TY - JOUR
T1 - Serum identification of at-risk MASH
T2 - The metabolomics-advanced steatohepatitis fibrosis score (MASEF)
AU - Noureddin, Mazen
AU - Truong, Emily
AU - Mayo, Rebeca
AU - Martínez-Arranz, Ibon
AU - Mincholé, Itziar
AU - Banales, Jesus M.
AU - Arrese, Marco
AU - Cusi, Kenneth
AU - Arias-Loste, María Teresa
AU - Bruha, Radan
AU - Romero-Gómez, Manuel
AU - Iruzubieta, Paula
AU - Aller, Rocio
AU - Ampuero, Javier
AU - Calleja, José Luis
AU - Ibañez-Samaniego, Luis
AU - Aspichueta, Patricia
AU - Martín-Duce, Antonio
AU - Kushner, Tatyana
AU - Ortiz, Pablo
AU - Harrison, Stephen A.
AU - Anstee, Quentin M.
AU - Crespo, Javier
AU - Mato, José M.
AU - Sanyal, Arun J.
N1 - Funding Information:
Marco Arrese received funding from Fondo Nacional de Ciencia y Tecnología de Chile (FONDECYT) under grant agreement No. 1191145. Quentin M. Anstee is supported by the Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS) consortium, which received funding from the Innovative Medicines Initiative 2 Joint Undertaking, under grant agreement No. 777377. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. He is a Newcastle NIHR Biomedical Research Centre investigator.
Publisher Copyright:
© 2024 John Wiley and Sons Inc.. All rights reserved.
PY - 2024/1
Y1 - 2024/1
N2 - Background: Early identification of those with NAFLD activity score ≥ 4 and significant fibrosis (≥F2) or at-risk metabolic dysfunction-associated steatohepatitis (MASH) is a priority as these patients are at increased risk for disease progression and may benefit from therapies. We developed and validated a highly specific metabolomics-driven score to identify at-risk MASH. Methods: We included derivation (n = 790) and validation (n = 565) cohorts from international tertiary centers. Patients underwent laboratory assessment and liver biopsy for metabolic dysfunction-associated steatotic liver disease. Based on 12 lipids, body mass index, aspartate aminotransferase, and alanine aminotransferase, the MASEF score was developed to identify at-risk MASH and compared to the FibroScan-AST (FAST) score. We further compared the performance of a FIB-4 + MASEF algorithm to that of FIB-4 + liver stiffness measurements (LSM) by vibration-controlled transient elastography (VCTE). Results: The diagnostic performance of the MASEF score showed an area under the receiver-operating characteristic curve, sensitivity, specificity, and positive and negative predictive values of 0.76 (95% CI 0.72-0.79), 0.69, 0.74, 0.53, and 0.85 in the derivation cohort, and 0.79 (95% CI 0.75-0.83), 0.78, 0.65, 0.48, and 0.88 in the validation cohort, while FibroScan-AST performance in the validation cohort was 0.74 (95% CI 0.68-0.79; p = 0.064), 0.58, 0.79, 0.67, and 0.73, respectively. FIB-4+MASEF showed similar overall performance compared with FIB-4 + LSM by VCTE (p = 0.69) to identify at-risk MASH. Conclusion: MASEF is a promising diagnostic tool for the assessment of at-risk MASH. It could be used alternatively to LSM by VCTE in the algorithm that is currently recommended by several guidance publications.
AB - Background: Early identification of those with NAFLD activity score ≥ 4 and significant fibrosis (≥F2) or at-risk metabolic dysfunction-associated steatohepatitis (MASH) is a priority as these patients are at increased risk for disease progression and may benefit from therapies. We developed and validated a highly specific metabolomics-driven score to identify at-risk MASH. Methods: We included derivation (n = 790) and validation (n = 565) cohorts from international tertiary centers. Patients underwent laboratory assessment and liver biopsy for metabolic dysfunction-associated steatotic liver disease. Based on 12 lipids, body mass index, aspartate aminotransferase, and alanine aminotransferase, the MASEF score was developed to identify at-risk MASH and compared to the FibroScan-AST (FAST) score. We further compared the performance of a FIB-4 + MASEF algorithm to that of FIB-4 + liver stiffness measurements (LSM) by vibration-controlled transient elastography (VCTE). Results: The diagnostic performance of the MASEF score showed an area under the receiver-operating characteristic curve, sensitivity, specificity, and positive and negative predictive values of 0.76 (95% CI 0.72-0.79), 0.69, 0.74, 0.53, and 0.85 in the derivation cohort, and 0.79 (95% CI 0.75-0.83), 0.78, 0.65, 0.48, and 0.88 in the validation cohort, while FibroScan-AST performance in the validation cohort was 0.74 (95% CI 0.68-0.79; p = 0.064), 0.58, 0.79, 0.67, and 0.73, respectively. FIB-4+MASEF showed similar overall performance compared with FIB-4 + LSM by VCTE (p = 0.69) to identify at-risk MASH. Conclusion: MASEF is a promising diagnostic tool for the assessment of at-risk MASH. It could be used alternatively to LSM by VCTE in the algorithm that is currently recommended by several guidance publications.
KW - Humans
KW - Liver/diagnostic imaging
KW - Liver Cirrhosis/pathology
KW - Non-alcoholic Fatty Liver Disease/pathology
KW - Fibrosis
KW - Predictive Value of Tests
KW - Elasticity Imaging Techniques
KW - Biopsy/adverse effects
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U2 - 10.1097/HEP.0000000000000542
DO - 10.1097/HEP.0000000000000542
M3 - Article
C2 - 37505221
AN - SCOPUS:85170672163
SN - 0270-9139
VL - 79
SP - 135
EP - 148
JO - Hepatology
JF - Hepatology
IS - 1
ER -