Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May 19;20(5):e0324000.
doi: 10.1371/journal.pone.0324000. eCollection 2025.

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study

Affiliations

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study

Hye Jun Lee et al. PLoS One. .

Abstract

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weight gain is augmenting the disease burden and healthcare expenditure. However, predictors of psychotropic drug-induced weight gain and the efficacy of anti-obesity drugs remain underexplored. This study aims to develop a machine learning algorithm to predict both psychotropic drugs-induced weight gain and metabolic changes, and the potential of anti-obesity drugs. We plan to enroll 300 patients with severe mental illnesses, including schizophrenia, bipolar disorder, and major depressive disorder. In Phase 1, the study will predict weight gain and metabolic changes after the psychotropic treatment. Data on demographics, lifestyle, medical history, psychological factors, anthropometrics, and laboratory results will be collected at baseline and re-evaluated 24 weeks post-treatment. Participants classified as obese (body mass index ≥ 25 kg/m²) or overweight (body mass index of 23-24.9 kg/m²) at the 24-week follow-up will proceed to Phase 2, which focuses on predicting the promise of anti-obesity drugs. The study participants will receive anti-obesity medications for 24 weeks, and the same variables from Phase 1 will be reassessed. A machine learning model will be developed to predict both psychotropic drug-induced weight gain and anti-obesity medications that will be effective. The algorithm will be tailored to each patient to guide clinicians in personalizing psychiatric and obesity treatment plans. The clinical trial is registered with the Clinical Research Information Service, part of the WHO International Clinical Trials Registry Platform (approval number: KCT0009769).

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schedule of enrolment, interventions, and assessments.
Note: -t1: -2 weeks; t0: baseline; t1: 12 weeks, t2: 24 weeks; t3: 36 weeks; t4: 48 weeks. Phase 1: Study predicting psychotropic drug-induced weight gain. Phase 2: Study assessing the efficacy of anti-obesity drugs in managing psychotropic-drug induced weight gain. A total of 300 participants will be enrolled. Participants with a BMI ≥ 25 kg/m² (obese) or 23–24.9 kg/m² (overweight) at the end of Phase 1 will be enrolled in Phase 2.

Similar articles

References

    1. Izquierdo-Torres E, Hernández-Oliveras A, Lozano-Arriaga D, Zarain-Herzberg Á. Obesity, the other pandemic: linking diet and carcinogenesis by epigenetic mechanisms. J Nutr Biochem. 2022;108:109092. doi: 10.1016/j.jnutbio.2022.109092 - DOI - PubMed
    1. World obesity atlas 2022. London: World Obesity Federation; 2022. [Cited 2024 September 3]. Available from: https://s3-eu-west-1.amazonaws.com/wof-files/World_Obesity_Atlas_2022.pdf.
    1. World Health Organization. Regional office for the Western Pacific. the Asia-Pacific perspective: redefining obesity and its treatment. Sydney: Health Communications; 2020. https://iris.who.int/handle/10665/206936
    1. Afzal M, Siddiqi N, Ahmad B, Afsheen N, Aslam F, Ali A, et al.. Prevalence of overweight and obesity in people with severe mental illness: systematic review and meta-analysis. Front Endocrinol (Lausanne). 2021;12:769309. doi: 10.3389/fendo.2021.769309 - DOI - PMC - PubMed
    1. Käräjämäki AJ, Korkiakoski A, Hukkanen J, Kesäniemi YA, Ukkola O. Long-term metabolic fate and mortality in obesity without metabolic syndrome. Ann Med. 2022;54(1):1432–43. doi: 10.1080/07853890.2022.2075915 - DOI - PMC - PubMed