Toluene inhalation in adolescent rats reduces flexible behaviour in adulthood and alters glutamatergic and GABAergic signalling.

Toluene is a commonly abused inhalant that is easily accessible to adolescents. Despite the increasing incidence of use, our understanding of its long-term impact remains limited. Here, we used a range of techniques to examine the acute and chronic effects of toluene exposure on glutameteric and GABAergic function, and on indices of psychological function in adult rats after adolescent exposure. Metabolomics conducted on cortical tissue established that acute exposure to toluene produces alterations in cellular metabolism indicative of a glutamatergic and GABAergic profile. Similarly, in vitro electrophysiology in Xenopus oocytes found that acute toluene exposure reduced NMDA receptor signalling. Finally, in an adolescent rodent model of chronic intermittent exposure to toluene (10 000 ppm), we found that, while toluene exposure did not affect initial learning, it induced a deficit in updating that learning when response-outcome relationships were reversed or degraded in an instrumental conditioning paradigm. There were also group differences when more effort was required to obtain the reward; toluene-exposed animals were less sensitive to progressive ratio schedules and to delayed discounting. These behavioural deficits were accompanied by changes in subunit expression of both NMDA and GABA receptors in adulthood, up to 10 weeks after the final exposure to toluene in the hippocampus, prefrontal cortex and ventromedial striatum; regions with recognized roles in behavioural flexibility and decision-making. Collectively, our data suggest that exposure to toluene is sufficient to induce adaptive changes in glutamatergic and GABAergic systems and in adaptive behaviour that may underlie the deficits observed following adolescent inhalant abuse, including susceptibility to further drug-use.

Ethanol, not detectably metabolized in brain, significantly reduces brain metabolism, probably via action at specific GABA(A) receptors and has measureable metabolic effects at very low concentrations.

Ethanol is a known neuromodulatory agent with reported actions at a range of neurotransmitter receptors. Here, we measured the effect of alcohol on metabolism of [3-¹³C]pyruvate in the adult Guinea pig brain cortical tissue slice and compared the outcomes to those from a library of ligands active in the GABAergic system as well as studying the metabolic fate of [1,2-¹³C]ethanol. Analyses of metabolic profile clusters suggest that the significant reductions in metabolism induced by ethanol (10, 30 and 60 mM) are via action at neurotransmitter receptors, particularly α4ß3δ receptors, whereas very low concentrations of ethanol may produce metabolic responses owing to release of GABA via GABA transporter 1 (GAT1) and the subsequent interaction of this GABA with local α5- or α1-containing GABA(A)R. There was no measureable metabolism of [1,2-¹³C]ethanol with no significant incorporation of ¹³C from [1,2-¹³C]ethanol into any measured metabolite above natural abundance, although there were measurable effects on total metabolite sizes similar to those seen with unlabelled ethanol.

A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection.

We have performed a metabolite quantitative trait locus (mQTL) study of the (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by (1)H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10(-11)

Latent biochemical relationships in the blood-milk metabolic axis of dairy cows revealed by statistical integration of 1H NMR spectroscopic data.

A detailed understanding of the relationships between the distinct metabolic compartments of blood and milk would be of potential benefit to our understanding of the physiology of lactation, and potentially for development of biomarkers for health and commercially relevant traits in dairy cattle. NMR methods were used to measure metabolic profiles from blood and milk samples from Holstein cows. Data were analyzed using PLS regression to identify quantitative relationships between metabolic profiles and important traits. Statistical Heterospectroscopy (SHY), a powerful approach to recovering latent biological information in NMR spectroscopic data sets from multiple complementary samples, was employed to explore the metabolic relationships between blood and milk from these animals. The study confirms milk is a distinct metabolic compartment with a metabolite composition largely not influenced by plasma composition under normal circumstances. However, several significant relationships were identified, including a high correlation for trimethylamine (TMA) and dimethylsulfone (DMSO(2)) across plasma and milk compartments, and evidence plasma valine levels are linked to differences in amino acid catabolism in the mammary gland. The findings provide insights into the physiological mechanisms underlying lactation and identification of links between key metabolites and milk traits such as the protein and fat content of milk. The approach has the potential to enable measurement of health, metabolic status and other important phenotypes with milk sampling.

1H NMR spectroscopy of serum reveals unique metabolic fingerprints associated with subtypes of surgically induced osteoarthritis in sheep.

Osteoarthritis (OA) is a highly prevalent joint disease. Its slow progressive nature and the correlation between pathological changes and clinical symptoms mean that OA is often well advanced by the time of diagnosis. In the absence of any specific pharmacological treatments, there is a pressing need to develop robust biomarkers for OA. We have adopted a nuclear magnetic resonance (NMR)-based metabolomic strategy to identify molecular responses to surgically induced OA in an animal model. Sheep underwent one of three types of surgical procedure (sham (control), meniscal destabilization, MD or anterior cruciate ligament transaction, ACLT), and for every animal a serum sample was collected both pre- and postoperatively, thus, affording two types of "control" data for comparison. 1D 1H NMR spectra were acquired from each sample at 800 MHz and the digitized spectral data were analyzed using principal components analysis and partial least-squares regression discriminant analysis. Our approach, combined with the study design, allowed us to separate the metabolic responses to surgical intervention from those associated with OA. We were able to identify dimethyl sulfone (DMSO2) as being increased in MD after 4 weeks, while ACLT-induced OA exhibited increased 3-methylhistidine and decreased branched chain amino acids (BCAAs). The findings are discussed in the context of interpretation of metabolomic results in studies of human disease, and the selection of appropriate "control" data sets.

Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra.

The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical total correlation spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.

Human metabolic profiles are stably controlled by genetic and environmental variation.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

Statistical integration of 1H NMR and MRS data from different biofluids and tissues enhances recovery of biological information from individuals with HIV-1 infection.

Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabonomics studies, but optimal recovery of latent biological information requires increasingly sophisticated statistical methods to identify quantitative relationships within these often highly complex data sets. Statistical heterospectroscopy (SHY) extracts latent relationships between NMR and mass spectrometry (MS) data from the same samples. Here we extend this concept to identify novel metabolic correlations between different biofluids and tissues from the same individuals. We acquired NMR data from blood plasma and cerebrospinal fluid (CSF) (N = 19) from HIV-1-infected individuals, who are known to be susceptible to neuropsychological dysfunction. We compared two computational approaches to SHY, namely the Pearson's product moment correlation and the Spearman's rank correlation. High correlations were observed for glutamine, valine, and polyethylene glycol, a drug delivery vehicle. Orthogonal projections to latent structures (OPLS) identified metabolites in blood plasma spectra that predicted the amounts of key CSF metabolites such as lactate, glutamine, and myo-inositol. Finally, brain metabolic data from magnetic resonance spectroscopy (MRS) measurements in vivo were integrated with CSF data to identify an association between 3-hydroxyvalerate and frontal white matter N-acetyl aspartate levels. The results underscore the utility of tools such as SHY and OPLS for coanalysis of high dimensional data sets to recover biological information unobtainable when such data are analyzed in isolation.

γ-Hydroxybutyrate and the GABAergic footprint: a metabolomic approach to unpicking the actions of GHB.

Gamma-hydroxybutyrate is found both naturally in the brain and self-administered as a drug of abuse. It has been reported to act at endogenous γ-hydroxybutyrate (GHB) receptors and GABA(B) receptors [GABA(B)R], and may also be metabolized to GABA. Here, the metabolic fingerprints of a range of concentrations of GHB were measured in brain cortical tissue slices and compared with those of ligands active at GHB and GABA-R using principal components analysis (PCA) to identify sites of GHB activity. Low concentrations of GHB (1.0 µM) produced fingerprints similar to those of ligands active at GHB receptors and α4-containing GABA(A)R. A total of 10 µM GHB clustered proximate to mainstream GABAergic synapse ligands, such as 1.0 µM baclofen, a GABA(B)R agonist. Higher concentrations of GHB (30 µM) clustered with GABA(C)R agonists and the metabolic responses induced by blockade of the GABA transporter-1 (GAT1). The metabolic responses induced by 60 and 100 µM GHB were mimicked by simultaneous blockade of GAT1 and GAT3, addition of low concentrations of GABA(C)R antagonists, or increasing cytoplasmic GABA concentrations by incubation with the GABA transaminase inhibitor vigabatrin. These data suggest that at concentrations > 30 µM, GHB may be active via metabolism to GABA, which is then acting upon an unidentified GABAergic master switch receptor (possibly a high-affinity extrasynaptic receptor), or GHB may itself be acting directly on an extrasynaptic GABA-R, capable of turning off large numbers of cells. These results offer an explanation for the steep dose-response curve of GHB seen in vivo, and suggest potential target receptors for further investigation.

Evaluation of full-resolution J-resolved 1H NMR projections of biofluids for metabonomics information retrieval and biomarker identification.

Spectroscopic profiling of biological samples is an integral part of metabolically driven top-down systems biology and can be used for identifying biomarkers of toxicity and disease. However, optimal biomarker information recovery and resonance assignment still pose significant challenges in NMR-based complex mixture analysis. The reduced signal overlap as achieved when projecting two-dimensional (2D) J-resolved (JRES) NMR spectra can be exploited to mitigate this problem and, here, full-resolution (1)H JRES projections have been evaluated as a tool for metabolic screening and biomarker identification. We show that the recoverable information content in JRES projections is intrinsically different from that in the conventional one-dimensional (1D) and Carr-Purcell-Meiboom-Gill (CPMG) spectra, because of the combined result of reduction of the over-representation of highly split multiplet peaks and relaxation editing. Principal component and correlation analyses of full-resolution JRES spectral data demonstrated that peak alignment is necessary. The application of statistical total correlation spectroscopy (STOCSY) to JRES projections improved the identification of previously overlapped small molecule resonances in JRES (1)H NMR spectra, compared to conventional 1D and CPMG spectra. These approaches are demonstrated using a galactosamine-induced hepatotoxicity study in rats and show that JRES projections have a useful and complementary role to standard one-dimensional experiments in complex mixture analysis for improved biomarker identification.

Dynamic biochemical information recovery in spontaneous human seminal fluid reactions via 1H NMR kinetic statistical total correlation spectroscopy.

Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz (1)H NMR spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assignment of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to determine the relationships between reaction rates and metabolite levels, revealing that choline, N-acetylglucosamine, and uridine are associated with higher peptidase activity. The novel approach adopted here has the potential to capture dynamic information in any complex mixture of reacting chemicals including other biofluids or cell extracts.

Statistical total correlation spectroscopy editing of 1H NMR spectra of biofluids: application to drug metabolite profile identification and enhanced information recovery.

Here we present a novel method for enhanced NMR spectral information recovery, utilizing a statistical total correlation spectroscopy editing (STOCSY-E) procedure for the identification of drug metabolite peaks in biofluids and for deconvolution of drug and endogenous metabolite signals. Structurally correlated peaks from drug metabolites and those from closely related drug metabolite pathways are first identified using STOCSY. Subsequently, this correlation information is utilized to scale the biofluid (1)H NMR spectra across these identified regions, producing a modified set of spectra in which drug metabolite contributions are reduced and, thus, facilitating analysis by pattern recognition methods without drug metabolite interferences. The application of STOCSY-E is illustrated with two exemplar (1)H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz (1)H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz (1)H NMR spectra of rat urine (n = 27) collected over 48 h following exposure to the renal papillary toxin 2-bromoethanamine (BEA). STOCSY-E efficiently identified and removed the major xenobiotic metabolite peaks in both data sets, providing enhanced visualization of endogenous changes via orthogonal to projection filtered partial least-squares discriminant analysis (OPLS-DA). OPLS-DA of the STOCSY-E spectral data from the BEA-treated rats revealed the gut bacterial-mammalian co-metabolite phenylacetylglycine as a previously unidentified surrogate biomarker of toxicity. STOCSY-E has a wide range of potential applications in clinical, epidemiology, toxicology, and nutritional studies where multiple xenobiotic metabolic interferences may confound biological interpretation. Additionally, this tool could prove useful for applications outside of metabolic analysis, for example, in process chemistry for following chemical reactions and equilibria and detecting impurities.

Large-scale human metabolic phenotyping and molecular epidemiological studies via 1H NMR spectroscopy of urine: investigation of borate preservation.

Borate is an antibacterial preservative widely used in clinical and large-scale epidemiological studies involving urine sample analysis. Since it readily forms covalent adducts and reversible complexes with hydroxyl and carboxylate groups, the effects of borate preservation in (1)H NMR-spectroscopy-based metabolic profiling of human urine samples have been assessed. Effects of various concentrations of borate (range 0-30 mM) on (1)H NMR spectra of urine were observed at sequential time points over a 12 month period. Consistent with known borate chemistry, the principal alterations in the (1)H resonance metabolite patterns were observed for compounds such as mannitol, citrate, and alpha-hydroxyisobutyrate and confirmed by ESI-MS analysis. These included line-broadening, T(1) and T(2) relaxation, and chemical shift changes consistent with complex formation and chemical exchange processes. To further investigate complexation behavior in the urinary metabolite profiles, a new tool for visualization of multicomponent relaxation variations in which the spectra were color-coded according to the T(1) and T(2) proton relaxation times respectively (T(1) or T(2) ordered projection spectroscopy, TOPSY) was also developed and applied. Addition of borate caused a general decrease in (1)H T(1) values, consistent with nonspecific effects such as solution viscosity changes. Minor changes in proton T(2) relaxation rates were observed for the most strongly complexing metabolites. From a molecular phenotyping and epidemiologic viewpoint, typical interpersonal biological variation was shown to be vastly greater than any variation introduced by the borate complexation, which had a negligible effect on the metabolic mapping and classification of samples. While caution is indicated in the assignment of biomarker signals where metabolites have diol groupings or where there are adjacent hydroxyl and carboxylate functions, it is concluded that borate preservation is "fit-for-purpose" for (1)H NMR-based epidemiological studies, since the essential biochemical classification features of the samples are robustly maintained.

1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies.

Statistical HeterospectroscopY (SHY) is a statistical strategy for the coanalysis of multiple spectroscopic data sets acquired in parallel on the same samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecular fingerprints measured by multiple spectroscopic techniques across cohorts of samples. Here, the method is applied to 600-MHz (1)H NMR and UPLC-TOF-MS (E) data obtained from human urine samples ( n = 86) from a subset of an epidemiological population unselected for any relevant phenotype or disease factor. We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/ z data from MS, together with fragment analysis from MS (E) scans, leads not only to the detection of numerous endogenous urinary metabolites but also the identification of drug metabolites that are part of the latent use of drugs by the population. We show previously unreported positive mode ions of ibuprofen metabolites with their NMR correlates and suggest the detection of new metabolites of disopyramide in the population samples. This approach is of great potential value in the description of population xenometabolomes and in population pharmacology studies, and indeed for drug metabolism studies in general.

Optimization of human plasma 1H NMR spectroscopic data processing for high-throughput metabolic phenotyping studies and detection of insulin resistance related to type 2 diabetes.

Optimizing NMR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the total biochemical information obtainable from (1)H NMR data, which includes concentration and molecular dynamics information. Here we have applied two different types of mathematical transformation (calculation of the first derivative of the NMR spectrum and Gaussian shaping of the free-induction decay) to attenuate broad spectral features from macromolecules and enhance the signals of small molecules. By application of chemometric methods such as principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (O-PLS-DA) and statistical spectroscopic tools such as statistical total correlation spectroscopy (STOCSY), we show that these methods successfully identify the same potential biomarkers as spin-echo (1)H NMR spectra in which broad lines are suppressed via T2 relaxation editing. Finally, we applied these methods for identification of the metabolic phenotype of patients with type 2 diabetes. This "virtual" relaxation-edited spectroscopy (RESY) approach can be particularly useful for high-throughput screening of complex mixtures such as human plasma and may be useful for extraction of latent biochemical information from legacy or archived NMR data sets for which only standard 1D data sets exist.

Application of the FLIPSY pulse sequence for increased sensitivity in 1H NMR-based metabolic profiling studies.

Metabolite profiling relies on optimal precision of the acquired data, which requires, among others, a high signal-to-noise ratio (S/N). In addition, increased S/N will increase the likelihood of identification of new biomarkers. Here we introduce, for the first time in metabolite profiling studies by 1H NMR, an approach to enhance the precision of multivariate regression models by use of the FLIPSY (flip angle adjustable one-dimensional NOESY) pulse sequence, augmented by a homospoil pulse after the presaturation period to provide superior baseline quality. Unlike NOESYPRESAT, the standard one-dimensional (1D) sequence generally used in metabonomic studies, FLIPSY incorporates a variable flip angle, allowing use of the Ernst angle for excitation and thus optimization of S/N ratios according to spin lattice relaxation times (T1) of individual resonances. T1 values of metabolites present in human urine were determined by inversion-recovery experiments and subsequently used in calculations of optimal experimental conditions. Comparison of human urine analysis by the FLIPSY and NOESYPRESAT demonstrated an increase of S/N ratio in the former case that amounts to approximately 7% when measured for the hippurate doublet at delta 7.84. An orthogonal projection to latent structures discriminant analysis (O-PLS-DA) model exhibited superior discrimination between controls and simulated phenylketonuria urines when using data generated by the FLIPSY as compared to NOESYPRESAT.

Seminal oligouridinosis: low uridine secretion as a biomarker for infertility in spinal neurotrauma.

BACKGROUND: Compromised sexual health is a major rehabilitative barrier for men with lower-spinal cord injury (SCI). Although studies have revealed decreased sperm motility, the quantitative biochemical changes that underlie the infertility mechanism remain poorly understood. METHODS: We employed a nontargeted approach combining 800 MHz hydrogen nuclear magnetic resonance ((1)H NMR) spectroscopy and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) with pattern recognition methods to analyze seminal fluid metabolite profiles in 10 men with and 8 without SCI above thoracic vertebra 10 (T10). RESULTS: The metabolic phenotype for SCI could be predicted from the (1)H NMR data. The median concentration of uridine in fertile controls was 1.55 mmol/L (range 1.0-5.0 mmol/L), but was undetectable by both NMR and MS in all but 2 individuals from the SCI group, one who later fathered a child without assisted fertility techniques. CONCLUSIONS: We hypothesize that uridine is likely to be an essential precursor to metabolites required for capacitation and is a potential marker for the prognosis of post-SCI functional fertility recovery. We derived the term "seminal oligouridinosis" to describe this newly identified condition.

The Gárdos channel: a review of the Ca2+-activated K+ channel in human erythrocytes.

Ca(2+)-dependent K(+) efflux from human erythrocytes was first described in the 1950s. Subsequent studies revealed that a K(+)-specific membrane protein (the Gárdos channel) was responsible for this phenomenon (the Gárdos effect). In recent years several types of Ca-activated K(+) channel have been identified and studied in a wide range of cells, with the erythrocyte Gárdos channel serving as both a model for a broader physiological perspective, and an intriguing component of erythrocyte function. The existence of this channel has raised a number of questions. For example, what is its role in the establishment and maintenance of ionic distribution across the red cell membrane? What role might it play in erythrocyte development? To what extent is it active in circulating erythrocytes? What are the cell-physiological implications of its dysfunction?This review summarises current knowledge of this membrane protein with respect to its function and structure, its physiological roles (some putative) and its contribution to various disease states, and it provides an introduction to adaptable NMR methods, which is our own area of technical expertise, for such ion transport analysis.

Applications of NMR in dairy research.

NMR is a robust analytical technique that has been employed to investigate the properties of many substances of agricultural relevance. NMR was first used to investigate the properties of milk in the 1950s and has since been employed in a wide range of studies; including properties analysis of specific milk proteins to metabolomics techniques used to monitor the health of dairy cows. In this brief review, we highlight the different uses of NMR in the dairy industry.

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 ß-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.