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Combining metabolomic non-targeted GC×GC–ToF–MS analysis and chemometric ASCA-based study of variances to assess dietary influence on type 2 diabetes development in a mouse model

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Abstract

Insulin resistance (IR) lies at the origin of type 2 diabetes. It induces initial compensatory insulin secretion until insulin exhaustion and subsequent excessive levels of glucose (hyperglycemia). A high-calorie diet is a major risk factor contributing to the development of this metabolic disease. For this study, a time-course experiment was designed that consisted of two groups of mice. The aim of this design was to reproduce the dietary conditions that parallel the progress of IR over time. The first group was fed with a high-fatty-acid diet for several weeks and followed by 1 week of a low-fatty-acid intake, while the second group was fed with a low-fatty-acid diet during the entire experiment. The metabolomic fingerprint of C3HeB/FeJ mice liver tissue extracts was determined by means of two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC–ToF–MS). This article addresses the application of ANOVA-simultaneous component analysis (ASCA) to the found metabolomic profile. By performing hyphenated high-throughput analytical techniques together with multivariate chemometric methodology on metabolomic analysis, it enables us to investigate the sources of variability in the data related to each experimental factor of the study design (defined as time, diet and individual). The contribution of the diet factor in the dissimilarities between the samples appeared to be predominant over the time factor contribution. Nevertheless, there is a significant contribution of the time–diet interaction factor. Thus, evaluating the influences of the factors separately, as it is done in classical statistical methods, may lead to inaccurate interpretation of the data, preventing achievement of consistent biological conclusions.

Time-course design of the study. The experimental design recreates the typical IR progression over time sketched in the upper graph. Data from samples measured with GC×GC–ToF–MS were processed and ASCA analysis was performed to discern the different sources of variation in them. 1392 × 816 mm (150 × 150 DPI)

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Authors and Affiliations

  1. Joint Mass Spectrometry Centre, Cooperation Group “Comprehensive Molecular Analytics”, Helmholtz Zentrum Muenchen, 85764, Neuherberg, Germany

    Saray Ly-Verdú, Thomas Maximilian Gröger, Jose Manuel Arteaga-Salas & Ralf Zimmermann

  2. Joint Mass Spectrometry Centre, Institute of Chemistry, Chair of Analytical Chemistry, University of Rostock, 18057, Rostock, Germany

    Saray Ly-Verdú, Thomas Maximilian Gröger & Ralf Zimmermann

  3. Institute of Epidemiology II, Helmholtz Zentrum Muenchen, 85764, Neuherberg, Germany

    Stefan Brandmaier

  4. Institute of Experimental Genetics, Helmholtz Zentrum Muenchen, 85764, Neuherberg, Germany

    Melanie Kahle, Susanne Neschen & Martin Harbě de Angelis

Authors
  1. Saray Ly-Verdú
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  2. Thomas Maximilian Gröger
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  3. Jose Manuel Arteaga-Salas
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  4. Stefan Brandmaier
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  5. Melanie Kahle
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  6. Susanne Neschen
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  7. Martin Harbě de Angelis
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  8. Ralf Zimmermann
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Correspondence to Thomas Maximilian Gröger.

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Published in the topical collection Multidimensional Chromatography with guest editors Torsten C. Schmidt, Oliver J. Schmitz, and Thorsten Teutenberg.

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Ly-Verdú, S., Gröger, T.M., Arteaga-Salas, J.M. et al. Combining metabolomic non-targeted GC×GC–ToF–MS analysis and chemometric ASCA-based study of variances to assess dietary influence on type 2 diabetes development in a mouse model. Anal Bioanal Chem 407, 343–354 (2015). https://doi.org/10.1007/s00216-014-8227-4

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  • DOI: https://doi.org/10.1007/s00216-014-8227-4

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