A Genetic Response Score for Hydrochlorothiazide UseNovelty and Significance
Insights From Genomics and Metabolomics Integration
Hydrochlorothiazide is among the most commonly prescribed antihypertensives; yet, <50% of hydrochlorothiazide-treated patients achieve blood pressure (BP) control. Herein, we integrated metabolomic and genomic profiles of hydrochlorothiazide-treated patients to identify novel genetic markers associated with hydrochlorothiazide BP response. The primary analysis included 228 white hypertensives treated with hydrochlorothiazide from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study. Genome-wide analysis was conducted using Illumina Omni 1 mol/L-Quad Chip, and untargeted metabolomics was performed on baseline fasting plasma samples using a gas chromatography-time-of-flight mass spectrometry platform. We found 13 metabolites significantly associated with hydrochlorothiazide systolic BP (SBP) and diastolic BP (DBP) responses (false discovery rate, <0.05). In addition, integrating genomic and metabolomic data revealed 3 polymorphisms (rs2727563 PRKAG2, rs12604940 DCC, and rs13262930 EPHX2) along with arachidonic acid, converging in the netrin signaling pathway (P=1×10−5), as potential markers, significantly influencing hydrochlorothiazide BP response. We successfully replicated the 3 genetic signals in 212 white hypertensives treated with hydrochlorothiazide and created a response score by summing their BP-lowering alleles. We found patients carrying 1 response allele had a significantly lower response than carriers of 6 alleles (∆SBP/∆DBP: −1.5/1.2 versus −16.3/−10.4 mm Hg, respectively, SBP score, P=1×10−8 and DBP score, P=3×10−9). This score explained 11.3% and 11.9% of the variability in hydrochlorothiazide SBP and DBP responses, respectively, and was further validated in another independent study of 196 whites treated with hydrochlorothiazide (DBP score, P=0.03; SBP score, P=0.07). This study suggests that PRKAG2, DCC, and EPHX2 might be important determinants of hydrochlorothiazide BP response.
Hydrochlorothiazide, a thiazide diuretic, is among the most commonly prescribed antihypertensive medications in the United States, with >50 million prescriptions annually.1 It is highly recommended as the first-line treatment for most patients with uncomplicated essential hypertension and for patients requiring >1 antihypertensive therapy for blood pressure (BP) control.2 Despite its importance, patients’ response to hydrochlorothiazide varies widely, and studies have shown that <50% of hydrochlorothiazide-treated patients achieve BP control.3,4 This wide interindividual variability in response to hydrochlorothiazide and other antihypertensive medications reveals that the current approach for therapy selection and BP control is suboptimal. Thus, identifying predictors of BP response to hydrochlorothiazide and other antihypertensive medications, which could be used in therapy selection, would help optimize antihypertensive treatment selection and improve BP control.
In the past decade, hypertension pharmacogenetic studies have advanced our understanding of the potential role of genetics in the observed variable response to antihypertensive medications.5,6 However, most of these studies focused on candidate genes, which revealed few reliable predictors of hydrochlorothiazide antihypertensive response.6,7 Recently, we have reported on genome-wide association studies (GWAS) using data generated in studies called Pharmacogenomics Evaluation of Antihypertensive Responses (PEAR) and Genetic Epidemiology of Responses to Antihypertensives (GERA) and have identified and replicated genetic signals associated with hydrochlorothiazide BP response, including protein kinase C, PRKC-α, and G-protein α-subunit-endothelia-3 (GNAS-EDN3) in European Americans (whites).8 However, these genetic signals only explain a small proportion of the variability associated with hydrochlorothiazide BP response, and many more remain to be found. In addition, in GWAS, the stringent significance threshold (P<5×10−8) limits our success in identifying additional significant single-nucleotide polymorphisms (SNPs) influencing hydrochlorothiazide BP response, particularly with the small sample sizes of the globally available hypertension pharmacogenomics studies. This suggests that the standard GWAS approach will not be able to yield all or even the majority of the genetic variance associated with variability in drug response.
In recent years, metabolomic approaches have been successfully used to identify novel biomarkers associated with different diseases and traits and bridge the gap between genomics and phenotype.9,10 In addition, integrating metabolomics with genomics has been successful in identifying novel key regulators and pathways for various diseases and traits, including drug response.11 Thus, in the current study, we aimed to extend the genetic findings from the PEAR study by incorporating metabolomic data with the genomic data on which we have previously reported8 and sought to (1) identify metabolites significantly associated with the BP response to hydrochlorothiazide, (2) use a metabolomic–genomic integrative approach to identify novel genetic variants associated with hydrochlorothiazide BP response, and then (3) create a response score using replicated genetic signals from this study to evaluate their relative contribution toward the interindividual variability in BP response to hydrochlorothiazide.
The primary analysis of the current study included clinical data and biological samples from white participants recruited as part of the PEAR trial (clinicaltrials.gov number, NCT00246519).12 In brief, PEAR was a prospective, multicenter study that recruited mild to moderate hypertensive participants, aged 17 to 65 years, with a primary goal of evaluating the role of genetics on the BP response of hydrochlorothiazide and atenolol monotherapy and combination therapy of both drugs. Further details of the PEAR study design are illustrated in Figure S1 in the online-only Data Supplement. Of note, the discovery analysis in this study included PEAR whites treated with hydrochlorothiazide monotherapy (n=228), which will be referred to as hydrochlorothiazide monotherapy. PEAR whites who started hydrochlorothiazide after atenolol will be referred to as hydrochlorothiazide add-on. Data from the latter group of participants (n=214) were used for replication efforts in this study.
A total of 196 white participants, from the GERA study (clinicaltrials.gov number, NCT00005520),13 were also used for the replication efforts within this study. In brief, the GERA study was a prospective, multicenter study that recruited hypertensive participants, aged 30 to 59 years, to examine the role of genetics on the BP response of hydrochlorothiazide and candesartan monotherapy. More details about the GERA study are presented in the online-only Data Supplement. The PEAR and GERA study protocols were approved by the institutional review board at each study site. All participants provided voluntary written informed consent before participation in the study.
BP Response Measurement
In the PEAR study, participants had their BP measured before hydrochlorothiazide (baseline) and after 9 weeks of hydrochlorothiazide. Home, office, and ambulatory BPs were measured as previously described12 (online-only Data Supplement). For the analysis of hydrochlorothiazide monotherapy and hydrochlorothiazide add-on participants, we used a weighted composite BP of the home, office, and ambulatory daytime and night time BP data, which we have shown to be a more accurate measurement of BP response with a better signal-to-noise ratio and more power to identify genetic predictors of BP response.14
In the GERA study, participants had their BP measured in triplicate by a trained assistant using a random zero sphygmomanometer (Hawksley and Sons, Ltd, Lancing, United Kingdom).13 Hydrochlorothiazide BP response was measured by calculating the difference between post– and pre–hydrochlorothiazide BP readings.
Untargeted Metabolomic Profiling
Baseline fasting plasma samples from 123 PEAR whites treated with hydrochlorothiazide were used for the metabolomic analysis. Samples were selected based on participants with a large waist circumference (men, ≥40 inches and women, ≥35 inches), and there was a good representation of hydrochlorothiazide BP response among participants within the metabolomic data set (Figure S2). Untargeted metabolite profiling was conducted using Gas Chromatography-Time-of-Flight Mass Spectrometry. Plasma samples were prepared for analysis using a 2-step methoximation/silylation protocol.15 For each metabolite, peak height was measured, which represents the maximum intensity of all data points forming the mass spectrometry peak. Further details are presented in the online-only Data Supplement.
Details of the genome-wide genotyping, quality control, and imputation performed on PEAR and GERA participants are described in the online-only Data Supplement.
The overall analyses framework used in this study included 6 steps, as illustrated in Figure 1. In brief, ingenuity pathway analysis software (Ingenuity Systems, http://www.ingenuity.com) was used to integrate metabolites significantly associated with hydrochlorothiazide BP response (false discovery rate [FDR], <0.05) with SNPs at P<5×10−5 from the GWAS analysis. Of note, this P value threshold was selected based on the quantile–quantile plots, which reveal that SNPs with P<5×10−5 deviated above the diagonal, that is, deviated from the expectation under the null hypothesis of no relationship between SNPs and hydrochlorothiazide BP response in PEAR white participants treated with hydrochlorothiazide (Figure S3). From the pathway analysis, we focused on significant SNPs/genes and metabolites converging within the significant pathways (FDR<0.05) and further confirmed their association with hydrochlorothiazide BP response by testing them for replication in PEAR hydrochlorothiazide add-on white participants treated with hydrochlorothiazide (n=212). In addition, to confirm the specificity of the genetic replicated signals to hydrochlorothiazide, we also tested their association in whites treated with atenolol monotherapy in the PEAR study (n=214) and with candesartan (angiotensin II receptor antagonist) monotherapy in the GERA study (n=198).
To assess the effect of multiple response alleles on hydrochlorothiazide BP response and to examine the relative contribution of our genetic findings toward our phenotype, we constructed a genetic response score based on replicated SNPs. Alleles with BP-lowering effect were summed up for inclusion in a regression model, with adjustment for age, sex, baseline BP, and principal components 1 and 2. To replicate the association of this score with hydrochlorothiazide BP response, we tested this score in data from hydrochlorothiazide-treated participants (n=196) within the GERA study. In silico analysis was conducted using HaploReg version 4.1 (http://www.broadinstitute.org/mammals/haploreg/haploreg.php) to test whether the replicated SNPs identified in this study affect gene expression levels. A separate analysis was also conducted to test another response score, including the replicated genetic signals identified in this study combined with other well-replicated SNPs (NEDD4L rs4149601, PRKC-α rs16960228, and GNAS-EDN3 rs2273359) that have previously been reported to be associated with hydrochlorothiazide BP response.7,8 More details about the analysis approach are illustrated in the online-only Data Supplement.
Baseline characteristics and hydrochlorothiazide BP responses of participants included in the genomic and metabolomic analyses are presented in the Table. Age, sex, and body mass index baseline characteristics were similar between the PEAR hydrochlorothiazide monotherapy (genomic and metabolomic datasets), PEAR hydrochlorothiazide add-on, and GERA hydrochlorothiazide studies. Pretreatment systolic BP (SBP) and diastolic BP (DBP) were lower within the PEAR hydrochlorothiazide add-on participants compared with the PEAR hydrochlorothiazide monotherapy and GERA participants because of atenolol treatment before starting hydrochlorothiazide therapy, whereas all other groups were untreated at baseline. Because we have previously shown that baseline BP is the most significant predictor of BP response,13 we adjusted for baseline BP in all the analyses. Of note, hydrochlorothiazide produced greater BP lowering when used as monotherapy in PEAR hydrochlorothiazide and GERA hydrochlorothiazide compared with its use when added to atenolol as hydrochlorothiazide add-on therapy.
Using a Gas Chromatography-Time-of-Flight Mass Spectro metry platform, we were able to identify 212 structurally known metabolites from PEAR hydrochlorothiazide monotherapy participants. Thirteen metabolites of the 212 were significantly associated with both DBP and SBP responses to hydrochlorothiazide (FDR<0.05), after adjustment for age, sex, and baseline BP (Table S1). Those 13 metabolites were then integrated with PEAR hydrochlorothiazide monotherapy GWAS top signals (P<5×10−5) using a pathway integrative approach as shown below.
Genomic–Metabolomic Integration (Step 3, Figure 1)
A total of 103 SNPs were selected, from PEAR hydrochlorothiazide monotherapy SBP and DBP GWAS analyses, based on our suggestive cutoff P value (ie, P<5×10−5) (Table S2). Integrating those 103 SNPs with the 13 significant metabolites identified the netrin signaling pathway as the only significant pathway (P=1.54×10−5), after adjusting for multiple comparisons (FDR<0.05), with rs2727563 in protein kinase, AMP-activated, gamma 2 non-catalytic subunite (PRKAG2) and rs12604940 in deleted in colorectal carcinoma (DCC) genes converging with the arachidonic acid metabolite in the same pathway (Figure S4). We found that carriers of the PRKAG2 rs2727563 C allele had better responses to hydrochlorothiazide in a manner consistent with an additive genetic model (P=2×10−5; Figure 2A). We also found that DCC rs12604940 carriers of the C/C genotypes had a better response to hydrochlorothiazide compared with participants with C/G and G/G genotypes (P=2×10−5; Figure 2B).
We also showed that arachidonic acid was involved in the netrin signaling pathway and had a significant association with hydrochlorothiazide BP response (SBP adjusted, P=1×10−4; DBP adjusted, P=7×10−4; Figure 3), after adjustment for age, sex, and baseline BP. Because arachidonic acid and its metabolites have been associated with cardiovascular traits and BP regulation,16,17 we hypothesized that the arachidonic acid association with hydrochlorothiazide BP response might also be mediated via polymorphisms within genes involved in the arachidonic acid metabolic pathway. Therefore, we tested our hypothesis by investigating SNPs within 11 genes directly involved in the synthesis and degradation of arachidonic acid and previously reported to be associated with BP regulation (Table S3). From this analysis, we were able to identify rs324425, within the candidate region of the fatty acid amide hydrolase gene, and rs7816586 and rs13262930 in the EPHX2 gene, with statistical significant association with hydrochlorothiazide BP response (FDR<0.05; Table S4). Those 3 SNPs along with the PRKAG2 rs2727563 and DCC rs12604940 SNPs were then moved for replication in PEAR hydrochlorothiazide add-on participants as shown below.
Replication and Validation (Step 4, Figure 1)
Three SNPs (PRKAG2 rs2727563, DCC rs12604940, and EPHX2 rs13262930) of the 5 tested SNPs were replicated in the same direction as shown in Figures 2A and 2D and 4, respectively. In silico analyses revealed that rs13262930 and rs2727563 affect the expression levels of EPHX2 and PRKAG2 in blood, respectively (Table S5). In addition, rs13262930 has been shown to affect the expression levels of EPHX2 in several other tissues, including the heart (Table S5). Interestingly, we also found that PRKAG2 rs2727563 has a significant association on arachidonic acid baseline levels (P=0.03; Figure S5). Similarly, EPHX2 rs13262930 has also shown a marginally significant association with arachidonic acid (P=0.055; Figure S5). For DCC rs12604940 SNP, we did not find any effect on DCC gene expression or on arachidonic acid levels.
We confirmed the specificity of these 3 replicated signals to hydrochlorothiazide BP response by testing them in whites treated with other antihypertensives, such as atenolol, in the PEAR study and candesartan in the GERA study. We found that neither of these SNPs were significantly associated with atenolol BP response (rs2727563: SBP, P=0.24 and DBP, P=0.47; rs12604940: SBP, P=0.96 and DBP, P=0.79; rs13262930: SBP, P=0.56 and DBP, P=0.93) nor with candesartan BP response (rs2727563: SBP, P=0.64 and DBP, P=0.71; rs12604940: SBP, P=0.37 and DBP, P=0.29; rs13262930: SBP, P=0.81 and DBP, P=0.77). These results suggest that these signals might be important determinants of hydrochlorothiazide BP response in particular.
Create a Response Score (Steps 5 and 6; Figure 1)
Linear regression analysis adjusting for age, sex, baseline BP, and principal components 1 and 2 revealed that individuals with a higher score had a better hydrochlorothiazide SBP (P=1×10−8) and DBP (P=3×10−9) responses compared with lower score participants (Figure 5A and 5B, respectively). We found that this genetic response score, by itself, explained 11.3% and 11.9% of hydrochlorothiazide SBP and DBP responses, respectively. In addition, we also found a significant association when we tested this response score with home BP, 24-hour ambulatory BP, and office BP responses measured (Figure S6). Moreover, we validated this score in whites treated with hydrochlorothiazide in the GERA study in which we found a significant association with DBP response (1-sided hypothesis, P=0.03; Figure 5C) and a trend toward significance with SBP response (1-sided hypothesis, P=0.07; Figure 5D).
In a separate analysis, we added another 3 well-replicated SNPs that have previously been reported to be associated with hydrochlorothiazide BP response in whites (NEDD4L rs4149601, PRKC-α rs16960228, and GNAS-EDN3 rs2273359)7,8 to our response score. Interestingly, adding those 3 SNPs to the response score improved the significance and the predictability of the score in both PEAR (DBP: P=5×10−10, r2=16.2% and SBP: P=5×10−11, r2=14.9%) and GERA (DBP: P=2×10−4, r2=4.5% and SBP: P=3×10−3, r2=3.3%) studies, as shown in Figure S7.
The genomic–metabolomic integrative approach used in this study helped identify 3 signals, PRKAG2 rs2727563, DCC rs12604940, and EPHX2 rs13262930, which were significantly associated with hydrochlorothiazide BP response, and their associations were replicated in a second cohort. Using these 3 replicated signals, we constructed a genetic response score with a stronger association with hydrochlorothiazide BP response compared with individual SNPs. This is not surprising for a complex phenotype like antihypertensive response because it is known to be affected by multiple genetic contributors. This response score by itself explained 11.3% and 11.9% of hydrochlorothiazide SBP and DBP responses, respectively, and was further validated in a third independent study, which emphasizes the importance of this response score and its signals to be considered in future models for guiding the selection of hydrochlorothiazide therapy.
Hydrochlorothiazide is known to inhibit the Na+/Cl− cotransporter in the distal convoluted tubule within the kidney. This inhibition initially contracts plasma volume and decreases cardiac output, leading to BP lowering; however, the plasma volume and cardiac output return to normal after 4 to 6 weeks of thiazide initiation. This suggests that the long-term BP-lowering effects of hydrochlorothiazide might be controlled by other unknown mechanisms.6 The genomic–metabolomic pathway analysis performed in this study highlighted the netrin signaling pathway as a significant pathway, including metabolic and genetic signatures associated with hydrochlorothiazide BP response. This pathway is activated by netrins, a class of proteins that play a crucial role in neuronal migration and in axon guidance. Netrin-1 is the most studied member of the family and has been shown as a potent endothelial mitogen-stimulating the production of nitric oxide via a DCC–extracellular signal–regulated kinase 1/2–dependent mechanism.18 In addition, a recent study has shown that netrin-1 and its receptor, DCC, control sympathetic arterial innervation and play an important role in the regulation of the blood flow to peripheral organs.19 Moreover, netrin-1 binding to specific receptors, such as DCC, has been shown to activate multiple pathways, including mitogen-activated protein kinases, protein kinase C, src, Rac and Rho kinases, and focal adhesion kinase,20–22 which all have been previously reported to be associated with hypertension and BP regulation.23–25 Furthermore, a recent study has demonstrated that netrin-1 activates PRKC-α and FAK/Fyn, which are important for the activation of the extracellular signal–regulated kinase, c-Jun N-terminal kinases, and nuclear factor kB.26 Of note, we recently identified and replicated a signal within the PRKC-α gene with a clinically significant influence on the BP response to hydrochlorothiazide.8 Collectively, this highlights the importance of the netrin signaling pathway and suggests that it might be a novel and substantial pathway through which hydrochlorothiazide produces its long-term antihypertensive effects.
The genomic–metabolomic integrative analyses have also identified rs2727563 SNP within the PRKAG2 with a significant association with hydrochlorothiazide BP response. PRKAG2 has been shown as an important regulator of cellular energy metabolism, including de novo biosynthesis of fatty acids, and also acts as a regulator of cellular polarity by remodeling the actin cytoskeleton.27 In addition, PRKAG2 has been previously shown to be significantly associated with BP,28 ventricular pre-excitation (Wolff–Parkinson–White syndrome), chronic kidney disease,29 and left ventricular hypertrophy resembling cardiomyopathy.30 Altogether, the literature evidence supporting the association of the PRKAG2 with BP and cardiovascular diseases and the evidence from our results that included the identification and replication of PRKAG2 rs2727563 association with hydrochlorothiazide BP response suggest PRKAG2 as a potential determinant of HCTZ BP response. Future work still needed to demonstrate the mechanistic relationship between this gene and hydrochlorothiazide BP response mechanism.
Our results have also revealed arachidonic acid, within the netrin signaling pathway, as a significant metabolomic signature influencing the BP response to hydrochlorothiazide therapy. Arachidonic acid and its metabolites have been well known for their role in the regulation of renal vascular tone, BP, and sodium transport.17,31 Testing genetic variants within genes, directly involved in the synthesis and degradation of arachidonic acid, revealed EPHX2 rs13262930 SNP significantly associated with hydrochlorothiazide BP response. EPHX2 is well known for encoding the soluble epoxide hydrolase enzyme, which converts epoxyeicosatrienoic acid, a strong vasodilator and anti-inflammatory compound, to the biologically less active compound, dihydroxyeicosatrienoic acid.32 Studies have shown that the expression of the soluble epoxide hydrolase enzyme is positively correlated with BP and inhibiting this enzyme increases the production of the epoxyeicosatrienoic acids and ultimately reduces BP.33 In addition, Ma et al34 recently reported that hydrochlorothiazide might be mediating its antihypertensive BP response through the inhibition of the soluble epoxide hydrolase.34 Collectively, our results along with the results from the study by Ma et al34 emphasize that EPHX2 might be involved in the BP-lowering mechanism of thiazide diuretics and suggest that it might be an important predictor of thiazide diuretics’ BP response.
Our study has several strengths. To our knowledge, this is the first study to use a genomic–metabolomic integrative approach to identify novel biomarkers associated with hydrochlorothiazide BP response. This integrative approach was successful in identifying novel genetic variants that we were not able to identify using GWAS data alone. We think that using such an approach can help us to take forward the large investment in GWAS and use the output of this approach to identify additional genetic variants and biologically relevant pathways associated with drug response. Second, replicating our genetic signals and further validating them in another independent study, as a combined allele response score, emphasize the importance of our findings and their significant influential effect on hydrochlorothiazide BP response.
Our study also has several limitations. First, our sample size was relatively small, which limited our power to identify additional SNPs within the GWAS analysis. However, integrating the metabolomic and the genomic profiles of participants treated with hydrochlorothiazide added to the breadth and the depth of our analyses and helped us to overcome this limitation and to identify novel genetic signals that we were not able to identify using GWAS data alone. Second, the approach that we used to identify EPHX2 rs13262930 might have some flaws, such as 1) neglecting important genes that have not been well studied with hypertension yet, or 2) obtaining false-positive results by adjusting for multiple comparisons in SNPs within certain genes. However, the EPHX2 rs13262930 SNP that we identified using this approach was confirmed by replicating it in 2 other independent studies. In addition, we showed that this SNP has biologically functional properties as it affects the expression levels of the EPHX2 gene in different tissues, including the heart, and has a marginally significant association with arachidonic acid in fasting plasma. These multiple levels of replication and validation highlight the legitimacy of our approach and its results and suggest that the EPHX2 rs13262930 SNP is likely a true and important genetic predictor of hydrochlorothiazide BP response.
In conclusion, this study illustrates the power of integrating different types of omic data to identify novel genetic variants underlying drug response. Future use of this approach would improve the breadth and depth of studying complex phenotypes, as antihypertensive response, and might provide more knowledge and insight into the mechanism underlying BP response. This knowledge might facilitate the development of new drugs and therapeutic approaches based on a deeper understanding of the determinants of the BP response.
Thiazide diuretics have been the mainstay antihypertension therapy for decades and are still ranked among the most commonly prescribed medications worldwide. However, the wide interindividual variability in response to this class of drugs highlights the need for identifying predictors that can be used for improving the BP response of this therapy. Using both genomic and metabolomic data in this study revealed the netrin signaling pathway as an important pathway associated with hydrochlorothiazide BP response. Future work on this pathway might provide more insights into the mechanism underlying hydrochlorothiazide antihypertensive effect and help in identifying novel drug targets of new antihypertensive medications. The results of this study have also shed light on DCC, PRKAG2, and EPHX2 genes as important determinants of hydrochlorothiazide BP response. The response score created using SNPs within these genes should be further tested in other independent cohorts to further confirm its use in guiding the selection of hydrochlorothiazide therapy.
We acknowledge the valuable contributions of the Pharmacogenomics Evaluation of Antihypertensive Responses (PEAR) and Genetic Epidemiology of Responses to Antihypertensives (GERA) study participants, support staff, and study physicians.
Sources of Funding
The Pharmacogenomics Evaluation of Antihypertensive Responses (PEAR) study was supported by the National Institute of Health Pharmacogenetics Research Network grant U01-GM074492 and the National Center for Advancing Translational Sciences under the award number UL1 TR000064 (University of Florida), UL1 TR000454 (Emory University), and UL1 TR000135 (Mayo Clinic). The PEAR study was also supported by funds from the Mayo Foundation. The metabolomics work was funded by National Institute of General Medical Sciences grant RC2-GM092729 “Metabolomics Network for Drug Response Phenotype.” Additional support for this work includes the following: M.H. Shahin was supported by AHA predoctoral fellowship award number 14PRE20460115, and O. Fiehn was funded by grant DK097154 from National Institutes of Health.
This article was sent to Theodore A. Kotchen, Guest Editor, for review by expert referees, editorial decision, and final disposition.
The online-only Data Supplement is available with this article at http://hyper.ahajournals.org/lookup/suppl/doi:10.1161/HYPERTENSIONAHA.116.07328/-/DC1.
- Received February 11, 2016.
- Revision received March 1, 2016.
- Accepted June 5, 2016.
- © 2016 American Heart Association, Inc.
- 1.↵IMS Institute: Medicines Use and Spending Shifts: A Review of the Use of Medicines in the U.S. in 2014. 2014. https://www.Imshealth.Com/en/thought-leadership/ims-institute/reports/medicines-use-in-the-us-2014. Accessed June 1, 2016.
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Novelty and Significance
What Is New?
To our knowledge, this is the first study to use a genomic–metabolomic integrative approach to identify novel genetic markers associated with blood pressure response to hydrochlorothiazide.
We identified and replicated 3 novel genetic signals, PRKAG2 rs2727563, DCC rs12604940, and EPHX2 rs13262930 with clinically relevant effects on the blood pressure response of European Americans treated with hydrochlorothiazide.
A response score to hydrochlorothiazide was created using the 3 replicated genetic signals and was further validated in independent participants treated with hydrochlorothiazide.
What Is Relevant?
The multiple level of replication for the 3 identified genetic signals, individually and combined in the response score, suggests their importance as predictors of hydrochlorothiazide blood pressure response, which may help in optimizing the selection of antihypertensive therapy. In addition, these signals may provide insight into the antihypertensive mechanisms underlying thiazide diuretics, which may lead to the identification of novel drug targets.
Integrating the genomic and metabolomic profiles of European American participants treated with hydrochlorothiazide succeeded in identifying novel genetic signals with clinically relevant effects on the blood pressure response to hydrochlorothiazide. Replicating these signals in an independent study substantiated the importance of these genetic signals as important determinants of hydrochlorothiazide blood pressure response. Combining the 3 replicated signals in a response score explained 11.3% to 11.9% of the blood pressure response to hydrochlorothiazide. Validating this response score in another independent study emphasized the importance of considering this score and its signals in future models for guiding the selection of hydrochlorothiazide therapy.