC (mmol l − 1) TG (mmol l − 1) HDL-C (mmol l − 1) LDL-C (mmol l − 1) FPG (mmol l − 1) Serum calcium (mmol l − 1) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg)
26.3 ± 21.2 29.3 ± 15.6 5.08 ± 1.15 69.0 ± 13.4 285.2 ± 72.3 5.45 ± 1.24 1.25 ± 0.90 1.65 ± 0.37 3.22 ± 0.82 4.88 ± 1.63 2.38 ± 0.11 122.0 ± 11.4 74.4 ± 8.5
30.8 ± 25.6 30.1 ± 13.7 4.84 ± 1.22 69.2 ± 14.0 310.6 ± 73.5 5.58 ± 1.19 1.46 ± 0.91 1.58 ± 0.35 3.30 ± 0.84 5.28 ± 1.51 2.38 ± 0.12 153.0 ± 15.1
0.58
0.003 0.38 0.21 0.74 o0.001 0.088 o0.001 0.002 0.12 o0.001 0.76 o0.001
84.8 ± 11.8 o0.001
Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; Cr, creatinine; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; URCA, blood uric acid. The data are presented as the means ± s.d. or n (%). Two-sample t-test was used to compare the means among groups. χ2-test was used for testing categorical data among groups.
Association results for blood pressure and hypertension in relation to each of the SNPs are presented in Table 2. Two SNPs (rs6567270 and rs4603673) in TNFRSF11A were associated with hypertension after adjusting for other risk factors (P = 0.010 and P = 0.013, respectively). When SBP and DBP were analyzed as the phenotype, the two SNPs (rs6567270 and rs4603673) were still significantly associated with SBP (P = 0.024 and P = 0.023, respectively). One SNP (rs9646629) in TNFRSF11A showed significant association with DBP (P = 0.031). However, after the Bonferroni correction, the associations of the SNPs with blood pressure and hypertension were no longer statistically significant. © 2015 Macmillan Publishers Limited
TNFRSF11A gene polymorphisms and hypertension P Duan et al
3 Table 2.
Associations of tagging SNPs of TNFSF11, TNFRSF11A and TNFRSF11B with blood pressure and hypertension
Gene
TNFRSF11B TNFRSF11B TNFRSF11B TNFRSF11B TNFRSF11B TNFRSF11B TNFSF11 TNFSF11 TNFSF11 TNFSF11 TNFSF11 TNFSF11 TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A TNFRSF11A
SNP
rs1485286 rs11573869 rs3102728 rs11573819 rs2073618 rs2073617 rs9525641 rs2277439 rs2324851 rs2875459 rs2200287 rs9533166 rs9962159 rs4603673 rs7239261 rs4500848 rs6567270 rs1805034 rs4303637 rs4941131 rs9646629
Allele
C/T A/G T/C G/A C/G A/G T/C A/G G/A C/T G/A T/C A/G C/G C/A C/T T/A T/C C/T T/C G/C
Function
Intron Intron Intron Intron Asn by Lys UTR-5 Intron Intron Intron Intron Intron Intron Intron Intron Intron Intron Intron Ala by Val Intron Intron Intron
HWE
0.52 0.60 0.49 0.54 0.63 0.63 0.16 0.34 0.41 0.50 0.55 0.13 0.75 0.93 0.22 0.44 0.19 0.42 0.97 0.54 0.25
MAF
0.40 0.17 0.14 0.16 0.25 0.37 0.47 0.30 0.30 0.22 0.22 0.13 0.43 0.17 0.23 0.26 0.40 0.30 0.47 0.33 0.46
SBP
DBP
Hypertension
Beta
P-value
P-Bonf
Beta
P-value
P-Bonf
OR
P-value
P-Bonf
− 1.4 1.9 − 0.11 − 1.0 − 0.67 0.15 − 0.97 -0.90 − 0.92 2.0 2.0 0.70 0.36 − 2.7 0.33 − 1.17 2.0 − 1.0 − 0.42 − 0.32 1.5
0.14 0.12 0.94 0.43 0.53 0.88 0.30 0.36 0.35 0.064 0.074 0.60 0.69 0.023 0.76 0.25 0.024 0.32 0.65 0.74 0.10
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.50 1.0 1. 0.51 1.0 1.0 1.0 1.0
− 0.51 0.52 − 0.44 − 0.079 − 0.27 0.21 0.59 − 0.53 − 0.58 0.11 0.10 0.051 − 0.21 − 1.2 0.63 − 0.28 0.33 − 0.68 0.61 0.50 1.1
0.34 0.44 0.56 0.91 0.65 0.69 0.26 0.34 0.29 0.86 0.87 0.95 0.68 0.086 0.29 0.63 0.52 0.24 0.23 0.37 0.031
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.64
0.87 1.2 1.1 0.86 0.88 0.97 1.0 0.91 0.91 1.0 1.0 0.95 1.1 0.74 1.1 0.91 1.3 0.91 0.96 0.99 1.1
0.12 0.10 0.57 0.22 0.22 0.73 0.83 0.33 0.33 0.73 0.81 0.73 0.50 0.013 0.65 0.35 0.010 0.31 0.64 0.88 0.26
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.27 1.0 1.0 0.21 1.0 1.0 1.0 1.0
Abbreviations: Beta, the regression coefficient; DBP, diastolic blood pressure; HWE, P-values for Hardy–Weinberg equilibrium; MAF, minor allele frequency; OR, odds ratio; P-Bonf, P-value by Bonferroni correction; SBP, systolic blood pressure; SNP, single-nucleotide polymorphism. Bold indicates significant (P o0.05).
Figure 1. Patterns of linkage disequilibrium in the TNFSF11, TNFRSF11A and TNFRSF11B genes. D’ values multiplied by 100 are shown as numbers in the diamonds. R2 values are indicated by the degree of darkness.
The linkage disequilibrium blocks of the genes were generated by the Haploview software and linkage disequilibrium structure of the genes are shown in Figure 1. Four candidate haplotype blocks, two in TNFRSF11A, one in TNFSF11 and one in TNFRSF11B were found in our study. Haplotype CG of block rs7239261-rs4603673 and AT of block rs6567270-rs1805034 of TNFRSF11A showed significant association with hypertension (P = 0.014 and P = 0.010, respectively) and SBP (P = 0.026 and P = 0.024, respectively) (Table 3), but no haplotype in the three genes was found to be significantly associated with DBP. Notably, in our study, all the significantly associated SNPs and haplotypes were observed in the TNFRSF11A, genetic variation in TNFSF11 and TNFRSF11B did not show significant association with blood pressure or hypertension. © 2015 Macmillan Publishers Limited
DISCUSSION In the present study, we demonstrated that two SNPs (rs6567270 and rs4603673) of TNFRSF11A were significantly associated with SBP and hypertension in post-menopausal Chinese women. The associations became nonsignificant after the Bonferroni correction, which is a common outcome for the Bonferroni method owing to its conservative nature, which can lead to a true association result being nullified unnecessarily. Furthermore, there was little evidence of an association between TNFSF11 or TNFRSF11B and blood pressure or hypertension in our cohort. Further haplotype analyses were in agreement with our individual SNP results. These findings are inconsistent with a previous report by Golledge et al.20 The authors found one SNP (rs11573901) of Journal of Human Hypertension (2015), 1 – 5
TNFRSF11A gene polymorphisms and hypertension P Duan et al
4 Table 3. Gene
Associations of haplotypes of TNFSF11, TNFRSF11A and TNFRSF11B with blood pressure and hypertension Haplotype
Frequency
SBP
DBP
Hypertension
P-value
Beta
P-value
χ2
TNFSF11: rs9525641-rs2277439-rs2324851-rs2875459-rs2200287-rs9533166 TAGTAC 0.13 0.84 TAGTAT 0.086 3.1 TGACGT 0.30 − 0.65 CAGCGT 0.46 − 0.73 TAGCGT 0.018 2.5
0.52 0.054 0.51 0.43 0.46
0.088 0.018 − 0.49 0.67 − 2.9
0.91 0.98 0.38 0.20 0.13
0.16 0.57 0.78 0.11 1.4
0.69 0.45 0.38 0.74 0.24
TNFRSF11A: rs4603673-rs7239261 CA GC CC
0.23 0.17 0.60
0.39 − 2.7 1.2
0.72 0.026 0.17
0.67 − 1.1 0.17
0.27 0.095 0.73
0.25 6.0 2.1
0.61 0.014 0.15
TNFRSF11A: rs6567270-rs1805034 TC AT TT
0.30 0.40 0.30
− 1.0 2.0 − 1.4
0.32 0.024 0.15
− 0.67 0.33 0.25
0.24 0.52 0.65
0.99 6.6 3.1
0.32 0.010 0.077
0.49 0.54 0.99 0.10 0.26 0.30
− 0.28 − 0.083 − 0.49 0.53 −0.52 0.98
0.64 0.91 0.52 0.43 0.47 0.21
1.7 1.3 0.27 3.0 0.23 0.99
0.19 0.25 0.60 0.085 0.63 0.32
Beta
TNFRSF11B: rs1485286-rs11573869-rs3102728-rs11573819-rs2073618 TATGG 0.25 − 0.74 CATAC 0.16 − 0.77 CACGC 0.14 − 0.016 CGTGC 0.17 1.9 TATGC 0.16 − 1.4 CATGC 0.13 1.4
P-value
Abbreviations: Beta, regression coefficient; DBP, diastolic blood pressure; SBP, systolic blood pressure; χ2, χ2-value. Bold indicates significant (Po0.05).
TNFRSF11B was significantly associated with diastolic blood pressure in Australian men. The inconsistency between the results of the present study and the other may be due to the proportion variation of genotypes among different race groups. Two recent studies reported that one SNP (rs9594782) of TNFSF11 showed significant association with acute coronary syndrome21 and aortic calcification.22 Brändström et al.23,24 found that polymorphisms in TNFRSF11B were associated with vascular morphology and function. In addition, a large amount of data had confirmed that vascular morphology and function were intimately linked to the regulation of blood pressure.25,26 The associations of genetic variations in TNFRSF11A with blood pressure and hypertension found in the present study can be explained by the possible roles of the encoded protein in vascular function. First of all, RANK, encoded by TNFRSF11A, is a key protein in the RANKL/RANK/OPG system, and previous studies have indicated that the RANKL/RANK/OPG system has an important role in vascular calcification.27 Vascular calcification is an active and regulated process, which is indispensable to cardiovascular diseases and may contribute to the mechanisms of hypertension by decreasing the elasticity of the artery.28 Observational studies have suggested that serum OPG level is associated with arterial stiffness29 and aortic calcification.30 On the other hand, evidence from animal models suggests that OPG may be protective against vascular calcification because OPG-deficient mice developed vascular calcification.31 The possible mechanism of vascular calcification regulated by the RANKL/RANK/OPG system is that RANK can modulate vascular smooth muscle cell calcification by regulating bone morphogenetic protein 4 production through activation of the nuclear factor-kappa B signaling pathway.32 In addition, studies have indicated that the RANKL/RANK/OPG system is closely related to endothelial cell dysfunction,33 which has an important role in hypertension by regulating the release of nitric oxide.34 In brief, the RANKL/RANK/OPG system may be involved in the hypertension through endothelial dysfunction and vascular calcification. Consequently, genetic variation in genes in Journal of Human Hypertension (2015), 1 – 5
the RANKL/RANK/OPG system may have a role in hypertension and blood pressure regulation. This study has some potential limitations. First, it is a case– control study. Therefore, recruitment and selection bias cannot be excluded. Second, as the associations between genes and complex traits may be race dependent, and different results can be obtained in different races, the results need to be confirmed in other racial populations. Furthermore, the exact mechanisms underlying the association observed and the specific functions of the gene polymorphisms in the RANKL/RANK/OPG system remain to be determined. In conclusion, the present study finds a significant association of genetic polymorphisms in TNFRSF11A with blood pressure and hypertension. The findings provide additional support for the genetic role of the RANKL/RANK/OPG system in hypertension and blood pressure regulation. Further studies with larger sample sizes and different races are needed to replicate and confirm our results. What is known about topic ● The RANKL/RANK/OPG system has an important role in vascular calcification. Vascular calcification is intimately linked to the regulation of blood pressure and contributory to various cardiovascular diseases. What this study adds ● Polymorphisms in TNFRSF11A gene in RANKL/RANK/OPG system are associated with hypertension and blood pressure. ● TNFRSF11A genotype status may provide additional support for the genetic role of RANKL/RANK/OPG system in hypertension and blood pressure regulation.
CONFLICT OF INTEREST The authors declare no conflict of interest.
© 2015 Macmillan Publishers Limited
TNFRSF11A gene polymorphisms and hypertension P Duan et al
ACKNOWLEDGEMENTS This study was supported by grants from the National Natural Science Foundation of China (no. 81260133) and the Key Projects of Health Department of Jiangxi province, China (no. 20114030).
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