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

作者：Laura K. M. Han, Yara J. Toenders, Xueyi Shen, Yuri Milaneschi, Heather C. Whalley, Philipp G. Saemann, Till F. M. Andlauer, Jochen Bauer, Klaus Berger, Tiana Borgers, James H. Cole, Udo Dannlowski, Kira Flinkenflügel, Hans J. Grabe, Oliver Gruber, Tim Hahn, J. Paul Hamilton, Sean N. Hatton, Marco Hermesdorf, Ian B. Hickie, Jan Homann, Tilo Kircher, Bernd Krämer, Anna Kraus, Axel Krug, Christina M. Lill, Sarah E. Medland, Susanne Meinert, Alana Castro Panzenhagen, Brenda W.J.H. Penninx, Nic J.A. van der Wee, Marie‐José van Tol, Uwe Völker, Henry Völzke, Antoine Weihs, Katharina Wittfeld, Sophia I. Thomopoulos, Neda Jahanshad, Paul M. Thompson, Elena Pozzi, Lianne Schmaal · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2025 · DOI：10.1101/2025.05.09.653064 · 被引用次数：3 · 研究领域：Health, Environment, Cognitive Aging、Tryptophan and brain disorders、Stress Responses and Cortisol

ABSTRACT Background Large-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. Methods Using 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). Results Higher brain age gaps were significantly associated with BMI (β=0.01, P FDR =0.02) and smoking (β=0.11, P FDR =0.02), while lower brain age gaps were linked to higher education (β=-0.02, P FDR =0.02). Higher childhood trauma scores predicted a higher brain age gap (β=0.04, P=0.01). Higher brain age gaps were positively associ...