CYP2D6 phenotypes are associated with adverse outcomes related to opioid medications

BACKGROUND: Variation in the CYP2D6 gene may affect response to opioids in both poor and ultrarapid metabolizers, but data demonstrating such associations have been mixed, and the impact of variants on toxicity-related symptoms (e.g., nausea) is unclear. Therefore, we examined the association between CYP2D6 phenotype and poor pain control or other adverse symptoms related to the use of opioids in a sample of primary care patients. MATERIALS AND METHODS: We identified all patients in the Mayo Clinic RIGHT Protocol who were prescribed an opioid medication between July 01, 2013 and June 30, 2015, and categorized patients into three phenotypes: poor, intermediate to extensive, or ultrarapid CYP2D6 metabolizers. We reviewed the electronic health record of these patients for indications of poor pain control or adverse symptoms related to medication use. Associations between phenotype and outcomes were assessed using Chi-square tests and logistic regression. RESULTS: Overall, 257 (25% of RIGHT Protocol participants) patients received at least one opioid prescription; of these, 40 (15%) were poor metabolizers, 146 (57%) were intermediate to extensive metabolizers, and 71 (28%) were ultrarapid metabolizers. We removed patients that were prescribed a CYP2D6 inhibitor medication (n=38). After adjusting for age and sex, patients with a poor or ultrarapid phenotype were 2.7 times more likely to experience either poor pain control or an adverse symptom related to the prescription compared to patients with an intermediate to extensive phenotype (odds ratio: 2.68; 95% CI: 1.39, 5.17; p=0.003). CONCLUSION: Our results suggest that >30% of patients with a poor or ultrarapid CYP2D6 phenotype may experience an adverse outcome after being prescribed codeine, tramadol, oxycodone, or hydrocodone. These medications are frequently prescribed for pain relief, and ~39% of the US population is expected to carry one of these phenotypes, suggesting that the population-level impact of these gene–drug interactions could be substantial.