# A Novel Multimodal Approach to Characterize NAFLD Severity and Prognosis

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $204,552

## Abstract

PROJECT SUMMARY
Nonalcoholic fatty liver disease (NAFLD) is an increasingly common cause of cirrhosis and on pace to be the
leading indication for liver transplantation in the United States.(1, 2) NAFLD presents as a spectrum of disease
ranging from isolated steatosis, which portends little risk of significant morbidity, to nonalcoholic steatohepatitis
(NASH), which is characterized by inflammation and cell death and has substantial risk of progression to cirrhosis
and liver-related mortality.(3) Unfortunately, liver biopsy remains the only way to accurately discriminate between
isolated steatosis and NASH; however the procedure is invasive and remains impractical to scale to the
estimated affected population of 60 million adults in the United States. Attempts to use individual or small
combinations of biomarkers to characterize risk in NAFLD have been largely unsuccessful leaving a tremendous
need for non-invasive risk stratification. My central hypothesis is that distinct subtypes of NAFLD can be identified
by combining multiple non-invasive biomarkers, genetic and clinical factors using advanced analytic techniques
for high dimensional data. Through my collaboration with the NIH-funded, multicenter NASH Clinical Research
Network (NASH CRN) I explored the association between 28 putative plasma biomarkers and NAFLD histology
and found that small sets of biomarkers were limited in discriminating between clinically significant stages of
histologic severity. However, by applying a novel statistical technique, latent class analysis (LCA), we generated
preliminary data identifying distinct subgroups of patients with NAFLD that are strongly associated with histologic
severity. The research goal of this application is to (1) combine clinical and dietary factors, genetic markers and
an expanded set of plasma biomarkers to refine distinct phenotypes of NAFLD using LCA, (2) validate the
association between LCA defined phenotypes and histologic severity in an independent cohort with biopsy
proven NAFLD, (3) build on an existing longitudinal cohort and test the ability of these phenotypes to predict
progression of fibrosis and inflammation. My long-term goal is to combine expertise in multimodal, non-invasive
biomarkers of NAFLD with advanced analytic techniques to personalize the management and treatment of
patients with NAFLD. In order to accomplish this goal, I have assembled an exceptional mentorship team
including my primary mentor, Dr. Rohit Loomba, who is an internationally renowned expert in NAFLD and
Director of the UCSD NAFLD Research Center. In addition, Dr. Ariel Feldstein, Chief of the Division of Pediatric
Gastroenterology, and an expert in translating NAFLD pathophysiology into biomarker development will serve
as a co-mentor. Professor Lily Xu, biostatistical director of the UCSD Clinical and Translational Research
Institute, will serve as my biostatistical mentor. Together, we formed a four-fold career development plan to gain
expertise in (...

## Key facts

- **NIH application ID:** 9997909
- **Project number:** 5K23DK119460-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Veeral Haresh Ajmera
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $204,552
- **Award type:** 5
- **Project period:** 2019-08-16 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9997909

## Citation

> US National Institutes of Health, RePORTER application 9997909, A Novel Multimodal Approach to Characterize NAFLD Severity and Prognosis (5K23DK119460-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9997909. Licensed CC0.

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