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

神经退行性疾病和绿色空间的作用：深度学习评估

基本信息

批准号：
10589100
负责人：
Marcia Ixchel Pescador Jimenez
金额：
$ 24.47万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10589100
关键词：
Acceleration Accounting Adult Affect African American African American population Age Air Pollutants Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Baltimore Biological Biometry Black race Cardiovascular Diseases Chicago Chinese Cities Clinical Complement Complex County Cross-Sectional Studies Data Data Sources Diagnosis Dimensions Disparity Elderly Environment Environmental Hazards Epidemiology Ethnic Origin Ethnic Population Exposure to Family Funding Geographic Factor Geography Goals Green space Health Health Benefit Healthcare Systems Hispanic Hispanic Populations Hypertension Image Imagery Impaired cognition Incidence Individual Intervention Life Cycle Stages Link Literature Longitudinal Studies Los Angeles Measurement Measures Mediating Mediation Mediator Mental Depression Mental Health Methodology Methods Minority Multi-Ethnic Study of Atherosclerosis Neurodegenerative Disorders New York Noise Not Hispanic or Latino Pathway interactions Patients Physical activity Plants Poaceae Population Population Heterogeneity Prevalence Prevention Prevention strategy Prospective cohort Prospective, cohort study Questionnaires Race Research Resolution Risk Risk Factors Risk Reduction Role Social Environment Social Impacts Techniques Time Training Trees United States National Institutes of Health Woman aged built environment career cognitive function cohort comorbidity deep learning deep learning algorithm dementia risk design economic impact epidemiology study ethnic minority healthy aging high resolution imaging indexing innovation insight longitudinal analysis men multidimensional data neurocognitive disorder new technology novel physical inactivity public health intervention racial disparity racial health disparity racial minority racial minority population racial population segmentation algorithm skills urban public health

项目摘要

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.

项目摘要阿尔茨海默氏病和相关痴呆症（ADRD）具有良好的危险因素，例如体育活动（PA），抑郁和高血压（HTN）。这些风险因素不成比例地影响少数族裔，但种族健康差异的基础机制尚不清楚。在此，地理因素可能是关键，因为PA，抑郁症和HTN受到包括绿色空间在内的地理暴露的强烈影响。但是，绿色空间是通常用问卷调查，这些问卷具有实质性错误，或基于卫星的索引，这些索引是非特异性和没有提供有关植被类型的信息（例如，树与草），也没有提供植被在街上是否在街上等级。结果，没有研究量化了绿色空间对ADRD种族分布的贡献。而新颖诸如Google Street Views（GSV）成像之类的技术是捕获独特测量的数据源绿色空间，管理，处理和分析高维数据呈现出明显的后勤和分析挑战，尤其是将这些数据与来自大型潜在人群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或城市风险的背景下，绿色空间 - 生命的整体 - 课程地理暴露（绿色空间，空气污染物，噪音，建筑环境和社交环境）估计哪些因素推动了健康。该建议将通过使用GSV成像来评估效果来解决这些挑战 PA，抑郁和HTN上的绿色空间以及随后的ADRD风险在多民族研究中动脉粥样硬化（MESA） - 对6,814名没有临床心血管疾病的男性和女性进行的10年纵向研究来自4个种族/族裔的基线（非西班牙裔白人，非裔美国人，中国和西班牙裔）。 AIM 1将量化绿色空间（例如树木，草，灌木，植物）的特定方面对ADRD和认知能力下降和评估的影响这些关联是否因种族/种族而有所不同。 AIM 2将确定绿色空间对通过PA，抑郁和HTN介导的ADRD。 AIM 3将量化与ADRD的展览体关联，并使用不靶向数据驱动的方法与降低技术结合使用并评估认知能力下降它们是否根据种族/种族有所不同。该研究计划是由一项培训计划完成的申请人在流行病学和生物统计学方面的背景，包括（1）实施深度学习的新培训分析高分辨率地理数据，（2）认知功能流行病学和（3）开发和完善的算法数据驱动的方法用于执行释放的流行病学研究。这些合并的计划将成功为申请人准备独立研究职业，重点是确定可修改的地理决定使用地理环境的创新度量在潜水员种群中的ADRD。