Blood Pressure Polygenic Scores Are Associated With Apparent Treatment-Resistant Hypertension

Individuals with apparent treatment-resistant hypertension (aTRH) have substantially higher hazards of coronary heart disease and stroke compared with controlled hypertensive individuals.¹ With better prediction of aTRH, hypertensive individuals’ therapeutic regimens could more quickly achieve stable blood pressure (BP) control, therefore, reducing comorbidity. We examined the associations between the genetic determinants of BP and aTRH by self-reported race and ethnicity to improve identification of individuals at higher risk for aTRH.

Summary statistics for genetic associations with systolic (SBP) and diastolic (DBP) BP were obtained from Giri et al,² Liang et al,³ and Biobank Japan (http://jenger.riken.jp/en/result; n_max=564 851). Results were meta-analyzed and variant weightings were adjusted using polygenic risk score (PRS) method PRS-CS, followed by P value thresholding, selecting the threshold explaining the most outcome variance.⁴ Associations between SBP and DBP PRSs and measured BP were validated in the UK Biobank, excluding overlap with Giri et al (n_max=341 930, SBP: P<2.31×10⁻³⁰⁴, DBP: P<1.17×10⁻²⁸⁷), adjusted for the covariates: age, age-squared, body mass index, sex, and the top 10 principal components. The study data are available from the corresponding author upon reasonable request. The PRS variants and weights have been uploaded to the PGS catalog (PGS002238 and PGS00229).

Controls, controlled hypertensive individuals, were defined in BioVU, Vanderbilt University Medical Center’s DNA biobank, through presence of an hypertension International Classification of Diseases, Ninth or Tenth Revisions code, treatment with 1 or 2 antihypertensive drugs, and BP measurements <135/90 mm Hg for 3 months following the initiation of the last antihypertensive medication. Patients with aTRH were identified using a previously validated algorithm, based on failure to achieve controlled BP on 3 antihypertensive drugs, including a thiazide diuretic, or prescribed 4 or more medications regardless of achieving control, excluding patients with chronic kidney disease stages 4 and 5 as well as causes of secondary hypertension.⁵ Data were extracted before August 2019. The Vanderbilt Institutional Review Board reviewed this project and deemed it exempt nonhuman subject research.

PRS associations with aTRH were modeled using logistic regression, adjusted for the covariates above and mean estimated glomerular filtration rate in the entire multi-ancestry BioVU population (n_max=37 978), as well as non-Hispanic White (NHW; n_max=28 545) and non-Hispanic Black (n_max=5026) subsets. To estimate the combined effect of SBP and DBP PRSs on aTRH, we exponentiated the sum of logistic regression coefficients for SBP and DBP PRSs and calculated SE by taking the square-root of the sum of the variance-covariance matrix of the regression coefficients. Bonferroni corrections are reported as significant (P<0.017), accounting for the 3 PRS models. Effects are presented as odds ratio and 95% CIs per one SD increase in PRS. The predictive model included both PRSs, covariates listed above, smoking status, and mean estimated glomerular filtration rate, and was developed separately in the NHW, non-Hispanic Black, and multi-ancestry BioVU populations using 10-fold cross validation in STATA 16. Model performance was assessed through calibration and discrimination, using the Brier Score and the area under the receiver operating characteristic curve concordance. A smaller Brier score, which ranges from 0 to 1, describes better model prediction and an area under the receiver operating characteristic curve >0.8 is generally required for clinical utility.

The SBP PRS was significantly associated with increased aTRH risk in the multi-ancestry (1.08 [1.04–1.13]) and NHW (1.08 [1.03–1.12]) populations (Table). The DBP PRS was not associated with aTRH risk in any population. The joint effect of both SBP and DBP PRS was associated with increased aTRH risk in the multi-ancestry (1.27 [1.13–1.42]) and NHW (1.20 [1.09–1.33]) populations. Brier scores indicated good model fit in all groups, however, area under the receiver operating characteristic curves were <0.8 (Table).

We previously observed that genetic determinants of BP are significantly associated with hypertension in a phenome-wide association study.² In the present study, we found that the genetic determinants of BP are significantly associated with aTRH risk, where increasing PRS is associated with increased aTRH risk in multi-ancestry and NHW populations. However, the PRSs were not associated with aTRH in the non-Hispanic Black population, despite significant prediction of BP in the validation set. The lack of association in the non-Hispanic Black population could be due to limited sample size. Inclusion of PRSs in the predictive model with other significant clinical predictors provides a modest improvement in predictive performance. Calibration and discrimination of the predictive models indicate a good fit in all evaluated populations. While the risk profile for aTRH is complex and the contribution of additional nongenetic factors must be evaluated, we demonstrate that further enhancement of aTRH prediction models is required for clinical application. Refined predictive models may lead to more effective screening strategies, resulting in more rapid BP control, thus reducing the risk of negative cardiometabolic outcomes.

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