Journal of Biological Chemistry
Volume 292, Issue 47, 24 November 2017, Pages 19135-19145
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Metabolism
Metabolomic analysis of insulin resistance across different mouse strains and diets

https://doi.org/10.1074/jbc.M117.818351Get rights and content
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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.

ceramide
glucose metabolism
insulin resistance
metabolomics
skeletal muscle metabolism
genetic diversity
metabolite signature
strain differences

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This work was supported in part by National Health and Medical Research Council (NHMRC) Project Grants GNT1061122, GNT1086851, and GNT1086850 (to D. E. J.) and National Institutes of Health Grants 2R01DK089312 and 2P01-DK058398 (to D. M. M.). The authors declare that they have no conflicts of interest with the contents of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or NHMRC.

This article contains supplemental Table S1.

1

These authors contributed equally to this work.

2

Supported by National Institutes of Health F32 Fellowship 1F32DK105665-01A1.

3

Present address: Centre for Exercise and Nutrition, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne VIC 3000, Australia.