A robust diagnostic model for high-risk MASH: integrating clinical parameters and circulating biomarkers through a multi-omics approach.

Abstract

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a critical health concern, with metabolic dysfunction-associated steatohepatitis (MASH) representing a severe subtype that poses significant risks. This study aims to develop a robust diagnostic model for high-risk MASH utilizing a multi-omics approach.

Methods: We initiated proteomic analysis to select differential proteins, followed by liver transcriptional profiling to localize these proteins. An intersection of differential proteins and liver-expressed genes facilitated the identification of candidate biomarkers. Subsequently, scRNA-seq data helped ascertain the subcellular localization of these biomarkers in kupffer cells. We then established two MASLD models to investigate the co-localization of F4/80 and the target proteins in Kupffer cells using immunofluorescence dual-labeling. Correlation analyses were performed using blood samples from a discovery cohort of 144 individuals with liver pathology to validate the relationships between candidate biomarkers and MASLD phenotypes. Using LASSO regression, we established the ABD-LTyG predictive model for high-risk MASH (NAS ≥ 4 + F ≥ 2) and validated its efficacy in an independent cohort of 171 individuals. Finally, we compared this model against three classic non-invasive liver fibrosis diagnostic methods.

Results: A proteo-transcriptomic comparison identified 58 consistent biomarkers in plasma and liver, with 25 closely associated with MASLD phenotype. Utilizing single-cell data and the HPA database, we delineated the localization of these biomarkers in liver cells, identifying TREM2, IL18BP, and LGALS3BP predominantly in the Kupffer cell subpopulation. Validation in animal models confirmed elevated expression and cellular localization of TREM2, IL18BP, and LGALS3BP in MASLD. To enhance diagnostic capability, we integrated clinical characteristics using LASSO regression to develop the ABD-LTyG model, comprising AST, BMI, total bilirubin (TB), vitamin D, TyG, and the biomarkers LGALS3BP and TREM2. This model demonstrated an AUC of 0.832 (95% CI 0.753-0.911) in the discovery cohort and 0.807 (95% CI 0.742-0.872) in the validation cohort for diagnosing high-risk MASH, outperforming traditional assessments such as FIB-4, NFS, and APRI.

Conclusion: The integration of circulating biomarkers and clinical parameters into the ABD-LTyG model offers a promising approach for diagnosing high-risk MASH. This study underscores the importance of multi-omics strategies in enhancing diagnostic accuracy and guiding clinical decision-making.