Relevance of bioaccumulating substances in the TTC concept.

Recently, the European Food Safety Authority (EFSA) stated that the Threshold of Toxicological Concern (TTC) thresholds should not be used for substances that are known or predicted to accumulate. Bioaccumulation of substances is usually considered unfavourable but so far a relation with toxicity at low dose exposure is insufficiently investigated to draw conclusions on the relevance of bioaccumulation at low dose exposure. In this manuscript it is investigated which physical chemical properties are related to bioaccumulation in order to predict accumulating properties of a substance, and is evaluated if the toxicity of known bioaccumulating substances is higher than for non-accumulating substances. Based on the evaluation it is concluded that the current TTC thresholds are derived with a dataset in which bioaccumulating substances are present, whereas the toxicity of the bioaccumulating substances is already taken into account in the TTC thresholds. The authors demonstrated that there is no need to exclude potential bioaccumulating substances from the TTC concept.

Integrated analysis of toxicity data of two pharmaceutical immunosuppressants and two environmental pollutants with immunomodulating properties to improve the understanding of side effects-A toxicopathologist׳s view.

Data in a toxicity test are evaluated generally per parameter. Information on the response per animal in addition to per parameter can improve the evaluation of the results. The results from the six studies in rats, described in the paper by Kemmerling, J., Fehlert, E., Rühl-Fehlert, C., Kuper, C.F., Stropp, G., Vogels, J., Krul, C., Vohr, H.-W., 2015. The transferability from rat subacute 4-week oral toxicity study to translational research exemplified by two pharmaceutical immunosuppressants and two environmental pollutants with immunomodulating properties (In this issue), have been subjected to principal component analysis (PCA) and principal component discriminant analysis (PC-DA). The two pharmaceuticals azathioprine (AZA) and cyclosporine A (CSA) and the two environmental pollutants hexachlorobenzene (HCB) and benzo(a)pyrene (BaP) all modulate the immune system, albeit that their mode of immunomodulation is quite diverse. PCA illustrated the similarities between the two independent studies with AZA (AZA1 and AZA2) and CSA (CSA1 and CSA2). The PC-DA on data of the AZA2 study did not increase substantially the information on dose levels. In general, the no-effect levels were lower upon single parameter analysis than indicated by the distances between the dose groups in the PCA. This was mostly due to the expert judgment in the single parameter evaluation, which took into account outstanding pathology in only one or two animals. The PCA plots did not reveal sex-related differences in sensitivity, but the key pathology for males and females differed. The observed variability in some of the control groups was largely a peripheral blood effect. Most importantly, PCA analysis identified several animals outside the 95% confidence limit indicating high-responders; also low-to-non-responders were identified. The key pathology enhanced the understanding of the response of the animals to the four model compounds.

The transferability from rat subacute 4-week oral toxicity study to translational research exemplified by two pharmaceutical immunosuppressants and two environmental pollutants with immunomodulating properties.

Exposure to chemicals may have an influence on the immune system. Often, this is an unwanted effect but in some pharmaceuticals, it is the intended mechanism of action. Immune function tests and in depth histopathological investigations of immune organs were integrated in rodent toxicity studies performed according to an extended OECD test guideline 407 protocol. Exemplified by two immunosuppressive drugs, azathioprine and cyclosporine A, and two environmental chemicals, hexachlorobenzene and benzo[a]pyrene, results of subacute rat studies were compared to knowledge in other species particular in humans. Although immune function has a high concordance in mammalian species, regarding the transferability from rodents to humans various factors have to be taken into account. In rats, sensitivity seems to depend on factors such as strain, sex, stress levels as well as metabolism. The two immunosuppressive drugs showed a high similarity of effects in animals and humans as the immune system was the most sensitive target in both. Hexachlorobenzene gave an inconsistent pattern of effects when considering the immune system of different species. In some species pronounced inflammation was observed, whereas in primates liver toxicity seemed more obvious. Generally, the immune system was not the most sensitive target in hexachlorobenzene-treatment. Immune function tests in rats gave evidence of a reaction to systemic inflammation rather than a direct impact on immune cells. Data from humans are likewise equivocal. In the case of benzo[a]pyrene, the immune system was the most sensitive target in rats. In the in vitro plaque forming cell assay (Mishell-Dutton culture) a direct comparison of cells from different species including rat and human was possible and showed similar reactions. The doses in the rat study had, however, no realistic relation to human exposure, which occurs exclusively in mixtures and in a much lower range. In summary, a case by case approach is necessary when testing immunotoxicity. Improvements for the translation from animals to humans related to immune cells can be expected from in vitro tests which offer direct comparison with reactions of human immune cells. This may lead to a better understanding of results and variations seen in animal studies.

Cross-species comparison of genes related to nutrient sensing mechanisms expressed along the intestine.

INTRODUCTION: Intestinal chemosensory receptors and transporters are able to detect food-derived molecules and are involved in the modulation of gut hormone release. Gut hormones play an important role in the regulation of food intake and the control of gastrointestinal functioning. This mechanism is often referred to as "nutrient sensing". Knowledge of the distribution of chemosensors along the intestinal tract is important to gain insight in nutrient detection and sensing, both pivotal processes for the regulation of food intake. However, most knowledge is derived from rodents, whereas studies in man and pig are limited, and cross-species comparisons are lacking. AIM: To characterize and compare intestinal expression patterns of genes related to nutrient sensing in mice, pigs and humans. METHODS: Mucosal biopsy samples taken at six locations in human intestine (n = 40) were analyzed by qPCR. Intestinal scrapings from 14 locations in pigs (n = 6) and from 10 locations in mice (n = 4) were analyzed by qPCR and microarray, respectively. The gene expression of glucagon, cholecystokinin, peptide YY, glucagon-like peptide-1 receptor, taste receptor T1R3, sodium/glucose cotransporter, peptide transporter-1, GPR120, taste receptor T1R1, GPR119 and GPR93 was investigated. Partial least squares (PLS) modeling was used to compare the intestinal expression pattern between the three species. RESULTS AND CONCLUSION: The studied genes were found to display specific expression patterns along the intestinal tract. PLS analysis showed a high similarity between human, pig and mouse in the expression of genes related to nutrient sensing in the distal ileum, and between human and pig in the colon. The gene expression pattern was most deviating between the species in the proximal intestine. Our results give new insights in interspecies similarities and provide new leads for translational research and models aiming to modulate food intake processes in man.

Instrument and process independent binning and baseline correction methods for liquid chromatography-high resolution-mass spectrometry deconvolution.

Setting appropriate bin sizes to aggregate hyphenated high-resolution mass spectrometry data, belonging to similar mass over charge (m/z) channels, is vital to metabolite quantification and further identification. In a high-resolution mass spectrometer when mass accuracy (ppm) varies as a function of molecular mass, which usually is the case while reading m/z from low to high values, it becomes a challenge to determine suitable bin sizes satisfying all m/z ranges. Similarly, the chromatographic process within a hyphenated system, like any other controlled processes, introduces some process driven systematic behavior that ultimately distorts the mass chromatogram signal. This is especially seen in liquid chromatogram-mass spectrometry (LC-MS) measurements where the gradient of the solvent and the washing step cycle-part of the chromatographic process, produce a mass chromatogram with a non-uniform baseline along the retention time axis. Hence prior to any automatic signal decomposition techniques like deconvolution, it is a equally vital to perform the baseline correction step for absolute metabolite quantification. This paper will discuss an instrument and process independent solution to the binning and the baseline correction problem discussed above, seen together, as an effective pre-processing step toward liquid chromatography-high resolution-mass spectrometry (LC-HR-MS) data deconvolution.

Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes.

BACKGROUND: Being able to visualize multivariate biological treatment effects can be insightful. However the axes in visualizations are often solely defined by variation and thus have no biological meaning. This makes the effects of treatment difficult to interpret. METHODS: A statistical visualization method is presented, which analyses and visualizes the effects of treatment in individual subjects. The visualization is based on predefined biological processes as determined by systems-biological datasets (metabolomics proteomics and transcriptomics). This allows one to evaluate biological effects depending on shifts of either groups or subjects in the space predefined by the axes, which illustrate specific biological processes. We built validated multivariate models for each axis to represent several biological processes. In this space each subject has his or her own score on each axis/process, indicating to which extent the treatment affects the related process. RESULTS: The health space model was applied to visualize the effects of a nutritional intervention, with the goal of applying diet to improve health. The model was therefore named the 'health space' model. The 36 study subjects received a 5-week dietary intervention containing several anti-inflammatory ingredients. Plasma concentrations of 79 proteins and 145 metabolites were quantified prior to and after treatment. The principal processes modulated by the intervention were oxidative stress, inflammation, and metabolism. These processes formed the axes of the 'health space'. The approach distinguished the treated and untreated groups, as well as two different response subgroups. One subgroup reacted mainly by modulating its metabolic stress profile, while a second subgroup showed a specific inflammatory and oxidative response to treatment. CONCLUSIONS: The 'health space' model allows visualization of multiple results and to interpret them. The model presents treatment group effects, subgroups and individual responses.

Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets.

Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets.

Evaluation of metabolite profiles as biomarkers for the pharmacological effects of thiazolidinediones in Type 2 diabetes mellitus patients and healthy volunteers.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: * Many studies have investigated the effects of thiazolidinediones on isolated biochemical markers (biomarkers) or sets of markers in Type 2 diabetes mellitus (T2DM) patients and healthy volunteers. * However, a limited number of parameters is not capable of capturing the broad response to pharmacological intervention with these types of (pleiotropic) drugs, which are known to activate the nuclear transcription factor peroxisome proliferator activated receptor gamma (PPARgamma). * Our study tested the new hypothesis (primary objective) that nuclear magnetic resonance (NMR)-based metabolomics, capable of providing a readout of global metabolite concentrations in biofluids, could provide a better (more holistic) picture of the the multiparametric response to pharmacological intervention with a PPARgamma agonist and thus yield a broad array of biomarkers ('fingerprint') that could be used to support and expedite clinical development of novel thiazolidinediones. WHAT THIS STUDY ADDS: * NMR-based metabolomics coupled with sophisticated bioinformatics is indeed capable of identifying rapid changes in global metabolite profiles in urine and plasma (treatment 'fingerprints'), which may be linked to the well-documented early changes in hepatic insulin senstitivity following thiazolidinedione intervention in T2DM patients. * Consequently, this approach (upon proper validation) comprises an important new addition to the early clinical development 'proof of concept' toolbox for thiazolidinediones, and may also be applicable to other classes of drugs. AIMS: To explore the usefulness of metabolomics as a method to obtain a broad array of biomarkers for the pharmacological effects of rosiglitazone (RSG) in plasma and urine samples from patients with type 2 diabetes mellitus (T2DM) and healthy volunteers (HVs). Additionally, we explored the differences in metabolite concentrations between T2DM patients and HVs to identify a putative metabolic disease fingerprint for T2DM. METHODS: (1)H nuclear magnetic resonance (NMR) spectroscopy was used to profile blood plasma and urine samples of 16 T2DM patients and 16 HVs receiving RSG 4 mg or placebo twice daily for 6 weeks. Multivariate analyses were employed to identify treatment- and disease-related effects on global endogenous metabolite profiles. RESULTS: RSG treatment led to a rapid relative reduction in urinary hippurate and aromatic amino acids as well as an increase in plasma branched chain amino acids and alanine, glutamine and glutamate in the T2DM group. No RSG treatment effects were noted in the HV group. Exploratory baseline analyses showed that urine and plasma metabolites discriminated between genders and disease state. T2DM patients showed a relative increase in urinary concentrations of several amino acids, citrate, phospho(enol)pyruvate and hippurate. Putative T2DM-related changes in plasma were largely attributable to increased plasma lipids. CONCLUSION: The results of this study indicate that NMR-based metabolomics of urine and blood plasma samples can yield a broad array of early responding biomarkers for the effects of RSG in T2DM patients, as well as nonglucose biomarkers that may reflect the T2DM state.

Uniform procedure of (1)H NMR analysis of rat urine and toxicometabonomics Part II: comparison of NMR profiles for classification of hepatotoxicity.

A procedure of nuclear magnetic resonance (NMR) urinalysis using pattern recognition is proposed for early detection of toxicity of investigational compounds in rats. The method is applied to detect toxicity upon administration of 13 toxic reference compounds and one nontoxic control compound (mianserine) in rats. The toxic compounds are expected to induce necrosis (bromobenzene, paracetamol, carbon tetrachloride, iproniazid, isoniazid, thioacetamide), cholestasis (alpha-naphthylisothiocyanate (ANIT), chlorpromazine, ethinylestradiol, methyltestosterone, ibuprofen), or steatosis (phenobarbital, tetracycline). Animals were treated daily for 2 or 4 days except for paracetamol and bromobenzene (1 and 2 days) and carbon tetrachloride (1 day only). Urine was collected 24 h after the first and second treatment. The animals were sacrificed 24 h after the last treatment, and NMR data were compared with liver histopathology as well as blood and urine biochemistry. Pathology and biochemistry showed marked toxicity in the liver at high doses of bromobenzene, paracetamol, carbon tetrachloride, ANIT, and ibuprofen. Thioacetamide and chlorpromazine showed less extensive changes, while the influences of iproniazid, isoniazid, phenobarbital, ethinylestradiol, and tetracycline on the toxic parameters were marginal or for methyltestosterone and mianserine negligible. NMR spectroscopy revealed significant changes upon dosing in 88 NMR biomarker signals preselected with the Procrustus Rotation method on principal component discriminant analysis (PCDA) plots. Further evaluation of the specific changes led to the identification of biomarker patterns for the specific types of liver toxicity. Comparison of our rat NMR PCDA data with histopathological changes reported in humans and/or rats suggests that rat NMR urinalysis can be used to predict hepatotoxicity.

Sensitivity of (1)H NMR analysis of rat urine in relation to toxicometabonomics. Part I: dose-dependent toxic effects of bromobenzene and paracetamol.

(1)H nuclear magnetic resonance (NMR) spectroscopy of rat urine in combination with pattern recognition analysis was evaluated for early noninvasive detection of toxicity of investigational chemical entities. Bromobenzene (B) and paracetamol (P) were administered at five single oral dosages between 2 and 500 mg/kg and between 6 and 1800 mg/kg, respectively. The sensitivity of the proposed method to detect changes in the NMR spectra 24 and 48 h after single dosing was compared with histopathology and biochemical parameters in plasma and urine. Both B and P applied at the highest dosages induced liver necrosis and markedly increased aspartate aminotransferase (AST) and alanine aminotransferase (ALT) plasma levels. At dosages of 125 mg/kg B and 450 mg/kg P, liver necrosis and changes in AST and ALT were less pronounced, while at lower dose levels these effects could not be detected. Changes in kidney pathology or standard urine biochemistry were not observed at any of these dosages. Evaluation of the total NMR dataset showed 80 signals to be sensitive for B and P dosing. Principal component analysis on the reduced dataset revealed that NMR spectra were significantly different at dosages above 8 mg/kg (B) and 110 mg/kg (P) at both sampling times. This implies a 4- to 16-fold increased sensitivity of NMR versus histopathology and clinical chemistry in recognizing early events of liver toxicity.

Integrative biological analysis of the APOE*3-leiden transgenic mouse.

Integrative (or systems biology) is a new approach to analyzing biological entities as integrated systems of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. Here, we demonstrate the application of intregrative biological analysis to a mammalian disease model, the apolipoprotein E3-Leiden (APO*E3) transgenic mouse. Mice selected for the study were fed a normal chow diet and sacrificed at 9 weeks of age-conditions under which they develop only mild type I and II atherosclerotic lesions. Hepatic mRNA expression analysis showed a 25% decrease in APO A1 and a 43% increase in liver fatty acid binding protein expression between transgenic and wild type control mice, while there was no change in PPAR-alpha expression. On-line high performance liquid chromatography-mass spectrometry quantitative profiling of tryptic digests of soluble liver proteins and liver lipids, coupled with principle component analysis, enabled rapid identification of early protein and metabolite markers of disease pathology. These included a 44% increase in L-FABP in transgenic animals compared to controls, as well as an increase in triglycerides and select bioactive lysophosphatidylcholine species. A correlation analysis of identified genes, proteins, and lipids was used to construct an interaction network. Taken together, these results indicate that integrative biology is a powerful tool for rapid identification of early markers and key components of pathophysiologic processes, and constitute the first application of this approach to a mammalian system.

Non-invasive measurement of brain damage in a primate model of multiple sclerosis.

Early recognition of whether a product has potential as a new therapy for treating multiple sclerosis (MS) relies upon the quality of the animal models used in the preclinical trials. The promising effects of new treatments in rodent models of experimental autoimmune encephalomyelitis (EAE) have rarely been reproduced in patients suffering from MS. EAE in outbred marmoset monkeys, Callithrix jacchus, is a valid new model, and might provide an experimental link between EAE in rodent models and human MS. Using magnetic resonance imaging techniques similar to those used in patients suffering from MS pathological abnormalities in the brain, white matter of the animal can be visualized and quantified. Moreover, NMR spectroscopy, in combination with pattern recognition, offers an advanced uroscopic technique for the identification of biomarkers of inflammatory demyelination.

Phenotype characterisation using integrated gene transcript, protein and metabolite profiling.

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels. This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes. These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying--well in advance of pathogenic manifestations of disease--the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model.

Multitiered quantitative analysis of biological systems is rapidly becoming the desired approach to study hierarchical functional interactions between proteins and metabolites. We describe here a novel systematic approach to analyze organisms with complex metabolic regulatory networks. By using precise analytical methods to measure biochemical constituents and their relative abundance in whole plasma of transgenic ApoE*3-Leiden mice and an isogenic wild-type control group, simultaneous snapshots of metabolic and protein states were obtained. Novel data processing and multivariate analysis tools such as Impurity Resolution Software (IMPRESS) and Windows-based linear fit program (WINLIN) were used to compare protein and metabolic profiles in parallel. Canonical correlations of the resulting data show quantitative relationships between heterogeneous components in the TG animals. These results, obtained solely from whole plasma analysis allowed us, in a rapid manner, to corroborate previous findings as well as find new events pertaining to dominant and peripheral events in lipoprotein metabolism of a genetically modified mammalian organism in relation to ApoE3, a key mediator of lipoprotein metabolism.

Proteome analysis reveals novel proteins associated with proliferation and differentiation of the colorectal cancer cell line Caco-2.

Here, we describe a proteomics approach to study protein expression changes in differentiating Caco-2 cells. Caco-2 is a colorectal carcinoma cell line, which upon differentiation loses its tumorigenic phenotype and displays characteristics of mature enterocytes, including brush borders with microvilli. Cells were grown in culture flasks and harvested at different stages of differentiation (days post-confluence: -3, 0, 3, 7, 10, 14, and 18). Two-dimensional gel electrophoresis was used to analyse proteome changes. Approximately 1400 protein spots were detected within the Caco-2 proteome, within the pH 4-7 range. Two-dimensional gel electrophoresis allowed for the detection of 18 proteins from which the levels of expression were found to be associated with differentiation. Of these proteins, 11 were identified by means of MALDI-TOF or NANO-ESI-MS/MS mass spectrometry and include liver fatty acid binding protein (FABL), three forms of alpha-enolase (ENOA), nucleoside diphosphate kinase A (NDKA), cofilin-1 (COF1), translationally controlled tumour protein (TCTP), mitochondrial 60-kDa heat shock protein (CH60), probable protein disulfide isomerase (ER60), creatine kinase B (KCRB), and glutathione S-transferase alpha (GTA1). Thus, proteomics revealed that the differentiation-related change in phenotype of Caco-2 involves changes in a variety of distinct biochemical pathways. Some of these proteins have not been shown before to be associated with Caco-2 differentiation (ER60; COF1; CH60; NDKA; TCTP and ENOA). Therefore, processes related to protein folding and disulfide bridge formation, cytoskeleton formation and maintenance, nucleotide metabolism, glycolysis as well as tumorigenesis-associated proteins may be involved in Caco-2 differentiation. Changes in the expression of CH60, TCTP, GTA1, NDKA, and FABL have also been reported to be associated with in vivo colon carcinogenesis. These findings illustrate that a combination of proteomics and cell culture is a useful approach to find markers for Caco-2 differentiation, which could contribute to the comprehension of the process of colon carcinogenesis.

1H-NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urines of MS patients and non-human primates with MS-like disease.

Proton nuclear magnetic resonance (1H-NMR) spectroscopy in combination with pattern recognition techniques were used to investigate the composition of organic compounds in urines from patients with multiple sclerosis (MS), patients with other neurological diseases (OND) and healthy controls (H). Using a valid animal model of MS, namely the common marmoset (Callithrix jacchus) model of experimental autoimmune encephalomyelitis (EAE), the relation of disease progression and alteration of the urine composition was investigated. Urine samples were collected during different stages of EAE, either induced with whole human myelin or with the myelin protein MOG in complete adjuvant. The urine samples were analysed with 1H-NMR spectroscopy allowing simultaneous detection of an array of compounds. Spectral differences between urines from EAE-affected and healthy monkeys were assessed with multivariate analysis. Evidence is provided that development of EAE is associated with changes in the chemical composition of the urine, in particular of compounds with NMR peaks in the region of the spectrum between 0.5 and 3.50 ppm. In addition, we found preliminary evidence for differences between urines from MS, OND and H groups.

Identification of disease- and nutrient-related metabolic fingerprints in osteoarthritic Guinea pigs.

Osteoarthritis (OA), one of the most common diseases among the elderly, is characterized by the progressive destruction of joint tissues. Its etiology is largely unclear and no effective disease-modifying treatment is currently available. Metabolic fingerprinting provides a novel tool for the identification of biomarkers. A metabolic fingerprint consists of a typical combination of metabolites in a biological fluid and is identified by a combination of (1)H NMR spectroscopy and multivariate data analysis (MVDA). The current feasibility study was aimed at identifying a metabolic fingerprint for OA and applying this in a nutritional intervention study. Urine samples were collected from osteoarthritic male Hartley guinea pigs (n = 44) at 10 and 12 mo of age, treated from 4 mo onward with variable vitamin C doses (2.5-3, 30 and 150 mg/d) and from healthy male Strain 13 guinea pigs (n = 8) at 12 mo of age, treated with 30 mg vitamin C/d. NMR measurements were performed on all urine samples. Subsequently, MVDA was carried out on the data obtained using NMR. An NMR fingerprint was identified that reflected the osteoarthritic changes in guinea pigs. The metabolites that comprised the fingerprint indicate that energy and purine metabolism are of major importance in OA. Metabolic fingerprinting also allowed detection of differences in OA-specific metabolites induced by different dietary vitamin C intakes. This study demonstrates the feasibility of metabolic fingerprinting to identify disease-specific profiles of urinary metabolites. NMR fingerprinting is a promising means of identifying new disease markers and of gaining fresh insights into the pathophysiology of disease.