Lipidomic profiling of MASLD: identifying lipid biomarkers for detection and stratification of disease severity

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease globally, particularly in Western countries, and is strongly linked to obesity, hyperlipidaemia, type 2 diabetes mellitus (T2DM), and metabolic syndrome[1]. MASLD includes a wide spectrum of conditions, from simple steatosis, often asymptomatic and benign, to metabolic dysfunction-associated steatohepatitis (MASH), a more aggressive phenotype characterized by hepatic fat accumulation, inflammation, and hepatocellular injury. MASH significantly increases the risk of fibrosis, cirrhosis, and hepatocellular carcinoma, and has become a leading indication for liver transplantation in many Western populations[2]. Despite its increasing clinical burden, early detection and accurate staging, particularly identifying those at risk of progression to MASH, remains challenging. Current non-invasive tests (NITs), such as blood-based scores and imaging techniques, are recommended as first-line diagnostic and risk stratification tools[3]. However, sensitive and specific biomarkers for reliably assessing disease severity and progression are still lacking. Against this backdrop, lipidomics offer promising avenues for improving disease characterization. In this study, high-risk metabolic patients with detailed clinical, biochemical, and imaging assessments, provided a well-characterized cohort (14 cases), covering the full MASLD spectrum, including advanced MASH. Lipids were extracted from serum and plasma collected from the patients in this cohort using a dual in-vial extraction with methyl-tert-butyl ether (MTBE)[4], then analyzed via UHPLC-HRMS using an Orbitrap Q Exactive system. Lipid annotation and identification were performed using Compound Discoverer (Thermo Fisher Scientific), supported by MS/MS-based structural validation. The analysis focused on lipid classes implicated in MASH pathogenesis, including ceramides, triglycerides, phospholipids, and sphingolipids. These findings revealed distinct lipid profiles and metabolic pathways associated with steatosis severity suggesting their potential in non-invasive disease stratification. While further validation in larger cohorts is needed, these findings, though preliminary, underscore the potential of lipidomic profiling to complement existing tools and advance precision medicine in MASLD.