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Metabolomics Reveals Biomarkers of Opioid Use Disorder Publisher Pubmed



Ghanbari R1, 2 ; Li Y1 ; Pathmasiri W1 ; Mcritchie S1 ; Etemadi A3 ; Pollock JD4 ; Poustchi H2 ; Rahimimovaghar A5 ; Aminesmaeili M5, 6 ; Roshandel G7 ; Shayanrad A2 ; Abaei B2 ; Malekzadeh R2 ; Sumner SCJ1
Authors
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Authors Affiliations
  1. 1. Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
  2. 2. Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Science, Tehran, Iran
  3. 3. Division of Cancer Epidemiology and Genetics, National Cancer Institute (NCI), Bethesda, MD, United States
  4. 4. Genetics, Epigenetics, and Developmental Neuroscience Branch, National Institute on Drug Abuse (NIDA), Bethesda, MD, United States
  5. 5. Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
  6. 6. Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
  7. 7. Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran

Source: Translational Psychiatry Published:2021


Abstract

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. © 2021, The Author(s).
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