Mapping the lipid landscape of MASLD: insights into disease progression through lipidomic patterns

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the most common chronic liver condition worldwide, particularly in Western countries, where it is strongly associated with obesity, hyperlipidaemia, type 2 diabetes mellitus (T2DM), and metabolic syndrome [1]. MASLD spectrum ranges from simple, often asymptomatic, steatosis to metabolic dysfunction-associated steatohepatitis (MASH), a more severe form marked by hepatic fat accumulation, inflammation, and cellular injury. MASH significantly increases the risk of fibrosis, cirrhosis, and hepatocellular carcinoma, and it is now one of the most common reasons for liver transplantation [2]. Despite its growing clinical burden, early detection and accurate staging, particularly identifying patients at risk of progression to MASH, remains challenging. Non-invasive tests (NITs), such as serum-based scores and imaging, are currently recommended for diagnosis and risk assessment [3]. However, sensitive and specific biomarkers for reliably assessing disease severity and progression are still lacking. In this perspective, lipidomics represents a promising approach for a deeper disease characterization. In this study, high-risk metabolic patients with detailed clinical, biochemical, and imaging assessments provided a well-characterized cohort, 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], and then they were analysed by UHPLC-HRMS using an Orbitrap Q Exactive mass analyser. Lipid annotation and identification were performed using Compound Discoverer (Thermo Fisher Scientific), supported by MS/MS-based structural validation. The untargeted analysis focused on lipid species related to MASH pathogenesis, including ceramides, triglycerides, phospholipids, and sphingolipids, revealing distinct lipid fingerprints tied to steatosis severity. These findings indicate lipidomics can be a highly efficient tool in non-invasive disease stratification. While further validation in larger cohorts is essential, this method shows strong potential to complement current tools and push MASLD care toward personalized medicine.