Polygenic scores for cardiovascular risk factors improve estimation of clinical outcomes in CCB treatment compared to

Related Experiment Video

Updated: Jun 28, 2025

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

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Deniz Türkmen1, Jack Bowden2,3, Jane A H Masoli1,4

  • 1Epidemiology & Public Health Group, Department of Clinical & Biomedical Science, Faculty of Health & Life Sciences, University of Exeter, Exeter, UK.

The Pharmacogenomics Journal
|April 17, 2024
PubMed

Insights

Genetic risk factors modify how patients respond to Calcium Channel Blockers (CCBs). Combining pharmacogenetic variants with polygenic scores for fat mass and lipoprotein A improves prediction of treatment discontinuation and heart failure risk.

Area of Science:

  • Cardiovascular Pharmacology
  • Genetics
  • Precision Medicine

Background:

  • Pharmacogenetic variants influence clinical outcomes in Calcium Channel Blocker (CCB) therapy.
  • The impact of genetically predicted clinical risk factors on these pharmacogenetic effects remains unclear.

Purpose of the Study:

  • To investigate whether genetically predicted clinical risk factors modify the association between pharmacogenetic variants and CCB treatment outcomes.
  • To assess the combined predictive value of pharmacogenetic variants and polygenic risk scores for treatment discontinuation and heart failure.

Main Methods:

  • Analysis of 32,000 UK Biobank participants treated with dihydropyridine CCBs.
  • Inclusion of 23 pharmacogenetic variants and calculation of polygenic scores for blood pressure, body fat mass, and other traits.
  • Evaluation of treatment discontinuation and heart failure as primary outcomes.

Main Results:

  • The NUMA1 variant (rs10898815-A) was associated with increased CCB treatment discontinuation, particularly in individuals with high polygenic scores for fat mass.
  • The RYR3 variant (rs877087 T-allele) independently increased heart failure risk, with substantially elevated risks observed in patients with high polygenic scores for fat mass, lean mass, and lipoprotein A.

Conclusions:

  • Genetically predicted risk factors, such as adiposity and lipoprotein A levels, significantly modify the impact of specific pharmacogenetic variants on CCB treatment outcomes.
  • Integrating polygenic scores with pharmacogenetic data may enhance the accuracy of risk prediction for treatment discontinuation and heart failure in CCB users.

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