Integrated liver-secreted and plasma proteomics identify a predictive model that stratifies MASH
William De Nardo, Olivia W. Lee, Yazmin Johari, Jacqueline Bayliss, M. Pensa, Paula M. Miotto, Stacey N. Keenan, Andrew Ryan, Amber Rucinski, Tessa M Svinos, Geraldine Ooi, Wendy A. Brown, William Kemp, Stuart K. Roberts, Benjamin L. Parker, Magdalene K. Montgomery, Mark Larance, Paul R. Burton, Matthew J. Watt
Abstract
Obesity is a major risk factor for metabolic-associated steatotic liver disease (MASLD), which can progress to metabolic-associated steatohepatitis (MASH). There are no validated non-invasive tests to stratify persons with obesity with a greater risk for MASH. Herein, we assess plasma and liver from 266 obese individuals spanning the MASLD spectrum. Ninety-six human livers were precision-cut, and mass spectrometry-based proteomics identifies 3,333 proteins in the liver-secretion medium, of which 107 are differentially secreted in MASH compared with no pathology. The plasma proteome is markedly remodeled in MASH but is not different between patients with steatosis and no pathology. The APASHA model, comprising plasma apolipoprotein F (APOF), proprotein convertase subtilisin/kexin type 9 (PCSK9), afamin (AFM), S100 calcium-binding protein A6 (S100A6), HbA1c, and zinc-alpha-2-glycoprotein (AZGP1), stratifies MASH (area under receiver operating characteristic [AUROC] = 0.88). Our investigations detail the evolution of liver-secreted and plasma proteins with MASLD progression, providing a rich resource defining human liver-secreted proteins and creating a predictive model to stratify patients with obesity at risk of MASH. • Catalog of liver-secreted and plasma proteins with MASLD progression in humans • Mass spectrometry analysis identifies remodeling of plasma proteins in human MASH • Protein secretion from the liver is altered in human MASH • The APASHA model effectively stratifies patients with obesity at risk of MASH De Nardo et al. profile the plasma and liver-secreted proteome from obese individuals spanning the spectrum of MASLD. They identify biomarkers for MASH in persons with obesity and develop and validate a biologically plausible non-invasive risk prediction model to predict or exclude MASH.