Untargeted metabolomics as a diagnostic tool in NAFLD: discrimination of steatosis, steatohepatitis and cirrhosis
Introduction Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method...
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Published in | Metabolomics Vol. 17; no. 2; p. 12 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.02.2021
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1573-3882 1573-3890 1573-3890 |
DOI | 10.1007/s11306-020-01756-1 |
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Abstract | Introduction
Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology.
Objectives
We applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls.
Methods
Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The “Partial-Least-Square Discriminant-Analysis”(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation.
Results
Several metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction.
Conclusions
Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology. |
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AbstractList | Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology.
We applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls.
Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The "Partial-Least-Square Discriminant-Analysis"(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation.
Several metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction.
Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology. IntroductionNon-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology.ObjectivesWe applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls.MethodsMetabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The “Partial-Least-Square Discriminant-Analysis”(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation.ResultsSeveral metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction.ConclusionsMetabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology. Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology.INTRODUCTIONNon-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology.We applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls.OBJECTIVESWe applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls.Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The "Partial-Least-Square Discriminant-Analysis"(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation.METHODSMetabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The "Partial-Least-Square Discriminant-Analysis"(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation.Several metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction.RESULTSSeveral metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction.Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology.CONCLUSIONSMetabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology. Introduction Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology. Objectives We applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls. Methods Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The “Partial-Least-Square Discriminant-Analysis”(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation. Results Several metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction. Conclusions Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology. |
ArticleNumber | 12 |
Author | Masarone, Mario Persico, Marcello Federico, Alessandro Aglitti, Andrea Torre, Pietro Balsano, Clara Dallio, Marcello Colucci, Angelo Troisi, Jacopo |
Author_xml | – sequence: 1 givenname: Mario surname: Masarone fullname: Masarone, Mario organization: Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno – sequence: 2 givenname: Jacopo surname: Troisi fullname: Troisi, Jacopo organization: Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno, Theoreo srl, European Biomedical Research Institute of Salerno (EBRIS), Hosmotic srl – sequence: 3 givenname: Andrea surname: Aglitti fullname: Aglitti, Andrea organization: Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno – sequence: 4 givenname: Pietro surname: Torre fullname: Torre, Pietro organization: Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno – sequence: 5 givenname: Angelo surname: Colucci fullname: Colucci, Angelo organization: Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno, Theoreo srl – sequence: 6 givenname: Marcello surname: Dallio fullname: Dallio, Marcello organization: Hepatogastroenterology Division, University of Campania “Luigi Vanvitelli” – sequence: 7 givenname: Alessandro surname: Federico fullname: Federico, Alessandro organization: Hepatogastroenterology Division, University of Campania “Luigi Vanvitelli” – sequence: 8 givenname: Clara surname: Balsano fullname: Balsano, Clara organization: MESVA Department, University of L’Aquila, F. Balsano Foundation – sequence: 9 givenname: Marcello orcidid: 0000-0002-1399-6498 surname: Persico fullname: Persico, Marcello email: mpersico@unisa.it organization: Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno |
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Snippet | Introduction
Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis... Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and... IntroductionNon-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis... |
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SubjectTerms | Biochemistry Biomedical and Life Sciences Biomedicine Cell Biology Cirrhosis Developmental Biology Disease Fatty liver Glutathione Hepatocellular carcinoma Learning algorithms Life Sciences Liver cancer Liver cirrhosis Liver diseases Mathematical models Metabolites Metabolomics Molecular Medicine Original Article Pathophysiology Phenylalanine Steatosis Taurocholic acid |
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Title | Untargeted metabolomics as a diagnostic tool in NAFLD: discrimination of steatosis, steatohepatitis and cirrhosis |
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