A1 Refereed original research article in a scientific journal
The BMIgap tool to quantify transdiagnostic brain signatures of current and future weight
Authors: Khuntia, Adyasha; Popovic, David; Sarisik, Elif; Buciuman, Madalina O.; Pedersen, Mads L.; Westlye, Lars T.; Andreassen, Ole A.; Meyer-Lindenberg, Andreas; Kambeitz, Joseph; Salokangas, Raimo K. R.; Hietala, Jarmo; Bertolino, Alessandro; Borgwardt, Stefan; Brambilla, Paolo; Upthegrove, Rachel; Wood, Stephen J.; Lencer, Rebekka; Meisenzahl, Eva; Falkai, Peter; Schwarz, Emanuel; Wiegand, Ariane; Koutsouleris, Nikolaos
Publisher: Springer Nature
Publication year: 2025
Journal: Nature Mental Health
Volume: 3
First page : 1395
Last page: 1406
eISSN: 2731-6076
DOI: https://doi.org/10.1038/s44220-025-00522-3
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1038/s44220-025-00522-3
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/505337010
Understanding the neurobiological underpinnings of weight gain could reduce excess mortality and improve long-term trajectories of psychiatric disorders. Using brain scans from healthy individuals (n = 1,504), we trained a model to predict body mass index (BMI) and applied it to individuals with schizophrenia (n = 146), clinical high-risk states for psychosis (n = 213) and recent-onset depression (ROD, n = 200). We computed BMIgap (BMIpredicted − BMImeasured), interrogated its brain-level overlaps with schizophrenia and explored whether BMIgap predicted weight gain at the 1-year and 2-year follow-ups. Schizophrenia (BMIgap = 1.05 kg m−2) and clinical high-risk individuals (BMIgap = 0.51 kg m−2) showed increased BMIgap and individuals with ROD (BMIgap = −0.82 kg m−2) showed decreased BMIgap. Shared brain patterns of BMI and schizophrenia were linked to illness duration, disease onset and hospitalization frequency. Higher BMIgap predicted future weight gain, particularly in younger individuals with ROD, and at 2-year follow-up. Here we show that BMIgap can serve as a potential brain-derived measure to stratify at-risk individuals and deliver tailored interventions for better metabolic risk control.
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Funding information in the publication:
This study was supported by the German Federal Ministry of Education and Research (01ZX1904E and 01ZX2204A) as a part of the COMorbidity Modeling via Integrative Transfer machine learning in MENTal illness project. PRONIA is a collaboration project funded by the European Union under the 7th Framework Programme (grant no. 602152). N.K. is supported through grants from the National Institutes of Health (U01MH124639-01; ProNET), the Wellcome Trust, the German Innovation Fund (CARE project), the German Federal Ministry of Education and Research (COMMITMENT and BEST projects), as well as ERA PerMed (IMPLEMENT project). A.K. is funded through the COMMITMENT project. D.P. was supported by the Else-Kröner-Fresenius-Foundation through the Clinician Scientist Program ‘EKFS-Translational Psychiatry’. E. Schwarz is supported through grants from the German Federal Ministry of Education and Research (COMMITMENT, grant no. 01ZX2204A), BEST (grant no. 01EK2101B) and IMPLEMENT (grant no. 01KU1905A)). The Norwegian study group is funded by the Research Council of Norway and the KG Jebsen Foundation. The funders were not involved in the design and conduct of the study; the collection, management, analysis and interpretation of the data; the preparation, review or approval of the manuscript; and the decision to submit the manuscript for publication.
