The Effect of Genetically Guided Mathematical Prediction and the Blood Pressure Response to Pharmacotherapy in Hypertension Patients.
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.
['Kelley|Eli F|EF|https://orcid.org/0000-0001-8337-2253', 'Olson|Thomas P|TP|', 'Curry|Timothy B|TB|', 'Sprissler|Ryan|R|', 'Snyder|Eric M|EM|']
genetics blood pressure hypertension algorithm polymorphism