Mostra i principali dati dell'item

dc.contributor.authorRosato, Valerio
dc.date.accessioned2022-10-28T10:37:26Z
dc.date.available2022-10-28T10:37:26Z
dc.date.issued2021-05-07
dc.identifier.urihttp://elea.unisa.it:8080/xmlui/handle/10556/6221
dc.identifier.urihttp://dx.doi.org/10.14273/unisa-4309
dc.description2019 - 2020it_IT
dc.description.abstractIntroduction: 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
dc.language.isoenit_IT
dc.publisherUniversita degli studi di Salernoit_IT
dc.subjectCirrhosisit_IT
dc.subjectNAFLDit_IT
dc.subjectUntargeted metabolomicsit_IT
dc.titleUntargeted metabolomics as a diagnostic tool in NAFLD: discrimination of steatosis, steatohepatitis and cirrhosisit_IT
dc.typeDoctoral Thesisit_IT
dc.subject.miurMED/09 MEDICINA INTERNAit_IT
dc.contributor.coordinatoreMonteleone, Palmieroit_IT
dc.description.cicloXXXIII cicloit_IT
dc.contributor.tutorPersico, Marcelloit_IT
dc.identifier.DipartimentoMedicina, Chirurgia ed Odontoiatria "Scuola medica salernitana"it_IT
 Find Full text

Files in questo item

Thumbnail
Thumbnail

Questo item appare nelle seguenti collezioni

Mostra i principali dati dell'item