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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，目前有580万人