Poster No:
836
Submission Type:
Abstract Submission
Authors:
Gabriella Alvarez1
Institutions:
1University of Pittsburgh, Pittsburgh, PA
First Author:
Introduction:
As individuals age, the human brain undergoes structural changes, such as volume reductions and cortical thinning (de Lange et al., 2020). These alterations are linked to cognitive decline and an increased risk of neurodegenerative disorders. While senescent brain deterioration is recognized, there is considerable variation in neurobiological aging trajectories among older populations, prompting neuroimaging studies to explore potential markers for brain aging.
In the context of racial disparities observed in brain-related diseases, especially Alzheimer's in Black populations (Babulal et al., 2018), researchers suggest that socioeconomic status (SES) may underlie racial differences in brain aging. However, the potential role of racism, a unique societal stressor experienced by marginalized populations, in influencing neurobiological aging remains underexplored.
This study addresses this gap by investigating links between racism exposure and brain aging in Black Americans, examining predicted brain age as a biomarker. Leveraging brain-age estimation methods, we explore associations while controlling for relevant covariates.
Methods:
Seventy-two Black American participants from the Midlife in the United States (MIDUS) dataset underwent a multi-stage assessment, completing discrimination exposure questionnaires and MRI scans. Discrimination exposure was measured comprehensively, and a composite score was derived. MRI scans occurred 9 months to 20 years post-questionnaire, providing high-resolution images for brain age estimation using the BrainageR package. Regression analyses explored the association between discrimination exposure and brain age, controlling for age, SES, and gender. The study also examined robustness to confounders and the impact of the time between survey and MRI on the discrimination exposure and brain age gap association.
Results:
Participant's ages ranged from 26.66-75.33 years (M=54.42, SD=12.53). Analyses revealed a significant main effect of discrimination exposure on brain age (b=4.59, SE=1.476, p=0.003), indicating that discrimination exposure was associated with accelerated brain age. This effect remained statistically significant after controlling for chronological age, SES, and gender. Gender differences revealed decelerated brain age in Black women (b=-5.836, SE=1.705, p=0.001). The time elapsed between survey completion and MRI scan significantly moderated the association between discrimination exposure and brain age gap (b=-1.957, SE=6.227, p=0.003). Specifically, simple slope analyses revealed a positive association between discrimination exposure and accelerated brain age among participants with a shorter lag between survey and MRI completion (b=4.73, se=1.70, p=0.01) while those with a longer lag demonstrated decelerated brain aging (b=-5.72 years, se=2.20, p=0.01). Associations between discrimination exposure and brain age remained robust even after accounting for potential confounders related to discrimination and aging, including symptoms of depression, anxiety, education, income, and neuroticism. Interestingly, neuroticism demonstrated a unique and negative association with brain age in this sample such that greater levels of neuroticism were associated with decelerated brain age (b=-1.957, se=3.67, p=0.001).
Conclusions:
This study provides compelling evidence of an association between discrimination exposure and accelerated brain aging in Black Americans. The temporal dynamics unveiled in our analysis, introduces a novel dimension to the understanding of this relationship. This temporal sensitivity suggests that the impact of discrimination on brain aging may not be static and may evolve over time, demanding a nuanced examination of the temporal aspects in future investigations. Future research should explore the intricate interplay of sociodemographic, psychosocial, and neurobiological factors to inform targeted interventions for mitigating discrimination's adverse effects on brain health in vulnerable populations.
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other 1
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Classification and Predictive Modeling
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Neuroanatomy Other
Keywords:
Aging
Degenerative Disease
NORMAL HUMAN
Social Interactions
STRUCTURAL MRI
Other - Social Factors
1|2Indicates the priority used for review
Provide references using author date format
Babulal, G. M., Quiroz, Y. T., Albensi, B. C., Arenaza‐Urquijo, E. M., Astell, A., Babiloni, C., … & O’Bryant, S. (2018). Perspectives on ethnic and racial disparities in alzheimer's disease and related dementias: update and areas of immediate need. Alzheimer's &Amp; Dementia, 15(2), 292-312. https://doi.org/10.1016/j.jalz.2018.09.009
Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 2017; 163C: 115-124.
de Lange, A. G., Anatürk, M., Suri, S., Kaufmann, T., Cole, J. H., Griffanti, L., Zsoldos, E., Jensen, D. E. A., Filippini, N., Singh-Manoux, A., Kivimäki, M., Westlye, L. T., & Ebmeier, K. P. (2020). Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. NeuroImage, 222, 117292. https://doi.org/10.1016/j.neuroimage.2020.117292
Franke, K. and Gaser, C. (2019). Ten years of brainage as a neuroimaging biomarker of brain aging: what insights have we gained?. Frontiers in Neurology, 10. https://doi.org/10.3389/fneur.2019.00789