Serum or Plasma (and Which Plasma), That Is the Question

Blood derivatives are the biofluids of choice formetabolomic clinical studies since blood can be collected with lowinvasiveness and is rich in biological information. However, the choiceof the blood collection tubes has an undeniable impact on the plasmaand serum metabolic content. Here, we compared the metabolomicand lipoprotein profiles of blood samples collected at the same timeand place from six healthy volunteers but using different collectiontubes (each enrolled volunteer provided multiple blood samples at adistance of a few weeks/months): citrate plasma, EDTA plasma, andserum tubes. All samples were analyzed via nuclear magnetic resonancespectroscopy. Several metabolites showed statistically significantalterations among the three blood matrices, and also metabolites'correlations were shown to be affected. The effects of blood collectiontubes on the lipoproteins'profiles are relevant too, but less marked. Overcoming the issue associated with different blood collectiontubes is pivotal to scale metabolomics and lipoprotein analysis at the level of epidemiological studies based on samples frommulticenter cohorts. We propose a statistical solution, based on regression, that is shown to be efficient in reducing the alterationsinduced by the different collection tubes for both the metabolomic and lipoprotein profile

KODAMA: an R package for knowledge discovery and data mining

Summary: KODAMA, a novel learning algorithm for unsuper-vised feature extraction, is specifically designed for analysing noisy and high-dimensional data sets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The pack-age requires no additional software and runs on all major plat-forms. Availability and Implementation: KODAMA is freely available from the R archive CRAN (http://cran.r-project.org). The soft-ware is distributed under the GNU General Public License (ver-sion 3 or later)

DHA-Induced Perturbation of Human Serum Metabolome. Role of the Food Matrix and Co-Administration of Oat β-glucan and Anthocyanins.

Docosahexaenoic acid (DHA) has been reported to have a positive impact on many diet-related disease risks, including metabolic syndrome. Although many DHA-enriched foods have been marketed, the impact of different food matrices on the effect of DHA is unknown. As well, the possibility to enhance DHA effectiveness through the co-administration of other bioactives has seldom been considered. We evaluated DHA effects on the serum metabolome administered to volunteers at risk of metabolic syndrome as an ingredient of three different foods. Foods were enriched with DHA alone or in combination with oat beta-glucan or anthocyanins and were administered to volunteers for 4 weeks. Serum samples collected at the beginning and end of the trial were analysed by NMR-based metabolomics. Multivariate and univariate statistical analyses were used to characterize modifications in the serum metabolome and to evaluate bioactive-bioactive and bioactive-food matrix interactions. DHA administration induces metabolome perturbation that is influenced by the food matrix and the co-presence of other bioactives. In particular, when co-administered with oat beta-glucan, DHA induces a strong rearrangement in the lipoprotein profile of the subjects. The observed modifications are consistent with clinical results and indicate that metabolomics represents a possible strategy to choose the most appropriate food matrices for bioactive enrichment

DHA-induced perturbation of human serum metabolome. Role of the food matrix and Co-administration of oat β-glucan and anthocyanins

Docosahexaenoic acid (DHA) has been reported to have a positive impact on many diet-related disease risks, including metabolic syndrome. Although many DHA-enriched foods have been marketed, the impact of different food matrices on the effect of DHA is unknown. As well, the possibility to enhance DHA effectiveness through the co-administration of other bioactives has seldom been considered. We evaluated DHA effects on the serum metabolome administered to volunteers at risk of metabolic syndrome as an ingredient of three different foods. Foods were enriched with DHA alone or in combination with oat beta-glucan or anthocyanins and were administered to volunteers for 4 weeks. Serum samples collected at the beginning and end of the trial were analysed by NMR-based metabolomics. Multivariate and univariate statistical analyses were used to characterize modifications in the serum metabolome and to evaluate bioactive-bioactive and bioactive-food matrix interactions. DHA administration induces metabolome perturbation that is influenced by the food matrix and the co-presence of other bioactives. In particular, when co-administered with oat beta-glucan, DHA induces a strong rearrangement in the lipoprotein profile of the subjects. The observed modifications are consistent with clinical results and indicate that metabolomics represents a possible strategy to choose the most appropriate food matrices for bioactive enrichment

A Metabolomic Perspective on Coeliac Disease

Metabolomics is an “omic” science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolis