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

  • Author Footnotes
    1 Indicates equal co-first authorship.
    Nikolaos Perakakis
    Correspondence
    Corresponding authors at: SL-418, 330 Brookline Avenue, East campus, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
    Footnotes
    1 Indicates equal co-first authorship.
    Affiliations
    Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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  • Author Footnotes
    1 Indicates equal co-first authorship.
    Stergios A. Polyzos
    Footnotes
    1 Indicates equal co-first authorship.
    Affiliations
    First Department of Pharmacology, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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  • Author Footnotes
    1 Indicates equal co-first authorship.
    Alireza Yazdani
    Footnotes
    1 Indicates equal co-first authorship.
    Affiliations
    Division of Applied Mathematics, Brown University, Providence, RI 02906, USA
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  • Aleix Sala-Vila
    Affiliations
    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
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  • Jannis Kountouras
    Affiliations
    Second Medical Clinic, Faculty of Medicine, Aristotle University of Thessaloniki, Ippokration Hospital, Thessaloniki, Greece
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  • Athanasios D. Anastasilakis
    Affiliations
    Department of Endocrinology, 424 General Military Hospital, Thessaloniki, Greece
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  • Christos S. Mantzoros
    Correspondence
    Corresponding authors at: SL-418, 330 Brookline Avenue, East campus, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
    Affiliations
    Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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  • Author Footnotes
    1 Indicates equal co-first authorship.
Published:November 08, 2019DOI:https://doi.org/10.1016/j.metabol.2019.154005

      Highlights

      • A lipidomic, glycomic and hormonal analysis was performed in healthy, NAFL and NASH subjects
      • Results were analyzed with 5 different machine learning techniques in two different platforms
      • Diagnostic models with excellent accuracy for diagnosing healthy vs. NAFL vs. NASH sumulatneously were developed
      • A predictive model consisting of 10 lipids showed perfect accuracy for detecting the presence of liver fibrosis

      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.

      Methods

      We performed a lipidomic, glycomic and free fatty acid analysis in serum samples of 49 healthy subjects and 31 patients with biopsy-proven NAFLD (15 with NAFL and 16 with NASH). The data from the above measurements combined with measurements of 4 hormonal parameters were analyzed with two different platforms and five different machine learning tools.

      Results

      365 lipids, 61 glycans and 23 fatty acids were identified with mass-spectrometry and liquid chromatography. Robust differences in the concentrations of specific lipid species were observed between healthy, NAFL and NASH subjects. One-vs-Rest (OvR) support vector machine (SVM) models with recursive feature elimination (RFE) including 29 lipids or combining lipids with glycans and/or hormones (20 or 10 variables total) could differentiate with very high accuracy (up to 90%) between the three conditions. In an exploratory analysis, a model consisting of 10 lipid species could robustly discriminate between the presence of liver fibrosis or not (98% accuracy).

      Conclusion

      We propose novel models utilizing lipids, hormones and glycans that can diagnose with high accuracy the presence of NASH, NAFL or healthy status. Additionally, we report a combination of lipids that can diagnose the presence of liver fibrosis. Both models should be further trained prospectively and validated in large independent cohorts.

      Abbreviations:

      NAFLD (non-alcoholic fatty liver disease), NAFL (non-alcoholic fatty liver), NASH (non-alcoholic steatohepatitis), HCC (hepatocellular carcinoma), IR (insulin resistance), TG (triglycerides), HDL (high-density lipoprotein), LDL (low-density lipoprotein), BMI (body mass index), SAM (significance analysis of microarrays), sPLS-DA (sparse Partial Least Squares – Discriminant Analysis), PCA (principal component analysis), t-SNE (t-distributed stochastic neighbor embedding), ROC (receiver operating characteristic), REF (recursive feature elimination), SVM (support vector machine), AUC (area under curve), OvR (One-vs-Rest), RBF (radial basis function), MCCV (Monte-Carlo cross validation), DG (diglycerides), PG (phosphatidylglycerols), PA (phosphatidic acids), AcCa (acylcarnitines), Che (cholesterol esters), Co (coenzyme Q10), LPC (lysophosphatidylcholines), SM (sphingomyelines), PE (Phosphatidylethanolamines), PC (Phosphatidylcholines), C16:0 (palmitic acid), C16:1n7cis (cis-palmitoleic acid), C18:2n6 (polyunsaturated linoleic acid), C20:4n6 (arachidonic acid), kNN (k-nearest neighbor), Ck-18f (cytokeratin-18 fragment), C18:1n9 (oleic acid), C18:3n6 (gamma-linoleic acid)

      Keywords

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