Relationship Between a Weighted Multi‐Gene Algorithm and Blood Pressure Control in Hypertension
Authors: Phelps Pamela K , Snyder Eric M , Walla Danielle M , Ross Jennifer K , Simmons Jerad J , Bulock Emma K , Ayres Audrie , Akre Monica K , Sprissler Ryan , Olson Thomas P 
Published: June 2021

Background

Hypertension (HTN) has a strong heritable component and the effectiveness of the treatment for HTN has been shown to be influenced by monogenic guidance. Each of the common HTN pharmacotherapies is ~50% effective in the population and layering blood pressure (BP) medications increases side effects and decreases medication adherence. HTN is a complex disease with primary interactions between the heart, vasculature, and kidney. Known and common genetic variants of each these organs have well‐described mechanisms. Despite the established bell‐curve response to treatment, the complexity of HTN, and functional variants of the major organ systems in HTN: no guidance, to date, utilizes a multi‐gene approach to guide BP therapy.

Purpose

To assess the association between a weighted multi‐gene algorithm and BP control in patients with HTN.

Methods

Non‐smokers with a family history of HTN were included in the analysis (n=384; age=61.0±0.9, 11% non‐white). 17 genotypes (ADRB1(2 SNPs), ADRB2 (2SNPs), CYP2D6, WNK (3SNPs), SLC12A3, SCNN1A, alpha‐adducin, renin, angiotensin (3 SNPs), ACE, and the angiotensin receptor) were weighted according to previous effect size in the literature (ΔBP when on the target therapy vs. not) as well as the number of peer‐reviewed papers of quality supporting each functional variant. After weighting, each genotype was entered into an algorithm that assessed pairing (e.g. homozygosity of functional ADRB1 is responsive to β‐blockade, heterozygosity is less responsive, and homozygosity of non‐functional is least responsive) within and between organ systems to predict the pharmacotherapy with greatest impact for each individual patient. Each pharmacotherapy was ranked from 1–4 as most to least likely to respond based on the algorithmic assessment of individual patient's genotypes. Three‐years of data was assessed at six‐month intervals for BP and medication history. Current and past pharmacotherapies were also assessed via chart review.

Results

There was no difference in initial BP at diagnosis between groups matching the top drug recommendation using the multi‐gene weighted algorithm (n=92) vs. those who did not match (n=292). However, from diagnosis to nadir, patients who matched the primary recommendation had a significantly greater drop in BP when compared to patients who did not (ΔSBP= −39.2±2.4 vs. −32.1±1.3mmHg , ΔDBP= −19.4±1.1 vs. −14.0±1.3mmHg , ΔMAP= −24.4±2.1 vs. −19.0±1.2mmHg, respectively, p<0.05 for SBP and DBP). Further, the difference between diagnosis to current 1yr. average BP was lower in the group that matched the top recommendation (ΔSBP= −33.2±2.3 vs. −27.4±1.2mmHg, ΔDBP=−14.8±1.1 vs. −11.5±1.2, ΔMAP= −21.2±2.3 vs. −15.6±1.8, respectively, p<0.05 for SBP and MAP).

Conclusions

These data suggest an association between a weighted multi‐gene algorithm on the BP response to pharmacotherapy. This magnitude of difference has previously been shown to be associated with a significant reduction in heart attack and stroke risk.

Support or Funding Information

Geneticure, Inc.

This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.