Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes

Perspective: a systems approach to diabetes research

We review here the status of human type 2 diabetes studies from a genetic, epidemiological, and clinical (intervention) perspective. Most studies limit analyses to one or a few omic technologies providing data of components of physiological processes. Since all chronic diseases are multifactorial and arise from complex interactions between genetic makeup and environment, type 2 diabetes mellitus (T2DM) is a collection of sub-phenotypes resulting in high fasting glucose. The underlying gene–environment interactions that produce these classes of T2DM are imperfectly characterized. Based on assessments of the complexity of T2DM, we propose a systems biology approach to advance the understanding of origin, onset, development, prevention, and treatment of this complex disease. This systems-based strategy is based on new study design principles and the integrated application of omics technologies: we pursue longitudinal studies in which each subject is analyzed at both homeostasis and after (healthy and safe) challenges. Each enrolled subject functions thereby as their own case and control and this design avoids assigning the subjects a priori to case and control groups based on limited phenotyping. Analyses at different time points along this longitudinal investigation are performed with a comprehensive set of omics platforms. These data sets are generated in a biological context, rather than biochemical compound class-driven manner, which we term “systems omics.”

Monitoring immune modulation by nutrition in the general population: identifying and substantiating effects on human health

Optimal functioning of the immune system is crucial to human health, and nutrition is one of the major exogenous factors modulating different aspects of immune function. Currently, no single marker is available to predict the effect of a dietary intervention on different aspects of immune function. To provide further guidance on the assessment and interpretation of the modulation of immune functions due to nutrition in the general population, International Life Sciences Institute Europe commissioned a group of experts from academia, government and the food industry to prepare a guidance document. A draft of this paper was refined at a workshop involving additional experts. First, the expert group defined criteria to evaluate the usefulness of immune function markers. Over seventy-five markers were scored within the context of three distinct immune system functions: defence against pathogens; avoidance or mitigation of allergy; control of low-grade (metabolic) inflammation. The most useful markers were subsequently classified depending on whether they by themselves signify clinical relevance and/or involvement of immune function. Next, five theoretical scenarios were drafted describing potential changes in the values of markers compared with a relevant reference range. Finally, all elements were combined, providing a framework to aid the design and interpretation of studies assessing the effects of nutrition on immune function. This stepwise approach offers a clear rationale for selecting markers for future trials and provides a framework for the interpretation of outcomes. A similar stepwise approach may also be useful to rationalise the selection and interpretation of markers for other physiological processes critical to the maintenance of health and well-being.

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.

Metabolite profiles during oral glucose challenge

To identify distinct biological pathways of glucose metabolism, we conducted a systematic evaluation of biochemical changes after an oral glucose tolerance test (OGTT) in a community-based population. Metabolic profiling was performed on 377 nondiabetic Framingham Offspring cohort participants (mean age 57 years, 42% women, BMI 30 kg/m(2)) before and after OGTT. Changes in metabolite levels were evaluated with paired Student t tests, cluster-based analyses, and multivariable linear regression to examine differences associated with insulin resistance. Of 110 metabolites tested, 91 significantly changed with OGTT (P ≤ 0.0005 for all). Amino acids, β-hydroxybutyrate, and tricarboxylic acid cycle intermediates decreased after OGTT, and glycolysis products increased, consistent with physiological insulin actions. Other pathways affected by OGTT included decreases in serotonin derivatives, urea cycle metabolites, and B vitamins. We also observed an increase in conjugated, and a decrease in unconjugated, bile acids. Changes in β-hydroxybutyrate, isoleucine, lactate, and pyridoxate were blunted in those with insulin resistance. Our findings demonstrate changes in 91 metabolites representing distinct biological pathways that are perturbed in response to an OGTT. We also identify metabolite responses that distinguish individuals with and without insulin resistance. These findings suggest that unique metabolic phenotypes can be unmasked by OGTT in the prediabetic state.

Human metabolomics: strategies to understand biology

Metabolomics provides a direct functional read-out of the physiological status of an organism and is in principle ideally suited to describe someone's health status. Whereas only a limited number of small metabolites are used in the clinics, in inborn errors of metabolism an extensive repertoire of metabolites are used as biomarkers. We discuss that the proper clinical phenotyping is crucial to find biomarkers and obtain biological insights for multifactorial diseases. This requires to study the phenotype dynamics including the concepts of homeostasis and allostasis, that is, the ability to adapt and cope with a challenge. We also elaborate that biology-driven metabolomics platforms (i.e. development of metabolomics technology driven by the need of studying and answering important biomedical questions) addressing clinically relevant pathways and at the same time providing absolute concentrations are key to allow discovery and validation of biomarkers across studies and labs. Following individuals over years will require high throughput metabolomics approaches, which are emerging for nuclear magnetic resonance spectroscopy and direct-infusion mass spectrometry, but should also include the biochemical networks needed for personalized health monitoring.

Use of nutrigenomics endpoints in dietary interventions

In this paper, the nutrigenomics approach is discussed as a research tool to study the physiological effects of nutrition and consequently how nutrition affects health and disease (endpoints). Nutrigenomics is the study of the effects of foods and food constituents on gene expression; the analyses include analysis of mRNA, proteins and metabolites. Nutrigenomics may be useful in dealing with the challenges that nutrition research is facing; by integrating the description of numerous active genes and metabolic pathways stronger evidence and new biomarkers for subtle nutritional effects may be obtained. Also, a new definition of disease and health may be needed. The use of tests challenging homoeostasis is being proposed to help define health. Challenge tests may be able to demonstrate in a better way subtle beneficial effects of nutrition on health. The paper describes some basic concepts relevant to nutrition research as well as some of the possibilities that are offered by nutrigenomics technology. Some of its applications are described.

A consideration of biomarkers to be used for evaluation of inflammation in human nutritional studies

To monitor inflammation in a meaningful way, the markers used must be valid: they must reflect the inflammatory process under study and they must be predictive of future health status. In 2009, the Nutrition and Immunity Task Force of the International Life Sciences Institute, European Branch, organized an expert group to attempt to identify robust and predictive markers, or patterns or clusters of markers, which can be used to assess inflammation in human nutrition studies in the general population. Inflammation is a normal process and there are a number of cells and mediators involved. These markers are involved in, or are produced as a result of, the inflammatory process irrespective of its trigger and its location and are common to all inflammatory situations. Currently, there is no consensus as to which markers of inflammation best represent low-grade inflammation or differentiate between acute and chronic inflammation or between the various phases of inflammatory responses. There are a number of modifying factors that affect the concentration of an inflammatory marker at a given time, including age, diet and body fatness, among others. Measuring the concentration of inflammatory markers in the bloodstream under basal conditions is probably less informative compared with data related to the concentration change in response to a challenge. A number of inflammatory challenges have been described. However, many of these challenges are poorly standardised. Patterns and clusters may be important as robust biomarkers of inflammation. Therefore, it is likely that a combination of multiple inflammatory markers and integrated readouts based upon kinetic analysis following defined challenges will be the most informative biomarker of inflammation.

Differential effects of drug interventions and dietary lifestyle in developing type 2 diabetes and complications: a systems biology analysis in LDLr-/- mice

Excess caloric intake leads to metabolic overload and is associated with development of type 2 diabetes (T2DM). Current disease management concentrates on risk factors of the disease such as blood glucose, however with limited success. We hypothesize that normalizing blood glucose levels by itself is insufficient to reduce the development of T2DM and complications, and that removal of the metabolic overload with dietary interventions may be more efficacious. We explored the efficacy and systems effects of pharmaceutical interventions versus dietary lifestyle intervention (DLI) in developing T2DM and complications. To mimic the situation in humans, high fat diet (HFD)-fed LDLr-/- mice with already established disease phenotype were treated with ten different drugs mixed into HFD or subjected to DLI (switch to low-fat chow), for 7 weeks. Interventions were compared to untreated reference mice kept on HFD or chow only. Although most of the drugs improved HFD-induced hyperglycemia, drugs only partially affected other risk factors and also had limited effect on disease progression towards microalbuminuria, hepatosteatosis and atherosclerosis. By contrast, DLI normalized T2DM risk factors, fully reversed hepatosteatosis and microalbuminuria, and tended to attenuate atherogenesis. The comprehensive beneficial effect of DLI was reflected by normalized metabolite profiles in plasma and liver. Analysis of disease pathways in liver confirmed reversion of the metabolic distortions with DLI. This study demonstrates that the pathogenesis of T2DM towards complications is reversible with DLI and highlights the differential effects of current pharmacotherapies and their limitation to resolve the disease.

Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach

Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21-0.44], factor 2 3.82 [2.64-5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.

Novel biomarkers for pre-diabetes identified by metabolomics

A targeted metabolomics approach was used to identify candidate biomarkers of pre-diabetes. The relevance of the identified metabolites is further corroborated with a protein-metabolite interaction network and gene expression data.

Three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine C2) were found with significantly altered levels in pre-diabetic individuals compared with normal controls.

Lower levels of glycine and LPC (18:2) were found to predict risks for pre-diabetes and type 2 diabetes (T2D).

Seven T2D-related genes (PPARG, TCF7L2, HNF1A, GCK, IGF1, IRS1 and IDE) are functionally associated with the three identified metabolites.

The unique combination of methodologies, including prospective population-based and nested case–control, as well as cross-sectional studies, was essential for the identification of the reported biomarkers.

Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10⁻⁴ to 2.1 × 10⁻¹³. Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

Molecular nutrition research: the modern way of performing nutritional science

In spite of amazing progress in food supply and nutritional science, and a striking increase in life expectancy of approximately 2.5 months per year in many countries during the previous 150 years, modern nutritional research has a great potential of still contributing to improved health for future generations, granted that the revolutions in molecular and systems technologies are applied to nutritional questions. Descriptive and mechanistic studies using state of the art epidemiology, food intake registration, genomics with single nucleotide polymorphisms (SNPs) and epigenomics, transcriptomics, proteomics, metabolomics, advanced biostatistics, imaging, calorimetry, cell biology, challenge tests (meals, exercise, etc.), and integration of all data by systems biology, will provide insight on a much higher level than today in a field we may name molecular nutrition research. To take advantage of all the new technologies scientists should develop international collaboration and gather data in large open access databases like the suggested Nutritional Phenotype database (dbNP). This collaboration will promote standardization of procedures (SOP), and provide a possibility to use collected data in future research projects. The ultimate goals of future nutritional research are to understand the detailed mechanisms of action for how nutrients/foods interact with the body and thereby enhance health and treat diet-related diseases.

Comparison of the effects of n-3 long chain polyunsaturated fatty acids and fenofibrate on markers of inflammation and vascular function, and on the serum lipoprotein profile in overweight and obese subjects

BACKGROUND AND AIMS

To compare the effects of n-3 long chain polyunsaturated fatty acids (n-3 LCPUFA), with those of fenofibrate, on markers of inflammation and vascular function, and on the serum lipoprotein profile in overweight and obese subjects.

METHODS AND RESULTS

Twenty overweight and obese subjects participated in a randomized, double-blind, placebo-controlled intervention trial and received 3.7 g/d n-3 fatty acids (providing 1.7 g/d EPA and 1.2 g/d DHA), 200 mg fenofibrate or placebo treatment for 6 weeks separated by a 2 weeks wash-out period. Fish oil and fenofibrate treatment reduced triglyceride (-0.61 ± 0.81 mmol/L, P < 0.001, and -0.34 ± 0.85 mmol/L, P = 0.048, respectively) and increased HDL cholesterol concentrations (0.13 ± 0.21 mmol/L, P = 0.013, and 0.10 ± 0.18 mmol/L, P = 0.076), as reflected by a decrease of large very VLDL particles and increases of large HDL particles and medium size HDL particles. Fish oil increased serum LDL cholesterol concentrations (0.34 ± 0.59 mmol/L, P = 0.013). Fenofibrate reduced concentrations of soluble endothelial selectin (sE-selectin) (-4.1 ± 7.5 ng/mL, P = 0.032), but increased those of macrophage chemoattractant protein 1 (MCP1) (28 ± 55 ng/mL, P = 0.034). Fish oil had no effects on these markers.

CONCLUSION

Although n-3 LCPUFA and fenofibrate can both activate PPARα, they have differential effects on cardiovascular risk markers. In overweight and obese subjects fenofibrate (200 mg/d) or n-3 LCPUFA (3.7 g/d) treatment for 6 weeks did not improve markers for low-grade systemic inflammation, while fenofibrate had more profound effects on plasma lipids and markers for vascular activity compared to fish oil. Registration number clinical trials EudraCT 2006-005743-28.

Perspectives for metabolomics in testosterone replacement therapy

Testosterone is the major circulating androgen in men but exhibits an age-related decline in the ageing male. Late-onset hypogonadism or androgen deficiency syndrome (ADS) is a 'syndromic' disorder including both a persistent low testosterone serum concentration and major clinical symptoms, including erectile dysfunction, low libido, decreased muscle mass and strength, increased body fat, decreased vitality or depressed mood. Given its unspecific symptoms, treatment goals and monitoring parameters, this review will outline the various uncertainties concerning the diagnosis, therapy and monitoring of ADS to date. Literature was identified primarily through searches for specific investigators in the PubMed database. No date or language limits were applied in the literature search for the present review. The current state of research, showing that metabolomics is starting to have an impact not only on disease diagnosis and prognosis but also on drug treatment efficacy and safety monitoring, will be presented, and the application of metabolomics to improve the clinical management of ADS will be discussed. Finally, the scientific opportunities presented by metabolomics and other -omics as novel and promising tools for biomarker discovery and individualised testosterone replacement therapy in men will be explored.

Nutritional metabolomics: progress in addressing complexity in diet and health

Nutritional metabolomics is rapidly maturing to use small-molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment, and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health, and complex pathobiology serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and nontargeted metabolic profiling, along with bioinformatics, pathway mapping, and computational modeling, are now used for nutrition research on diet, metabolism, microbiome, and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics, and health phenotyping, to support the integrated models required for personalized diet and nutrition forecasting.

Responses to high-fat challenges varying in fat type in subjects with different metabolic risk phenotypes: a randomized trial

Background

The ability of subjects to respond to nutritional challenges can reflect the flexibility of their biological system. Nutritional challenge tests could be used as an indicator of health status but more knowledge on metabolic and immune responses of different subjects to nutritional challenges is needed. The aim of this study was to compare the responses to high-fat challenges varying in fat type in subjects with different metabolic risk phenotypes.

Methodology/Principal Findings

In a cross-over design 42 men (age 50–70 y) consumed three high-fat shakes containing saturated fat (SFA), monounsaturated fat (MUFA) or n-3 polyunsaturated (PUFA). Men were selected on BMI and health status (lean, obese or obese diabetic) and phenotyped with MRI for adipose tissue distribution. Before and 2 and 4 h after shake consumption blood was drawn for measurement of expression of metabolic and inflammation-related genes in peripheral blood mononuclear cells (PBMCs), plasma triglycerides (TAG), glucose, insulin, cytokines and ex vivo PBMC immune response capacity. The MUFA and n-3 PUFA challenge, compared to the SFA challenge, induced higher changes in expression of inflammation genes MCP1 and IL1β in PBMCs. Obese and obese diabetic subjects had different PBMC gene expression and metabolic responses to high-fat challenges compared to lean subjects. The MUFA challenge induced the most pronounced TAG response, mainly in obese and obese diabetic subjects.

Conclusion/Significance

The PBMC gene expression response and metabolic response to high-fat challenges were affected by fat type and metabolic risk phenotype. Based on our results we suggest using a MUFA challenge to reveal differences in response capacity of subjects.

Trial Registration

ClinicalTrials.gov NCT00977262

Application of metabolomics in traditional chinese medicine differentiation of deficiency and excess syndromes in patients with diabetes mellitus

Metabolic profiling is widely used as a probe in diagnosing diseases. In this study, the metabolic profiling of urinary carbohydrates was investigated using gas chromatography/mass spectrometry (GC/MS) and multivariate statistical analysis. The kernel-based orthogonal projections to latent structures (K-OPLS) model were established and validated to distinguish between subjects with and without diabetes mellitus (DM). The model was combined with subwindow permutation analysis (SPA) in order to extract novel biomarker information. Furthermore, the K-OPLS model visually represented the alterations in urinary carbohydrate profiles of excess and deficiency syndromes in patients with diabetes. The combination of GC/MS and K-OPLS/SPA analysis allowed the urinary carbohydrate metabolic characterization of DM patients with different traditional Chinese medicine (TCM) syndromes, including biomarkers different from non-DM patients. The method presented in this study might be a complement or an alternative to TCM syndrome research.

Metabolite profiling identifies pathways associated with metabolic risk in humans

BACKGROUND

Although metabolic risk factors are known to cluster in individuals who are prone to developing diabetes mellitus and cardiovascular disease, the underlying biological mechanisms remain poorly understood.

METHODS AND RESULTS

To identify pathways associated with cardiometabolic risk, we used liquid chromatography/mass spectrometry to determine the plasma concentrations of 45 distinct metabolites and to examine their relation to cardiometabolic risk in the Framingham Heart Study (FHS; n=1015) and the Malmö Diet and Cancer Study (MDC; n=746). We then interrogated significant findings in experimental models of cardiovascular and metabolic disease. We observed that metabolic risk factors (obesity, insulin resistance, high blood pressure, and dyslipidemia) were associated with multiple metabolites, including branched-chain amino acids, other hydrophobic amino acids, tryptophan breakdown products, and nucleotide metabolites. We observed strong associations of insulin resistance traits with glutamine (standardized regression coefficients, -0.04 to -0.22 per 1-SD change in log-glutamine; P<0.001), glutamate (0.05 to 0.14; P<0.001), and the glutamine-to-glutamate ratio (-0.05 to -0.20; P<0.001) in the discovery sample (FHS); similar associations were observed in the replication sample (MDC). High glutamine-to-glutamate ratio was associated with lower risk of incident diabetes mellitus in FHS (odds ratio, 0.79; adjusted P=0.03) but not in MDC. In experimental models, administration of glutamine in mice led to both increased glucose tolerance (P=0.01) and decreased blood pressure (P<0.05).

CONCLUSIONS

Biochemical profiling identified circulating metabolites not previously associated with metabolic traits. Experimentally interrogating one of these pathways demonstrated that excess glutamine relative to glutamate, resulting from exogenous administration, is associated with reduced metabolic risk in mice.

Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500 ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6 h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0320-5) contains supplementary material, which is available to authorized users.

Urine metabolomics combined with the personalized diagnosis guided by Chinese medicine reveals subtypes of pre-diabetes

The prevalence of type 2 diabetes continuously increases globally. A personalized strategy applied in the pre-diabetic stage is vital for diabetic prevention and management. The personalized diagnosis of Chinese Medicine (CM) may help to stratify the diabetics. Metabolomics is regarded as a potential platform to provide biomarkers for disease-subtypes. We designed an explorative study of 50 pre-diabetic males, combining GC-MS urine metabolomics with CM diagnosis in order to identify diagnostic biomarkers for pre-diabetic subtypes. Three CM physicians reached 85% diagnosis consistency resulting in the classification of 3 pre-diabetic groups. The urine metabolic patterns of groups 1 'Qi-Yin deficiency' and 2 'Qi-Yin deficiency with dampness' (subtype A) and group 3 'Qi-Yin deficiency with stagnation' (subtype B) were clearly discriminated. The majority of metabolites (51%), mainly sugars and amino acids, showed higher urine levels in subtype B compared with subtype A. This indicated more disturbances of carbohydrate metabolism and renal function in subtype B compared with subtype A. No differences were found for hematological and biochemical parameters except for levels of glucose and γ-glutamyltransferase that were significantly higher in subtype B compared with subtype A. This study proved that combining metabolomics with CM diagnosis can reveal metabolic signatures for pre-diabetic subtypes. The identified urinary metabolites may be of special clinical relevance for non-invasive screening for subtypes of pre-diabetes, which could lead to an improvement in personalized interventions for diabetics.

LC-MS-based metabolomics

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

Lipidomics reveals multiple pathway effects of a multi-components preparation on lipid biochemistry in ApoE*3Leiden.CETP mice

BACKGROUND

Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses.

METHODOLOGY/PRINCIPAL FINDINGS

ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (-49%, p<0.001), CETP levels (-31%, p<0.001), CETP activity (-74%, p<0.001) and increased HDL-C (39%, p<0.05). It influenced lipidomics classes of cholesterol esters and triglycerides the most. Rimonabant induced a weight loss (-9%, p<0.05), but only a moderate improvement of lipid profiles. In vitro, SUB885C extract caused adipolysis stimulation and adipogenesis inhibition in 3T3-L1 cells.

CONCLUSIONS

SUB885C, a multi-components preparation, is able to produce anti-atherogenic changes in lipids of the ApoE*3Leiden.CETP mice, which are comparable to those obtained with compounds belonging to known drugs (e.g. rimonabant, atorvastatin, niacin). This study successfully illustrated the power of lipidomics in unraveling intervention effects and to help finding new targets or ingredients for lifestyle-related metabolic abnormality.

Comparative transcriptomic and metabolomic analysis of fenofibrate and fish oil treatments in mice

Elevated circulating triglycerides, which are considered a risk factor for cardiovascular disease, can be targeted by treatment with fenofibrate or fish oil. To gain insight into underlying mechanisms, we carried out a comparative transcriptomics and metabolomics analysis of the effect of 2 wk treatment with fenofibrate and fish oil in mice. Plasma triglycerides were significantly decreased by fenofibrate (−49.1%) and fish oil (−21.8%), whereas plasma cholesterol was increased by fenofibrate (+29.9%) and decreased by fish oil (−32.8%). Levels of various phospholipid species were specifically decreased by fish oil, while levels of Krebs cycle intermediates were increased specifically by fenofibrate. Plasma levels of many amino acids were altered by fenofibrate and to a lesser extent by fish oil. Both fenofibrate and fish oil upregulated genes involved in fatty acid metabolism and downregulated genes involved in blood coagulation and fibrinolysis. Significant overlap in gene regulation by fenofibrate and fish oil was observed, reflecting their property as high or low affinity agonist for peroxisome proliferator-activated receptor-α, respectively. Fenofibrate specifically downregulated genes involved in complement cascade and inflammatory response. Fish oil specifically downregulated genes involved in cholesterol and fatty acid biosynthesis and upregulated genes involved in amino acid and arachidonic acid metabolism. Taken together, the data indicate that despite being similarly potent toward modulating plasma free fatty acids, cholesterol, and triglyceride levels, fish oil causes modest changes in gene expression likely via activation of multiple mechanistic pathways, whereas fenofibrate causes pronounced gene expression changes via a single pathway, reflecting the key difference between nutritional and pharmacological intervention.

Plasma and liver lipidomics response to an intervention of rimonabant in ApoE*3Leiden.CETP transgenic mice

Background

Lipids are known to play crucial roles in the development of life-style related risk factors such as obesity, dyslipoproteinemia, hypertension and diabetes. The first selective cannabinoid-1 receptor blocker rimonabant, an anorectic anti-obesity drug, was frequently used in conjunction with diet and exercise for patients with a body mass index greater than 30 kg/m² with associated risk factors such as type II diabetes and dyslipidaemia in the past. Less is known about the impact of this drug on the regulation of lipid metabolism in plasma and liver in the early stage of obesity.

Methodology/Principal Findings

We designed a four-week parallel controlled intervention on apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) transgenic mice with mild overweight and hypercholesterolemia. A liquid chromatography–linear ion trap-Fourier transform ion cyclotron resonance-mass spectrometric approach was employed to investigate plasma and liver lipid responses to the rimonabant intervention. Rimonabant was found to induce a significant body weight loss (9.4%, p<0.05) and a significant plasma total cholesterol reduction (24%, p<0.05). Six plasma and three liver lipids in ApoE*3Leiden.CETP transgenic mice were detected to most significantly respond to rimonabant treatment. Distinct lipid patterns between the mice were observed for both plasma and liver samples in rimonabant treatment vs. non-treated controls. This study successfully applied, for the first time, systems biology based lipidomics approaches to evaluate treatment effects of rimonabant in the early stage of obesity.

Conclusion

The effects of rimonabant on lipid metabolism and body weight reduction in the early stage obesity were shown to be moderate in ApoE*3Leiden.CETP mice on high-fat diet.

Metabolomics study of diabetic retinopathy using gas chromatography-mass spectrometry: a comparison of stages and subtypes diagnosed by Western and Chinese medicine

Diabetic retinopathy (DR) is a serious microvascular syndrome of diabetes, and is one of the most frequent causes of blindness in the world. It has three progressive stages with complex metabolic deregulations in the holistic system of Western medicine. Chinese medicine classifies DR into two different syndrome types; integrating Western and Chinese medicine to treat DR is a validated therapeutic approach in China. In this research, the systemic metabolite change of DR was investigated from the viewpoint of both Western and Chinese medicine, using metabolomics based on gas chromatography-mass spectrometry. The data revealed both perspectives can reflect the metabolic patterns, development and differentiation of DR, and the data also had good correlation and complementarity in characterizing the process of DR. Potential biomarkers of DR based on the two perspectives indicated the alterative modes of metabolites and metabolic pathways in the disease, e.g. the disturbance in fatty acids, amino acids and glucose, etc. The results showed the usefulness and validity of combining both Western and Chinese medicine to study the subtypes of DR and the mechanisms involved.

Metabolite profiles and the risk of developing diabetes

Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.

Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans

Dyslipidemia is an independent risk factor for type 2 diabetes, although exactly which of the many plasma lipids contribute to this remains unclear. We therefore investigated whether lipid profiling can inform diabetes prediction by performing liquid chromatography/mass spectrometry-based lipid profiling in 189 individuals who developed type 2 diabetes and 189 matched disease-free individuals, with over 12 years of follow up in the Framingham Heart Study. We found that lipids of lower carbon number and double bond content were associated with an increased risk of diabetes, whereas lipids of higher carbon number and double bond content were associated with decreased risk. This pattern was strongest for triacylglycerols (TAGs) and persisted after multivariable adjustment for age, sex, BMI, fasting glucose, fasting insulin, total triglycerides, and HDL cholesterol. A combination of 2 TAGs further improved diabetes prediction. To explore potential mechanisms that modulate the distribution of plasma lipids, we performed lipid profiling during oral glucose tolerance testing, pharmacologic interventions, and acute exercise testing. Levels of TAGs associated with increased risk for diabetes decreased in response to insulin action and were elevated in the setting of insulin resistance. Conversely, levels of TAGs associated with decreased diabetes risk rose in response to insulin and were poorly correlated with insulin resistance. These studies identify a relationship between lipid acyl chain content and diabetes risk and demonstrate how lipid profiling could aid in clinical risk assessment.

Beyond glucose: metabolic shifts in responses to the effects of the oral glucose tolerance test and the high-fructose diet in rats

High-fructose diet-fed rats as one of the insulin resistant models was used widely for understanding the mechanisms of type 2 diabetes mellitus. Systems-level metabolic profiling of the rat model, however, has not been deciphered clearly. To address this issue, mass spectrometry-based metabolomics was employed to unlock the metabolic snapshots of the oral glucose tolerance test (oGTT) effect in either healthy or diabetic rats, as well as to delineate the metabolic signatures in tissues of rats fed with high-fructose diet. Several differentiating metabolites were highlighted to reveal the metabolic perturbation of the oGTT effects in healthy and diabetic rats, which involved amino acid biosynthesis, polyunsaturated fatty acids, phospholipids and purine metabolism. Surprisingly, the patterns of relationships for the metabolic phenotypes by using data mining revealed that glucose ingestion might induce the healthy group to display its trajectory towards diabetic status, while only a very slight influence was observed on the high-fructose diet-fed rats 120 min after glucose ingestion. The data treatment for liver, skeletal muscle and brain tissues suggested that oxidative stress, such as lipid peroxidation and the declined antioxidant, the elevated amino acids and the perturbation of fatty acids, were caused by the high-fructose diet in liver and skeletal muscle tissues. On the other hand, the up-regulation in purine biosynthesis and the decreased concentrations for amino acids were observed in the cerebral cortex and hippocampus tissues. Collectively, the obtained results might provide a new insight not only for the impairment of glucose tolerance but also for the dietary style in rats.

Insulin sensitivity is reflected by characteristic metabolic fingerprints--a Fourier transform mass spectrometric non-targeted metabolomics approach

Background

A decline in body insulin sensitivity in apparently healthy individuals indicates a high risk to develop type 2 diabetes. Investigating the metabolic fingerprints of individuals with different whole body insulin sensitivity according to the formula of Matsuda, et al. (ISI_Matsuda) by a non-targeted metabolomics approach we aimed a) to figure out an unsuspicious and altered metabolic pattern, b) to estimate a threshold related to these changes based on the ISI, and c) to identify the metabolic pathways responsible for the discrimination of the two patterns.

Methodology and Principal Findings

By applying infusion ion cyclotron resonance Fourier transform mass spectrometry, we analyzed plasma of 46 non-diabetic subjects exhibiting high to low insulin sensitivities. The orthogonal partial least square model revealed a cluster of 28 individuals with alterations in their metabolic fingerprints associated with a decline in insulin sensitivity. This group could be separated from 18 subjects with an unsuspicious metabolite pattern. The orthogonal signal correction score scatter plot suggests a threshold of an ISI_Matsuda of 15 for the discrimination of these two groups. Of note, a potential subgroup represented by eight individuals (ISI_Matsuda value between 8.5 and 15) was identified in different models. This subgroup may indicate a metabolic transition state, since it is already located within the cluster of individuals with declined insulin sensitivity but the metabolic fingerprints still show some similarities with unaffected individuals (ISI >15). Moreover, the highest number of metabolite intensity differences between unsuspicious and altered metabolic fingerprints was detected in lipid metabolic pathways (arachidonic acid metabolism, metabolism of essential fatty acids and biosynthesis of unsaturated fatty acids), steroid hormone biosyntheses and bile acid metabolism, based on data evaluation using the metabolic annotation interface MassTRIX.

Conclusions

Our results suggest that altered metabolite patterns that reflect changes in insulin sensitivity respectively the ISI_Matsuda are dominated by lipid-related pathways. Furthermore, a metabolic transition state reflected by heterogeneous metabolite fingerprints may precede severe alterations of metabolism. Our findings offer future prospects for novel insights in the pathogenesis of the pre-diabetic phase.

Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits

Impaired glucose tolerance (IGT) which precedes overt type 2 diabetes (T2DM) for decades is associated with multiple metabolic alterations in insulin sensitive tissues. In an UPLC-qTOF-mass spectrometry-driven non-targeted metabonomics approach we investigated plasma as well as spot urine of 51 non-diabetic, overnight fasted individuals aiming to separate subjects with IGT from controls thereby identify pathways affected by the pre-diabetic metabolic state. We could clearly demonstrate that normal glucose tolerant (NGT) and IGT subjects clustered in two distinct groups independent of the investigated metabonome. These findings reflect considerable differences in individual metabolite fingerprints, both in plasma and urine. Pre-diabetes associated alterations in fatty acid-, tryptophan-, uric acid-, bile acid-, and lysophosphatidylcholine-metabolism, as well as the TCA cycle were identified. Of note, individuals with IGT also showed decreased levels of gut flora-associated metabolites namely hippuric acid, methylxanthine, methyluric acid, and 3-hydroxyhippuric acid. The findings of our non-targeted UPLC-qTOF-MS metabonomics analysis in plasma and spot urine of individuals with IGT vs NGT offers novel insights into the metabolic alterations occurring in the long, asymptomatic period preceding the manifestation of T2DM thereby giving prospects for new intervention targets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0203-1) contains supplementary material, which is available to authorized users.

An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach

BACKGROUND

Low-grade chronic inflammation in overweight subjects is thought to play an important role in disease development.

OBJECTIVE

It was hypothesized that specific dietary components are able to reduce low-grade inflammation as well as metabolic and oxidative stress.

DESIGN

Dietary products [resveratrol, green tea extract, alpha-tocopherol, vitamin C, n-3 (omega-3) polyunsaturated fatty acids, and tomato extract] selected for their evidence-based antiinflammatory properties were combined and given as supplements to 36 healthy overweight men with mildly elevated plasma C-reactive protein concentrations in a double-blind, placebo-controlled, crossover study with treatment periods of 5 wk. Inflammatory and oxidative stress defense markers were quantified in plasma and urine. Furthermore, 120 plasma proteins, 274 plasma metabolites (lipids, free fatty acids, and polar compounds), and the transcriptomes of peripheral blood mononuclear cells and adipose tissue were quantified.

RESULTS

Plasma adiponectin concentrations increased by 7%, whereas C-reactive protein (principal inflammation marker) was unchanged. However, a multitude of subtle changes were detected by an integrated analysis of the "omics" data, which indicated modulated inflammation of adipose tissue, improved endothelial function, affected oxidative stress, and increased liver fatty acid oxidation.

CONCLUSION

An intervention with selected dietary products affected inflammatory processes, oxidative stress, and metabolism in humans, as shown by large-scale profiling of genes, proteins, and metabolites in plasma, urine, and adipose tissue. This trial was registered at clinical trials.gov as NCT00655798.

Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study

Background

Chronic systemic low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy.

Methods

To address this limitation, we used an integrative omics approach to characterize modulation of inflammation in overweight men during an intervention with the non-steroidal anti-inflammatory drug diclofenac. Measured parameters included 80 plasma proteins, >300 plasma metabolites (lipids, free fatty acids, oxylipids and polar compounds) and an array of peripheral blood mononuclear cells (PBMC) gene expression products. These measures were submitted to multivariate and correlation analysis and were used for construction of biological response networks.

Results

A panel of genes, proteins and metabolites, including PGE₂ and TNF-alpha, were identified that describe a diclofenac-response network (68 genes in PBMC, 1 plasma protein and 4 plasma metabolites). Novel candidate markers of inflammatory modulation included PBMC expression of annexin A1 and caspase 8, and the arachidonic acid metabolite 5,6-DHET.

Conclusion

In this study the integrated analysis of a wide range of parameters allowed the development of a network of markers responding to inflammatory modulation, thereby providing insight into the complex process of inflammation and ways to assess changes in inflammatory status associated with obesity.

Trial registration

The study is registered as NCT00221052 in clinicaltrials.gov database.

Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping

Analytical errors caused by suboptimal performance of the chosen platform for a number of metabolites and instrumental drift are a major issue in large-scale metabolomics studies. Especially for MS-based methods, which are gaining common ground within metabolomics, it is difficult to control the analytical data quality without the availability of suitable labeled internal standards and calibration standards even within one laboratory. In this paper, we suggest a workflow for significant reduction of the analytical error using pooled calibration samples and multiple internal standard strategy. Between and within batch calibration techniques are applied and the analytical error is reduced significantly (increase of 25% of peaks with RSD lower than 20%) and does not hamper or interfere with statistical analysis of the final data.

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.

Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction

AIMS

In this review metabolomics is introduced in historic perspective, with key platforms and bioinformatics methodologies described. An overview is provided covering recent applications of metabolomics and lipidomics in the context of human physiology, lipid metabolism and nutrition.

DATA SYNTHESIS

Global coverage of human metabolome requires application of multiple analytical platforms. The choice of a particular targeted or non-targeted analytical strategy depends on the hypothesis tested, state-of-the-art in the field, as well as on sample availability. Human metabolome has been shown to be sensitive to age, gut microbial composition, and lifestyle. Several studies have shown that, given the appropriate experimental design, subtle effects of interventions such as change of diet or weight loss can be detected by metabolomics and studied in the context of human physiology and health status.

CONCLUSION

Metabolome provides a sensitive intermediate phenotype linking the genotype, gut microbial composition and personal health status. Innovative experimental designs combined with novel computational tools for handling metabolomics data offer new opportunities for early disease detection as well as for characterization of dietary and therapeutic interventions in the context of human physiology.