Sprissler, Ryan

Introduction Several common and functional genes are known to contribute to renal, blood vessel, and cardiac functional characteristics associated with sodium (Na + ) handling, vascular reactivity, and cardiac output, respectively. Many of these same genes are suggested to contribute to differential responsiveness to blood pressure (BP) therapy. Few studies have explored the simultaneous phenotypic consequence of multiple genes critical for BP control. Despite a significant impulse of the medical community towards personalized medicine, BP therapy is typically guided by algorithms that do not include a patient's genetic information. Purpose The purpose of this study was to determine the impact of 14 genes on time to BP control in response to common BP therapy, with a specific focus on genes associated with drug metabolism and protein function in the kidney, the blood vessels, and the heart. Methods Eighty‐six patients completed one study visit consisting of a buccal swab for DNA extraction and genotyping, measurement of ambulatory BP, and a medical chart review for objective BP history. Genes in the analysis included those that encode for one drug metabolizing enzyme (CYP2D6), renal Na + handling (SCNN1A, WNK1, SLC12A3, and Alpha Adducin), vascular function (angiotensin, ACE, angiotensin‐II receptor type‐I, and renin), and cardiac function (the β 1 and β 2 adrenergic receptors ADRB1 and ADRB2, respectively). Results Genetic variation of WNK1 (rs1159744) demonstrated differential responses to diuretic therapy (determined as those with blood pressure under 140/90 vs. those above 140/90 according to genotype)(WNK1=GG>CG/CC, P<0.05). Genes that encode ADRB1 (rs1801252), ADRB2 (rs1042713), and CYP2D6 (rs3892097) demonstrated differential response to β‐blocker therapy (ADRB1=Ser/Ser>SerThr/ThrThr, ADRB2=GG>AG/AA, CYP2D6=CC>CT/TT, P<0.05). Genes that encode angiotensin (rs699), ACE (rs1799752), and renin (rs12750834) demonstrated differential response to ACE‐inhibition and angiotensin receptor blockade (angiotensin=CT/TT>CC, ACE=Ins/Ins>InsDel/DelDel, renin=AG/AA>GG, p<0.05). Conclusion These results demonstrate that genetic variants encoding proteins involved in renal Na + handling, vascular function, and cardiac output influence response to BP therapy for their respective drug classes. Support or Funding Information Funded by Geneticure

Introduction Hypertension is one of the most important preventable contributors to disease and death worldwide and represents the most common condition seen in the primary care setting. Approximately one in three adults living in the US has hypertension. Despite the prevalence and impact of hypertension, little data exists on the average time to blood pressure (BP) control in US patients and time to control according to common BP medication classes. Purpose Therefore, the purpose of this study was to assess the average time to BP control in adults with controlled hypertension, according to drug class. Methods For this study we assessed how long (in months) and how many clinic visits were needed in order to control BP according to common medication classes in patients with existing hypertension. The study was performed at two clinical sites where patients came in for one visit for an in‐clinic assessment of BP and for completion of a patient survey regarding their history of BP. The primary care physician performed a chart review for a detailed objective BP history. Results Ninety‐nine patients completed all aspects of the study (age=58±0.8yrs., %female=46, height=169±1.0cm, weight=86±1.4kg, BMI=29.9±0.4m/kg 2 ). The average initial BP was 151±2mmHg systolic (SBP), 91±1mmHg diastolic, and 111±1mmHg mean arterial (MAP). Patients demonstrated a significant drop in BP with therapy (SBP=16mmHg, DBP=10mmHg, MAP=12mmHg). Patients were prescribed an average of 1.8 BP medications (62% on ACE‐inhibitor, 42% on diuretic, 33% on β‐blocker, 27% on angiotensin receptor blocker, and 16% on Ca + channel blocker). The average time to BP control was 14.9 months or 3.6 clinic visits. Patients on a diuretic had the longest time to BP control, while patients on a Ca + channel blocker had the shortest time to control (diuretic=19.5±10.4, angiotensin receptor blocker=22.8±13.1, ACE‐inhibitor=22.2±11.4, β‐blocker=24.5±16.9, Ca + channel blocker=9.9±4.5 months, pANOVA<0.05). In contrast, patients on a Ca + channel blocker had significantly more clinic visits to attain BP control (diuretic=4.5±0.6, ACE‐inhibitor=3.1±0.4, angiotensin receptor blocker=3.9±0.6, β‐blocker=4.9±0.7, Ca + channel blocker=5.1±0.7 visits). Conclusion These results suggest important differences in the time to BP control, and the number of office visits necessary for BP control according to common classes of hypertension therapies. Funding was provided by Geneticure, LLC Support or Funding Information Funded by Geneticure

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 .

Purpose: The purpose of this study was to determine the effectiveness of a simple algorithm to mathematically predict a patients’ response to blood pressure (BP) therapy using functional genes in the 3 major organ systems involved in hypertension. Methods: Eighty-six patients with controlled hypertension completed 1 study visit consisting of a buccal swab collection, measurement of office BP, and a medical chart review for BP history. Genes in the analysis included 14 functional alleles in 11 genes. These genotypes were mathematically summed per organ system to determine whether a patient would likely respond to target therapy. Results: Patients recommended to and taking a diuretic had significantly higher rates of control (<120/<80) than patients recommended but not taking this drug class (0.2 ± 0.1 and 0.03 ± 0.03, respectively). Furthermore, there was a difference between patients genetically recommended and taking an angiotensin receptor blocker (ARB) vs patients recommended but not taking an ARB for the lowest diastolic blood pressure (DBP) and mean arterial pressure (MAP) recorded in the past 2 years (DBP = 66.2 ± 2.9 and 75.3 ± 1.7, MAP = 82.3 ± 2.8 and 89.3 ± 1.5, respectively). In addition, there was a nonsignificant trend for greater reductions in ΔSBP, ΔDBP, and ΔMAP in patients on recommended drug class for beta-blockers, diuretics, and angiotensin II receptor blockers vs patients not on these classes. Conclusion: The present study suggests that simple mathematical weighting of functional genotypes known to control BP may be ineffective in predicting control. This study demonstrates the need for a more complex, weighted, multigene algorithm to more accurately predict BP therapy response.

Hypertension (HTN) is a complex disease with interactions among multiple organ systems, including the heart, vasculature, and kidney with a strong heritable component. Despite the multifactorial nature of HTN, no clinical guidelines utilize a multi-gene approach to guide blood pressure (BP) therapy. Non-smokers with a family history of HTN were included in the analysis (n = 384; age = 61.0 ± 0.9, 11% non-white). A total of 17 functional genotypes were weighted according to the previous effect size in the literature and entered into an algorithm. 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 were assessed at six-month intervals for BP and medication history. There was no difference in 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. Further, the difference between diagnosis to current 1-year average BP was lower in the group that matched the top recommendation. These data suggest an association between a weighted multi-gene algorithm on the BP response to pharmacotherapy.