In the late 1990s, the field of metabolic profiling evolved into metabolomics following the general move towards systems biology and other omics techniques. Using sensitive, analytical platforms such as NMR, metabolomics aims to gather an unbiased, broad perspective of the active biochemistry in biofluids. The result was an explosive growth in the data available to study short term physiological effects, followed perforce by the application of multivariate pattern-recognition techniques to aid in its interpretation.
Given the sensitive and comprehensive nature of the technique, it quickly became apparent that any number of artifactual or spurious relationships appear in the results. To alleviate those concerns, a variety of improved experimental designs, analytical techniques, and validation paradigms can be applied. Starting with a basic experimental design, the aim of this work is to explore the ability of properly validated metabolomics to provide useful information about the metabolic shifts seen in established animal models of insulin resistance, a human disease with increasing medical significance. Different two-factor experimental designs are used to refine the results of this early study, validate the resulting hypothesis and reinforce its interpretation.
Having seen significant differences in ostensibly identical batches of animals in the first three experiments, further analysis of the differences are performed. Techniques for comparing batch models, as a form of multivariate hypothesis validation, are evaluated and the ability of statistical techniques to predict or ameiliorate these “batch effects” is studied. Finally, a rat model of vitamin C deficiency, another condition with ongoing pathological implications in the third world, is studied using the same metabolomic techniques. The identified metabolic shifts are subjected to a complete pathway analysis, the context of which provides a potentially interesting insight into the regulation of an important human oxidative damage control mechanism.