Distinct Metabolomic Profiling of Serum Samples from High-Fat-Diet-Induced Insulin-Resistant Mice

High-fat-diet (HFD)-induced obesity is associated with an elevated risk of insulin resistance (IR), which may precede the onset of type 2 diabetes mellitus and associated metabolic complications. Being a heterogeneous metabolic condition, it is pertinent to understand the metabolites and metabolic pathways that are altered during the development and progression of IR toward T2DM. Serum samples were collected from C57BL/6J mice fed with HFD or chow diet (CD) for 16 weeks. Collected samples were analyzed by gas chromatography-tandem mass spectrometry (GC-MS/MS). Data on the identified raw metabolites were evaluated using a combination of univariate and multivariate statistical methods. Mice fed with HFD had glucose and insulin intolerance associated with impairment of insulin signaling in key metabolic tissues. From the GC-MS/MS analysis of serum samples, a total of 75 common annotated metabolites were identified between HFD- and CD-fed mice. In the t-test, 22 significantly altered metabolites were identified. Out of these, 16 metabolites were up-accumulated, whereas 6 metabolites were down-accumulated. Pathway analysis identified 4 significantly altered metabolic pathways. In particular, primary bile acid biosynthesis and linoleic acid metabolism were upregulated, whereas the TCA cycle and pentose and glucuronate interconversion were downregulated in HFD-fed mice in comparison to CD-fed mice. These results show the distinct metabolic profiles associated with the onset of IR that could provide promising metabolic biomarkers for diagnostic and clinical applications.

Automated summary

High-fat-diet-induced obesity increases the risk of insulin resistance, which may lead to type 2 diabetes and related metabolic complications. Gas chromatography-tandem mass spectrometry analysis of serum samples from mice fed with high-fat-diet and chow diet revealed differences in metabolites and metabolic pathways. These findings provide potential metabolic biomarkers for diagnosing insulin resistance and clinical applications.