gms | German Medical Science

MAINZ//2011: 56. GMDS-Jahrestagung und 6. DGEpi-Jahrestagung

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V.
Deutsche Gesellschaft für Epidemiologie e. V.

26. - 29.09.2011 in Mainz

Associations between fat-free mass and fat mass and the serum metabolite profile – results from KORA F4

Meeting Abstract

  • Carolin Jourdan - Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg
  • Ann-Kristin Petersen - Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg
  • Christian Gieger - Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg
  • Angela Döring - Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg
  • Thomas Illig - Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg
  • Annette Peters - Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg
  • Cornelia Prehn - Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg
  • Jerzy Adamski - Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg
  • Suhre Karsten - Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg
  • Andreas Mielck - Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg
  • H.-Erich Wichmann - Helmholtz Zentrum München, Neuherberg
  • Jakob Linseisen - Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg

Mainz//2011. 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi). Mainz, 26.-29.09.2011. Düsseldorf: German Medical Science GMS Publishing House; 2011. Doc11gmds385

doi: 10.3205/11gmds385, urn:nbn:de:0183-11gmds3851

Veröffentlicht: 20. September 2011

© 2011 Jourdan et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Objective: Body fat is not only a risk factor for various chronic diseases but also known as endocrine organ. Also the skeletal muscle, a major determinant of energy requirement, produces and releases myokines. However, more metabolic pathways might be affected than currently known. The present analyses aimed to characterise the influence of body compartments on the metabolite profile in serum samples of KORA F4 (Cooperative Health Research in the Region of Augsburg) participants.

Subjects and methods: Within KORA F4, the serum metabolome of 3061 subjects (2006-2008, aging 32-81 years) was quantified by means of targeted metabolomics assay with AbsoluteIDQ kit (BIOCRATES life science AG, Austria). The present analyses were based on a subsample (n=890) including only healthy weight-stable subjects. Associations between serum metabolite concentrations and the fat free mass index (FFMI) – defined as fat free mass in kg divided by height in m2 - as well as the body fat mass index (BFMI; kg/m2) were analysed using linear regression models.

Results: Several significant associations were found for FFMI as well as BFMI and different metabolites characterising the effects of the two body compartments on the metabolic profile in serum. The strongest associations were found for glycine, valine, leucine/isoleucine as well as octenoylcarnitine (C8:1) with FFMI (with p-values ranging from 1.45x10-05 to 3.38x10-02 after adjustment for multiple testing) and the lyso-phosphatidylcholine acyl esters C17:0, C18:1 and C18:2 with BFMI (with adjusted p-values ranging from 7.60x10-21 to 4.93x10-18). In order to understand the relationships among the metabolites, a partial correlation network was established.

Conclusion: We have identified serum metabolites which are significantly associated only with FFMI or only with BFMI as well as a network explaining the interrelationships between metabolites.