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The specific nutritional composition of nuts could affect different metabolic pathways involved in a broad range of metabolic diseases. We therefore investigated whether chronic consumption of pistachio nuts modifies the urine metabolome in prediabetic subjects. We designed a randomized crossover clinical trial in 39 prediabetic subjects. They consumed a pistachio-supplemented diet (PD, 50% carbohydrates, 33% fat, including 57 g/d of pistachios daily) and a control diet (CD, 55% carbohydrates, 30% fat) for 4 months each, separated by a 2-week wash-out. Nuclear magnetic resonance (NRM) was performed to determine changes in 24-h urine metabolites. Significant changes in urine metabolites according to the different intervention periods were found in uni- and multivariate analysis. Score plot of the first two components of the multilevel partial least squares discriminant analysis (ML-PLS-DA) showed a clear separation of the intervention periods. Three metabolites related with gut microbiota metabolism (i.e., hippurate, p-cresol sulfate and dimethylamine) were found decreased in PD compared with CD (P

The quality of automatic metabolite profiling in NMR datasets in complex matrices can be compromised by the multiple sources of variability in the samples. These sources cause uncertainty in the metabolite signal parameters and the presence of multiple low-intensity signals. Lineshape fitting approaches might produce suboptimal resolutions or distort the fitted signals to adapt them to the complex spectrum lineshape. As a result, tools tend to restrict their use to specific matrices and strict protocols to reduce this uncertainty. However, the analysis and modelling of the signal parameters collected during a first profiling iteration can further reduce the uncertainty by the generation of narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted and the performance of automatic profiling can be maximized. Thanks to the ability of our workflow to learn and model the sample properties, restrictions in the matrix or protocol and limitations of lineshape fitting approaches can be overcome.

Fetal growth may be impaired by poor placental function or maternal conditions, each of which can influence the transfer of nutrients and oxygen from the mother to the developing fetus. Large-scale studies of metabolites (metabolomics) are key to understand cellular metabolism and pathophysiology of human conditions. Herein, maternal and cord blood plasma samples were used for NMR-based metabolic fingerprinting and profiling, including analysis of the enrichment of circulating lipid classes and subclasses, as well as the number of sub-fraction particles and their size. Changes in phosphatidylcholines and glycoproteins were prominent in growth-restricted fetuses indicating significant alterations in their abundance and biophysical properties. Lipoprotein profiles showed significantly lower plasma concentrations of cholesterol-intermediate density lipoprotein (IDL), triglycerides-IDL and high-density lipoprotein (HDL) in mothers of growth-restricted fetuses compared to controls (p

NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical shift of metabolite signals. This information is typically not used even though it can enhance sample discrimination, since many conditions show pH or ionic imbalance. Here, we demonstrate how chemical shift information can be used to improve the quality of the discrimination between case and control samples in three public datasets of different human matrices. In two of these datasets, chemical shift information helped to provide an AUROC value higher than 0.9 during sample classification. In the other dataset, the chemical shift also showed discriminant potential (AUROC 0.831). These results are consistent with the pH imbalance characteristic of the condition studied in the datasets. In addition, we show that this signal misalignment dependent on sample class can alter the results of fingerprinting approaches in the three datasets. Our results show that it is possible to use chemical shift information to enhance the diagnostic and predictive properties of NMR.

Prebiotics and diets low in fermentable oligo-, di-, mono-saccharides and polyols (low-FODMAP diet) might reduce symptoms in patients with functional gastrointestinal disorders, despite reports that some nonabsorbable, fermentable meal products (prebiotics) provide substrates for colonic bacteria and thereby increase gas production. We performed a randomized, parallel, double-blind study of patients with functional gastrointestinal disorders with flatulence. We compared the effects of a prebiotic supplement (2.8 g/d Bimuno containing 1.37 g beta-galactooligosaccharide) plus a placebo (Mediterranean-type diet (prebiotic group, n = 19) vs a placebo supplement (2.8 g xylose) plus a diet low in FODMAP (low-FODMAP group, n = 21) for 4 weeks; patients were then followed for 2 weeks. The primary outcome was effects on composition of the fecal microbiota, analyzed by 16S sequencing. Secondary outcomes were intestinal gas production and digestive sensations. After 4 weeks, we observed opposite effects on microbiota in each group, particularly in relation to the abundance of Bifidobacterium sequences (increase in the prebiotic group and decrease in the low-FODMAP group; P = .042), and Bilophila wadsworthia (decrease in the prebiotic group and increase in the low-FODMAP group; P = .050). After 4 weeks, both groups had statistically significant reductions in all symptom scores, except reductions in flatulence and borborygmi were not significant in the prebiotic group. Although the decrease in symptoms persisted for 2 weeks after patients discontinued prebiotic supplementation, symptoms reappeared immediately after patients discontinued the low-FODMAP diet. Intermittent prebiotic administration might therefore be an alternative to dietary restrictions for patients with functional gut symptoms. ClinicalTrials.gov no.: NCT02210572.

Adoption of automatic profiling tools for 1H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices. To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process. rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality. The information and quality achieved in two public datasets of complex matrices are maximized. rDolphin is an open-source R package ( http://github.com/danielcanueto/rDolphin ) able to provide the best balance between accuracy, reproducibility and ease of use.

SCOPE: To examine whether a low‐glycemic index (LGI) diet improves a set of plasma metabolites related to different metabolic diseases, and comparison to a high‐glycemic index (HGI) diet and a low‐fat (LF) diet. METHODS AND RESULTS: A parallel, randomized trial with three intervention diets: an LGI diet, an HGI diet, and an LF diet. A total of 122 adult overweight and obese subjects were enrolled in the study for 6 months. Blood samples were collected at baseline and at the end of the intervention. The plasma metabolomic profile of 102 subjects was analyzed using three different approaches: GC/quadrupole‐TOF, LC/quadrupole‐TOF, and nuclear magnetic resonance. Both univariate and multivariate analysis were performed. Serine levels were significantly higher following the LGI diet compared to both the HGI and LF diets (q = 0.002), whereas leucine (q = 0.015) and valine (q = 0.024) were lower in the LGI diet compared to the LF diet. A set of two sphingomyelins, two lysophosphatidylcholines, and six phosphatidylcholines were significantly modulated after the LGI diet compared to the HGI and LF diets (q

Introduction The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools. Objectives To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics. Methods The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for. Results A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary. Conclusion This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking. Electronic supplementary material The online version of this article (doi:10.1007/s11306-017-1242-7) contains supplementary material, which is available to authorized users.

Adoption of automatic profiling tools forTo provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.The information and quality achieved in two public datasets of complex matrices are maximized.rDolphin is an open-source R package ( http://github.com/danielcanueto/rDolphin ) able to provide the best balance between accuracy, reproducibility and ease of use.