Serum identification of at-risk MASH: The metabolomics-advanced steatohepatitis fibrosis score (MASEF)

Author:

Noureddin Mazen12ORCID,Truong Emily3ORCID,Mayo Rebeca4ORCID,Martínez-Arranz Ibon4ORCID,Mincholé Itziar4ORCID,Banales Jesus M.56ORCID,Arrese Marco7,Cusi Kenneth8,Arias-Loste María Teresa9,Bruha Radan10,Romero-Gómez Manuel11,Iruzubieta Paula9ORCID,Aller Rocio12,Ampuero Javier13,Calleja José Luis14ORCID,Ibañez-Samaniego Luis15,Aspichueta Patricia161718,Martín-Duce Antonio19ORCID,Kushner Tatyana20,Ortiz Pablo4ORCID,Harrison Stephen A.21,Anstee Quentin M.22ORCID,Crespo Javier9ORCID,Mato José M.1823ORCID,Sanyal Arun J.24ORCID

Affiliation:

1. Houston Methodist Hospital, Houston Research Institute Houston, Texas, USA

2. Houston Research Institute, Houston, Texas, USA

3. Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA

4. OWL Metabolomics, Derio, Spain

5. Biodonostia Research Institute, Donostia University Hospital, University of the Basque Country (UPV-EHU), CIBERehd, IKERBASQUE, Donostia, Spain

6. Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain

7. Department of Gastroenterology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

8. University of Florida, Gainesville, Florida, USA

9. Marqués de Valdecilla University Hospital, Cantabria University, IDIVAL, Santander, Spain

10. General University Hospital and the First Faculty of Medicine, Charles University, Prague, Czech Republic

11. Valme University Hospital, CIBERehd, Seville, Spain

12. Clinic University Hospital, University of Valladolid, Valladolid, Spain

13. Virgen del Rocío University Hospital, Sevilla, Spain

14. Puerta del Hierro University Hospital, Madrid, Spain

15. Gregorio Marañón University Hospital, Madrid, Spain

16. Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country UPV/EHU, Leioa, Spain

17. Biocruces Bizkaia Health Research Institute, Barakaldo, Spain

18. National Institute for the Study of Liver and Gastrointestinal Diseases (CIBERehd, Instituto de Salud Carlos III), Madrid, Spain

19. Príncipe de Asturias University Hospital, Alcalá University, Madrid, Spain

20. Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York City, New York, USA

21. Pinnacle Clinical Research, San Antonio, Texas, USA

22. Translational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom

23. CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Derio, Spain

24. Virginia Commonwealth University Medical Center, Richmond, Virginia, USA

Abstract

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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Hepatology

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