scholarly journals Gender differences among treatment-seeking adults with cannabis use disorder: Clinical profiles of women and men enrolled in the achieving cannabis cessation-evaluating N-acetylcysteine treatment (ACCENT) study

2017 ◽  
Vol 26 (2) ◽  
pp. 136-144 ◽  
Author(s):  
Brian J. Sherman ◽  
Aimee L. McRae-Clark ◽  
Nathaniel L. Baker ◽  
Susan C. Sonne ◽  
Therese K. Killeen ◽  
...  
Keyword(s):  
Gender Differences ◽  
Cannabis Use ◽  
Treatment Seeking ◽  
Cannabis Use Disorder ◽  
Clinical Profiles

scholarly journals Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

2021 ◽  
Author(s):  
Gregory Niklason ◽  
Eric Rawls ◽  
Sisi Ma ◽  
Erich Kummerfeld ◽  
Andrea M. Maxwell ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Gender Differences ◽  
Community Sample ◽  
Cannabis Use ◽  
Data Driven ◽  
Cannabis Use Disorder ◽  
Cannabis Dependence ◽  
Human Connectome Project

Background: Cannabis Use Disorder (CUD) has been linked to environmental, personality, mental health, neurocognitive and neurobiological risk factors. While many studies have revealed gender differences in CUD, the relative importance of these complex factors by gender has not been described. Methods: We conducted a data-driven examination of gender differences in CUD in a community sample of young adults (Human Connectome Project [HCP]; n = 1204, 54% female). We employed state-of-the-art machine learning methods [gradient tree boosting, XGBoost] in combination with novel factor ranking tools [SHapley's Additive exPlanations (SHAP)] as an 'explainable machine learning approach' in the multimodal data collected by the HCP (phenotypic and brain data). Results: We were able to successfully classify both cannabis dependence and cannabis use levels. Previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification. Predominantly-male risk factors included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, predominantly-female risk factors included environmental (low education level, low instrumental support) factors. Conclusions: Our data-driven analysis of gender differences in the multimodal risk factors underlying cannabis dependence and use levels demonstrate that environmental factors contribute more strongly to CUD in women, whereas individual factors such as personality, mental health and neurocognitive factors have a larger importance in men. This warrants further investigations, and suggests the importance of understanding how these differences relate to the development of effective treatment approaches.

Genetic Influences in Cannabis Use Disorder and Psychosis: Dopamine and Beyond

2017 ◽  
Vol 22 (42) ◽  
pp. 6392-6396 ◽  
Author(s):  
Amine Benyamina ◽  
Laurent Karila ◽  
Geneviève Lafaye ◽  
Lisa Blecha
Keyword(s):  
Cannabis Use ◽  
Genetic Influences ◽  
Cannabis Use Disorder

scholarly journals Genetic Liability to Cannabis Use Disorder and COVID-19 Hospitalization

2021 ◽  
Author(s):  
Alexander S. Hatoum ◽  
Claire L. Morrison ◽  
Sarah M.C. Colbert ◽  
Evan A. Winiger ◽  
Emma C. Johnson ◽  
...  
Keyword(s):  
Cannabis Use ◽  
Cannabis Use Disorder ◽  
Genetic Liability
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