Abstract
Structural brain changes are commonly detectable on MRI before the progressive loss of cognitive function that occurs in individuals with Alzheimer’s disease and related dementias (ADRD). Some proportion of ADRD risk may be modifiable through lifestyle. Certain lifestyle factors may be associated with slower brain atrophy rates, even for individuals at high genetic risk for dementia. Here, we evaluated 44,100 T1-weighted brain MRIs and detailed lifestyle reports from UK Biobank participants who had one or more genetic risk factors for ADRD, including family history of dementia, or one or two ApoE4 risk alleles. In this cross-sectional dataset, we use a machine-learning based metric of age predicted from cross-sectional brain MRIs - or ‘brain age’ - which when compared to the participant's chronological age, may be considered a proxy for abnormal brain aging and degree of atrophy. We used a 3D convolutional neural network trained on T1w brain MRIs to identify the subset of genetically high-risk individuals with a substantially lower brain age than chronological age, which we interpret as resilient to neurodegeneration. We used association rule learning to identify sets of lifestyle factors that were frequently associated with brain-age resiliency. Never or rarely adding salt to food was consistently associated with resiliency. Sex-stratified analyses showed that anthropometry measures and alcohol consumption contribute differently to male vs female resilience. These findings may shed light on distinctive risk profile modifications that can be made to mitigate accelerated aging and risk for ADRD.
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Acknowledgments
This work was supported in part by: R01AG059874, U01AG068057, P41EB05922. This research has been conducted using the UK Biobank Resource under Application Number ‘11559’.
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Haddad, E. et al. (2022). Lifestyle Factors That Promote Brain Structural Resilience in Individuals with Genetic Risk Factors for Dementia. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2022. Lecture Notes in Computer Science, vol 13596. Springer, Cham. https://doi.org/10.1007/978-3-031-17899-3_11
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DOI: https://doi.org/10.1007/978-3-031-17899-3_11
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