Who is alcohol cue-reactive? A machine learning approach

Background The alcohol cue-exposure paradigm is widely used in alcohol use disorder (AUD) research. Individuals with AUD exhibit considerable variability in their alcohol cue-reactivity, highlighting the need to identify characteristics that contribute to this heterogeneity. This study applied machine learning models to identify clinical and sociodemographic predictors of subjective alcohol cue-reactivity (ALCUrge). Methods Individuals with AUD (N = 139; 83 M/56F) completed an alcohol cue-exposure paradigm and a battery of clinical and sociodemographic measures. ALCUrge (primary outcome variable) was assessed using the Alcohol Urge Questionnaire following alcohol cue-exposure. We implemented three machine learning models (Lasso regression, Ridge regression, Random Forest) to identify clinical and sociodemographic predictors of ALCUrge and compared model performance (i.e. predictive accuracy). Results Lasso regression had the strongest predictive accuracy, with a Root Mean Square Error (RMSE) of 9.48, followed by Random Forest (RMSE = 9.95), and Ridge regression (RMSE = 10.40). All models outperformed chance-level prediction (null baseline model RMSE = 14.80). Top predictors of ALCUrge across multiple models were alcohol urge prior to cue-exposure, compulsive alcohol-related behaviors/thoughts, tonic alcohol craving, cigarette smoking status, and biological sex. Higher pre-cue exposure alcohol urge, more compulsive alcohol-related tendencies, greater tonic craving, and occasional cigarette use was associated with greater predicted ALCUrge, while being female was associated with lower predicted ALCUrge. Conclusion This study advances our understanding of the phenotypic overlap in the compulsive aspects of tonic craving and phasic cue-induced alcohol urge, and offers insight into additional factors, such as biological sex and cigarette smoking, that may contribute to variability in alcohol cue-reactivity.