BMIgap: a new tool to quantify transdiagnostic brain signatures of current and future weight

Understanding the neurobiological underpinnings of weight gain could reduce excess mortality and improve long-term trajectories of psychiatric disorders. We used support-vector machines and whole-brain voxel-wise grey matter volume to generate and validate a BMI predictor in healthy individuals (N = 1504) and applied it to individuals with schizophrenia (SCZ,N = 146), clinical high-risk states for psychosis (CHR,N = 213) and recent-onset depression (ROD,N = 200). We computed BMIgap (BMI_predicted-BMI_measured), interrogated its brain-level overlaps with SCZ and explored whether BMIgap predicted weight gain at 1- and 2-year follow-up. SCZ (BMIgap = 1.05kg/m²) and CHR individuals (BMIgap = 0.51 kg/m²) showed increased and ROD individuals (BMIgap=-0.82 kg/m²) decreased BMIgap. Shared brain patterns of BMI and SCZ were linked to illness duration, disease onset, and hospitalization frequency. Higher BMIgap predicted future weight gain, particularly in younger ROD individuals, and at 2-year follow-up. Therefore, we propose BMIgap as a potential brain-derived measure to stratify at-risk individuals and deliver tailored interventions for better metabolic risk control.