Metabolite profiling reveals new insights into the regulation of serum urate in humans

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• 作者：Eva Albrecht (1)
  Melanie Waldenberger (2)
  Jan Krumsiek (3)
  Anne M. Evans (4)
  Ulli Jeratsch (3)
  Michaela Breier (2)
  Jerzy Adamski (17) (5) (6)
  Wolfgang Koenig (7)
  Sonja Zeilinger (2)
  Christiane Fuchs (3)
  Norman Klopp (2) (8)
  Fabian J. Theis (3)
  H.-Erich Wichmann (10) (11) (9)
  Karsten Suhre (12) (3)
  Thomas Illig (2) (8)
  Konstantin Strauch (13) (18)
  Annette Peters (14) (15) (2)
  Christian Gieger (18)
  Gabi Kastenmüller (3)
  Angela Doering (14) (9)
  Christa Meisinger (14) (16)
  
• 关键词：Gaussian Graphical Modeling ; Metabolite network ; Pathway reconstruction ; Allopurinol ; Uric acid ; Purine metabolism
• 刊名：Metabolomics
• 出版年：2014
• 出版时间：February 2014
• 年：2014
• 卷：10
• 期：1
• 页码：141-151
• 全文大小：437 KB
• 作者单位：Eva Albrecht (1)
  Melanie Waldenberger (2)
  Jan Krumsiek (3)
  Anne M. Evans (4)
  Ulli Jeratsch (3)
  Michaela Breier (2)
  Jerzy Adamski (17) (5) (6)
  Wolfgang Koenig (7)
  Sonja Zeilinger (2)
  Christiane Fuchs (3)
  Norman Klopp (2) (8)
  Fabian J. Theis (3)
  H.-Erich Wichmann (10) (11) (9)
  Karsten Suhre (12) (3)
  Thomas Illig (2) (8)
  Konstantin Strauch (13) (18)
  Annette Peters (14) (15) (2)
  Christian Gieger (18)
  Gabi Kastenmüller (3)
  Angela Doering (14) (9)
  Christa Meisinger (14) (16)
  
  1. Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolst?dter Landstr. 1, 85764, Neuherberg, Germany
  2. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
  3. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
  4. Metabolon, Inc., 617 Davis Drive, Suite 400, Durham, NC, 27713, USA
  17. Member of German Center for Diabetes Research (DZD), Neuherberg, Germany
  5. Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
  6. Lehrstuhl für Experimentelle Genetik, Technische Universit?t München, Munich, Germany
  7. Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Ulm, Germany
  8. Hanover Unified Biobank, Hanover Medical School, Hanover, Germany
  10. Institute of Medical Informatics, Biometry, and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universit?t, Munich, Germany
  11. Klinikum Grosshadern, Munich, Germany
  9. Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
  12. Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City-Qatar Foundation, Doha, Qatar
  13. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universit?t, Munich, Germany
  18. Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
  14. Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
  15. Munich Heart Alliance, Munich, Germany
  16. Central Hospital of Augsburg, Monitoring Trends and Determinants on Cardiovascular Diseases/Cooperative Research in the Region of Augsburg Myocardial Infarction Registry, Augsburg, Germany
  
• ISSN：1573-3890

文摘

Serum urate, the final breakdown product of purine metabolism, is causally involved in the pathogenesis of gout, and implicated in cardiovascular disease and type 2 diabetes. Serum urate levels highly differ between men and women; however the underlying biological processes in its regulation are still not completely understood and are assumed to result from a complex interplay between genetic, environmental and lifestyle factors. In order to describe the metabolic vicinity of serum urate, we analyzed 355 metabolites in 1,764 individuals of the population-based KORA F4 study and constructed a metabolite network around serum urate using Gaussian Graphical Modeling in a hypothesis-free approach. We subsequently investigated the effect of sex and urate lowering medication on all 38 metabolites assigned to the network. Within the resulting network three main clusters could be detected around urate, including the well-known pathway of purine metabolism, as well as several dipeptides, a group of essential amino acids, and a group of steroids. Of the 38 assigned metabolites, 25 showed strong differences between sexes. Association with uricostatic medication intake was not only confined to purine metabolism but seen for seven metabolites within the network. Our findings highlight pathways that are important in the regulation of serum urate and suggest that dipeptides, amino acids, and steroid hormones are playing a role in its regulation. The findings might have an impact on the development of specific targets in the treatment and prevention of hyperuricemia.