A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human

A nutrient-dense, high-fiber, fruit-based supplement bar increases HDL cholesterol, particularly large HDL, lowers homocysteine, and raises glutathione in a 2-wk trial

Dietary intake modulates disease risk, but little is known how components within food mixtures affect pathophysiology. A low-calorie, high-fiber, fruit-based nutrient-dense bar of defined composition (e.g., vitamins and minerals, fruit polyphenolics, β-glucan, docosahexaenoic acid) appropriate for deconstruction and mechanistic studies is described and evaluated in a pilot trial. The bar was developed in collaboration with the U.S. Department of Agriculture. Changes in cardiovascular disease and diabetes risk biomarkers were measured after 2 wk twice-daily consumption of the bar, and compared against baseline controls in 25 healthy adults. Plasma HDL-cholesterol (HDL-c) increased 6.2% (P=0.001), due primarily to a 28% increase in large HDL (HDL-L; P<0.0001). Total plasma homocysteine (Hcy) decreased 19% (P=0.017), and glutathione (GSH) increased 20% (P=0.011). The changes in HDL and Hcy are in the direction associated with decreased risk of cardiovascular disease and cognitive decline; increased GSH reflects improved antioxidant defense. Changes in biomarkers linked to insulin resistance and inflammation were not observed. A defined food-based supplement can, within 2 wk, positively impact metabolic biomarkers linked to disease risk. These results lay the groundwork for mechanistic/deconstruction experiments to identify critical bar components and putative synergistic combinations responsible for observed effects.

Biomarkers of inflammation, immunosuppression and stress with active disease are revealed by metabolomic profiling of tuberculosis patients

Although tuberculosis (TB) causes more deaths than any other pathogen, most infected individuals harbor the pathogen without signs of disease. We explored the metabolome of >400 small molecules in serum of uninfected individuals, latently infected healthy individuals and patients with active TB. We identified changes in amino acid, lipid and nucleotide metabolism pathways, providing evidence for anti-inflammatory metabolomic changes in TB. Metabolic profiles indicate increased activity of indoleamine 2,3 dioxygenase 1 (IDO1), decreased phospholipase activity, increased abundance of adenosine metabolism products, as well as indicators of fibrotic lesions in active disease as compared to latent infection. Consistent with our predictions, we experimentally demonstrate TB-induced IDO1 activity. Furthermore, we demonstrate a link between metabolic profiles and cytokine signaling. Finally, we show that 20 metabolites are sufficient for robust discrimination of TB patients from healthy individuals. Our results provide specific insights into the biology of TB and pave the way for the rational development of metabolic biomarkers for TB.

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.

Streptozotocin-induced dynamic metabonomic changes in rat biofluids

Diabetes mellitus is a complex polygenic disease caused by gene-environment interactions with multiple complications, and metabonomic analysis is crucial for pathogenesis, early diagnosis, and timely interventions. Here, we comprehensively analyzed the dynamic metabolic changes in rat urine and plasma, which were induced by the well-known diabetogenic chemical streptozotocin (STZ), using (1)H NMR spectroscopy in conjunction with multivariate data analysis. The results showed that a single intraperitoneal injection of STZ with a moderate dosage (55 mg/kg) induced significant urinary metabonomic changes within 24 h. These changes showed time-dependence and heterogeneity among the treated animals with an animal recovered within 11 days. STZ-induced metabonomic alterations were related to suppression of glycolysis and TCA cycle, promotion of gluconeogenesis and oxidation of amino acids, alterations in metabolisms of basic amino acids associated with diabetic complications, and disruption of lipid metabolism and gut microbiota functions. With diffusion-edited NMR spectral data, we further observed the STZ-induced significant elevation of monounsaturated fatty acids and total unsaturated fatty acids together with reductions in PUFA-to-MUFA ratio in the blood plasma. These findings provided details of the time-dependent metabonomic changes in the progressive development of the STZ-induced diabetes mellitus and showed the possibility of detecting the biochemical changes in the early stage of type 1 diabetic genesis.

Applications of metabolomics for understanding the action of peroxisome proliferator-activated receptors (PPARs) in diabetes, obesity and cancer

The peroxisome proliferator-activated receptors (PPARs) are a set of three nuclear hormone receptors that together play a key role in regulating metabolism, particularly the switch between the fed and fasted state and the metabolic pathways involving fatty-acid oxidation and lipid metabolism. In addition, they have a number of important developmental and regulatory roles outside metabolism. The PPARs are also potent targets for treating type II diabetes, dyslipidemia and obesity, although a number of individual agonists have also been linked to unwanted side effects, and there is a complex relationship between the PPARs and the development of cancer. This review examines the part that metabolomics, including lipidomics, has played in elucidating the roles PPARs have in regulating systemic metabolism, as well as their role in aspects of drug-induced cancer and xenobiotic metabolism. These studies have defined the role PPARδ plays in regulating fatty-acid oxidation in adipose tissue and the interaction between aging and PPARα in the liver. The potential translational benefits of these approaches include widening the role of PPAR agonists and improved monitoring of drug efficacy.

Differences in metabolomic profiles of male db/db and s/s, leptin receptor mutant mice

Leptin, a protein hormone secreted by adipose tissue, plays an important role in regulating energy metabolism and the immune response. Despite similar extremes of adiposity, mutant mouse models, db/db, carrying spontaneous deletion of the active form of the leptin receptor (LEPR-B) intracellular signaling domain, and the s/s, carrying a specific point mutation leading to a dysfunctional LEPR-B-STAT3 signaling pathway, have been shown to have robust differences in glucose homeostasis. This suggests specific effects of leptin, mediated by non-STAT3 LEPR-B pathways. Differences in the LEPR-B signaling pathways in these two LEPR-B mutant mice models are expected to lead to differences in metabolism. In the current study, the hypothesized differences in metabolism were investigated using the metabolomics approach. Proton nuclear magnetic resonance spectroscopy ((1)HNMR) was conducted on 24 h urine samples in deuterium oxide using a 500 MHz instrument at 25°C. Principle Component Analysis showed clear separation of urine NMR spectra between the groups (P < 0.05). The CHENOMX metabolite database was used to identify several metabolites that differed between the two mouse models. Significant differences (P < 0.05) in metabolites associated with the glycine, serine, and homocysteine metabolism were observed. The results demonstrate that the metabolomic profile of db/db and s/s mice are fundamentally different and provide insight into the unique metabolic effects of leptin exerted through non-STAT3 LEPR-B pathways.

Metabolomic biomarkers of impaired glucose tolerance and type 2 diabetes mellitus with a potential for risk stratification in women with polycystic ovary syndrome

There is a need to identify biomarkers of impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM) risk in women with PCOS to facilitate screening and the development of novel strategies to prevent disease progression. Metabolomic technologies may address this need. All published studies on metabolomic biomarkers of IGT and/or T2DM identified through MEDLINE (1966-December 2010), EMBASE (1980-December 2010) and Cochrane (1993-December 2010) were retrieved. Eligible studies were screened and specific study characteristics recorded including study design, number of participants, selection criteria, type of metabolomic technique used, site of sample collection, and a list of metabolites identified to have been altered in IGT and/or T2DM versus healthy controls was created. Nine metabolomic biomarkers that could potentially be used to identify women with PCOS at risk of developing IGT and/or T2DM were identified including leucine, isoleucine, citrate, glucose, creatinine, valine, glutamine, alanine and HDL. Of these biomarkers, a panel of four biomarkers were consistently either elevated or reduced including glucose (elevated), valine (reduced), HDL (reduced) and alanine (reduced) in IGT/T2DM compared with controls. These biomarkers may predict the development of IGT/T2DM in young women with PCOS. More studies are required to test this hypothesis and translate the findings into patient benefit by reducing the morbidity/mortality associated with IGT/T2DM in PCOS.

Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state

Dysregulation of insulin action is most often considered in the context of impaired glucose homeostasis, with the defining feature of diabetes mellitus being elevated blood glucose concentration. Complications arising from the hyperglycemia accompanying frank diabetes are well known and epidemiological studies point to higher risk toward development of metabolic disease in persons with impaired glucose tolerance. Although the central role of proper blood sugar control in maintaining metabolic health is well established, recent developments have begun to shed light on associations between compromised insulin action [obesity, prediabetes, and type 2 diabetes mellitus (T2DM)] and altered intermediary metabolism of fats and amino acids. For amino acids, changes in blood concentrations of select essential amino acids and their derivatives, in particular BCAA, sulfur amino acids, tyrosine, and phenylalanine, are apparent with obesity and insulin resistance, often before the onset of clinically diagnosed T2DM. This review provides an overview of these changes and places recent observations from metabolomics research into the context of historical reports in the areas of biochemistry and nutritional biology. Based on this synthesis, a model is proposed that links the FFA-rich environment of obesity/insulin resistance and T2DM with diminution of BCAA catabolic enzyme activity, changes in methionine oxidation and cysteine/cystine generation, and tissue redox balance (NADH/NAD+).

A Metadata description of the data in "A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human."

Background

Metabolomics is a rapidly developing functional genomic tool that has a wide range of applications in diverse fields in biology and medicine. However, unlike transcriptomics and proteomics there is currently no central repository for the depositing of data despite efforts by the Metabolomics Standard Initiative (MSI) to develop a standardised description of a metabolomic experiment.

Findings

In this manuscript we describe how the MSI description has been applied to a published dataset involving the identification of cross-species metabolic biomarkers associated with type II diabetes. The study describes sample collection of urine from mice, rats and human volunteers, and the subsequent acquisition of data by high resolution 1H NMR spectroscopy. The metadata is described to demonstrate how the MSI descriptions could be applied in a manuscript and the spectra have also been made available for the mouse and rat studies to allow others to process the data.

Conclusions

The intention of this manuscript is to stimulate discussion as to whether the MSI description is sufficient to describe the metadata associated with metabolomic experiments and encourage others to make their data available to other researchers.

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

Role of pyruvate dehydrogenase inhibition in the development of hypertrophy in the hyperthyroid rat heart: a combined magnetic resonance imaging and hyperpolarized magnetic resonance spectroscopy study

BACKGROUND

Hyperthyroidism increases heart rate, contractility, cardiac output, and metabolic rate. It is also accompanied by alterations in the regulation of cardiac substrate use. Specifically, hyperthyroidism increases the ex vivo activity of pyruvate dehydrogenase kinase, thereby inhibiting glucose oxidation via pyruvate dehydrogenase. Cardiac hypertrophy is another effect of hyperthyroidism, with an increase in the abundance of mitochondria. Although the hypertrophy is initially beneficial, it can eventually lead to heart failure. The aim of this study was to use hyperpolarized magnetic resonance spectroscopy to investigate the rate and regulation of in vivo pyruvate dehydrogenase flux in the hyperthyroid heart and to establish whether modulation of flux through pyruvate dehydrogenase would alter cardiac hypertrophy.

METHODS AND RESULTS

Hyperthyroidism was induced in 18 male Wistar rats with 7 daily intraperitoneal injections of freshly prepared triiodothyronine (0.2 mg x kg(-1) x d(-1)). In vivo pyruvate dehydrogenase flux, assessed with hyperpolarized magnetic resonance spectroscopy, was reduced by 59% in hyperthyroid animals (0.0022 ± 0.0002 versus 0.0055 ± 0.0005 second(-1); P=0.0003), and this reduction was completely reversed by both short- and long-term delivery of dichloroacetic acid, a pyruvate dehydrogenase kinase inhibitor. Hyperpolarized [2-(13)C]pyruvate was also used to evaluate Krebs cycle metabolism and demonstrated a unique marker of anaplerosis, the level of which was significantly increased in the hyperthyroid heart. Cine magnetic resonance imaging showed that long-term dichloroacetic acid treatment significantly reduced the hypertrophy observed in hyperthyroid animals (100 ± 20 versus 200 ± 30 mg; P=0.04) despite no change in the increase observed in cardiac output.

CONCLUSIONS

This work has demonstrated that inhibition of glucose oxidation in the hyperthyroid heart in vivo is mediated by pyruvate dehydrogenase kinase. Relieving this inhibition can increase the metabolic flexibility of the hyperthyroid heart and reduce the level of hypertrophy that develops while maintaining the increased cardiac output required to meet the higher systemic metabolic demand.

1H-NMR-based metabonomic analysis of metabolic profiling in diabetic nephropathy rats induced by streptozotocin

Elucidation of the metabolic profiling in diabetic nephropathy (DN) rats is of great assistance for understanding the pathogenesis of DN. In this study, 1H-nuclear magnetic resonance (NMR)-based metabonomics combined with HPLC measurements was used to quantitatively analyze the metabolic changes in urine and kidney extracts from diabetic 2-wk and 8-wk rats induced by streptozotocin (STZ). Pattern recognition analysis of either urine or kidney extracts indicated that the two diabetic groups were separated obviously from the control group, suggesting that the metabolic profiles of the diabetic groups were markedly different from the control. The diabetic 8-wk rats showed lower levels of creatine, dimethylamine, and higher levels of ascorbate, succinate, lactate, citrate, allantoin, 2-ketoglutarate, and 3-hydrobutyrate (3-HB) in the urine samples. Moreover, the diabetic 8-wk rats displayed lower levels of succinate, creatine, myo-inositol, alanine, lactate, and ATP, and higher levels of 3-HB and glucose in the kidney extracts. The observed metabolic changes imply the enhanced pathways of either lipid or ketone body synthesis and decreased pathways of either tricarboxylic acid cycle or glycolysis in DN rats compared with the control. Our results suggest that the energy metabolic changes are associated with the pathogenic process of DN.

Comparison of serum metabolite compositions between obese and lean growing pigs using an NMR-based metabonomic approach

Childhood obesity has become a prevalent risk to health of children and teenagers. To develop biomarkers in serum for altered lipid metabolism, genetically obese (Ningxiang strain) and lean (Duroc×Landrace×Large Yorkshire strain) growing pigs were used as models to identify potential differences in the serum metabonome between the two strains of pigs after consuming the same diet for 46 days. At the end of the study, pigs were euthanized for analysis of the serum metabonome and determination of body composition. Obese pigs had higher fat mass (42.3±8.8% vs. 21.9±4.5%) and lower muscle mass (35.4±4.5% vs. 58.9±2.5%) than lean pigs (P<.01). Serum concentrations of insulin and glucagon were higher (P<.02) in obese than in lean pigs. With the use of an NMR-based metabonomic technology, orthogonal projection to latent structure with discriminant analysis showed that serum HDL, VLDL, lipids, unsaturated lipids, glycoprotein, myo-inositol, pyruvate, threonine, tyrosine and creatine were higher in obese than in lean pigs (P<.05), while serum glucose and urea were lower in obese pigs (P<.05). In addition, changes in gut microbiota-related metabolites, including trimethylamine-N-oxide and choline, were observed in sera of obese pigs relatively to lean pigs (P<.05). These novel findings indicate that obese pigs have distinct metabolism, including lipogenesis, lipid oxidation, energy utilization and partition, protein and amino acid metabolism, and fermentation of gastrointestinal microbes, compared with lean pigs. The obese Ningxiang pig may be a useful model for childhood obesity research.

Tumour xenograft detection through quantitative analysis of the metabolic profile of urine in mice

The metabolic content of urine from NIH III nude mice (n = 22) was analysed before and after inoculation with human glioblastoma multiforme (GBM) cancer cells. An age- and gender-matched control population (n = 14) was also studied to identify non-tumour-related changes. Urine samples were collected daily for 6 weeks, beginning 1 week before cell injection. Metabolite concentrations were obtained via targeted profiling with Chenomx Suite 5.1, based on nuclear magnetic resonance (NMR) spectra acquired on an Oxford 800 MHz cold probe NMR spectrometer. The Wilcoxon rank sum test was used to evaluate the significance of the change in metabolite concentration between the two time points. Both the metabolite concentrations and the ratios of pairs of metabolites were studied. The complicated inter-relationships between metabolites were assessed through partial least-squares discriminant analysis (PLS-DA). Receiver operating characteristic (ROC) curves were generated for all variables and the area under the curve (AUC) calculated. The data indicate that the number of statistically significant changes in metabolite concentrations was more pronounced in the tumour-bearing population than in the control animals. This was also true of the ratios of pairs of metabolites. ROC analysis suggests that the ratios were better able to differentiate between the pre- and post-injection samples compared to the metabolite concentrations. PLS-DA models produced good separation between the populations and had the best AUC results (all models exceeded 0.937). These results demonstrate that metabolomics may be used as a screening tool for GBM cells grown in xenograft models in mice.

¹H nuclear magnetic resonance based metabolic urinary profiling of patients with ischemic heart failure

OBJECTIVES

We sought to identify metabolic pathways characterizing human heart failure (HF) using ¹NMR based urinary metabolomic analysis in conjunction with multivariate statistics.

DESIGN AND METHODS

Patients with systolic HF of ischemic origin (n=15) and healthy controls (n=20) participated in this study. Patients with type 2 diabetes mellitus were excluded.

RESULTS

The results showed that the urine of the HF patients had higher levels of metabolites for acetate (p<0.05) and acetone (p<0.01) compared to the healthy controls. In addition, there was a perturbation in methylmalonate metabolism as shown by increased urinary levels of methylmalonic acid (p<0.001) in the HF patients. HF patients also had increased urinary levels of cytosine (p<0.01) and phenylacetylglycine (p<0.01) and decreased 1-methylnicotinamide (p<0.05) compared to healthy controls.

CONCLUSIONS

TCA cycle metabolites and fatty acid metabolism were modified in the HF patients, indicating altered energy metabolism. Moreover, perturbations of metabolism in nucleotide and methylmalonate were observed.

Quantification of high-resolution (1)H NMR spectra from rat brain extracts

Extracting quantitative information about absolute concentrations from high-resolution (1)H NMR spectra of complex mixtures such as brain extracts remains challenging. Partial overlap of resonances complicates integration, whereas simple line fitting algorithms cannot accommodate the spectral complexity of coupled spin systems. Here, it is shown that high-resolution (1)H NMR spectra of rat brain extracts from 11 distinct brain regions can be reproducibly quantified using a basis set of 29 compounds. The basis set is simulated with the density matrix formalism using complete prior knowledge of chemical shifts and scalar couplings. A crucial aspect to obtain reproducible results was the inclusion of a line shape distortion common among all 73 resonances of the 29 compounds. All metabolites could be quantified with <10% and <3% inter- and intrasubject variation, respectively.

Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women

Insulin resistance progressing to type 2 diabetes mellitus (T2DM) is marked by a broad perturbation of macronutrient intermediary metabolism. Understanding the biochemical networks that underlie metabolic homeostasis and how they associate with insulin action will help unravel diabetes etiology and should foster discovery of new biomarkers of disease risk and severity. We examined differences in plasma concentrations of >350 metabolites in fasted obese T2DM vs. obese non-diabetic African-American women, and utilized principal components analysis to identify 158 metabolite components that strongly correlated with fasting HbA1c over a broad range of the latter (r = −0.631; p<0.0001). In addition to many unidentified small molecules, specific metabolites that were increased significantly in T2DM subjects included certain amino acids and their derivatives (i.e., leucine, 2-ketoisocaproate, valine, cystine, histidine), 2-hydroxybutanoate, long-chain fatty acids, and carbohydrate derivatives. Leucine and valine concentrations rose with increasing HbA1c, and significantly correlated with plasma acetylcarnitine concentrations. It is hypothesized that this reflects a close link between abnormalities in glucose homeostasis, amino acid catabolism, and efficiency of fuel combustion in the tricarboxylic acid (TCA) cycle. It is speculated that a mechanism for potential TCA cycle inefficiency concurrent with insulin resistance is “anaplerotic stress” emanating from reduced amino acid-derived carbon flux to TCA cycle intermediates, which if coupled to perturbation in cataplerosis would lead to net reduction in TCA cycle capacity relative to fuel delivery.

Bioactive food components and cancer-specific metabonomic profiles

Cancer cells possess unique metabolic signatures compared to normal cells, including shifts in aerobic glycolysis, glutaminolysis, and de novo biosynthesis of macromolecules. Targeting these changes with agents (drugs and dietary components) has been employed as strategies to reduce the complications associated with tumorigenesis. This paper highlights the ability of several food components to suppress tumor-specific metabolic pathways, including increased expression of glucose transporters, oncogenic tyrosine kinase, tumor-specific M2-type pyruvate kinase, and fatty acid synthase, and the detection of such effects using various metabonomic technologies, including liquid chromatography/mass spectrometry (LC/MS) and stable isotope-labeled MS. Stable isotope-mediated tracing technologies offer exciting opportunities for defining specific target(s) for food components. Exposures, especially during the early transition phase from normal to cancer, are critical for the translation of knowledge about food components into effective prevention strategies. Although appropriate dietary exposures needed to alter cellular metabolism remain inconsistent and/or ill-defined, validated metabonomic biomarkers for dietary components hold promise for establishing effective strategies for cancer prevention.

Interactions between immunity and metabolism - contributions from the metabolic profiling of parasite-rodent models

A combined interdisciplinary research strategy is even more crucial in immunology than in many other biological sciences in order to comprehend the closely linked interactions between cell proliferation, molecular signalling and gene rearrangements. Because of the multi-dimensional nature of the immune system, an abundance of different experimental approaches has developed, with a main focus on cellular and molecular mechanisms. The role of metabolism in immunity has been underexplored so far, and yet researchers have made important contributions in describing associations of immune processes and metabolic pathways, such as the central role of the l-arginine pathway in macrophage activation or the immune regulatory functions of the nucleotides. Furthermore, metabolite supplement studies, including nutritional administration and labelled substrates, have opened up new means of manipulating immune mechanisms. Metabolic profiling has introduced a reproducible platform for systemic assessment of changes at the small-molecule level within a host organism, and specific metabolic fingerprints of several parasitic infections have been characterized by 1H NMR spectroscopy. The application of multivariate statistical methods to spectral data has facilitated recovery of biomarkers, such as increased acute phase protein signals, and enabled direct correlation to the relative cytokine levels, which encourages further application of metabolic profiling to explore immune regulatory systems.

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.

A metabonomic comparison of urinary changes in Zucker and GK rats

To further investigate pathogenesis and pathogenic process of type 2 diabetes mellitus (T2DM), we compared the urinary metabolic profiling of Zucker obese and Goto-kakizaki (GK) rats by NMR-based metabonomics. Principal component analysis (PCA) on urine samples of both models rats indicates markedly elevated levels of creatine/creatinine, dimethylamine, and acetoacetate, with concomitantly declined levels of citrate, 2-ketoglurarate, lactate, hippurate, and succinate compared with control rats, respectively. Simultaneously, compared with Zucker obese rats, the GK rats show decreased levels of trimethylamine, acetate, and choline, as well as increased levels of creatine/creatinine, acetoacetate, alanine, citrate, 2-ketoglutarate, succinate, lactate, and hippurate. This study demonstrates metabolic similarities between the two stages of T2DM, including reduced tricarboxylic acid (TCA) cycle and increased ketone bodies production. In addition, compared with Zucker obese rats, the GK rats have enhanced concentration of energy metabolites, which indicates energy metabolic changes produced in hyperglycemia stage more than in insulin resistance stage.

Navigating the human metabolome for biomarker identification and design of pharmaceutical molecules

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics. Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants.

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.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population

BACKGROUND

Insulin resistance is a risk factor for type 2 diabetes and cardiovascular disease progression. Current diagnostic tests, such as glycemic indicators, have limitations in the early detection of insulin resistant individuals. We searched for novel biomarkers identifying these at-risk subjects.

METHODS

Using mass spectrometry, non-targeted biochemical profiling was conducted in a cohort of 399 nondiabetic subjects representing a broad spectrum of insulin sensitivity and glucose tolerance (based on the hyperinsulinemic euglycemic clamp and oral glucose tolerance testing, respectively).

RESULTS

Random forest statistical analysis selected alpha-hydroxybutyrate (alpha-HB) as the top-ranked biochemical for separating insulin resistant (lower third of the clamp-derived M(FFM) = 33 [12] micromol x min(-1) x kg(FFM) (-1), median [interquartile range], n = 140) from insulin sensitive subjects (M(FFM) = 66 [23] micromol x min(-1) x kg(FFM) (-1)) with a 76% accuracy. By targeted isotope dilution assay, plasma alpha-HB concentrations were reciprocally related to M(FFM); and by partition analysis, an alpha-HB value of 5 microg/ml was found to best separate insulin resistant from insulin sensitive subjects. alpha-HB also separated subjects with normal glucose tolerance from those with impaired fasting glycemia or impaired glucose tolerance independently of, and in an additive fashion to, insulin resistance. These associations were also independent of sex, age and BMI. Other metabolites from this global analysis that significantly correlated to insulin sensitivity included certain organic acid, amino acid, lysophospholipid, acylcarnitine and fatty acid species. Several metabolites are intermediates related to alpha-HB metabolism and biosynthesis.

CONCLUSIONS

alpha-hydroxybutyrate is an early marker for both insulin resistance and impaired glucose regulation. The underlying biochemical mechanisms may involve increased lipid oxidation and oxidative stress.

Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.

Type 2 diabetes mellitus is a complex metabolic disease recognized as one of the main threats to human health in the 21^st century. Recent studies of gene expression levels in human tissue samples have indicated that multiple metabolic pathways are dysregulated in diabetes and in individuals at risk for diabetes; which of these are primary, or central to disease pathogenesis, remains a key question. Cellular metabolic networks are highly interconnected and often tightly regulated; any perturbations at a single node can thus rapidly diffuse to the rest of the network. Such complexity presents a considerable challenge in pinpointing key molecular mechanisms and biomarkers associated with insulin resistance and type 2 diabetes. In this study, we address this problem by using a methodology that integrates gene expression data with the human cellular metabolic network. We demonstrate our approach by analyzing gene expression patterns in skeletal muscle. The analysis identified transcription factors and metabolites that represent potential targets for therapeutic agents and future clinical diagnostics for type 2 diabetes and impaired glucose metabolism. In a broader perspective, the study provides a framework for analysis of gene expression datasets from complex diseases in the context of changes in cellular metabolism.

Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype

Obesity is a complex multifactorial disease involving genetic and environmental factors and influencing several different metabolic pathways. In this regard, metabonomics, that is the study of complex metabolite profiles in biological samples, may provide a systems approach to understand the global metabolic regulation of the organism in relation to this peculiar pathology. In this pilot study, we have applied a nuclear magnetic resonance (NMR)-based metabolomic approach on urinary samples of morbidly obese subjects. Urine samples of 15 morbidly obese insulin-resistant (body mass index>40; homeostasis assessment model of insulin resistance>3) male patients and 10 age-matched controls were collected, frozen and analyzed by high-resolution (1)H-NMR spectroscopy combined with partial least squares-discriminant analysis. Furthermore, two obese patients who underwent bariatric surgery (biliopancreatic diversion and gastric bypass, respectively) were monitored during the first 3 months after surgery and their urinary metabolic profiles were characterized. NMR-based metabolomic analysis allowed us to identify an obesity-associated metabolic phenotype (metabotype) that differs from that of lean controls. Gut flora-derived metabolites such as hippuric acid, trigonelline, 2-hydroxyisobutyrate and xanthine contributed most to the classification model and were responsible for the discrimination. These preliminary results confirmed that in humans the gut microflora metabolism is strongly linked to the obesity phenotype. Moreover, the typical obese metabotype is lost after weight loss induced by bariatric surgery.

Introduction to omics

Exploiting the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes has currently been receiving a lot of attention. In recent years, most of the effort has been put into demonstrating the possible clinical applications of the various omics fields. The cost-effectiveness analysis has been, so far, rather neglected. The cost of omics-derived applications is still very high, but future technological improvements are likely to overcome this problem. In this chapter, we will give a general background of the main omics fields and try to provide some examples of the most successful applications of omics that might be used in clinical diagnosis and in a therapeutic context.

NMR metabolic analysis of samples using fuzzy K-means clustering

The global analysis of metabolites can be used to define the phenotypes of cells, tissues or organisms. Classifying groups of samples based on their metabolic profile is one of the main topics of metabolomics research. Crisp clustering methods assign each feature to one cluster, thereby omitting information about the multiplicity of sample subtypes. Here, we present the application of fuzzy K-means clustering method for the classification of samples based on metabolomics 1D (1)H NMR fingerprints. The sample classification was performed on NMR spectra of cancer cell line extracts and of urine samples of type 2 diabetes patients and animal models. The cell line dataset included NMR spectra of lipophilic cell extracts for two normal and three cancer cell lines with cancer cell lines including two invasive and one non-invasive cancers. The second dataset included previously published NMR spectra of urine samples of human type 2 diabetics and healthy controls, mouse wild type and diabetes model and rat obese and lean phenotypes. The fuzzy K-means clustering method allowed more accurate sample classification in both datasets relative to the other tested methods including principal component analysis (PCA), hierarchical clustering (HCL) and K-means clustering. In the cell line samples, fuzzy clustering provided a clear separation of individual cell lines, groups of cancer and normal cell lines as well as non-invasive and invasive tumour cell lines. In the diabetes dataset, clear separation of healthy controls and diabetics in all three models was possible only by using the fuzzy clustering method.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

NMR-based metabolomics of urine for the atherosclerotic mouse model using apolipoprotein-E deficient mice

NMR-based metabolomics of mouse urine was used in conjunction with the traditional staining and imaging of aortas for the characterization of disease advancement, that is, plaque formation in untreated and drug-treated apolipoprotein-E (apoE) knockout mice. The metabolomics approach with multivariate analysis was able to differentiate the captopril-treated from the untreated mice in general agreement with the staining results. Principal component analysis showed a pattern shift in both the drug-treated and untreated samples as a function of time that could possibly be explained as the effect of aging. Allantoin, a marker attributed to captopril treatment was elevated in the drug-treated mice. From partial least squares-discriminant analysis, xanthine and ascorbate were elevated in the untreated mice and were possible markers of plaque formation in the apoE knockout mice. Several additional peaks in the spectra characterizing the study endpoint were found but their respective metabolite identities were unknown.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Combination of 1H nuclear magnetic resonance spectroscopy and liquid chromatography/mass spectrometry with pattern recognition techniques for evaluation of metabolic profile associated with albuminuria

A method using 1H NMR and LC/MS with pattern recognition tools such as principal component analysis (PCA) and orthogonal projection to latent structure discriminant analysis (O-PLS-DA) was used to study the urinary metabolic profiles associated with an increase in urinary albumin in a general population. The normalized peak intensities obtained from 1H NMR and LC/MS with nonparametric two-tailed Mann-Whitney analysis was used for the identification of network of potential biomarkers corresponding to the increase of albumin in urine. The specificity of detecting the stated metabolites by 1H NMR and LC/MS was demonstrated. Our preliminary data obtained demonstrated that LC/MS may produce more distinctive metabolic profiles. For the patient group, changes in alanine, kyneurnic acid, and xanthurenic acid might be associated with changes in the tryptophan metabolism. At the same time, other metabolites that were involved in citric acid cycle, amino acid metabolism, and cellular functions were affected in the patient group. The proposed approach provided a comprehensive picture of the metabolic changes induced by the increase of protein in urine and demonstrated the advantages of using multiple diagnostic biomarkers. At the same time, the current work was demonstrated as a potential cost-effective solution of high-throughput analysis with pattern recognition tools as applied here in a real clinical situation.

Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling

Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in (1)H NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.

Metabolomics in evaluation of glucose disorders

PURPOSE OF REVIEW

Recent advances in metabolomic tools now permit to characterize dysregulated metabolic pathways in various diseases associated with the identification of sensitive and specific early responding biomarkers that are critical both for the diagnosis of the type of insult as well as for the selection and evaluation of therapy.

RECENT FINDINGS

This short review describes progresses made in analytical science and their applications in the field of glucose disorders. Recent studies focused mainly on type 2 diabetes both in human and animal models in order to validate early biomarkers and effects of drugs on disease progression. The potential of using the metabolomic approach was also demonstrated for diagnosing diabetic complications such as diabetic nephropathy.

SUMMARY

In addition to its application in the discovery of disease biomarkers, metabolomics can contribute to the elucidation of pathophysiological mechanisms.

1H NMR-based metabonomics for investigating diabetes

Diabetes is characterized by hyperglycemia due to dysfunction of insulin secretion or action. The two most common forms are Type 1 diabetes, in which pancreatic β-cells are destroyed, and Type 2 diabetes, in which a combination of disordered insulin action and secretion results in abnormal carbohydrate, lipid and protein metabolism. Metabonomics employs analytical technologies to measure ‘global’ metabolic responses to a disease state. With the aid of statistical pattern recognition, this can reveal novel insights into the biochemical consequences of diabetes. The metabonomic method can be divided into four stages: sample collection; preparation; data acquisition and processing; and statistical analyses. In this review, we describe the most recent developments at each experimental stage in detail, and comment on specific precautions or improvements that should be taken into account when studying diabetes. Finally, we end with speculations as to where and how the field will develop in the future. Metabonomics provides a logical framework for understanding the global metabolic effects of diabetes. Continuing technological improvements will expand our knowledge of the causes and progression of this disease, and enhance treatment options for individuals.

Characterization of inflammatory bowel disease with urinary metabolic profiling

OBJECTIVES

Distinguishing between the inflammatory bowel disease (IBD), Crohn's disease (CD), and ulcerative colitis (UC) is important for both management and prognostic reasons. Discrimination using noninvasive techniques could be an adjunct to conventional diagnostics. Differences have been shown between the intestinal microbiota of CD and UC patients and controls; the gut bacteria influence specific urinary metabolites that are quantifiable using proton high-resolution nuclear magnetic resonance (NMR) spectroscopy. This study tested the hypothesis that such metabolites differ between IBD and control cohorts, and that using multivariate pattern-recognition analysis, the cohorts could be distinguished by urine NMR spectroscopy.

METHODS

NMR spectra were acquired from urine samples of 206 Caucasian subjects (86 CD patients, 60 UC patients, and 60 healthy controls). Longitudinal samples were collected from 75 individuals. NMR resonances specific for metabolites influenced by the gut microbes were studied, including hippurate, formate, and 4-cresol sulfate. Multivariate analysis of all urinary metabolites involved principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

RESULTS

Hippurate levels were lowest in CD patients and differed significantly between the three cohorts (P<0.0001). Formate levels were higher and 4-cresol sulfate levels lower in CD patients than in UC patients or controls (P=0.0005 and P=0.0002, respectively). PCA revealed clustering of the groups; PLS-DA modeling was able to distinguish the cohorts. These results were independent of medication and diet and were reproducible in the longitudinal cohort.

CONCLUSIONS

Specific urinary metabolites related to gut microbial metabolism differ between CD patients, UC patients, and controls. The emerging technique of urinary metabolic profiling with multivariate analysis was able to distinguish these cohorts.

Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system

Ultra performance liquid chromatography (UPLC) coupled with orthogonal acceleration time-of-flight (oaTOF) mass spectrometry has showed great potential in diabetes research. In this paper, a UPLC-oaTOF-MS system was employed to distinguish the global serum profiles of 8 diabetic nephropathy (DN) patients, 33 type 2 diabetes mellitus (T2DM) patients and 25 healthy volunteers, and tried to find potential biomarkers. The UPLC system produced information-rich chromatograms with typical measured peak widths of 4 s, generating peak capacities of 225 in 15 min. Furthermore, principal component analysis (PCA) was used for group differentiation and marker selection. As shown in the scores plot, the distinct clustering between the patients and controls was observed, and DN and T2DM patients were also separated into two individual groups. Several compounds were tentatively identified based on accurate mass, isotopic pattern and MS/MS information. In addition, significant changes in the serum level of leucine, dihydrosphingosine and phytosphingosine were noted, indicating the perturbations of amino acid metabolism and phospholipid metabolism in diabetic diseases, which having implications in clinical diagnosis and treatment.

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Prolonged L-alanine exposure induces changes in metabolism, Ca2+ handling and desensitization of insulin secretion in clonal pancreatic β-cells

Acute insulin-releasing actions of amino acids have been studied in detail, but comparatively little is known about the β-cell effects of long-term exposure to amino acids. The present study examined the effects of prolonged exposure of β-cells to the metabolizable amino acid L-alanine. Basal insulin release or cellular insulin content were not significantly altered by alanine culture, but acute alanine-induced insulin secretion was suppressed by 74% (P<0.001). Acute stimulation of insulin secretion with glucose, KCl or KIC (2-oxoisocaproic acid) following alanine culture was not affected. Acute alanine exposure evoked strong cellular depolarization after control culture, whereas AUC (area under the curve) analysis revealed significant (P<0.01) suppression of this action after culture with alanine. Compared with control cells, prior exposure to alanine also markedly decreased (P<0.01) the acute elevation of [Ca2+]i (intracellular [Ca2+]) induced by acute alanine exposure. These diminished stimulatory responses were partially restored after 18 h of culture in the absence of alanine, indicating reversible amino-acid-induced desensitization. 13C NMR spectra revealed that alanine culture increased glutamate labelling at position C4 (by 60%; P<0.01), as a result of an increase in the singlet peak, indicating increased flux through pyruvate dehydrogenase. Consistent with this, protein expression of the pyruvate dehydrogenase kinases PDK2 and PDK4 was significantly reduced. This was accompanied by a decrease in cellular ATP (P<0.05), consistent with diminished insulin-releasing actions of this amino acid. Collectively, these results illustrate the phenomenon of β-cell desensitization by amino acids, indicating that prolonged exposure to alanine can induce reversible alterations to metabolic flux, Ca2+ handling and insulin secretion.

Metabolomic profiling of dietary-induced insulin resistance in the high fat-fed C57BL/6J mouse

The predictive ability of metabolic profiling to detect obesity-induced perturbations in metabolism has not been clearly established. Complex aetiologies interacting with environmental factors highlight the need to understand how specific manipulations alter metabolite profiles in this state. The aim of this study was to determine if targeted metabolomic profiling could be employed as a reliable tool to detect dietary-induced insulin resistance in a small subset of experimental animals (n = 10/treatment). Following weaning, male C57BL/6J littermates were randomly divided into two dietary groups: chow and high fat. Following 12 weeks of dietary manipulation, mice were fasted for 5 h prior to serum collection. The resultant high fat-fed animals were obese and insulin resistant as shown by a euglycaemic-hyperinsulinaemic clamp. Sera were analysed by proton nuclear magnetic resonance spectroscopy, and 46 known compounds were identified and quantified. Multivariate analysis by orthogonal partial least squares discriminant analysis, a projection method for class separation, was then used to establish models of each treatment. Models were able to predict class separation between diets with 90% accuracy. Variable importance plots revealed the most important metabolites in this discrimination to include lysine, glycine, citrate, leucine, suberate and acetate. These metabolites are involved in energy metabolism and may be representative of the perturbations taking place with insulin resistance. Results show metabolomics to reliably describe the metabolic effects of insulin resistance in a small subset of samples and are an initial step in establishing metabolomics as a tool to understand the biochemical signature of insulin resistance.