Abstract
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.
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Supplementary materials
Table S1 Ingredient names and RxNorm codes of opioid prescription medications included in the study
Table S2 Ingredient name and count of additional prescriptions received on the same day as the opioid prescription
Table S3 Ingredient names and RxNorm codes of strong or moderately strong CYP2D6 inhibitors included in the study
Acknowledgments
The authors thank Ms. Robin Adams for her assistance in typing and formatting the manuscript, and Ms. Ruoxiang Jiang for analytic support and review of this manuscript. The authors also thank Dr. Muhamad Elrashidi and Ms. Carolyn Roer Vitek for expertise regarding the likely impact of pharmacogenomics-guided opioid prescribing on primary care patients and physicians, and for their review and revision of this manuscript.
This work was supported in part by Mayo Clinic Center for Individualized Medicine, the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, National Institutes of Health grants U19 GM61388 (The Pharmacogenomics Research Network), R01 GM28157, U01 HG005137, R01 CA138461, R01 AG034676 (The Rochester Epidemiology Project), and U01 HG06379 and U01 HG06379 Supplement (The Electronic Medical Record and Genomics [eMERGE] Network).
Disclosure
John L Black III has licensed intellectual property to the companies AssureX Health and OneOme. In addition, he has stock ownership in OneOme. The other authors report no conflicts of interest in this work.