Who is at risk for weight gain after weight-gain associated treatment with antipsychotics, antidepressants, and mood stabilizers: A machine learning approach.

Abstract

Weight gain is a common side effect in psychopharmacology; however, targeted therapeutic interventions and prevention strategies are currently absent in day-to-day clinical practice. To promote the development of such strategies, the identification of factors indicative of patients at risk is essential. In this study, we developed a transdiagnostic model using and comparing decision tree classifiers, logistic regression, XGboost, and a support vector machine to predict weight gain of ≥5% of body weight during the first 4 weeks of treatment with psychotropic drugs associated with weight gain in 103 psychiatric inpatients. We included established variables from the literature as well as an extended set with additional clinical variables and questionnaires. Baseline BMI, premorbid BMI, and age are known risk factors and were confirmed by our models. Additionally, waist circumference has emerged as a new and significant risk factor. Eating behavior next to blood glucose were found as additional potential predictor that may underlie therapeutic interventions and could be used for preventive strategies in a cohort at risk for psychotropics induced weight gain (PIWG). Our models validate existing findings and further uncover previously unknown modifiable factors, such as eating behavior and blood glucose, which can be used as targets for preventive strategies. These findings underscore the imperative for continued research in this domain to establish effective preventive measures for individuals undergoing psychotropic drug treatments.