Development of a genetic risk score to predict the risk of hypertension in European adolescents from the HELENA study

doi:10.21203/rs.3.rs-1978509/v2

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Development of a genetic risk score to predict the risk of hypertension in European adolescents from the HELENA study

https://doi.org/10.21203/rs.3.rs-1978509/v2

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Background

Several single nucleotide polymorphisms (SNPs) are associated with blood pressure (BP) levels. A combination of SNPs, forming a genetic risk score (GRS) could be considered a useful genetic tool to identify individuals at risk of developing hypertension from early stages in life. Therefore, the study aims to predict the genetic predisposition to hypertension in European adolescents from a GRS.

Methods

A total of 869 adolescents (53% female), aged 12.5–17.5, were included from the “Healthy Lifestyle in Europe by Nutrition in Adolescence” (HELENA) cross-sectional study. The sample was divided into altered (≥ 130 mmHg for systolic and/or ≥ 80 mmHg for diastolic) or normal BP.

Results

From 1534 SNPs available, An initial screening of SNPs univariately associated with BP (p < 0.10) was established, to finally obtain a number of 16 SNPs significantly associated with BP (p < 0.05) in the multivariate model. Both unweighted GRS (uGRS) and weighted GRS (wGRS) were associated with altered BP. The area under the curve was explored using ten-fold internal cross-validation for uGRS (0.796) and wGRS (0.803). Further covariates were added to obtain a higher predictive ability.

Conclusions

Both GRSs, could be useful to evaluate the genetic predisposition to hypertension in European adolescents.

During the last decades there has been an increase in hypertension (HTN) prevalence throughout the world, both in children and adolescents ¹. A recent meta-analysis found higher HTN prevalence (9.12%) in children older than 10 years, compared with children 6–9 years old (4.06%) ¹. The increase in HTN prevalence has been related to other cardiometabolic disturbances such as obesity and dyslipidemia ^2,3, as some physiological mechanisms are shared ⁴. However, the etiology of essential HTN has not been fully elucidated ⁵.

The absence of symptoms together with the lack of time during routine outpatient visits have become a challenge for the early HTN detection and diagnosis ⁶. In addition, adolescents with HTN often remain hypertensive even in adulthood with possible irreversible consequences on their health status ⁷. In order to prevent future health complications, the American Academy of Pediatrics (AAP) updated in 2017 the Clinical Practice Guideline for Screening and Management related to high BP in children and adolescents. Fixed cut-off points independent of age, sex and height were established for children over 13 years. HTN was defined when BP levels are greater than or equal to 130 mmHg for systolic BP (SBP), or greater than or equal to 80 mmHg for diastolic BP (DBP) ⁸.

Several risk factors affecting BP levels, such as dietary habits, salt intake, excess adiposity, low levels of physical activity, sleep quality and secondhand smoking, can be modified ^9,10, while others, including genetics, seem to remain unmodified ¹¹. Therefore, early identification of risk factors, not only environmental but also genetics, is essential for the prevention of HTN-related future cardiovascular events ¹¹.

HTN is a highly heritable trait, almost 40–60% on individual differences in BP have a genetic basis ¹². Since the first genome-wide association studies (GWAS) in 2009 to present, several genetic variants associated with BP have been deciphered ¹³. Due to the great amount of candidate loci, there is limited potential to carry out the functional investigation of each locus. However, some of them have been fairly well characterized. Some of these plausible candidates are angiotensinogen (AGT), Angiotensin-I-Converting Enzyme (ACE) and Angiotensin-I-Converting Enzyme 2 (ACE2), genes encoding enzymes responsible for the control of the renin-angiotensin system ¹¹. In Chinese populations, most studies have found significant associations between BP and single nucleotide polymorphisms (SNPs) related with theses loci ¹⁴. Other loci such as ATPase Plasma Membrane Ca²⁺ Transporting 1 (ATP2B1) and Calcium Voltage-Gated Channel Auxiliary Subunit Beta 2 (CACNB2) genes, that play a critical role in the intracellular calcium homeostasis, have shown several SNPs associated with higher BP ^15,16. Moreover, there are also studies that have found associations between the fat mass and obesity associated gene (FTO) and HTN risk, in which BMI plays a moderating role ¹⁷.

Although there is a large number of SNPs associated with BP or HTN, each of them independently has a limited predictive ability ¹⁸. Thus, the development of a GWAS-based genetic risk score (GRS), to predict the risk of hypertension, could be a useful genetic tool to assess its genetic susceptibility. A GRS is built by combining SNPs, either by summing the number of risk alleles: unweighted GRS (uGRS); or by multiplying the number of risk alleles by each estimated coefficient: weighted GRS (wGRS).

In European adults, several GRS have been associated with BP and HTN, also being prognostic markers for future cardiovascular events ^19,20. For example, a GRS based on 29 genetic variants developed in adults ²⁰ was also analyzed in European children and adolescents ^21,22. Moreover, another study carried out in European adolescents used an adult-based GRS to explain the association with different traits, including BP, in adolescents ²³. To date, only two GWAS have been performed in children and adolescents, identifying novel loci associated with BP ^24,25. In addition, to the best of our knowledge, no GRSs developed in European adolescents have considered GWAS BP-associated loci in both adult population and children and adolescents. Thus, the present study considered loci associated with BP or elevated BP previously found in the literature, with special emphasis in those studies based on Caucasian adolescent populations. Therefore, the aim of the present study was to develop a GRS as a predictive tool for HTN in European adolescents of the “The Healthy Lifestyle in European by Nutrition in Adolescence (HELENA)” cross-sectional study.

Study design and population

The HELENA-cross-sectional and multicentric study was conducted in 10 European cities between 2006 and 2007 (Athens, Greece; Dortmund, Germany; Ghent, Belgium; Heraklion, Crete; Lille, France; Pécs, Hungary; Rome, Italy; Stockholm, Sweden; Vienna, Austria; and Zaragoza, Spain) with large enough sample to represent population diversity ²⁶. The main objective of the HELENA study was to obtain a representative data of European adolescents in terms of nutrition, lifestyle and health-related parameters. The HELENA study was implemented following the Declaration of Helsinki 1964 recommendations (revision of 2000) and with the approval of the Ethics Committees of each of the ten participating countries ²⁷. The protocol was approved by the Ethical Committee (Comité de Ética de la Investigación de la Comunidad Autónoma de Aragón: CEICA). Moreover, parents of the participants enrolled in the study read and signed an informed consent. Also, a verbal informed consent was read to the participants whose age was above 16 years old.

A randomly selected sample from the total of participants was assigned for blood sampling ²⁸, selecting a total of 869 adolescents (52.9% females) with complete genetic and BP information (Supplementary Fig. 1).

Blood pressure assessment

The SBP and DBP were measured twice in sitting position with a 10-minute interval in between, using a previously validated automated digital BP device for clinical use (OMRON M6) ²⁹. The blood pressure measure was measured by a trained professionals in a quiet room. The cuff-size was adapted to the arm circumference of participant. The complete procedures of BP measurement have been previously described ³⁰. In the present study, the lowest BP measurement, in both SBP and DBP, was selected as the final value. A cut-off point for BP levels was established according to the AAP CPG 2017 recommendations. Adolescents aged 13 years or more were classified as stage 1 HTN when either SBP was above or equal to 130 mmHg and DBP was above or equal to 80 mmHg ⁸. Although, three BP reading separated in time are necessary to confirm HTN. In the present study, the term HTN or risk of HTN has been used interchangeably to refer to those participants who had BP levels above or equal the cut-off-points at the time of the BP measurement.

Anthropometric measurements

Weight and height were measured by trained researchers following standardized procedures. BMI was calculated from height and weight (kg/m²). Further, the BMI z-score was calculated using the recommendations of Cole et al., ³¹.

Blood collection and genotyping

A standardized methodology for blood collection, transport and analysis was performed by a certified laboratory ³². Blood samples were collected after a 10–12 hour overnight fast in EDTA K3 tubes. Then, the DNA was extracted and stored at the Institute of Nutritional and Food Sciences (IEL) of the University of Bonn, and sent to the Laboratoire d’Analyse Genomique Centre de Ressources Biologiques (LAG-CRB) e BB- 0033–00071 Institut Pasteur de Lille, F-59000 Lille, France. DNA was extracted from white blood cells with the Puregene kit (QIAGEN, Courtaboeuf, France) and stored at − 20 ºC. Samples were genotyped using the Illumina Global Screening Array chip. After quality control, ~ 600,000 genotyped SNPs were available. Additionally, around 7 million SNPs were obtained with imputation using the Haplotype Reference Consortium reference panel. SNPs were excluded if imputation quality was < 0.3.

Based on the literature, a total of 57 genes related with BP, specifically in adolescents of European origin, were identified. Among these genes, 13428 SNPs are available in the HELENA genetic dataset. Unfortunately, the gene ACE2 was not available due to its location on the X chromosome which was not analyzed in the HELENA study. SNPs quality control Linkage Disequilibrium (r² > 0.8) was applied, leading to 4770 independent SNPs (572 genotyped SNPs and 4198 imputed) (Supplementary Fig. 1).

Statistical analyses

Anthropometric, BP and pure fructose from non-natural foods consumption variables were descriptively analyzed by sex. Continuous variables (age, height, body weight, BMI, consumption of pure fructose from non-natural foods, SBP and DBP) were reported as median and interquartile range. Differences between groups were analyzed using the Mann-Whitney U test.

SBP and DBP were dichotomized. Then, a new HTN variable was created based on whether or not participants had abnormal SBP or DBP values. The newly created BP variable was used as the model outcome. Lastly, the chi-square test was used to compare BP categories in both sex groups.

All statistical analyses were performed using Rstudio Version 1.2.5019 (Rstudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/) and the significance level was set at p < 0.05.

Development of the genetic risk score

After quality control check, a total of 4770 SNPs related to BP were obtained from the HELENA GWAS analyses. The SNPAssoc R package was used to establish genetic filters: a Hardy-Weinberg equilibrium (HWE) < 0.05 and minor allele frequency (MAF) > 0.1. A total of 1534 SNPs were candidates to be included in the GRS. Each SNP was recoded as 0, 1 or 2, depending on the number of risk alleles defined in previous literature, and the additive genetic model was used. Then, using univariate logistic regression analysis with a cut-off p-value of 0.1, 166 SNPs were found. Next, a backward/forward stepwise logistic regression algorithm was carried out to select the significant SNPs at p < 0.05 (n = 16). From the selected SNPs, two methodological approaches were used to create the multivariable GRS, the unweighted method (uGRS) and the weighted method (wGRS). The uGRS was generated by summing the number of risk alleles of each SNP (0, 1 or 2) with a rescaling,for the SNPs that appears as protector factors. The wGRS was the result of multiplying the number of risk alleles at each locus (0, 1, 2) for each estimated coefficient of the multivariate model. Those SNPs that acts as protector factors have a negative coefficient. So when adding them to the score they always subtract in the GRS.

Validation of the genetic risk score

Receiver operating characteristics (ROC) curves analysis was applied to test the diagnostic accuracy of the GRS to classify potential participants with risk of predisposition to HTN or not. For both uGRS and wGRS an area under the curve (AUC) was obtained considering BP as a binary variable. A principal component analysis (PCAs) were calculated on the total sample of SNPs available in order to explain the variability related to ethnicity and center. Additionally, both GRSs were adjusted by PCAs, sex and age.

The models developed were validated internally by 10-fold cross validation. The validation process divided the initial dataset in 10 groups, 9 of them were used to build the predictive model and the last one to validate this model, thus ensuring the validation of models with data not used in their construction

Moreover, a boxplot was created to graphically show the discrimination ability of the GRS, presenting the distribution of uGRS and wGRS in participants with normal BP and with risk to HTN in a comparison way. Finally, the Youden index was calculated to obtain the best cut-off for the dichotomization of the GRS, and additionally we showed the sensitivity, specificity, negative predictive value, positive predictive value and accuracy for different thresholds for uGRS and wGRS.

Dietary assessment

Dietary intake was determined from two no-consecutive self-administered 24 h recalls at any point in the week. Then, was computerized by a tool previously validated in Flemish adolescents³³: the HELENA Dietary Assessment Tool (HELENA-DIAT). This tool allows participants to select all foods and beverages consumed in six meals (breakfast, morning snacks, lunch, afternoon snacks, dinner and evening snacks). Then, the multiple source method (MSM) was used to calculated individual usual dietary intake. Thus allowing to correct the variability of dietary data between and within participants ³⁴. Additionally, based on the meals salt content reported in the 24-h recalls sodium intake was calculated.

Regarding the assessment of daily fructose intake, the variable created by Béghin et al.,³⁵ in the HELENA study was used. Total fructose exposure was calculated as fructose from monosaccharide form plus fructose from sucrose (0.5 × sucrose per 100 g). The consumption of pure fructose from non-natural foods included food extrinsically fructose rich such as: sugar-sweetened beverages, nonchocolate confectionary, chocolate, cakes/pies/biscuits, desserts and puddings, breakfast cereals and others sources categories³³.

Covariate analysis

BMI z-score, consumption of pure fructose from non-natural foods, and sodium intake, were added to the two GRSs in order to observe whether the inclusion of other covariates affects GRS predictive ability (uGRS and wGRS). General linear model (GLMs) were performed to evaluate the association between BMI z-score, pure fructose and/or sodium with both uGRS and wGRS. Also, the added predictive ability of covariates was analyzed by significant differences between AUCs using a permutation test, obtained in GRSs with covariates and without them.

Description of the study sample

A total of 869 participants were studied (53% females). Supplementary Table 1 shows the main characteristics of the adolescents participating in the study by sex. All variables showed significant differences except for age, DBP and BMI. A number of 16.7% of the participants showed BP levels above the cut-off points used in our study. Moreover, that percentage was higher in male (11.5%) than in female adolescents (5.2%).

Associations between SNPs and HTN

First, a total of 166 SNPs were associated with HTN in univariate analyses. After the multivariate model developed to build the GRS, the number of SNPs significantly associated with risk to HTN was reduced to 16 (Table 1). Moreover, the univariate and multivariate model´s odds ratio (OR) were analyzed for each of the SNPs included in the GRS (Table 2). Supplementary Fig. 2 shows the direction of each SNP (protective/risk), represented in a forest plot. A number of protector SNPs were obtained: STK39 rs6433023, ULK4 rs4580521, ULK4 rs4973982, SMARCA2 rs76973157, CACNB2 rs76466243, PLEKHA7 rs75351046, PLEKHA7 rs72865722, PLEKHA7 rs10832706, FTO rs8057044 and UMODL1 rs113087295. On the other hand, the SNPsthat appeared as risk factors of HTN were: ADRB2 rs17108817, SMARCA2 rs7048826, SMARCA2 rs10965093, PAX5 rs62533676, ITGA11 rs17320635 and ITGA11 rs895135.

Table 1

Genetic information about the 16 single nucleotide polymorphisms (SNPs) forming the genetic risk score.
rs number	Chromosome	Nearest gene	Alleles (major/minor)	MAF	Imputation score	HWE
rs6433023	2	STK39	T/C	0.249	0.98477	0.069
rs4580521	3	ULK4	A/C	0.346	0.99459	0.1
rs4973982	3	ULK4	G/C	0.101	0.99422	0.353
rs17108817	5	ADRB2	T/C	0.488	0.94183	0.541
rs76973157	9	SMARCA2	A/C	0.117	0.99783	0.870
rs7048826	9	SMARCA2	G/C	0.13	0.99179	0.881
rs10965093	9	SMARCA2	C/G	0.433	0.9665	0.407
rs62533676	9	PAX5	T/G	0.25	0.97257	0.415
rs76466243	10	CACNB2	C/G	0.138	0.99581	0.568
rs75351046	11	PLEKHA7	C/T	0.244	0.9783	0.927
rs72865722	11	PLEKHA7	G/A	0.129	0.9939	0.880
rs10832706	11	PLEKHA7	C/T	0.269	0.99972	0.684
rs17320635	15	ITGA11	A/G	0.178	0.99943	0.487
rs895135	15	ITGA11	G/T	0.361	0.92715	0.339
rs8057044	16	FTO	G/A	0.465	0.99549	0.453
rs113087295	21	UMODL1	C/T	0.115	0.99787	0.316
ADRB2 (Adrenergic receptor Beta2); CACNB2 (Calcium Voltage-Gated Channel Auxiliary Subunit Beta 2);FTO (Fat mass and obesity-associated gene); HWE (Hardy-Weinberg Equilibrium); ITGA11(Integrin Subunit Alpha 11); MAF (Minor allele frequency); PAX5 (Paired Box 5); PLEKHA7 (Pleckstrin Homology Domain Containing A7); SMARCA2 (SWI/SNF Related, Matrix Associated, Actin Dependent Regulator Of Chromatin, Subfamily A, Member 2); STK39 (Serine/Threonine Kinase 39); ULK4 (Unc-51 Like Kinase 4); UMODL1 (Uromodulin Like 1).

Table 2

Association between the risk of hypertension and the 16 single nucleotide polymorphisms included in the genetic risk score, ordered by chromosome number and protector/risk nature.
		Univariate*		Multivariate*
rs number	Chromosome	O.R. (95% C.I.)	p	O.R. (95% C.I.)	p
rs6433023	2	0.730 (0.558–0.959)	0.022	0.636 (0.470–0.861)	0.003
rs4580521	3	0.688 (0.515–0.913)	0.011	0.592 (0.430–0.806)	0.001
rs4973982	3	0.606 (0.414–0.899)	0.011	0.443 (0.289–0.685)	< 0.001
rs76973157	9	0.666 (0.419–1.017)	0.071	0.529 (0.317–0.846)	0.01
rs76466243	10	0.591 (0.378–0.891)	0.016	0.495 (0.305–0.775)	0.003
rs75351046	11	0.702 (0.508–0.956)	0.028	0.632 (0.448–0.878)	0.007
rs72865722	11	0.721 (0.511–1.031)	0.067	0.483 (0.319–0.734)	< 0.001
rs10832706	11	0.748 (0.555–0.996)	0.051	0.570 (0.401–0.798)	0.001
rs8057044	16	0.688 (0.534–0.884)	0.004	0.623 (0.468–0.825)	0.001
rs113087295	21	0.574 (0.350–0.9)	0.021	0.417 (0.242–0.687)	0.001
rs17108817	5	1.313 (1.022–1.694)	0.034	1.497 (1.132–1.989)	0.005
rs7048826	9	1.506 (1.059–2.114)	0.02	1.724 (1.177–2.507)	0.005
rs10965093	9	1.397 (1.088–1.796)	0.009	1.66 (1.256–2.210)	< 0.001
rs62533676	9	1.580 (1.159–2.192)	0.005	1.659 (1.186–2.362)	0.004
rs17320635	15	1.363 (0.986–1.865)	0.056	1.609 (1.132–2.276)	0.007
rs895135	15	1.255 (0.963–1.633)	0.092	1.503 (1.125–2.011)	0.006
*Univariate and Multivariate model of SNP associated with risk of hypertension shown as: Odds Ratio (O.R.) and 95% confidence interval (C.I.).

The predictABEL R package ³⁶ was used to create the uGRS and a wGRS from the multivariate model aiming to predict the risk of HTN. Figure 1 shows the ROC curve and the AUC obtained by comparing the probability of risk of HTN and the real outcome in both GRSs adjusted by PCAs, sex and age (uGRS: 0.830, wGRS: 0.838). The discrimination ability of wGRS and uGRS, adjusted by PCAs, sex and age, was internally validated by 10-fold cross-validation. Both GRSs gives robust predictions (AUC = 0.796 uGRS and AUC = 0.803 wGRS). In addition, a boxplot was created to show the uGRS and wGRS distribution in the different groups, adolescents with normal BP and with risk o HTN (Fig. 2). Both GRSs showed differences between the groups. The uGRS showed a possible cut-off point that would differentiate those adolescents with normal BP levels from those with above or equal to the cut-off points established by AAP CPG for HTN. Lastly, the cut-off point with the highest sum of sensitivity and specificity (Youden index) for uGRS was 16.5 and for wGRS was − 2.98. Finally, Table 3 shows additional information regarding sensitivity, specificity, positive and negative predictive value and accuracy of the GRS.

Table 3

Performance of diagnostic tests in the two GRSs expressed in percentage.
Youden index	Spec	Sens	NPV	PPV	Acc	Youden index	Spec	Sens	NPV	PPV	Acc
uGRS						wGRS
23	100	0.69	83.41	100	83.43	0.18	100	0.69	83.41	100	83.43
22	99.72	2.07	83.56	100	83.43	0.03	99.86	0.69	83.39	50.00	83.31
21	99.72	3.45	83.76	60.00	83.66	-0.59	99.72	3.45	83.76	71.43	83.66
20	96.69	15.17	85.05	71.43	83.08	-0.90	99.45	5.52	84.01	66.67	83.77
19	90.61	33.10	87.12	47.83	81.01	-1.10	98.76	10.34	84.62	62.50	84.00
18	80.93	55.17	90.02	41.38	76.64	-2.01	88.12	35.86	87.28	37.68	79.40
17	62.30	76.55	92.99	36.70	64.67	-2.68	71.27	64.83	91.01	31.13	70.20
16	44.61	88.28	95.00	28.91	51.90	-2.97	60.91	79.31	93.63	28.89	63.98
15	25.27	96.55	97.34	24.20	37.17	-3.15	55.52	83.45	94.37	27.31	60.18
14	14.09	100	100	20.56	28.42	-3.49	43.23	90.34	95.72	24.17	51.09
13	6.07	100	100	18.90	21.75	-4.02	25.83	97.24	97.91	20.80	37.74
12	1.38	100	100	17.58	17.84	-4.84	10.08	100	100	18.22	25.09
11	0.41	100	100	16.88	17.03	-5.16	5.66	100	100	17.51	21.40
10	0.00	100	100	16.74	16.80	-6.58	0.00	100	100	16.69	16.69
Abbreviations: Acc: accuracy; NPV: negative predictive value; PPV: positive predictive value; Sens: sensitivity; Spec: specificity; uGRS: unweighted genetic risk score; wGRS: weighted genetic risk score.

Associations between GRS and other covariates

GLMs showed a significant association between GRS and BMI z-score (p < 0.001) both in unweighted and weighted approaches (β = 0.767, β = 0.751), respectively. Also, pure fructose added from non-natural foods showed a significant association with the uGRS (p = 0.01) β = 0.014 and wGRS (p = 0.013) β = 0.026 models.

The predictive ability of ROC curves increased when BMI z-score was included to the model, obtaining an AUC = 0.872 in the uGRS and AUC = 0.877 in the wGRS (Supplementary Fig. 3). Moreover, combining BMI z-score and pure fructose in the same model, an even greater increase in the predictive ability of the GRSs was observed (uGRS: 0.885, wGRS: 0.888) (Supplementary Fig. 4).

In contrast, the addition of sodium covariate does not affect to predictive capacity of the model (uGRS: 0.885, wGRS: 0.888) compared with the AUC obtained with model with BMI z-score + fructose covariates (p > 0.05). The inclusion of the sodium variable in the model was not significant either (p > 0.05).

In addition, significant differences (p < 0.05) were observed when comparing the AUC obtained in the GRSs model adjusted by PCAs, sex and age with that obtained in the model with covariables (p(uGRS) = 0.001, p(wGRS) = 0.003).

In the present study, an uGRS and a wGRS, considering 16 SNPs associated with BP, were developed in European adolescents. To our knowledge, this is the first study performed in European adolescents considering a combination of SNPs significantly associated with BP-related traits in Caucasian youth. Other GRSs have been developed from adults GWAS and then employed in childhood studies ^21,22. In a Finnish follow-up study of children with European ancestry, a weighted GRS was developed, based on 13 SNPs previously associated with BP in adults; the participants with the highest score in the GRS had significant higher diastolic BP at 9 years and the effect was maintained from childhood to adulthood (OR = 1.82) ²¹. Additionally, a cohort study, evaluated the variance explained by an adult-based GRSs developed for traits related to anthropometry, cardiovascular and renal function, metabolism, and inflammation in Dutch adolescents. For BP levels, the variance explained in adolescents was similar to those observed in adults ²³. In contrast, other studies, which analyzed SNPs previously associated with BP in adults, did not find the same associations in adolescents with European ancestry ^21,24 reinforcing the hypothesis that genetic influences on BP change with age ³⁷. The transition from infancy to adulthood comprehends a number of changes in growth and BP. These changes are determined by interactions with age, lifestyle and behavioral risk factors ³⁸. Adolescence is a critical period when individuals with elevated BP levels are more likely to maintain higher BP levels when entering into the adulthood period ³⁹. It is also the stage in which most divergences in BP trajectories are observed ³⁸. For this reason, it is important to know whether there is a difference between the genes that modulate BP in adults and in children. Therefore, for the development of GRSs in adolescents, the SNPs significantly associated with BP found in BP-related GWAS performed in children and adolescents should be considered ^24,25.

There are several genetic polymorphisms involved in metabolic pathways related to BP. In the present study 16 SNPs were included in the GRS. Except for rs8057044, the rest of the SNPs included in the GRS had not been associated with BP in previous studies, despite the previous association between their gene and BP ^21,22,24,25.

One SNP was previously associated with obesity, the rs8057044 in the FTO gene, that has also been associated with high BP in a study performed in Tunisian adults. Specifically, G carriers of rs8057044 showed an association with high DBP ⁴⁰. In the present GRSs, the rs8057044 appeared as protective SNP for the development of HTN. This may be explained by the differences in both ethnicity and age between the two studies. Paired box 5 (PAX5) genes were also associated with elevated BP in French Canadian adolescents with obesity (12–18 years old) ⁴¹. In the mentioned study, those carriers of the rs16933812 showed higher SBP levels. Similarly, in the present study the PAX5rs62533676 had a risk role for HTN.

Other SNPs were observed to be associated with BP. For example, the CACNB2 rs12258967 was analyzed in a study conducted in Lithuanian children and adolescents, showing high odds ratio of high BP in those children with this variant ¹⁵. In contrast, in our study, the rs12258967 was not associated with BP in European adolescents. However, another SNP, related to the CACNB2 rs76466243, was significantly associated with BP and was included in the GRS. There was only one article showing a relationship of the CACNB2 gene (O.R < 1) with diabetic retinopathy ⁴². Similarly, we have observed an association between this SNP and a protective effect on the development of HTN.

Another gene related to BP and HTN is the Serine/Threonine Kinase 39 (STK39) gene. This gene regulates the Na/Cl cotransporter. Several studies conducted in adult populations have found associations between different STK39 SNPs and BP or HTN ⁴³. However, a meta-analysis performed in adults from Asian and European populations, which analyzed the most frequent SNPs of the STK39 gene and their association with HTN did not find conclusive results ⁴⁴. In children and adolescents the only study found, performed in Chinese children, showed no associations between the most frequent SNP of STK39 rs3754777 and the risk of HTN ⁴⁵. Nevertheless, in the GRS developed in this study, a SNP related with the STRK39 rs6433023 gene has been included. This SNP is a possible protective factor for the development of HTN, hypothesizing that maybe the presence of this SNP could increase the sodium excretion in the renal tubule, reducing the BP levels.

Another widely studied gene has been the Beta 2 adrenergic receptor (ADRβ2); it appears to play an important role in salt-sensitive hypertensive patients ⁴⁶. In the developed GRS, ADRβ2 rs17108817 was also a risk factor for the development of HTN.

In addition, genes such as Pleckstrin Homology Domain Containing Family A Member 7 (PLEKHA7) and Serine/Threonine-protein kinase (ULK4) were found associated with BP ¹⁶. However, the SNPs included in that GWAS differ from those obtained in our GRS. In our GRS, all the SNPs associated with these two genes act as a protector factors: rs75351046, rs72865722, rs10832706 for PLEKHA7 and rs4580521, ULK4 rs4973982 for ULK4.

Interestingly, from GWAS developed in children and adolescents, new loci for BP were identified, such as Integrin Subunit Alpha 11 ITGA11 rs1563894 associated with SBP assessed in prepuberty, and rs872256 associated with BP during puberty. These loci were not found to be associated with BP in adults. In the GRS developed, two risk ITGA11 SNPs, rs17320635 and rs895135, were also associated with risk of HTN. Furthermore, in the same GWAS, a SNP (rs872256) near to the SWI/SNF-related Matrix-associated, Actin-dependent Regulator Chromatin group A 2 (SMARCA2) gene was significantly associated with SBP in puberty children ²⁴. We also found an association between 3 SNPs of the SMARCA2 gene and BP. Two of them, rs7048826 and rs10965093, are risk SNPs for the development of HTN, whereas rs76973157 had a protective effect.

The GRS developed in the present study showed a good ability to predict the risk of HTN in adolescents. The use of external weights is a practice commonly used in the GRSs development ⁴⁷. However, in this study, internal weights were used instead. In addition, an uGRS was also built because it is more intuitive to understand than a weighted one. The wGRS and the uGRS, adjusted by principal components analyses, sex and age, both showed an AUC above 0.8 (uGRS: 0.830, wGRS: 0.838). In the literature, an AUC around or above 0.8 is an accepted value to use for clinical diagnosis ⁴⁸. In addition, when BMI z-score and pure fructose from non-natural foods were added to the GRS, the AUC increased, reaching levels close to 0.9. Both BMI z-score and pure fructose from non-natural foods showed a significant association with risk of HTN (p < 0.001). BMI and BP are widely related factors and it is expected that they are significantly related. However, the relationship between fructose and BP is controversial ³⁴. A study carried out in European adolescents observed an association between diastolic BP and a high consumption of pure fructose from non-natural foods ³⁴. The relationship between fructose and BP could be explained through the metabolism of this monosaccharide. Fructose is the only carbohydrate that can increases the uric acid levels. This increase causes hemodynamic effects on the organism (increased oxidative stress, endothelial dysfunction and activation of the renin–angiotensin–aldosterone system) and contributes to the BP increase ⁴⁹.

The present study has some limitations. First, the results should be validated in other populations with larger sample size, similar age and with different ethnic origins. This would allow to test the validity of this BP-specific GRS. Instead, the GRS was internally validated, using cross-validation. Ideally, we should have performed an external validation in an independent cohort. Unfortunately, we did not have the genetic information necessary to carry it out. Second, the HELENA is a cross-sectional study and the long-term effects related with possible cardiovascular complications are not available. Moreover, repeated BP measurements over time were not performed, as suggested by the AAP guideline; therefore, it is not possible to confirm a diagnosis for HTN in this study. In addition, lack of repeated BP measurements over time is one of the reasons why we obtain a high percentage of adolescents with HTN compared with the prevalence observed in other studies. Third, we did not have genetic information of the X chromosome where the ACE2 gene is located. Perhaps the inclusion of the polymorphisms related to this gene may vary the predictive capacity of the GRS. Fourth, the 24-h recalls assess the sodium content in foods. However, this method does not considered the source of sodium from added salt. According to a recent systematic review a meta-analysis, the monitoring of sodium intake with a 24-hour urine is the most accurate method of sodium assessment. Nevertheless, studies developed with high-quality 24-h recalls and in high-income countries showed smaller differences in sodium intake comparing the two methods ⁵⁰.

In contrast, our study showed some strengths; one was the inclusion of 10 cities from different European countries in the HELENA study. Moreover, a limited number of GRSs were developed to predict the risk of HTN considering both protective and risk SNPs. In addition, the GRS developed is formed by several SNPs of genes that have been related to BP in a GWAS performed in children and adolescents, reinforcing the importance of using specific GWAS and GRS for each stage of development. In this line, all SNPs related to all genes found in the scientific literature that are related to BP were included in the analyses, with special interest in those associated with BP in children and adolescents and those previously identified in the adult population. For future research we encourage to validate the present GRS developed in other cohorts with the same genetic ancestries and characteristics in youth.

In conclusion, the GRSs developed in this study could be used as genetic tools to detect adolescents with an increased risk of HTN. Therefore, the BP- specific GRSs may have the potential to guide preventative measures for HTN in youth. Personalized lifestyle interventions, such as diet and physical activity, could target individuals at high genetic risk already from early stages in life.

Acknowledgements

We are grateful to all participants and their families in the HELENA study. The HELENA Study (http://www.helenastudy.com/) was financial support of the European Community Sixth RTD Framework Programme (contract FOOD-CT-2005-007034). Gloria Pérez-Gimeno, has receiving a predoctoral fellowship from the Government of Aragón. Diego F. Salazar-Tortosa was supported by a Marie S. Curie Global Fellowship within the European Union research and innovation framework programme (2014-2020; ClimAHealth: 101030971). Miguel Seral-Cortes has received funding from the Iberus Talent Pre-doctoral fellowships 2018, under the European Union’s H2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No 801586.

Author contributions

G.P-G, M.S-C, S.S-L and L.E. contributed to the GRS analysis and development. A.M and M.M supervised the genomic data. All the remaining authors contributed to data interpretation. The first draft was written by G.P-G and L.M, M-S-C, S.S-L, L.E, G.B-L and A.I.R. editing the first draft. All authors reviewed, commented on, and approved the final version of the article.

Competing interests

The authors declare no conflict of interest

Dara Availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

HELENA Study Group

Co-ordinator: Luis A. Moreno.

Core Group members: Luis A. Moreno, Fréderic Gottrand, Stefaan De Henauw, Marcela González-Gross, Chantal Gilbert.

Steering Committee: Anthony Kafatos (President), Luis A. Moreno, Christian Libersa, Stefaan De Henauw, Sara Castelló, Fréderic Gottrand, Mathilde Kersting, Michael Sjöstrom, Dénes Molnár, Marcela González-Gross, Jean Dallongeville, Chantal Gilbert, Gunnar Hall, Lea Maes, Luca Scalfi.

Project Manager: Pilar Meléndez.

1. Universidad de Zaragoza (Spain)

Luis A. Moreno, José A. Casajús, Jesús Fleta, Gerardo Rodríguez, Concepción Tomás, María I. Mesana, Germán Vicente-Rodríguez, Adoración Villarroya, Carlos M. Gil, Ignacio Ara, Juan Fernández Alvira, Gloria Bueno, Olga Bueno, Juan F. León, Jesús Mª Garagorri, Idoia Labayen, Iris Iglesia, Silvia Bel, Luis A. Gracia Marco, Theodora Mouratidou, Alba Santaliestra-Pasías, Iris Iglesia, Esther González-Gil, Pilar De Miguel-Etayo, Cristina Julián, Mary Miguel-Berges, Isabel Iguacel, Azahara Rupérez, Miguel Seral-Cortes and Gloria Pérez-Gimeno.

2. Consejo Superior de Investigaciones Científicas (Spain)

Ascensión Marcos, Julia Wärnberg, Esther Nova, Sonia Gómez, Ligia Esperanza Díaz, Javier Romeo, Ana Veses, Belén Zapatera, Tamara Pozo, David Martínez.

3. Université de Lille 2 (France)

Laurent Beghin, Christian Libersa, Frédéric Gottrand, Catalina Iliescu, Juliana Von Berlepsch.

4. Research Institute of Child Nutrition Dortmund, Rheinische Friedrich-Wilhelms-Universität Bonn (Germany)

Mathilde Kersting, Wolfgang Sichert-Hellert, Ellen Koeppen.

5. Pécsi Tudományegyetem (University of Pécs) (Hungary)

Dénes Molnar, Eva Erhardt, Katalin Csernus, Katalin Török, Szilvia Bokor, Mrs. Angster, Enikö Nagy, Orsolya Kovács, Judit Répasi.

6. University of Crete School of Medicine (Greece)

Anthony Kafatos, Caroline Codrington, María Plada, Angeliki Papadaki, Katerina Sarri, Anna Viskadourou, Christos Hatzis, Michael Kiriakakis, George Tsibinos, Constantine Vardavas, Manolis Sbokos, Eva Protoyeraki, Maria Fasoulaki.

7. Institut für Ernährungs- und Lebensmittelwissenschaften – Ernährungphysiologie. Rheinische Friedrich Wilhelms Universität (Germany)

Peter Stehle, Klaus Pietrzik, Marcela González-Gross, Christina Breidenassel, Andre Spinneker, Jasmin Al-Tahan, Miriam Segoviano, Anke Berchtold, Christine Bierschbach, Erika Blatzheim, Adelheid Schuch, Petra Pickert.

8. University of Granada (Spain)

Manuel J. Castillo, Ángel Gutiérrez, Francisco B Ortega, Jonatan R Ruiz, Enrique G Artero, Vanesa España, David Jiménez-Pavón, Palma Chillón, Cristóbal Sánchez-Muñoz, Magdalena Cuenca

9. Istituto Nazionalen di Ricerca per gli Alimenti e la Nutrizione (Italy)

Davide Arcella, Elena Azzini, Emma Barrison, Noemi Bevilacqua, Pasquale Buonocore, Giovina Catasta, Laura Censi, Donatella Ciarapica, Paola D'Acapito, Marika Ferrari, Myriam Galfo, Cinzia Le Donne, Catherine Leclercq, Giuseppe Maiani, Beatrice Mauro, Lorenza Mistura, Antonella Pasquali, Raffaela Piccinelli, Angela Polito, Romana Roccaldo, Raffaella Spada, Stefania Sette, Maria Zaccaria.

10. University of Napoli "Federico II" Dept of Food Science (Italy)

Luca Scalfi, Paola Vitaglione, Concetta Montagnese.

11. Ghent University (Belgium)

Ilse De Bourdeaudhuij, Stefaan De Henauw, Tineke De Vriendt, Lea Maes, Christophe Matthys, Carine Vereecken, Mieke de Maeyer, Charlene Ottevaere, Inge Huybrechts.

12. Medical University of Vienna (Austria)

Kurt Widhalm, Katharina Phillipp, Sabine Dietrich, Birgit Kubelka
Marion Boriss-Riedl.

13. Harokopio University (Greece)

Yannis Manios, Eva Grammatikaki, Zoi Bouloubasi, Tina Louisa Cook, Sofia Eleutheriou, Orsalia Consta, George Moschonis, Ioanna Katsaroli, George Kraniou, Stalo Papoutsou, Despoina Keke, Ioanna Petraki, Elena Bellou, Sofia Tanagra, Kostalenia Kallianoti, Dionysia Argyropoulou, Stamatoula Tsikrika, Christos Karaiskos.

14. Institut Pasteur de Lille (France)

Jean Dallongeville, Aline Meirhaeghe.

15. Karolinska Institutet (Sweden)

Michael Sjöstrom, Jonatan R Ruiz, Francisco B. Ortega, María Hagströmer, Anita Hurtig Wennlöf, Lena Hallström, Emma Patterson, Lydia Kwak, Julia Wärnberg, Nico Rizzo.

16. Asociación de Investigación de la Industria Agroalimentaria (Spain)

Jackie Sánchez-Molero, Sara Castelló, Elena Picó, Maite Navarro, Blanca Viadel, José Enrique Carreres, Gema Merino, Rosa Sanjuán, María Lorente, María José Sánchez.

17. Campden BRI (United Kingdom)

Chantal Gilbert, Sarah Thomas, Elaine Allchurch, Peter Burgess.

18. SIK - Institutet foer Livsmedel och Bioteknik (Sweden)

Gunnar Hall, Annika Astrom, Anna Sverkén, Agneta Broberg.

19. Meurice Recherche & Development asbl (Belgium)

Annick Masson, Claire Lehoux, Pascal Brabant, Philippe Pate, Laurence Fontaine.

20. Campden & Chorleywood Food Development Institute (Hungary)

Andras Sebok, Tunde Kuti, Adrienn Hegyi.

21. Productos Aditivos SA (Spain)

Cristina Maldonado, Ana Llorente.

22. Cárnicas Serrano SL (Spain)

Emilio García.

23. Cederroth International AB (Sweden)

Holger von Fircks, Marianne Lilja Hallberg, Maria Messerer

24. Lantmännen Food R&D (Sweden)

Mats Larsson, Helena Fredriksson, Viola Adamsson, Ingmar Börjesson.

25. European Food Information Council (Belgium)

Laura Fernández, Laura Smillie, Josephine Wills.

26. Universidad Politécnica de Madrid (Spain)

Marcela González-Gross, Raquel Pedrero-Chamizo, Agustín Meléndez, Jara Valtueña, David Jiménez-Pavón, Ulrike Albers, Pedro J. Benito, Juan José Gómez Lorente, David Cañada, Alejandro Urzanqui, Rosa María Torres, Paloma Navarro.

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No competing interests reported.

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Development of a genetic risk score to predict the risk of hypertension in European adolescents from the HELENA study

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Study design and population

Blood pressure assessment

Anthropometric measurements

Blood collection and genotyping

Statistical analyses

Development of the genetic risk score

Validation of the genetic risk score

Dietary assessment

Covariate analysis

Results

Description of the study sample

Associations between SNPs and HTN

Associations between GRS and other covariates

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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