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Association of Pharmacogenomic Phenotypes in CYP2D6, CYP2C9, CYP2C19, and CYP3A5 on Polypharmacy in Veterans.

Linas KrulikasJill S BatesCatherine ChanfreauHeather ColemanShawn DaltonDeepak Voora
Published in: Clinical pharmacology and therapeutics (2024)
The Department of Veterans Affairs (VA) utilizes a pharmacogenomic (PGx) program that analyzes specific "pharmacogenes." This study evaluates the effect that pharmacogenes may have on prevalence of polypharmacy. This retrospective cohort study included patients with VA prescriptions who underwent PGx testing. We quantified prescriptions active or recently expired at the time of PGx testing. We constructed two co-primary polypharmacy (≥10 medications) end points: (i) based on all medications and (ii) requiring that at least one medication was affected by a pharmacogene of interest. Pharmacogenes and actionable phenotypes of interest included poor and ultrarapid metabolizers for CYP2D6, CYP2C9, and CYP2C19 and intermediate and normal metabolizers for CYP3A5. Patients were classified as having 0, 1, and 2+ total phenotypes across all genes. Of the 15,144 patients screened, 13,116 met eligibility criteria. Across phenotype cohorts, there was no significant association with polypharmacy using all medications, number of total medications, or number of medications affected by phenotypes. However, there was a significant difference in patients with polypharmacy prescribed ≥1 medication impacted by PGx across phenotype groups: 2,514/4,949 (51%), 1,349/2,595 (52%), 204/350 (58%) (P = 0.03, OR 1.31, 95% CI 1.02-1.67). The median number of medications affected by PGx phenotypes with ≥1 PGx-impacted medication across phenotype groups was a median of 0 (IQR 0, 0), 0 (IQR 0, 0), and 1 (IQR 0, 1) (P < 0.001). In patients prescribed ≥1 medication impacted by PGx, those with more actionable pharmacogenomic phenotypes were more likely to meet polypharmacy criteria.
Keyphrases
  • end stage renal disease
  • adverse drug
  • newly diagnosed
  • ejection fraction
  • chronic kidney disease
  • healthcare
  • prognostic factors
  • gene expression
  • dna methylation
  • tyrosine kinase