Background and Aims: Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from
simple steatosis to NASH and cirrhosis/HCC. The challenge in this field is to recognize
the more severe and/or progressive pathology. A reliable non-invasive method based
on biomarkers does not exist at the moment. Metabolomics technique has a great potential
for this task, because it can non-invasively perform a complete “metabolic fingerprint”
of a disease and, in turn, potentially detect all its evolution steps. With this aim,
we performed a serum metabolomics characterization of several NAFLD forms and then
tested its accuracy confronting it with an independent cohort by mean of machine-learning
models’ approach. Moreover, we performed a time-series analysis to verify if there
were metabolomic profiles that change during the evolutionary steps of NAFLD.
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© 2019 Published by Elsevier Inc.
