How life experiences shape Alzheimer's risk across racial and ethnic groups
A Life Course Approach to Understanding Racial and Ethnic Disparities in Alzheimer's Disease and Related Dementias and Health Care
This project uses long-term health, neighborhood, and life-history data plus machine learning to look for early- and midlife factors that change Alzheimer's and related dementia risk for older U.S. adults from different racial and ethnic groups.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project ID | NIH-11128581 on NIH RePORTER |
What this research studies
From a patient's point of view, researchers will combine decades of survey answers, neighborhood information, and medical claims from the national Health and Retirement Study to trace how childhood, midlife, and later-life experiences add up to affect dementia risk. They will apply machine learning to detect patterns and early warning signs that may differ across racial and ethnic groups and to link risk factors with differences in dementia-related health care use. The team aims to pinpoint times and factors where prevention or better care could reduce disparities and delay dementia onset. This work analyzes existing data and does not require new tests or clinic visits by current participants.
Who could benefit from this research
Good fit: This research most directly applies to older U.S. adults from diverse racial and ethnic backgrounds whose long-term survey, neighborhood, and health-claims data are represented in the Health and Retirement Study.
Not a fit: People who live outside the United States, are much younger than typical HRS participants, or whose health questions don't relate to dementia may not directly benefit from these findings.
Why it matters
Potential benefit: If successful, the work could reveal modifiable life-course factors to target for prevention and earlier detection of Alzheimer's in groups that face higher risk.
How similar studies have performed: Other studies have used machine learning to spot early signs of dementia with promising results, but using a life-course approach to explain racial and ethnic gaps in ADRD is a newer and less-tested direction.
Where this research is happening
New Haven, United States
- Yale University — New Haven, United States (Active)
Researchers
- Principal investigator: Chen, Xi — Yale University
- Study coordinator: Chen, Xi
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.