Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an “explainable” machine learning approach that...

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Published inScientific reports Vol. 12; no. 1; pp. 15624 - 14
Main Authors Niklason, Gregory R., Rawls, Eric, Ma, Sisi, Kummerfeld, Erich, Maxwell, Andrea M., Brucar, Leyla R., Drossel, Gunner, Zilverstand, Anna
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 17.09.2022
Nature Publishing Group
Nature Portfolio
Subjects
692/53
692/699/476
692/699/476/5
Cannabis
Child
Children
Cognition
Cognitive ability
Drug addiction
Environmental factors
Female
Gender
Gender differences
Hippocampus
Humanities and Social Sciences
Humans
Learning algorithms
Machine Learning
Male
Marijuana
Marijuana Abuse - diagnosis
Mental health
multidisciplinary
Personality
Personality Disorders
Risk factors
Risk taking
Science
Science (multidisciplinary)
Sex differences
Sex Factors
Short term memory
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Summary:Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an “explainable” machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley’s Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men 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, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-19804-2