Understanding different types of Alzheimer's disease progression for better treatment
Official title: Data-driven Subtypes of Alzheimer's disease progression for targeted treatment
Funded at University of Texas Hlth Sci Ctr Houston · NIH-10977900 · Data updated 2026-11-12
This study is looking at how Alzheimer's disease affects different people in unique ways to find out which groups might benefit most from specific treatments, so if you join, you could help improve understanding and options for everyone with the condition.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Hlth Sci Ctr Houston NIH-funded |
| Lab location | 1 site (Houston, United States) |
| Project start | September 30, 2024 |
| Project end | June 30, 2029 |
| Funding year | 2024 |
| Award (this year) | $778,499 |
| NIH project number | 1R01AG084637-01A1 |
| Project ID | NIH-10977900 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with Alzheimer's disease, particularly those experiencing varying rates of cognitive decline.
✕Less likely to help
Patients with other forms of dementia or those who do not have a diagnosis of Alzheimer's disease may not benefit from this research.
What this research studies
This research investigates the varying rates of progression in Alzheimer's disease (AD) patients to identify specific subtypes that may respond better to targeted treatments. By utilizing advanced machine learning techniques, the study aims to analyze a vast array of patient data, including biomarkers and clinical characteristics, to create a more personalized approach to therapy. The goal is to enhance the effectiveness of clinical trials by selecting more homogeneous patient groups, which could lead to faster development of new treatments. Patients participating in this research may contribute to a better understanding of their condition and potential treatment options.
Why it matters
Potential benefit: If successful, this research could lead to more effective and personalized treatments for Alzheimer's disease patients.
How similar studies have gone: Previous research has shown promise in using big data and machine learning to understand disease heterogeneity, suggesting that this approach could yield valuable insights.
Where this research is happening
-
University of Texas Hlth Sci Ctr Houston
Houston, United States Active
Researchers
- Principal investigator: Kim, Yejin — University of Texas Hlth Sci Ctr Houston
- Study coordinator: Kim, Yejin
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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.