Identifying risk factors for cirrhosis in patients with non-alcoholic fatty liver disease.
Official title: Predictive models for incident cirrhosis in non-alcoholic fatty liver disease using genetic and electronic medical record-based risk factors
Funded at University of Michigan at Ann Arbor · NIH-11060022 · Data updated 2026-11-12
This study is looking at how your genes and medical records can help figure out which people with non-alcoholic fatty liver disease are more likely to develop serious liver damage, so we can find better ways to help those at higher risk.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Michigan at Ann Arbor NIH-funded |
| Lab location | 1 site (Ann Arbor, United States) |
| Project start | July 1, 2022 |
| Project end | March 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $165,240 |
| NIH project number | 5K08DK132312-04 |
| Project ID | NIH-11060022 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with non-alcoholic fatty liver disease, particularly those with a family history of liver disease or other risk factors.
✕Less likely to help
Patients who do not have non-alcoholic fatty liver disease or those with advanced cirrhosis may not benefit from this research.
What this research studies
This research investigates how genetic factors and electronic medical records can help predict which patients with non-alcoholic fatty liver disease (NAFLD) are most likely to develop cirrhosis. By analyzing genetic variants and utilizing machine learning techniques on complex medical data, the study aims to create more accurate models for assessing disease progression. This could lead to better-targeted interventions for those at highest risk, improving patient outcomes. The research seeks to enhance current methods that rely heavily on existing fibrosis stages, which may not be readily available to all patients.
Why it matters
Potential benefit: If successful, this research could lead to earlier identification and intervention for patients at risk of developing cirrhosis, potentially reducing liver-related complications and deaths.
How similar studies have gone: Previous research has shown promise in using genetic and electronic medical record data for predicting disease outcomes, suggesting that this approach could be effective.
Where this research is happening
-
University of Michigan at Ann Arbor
Ann Arbor, United States Active
Researchers
- Principal investigator: Chen, Vincent Lingzhi — University of Michigan at Ann Arbor
- Study coordinator: Chen, Vincent Lingzhi
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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.