Early detection and outcome prediction for fatty liver (NAFLD)
Official title: NAFLD Diagnosis and Outcomes
Funded at Mayo Clinic Rochester · NIH-11247579 · Data updated 2026-11-12
Using AI on medical records to find which adults with fatty liver are more likely to develop serious liver problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Mayo Clinic Rochester NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | February 15, 2024 |
| Project end | December 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $530,351 |
| NIH project number | 5R01DK134448-03 |
| Project ID | NIH-11247579 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults aged 21 and older whose electronic health records include relevant labs, diagnoses, medications, or primary care follow-up—especially people with obesity, diabetes, or abnormal liver tests—are ideal candidates for contributing data or participating.
✕Less likely to help
Children, people under 21, those without accessible electronic health records, or patients with non-NAFLD liver diseases are unlikely to benefit directly from this specific project.
What this research studies
You would allow researchers to use information from your medical records—like diagnoses, lab tests, medications, height/weight, and basic demographics—to look for patterns linked to fatty liver and its worsening. The team will train machine-learning models on large electronic health record datasets to spot people with NAFLD and predict who may progress to cirrhosis or other liver events. The goal is tools that can run in primary care so doctors can identify higher-risk patients earlier and tailor follow-up or treatments. Your data may help create screening flags that work across routine healthcare visits.
Why it matters
Potential benefit: If successful, this could help doctors catch risky fatty liver earlier and direct patients to closer monitoring or treatments before severe liver damage occurs.
How similar studies have gone: Other teams have used EHRs and AI to identify liver disease with promising early results, but using these methods to reliably predict long-term progression is still relatively new.
Where this research is happening
-
Mayo Clinic Rochester
Rochester, United States Active
Researchers
- Principal investigator: Allen, Alina M — Mayo Clinic Rochester
- Study coordinator: Allen, Alina M
Related research
Other NIH-funded projects on the same topics.
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Lipid signals that control acetyl-CoA and fat metabolism in NAFLD
A study exploring how membrane lipids influence acetyl-CoA metabolism to understand and potentially treat nonalcoholic fatty liver disease (NAFLD).
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Long-term study of NAFLD progression and gut bacteria in diverse groups
We’re following people over time to see how the gut microbiome and diet relate to fatty liver disease progression in diverse racial/ethnic groups.
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Genetic and environmental risk for fatty liver cancer in Latinos (GENIAL)
A study to understand why Latino people with fatty liver disease might be more likely to develop liver cancer and to use genetic and environmental clues to predict who is at higher risk.
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Toxic fats, vitamin B12 loss, and AHR may drive fatty liver disease
This grant looks at how certain environmental chemicals may trigger liver damage linked to fatty liver disease and how a vitamin found in the blood could be affected.
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Role of cholesterol metabolism in toxin-driven liver disease
This grant explores how a liver toxin called TCDD affects cholesterol and fatty liver disease, and how a key enzyme (HMGCR) might change that risk in adults.
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Small blood particle signals and fatty liver in children and teens
Researchers are looking at whether molecules carried in tiny blood particles can explain or signal fatty liver disease in children and adolescents with excess weight.
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.