Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study.

Journal: Addiction (Abingdon, England)

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Affiliated Institutions:  Neuroscience Institute, University of Cape Town, Cape Town, South Africa. Department of Psychiatry, University of Vermont College of Medicine, Burlington, USA. Department of Pediatrics, Division of Endocrinology, Diabetes, and Metabolism, Children's Hospital Los Angeles, Los Angeles, USA. Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA. Department of Family Medicine, UCLA, Los Angeles, CA, USA. Department of Psychiatry, Université de Montreal, CHU Ste Justine Hospital, Montreal, Canada.

Abstract summary 

The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls.This was a cross-sectional study.This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites.This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls.This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls.Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models.People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Authors & Co-authors:  Scheffler Freda F Ipser Jonathan J Pancholi Devarshi D Murphy Alistair A Cao Zhipeng Z Ottino-González Jonatan J Thompson Paul M PM Shoptaw Steve S Conrod Patricia P Mackey Scott S Garavan Hugh H Stein Dan J DJ

Study Outcome 

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Statistics
Citations :  Cole JH, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 2017;40(12):681–690. https://doi.org/10.1016/j.tins.2017.10.001
Authors :  13
Identifiers
Doi : 10.1111/add.16621
SSN : 1360-0443
Study Population
Male,Female
Mesh Terms
Other Terms
ENIGMA;addiction;brain age;machine learning;neuroimaging;predicted brain age difference;substance use disorder
Study Design
Cross Sectional Study
Study Approach
Mixed Methods
Country of Study
Publication Country
England