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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
- Ly-Verdú, Saray, Gröger, Thomas Maximilian, Arteaga-Salas, Jose Manuel, Brandmaier, Stefan, Kahle, Melanie, Neschen, Susanne, Harbě de Angelis, Martin, Zimmermann, Ralf
- Analytical and bioanalytical chemistry 2015 v.407 no.1 pp. 343-354
- animal models, chemometrics, comprehensive two-dimensional gas chromatography, experimental design, glucose, high energy diet, hyperglycemia, insulin, insulin resistance, insulin secretion, liver, mass spectrometry, metabolomics, mice, noninsulin-dependent diabetes mellitus, risk factors, statistical analysis
- 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.