2026 ECNP Congress

PS03-3169

Brain age gap across psychiatric diagnoses: a transdiagnostic comparison of schizophrenia, bipolar disorder type 1, and major depressive disorder

Ibrahim Sungur — K. Keskin, E. Key, M.C. Eker, A.S. Gonul · Ege University School of Medicine, Psychiatry- SoCAT Lab, Izmir, Türkiye

2026 ECNP Congress

Introduction

Brain age gap (BAG), defined as the discrepancy between estimated biological brain age and chronological age derived from structural magnetic resonance imaging (sMRI), has emerged as a promising transdiagnostic neuroimaging biomarker [1]. Prior studies consistently report increased BAG in schizophrenia (SCH); however, evidence in bipolar disorder (BD) and major depressive disorder (MDD) is more limited and heterogeneous compared to SCH [2], [3], [4]. Elevated BAG is hypothesised to reflect accelerated cortical thinning, synaptic overpruning, and inflammatory processes that differ in nature and extent across diagnostic boundaries [1]. Whether age-related BAG trajectories differ across psychiatric diagnoses remains underexplored.

Aims

In this study, we aimed to compare BAG across SCH, BD, MDD, and healthy controls (HC) within a transdiagnostic framework, and to examine whether age-related BAG trajectories differ across diagnostic groups.

Methods

Participants included SCH (n=52), BD (n=44), MDD (n=37), and HC (n=74). T1-weighted sMRI data were acquired on a 3T-Siemens scanner and processed using FreeSurfer v7.4.1 following the Enhancing Neuro Imaging Genetics by Meta-Analysis (ENIGMA) consortium protocol. Cortical thickness and surface area (Desikan-Killiany atlas, 68 bilateral regions) and subcortical volumes (16 regions) were entered into the Centile BrainAGE algorithm (https://centilebrain.org/#/brainAGE), which was trained over 40000 ENIGMA participants ranging between 3 and 90 years old. Age-related bias-corrected BAG scores were computed separately by sex and age. Groups were comparable in age (F(3,203)=2.49, p=0.141). Group differences in BAG were examined using one-way ANOVA with Tukey post-hoc correction. Sex was included as a covariate in the sensitivity ANCOVA. Age-related BAG trajectories were examined via linear regression including age, diagnosis, and their interaction (Age*Diagnosis) with HC as the reference group (N=207, R²=0.170).

Results

A significant main effect of diagnosis on BAG was observed (F(3,203)=5.02, p=0.002). Mean BAG values were: SCH=+2.27 years, HC=+0.22 years, MDD=-0.44 years, BD=-0.72 years. Post-hoc analyses revealed SCH showed significantly higher BAG compared to HC (mean difference=+2.05 years, p=0.038), BD (mean difference=+2.99 years, p=0.003), and MDD (mean difference=+2.71 years, p=0.016). No significant differences were observed among HC, BD, and MDD (all p>0.05). Sensitivity ANCOVA with sex as covariate confirmed these findings (Diagnosis: F(3,202)=5.70, p=0.001; Sex: p=0.161), indicating results were independent of sex differences across groups. A significant Age × Diagnosis interaction was observed for SCH (β=+0.271, p=0.001) but not for BD (p=0.873) or MDD (p=0.070), indicating that BAG increased significantly faster with age in SCH relative to HC, consistent with progressive acceleration of brain ageing. This trajectory was not observed in BD or MDD.

Conclusions

SCH was associated with both the highest BAG and the steepest age-related BAG acceleration across all diagnostic groups, consistent with progressive neurobiological deterioration in psychotic disorders. In contrast to SCH, both BD and MDD showed BAG values comparable to HC, raising the intriguing possibility that mood disorders are not associated with accelerated brain ageing. The divergent age-BAG trajectories across diagnoses suggest disorder-specific mechanisms of brain ageing beyond a simple severity gradient. These transdiagnostic findings highlight the potential of normative brain age modelling to dissociate neurobiological profiles across psychiatric diagnoses and may inform future biomarker-driven stratification approaches.

References

  1. Seitz-Holland, J., Haas, S.S., Penzel, N., Reichenberg, A., Pasternak, O., 2024. BrainAGE, brain health, and mental disorders: A systematic review. Neurosci Biobehav Rev. 159, 105581.
  2. Blake, K.V., Ntwatwa, Z., Kaufmann, T., Stein, D.J., Ipser, J.C., Groenewold, N.A., 2023. Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies. J Psychiatr Res. 157, 180-191.
  3. Ballester, P.L., Romano, M.T., de Azevedo Cardoso, T., Hassel, S., Strother, S.C., Kennedy, S.H., Frey, B.N., 2022. Brain age in mood and psychotic disorders: a systematic review and meta-analysis. Acta Psychiatr Scand. 145 (1), 42-55.
  4. Hajek, T., Franke, K., Kolenic, M., Capkova, J., Matejka, M., Propper, L., Uher, R., Stopkova, P., Novak, T., Paus, T., Kopecek, M., Spaniel, F., Alda, M., 2019. Brain Age in Early Stages of Bipolar Disorders or Schizophrenia. Schizophr Bull. 45 (1), 190-198.