Nikolaos Perakakis1, Stergios A Polyzos2, Alireza Yazdani3, Aleix Sala-Vila4, Jannis Kountouras5, Athanasios D Anastasilakis6, Christos S Mantzoros7. 1. Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. Electronic address: nperakak@bidmc.harvard.edu. 2. First Department of Pharmacology, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece. 3. Division of Applied Mathematics, Brown University, Providence, RI 02906, USA. 4. 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. 5. Second Medical Clinic, Faculty of Medicine, Aristotle University of Thessaloniki, Ippokration Hospital, Thessaloniki, Greece. 6. Department of Endocrinology, 424 General Military Hospital, Thessaloniki, Greece. 7. Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. Electronic address: cmantzor@bidmc.harvard.edu.
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.
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.
Authors: Matt Docherty; Stephane A Regnier; Gorana Capkun; Maria-Magdalena Balp; Qin Ye; Nico Janssens; Andreas Tietz; Jürgen Löffler; Jennifer Cai; Marcos C Pedrosa; Jörn M Schattenberg Journal: J Am Med Inform Assoc Date: 2021-06-12 Impact factor: 4.497
Authors: Vanda Marques; Marta B Afonso; Nina Bierig; Filipa Duarte-Ramos; Álvaro Santos-Laso; Raul Jimenez-Agüero; Emma Eizaguirre; Luis Bujanda; Maria J Pareja; Rita Luís; Adília Costa; Mariana V Machado; Cristina Alonso; Enara Arretxe; José M Alustiza; Marcin Krawczyk; Frank Lammert; Dina G Tiniakos; Bertram Flehmig; Helena Cortez-Pinto; Jesus M Banales; Rui E Castro; Andrea Normann; Cecília M P Rodrigues Journal: Front Med (Lausanne) Date: 2021-06-23