There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at

The purpose of this study was to compare the effects of black and green tea consumption on human metabolism. Seventeen healthy male volunteers consumed black tea, green tea, or caffeine in a randomized crossover study. Twenty-four-hour urine and blood plasma samples were analyzed by NMR-based metabonomics, that is, high-resolution 1H NMR metabolic profiling combined with multivariate statistics. Green and black tea consumption resulted in similar increases in urinary excretion of hippuric acid and 1,3-dihydroxyphenyl-2-O-sulfate, both of which are end products of tea flavonoid degradation by colonic bacteria. Several unidentified aromatic metabolites were detected in urine specifically after green tea intake. Interestingly, green and black tea intake also had a different impact on endogenous metabolites in urine and plasma. Green tea intake caused a stronger increase in urinary excretion of several citric acid cycle intermediates, which suggests an effect of green tea flavanols on human oxidative energy metabolism and/or biosynthetic pathways.

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Epidemiological studies indicate that a high intake of flavonoids is associated with an improved health status. Tea is one of the most abundant sources of flavonoids in the human diet. The bioavailability and biotransformation of tea flavonoids are, however, not clearly understood. The aim of the present study was to investigate the metabolism of black tea via a nonspecific screening method. (1)H nuclear magnetic resonance (NMR) spectroscopy was used to obtain nonselective profiles of urine samples collected from three human volunteers before and after a single dose of black tea. The complex spectroscopic profiles were interpreted with the use of pattern recognition techniques. Hippuric acid was confirmed as the major urinary black tea metabolite. One previously unknown metabolite was detected and identified as 1,3-dihydroxyphenyl-2-O-sulfate (sulfate conjugate of pyrogallol) using HPLC directly coupled to mass spectrometry and (1)H NMR spectroscopy. This study shows that NMR-pattern recognition studies can be used for the discovery of unknown flavonoid metabolites in humans.

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With the increasing production of metabolomic data there is an awareness of a need for a standardised description of this data to aid assessment, exchange, storage and curation of information from metabolomic studies. In this manuscript the first draft of a minimum requirement for the description of the biological context of a metabolomic study involving mammalian subjects is described. This recommendation has been produced by the Metabolomics Standards Initiative-Mammalian Context Working Sub-Group (MSI-MCWSG) as part of the wider standardisation initiative led by the Metabolomics society. The experiments considered include functional genomic studies, drug toxicology, nutrigenomics, clinical trials, and other human studies. Two reporting requirements are described for pre-clinical (e.g. functional genomics, toxicology) and clinical (e.g. clinical trials, nutrigenomics) studies. It is planned that this will lead to the development of a tool for the description of metabolomic experiments that enables storage, retrieval and manipulation of large amounts of data. This will benefit the assessment and dissemination of metabolomic data from mammalian studies.

The aim of this study was to investigate whether women with polycystic ovary syndrome (PCOS) had a unique metabolomic profile that was different from controls and to assess the feasibility of a definitive study. Twelve women with PCOS and 10 healthy women  as controls had measurements of demographic and anthropometric data, venepunctures and assays on plasma samples for metabolomic profiles using hydrogen-1, nuclear magnetic resonance ((1)H NMR) spectroscopy. There did not appear to be any clear differences between the metabolomic profiles of women with PCOS compared with controls when the NMR spectra were visually inspected and initial principal component analysis showed only a subtle differentiation between the two groups which was spread over three principal components. However, 'supervised' data analysis in the form of partial least-squares discriminant analysis (PLS-DA) and non-parametric univariate analysis allowed a stable PLS-DA model to be built, which appeared to differentiate between the two groups in a robust manner. Peak assignments for those spectral regions which appeared to differentiate between control and PCOS were consistent with amino acids (arginine, lysine, proline, glutamate and histidine), organic acids (citrate) and potentially lipids (CH(2)-CH(2)-C=C) with significant decreases noted in the levels of citrulline, lipid (CH(2)-CH(2)-C=C), arginine, lysine, ornithine, proline, glutamate, acetone, citrate and histidine in PCOS compared with controls. Women with PCOS may have a unique metabolomic finger print and a definitive study is feasible. These findings may enable sample size calculations for confirmatory studies and stimulate further research using metabolomics to improve the understanding and management of PCOS.

The aim of the study was to evaluate metabolite variability in human eccrine sweat using a metabonomics based approach. Eccrine sweat is a dilute electrolyte solution whose primary function is to control body temperature via evaporative cooling. Although the composition of sweat is primarily water, previous studies have shown that a diverse array of organic and inorganic compounds are also present. Human eccrine sweat samples from 30 female and 30 male subjects were analysed using highresolution 1 H nuclear magnetic resonance (NMR) spectroscopy in conjunction with statistical pattern recognition. High-resolution 1 H NMR spectroscopy produced spectra of the sweat samples that readily identified and quantified many different metabolites. The major metabolite classes found to be present were lactate, amino acids and lipids, with lactate being by far the most dominant metabolite found in all samples. Principal Components Analysis, Principal Components-Discriminant Analysis and Partial Least Squares-Discriminant Analysis of the eccrine sweat samples, revealed no significant differences in metabolite composition and concentration between female and male subjects. Also, the variation between subjects did not appear to be correlated with any other clinical information provided by the subjects. Overall, the spectra data set demonstrates the large physiological variability in terms of number of metabolites present and concentrations between subjects i.e. human eccrine sweat samples exhibit a high degree of inter-individual variability.