Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study.

Journal: Metabolism: clinical and experimental
Published Date: Dec 1, 2019

Abstract

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis and cirrhosis leading to hepatocellular carcinoma. To date, liver biopsy is the gold standard for the diagnosis of NASH and for staging liver fibrosis. This study aimed to train models for the non-invasive diagnosis of NASH and liver fibrosis based on measurements of lipids, glycans and biochemical parameters in peripheral blood and with the use of different machine learning methods.

Authors

  • Nikolaos Perakakis
    Department of Endocrinology, VA Boston Healthcare System, Jamaica Plain, Boston, MA 02130, USA; Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
  • Stergios A Polyzos
    First Department of Pharmacology, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
  • Alireza Yazdani
    Division of Applied Mathematics, Brown University, Providence, RI 02906, USA.
  • Aleix Sala-Vila
    CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Villarroel 170, Barcelona 08036, Spain.
  • Jannis Kountouras
    Second Medical Clinic, Faculty of Medicine, Aristotle University of Thessaloniki, Ippokration Hospital, Thessaloniki, Greece.
  • Athanasios D Anastasilakis
    Department of Endocrinology, 424 General Military Hospital, Thessaloniki, Greece.
  • Christos S Mantzoros
    Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.

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