[Show abstract][Hide abstract] ABSTRACT: Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.
PLoS ONE 05/2014; 9(5):e96955. DOI:10.1371/journal.pone.0096955 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Patients with CKD display altered plasma amino acid profiles. This study estimated the association between the estimated GFR and urinary and plasma amino acid profiles in CKD patients.
Urine and plasma samples were taken from 52 patients with different stages of CKD, and plasma samples only were taken from 25 patients on maintenance hemodialysis. Metabolic profiling was performed by liquid chromatography coupled with tandem mass spectrometry after phenylisothiocyanate derivatization.
Most plasma amino acid concentrations were decreased in hemodialysis patients, whereas proline, citrulline, asparagine, asymmetric dimethylarginine, and hydroxykynurenine levels were increased (P<0.05). Both plasma levels and urinary excretion of citrulline were higher in the group of patients with advanced CKD (CKD stages 2 and 3 versus CKD stages 4 and 5; in plasma: 35.9±16.3 versus 61.8±23.6 µmol/L, P<0.01; in urine: 1.0±1.2 versus 7.1±14.3 µmol/mol creatinine, P<0.001). Plasma asymmetric dimethylarginine levels were higher in advanced CKD (CKD stages 2 and 3, 0.57±0.29; CKD stages 4 and 5, 1.02±0.48, P<0.001), whereas urinary excretion was lower (2.37±0.93 versus 1.51±1.43, P<0.001). Multivariate analyses adjusting on estimated GFR, serum albumin, proteinuria, and other covariates revealed associations between diabetes and plasma citrulline (P=0.02) and between serum sodium and plasma asymmetric dimethylarginine (P=0.03). Plasma tyrosine to phenylalanine and valine to glycine ratios were lower in advanced CKD stages (P<0.01).
CKD patients have altered plasma and urinary amino acid profiles that are not corrected by dialysis. Depending on solutes, elevated plasma levels were associated with increased or decreased urinary excretion, depicting situations of uremic retention (asymmetric dimethylarginine) or systemic overproduction (citrulline). These results give some insight in the CKD-associated modifications of amino acid metabolism, which may help improve their handling.
Clinical Journal of the American Society of Nephrology 11/2013; 9(1). DOI:10.2215/CJN.06000613 · 5.25 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Abstract Pichler Hefti, Jacqueline, Denise Sonntag, Urs Hefti, Lorenz Risch, Otto D. Schoch, Alexander J. Turk, Thomas Hess, Konrad E Bloch, Marco Maggiorini, Tobias M. Merz, Klaus M. Weinberger, and Andreas R. Huber. Oxidative stress in hypobaric hypoxia and influence on vessel-tone modifying mediators. High Alt Med Biol. 14:273-279, 2013.-Increased pulmonary artery pressure is a well-known phenomenon of hypoxia and is seen in patients with chronic pulmonary diseases, and also in mountaineers on high altitude expedition. Different mediators are known to regulate pulmonary artery vessel tone. However, exact mechanisms are not fully understood and a multimodal process consisting of a whole panel of mediators is supposed to cause pulmonary artery vasoconstriction. We hypothesized that increased hypoxemia is associated with an increase in vasoconstrictive mediators and decrease of vasodilatators leading to a vasoconstrictive net effect. Furthermore, we suggested oxidative stress being partly involved in changement of these parameters. Oxygen saturation (Sao2) and clinical parameters were assessed in 34 volunteers before and during a Swiss research expedition to Mount Muztagh Ata (7549 m) in Western China. Blood samples were taken at four different sites up to an altitude of 6865 m. A mass spectrometry-based targeted metabolomic platform was used to detect multiple parameters, and revealed functional impairment of enzymes that require oxidation-sensitive cofactors. Specifically, the tetrahydrobiopterin (BH4)-dependent enzyme nitric oxide synthase (NOS) showed significantly lower activities (citrulline-to-arginine ratio decreased from baseline median 0.21 to 0.14 at 6265 m), indicating lower NO availability resulting in less vasodilatative activity. Correspondingly, an increase in systemic oxidative stress was found with a significant increase of the percentage of methionine sulfoxide from a median 6% under normoxic condition to a median level of 30% (p<0.001) in camp 1 at 5533 m. Furthermore, significant increase in vasoconstrictive mediators (e.g., tryptophan, serotonin, and peroxidation-sensitive lipids) were found. During ascent up to 6865 m, significant altitude-dependent changes in multiple vessel-tone modifying mediators with excess in vasoconstrictive metabolites could be demonstrated. These changes, as well as highly significant increase in systemic oxidative stress, may be predictive for increase in acute mountain sickness score and changes in Sao2.
High altitude medicine & biology 09/2013; 14(3):273-9. DOI:10.1089/ham.2012.1110 · 1.82 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Alcohol consumption is one of the world's major risk factors for disease development. But underlying mechanisms by which moderate-to-heavy alcohol intake causes damage are poorly understood and biomarkers are sub-optimal. Here, we investigated metabolite concentration differences in relation to alcohol intake in 2090 individuals of the KORA F4 and replicated results in 261 KORA F3 and up to 629 females of the TwinsUK adult bioresource. Using logistic regression analysis adjusted for age, body mass index, smoking, high-density lipoproteins and triglycerides, we identified 40/18 significant metabolites in males/females with P-values <3.8E-04 (Bonferroni corrected) that differed in concentrations between moderate-to-heavy drinkers (MHD) and light drinkers (LD) in the KORA F4 study. We further identified specific profiles of the 10/5 metabolites in males/females that clearly separated LD from MHD in the KORA F4 cohort. For those metabolites, the respective area under the receiver operating characteristic curves were 0.812/0.679, respectively, thus providing moderate-to-high sensitivity and specificity for the discrimination of LD to MHD. A number of alcohol-related metabolites could be replicated in the KORA F3 and TwinsUK studies. Our data suggests that metabolomic profiles based on diacylphosphatidylcholines, lysophosphatidylcholines, ether lipids and sphingolipids form a new class of biomarkers for excess alcohol intake and have potential for future epidemiological and clinical studies.
[Show abstract][Hide abstract] ABSTRACT: National Kidney Foundation CKD staging has allowed uniformity in studies on CKD. However, early diagnosis and predicting progression to end stage renal disease are yet to be improved. Seventy six patients with different levels of CKD, including outpatients and dialysed patients were studied for transcriptome, metabolome and proteome description. High resolution urinary proteome analysis was blindly performed in the 53 non-anuric out of the 76 CKD patients. In addition to routine clinical parameters, CKD273, a urinary proteomics-based classifier and its peptides were quantified. The baseline values were analyzed with regard to the clinical parameters and the occurrence of death or renal death during follow-up (3.6 years) as the main outcome measurements. None of the patients with CKD273<0.55 required dialysis or died while all fifteen patients that reached an endpoint had a CKD273 score >0.55. Unsupervised clustering analysis of the CKD273 peptides separated the patients into two main groups differing in CKD associated parameters. Among the 273 biomarkers, peptides derived from serum proteins were relatively increased in patients with lower glomerular filtration rate, while collagen-derived peptides were relatively decreased (p<0.05; Spearman). CKD273 was different in the groups with different renal function (p<0.003). The CKD273 classifier separated CKD patients according to their renal function and informed on the likelihood of experiencing adverse outcome. Recently defined in a large population, CKD273 is the first proteomic-based classifier successfully tested for prognosis of CKD progression in an independent cohort.
PLoS ONE 05/2013; 8(5):e62837. DOI:10.1371/journal.pone.0062837 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.
[Show abstract][Hide abstract] ABSTRACT: In metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity.
In this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise.
We propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/.
[Show abstract][Hide abstract] ABSTRACT: Nutrition plays an important role in human metabolism and health. However, it is unclear in how far self-reported nutrition intake reflects de facto differences in body metabolite composition. To investigate this question on an epidemiological scale we conducted a metabolomics study analyzing the association of self-reported nutrition habits with 363 metabolites quantified in blood serum of 284 male participants of the KORA population study, aged between 55 and 79 years. Using data from an 18-item food frequency questionnaire, the consumption of 18 different food groups as well as four derived nutrition indices summarizing these food groups by their nutrient content were analyzed for association with the measured metabolites. The self-reported nutrition intake index "polyunsaturated fatty acids" associates with a decrease in saturation of the fatty acid chains of glycero-phosphatidylcholines analyzed in serum samples. Using a principal component analysis dietary patterns highly associating with serum metabolite concentrations could be identified. The first principal component, which was interpreted as a healthy nutrition lifestyle, associates with a decrease in the degree of saturation of the fatty acid moieties of different glycero-phosphatidylcholines. In summary, this analysis shows that on a population level metabolomics provides the possibility to link self-reported nutrition habits to changes in human metabolic profiles and that the associating metabolites reflect the self-reported nutritional intake. Moreover, we could show that the strength of association increases when composed nutrition indices are used. Metabolomics may, thus, facilitate evaluating questionnaires and improving future questionnaire-based epidemiological studies on human health.
European Journal of Epidemiology 02/2011; 26(2):145-56. DOI:10.1007/s10654-010-9524-7 · 5.15 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The broad view of the state of biological systems cannot be complete without the added value of integrating proteomic and genomic data with metabolite measurement. By definition, metabolomics aims at quantifying not less than the totality of small molecules present in a biofluid, tissue, organism, or any material beyond living systems. To cope with the complexity of the task, mass spectrometry (MS) is the most promising analytical environment to fulfill increasing appetite for more accurate and larger view of the metabolome while providing sufficient data generation throughput. Bioinformatics and associated disciplines naturally play a central role in bridging the gap between fast evolving technology and domain experts. Here, we describe the strategies to translate crude MS information into features characteristics of metabolites, and resources available to guide scientists along the metabolomics pipeline. A particular emphasis is put on pragmatic solutions to interpret the outcome of metabolomics experiments at the level of signal processing, statistical treatment, and biochemical understanding.
[Show abstract][Hide abstract] ABSTRACT: Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.
40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).
Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.
PLoS ONE 11/2010; 5(11):e13953. DOI:10.1371/journal.pone.0013953 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Identifying trait-associated genetic variation offers new prospects to reveal novel physiological pathways modulating complex traits. Taking advantage of a unique animal model, we identified the I442M mutation in the non-SMC condensin I complex, subunit G (NCAPG) gene and the Q204X mutation in the growth differentiation factor 8 (GDF8) gene as substantial modulators of pre- and/or postnatal growth in cattle. In a combined metabolomic and genotype association approach, which is the first respective study in livestock, we surveyed the specific physiological background of the effects of both loci on body-mass gain and lipid deposition. Our data provided confirming evidence from two historically and geographically distant cattle populations that the onset of puberty is the key interval of divergent growth. The locus-specific metabolic patterns obtained from monitoring 201 plasma metabolites at puberty mirror the particular NCAPG I442M and GDF8 Q204X effects and represent biosignatures of divergent physiological pathways potentially modulating effects on proportional and disproportional growth, respectively. While the NCAPG I442M mutation affected the arginine metabolism, the 204X allele in the GDF8 gene predominantly raised the carnitine level and had concordant effects on glycerophosphatidylcholines and sphingomyelins. Our study provides a conclusive link between the well-described growth-regulating functions of arginine metabolism and the previously unknown specific physiological role of the NCAPG protein in mammalian metabolism. Owing to the confirmed effect of the NCAPG/LCORL locus on human height in genome-wide association studies, the results obtained for bovine NCAPG might add valuable, comparative information on the physiological background of genetically determined divergent mammalian growth.
[Show abstract][Hide abstract] ABSTRACT: The effect of coffee consumption on human health is still discussed controversially. Here, we report results from a metabolomics study of coffee consumption, where we measured 363 metabolites in blood serum of 284 male participants of the Cooperative Health Research in the Region of Augsburg study population, aged between 55 and 79 years. A statistical analysis of the association of metabolite concentrations and the number of cups of coffee consumed per day showed that coffee intake is positively associated with two classes of sphingomyelins, one containing a hydroxy-group (SM(OH)) and the other having an additional carboxy-group (SM(OH,COOH)). In contrast, long- and medium-chain acylcarnitines were found to decrease with increasing coffee consumption. It is noteworthy that the concentration of total cholesterol also rises with an increased coffee intake in this study group. The association observed here between these hydroxylated and carboxylated sphingolipid species and coffee intake may be induced by changes in the cholesterol levels. Alternatively, these molecules may act as scavengers of oxidative species, which decrease with higher coffee intake. In summary, we demonstrate strong positive associations between coffee consumption and two classes of sphingomyelins and a negative association between coffee consumption and long- and medium-chain acylcarnitines.
[Show abstract][Hide abstract] ABSTRACT: The oxidation of fatty acids in mitochondria plays an important role in energy metabolism and genetic disorders of this pathway may cause metabolic diseases. Enzyme deficiencies can block the metabolism at defined reactions in the mitochondrion and lead to accumulation of specific substrates causing severe clinical manifestations. Ten of the disorders directly affecting mitochondrial fatty acid oxidation have been well-defined, implicating episodic hypoketotic hypoglycemia provoked by catabolic stress, multiple organ failure, muscle weakness, or hypertrophic cardiomyopathy. Additionally, syndromes of severe maternal illness (HELLP syndrome and AFLP) have been associated with pregnancies carrying a fetus affected by fatty acid oxidation deficiencies. However, little is known about fatty acids kinetics, especially during fasting or exercise when the demand for fatty acid oxidation is increased (catabolic stress).
A computational kinetic network of 64 reactions with 91 compounds and 301 parameters was constructed to study dynamic properties of mitochondrial fatty acid beta-oxidation. Various deficiencies of acyl-CoA dehydrogenase were simulated and verified with measured concentrations of indicative metabolites of screened newborns in Middle Europe and South Australia. The simulated accumulation of specific acyl-CoAs according to the investigated enzyme deficiencies are in agreement with experimental data and findings in literature. Investigation of the dynamic properties of the fatty acid beta-oxidation reveals that the formation of acetyl-CoA - substrate for energy production - is highly impaired within the first hours of fasting corresponding to the rapid progress to coma within 1-2 hours. LCAD deficiency exhibits the highest accumulation of fatty acids along with marked increase of these substrates during catabolic stress and the lowest production rate of acetyl-CoA. These findings might confirm gestational loss to be the explanation that no human cases of LCAD deficiency have been described.
In summary, this work provides a detailed kinetic model of mitochondrial metabolism with specific focus on fatty acid beta-oxidation to simulate and predict the dynamic response of that metabolic network in the context of human disease. Our findings offer insight into the disease process (e.g. rapid progress to coma) and might confirm new explanations (no human cases of LCAD deficiency), which can hardly be obtained from experimental data alone.
BMC Systems Biology 02/2009; 3:2. DOI:10.1186/1752-0509-3-2 · 2.85 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10(-16) to 10(-21)). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.
[Show abstract][Hide abstract] ABSTRACT: Fatty acid metabolites play a key role in numerous physiological and pathological processes. A rapid liquid chromatography-mass spectrometry assay for the simultaneous determination of prostanoids, isoprostane and lipoxygenase (LOX) derived fatty acid metabolites in a small biological sample of only 20 microL was developed.
Human plasma samples were applied to a filter spot, extracted without prior derivatization and analyzed within 13 min. Detection of metabolites was performed on a triple quadrupole mass spectrometer in negative multiple-reaction monitoring detection mode. Application of this assay to various biological matrices was performed.
The validated assay was linear over the concentration range of 5-500 nmol/L for prostanoids and isoprostane, 50-5000 nmol/L for LOX-derived metabolites and 400-40,000 nmol/L for fatty acids. Limits of quantitation were 0.4-233 nmol/L, depending on the metabolite. Plasma samples from diabetic patients and controls showed significant increases in (+/-)9-HODE and 15(S)-HETE with p-values of 0.019 and 0.024, respectively.
The small amount of 20 microL sample volume used in this assay and the demonstrated application to various sample types makes it an ideal routine analysis method for fatty acid metabolites. The resulting values for LOX-derived metabolites in diabetes mellitus type 2 samples support earlier findings about the role of lipid oxidation products in diabetes.
Clinical Chemistry and Laboratory Medicine 11/2008; 46(11):1589-97. DOI:10.1515/CCLM.2008.323 · 2.96 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Metabolomics, i.e. the systematic identification and quantitation of low-molecular weight compounds in a certain cell, a tissue or a body fluid is increasingly recognized as the most relevant approach in functional genomics. Long-standing positive experience in routine diagnostics of inborn errors of metabolism and analytical innovations leading to improved sensitivity and reproducibility of quantitative multiparametric assays now pave the way for further diagnostic use of metabolomics. This article summarizes the scientific and technological background of the current boost in metabolomics and describes some use cases in which high-resolution metabolic analyses demonstrate their potential for future clinical applications.
[Show abstract][Hide abstract] ABSTRACT: Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management.
We developed a novel feature selection algorithm termed as associative voting (AV) for identifying biomarker candidates in prostate cancer data measured via targeted metabolite profiling MS/MS analysis. We benchmarked our algorithm against two standard entropy-based and correlation-based feature selection methods [Information Gain (IG) and ReliefF (RF)] and observed that, on a variety of classification tasks in prostate cancer diagnosis, our algorithm identified subsets of biomarker candidates that are both smaller and show higher discriminatory power than the subsets identified by IG and RF. A literature study confirms that the highest ranked biomarker candidates identified by AV have independently been identified as important factors in prostate cancer development.
The algorithm can be downloaded from the following http://biomed.umit.at/page.cfm?pageid=516.
[Show abstract][Hide abstract] ABSTRACT: Metabolomics is a powerful tool for identifying both known and new disease-related perturbations in metabolic pathways. In preclinical drug testing, it has a high potential for early identification of drug off-target effects. Recent advances in high-precision high-throughput mass spectrometry have brought the metabolomic field to a point where quantitative, targeted, metabolomic measurements with ready-to-use kits allow for the automated in-house screening for hundreds of different metabolites in large sets of biological samples. Today, the field of metabolomics is, arguably, at a point where transcriptomics was about 5 yr ago. This being so, the field has a strong need for adapted bioinformatics tools and methods. In this paper we describe a systematic analysis of a targeted quantitative characterization of more than 800 metabolites in blood plasma samples from healthy and diabetic mice under rosiglitazone treatment. We show that known and new metabolic phenotypes of diabetes and medication can be recovered in a statistically objective manner. We find that concentrations of methylglutaryl carnitine are oppositely impacted by rosiglitazone treatment of both healthy and diabetic mice. Analyzing ratios between metabolite concentrations dramatically reduces the noise in the data set, allowing for the discovery of new potential biomarkers of diabetes, such as the N-hydroxyacyloylsphingosyl-phosphocholines SM(OH)28:0 and SM(OH)26:0. Using a hierarchical clustering technique on partial eta(2) values, we identify functionally related groups of metabolites, indicating a diabetes-related shift from lysophosphatidylcholine to phosphatidylcholine levels. The bioinformatics data analysis approach introduced here can be readily generalized to other drug testing scenarios and other medical disorders.