Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements.

Health has been defined as the capability of the organism to adapt to challenges. In this study, we tested to what extent comprehensively phenotyped individuals reveal differences in metabolic responses to a standardized mixed meal tolerance test (MMTT) and how these responses change when individuals experience moderate weight loss. Metabolome analysis was used in 70 healthy individuals. with profiling of ∼300 plasma metabolites during an MMTT over 8 h. Multivariate analysis of plasma markers of fatty acid catabolism identified 2 distinct metabotype clusters (A and B). Individuals from metabotype B showed slower glucose clearance, had increased intra-abdominal adipose tissue mass and higher hepatic lipid levels when compared with individuals from metabotype A. An NMR-based urine analysis revealed that these individuals also to have a less healthy dietary pattern. After a weight loss of ∼5.6 kg over 12 wk, only the subjects from metabotype B showed positive changes in the glycemic response during the MMTT and in markers of metabolic diseases. Our study in healthy individuals demonstrates that more comprehensive phenotyping can reveal discrete metabotypes with different outcomes in a dietary intervention and that markers of lipid catabolism in plasma could allow early detection of the metabolic syndrome.-Fiamoncini, J., Rundle, M., Gibbons, H., Thomas, E. L., Geillinger-Kästle, K., Bunzel, D., Trezzi, J.-P., Kiselova-Kaneva, Y., Wopereis, S., Wahrheit, J., Kulling, S. E., Hiller, K., Sonntag, D., Ivanova, D., van Ommen, B., Frost, G., Brennan, L., Bell, J. Daniel, H. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements.

Associations of vitamin D status with dietary intakes and physical activity levels among adults from seven European countries: the Food4Me study.

PURPOSE: To report the vitamin D status in adults from seven European countries and to identify behavioural correlates. METHODS: In total, 1075 eligible adult men and women from Ireland, Netherlands, Spain, Greece, UK, Poland and Germany, were included in the study. RESULTS: Vitamin D deficiency and insufficiency, defined as 25-hydroxy vitamin D3 (25-OHD3) concentration of <30 and 30-49.9 nmol/L, respectively, were observed in 3.3 and 30.6% of the participants. The highest prevalence of vitamin D deficiency was found in the UK and the lowest in the Netherlands (8.2 vs. 1.1%, P < 0.05). In addition, the prevalence of vitamin D insufficiency was higher in females compared with males (36.6 vs. 22.6%, P < 0.001), in winter compared with summer months (39.3 vs. 25.0%, P < 0.05) and in younger compared with older participants (36.0 vs. 24.4%, P < 0.05). Positive dose-response associations were also observed between 25-OHD3 concentrations and dietary vitamin D intake from foods and supplements, as well as with physical activity (PA) levels. Vitamin D intakes of ≥5 µg/day from foods and ≥5 µg/day from supplements, as well as engagement in ≥30 min/day of moderate- and vigorous-intensity PA were associated with higher odds (P < 0.05) for maintaining sufficient (≥50 nmol/L) 25-OHD3 concentrations. CONCLUSIONS: The prevalence of vitamin D deficiency varied considerably among European adults. Dietary intakes of ≥10 µg/day of vitamin D from foods and/or supplements and at least 30 min/day of moderate- and vigorous-intensity PA were the minimum thresholds associated with vitamin D sufficiency.

Determinants of postprandial plasma bile acid kinetics in human volunteers.

Bile acids (BA) are signaling molecules with a wide range of biological effects, also identified among the most responsive plasma metabolites in the postprandial state. We here describe this response to different dietary challenges and report on key determinants linked to its interindividual variability. Healthy men and women (n = 72, 62 ± 8 yr, mean ± SE) were enrolled into a 12-wk weight loss intervention. All subjects underwent an oral glucose tolerance test and a mixed-meal tolerance test before and after the intervention. BA were quantified in plasma by liquid chromatography-tandem mass spectrometry combined with whole genome exome sequencing and fecal microbiota profiling. Considering the average response of all 72 subjects, no effect of the successful weight loss intervention was found on plasma BA profiles. Fasting and postprandial BA profiles revealed high interindividual variability, and three main patterns in postprandial BA response were identified using multivariate analysis. Although the women enrolled were postmenopausal, effects of sex difference in BA response were evident. Exome data revealed the contribution of preselected genes to the observed interindividual variability. In particular, a variant in the SLCO1A2 gene, encoding the small intestinal BA transporter organic anion-transporting polypeptide-1A2 (OATP1A2), was associated with delayed postprandial BA increases. Fecal microbiota analysis did not reveal evidence for a significant influence of bacterial diversity and/or composition on plasma BA profiles. The analysis of plasma BA profiles in response to two different dietary challenges revealed a high interindividual variability, which was mainly determined by genetics and sex difference of host with minimal effects of the microbiota.NEW & NOTEWORTHY Considering the average response of all 72 subjects, no effect of the successful weight loss intervention was found on plasma bile acid (BA) profiles. Despite high interindividual variability, three main patterns in postprandial BA response were identified using multivariate analysis. A variant in the SLCO1A2 gene, encoding the small intestinal BA transporter organic anion-transporting polypeptide-1A2 (OATP1A2), was associated with delayed postprandial BA increases in response to both the oral glucose tolerance test and the mixed-meal tolerance test.

The insulin resistance phenotype (muscle or liver) interacts with the type of diet to determine changes in disposition index after 2 years of intervention: the CORDIOPREV-DIAB randomised clinical trial.

AIMS/HYPOTHESIS: The aim of the study was to determine whether basal insulin resistance (IR) phenotype (muscle and/or liver) determines the effect of long-term consumption of a Mediterranean diet or a low-fat diet on tissue-specific IR and beta cell function. METHODS: The study was performed in 642 patients included in The effect of an olive oil rich Mediterranean diet on type 2 diabetes mellitus risk and incidence study (CORDIOPREV-DIAB). A total of 327 patients were randomised to a Mediterranean diet (35% fat; 22% from monounsaturated fatty acids) and 315 to a low-fat diet (<28% fat). At baseline, the patients were classified into four phenotypes according to the type of IR: (1) no IR; (2) muscle IR; (3) liver IR; (4) muscle + liver IR. The hepatic insulin resistance index (HIRI), muscular insulin sensitivity index (MISI) and disposition index were analysed at baseline and after 2 years of follow-up. RESULTS: At baseline, 322 patients presented no IR, 106 presented muscle IR, 109 presented liver IR, and 105 presented muscle + liver IR. With both dietary interventions, HIRI decreased in all patients (p < 0.001) and MISI increased in muscle IR and muscle + liver IR patients (p < 0.01). Long-term intake of the Mediterranean diet increased the disposition index and insulinogenic index in the muscle IR patients (p = 0.042 and p = 0.044, respectively) and the disposition index in the muscle + liver IR patients (p = 0.048), whereas the low-fat diet increased the disposition index in the liver IR patients (p = 0.017). CONCLUSIONS/INTERPRETATION: Although both diets improve insulin sensitivity, there are differences based on basal IR phenotypes. Moreover, according to insulinogenic and disposition index data, a low-fat diet might be more beneficial to patients with liver IR, whereas patients with muscle IR and muscle + liver IR might benefit more from a Mediterranean diet. Trial registration ClinicalTrials.gov NCT00924937 FUNDING: The study was supported by the Ministerio de Economia y Competitividad (AGL2012/39615) and by the Ministerio de Ciencia e Innovacion (PIE14/00005 and PI13/00023).

Quantifying phenotypic flexibility as the response to a high-fat challenge test in different states of metabolic health.

Metabolism maintains homeostasis at chronic hypercaloric conditions, activating postprandial response mechanisms, which come at the cost of adaptation processes such as energy storage, eventually with negative health consequences. This study quantified the metabolic adaptation capacity by studying challenge response curves. After a high-fat challenge, the 8 h response curves of 61 biomarkers related to adipose tissue mass and function, systemic stress, metabolic flexibility, vascular health, and glucose metabolism was compared between 3 metabolic health stages: 10 healthy men, before and after 4 wk of high-fat, high-calorie diet (1300 kcal/d extra), and 9 men with metabolic syndrome (MetS). The MetS subjects had increased fasting concentrations of biomarkers representing the 3 core processes, glucose, TG, and inflammation control, and the challenge response curves of most biomarkers were altered. After the 4 wk hypercaloric dietary intervention, these 3 processes were not changed, as compared with the preintervention state in the healthy subjects, whereas the challenge response curves of almost all endocrine, metabolic, and inflammatory processes regulating these core processes were altered, demonstrating major molecular physiologic efforts to maintain homeostasis. This study thus demonstrates that change in challenge response is a more sensitive biomarker of metabolic resilience than are changes in fasting concentrations.

Inflammation and Nutritional Science for Programs/Policies and Interpretation of Research Evidence (INSPIRE).

An increasing recognition has emerged of the complexities of the global health agenda­specifically, the collision of infections and noncommunicable diseases and the dual burden of over- and undernutrition. Of particular practical concern are both 1) the need for a better understanding of the bidirectional relations between nutritional status and the development and function of the immune and inflammatory response and 2) the specific impact of the inflammatory response on the selection, use, and interpretation of nutrient biomarkers. The goal of the Inflammation and Nutritional Science for Programs/Policies and Interpretation of Research Evidence (INSPIRE) is to provide guidance for those users represented by the global food and nutrition enterprise. These include researchers (bench and clinical), clinicians providing care/treatment, those developing and evaluating programs/interventions at scale, and those responsible for generating evidence-based policy. The INSPIRE process included convening 5 thematic working groups (WGs) charged with developing summary reports around the following issues: 1) basic overview of the interactions between nutrition, immune function, and the inflammatory response; 2) examination of the evidence regarding the impact of nutrition on immune function and inflammation; 3) evaluation of the impact of inflammation and clinical conditions (acute and chronic) on nutrition; 4) examination of existing and potential new approaches to account for the impact of inflammation on biomarker interpretation and use; and 5) the presentation of new approaches to the study of these relations. Each WG was tasked with synthesizing a summary of the evidence for each of these topics and delineating the remaining gaps in our knowledge. This review consists of a summary of the INSPIRE workshop and the WG deliberations.

Looking back into the future: 30 years of metabolomics at TNO.

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

Exploring the benefits and challenges of establishing a DRI-like process for bioactives.

Bioactives can be defined as: "Constituents in foods or dietary supplements, other than those needed to meet basic human nutritional needs, which are responsible for changes in health status" (Office of Disease Prevention and Health Promotion, Office of Public Health and Science, Department of Health and Human Services in Fed Reg 69:55821-55822, 2004). Although traditional nutrients, such as vitamins, minerals, protein, essential fatty acids and essential amino acids, have dietary reference intake (DRI) values, there is no such evaluative process for bioactives. For certain classes of bioactives, substantial scientific evidence exists to validate a relationship between their intake and enhanced health conditions or reduced risk of disease. In addition, the study of bioactives and their relationship to disease risk is a growing area of research supported by government, academic institutions, and food and supplement manufacturers. Importantly, consumers are purchasing foods containing bioactives, yet there is no evaluative process in place to let the public know how strong the science is behind the benefits or the quantitative amounts needed to achieve these beneficial health effects. This conference, Bioactives: Qualitative Nutrient Reference Values for Life-stage Groups?, explored why it is important to have a DRI-like process for bioactives and challenges for establishing such a process.

Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations.

We used a previously developed physiologically based kinetic (PBK) model to analyze the effect of individual variations in metabolism and transport of cholesterol on pravastatin response. The PBK model is based on kinetic expressions for 21 reactions that interconnect eight different body cholesterol pools including plasma HDL and non-HDL cholesterol. A pravastatin pharmacokinetic model was constructed and the simulated hepatic pravastatin concentration was used to modulate the reaction rate constant of hepatic free cholesterol synthesis in the PBK model. The integrated model was then used to predict plasma cholesterol concentrations as a function of pravastatin dose. Predicted versus observed values at 40 mg/d pravastatin were 15 versus 22 % reduction of total plasma cholesterol, and 10 versus 5.6 % increase of HDL cholesterol. A population of 7,609 virtual subjects was generated using a Monte Carlo approach, and the response to a 40 mg/d pravastatin dose was simulated for each subject. Linear regression analysis of the pravastatin response in this virtual population showed that hepatic and peripheral cholesterol synthesis had the largest regression coefficients for the non-HDL-C response. However, the modeling also showed that these processes alone did not suffice to predict non-HDL-C response to pravastatin, contradicting the hypothesis that people with high cholesterol synthesis rates are good statin responders. In conclusion, we have developed a PBK model that is able to accurately describe the effect of pravastatin treatment on plasma cholesterol concentrations and can be used to provide insight in the mechanisms behind individual variation in statin response.

EURRECA-Principles and future for deriving micronutrient recommendations.

The EURopean micronutrient RECommendations Aligned (EURRECA) Network of Excellence (NoE) explored an approach for setting micronutrient recommendations, which would address the variation in recommendations across Europe. Therefore, a framework for deriving and using micronutrient Dietary Reference Values (DRVs) has been developed. This framework comprises four stages (defining the problem-monitoring and evaluating-deriving dietary reference values-using dietary reference values in policy making). The aim of the present paper is to use this framework to identify specific research gaps and needs related to (1) knowledge available on specific micronutrients (folate, iodine, iron, selenium, vitamin B12, vitamin D, and zinc) and (2) the methodology presented in the framework. Furthermore, the paper describes the different outputs that support the process like protocols, guidelines, systematic review databases, and peer-reviewed publications, as well as the principal routes of dissemination of these outputs to ensure their optimal uptake in policy, practice, and research collaborations. The importance of ensuring transparency in risk assessment and risk management, systematic searching the literature, and taking into account policy options is highlighted. [Supplementary materials are available for this article. Go to the publisher's online edition of Critical Reviews in Food Science and Nutrition for the following free supplemental files: Additional tables.].

Diet-induced Weight Loss and Phenotypic Flexibility Among Healthy Overweight Adults: A Randomized Trial.

BACKGROUND: The capacity of an individual to respond to changes in food intake so that postprandial metabolic perturbations are resolved, and metabolism returns to its pre-prandial state, is called phenotypic flexibility. This ability may be a more important indicator of current health status than metabolic markers in a fasting state. AIM: In this parallel randomized controlled trial study, an energy-restricted healthy diet and 2 dietary challenges were used to assess the effect of weight loss on phenotypic flexibility. METHODS: Seventy-two volunteers with overweight and obesity underwent a 12-wk dietary intervention. The participants were randomized to a weight loss group (WLG) with 20% less energy intake or a weight-maintenance group (WMG). At weeks 1 and 12, participants were assessed for body composition by MRI. Concurrently, markers of metabolism and insulin sensitivity were obtained from the analysis of plasma metabolome during 2 different dietary challenges-an oral glucose tolerance test (OGTT) and a mixed-meal tolerance test. RESULTS: Intended weight loss was achieved in the WLG (-5.6 kg, P < 0.0001) and induced a significant reduction in total and regional adipose tissue as well as ectopic fat in the liver. Amino acid-based markers of insulin action and resistance such as leucine and glutamate were reduced in the postprandial phase of the OGTT in the WLG by 11.5% and 28%, respectively, after body weight reduction. Weight loss correlated with the magnitude of changes in metabolic responses to dietary challenges. Large interindividual variation in metabolic responses to weight loss was observed. CONCLUSION: Application of dietary challenges increased sensitivity to detect metabolic response to weight loss intervention. Large interindividual variation was observed across a wide range of measurements allowing the identification of distinct responses to the weight loss intervention and mechanistic insight into the metabolic response to weight loss.

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans.

Increased plasma cholesterol concentration is associated with increased risk of cardiovascular disease. This study describes the development, validation, and analysis of a physiologically based kinetic (PBK) model for the prediction of plasma cholesterol concentrations in humans. This model was directly adapted from a PBK model for mice by incorporation of the reaction catalyzed by cholesterol ester transfer protein and contained 21 biochemical reactions and eight different cholesterol pools. The model was calibrated using published data for humans and validated by comparing model predictions on plasma cholesterol levels of subjects with 10 different genetic mutations (including familial hypercholesterolemia and Smith-Lemli-Opitz syndrome) with experimental data. Average model predictions on total cholesterol were accurate within 36% of the experimental data, which was within the experimental margin. Sensitivity analysis of the model indicated that the HDL cholesterol (HDL-C) concentration was mainly dependent on hepatic transport of cholesterol to HDL, cholesterol ester transfer from HDL to non-HDL, and hepatic uptake of cholesterol from non-HDL-C. Thus, the presented PBK model is a valid tool to predict the effect of genetic mutations on cholesterol concentrations, opening the way for future studies on the effect of different drugs on cholesterol levels in various subpopulations in silico.

A physiologically-based kinetic model for the prediction of plasma cholesterol concentrations in the mouse.

The LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C) concentrations are determined by the activity of a complex network of reactions in several organs. Physiologically-based kinetic (PBK) computational models can be used to describe these different reactions in an integrated, quantitative manner. A PBK model to predict plasma cholesterol levels in the mouse was developed, validated, and analyzed. Kinetic parameters required for defining the model were obtained using data from published experiments. To construct the model, a set of appropriate submodels was selected from a set of 65,536 submodels differing in the kinetic expressions of the reactions. A submodel was considered appropriate if it had the ability to correctly predict an increased or decreased plasma cholesterol level for a training set of 5 knockout mouse strains. The model thus defined consisted of 8 appropriate submodels and was validated using data from an independent set of 9 knockout mouse strains. The model prediction is the average prediction of 8 appropriate submodels. Remarkably, these submodels had in common that the rate of cholesterol transport from the liver to HDL was not dependent on hepatic cholesterol concentrations. The model appeared able to accurately predict in a quantitative way the plasma cholesterol concentrations of all 14 knockout strains considered, including the frequently used Ldlr-/- and Apoe-/- mouse strains. The model presented is a useful tool to predict the effect of knocking out genes that act in important steps in cholesterol metabolism on total plasma cholesterol, HDL-C and LDL-C in the mouse.

Systems biology analysis unravels the complementary action of combined rosuvastatin and ezetimibe therapy.

AIMS: Combination-drug therapy takes advantage of the complementary action of their individual components, thereby potentiating its therapeutic effect. Potential disadvantages include side effects that are not foreseen on basis of the data available from drug monotherapy. Here, we used a systems biology approach to understand both the efficacy and the side effects of a cholesterol-lowering drug-combination therapy on the basis of the biological pathways and molecular processes affected by each drug alone or in combination. METHODS AND RESULTS: ApoE*3Leiden transgenic mice, a mouse model with human-like cholesterol-lowering drug responses, were treated with rosuvastatin and ezetimibe, alone and in combination. Analyses included functional responses, viz. effects on cardiovascular risk factors, inflammation, and atherosclerosis, and measurement of global gene expression, and identification of enriched biological pathways and molecular processes. Combination therapy reduced plasma cholesterol, plasma inflammation markers, and atherosclerosis stronger than the single drugs did. Systems biology analysis at the level of biological processes shows that the therapeutic benefit of combined therapy is largely the result of additivity of the complementary mechanisms of action of the two single drugs. Importantly, combination therapy also exerted a significant effect on 16 additional and mostly NF-κB-linked signaling processes, 11 of which tended to be regulated in a similar direction with monotherapy. CONCLUSION: This study shows that gene expression analysis together with bioinformatics pathway analysis has the potential to help predict and identify drug combination-specific complementary and side effects.

Developing computational model-based diagnostics to analyse clinical chemistry data.

This article provides methodological and technical considerations to researchers starting to develop computational model-based diagnostics using clinical chemistry data. These models are of increasing importance, since novel metabolomics and proteomics measuring technologies are able to produce large amounts of data that are difficult to interpret at first sight, but have high diagnostic potential. Computational models aid interpretation and make the data accessible for clinical diagnosis. We discuss the issues that a modeller has to take into account during the design, construction and evaluation phases of model development. We use the example of Particle Profiler development, a model-based diagnostic tool for lipoprotein disorders, as a case study, to illustrate our considerations. The case study also offers techniques for efficient model formulation, model calculation, workflow structuring and quality control.

Transcriptomic coordination in the human metabolic network reveals links between n-3 fat intake, adipose tissue gene expression and metabolic health.

Understanding the molecular link between diet and health is a key goal in nutritional systems biology. As an alternative to pathway analysis, we have developed a joint multivariate and network-based approach to analysis of a dataset of habitual dietary records, adipose tissue transcriptomics and comprehensive plasma marker profiles from human volunteers with the Metabolic Syndrome. With this approach we identified prominent co-expressed sub-networks in the global metabolic network, which showed correlated expression with habitual n-3 PUFA intake and urinary levels of the oxidative stress marker 8-iso-PGF(2α). These sub-networks illustrated inherent cross-talk between distinct metabolic pathways, such as between triglyceride metabolism and production of lipid signalling molecules. In a parallel promoter analysis, we identified several adipogenic transcription factors as potential transcriptional regulators associated with habitual n-3 PUFA intake. Our results illustrate advantages of network-based analysis, and generate novel hypotheses on the transcriptomic link between habitual n-3 PUFA intake, adipose tissue function and oxidative stress.

Effects of a 13-Week Personalized Lifestyle Intervention Based on the Diabetes Subtype for People with Newly Diagnosed Type 2 Diabetes.

A type 2 diabetes mellitus (T2DM) subtyping method that determines the T2DM phenotype based on an extended oral glucose tolerance test is proposed. It assigns participants to one of seven subtypes according to their ß-cell function and the presence of hepatic and/or muscle insulin resistance. The effectiveness of this subtyping approach and subsequent personalized lifestyle treatment in ameliorating T2DM was assessed in a primary care setting. Sixty participants, newly diagnosed with (pre)diabetes type 2 and not taking diabetes medication, completed the intervention. Retrospectively collected data of 60 people with T2DM from usual care were used as controls. Bodyweight (p < 0.01) and HbA1c (p < 0.01) were significantly reduced after 13 weeks in the intervention group, but not in the usual care group. The intervention group achieved 75.0% diabetes remission after 13 weeks (fasting glucose ≤ 6.9 mmol/L and HbA1c < 6.5% (48 mmol/mol)); for the usual care group, this was 22.0%. Lasting (two years) remission was especially achieved in subgroups with isolated hepatic insulin resistance. Our study shows that a personalized diagnosis and lifestyle intervention for T2DM in a primary care setting may be more effective in improving T2DM-related parameters than usual care, with long-term effects seen especially in subgroups with hepatic insulin resistance.

An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study.

Introduction: A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). Objectives: We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods: All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results: A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion: These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

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.

Systematic construction of a conceptual minimal model of plasma cholesterol levels based on knockout mouse phenotypes.

Elevated plasma cholesterol, a well-known risk factor for cardiovascular diseases, is the result of the activity of many genes and their encoded proteins in a complex physiological network. We aim to develop a minimal kinetic computational model for predicting plasma cholesterol levels. To define the scope of this model, it is essential to discriminate between important and less important processes influencing plasma cholesterol levels. To this end, we performed a systematic review of mouse knockout strains and used the resulting dataset, named KOMDIP, for the identification of key genes that determine plasma cholesterol levels. Based on the described phenotype of mouse knockout models, 36 of the 120 evaluated genes were marked as key genes that have a pronounced effect on the plasma cholesterol concentration. The key genes include well-known genes, e.g., Apoe and Ldlr, as well as genes hardly linked to cholesterol metabolism so far, e.g., Plagl2 and Slc37a4. Based on the catalytic function of the genes, a minimal conceptual model was defined. A comparison with nine conceptual models from literature revealed that each of the individual published models is less complete than our model. Concluding, we have developed a conceptual model that can be used to develop a physiologically based kinetic model to quantitatively predict plasma cholesterol levels.