Alzheimer's disease (AD) affects more women than men and burdens millions of aging Americans and their families. While our knowledge of modifiable risk factors for AD remains limited, there is growing evidence that exposure to ambient air pollutants, including particulate matter (PM) and ozone, accelerates brain aging. Built on our collaborative work on neurotoxic effects of ambient air pollution and cognitive aging in Women's Health Initiative Study (WHIMS), this application will address two critical knowledge gaps in this promising area of air pollution-neuroepidemiology: (1) It is unclear whether brain region- and/or site-specific neurotoxicity occurs from ambient air pollution exposure; and (2) Strong epidemiologic evidence linking ambient air pollution to increased AD risk are still lacking. These knowledge gaps reflect limitations of previous research with conventional analyses of neuroimaging data and insufficient power in linking AD risk with exposures. This application will overcome these challenges by combing three unique data sources (WHIMS air pollution exposure; WHIMS MRI; and the Alzheimer's Disease Neuroimaging Initiative [ADNI]). Leveraging the longitudinal brain structure data from WHIMS MRI (gathered in 2005-6; 2010-2011) plus air pollution exposure estimates generated for WHIMS (gathered in 1993-2010), we will apply high-dimensional computational methods to conduct voxel-based analyses with a systematic and agnostic approach to examining the brain region-specific neurotoxicity of air pollution. This exploratory R21 application will also test the hypothesized adverse effect of ambient air pollution on the pattern of brain structure predictive of increased AD risk in an optimal population context, where ambient air pollutants affected the cognitive aging in older women. ADNI provides a well-characterized clinical database of cognitive normal and AD phenotypes, enabling our development/validation of novel AD risk metrics based on neuroimaging data applicable to WHIMS MRI. Thus, our Specific Aims are:
Aim 1) Using structural brain MRI and air pollution data from the WHIMS-MRI cohort, to examine whether spatial patterns of grey matter/white matter atrophy at baseline and their changes over time are associated with air pollutant exposures;
and Aim 2) Using novel metrics of AD risk to conduct a quantitative assessment linking AD risk with ambient air pollution. We have assembled a multi-disciplinary team with complementary expertise in high-dimensional machining learning for neuroimaging data analyses, cognitive aging, and air pollution neurotoxicology/epidemiology. Our study will advance the field of environmental epidemiology of aging by using advanced analytical tools to link air pollution exposures to spatial patterns of brain structure and the predicted risk of AD.

Public Health Relevance

Long-term exposure to air pollutants may adversely impact the cognitive health of older individuals, however this link is not well-established and whether this conveys an increased risk for Alzheimer's disease is unknown. Leveraging brain magnetic resonance imaging data collected at geographically dispersed US sites and geocoded estimates of person-specific long-term environmental exposures, we will use sophisticated machine learning methods to characterize associations that exposure to air pollution has with brain structure and the risk of Alzheimer's disease (AD). Determining the nature and extent of these relationships, and their consistency across subgroups of older women, we will identify targets towards improving the health of seniors.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Neurological, Aging and Musculoskeletal Epidemiology (NAME)
Program Officer
Wise, Bradley C
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Wake Forest University Health Sciences
Biostatistics & Other Math Sci
Schools of Medicine
United States
Zip Code
Espeland, Mark A; Chen, Jiu-Chiuan; Weitlauf, Julie et al. (2018) Trajectories of Relative Performance with 2 Measures of Global Cognitive Function. J Am Geriatr Soc 66:1575-1580
Casanova, Ramon; Barnard, Ryan T; Gaussoin, Sarah A et al. (2018) Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases. Neuroimage 183:401-411
Casanova, Ramon; Wang, Xinhui; Reyes, Jeanette et al. (2016) A Voxel-Based Morphometry Study Reveals Local Brain Structural Alterations Associated with Ambient Fine Particles in Older Women. Front Hum Neurosci 10:495