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

阿尔茨海默病(AD)是美国第六大死因，目前有580万人 人们患有痴呆症，预计到2050年，这一数字将增加近两倍，达到1380万人。在……里面 此外，AD和相关痴呆症(AD/ADRD)的医疗保健成本为65年≥估计为277美元 2018年将达到10亿。尽管已经确定了假定的风险和保护因素，但已发表的研究表明 无法确定如何预防和缓解疾病进展以及谁最脆弱，主要是因为 现有的研究是基于小样本量的，缺乏统计能力来理清 不同的因素。暴露于细颗粒物(PM2.5)(一种无处不在但可改变的暴露，对 大脑老化)与认知功能下降、认知衰退更快、痴呆、阿尔茨海默病、 和轻度认知障碍(MCI)。毒理学和人体研究提供了证据表明 空气污染和神经退化之间的关系，强调潜在的生物途径，包括系统性 炎症和氧化应激。为了响应PAR-17-054，我们的目标是在全国范围内利用 数据集(Medicare和Medicaid)与高级统计方法相结合，以克服 现有研究并阐明AD/ADRD的风险和保护因素。具体来说，在目标1中，我们将进行 2000-2021年期间使用医疗保险和医疗补助报销的大陆国家流行病学研究 美国，估计暴露于空气污染与第一次AD/ADRD时间之间的关系 住院治疗。在因AD/ADRD或MCI住院的参与者中，我们将评估AIR 污染暴露会增加AD/ADRD的死亡风险，和/或加速再住院。在《目标2》中，我们 将应用机器学习方法来确定个人层面的共同发生(既往住院、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AD/ADRD住院和进展的风险最大/风险最小 在暴露于空气污染之后。在目标3中，我们将制定克服统计挑战的方法，包括 (1)通过利用以下方法将暴露于空气污染的影响与其他混杂因素区分开来 因果推理，以及(2)纠正潜在的结果错误分类。我们将并肩进行 使用传统方法(如回归)、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。为了确保 透明度和可重复性，我们将提供同行评审的开源软件，以便其他调查人员可以 执行我们的方法。总之，这项提案的结果将表征空气污染之间的联系 暴露和AD/ADRD住院和进展，将确定多重可改变的风险和保护 确定AD/ADRD漏洞的因素，并为可实施的预防措施提供基础 并减轻这一巨大的健康负担。

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.