Abstract
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.
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Acknowledgements
We thank all participants of the PopGen control cohort study for their invaluable contribution to the study. This work was supported by grants from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) Excellence Cluster ‘Inflammation at Interfaces’ (EXC306, EXC306/2), sysINFLAME (01ZX1306A) and through grants from the German Federal Ministry of Education and Research (01GR0468). The PopGen 2.0 network is supported by a grant from the German Ministry for Education and Research (01EY1103). Manja Koch is recipient of a postdoctoral research fellowship from the DFG (KO 5187/1-1).
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Koch, M., Freitag-Wolf, S., Schlesinger, S. et al. Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample. Eur J Clin Nutr 71, 995–1001 (2017). https://doi.org/10.1038/ejcn.2017.43
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DOI: https://doi.org/10.1038/ejcn.2017.43
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