Uncontrolled eating and sensation-seeking partially explain the prediction of future binge drinking from adolescent brain structure

• Verfasst von: Rane, Roshan Prakash [VerfasserIn]
• Verfasst von: Musial, Milena Philomena Maria [VerfasserIn]
• Verfasst von: Beck, Anne [VerfasserIn]
• Verfasst von: Rapp, Michael [VerfasserIn]
• Verfasst von: Schlagenhauf, Florian [VerfasserIn]
• Verfasst von: Banaschewski, Tobias [VerfasserIn]
• Verfasst von: Bokde, Arun L. W. [VerfasserIn]
• Verfasst von: Paillère Martinot, Marie-Laure [VerfasserIn]
• Verfasst von: Artiges, Eric [VerfasserIn]
• Verfasst von: Nees, Frauke [VerfasserIn]
• Verfasst von: Lemaitre, Herve [VerfasserIn]
• Verfasst von: Hohmann, Sarah [VerfasserIn]
• Verfasst von: Schumann, Gunter [VerfasserIn]
• Verfasst von: Walter, Henrik [VerfasserIn]
• Verfasst von: Heinz, Andreas [VerfasserIn]
• Verfasst von: Ritter, Kerstin [VerfasserIn]
• Titel: Uncontrolled eating and sensation-seeking partially explain the prediction of future binge drinking from adolescent brain structure
• Verf.angabe: Roshan Prakash Rane, Milena Philomena Maria Musial, Anne Beck, Michael Rapp, Florian Schlagenhauf, Tobias Banaschewski, Arun LW Bokde, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Herve Lemaitre, Sarah Hohmann, Gunter Schumann, Henrik Walter, Andreas Heinz, Kerstin Ritter, Imagen consortium
• E-Jahr: 2023
• Jahr: 30 September 2023
• Umfang: 9 S.
• Illustrationen: Illustrationen
• Fussnoten: Online verfügbar: 30. September, 2023, Artikelversion: 12. Oktober, 2023 ; Gesehen am 01.02.2024
• Titel Quelle: Enthalten in: NeuroImage: Clinical
• Ort Quelle: [Amsterdam u.a.] : Elsevier, 2012
• Jahr Quelle: 2023
• Band/Heft Quelle: 40(2023), Seite 103520
• ISSN Quelle: 2213-1582
• DOI: doi:10.1016/j.nicl.2023.103520
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Addiction
• Sach-SW: Binge drinking
• Sach-SW: Confound detection
• Sach-SW: Eating behavior
• Sach-SW: Machine learning
• Sach-SW: Sensation-seeking
• K10plus-PPN: 1879726092

Abstract

Binge drinking behavior in early adulthood can be predicted from brain structure during early adolescence with an accuracy of above 70%. We investigated whether this accurate prospective prediction of alcohol misuse behavior can be explained by psychometric variables such as personality traits or mental health comorbidities in a data-driven approach. We analyzed a subset of adolescents who did not have any prior binge drinking experience at age 14 (IMAGEN dataset, n = 555, 52.61% female). Participants underwent structural magnetic resonance imaging at age 14, binge drinking assessments at ages 14 and 22, and psychometric questionnaire assessments at ages 14 and 22. We derived structural brain features from T1-weighted magnetic resonance and diffusion tensor imaging. Using Machine Learning (ML), we predicted binge drinking (age 22) from brain structure (age 14) and used counterbalancing with oversampling to systematically control for 110 + variables from a wide range of social, personality, and other psychometric characteristics potentially associated with binge drinking. We evaluated if controlling for any variable resulted in a significant reduction in ML prediction accuracy. Sensation-seeking (-13.98 ± 1.68%), assessed via the Substance Use Risk Profile Scale at age 14, and uncontrolled eating (-13.98 ± 3.28%), assessed via the Three-Factor-Eating-Questionnaire at age 22, led to significant reductions in mean balanced prediction accuracy upon controlling for them. Thus, sensation-seeking and binge eating could partially explain the prediction of future binge drinking from adolescent brain structure. Our findings suggest that binge drinking and binge eating at age 22 share common neurobiological precursors discovered by the ML model. These neurobiological precursors seem to be associated with sensation-seeking at age 14. Our results facilitate early detection of increased risk for binge drinking and inform future clinical research in trans-diagnostic prevention approaches for adolescent alcohol misuse.