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...

Full description

Saved in:
Bibliographic Details
Published inMetabolomics Vol. 17; no. 2; p. 12
Main Authors Masarone, Mario, Troisi, Jacopo, Aglitti, Andrea, Torre, Pietro, Colucci, Angelo, Dallio, Marcello, Federico, Alessandro, Balsano, Clara, Persico, Marcello
Format Journal Article
LanguageEnglish
Published New York Springer US 01.02.2021
Springer Nature B.V
Subjects
Online AccessGet full text
ISSN1573-3882
1573-3890
1573-3890
DOI10.1007/s11306-020-01756-1

Cover

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.
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
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33458794$$D View this record in MEDLINE/PubMed
BookMark eNp9kU9PGzEQxa0qqAmUL9BDZamXHlgY_9m1tzdES4sUwaWcLa_XG4x27cR2Dnz7miSAxAHJkkea3xu9mXeMZj54i9BXAucEQFwkQhg0FVCogIi6qcgntCC1YBWTLcxea0nn6DilRwDOWwGf0ZwxXkvR8gXa3Pus48pm2-PJZt2FMUzOJKzLw73TKx9SdgbnEEbsPL69vF7--lk6yUQ3Oa-zCx6HAadsdQ7JpbND-WDXpZldGeR7bFyMD8_tL-ho0GOyp4f_BN1f__539bda3v25ubpcVoZzkivd9Rr6hgjTGEI0lQ3vZGtAtkwAoRyM7TQz1PBGcDt0pOnNwNhgWVtz2jJ2gn7s565j2Gxtymoqnu04am_DNinKhRRC0hoK-v0d-hi20Rd3O4oVrK4L9e1AbbvJ9mpd9tfxSb0cswByD5gYUop2UMbl3X1y1G5UBNRzbmqfmyq5qV1uihQpfSd9mf6hiO1FqcB-ZeOb7Q9U_wGrpKpQ
CitedBy_id crossref_primary_10_1016_j_isci_2024_109345
crossref_primary_10_1016_j_jpba_2024_116541
crossref_primary_10_3390_antiox13121461
crossref_primary_10_3390_ijms24129789
crossref_primary_10_3390_ijms24043563
crossref_primary_10_15407_internalmed2023_02_004
crossref_primary_10_3390_diagnostics12020407
crossref_primary_10_3390_genes13112142
crossref_primary_10_1016_j_heliyon_2024_e27325
crossref_primary_10_1016_j_cca_2024_120038
crossref_primary_10_1042_BSR20220319
crossref_primary_10_22416_1382_4376_2022_32_1_46_52
crossref_primary_10_1007_s11306_022_01960_1
crossref_primary_10_1038_s41598_022_09056_5
crossref_primary_10_1055_a_2364_2928
crossref_primary_10_29296_25877305_2023_06_12
crossref_primary_10_3390_metabo11100694
crossref_primary_10_11569_wcjd_v32_i8_561
crossref_primary_10_3389_fphar_2022_971561
crossref_primary_10_3390_metabo13040536
crossref_primary_10_1016_j_heliyon_2023_e22151
crossref_primary_10_3390_biomedicines12081904
crossref_primary_10_1021_acs_analchem_4c02369
crossref_primary_10_3390_biomedicines10010015
crossref_primary_10_3389_frai_2022_1050439
crossref_primary_10_3390_metabo11060353
crossref_primary_10_3350_cmh_2021_0127
crossref_primary_10_1186_s12916_023_03185_y
crossref_primary_10_3390_ijms252312809
crossref_primary_10_1007_s11306_024_02122_1
crossref_primary_10_3390_cancers15184566
crossref_primary_10_1016_j_jtemb_2024_127397
crossref_primary_10_1186_s12859_023_05383_0
crossref_primary_10_1016_j_biopha_2021_112425
crossref_primary_10_3390_biom12010056
crossref_primary_10_4239_wjd_v12_i12_2027
crossref_primary_10_1007_s11739_024_03626_3
crossref_primary_10_3390_ijms22136900
crossref_primary_10_1002_cld_1199
crossref_primary_10_3390_diagnostics12051199
crossref_primary_10_3748_wjg_v30_i47_5055
crossref_primary_10_3390_biomedicines10030550
crossref_primary_10_1016_j_heliyon_2024_e27075
crossref_primary_10_3389_fendo_2024_1323647
crossref_primary_10_1080_00365521_2023_2225667
crossref_primary_10_3390_ijms242015188
Cites_doi 10.1038/s41598-017-11759-z
10.1039/C5RA13417J
10.1038/nrm3314
10.3390/nu9050485
10.2174/1389450116666150427155342
10.1016/j.jhep.2015.01.019
10.15403/jgld.2014.1121.271.ald
10.1371/journal.pone.0127299
10.1111/j.1572-0241.1999.01377.x
10.1016/j.jhep.2020.02.020
10.2337/dc07-1268
10.1016/j.cbi.2019.02.015
10.15403/jgld.2014.1121.234.bna
10.1007/s11306-017-1274-z
10.1002/hep.24322
10.1038/nrgastro.2016.85
10.2174/13816128113199990344
10.1002/hep.25762
10.1186/1471-230X-6-33
10.3390/nu11020274
10.1016/j.cgh.2012.10.001
10.1007/s00726-014-1894-9
10.1038/nrgastro.2017.109
10.1042/BST20140138
10.1081/DMR-120037569
10.1001/jama.2013.281053
10.1111/1753-0407.12639
10.1016/j.phrs.2019.01.029
10.1002/hep.21496
10.2174/1574887109666141216111143
10.1016/j.metabol.2010.03.006
10.1056/NEJMra011775
10.1371/journal.pone.0193138
10.1016/j.jhep.2017.11.018
10.1155/2014/169216
10.1002/hep4.1188
10.1007/s11306-007-0082-2
10.1007/s11306-018-1370-8
10.1093/bioinformatics/btr661
10.5114/ceh.2019.83151
10.3748/wjg.v21.i24.7529
10.1016/S0016-5085(99)70506-8
10.1021/acs.jproteome.7b00503
10.1111/dom.13285
10.7551/mitpress/3927.001.0001
10.2307/2531595
10.1038/nrgastro.2010.21
10.1016/j.dld.2006.03.021
10.3390/nu9101124
10.1002/hep.29465
10.1002/hep.20701
10.12688/f1000research.4524.1
10.1210/jc.2009-1235
10.1155/2016/2794591
ContentType Journal Article
Copyright The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021
The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.
Copyright_xml – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021
– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.
DBID AAYXX
CITATION
NPM
3V.
7X7
7XB
8FE
8FH
8FI
8FJ
8FK
ABUWG
AFKRA
AZQEC
BBNVY
BENPR
BHPHI
CCPQU
DWQXO
FYUFA
GHDGH
GNUQQ
HCIFZ
K9.
LK8
M0S
M7P
PHGZM
PHGZT
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
7X8
DOI 10.1007/s11306-020-01756-1
DatabaseName CrossRef
PubMed
ProQuest Central (Corporate)
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
ProQuest SciTech Collection
ProQuest Natural Science Collection
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials
Biological Science Collection
ProQuest Central
Natural Science Collection
ProQuest One Community College
ProQuest Central Korea
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Central Student
SciTech Premium Collection
ProQuest Health & Medical Complete (Alumni)
Biological Sciences
ProQuest Health & Medical Collection
Biological Science Database
ProQuest Central Premium
ProQuest One Academic (New)
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
MEDLINE - Academic
DatabaseTitle CrossRef
PubMed
ProQuest Central Student
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Natural Science Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
Natural Science Collection
ProQuest Central Korea
Biological Science Collection
ProQuest Central (New)
ProQuest Biological Science Collection
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
Biological Science Database
ProQuest SciTech Collection
ProQuest Hospital Collection (Alumni)
ProQuest Health & Medical Complete
ProQuest One Academic UKI Edition
ProQuest One Academic
ProQuest Central (Alumni)
ProQuest One Academic (New)
MEDLINE - Academic
DatabaseTitleList PubMed
ProQuest Central Student
MEDLINE - Academic

Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: BENPR
  name: ProQuest Central
  url: http://www.proquest.com/pqcentral?accountid=15518
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Anatomy & Physiology
EISSN 1573-3890
ExternalDocumentID 33458794
10_1007_s11306_020_01756_1
Genre Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: Regione Campania
  grantid: ESR 2014/2020 Asse 1 - Obiettivo specifico 1.2 - Azione1 .2. Progetto: Campania Onco Terapie CUP: B61G18000470007
  funderid: http://dx.doi.org/10.13039/501100003852
GroupedDBID ---
-56
-5G
-BR
-EM
-Y2
-~C
.86
.VR
06C
06D
0R~
0VY
123
199
1N0
203
29M
29~
2J2
2JN
2JY
2KG
2KM
2LR
2P1
2VQ
2~H
30V
3V.
4.4
406
408
409
40D
40E
53G
5VS
67N
67Z
6NX
7X7
8FE
8FH
8FI
8FJ
8TC
8UJ
95-
95.
95~
96X
AAAVM
AABHQ
AACDK
AAHNG
AAIAL
AAJBT
AAJKR
AANXM
AANZL
AARHV
AARTL
AASML
AATNV
AATVU
AAUYE
AAWCG
AAYIU
AAYQN
AAYTO
AAYZH
ABAKF
ABBBX
ABBXA
ABDBF
ABDZT
ABECU
ABFTV
ABHFT
ABHLI
ABHQN
ABJNI
ABJOX
ABKCH
ABKTR
ABMNI
ABMQK
ABNWP
ABPLI
ABQBU
ABQSL
ABSXP
ABTEG
ABTHY
ABTKH
ABTMW
ABULA
ABUWG
ABWNU
ABXPI
ACAOD
ACDTI
ACGFO
ACGFS
ACHSB
ACHXU
ACIHN
ACKNC
ACMDZ
ACMLO
ACOKC
ACOMO
ACPIV
ACPRK
ACREN
ACSNA
ACUHS
ACZOJ
ADBBV
ADHHG
ADHIR
ADINQ
ADKNI
ADKPE
ADRFC
ADTPH
ADURQ
ADYFF
ADYOE
ADZKW
AEAQA
AEBTG
AEFQL
AEGAL
AEGNC
AEJHL
AEJRE
AEKMD
AEMSY
AENEX
AEOHA
AEPYU
AESKC
AETLH
AEVLU
AEXYK
AFBBN
AFGCZ
AFKRA
AFLOW
AFQWF
AFWTZ
AFYQB
AFZKB
AGAYW
AGDGC
AGJBK
AGMZJ
AGQEE
AGQMX
AGRTI
AGWIL
AGWZB
AGYKE
AHAVH
AHBYD
AHKAY
AHMBA
AHSBF
AHYZX
AIAKS
AIGIU
AIIXL
AILAN
AITGF
AJBLW
AJRNO
AJZVZ
AKMHD
ALMA_UNASSIGNED_HOLDINGS
ALWAN
AMKLP
AMTXH
AMXSW
AMYLF
AMYQR
AOCGG
ARMRJ
ASPBG
AVWKF
AXYYD
AZFZN
B-.
BA0
BBNVY
BDATZ
BENPR
BGNMA
BHPHI
BPHCQ
BSONS
BVXVI
CAG
CCPQU
COF
CS3
CSCUP
DDRTE
DL5
DNIVK
DPUIP
DU5
EBD
EBLON
EBS
EIOEI
EJD
EN4
ESBYG
ESX
FEDTE
FERAY
FFXSO
FIGPU
FINBP
FNLPD
FRRFC
FSGXE
FWDCC
FYUFA
G-Y
G-Z
GGCAI
GGRSB
GJIRD
GNWQR
GQ6
GQ7
GQ8
GXS
H13
HCIFZ
HF~
HG5
HG6
HLICF
HMCUK
HMJXF
HQYDN
HRMNR
HVGLF
HZ~
I09
IHE
IJ-
IKXTQ
IWAJR
IXC
IXD
IXE
IZIGR
IZQ
I~X
I~Z
J-C
J0Z
JBSCW
JCJTX
JZLTJ
KDC
KOV
KPH
LAK
LK8
LLZTM
LMP
M4Y
M7P
MA-
N2Q
NB0
NPVJJ
NQJWS
NU0
O9-
O93
O9I
O9J
OAM
P2P
PF0
PQQKQ
PROAC
PT4
Q2X
QOR
QOS
R89
R9I
RIG
ROL
RPX
RSV
S16
S1Z
S27
S3A
S3B
SAP
SBL
SDH
SHX
SISQX
SJYHP
SNE
SNPRN
SNX
SOHCF
SOJ
SPISZ
SRMVM
SSLCW
SSXJD
STPWE
SZN
T13
TSG
TSK
TSV
TUC
TUS
U2A
U9L
UG4
UKHRP
UOJIU
UTJUX
UZXMN
VC2
VFIZW
W23
W48
WJK
WK8
YLTOR
Z45
Z7U
Z7V
Z7W
Z7Y
Z82
Z83
Z87
ZMTXR
ZOVNA
~A9
~KM
AAPKM
AAYXX
ABBRH
ABDBE
ABFSG
ABRTQ
ACSTC
ADHKG
AEZWR
AFDZB
AFHIU
AFOHR
AGQPQ
AHPBZ
AHWEU
AIXLP
ATHPR
AYFIA
CITATION
PHGZM
PHGZT
PQGLB
PUEGO
NPM
7XB
8FK
AZQEC
DWQXO
GNUQQ
K9.
PKEHL
PQEST
PQUKI
PRINS
7X8
ID FETCH-LOGICAL-c441t-abda0d617c6c11a2864b89c0893701240ceba3c2c4674efb16dcf33fe39542933
IEDL.DBID 7X7
ISSN 1573-3882
1573-3890
IngestDate Sun Sep 28 07:46:06 EDT 2025
Fri Jul 25 18:57:31 EDT 2025
Wed Feb 19 02:28:16 EST 2025
Wed Oct 01 02:40:50 EDT 2025
Thu Apr 24 23:06:13 EDT 2025
Fri Feb 21 02:48:50 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 2
Keywords NAFLD
NASH
Untargeted metabolomics
Ensemble machine learning diagnostics
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c441t-abda0d617c6c11a2864b89c0893701240ceba3c2c4674efb16dcf33fe39542933
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0002-1399-6498
PMID 33458794
PQID 2478377855
PQPubID 326279
ParticipantIDs proquest_miscellaneous_2478778250
proquest_journals_2478377855
pubmed_primary_33458794
crossref_citationtrail_10_1007_s11306_020_01756_1
crossref_primary_10_1007_s11306_020_01756_1
springer_journals_10_1007_s11306_020_01756_1
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2021-02-01
PublicationDateYYYYMMDD 2021-02-01
PublicationDate_xml – month: 02
  year: 2021
  text: 2021-02-01
  day: 01
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
– name: United States
– name: Heidelberg
PublicationTitle Metabolomics
PublicationTitleAbbrev Metabolomics
PublicationTitleAlternate Metabolomics
PublicationYear 2021
Publisher Springer US
Springer Nature B.V
Publisher_xml – name: Springer US
– name: Springer Nature B.V
References Nishida, Ono, Kanaya, Takahashi (CR35) 2014; 3
Ballestri, Lonardo, Loria (CR5) 2011; 53
Matteoni, Younossi, Gramlich, Boparai, Liu, McCullough (CR31) 1999; 116
Nezami Ranjbar, Luo, Di Poto, Varghese, Ferrarini, Zhang (CR34) 2015; 10
Voiculescu, Nanau, Neuman (CR49) 2014; 23
Brunt (CR8) 2010; 7
Chalasani, Younossi, Lavine, Diehl, Brunt, Cusi (CR12) 2012; 55
Mayo, Crespo, Martinez-Arranz, Banales, Arias, Minchole (CR32) 2018; 2
Liang, Wang, Li, Zhang (CR26) 2015
Romero-Ibarguengoitia, Vadillo-Ortega, Caballero, Ibarra-Gonzalez, Herrera-Rosas, Serratos-Canales (CR38) 2018; 13
White, Kanwal, El-Serag (CR50) 2012; 10
Ma, Zhou, Li (CR28) 2017; 9
Trépo, Valenti (CR43) 2020; 72
Troisi, Landolfi, Sarno, Richards, Symes, Adair (CR45) 2018; 14
Zhao, Han, Liu, Sun, Gang, Wang (CR54) 2016
Troisi, Sarno, Landolfi, Scala, Martinelli, Venturella (CR47) 2018; 17
Gougeon, Morais, Chevalier, Pereira, Lamarche, Marliss (CR18) 2008; 31
Angulo, Hui, Marchesini, Bugianesi, George, Farrell (CR2) 2007; 45
Palmentieri, de Sio, La Mura, Masarone, Vecchione, Bruno (CR36) 2006; 38
Patti, Yanes, Siuzdak (CR37) 2012; 13
Cano, Alonso (CR11) 2014; 42
Kleiner, Brunt, Van Natta, Behling, Contos, Cummings (CR23) 2005; 41
Ding, Yanagi, Cheng, Alaniz, Lee, Jayaraman (CR16) 2019; 141
Thuluvath, Kantsevoy, Thuluvath, Savva (CR42) 2018; 68
Kalhan, Guo, Edmison, Dasarathy, McCullough, Hanson, Milburn (CR21) 2011; 60
Mitchell (CR33) 1998
Troisi, Belmonte, Bisogno, Pierri, Colucci, Scala (CR44) 2019
Chen, Golla, Garcia-Milian, Thompson, Gonzalez, Vasiliou (CR14) 2019; 303
Brunt, Janney, Di Bisceglie, Neuschwander-Tetri, Bacon (CR9) 1999; 94
Younossi, Anstee, Marietti, Hardy, Henry, Eslam (CR53) 2018; 15
Masarone, Federico, Abenavoli, Loguercio, Persico (CR30) 2014; 9
Augustyn, Grys, Kukla (CR4) 2019; 5
Kalhan (CR20) 2009; 94
Karnovsky, Weymouth, Hull, Tarcea, Scardoni, Laudanna (CR22) 2012; 28
Marra, Lotersztajn (CR29) 2013; 19
Bril, Millan, Kalavalapalli, McPhaul, Caulfield, Martinez-Arranz (CR7) 2018; 20
Harrison (CR19) 2004; 36
Asghari, Farhadnejad, Teymoori, Mirmiran, Tohidi, Azizi (CR3) 2018; 10
Troisi, Pierri, Landolfi, Marciano, Bisogno, Belmonte (CR46) 2017
Troisi, Sarno, Martinelli, Di Carlo, Landolfi, Scala (CR48) 2017; 13
Leung, Rivera, Furness, Angus (CR25) 2016; 13
Liu, Baker, Baker, Zhu (CR27) 2015; 16
Angulo (CR1) 2002; 346
Sumner, Amberg, Barrett, Beale, Beger, Daykin (CR41) 2007; 3
(CR51) 2013; 310
Calvo, Beltran-Debon, Rodriguez-Gallego, Hernandez-Aguilera, Guirro, Marine-Casado (CR10) 2015; 21
Bedogni, Bellentani, Miglioli, Masutti, Passalacqua, Castiglione, Tiribelli (CR6) 2006; 6
Lake, Novak, Shipkova, Aranibar, Robertson, Reily (CR24) 2015; 47
Chang, Meng, Liu, Wang, Yang, Lu, Wang (CR13) 2017; 7
Suciu, Crisan, Procopet, Radu, Socaciu, Tantau (CR40) 2018; 27
Xu, Wan, Xu, Weng, Yan, Miao (CR52) 2015; 62
Gaggini, Carli, Rosso, Buzzigoli, Marietti, Della Latta (CR17) 2018; 67
Stiuso, Scognamiglio, Murolo, Ferranti, Simone, Rizzo (CR39) 2014
DeLong, DeLong, Clarke-Pearson (CR15) 1988; 44
S Ballestri (1756_CR5) 2011; 53
M Masarone (1756_CR30) 2014; 9
Y Chen (1756_CR14) 2019; 303
M Mitchell (1756_CR33) 1998
MR Nezami Ranjbar (1756_CR34) 2015; 10
CA Matteoni (1756_CR31) 1999; 116
H Chang (1756_CR13) 2017; 7
E Trépo (1756_CR43) 2020; 72
J Troisi (1756_CR46) 2017
M Voiculescu (1756_CR49) 2014; 23
K Nishida (1756_CR35) 2014; 3
Z Younossi (1756_CR53) 2018; 15
F Marra (1756_CR29) 2013; 19
PJ Thuluvath (1756_CR42) 2018; 68
EM Brunt (1756_CR9) 1999; 94
ER DeLong (1756_CR15) 1988; 44
LW Sumner (1756_CR41) 2007; 3
DL White (1756_CR50) 2012; 10
DE Kleiner (1756_CR23) 2005; 41
J Ma (1756_CR28) 2017; 9
P Stiuso (1756_CR39) 2014
A Cano (1756_CR11) 2014; 42
GJ Patti (1756_CR37) 2012; 13
Q Liang (1756_CR26) 2015
M Augustyn (1756_CR4) 2019; 5
R Harrison (1756_CR19) 2004; 36
AD Lake (1756_CR24) 2015; 47
P Angulo (1756_CR2) 2007; 45
N Calvo (1756_CR10) 2015; 21
R Gougeon (1756_CR18) 2008; 31
SC Kalhan (1756_CR20) 2009; 94
C Leung (1756_CR25) 2016; 13
W Liu (1756_CR27) 2015; 16
R Mayo (1756_CR32) 2018; 2
Y Ding (1756_CR16) 2019; 141
N Chalasani (1756_CR12) 2012; 55
J Troisi (1756_CR44) 2019
P Angulo (1756_CR1) 2002; 346
EM Brunt (1756_CR8) 2010; 7
World Medical Association (1756_CR51) 2013; 310
C Xu (1756_CR52) 2015; 62
M Gaggini (1756_CR17) 2018; 67
F Bril (1756_CR7) 2018; 20
J Troisi (1756_CR45) 2018; 14
X Zhao (1756_CR54) 2016
J Troisi (1756_CR47) 2018; 17
G Bedogni (1756_CR6) 2006; 6
AM Suciu (1756_CR40) 2018; 27
G Asghari (1756_CR3) 2018; 10
SC Kalhan (1756_CR21) 2011; 60
B Palmentieri (1756_CR36) 2006; 38
ME Romero-Ibarguengoitia (1756_CR38) 2018; 13
A Karnovsky (1756_CR22) 2012; 28
J Troisi (1756_CR48) 2017; 13
References_xml – volume: 7
  start-page: 11433
  year: 2017
  ident: CR13
  article-title: Identification of key metabolic changes during liver fibrosis progression in rats using a urine and serum metabolomics approach
  publication-title: Scientific Reports
  doi: 10.1038/s41598-017-11759-z
– year: 2015
  ident: CR26
  article-title: Metabolomics of alcoholic liver disease: A clinical discovery study
  publication-title: RSC Advances
  doi: 10.1039/C5RA13417J
– volume: 13
  start-page: 263
  issue: 4
  year: 2012
  end-page: 269
  ident: CR37
  article-title: Innovation: Metabolomics: The apogee of the omics trilogy
  publication-title: Nature Reviews Molecular Cell Biology
  doi: 10.1038/nrm3314
– year: 2017
  ident: CR46
  article-title: Urinary metabolomics in pediatric obesity and NAFLD identifies metabolic pathways/metabolites related to dietary habits and gut-liver axis perturbations
  publication-title: Nutrients
  doi: 10.3390/nu9050485
– volume: 16
  start-page: 1301
  issue: 12
  year: 2015
  end-page: 1314
  ident: CR27
  article-title: Antioxidant mechanisms in nonalcoholic fatty liver disease
  publication-title: Current Drug Targets
  doi: 10.2174/1389450116666150427155342
– volume: 62
  start-page: 1412
  issue: 6
  year: 2015
  end-page: 1419
  ident: CR52
  article-title: Xanthine oxidase in non-alcoholic fatty liver disease and hyperuricemia: One stone hits two birds
  publication-title: Journal of Hepatology
  doi: 10.1016/j.jhep.2015.01.019
– volume: 27
  start-page: 51
  issue: 1
  year: 2018
  end-page: 58
  ident: CR40
  article-title: What’s in metabolomics for alcoholic liver disease?
  publication-title: Journal of Gastrointestinal and Liver Diseases
  doi: 10.15403/jgld.2014.1121.271.ald
– volume: 10
  start-page: e0127299
  issue: 6
  year: 2015
  ident: CR34
  article-title: GC-MS based plasma metabolomics for identification of candidate biomarkers for hepatocellular carcinoma in Egyptian Cohort
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0127299
– volume: 94
  start-page: 2467
  issue: 9
  year: 1999
  end-page: 2474
  ident: CR9
  article-title: Nonalcoholic steatohepatitis: A proposal for grading and staging the histological lesions
  publication-title: The American Journal of Gastroenterology
  doi: 10.1111/j.1572-0241.1999.01377.x
– volume: 72
  start-page: 1196
  issue: 6
  year: 2020
  end-page: 1209
  ident: CR43
  article-title: Update on NAFLD genetics: From new variants to the clinic
  publication-title: Journal of Hepatology
  doi: 10.1016/j.jhep.2020.02.020
– volume: 31
  start-page: 128
  issue: 1
  year: 2008
  end-page: 133
  ident: CR18
  article-title: Determinants of whole-body protein metabolism in subjects with and without type 2 diabetes
  publication-title: Diabetes Care
  doi: 10.2337/dc07-1268
– volume: 303
  start-page: 1
  year: 2019
  end-page: 6
  ident: CR14
  article-title: Hepatic metabolic adaptation in a murine model of glutathione deficiency
  publication-title: Chemico-Biological Interactions
  doi: 10.1016/j.cbi.2019.02.015
– volume: 23
  start-page: 425
  issue: 4
  year: 2014
  end-page: 429
  ident: CR49
  article-title: Non-invasive biomarkers in non-alcoholic steatohepatitis-induced hepatocellular carcinoma
  publication-title: Journal of Gastrointestinal & Liver Diseases
  doi: 10.15403/jgld.2014.1121.234.bna
– volume: 13
  start-page: 140
  issue: 11
  year: 2017
  ident: CR48
  article-title: A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies
  publication-title: Metabolomics
  doi: 10.1007/s11306-017-1274-z
– volume: 53
  start-page: 2142
  issue: 6
  year: 2011
  end-page: 2143
  ident: CR5
  article-title: Nonalcoholic fatty liver disease activity score and Brunt’s pathologic criteria for the diagnosis of nonalcoholic steatohepatitis: What do they mean and do they agree?
  publication-title: Hepatology
  doi: 10.1002/hep.24322
– volume: 13
  start-page: 412
  issue: 7
  year: 2016
  end-page: 425
  ident: CR25
  article-title: The role of the gut microbiota in NAFLD
  publication-title: Nature Reviews. Gastroenterology & Hepatology
  doi: 10.1038/nrgastro.2016.85
– volume: 19
  start-page: 5250
  issue: 29
  year: 2013
  end-page: 5269
  ident: CR29
  article-title: Pathophysiology of NASH: Perspectives for a targeted treatment
  publication-title: Current Pharmaceutical Design
  doi: 10.2174/13816128113199990344
– volume: 55
  start-page: 2005
  issue: 6
  year: 2012
  end-page: 2023
  ident: CR12
  article-title: The diagnosis and management of non-alcoholic fatty liver disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association
  publication-title: Hepatology
  doi: 10.1002/hep.25762
– volume: 6
  start-page: 33
  year: 2006
  ident: CR6
  article-title: The Fatty Liver Index: A simple and accurate predictor of hepatic steatosis in the general population
  publication-title: BMC Gastroenterology
  doi: 10.1186/1471-230X-6-33
– year: 2019
  ident: CR44
  article-title: Metabolomic salivary signature of pediatric obesity related liver disease and metabolic syndrome
  publication-title: Nutrients
  doi: 10.3390/nu11020274
– volume: 10
  start-page: 1342
  issue: 12
  year: 2012
  end-page: 1359.e2
  ident: CR50
  article-title: Association between nonalcoholic fatty liver disease and risk for hepatocellular cancer, based on systematic review
  publication-title: Clinical Gastroenterology and Hepatology: The Official Clinical Practice Journal of the American Gastroenterological Association
  doi: 10.1016/j.cgh.2012.10.001
– volume: 47
  start-page: 603
  issue: 3
  year: 2015
  end-page: 615
  ident: CR24
  article-title: Branched chain amino acid metabolism profiles in progressive human nonalcoholic fatty liver disease
  publication-title: Amino Acids
  doi: 10.1007/s00726-014-1894-9
– volume: 15
  start-page: 11
  issue: 1
  year: 2018
  end-page: 20
  ident: CR53
  article-title: Global burden of NAFLD and NASH: Trends, predictions, risk factors and prevention
  publication-title: Nature Reviews. Gastroenterology & Hepatology
  doi: 10.1038/nrgastro.2017.109
– volume: 42
  start-page: 1447
  issue: 5
  year: 2014
  end-page: 1452
  ident: CR11
  article-title: Deciphering non-alcoholic fatty liver disease through metabolomics
  publication-title: Biochemical Society Transactions
  doi: 10.1042/BST20140138
– volume: 36
  start-page: 363
  issue: 2
  year: 2004
  end-page: 375
  ident: CR19
  article-title: Physiological roles of xanthine oxidoreductase
  publication-title: Drug Metabolism Reviews
  doi: 10.1081/DMR-120037569
– volume: 310
  start-page: 2191
  issue: 20
  year: 2013
  end-page: 2194
  ident: CR51
  article-title: World medical association declaration of helsinki: Ethical principles for medical research involving human subjects
  publication-title: JAMA
  doi: 10.1001/jama.2013.281053
– volume: 10
  start-page: 357
  issue: 5
  year: 2018
  end-page: 364
  ident: CR3
  article-title: High dietary intake of branched-chain amino acids is associated with an increased risk of insulin resistance in adults
  publication-title: Journal of Diabetes
  doi: 10.1111/1753-0407.12639
– volume: 141
  start-page: 521
  year: 2019
  end-page: 529
  ident: CR16
  article-title: Interactions between gut microbiota and non-alcoholic liver disease: The role of microbiota-derived metabolites
  publication-title: Pharmacological Research
  doi: 10.1016/j.phrs.2019.01.029
– volume: 45
  start-page: 846
  issue: 4
  year: 2007
  end-page: 854
  ident: CR2
  article-title: The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD
  publication-title: Hepatology
  doi: 10.1002/hep.21496
– volume: 9
  start-page: 126
  issue: 3
  year: 2014
  end-page: 133
  ident: CR30
  article-title: Non alcoholic fatty liver: Epidemiology and natural history
  publication-title: Reviews on Recent Clinical Trials
  doi: 10.2174/1574887109666141216111143
– volume: 60
  start-page: 404
  issue: 3
  year: 2011
  end-page: 413
  ident: CR21
  article-title: Plasma metabolomic profile in non-alcoholic fatty liver disease
  publication-title: Metabolism, Clinical and Experimental
  doi: 10.1016/j.metabol.2010.03.006
– volume: 346
  start-page: 1221
  issue: 16
  year: 2002
  end-page: 1231
  ident: CR1
  article-title: Nonalcoholic fatty liver disease
  publication-title: The New England Journal of Medicine
  doi: 10.1056/NEJMra011775
– volume: 13
  start-page: e0193138
  issue: 2
  year: 2018
  ident: CR38
  article-title: Family history and obesity in youth, their effect on acylcarnitine/aminoacids metabolomics and non-alcoholic fatty liver disease (NAFLD). Structural equation modeling approach
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0193138
– volume: 68
  start-page: 519
  issue: 3
  year: 2018
  end-page: 525
  ident: CR42
  article-title: Is cryptogenic cirrhosis different from NASH cirrhosis?
  publication-title: Journal of Hepatology
  doi: 10.1016/j.jhep.2017.11.018
– year: 2014
  ident: CR39
  article-title: Serum oxidative stress markers and lipidomic profile to detect NASH patients responsive to an antioxidant treatment: A pilot study
  publication-title: Oxidative Medicine and Cellular Longevity
  doi: 10.1155/2014/169216
– volume: 2
  start-page: 807
  issue: 7
  year: 2018
  end-page: 820
  ident: CR32
  article-title: Metabolomic-based noninvasive serum test to diagnose nonalcoholic steatohepatitis: Results from discovery and validation cohorts
  publication-title: Hepatology Communications
  doi: 10.1002/hep4.1188
– volume: 3
  start-page: 211
  issue: 3
  year: 2007
  end-page: 221
  ident: CR41
  article-title: Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)
  publication-title: Metabolomics: Official Journal of the Metabolomic Society
  doi: 10.1007/s11306-007-0082-2
– volume: 14
  start-page: 77
  issue: 6
  year: 2018
  ident: CR45
  article-title: A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies
  publication-title: Metabolomics
  doi: 10.1007/s11306-018-1370-8
– volume: 28
  start-page: 373
  issue: 3
  year: 2012
  end-page: 380
  ident: CR22
  article-title: Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btr661
– volume: 5
  start-page: 1
  issue: 1
  year: 2019
  end-page: 10
  ident: CR4
  article-title: Small intestinal bacterial overgrowth and nonalcoholic fatty liver disease
  publication-title: Clinical and Experimental Hepatology
  doi: 10.5114/ceh.2019.83151
– volume: 21
  start-page: 7529
  issue: 24
  year: 2015
  end-page: 7544
  ident: CR10
  article-title: Liver fat deposition and mitochondrial dysfunction in morbid obesity: An approach combining metabolomics with liver imaging and histology
  publication-title: World Journal of Gastroenterology
  doi: 10.3748/wjg.v21.i24.7529
– volume: 116
  start-page: 1413
  issue: 6
  year: 1999
  end-page: 1419
  ident: CR31
  article-title: Nonalcoholic fatty liver disease: A spectrum of clinical and pathological severity
  publication-title: Gastroenterology
  doi: 10.1016/S0016-5085(99)70506-8
– volume: 17
  start-page: 804
  issue: 2
  year: 2018
  end-page: 812
  ident: CR47
  article-title: Metabolomic signature of endometrial cancer
  publication-title: Journal of Proteome Research
  doi: 10.1021/acs.jproteome.7b00503
– volume: 20
  start-page: 1702
  issue: 7
  year: 2018
  end-page: 1709
  ident: CR7
  article-title: Use of a metabolomic approach to non-invasively diagnose non-alcoholic fatty liver disease in patients with type 2 diabetes mellitus
  publication-title: Diabetes, Obesity & Metabolism
  doi: 10.1111/dom.13285
– year: 1998
  ident: CR33
  publication-title: An introduction to genetic algorithms
  doi: 10.7551/mitpress/3927.001.0001
– volume: 44
  start-page: 837
  issue: 3
  year: 1988
  end-page: 845
  ident: CR15
  article-title: Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach
  publication-title: Biometrics
  doi: 10.2307/2531595
– volume: 7
  start-page: 195
  issue: 4
  year: 2010
  end-page: 203
  ident: CR8
  article-title: Pathology of nonalcoholic fatty liver disease
  publication-title: Nature reviews. Gastroenterology & hepatology
  doi: 10.1038/nrgastro.2010.21
– volume: 38
  start-page: 485
  issue: 7
  year: 2006
  end-page: 489
  ident: CR36
  article-title: The role of bright liver echo pattern on ultrasound B-mode examination in the diagnosis of liver steatosis
  publication-title: Digestive and Liver Disease
  doi: 10.1016/j.dld.2006.03.021
– volume: 9
  start-page: 1124
  issue: 10
  year: 2017
  ident: CR28
  article-title: Gut microbiota and nonalcoholic fatty liver disease: Insights on mechanisms and therapy
  publication-title: Nutrients
  doi: 10.3390/nu9101124
– volume: 67
  start-page: 145
  issue: 1
  year: 2018
  end-page: 158
  ident: CR17
  article-title: Altered amino acid concentrations in NAFLD: Impact of obesity and insulin resistance
  publication-title: Hepatology
  doi: 10.1002/hep.29465
– volume: 41
  start-page: 1313
  issue: 6
  year: 2005
  end-page: 1321
  ident: CR23
  article-title: Design and validation of a histological scoring system for nonalcoholic fatty liver disease
  publication-title: Hepatology
  doi: 10.1002/hep.20701
– volume: 3
  start-page: 144
  year: 2014
  ident: CR35
  article-title: KEGGscape: A Cytoscape app for pathway data integration
  publication-title: F1000Research
  doi: 10.12688/f1000research.4524.1
– volume: 94
  start-page: 2725
  issue: 8
  year: 2009
  end-page: 2727
  ident: CR20
  article-title: Fatty acids, insulin resistance, and protein metabolism
  publication-title: The Journal of Clinical Endocrinology and Metabolism
  doi: 10.1210/jc.2009-1235
– year: 2016
  ident: CR54
  article-title: The relationship between branched-chain amino acid related metabolomic signature and insulin resistance: A systematic review
  publication-title: Journal of Diabetes Research
  doi: 10.1155/2016/2794591
– volume: 10
  start-page: 357
  issue: 5
  year: 2018
  ident: 1756_CR3
  publication-title: Journal of Diabetes
  doi: 10.1111/1753-0407.12639
– year: 2019
  ident: 1756_CR44
  publication-title: Nutrients
  doi: 10.3390/nu11020274
– volume: 31
  start-page: 128
  issue: 1
  year: 2008
  ident: 1756_CR18
  publication-title: Diabetes Care
  doi: 10.2337/dc07-1268
– volume: 41
  start-page: 1313
  issue: 6
  year: 2005
  ident: 1756_CR23
  publication-title: Hepatology
  doi: 10.1002/hep.20701
– volume: 13
  start-page: 412
  issue: 7
  year: 2016
  ident: 1756_CR25
  publication-title: Nature Reviews. Gastroenterology & Hepatology
  doi: 10.1038/nrgastro.2016.85
– volume: 6
  start-page: 33
  year: 2006
  ident: 1756_CR6
  publication-title: BMC Gastroenterology
  doi: 10.1186/1471-230X-6-33
– volume: 19
  start-page: 5250
  issue: 29
  year: 2013
  ident: 1756_CR29
  publication-title: Current Pharmaceutical Design
  doi: 10.2174/13816128113199990344
– volume: 17
  start-page: 804
  issue: 2
  year: 2018
  ident: 1756_CR47
  publication-title: Journal of Proteome Research
  doi: 10.1021/acs.jproteome.7b00503
– volume: 94
  start-page: 2467
  issue: 9
  year: 1999
  ident: 1756_CR9
  publication-title: The American Journal of Gastroenterology
  doi: 10.1111/j.1572-0241.1999.01377.x
– volume: 14
  start-page: 77
  issue: 6
  year: 2018
  ident: 1756_CR45
  publication-title: Metabolomics
  doi: 10.1007/s11306-018-1370-8
– volume: 10
  start-page: 1342
  issue: 12
  year: 2012
  ident: 1756_CR50
  publication-title: Clinical Gastroenterology and Hepatology: The Official Clinical Practice Journal of the American Gastroenterological Association
  doi: 10.1016/j.cgh.2012.10.001
– year: 2014
  ident: 1756_CR39
  publication-title: Oxidative Medicine and Cellular Longevity
  doi: 10.1155/2014/169216
– volume: 13
  start-page: e0193138
  issue: 2
  year: 2018
  ident: 1756_CR38
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0193138
– volume: 62
  start-page: 1412
  issue: 6
  year: 2015
  ident: 1756_CR52
  publication-title: Journal of Hepatology
  doi: 10.1016/j.jhep.2015.01.019
– volume: 3
  start-page: 211
  issue: 3
  year: 2007
  ident: 1756_CR41
  publication-title: Metabolomics: Official Journal of the Metabolomic Society
  doi: 10.1007/s11306-007-0082-2
– volume: 16
  start-page: 1301
  issue: 12
  year: 2015
  ident: 1756_CR27
  publication-title: Current Drug Targets
  doi: 10.2174/1389450116666150427155342
– year: 2015
  ident: 1756_CR26
  publication-title: RSC Advances
  doi: 10.1039/C5RA13417J
– volume: 36
  start-page: 363
  issue: 2
  year: 2004
  ident: 1756_CR19
  publication-title: Drug Metabolism Reviews
  doi: 10.1081/DMR-120037569
– volume: 47
  start-page: 603
  issue: 3
  year: 2015
  ident: 1756_CR24
  publication-title: Amino Acids
  doi: 10.1007/s00726-014-1894-9
– volume: 303
  start-page: 1
  year: 2019
  ident: 1756_CR14
  publication-title: Chemico-Biological Interactions
  doi: 10.1016/j.cbi.2019.02.015
– volume: 21
  start-page: 7529
  issue: 24
  year: 2015
  ident: 1756_CR10
  publication-title: World Journal of Gastroenterology
  doi: 10.3748/wjg.v21.i24.7529
– volume-title: An introduction to genetic algorithms
  year: 1998
  ident: 1756_CR33
  doi: 10.7551/mitpress/3927.001.0001
– volume: 28
  start-page: 373
  issue: 3
  year: 2012
  ident: 1756_CR22
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btr661
– volume: 68
  start-page: 519
  issue: 3
  year: 2018
  ident: 1756_CR42
  publication-title: Journal of Hepatology
  doi: 10.1016/j.jhep.2017.11.018
– volume: 13
  start-page: 263
  issue: 4
  year: 2012
  ident: 1756_CR37
  publication-title: Nature Reviews Molecular Cell Biology
  doi: 10.1038/nrm3314
– volume: 23
  start-page: 425
  issue: 4
  year: 2014
  ident: 1756_CR49
  publication-title: Journal of Gastrointestinal & Liver Diseases
  doi: 10.15403/jgld.2014.1121.234.bna
– year: 2016
  ident: 1756_CR54
  publication-title: Journal of Diabetes Research
  doi: 10.1155/2016/2794591
– volume: 7
  start-page: 195
  issue: 4
  year: 2010
  ident: 1756_CR8
  publication-title: Nature reviews. Gastroenterology & hepatology
  doi: 10.1038/nrgastro.2010.21
– volume: 5
  start-page: 1
  issue: 1
  year: 2019
  ident: 1756_CR4
  publication-title: Clinical and Experimental Hepatology
  doi: 10.5114/ceh.2019.83151
– volume: 9
  start-page: 126
  issue: 3
  year: 2014
  ident: 1756_CR30
  publication-title: Reviews on Recent Clinical Trials
  doi: 10.2174/1574887109666141216111143
– volume: 15
  start-page: 11
  issue: 1
  year: 2018
  ident: 1756_CR53
  publication-title: Nature Reviews. Gastroenterology & Hepatology
  doi: 10.1038/nrgastro.2017.109
– volume: 38
  start-page: 485
  issue: 7
  year: 2006
  ident: 1756_CR36
  publication-title: Digestive and Liver Disease
  doi: 10.1016/j.dld.2006.03.021
– volume: 10
  start-page: e0127299
  issue: 6
  year: 2015
  ident: 1756_CR34
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0127299
– volume: 7
  start-page: 11433
  year: 2017
  ident: 1756_CR13
  publication-title: Scientific Reports
  doi: 10.1038/s41598-017-11759-z
– volume: 67
  start-page: 145
  issue: 1
  year: 2018
  ident: 1756_CR17
  publication-title: Hepatology
  doi: 10.1002/hep.29465
– volume: 27
  start-page: 51
  issue: 1
  year: 2018
  ident: 1756_CR40
  publication-title: Journal of Gastrointestinal and Liver Diseases
  doi: 10.15403/jgld.2014.1121.271.ald
– year: 2017
  ident: 1756_CR46
  publication-title: Nutrients
  doi: 10.3390/nu9050485
– volume: 42
  start-page: 1447
  issue: 5
  year: 2014
  ident: 1756_CR11
  publication-title: Biochemical Society Transactions
  doi: 10.1042/BST20140138
– volume: 3
  start-page: 144
  year: 2014
  ident: 1756_CR35
  publication-title: F1000Research
  doi: 10.12688/f1000research.4524.1
– volume: 20
  start-page: 1702
  issue: 7
  year: 2018
  ident: 1756_CR7
  publication-title: Diabetes, Obesity & Metabolism
  doi: 10.1111/dom.13285
– volume: 94
  start-page: 2725
  issue: 8
  year: 2009
  ident: 1756_CR20
  publication-title: The Journal of Clinical Endocrinology and Metabolism
  doi: 10.1210/jc.2009-1235
– volume: 53
  start-page: 2142
  issue: 6
  year: 2011
  ident: 1756_CR5
  publication-title: Hepatology
  doi: 10.1002/hep.24322
– volume: 2
  start-page: 807
  issue: 7
  year: 2018
  ident: 1756_CR32
  publication-title: Hepatology Communications
  doi: 10.1002/hep4.1188
– volume: 346
  start-page: 1221
  issue: 16
  year: 2002
  ident: 1756_CR1
  publication-title: The New England Journal of Medicine
  doi: 10.1056/NEJMra011775
– volume: 9
  start-page: 1124
  issue: 10
  year: 2017
  ident: 1756_CR28
  publication-title: Nutrients
  doi: 10.3390/nu9101124
– volume: 116
  start-page: 1413
  issue: 6
  year: 1999
  ident: 1756_CR31
  publication-title: Gastroenterology
  doi: 10.1016/S0016-5085(99)70506-8
– volume: 60
  start-page: 404
  issue: 3
  year: 2011
  ident: 1756_CR21
  publication-title: Metabolism, Clinical and Experimental
  doi: 10.1016/j.metabol.2010.03.006
– volume: 310
  start-page: 2191
  issue: 20
  year: 2013
  ident: 1756_CR51
  publication-title: JAMA
  doi: 10.1001/jama.2013.281053
– volume: 55
  start-page: 2005
  issue: 6
  year: 2012
  ident: 1756_CR12
  publication-title: Hepatology
  doi: 10.1002/hep.25762
– volume: 44
  start-page: 837
  issue: 3
  year: 1988
  ident: 1756_CR15
  publication-title: Biometrics
  doi: 10.2307/2531595
– volume: 141
  start-page: 521
  year: 2019
  ident: 1756_CR16
  publication-title: Pharmacological Research
  doi: 10.1016/j.phrs.2019.01.029
– volume: 45
  start-page: 846
  issue: 4
  year: 2007
  ident: 1756_CR2
  publication-title: Hepatology
  doi: 10.1002/hep.21496
– volume: 72
  start-page: 1196
  issue: 6
  year: 2020
  ident: 1756_CR43
  publication-title: Journal of Hepatology
  doi: 10.1016/j.jhep.2020.02.020
– volume: 13
  start-page: 140
  issue: 11
  year: 2017
  ident: 1756_CR48
  publication-title: Metabolomics
  doi: 10.1007/s11306-017-1274-z
SSID ssj0044970
Score 2.4876077
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...
SourceID proquest
pubmed
crossref
springer
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 12
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
SummonAdditionalLinks – databaseName: SpringerLINK - Czech Republic Consortium
  dbid: AGYKE
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB61y6WXFgptw0tGqnopQUnsOFluK2BBQDl1JXqKbMcRK5YENtkD_Hpm8thVeUlIOUTKeOLY45lx_M0MwE8js1R6EQqvDJUrdCBdtIq-GweZNkpoxWOKRv5zIU9G4vQyvGyDwsoO7d4dSdaaehHshuqWALMEpIooe95HWAppg9KDpcHxv7OjTgML0a-LxPlhxF2OLmQbLPMyl_8N0jMv89kJaW14hl9g1HW5wZtc780qvWcenmRzfO83LcPn1hNlg0Z0VuCDzb_C6iDHXfjNPfvFamxo_dN9Fe5GeQMZtym7sRUKzoSimUum8GJpA9dDPqwqigkb5-xiMDw_3GcU89vUDaP5Z0XGSKiqohyXu-3tlSVMdzVGRnnKzHg6vaLHazAaHv09OHHbYg2uQY-qcpVOlZeiP2Sk8X0VxFLouG888ofQCArPWJx4Exgqb2Iz7cvUZJxnlvepZBbn36CXF7n9AUyiybQqtqEKtBBZpEXMUQ8GnkGxwrYO-N2MJabNZE4FNSbJIgczjWuC45rU45r4Dvyet7lt8ni8Sb3ZCULSrukyCUSEu_koDkMHduaPcTXSEYvKbTFraJAE_UoHvjcCNH8d5yKMUf05sNsJw4L5631Zfx_5BnwKCHZTA8s3oVdNZ3YL_aZKb7fL5BHnsgvx
  priority: 102
  providerName: Springer Nature
Title Untargeted metabolomics as a diagnostic tool in NAFLD: discrimination of steatosis, steatohepatitis and cirrhosis
URI https://link.springer.com/article/10.1007/s11306-020-01756-1
https://www.ncbi.nlm.nih.gov/pubmed/33458794
https://www.proquest.com/docview/2478377855
https://www.proquest.com/docview/2478778250
Volume 17
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVEBS
  databaseName: EBSCOhost Academic Search Ultimate
  customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn
  eissn: 1573-3890
  dateEnd: 20241003
  omitProxy: true
  ssIdentifier: ssj0044970
  issn: 1573-3882
  databaseCode: ABDBF
  dateStart: 20100301
  isFulltext: true
  titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn
  providerName: EBSCOhost
– providerCode: PRVLSH
  databaseName: SpringerLink Journals
  customDbUrl:
  mediaType: online
  eissn: 1573-3890
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0044970
  issn: 1573-3882
  databaseCode: AFBBN
  dateStart: 20050301
  isFulltext: true
  providerName: Library Specific Holdings
– providerCode: PRVAVX
  databaseName: SpringerLINK - Czech Republic Consortium
  customDbUrl:
  eissn: 1573-3890
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0044970
  issn: 1573-3882
  databaseCode: AGYKE
  dateStart: 20050101
  isFulltext: true
  titleUrlDefault: http://link.springer.com
  providerName: Springer Nature
– providerCode: PRVAVX
  databaseName: SpringerLink Journals (ICM)
  customDbUrl:
  eissn: 1573-3890
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0044970
  issn: 1573-3882
  databaseCode: U2A
  dateStart: 20050302
  isFulltext: true
  titleUrlDefault: http://www.springerlink.com/journals/
  providerName: Springer Nature
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fa9swED629mUvY133w11XNBh7Wc1sSZadvgxvTVb2I4yxQPZkJFmmgdRuY_dh_33vbCWhlBYCDrGsGH_nu0_SdzqA91ZVpYpSNF6V6FAarkKMinGY8cpYLY0WGWUj_5qqs5n8Pk_mfsKt9bLKtU_sHXXZWJoj_8RlimOpNEuSz5dXIVWNotVVX0LjMezGSFXIqtP5ZsAl5agvFhcnqQgFUkmfNDOkzqHzJvktybJS2ovvdmC6wzbvrJT2AWjyDJ565sjyAeo9eOTq57Cf1zhqvvjPPrBey9lPku_D1aweJN6uZBeuQ6CXlH3cMo0fVg7yOuyHdU2zZIuaTfPJz9MTRjm6Q50vwos1FSMj6Jp20R77r-eONNjdAjuqS2YXq9U5nX4Bs8n479ez0BdXCC0yoC7UptRRifzFKhvHmmdKmmxkI-IvGLRkZB0CZbmlciSuMrEqbSVE5cSISlwJ8RJ26qZ2r4EpDHFOZy7R3EhZpUZmAv0WjyyaAV4bQLx-soX1O49TAYxlsd0zmdAoEI2iR6OIA_i4ueZy2HfjwdaHa8AK_w62xdZiAni3OY1vDy2J6No110MbbII8MIBXA9CbvxNCJhm6qwCO18hvO7__Xg4evpc38ISTLKYXfh_CTre6dm-R13TmqDfeI9jNv_37Mcbjl_H09x_8dcbzG2oU9lg
linkProvider ProQuest
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9wwEB5CemgvpU36cJumKrS9NKa2JD-2EMLSdNk0mz1lYW-uJMtkYWMna4eSP9Xf2BnL3qWE5hbwwWBZFp5PMyPpmxmAjyYu8jhIELxxpHypeeyjVQz9lBfaKKmVSCka-Wwaj2fy5zyab8GfPhaGaJW9TmwVdV4Z2iP_ymWCa6kkjaKjq2ufqkbR6WpfQsPB4tTe_sYlW314cozy_cT56Mf597HfVRXwDZr-xlc6V0GOhtvEJgwVT2Op04EJyHCjtpaBsThCww3V4bCFDuPcFEIUVgyothNtgKLKfyRFIClXfzJfL_CkHLTF6cIoEb5A17UL0nGhemgsiO5LNLCEcv_9awjveLd3TmZbgzd6Bk87T5UNHbSew5Ytd2B3WOIq_fKWfWYtd7TdlN-F61npKOU2Z5e2QWAtKdq5Zgovljs6H_bDmqpaskXJpsPR5Pgbo5hgV1eM8MGqghHomqpe1Afd7YUlznezwI7KnJnFanVBj1_A7EF--0vYLqvSvgYWo0m1KrWR4lrKItEyFagneWAQdviuB2H_ZzPTZTqnghvLbJOjmaSRoTSyVhpZ6MGX9TtXLs_Hva33eoFl3Zyvsw1CPfiwfoyzlY5gVGmrG9cGm6Df6cErJ-j154SQUYrq0YODXvKbzv8_ljf3j-U9PB6fn02yycn09C084UTJaUnne7DdrG7sO_SpGr3fApnBr4eeOX8BX6YuzA
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9wwEB5CAqWX0jZ9uE0bFdpeGhNbkmVvoJSl2yVp0qWHLuzNlWSZLGzsZO0Q8tf66zLjxy4hNLeADwZLstB8mhlJ32gAPlqVZyqIEbwq0r40XPloFUM_4bmxWhotEopG_jVRh1P5cxbNNuBfHwtDtMpeJzaKOist7ZHvcxnjWipOomg_72gRv0fjb-cXPmWQopPWPp1GC5Fjd32Fy7fq69EIZf2J8_GPP98P_S7DgG_RDah9bTIdZGjErbJhqHmipEkGNiAjjppbBtZhby23lJPD5SZUmc2FyJ0YUJ4n2gxF9b8VCymIThbPVos9KQdNorowioUv0I3tAnbasD00HET9JUpYTPcA3jaKdzzdO6e0jfEbP4UnndfKhi3MnsGGK57D9rDAFfvZNfvMGh5ps0G_DRfToqWXu4yduRpBtqDI54ppfFjWUvuwHVaX5YLNCzYZjk9GB4zig9scY4QVVuaMAFiX1bza615PHfG_6zk2VGTMzpfLU_r8AqYPMuwvYbMoC_camELz6nTiIs2NlHlsZCJQZ_LAIgSxrgdhP7Kp7W49p-Qbi3R9XzNJI0VppI000tCDL6s65-2dH_eW3ukFlnbzv0rXaPXgw-ozzlw6jtGFKy_bMlgEfVAPXrWCXv1OCBklqCo92Oslv278_315c39fduERzpn05Ghy_BYec2LnNPzzHdisl5fuHbpXtXnf4JjB34eeODevyjMH
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Untargeted+metabolomics+as+a+diagnostic+tool+in+NAFLD%3A+discrimination+of+steatosis%2C+steatohepatitis+and+cirrhosis&rft.jtitle=Metabolomics&rft.au=Masarone%2C+Mario&rft.au=Troisi%2C+Jacopo&rft.au=Aglitti%2C+Andrea&rft.au=Torre%2C+Pietro&rft.date=2021-02-01&rft.eissn=1573-3890&rft.volume=17&rft.issue=2&rft.spage=12&rft_id=info:doi/10.1007%2Fs11306-020-01756-1&rft_id=info%3Apmid%2F33458794&rft.externalDocID=33458794
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1573-3882&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1573-3882&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1573-3882&client=summon