Romina di Giuseppe1, Manja Koch2, Ute Nöthlings3, Gabi Kastenmüller4,5, Anna Artati6, Jerzy Adamski5,6,7, Gunnar Jacobs8,9, Wolfgang Lieb8,9. 1. Institute of Epidemiology, Kiel University, Kiel, Germany. romina.digiuseppe@epi.uni-kiel.de. 2. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 3. Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany. 4. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. 5. Deutsches Zentrum für Diabetesforschung (DZD), Neuherberg, Germany. 6. Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany. 7. Experimental Genetics, Technical University of Munich, Freising, Germany. 8. Institute of Epidemiology, Kiel University, Kiel, Germany. 9. Biobank PopGen, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
Abstract
PURPOSE: Selenoprotein P (SELENOP) has been previously related to various metabolic traits with partially conflicting results. The identification of SELENOP-associated metabolites, using an untargeted metabolomics approach, may provide novel biological insights relevant to disentangle the role of SELENOP in human health. METHODS: In this cross-sectional study, 572 serum metabolites were identified by comparing the obtained LC-MS/MS spectra with spectra stored in Metabolon's spectra library. Serum SELENOP levels were measured in 832 men and women using an ELISA kit. RESULTS: Circulating SELENOP levels were associated with 24 out of 572 metabolites after accounting for the number of independent dimensions in the metabolomics data, including inverse associations with alanine, glutamate, leucine, isoleucine and valine, an unknown compound X-12063, urate and the peptides gamma-glutamyl-leucine, and N-acetylcarnosine. Positive associations were observed between SELENOP and several lipid compounds. Of the identified metabolites, each standard deviation increase in the branched-chain amino acids (isoleucine, leucine, valine), alanine and gamma-glutamyl-leucine was related to higher odds of having T2DM [OR (95% CI): 1.96 (1.41-2.73); 1.62 (1.15-2.28); 1.94 (1.45-2.60), 1.57 (1.17-2.11), and 1.52 (1.13-2.05), respectively]. CONCLUSIONS: Higher serum SELENOP levels were associated with an overall healthy metabolomics profile, which may provide further insights into potential mechanisms of SELENOP-associated metabolic disorders.
PURPOSE:Selenoprotein P (SELENOP) has been previously related to various metabolic traits with partially conflicting results. The identification of SELENOP-associated metabolites, using an untargeted metabolomics approach, may provide novel biological insights relevant to disentangle the role of SELENOP in human health. METHODS: In this cross-sectional study, 572 serum metabolites were identified by comparing the obtained LC-MS/MS spectra with spectra stored in Metabolon's spectra library. Serum SELENOP levels were measured in 832 men and women using an ELISA kit. RESULTS: Circulating SELENOP levels were associated with 24 out of 572 metabolites after accounting for the number of independent dimensions in the metabolomics data, including inverse associations with alanine, glutamate, leucine, isoleucine and valine, an unknown compound X-12063, urate and the peptides gamma-glutamyl-leucine, and N-acetylcarnosine. Positive associations were observed between SELENOP and several lipid compounds. Of the identified metabolites, each standard deviation increase in the branched-chain amino acids (isoleucine, leucine, valine), alanine and gamma-glutamyl-leucine was related to higher odds of having T2DM [OR (95% CI): 1.96 (1.41-2.73); 1.62 (1.15-2.28); 1.94 (1.45-2.60), 1.57 (1.17-2.11), and 1.52 (1.13-2.05), respectively]. CONCLUSIONS: Higher serum SELENOP levels were associated with an overall healthy metabolomics profile, which may provide further insights into potential mechanisms of SELENOP-associated metabolic disorders.
Authors: Walter E Gall; Kirk Beebe; Kay A Lawton; Klaus-Peter Adam; Matthew W Mitchell; Pamela J Nakhle; John A Ryals; Michael V Milburn; Monica Nannipieri; Stefania Camastra; Andrea Natali; Ele Ferrannini Journal: PLoS One Date: 2010-05-28 Impact factor: 3.240
Authors: Christopher B Newgard; Jie An; James R Bain; Michael J Muehlbauer; Robert D Stevens; Lillian F Lien; Andrea M Haqq; Svati H Shah; Michelle Arlotto; Cris A Slentz; James Rochon; Dianne Gallup; Olga Ilkayeva; Brett R Wenner; William S Yancy; Howard Eisenson; Gerald Musante; Richard S Surwit; David S Millington; Mark D Butler; Laura P Svetkey Journal: Cell Metab Date: 2009-04 Impact factor: 27.287
Authors: Ron Wehrens; Jos A Hageman; Fred van Eeuwijk; Rik Kooke; Pádraic J Flood; Erik Wijnker; Joost J B Keurentjes; Arjen Lommen; Henriëtte D L M van Eekelen; Robert D Hall; Roland Mumm; Ric C H de Vos Journal: Metabolomics Date: 2016-03-18 Impact factor: 4.290