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Clinical relevance of a genetic predisposition to elevated blood pressure was quantified during the transition from childhood to adulthood in a population-based Finnish cohort (N=2,357). Blood pressure was measured at baseline in 1980 (age 3–18 years) and in follow-ups in 1983, 1986, 2001 and 2007. Thirteen single nucleotide polymorphisms associated with blood pressure were genotyped and three genetic risk scores associated with systolic and diastolic blood pressure and their combination were derived for all participants. Effects of the genetic risk score were 0.47 mmHg for systolic and 0.53 mmHg for diastolic blood pressure (both p<0.01). The combination genetic risk score was associated with diastolic blood pressure from age 9 onwards (β=0.68 mmHg, p=0.015). Replications in 1194 participants of the Bogalusa Heart Study showed essentially similar results. The participants in the highest quintile of the combination genetic risk score had a 1.82-fold risk of hypertension in adulthood (p<0.0001) compared with the lowest quintile, independent of a family history of premature hypertension. These findings show that genetic variants are associated with preclinical blood pressure traits in childhood, individuals with several susceptibility alleles have on average a 0.5 mmHg higher blood pressure and this trajectory continues from childhood to adulthood.
High blood pressure (BP), especially systolic BP increases the risk of heart disease and stroke by affecting arterial structure and function in adults. Raised BP is the cause of around 13% of total mortality and is responsible for over 7.5 million deaths annually worldwide (1). Atherosclerosis begins in early childhood, and elevated BP is an important risk factor in the pathogenesis of atherosclerosis and cardiovascular events (2–5). Globally, childhood hypertension is an expanding health issue and largely attributed to the obesity epidemic as well as high dietary salt intake (6). Genetic factors are clearly implicated in the pathogenesis of elevated BP as the heritability of BP and hypertension has been estimated to be 31–68% (7). Genome-wide association studies (GWAS) have revealed several variants associated with adult BP, supporting the idea that BP is a complex polygenic trait (8–11). Our aim was to examine whether the genetic variants known to be associated with BP levels or hypertension in adults are also associated with higher systolic and diastolic BP in children and early adolescence thus exposing the carriers to life-long effects of elevated BP. To this end, we identified thirteen common single-nucleotide polymorphisms (SNPs) independently associated with BP levels in recently published GWAS (12, 13). We used BP and lifestyle data from the Young Finns Study, a randomly selected population of Finns with a long clinical follow-up beginning in childhood and adolescence (5, 14). The analyses were replicated in the multi-ethnic population of the Bogalusa Heart Study with long clinical follow-up data starting from early childhood (15).
The participants were 2,357 persons (1,234 women and 1,123 men) all ethnically homogenous Finns participating in the ongoing population-based cohort follow-up study, the Cardiovascular Risk in Young Finns. The first cross-sectional survey was conducted in 1980 in five Finnish cities (Helsinki, Kuopio, Oulu, Tampere, and Turku). Participants were randomly selected from the cities and their rural vicinities to form six cohorts aged 3, 6, 9, 12, 15, and 18 years in 1980. Follow-up studies of the cohorts were carried out in 1983, 1986, 2001 and 2007. The study was approved by an institutional ethics committee and the subjects gave an informed consent. Details of the study protocol and population have been published previously (5, 14, 15). Women who were pregnant in 2001 or 2007 were excluded from the present study.
BP, weight and height measurements were made according to a standardized protocol (14, 15, 16). Detailed methods regarding BP measurements are given in the online data supplement (please see http://hyper.ahajournals.org). Body mass index (BMI) was calculated as participants’ weight in kilograms divided by the square of height in meters. In adulthood, data on antihypertensive treatment, a family history of premature hypertension (before the age of 55 years in either parent), current smoking status (regular cigarette smoking on a weekly basis or more often, or nonsmoker) and alcohol use (in standard drinks per day) were obtained via a self-administered questionnaire in 2001 and 2007. Dietary sodium intake was assessed in 2007 using a modified 131 item food frequency questionnaire developed by the Finnish National Institute for Health and Welfare (17). Pediatric hypertension for subjects between the ages of 3 and 15 was defined according to the 95th percentile thresholds for systolic and diastolic BP (International Pediatric Hypertension Association 2006). A binary variable for prevalent hypertension in adulthood (age 18 or older) was defined as the use of antihypertensive medication or systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg in 2007. We applied a substitution method for those subjects who were under antihypertensive treatment in 2001 (N=58) or 2007 (N=143) by adding 15 mmHg to the systolic and 10 mmHg to the diastolic BP measured in 2001 or 2007 (18).
Common genetic variants associated with BP levels were identified from recently published GWA studies (10, 11). To ascertain that there were no interactions between the SNPs in the GRS, the SNPs were first paired according to the highest LD values, and from each pair the SNP was chosen which had the lowest p value in the original GWA studies. Finally, we chose thirteen BP-associated SNPs which contributed independently to BP (please see Table S1 at http://hyper.ahajournals.org). Whole-genome SNP data of the study subjects were obtained using an Illumina BeadChip Human 670K including 546,677 SNPs. We included all 2,357 subjects with complete data on the 13 SNPs. All SNPs were in Hardy-Weinberg equilibrium. The allele count for the five directly genotyped SNPs was coded 0/1/2, and the expected allele count for the eight imputed SNPs ranged from 0.0 to 2.0. The genetic risk scores (GRS) were calculated for each individual as weighted sums of allele count (expected or real), weighted with the effect sizes from reported GWA studies (12, 13). Weighted GRS model is an approach for evaluating multiple genetic markers simultaneously in association testing for clinical phenotypes. Aggregation of polygenic information in a GRS weighted by the estimated effect of each genetic marker on the risk phenotype is an effective means to encapsulate risk-associated genetic information (19, 20). To explore additive effects of the loci associated more strongly with systolic or diastolic BP in the discovery studies, three different GRS were derived: the combination GRS from all 13 BP-associated alleles, an 8-SNP diastolic GRS (rs16998073, rs1530440, rs3184504, rs1378942, rs16948048, rs9815354, rs11014166 and rs2384550) that associated more strongly with diastolic BP and a 5-SNP systolic GRS (rs17367504, rs11191548, rs12946454, rs381815 and rs2681492). Subjects were divided into five groups according to quintiles of the combination GRS. The combination GRS had a normal distribution in the Young Finns population (please see Figure S1 in the online Data Supplement at http://hyper.ahajournals.org).
The analyses were replicated in the Bogalusa Heart Study, an independent follow-up study with existing knowledge of the genetic composition of the participants and blood pressure measurements starting from childhood. Details of the project have been described elsewhere (15). Briefly, between 1973 and 2010, nine cross-sectional surveys of children aged 4–17 years and ten cross-sectional surveys of adults aged 18–50 years, previously examined as children were conducted in Bogalusa, Louisiana. This panel design of repeated cross-sectional examinations has resulted in serial observations from childhood to adulthood, with 3929 individuals (2374 of European ancestry and 1555 of African-American ancestry) having 4–16 serial measurements.
The primary phenotype of interest was the longitudinal trend in BP from 1980 through 1983, 1986, 2001 and 2007. The GRSs were standardized (mean=0, standard deviation=1) and the effect estimates (β) indicate the change in BP in mmHg per a 1-sd change in GRS. Models were adjusted for age, sex and BMI. The longitudinal mixed models were adjusted for each study year separately to account for a secular trend in BP and the change in measurement device. The longitudinal models for systolic blood pressure included a term for age squared, because the association with age was non-linear. Repeated measurements from same persons are likely to be autocorrelated which must be taken into account using appropriate longitudinal data analysis methods. The strength of autocorrelation is assumed to be inversely associated with time between measurements. In the longitudinal analysis, we took into account the various time intervals between measurements (three years between 1980, 1983 and 1986, fifteen years between 1986 and 2001 and six years between 2001 and 2007) by using a continuous autoregressive covariance structure.
Using the same analysis methods and a first-order autoregressive covariance structure in the longitudinal models, we tested whether the GRS was associated with in the Bogalusa study. All SNPs included in the original analyses were available in the Bogalusa Heart Study population of European ancestry, but SNPs rs3184504 and rs1378942 were not available for the African-American population so that the racial groups were analysed separately. Imputed genotype dosages were used when direct genotype data was not available. The participants of Bogalusa Heart Study were divided into five age groups with equal numbers of participants. Only individuals with complete allelic information of the SNPs in the GRSs were included and the final population from the Bogalusa Heart Study included 1194 individuals (826 European and 368 African-American).
As secondary analyses, we performed cross-sectional linear regression analyses at baseline and at the 2007 follow-up for all GRSs and the individual SNPs. Since the analyses of selected individual SNPs are of exploratory nature, we present nominal p-values without correction for multiple testing. Age-stratified linear regression analyses were performed to ascertain the age when a genetic effect was first detectable. The effects of the GRSs and the SNPs on the risk of hypertension in adulthood were studied with logistic regression. We analysed the hypertension risk for the GRSs, quintiles of the combination GRS (assuming a linear effect across quintiles of the combination GRS) and for the individual SNPs adjusting for the family history of premature hypertension. We evaluated the predicted probability of adult hypertension with two logistic regression models. One fitted with the GRS, age, sex and BMI, and the other with only age, sex and BMI. We calculated the areas under the receiver operating characteristic curves (AUC) and compared their difference by using the C statistics (21). Finally, we studied to what extent sex and dietary salt intake could modify the results by including the interaction terms sex*genetic variable and sodium intake*genetic variable in the models for adult hypertension and systolic and diastolic BP in 2007. The statistical analyses were performed with R version 2.11.1 and SAS version 9.2.
At baseline in 1980, the median and interquartile range (IQR) for systolic BP was 112, IQR=15, diastolic BP median=69, IQR=13 and BMI median=17.3, IQR=4.4 and at the 2007 follow-up the median and IQR for systolic BP was 120, IQR=20, diastolic BP median=76, IQR=15 and BMI median=25.3, IQR=5.7. In 2007, the smoking prevalence was 19% and average alcohol use 1.0 drinks per day. There were no differences in 2001 or 2007 smoking prevalence or alcohol consumption according to the GRS, and smoking and alcohol use were omitted from further analyses. The age-specific average BP and BMI and the sizes of the age groups are described in Table 1.
The GRSs were significantly associated with BP both in the longitudinal analyses spanning from childhood and adolescence to adult age and in the cross-sectional analyses at baseline and at 2007 (Table 2). In the longitudinal analyses, systolic BP was associated with the combination GRS (β=0.47 mmHg, p=0.008) and diastolic BP with both the combination GRS (β=0.53 mmHg, p=0.0003) and the diastolic GRS (β=0.50 mmHg, p=0.0005). The effect sizes were 0.39–0.49 mmHg per a 1-SD change in the GRS, explaining 0.1–0.2% of the variation in BP. In the cross-sectional analyses the combination GRS explained 0.5% of the variation in diastolic BP in the age group of 9-year olds. The individual SNPs rs16948048, rs11014166 and rs11191548 were also associated with BP traits in the longitudinal models (please see Figure S2 in the Online Data Supplement at http://hyper.ahajournals.org).
In age-stratified analyses, the combination GRS was significantly associated with diastolic BP at the age of 9 years (β=0.68 mmHg, p=0.02, Figure 1) and the SNPs rs11014166, rs16948048 and rs11191548 had the most significant effects (please see Table S2 in the Online Data Supplement at http://hyper.ahajournals.org). The cross-sectional effects on BP at baseline and the 2007 follow-up as well as the OR for adult hypertension for all GRSs are presented in Table 2 (for the individual SNPs, please see Table S2 at http://hyper.ahajournals.org).
The GRSs were independent risk factors for hypertension in adulthood even when adjusted for a family history of premature hypertension (Table 2). There was a significant difference in adulthood between the extreme quintiles of the combination GRS in systolic BP (119 mmHg in the lowest and 123 mmHg in the highest quintile, p=0.0008) and diastolic BP (76 mmHg and 78 mmHg, p=0.003). In age-, sex- and BMI-adjusted logistic regression, the risk of hypertension in adulthood was significantly higher in the highest quintile compared with the lowest (OR=1.82, 95% CI= 1.53–2.16, p<0.0001). To avoid overstating effect sizes associated with a specified subject category, we also compared the highest quintile with the quintile containing the mean value of GRS of the population. In such a comparison, the risk of hypertension remained elevated in the highest quintile (OR=1.44, 95% CI=1.20–1.71, p<0.0001) (Figure 2). The proportions of normotensive subjects in the highest and lowest quintiles of the combination GRS from childhood and adolescence through adulthood are shown in the Online Supplement (please see Figure S3 at http://hyper.ahajournals.org). To explore the discriminative power of the combination GRS, we compared receiver operating characteristic curves for two models. Model 1 included age, sex and BMI, and model 2 also the combination GRS (please see Figure S4 http://hyper.ahajournals.org). The model that included the combination GRS had non-significantly higher AUC value than the model that included only age, sex and BMI (AUCs 0.72 vs. 0.71, P=0.33).
We tested the possibility of a sex difference by including a SNP*sex- or GRS*sex -interaction term in the models. There was a significant difference between men and women for the SNP rs11191548 (SNP*sex p=0.005). In sex-stratified regression analysis, the effect estimate for women was 0.9 mmHg/risk allele (p=0.003). In men, the effect was not significant (β=−0.7, p=0.13). For the GRSs or other individual SNPs with significant effects we did not find evidence for sex differences. Similarly, the dietary sodium intake measured in 2007 did not modify the relation between blood pressure/hypertension and individual SNPs of GRSs (interaction term p-values always p>0.5).
In the longitudinal replication analyses with the Bogalusa Heart Study participants, the combination GRS was directly and significantly associated with systolic and diastolic BP in African-Americans (beta=1.22 mmHg, P=0.002 and beta=0.78 mmHg, p=0.005, respectively), and non-significantly in Europeans (beta=0.18, P=0.30 and beta=0.01, p=0.94) (Table 2). Systolic GRS was directly associated with systolic BP both in African-American (beta=1.02, p=0.09) and Europeans (beta=0.42 mmHg, p=0.048) (Table 2). Diastolic GRS was directly associated with diastolic BP in African-Americans (beta=1.76 mmHg, p=0.002) but not in Europeans (beta=−0.1, p=0.60) (Table 2). The effects of individual SNPs in Bogalusa data are shown in Table S2 (please see Table S2 at http://hyper.ahajournals.org). Among the SNP effects, 12 out of 13 markers in Europeans of the Bogalusa Heart Study showed the same direction of effect on diastolic and 10 of 13 on systolic blood pressure as in the Young Finns study. The age-stratified associations of the GRSs in the Bogalusa data are presented in Figure S7 (please see Figure S5 at http://hyper.ahajournals.org). In African-Americans, the diastolic GRS predicted hypertension in adulthood (OR=1.84, 95% CI=1.13–3.02, p=0.02) (Table 2).
These longitudinal data demonstrate that the combination GRS associated with higher BP levels in youth and early adulthood. Individuals with the highest combination GRS had significantly higher diastolic BP at the age of 9 years and the effect was persistent from childhood through adult age. Common variants generally have a small effect size, but in adults an effect size of 1 mmHg could translate to a 10% higher mortality risk (22). The original GWA studies were conducted in older populations, and the extent to which their findings can be validated in the early age groups has not been clarified. Therefore we conducted individual replications for the effects on BP of the GRSs and each of the 13 SNPs in young individuals between 3 and 18 years of age. In older adults systolic blood pressure may be directly associated with cardiovascular risk and mortality (23), but in a recent analysis of blood pressure data collected from Swedish conscripts, elevated diastolic blood pressure in late adolescence contributed more to subsequent mortality in middle age compared with systolic blood pressure (24).
Thanassoulis et al. regarded family history as the best marker for the genetic risk of complex traits (25). In our cohort, individuals with family history of premature hypertension had 2.46-fold increased odds for hypertension (p<0.0001), demonstrating the importance of both lifestyle and unknown gene-environment interactions. In our study, the GRSs were independent risk factors for hypertension in adulthood, even when adjusted for family history of premature hypertension. A similar finding was reported recently for GRS based on coronary heart disease loci (26). These results show that even though effects of individual SNPs are small, they jointly add precision to the risk profiling of individuals over family history.
Several important genomic regions are connected to the SNPs used for calculation of the GRSs in this study. The individual SNPs with nominally significant effect sizes in this study were rs11191548, rs16948048, rs17367504, rs9815354 and rs2681492. In secondary analyses two SNPs, rs11014166 (OR=1.21, CI=1.01–1.45, p=0.04) and rs11191548 (OR=1.39, CI=1.01–1.93, p=0.046) were associated with an increased risk of adult hypertension. We also found a sex difference in the effect of the rs11191548, where the effect estimate was 0.9 mmHg per risk allele in women but there was no significant effect in men in this sample. The rs11191548 is located near the gene CYP17A1 encoding cytosolic purine 5′-nucleotidase, an enzyme in the cytochrome P450 family involved in catalyzing the synthesis of cholesterol, steroids and other lipids. However, this locus contains many genes other than CYP17A1, such as the CYP17 gene where mutations cause congenital adrenal hyperplasia, a rare (Mendelian) cause of hypertension. Little else is known about how this locus might influence BP in humans. Therefore, we calculated the level of linkage disequilibrium (LD) of rs11191548 with other SNPs located within CYP17A1 in our population (please see Table S3 in the online Data Supplement at http://hyper.ahajournals.org). rs11191548 was in strong LD with two other SNPs located within CYP17A1. However, as none of the SNPs in this study have a known functional role and there are no missense variants, no eQTL mapping was available to further support the possible involvement of CYP17A1 and therefore variants in other nearby genes could underlie the observed association between rs11191548 and adult hypertension.
The SNP rs16948048 was also associated with diastolic BP in women in our study cohort. The exact mechanism by which this variant can increase BP remains as yet unknown. The rs16948048 is a variant of the ZNF652 gene encoding the zinc finger protein 652, which belongs to a family of transcription-modulating proteins, able to switch genes on and off (27). Zinc finger proteins are widespread in nature and comprise some 3% of the human genome, and as they are uniquely suited to specifically recognizing large sequences of the genome they are currently a target of intensive research for genetic engineering and gene therapy. Interestingly, the same variant (rs16948048) was associated with a significantly lower risk of hypertension in a recent Chinese Han population-based study by Niu et al, highlighting the need for more research on the pathophysiological mechanisms by which minor genetic differences may act upon the variation of BP both between individuals and populations (28).
The SNP rs17367504 near the gene encoding natriuretic peptide B was significantly associated with BP in this study. Common genetic variants at the NPPA-NPPB loci (natriuretic peptide precursor A and B) have previously been associated with inter-individual variation in plasma natriuretic peptides and BP (9). The natriuretic peptides may lower BP by promoting the urinary excretion of sodium or through vasodilation, and they are synthesized in response to high BP or volume overload. The SNPs rs9815354 near the gene ULK4 (Unc-51-like kinase 4; serine/threonine protein kinase) and rs2681492 near ATP2B1 (ATPase, Ca++ transporting, plasma membrane 1) were also associated with BP traits in the cross-sectional analyses.
A limitation of this study is that the combination of risk alleles was based on statistical analysis, and the biological significance of the genetic variants need further clarification before assigning a clinical value to a genetic risk score. Not all of the individual SNPs proved to be significantly associated with blood pressure in our data and the effect of GRS may be “diluted” by the inclusion of SNPs which have little or no effect. Because the GRSs in this study included SNPs which have been discovered in very large international consortia, no correction for multiple testing was performed, and the significance of individual SNPs should be interpreted with caution. Although they were statistically not significant, the smaller effect sizes at ages 27 and 42 possibly arose from cohort effects. We did not include the potential confounders or effect modifiers such as smoking or alcohol consumption in adulthood in this study, but antihypertensive treatment was accounted for by a substitution method. Although the combination GRS was statistically significantly associated with hypertension in multivariable models, it did not significantly improve AUC in addition to age, sex, and BMI as judged by the C-statistics.
Several gene-environment interactions and physiological pathways through which the genetic effects on BP could be mediated are currently under investigation, including salt sensitivity, the renin-angiotensin-aldosterone system as well as endothelial dysfunction (29–32). We had cross-sectional data on sodium intakes collected with food frequency questionnaires in adulthood but modification of the association between the genetic markers and blood pressure by salt intake was not detectable. However, the used food frequency questionnaires were not designed to precisely capture salt intake or cumulative salt exposure, and with a more valid assessment method such as repeated 24-h urine collection the intake of salt could possibly have shown to play a more significant role in these analyses (33).
The disease probability of complex polygenic traits such as BP can be studied with whole-genome prediction methods, as more SNPs will probably be recognized in future (34–37). Recently, Ho et al. used a novel approach combining both GWAS-derived SNPs and a gene expression-guided approach in the identification of a novel locus associated with BP in a large cohort of middle-aged women (38). Their strategy highlights the importance of identifying biologically functional SNPs and looking for associations beyond the conventional threshold for genome-wide significance (P<5*10−8).
The genetic variants had a quantifiable effect on blood pressure traits from an early age and this was independent of age, sex and BMI in our randomly selected population-based cohort. The combination genetic risk score was an independent predictor of hypertension in adulthood. Our population consisted entirely of ethnically homogenous Finns, and therefore the analyses were repeated in the racially mixed population of the Bogalusa Heart Study. In participants of European ancestry, the relations between genetic risk scores and blood pressure were to the same direction in the Bogalusa data, but somewhat stronger genetic effects were seen among African-Americans. Based on our findings, population-based public health measures, such as preventive lifestyle advice, BP monitoring and, if needed, early treatment could be targeted to high-risk individuals if the genetic risk could be detected more effectively in childhood. However, given the very low proportion of variance in BP explained by these genetic markers, it may well be that population-wide measures, such as reducing the salt content of the food supply, would prove more effective in reducing the burden of disease due to high blood pressure (39). Comparative analyses beyond the replication study included here are needed to assess whether our observations are applicable to other more heterogenous groups of young Caucasians and other ethnicities before genetic testing can be recommended to predict future risk of hypertension.
The authors would like to thank all participants of the Young Finns Study and the Bogalusa Heart Study. The expert technical assistance in the statistical analyses by Irina Lisinen and Ville Aalto is gratefully acknowledged.
Sources of funding
The Young Finns Study has been financially supported by the Academy of Finland (grants 126925, 121584 and 124282), the Social Insurance Institution of Finland, the Tampere (T.L. and M.K.), Kuopio and Turku University Hospital Medical Funds, Juho Vainio Foundation, Paavo Nurmi Foundation, Emil Aaltonen Foundation (T.L.), Finnish Foundation of Cardiovascular Research and Finnish Cultural Foundation. C.N.-C. is supported by grants from the Burroughs Wellcome Fund, Doris Duke Charitable Foundation and the National Institutes of Health. VS was supported by grants number 129494 and 139635 from the Academy of Finland. S.R. and L.P. were supported by the Academy of Finland Center of Excellence for Complex Disease Genetics (grants 213506, 129680). Genotyping was done with the support of the Wellcome Trust. The Bogalusa Heart Study was supported by grants 0855082E from American Heart Association, HD-061437 and HD-062783 from the National Institute of Child Health and Human Development, and AG-16592 from the National Institute on Aging.
Conflicts of interest/Disclosures
There are no conflicts of interest.