A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.

Inflammatory bowel diseases (IBD) including Crohn's disease (CD) and ulcerative colitis (UC) have a major impact on the health of individuals and populations. Accurate diagnosis of inflammatory bowel disease (IBD) at an early stage, and correct differentiation between Crohn's disease (CD) and ulcerative colitis (UC), is important for optimum treatment and prognosis. We present here the first characterization of fecal extracts obtained from patients with CD and UC by employing a noninvasive metabonomics approach, which combines high resolution 1H NMR spectroscopy and multivariate pattern recognition techniques. The fecal extracts of both CD and UC patients were characterized by reduced levels of butyrate, acetate, methylamine, and trimethylamine in comparison with a control population, suggesting changes in the gut microbial community. Also, elevated quantities of amino acids were present in the feces from both disease groups, implying malabsorption caused by the inflammatory disease or an element of protein losing enteropathy. Metabolic differences in fecal profiles were more marked in the CD group in comparison with the control group, indicating that the inflammation caused by CD is more extensive in comparison with UC and involves the whole intestine. Furthermore, glycerol resonances were a dominant feature of fecal spectra from patients with CD but were present in much lower intensity in the control and UC groups. This work illustrates the potential of metabonomics to generate novel noninvasive diagnostics for gastrointestinal diseases and may further our understanding of disease mechanisms.