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Epidemiology

Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample

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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References

  1. Szczepaniak LS, Nurenberg P, Leonard D, Browning JD, Reingold JS, Grundy S et al. Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. Am J Physiol Endocrinol Metab 2005; 288: e462–e468.

    Article  CAS  Google Scholar 

  2. Markus MR, Baumeister SE, Stritzke J, Dorr M, Wallaschofski H, Volzke H et al. Hepatic steatosis is associated with aortic valve sclerosis in the general population: the Study of Health in Pomerania (SHIP). Arterioscler Thromb Vasc Biol 2013; 33: 1690–1695.

    Article  CAS  Google Scholar 

  3. Portillo Sanchez P, Bril F, Maximos M, Lomonaco R, Biernacki D, Orsak B et al. High prevalence of nonalcoholic fatty liver disease in patients with type 2 diabetes mellitus and normal plasma aminotransferase levels. J Clin Endocrinol Metab 2015; 100: 2231–2238.

    Article  CAS  Google Scholar 

  4. Anstee QM, Targher G, Day CP . Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat Rev Gastroenterol Hepatol 2013; 10: 330–344.

    Article  CAS  Google Scholar 

  5. Dunn W, Xu R, Wingard DL, Rogers C, Angulo P, Younossi ZM et al. Suspected nonalcoholic fatty liver disease and mortality risk in a population-based cohort study. Am J Gastroenterol 2008; 103: 2263–2271.

    Article  Google Scholar 

  6. Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV et al. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology 2010; 51: 1979–1987.

    Article  CAS  Google Scholar 

  7. Cohen JC, Horton JD, Hobbs HH . Human fatty liver disease: old questions and new insights. Science 2011; 332: 1519–1523.

    Article  CAS  Google Scholar 

  8. Dumas ME, Kinross J, Nicholson JK . Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease. Gastroenterology 2014; 146: 46–62.

    Article  Google Scholar 

  9. Kalhan SC, Guo L, Edmison J, Dasarathy S, McCullough AJ, Hanson RW et al. Plasma metabolomic profile in nonalcoholic fatty liver disease. Metabolism 2011; 60: 404–413.

    Article  CAS  Google Scholar 

  10. Barr J, Caballeria J, Martinez-Arranz I, Dominguez-Diez A, Alonso C, Muntane J et al. Obesity-dependent metabolic signatures associated with nonalcoholic fatty liver disease progression. J Proteome Res 2012; 11: 2521–2532.

    Article  CAS  Google Scholar 

  11. Rodriguez-Gallego E, Guirro M, Riera-Borrull M, Hernandez-Aguilera A, Marine-Casado R, Fernandez-Arroyo S et al. Mapping of the circulating metabolome reveals alpha-ketoglutarate as a predictor of morbid obesity-associated non-alcoholic fatty liver disease. Int J Obes 2014; 39: 279–287.

    Article  Google Scholar 

  12. Soga T, Sugimoto M, Honma M, Mori M, Igarashi K, Kashikura K et al. Serum metabolomics reveals gamma-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J Hepatol 2011; 55: 896–905.

    Article  CAS  Google Scholar 

  13. Nöthlings U, Krawczak M . [PopGen: A population-based biobank with prospective follow-up of a control group]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2012; 55: 831–835.

    Article  Google Scholar 

  14. Koch M, Borggrefe J, Barbaresko J, Groth G, Jacobs G, Siegert S et al. Dietary patterns associated with magnetic resonance imaging-determined liver fat content in a general population study. Am J Clin Nutr 2014; 99: 369–377.

    Article  CAS  Google Scholar 

  15. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C et al2011. Compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc 2011 43: 1575–1581.

    Article  Google Scholar 

  16. International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 2009; 32: 1327–1334.

    Article  Google Scholar 

  17. Nöthlings U, Hoffmann K, Bergmann MM, Boeing H . Fitting portion sizes in a self-administered food frequency questionnaire. J Nutr 2007; 137: 2781–2786.

    Article  Google Scholar 

  18. Dehne LI, Klemm C, Henseler G, Hermann-Kunz E . The german food code and nutrient data base (BLS II.2). Eur J Epidemiol 1999; 15: 355–359.

    Article  CAS  Google Scholar 

  19. Stefan N, Kantartzis K, Haring HU . Causes and metabolic consequences of fatty liver. Endocr Rev 2008; 29: 939–960.

    Article  CAS  Google Scholar 

  20. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E . Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem 2009; 81: 6656–6667.

    Article  CAS  Google Scholar 

  21. Hoffmann K, Schulze MB, Schienkiewitz A, Nothlings U, Boeing H . Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004; 159: 935–944.

    Article  Google Scholar 

  22. Kotronen A, Yki-Jarvinen H . Fatty liver: a novel component of the metabolic syndrome. Arterioscler Thromb Vasc Biol 2008; 28: 27–38.

    Article  CAS  Google Scholar 

  23. Xia J, Wishart DS . MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 2010; 38: W71–W77.

    Article  CAS  Google Scholar 

  24. Xia J, Wishart DS . MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 2010; 26: 2342–2344.

    Article  CAS  Google Scholar 

  25. Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS . MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis. Nucleic Acids Res 2012; 40: W127–W133.

    Article  CAS  Google Scholar 

  26. Mehmood T, Martens H, Saebo S, Warringer J, Snipen L . A Partial Least Squares based algorithm for parsimonious variable selection. Algorithms Mol Biol 2011; 6: 27.

    Article  Google Scholar 

  27. Xia J, Wishart DS . Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protoc 2011; 6: 743–760.

    Article  CAS  Google Scholar 

  28. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N et al. HMDB: the human metabolome database. Nucleic Acids Res 2007; 35: D521–D526.

    Article  CAS  Google Scholar 

  29. Barr J, Vazquez-Chantada M, Alonso C, Perez-Cormenzana M, Mayo R, Galan A et al. Liquid chromatography-mass spectrometry-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of nonalcoholic fatty liver disease. J Proteome Res 2010; 9: 4501–4512.

    Article  CAS  Google Scholar 

  30. Kaikkonen JE, Wurtz P, Suomela E, Lehtovirta M, Kangas AJ, Jula A et al. Metabolic profiling of fatty liver in young and middle-aged adults: Cross-sectional and prospective analyses of the Young Finns Study. Hepatology 2017; 65: 491–500.

    Article  CAS  Google Scholar 

  31. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 2012; 125: 2222–2231.

    Article  CAS  Google Scholar 

  32. Siegert S, Yu Z, Wang-Sattler R, Illig T, Adamski J, Hampe J et al. Diagnosing fatty liver disease: a comparative evaluation of metabolic markers, phenotypes, genotypes and established biomarkers. PLoS ONE 2013; 8: e76813.

    Article  CAS  Google Scholar 

  33. Rinella ME . Nonalcoholic fatty liver disease: a systematic review. JAMA 2015; 313: 2263–2273.

    Article  CAS  Google Scholar 

  34. Puri P, Wiest MM, Cheung O, Mirshahi F, Sargeant C, Min HK et al. The plasma lipidomic signature of nonalcoholic steatohepatitis. Hepatology 2009; 50: 1827–1838.

    Article  CAS  Google Scholar 

  35. Floegel A, Drogan D, Wang-Sattler R, Prehn C, Illig T, Adamski J et al. Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS ONE 2011; 6: e21103.

    Article  CAS  Google Scholar 

  36. Yousri NA, Kastenmuller G, Gieger C, Shin SY, Erte I, Menni C et al. Long term conservation of human metabolic phenotypes and link to heritability. Metabolomics 2014; 10: 1005–1017.

    Article  CAS  Google Scholar 

Download references

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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