Neurodegenerative diseases and the role of green space: A deep learning assessment - PROJECT SUMMARY Alzheimer’s disease and related dementias (ADRD) have well-established risk factors such as physical activity (PA), depression, and hypertension (HTN). These risk factors disproportionately affect racial minority populations, but the mechanisms underlying racial health disparities are not well understood. In this, geographic factors could be key, as PA, depression and HTN are strongly affected by geographic exposures, including green space. However, green space is typically measured with questionnaires, which have substantial error, or satellite-based indexes that are nonspecific and provide no information on the type of vegetation (e.g., tree vs. grass), nor whether the vegetation is within view at the street level. As a result, no study has quantified the contribution of green space to racial disparities in ADRD. And while novel technologies such as Google Street Views (GSV) imaging are promising data sources for capturing unique measures of green space, managing, processing, and analyzing high-dimensional data present significant logistical and analytical challenges, especially when linking these data to existing data from large prospective cohorts. Finally, we need to understand green space in the context of other potentially correlated geographic exposures, or the urban exposome—the totality of life- course geographic exposures (the set of green space, air pollutants, noise, built environment, and social environment)—to estimate which factors drive health. This proposal will address these challenges by using GSV imaging to assess the effect of green space on PA, depression, and HTN, as well as subsequent ADRD risk within the Multi-Ethnic Study of Atherosclerosis (MESA)—a 10-year longitudinal study of 6,814 men and women without clinical cardiovascular disease at baseline from 4 racial/ethnic groups (Non-Hispanic White, African-American, Chinese, and Hispanic). Aim 1 will quantify the effect of specific aspects of green space (e.g. trees, grass, shrubs, plants) on ADRD and cognitive decline and evaluate whether these associations differ according to race/ethnicity. Aim 2 will determine the indirect effect of green space on ADRD that is mediated through PA, depression, and HTN. Aim 3 will quantify exposome associations with ADRD and cognitive decline using untargeted data-driven approaches in conjunction with dimension reduction techniques and evaluate whether they differ according to race/ethnicity. This research plan is complemented by a training plan that builds on the applicant’s background in epidemiology and biostatistics and includes new training in (1) implementing deep learning algorithms to analyze high-resolution geographic data, (2) cognitive function epidemiology, and (3) developing and refining data-driven approaches to perform exposome-informed epidemiological studies. These combined plans will successfully prepare the applicant for an independent research career focused on identifying modifiable geographic determinants of ADRD in diverse populations using innovative measures of geographic context.
