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Assessing blood pressure is a standard procedure executed in virtually all medical environments. The method utilized today was established over a hundred years ago by an innovative Russian doctor, Dr. Nikolai Korotkoff. The soft ticking noise, known as Korotkoff sounds, heard while taking blood pressure readings results from turbulent blood flow within the vessels. The apparatus required for this procedure includes a sphygmomanometer, a blood pressure cuff attached to a gauge, and a...
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Related Experiment Video

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Relationship between a Weighted Multi-Gene Algorithm and Blood Pressure Control in Hypertension.

Pamela K Phelps1, Eli F Kelley2, Danielle M Walla3

  • 1Medical Center, University of Minnesota, Fairview, Minneapolis, MN 55455, USA. pphelps2@fairview.org.

Journal of Clinical Medicine
|March 3, 2019
PubMed
Summary

A novel multi-gene algorithm may improve hypertension treatment. This approach, analyzing genetic data, showed patients matching the top drug recommendation experienced a greater blood pressure drop, suggesting personalized therapy potential.

Keywords:
blood pressuregeneticshypertensionpharmacogeneticspharmacotherapytreatment

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Area of Science:

  • Genetics
  • Cardiovascular Medicine
  • Pharmacogenomics

Background:

  • Hypertension (HTN) is a complex, heritable cardiovascular disease impacting multiple organ systems.
  • Current clinical guidelines lack a multi-gene approach for guiding blood pressure (BP) pharmacotherapy.
  • Genetic factors significantly influence individual responses to antihypertensive medications.

Purpose of the Study:

  • To investigate the utility of a weighted multi-gene algorithm in predicting pharmacotherapy response for hypertension.
  • To assess if a multi-gene based algorithm can personalize drug selection for better BP management.

Main Methods:

  • A cohort of 384 non-smokers with a family history of HTN was analyzed.
  • Seventeen functional genotypes were weighted by prior effect sizes and incorporated into an algorithm.
  • Pharmacotherapy recommendations were ranked (1-4) based on algorithmic genotype assessment; BP and medication data were tracked over three years.

Main Results:

  • No significant difference in BP at diagnosis between patients matching the algorithm's top recommendation (n=92) and those who did not (n=292).
  • Patients matching the top recommendation showed a significantly greater BP reduction from diagnosis to nadir.
  • The difference between diagnosis and 1-year average BP was lower in patients who matched the algorithm's primary drug recommendation.

Conclusions:

  • A weighted multi-gene algorithm shows potential association with improved blood pressure response to pharmacotherapy in hypertensive patients.
  • This genetic-based approach may offer a pathway toward more personalized hypertension treatment strategies.
  • Further research is warranted to validate and implement multi-gene algorithms in clinical practice for hypertension management.