Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatographymass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of Nitin Purandare is now deceased.Electronic supplementary material The online version of this article

In clinical analyses, the most appropriate biofluid should be analyzed for optimal assay performance. For biological fluids, the most readily accessible is blood, and metabolomic analyses can be performed either on plasma or serum. To determine the optimal agent for analysis, metabolic profiles of matched human serum and plasma were assessed by gas chromatography/time-of-flight mass spectrometry and ultrahigh-performance liquid chromatography mass spectrometry (in positive and negative electrospray ionization modes). Comparison of the two metabolomes, in terms of reproducibility, discriminative ability and coverage, indicated that they offered similar analytical opportunities. An analysis of the variation between 29 small-cell lung cancer (SCLC) patients revealed that the differences between individuals are markedly similar for the two biofluids. However, significant differences between the levels of some specific metabolites were identified, as were differences in the intersubject variability of some metabolite levels. Glycerophosphocholines, erythritol, creatinine, hexadecanoic acid, and glutamine in plasma, but not in serum, were shown to correlate with life expectancy for SCLC patients, indicating the utility of metabolomic analyses in clinical prognosis and the particular utility of plasma in relation to the clinical management of SCLC.

BackgroundObservational studies suggest weight loss and energy restriction reduce breast cancer risk. Intermittent energy restriction (IER) reduces weight to the same extent as, or more than equivalent continuous energy restriction (CER) but the effects of IER on normal breast tissue and systemic metabolism as indicators of breast cancer risk are unknown.MethodsWe assessed the effect of IER (two days of 65 % energy restriction per week) for one menstrual cycle on breast tissue gene expression using Affymetrix GeneChips, adipocyte size by morphometry, and systemic metabolism (insulin resistance, lipids, serum and urine metabolites, lymphocyte gene expression) in 23 overweight premenopausal women at high risk of breast cancer. Unsupervised and supervised analyses of matched pre and post IER biopsies in 20 subjects were performed, whilst liquid and gas chromatography mass spectrometry assessed corresponding changes in serum and urine metabolites in all subjects after the two restricted and five unrestricted days of the IER.ResultsWomen lost 4.8 % (±2.0 %) of body weight and 8.0 % (±5.0 %) of total body fat. Insulin resistance (homeostatic model assessment (HOMA)) reduced by 29.8 % (±17.8 %) on the restricted days and by 11 % (±34 %) on the unrestricted days of the IER. Five hundred and twenty-seven metabolites significantly increased or decreased during the two restricted days of IER. Ninety-one percent of these returned to baseline after 5 days of normal eating. Eleven subjects (55 %) displayed reductions in energy restriction-associated metabolic gene pathways including lipid synthesis, gluconeogenesis and glycogen synthesis. Some of these women also had increases in genes associated with breast epithelial cell differentiation (secretoglobulins, milk proteins and mucins) and decreased collagen synthesis (TNMD, PCOLCE2, TIMP4). There was no appreciable effect of IER on breast gene expression in the other nine subjects. These groups did not differ in the degree of changes in weight, total body fat, fat cell size or serum or urine metabolomic markers. Corresponding gene changes were not seen in peripheral blood lymphocytes.ConclusionThe transcriptional response to IER is variable in breast tissue, which was not reflected in the systemic response, which occurred in all subjects. The mechanisms of breast responsiveness/non-responsiveness require further investigation.Trial registrationISRCTN77916487 31/07/2012.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0714-4) contains supplementary material, which is available to authorized users.

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Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365-372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56% accuracy) and SVM with a polynomial kernel (91.66% accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data.