M Koch1,2, S Freitag-Wolf3, S Schlesinger1, J Borggrefe4, J R Hov5,6,7,8, M K Jensen2, J Pick9, M R P Markus10,11,12, T Höpfner1, G Jacobs1,13, S Siegert14, A Artati15, G Kastenmüller16,17, W Römisch-Margl16, J Adamski15,17,18, T Illig19,20, M Nothnagel14, T H Karlsen5,6,7,8, S Schreiber21,22, A Franke21, M Krawczak3, U Nöthlings9, W Lieb1,13. 1. Institute of Epidemiology, Kiel University, Kiel, Germany. 2. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 3. Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany. 4. Department of Radiology, University of Cologne, Cologne, MA, USA. 5. Division of Cancer Medicine, Department of Transplantation Medicine, Surgery and Transplantation, Norwegian PSC Research Center, Oslo University Hospital, Rikshospitalet, Oslo, Norway. 6. K.G. Jebsen Inflammation Research Center, Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 7. Division of Cancer Medicine, Surgery and Transplantation, Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway. 8. Division of Cancer Medicine, Section of Gastroenterology, Department of Transplantation Medicine, Surgery and Transplantation, Oslo University Hospital, Rikshospitalet, Oslo, Norway. 9. Nutritional Epidemiology, Department of Nutrition and Food Science, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, Germany. 10. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany. 11. DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany. 12. Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. 13. PopGen Biobank, University Medical Center Schleswig-Holstein, Kiel, Germany. 14. Cologne Center for Genomics, University of Cologne, Cologne, Germany. 15. Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany. 16. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. 17. Deutsches Zentrum für Diabetesforschung (DZD), German Centere for Diabetes Research, Neuherberg, Germany. 18. Experimental Genetics, Technical University of Munich, Freising, Germany. 19. Hannover Unified Biobank, Hannover Medical School, Hannover, Germany. 20. Institute for Human Genetics, Hannover Medical School, Hanover, Germany. 21. Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany. 22. Institute of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany.
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.
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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