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The rapid expansion of food and nutrition information requires new ways of data sharing and dissemination. Interactive platforms integrating data portals and visualization dashboards have been effectively utilized to describe, monitor, and track information related to food and nutrition; however, a comprehensive evaluation of emerging interactive systems is lacking. We conducted a systematic review on publicly available dashboards using a set of 48 evaluation metrics for data integrity, completeness, granularity, visualization quality, and interactivity based on 4 major principles: evidence, efficiency, emphasis, and ethics. We evaluated 13 dashboards, summarized their characteristics, strengths, and limitations, and provided guidelines for developing nutrition dashboards. We applied mixed effects models to summarize evaluation results adjusted for interrater variability. The proposed metrics and evaluation principles help to improve data standardization and harmonization, dashboard performance and usability, broaden information and knowledge sharing among researchers, practitioners, and decision makers in the field of food and nutrition, and accelerate data literacy and communication.

This study evaluated the validity of nutrient and food group intakes estimated by an FFQ against biomarkers. A 71-item semiquantitative FFQ was administered to 210 Brazilian children and adolescents aged 9–13 years. Intakes were correlated with biomarkers in plasma and red blood cells. Correlations between nutrients and their biomarkers were presented for animal protein, myristic acid (C14:0), EPA, DHA, β-carotene, folate, and vitamins B3, B5 and B6. Food groups and biomarkers were correlated as follows: fish products with EPA and DHA; milk and dairy with C14:0, pyridoxal 5'-phosphate and vitamin B12; total vegetables and dark green and orange vegetables with β-carotene; 5-methyltetrahydrofolate with green vegetables; and flour products with para-aminobenzoylglutamic acid. This FFQ is a valid tool for ranking Brazilian children and adolescents according to their intake of several nutrients and food groups.

The rapid development of nutrition science is embracing digital transformation to generate large amounts of data. Precision nutrition and "Big Data" place increasing demand for data repositories and visualization, which enhances the digital transformation. We defined the need for an integrated nutrition data platform as a web-based platform that can collect, store, track, analyze, monitor, and visually display key metrics in nutrition and health while allowing users to interact with visuals and download data provided in the platform. Interactive dashboards create new opportunities for scholars and practitioners to generate and test hypotheses. We present the development and implementation of the Global Nutrition and Health Atlas (GNHA; https://sites.tufts.edu/gnha/), an open-access online platform covering nutrition and health data with 26 themes and 500+ indicators from 190+ countries up to 30 y. We view GNHA as an interactive tool aiming to share information and perspectives and foster collaborations and innovations.

Systems biology approaches have been increasingly employed in clinical studies to enhance our understanding of the role of genetics, environmental factors and their interactions on nutritional, health and disease status. Amongst the new omics technologies, metabonomics has emerged as a robust platform to capture metabolic and nutritional requirements by enabling, in a minimally invasive fashion, the monitoring of a wide range of biochemical compounds. Their variations reflect comprehensively the various molecular regulatory processes, which are tightly controlled and under the influence of genetics, diet, gut microbiota and other environmental factors. They are providing key insights into complex metabolic phenomena as well as into differences and specificities at individual and population level. The aim of this review is to evaluate promising metabolic insights towards understanding metabolism of a long and healthy life from pre-clinical and clinical metabonomics studies. We will also discuss analytical approaches to enable data integration, with an emphasis on the longitudinal component. Herein, we will illustrate current examples, challenges and perspectives in the applications of metabonomics monitoring and modelling approaches in the context of healthy ageing research.

Polyunsaturated fatty acids (PUFA) contained in fish oil (FO) are ligands for peroxisome proliferator-activated receptors (PPAR) that may induce changes in cardiometabolic markers. Variation in PPAR genes may influence the beneficial responses linked to FO supplementation in young adults. The study aimed to analyze the effect of FO supplementation on glucose metabolism, circulating lipids and inflammation according to PPARα L162V and PPARγ2 P12A genotypes in young Mexican adults. 191 young, non-smoking subjects between 18 and 40 years were included in a one-arm study. Participants were supplemented with 2.7 g/day of EPA+DHA, during six weeks. Dietary analysis, body composition measurements and indicators for glucose metabolism, circulating lipids, and markers for inflammation were analyzed before and after intervention. An overall decrease in triglycerides (TG) and an increase in HS-ω3 index were observed in all subjects [-4.1 mg/dL, (SD:±51.7), P=.02 and 2.6%, (SD:±1.2), P

Aim: There is increasing interest in the profiling and quantitation of methionine pathway metabolites for health management research. Currently, several analytical approaches are required to cover metabolites and co-factors. Results: We report the development and the validation of a method for the simultaneous detection and quantitation of 13 metabolites in red blood cells. The method, validated in a cohort of healthy human volunteers, shows a high level of accuracy and reproducibility. Conclusion: This high-throughput protocol provides a robust coverage of central metabolites and co-factors in one single analysis and in a high-throughput fashion. In large-scale clinical settings, the use of such an approach will significantly advance the field of nutritional research in health and disease.

Quinic acid in its free form is broadly abundant in plants, and can accumulate in copious amounts in coffee, tea, and certain fruits. However, it has been mostly studied as chlorogenic acid, an ester of caffeic and quinic acids. When chlorogenic acid reaches the colon, it is hydrolyzed by microbial esterases releasing caffeic and quinic acids. While biotransformation of chlorogenic and caffeic acids have been elucidated by in vitro and in vivo studies, the gut metabolism of quinic acid has been so far overlooked.[U-Two parallel degradation pathways could be proposed: (1) an oxidative route, leading to aromatization and accumulation of protocatechuic acid, and a (2) reductive route, including dehydroxylation to cyclohexane carboxylic acid. Elucidating the biotransformation of food bioactives by the gut microbiota is of relevance for understanding nutrition, interindividual variability and potential effects on human metabolism.

In longitudinal clinical studies, methodologies available for the analysis of multivariate data with multivariate methods are relatively limited. Here, we present Consensus Clustering (CClust) a new computational method based on clustering of time profiles and posterior identification of correlation between clusters and predictors. Subjects are first clustered in groups according to a response variable temporal profile, using a robust consensus-based strategy. To discover which of the remaining variables are associated with the resulting groups, a non-parametric hypothesis test is performed between groups at every time point, and then the results are aggregated according to the Fisher method. Our approach is tested through its application to the EarlyBird cohort database, which contains temporal variations of clinical, metabolic, and anthropometric profiles in a population of 150 children followed-up annually from age 5 to age 16. Our results show that our consensus-based method is able to overcome the problem of the approach-dependent results produced by current clustering algorithms, producing groups defined according to Insulin Resistance (IR) and biological age (Tanner Score). Moreover, it provides meaningful biological results confirmed by hypothesis testing with most of the main clinical variables. These results position CClust as a valid alternative for the analysis of multivariate longitudinal data.

Scope: Quinic acid in its free form is broadly abundant in plants, and can accumulate in copious amounts in coffee, tea, and certain fruits. However, it has been mostly studied as chlorogenic acid, an ester of caffeic and quinic acids. When chlorogenic acid reaches the colon, it is hydrolyzed by microbial esterases releasing caffeic and quinic acids. While biotransformation of chlorogenic and caffeic acids have been elucidated by in vitro and in vivo studies, the gut metabolism of quinic acid has been so far overlooked. Methods and Results: [U-13C]-Quinic acid is submitted to a colonic model using human fecal microbiota for assessing its metabolic fate. The metabolite profiles formed along microbial biotransformation are monitored by a combined metabolomics approach, using both 2D GC– and ultra-HPLC–MS. Six metabolic intermediates are identified by incorporation of isotopic label. Conclusion: Two parallel degradation pathways could be proposed: (1) an oxidative route, leading to aromatization and accumulation of protocatechuic acid, and a (2) reductive route, including dehydroxylation to cyclohexane carboxylic acid. Elucidating the biotransformation of food bioactives by the gut microbiota is of relevance for understanding nutrition, interindividual variability and potential effects on human metabolism.

Calorie restriction (CR) has long been used to study lifespan effects and oppose the development of a broad array of age-related biological and pathological changes (increase healthspan). Yet, a comprehensive comparison of the metabolic phenotype across different genetic backgrounds to identify common metabolic markers affected by CR is still lacking. Using a system biology approach comprising metabonomics and liver transcriptomics we revealed the effect of CR across multiple mouse strains (129S1/SvlmJ, C57BL6/J, C3H/HeJ, CBA/J, DBA/2J, JC3F1/J). Oligonucleotide microarrays identified 76 genes as differentially expressed in all six strains confirmed. These genes were subjected to quantitative RT-PCR analysis in the C57BL/6J mouse strain, and a CR-induced change expression was confirmed for 14 genes. To fully depict the metabolic pathways affected by CR and complement the changes observed through differential gene expression, the metabolome of C57BL6/J was further characterized in liver tissues, urine and plasma levels using a combination or targeted mass spectrometry and proton nuclear magnetic resonance spectroscopy. Overall, our integrated approach commonly confirms that energy metabolism, stress response, lipids regulators and the insulin/IGF-1 are key determinants factors involved in CR regulation.

Epidemiological studies consistently find that diets rich in whole-grain (WG) cereals lead to decreased risk of disease compared with refined grain (RG)-based diets. Aside from a greater amount of fiber and micronutrients, possible mechanisms for why WGs may be beneficial for health remain speculative. In an exploratory, randomized, researcher-blinded, crossover trial, we measured metabolic profile differences between healthy participants eating a diet based on WGs compared with a diet based on RGs. Seventeen healthy adult participants (11 female, 6 male) consumed a controlled diet based on either WG-rich or RG-rich foods for 2 wk, followed by the other diet after a 5-wk washout period. Both diets were the same except for the use of WG (150 g/d) or FIG foods. The metabolic profiles of plasma, urine, and fecal water were measured using H-1-nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry (plasma only). After 1 wk of intervention, the WG diet led to decreases in urinary excretion of metabolites related to protein catabolism (urea, methylguanadine), lipid (carnitine and acylcarnitines) and gut microbial (4-hydroxyphenylacetate, trimethylacetate, dimethylacetate) metabolism in men compared with the same time point during the FIG intervention. There were no differences between the interventions after 2 wk. Urinary urea, carnitine, and acylcarnitine were lower at wk 1 of the WG intervention relative to the FIG intervention in all participants. Fecal water short-chain fatty acids acetate and butyrate were relatively greater after the WG diet compared to the RG diet. Although based on a small population and for a short time period, these observations suggest that a WG diet may affect protein metabolism.

We investigated the short-term (7 days) and long-term (60 days) metabolic effect of high fat diet induced obesity (DIO) and weight gain in isogenic C57BL/6 mice and examined the specific metabolic differentiation between mice that were either strong-responders (SR), or non-responders (NR) to weight gain. Mice (n = 80) were fed a standard chow diet for 7 days prior to randomization into a high-fat (HF) (n = 56) or a low-fat (LF) (n = 24) diet group. The (1)H NMR urinary metabolic profiles of LF and HF mice were recorded 7 and 60 days after the diet switch. On the basis of the body weight gain (BWG) distribution of HF group, we identified NR mice (n = 10) and SR mice (n = 14) to DIO. Compared with LF, HF feeding increased urinary excretion of glycine conjugates of β-oxidation intermediate (hexanoylglycine), branched chain amino acid (BCAA) catabolism intermediates (isovalerylglycine, α-keto-β-methylvalerate and α-ketoisovalerate) and end-products of nicotinamide adenine dinucleotide (NAD) metabolism (N1-methyl-2-pyridone-5-carboxamide, N1-methyl-4-pyridone-3-carboxamide) suggesting up-regulation of mitochondrial oxidative pathways. In the HF group, NR mice excreted relatively more hexanoylglycine, isovalerylglycine, and fewer tricarboxylic acid (TCA) cycle intermediate (succinate) in comparison to SR mice. Thus, subtle regulation of ketogenic pathways in DIO may alleviate the saturation of the TCA cycle and mitochondrial oxidative metabolism.

Background Hyperhomocysteinemia is a risk factor for cognitive decline and dementia, including Alzheimer disease (AD). Homocysteine (Hcy) is a sulfur-containing amino acid and metabolite of the methionine pathway. The interrelated methionine, purine, and thymidylate cycles constitute the one-carbon metabolism that plays a critical role in the synthesis of DNA, neurotransmitters, phospholipids, and myelin. In this study, we tested the hypothesis that one-carbon metabolites beyond Hcy are relevant to cognitive function and cerebrospinal fluid (CSF) measures of AD pathology in older adults. Methods Cross-sectional analysis was performed on matched CSF and plasma collected from 120 older community-dwelling adults with (n = 72) or without (n = 48) cognitive impairment. Liquid chromatography-mass spectrometry was performed to quantify one-carbon metabolites and their cofactors. Least absolute shrinkage and selection operator (LASSO) regression was initially applied to clinical and biomarker measures that generate the highest diagnostic accuracy of a priori-defined cognitive impairment (Clinical Dementia Rating-based) and AD pathology (i.e., CSF tau phosphorylated at threonine 181 [p-tau181]/β-Amyloid 1–42 peptide chain [Aβ1–42] >0.0779) to establish a reference benchmark. Two other LASSO-determined models were generated that included the one-carbon metabolites in CSF and then plasma. Correlations of CSF and plasma one-carbon metabolites with CSF amyloid and tau were explored. LASSO-determined models were stratified by apolipoprotein E (APOE) ε4 carrier status. Results The diagnostic accuracy of cognitive impairment for the reference model was 80.8% and included age, years of education, Aβ1–42, tau, and p-tau181. A model including CSF cystathionine, methionine, S-adenosyl-L-homocysteine (SAH), S-adenosylmethionine (SAM), serine, cysteine, and 5-methyltetrahydrofolate (5-MTHF) improved the diagnostic accuracy to 87.4%. A second model derived from plasma included cystathionine, glycine, methionine, SAH, SAM, serine, cysteine, and Hcy and reached a diagnostic accuracy of 87.5%. CSF SAH and 5-MTHF were associated with CSF tau and p-tau181. Plasma one-carbon metabolites were able to diagnose subjects with a positive CSF profile of AD pathology in APOE ε4 carriers. Conclusions We observed significant improvements in the prediction of cognitive impairment by adding one-carbon metabolites. This is partially explained by associations with CSF tau and p-tau181, suggesting a role for one-carbon metabolism in the aggregation of tau and neuronal injury. These metabolites may be particularly critical in APOE ε4 carriers. Electronic supplementary material The online version of this article (doi:10.1186/s13195-017-0270-x) contains supplementary material, which is available to authorized users.

Longitudinal studies aim typically at following populations of subjects over time and are important to understand the global evolution of biological processes. When it comes to longitudinal omics data, it will often depend on the overall objective of the study, and constraints imposed by the data, to define the appropriate modeling tools. Here, we report the use of multilevel simultaneous component analysis (MSCA), orthogonal projection on latent structures (OPLS), and regularized canonical correlation analysis (rCCA) to study associations between specific longitudinal urine metabonomics data and microbiome data in a diet-induced obesity model using C57BL/6 mice. (1)H NMR urine metabolic profiling was performed on samples collected weekly over a period of 13 weeks, and stool microbial composition was assessed using 16S rRNA gene sequencing at three specific time periods (baseline, first week response, end of study). MSCA and OPLS allowed us to explore longitudinal urine metabonomics data in relation to the dietary groups, as well as dietary effects on body weight. In addition, we report a data integration strategy based on regularized CCA and correlation analyses of urine metabonomics data and 16S rRNA gene sequencing data to investigate the functional relationships between metabolites and gut microbial composition. Thanks to this workflow enabling the breakdown of this data set complexity, the most relevant patterns could be extracted to further explore physiological processes at an anthropometric, cellular, and molecular level.

The underlying biochemical consequences of inflammatory bowel disease (IBD) on the systemic and gastrointestinal metabolism have not yet been fully elucidated but could help to better understand the disease pathogenesis and to identify tissue-specific markers associated with the different disease stages. Here, we applied a metabonomic approach to monitor metabolic events associated with the gradual development of Crohn's disease (CD)-like ileitis in the TNF(ΔARE/WT) mouse model. Metabolic profiles of different intestinal compartments from the age of 4 up to 24 weeks were generated by combining proton nuclear magnetic resonance ((1)H NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). From 8 weeks onward, mice developed CD similar to the immune and tissue-related phenotype of human CD with ileal involvement, including ileal histological abnormalities, reduced fat mass and body weight, as well as hallmarks of malabsorption with higher energy wasting. The metabonomic approach highlighted shifts in the intestinal lipid metabolism concomitant to the histological onset of inflammation. Moreover, the advanced disease status was characterized by a significantly altered metabolism of cholesterol, triglycerides, phospholipids, plasmalogens, and sphingomyelins in the inflamed tissue (ileum) and the adjacent intestinal parts (proximal colon). These results describe different biological processes associated with the disease onset, including modifications of the general cell membrane composition, alteration of energy homeostasis, and finally the generation of inflammatory lipid mediators. Taken together, this provides novel insights into IBD-related alterations of specific lipid-dependant processes during inflammatory states.

Insulin/IGF-like signaling (IIS) and nutrient sensing are among the most potent regulators of health status and aging. Here, a global view of the metabolic changes in C. elegans with impaired function of IIS represented by daf-2 and daf-16 and the intestinal di- and tripeptide transport pept-1 was generated using (1)H nuclear magnetic resonance spectroscopic analysis of worm extracts and spent culture media. We showed that specific metabolic profiles were significantly associated with each type of mutant. On the basis of the metabonomics data, selected underlying processes were further investigated using proteomic and transcriptomic approaches. The observed changes suggest a decreased activity of the one carbon metabolism in pept-1(lg601) mutants. Higher concentration of branched-chain amino acids (BCAA) and altered transcript levels of genes involved in BCAA metabolism were observed in long-living strains daf-2(e1370) and daf-2(e1370);pept-1(lg601) when compared to wild types and daf-16(m26);daf-2(e1370);pept-1(lg601) C. elegans, suggesting a DAF-16-dependent mechanism.

In man, living environment and lifestyle influence host physiology and microbial populations, and these interactions determine the health of individuals. In particular, dietary preferences are partially triggered by genetically-mediated taste responses to specific foods and may be reflected in the metabolic phenotype. However, identification of dietary metabolic signatures in free-living populations is made difficult by the relatively low amplitude of nutritional modulations when compared to inherent intra- and inter-individual variability that results from numerous confounding factors, e.g. sampling, lifestyle, gender and ageing. The identification of dietary biomarkers lies in the application of chemometrics to recover key metabolic information from the high density and highly variable metabolic phenotypes. One preferred approach is the use of linear regression models such as Partial Least Squares – Discriminant Analysis, whose reliability depends on appropriate validation procedures. However, the high inter- and intra-individual variability in free-living populations makes difficult the direct application of consolidated validation methodologies such as n-fold cross-validation that often leads to unstable solutions. In the present manuscript, we propose a new validation strategy based on the combination of several methodologies, merging n-fold cross validation and bootstrapping procedures with the confirmation of the reliability of the model through the generation of a set of replicate models. The proposed method was employed to validate the identification of dietary specific metabolic biomarkers in 1 H NMR and UPLC TOFMS urine metabolic profiles obtained from 21 free-living subjects stratified according to their sensitivity to propylthiouracil.

Multicompartmental metabolic profiling combined with multivariate data analysis offers a unique opportunity to explore the multidimensional metabolic relationships between various biological matrices. Here, we applied unsupervised chemometric methods for integrating 1H NMR metabolic profiles from mouse plasma, liver, pancreas, adrenal gland and kidney cortex matrices in order to infer intercompartments functional links. Principal Component Analysis (PCA) revealed metabolic differences between matrices but contained limited information on intercompartment metabolic relationships. Multiway PCA enabled the assessment of interindividual metabolic variability across multiple compartments in a single model and, therefore, metabolic correlations between different organs and circulating biofluids. However, this approach does not provide information on the relative contribution of one compartment to another. Integration of metabolic profiles using Multivariate Curve Resolution (MCR) and Parallel Factor Analysis (PARAFAC) methods provided an overview of functional relationships across matrices and enabled the characterization of compartment-specific metabolite signatures, the spectrotypes. In particular, the spectrotypes describe biochemical profiles specific or common to different biological compartments. Consequently, MCR-ALS and PARAFAC appeared to be better adapted for stepwise variable and compartment selection for further correlation analysis. Such a combination of chemometric techniques could provide new research avenues to assess the efficacy of drug or nutritional interventions on targeted organs.

In this article we highlight a novel variation on dynamic time warping (DTW) for aligning chromatogram signals. We are interested in sets of signals that can be aligned well locally, but not globally, by shifting individual signals in time. This kind of alignment is often sufficient for aligning gas chromatography data. Regular DTW often "over-warps" signals and introduces artificial features into the aligned data. To overcome this we introduce a variable penalty into the DTW process. The penalty is added to the distance metric whenever a nondiagonal step is taken. We select our penalty based on a morphological dilation of the two signals. We showcase our method by aligning GC/MS datafiles from 712 blood plasma samples processed in 23 batches over the course of 6 months. The use of variable penalty DTW significantly reduces the number of nondiagonal moves. In the examples presented here, this reduction is by a factor of 30, with no cost to visual quality of the alignment.

Although the prevalence of main idiopathic forms of inflammatory bowel disease (IBD) has risen considerably over the last decades, their clinical features do not allow accurate prediction of prognosis, likelihood of disease progression, or response to specific therapy. Through a better understanding of the molecular pathways involved in IBD and the promise of more targeted therapies, the personalized approach to the management of IBD shows potential. To achieve this, there remains a significant need to better understand the disease process at cellular and molecular levels for any given individual with IBD. The complexity of biological functional networks behind the etiology of IBD highlights the need for their comprehensive analysis. In this, omics technologies can generate a systemic view of IBD pathogenesis on which to base novel, multiple pathway-integrated therapies. Omics sciences have just started to contribute here by generating gene, protein expression, metabolite data at global level and large scale, and more recently by offering new opportunities to explore gut functional ecology. In particular, there is much expectation regarding the putative role of the gut microbiome in IBD. No doubt it will provide additional insights and lead to the development of alternative, hopefully better, diagnostic, prognostic, and monitoring tools in the management of IBD. This review discusses perspectives of relevance to clinical translation with emphasis on gut microbial metabolic activities.

Objectives: Exclusive enteral nutrition (EEN) is one of the first-line therapeutic options for pediatric patients with active Crohn’s disease (CD). However, only sparse data exist on plasma metabolic changes from patients on EEN therapy. Thus to gain mechanistic insights, we have characterized selected markers in pediatric CD patients treated with EEN for induction of disease remission. Methods: Plasma levels of 18 cytokines and chemokines and 163 metabolites in 10 pediatric CD patients receiving 8 weeks of EEN therapy were measured. Measurements were performed at three time points: a) at baseline (V0); b) during EEN intervention at week 4 (V4) and; c) at 4 weeks after completion of EEN therapy at week 12 (V12). Results: Comparisons between V0 and V4 levels identified changes in 20 different molecules, including increases in 7 metabolites of the diacyl phosphatidylcholine (PC) class and 6 metabolites of the alkylacyl PC class. However, most of these changes were not sustained after returning to a normal diet, i.e. only 7 out of the 20 molecules with significant changes between V0 and V4 still retained significance upon comparing V0 and V12. Conclusions: The changes in the plasma levels of various phospholipids at different time-points reflect the nutritional intervention and improved health status of pediatric CD patients.

The Brazilian Healthy Eating Index-Revised (BHEI-R) can be used to determine overall dietary patterns. We assessed the BHEI-R scores in children and adolescents, aged from 9 to 13 years old, and associated its component scores with biomarkers of health and dietary exposure. Three 24-h recalls were used to generate BHEI-R. Biomarkers were analyzed in plasma and red blood cells. Correlation tests, agreement, and covariance analyses were used to associate BHEI-R components with biomarkers. Data from 167 subjects were used. The strongest correlations were between fruits, vegetables and legumes with omega-6 and omega-3 fatty acids, and β-carotene intakes. Milk and dairy correlated with plasma retinol and pyridoxine. All components rich in vegetable and animal protein sources correlated with plasma creatine. Total BHEI-R scores were positively associated with intakes of omega-6, omega-3, fiber and vitamin C, and inversely associated with energy and saturated fat intakes of individuals. Plasma β-carotene and riboflavin biomarkers were positively associated with total BHEI-R. An inadequate food consumption pattern was captured by both biomarkers of health and dietary exposure. BHEI-R was validated for the above dietary components and can be associated with metabolomics and nutritional epidemiological data in future pediatric studies.

This chapter describes the role of machine learning approaches such as random forests in holistic discovery applications and provides a background for its better understanding. Their suitability for feature selection, data integration, and network modelling are also evaluated through recent examples in the literature. These examples cover a variety of fields, ranging from ecology to metabolomics.

Although the prevalence of main idiopathic forms of inflammatory bowel disease (IBD) has risen considerably over the last decades, their clinical features do not allow accurate prediction of prognosis, likelihood of disease progression, or response to specific therapy. Through a better understanding of the molecular pathways involved in IBD and the promise of more targeted therapies, the personalized approach to the management of IBD shows potential. To achieve this, there remains a significant need to better understand the disease process at cellular and molecular levels for any given individual with IBD. The complexity of biological functional networks behind the etiology of IBD highlights the need for their comprehensive analysis. In this, omics technologies can generate a systemic view of IBD pathogenesis on which to base novel, multiple pathway-integrated therapies. Omics sciences have just started to contribute here by generating gene, protein expression, metabolite data at global level and large scale, and more recently by offering new opportunities to explore gut functional ecology. In particular, there is much expectation regarding the putative role of the gut microbiome in IBD. No doubt it will provide additional insights and lead to the development of alternative, hopefully better, diagnostic, prognostic, and monitoring tools in the management of IBD. This review discusses perspectives of relevance to clinical translation with emphasis on gut microbial metabolic activities.

A lipidomic approach was employed in a clinically well-defined cohort of healthy obese women to explore blood lipidome phenotype ascribed to body fat deposition, with emphasis on epicardial adipose tissue (EAT).The present investigation delivered a lipidomics signature of epicardial adiposity under healthy clinical conditions using a cohort of 40 obese females (age: 25-45 years, BMI: 28-40 kg/m(2) ) not showing any metabolic disease traits. Lipidomics analysis of blood plasma was employed in combination with in vivo quantitation of mediastinal fat depots by computerized tomography.All cardiac fat depots correlated to indicators of hepatic dysfunctions (ALAT and ASAT), which describe physiological connections between hepatic and cardiac steatosis. Plasma lipidomics encompassed overall levels of lipid classes, fatty acid profiles, and individual lipid species. EAT and visceral fat associated with diacylglycerols (DAG), triglycerides, and distinct phospholipid and sphingolipid species. A pattern of DAG and phosphoglycerols was specific to EAT.Human blood plasma lipidomics appears to be a promising clinical and potentially diagnostic readout for patient stratification and monitoring. Association of blood lipidomics signature to regio-specific mediastinal and visceral adiposity under healthy clinical conditions may help provide more biological insights into obese patient stratification for cardiovascular disease risks.

As centenarians well represent the model of healthy aging, there are many important implications in revealing the underlying molecular mechanisms behind such successful aging. By combining NMR metabonomics and shot-gun lipidomics in serum we analyzed metabolome and lipidome composition of a group of centenarians with respect to elderly individuals. Specifically, NMR metabonomics profiling of serum revealed that centenarians are characterized by a metabolic phenotype distinct from that of elderly subjects, in particular regarding amino acids and lipid species. Shot- gun lipidomics approach displays unique changes in lipids biosynthesis in centenarians, with 41 differently abundant lipid species with respect to elderly subjects. These findings reveal phospho/sphingolipids as putative markers and biological modulators of healthy aging, in humans. Considering the particular actions of these metabolites, these data are suggestive of a better counteractive antioxidant capacity and a well-developed membrane lipid remodelling process in the healthy aging phenotype.

Background:'You are what you eat' is an accurate summary for humans and animals when it comes to carbon isotope abundance. In biological material, natural13C/12C ratio is subject to minute variations due to diet composition (mainly from ingestion of C3 and C4 metabolism plants) and to the discrimination between 'light' and 'heavy' isotopes during biochemical reactions (isotope effects and isotopic fractionation).Methodology/Principal Findings:Carbon isotopic abundance was measured in ZDF (fa/+) and ZDF (fa/fa), (lean and obese-diabetic rats respectively) fed the same diet. By analysing plasma metabolites (glucose and non-esterified fatty acids), breath and liver tissue by high-precision isotope ratio mass spectrometry, we demonstrate for the first time statistically distinguishable metabolic carbon isotope abundance between ZDF (fa/+) and ZDF (fa/fa) rats based on plasma glucose, palmitic, oleic, linoleic, arachidonic acids and bulk analysis of liver tissue (P

BACKGROUND: The combination of maternal obesity in early pregnancy and high protein intake in infant formula feeding might predispose to obesity risk in later life. METHODS: This study assesses the impact of breast- or formula-feeding (differing in protein content by 1.65 or 2.7g/100 kcal) on the metabolism of term infants from overweight and obese mothers. From birth to 3 mo of age, infants received exclusively either breast- or starter formula-feeding and until 6 mo, exclusively either a formula designed for this study or breast-feeding. From 6 to 12 mo, infants received complementary weaning food. Metabonomics was conducted on the infants' urine and stool samples collected at the age of 3, 6, and 12 mo. RESULTS: Infant formula-feeding resulted in higher protein-derived short-chain fatty acids and amino acids in stools. Urine metabonomics revealed a relationship between bacterial processing of dietary proteins and host protein metabolism stimulated with increasing protein content in the formula. Moreover, formula-fed infants were metabolically different from breast-fed infants, at the level of lipid and energy metabolism (carnitines, ketone bodies, and Krebs cycle). CONCLUSION: Noninvasive urine and stool metabolic monitoring of responses to early nutrition provides relevant readouts to assess nutritional requirements for infants' growth.

The aging phenotype in humans has been thoroughly studied but a detailed metabolic profiling capable of shading light on the underpinning biological processes of longevity is still missing. Here using a combined metabonomics approach compromising holistic 1H-NMR profiling and targeted MS approaches, we report for the first time the metabolic phenotype of longevity in a well characterized human aging cohort compromising mostly female’s centenarian, elderly, and young individuals. Within increasing age targeted MS profiling of blood serum displayed a marked decrease in tryptophan concentration, while an unique alteration of specific glycerophospholipids and sphingolipids are seen in the longevity phenotype.We hypothesized that the overall lipidome changes specific to longevity putatively reflect centenarians’ unique capacity to adapt/respond to the accumulating oxidative and chronic inflammatory conditions characteristics of their extreme aging phenotype. Our data in centenarians support promotion of a cellular detoxification mechanisms through specific modulation of the arachidonic acid metabolic cascade as we underpinned increased concentration of 8,9-EpETrE, suggesting enhanced cytochrome P450 (CYP) enzyme activity. Such effective mechanism might results in the activation of an anti-oxidative response, as displayed by decreased circulating levels of 9-HODE, and 9-oxoODE, markers of lipid peroxidation and oxidative products of linoleic acid. Lastly, we also revealed that the longevity process deeply affects the structure and composition of the human gut microbiota as shown by the increased extrection of phenylacetylglutamine (PAG) and p-cresol sulfate (PCS) in urine of centenarians. Together, our novel approach in this representative Italian longevity cohort support the hypothesis that a complex remodeling of lipid, amino acid metabolism, and of gut microbiota functionality are key regulatory processes marking exceptional longevity in humans.

Systems biology approaches are providing novel insights into the role of nutrition for the management of health and disease. In the present study, we investigated if dietary preference for dark chocolate in healthy subjects may lead to different metabolic response to daily chocolate consumption. Using NMR- and MS-based metabolic profiling of blood plasma and urine, we monitored the metabolic response of 10 participants stratified as chocolate desiring and eating regularly dark chocolate (CD) and 10 participants stratified as chocolate indifferent and eating rarely dark chocolate (CI) to a daily consumption of 50 g of dark chocolate as part of a standardized diet over a one week period. We demonstrated that preference for chocolate leads to different metabolic response to chocolate consumption. Daily intake of dark chocolate significantly increased HDL cholesterol by 6% and decreased polyunsaturated acyl ether phospholipids. Dark chocolate intake could also induce an improvement in the metabolism of long chain fatty acid, as noted by a compositional change in plasma fatty acyl carnitines. Moreover, a relationship between regular long-term dietary exposure to a small amount of dark chocolate, gut microbiota, and phenolics was highlighted, providing novel insights into biological processes associated with cocoa bioactives.

Genome-wide association studies (GWASs) have become a very important tool to address the genetic origin of phenotypic variability, in particular associated with diseases. Nevertheless, these types of studies provide limited information about disease etiology and the molecular mechanisms involved. Recently, the incorporation of metabolomics into the analysis has offered novel opportunities for a better understanding of disease-related metabolic deregulation. The pattern emerging from this work is that gene-driven changes in metabolism are prevalent and that common genetic variations can have a profound impact on the homeostatic concentrations of specific metabolites. A particularly interesting aspect of this work takes into account interactions of environment and lifestyle with the genome and how this interaction translates into changes in the metabolome. For instance, the role of PYROXD2 in trimethylamine metabolism points to an interaction between host and microbiome genomes (host/microbiota). Often, these findings reveal metabolic deregulations, which could eventually be tuned with a nutritional intervention. Here we review the development of gene–metabolism association studies from a single-gene/single-metabolite to a genome-wide/metabolome-wide approach and highlight the conceptual changes associated with this ongoing transition. Moreover, we report some of our recent GWAS results on a cohort of 265 individuals from an ethnically diverse population that validate and refine previous findings on gene–urine metabolism interactions. Specifically, our results confirm the effect of PYROXD2 polymorphisms on trimethylamine metabolism and suggest that a previously reported association of N-acetylated compounds with the ALMS1/NAT8 locus is driven by SNPs in the ALMS1 gene.

A major source of intestinal metabolites results from both host and microbial processing of dietary nutrients. (1)H NMR-based metabolic profiling of mouse feces was carried out over time in different microbiome mouse models, including conventional (n = 9), conventionalized (n = 10), and "humanized" gnotobiotic mice inoculated with a model of human baby microbiota (HBM, n = 17). HBM mice were supplemented with Lactobacillus paracasei with (n = 10) and without (n = 7) prebiotics. Animals not supplemented with prebiotics received a diet enriched in glucose and lactose as placebo. In conventionalized animals, microbial populations and activities converged in term of multivariate mapping toward conventional mice. Both groups decreased bacterial processing of dietary proteins when switching to a diet enriched in glucose and lactose, as described with low levels of 5-aminovalerate, acetate, and propionate and high levels of lysine and arginine. The HBM model differs from conventional and conventionalized microbiota in terms of type, proportion, and metabolic activity of gut bacteria (lower short chain fatty acids (SCFAs), lactate, 5-aminovalerate, and oligosaccharides, higher bile acids and choline). The probiotics supplementation of HBM mice was associated with a specific amino acid pattern that can be linked to L. paracasei proteolytic activities. The combination of L. paracasei with the galactosyl-oligosaccharide prebiotics was related to the enhanced growth of bifidobacteria and lactobacilli, and a specific metabolism of carbohydrates, proteins, and SCFAs. The present study describes how the assessment of metabolic changes in feces may provide information for studying nutrient-microbiota relationships in different microbiome mouse models.

A global metabolic profiling methodology based on gas chromatography coupled to time of flight mass spectrometry (GC TOFMS) for human plasma was applied to a human exercise study focused on the effects of beverages containing glucose galactose or fructose taken after exercise and throughout a recovery period of 6. h and 45. min. One group of 10 well trained male cyclists performed 3 experimental sessions on separate days (randomized single center). After performing a standardized depletion protocol on a bicycle subjects consumed one of three different beverages: maltodextrin (MD). +. glucose (2:1 ratio) MD. +. galactose (2:1) and MD. +. fructose (2:1) consumed at an average of 1.25. g of carbohydrate (CHO) ingested per minute. Blood was taken straight after exercise and every 45. min within the recovery phase. With the resulting blood plasma insulin free fatty acid (FFA) profile glucose and GC TOFMS global metabolic profiling measurements were performed. The resulting profiling data was able to match the results obtained from the other clinical measurements with the addition of being able to follow many different metabolites throughout the recovery period. The data quality was assessed with all the labelled internal standards yielding values of

Metal oxide (MOX) gas sensors have been widely used for years. Temperature modulation of gas sensors is as an alternative to increase their sensitivity and selectivity to different gas species. In order to enhance the extraction of useful information from this kind of signals, data processing techniques are needed. In this work, the use of self-modelling curve resolution techniques, in particular multivariate curve resolution-alternating least squares (MCR-ALS), is presented for the analysis of these signals. First, the performance of MCR in a synthetic dataset generated from temperature-modulated gas sensor response models has been evaluated, showing good results both in the resolution of gas mixtures and in the determination of concentration/sensitivity profiles. Secondly, experimental confirmation of previously obtained conclusions is attempted using temperature-modulated MOX sensors together with MCR-ALS for the analysis of carbon monoxide (CO) and methane (CH4) gas mixtures in dry air. Results allow confirming the possibility of using the proposed approach as a quantitative technique for gas mixtures analysis, and also reveal some limitations., GOSPEL NoE FP6-IST 507610 for their support and CyCIT project TEC2004-07853-c02-01.

Background Dietary long-chain polyunsaturated fatty acids (LC-PUFA) are of crucial importance for the development of neural tissues. The aim of this study was to evaluate the impact of a dietary supplementation in n-3 fatty acids in female rats during gestation and lactation on fatty acid pattern in brain glial cells phosphatidylethanolamine (PE) and phosphatidylserine (PS) in the neonates. Methods Sprague-Dawley rats were fed during the whole gestation and lactation period with a diet containing either docosahexaenoic acid (DHA, 0.55%) and eicosapentaenoic acid (EPA, 0.75% of total fatty acids) or α-linolenic acid (ALA, 2.90%). At two weeks of age, gastric content and brain glial cell PE and PS of rat neonates were analyzed for their fatty acid and dimethylacetal (DMA) profile. Data were analyzed by bivariate and multivariate statistics. Results In the neonates from the group fed with n-3 LC-PUFA, the DHA level in gastric content (+65%, P < 0.0001) and brain glial cell PE (+18%, P = 0.0001) and PS (+15%, P = 0.0009) were significantly increased compared to the ALA group. The filtered correlation analysis (P < 0.05) underlined that levels of dihomo-γ-linolenic acid (DGLA), DHA and n-3 docosapentaenoic acid (DPA) were negatively correlated with arachidonic acid (ARA) and n-6 DPA in PE of brain glial cells. No significant correlation between n-3 and n-6 LC-PUFA were found in the PS dataset. DMA level in PE was negatively correlated with n-6 DPA. DMA were found to occur in brain glial cell PS fraction; in this class DMA level was correlated negatively with DHA and positively with ARA. Conclusion The present study confirms that early supplementation of maternal diet with n-3 fatty acids supplied as LC-PUFA is more efficient in increasing n-3 in brain glial cell PE and PS in the neonate than ALA. Negative correlation between n-6 DPA, a conventional marker of DHA deficiency, and DMA in PE suggests n-6 DPA that potentially be considered as a marker of tissue ethanolamine plasmalogen status. The combination of multivariate and bivariate statistics allowed to underline that the accretion pattern of n-3 LC-PUFA in PE and PS differ.