Metabolomic analysis of insulin resistance across different mouse strains and diets.

Published

Journal Article

Insulin resistance is a major risk factor for many diseases. However, its underlying mechanism remains unclear in part because it is triggered by a complex relationship between multiple factors, including genes and the environment. Here, we used metabolomics combined with computational methods to identify factors that classified insulin resistance across individual mice derived from three different mouse strains fed two different diets. Three inbred ILSXISS strains were fed high-fat or chow diets and subjected to metabolic phenotyping and metabolomics analysis of skeletal muscle. There was significant metabolic heterogeneity between strains, diets, and individual animals. Distinct metabolites were changed with insulin resistance, diet, and between strains. Computational analysis revealed 113 metabolites that were correlated with metabolic phenotypes. Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sensitivity, we identified C22:1-CoA, C2-carnitine, and C16-ceramide as the best classifiers. Strikingly, when these three metabolites were combined into one signature, they classified mice based on insulin sensitivity more accurately than each metabolite on its own or other published metabolic signatures. Furthermore, C22:1-CoA was 2.3-fold higher in insulin-resistant mice and correlated significantly with insulin resistance. We have identified a metabolomic signature composed of three functionally unrelated metabolites that accurately predicts whole-body insulin sensitivity across three mouse strains. These data indicate the power of simultaneous analysis of individual, genetic, and environmental variance in mice for identifying novel factors that accurately predict metabolic phenotypes like whole-body insulin sensitivity.

Full Text

Duke Authors

Cited Authors

  • Stöckli, J; Fisher-Wellman, KH; Chaudhuri, R; Zeng, X-Y; Fazakerley, DJ; Meoli, CC; Thomas, KC; Hoffman, NJ; Mangiafico, SP; Xirouchaki, CE; Yang, C-H; Ilkayeva, O; Wong, K; Cooney, GJ; Andrikopoulos, S; Muoio, DM; James, DE

Published Date

  • November 24, 2017

Published In

Volume / Issue

  • 292 / 47

Start / End Page

  • 19135 - 19145

PubMed ID

  • 28982973

Pubmed Central ID

  • 28982973

Electronic International Standard Serial Number (EISSN)

  • 1083-351X

Digital Object Identifier (DOI)

  • 10.1074/jbc.M117.818351

Language

  • eng

Conference Location

  • United States