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. 2025 Mar;151(3):231-244.
doi: 10.1111/acps.13684. Epub 2024 Apr 1.

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

Julia Eder  1   2 , Catherine Glocker  1 , Barbara Barton  1 , Elif Sarisik  1   3   4 , David Popovic  1   3   4 , Jana Lämmermann  1   2 , Alexandra Knaf  1   2 , Anja Beqiri-Zagler  1 , Katharina Engl  1 , Leonie Rihs  1 , Lisa Pfeiffer  1   2 , Andrea Schmitt  1   2   5 , Peter Falkai  1   2   3   4 , Maria S Simon  1 , Richard Musil  1   6
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Who is at risk for weight gain after weight-gain associated treatment with antipsychotics, antidepressants, and mood stabilizers: A machine learning approach

Julia Eder et al. Acta Psychiatr Scand. 2025 Mar.

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Keywords: antidepressants; antipsychotics; mood‐stabilizer; predictor; weight gain.

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Conflict of interest statement

Richard Musil has received financial research support from the EU (H2020 No. 754740), and served as PI in clinical trials from Abide Therapeutics, Böhringer‐Ingelheim, Emalex Biosciences, Lundbeck GmbH, Nuvelution TS Pharma Inc., Oryzon, Otsuka Pharmaceuticals and Therapix Biosciences. Peter Falkai received research support/honoraria for lectures or advisory activities from: Boehringer‐Ingelheim, Janssen, Lundbeck, Otsuka, Recordati, and Richter. All other authors declare that they have no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Illustration of the 5 × 5 nested cross‐validation framework employed for training the machine learning algorithms.
FIGURE 2
FIGURE 2
The decision tree based on factors retrieved from a literature search.
FIGURE 3
FIGURE 3
The decision tree based on the addition of further factors to the ones from literature.
See this image and copyright information in PMC

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