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 阿尔茨海默病和相关痴呆（ADRD）的患病率上升是一个主要的公共卫生问题， 美国的临床挑战。确定ADRD原因以告知预防和政策是 最有效的方法来应对这些挑战。到目前为止，大多数研究都集中在识别遗传 然而，最近的人口规模研究表明，环境暴露，如 铅和镉也是ADRD风险和病因。与环境因素有关的初步调查结果 ADRD风险是有希望的，但人类证据有限。广泛的环境暴露（麻烦） 从未对ADRD事件进行过系统评价。虽然有越来越多的需求， 为了更准确地预测ADRS的未来风险，药物组学数据在改善ADRD风险预测中的作用已经 从未被评估过。为了解决这些差距，我们提出了一项前瞻性队列研究， 现有的大规模，美国全国代表性，多种族人口为基础的数据。国家 健康和营养检查调查（NHANES，1998-2010年，n> 15，000）有各种各样的 环境化学品暴露测量、行为风险因素和临床表型，以及 与医疗保险数据相关联，提供长达25年的ADRD事件。我们的目标是（1）进行生物学 基于假设的方法来检验慢性铅和镉暴露与事件的关系 ADRD;（2）进行数据驱动的全环境关联研究，以系统地评估广泛的 环境毒物与事件ADRD;和（3）开发和验证基于麻烦的风险 使用机器学习方法建立ADRD的预测模型。这项研究将推动科学 了解可改变的和目前普遍存在的环境神经毒物如何导致 ADRD的发展。这项研究评估了该基因组，以改善对未来疾病风险的预测， 更准确地界定弱势群体。这项研究将突出个人水平和群体水平 干预措施（即精准健康），以有效预防或降低美国人群中ADRD的风险。