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<p>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 (<i>n</i> = 1,504), we trained a model to predict body mass index (BMI) and applied it to individuals with schizophrenia (<i>n</i> = 146), clinical high-risk states for psychosis (<i>n</i> = 213) and recent-onset depression (ROD, <i>n</i> = 200). We computed BMIgap (BMI<sub>predicted</sub> − BMI<sub>measured</sub>), 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<sup>−</sup><sup>2</sup>) and clinical high-risk individuals (BMIgap = 0.51 kg m<sup>−</sup><sup>2</sup>) showed increased BMIgap and individuals with ROD (BMIgap = −0.82 kg m<sup>−</sup><sup>2</sup>) 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.</p>

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The BMIgap tool to quantify transdiagnostic brain signatures of current and future weight

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月20日

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  • 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

摘要

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.