Neurodegenerative diseases and the role of green space: A deep learning assessment

项目总结 阿尔茨海默病和相关痴呆症(ADRD)有公认的危险因素，如体力活动(PA)、 抑郁症和高血压(HTN)。这些风险因素对少数族裔人口的影响不成比例，但 种族健康差异背后的机制还没有被很好地理解。在这方面，地理因素可能是关键，因为PA， 抑郁症和HTN受到地理暴露的强烈影响，包括绿地。然而，绿地是 通常用有很大误差的问卷来衡量，或者用非特定和基于卫星的指数来衡量 没有提供关于植被类型的信息(例如，树木与草)，也没有提供街道上是否可以看到植被的信息 水平。因此，还没有研究量化绿地对ADRD种族差异的贡献。虽然很新奇 谷歌街景(GSV)成像等技术是很有前途的数据源，可以用来捕获 绿地、管理、处理和分析高维数据提供了重要的后勤和分析 挑战，特别是在将这些数据与来自大型预期队列的现有数据联系起来时。最后，我们需要了解 在其他潜在相关地理暴露的背景下的绿地，或城市暴露--生命的整体-- 课程地理暴露(绿地、空气污染物、噪音、建筑环境和社会环境) 估计哪些因素会推动健康。这项提案将通过使用GSV成像来评估效果来应对这些挑战 绿地对PA、抑郁症和HTN的影响，以及随后的ADRD风险 动脉粥样硬化(MESA)-对6814名没有临床心血管疾病的男性和女性进行了为期10年的纵向研究 4个种族/民族(非西班牙裔白人、非裔美国人、中国人和西班牙裔)的基线。目标1将量化 绿地特定方面(如乔木、草、灌木、植物)对ADRD和认知衰退的影响及评估 这些关联是否因种族/民族而异。目标2将确定绿地对 通过PA、抑郁症和HTN介导的ADRD。目标3将量化曝光组与ADRD和 使用非定向数据驱动方法与降维技术相结合的认知能力下降 是否因种族/民族不同而不同。这项研究计划得到了一项建立在 申请人的流行病学和生物统计学背景，包括(1)实施深度学习方面的新培训 分析高分辨率地理数据的算法，(2)认知功能流行病学，以及(3)开发和改进 数据驱动的方法，以执行暴露和知情的流行病学研究。这些合并的计划将成功 为申请人的独立研究生涯做好准备，专注于确定可修改的地理决定因素 使用创新的地理环境测量方法在不同人群中开展ADRD。

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.