Identifying risk factors for cirrhosis in patients with non-alcoholic fatty liver disease.
Predictive models for incident cirrhosis in non-alcoholic fatty liver disease using genetic and electronic medical record-based risk factors
['FUNDING_CAREER'] · UNIVERSITY OF MICHIGAN AT ANN ARBOR · NIH-11060022
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
| Phase | ['FUNDING_CAREER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF MICHIGAN AT ANN ARBOR (nih funded) |
| Locations | 1 site (ANN ARBOR, UNITED STATES) |
| Trial ID | NIH-11060022 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: Patients who do not have non-alcoholic fatty liver disease or those with advanced cirrhosis may not benefit from this research.
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 performed: 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
ANN ARBOR, UNITED STATES
- 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
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.