Skip to main content
BMJ Open logoLink to BMJ Open
. 2018 Jul 7;8(7):e019902. doi: 10.1136/bmjopen-2017-019902

Association between ATP2B1 and CACNB2 polymorphisms and high blood pressure in a population of Lithuanian children and adolescents: a cross-sectional study

Sandrita Simonyte 1, Renata Kuciene 1, Virginija Dulskiene 1, Vaiva Lesauskaite 1
PMCID: PMC6042568  PMID: 29982197

Abstract

Objectives

Recently, genome-wide associated studies have identified several genetic loci that are associated with elevated blood pressure and could play a critical role in intracellular calcium homeostasis. The aim of this study was to assess the associations of ATP2B1 rs2681472 and CACNB2 rs12258967 gene polymorphisms with high blood pressure (HBP) among Lithuanian children and adolescents aged 12–15 years.

Study design and participants

This was a cross-sectional study of a randomly selected sample of 646 12–15-year-old adolescents who participated in the survey ‘The Prevalence and Risk Factors of HBP in 12–15 Year-Old Lithuanian Children and Adolescents (from November 2010 to April 2012)’. Anthropometric parameters and BP were measured. The participants with HBP were screened on two separate occasions. Subjects were genotyped ATP2B1 rs2681472 and CACNB2 rs12258967 gene polymorphisms using real-time PCR method.

Results

The prevalence of HBP was 36.7%, significantly higher for boys than for girls. In the multivariate analysis, after adjustment for body mass index and waist circumference, boys with CACNB2 CG genotype, CACNB2 GG genotype and CACNB2 CG +GG genotype had higher odds of having HBP in codominant (adjusted OR (aOR)=1.92; 95% CI 1.16 to 3.18, p=0.011; and aOR=2.64; 95% CI 1.19 to 5.90, p=0.018) and in dominant (aOR=2.05; 95% CI 1.27 to 3.30, p=0.003) inheritance models. Girls carrying CACNB2 CG genotype and CACNB2 CG +GG genotype had increased odds of HBP in codominant (aOR=1.82; 95% CI 1.02 to 3.24, p=0.044) and in dominant (aOR=1.89; 95% CI 1.09 to 3.28, p=0.023) inheritance models. Furthermore, significant associations were found in additive models separately for boys (aOR=1.72; 95% CI 1.20 to 2.46, p=0.003) and girls (aOR=1.52; 95% CI 1.05 to 2.20, p=0.027). No significant association was found between ATP2B1 gene polymorphism and the odds of HBP.

Conclusions

Our results indicate that CACNB2 gene polymorphism was significantly associated with higher odds of HBP in Lithuanian adolescents aged 12–15 years.

Keywords: cardiac epidemiology, hypertension, molecular biology, paediatric cardiology


Strengths and limitations of this study.

  • To our knowledge, this is the first study to determine the association of the ATP2B1 rs2681472 polymorphism with high blood pressure (HBP) in adolescents aged 12–15 years in the Baltic states.

  • Multivariate logistic regression analyses were conducted separately for boys and girls to evaluate the associations between ATP2B1 rs2681472 and CACNB2 rs12258697 gene polymorphisms and HBP under different inheritance models.

  • As our study was a cross-sectional study in its design, we cannot determine a cause–effect relationship.

Introduction

Hypertension is one of the major risk factors of cardiovascular disease, and does not only affect middle-aged and elderly people, but is also increasing in prevalence among children and adolescents worldwide.1 2 A well-known fact is that overweight and obesity, like family history of hypertension and low socioeconomic status, are related to elevated blood pressure (BP) in childhood, which is an important predictor of hypertension in adult life.3 4 According to various epidemiological studies, the prevalence of elevated BP in children and adolescents ranges from 0.8% up to 5%.5–7 Studies conducted in Lithuania have shown a higher prevalence of an increased BP among 3–7-year-old children (21.4%)8 and a higher prevalence of prehypertension and hypertension among 12–15-year-old adolescents (12.6% and 22.5%, respectively).9 The mortality rate from cardiovascular diseases in Lithuania is one of the highest in Europe.10 Hypertension is a highly heritable trait, and around 40%–60% of individual differences in BP have a genetic basis.11 Therefore, early identification of not only potential environmental but also genetic risk factors for the development of hypertension is essential, as they are important diagnostic and prognostic molecular markers.

Genome-wide associated studies have identified several genetic loci that are associated with elevated systolic (SBP) or diastolic blood pressure (DBP),12–14 but the mechanism by which a certain gene variant influences the risk of hypertension remains unclear. In our study, we selected genes (ATP2B1 and CACNB2) that play a critical role in intracellular calcium homeostasis15 and could be also involved in the pathogenic processes of hypertension. The ATP2B1 gene encodes the plasma membrane calcium ATPase isoform 1, which removes bivalent calcium ions from eucaryotic cells against very large concentration gradients16 and is responsible for controlling the contraction and dilation of vascular smooth muscles.17 The CACNB2 gene encodes the beta-2 subunit of a voltage-gated calcium channel and is a member of high voltage-gated calcium channel genes. The beta-2 subunit could interact with a pore-forming subunit of the calcium channel and could modulate calcium channel activity and blood pressure.18

The aim of our study was to investigate the associations of ATP2B1 rs2681472 and CACNB2 rs12258967 gene polymorphisms with HBP among Lithuanian children and adolescents aged 12–15 years.

Methods

Study design and population

Blood pressure and anthropometric measurements performed in this study have been described in our previous publication.19

This was a cross-sectional study of children and adolescents who had participated in the baseline survey ‘The Prevalence and Risk Factors of HBP in 12–15 Year-Old Lithuanian Children and Adolescents (Study 1, 2010–2012)’ in Kaunas City and Kaunas District, which are the second-largest city and district in Lithuania.19 The baseline survey that was based on a two-stage cluster sampling design enrolled 7638 children and adolescents aged 12 to 15 years who at the time of the examination (from November 2010 to April 2012) attended gymnasiums or secondary schools of all classes (grades 6, 7, 8 and 9). Data on clinically verified health disorders were collected from the subjects’ medical records (Form No. 027–1/a). The present study examined data of a randomly selected sample of 646 participants (313 boys and 333 girls) aged 12–15 years who underwent BP and anthropometric examinations as well as a genetic examination of the saliva.

Measurements

Blood pressure measurement

BP was measured in the morning hours by a physician who was not wearing a white coat in a quiet environment. The adolescents were advised to avoid coffee, tea, energy drinks and physical exercises until the measurements were taken. Before the BP measurement, the participants were asked to sit still for 10 min. BP measurements were performed with an automatic BP monitor (Omron M6; Omron Halthcare Co., Kyoto, Japan) using a cuff of the appropriate size. BP was measured three times with a 5 min rest interval between the measurements, with the participant being in a sitting position. The average of three BP measurements was calculated and used in the analysis. According to ‘The Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents’, normal BP (NBP) was defined as SBP and DBP less than the 90th percentile for sex, age and height, and HBP was defined as SBP and DBP ≥90th percentile for sex, age and height.20 Prehypertension was defined as SBP or DBP levels between the 90th and the 95th percentile for sex, age and height, or if BP levels were ≥120/80 mm Hg even if below the 90th percentile.20 All subjects with high BP (BP was in the ≥90th percentile) during the first screening underwent a second evaluation of BP measurements within 2–3 weeks.

Anthropometric measurement

All participants underwent anthropometric measurements while being barefoot and wearing only light clothes. Body weight and height of the participants were measured to the nearest 0.1 kg and 0.1 cm, respectively, by using a balance beam scale (Seca) and a portable stadiometer. The participants were grouped into three categories: normal weight, overweight, and obese, using the cut-off points of body mass index (BMI) by age and sex for children and adolescents proposed by the International Obesity Task Force (IOTF).21

Waist circumference (WC) was measured to the nearest 0.5 cm by using a flexible measuring tape (Seca) at a level midway between the lower rib margin and the iliac crest. Using the cut-off values of the percentiles of the WC according to the criteria of the National Health and Nutrition Examination Survey III,22 the participants were grouped into the categories on the basis of their WC: below the 75th percentile (normal waist value), 75th<90th percentile and ≥90th percentile. High waist value was defined as WC ≥75th percentile.

Both BP and anthropometric measurements were performed at the subjects’ schools by the same team of trained research personnel (physicians and research assistants).

Genetic analysis

Saliva samples were collected into tubes from each individual for DNA extraction. Genomic DNA was extracted using a commercial DNA isolation kit—the ‘Genomic DNA Purification Kit’ (Thermo Fisher Scientific, Lithuania) according to the manufacturer’s instructions.

ATP2B1 rs2681472 and CACNB2 rs12258967 gene polymorphisms in the subjects were genotyped using the real-time PCR technique with TaqMan allelic discrimination Assay-By-Design genotyping assays C_30872739_10 and C_31302374_10, according to the manufacturer’s instructions (Applied Biosystems, Foster City, California, USA). Allele-specific fluorescence was analysed on the ABI 7900HT Sequence Detection System with SDS V.2.1 (Applied Biosystems).

Statistical analysis

Statistical analysis was performed using the statistical software package SPSS V.20 for Windows. Categorical variables were expressed as numbers and percentage. The z-test was used to compare differences between the groups. Continuous variables were presented as mean values±SD. The normality of the distribution of the continuous variables was assessed by applying the Kolmogorov-Smirnov test. Comparisons between the groups were performed by applying the χ2 test, Student’s t-test and analysis of variance with Bonferroni post hoc test. The z-test was used to compare the proportions. The χ2 test was used for the assessment of the Hardy-Weinberg equilibrium (HWE) for the distribution of genotypes. Logistic regression analyses were used to test for the associations of ATP2B1 (rs2681472) and CACNB2 (rs12258967) polymorphisms with HBP under five inheritance models based on the Akaike information criterion (AIC) to calculate the OR (95% CI) for CACNB2 and ATP2B1 polymorphism. ORs were adjusted for BMI and WC. The data were analysed separately for boys and girls. P values <0.05 were considered statistically significant.

Patient and public involvement

The schoolchildren and their parents or guardians were not involved in the design and conduct of the study.

Results

The characteristics of the study population are given in table 1. Among 646 study subjects aged 12–15 years (mean±SD age: 13.27±1.14 years), 48.5% (n=313) were boys, and 51.5% (n=333) were girls. Boys were significantly heavier and taller, and had a significantly greater mean WC. They had significantly higher mean SBP and significantly lower mean DBP, compared with girls. There was no significant difference in mean age and BMI between these groups.

Table 1.

Demographic, anthropometric and BP characteristics of the study subjects by sex

Variables Total
(n=646)
Boys
(n=313)
Girls
(n=333)
P values*
Age (years) 13.27 (1.14) 13.31 (1.16) 13.24 (1.13) 0.607
Weight (kg) 52.50 (12.19) 54.03 (13.33) 51.07 (10.85) 0.015
Height (cm) 162.86 (9.45) 164.42 (10.51) 161.40 (8.09) <0.001
BMI (kg/m2) 19.61 (3.35) 19.76 (3.49) 19.46 (3.22) 0.429
WC (cm) 67.41 (8.66) 69.63 (8.93) 65.33 (7.87) <0.001
SBP (mm Hg) 119.97 (14.75) 123.20 (16.13) 116.92 (12.61) <0.001
DBP (mm Hg) 64.99 (8.01) 64.34 (7.99) 65.61 (7.99) 0.043

Values are mean±SD.

*Boys versus girls (t-test).

BP, blood pressure; BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure; WC, waist circumference.

The characteristics of the study participants according to BP levels are presented in table 2. The overall prevalence of HBP was 36.7%. HBP was found more frequently in boys than in girls (45.7% vs 28.2%; p<0.001). Subjects aged 14 to 15 years were more likely to have HBP than subjects aged 12 to 13 years, but a significant difference was found only between the boys (56.3% vs 38.5%, p<0.05) (data not shown). The subjects with HBP had significantly higher mean values of weight, height, BMI and WC, compared with subjects with NBP. The prevalence of overweight, obesity and large WC (≥75th percentile) were much more common in participants with HBP than in the NBP group.

Table 2.

Characteristics of the study population according to the BP level

Characteristics HBP
(n=237)
NBP
(n=409)
P value
Age, years, mean (SD) 13.30 (1.12) 13.26 (1.16) 0.458
Weight, kg, mean (SD) 58.33 (12.37) 49.13 (10.75) <0.001
Height, cm, mean (SD) 165.18 (8.77) 161.52 (9.58) <0.001
BMI, kg/m2, mean (SD) 21.24 (3.54) 18.66 (2.84) <0.001
WC, cm, mean (SD) 72.33 (9.15) 64.56 (6.93) <0.001
SBP, mm Hg, mean (SD) 135.90 (9.84) 110.73 (7.40) <0.001
DBP, mm Hg, mean (SD) 69.76 (8.04) 62.24 (6.58) <0.001
Sex, n (%)
 Boys 143 (45.7)* 170 (54.3) <0.001
 Girls 94 (28.2)* 239 (71.8)
Age, years, n (%)
 12–13 134 (34.6) 253 (65.4) 0.212
 14–15 103 (39.8) 156 (60.2)
BMI categories, n (%)
 Normal weight 172 (31.6)* 372 (68.4) <0.001
 Overweight 47 (58.8)* 33 (41.2)
 Obesity 18 (81.8)* 4 (18.2)
WC percentile categories, n (%)
 <75th 176 (31.3)* 386 (68.7) <0.001
 75th-<90th 41 (67.2)* 20 (32.8)
 ≥90th 20 (87.0)* 3 (13.0)

The means were compared using t-test.

Categorical variables were compared using the χ2 test.

*P<0.05 in comparison with NBP group (z-test).

BMI, body mass index; DBP, diastolic blood pressure; HBP, high blood pressure; NBP, normal blood pressure; SBP, systolic blood pressure; WC, waist circumference.

The distribution of the analysed genotypes was in the HWE (p>0.05) (table 3). No significant differences in the frequencies of ATP2B1 and CACNB2 genotypes or alleles between boys and girls were found.

Table 3.

Distribution of the ATP2B1 and CACNB2 genotypes and frequency of alleles in the study population

Characteristics HBP NBP
Boys Girls Boys Girls
ATP2B1 genotypes, n (%)
 AA 106 (74.1) 73 (77.7) 121 (71.2) 166 (69.5)
 AG 37 (25.9) 19 (20.2) 45 (26.4) 68 (28.4)
 GG 0 (0) 2 (2.1) 4 (2.4) 5 (2.1)
ATP2B1 allele frequency
 A 0.87 0.88 0.84 0.84
 G 0.13 0.12 0.16 0.16
CACNB2 genotypes, n (%)
 CC 56 (39.2)* 33 (35.1) 92 (54.2) 106 (44.4)
 CG 67 (46.8)† 46 (48.9) 65 (38.2)‡ 99 (41.4)
 GG 20 (14.0)§ 15 (16.0) 13 (7.6)¶ 34 (14.2)
CACNB2 allele frequency
 C 0.63 0.60 0.73 0.65
 G 0.37 0.40 0.27 0.35

*P<0.05 in comparison with NBP group (z-test).

†P<0.05 between CACNB2 CC genotype and CACNB2 CG genotype in boys with HBP (z-test).

‡P<0.05 between CACNB2 CC genotype and CACNB2 CG genotype in boys with NBP (z-test).

§P<0.05 between CACNB2 CC genotype and CACNB2 GG genotype in boys with HBP (z-test).

¶P<0.05 between CACNB2 CC genotype and CACNB2 GG genotype in boys with NBP (z-test).

ATP2B1, the plasma membrane calcium-transporting ATPase 1 gene; CACNB2, beta-2 subunit of voltage-gated calcium channel; HBP, high blood pressure; NBP, normal blood pressure.

The characteristics of the study subjects according to the ATP2B1 genotypes are presented in table 4. Mean values of SBP and DBP were similar in groups with different genotypes separately for both sexes. Statistically significant differences between ATP2B1 genotypes with respect to the mean values of age, weight, height and WC were observed only among boys. The subjects with ATP2B1 AA genotype had higher mean values of anthropometric variables and SBP than ATP2B1 AG and GG genotype carriers.

Table 4.

Characteristics of the population (means and SD) according to ATP2B1 genotypes

Characteristics ATP2B1 genotypes P values
AA AG GG
Boys n=227 n=82 n=4
 Age, years, mean (SD) 13.41 (1.14) 13.04 (1.20)* 12.75 (0.50) 0.021
 Weight, kg, mean (SD) 55.40 (13.58) 50.41 (12.22)* 50.38 (5.19) 0.012
 Height, cm, mean (SD) 165.47 (10.27) 161.63 (10.83)* 161.75 (7.93) 0.015
 BMI, kg/m2, mean (SD) 20.02 (3.64) 19.07 (3.02) 19.23 (1.15) 0.303
 WC, cm, mean (SD) 70.54 (9.35) 67.40 (7.35)* 64.00 (1.41) 0.012
 SBP, mm Hg, mean (SD) 123.90 (15.89) 121.89 (16.84) 110.58 (8.16) 0.218
 DBP, mm Hg, mean (SD) 64.33 (7.95) 64.36 (8.36) 64.42 (3.34) 0.985
Girls n=239 n=87 n=7
 Age, years, mean (SD) 13.22 (1.16) 13.31 (1.05) 13.14 (1.07) 0.751
 Weight, kg, mean (SD) 51.64 (11.13) 49.92 (9.99) 46.00 (10.16) 0.205
 Height, cm, mean (SD) 161.87 (7.83) 160.47 (8.74) 157.14 (7.34) 0.143
 BMI, kg/m2, mean (SD) 19.57 (3.39) 19.23 (2.75) 18.45 (2.75) 0.489
 WC, cm, mean (SD) 65.69 (8.07) 64.67 (7.24) 61.29 (7.50) 0.152
 SBP, mm Hg, mean (SD) 117.29 (12.65) 116.07 (12.82) 115.14 (8.93) 0.460
 DBP, mm Hg, mean (SD) 65.86 (8.19) 65.20 (7.58) 62.52 (5.64) 0.472

*P<0.05 between ATP2B1 AA genotype and ATP2B1 AG genotype in boys (t-test).

ATP2B1, the plasma membrane calcium-transporting ATPase 1 gene; BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure; WC, waist circumference.

The characteristics of the study subjects according to CACNB2 genotypes are presented in table 5. Mean values of weight, height, BMI, WC and DBP were similar in groups with different genotypes for both sexes separately, except for age and SBP among the boys. Furthermore, boys with the CACNB2 GG genotype had the highest level of SBP.

Table 5.

Characteristics of the population (means and SD) according to CACNB2 genotypes

Characteristics CACNB2 genotypes P values
CC CG GG
Boys n=148 n=132 n=33
 Age, years, mean (SD) 13.11 (1.10) 13.45 (1.22)* 13.61 (1.09)† 0.017
 Weight, kg, mean (SD) 54.10 (14.16) 53.27 (12.22) 56.75 (13.79) 0.405
 Height, cm, mean (SD) 163.49 (10.88) 164.79 (9.68) 167.08 (11.71) 0.180
 BMI, kg/m2, mean (SD) 19.99 (3.69) 19.44 (3.33) 20.06 (3.11) 0.426
 WC, cm, mean (SD) 69.98 (9.23) 68.72 (8.60) 71.73 (8.63) 0.172
 SBP, mm Hg, mean (SD) 121.06 (16.13) 124.78 (16.27)* 126.52 (14.60)† 0.037
 DBP, mm Hg, mean (SD) 64.06 (7.84) 64.43 (7.93) 65.25 (9.08) 0.730
Girls n=139 n=145 n=49
 Age, years, mean (SD) 13.15 (1.16) 13.23 (1.07) 13.53 (1.16)‡ 0.111
 Weight, kg, mean (SD) 50.61 (11.42) 51.66 (10.89) 50.62 (8.99) 0.687
 Height, cm, mean (SD) 161.33 (8.69) 161.88 (7.40) 160.20 (8.32) 0.455
 BMI, kg/m2, mean (SD) 19.30 (3.56) 19.56 (3.13) 19.60 (2.37) 0.239
 WC, cm, mean (SD) 65.68 (8.71) 65.46 (7.60) 63.93 (5.81) 0.763
 SBP, mm Hg, mean (SD) 115.63 (12.12) 118.40 (13.27) 116.23 (11.69) 0.225
 DBP, mm Hg, mean (SD) 64.87 (8.03) 66.05 (7.94) 66.46 (8.01) 0.335

*P<0.05 between CACNB2 CC genotype and CACNB2 CG genotype in boys (t-test).

†P<0.05 between CACNB2 CC genotype and CACNB2 GG genotype in boys (t-test).

‡P<0.05 between CACNB2 CC genotype and CACNB2 GG genotype in girls (t-test).

BMI, body mass index; CACNB2, beta-2 subunit of voltage-gated calcium channel; DBP, diastolic blood pressure; SBP, systolic blood pressure; WC, waist circumference

The mean SBP and DBP levels of the subjects according to genotypes are given in table 6. No significant differences were observed comparing the mean values of SBP and DBP between the ATP2B1 genotypes (AG vs AA, GG vs AA or GG vs AG), the CACNB2 genotypes (CG vs CC, GG vs CC or GG vs CG) separately for both sexes within each BP group.

Table 6.

Distribution of ATP2B1 and CACNB2 genotypes according to systolic and diastolic blood pressure in the study population

Characteristics HBP NBP
SBP (mm Hg) DBP (mm Hg) SBP (mm Hg) DBP (mm Hg)
Boys
ATP2B1 genotypes, mean (SD)
 AA 138.06 (10.10) 67.58 (7.51) 111.50 (7.36) 61.48 (7.22)
 AG 137.48 (8.63) 68.14 (7.95) 109.07 (9.43) 61.24 (7.41)
 GG 0 (0) 0 (0) 110.58 (8.16) 64.42 (3.34)
CACNB2 genotypes, mean (SD)
 CC 138.60 (9.82) 68.21 (7.11) 110.38 (7.57) 61.53 (7.20)
 CG 138.15 (9.38) 67.57 (7.52) 111.00 (8.44) 61.19 (7.02)
 GG 135.13 (10.57) 66.88 (9.36) 113.26 (8.78) 62.74 (8.38)
Girls
ATP2B1 genotypes, mean (SD)
 AA 132.67 (8.03) 73.25 (7.62) 110.52 (7.20) 62.61 (6.05)
 AG 134.35 (13.35) 72.05 (8.04) 110.97 (6.46) 63.28 (6.27)
 GG 125.67 (0.00) 66.00 (10.37) 110.93 (6.48) 61.13 (3.51)
CACNB2 genotypes, mean (SD)
 CC 132.24 (9.60) 73.22 (7.24) 110.46 (7.16) 62.26 (6.33)
 CG 134.17 (9.72) 72.07 (8.85) 111.07 (6.67) 63.25 (5.64)
 GG 130.20 (6.58) 74.44 (4.63) 110.07 (7.26) 62.93 (6.51)

ATP2B1, the plasma membrane calcium-transporting ATPase 1 gene; CACNB2, beta-2 subunit of voltage-gated calcium channel; DBP, diastolic blood pressure; HBP, high blood pressure; SBP, systolic blood pressure; NBP, normal blood pressure.

The boys who carried one or two copies of the CACNB2 G allele were by 1.64 times more likely to have HBP than boys who had one or two copies of the CACNB2 C allele (table 7).

Table 7.

Associations between ATP2B1 and CACNB2 polymorphisms and high blood pressure by sex (allelic models)

Boys OR
(95 % CI)
P value Girls OR
(95 % CI)
P value
HBP NBP HBP NBP
N (%) N (%) N (%) N (%)
ATP2B1 alleles
 A 249 (87.1) 287 (84.4) 1.00 165 (84.4) 400 (87.8) 1.00
 G 37 (12.9) 53 (15.6) 0.80 (0.50 to 1.30) 0.346 23 (15.6) 78 (12.2) 0.71 (0.42 to 1.21) 0.186
CACNB2 alleles
 C 179 (62.6) 249 (73.2) 1.00 112 (59.6) 311 (65.1) 1.00
 G 107 (37.4) 91 (26.8) 1.64 (1.15 to 2.33) 0.004 76 (40.4) 167 (34.9) 1.26 (0.88 to 1.81) 0.185

ATP2B1, the plasma membrane calcium-transporting ATPase 1 gene; CACNB2, beta-2 subunit of voltage-gated calcium channel; HBP, high blood pressure; NBP, normal blood pressure.

ORs in bold text are statistically significant (p< 0.05).

The results of multivariate logistic regression analyses are presented in table 8. To examine the associations with HBP, we calculated adjusted ORs (aORs) adjusted by the BMI and WC for boys and girls separately. No significant association between ATP2B1 genotypes and HBP was found either for boys or girls.

Table 8.

Associations between ATP2B1 and CACNB2 polymorphisms and high blood pressure by sex (multivariate analyses)

Models Genotypes Boys P value AIC Girls P value AIC
aOR*
(95 % CI)
aOR*
(95 % CI)
CACNB2
 Codominant CC 1.00 1.00
CG 1.92 (1.16 to 3.18) 0.011 412.01 1.82 (1.02 to 3.24) 0.044 367.15
GG 2.64 (1.19 to 5.90) 0.018 2.15 (0.99 to 4.66) 0.054
 Dominant CC 1.00 1.00
CG+GG 2.05 (1.27 to 3.30) 0.003 410.62 1.89 (1.09 to 3.28) 0.023 365.35
 Recessive CC+CG 1.00 1.00
GG 1.93 (0.90 to 4.14) 0.090 416.52 1.53 (0.77 to 3.05) 0.228 369.32
 Overdominant CC+GG 1.00 1.00
CG 1.59 (0.99 to 2.56) 0.055 415.74 1.44 (0.86 to 2.41) 0.162 368.76
 Additive 1.72 (1.20 to 2.46) 0.003 410.37 1.52 (1.05 to 2.20) 0.027 365.78
ATP2B1
 Codominant AA 1.00 1.00
AG 1.11 (0.66 to 1.89) 0.691 417.10 0.72 (0.39 to 1.34) 0.302 389.63
GG 0.93 (0.16 to 5.59) 0.941
 Dominant AA 1.00 1.00
AG+GG 1.02 (0.61 to 1.72) 0.932 419.43 0.74 (0.41 to 1.34) 0.318 369.70
 Recessive AA+AG 1.00 1.00
GG 415.26 1.01 (0.17 to 6.04) 0.992 370.72
 Overdominant AA+GG 1.00 1.00
AG 1.15 (0.68 to 1.95) 0.602 419.17 0.72 (0.39 to 1.34) 0.304 369.63
 Additive 0.92 (0.57 to 1.50) 0.750 419.34 0.79 (0.46 to 1.34) 0.375 369.91

*Adjusted for body mass index and waist circumference.

AIC, Akaike information criterion; aOR, adjusted OR; ATP2B1, the plasma membrane calcium-transporting ATPase 1 gene; CACNB2, beta-2 subunit of voltage-gated calcium channel.

ORs in bold text are statistically significant (p< 0.05).

The results showed that boys who carried the CACNB2 CG genotype, the CACNB2 GG genotype and the CACNB2 CG +GG genotypes had higher odds of having HBP than carriers of the CACNB2 CC genotype did (in the codominant model: aOR=1.92; p=0.011 and aOR=2.64; p=0.018; in the dominant model: aOR=2.05; p=0.003, respectively). In girls, carriers of the CACNB2 CG genotype and the CACNB2 CG +GG genotypes had significantly higher odds of HBP than girls with the CACNB2 CC genotype did (in the codominant model: aOR=1.82; p=0.044, and in dominant model: aOR=1.89; p=0.023). In addition, significant associations were found in additive models separately for both sexes.

Discussion

Though hypertension in adolescents is less common than in adults, it has become a major concern in some countries over the past decade because of the evidence suggesting that HBP tracks from childhood to adulthood.23 Children become hypertensive in adult years, particularly if they are obese as children or become obese as young adults.3 4 In adults, primary hypertension is more common than secondary hypertension in the paediatric population.24 In order to promote cardiovascular health in adults, it is strongly recommended to measure BP in all children ≥3 years of age.25 It was examined that the model of prediction of adult hypertension consists of known physical and environmental childhood risk factors, family history of hypertension and novel genetic variants.4 However, new findings of genome-wide association studies on adult hypertension are still little reflected in paediatric research. In the present study, we examined two newly identified genes together with measured hypertension risk factors among Lithuanian children and adolescents aged 12–15 years.

The prevalence of hypertension in the paediatric population varies because BP relates to sex, age and height during childhood.20 26 27 Nevertheless, many epidemiological studies reported the increasing prevalence of hypertension (from 11% to 22%) in children and adolescents between the ages of 3 and 18 years.1 28–31 In our study, the prevalence of HBP was 36.7%, and was significantly higher in boys than in girls. Our results are consistent with those of other studies showing sex differences in BP.32 33 Many studies have shown that obesity was associated with elevated BP.23 34–36 In this study, we found that the prevalence of overweight, obesity and high WC (≥75th percentile) was much more common in participants with HBP than in the NBP group, which is in line with the findings of other researchers.

There is no doubt that the genetic element in the evaluation of hypertension is an important factor. However, the pathophysiological mechanisms of the association between gene polymorphisms and variations of BP are poorly understood. We examined ATP2B1 rs2681472 and CACNB2 rs12258967 variants located in an intron region of these genes. According to numerous Genome-wide association studies (GWAS) reports,12–14 ATP2B1 and CACNB2 are involved in calcium transportation. In addition to well-known biological functions, calcium triggers muscle contraction and is the second messenger of hormones and growth factors.37 A defect in the regulation of calcium and calcium signalling plays an important role in hypertension-associated vascular dysfunction.38 The greatest amount of cellular calcium resides in the sarcoplasmic reticulum and mitochondria, resulting in its lower intracellular than extracellular concentration.39 After depolarisation, calcium enters cardiomyocytes via L-type calcium channels. It activates ryanodine receptors on the sarcoplasmic reticulum to release calcium, and triggers muscle contraction. As the contraction ends, intracellular calcium returns to the sarcoplasmic reticulum via calcium ATPase or plasma membrane Ca2+ ATPase isoform 1 (PMCA1), which is encoded by ATP2B1.40 The plasma membrane calcium/calmodulin-dependent ATPase is involved in calcium pumping from the cytosol to the extracellular compartment41 and regulates the homeostasis of cellular calcium levels, which is important in controlling the contraction and dilatation of vascular smooth muscles.42

CACNB2 encodes the intracellular beta-2 subunit of a calcium channel (voltage-dependent L-type calcium channel), which is a member of the high voltage-gated calcium channel genes and serves as a target of calcium channel blockers.43 These blockers, also known as calcium antagonists, act both on the cardiac tissue and on vascular smooth muscles. In cardiac tissues, a decrease in calcium levels directly results in a decrease of the force of the contraction of the heart, and thus a decrease in blood pressure. It was suggested that the beta-2 subunit could interact with the pore-forming alpha-1 subunit to modulate activity through conformational changes in the calcium channel. Mutations in genes could result in channel activation at more hyperpolarised membrane potentials, implicating an increased calcium influx in disease pathogenesis.44 This may explain why mutations in CACNB2 can influence intracellular calcium homeostasis and alter blood pressure.18 45 46

Studies have shown that CACNB2 was associated with DBP,12 13 23 47 with systolic pressure,13 48–52 mean arterial pressure53 and hypertension.13 49–52 The Kaunas Cardiovascular Risk cohort study found that CACNB2 CC carriers had the highest values of DBP in childhood. However, the logistic regression analysis failed to show significant associations between CACNB2 (rs12258967) and the risk of hypertension.23 CACNB2 polymorphism (rs4373814) was found to be significantly associated with a decreased risk (OR=0.70, 95% CI 0.51 to 0.95) of hypertension in the Han Chinese population.51 The association of the CACNB2 polymorphism (rs11014166) with hypertension (OR=0.79, 95% CI 0.65 to 0.97) was also observed in a study of China.52 A CACNB2 intronic single-nucleotide polymorphism (SNP), rs1571787, had the most significant association with pulse pressure (minor allele frequency (MAF)=0.27, p=0.0003) in a large cohort study.54 Our data showed that boys with the CACNB2 GG genotype had the highest level of SBP. The dominant model of CACNB2 adjusted for the BMI and WC showed that carriers of CG +GG genotypes had a significantly higher odds of HBP than carriers of the CACNB2 CC genotype did (in boys and girls: aOR=2.05; p=0.003 and aOR=1.89; p=0.023, respectively).

It remains unclear how ATP2B1 could result in an increased risk of hypertension, but a significant association of a variant of the ATP2B1 gene with blood pressure and hypertension was identified among Asian and European adults in GWAS.12 13 55 56 The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium GWAS analysis showed that for rs2681472 in ATP2B1, the strongest signal for diastolic pressure – p=3.7×10-8 and with an OR for hypertension of 1.17 per risk allele.13 A meta-analysis involving 15 909 cases and 18 529 controls confirmed that rs2681472 was significantly associated with the risk of hypertension in East Asians (OR=1.18, 95% CI 1.10 to 1.27, p<0.0001).17 Another study among Chinese children confirmed a significant association of ATP2B1 rs17249754 with the risk of hypertension (allelic OR=1.25, 95% CI 1.08 to 1.44, p=0.003).57 However, five studies conducted in Korean,58 Chinese59 and Japanese populations16 60 61 yielded inconsistent results. Our study found no significant association of ATP2B1 with BP or with the risk of hypertension.

BP could be increased during puberty, which is related to rapid physical growth, the endocrine system and hormonal changes.62 Epidemiological studies have demonstrated that adolescents’ age was significantly associated with HBP.63–65 A cross-sectional study in China including children and adolescents aged 5–18 years reported that subjects aged 12–14 and 15–18 years had a significantly increased risk of prehypertension, compared with those aged 5–8 years.63 Data from the Healthy Kids Project showed that 12–17-year-old both boys and girls separately had a greater risk for prehypertension, hypertension and prehypertension/hypertension compared with 5–11-year-old participants.64 Results from the ‘Beijing children and adolescents (BP) study’ showed that adolescents with hypertension during puberty had a higher risk of hypertension in adulthood than children with hypertension in prepuberty did.65

Elevated blood pressure at a young age predicts HBP in adulthood, which is the leading risk factor for cardiovascular diseases. Studies have shown that HBP in childhood correlates with carotid intima-media thickness, atherosclerosis, left ventricular hypertrophy and kidney failure in adulthood.66–68 Childhood BP, overweight and obesity showed the largest OR for adult hypertension, followed by parental hypertension status in different longitudinal studies.4 23 69 Screening children and adolescents for elevated blood pressure could identify hypertension at an early stage, decreasing the rate of progression of hypertension from childhood to adulthood and reducing the risk of cardiovascular disease in adulthood.70

Strengths and limitations

To our knowledge, this is the first study investigating the relationships between ATP2B1 rs2681472 polymorphism and HBP among children and adolescents aged 12–15 years in the Baltic countries. Furthermore, we used multivariate logistic regression analyses to evaluate the associations under different inheritance models, separately for boys and girls. However, the present research has some limitations. The subject selection was limited to 12–15-year-old children and adolescents. Therefore, these results need to be confirmed in a large-scale study involving a larger population of younger children and older adolescents. In this study, BP readings were obtained by an automatic oscillometric BP monitor, although, according to the Fourth Report of the National High Blood Pressure Education Program (NHBPEP), HBP readings should be repeated by using auscultation.20 The present study did not evaluate pubertal status and biochemical parameters. In addition, there was no adjustment for family history of hypertension because information obtained from self-reports was lacking. The design of our study did not allow us to determine the cause–effect relationship.

Conclusion

In conclusion, CACNB2 rs12258967 gene polymorphism was significantly associated with higher odds of HBP in Lithuanian adolescents aged 12–15 years. ATP2B1 rs2681472 gene polymorphism was not associated with the odds of HBP. Further studies, particularly those evaluating the effect of genetic–anthropometric–environmental interactions on blood pressure, could help us to understand new pathophysiological mechanisms of the regulation of BP and to find potential targets for treatments.

Supplementary Material

Reviewer comments
Author's manuscript

Acknowledgments

The authors would like to thank Jurate Medzioniene for carrying out the statistical analysis. The authors also would like to thank the participants of the present study.

Footnotes

Contributors: SS, RK and VD contributed to the conception or design of the work. SS, RK and VL contributed to the acquisition, analysis or interpretation of data for the work. SS and RK drafted the manuscript. VL critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.

Funding: This research was funded by a grant (no. LIG-02/2011) from the Research Council of Lithuania.

Competing interests: None declared.

Patient consent: Parental/guardian consent obtained.

Ethics approval: The study was approved by Kaunas Regional Biomedical Research Ethics Committee at the Lithuanian University of Health Sciences (protocol No. BE-2-69).

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: No additional data are available.

References

  • 1. Rosner B, Cook NR, Daniels S, et al. Childhood blood pressure trends and risk factors for high blood pressure: the NHANES experience 1988-2008. Hypertension 2013;62:247–54. 10.1161/HYPERTENSIONAHA.111.00831 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Liang YJ, Xi B, Hu YH, et al. Trends in blood pressure and hypertension among Chinese children and adolescents: China Health and Nutrition Surveys 1991-2004. Blood Press 2011;20:45–53. 10.3109/08037051.2010.524085 [DOI] [PubMed] [Google Scholar]
  • 3. Chen X, Wang Y. Tracking of blood pressure from childhood to adulthood: a systematic review and meta-regression analysis. Circulation 2008;117:3171–80. 10.1161/CIRCULATIONAHA.107.730366 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Juhola J, Magnussen CG, Viikari JS, et al. Tracking of serum lipid levels, blood pressure, and body mass index from childhood to adulthood: the Cardiovascular Risk in Young Finns Study. J Pediatr 2011;159:584–90. 10.1016/j.jpeds.2011.03.021 [DOI] [PubMed] [Google Scholar]
  • 5. Sorof JM, Lai D, Turner J, et al. Overweight, ethnicity, and the prevalence of hypertension in school-aged children. Pediatrics 2004. 113(3 Pt 1):475–82. [DOI] [PubMed] [Google Scholar]
  • 6. Moore WE, Stephens A, Wilson T, et al. Body mass index and blood pressure screening in a rural public school system: the Healthy Kids Project. Prev Chronic Dis 2006;3:A114. [PMC free article] [PubMed] [Google Scholar]
  • 7. Hansen ML, Gunn PW, Kaelber DC. Underdiagnosis of hypertension in children and adolescents. JAMA 2007;298:874–9. 10.1001/jama.298.8.874 [DOI] [PubMed] [Google Scholar]
  • 8. Zaborskis A, Petrauskiene A, Gradeckiene S, et al. Overweight and increased blood pressure in preschool-aged children. Medicina 2003;39:1200–7. [PubMed] [Google Scholar]
  • 9. Kuciene R, Dulskiene V. Associations of short sleep duration with prehypertension and hypertension among Lithuanian children and adolescents: a cross-sectional study. BMC Public Health 2014;14:255 10.1186/1471-2458-14-255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Grabauskas V, Klumbiene J, Petkeviciene J, et al. Risk factors for noncommunicable diseases in Lithuanian rural population: CINDI survey 2007. Medicina 2008;44:633–9. [PubMed] [Google Scholar]
  • 11. Kupper N, Willemsen G, Riese H, et al. Heritability of daytime ambulatory blood pressure in an extended twin design. Hypertension 2005;45:80–5. 10.1161/01.HYP.0000149952.84391.54 [DOI] [PubMed] [Google Scholar]
  • 12. Ehret GB, Munroe PB, Rice KM, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011;478:103–9. 10.1038/nature10405 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Levy D, Ehret GB, Rice K, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet 2009;41:677–87. 10.1038/ng.384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Newton-Cheh C, Johnson T, Gateva V, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet 2009;41:666–76. 10.1038/ng.361 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Lu X, Wang L, Lin X, et al. Genome-wide association study in Chinese identifies novel loci for blood pressure and hypertension. Hum Mol Genet 2015;24:865–74. 10.1093/hmg/ddu478 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Tabara Y, Kohara K, Kita Y, et al. Common variants in the ATP2B1 gene are associated with susceptibility to hypertension: the Japanese Millennium Genome Project. Hypertension 2010;56:973–80. 10.1161/HYPERTENSIONAHA.110.153429 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Xi B, Tang W, Wang Q. Polymorphism near the ATP2B1 gene is associated with hypertension risk in East Asians: a meta-analysis involving 15 909 cases and 18 529 controls. Blood Press 2012;21:134–8. 10.3109/08037051.2012.632845 [DOI] [PubMed] [Google Scholar]
  • 18. Lao QZ, Kobrinsky E, Harry JB, et al. New Determinant for the CaVbeta2 subunit modulation of the CaV1.2 calcium channel. J Biol Chem 2008;283:15577–88. 10.1074/jbc.M802035200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Dulskiene V, Kuciene R, Medzioniene J, et al. Association between obesity and high blood pressure among Lithuanian adolescents: a cross-sectional study. Ital J Pediatr 2014;40:102 10.1186/s13052-014-0102-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004;114(2 Suppl 4th Report):555–76. [PubMed] [Google Scholar]
  • 21. Cole TJ, Bellizzi MC, Flegal KM, et al. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320:1240–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Fernández JR, Redden DT, Pietrobelli A, et al. Waist circumference percentiles in nationally representative samples of African-American, European-American, and Mexican-American children and adolescents. J Pediatr 2004;145:439–44. 10.1016/j.jpeds.2004.06.044 [DOI] [PubMed] [Google Scholar]
  • 23. Petkeviciene J, Klumbiene J, Simonyte S, et al. Physical, behavioural and genetic predictors of adult hypertension: the findings of the Kaunas Cardiovascular Risk Cohort study. PLoS One 2014;9:e109974 10.1371/journal.pone.0109974 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Ejike CE, Ugwu CE, Ezeanyika LU, et al. Blood pressure patterns in relation to geographic area of residence: a cross-sectional study of adolescents in Kogi state, Nigeria. BMC Public Health 2008;8:411 10.1186/1471-2458-8-411 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Falkner B, Daniels SR. Summary of the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents. Hypertension 2004;44:387–8. 10.1161/01.HYP.0000143545.54637.af [DOI] [PubMed] [Google Scholar]
  • 26. Redwine KM, Acosta AA, Poffenbarger T, et al. Development of hypertension in adolescents with pre-hypertension. J Pediatr 2012;160:98–103. 10.1016/j.jpeds.2011.07.010 [DOI] [PubMed] [Google Scholar]
  • 27. Kollias A, Dafni M, Poulidakis E, et al. Out-of-office blood pressure and target organ damage in children and adolescents: a systematic review and meta-analysis. J Hypertens 2014;32:2315–31. discussion 2331 10.1097/HJH.0000000000000384 [DOI] [PubMed] [Google Scholar]
  • 28. Maldonado J, Pereira T, Fernandes R, et al. An approach of hypertension prevalence in a sample of 5381 Portuguese children and adolescents. The AVELEIRA registry. "Hypertension in children". Blood Press 2011;20:153–7. 10.3109/08037051.2010.542649 [DOI] [PubMed] [Google Scholar]
  • 29. de Moraes AC, Lacerda MB, Moreno LA, et al. Prevalence of high blood pressure in 122,053 adolescents: a systematic review and meta-regression. Medicine 2014;93:e232 10.1097/MD.0000000000000232 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Karatzi K, Protogerou AD, Moschonis G, et al. Prevalence of hypertension and hypertension phenotypes by age and gender among schoolchildren in Greece: The Healthy Growth Study. Atherosclerosis 2017;259:128–33. 10.1016/j.atherosclerosis.2017.01.027 [DOI] [PubMed] [Google Scholar]
  • 31. Guo X, Zhang X, Li Y, et al. Differences in healthy lifestyles between prehypertensive and normotensive children and adolescents in Northern China. Pediatr Cardiol 2012;33:222–8. 10.1007/s00246-011-0112-8 [DOI] [PubMed] [Google Scholar]
  • 32. Rosner B, Prineas R, Daniels SR, et al. Blood pressure differences between blacks and whites in relation to body size among US children and adolescents. Am J Epidemiol 2000;151:1007–18. [DOI] [PubMed] [Google Scholar]
  • 33. Peters RM, Flack JM. Diagnosis and treatment of hypertension in children and adolescents. J Am Acad Nurse Pract 2003;15:56–63. 10.1111/j.1745-7599.2003.tb00352.x [DOI] [PubMed] [Google Scholar]
  • 34. Mehdad S, Hamrani A, El Kari K, et al. Prevalence of elevated blood pressure and its relationship with fat mass, body mass index and waist circumference among a group of Moroccan overweight adolescents. Obes Res Clin Pract 2013;7:e284–9. 10.1016/j.orcp.2012.02.006 [DOI] [PubMed] [Google Scholar]
  • 35. Menard SW, Park MK, Scholfield J. The San Antonio Biethnic Children’s Blood Pressure Study: anthropometric findings. Clin Excell Nurse Pract 1999;3:19–27. [PubMed] [Google Scholar]
  • 36. Fixler DE, Kautz JA, Dana K. Systolic blood pressure differences among pediatric epidemiological studies. Hypertension 1980;2(4 Pt 2):I3–7. 10.1161/01.HYP.2.4_Pt_2.I3 [DOI] [PubMed] [Google Scholar]
  • 37. Bootman MD, Rietdorf K, Hardy H, et al. Calcium Signalling and Regulation of Cell Function. In: eLS. Chichester: John Wiley & Sons Ltd, 2012. [Google Scholar]
  • 38. Supiano MA, Hogikyan RV, Sidani MA, et al. Sympathetic nervous system activity and alpha-adrenergic responsiveness in older hypertensive humans. Am J Physiol 1999;276:E519–E528. [DOI] [PubMed] [Google Scholar]
  • 39. Dibb KM, Graham HK, Venetucci LA, et al. Analysis of cellular calcium fluxes in cardiac muscle to understand calcium homeostasis in the heart. Cell Calcium 2007;42:503–12. 10.1016/j.ceca.2007.04.002 [DOI] [PubMed] [Google Scholar]
  • 40. Petrovic MM, Vales K, Putnikovic B, et al. Ryanodine receptors, voltage-gated calcium channels and their relationship with protein kinase A in the myocardium. Physiol Res 2008;57:141–9. [DOI] [PubMed] [Google Scholar]
  • 41. Monteith GR, Roufogalis BD. The plasma membrane calcium pump–a physiological perspective on its regulation. Cell Calcium 1995;18:459–70. [DOI] [PubMed] [Google Scholar]
  • 42. Pande J, Mallhi KK, Sawh A, et al. Aortic smooth muscle and endothelial plasma membrane Ca2+ pump isoforms are inhibited differently by the extracellular inhibitor caloxin 1b1. Am J Physiol Cell Physiol 2006;290:1341–9. 10.1152/ajpcell.00573.2005 [DOI] [PubMed] [Google Scholar]
  • 43. Triggle DJ. Drug targets in the voltage-gated calcium channel family: why some are and some are not. Assay Drug Dev Technol 2003. 1:719–33. 10.1089/154065803770381075 [DOI] [PubMed] [Google Scholar]
  • 44. Scholl UI, Goh G, Stölting G, et al. Somatic and germline CACNA1D calcium channel mutations in aldosterone-producing adenomas and primary aldosteronism. Nat Genet 2013;45:1050–4. 10.1038/ng.2695 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Moosmang S, Schulla V, Welling A, et al. Dominant role of smooth muscle L-type calcium channel Cav1.2 for blood pressure regulation. Embo J 2003;22:6027–34. 10.1093/emboj/cdg583 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Murakami M, Yamamura H, Murakami A, et al. Conserved smooth muscle contractility and blood pressure increase in response to high-salt diet in mice lacking the beta3 subunit of the voltage-dependent calcium channel. J Cardiovasc Pharmacol 2000;36 Suppl 2:S69–73. 10.1097/00005344-200000006-00015 [DOI] [PubMed] [Google Scholar]
  • 47. Arora P, Newton-Cheh C. Blood pressure and human genetic variation in the general population. Curr Opin Cardiol 2010;25:229–37. 10.1097/HCO.0b013e3283383e2c [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. de Las Fuentes L, Sung YJ, Schwander KL, et al. The role of SNP-loop diuretic interactions in hypertension across ethnic groups in HyperGEN. Front Genet 2013;4:304 10.3389/fgene.2013.00304 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Luke MM, O’Meara ES, Rowland CM, et al. Gene variants associated with ischemic stroke: the cardiovascular health study. Stroke 2009;40:363–8. 10.1161/STROKEAHA.108.521328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Ganesh SK, Chasman DI, Larson MG, et al. Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations. Am J Hum Genet 2014;95:49–65. 10.1016/j.ajhg.2014.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Hong GL, Chen XZ, Liu Y, et al. Genetic variations in MOV10 and CACNB2 are associated with hypertension in a Chinese Han population. Genet Mol Res 2013;12:6220–7. 10.4238/2013.December.4.9 [DOI] [PubMed] [Google Scholar]
  • 52. Lin Y, Lai X, Chen B, et al. Genetic variations in CYP17A1, CACNB2 and PLEKHA7 are associated with blood pressure and/or hypertension in she ethnic minority of China. Atherosclerosis 2011;219:709–14. 10.1016/j.atherosclerosis.2011.09.006 [DOI] [PubMed] [Google Scholar]
  • 53. Wain LV, Verwoert GC, O’Reilly PF, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet 2011;43:1005–11. 10.1038/ng.922 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Morrison AC, Bis JC, Hwang SJ, et al. Sequence analysis of six blood pressure candidate regions in 4,178 individuals: the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) targeted sequencing study. PLoS One 2014;9:e109155 10.1371/journal.pone.0109155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Kato N, Takeuchi F, Tabara Y, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet 2011;43:531–8. 10.1038/ng.834 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Cho YS, Go MJ, Kim YJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527–34. 10.1038/ng.357 [DOI] [PubMed] [Google Scholar]
  • 57. Xi B, Shen Y, Zhao X, et al. Association of common variants in/near six genes (ATP2B1, CSK, MTHFR, CYP17A1, STK39 and FGF5) with blood pressure/hypertension risk in Chinese children. J Hum Hypertens 2014;28:32–6. 10.1038/jhh.2013.50 [DOI] [PubMed] [Google Scholar]
  • 58. Hong KW, Go MJ, Jin HS, et al. Genetic variations in ATP2B1, CSK, ARSG and CSMD1 loci are related to blood pressure and/or hypertension in two Korean cohorts. J Hum Hypertens 2010;24:367–72. 10.1038/jhh.2009.86 [DOI] [PubMed] [Google Scholar]
  • 59. Liu C, Li H, Qi Q, et al. Common variants in or near FGF5, CYP17A1 and MTHFR genes are associated with blood pressure and hypertension in Chinese Hans. J Hypertens 2011;29:70–5. 10.1097/HJH.0b013e32833f60ab [DOI] [PubMed] [Google Scholar]
  • 60. Takeuchi F, Isono M, Katsuya T, et al. Blood pressure and hypertension are associated with 7 loci in the Japanese population. Circulation 2010;121:2302–9. 10.1161/CIRCULATIONAHA.109.904664 [DOI] [PubMed] [Google Scholar]
  • 61. Miyaki K, Htun NC, Song Y, et al. The combined impact of 12 common variants on hypertension in Japanese men, considering GWAS results. J Hum Hypertens 2012;26:430–6. 10.1038/jhh.2011.50 [DOI] [PubMed] [Google Scholar]
  • 62. Rogol AD, Roemmich JN, Clark PA. Growth at puberty. J Adolesc Health 2002;31(6 Suppl):192–200. [DOI] [PubMed] [Google Scholar]
  • 63. Guo X, Zheng L, Li Y, et al. Gender-specific prevalence and associated risk factors of prehypertension among rural children and adolescents in Northeast China: a cross-sectional study. Eur J Pediatr 2013;172:223–30. 10.1007/s00431-012-1873-7 [DOI] [PubMed] [Google Scholar]
  • 64. Moore WE, Eichner JE, Cohn EM, et al. Blood pressure screening of school children in a multiracial school district: the Healthy Kids Project. Am J Hypertens 2009;22:351–6. 10.1038/ajh.2009.13 [DOI] [PubMed] [Google Scholar]
  • 65. Liang Y, Mi J. Pubertal hypertension is a strong predictor for the risk of adult hypertension. Biomed Environ Sci 2011;24:459–66. 10.3967/0895-3988.2011.05.002 [DOI] [PubMed] [Google Scholar]
  • 66. Juonala M, Magnussen CG, Venn A, et al. Influence of age on associations between childhood risk factors and carotid intima-media thickness in adulthood: the Cardiovascular Risk in Young Finns Study, the Childhood Determinants of Adult Health Study, the Bogalusa Heart Study, and the Muscatine Study for the International Childhood Cardiovascular Cohort (i3C) Consortium. Circulation 2010;122:2514–20. 10.1161/CIRCULATIONAHA.110.966465 [DOI] [PubMed] [Google Scholar]
  • 67. Litwin M, Niemirska A, Sladowska-Kozlowska J, et al. Regression of target organ damage in children and adolescents with primary hypertension. Pediatr Nephrol 2010;25:2489–99. 10.1007/s00467-010-1626-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68. Hartiala O, Magnussen CG, Kajander S, et al. Adolescence risk factors are predictive of coronary artery calcification at middle age: the cardiovascular risk in young Finns study. J Am Coll Cardiol 2012;60:1364–70. 10.1016/j.jacc.2012.05.045 [DOI] [PubMed] [Google Scholar]
  • 69. Juhola J, Oikonen M, Magnussen CG, et al. Childhood physical, environmental, and genetic predictors of adult hypertension: the cardiovascular risk in young Finns study. Circulation 2012;126:402–9. 10.1161/CIRCULATIONAHA.111.085977 [DOI] [PubMed] [Google Scholar]
  • 70. Sun SS, Grave GD, Siervogel RM, et al. Systolic blood pressure in childhood predicts hypertension and metabolic syndrome later in life. Pediatrics 2007;119:237–46. 10.1542/peds.2006-2543 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Reviewer comments
Author's manuscript

Articles from BMJ Open are provided here courtesy of BMJ Publishing Group

RESOURCES