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Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample.
Eur. J. Clin. Nutr. 71, 995–1001 (2017)
BACKGROUND/OBJECTIVES: Fatty liver disease (FLD) is an important intermediate trait along the cardiometabolic disease spectrum and strongly associates with type 2 diabetes. Knowledge of biological pathways implicated in FLD is limited. An untargeted metabolomic approach might unravel novel pathways related to FLD. SUBJECTS/METHODS: In a population-based sample (n=555) from Northern Germany, liver fat content was quantified as liver signal intensity using magnetic resonance imaging. Serum metabolites were determined using a non-targeted approach. Partial least squares regression was applied to derive a metabolomic score, explaining variation in serum metabolites and liver signal intensity. Associations of the metabolomic score with liver signal intensity and FLD were investigated in multivariable-adjusted robust linear and logistic regression models, respectively. Metabolites with a variable importance in the projection >1 were entered in in silico overrepresentation and pathway analyses. RESULTS: In univariate analysis, the metabolomics score explained 23.9% variation in liver signal intensity. A 1-unit increment in the metabolomic score was positively associated with FLD (n=219; odds ratio: 1.36; 95% confidence interval: 1.27-1.45) adjusting for age, sex, education, smoking and physical activity. A simplified score based on the 15 metabolites with highest variable importance in the projection statistic showed similar associations. Overrepresentation and pathway analyses highlighted branched-chain amino acids and derived gamma-glutamyl dipeptides as significant correlates of FLD. CONCLUSIONS: A serum metabolomic profile was associated with FLD and liver fat content. We identified a simplified metabolomics score, which should be evaluated in prospective studies.European Journal of Clinical Nutrition advance online publication, 5 April 2017; doi:10.1038/ejcn.2017.43.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
3.057
0.000
11
16
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Hepatic Steatosis; Disease; Progression; Patterns; Metaboanalyst; Biomarkers; Systems; Risk; Tool
Sprache
englisch
Veröffentlichungsjahr
2017
HGF-Berichtsjahr
2017
ISSN (print) / ISBN
0954-3007
e-ISSN
1476-5640
Zeitschrift
European Journal of Clinical Nutrition
Quellenangaben
Band: 71,
Heft: 8,
Seiten: 995–1001
Verlag
Nature Publishing Group
Verlagsort
London
Begutachtungsstatus
Peer reviewed
Institut(e)
CF Metabolomics & Proteomics (CF-MPC)
Institute of Computational Biology (ICB)
Molekulare Endokrinologie und Metabolismus (MEM)
Institute of Bioinformatics and Systems Biology (IBIS)
Institute of Computational Biology (ICB)
Molekulare Endokrinologie und Metabolismus (MEM)
Institute of Bioinformatics and Systems Biology (IBIS)
POF Topic(s)
30505 - New Technologies for Biomedical Discoveries
30205 - Bioengineering and Digital Health
30201 - Metabolic Health
30205 - Bioengineering and Digital Health
30201 - Metabolic Health
Forschungsfeld(er)
Enabling and Novel Technologies
Genetics and Epidemiology
Genetics and Epidemiology
PSP-Element(e)
A-630710-001
G-503891-001
G-505600-003
G-503700-001
G-503891-001
G-505600-003
G-503700-001
WOS ID
WOS:000406963700011
Scopus ID
85017138983
PubMed ID
28378853
Erfassungsdatum
2017-06-08