Association Between Genetic Algorithm to Predict Hypertension Therapy and Response to Treatment

Study Overview
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Brief Summary
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To assess the effectiveness of the use of a patient's genes to predict which hypertension therapy is successful

Detailed Description
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Hypertension is known to have a strong heritable component. Previous work has demonstrated that sons of hypertensive patients are more likely to be hypertensive when compared to sons of normotensive individuals. Additionally, monozygotic twins are more likely to share hypertension than dizygotic twins who are more likely than non-twin siblings to share hypertension. Each of these previous studies demonstrate that genetics plays a role in the development of hypertension. For each major class of drugs (diuretic, vasodilator, and β-blocker) the effectiveness rate ranges from 40-60%. Contrary to common belief, even a small  10-20% of patients have an increase in blood pressure with a given anti-hypertensive medication. These effectiveness rates go far beyond adherence in that these previous trials have controlled for medication adherence. In addition to this controlled studies, epidemiologic data has demonstrated that 40% of patients who take their medication, as prescribed by their clinician, do not have their blood pressure under control. Unfortunately, despite a significant impulse in the medical community to move towards an individualized medicine approach to patient centered treatment, the current clinical treatment strategy is based on a set algorithm which does not take into account individual patient differences. Rather, physicians are guided to choose a drug (one out of many options) in a given class of drugs and use that specific drug as a first line therapy (typically initiating with the diuretic class) and titrate that specific drug of choice to therapeutic dosage regardless of efficacy2. It is only after a prolonged course of treatment with that specific class of drug that clinical efficacy is determined (typically three months). At this stage, if clinical guideline goals for blood pressure have not been met, it is often recommended that the patient remain on the first line therapy whilst an additional drug from a different class of drugs (typically an Angiotensin converting enzyme inhibitor (ACE inhibitor) or Angiotensin II receptor blocker (ARB)) is added to the pharmacologic regimen. Again, this drug is titrated to recommended therapeutic dosage and another prolonged course of treatment is initiated before clinical efficacy is determined (an additional three months - six months since initiation of treatment). If at this point, clinical guideline goals for blood pressure have not been met, a third drug from a third class of drugs (typically a beta-blocker) is added and the process is repeated (another three months - nine months from initiation of treatment). Further, if clinical guideline goals have continued to be elusive, the diagnosis of refractory hypertension is added and the process is reinitiated with a different combination of drugs, different classes of drugs, different drug options within a given class of drugs, different dosages, or all of the above. Thus, from the time of initial diagnosis and the start of treatment to the point in which blood pressure is adequately controlled may take anywhere from three months to well over one year. This trial-and-error standard of care is clearly not optimal. The blood pressure panel created by Geneticure has been created to comprehensively assess seventeen common genetic variants in the liver (drug metabolizing enzyme) cardiac, vascular, and renal systems that can improve therapeutic guidance for the clinician based on known functional alterations of the protein through these genetic changes, as well as demonstrated effects of certain drug classes on these various genotypes. Based on this information, a clinician can guide therapy with knowledge specific to their patient, rather than trial-and-error based on population data and using drugs with least side effects initially. To assess the effectiveness of the use of a patient's genes to predict which hypertension therapy is successful, as measured by: Level of blood pressure control (<140/<90) Change in blood pressure from baseline to control

Official Title
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Association Between a Pharmacogenetic Algorithm to Predict Blood Pressure Therapy With Blood Pressure Response to Anti-Hypertensive Therapy

Conditions
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Hypertension

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Subject is able and willing to provide informed consent Subject is ≥ 20 and ≤ 85 years of age Subject with diagnosis of Hypertension for a minimum of 1 year Subject has been on the same class/classes of blood pressure medication for a minimum of 6 months. Note: A change in dosage, frequency, or specific medication is acceptable as long as there have been no changes to the class/classes of medications prescribed. Subject with a Body Mass Index (BMI) ≥ 19 and ≤ 45 Subject is currently prescribed and taking one of the following classes of medications alone or in combination with each other. Diuretics (thiazide or thiazide-like) ACE Inhibitors Angiotensin Receptor Blocker (ARB) Beta-blockers Ca+ Channel Blockers Exclusion Criteria: Subject has a diagnosis of secondary hypertension or is experiencing a complication of pregnancy. Subject is currently prescribed and taking any additional class of medication(s) for high blood pressure not included in the list above Subject has Systolic BP > 190 or Diastolic BP > 120 documented within the six months prior to visit. Any other reason that the subject is inappropriate for study enrollment in the opinion of the Investigator.

Ages Eligible for Study
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Minimum Age: 30 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Level of Blood Pressure Control | how many participants are <140/<90 with genetic prediction | 5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of medications needed to obtain blood pressure control | Do those whose genes match therapy need fewer medications | 5 years | 
| Time to blood pressure control | If control faster if associated with genes that predict control | 5 years | 
| Number of office visits to obtain blood pressure control | Are office visits fewer if genes would have been used to predict control | 5 years | 
| side effects from hypertension therapy | Do patients have more side effects on therapies that do not align with their predictive genes | 5 years | 
| Hypertension associated adverse events during the course of treatment | Do patients have more side adverse events on therapies that do not align with their predictive genes | 5 years | 
| Change in BP from treatment to control | Modeled by BP genes | 5 years |