Metabolite profiling: development and application of an UHR-QTOF-MS(/MS) method approach for the assessment of metabolic changes in high fat diet fed mice
Introduction: The metabolic alterations accompanying the development of insulin resistance and type 2 diabetes mellitus (T2DM) are complex, not coherently understood and only partially represented by conventional clinical tests like the oral glucose tolerance test. Changes in plasma metabolite concentrations preceding insulin resistance or overt T2DM may help understand the etiology of metabolic disorders and they are potential predictive risk markers. Objectives: Here, we describe a non-targeted metabolomics platform based on UPLC-UHR-QToF-MS(/MS) for the assessment of plasma non-polar metabolites. Methods: This method was applied to a longitudinal mouse obesity study comparing mice on control and high fat diet (HFD), respectively. Plasma metabolites were assessed 2, 4, 8 and 16 weeks after initiation of feeding. Multivariate analysis of the metabolite dataset showed clear differentiation of the feeding groups after 8 weeks when the HFD-fed mice exhibited clear signs of insulin resistance. Results: The discrimination of the groups was due to changes in various metabolic pathways including, among others, glycerophospholipid, sphingolipid and cholesterol metabolism. Conclusion: From 81 compounds with a p-value lower than 0.05, a total of 19 metabolites could be putatively identified due to their accurate mass, isotope and fragmentation pattern. Thirteen of these observed metabolites are known key metabolites to diabetes or its secondary diseases like diabetic nephropathy and neuropathy (Meiss, Werner, John, Scheja, Herbach, Heeren, Fischer 2015). The compounds putatively identified here may provide valuable starting points for further investigations and developments of clinical diagnostics and prediagnostics for T2DM and related diseases.