Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) remains a prevalent condition with limited diagnostic and therapeutic options. This study aims to identify metabolic signatures of disease progression and develop non-invasive diagnostic models through three independent cohorts (including two cohorts confirmed by biopsy and one cohort confirmed by ultrasound) involving 293 participants for detecting significant fibrosis (≥F2) and mild to severe inflammatory activity (≥I2) using multiple machine learning techniques. The fibrosis panel shows area under the receiver operating characteristic curve (AUROC) of 0.928 (95% confidence interval [CI]: 0.835-0.978), 0.829 (0.732-0.902), and 0.806 (0.724-0.872) in the discovery cohort, validation cohort 1, and validation cohort 2, respectively, outperforming the fibrosis-4 index (FIB-4), aspartate aminotransferase-to-platelet ratio index (APRI), non-alcoholic fatty liver disease fibrosis score (NFS), liver stiffness measurement (LSM), and combination of hoMa, Ast and CK18 (MACK-3). The inflammation panel achieves AUROCs of 0.894 (0.791-0.957) and 0.776 (0.673-0.859) in the discovery cohort and validation cohort 1, respectively. The key metabolites guanidinoacetic acid (GAA) and sebacic acid (SA) demonstrate therapeutic efficacy in mice. These validated panels provide accurate stratification of MASLD severity, and GAA/SA offer therapeutic potential, advancing both diagnosis and treatment strategies.