Alcohol and alcoholism (Oxford, Oxfordshire) · 2025
Who is alcohol cue-reactive? A machine learning approach.
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Kirsch Dylan E, McManus Kaitlin R, Grodin Erica N, Nieto Steven J, Miranda Robert, O'Malley Stephanie S, Schacht Joseph P, Ray Lara A
Аннотация
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.
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.