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| Funder | NATIONAL INSTITUTE ON AGING |
|---|---|
| Recipient Organization | University of Michigan At Ann Arbor |
| Country | United States |
| Start Date | Mar 15, 2021 |
| End Date | Feb 28, 2026 |
| Duration | 1,811 days |
| Number of Grantees | 2 |
| Roles | Co-Investigator; Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10371214 |
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.
University of Michigan At Ann Arbor
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