Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium

Han, L. K. M.; Toenders, Y. J.; Shen, X.; Milaneschi, Y.; Whalley, H. C.; Saemann, P. G.; Andlauer, T. F. M.; Bauer, J.; Berger, K.; Borgers, T.; Cole, J. H.; Dannlowski, U.; Flinkenfluegel, K.; Grabe, H. J.; Grotegerd, D.; Gruber, O.; Hahn, T.; Hamilton, P. J.; Hatton, S. N.; Hermesdorf, M.; Hickie, I.; Homann, J.; Kircher, T. T. J.; Kraemer, B.; Kraus, A.; Krug, A.; Lill, C. M.; Medland, S. E.; Meinert, S.; Panzenhagen, A. C.; Penninx, B. W. J. H.; van der Wee, N. J. A.; van Tol, M.-J.; Volker, U.; Volzke, H.; Weihs, A.; Wittfeld, K.; Thomopoulos, S. I.; Jahanshad, N.; Thompson, P. M.; Pozzi

doi:10.1101/2025.05.09.653064

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bioRxiv · 10.1101/2025.05.09.653064

Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium

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Abstract

BackgroundLarge-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. MethodsUsing an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). ResultsHigher brain age gaps were significantly associated with BMI ({beta}=0.01, PFDR=0.02) and smoking ({beta}=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education ({beta}=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap ({beta}=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS ({beta}s=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors--BMI, smoking, and education--remained uniquely associated with brain age gaps. ConclusionsGenetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

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Han, L. K. M., Toenders, Y. J., Shen, X., Milaneschi, Y., Whalley, H. C., Saemann, P. G., Andlauer, T. F. M., Bauer, J., Berger, K., Borgers, T., Cole, J. H., Dannlowski, U., Flinkenfluegel, K., Grabe, H. J., Grotegerd, D., Gruber, O., Hahn, T., Hamilton, P. J., Hatton, S. N., Hermesdorf, M., Hickie, I., Homann, J., Kircher, T. T. J., Kraemer, B., Kraus, A., Krug, A., Lill, C. M., Medland, S. E., Meinert, S., Panzenhagen, A. C., Penninx, B. W. J. H., van der Wee, N. J. A., van Tol, M.-J., Volker, U., Volzke, H., Weihs, A., Wittfeld, K., Thomopoulos, S. I., Jahanshad, N., Thompson, P. M., Pozzi. 2025-05-11. Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium. https://doi.org/10.1101/2025.05.09.653064

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