Kamzolas, I.; Koutsandreas, T.; Barker, C. G.; Vathrakokoili Pournara, A.; Weston, H. N.; Vacca, M.; Papatheodorou, I.; Vidal-Puig, A.; Petsalaki, E.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) has emerged as a silent pandemic, affecting nearly one-third of the global population. This condition encompasses a spectrum of liver disorders, ranging from simple steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), which is characterised by liver steatosis, lipotoxicity, hepatocellular damage, inflammation, and fibrosis. Left unchecked, MASLD/MASH can progress to cirrhosis and hepatocellular carcinoma. Despite the progressive nature of MASLD/MASH, current research primarily relies on static, histopathologically defined stages, which fail to capture the dynamic disease continuum. In this study, we present an integrative stratification approach that combines patient pseudo-temporal ordering, network analysis, and cell-type deconvolution to map the continuous disease trajectory. By analyzing transcriptomic profiles, we predict patients\' positions along this trajectory, moving beyond conventional stage-based classifications. This approach reveals the sequence of critical molecular events driving MASLD/MASH progression, providing new insights into the disease\'s pathophysiology. Furthermore, we identify novel trajectory-specific biomarkers that support a more refined, personalised strategy for managing MASLD. This work highlights the potential of trajectory-based frameworks in advancing our understanding and treatment of complex metabolic diseases.