# NAFLD Diagnosis and Outcomes

> **NIH DK R01** · MAYO CLINIC ROCHESTER · 2026 · $530,351

## Abstract

PROJECT SUMMARY
Nonalcoholic fatty liver disease (NAFLD) affects 25-30% of US adults and has a major impact on healthcare
burden and population health due to higher morbidity and mortality than the general population. Even though
NAFLD can progress to cirrhosis, decompensation, and liver cancer, these outcomes affect a small proportion
of patients, but there are no accurate methods that are accessible in primary care to identify these patients
early. The heterogeneity of clinical phenotypes, lack of universal screening, and risk-stratification approaches
lead to delayed diagnosis, phenotype-specific prophylactic or therapeutic intervention, and poor patient
outcomes. The long-term goal is to develop easily accessible methods for NAFLD screening and prediction of
disease trajectory. The objective of this application is to leverage large electronic health record (EHR) datasets
and analytics to enhance the early identification of NAFLD in general healthcare settings. The central
hypothesis is that targeted screening with machine-learning (ML)/artificial intelligence (AI) models applied to
longitudinal healthcare data (diagnoses, laboratory values, medications, anthropometrics, demographics) can
identify predictors of NAFLD risk and, subsequently, a progressive phenotype toward liver events. The
rationale that underlies the proposed research is that EHR-based clinical algorithms which identify NAFLD and
phenotype the disease trajectory will guide clinicians in selecting patients who need liver-related diagnostic
evaluation and enable timely intervention to prevent hard outcomes. Guided by strong preliminary data, the
hypothesis will be tested by pursuing two specific aims. In Aim 1, a predictive model for NAFLD will be
developed using retrospective data from a population-based EHR-linkage system with validated diagnosed
NAFLD and non-NAFLD controls by chart review. Among those with NAFLD, an EHR-based model to predict a
clinical phenotype at risk for future liver-relate

## Key facts

- **NIH application ID:** 11247579
- **Project number:** 5R01DK134448-03
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Alina M Allen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** DK
- **Fiscal year:** 2026
- **Award amount:** $530,351
- **Award type:** 5
- **Project period:** 2024-02-15T00:00:00 → 2028-12-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11247579

## Citation

> US National Institutes of Health, RePORTER application 11247579, NAFLD Diagnosis and Outcomes (5R01DK134448-03). Retrieved via AI Analytics 2026-05-20 from https://api.ai-analytics.org/grant/nih/11247579. Licensed CC0.

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