Profiling and biomarker identification in plasma from different Zucker rat strains via high mass accuracy multistage mass spectrometric analysis using liquid chromatography/mass spectrometry with a quadrupole ion trap-time of flight mass spectrometer

Obesity and Cage Environment Modulate Metabolism in the Zucker Rat: A Multiple Biological Matrix Approach to Characterizing Metabolic Phenomena

Obesity and its comorbidities are increasing worldwide imposing a heavy socioeconomic burden. The effects of obesity on the metabolic profiles of tissues (liver, kidney, pancreas), urine, and the systemic circulation were investigated in the Zucker rat model using ¹H NMR spectroscopy coupled to multivariate statistical analysis. The metabolic profiles of the obese ( fa/ fa) animals were clearly differentiated from the two phenotypically lean phenotypes, ((+/+) and ( fa/+)) within each biological compartment studied, and across all matrices combined. No significant differences were observed between the metabolic profiles of the genotypically distinct lean strains. Obese Zucker rats were characterized by higher relative concentrations of blood lipid species, cross-compartmental amino acids (particularly BCAAs), urinary and liver metabolites relating to the TCA cycle and glucose metabolism; and lower amounts of urinary gut microbial-host cometabolites, and intermatrix metabolites associated with creatine metabolism. Further to this, the obese Zucker rat metabotype was defined by significant metabolic alterations relating to disruptions in the metabolism of choline across all compartments analyzed. The cage environment was found to have a significant effect on urinary metabolites related to gut-microbial metabolism, with additional cage-microenvironment trends also observed in liver, kidney, and pancreas. This study emphasizes the value in metabotyping multiple biological matrices simultaneously to gain a better understanding of systemic perturbations in metabolism, and also underscores the need for control or evaluation of cage environment when designing and interpreting data from metabonomic studies in animal models.

Metabolic Profiling of Liver Tissue in Diabetic Mice Treated with Artemisia Capillaris and Alisma Rhizome Using LC-MS and CE-MS

Artemisia Capillaris (AC) and Alisma Rhizome (AR) are natural products for the treatment of liver disorders in oriental medicine clinics. Here, we report metabolomic changes in the evaluation of the treatment effects of AC and AR on fatty livers in diabetic mice, along with a proposition of the underlying metabolic pathway. Hydrophobic and hydrophilic metabolites extracted from mouse livers were analyzed using HPLC-QTOF and CE-QTOF, respectively, to generate metabolic profiles. Statistical analysis of the metabolites by PLS-DA and OPLA-DA fairly discriminated between the diabetic, and the AC- and AR-treated mice groups. Various PEs mostly contributed to the discrimination of the diabetic mice from the normal mice, and besides, DG (18:1/16:0), TG (16:1/16:1/20:1), PE (21:0/20:5), and PA (18:0/21:0) were also associated with discrimination by s-plot. Nevertheless, the effects of AC and AR treatment were indistinct with respect to lipid metabolites. Of the 97 polar metabolites extracted from the CE-MS data, 40 compounds related to amino acid, central carbon, lipid, purine, and pyrimidine metabolism, with [Formula: see text] values less than 0.05, were shown to contribute to liver dysregulation. Following treatment with AC and AR, the metabolites belonging to purine metabolism preferentially recovered to the metabolic state of the normal mice. The AMP/ATP ratio of cellular energy homeostasis in AR-treated mice was more apparently increased ([Formula: see text]) than that of AC-treated mice. On the other hand, amino acids, which showed the main alterations in diabetic mice, did not return to the normal levels upon treatment with AR or AC. In terms of metabolomics, AR was a more effective natural product in the treatment of liver dysfunction than AC. These results may provide putative biomarkers for the prognosis of fatty liver disorder following treatment with AC and AR extracts.

Metabolites software-assisted flavonoid hunting in plants using ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry

Plant secondary metabolism drives the generation of metabolites used for host plant resistance, as biopesticides and botanicals, even for the discovery of new therapeutics for human diseases. Flavonoids are one of the largest and most studied classes of specialized plant metabolites. To quickly identify the potential bioactive flavonoids in herbs, a metabolites software-assisted flavonoid hunting approach was developed, which mainly included three steps: firstly, utilizing commercial metabolite software, a flavonoids database was established based on the biosynthetic pathways; secondly, mass spectral data of components in herbs were acquired by ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF-MS); and finally, the acquired LC-MS data were imported into the database and the compounds in the herbs were automatically identified by comparison of their mass spectra with the theoretical values. As a case study, the flavonoids in Smilax glabra were profiled using this approach. As a result, 104 flavonoids including 27 potential new compounds were identified. To our knowledge, this is the first report on profiling the components in the plants utilizing the plant metabolic principles with the assistance of metabolites software. This approach can be extended to the analysis of flavonoids in other plants.

Prehypertension-associated elevation in circulating lysophosphatidlycholines, Lp-PLA2 activity, and oxidative stress

Prehypertension is a risk factor for atherosclerosis. We investigated alterations in plasma metabolites that are associated with prehypertension. A group of 53 individuals was identified who remained within the range of prehypertension during repeated measurements in a 3-year period. This group was compared with the control group of 53 normotensive subjects who were matched for age and gender. Metabolomic profiles were analyzed with UPLC-LTQ-Orbitrap mass spectrometry. The prehypertensive group showed higher levels of lysophosphatidylcholines (lysoPCs) containing C14:0, C16:1, C16:0, C18:2, C18:1, C18:0, C20:5, C20:4, C20:3, and C22:6, higher circulating Lp-PLA₂ activity, oxidized LDL (ox-LDL), interleukin 6 (IL-6), urinary 8-epi-PGF_2α, and higher brachial-ankle pulse wave velocity (ba-PWV), before and after adjusting for BMI, WHR, smoking, alcohol consumption, serum lipid profiles, glucose, and insulin. LysoPC (16:0) was the most important plasma metabolite for evaluating the difference between control and prehypertensive groups, with a variable important in the projection (VIP) value of 17.173, and it showed a positive and independent association with DBP and SBP. In the prehypertensive group, the levels of lysoPC (16:0) positively and significantly correlated with ox-LDL, Lp-PLA₂ activity, 8-epi-PGF_2α, ba-PWV, and IL-6 before and after adjusting for confounding variables. Prehypertension-associated elevations in lysoPCs, Lp-PLA₂ activity, ox-LDL, urinary 8-epi-PGF_2α, IL-6, and ba-PWV could indicate increased oxidative stress from Lp-PLA₂-catalyzed PC hydrolysis during increased LDL oxidation, thereby enhancing proinflammation and arterial stiffness.

Nutritional modulation of the metabonome: applications of metabolic phenotyping in translational nutritional research

PURPOSE OF REVIEW

Metabolic profiling technologies provide a global overview of complex dietary processes. Metabonomic analytical approaches have now been translated into multiple areas of clinical nutritional research based on the widespread adoption of high-throughput mass spectrometry and proton nuclear magnetic resonance spectroscopy. This has generated novel insights into the molecular mechanisms that shape the microbiome-dietary-chronic disease axis.

RECENT FINDINGS

Metabolome-wide association studies have created a new paradigm in nutritional molecular epidemiology and they have highlighted the importance of gut microbial cometabolic processes in the development of cardiovascular disease and diabetes. Targeted analyses are helping to explain the mechanisms by which high-risk diets (such as red meat) modulate disease risk and they are generating novel biomarkers that will serve to re-define how the efficacy of nutritional interventions is assessed. Nutritional metabonome-microbiome interactions have also been defined in extreme dietary states such as obesity and starvation, and they also serve as important models for understanding how the gut microbiome modifies disease risk. Finally, nutritional systems medicine approaches are creating novel insights into the functional components of our diet, and the mechanisms by which they cause disease.

SUMMARY

Diet is an important modulator of the human metabolic phenotype and the analysis of the nutritional metabolome will drive future development of personalized nutritional interventions.

Liquid Chromatographic Techniques in Metabolomics

In the past decade, LC‐MS‐based metabolomic/metabonomic profiling has become a major analytical focus for biomarker research. Chromatographic resolution is continually improving with the development of more advanced separation platforms based on smaller particle sizes, new types of stationary phase and miniaturized systems allowing the profiling of biological samples for metabolites in ways that were simply not possible before. Chromatographic advances, combined with increased mass resolution instruments that provide sub‐2 ppm mass accuracy and high sensitivity, have greatly facilitated the detection and identification of potential biomarkers. In this chapter, the most common LC(‐MS) methods utilized in metabolic analyses are presented, with emphasis on novel high‐efficiency and high‐throughput analyses and their suitability for metabolic analyses. Guidelines for the selection of the appropriate method for different applications are given, with emphasis on the use of LC‐MS.

Association of age-related changes in circulating intermediary lipid metabolites, inflammatory and oxidative stress markers, and arterial stiffness in middle-aged men

The relationships between age-related changes in circulating endogenous metabolites, inflammatory and oxidative stress markers, and arterial stiffness in 57 middle-aged (34-55 years), nonobese men were studied over the course of 3 years. Arterial stiffness was measured using brachial-ankle pulse wave velocities (ba-PWV). Plasma metabolomic profiling was performed using ultra-performance liquid chromatography and quadrupole time-of-flight mass spectrometry. After 3 years, decreased HDL cholesterol and increased malondialdehyde (MDA) and ox-LDL levels were observed. Among 15 identified lipids, lysoPCs (C16:0, C18:0, C18:2, C20:4, and C20:5) and linoleyl carnitine were the major plasma metabolites that contributed to the age-related differences. LysoPC16:0 (variable importance in the projection value, 6.2029) was found as the most important plasma metabolite for evaluating these changes. Changes in lysoPC16:0 levels positively correlated with the changes in 8-epi-PGF2α (r = 0.608), MDA (r = 0.413), high-sensitivity C-reactive protein (r = 0.509), IL-6 (r = 0.497), and ba-PWV (r = 0.283) levels. ba-PWV levels positively correlated with the changes in waist-to-hip ratios (WHR), inflammatory and oxidative stress markers. In a subgroup analysis of subjects with decreased ba-PWVs vs. increased ba-PWVs, changes in WHR and levels of lysoPC16:0, ba-PWV, IL-6, 8-epi-PGF2α, MDA, and P-selectin were significantly different. Our results suggest that age-related increases in lysoPC16:0 may contribute to lipid peroxidation, thereby activating proinflammatory phenotypes and arterial stiffness.

Global metabolite profiling of human colorectal cancer xenografts in mice using HPLC-MS/MS

Reversed-phase gradient LC-MS was used to perform untargeted metabonomic analysis on extracts of human colorectal cancer (CRC) cell lines (COLO 205, HT-29, HCT 116 and SW620) subcutaneously implanted into age-matched athymic nude male mice to study small molecule metabolic profiles and examine possible correlations with human cancer biopsies. Following high mass accuracy data analysis using MS and MS/MS, metabolites were identified by searching against major metabolite databases including METLIN, MASSBANK, The Human Metabolome Database, PubChem, Biospider, LipidMaps and KEGG. HT-29 and COLO 205 tumor xenografts showed a distribution of metabolites that differed from SW620 and HCT 116 xenografts (predominantly on the basis of relative differences in the amounts of amino acids and lipids detected). This finding is consistent with NMR-based analysis of human colorectal tissue, where the metabolite profiles of HT-29 tumors exhibit the greatest similarity to human rectal cancer tissue with respect to changes in the relative amounts of lipids and choline-containing compounds. As the metabolic signatures of cancer cells result from oncogene-directed metabolic reprogramming, the HT-29 xenografts in mice may prove to be a useful model to further study the tumor microenvironment and cancer biology.

An introduction to hybrid ion trap/time-of-flight mass spectrometry coupled with liquid chromatography applied to drug metabolism studies

Metabolism studies play an important role at various stages of drug discovery and development. Liquid chromatography combined with mass spectrometry (LC/MS) has become a most powerful and widely used analytical tool for identifying drug metabolites. The suitability of different types of mass spectrometers for metabolite profiling differs widely, and therefore, the data quality and reliability of the results also depend on which instrumentation is used. As one of the latest LC/MS instrumentation designs, hybrid ion trap/time-of-flight MS coupled with LC (LC-IT-TOF-MS) has successfully integrated ease of operation, compatibility with LC flow rates and data-dependent MS(n) with high mass accuracy and mass resolving power. The MS(n) and accurate mass capabilities are routinely utilized to rapidly confirm the identification of expected metabolites or to elucidate the structures of uncommon or unexpected metabolites. These features make the LC-IT-TOF-MS a very powerful analytical tool for metabolite identification. This paper begins with a brief introduction to some basic principles and main properties of a hybrid IT-TOF instrument. Then, a general workflow for metabolite profiling using LC-IT-TOF-MS, starting from sample collection and preparation to final identification of the metabolite structures, is discussed in detail. The data extraction and mining techniques to find and confirm metabolites are discussed and illustrated with some examples. This paper is directed to readers with no prior experience with LC-IT-TOF-MS and will provide a broad understanding of the development and utility of this instrument for drug metabolism studies.

Surface-activated chemical ionization-electrospray ionization source improves biomarker discovery with mass spectrometry

RATIONALE

Mass spectrometry (MS) is increasingly employed for the discovery of clinical biomarkers. However, due to sensitivity limitations related to in-source ionization yield, many potential biomarkers are not detected by standard mass spectrometers. Therefore, more efficient ion-source technologies are needed to improve MS applications in biomarker discovery.

METHODS

Among novel ion-source technologies, Surface-Activated Chemical Ionization (SACI), although endowed with high sensitivity linked to its ability to reduce chemical noise in mass spectra, has seen limited application in biomarker discovery to date, due to its selectivity for highly polar compounds. However, in combination with an Electrospray Ionization (ESI) source, SACI selectivity can be enlarged in the range of less polar compounds. To validate the new SACI-ESI approach in biomarker discovery, we applied it to a translational setting in oncology. We performed MS profiles of 101 human serum samples from a male population, aged 40 or older, coming to the clinic for prostate cancer evaluation based on multiple PSA exams, digital rectal examination and echography. The SACI-ESI MS spectra were analyzed and classified with an innovative bioinformatic approach based on the MS-search freeware developed in house.

RESULTS

Here we demonstrate that the SACI-ESI combination can produce MS spectra with greater sensitivity and lower noise than those obtained with the common ESI alone. We found that the SACI-ESI combination increased the number of detectable compounds and produced better quality of profiles in liquid chromatography (LC) coupled with MS (LC/MS) analysis of human serum samples, improving disease prediction potential.

CONCLUSIONS

SACI-ESI can facilitate MS-based discovery of potential biomarkers in human serum. Combined with the proposed bioinformatic approach (based on XCMS and NIST data elaboration) for the analysis of the MS spectra obtained, the potential for developing biomarkers with diagnostic capabilities are demonstrated in a prostate cancer diagnosis clinical setting.

Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

Sample preparation prior to the LC–MS-based metabolomics/metabonomics of blood-derived samples

Blood represents a very important biological fluid and has been the target of continuous and extensive research for diagnostic, or health and drug monitoring reasons. Recently, metabonomics/metabolomics have emerged as a new and promising ‘omics’ platform that shows potential in biomarker discovery, especially in areas such as disease diagnosis, assessment of drug efficacy or toxicity. Blood is collected in various establishments in conditions that are not standardized. Next, the samples are prepared and analyzed using different methodologies or tools. When targeted analysis of key molecules (e.g., a drug or its metabolite[s]) is the aim, enforcement of certain measures or additional analyses may correct and harmonize these discrepancies. In omics fields such as those performed by holistic analytical approaches, no such rules or tools are available. As a result, comparison or correlation of results or data fusion becomes impractical. However, it becomes evident that such obstacles should be overcome in the near future to allow for large-scale studies that involve the assaying of samples from hundreds of individuals. In this case the effect of sample handling and preparation becomes very serious, in order to avoid wasting months of work from experts and expensive instrument time. The present review aims to cover the different methodologies applied to the pretreatment of blood prior to LC–MS metabolomic/metabonomic studies. The article tries to critically compare the methods and highlight issues that need to be addressed.

Computational strategies for metabolite identification in metabolomics

Most metabolomic data are characterized by complex spectra or chromatograms containing hundreds of peaks or features. While there are many methods for aligning or comparing these spectral features, there are few approaches for actually identifying which peaks match to which compounds. Indeed, one of the biggest unmet needs in the field of metabolomics lies in the problem of compound identification. This review describes some of the newly emerging computational strategies in metabolomics that are being used to aid in the identification of metabolites from biofluid mixtures analyzed by NMR and MS. The most successful compound-identification strategies typically involve matching spectral features of the unknown compound(s) to curated spectral databases of reference compounds. This approach is known as the identification of 'known unknowns'. However, the identification of truly novel compounds (the 'unknown unknowns') is particularly challenging and requires the use of computer-aided structure elucidation methods being applied to the purified compound. The strengths and limitations of these approaches as applied to different analytical technologies (GC-MS, LC-MS and NMR) will be discussed, as will prospects for potential improvements to existing strategies.

Practical analytical approach for the identification of biomarker candidates in prediabetic state based upon metabonomic study by ultraperformance liquid chromatography coupled to electrospray ionization time-of-flight mass spectrometry

The number of diabetic patients has recently been increasing worldwide. Thus, the discovery of potential diabetic biomarker(s), leading to the early detection and/or prevention of diabetes mellitus, is strongly required. The diagnosis of the prediabetic state in humans is a very difficult issue because of the lifestyle differences in each person and ethical consideration. Upon the basis of these considerations, animal experiments using ddY strain mice (ddY-H), which undergo naturally occurring diabetes along with age, were carried out in this study. Biomarker discovery based upon a metabonome study is now quite common, the same as that in the proteome analysis. Reversed-phase liquid chromatography-mass spectrometry (LC-MS) has mainly been used for the extensive analysis of low-molecular mass compounds including metabolites. The metabolites in the plasma of diabetic mice (ddY-H) and normal mice (ddY-L) were exhaustively separated and detected by ultraperformance liquid chromatography along with electrospray ionization time-of-flight mass spectrometry (UPLC-ESI-TOF-MS) using T3-C18 and HS-F5 columns. The biomarker candidates related to diabetes mellitus were extracted from the metabolite profiling of ddY-H and ddY-L at 5, 9 13, and 20 weeks old using a multivariate statistical analysis such as orthogonal partial least-squares-discriminant analysis (OPLS-DA). Various metabolites and unknown compounds were detected as biomarker candidates related to diabetic mellitus. Furthermore, the concentration of several metabolites on Lysine biosynthesis and Lysine degradation pathways were remarkably changed between the 9-week old ddY-H and ddY-L mice. Because a couple of biomarker candidates related to the prediabetic state were identified using the present approach, the metabolite profiling study could be helpful for understanding the abnormal state of various diseases.

Metabolomic analysis of livers and serum from high-fat diet induced obese mice

Liver and serum metabolites of obese and lean mice fed on high fat or normal diets were analyzed using ultraperformance liquid chromatography-quadrupole-time-of-flight mass spectrometry, gas chromatography-mass spectrometry, and partial least-squares-discriminant analysis (PLS-DA). Obese and lean groups were clearly discriminated from each other on PLS-DA score plot and major metabolites contributing to the discrimination were assigned as lipid metabolites (fatty acids, phosphatidylcholines (PCs), and lysophosphatidylcholines (lysoPCs)), lipid metabolism intermediates (betaine, carnitine, and acylcarnitines), amino acids, acidic compounds, monosaccharides, and serotonin. A high-fat diet increased lipid metabolites but decreased lipid metabolism intermediates and the NAD/NADH ratio, indicating that abnormal lipid and energy metabolism induced by a high-fat diet resulted in fat accumulation via decreased β-oxidation. In addition, this study revealed that the levels of many metabolites, including serotonin, betaine, pipecolic acid, and uric acid, were positively or negatively related to obesity-associated diseases. On the basis of these metabolites, we proposed a metabolic pathway related to high-fat diet-induced obesity. These metabolites can be used to better understand obesity and related diseases induced by a hyperlipidic diet. Furthermore, the level changes of these metabolites can be used to assess the risk of obesity and the therapeutic effect of obesity management.

Liquid chromatography/mass spectrometry for investigating the biochemical effects induced by aristolochic acid in rats: the plasma metabolome

In this study, high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry in conjunction with chemometric methods including principal components analysis was used to investigate metabolic profiling in plasma samples from rats dosed with aristolochic acid (AA). Differentiating metabolites were identified by high-resolution mass spectrometry and tandem mass spectrometry analyses, database searching and comparison with the analytical results of authentic standards. Several metabolites in plasma including a glucuronide conjugate, bile acids, lysophosphatidylcholines and fatty acids were detected in rats exposed to AA. To improve visualization, a z-score plot and a cluster heat map were generated for the concentration fluctuations of the metabolites in different dosage groups. The results obtained from this study indicated that class-specific metabolomic patterns were obviously differentiated for each metabolite in the different dosage groups of AA.

A maternal high-fat diet is accompanied by alterations in the fetal primate metabolome

OBJECTIVE

To characterize the serum metabolome of a primate model of in utero high-fat exposure.

STUDY DESIGN

Serum from maternal and fetal (e130) macaque monkeys exposed to either a high-fat or control diet were analyzed by gas chromatography-mass spectrometry. Multivariate data analysis was performed to reduce the generated data set. Candidate metabolites were further analyzed for significance by using the analysis of variance and comparative t tests.

RESULTS

Approximately 1300 chromatographic features were detected. Through multivariate data analysis this number was reduced to 60 possible metabolites. With the use of comparative t tests, 22 metabolites had statistical significance (P < .05) over the entire study. By virtue of maternal high-fat diet alone, fetal phenotypic differences are accompanied by altered metabolite concentrations of 7 metabolites (P < .05).

CONCLUSION

In utero high-fat diet exposure is associated with an altered fetal epigenome and parlays a characteristic modification in the fetal metabolite profile.

Metabolomics in evaluation of glucose disorders

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

High-resolution extracted ion chromatography, a new tool for metabolomics and lipidomics using a second-generation orbitrap mass spectrometer

Most analytical methods in metabolomics are based on one of two strategies. The first strategy is aimed at specifically analysing a limited number of known metabolites or compound classes. Alternatively, an unbiased approach can be used for profiling as many features as possible in a given metabolome without prior knowledge of the identity of these features. Using high-resolution mass spectrometry with instruments capable of measuring m/z ratios with sufficiently low mass measurement uncertainties and simultaneous high scan speeds, it is possible to combine these two strategies, allowing unbiased profiling of biological samples and targeted analysis of specific compounds at the same time without compromises. Such high mass accuracy and mass resolving power reduces the number of candidate metabolites occupying the same retention time and m/z ratio space to a minimum. In this study, we demonstrate how targeted analysis of phospholipids as well as unbiased profiling is achievable using a benchtop orbitrap instrument after high-speed reversed-phase chromatography. The ability to apply both strategies in one experiment is an important step forward in comprehensive analysis of the metabolome.

From differentiating metabolites to biomarkers

The current developments in metabolomics and metabolic profiling technologies have led to the discovery of several new metabolic biomarkers. Finding metabolites present in significantly different levels between sample sets, however, does not necessarily make these metabolites useful biomarkers. The route to valid and applicable biomarkers (biomarker qualification) is long and demands a significant amount of work. In this overview, we critically discuss the current state-of-the-art of metabolic biomarker discovery, with highlights and shortcomings, and suggest a pathway to clinical usefulness.