Sex-specific aging trajectories in human brain
Poster No:
907
Submission Type:
Abstract Submission
Authors:
Yang Xiao1, Meng Wang2, Bing Liu2, Hao Yan3
Institutions:
1Institute of Mental Health, Peking University Sixth Hospital, Beijing, China, 2State Key Laboratory of Cognitive Neuroscience and Learning, Peking Normal University, BeiJing, China, 3Institute of Mental Health, Peking University Sixth Hospital, BeiJing, China
First Author:
Co-Author(s):
Meng Wang
State Key Laboratory of Cognitive Neuroscience and Learning, Peking Normal University
BeiJing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Peking Normal University
BeiJing, China
Bing Liu
State Key Laboratory of Cognitive Neuroscience and Learning, Peking Normal University
BeiJing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Peking Normal University
BeiJing, China
Introduction:
Brain aging, the progressive and widespread decline of brain function that occurs with age, is a primary risk factor for many neurological and psychiatric disorders (Niccoli & Partridge, 2012). Brain aging varies depending on individuals, populations and sexes, highlighting the heterogeneity of brain aging trajectories (Reicher et al., 2024). The patterns of brain aging in males and females may differ significantly, leading to substantial variations in cognitive decline, neurodegenerative changes, and overall health outcomes (Moguilner et al., 2024). However, previous research has often overlooked sex differences in brain aging patterns, and the potential spatiotemporal trajectories of brain aging across sexes remain unknown.
Methods:
This final sample included 21, 992 healthy participants (10, 996 females) aged 45-85 from the UK Biobank (UKB) to ensure strict age matching between males and females across different age groups. Using a light gradient boosting machine (LightGBM) (Ke et al., 2017), we developed a machine learning model for chronological age (CA) prediction and evaluated the predictive accuracy of structural measurements from 7 brain systems separately in males and females. Brain age (BA), representing the aging of different brain systems, was quantified and used to reveal distinct aging patterns between sexes. To further assess the intricate progression trajectories underlying brain aging, an event-based modeling (Young et al., 2018) approach was used to capture the spatiotemporal patterns of brain aging from cross-sectional data separately in males and females.
Results:
The results exhibited that all 7 brain systems significantly predicted CA in males and females. However, the prediction accuracy was significantly higher in males than in females in cingulate gyrus, medial temporal lobe (MTL) systems, and subcortex (Figure 1A). This suggested that brain systems age differently in men and women. The results from age subgroups revealed that the sex difference of brain aging was age dependent, the aging patterns of different brain systems appear sex-specific differences at about the age of 60 years (Figure 1B). Focusing on the aging after 60 years, we further founded that males and females exhibit similar spatial patterns of brain aging, starting in the subcortex and then spreading to the cortex, but the temporal progression differs significantly between the sexes. In males, brain aging occurs as a long-term process involving multi-system parallel aging, while the brain aging progression of females were characterized by rapid aging that occurs independently within single brain systems. Besides, the subcortex in females was found to reach a scale of large aging earlier than in males, and progressed to pathological aging in later stages (Figure 2). This may suggest an accelerated subcortical aging in females may increase the risk of pathological changes at late aging stages.
·Figure 1. Performance of aging models based on distinct brain systems and their correlations with chronological age.
·Figure 2. Spatiotemporal progression of brain aging in males and females.
Conclusions:
This study explores the dynamic trajectories of brain aging across sexes for the first time. We identify large differences in the brain aging patterns between males and females and revealed the sex-dimorphic patterns of brain aging progression. Our findings provide a more comprehensive perspective on the brain aging and providing direct evidence for advancing the understanding of brain-related disorders and developing personalized medical intervention strategies.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Keywords:
Aging
Data analysis
Sexual Dimorphism
STRUCTURAL MRI
Sub-Cortical
1|2Indicates the priority used for review
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Provide references using APA citation style.
Moguilner, S., Baez, S., Hernandez, H., Migeot, J., Legaz, A., Gonzalez-Gomez, R., . . . Ibanez, A. (2024). Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat Med, 30(12), 3646-3657. doi:10.1038/s41591-024-03209-x
Niccoli, T., & Partridge, L. (2012). Ageing as a risk factor for disease. Curr Biol, 22(17), R741-752. doi:10.1016/j.cub.2012.07.024
Reicher, L., Bar, N., Godneva, A., Reisner, Y., Zahavi, L., Shahaf, N., . . . Segal, E. (2024). Phenome-wide associations of human aging uncover sex-specific dynamics. Nat Aging, 4(11), 1643-1655. doi:10.1038/s43587-024-00734-9
Young, A. L., Marinescu, R. V., Oxtoby, N. P., Bocchetta, M., Yong, K., Firth, N. C., . . . The Alzheimer’s Disease Neuroimaging, I. (2018). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun, 9(1), 4273. doi:10.1038/s41467-018-05892-0