A pilot study to identify pharmacogenomic and clinical risk factors associated with opioid related falls and adverse effects in older adults.

Given the high prevalence of pain in older adults and current trends in opioid prescribing, inclusion of genetic information in risk prediction tools may improve opioid risk assessment. Our objectives were to (1) determine the feasibility of recruiting socioeconomically disadvantaged and racially diverse middle aged and older adult populations for a study seeking to identify risk factors for opioid-related falls and other serious adverse effects and (2) explore potential associations between the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (CIP-RIOSORD) risk class and other patient factors with falls and serious opioid adverse effects. This was an observational study of 44 participants discharged home from the emergency department with an opioid prescription for acute pain and followed for 30 days. We found pain interference may predict opioid-related falls or serious adverse effects within older, opioid-treated patients. If validated, pain interference may prove to be a beneficial marker for risk stratification of older adults initiated on opioids for acute pain.

Characterizing OPRM1 DNA methylation in prescription opioid users with chronic musculoskeletal pain.

Introduction: Many patients with chronic pain use prescription opioids. Epigenetic modification of the µ-opioid receptor 1 (OPRM1) gene, which codes for the target protein of opioids, may influence vulnerability to opioid abuse and response to opioid pharmacotherapy, potentially affecting pain outcomes. Objective: Our objective was to investigate associations of clinical and sociodemographic factors with OPRM1 DNA methylation in patients with chronic musculoskeletal pain on long-term prescription opioids. Methods: Sociodemographic variables, survey data (Rapid Estimate of Adult Health Literacy in Medicine-Short Form, Functional Comorbidity Index [FCI], PROMIS 43v2.1 Profile, Opioid Risk Tool, and PROMIS Prescription Pain Medication Misuse), and saliva samples were collected. The genomic DNA extracted from saliva samples were bisulfite converted, amplified by polymerase chain reaction, and processed for OPRM1-targeted DNA methylation analysis on a Pyrosequencing instrument (Qiagen Inc, Valencia, CA). General linear models were used to examine the relationships between the predictors and OPRM1 DNA methylation. Results: Data from 112 patients were analyzed. The best-fitted multivariable model indicated, compared with their counterparts, patients with > eighth grade reading level, degenerative disk disease, substance abuse comorbidity, and opioid use < 1 year (compared with >5 years), had average methylation levels that were 7.7% (95% confidence interval [CI] 0.95%, 14.4%), 11.7% (95% CI 2.7%, 21.1%), 21.7% (95% CI 10.7%, 32.5%), and 16.1% (95% CI 3.3%, 28.8%) higher than the reference groups, respectively. Methylation levels were 2.2% (95% CI 0.64%, 3.7%) lower for every 1 unit increase in FCI and greater by 0.45% (95% CI 0.08%, 0.82%) for every fatigue T score unit increase. Conclusions: OPRM1 methylation levels varied by several patient factors. Further studies are warranted to replicate these findings and determine potential clinical utility.