The BMIgap tool to quantify transdiagnostic brain signatures of current and future weight
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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 (BMI predicted − BMI measured ), 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.
Source: Nature Mental Health
Authors: Adyasha Khuntia, David Popovic, Elif Sarisik, Madalina O. Buciuman, Mads L. Pedersen, Lars T. Westlye, Ole A. Andreassen, Andreas Meyer-Lindenberg, Joseph Kambeitz, Raimo K. R. Salokangas, Jarmo Hietala, Alessandro Bertolino, Stefan Borgwardt, Paolo Brambilla, Rachel Upthegrove, Stephen J. Wood, Rebekka Lencer, Eva Meisenzahl, Peter Falkai, Emanuel Schwarz, Ariane Wiegand, Nikolaos Koutsouleris
Institutions: Max Planck Institute of Psychiatry, Ludwig-Maximilians-Universität München, University of Oslo, Oslo University Hospital, Heidelberg University, Central Institute of Mental Health, Medizinische Fakultät Mannheim, Deutsches Zentrum für Psychische Gesundheit, University of Cologne, University of Turku, University of Bari Aldo Moro, University of Basel, Universitäre Psychiatrische Kliniken Basel, University of Lübeck, University of Milan, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Oxford, Birmingham Women’s and Children’s NHS Foundation Trust, University of Birmingham, The University of Melbourne, Orygen, University of Münster, Heinrich Heine University Düsseldorf, King's College London
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Funders: Universität Basel, Sunovion, Wellcome Trust, Università degli Studi di Milano, Birmingham Women's and Children's NHS Foundation Trust, National Institute for Health and Care Research, University of Oxford, European Commission, Bundesministerium für Bildung und Forschung, Else Kröner-Fresenius-Stiftung, Universität zu Lübeck, Universita degli Studi di Bari Aldo Moro, Universitetet i Oslo, Norges Forskningsråd, Turun Yliopisto, Universität zu Köln, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, International Max Planck Research School for Advanced Methods in Process and Systems Engineering, H. Lundbeck A/S, National Institutes of Health
Awards: G3882453451, G4016299606, Personalised Prognostic Tools for Early Psychosis Management, COMMITMENT - Modellierung von Komorbiditäts-Prozessen durch integratives, maschinelles Transfer-Lernen für psychiatrische Erkrankungen - Entwicklung einer Informationstechnologielösung zur Anwendung von verteiltem maschinellem Lernen, G8367616569, G9000073410
OpenAlex ID: W4415349270