Genetic Associations with a BrainAGE Model Incorporating Structural Neuroimaging and Exercise
Poster No:
890
Submission Type:
Abstract Submission
Authors:
Karen Ardila1,2,3, Aashka Mohite2,3,4, Abdoljalil Addeh1,2,3, Amanda Tyndall2,3,5, Cindy Barha2,3,6, Rebecca J Williams2,7,8, Quan Long2,3,5,9, M. Ethan MacDonald1,2,3,4,8
Institutions:
1Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada, 2Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada, 3Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, 4Department of Electrical Engineering & Software Engineering, University of Calgary, Calgary, Alberta, Canada, 5Department of Medical Genetics, University of Calgary, Calgary, Alberta, Canada, 6Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada, 7Brain-Behaviour Research Group, University of New England, New South Wales, Australia, 8Department of Radiology, University of Calgary, Calgary, Alberta, Canada, 9Department of Biochemistry & Molecular Biology, University of Calgary, Calgary, Alberta, Canada
First Author:
Karen Ardila
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Co-Author(s):
Aashka Mohite
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Electrical Engineering & Software Engineering, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Electrical Engineering & Software Engineering, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Abdoljalil Addeh
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Amanda Tyndall
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Medical Genetics, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Medical Genetics, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Cindy Barha
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Faculty of Kinesiology, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Faculty of Kinesiology, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Rebecca J Williams
Hotchkiss Brain Institute, University of Calgary|Brain-Behaviour Research Group, University of New England|Department of Radiology, University of Calgary
Calgary, Alberta, Canada|New South Wales, Australia|Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary|Brain-Behaviour Research Group, University of New England|Department of Radiology, University of Calgary
Calgary, Alberta, Canada|New South Wales, Australia|Calgary, Alberta, Canada
Quan Long
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Medical Genetics, University of Calgary|Department of Biochemistry & Molecular Biology, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Medical Genetics, University of Calgary|Department of Biochemistry & Molecular Biology, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
M. Ethan MacDonald
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Electrical Engineering & Software Engineering, University of Calgary|Department of Radiology, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, University of Calgary|Alberta Children’s Hospital Research Institute, University of Calgary|Department of Electrical Engineering & Software Engineering, University of Calgary|Department of Radiology, University of Calgary
Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada|Calgary, Alberta, Canada
Introduction:
Brain aging is shaped by a complex interplay of genetic and lifestyle factors (Pang et al., 2019; Niechcial, Vaportzis and Gow, 2022). Structural magnetic resonance imaging (MRI) provides a suitable tool for quantifying age-related changes in brain structure, enabling precise measurements of regional brain volumes and other biomarkers (Franke et al., 2010; Beheshti et al., 2020; MacDonald et al., 2020). Advances in neuroimaging and machine learning have furthered this field by developing models such as a Brain Age Gap Estimate (BrainAGE) model (Franke and Gaser, 2012, 2019). However, a large portion of the variance remains unaccounted for in BrainAGE models, is likely attributable to genetic and lifestyle factors.
We have previously used neuroimaging-only BrainAGE models to explore the genetics, and in this work, we incorporate a key lifestyle factor, vigorous physical activity, into the BrainAGE model to remove additional variance before conducting a genome-wide association study.
We have previously used neuroimaging-only BrainAGE models to explore the genetics, and in this work, we incorporate a key lifestyle factor, vigorous physical activity, into the BrainAGE model to remove additional variance before conducting a genome-wide association study.
Methods:
This cross-sectional study utilized data from 40,958 healthy participants (aged 45-83 years, mean age 64.33, 52.8% female) in the UK Biobank. Structural MRI-derived metrics, including total brain volume (TBV), total hippocampal volume (THV), and total ventricular volume (TVV), were analyzed to compute BrainAGE with a random forest regression model and a bias correction method (Beheshti et al., 2019). Self-reported vigorous physical activity (VPA) was included as a lifestyle feature in the model to evaluate its contribution to predicting brain aging outcomes. VPA was defined as activities lasting at least 10 continuous minutes that made participants sweat or breathe hard, such as fast cycling, aerobics, or heavy lifting. Participants were then categorized into low (0-2 days/week), moderate (3-4 days/week), and high (5 or more days/week) activity levels based on their responses. Additionally, this lifestyle factor was statistically analyzed to evaluate its influence on BrainAGE. Lastly, genome-wide association studies (GWAS) were conducted with BrainAGE as the phenotype for each brain structure, employing PLINK v1.9 for quality control and association testing (Chang et al., 2015; Marees et al., 2018).
Results:
The BrainAGE models achieved R-squared values of 0.76 (MAE: 3.06) for TBV, 0.91 (MAE: 1.88) for THV, and 0.79 (MAE: 2.85) for LVV. Moderate and high levels of VPA showed consistent protective effects on BrainAGE (Figure 1). The strongest effect occurred in THV, where high activity reduced BrainAGE by -0.24 years and low activity increased BrainAGE by 0.30 years (p<3.2e-83). For TBV, high activity significantly slowed brain aging (p<6.3e-14). In LVV, high activity provided measurable protection (p<7.5e-13).
GWAS identified 13 significant single-nucleotide polymorphisms (SNPs) for TBV, 1 SNP for THV, and 20 SNPs for LVV. Key genes linked to brain-related traits included L3MBTL3, WNT16, and RNA5SP375 for TBV (Alzheimer's disease (AD) non-APOE e4 carriers, brain volume measurement, body mass index); for LVV, GMNC-OSTN (AD, t-tau levels, nicotine dependence) and C16orf95 (AD, p-tau levels, lateral ventricular volume in normal aging); lastly, KANSL1 for THV (AD, cognitive function, body mass index, Parkinson's disease). The phenogram illustrates SNPs effects across the genome (Figure 2).
GWAS identified 13 significant single-nucleotide polymorphisms (SNPs) for TBV, 1 SNP for THV, and 20 SNPs for LVV. Key genes linked to brain-related traits included L3MBTL3, WNT16, and RNA5SP375 for TBV (Alzheimer's disease (AD) non-APOE e4 carriers, brain volume measurement, body mass index); for LVV, GMNC-OSTN (AD, t-tau levels, nicotine dependence) and C16orf95 (AD, p-tau levels, lateral ventricular volume in normal aging); lastly, KANSL1 for THV (AD, cognitive function, body mass index, Parkinson's disease). The phenogram illustrates SNPs effects across the genome (Figure 2).
Conclusions:
This study demonstrates that higher levels of VPA consistently provide protective effects against brain aging across brain structures. The findings emphasize genetic contributions to brain aging and suggest genetic susceptibility may influence the protective effects of VPA. Notably, the strongest effect was seen in THV, with high activity levels reducing BrainAGE by -0.24 years. These findings underscore the importance of integrating genetic and environmental data to develop targeted interventions for promoting brain health. Future research should explore longitudinal designs and assess additional lifestyle factors to deepen our understanding of their interplay with brain aging.
Genetics:
Genetic Association Studies 2
Lifespan Development:
Aging 1
Keywords:
Aging
Other - Machine learning, Neurogenetics, Lifestyle Factors, Structural MRI
1|2Indicates the priority used for review
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2. Beheshti, I. et al. (2020) ‘T1-weighted MRI-driven Brain Age Estimation in Alzheimer’s Disease and Parkinson’s Disease’, Aging and disease, 11(3), pp. 618–628. doi.org/10.14336/AD.2019.0617.
3. Chang, C.C. et al. (2015) ‘Second-generation PLINK: Rising to the challenge of larger and richer datasets’, GigaScience, 4(1), p. 7. doi.org/10.1186/S13742-015-0047-8/2707533.
4. Franke, K. et al. (2010) ‘Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters’, NeuroImage, 50(3), pp. 883–892. doi.org/10.1016/J.NEUROIMAGE.2010.01.005.
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8. Marees, A.T. et al. (2018) ‘A tutorial on conducting genome-wide association studies: Quality control and statistical analysis’. doi.org/10.1002/mpr.1608.
9. Niechcial, M.A. et al. (2022) ‘Genes Versus Lifestyles: Exploring Beliefs About the Determinants of Cognitive Ageing’, Frontiers in Psychology, 13, p. 838323. doi.org/10.3389/FPSYG.2022.838323/BIBTEX.
10. Pang, S.Y.Y. et al. (2019) ‘The interplay of aging, genetics and environmental factors in the pathogenesis of Parkinson’s disease’, Translational Neurodegeneration 2019 8:1, 8(1), pp. 1–11. doi.org/10.1186/S40035-019-0165-9.