Improving warfarin dosing for African American and Latino patients using gut microbiome and ancestry data
Leveraging the Microbiome, Local Admixture, and Machine Learning to Optimize Anticoagulant Pharmacogenomics in Medically Underserved Patients
This project will use gut bacteria, genetic ancestry near drug genes, and machine learning to help make warfarin dosing safer and more accurate for African American and Latino patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Arizona NIH-funded |
| Lab location | 1 site (Tucson, United States) |
| Project ID | NIH-11097151 on NIH RePORTER |
What this research studies
If you join, researchers will collect blood and stool samples plus information about your medication history and family background. They will sequence the gut bacteria (16S rRNA), measure local genetic ancestry at key warfarin genes, and combine those data with clinical information using machine learning. The team aims to build a dosing tool that works better for African American and Latino patients who have been underrepresented in past studies. Participation may require clinic visits and sample collection and could help guide safer warfarin dosing options for people like you.
Who could benefit from this research
Good fit: Adults who are African American or Latino and who are starting warfarin or already taking warfarin would be ideal candidates for participation.
Not a fit: People who are not taking warfarin, children, or those unwilling to provide blood or stool samples are unlikely to receive direct benefit from this project.
Why it matters
Potential benefit: If successful, this work could lead to more accurate starting doses of warfarin and fewer bleeding or clotting complications for underserved patients.
How similar studies have performed: Existing genetic-based warfarin dosing tools have helped some patients but often perform poorly in African American and Latino groups, and adding microbiome and local ancestry data with machine learning is a newer, less-tested approach.
Where this research is happening
Tucson, United States
- University of Arizona — Tucson, United States (Active)
Researchers
- Principal investigator: Karnes, Jason Hansen — University of Arizona
- Study coordinator: Karnes, Jason Hansen
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.