National Cohort Studies of Alzheimer's Disease, Related Dementias and Air Pollution

阿尔茨海默病、相关痴呆症和空气污染的国家队列研究

• 批准号: 10338186
• 负责人: Antonella Zanobetti
• 金额: $ 73.3万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10338186
• 关键词: Acceleration; Address; Age; Air Pollution; Alzheimer' s Disease; Alzheimer' s disease related dementia; Biological; Cause of Death; Cessation of life; Cohort Studies; Complex; Computer software; Coupled; Dangerousness; Data; Data Set; Dementia; Disease Progression; Ensure; Environmental Risk Factor; Epidemiology; Exposure to; Face; Foundations; Funding; General Population; Goals; Green space; Health; Health Care Costs; Healthcare; Hospitalization; Human; Impaired cognition; Individual; Link; Long-Term Effects; Longitudinal cohort; Machine Learning; Medicaid; Medicare claim; Medicare/Medicaid; Methods; Neighborhoods; Nerve Degeneration; Neuroglia; Neurons; Nitrogen Dioxide; Noise; Outcome; Oxidative Stress; Oxidative Stress Pathway; Ozone; Pathologic; Pathway interactions; Peer Review; Persons; Population; Prevention; Publishing; Race; Reproducibility; Research; Research Personnel; Risk; Risk Factors; Sample Size; Side; Societal Factors; Statistical Methods; Subgroup; Time; Toxicology; United States; United States National Institutes of Health; Update; Weather; aging brain; cognitive function; cognitive testing; cohort; dementia risk; epidemiology study; fine particles; high risk; hospital readmission; improved; innovation; low socioeconomic status; machine learning method; mild cognitive impairment; modifiable risk; mortality risk; nanoparticle; open source; prevent; protective factors; response; sex; socioeconomics; systemic inflammatory response

Alzheimer's disease (AD) is the sixth leading cause of death in the United States, where currently 5.8 million people are living with AD dementias, and this number is projected to almost triple to 13.8 million by 2050. In addition AD and related dementias (AD/ADRD) healthcare costs for people ≥65 years were an estimated $277 billion in 2018. Although putative risk and protective factors have been identified, published studies have been unable to identify how to prevent and mitigate disease progression and who is most vulnerable mostly because the existing studies are based on small sample size and lack statistical power to disentangle the effects of different factors. Exposure to fine particulate matter (PM2.5) (a ubiquitous yet modifiable exposure dangerous to the aging brain) has been associated with decreased cognitive function, faster cognitive decline, dementia, AD, and mild cognitive impairment (MCI). Toxicological and human studies provide evidence of an association between air pollution and neurodegeneration, highlighting potential biological pathways that include systemic inflammation and oxidative stress. In response to PAR-17-054, our goal is to leverage massive nationwide datasets (Medicare and Medicaid), coupled with advanced statistical methods, to overcome the limitations of existing studies and clarify risk and protective factors for AD/ADRD. Specifically, in Aim 1 we will conduct national epidemiological studies using Medicare and Medicaid claims for the period 2000-2021 for the continental US, to estimate the association between exposure to air pollution and the time to the first AD/ADRD hospitalization. Among enrollees that have been hospitalized for AD/ADRD or MCI we will assess whether air pollution exposure increases risk of mortality, and/or accelerates re-hospitalization for AD/ADRD. In Aim 2 we will apply machine learning methods to identify co-occurrence of individual-level (previous hospitalizations, race, age, and sex), environmental (weather, green space, and noise), and SES risk (or protective) factors to determine which population subgroups are most/least at risk for AD/ADRD hospitalization and progression following air pollution exposure. In Aim 3 we will develop methods to overcome statistical challenges including (1) disentangle the effects of air pollution exposure from other confounding factors by leveraging approaches for causal inference, and (2) correct for potential outcome misclassification. We will conduct side-by-side epidemiological analyses using traditional methods (e.g. regression) and causal inference and machine learning approaches to understand which statistical challenges require more sophisticated approaches. To ensure transparency and reproducibility, we will provide peer-reviewed open-source software so other investigators may implement our methods. In summary the results of this proposal will characterize the link between air pollution exposure and AD/ADRD hospitalization and progression, will identify the multiple modifiable risk and protective factors that determine vulnerability in AD/ADRD, and provide the foundation for implementable actions to prevent and reduce this enormous health burden.

阿尔茨海默氏病（AD）是美国第六大死亡原因，目前有580万 人们与痴呆症患者相处，到2050年，这个数字预计将近三倍至1380万。 ≥65岁的人的添加广告和相关痴呆症（AD/ADRD）的医疗费用估计为277美元 2018年十亿美元。尽管已经确定了假定的风险和受保护因素，但已发表的研究一直是 无法识别如何预防和减轻疾病进展，谁最脆弱，主要是因为 现有研究是基于样本量较小的，缺乏统计能力来消除 不同的因素。暴露于细节的特定物质（PM2.5）（一种无处不在但可修改的暴露危险的暴露 衰老的大脑）与认知功能降低，认知能力下降，痴呆症，AD， 和轻度认知障碍（MCI）。毒理学和人类研究提供了关联的证据 在空气污染和神经变性之间，突出了包括全身性的潜在生物学途径 为了响应17-054帕尔，我们的目标是在全国范围内利用大量 数据集（Medicare和Medicaid），再加上高级统计方法，以克服局限性 现有研究并澄清风险和保护AD/ADRD的因素。具体来说，在AIM 1中，我们将进行 国家流行病学研究使用Medicare和Medicaid在2000 - 2021年期间进行连续的 我们，估计暴露于空气污染与第一个广告/ADRD的时间之间的关联 住院。在已住院的AD/ADRD或MCI住院的注册中，我们将评估是否播放 污染暴露会增加死亡率的风险和/或加速AD/ADRD的重新住院。在目标2中我们 将采用机器学习方法来识别个人级别的同时出现（以前的住院，种族， 年龄和性别），环境（天气，绿色空间和噪音）以及SES风险（或保护性）因素 确定哪些人口亚组最少/最少有AD/ADRD住院和进展的风险 在空气污染暴露之后。在AIM 3中，我们将开发克服统计挑战的方法 （1）通过利用方法来解除空气污染的影响与其他混杂因素的影响 因果推断，（2）纠正潜在结果错误分类。我们将并排进行 使用传统方法（例如回归）和因果推理和机器学习的流行病学分析 了解哪些统计挑战需要更复杂的方法。确保 透明度和可重复性，我们将提供经同行评审的开源软件，以便其他调查人员可以 实施我们的方法。总之，该提案的结果将表征空气污染之间的联系 曝光以及AD/ADRD住院和进步将确定多个可修改的风险和保护性 决定AD/ADRD脆弱性的因素，并为防止可行措施提供基础 并减少这种巨大的健康伯恩。