Gut microbiota as a driver of the interindividual variability of cardiometabolic effects from tea polyphenols.

Tea polyphenols have been extensively studied for their preventive properties against cardiometabolic diseases. Nevertheless, the evidence of these effects from human intervention studies is not always consistent, mainly because of a large interindividual variability. The bioavailability of tea polyphenols is low, and metabolism of tea polyphenols highly depends on individual gut microbiota. The accompanying reciprocal relationship between tea polyphenols and gut microbiota may result in alterations in the cardiometabolic effects, however, the underlying mechanism of which is little explored. This review summarizes tea polyphenols-microbiota interaction and its contribution to interindividual variability in cardiometabolic effects. Currently, only a few bacteria that can biodegrade tea polyphenols have been identified and generated metabolites and their bioactivities in metabolic pathways are not fully elucidated. A deeper understanding of the role of complex interaction necessitates fully individualized data, the ntegration of multiple-omics platforms and development of polyphenol-centered databases. Knowledge of this microbial contribution will enable the functional stratification of individuals in the gut microbiota profile (metabotypes) to clarify interindividual variability in the health effects of tea polyphenols. This could be used to predict individual responses to tea polyphenols consumption, hence bringing us closer to personalized nutrition with optimal dose and additional supplementation of specific microorganisms.

Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches.

PURPOSE: Extensive inter-individual variability exists in the production of flavan-3-ol metabolites. Preliminary metabolic phenotypes (metabotypes) have been defined, but there is no consensus on the existence of metabotypes associated with the catabolism of catechins and proanthocyanidins. This study aims at elucidating the presence of different metabotypes in the urinary excretion of main flavan-3-ol colonic metabolites after consumption of cranberry products and at assessing the impact of the statistical technique used for metabotyping. METHODS: Data on urinary concentrations of phenyl-γ-valerolactones and 3-(hydroxyphenyl)propanoic acid derivatives from two human interventions has been used. Different multivariate statistics, principal component analysis (PCA), cluster analysis, and partial least square-discriminant analysis (PLS-DA), have been considered. RESULTS: Data pre-treatment plays a major role on resulting PCA models. Cluster analysis based on k-means and a final consensus algorithm lead to quantitative-based models, while the expectation-maximization algorithm and clustering according to principal component scores yield metabotypes characterized by quali-quantitative differences in the excretion of colonic metabolites. PLS-DA, together with univariate analyses, has served to validate the urinary metabotypes in the production of flavan-3-ol metabolites and to confirm the robustness of the methodological approach. CONCLUSIONS: This work proposes a methodological workflow for metabotype definition and highlights the importance of data pre-treatment and clustering methods on the final outcomes for a given dataset. It represents an additional step toward the understanding of the inter-individual variability in flavan-3-ol metabolism. TRIAL REGISTRATION: The acute study was registered at clinicaltrials.gov as NCT02517775, August 7, 2015; the chronic study was registered at clinicaltrials.gov as NCT02764749, May 6, 2016.

Metabotyping and its role in nutrition research.

Personalised nutrition is at its simplest form the delivery of dietary advice at an individual level. Incorporating response to different diets has resulted in the concept of precision nutrition. Harnessing the metabolic phenotype to identify subgroups of individuals that respond differentially to dietary interventions is becoming a reality. More specifically, the classification of individuals in subgroups according to their metabolic profile is defined as metabotyping and this approach has been employed to successfully identify differential response to dietary interventions. Furthermore, the approach has been expanded to develop a framework for the delivery of targeted nutrition. The present review examines the application of the metabotype approach in nutrition research with a focus on developing personalised nutrition. Application of metabotyping in longitudinal studies demonstrates that metabotypes can be associated with cardiometabolic risk factors and diet-related diseases while application in interventions can identify metabotypes with differential responses. In general, there is strong evidence that metabolic phenotyping is a promising strategy to identify groups at risk and to potentially improve health promotion at a population level. Future work should verify if targeted nutrition can change behaviours and have an impact on health outcomes.

Inter-individual variability in the production of flavan-3-ol colonic metabolites: preliminary elucidation of urinary metabotypes.

PURPOSE: There is much information on the bioavailability of (poly)phenolic compounds following acute intake of various foods. However, there are only limited data on the effects of repeated and combined exposure to specific (poly)phenol food sources and the inter-individual variability in their bioavailability. This study evaluated the combined urinary excretion of (poly)phenols from green tea and coffee following daily consumption by healthy subjects in free-living conditions. The inter-individual variability in the production of phenolic metabolites was also investigated. METHODS: Eleven participants consumed both tablets of green tea and green coffee bean extracts daily for 8 weeks and 24-h urine was collected on five different occasions. The urinary profile of phenolic metabolites and a set of multivariate statistical tests were used to investigate the putative existence of characteristic metabotypes in the production of flavan-3-ol microbial metabolites. RESULTS: (Poly)phenolic compounds in the green tea and green coffee bean extracts were absorbed and excreted after simultaneous consumption, with green tea resulting in more inter-individual variability in urinary excretion of phenolic metabolites. Three metabotypes in the production of flavan-3-ol microbial metabolites were tentatively defined, characterized by the excretion of different amounts of trihydroxyphenyl-γ-valerolactones, dihydroxyphenyl-γ-valerolactones, and hydroxyphenylpropionic acids. CONCLUSIONS: The selective production of microbiota-derived metabolites from flavan-3-ols and the putative existence of characteristic metabotypes in their production represent an important development in the study of the bioavailability of plant bioactives. These observations will contribute to better understand the health effects and individual differences associated with consumption of flavan-3-ols, arguably the main class of flavonoids in the human diet.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling.

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

Metabotyping and its application in targeted nutrition: an overview.

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping - that is, grouping metabolically similar individuals - tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term 'metabotype' has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Chronic Diseases and Lifestyle Biomarkers Identification by Metabolomics.

Chronic diseases, also known as noncommunicable diseases (NCDs), are complex disorders that last for long periods of time and progress slowly. They currently account for the major cause of death worldwide with an alarming increase in rate both in developed and developing countries. In this chapter, the principal metabolomic-based investigations on chronic diseases (cardiovascular diseases, diabetes, and respiratory chronic diseases) and their major risk factors (particularly overweight/obesity) are described by focusing both on metabolites and metabolic pathways. Additional information on the contribution of metabolomics strategies in the ambit of the biomarker discovery for NCDs is also provided by exploring the major prospective studies of the last years (i.e., Framingham Heart Study, EPIC, MONICA, KORA, FINRIK, ECLIPSE). The metabolic signature of diseases, which arises from the metabolomic-based investigation, is therefore depicted in the chapter by pointing out the potential of metabolomics to explain the pathophysiological mechanisms underlying a disease, as well as to propose new therapeutic targets for alternative treatments.

Application of quantitative metabolomics in systems genetics in rodent models of complex phenotypes.

Genome-wide association studies (GWAS) have provided remarkable advances in our understanding of the etiology of complex diseases in humans and have underlined the need to improve patients' phenotype characterization with intermediate molecular phenotypes. High resolution metabolomics is becoming an increasingly popular and robust strategy for metabolic phenotyping large cohorts of patients and controls in genetic studies, in order to map the genetic control of metabotypes in various biological matrices (organ extracts and biofluids) through Quantitative Trait Locus (mQTL) analysis. This article reviews results from ongoing research in mQTL mapping in rodent models of human complex traits, with a specific focus on the cardiometabolic syndrome, and prospects of applications of untargeted metabolomics to improve knowledge of multilevel genome expression control in health and disease and to detect potential novel biomarkers for complex phenotypes in experimental systems in mice and rats.

Improvement of Urolithin A Yield by In Vitro Cofermentation of Streptococcus thermophilus FUA329 with Human Gut Microbiota from Different Urolithin Metabotypes.

Streptococcus thermophilus FUA329 converts ellagic acid (EA) to urolithin A (Uro-A), which is not autonomously converted by the gut microbiota to produce highly bioavailable and multibiologically active Uro-A in urolithin metabotype 0 (UM-0) populations. We consider that Streptococcus thermophilus FUA329 has the potential to be developed as a probiotic. Therefore, we utilized S. thermophilus FUA329 for in vitro cofermentation with gut microbiota. The results revealed that strain FUA329 increased the production of EA-converted Uro-A during in vitro cofermentation with the human gut microbiota of different urolithin metabotypes (UMs), with a significant increase in the production of Uro-A in the experimental group of UM-0. In addition, changes in the in vitro cofermentation microbial community were determined using high-throughput sequencing. Strain FUA329 modulated the structure and composition of the gut microbiota in different UMs, thereby significantly increasing the abundance of beneficial microbiota in the gut microbiota while decreasing the abundance of harmful microbiota. Of greatest interest was the significant increase in the abundance of Actinobacteria phylum after the cofermentation of strain FUA329 with UM-0 gut microbiota, which might be related to the significant increase in the production of Uro-A.

Fasting and postprandial plasma metabolite responses to a 12-wk dietary intervention in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial.

BACKGROUND: We previously showed that dietary intervention effects on cardiometabolic health were driven by tissue-specific insulin resistance (IR) phenotype: individuals with predominant muscle IR (MIR) benefited more from a low-fat, high-protein, and high-fiber (LFHP) diet, whereas individuals with predominant liver insulin resistance (LIR) benefited more from a high-monounsaturated fatty acid (HMUFA) diet. OBJECTIVES: To further characterize the effects of LFHP and HMUFA diets and their interaction with tissue-specific IR, we investigated dietary intervention effects on fasting and postprandial plasma metabolite profile. METHODS: Adults with MIR or LIR (40-75 y, BMI 25-40 kg/m2) were randomly assigned to a 12-wk HMUFA or LFHP diet (n = 242). After the exclusion of statin use, 214 participants were included in this prespecified secondary analysis. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, and 240 min) a high-fat mixed meal for quantification of 247 metabolite measures using nuclear magnetic resonance spectroscopy. RESULTS: A larger reduction in fasting VLDL-triacylglycerol (TAG) and VLDL particle size was observed in individuals with MIR following the LFHP diet and those with LIR following the HMUFA diet, although no longer statistically significant after false discovery rate (FDR) adjustment. No IR phenotype-by-diet interactions were found for postprandial plasma metabolites assessed as total area under the curve (tAUC). Irrespective of IR phenotype, the LFHP diet induced greater reductions in postprandial plasma tAUC of the larger VLDL particles and small HDL particles, and TAG content in most VLDL subclasses and the smaller LDL and HDL subclasses (for example, VLDL-TAG tAUC standardized mean change [95% CI] LFHP = -0.29 [-0.43, -0.16] compared with HMUFA = -0.04 [-0.16, 0.09]; FDR-adjusted P for diet × time = 0.041). CONCLUSIONS: Diet effects on plasma metabolite profiles were more pronounced than phenotype-by-diet interactions. An LFHP diet may be more effective than an HMUFA diet for reducing cardiometabolic risk in individuals with tissue-specific IR, irrespective of IR phenotype. Am J Clin Nutr 20xx;x:xx. This trial was registered at the clinicaltrials.gov registration (https://clinicaltrials.gov/study/NCT03708419?term=NCT03708419&rank=1) as NCT03708419 and CCMO registration (https://www.toetsingonline.nl/to/ccmo_search.nsf/fABRpop?readform&unids=3969AABCD9BA27FEC12587F1001BCC65) as NL63768.068.17.

Metabotyping: a tool for identifying subgroups for tailored nutrition advice.

Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet­disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.

Associations between dietary intake and glucose tolerance in clinical and metabolomics-based metabotypes.

BACKGROUND: Metabotyping is a novel concept to group metabolically similar individuals. Different metabotypes may respond differently to dietary interventions; hence, metabotyping may become an important future tool in precision nutrition strategies. However, it is not known if metabotyping based on comprehensive omic data provides more useful identification of metabotypes compared to metabotyping based on only a few clinically relevant metabolites. AIM: This study aimed to investigate if associations between habitual dietary intake and glucose tolerance depend on metabotypes identified from standard clinical variables or comprehensive nuclear magnetic resonance (NMR) metabolomics. METHODS: We used cross-sectional data from participants recruited through advertisements aimed at people at risk of type 2 diabetes mellitus (n = 203). Glucose tolerance was assessed with a 2-h oral glucose tolerance test (OGTT), and habitual dietary intake was recorded with a food frequency questionnaire. Lipoprotein subclasses and various metabolites were quantified with NMR spectroscopy, and plasma carotenoids were quantified using high-performance liquid chromatography. We divided participants into favorable and unfavorable clinical metabotypes based on established cutoffs for HbA1c and fasting and 2-h OGTT glucose. Favorable and unfavorable NMR metabotypes were created using k-means clustering of NMR metabolites. RESULTS: While the clinical metabotypes were separated by glycemic variables, the NMR metabotypes were mainly separated by variables related to lipoproteins. A high intake of vegetables was associated with a better glucose tolerance in the unfavorable, but not the favorable clinical metabotype (interaction, p = 0.01). This interaction was confirmed using plasma concentrations of lutein and zeaxanthin, objective biomarkers of vegetable intake. Although non-significantly, the association between glucose tolerance and fiber intake depended on the clinical metabotypes, while the association between glucose tolerance and intake of saturated fatty acids and dietary fat sources depended on the NMR metabotypes. CONCLUSION: Metabotyping may be a useful tool to tailor dietary interventions that will benefit specific groups of individuals. The variables that are used to create metabotypes will affect the association between dietary intake and disease risk.

Microbial Metabolism of the Soy Isoflavones Daidzein and Genistein in Postmenopausal Women: Human Intervention Study Reveals New Metabotypes.

BACKGROUND: Soy isoflavones belong to the group of phytoestrogens and are associated with beneficial health effects but are also discussed to have adverse effects. Isoflavones are intensively metabolized by the gut microbiota leading to metabolites with altered estrogenic potency. The population is classified into different isoflavone metabotypes based on individual metabolite profiles. So far, this classification was based on the capacity to metabolize daidzein and did not reflect genistein metabolism. We investigated the microbial metabolite profile of isoflavones considering daidzein and genistein. METHODS: Isoflavones and metabolites were quantified in the urine of postmenopausal women receiving a soy isoflavone extract for 12 weeks. Based on these data, women were clustered in different isoflavone metabotypes. Further, the estrogenic potency of these metabotypes was estimated. RESULTS: Based on the excreted urinary amounts of isoflavones and metabolites, the metabolite profiles could be calculated, resulting in 5 metabotypes applying a hierarchical cluster analysis. The metabotypes differed in part strongly regarding their metabolite profile and their estimated estrogenic potency.

Using a Metabotype Framework to Deliver Personalized Nutrition Improves Dietary Quality and Metabolic Health Parameters: A 12-Week Randomized Controlled Trial.

SCOPE: Effective strategies for tailoring dietary advice to individuals are urgently needed. The effectiveness of personalized nutrition advice delivered using a metabotype framework in improving dietary quality and metabolic health biomarkers compared to population-level advice is investigated. MATERIALS AND RESULTS: A 12-week parallel randomized controlled trial is performed with 107 healthy adults. Individuals in the personalized group are classified into metabotypes using four markers (triacylglycerol, high-density lipoprotein [HDL]-cholesterol, total cholesterol [TC], and glucose) and received dietary advice from decision tree algorithms containing metabotypes characteristics and individual traits. Individuals in the control group received generic dietary advice based on national guidelines. The personalized approach results in higher dietary quality assessed by the Alternate Mediterranean Diet Score (effect size [95% confidence interval, CI], 0.77 [0.07, 1.48], 12% versus 3% increase) and significantly lower concentrations of triacylglycerol (-0.17 [-0.28, -0.06] log10 mmol L-1 ), TC (-0.42 [-0.74, -0.10] mmol L-1 ), low-density lipoprotein (LDL)-cholesterol (-0.34, [-0.60, -0.09] mmol L-1 ), and lower triacylglycerol-glucose index (-0.40, [-0.67, -0.13]). Sixteen phosphatidylcholines and six lysophosphatidylcholines, predominately with chain lengths of 30-36 carbons, are lower in the personalized group. CONCLUSIONS: Personalized nutrition advice delivered using the metabotype framework is effective to improve dietary quality, which could result in reduced CVD risk, and metabolic heath biomarkers.

Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial.

Background: In a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework. Methods: Forty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated. Results: Two clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (p ≤ 0.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines). Conclusion: The findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health. Clinical trial registration: isrctn.com, identifier ISRCTN15305840.

Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition.

Motivation: In the field of precision nutrition, predicting metabolic response to diet and identifying groups of differential responders are two highly desirable steps toward developing tailored dietary strategies. However, data analysis tools are currently lacking, especially for complex settings such as crossover studies with repeated measures.Current methods of analysis often rely on matrix or tensor decompositions, which are well suited for identifying differential responders but lacking in predictive power, or on dynamical systems modeling, which may be used for prediction but typically requires detailed mechanistic knowledge of the system under study. To remedy these shortcomings, we explored dynamic mode decomposition (DMD), which is a recent, data-driven method for deriving low-rank linear dynamical systems from high dimensional data.Combining the two recent developments "parametric DMD" (pDMD) and "DMD with control" (DMDc) enabled us to (i) integrate multiple dietary challenges, (ii) predict the dynamic response in all measured metabolites to new diets from only the metabolite baseline and dietary input, and (iii) identify inter-individual metabolic differences, i.e., metabotypes. To our knowledge, this is the first time DMD has been applied to analyze time-resolved metabolomics data. Results: We demonstrate the potential of pDMDc in a crossover study setting. We could predict the metabolite response to unseen dietary exposures on both measured (R2 = 0.40) and simulated data of increasing size (Rmax2= 0.65), as well as recover clusters of dynamic metabolite responses. We conclude that this method has potential for applications in personalized nutrition and could be useful in guiding metabolite response to target levels. Availability and implementation: The measured data analyzed in this study can be provided upon reasonable request. The simulated data along with a MATLAB implementation of pDMDc is available at https://github.com/FraunhoferChalmersCentre/pDMDc.

The relevance of urolithins-based metabotyping for assessing the effects of a polyphenol-rich dietary intervention on intestinal permeability: A post-hoc analysis of the MaPLE trial.

A polyphenol-rich diet reduced intestinal permeability (IP) in older adults. Our aim was to evaluate if participants categorized according to urolithin metabotypes (UMs) exhibited different responses in the MaPLE trial. Fifty-one older adults (mean age: 78 years) completed an 8-week randomized-controlled-crossover trial comparing the effects of a polyphenol-rich vs. a control diet on IP, assessed through zonulin levels. Plasma and urinary metabolomics were evaluated with a semi-targeted UHPLC-MS/MS method. Gut microbiota was characterized by 16S rRNA gene profiling. UMs were determined according to urolithin excretion in 24 h urine samples. Multivariate statistics were used to characterize the differences in metabolomic and metataxonomic responses across UMs. Thirty-three participants were classified as urolithin metabotype A (UMA), 13 as urolithin metabotype B (UMB), and 5 as urolithin metabotype 0 (UM0) according to their urinary excretion of urolithins. Clinical, dietary, and biochemical characteristics at baseline were similar between UMs (all p > 0.05). After the polyphenol-rich diet, UMB vs. UMA participants showed a 2-fold higher improvement of zonulin levels (p for interaction = 0.033). Moreover, UMB vs. UMA participants were characterized for alterations in fatty acid metabolism, kynurenine pathway of tryptophan catabolism, and microbial metabolization of phenolic acids. These changes were correlated with the reduction of zonulin levels and modifications of gut microbes (increased Clostridiales, including, R. lactaris, and G. formicilis). In conclusion, urolithin-based metabotyping identified older adults with a higher improvement of IP after a polyphenol-rich diet. Our results reinforce the concept that UMs may contribute to tailor personalized nutrition interventions.

Plasma Metabolic Signatures of Healthy Overweight Subjects Challenged With an Oral Glucose Tolerance Test.

Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as "at risk" for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.

Metabolomics and the Multi-Omics View of Cancer.

Cancer is widely regarded to be a genetic disease. Indeed, over the past five decades, the genomic perspective on cancer has come to almost completely dominate the field. However, this genome-only view is incomplete and tends to portray cancer as a disease that is highly heritable, driven by hundreds of complex genetic interactions and, consequently, difficult to prevent or treat. New evidence suggests that cancer is not as heritable or purely genetic as once thought and that it really is a multi-omics disease. As highlighted in this review, the genome, the exposome, and the metabolome all play roles in cancer's development and manifestation. The data presented here show that >90% of cancers are initiated by environmental exposures (the exposome) which lead to cancer-inducing genetic changes. The resulting genetic changes are, then, propagated through the altered DNA of the proliferating cancer cells (the genome). Finally, the dividing cancer cells are nourished and sustained by genetically reprogrammed, cancer-specific metabolism (the metabolome). As shown in this review, all three "omes" play roles in initiating cancer. Likewise, all three "omes" interact closely, often providing feedback to each other to sustain or enhance tumor development. Thanks to metabolomics, these multi-omics feedback loops are now much more evident and their roles in explaining the hallmarks of cancer are much better understood. Importantly, this more holistic, multi-omics view portrays cancer as a disease that is much more preventable, easier to understand, and potentially, far more treatable.

Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy.

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography−high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.