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Subjective Response to Opioids Predicts Risk for Opioid Use Disorder

Gonzalez, J.; Tran, V.; Meredith, J.; Xu, I.; Penchala, R.; Vilar Ribo, L.; Courchesne-Krak, N. S.; Zoleikhaeian, D.; 23andMe Research Team, ; McIntyre, M.; Fontanillas, P.; Kukar Bond, K.; Johnson, E. O.; Jeffery, A.; MacKillop, J.; Marienfeld, C.; de Wit, H.; Palmer, A. A.; Sanchez-Roige, S.

2025-03-23 addiction medicine
10.1101/2025.03.21.25324409 medRxiv
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BackgroundOpioid use disorder (OUD) is a major public health crisis. Patients initial exposure to opioids often comes from prescribed medications. Predicting which of these patients will develop OUD remains challenging. Prior evidence from various substances suggest that initial subjective responses influence addiction risk, however these studies have used relatively small cohorts and have not led to the development of widespread tools to predict OUD risk. MethodsWe used a cohort of 141,897 adult research participants to perform a retrospective observational study of self-reported subjective responses to prescription opioids. We collected demographics, subjective positive (e.g., euphoria), subjective negative (e.g., nausea), and analgesic responses as well as self-reported OUD. ResultsPositive subjective effects, particularly "Like Overall", "Euphoric", and "Energized", were the strongest predictors of OUD. For example, the odds-ratio for individuals responding "Extremely" for "Like Overall" was 36.5. The sensitivity and specificity of this single question was excellent (ROC=0.87). Negative effects and analgesic effects were much less predictive. We developed a two-question decision tree ("When you first took opioid pain medication, to what extent did you like the way they made you feel overall?" and "When you first took opioid pain medication, to what extent did you experience an unpleasant itchy feeling?"), that can identify a small high-risk subset with 78.5% prevalence of OUD and a much larger low-risk subset with 1.2% prevalence of OUD. ConclusionsScreening for subjective responses can identify high-risk individuals who would benefit from tailored interventions.

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