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Identifying Biomarkers Related to Opioid Use and Pain Response Following Traumatic Injury and Surgery

University of California, San Diego · 2026
ClinicalTrials.gov · Datasets · License: Public Domain · 2026
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artificial-intelligenceopioid-use-disorder
The mechanisms leading to opioid tolerance or dependence are not well understood and there are currently no biomarkers for predicting who is at risk for development of OUD. The purpose of this project is to support the feasibility of detecting a miRNA bio-behavioral signature of opioid misuse risk that will demonstrate improved predictive precision compared to current tools such as polygenic risk scores (PRS). Specifically, the study will address key goals of the Wellcome Leap program to develop scalable measures assessing individual addiction susceptibility and quantifying addiction risk and progression during prescription drug use. The study will utilize behavioral, genomics, and bioinformatics pipelines to characterize opioid-induced miRNA expression dynamics among trauma and surgical patients prescribed opioids at discharge. Based on results from this study, the research will develop a high-throughput screening assay to predict risk for opioid use disorder (OUD). The hypothesis is that miRNAs will serve as powerful bio-signatures for predicting long-term clinical outcomes in patients treated with opioids for pain management following trauma injury and/or surgery.
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