Finding Alzheimer’s progression types to match people with targeted treatments
Data-driven Subtypes of Alzheimer's disease progression for targeted treatment
This project uses medical records, brain scans, lab biomarkers, and past trial data to group people with Alzheimer’s who follow similar courses so treatments can be better matched to each group.
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 ID | NIH-11193507 on NIH RePORTER |
What this research studies
If you have Alzheimer’s, this work looks across medical records, brain scans, lab biomarkers, and cognitive tests from many people to find groups who get worse faster or slower. The team will apply advanced machine-learning models to data pooled from 14 randomized trials plus several observational registries to identify consistent progression subtypes. By combining many kinds of information and following people over time, they aim to create more precise patient groups for future targeted therapies. Those groups could help future trials enroll the right people and speed up development of treatments that work for specific types of Alzheimer’s.
Who could benefit from this research
Good fit: People diagnosed with Alzheimer’s disease who have clinical records and biomarker information (for example cognitive tests, brain imaging, CSF or blood biomarkers) or who previously participated in Alzheimer’s trials or registries are the most relevant candidates for this work.
Not a fit: People without Alzheimer’s or those who have no clinical or biomarker data available in the included datasets are unlikely to benefit directly from this project.
Why it matters
Potential benefit: Could help match patients to treatments suited to their specific Alzheimer’s subtype and make clinical trials more likely to show benefit.
How similar studies have performed: Some earlier machine-learning efforts have suggested possible Alzheimer’s subtypes but findings are mixed, and this project's use of many randomized-trial and registry datasets for longitudinal, multi-modal modeling is more comprehensive and relatively novel.
Where this research is happening
Houston, United States
- 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
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.