Identifying lipid patterns through lipidomic profiling for MASLD detection and severity stratification

The increasing global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) has placed this condition at the forefront of chronic liver diseases, especially in Western populations. Strongly associated with metabolic comorbidities such as obesity, dyslipidemia, insulin resistance, and type 2 diabetes [1], MASLD follows a continuum that ranges from asymptomatic hepatic steatosis to the more advanced metabolic dysfunction-associated steatohepatitis (MASH). This progression, characterized by hepatocellular injury and inflammation, significantly increases the risk of fibrosis, cirrhosis, and hepatocellular carcinoma [2]. Although current non-invasive tools including imaging and serum-based biomarkers are helpful in initial evaluations, their limited sensitivity and specificity reduce their effectiveness in accurately staging the disease or predicting its progression [3]. To address this gap, we applied lipidomic profiling to investigate the molecular features associated with MASLD severity. In this pilot study, we conducted untargeted lipidomic analyses on serum and plasma samples from a metabolically defined patient cohort spanning the full MASLD spectrum, including cases with advanced MASH. Lipids were extracted using a biphasic methyl-tert-butyl ether (MTBE) protocol [4] and analyzed by UHPLC coupled with high-resolution Orbitrap mass spectrometry. Lipid species were annotated using Compound Discoverer software and structurally validated through MS/MS fragmentation. Our analysis revealed distinct lipidomic profiles corresponding to different stages of MASLD. Key changes were observed in ceramides, sphingolipids and phospholipids, with specific patterns reflecting the extent of steatosis and inflammatory activity. These results point to a gradual reprogramming of lipid metabolism as the disease progresses, highlighting lipidomic alterations as potential indicators of disease status. Overall, our findings underscore the value of lipidomics as a high-resolution molecular tool for improving early detection, patient stratification, and personalized monitoring in MASLD. While larger validation studies are needed, this approach may enrich current diagnostic frameworks and support the transition toward a more personalized model of care.