A novel method for the analysis of clinical biomarkers to investigate the effect of diet on health in a rat model

Experiments into the relationship between diet and health have been an area of high interest for a long time. In this study, we investigate the application of multivariate data analysis to differentiate between rat populations fed on two different diets: normal rat diet (control) and Western affluent diet (WAD). Two sets of data were acquired and analysed: one from a biochemical clinical analyser, taking measurements of blood-based biochemical markers; the other from the analysis of the volatile organic compounds (VOCs) emitted from faecal samples from the same animals using selected ion flow tube mass spectrometry (SIFT-MS). Five classes were considered: weanlings, 12 month controls, 12 month WADs, 18 month controls, and 18 month WADs. Data from the biochemical analyser, weanlings and 18 month WAD fed rats showed significant differences from the other measurement classes. This was shown in both the exploratory analysis and through multivariate classification. Classification of control diet versus WAD diets suggested there are differences between classes with 92% accuracy for the 12 month classes and 91% for the 18 month classes. Cholesterol markers, especially as low density lipoprotein-cholesterol (LDL), were the main factor in influencing WAD samples. The data from the SIFT-MS analysis also produced very good classification accuracies. Classification of control diet versus WAD diets using the H3O+ precursor ion data suggested there are differences between classes with 71% accuracy for the 12 month classes and 100% for the 18 month classes. These findings confirm that total cholesterol and LDL-cholesterol are elevated in the 18 month WAD-fed rats. We therefore suggest that the analysis of VOCs from faecal samples in conjunction with multivariate data analysis may be a useful alternative to blood analysis for the detection of parameters of health

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided

An automated Pearson's correlation change classification (APC3) approach for GC/MS metabonomic data using total ion chromatograms (TICs)

10.1039/c3an00048fAnalyst138102883-2889ANAL

Metabolic footprinting of tumorigenic and nontumorigenic uroepithelial cells using two-dimensional gas chromatography time-of-flight mass spectrometry

10.1007/s00216-010-4055-3Analytical and Bioanalytical Chemistry39831285-1293ABCN

Metabonomic profiling of bladder cancer

10.1021/pr500966hJournal of Proteome Research142587-60

Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry

10.1021/pr4000448Journal of Proteome Research1293865-3873JPRO

Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry

10.1038/nprot.2011.375Nature Protocols6101483-149

Exploratory GC/MS-Based Metabolomics of Body Fluids

Part of the Methods in Molecular Biology book series (MIMB, volume 1730)GC/MS-based metabolomics is a powerful tool for metabolic phenotyping and biomarker discovery from body biofluids. In this chapter, we describe an untargeted metabolomic approach for plasma/serum and fecal water sample profiling. It describes a multistep procedure, from sample preparation, oximation/silylation derivatization, and data acquisition using GC/QToF to data processing consisting in data extraction and identification of metabolites

Use of urine volatile organic compounds to discriminate tuberculosis patients from healthy subjects

10.1021/ac200265gAnalytical Chemistry83145526-5534ANCH