The Study of the Environment and Alzheimer's disease and related Dementias (SEAD)

环境与阿尔茨海默病和相关痴呆症的研究 (SEAD)

• 批准号: 10371214
• 负责人: Kelly Marie Bakulski
• 金额: $ 66.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10371214
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; American; Animal Model; Behavioral; Biological; Biological Markers; Biology; Blood; Cadmium; Case-Control Studies; Chemicals; Clinical; Cognition; Cohort Studies; Complex; Data; Development; Diet; Discrimination; Early Intervention; Environment; Environmental Exposure; Environmental Risk Factor; Etiology; Evaluation; Exposure to; Future; Genetic; Genotype; Goals; Human; Individual; Infection; Intervention; Lead; Life Style; Link; Measurement; Measures; Medicare; Modeling; Modernization; National Health and Nutrition Examination Survey; National Institute on Aging; Neurodegenerative Disorders; Neurotoxins; Participant; Policies; Population; Precision Health; Prevalence; Prevention; Prospective cohort study; Public Health; Recommendation; Research; Risk; Risk Factors; Role; Scientific Advances and Accomplishments; Strategic Planning; Testing; Time; Toxic Environmental Substances; United States; Urine; Vulnerable Populations; abeta deposition; advanced disease; base; bone; chronic Pb exposure; clinical phenotype; dementia risk; demographics; disorder prevention; disorder risk; effective intervention; environmental chemical; environmental chemical exposure; follow-up; human population study; improved; lead exposure; machine learning method; modifiable risk; molecular phenotype; multi-ethnic; neurotoxic; neurotoxicity; novel; population based; precision medicine; prevent; prospective; risk prediction; risk prediction model; risk stratification; symposium; tau expression; toxicant

PROJECT SUMMARY The rising prevalence of Alzheimer's disease and related dementias (ADRD) is a major public health and clinical challenge in the United States. Identification of ADRD causes to inform prevention and policy is the most efficient way to address these challenges. Most research to date has focused on identifying genetic causes of ADRD, however, recent population-scale studies have shown that environmental exposures, such as lead and cadmium, also contribute to ADRD risk and etiology. Initial findings on environmental factors linked to ADRD risk is promising, but human evidence is limited. A wide range of environmental exposures (exposome) have never been evaluated systematically in relation to incident ADRD. While there is a growing demand to predict future risk for ADRS more precisely, the role of exposomic data in improving ADRD risk prediction has never been evaluated. To address these gaps, we propose a prospective cohort study by capitalizing on existing large-scale, United States nationally representative, multi-ethnic population-based data. The National Health and Nutrition Examination Survey (NHANES, from 1998-2010, n>15,000) has a variety of environmental chemical exposure measurements, behavioral risk factors, and clinical phenotypes, and when linked to Medicare data, provides up to 25 years of incident ADRD. We aim to (1) conduct a biologic hypothesis-based approach to test the associations of chronic exposure to lead and cadmium with incident ADRD; (2) conduct a data-driven environment-wide association study to systematically evaluate a wide-range of environmental toxicants with incident ADRD; and (3) develop and validate an exposome-based risk prediction model for ADRD using machine learning methods. The proposed study will advance scientific understanding on how modifiable and currently ubiquitous environmental neurotoxicants can lead to the development of ADRD. This study assesses the exposome to improve prediction of future disease risk and define vulnerable populations more precisely. This research will highlight individual-level and population-level interventions (i.e. precision health) to effectively prevent or reduce the risk of ADRD in the US population.

项目摘要 阿尔茨海默氏病和相关痴呆症（ADRD）的患病率上升是一种主要的公共卫生， 美国的临床挑战。确定为预防和政策提供信息的ADRD原因是 解决这些挑战的最有效方法。迄今为止，大多数研究都集中于识别遗传 但是，ADRD的原因，最近的人口规模研究表明，环境暴露，例如 铅和镉，也有助于ADRD风险和病因。关于与环境因素有关的初步发现 ADRD风险很有希望，但人类证据是有限的。广泛的环境暴露（EXPOSOME） 从未与事件ADRD进行系统的评估。虽然需求越来越大 更准确地说，预测ADR的未来风险，展开数据在改善ADRD风险预测中的作用 从未评估过。为了解决这些差距，我们提出了一项潜在的队列研究 现有的大规模，美国国家代表，基于多种族的数据。国民 健康和营养检查调查（NHANES，1998 - 2010年，n> 15,000）有多种 环境化学暴露测量，行为风险因素和临床表型以及何时 链接到Medicare数据，提供多达25年的事件ADRD。我们的目标是（1）进行生物学 基于假设的方法测试慢性暴露与铅和镉与事件的关联的方法 adrd; （2）进行数据驱动的环境关联研究，以系统地评估广泛的范围 伴有事件的环境有毒物质； （3）发展和验证基于Exposome的风险 使用机器学习方法的ADRD预测模型。拟议的研究将提高科学 了解如何修改和目前无处不在的环境神经毒性会导致 Adrd的发展。这项研究评估了提高未来疾病风险预测和 更精确地定义脆弱人群。这项研究将重点介绍个人级别和人口级别 干预措施（即精确健康）有效预防或减少美国人口中ADRD的风险。