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

## TL;DR

This study uses brain scans to predict BMI and identifies a brain-based measure called BMIgap that could help identify people at risk of weight gain and psychiatric disorders.

## Contribution

The novel BMIgap tool quantifies brain signatures of weight and links them to psychiatric conditions and future weight gain.

## Key findings

- Individuals with schizophrenia and clinical high-risk states showed increased BMIgap compared to measured BMI.
- Higher BMIgap predicted future weight gain, especially in younger individuals with depression.
- BMIgap brain patterns overlap with schizophrenia and correlate with disease progression markers.

## 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 (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.

This research investigates the neurobiological factors influencing weight gain, using brain scans from diverse cohorts to develop a predictive model. The findings indicate that BMIgap correlates with psychiatric conditions, suggesting its potential for identifying at-risk individuals and guiding personalized interventions.