Exposure to prescription opioids following traumatic injury can increase the risk of developing tolerance, persistent opioid use and opioid use disorder. The mechanisms underlying opioid tolerance or dependence are not well understood, and no biomarkers predict risk. Opioid exposure causes epigenetic modifications, including alterations in microRNA (miRNA) expression. Several miRNAs, which regulate synaptic plasticity, are hypothesised to underlie substance use disorders and influence µ-opioid receptor levels, modulating opioid tolerance. This project aims to develop a bio-behavioural signature to predict persistent opioid use and chronic pain up to 6 months post-discharge.

The study will use a prospective cohort design, enrolling 180 adult patients at a Level I Trauma Center who are prescribed opioids at discharge. Prospective data will be collected in the hospital and at 7 days and 1, 3 and 6 months post-discharge. Biological data (genotyping and miRNA levels) and clinical measures of opioid use, pain, pain sensitivity (EEG) and psychosocial functioning will be collected at each time point. Bayesian regression methods will be used to identify baseline clinical, genetic, epigenetic and psychosocial predictors of opioid use and pain outcomes at 6 months post-discharge. Growth mixture modelling will identify distinct subgroups with varying trajectories, followed by Bayesian hierarchical modelling to predict trajectory classification based on predictor variables.

Ethics approval for this study was obtained from the University of Texas Health Science Center at Houston Committee for the Protection of Human Subjects (HSC-MS-24–0314). Findings will be disseminated in peer-reviewed scientific journals and at national and international conferences.