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478 Magnetic Resonance Biomarkers of Metabolic Dysfunction-Associated Steatotic Liver Disease

Published online by Cambridge University Press:  03 April 2024

Marissa Brown
Affiliation:
University of Texas Health Science Center San Antonio
Alexander Moody
Affiliation:
University of Texas Health Science Center San Antonio
Juan Vasquez
Affiliation:
University of Texas Health Science Center San Antonio
John Blangero
Affiliation:
University of Texas Rio Grande Valley
Luke Norton
Affiliation:
University of Texas Health Science Center San Antonio
Geoffrey Clarke
Affiliation:
University of Texas Health Science Center San Antonio
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Abstract

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OBJECTIVES/GOALS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major public health concern due to its increasing prevalence and association with type 2 diabetes mellitus. Non-invasive magnetic resonance-based biomarkers can aid in the monitoring of disease progression and identification of patients at risk for chronic liver disease. METHODS/STUDY POPULATION: Over 600 subjects will be recruited from the San Antonio Mexican American Family Study and from a second study, which consists of (i) T2DM patients diagnosed with either MASLD or metabolic dysfunction-associated steatohepatitis (MASH) or (ii) metabolically healthy controls. Hydrogen-1 MRS and diffusion-weighted MRI (DW-MRI) will be used to measure liver fat fraction and liver stiffness biomarkers, respectively. Several potential biomarkers of liver stiffness will be evaluated in vivo using the intravoxel incoherent motion (IVIM) model for DW-MRI. To further improve the diagnostic accuracy of patients with liver fibrosis, we will integrate MRI/MRS data with relevant clinical indicators of hepatic metabolism. Results will be compared to biopsy samples to evaluate the model’s diagnostic accuracy. RESULTS/ANTICIPATED RESULTS: Based on preliminary data, we predict that IVIM will be able to accurately diagnose hepatic fibrosis in patients with MASLD, allowing it to be implemented in clinics with high-field MRI units easily. Previous studies have shown correlations between IVIM estimates and fibrosis stages, however, none included additional clinical indicators of liver disease in their models. We have already found significant differences in metabolic measurements such as fasting plasma glucose and HbA1c levels. Additionally, the use of machine learning in developing these models has shown improvements in the ability to extract features from the data. The aim is to achieve high accuracy and robustness in the staging of liver fibrosis. DISCUSSION/SIGNIFICANCE: Over 100 million people in the US are affected by MASLD. Without treatment, it progresses from hepatic steatosis to MASH, fibrosis (liver stiffening), and ultimately to hepatic cirrhosis and hepatocellular carcinoma (HCC). Continued research efforts and clinical implementation of MRI and MRS are vital in combating the growing burden of MASLD.

Type
Precision Medicine/Health
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. The Association for Clinical and Translational Science