Search

An increasing number of scientific studies support that flavanol-rich foods and beverages such as cocoa can promote human health, and are beneficial agents for the prevention of some diseases. Our previous studies showed that long-term cocoa intake enhances the antioxidant status in lymphoid organs and also modulates lymphocyte functionality in healthy young rats. Cocoa polyphenolic antioxidants seem to be the best candidates for those effects. However, data regarding polyphenol metabolites in tissues after a long-term cocoa intake are scarce. In the present study we mainly focus on the uptake and accumulation of epicatechin metabolites in lymphoid organs, including the thymus, spleen and mesenteric lymphoid nodes, as well as in the liver and testes after a diet rich in cocoa. Ten young weaned Wistar rats were fed randomly with a 10% (w/w) cocoa diet or a control diet for 3 weeks, corresponding to their infancy and youth. Tissues were treated with a solid-phase extraction and analysed by liquid chromatography-tandem MS. The major compounds recovered in these tissues were glucuronide derivatives of epicatechin and methylepicatechin. The highest concentration of these metabolites was found in the thymus, testicles and liver, followed by lymphatic nodes and spleen. The high amount of epicatechin metabolites found in the thymus supports our previous findings showing its high antioxidant capacity compared with other tissues such as the spleen. Moreover, this is the first time that epicatechin metabolites have been found in high concentrations in the testes, confirming other studies that have suggested the testes as an important site of oxidation. [ABSTRACT FROM AUTHOR]

BACKGROUND & AIMS: Punicalagin is an antioxidant ellagitannin of pomegranate juice. This compound is responsible for the high antioxidant activity of this juice. Nothing is known about the bioavailability and metabolism of punicalagin or other food ellagitannins. The present work aims to evaluate the bioavailability and metabolism of punicalagin in the rat as an animal model. DESIGN: Two groups of rats were studied. One fed with standard rat diet (n = 5) and another with the same diet plus 6 % punicalagin (n = 5). Samples of urine and faeces were taken during 37 days and plasma every week. The different metabolites were analysed by HPLC-MS-MS. RESULTS: The daily intake of punicalagin ranged from 0.6 to 1.2 g. Values around 3-6 % the ingested punicalagin were excreted as identified metabolites in faeces and urine. In faeces, punicalagin is transformed to hydrolysis products and partly metabolites by the rat microflora to 6H-dibenzo[b,d]pyran-6-one derivatives. In plasma, punicalagin was detected at concentrations around 30 microg/mL, and glucuronides of methyl ether derivatives of ellagic acid were also detected. 6H-Dibenzo[b,d]pyran-6-one derivatives were also detected especially during the last few weeks of the experiment. In urine, the main metabolites observed were the 6H-dibenzo[b,d]pyran-6-one derivatives, as aglycones or glucuronides. CONCLUSION: As only 3-6 % of the ingested punicalagin was detected as such or as metabolites in urine and faeces, the majority of this ellagitannin has to be converted to undetectable metabolites (i. e. CO(2)) or accumulated in non-analysed tissues, however with only traces of punicalagin metabolites being detected in liver or kidney. This is the first report on the absorption of an ellagitannin and its presence in plasma. In addition, the transformation of ellagic acid derivatives to 6H-dibenzo[b,d]pyran-6-one derivatives in the rat is also confirmed. [ABSTRACT FROM AUTHOR]

In inborn errors of intermediate protein metabolism (IEM), the effect of special low-protein foods (SLPFs) on dietary intake has been scarcely studied. The aim of this study was to compare the nutritional profile of SLPFs with usual foods and to assess whether their intake determines the dietary pattern and affects the plasma biochemical profile in children with IEMs with different protein restrictions. A database with the nutritional composition of 250 SLPFs was created. A total of 59 children with IEMs were included in this cross-sectional observational study. The greatest significant differences in macronutrient composition were observed between dairy, meat, fish, and egg SLPFs and regular foods. After stratifying subjects by SLPFs, the participants with the highest intake (>32%) had a higher total energy intake and lower intake of natural protein than those in the lowest tertile (<24%) ( p < 0.05). However, when stratifying subjects by dairy SLPF intake, children in the highest tertile (>5%) showed a higher intake of sugars, total and saturated fats, and higher plasma levels of total and low-density lipoprotein cholesterol than those in the first tertile (<1%) ( p < 0.05). The variability in the nutritional composition of SLPFs highlights the need for up-to-date databases which would greatly assist in optimizing individualized recommendations for children with IEMs and protein restrictions.

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Madrid-Gambin, Oller, Marco, Pozo, Andres-Lacueva and Llorach.)

The intervention with the Mediterranean diet (MD) pattern has evidenced short-term anti-inflammatory effects, but little is known about its long-term anti-inflammatory properties at molecular level. This study aims to investigate the 3-year effect of MD interventions compared to low-fat diet (LFD) on changes on inflammatory biomarkers related to atherosclerosis in a free-living population with a high-risk of cardiovascular disease (CD). Participants ( n = 285) in the PREDIMED trial were randomly assigned into three intervention groups: MD with extra-virgin olive oil (EVOO) or MD-Nuts, and a LFD. Fourteen plasma inflammatory biomarkers were determined by Luminex assays. An additional pilot study of gene expression (GE) was determined by RT-PCR in 35 participants. After 3 years, both MDs showed a significant reduction in the plasma levels of IL-1β, IL-6, IL-8, TNF-α, IFN-γ, hs-CRP, MCP-1, MIP-1β, RANTES, and ENA78 ( p < 0.05; all). The decreased levels of IL-1β, IL-6, IL-8, and TNF-α after MD significantly differed from those in the LFD ( p < 0.05). No significant changes were observed at the gene level after MD interventions, however, the GE of CXCR2 and CXCR3 tended to increase in the control LFD group ( p = 0.09). This study supports the implementation of MD as a healthy long-term dietary pattern in the prevention of CD in populations at high cardiovascular risk.

Legumes are an excellent source of nutrients and phytochemicals. They have been recognized for their contributions to health, sustainability, and the economy. Although legumes comprise several species and varieties, little is known about the differences in their phytochemical composition and the magnitude of these. Therefore, the aim of this review is to describe and compare the qualitative profile of phytochemicals contained in legumes and identified through LC-MS and GC-MS methods. Among the 478 phytochemicals reported in 52 varieties of legumes, phenolic compounds were by far the most frequently described ( n = 405, 85%). Metabolomics data analysis tools were used to visualize the qualitative differences, showing beans to be the most widely analyzed legumes and those with the highest number of discriminant phytochemicals ( n = 180, 38%). A Venn diagram showed that lentils, beans, soybeans, and chickpeas shared only 7% of their compounds. This work highlighted the huge chemical diversity among legumes and identified the need for further research in this field and the use of metabolomics as a promising tool to achieve it.

Legumes are a well-known source of phytochemicals and are commonly believed to have similar composition between different genera. To date, there are no studies evaluating changes in legumes to discover those compounds that help to discriminate for food quality and authenticity. The aim of this work was to characterize and make a comparative analysis of the composition of bioactive compounds between Cicer arietinum L. (chickpea), Lens culinaris L. (lentil) and Phaseolus vulgaris L. (white bean) through an LC-MS-Orbitrap metabolomic approach to establish which compounds discriminate between the three studied legumes. Untargeted metabolomic analysis was carried out by LC-MS-Orbitrap from extracts of freeze-dried legumes prepared from pre-cooked canned legumes. The metabolomic data treatment and statistical analysis were realized by using MAIT R's package, and final identification and characterization was done using MS n experiments. Fold-change evaluation was made through Metaboanalyst 4.0. Results showed 43 identified and characterized compounds displaying differences between the three legumes. Polyphenols, mainly flavonol and flavanol compounds, were the main group with 30 identified compounds, followed by α-galactosides (n = 5). Fatty acyls, prenol lipids, a nucleoside and organic compounds were also characterized. The fold-change analysis showed flavanols as the wider class of discriminative compounds of lentils compared to the other legumes; prenol lipids and eucomic acids were the most discriminative compounds of beans versus other legumes and several phenolic acids (such as primeveroside salycilic), kaempferol derivatives, coumesterol and α-galactosides were the most discriminative compounds of chickpeas. This study highlights the applicability of metabolomics for evaluating which are the characteristic compounds of the different legumes. In addition, it describes the future application of metabolomics as tool for the quality control of foods and authentication of different kinds of legumes., (Copyright © 2019. Published by Elsevier Ltd.)

Background/objectives: Cereal foods are major contributors to the daily energy, protein, and dietary fiber intake all over the world. The role of cereals in human health is dependent on whether they are consumed as refined or whole grain and on cereal species. To unravel the underlying mechanisms of health effects attributed to specific cereal foods and to provide more precise dietary advice, there is a need for improved dietary assessment of whole-grain intake. Dietary biomarkers of specific cereals, different fractions or cereal-containing foods could offer such a possibility. The aim of this review was to summarize the current status on biomarkers of different cereals, fractions, and specific cereal foods., Subjects and Methods: A literature review was conducted and putative biomarkers of different cereals and pseudo-cereals (wheat, oats, rye, barley, rice, and quinoa) as well as for different grain fractions (whole grain, refined grain, bran) and foods were summarized and discussed., Results: Several putative biomarkers have been suggested for different cereals, due to their unique presence in these grains. Among the biomarkers, odd-numbered alkylresorcinols are the most well-studied and -evaluated biomarkers and reflect whole-grain wheat and rye intake. Even-numbered alkylresorcinols have been suggested to reflect quinoa intake. Recent studies have also highlighted the potential of avenanthramides and avenacosides as specific biomarkers of oat intake, and a set of biomarkers have been suggested to reflect rice bran intake. However, there are yet no specific biomarkers of refined grains. Most biomarker candidates remain to be evaluated in controlled interventions and free-living populations before applied as biomarkers of intake in food and health studies., Conclusion: Several putative biomarkers of different cereals have been suggested and should be validated in human studies using recently developed food intake biomarker validation criteria., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2019.)

Covering: 1958 to June 2018 Phenyl-γ-valerolactones (PVLs) and their related phenylvaleric acids (PVAs) are the main metabolites of flavan-3-ols, the major class of flavonoids in the human diet. Despite their presumed importance, these gut microbiota-derived compounds have, to date, in terms of biological activity, been considered subordinate to their parent dietary compounds, the flavan-3-ol monomers and proanthocyanidins. In this review, the role and prospects of PVLs and PVAs as key metabolites in the understanding of the health features of flavan-3-ols have been critically assessed. Among the topics covered, are proposals for a standardised nomenclature for PVLs and PVAs. The formation, bioavailability and pharmacokinetics of PVLs and PVAs from different types of flavan-3-ols are discussed, taking into account in vitro and animal studies, as well as inter-individual differences and the existence of putative flavan-3-ol metabotypes. Synthetic strategies used for the preparation of PVLs are considered and the methodologies for their identification and quantification assessed. Metabolomic approaches unravelling the role of PVLs and PVAs as biomarkers of intake are also described. Finally, the biological activity of these microbial catabolites in different experimental models is summarised. Knowledge gaps and future research are considered in this key area of dietary (poly)phenol research.

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Fruit is a key component of a healthy diet. However, it is still not clear whether some classes of fruit may be more beneficial than others and whether all individuals whatever their age, gender, health status, genotype, or gut microbiota composition respond in the same way to fruit consumption. Such questions require further observational and intervention studies in which the intake of a specific fruit can be precisely assessed at the population and individual levels. Within the Food Biomarker Alliance Project (FoodBAll Project) under the Joint Programming Initiative "A Healthy Diet for a Healthy Life", an ambitious action was undertaken aiming at reviewing existent literature in a systematic way to identify validated and promising biomarkers of intake for all major food groups, including fruits. This paper belongs to a series of reviews following the same BFIRev protocol and is focusing on biomarkers of pome and stone fruit intake. Selected candidate biomarkers extracted from the literature search went through a validation process specifically developed for food intake biomarkers., Competing Interests: Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Bioactive compounds present in plant-based foods, and their metabolites derived from gut microbiota and endogenous metabolism, represent thousands of chemical structures of potential interest for human nutrition and health. State-of-the-art analytical methodologies, including untargeted metabolomics based on high-resolution mass spectrometry, are required for the profiling of these compounds in complex matrices, including plant food materials and biofluids. The aim of this project was to compare the analytical coverage of untargeted metabolomics methods independently developed and employed in various European platforms. In total, 56 chemical standards representing the most common classes of bioactive compounds spread over a wide chemical space were selected and analyzed by the participating platforms ( n = 13) using their preferred untargeted method. The results were used to define analytical criteria for a successful analysis of plant food bioactives. Furthermore, they will serve as a basis for an optimized consensus method., Competing Interests: The authors declare no conflict of interest.

The study of biomarkers of dietary patterns including the Mediterranean diet (MedDiet) is scarce and could improve the assessment of these patterns. Moreover, it could provide a better understanding of health benefits of dietary patterns in nutritional epidemiology. We aimed to determine a robust and accurate biomarker associated with a high adherence to a MedDiet pattern that included dietary assessment and its biological effect. In this cross-sectional study, we included 56 and 63 individuals with high (H-MDA) and low (L-MDA) MedDiet adherence categories, respectively, all from the Prevención con Dieta Mediterránea trial. A 1 H-NMR-based untargeted metabolomics approach was applied to urine samples. Multivariate statistical analyses were conducted to determine the metabolite differences between groups. A stepwise logistic regression and receiver operating characteristic curves were used to build and evaluate the prediction model for H-MDA. Thirty-four metabolites were identified as discriminant between H-MDA and L-MDA. The fingerprint associated with H-MDA included higher excretion of proline betaine and phenylacetylglutamine, among others, and decreased amounts of metabolites related to glucose metabolism. Three microbial metabolites - phenylacetylglutamine, p-cresol and 4-hydroxyphenylacetate - were included in the prediction model of H-MDA (95% specificity, 95% sensitivity and 97% area under the curve). The model composed of microbial metabolites was the biomarker that defined high adherence to a Mediterranean dietary pattern. The overall metabolite profiling identified reflects the metabolic modulation produced by H-MDA. The proposed biomarker may be a better tool for assessing and aiding nutritional epidemiology in future associations between H-MDA and the prevention or amelioration of chronic diseases., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils, and beans), spot urine samples from a subcohort from the PREDIMED study were stratified using a validated food frequency questionnaire. Urine samples of nonpulse consumers (≤4 g/day of pulse intake) and habitual pulse consumers (≥25 g/day of pulse intake) were analyzed using a 1 H nuclear magnetic resonance (NMR) metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine, and 3-methylhistidine. This model was evaluated by a receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to reported pulse exposure highlighted new candidates for biomarkers of pulse consumption and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings.

Scope: The Phenol-Explorer web database details 383 polyphenol metabolites identified in human and animal biofluids from 221 publications. Here, we exploit these data to characterize and visualize the polyphenol metabolome, the set of all metabolites derived from phenolic food components., Methods and Results: Qualitative and quantitative data on 383 polyphenol metabolites as described in 424 human and animal intervention studies were systematically analyzed. Of these metabolites, 301 were identified without prior enzymatic hydrolysis of biofluids, and included glucuronide and sulfate esters, glycosides, aglycones, and O-methyl ethers. Around one-third of these compounds are also known as food constituents and corresponded to polyphenols absorbed without further metabolism. Many ring-cleavage metabolites formed by gut microbiota were noted, mostly derived from hydroxycinnamates, flavanols, and flavonols. Median maximum plasma concentrations (C(max)) of all human metabolites were 0.09 and 0.32 μM when consumed from foods or dietary supplements, respectively. Median time to reach maximum plasma concentration in humans (T(max)) was 2.18 h., Conclusion: These data show the complexity of the polyphenol metabolome and the need to take into account biotransformations to understand in vivo bioactivities and the role of dietary polyphenols in health and disease., (© 2015 The Authors. Molecular Nutrition & Food Research published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Grape-derived polyphenols are considered to be one of the most promising ingredients for functional foods due to their health-promoting activities. We applied a HPLC-MS-based untargeted metabolomic approach in order to evaluate the impact of a functional food based on grape skin polyphenols on the urinary metabolome of healthy subjects. Thirty-one volunteers participated in two dietary crossover randomized intervention studies: with a single-dose intake (187 mL) and with a 15-day sustained consumption (twice per day, 187 mL per day in total) of a functional beverage (FB). Postprandial (4-hour) and 24-hour urine samples collected after acute consumption and on the last day of sustained FB consumption, respectively, were analysed using an untargeted HPLC-qTOF-MS approach. Multivariate modelling with subsequent application of an S-plot revealed differential mass features related to acute and prolonged consumption of FB. More than half of the mass features were shared between the two types of samples, among which several phase II metabolites of grape-derived polyphenols were identified at confidence level II. Prolonged consumption of FB was specifically reflected in urine metabolome by the presence of first-stage microbial metabolites of flavanols: hydroxyvaleric acid and hydroxyvalerolactone derivatives. Overall, several epicatechin and phenolic acid metabolites both of tissular and microbiota origin were the most representative markers of FB consumption. To our knowledge, this is one of the first studies where an untargeted LC-MS metabolomic approach has been applied in nutrition research on a grape-derived FB.

Although LC-MS untargeted metabolomics continues to expand into exiting research domains, methodological issues have not been solved yet by the definition of unbiased, standardized and globally accepted analytical protocols. In the present study, the response of the plasma metabolome coverage to specific methodological choices of the sample preparation (two SPE technologies, three sample-to-solvent dilution ratios) and the LC-ESI-MS data acquisition steps of the metabolomics workflow (four RP columns, four elution solvent combinations, two solvent quality grades, postcolumn modification of the mobile phase) was investigated in a pragmatic and decision tree-like performance evaluation strategy. Quality control samples, reference plasma and human plasma from a real nutrimetabolomic study were used for intermethod comparisons. Uni- and multivariate data analysis approaches were independently applied. The highest method performance was obtained by combining the plasma hybrid extraction with the highest solvent proportion during sample preparation, the use of a RP column compatible with 100% aqueous polar phase (Atlantis T3), and the ESI enhancement by using UHPLC-MS purity grade methanol as both organic phase and postcolumn modifier. Results led to the following considerations: submit plasma samples to hybrid extraction for removal of interfering components to minimize the major sample-dependent matrix effects; avoid solvent evaporation following sample extraction if loss in detection and peak shape distortion of early eluting metabolites are not noticed; opt for a RP column for superior retention of highly polar species when analysis fractionation is not feasible; use ultrahigh quality grade solvents and "vintage" analytical tricks such as postcolumn organic enrichment of the mobile phase to enhance ESI efficiency. The final proposed protocol offers an example of how novel and old-fashioned analytical solutions may fruitfully cohabit in untargeted metabolomics protocols.

Scope: The aim of the current study was to apply an untargeted metabolomics strategy to characterize a model of cocoa intake biomarkers in a free-living population., Methods and Results: An untargeted HPLC-q-ToF-MS based metabolomics approach was applied to human urine from 32 consumers of cocoa or derived products (CC) and 32 matched control subjects with no consumption of cocoa products (NC). The multivariate statistical analysis (OSC-PLS-DA) showed clear differences between CC and NC groups. The discriminant biomarkers identified were mainly related to the metabolic pathways of theobromine and polyphenols, as well as to cocoa processing. Consumption of cocoa products was also associated with reduced urinary excretions of methylglutarylcarnitine, which could be related to effects of cocoa exposure on insulin resistance. To improve the prediction of cocoa consumption, a combined urinary metabolite model was constructed. ROC curves were performed to evaluate the model and individual metabolites. The AUC values (95% CI) for the model were 95.7% (89.8-100%) and 92.6% (81.9-100%) in training and validation sets, respectively, whereas the AUCs for individual metabolites were <90%., Conclusions: The metabolic signature of cocoa consumption in free-living subjects reveals that combining different metabolites as biomarker models improves prediction of dietary exposure to cocoa., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The Mediterranean diet (MD) is considered a dietary pattern with beneficial effects on human health. The aim of this study was to assess the effect of an MD on urinary metabolome by comparing subjects at 1 and 3 years of follow-up, after an MD supplemented with either extra-virgin olive oil (MD + EVOO) or nuts (MD + Nuts), to those on advice to follow a control low-fat diet (LFD). Ninety-eight nondiabetic volunteers were evaluated, using metabolomic approaches, corresponding to MD + EVOO (n = 41), MD + Nuts (n = 27), or LFD (n = 30) groups. The (1)H NMR urinary profiles were examined at baseline and after 1 and 3 years of follow-up. Multivariate data analysis (OSC-PLS-DA and HCA) methods were used to identify the potential biomarker discriminating groups, exhibiting a urinary metabolome separation between MD groups against baseline and LFD. Results revealed that the most prominent hallmarks concerning MD groups were related to the metabolism of carbohydrates (3-hydroxybutyrate, citrate, and cis-aconitate), creatine, creatinine, amino acids (proline, N-acetylglutamine, glycine, branched-chain amino acids, and derived metabolites), lipids (oleic and suberic acids), and microbial cometabolites (phenylacetylglutamine and p-cresol). Otherwise, hippurate, trimethylamine-N-oxide, histidine and derivates (methylhistidines, carnosine, and anserine), and xanthosine were predominant after LFD. The application of NMR-based metabolomics enabled the classification of individuals regarding their dietary pattern and highlights the potential of this approach for evaluating changes in the urinary metabolome at different time points of follow-up in response to specific dietary interventions.

Unlabelled: Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context., Availability and Implementation: The source code of the methods is available as the R package intCor at http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Infectious mastitis is a common condition among lactating women, with staphylococci and streptococci being the main aetiological agents. In this context, some lactobacilli strains isolated from breast milk appear to be particularly effective for treating mastitis and, therefore, constitute an attractive alternative to antibiotherapy. A (1)H NMR-based metabolomic approach was applied to detect metabolomic differences after consuming a probiotic strain (Lactobacillus salivarius PS2) in women with mastitis. 24h urine of women with lactational mastitis was collected at baseline and after 21 days of probiotic (PB) administration. Multivariate analysis (OSC-PLS-DA and hierarchical clustering) showed metabolome differences after PB treatment. The discriminant metabolites detected at baseline were lactose, and ibuprofen and acetaminophen (two pharmacological drugs commonly used for mastitis pain), while, after PB intake, creatine and the gut microbial co-metabolites hippurate and TMAO were detected. In addition, a voluntary desertion of the pharmacological drugs ibuprofen and acetaminophen was observed after probiotic administration. The application of NMR-based metabolomics enabled the identification of the overall effects of probiotic consumption among women suffering from mastitis and highlighted the potential of this approach in evaluating the outcomes of probiotics consumption. To our knowledge, this is the first time that this approach has been applied in women with mastitis during lactation., (Copyright © 2014. Published by Elsevier Ltd.)