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

基本信息

批准号：
10467527
负责人：
Antonella Zanobetti
金额：
$ 23.93万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
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：
2020-04-01 至 2025-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10467527
关键词：
Address Age Air Pollution Alzheimer&apos s Disease Alzheimer&apos 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 Lead Link Long-Term Effects Longitudinal cohort Machine Learning Medicaid Medicare Medicare claim Medicare/Medicaid Methods Neighborhoods Nerve Degeneration Neuroglia Neurons Nitrogen Dioxide Noise Outcome Oxidative Stress Ozone Particulate Matter 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 base cognitive function 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

项目摘要

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.

阿尔茨海默病 (AD) 是美国第六大死因，目前美国有 580 万人患有阿尔茨海默病 (AD) 患有 AD 痴呆症的人预计到 2050 年这一数字将增加近三倍，达到 1380 万人。另外，≥65 岁人群的 AD 和相关痴呆症 (AD/ADRD) 医疗费用估计为 277 美元 2018 年为 10 亿美元。尽管已经确定了假定的风险和保护因素，但已发表的研究无法确定如何预防和减轻疾病进展以及谁最脆弱，主要是因为现有的研究样本量较小，缺乏统计能力来理清以下因素的影响：接触细颗粒物 (PM2.5)（一种普遍存在但可改变的接触方式，对人体有害。大脑老化）与认知功能下降、认知能力下降更快、痴呆、AD、和轻度认知障碍 (MCI) 的毒理学和人体研究提供了相关性的证据。空气污染和神经退行性疾病之间的关系，强调了潜在的生物途径，包括系统性为了应对 PAR-17-054，我们的目标是在全国范围内进行大规模的应对。数据集（医疗保险和医疗补助），加上先进的统计方法，克服了具体而言，我们将在目标 1 中进行现有研究并阐明 AD/ADRD 的风险和保护因素。使用 2000 年至 2021 年期间医疗保险和医疗补助索赔对大陆地区进行的国家流行病学研究美国，估计空气污染暴露与首次 AD/ADRD 时间之间的关联在因 AD/ADRD 或 MCI 住院的参与者中，我们将评估是否接受空气治疗。在目标 2 中，污染暴露会增加死亡风险和/或加速 AD/ADRD 的再住院。将应用机器学习方法来识别个人层面的共现（既往住院史、种族、年龄和性别）、环境（天气、绿地和噪音）和 SES 风险（或保护性）因素确定哪些人群亚组患 AD/ADRD 住院和进展的风险最大/最小在空气污染暴露之后，我们将开发克服统计挑战的方法，包括 (1) 通过利用以下方法，将空气污染暴露的影响与其他混杂因素分开：因果推断，以及（2）纠正潜在的结果错误分类。我们将并行进行。使用传统方法（例如回归）以及因果推理和机器学习进行流行病学分析了解哪些统计挑战需要更复杂的方法。透明度和可重复性，我们将提供经过同行评审的开源软件，以便其他研究人员可以总之，该提案的结果将描述空气污染之间的联系。暴露以及 AD/ADRD 住院和进展，将确定多种可改变的风险和保护措施决定 AD/ADRD 脆弱性的因素，并为预防 AD/ADRD 的可实施行动提供基础并减轻这一巨大的健康负担。