Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat Fraction

Background and Aims: Magnetic resonance imaging–proton density fat fraction (MRI-PDFF) is an accurate, noninvasive tool for diagnosing metabolic dysfunction–associated steatotic liver disease, but its feasibility is limited in routine clinical practice. We aimed to assess the clinical utility of alt...

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Main Authors: Santos Carvajal-Gonzalez, Theresa Tuthill, Vincent Wai-Sun Wong, Amy Lauren Ashworth, Zeid Kayali, Céline Fournier-Poizat, Neeta B. Amin
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Gastro Hep Advances
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772572325000561
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author Santos Carvajal-Gonzalez
Theresa Tuthill
Vincent Wai-Sun Wong
Amy Lauren Ashworth
Zeid Kayali
Céline Fournier-Poizat
Neeta B. Amin
author_facet Santos Carvajal-Gonzalez
Theresa Tuthill
Vincent Wai-Sun Wong
Amy Lauren Ashworth
Zeid Kayali
Céline Fournier-Poizat
Neeta B. Amin
author_sort Santos Carvajal-Gonzalez
collection DOAJ
description Background and Aims: Magnetic resonance imaging–proton density fat fraction (MRI-PDFF) is an accurate, noninvasive tool for diagnosing metabolic dysfunction–associated steatotic liver disease, but its feasibility is limited in routine clinical practice. We aimed to assess the clinical utility of alternative, cost-efficient approaches for assessing liver fat changes and their relationship with MRI-PDFF changes. Methods: This is a secondary analysis of a phase 2a study that included adults with metabolic dysfunction–associated steatotic liver disease who received clesacostat, a selective, reversible inhibitor of acetyl-CoA carboxylase. In this secondary analysis, responders were defined as those in whom a ≥30% decrease in liver fat by MRI-PDFF was observed with clesacostat or placebo. Other endpoints were evaluated for their ability to predict MRI-PDFF responder status, including controlled attenuation parameter (CAP), liver enzymes (alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase), metabolic dysfunction–associated steatohepatitis–related biomarkers (liver stiffness measurement by vibration-controlled transient elastography, cytokeratin 18-M30, and cytokeratin 18-M65), and markers of hepatic steatosis (hepatic steatosis index and fatty liver index). These relationships were investigated through correlation, univariate, and multivariable regression analyses. Results: Of 260 participants with a baseline and on-treatment measure at week 12 or week 16, 143 were responders. Based on correlation analyses, a significant but weak positive correlation between MRI-PDFF and CAP measurements of relative percentage change from baseline in liver fat was observed. By combining the selected 6 parameters (CAP, hepatic steatosis index, fatty liver index, alanine aminotransferase, gamma-glutamyl transferase, and cytokeratin 18-M65) through multivariable regression modeling, responders can be predicted with a high level of sensitivity and specificity (mean area under the receiver operating characteristic curve = 0.831 from 10-fold cross-validation). Conclusion: Modeling multiple noninvasive assessments of liver fat closely aligned with MRI-PDFF measurements. These data support further assessment of its suitability in real-world clinical practice.
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spelling doaj-art-97ea0783cb78472e8902c40e498240b52025-08-20T03:53:47ZengElsevierGastro Hep Advances2772-57232025-01-014710066910.1016/j.gastha.2025.100669Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat FractionSantos Carvajal-Gonzalez0Theresa Tuthill1Vincent Wai-Sun Wong2Amy Lauren Ashworth3Zeid Kayali4Céline Fournier-Poizat5Neeta B. Amin6Pfizer Research and Development, Pfizer Inc, Cambridge, MassachusettsPfizer Research and Development, Pfizer Inc, Cambridge, MassachusettsMedical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, ChinaPfizer Research and Development, Pfizer Inc, Walton Oaks, UKInland Empire Liver Foundation, Rialto, CaliforniaEchosens SA, Paris, FrancePfizer Research and Development, Pfizer Inc, Cambridge, Massachusetts; Correspondence: Address correspondence to: Neeta B. Amin, PharmD, Pfizer Research and Development, Pfizer Inc, 1 Portland Street, Cambridge, Massachusetts 02139.Background and Aims: Magnetic resonance imaging–proton density fat fraction (MRI-PDFF) is an accurate, noninvasive tool for diagnosing metabolic dysfunction–associated steatotic liver disease, but its feasibility is limited in routine clinical practice. We aimed to assess the clinical utility of alternative, cost-efficient approaches for assessing liver fat changes and their relationship with MRI-PDFF changes. Methods: This is a secondary analysis of a phase 2a study that included adults with metabolic dysfunction–associated steatotic liver disease who received clesacostat, a selective, reversible inhibitor of acetyl-CoA carboxylase. In this secondary analysis, responders were defined as those in whom a ≥30% decrease in liver fat by MRI-PDFF was observed with clesacostat or placebo. Other endpoints were evaluated for their ability to predict MRI-PDFF responder status, including controlled attenuation parameter (CAP), liver enzymes (alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase), metabolic dysfunction–associated steatohepatitis–related biomarkers (liver stiffness measurement by vibration-controlled transient elastography, cytokeratin 18-M30, and cytokeratin 18-M65), and markers of hepatic steatosis (hepatic steatosis index and fatty liver index). These relationships were investigated through correlation, univariate, and multivariable regression analyses. Results: Of 260 participants with a baseline and on-treatment measure at week 12 or week 16, 143 were responders. Based on correlation analyses, a significant but weak positive correlation between MRI-PDFF and CAP measurements of relative percentage change from baseline in liver fat was observed. By combining the selected 6 parameters (CAP, hepatic steatosis index, fatty liver index, alanine aminotransferase, gamma-glutamyl transferase, and cytokeratin 18-M65) through multivariable regression modeling, responders can be predicted with a high level of sensitivity and specificity (mean area under the receiver operating characteristic curve = 0.831 from 10-fold cross-validation). Conclusion: Modeling multiple noninvasive assessments of liver fat closely aligned with MRI-PDFF measurements. These data support further assessment of its suitability in real-world clinical practice.http://www.sciencedirect.com/science/article/pii/S2772572325000561MASHcontrolled attenuation parameterHepatic SteatosisClesacostat
spellingShingle Santos Carvajal-Gonzalez
Theresa Tuthill
Vincent Wai-Sun Wong
Amy Lauren Ashworth
Zeid Kayali
Céline Fournier-Poizat
Neeta B. Amin
Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat Fraction
Gastro Hep Advances
MASH
controlled attenuation parameter
Hepatic Steatosis
Clesacostat
title Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat Fraction
title_full Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat Fraction
title_fullStr Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat Fraction
title_full_unstemmed Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat Fraction
title_short Modeling Reductions in Liver Fat: Comparing Noninvasive Tests to Magnetic Resonance Imaging–Proton Density Fat Fraction
title_sort modeling reductions in liver fat comparing noninvasive tests to magnetic resonance imaging proton density fat fraction
topic MASH
controlled attenuation parameter
Hepatic Steatosis
Clesacostat
url http://www.sciencedirect.com/science/article/pii/S2772572325000561
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