Comparative analysis of plasma metabolomics response to metabolic challenge tests in healthy subjects and influence of the FTO obesity risk allele
- 842 Downloads
The measurement of metabolites during intravenous or nutritional challenges may improve the identification of novel metabolic signatures which are not detectable in the fasting state. Here, we comprehensively characterized the plasma metabolomics response to five defined challenge tests and explored their use to identify interactions with the FTO rs9939609 obesity risk genotype. Fifty-six non-diabetic male participants of the KORA S4/F4 cohort, including 25 homozygous carriers of the FTO risk allele (AA genotype) and 31 carriers of the TT genotype were recruited. Challenges comprised an oral glucose tolerance test, a standardized high-fat high-carbohydrate meal and a lipid tolerance test, as well as an intravenous glucose tolerance test and a euglycemic hyperinsulinemic clamp. Blood was sampled for biochemical and metabolomics measurement before and during the challenges. Plasma samples were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Linear mixed-effects models and cluster analysis were performed. In both genotype groups, we observed significant challenge-induced changes for all major metabolite classes (amino acids, hexose, acylcarnitines, phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, with corrected p-values ranging from 0.05 to 6.7E−37), which clustered in five distinct metabolic response profiles. Our data contribute to the understanding of plasma metabolomics response to diverse metabolic challenges, including previously unreported metabolite changes in response to intravenous challenges. The FTO genotype had only minor effects on the metabolite fluxes after standardized metabolic challenges.
KeywordsMetabolomics Metabolite profile Nutritional challenge Metabolic challenge Oral glucose tolerance test Oral lipid tolerance test Intravenous glucose tolerance test Clamp Obesity FTO Gene-environment interaction
This work was funded by the Else Kroener-Fresenius Foundation, Bad Homburg v. d. H, Germany, the grant Virtual Institute ‘Molecular basis of glucose regulation and type 2 diabetes’ received from the Helmholtz Zentrum München, Neuherberg, Germany, the grant Clinical Cooperation Group ‘Nutrigenomics and type 2 diabetes’ received from the Helmholtz Zentrum München, Neuherberg, Germany, the Technische Universität München, Freising-Weihenstephan, and by funding from the German Federal Ministry for Education and Research (BMBF) to the German Center for Diabetes Research (DZD e. V.). WRM is funded by BMBF Grant no. 03IS2061B (project Gani_Med). KS is supported by ‘Biomedical Research Program’ funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. The KORA study group consists of A. Peters (speaker), R. Leidl, R. Holle, J. Heinrich, C. Meisinger, C. Strauch, and their coworkers, who are responsible for the design and conduct of the KORA studies. We gratefully acknowledge the contribution of all members of field staffs conducting the KORA study and thank all study probands participating in the study. The KORA research platform studies were initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Science, Research and Technology and by the State of Bavaria. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Julia Scarpa, Katharina Sckell and Arsin Sabunchi for metabolomics measurement performed at the Helmholtz Zentrum München, Genome Analysis Center, Metabolomics Core Facility, Neuherberg, Germany, and Jan Krumsiek for advice concerning weighted enrichment analysis.
Conflict of interest
All authors declare that there is no conflict of interest.
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, 57, 289–300.Google Scholar
- Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap (1st ed.). Boca Raton: Chapman and Hall/CRC.Google Scholar
- Kryszczuk, K., & Hurley, P. (2010). Estimation of the number of clusters using multiple clustering validity indices. In N. Gayar, J. Kittler, & F. Roli (Eds.), Multiple classifier systems. Berlin: Springer.Google Scholar
- Luís, P. B. M., Ruiter, J. P. N., Ijlst, L., et al. (2011). Role of isovaleryl-CoA dehydrogenase and short branched-chain acyl-CoA dehydrogenase in the metabolism of valproic acid: implications for the branched-chain amino acid oxidation pathway. Drug Metabolism and Disposition, 39, 1155–1160.CrossRefPubMedPubMedCentralGoogle Scholar
- Pannacciulli, N., Bunt, J. C., Koska, J., Bogardus, C., & Krakoff, J. (2006). Higher fasting plasma concentrations of glucagon-like peptide 1 are associated with higher resting energy expenditure and fat oxidation rates in humans. The American Journal of Clinical Nutrition, 84, 556–560.PubMedGoogle Scholar
- R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.
- Sancho, V., Trigo, M. V., González, N., Valverde, I., Malaisse, W. J., & Villanueva-Peñacarrillo, M. L. (2005). Effects of glucagon-like peptide-1 and exendins on kinase activity, glucose transport and lipid metabolism in adipocytes from normal and type-2 diabetic rats. Journal of Molecular Endocrinology, 35(1), 27–38.CrossRefPubMedGoogle Scholar
- Standaert, M. L., Avignon, A., Yamada, K., Bandyopadhyay, G., & Farese, R. V. (1996a). The phosphatidylinositol 3-kinase inhibitor, wortmannin, inhibits insulin-induced activation of phosphatidylcholine hydrolysis and associated protein kinase C translocation in rat adipocytes. The Biochemical Journal, 313, 1039–1046.CrossRefPubMedPubMedCentralGoogle Scholar
- Standaert, M. L., Bandyopadhyay, G., Zhou, X., Galloway, L., & Farese, R. V. (1996b). Insulin stimulates phospholipase D-dependent phosphatidylcholine hydrolysis, Rho translocation, de novo phospholipid synthesis, and diacylglycerol/protein kinase C signaling in L6 myotubes. Endocrinology, 137, 3014–3020.PubMedGoogle Scholar
- Svegliati-Baroni, G., Saccomanno, S., Rychlicki, C., et al. (2011). Glucagon-like peptide-1 receptor activation stimulates hepatic lipid oxidation and restores hepatic signalling alteration induced by a high-fat diet in nonalcoholic steatohepatitis. Liver International, 31, 1285–1297.CrossRefPubMedGoogle Scholar
- Weickert, M. O., Loeffelholz, C., Roden, V. M., et al. (2007). A Thr94Ala mutation in human liver fatty acid-binding protein contributes to reduced hepatic glycogenolysis and blunted elevation of plasma glucose levels in lipid-exposed subjects. American Journal of Physiology, Endocrinology and Metabolism, 293, E1078–E1084.CrossRefPubMedGoogle Scholar
- Westphal, S., Orth, M., Ambrosch, A., Osmundsen, K., & Luley, C. (2000). Postprandial chylomicrons and VLDLs in severe hypertriacylglycerolemia are lowered more effectively than are chylomicron remnants after treatment with n-3 fatty acids. The American Journal of Clinical Nutrition, 71, 914–920.PubMedGoogle Scholar