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
Methods
Results
Conclusion
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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