| dc.description.abstract | Introduction: Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma(HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology. Objectives: we applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls. Methods: Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis). The “Partial-Least-Square Discriminant-Analysis” (PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation. Results: Several metabolites were selected as relevant, in particular: Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p<0.001 for each). Moreover, an ensemble machine learning (EML) model was built using 10 different classification models. EML showed accuracy>80% in NAFLD evolution steps prediction. Conclusions: Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology. [edited by Author] | it_IT |
