Genetic Liabilities Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals
Poster No:
508
Submission Type:
Abstract Submission
Authors:
Laura Han1, Yara Toenders2, Xueyi Shen3, Heather Whalley4, Yuri Milaneschi5, Klaus Berger6, James Cole7, Jan Homann8, Christina Lill9, Philipp Sämann10, Sophia Thomopoulos11, Neda Jahanshad12, Paul Thompson11, Elena Pozzi13, Lianne Schmaal13, ENIGMA MDD Working Group14
Institutions:
1Amsterdam UMC, Amsterdam, Noord-Holland, 2Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands, Rotterdam, Netherlands, 3University of Edinburgh, Edinburgh, United Kingdom, 4Department of Psychiatry, University of Edinburgh, Edinburgh, Scotland, 5Amsterdam UMC, Amsterdam, Netherlands, 6Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany, 7University College London, London, London, 8Institute of Epidemiology and Social Medicine, Münster, Germany, Munster, Germany, 9Institute of Epidemiology and Social Medicine, University of Münster, Munster, Germany, 10Max Planck Institute of Psychiatry, Munich, Germany, 11University of Southern California, Los Angeles, CA, 12University of Southern California,, Marina del Rey, CA, 13The University of Melbourne, Parkville, Victoria, 14USC, Los Angeles, CA
First Author:
Co-Author(s):
Yara Toenders
Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands
Rotterdam, Netherlands
Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands
Rotterdam, Netherlands
Klaus Berger
Institute of Epidemiology and Social Medicine, University of Muenster
Muenster, Germany
Institute of Epidemiology and Social Medicine, University of Muenster
Muenster, Germany
Christina Lill
Institute of Epidemiology and Social Medicine, University of Münster
Munster, Germany
Institute of Epidemiology and Social Medicine, University of Münster
Munster, Germany
Introduction:
Research reveals a link between major depressive disorder (MDD) and a higher brain age gap[1], potentially shedding light on the increased risk of premature mortality in individuals with MDD. However, brain aging and MDD are both individually complex phenomena, highlighting the important need to further dissect the nature and direction of their connections. Large within-group variability and small between-group differences demonstrate that not all patients show abnormal age-related brain signatures compared to controls. In this study, we examined whether individual genetic liability for depression and depression-related traits contributes to individual variations in the brain age gap. To further explore the connection between peripheral and brain imaging age-related signatures, we investigated associations between the brain age gap and genetic liability for DNA methylation-derived biological age indicators (i.e., epigenetic clocks).
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 MDD, C-reactive protein, BMI, and four epigenetic clocks (i.e., GrimAge, Hannum, Horvath, and PhenoAge) using large-scale Genome-Wide Association Study results [2,3,4,5]. To calculate PRS, we used a clumping and thresholding (CT) method. For each phenotype PRS were calculated using PRSice 2.0 based on ten p-value thresholds (pT = 5×10-8, 1×10-6, 1×10-4, 1×10-3, 0.01, 0.05, 0.1, 0.2, 0.5 and 1). Scripts used to calculate PRS are publicly available at https://github.com/xshen796/ENIGMA_mdd_prs/blob/main/script/PREP_PRS/Calculate_PRS.md. Individual-level brain age gap scores, PRS, and demographic measures from each cohort were pooled for a mega-analysis using linear mixed models. PRS were adjusted for population stratification using the first ten genetic principal components and brain age gap scores were adjusted for linear and quadratic age effects. Sex was included as a covariate. To account for site-related variability, random intercepts for scanning sites were incorporated.
Results:
A higher brain age gap was associated with higher PRS for BMI at one out of ten thresholds (β=0.039 [SE=0.018], p=0.036), higher PRS for CRP at six out of ten thresholds (β-values ranged from 0.032 to 0.046, P-values ranged from 0.001 to 0.016), and higher PRS for MDD at eight out of ten thresholds (β-values ranged from 0.029 to 0.038, P-values ranged from 0.001 to 0.016), Figure 1. When analyzing the genetic liability for epigenetic aging in relation to the brain age gap, we observed distinct patterns: a higher brain age gap was associated with a lower PRS for epigenetic aging as measured by Horvath acceleration residuals (at two of ten thresholds; β-values ranging from 0.08 to 0.12 and p-values between 0.007 and 0.038), but with a higher PRS as measured by Hannum acceleration residuals (at two of ten thresholds; β-values ranging from 0.08 to 0.12 and p-values between 0.007 and 0.038), Figure 2. There were no significant interactions between any of the PRS and diagnostic status (Control/MDD) on the brain age gap at any of the thresholds.
·Associations between the brain age gap and genetic liability for BMI, C-reactive protein, and major depression. The x-axis represents the PRS threshold. The y-axis represents the standardized beta.
·Associations between the brain age gap and genetic liability for epigenetic age acceleration residuals. The x-axis represents the PRS threshold. The y-axis represents the standardized beta.
Conclusions:
Our findings indicate that higher genetic liabilities for MDD, higher BMI and elevated inflammation, significantly contributed to a higher brain age gap. This suggests that advanced brain age may partly result from a genetic predisposition to depression and traits commonly seen depression. However, the relationship with genetic liability for peripheral aging markers (epigenetic clocks) was less consistent. We found no associations between the brain age gap and genetic liability of "second-generation" epigenetic clocks (i.e., GrimAge, PhenoAge), and observed agreement with genetic liability for the DNA methylation-based age predictor derived from blood (i.e., Hannum) but not from multiple tissues (i.e., Horvath).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetics Other 2
Lifespan Development:
Aging
Modeling and Analysis Methods:
Multivariate Approaches
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Affective Disorders
Aging
Machine Learning
MRI
Open-Source Code
Psychiatric Disorders
STRUCTURAL MRI
1|2Indicates the priority used for review
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[2] Howard, D. M. et al. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience,22(3):343.
[3] Locke, A.E., et al. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538):197-206.
[4] Ligthart, S., et al. (2018). Genome analyses of> 200,000 individuals identify 58 loci for chronic inflammation and highlight pathways that link inflammation and complex disorders. American Journal of Human Genetics, 103(5):691-706.
[5] McCartney, D.L., et al. (2021) Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging. Genome Biology, 22(1):194.