Metabolomics reveals biomarkers of opioid use disorder

• Published in: Translational psychiatry Vol. 11; no. 1; pp. 103 - 10
• Main Authors: Ghanbari, Reza, Li, Yuanyuan, Pathmasiri, Wimal, McRitchie, Susan, Etemadi, Arash, Pollock, Jonathan D., Poustchi, Hossein, Rahimi-Movaghar, Afarin, Amin-Esmaeili, Masoumeh, Roshandel, Gholamreza, Shayanrad, Amaneh, Abaei, Behrouz, Malekzadeh, Reza, Sumner, Susan C. J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04-02-2021; Nature Publishing Group
• Subjects: 692/53/2421; 706/648; 82/58; Analgesics, Opioid - therapeutic use; Behavioral Sciences; Biological Psychology; Biomarkers; Cohort Studies; Drug addiction; Humans; Medicine; Medicine & Public Health; Metabolomics; Narcotics; Neurosciences; Opioid-Related Disorders; Pharmacotherapy; Psychiatry
• ISSN: 2158-3188; 2158-3188
• DOI: 10.1038/s41398-021-01228-7

Opioid use disorder (OUD) is diagnosed using the qualitative criteria defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Diagnostic biomarkers for OUD do not currently exist. Our study focused on developing objective biological markers to differentiate chronic opiate users with OUD from chronic opiate users without OUD. Using biospecimens from the Golestan Cohort Study, we compared the metabolomics profiles of high opium users who were diagnosed as OUD positive with high opium users who were diagnosed as OUD negative. High opium use was defined as maximum weekly opium usage greater than or equal to the median usage (2.4 g per week), and OUD was defined as having 2 or more DSM-5 criteria in any 12-month period. Among the 218 high opium users in this study, 80 were diagnosed as OUD negative, while 138 were diagnosed as OUD positive. Seven hundred and twelve peaks differentiated high opium users diagnosed as OUD positive from high opium users diagnosed as OUD negative. Stepwise logistic regression modeling of subject characteristics data together with the 712 differentiating peaks revealed a signature that is 95% predictive of an OUD positive diagnosis, a significant (
p
 < 0.0001) improvement over a 63% accurate prediction based on subject characteristic data for these samples. These results suggest that a metabolic profile can be used to predict an OUD positive diagnosis.