A laboratory approach for characterizing chronic fatigue: what does metabolomics tell us?

INTRODUCTION: Manifestations of fatigue range from chronic fatigue up to a severe syndrome and myalgic encephalomyelitis. Fatigue grossly affects the functional status and quality of life of affected individuals, prompting the World Health Organization to recognize it as a chronic non-communicable condition. OBJECTIVES: Here, we explore the potential of urinary metabolite information to complement clinical criteria of fatigue, providing an avenue towards an objective measure of fatigue in patients presenting with the full spectrum of fatigue levels. METHODS: The experimental group consisted of 578 chronic fatigue female patients. The measurement design was composed of (1) existing clinical fatigue scales, (2) a hepatic detoxification challenge test, and (3) untargeted proton nuclear magnetic resonance (1H-NMR) procedure to generate metabolomics data. Data analysed via an in-house Matlab script that combines functions from a Statistics and a PLS Toolbox. RESULTS: Multivariate analysis of the original 459 profiled 1H-NMR bins for the low (control) and high (patient) fatigue groups indicated complete separation following the detoxification experimental challenge. Important bins identified from the 1H-NMR spectra provided quantitative metabolite information on the detoxification challenge for the fatigue groups. CONCLUSIONS: Untargeted 1H-NMR metabolomics proved its applicability as a global profiling tool to reveal the impact of toxicological interventions in chronic fatigue patients. No clear potential biomarker emerged from this study, but the quantitative profile of the phase II biotransformation products provide a practical visible effect directing to up-regulation of crucial phase II enzyme systems in the high fatigue group in response to a high xenobiotic-load.

Biotransformation profiles from a cohort of chronic fatigue women in response to a hepatic detoxification challenge.

Chronic fatigue, in its various manifestations, frequently co-occur with pain, sleep disturbances and depression and is a non-communicable condition which is rapidly becoming endemic worldwide. However, it is handicapped by a lack of objective definitions and diagnostic measures. This has prompted the World Health Organization to develop an international instrument whose intended purpose is to improve quality of life (QOL), with energy and fatigue as one domain of focus. To complement this objective, the interface between detoxification, the exposome, and xenobiotic-sensing by nuclear receptors that mediate induction of biotransformation-linked genes, is stimulating renewed attention to a rational development of strategies to identify the metabolic profiles in complex multifactorial conditions like fatigue. Here we present results from a seven-year study of a cohort of 576 female patients suffering from low to high levels of chronic fatigue, in which phase I and phase II biotransformation was assessed. The biotransformation profiles used were based on hepatic detoxification challenge tests through oral caffeine, acetaminophen and acetylsalicylic acid ingestion coupled with oxidative stress analyses. The interventions indicated normal phase I but increased phase II glucuronidation and glycination conjugation. Complementarity was indicated between a fatigue scale, medical symptoms and associated energy-related parameters by application of Chi-square Automatic Interaction Detector (CHAID) analysis. The presented study provides a cluster of data from which we propose that multidisciplinary inputs from the combination of a fatigue scale, medical symptoms and biotransformation profiles provide the rationale for the development of a comprehensive laboratory instrument for improved diagnostics and personalized interventions in patients with chronic fatigue with a view to improving their QOL.

The GC-MS metabolomics signature in patients with fibromyalgia syndrome directs to dysbiosis as an aspect contributing factor of FMS pathophysiology.

INTRODUCTION: Fibromyalgia syndrome (FMS) is a chronic pain syndrome. Previous analyses of untargeted metabolomics data indicated altered metabolic profile in FMS patients. OBJECTIVES: We report a semi-targeted explorative metabolomics study on the urinary metabolite profile of FMS patients; exploring the potential of urinary metabolite information to augment existing medical diagnosis. METHODS: All cases were females. Patients had a medical history of persistent FMS (n = 18). Control groups were first-generation family members of the patients (n = 11), age-related individuals without indications of FMS (n = 10), and healthy, young (18-22 years) individuals (n = 41). The biofluid investigated was early morning urine samples. Data generation was done through gas chromatography-mass spectrometry (GC-MS) analysis and data processing and analyses were performed using Matlab, R, SPSS and SAS software. RESULTS: Quantitative analysis revealed the presence of 196 metabolites. Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, which could be related to 14 significantly increased metabolites. These metabolites are associated with energy metabolism, digestion and metabolism of carbohydrates and other host and gut metabolites. CONCLUSIONS: Overall, urinary metabolite profiles in the FMS patients suggest: (1) energy utilization is a central aspect of this pain disorder, (2) dysbiosis seems to prevail in FMS patients, indicated by disrupted microbiota metabolites, supporting the model that microbiota may alter brain function through the gut-brain axis, with the gut being a gateway to generalized pain, and (3) screening of urine from FMS is an avenue to explore for adding non-invasive clinical information for diagnosis and treatment of FMS.

The 1H-NMR-based metabolite profile of acute alcohol consumption: A metabolomics intervention study.

Metabolomics studies of disease conditions related to chronic alcohol consumption provide compelling evidence of several perturbed metabolic pathways underlying the pathophysiology of alcoholism. The objective of the present study was to utilize proton nuclear magnetic resonance (1H-NMR) spectroscopy metabolomics to study the holistic metabolic consequences of acute alcohol consumption in humans. The experimental design was a cross-over intervention study which included a number of substances to be consumed-alcohol, a nicotinamide adenine dinucleotide (NAD) supplement, and a benzoic acid-containing flavoured water vehicle. The experimental subjects-24 healthy, moderate-drinking young men-each provided six hourly-collected urine samples for analysis. Complete data sets were obtained from 20 of the subjects and used for data generation, analysis and interpretation. The results from the NMR approach produced complex spectral data, which could be resolved sufficiently through the application of a combination of univariate and multivariate methods of statistical analysis. The metabolite profiles resulting from acute alcohol consumption indicated that alcohol-induced NAD+ depletion, and the production of an excessive amount of reducing equivalents, greatly perturbed the hepatocyte redox homeostasis, resulting in essentially three major metabolic disturbances-up-regulated lactic acid metabolism, down-regulated purine catabolism and osmoregulation. Of these, the urinary excretion of the osmolyte sorbitol proved to be novel, and suggests hepatocyte swelling due to ethanol influx following acute alcohol consumption. Time-dependent metabolomics investigations, using designed interventions, provide a way of interpreting the variation induced by the different factors of a designed experiment, thereby also giving methodological significance to this study. The outcomes of this approach have the potential to significantly advance our understanding of the serious impact of the pathophysiological perturbations which arise from the consumption of a single, large dose of alcohol-a simulation of a widespread, and mostly naive, social practice.

GC-MS-based urinary organic acid profiling reveals multiple dysregulated metabolic pathways following experimental acute alcohol consumption.

Metabolomics studies of diseases associated with chronic alcohol consumption provide compelling evidence of several perturbed metabolic pathways. Moreover, the holistic approach of such studies gives insights into the pathophysiological risk factors associated with chronic alcohol-induced disability, morbidity and mortality. Here, we report on a GC-MS-based organic acid profiling study on acute alcohol consumption. Our investigation - involving 12 healthy, moderate-drinking young men - simulated a single binge drinking event, and indicated its metabolic consequences. We generated time-dependent data that predicted the metabolic pathophysiology of the alcohol intervention. Multivariate statistical modelling was applied to the longitudinal data of 120 biologically relevant organic acids, of which 13 provided statistical evidence of the alcohol effect. The known alcohol-induced increased NADH:NAD+ ratio in the cytosol of hepatocytes contributed to the global dysregulation of several metabolic reactions of glycolysis, ketogenesis, the Krebs cycle and gluconeogenesis. The significant presence of 2-hydroxyisobutyric acid supports the emerging paradigm that this compound is an important endogenous metabolite. Its metabolic origin remains elusive, but recent evidence indicated 2-hydroxyisobutyrylation as a novel regulatory modifier of histones. Metabolomics has thus opened an avenue for further research on the reprogramming of metabolic pathways and epigenetic networks in relation to the severe effects of alcohol consumption.

Cerebrospinal Fluid Amino Acid Profiling of Pediatric Cases with Tuberculous Meningitis.

Background: In Africa, tuberculosis is generally regarded as persisting as one of the most devastating infectious diseases. The pediatric population is particularly vulnerable, with infection of the brain in the form of tuberculous meningitis (TBM) being the most severe manifestation. TBM is often difficult to diagnose in its early stages because of its non-specific clinical presentation. Of particular concern is that late diagnosis, and subsequent delayed treatment, leads to high risk of long-term neurological sequelae, and even death. Using advanced technology and scientific expertise, we are intent on further describing the biochemistry behind this devastating neuroinflammatory disease, with the goal of improving upon its early diagnosis. Method: We used the highly sensitive analytical platform of gas chromatography-mass spectrometry (GC-MS) to analyze amino acid profiles of cerebrospinal fluid (CSF) collected from a cohort of 33 South African pediatric TBM cases, compared to 34 controls. Results: Through the use of a stringent quality assurance procedure and various statistical techniques, we were able to confidently identify five amino acids as being significantly elevated in TBM cases, namely, alanine, asparagine, glycine, lysine, and proline. We found also in an earlier untargeted metabolomics investigation that alanine can be attributed to increased CSF lactate levels, and lysine as a marker of lipid peroxidation. Alanine, like glycine, is an inhibitory neurotransmitter in the brain. Asparagine, as with proline, is linked to the glutamate-glutamine cycle. Asparagine is associated with the removal of increased nitrites in the brain, whereas elevated proline coincides with the classic biochemical marker of increased CSF protein in TBM. All five discriminatory amino acids are linked to ammonia due to increased nitrites in TBM. Conclusion: A large amount of untapped biochemical information is present in CSF of TBM cases, of which amino acid profiling through GC-MS has potential in aiding in earlier diagnosis, and hence crucial earlier treatment.

1H NMR spectral identification of medication in cerebrospinal fluid of pediatric meningitis.

Exploratory metabolomics studies of cerebrospinal fluid (CSF), using proton nuclear magnetic resonance (1H NMR) spectroscopy, hold major potential application in neurodiagnostics. Such studies, however, rely upon established databases of known metabolites. Here we address the 'unknowns' in the 1H NMR spectra of CSF from treated pediatric meningitis cases. Through knowledge of the clinical information given by the pediatrician and analytical application of 1H NMR spectroscopy on pure reference compounds of the medication used, we identified four of the previously unknown compounds in the 1H NMR CSF spectra - the drugs pyrazinamide, isoniazid, acyclovir, and sulfamethoxazole. We report on the one- and two-dimensional 1H NMR spectral data and chemical information of these four compounds. By expanding our knowledge of 1H NMR CSF spectra from treated meningitis cases, we are able to bring 1H NMR closer to the forefront of neurodiagnostics.

A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls.

BACKGROUND: Fibromyalgia syndrome (FMS) is a chronic pain syndrome. A plausible pathogenesis of the disease is uncertain and the pursuit of measurable biomarkers for objective identification of affected individuals is a continuing endeavour in FMS research. Our objective was to perform an explorative metabolomics study (1) to elucidate the global urinary metabolite profile of patients suffering from FMS, and (2) to explore the potential of this metabolite information to augment existing medical practice in diagnosing the disease. METHODS: We selected patients with a medical history of persistent FMS (n = 18), who described their recent state of the disease through the Fibromyalgia Impact Questionnaire (FIQR) and an in-house clinical questionnaire (IHCQ). Three control groups were used: first-generation family members of the patients (n = 11), age-related individuals without any indications of FMS or related conditions (n = 10), and healthy young (18-22 years) individuals (n = 20). All subjects were female and the biofluid under investigation was urine. Correlation analysis of the FIQR showed the FMS patients represented a well-defined disease group for this metabolomics study. Spectral analyses of urine were conducted using a 500 MHz 1H nuclear magnetic resonance (NMR) spectrometer; data processing and analyses were performed using Matlab, R, SPSS and SAS software. RESULTS AND DISCUSSION: Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, and significant increases in metabolites related to the gut microbiome (hippuric, succinic and lactic acids) were observed. We have developed an algorithm for the diagnosis of FMS consisting of three metabolites - succinic acid, taurine and creatine - that have a good level of diagnostic accuracy (Receiver Operating Characteristic (ROC) analysis - area under the curve 90%) and on the pain and fatigue symptoms for the selected FMS patient group. CONCLUSION: Our data and comparative analyses indicated an altered metabolic profile of patients with FMS, analytically detectable within their urine. Validation studies may substantiate urinary metabolites to supplement information from medical assessment, tender-point measurements and FIQR questionnaires for an improved objective diagnosis of FMS.

Metabolomics variable selection and classification in the presence of observations below the detection limit using an extension of ERp.

BACKGROUND: ERp is a variable selection and classification method for metabolomics data. ERp uses minimized classification error rates, based on data from a control and experimental group, to test the null hypothesis of no difference between the distributions of variables over the two groups. If the associated p-values are significant they indicate discriminatory variables (i.e. informative metabolites). The p-values are calculated assuming a common continuous strictly increasing cumulative distribution under the null hypothesis. This assumption is violated when zero-valued observations can occur with positive probability, a characteristic of GC-MS metabolomics data, disqualifying ERp in this context. This paper extends ERp to address two sources of zero-valued observations: (i) zeros reflecting the complete absence of a metabolite from a sample (true zeros); and (ii) zeros reflecting a measurement below the detection limit. This is achieved by allowing the null cumulative distribution function to take the form of a mixture between a jump at zero and a continuous strictly increasing function. The extended ERp approach is referred to as XERp. RESULTS: XERp is no longer non-parametric, but its null distributions depend only on one parameter, the true proportion of zeros. Under the null hypothesis this parameter can be estimated by the proportion of zeros in the available data. XERp is shown to perform well with regard to bias and power. To demonstrate the utility of XERp, it is applied to GC-MS data from a metabolomics study on tuberculosis meningitis in infants and children. We find that XERp is able to provide an informative shortlist of discriminatory variables, while attaining satisfactory classification accuracy for new subjects in a leave-one-out cross-validation context. CONCLUSION: XERp takes into account the distributional structure of data with a probability mass at zero without requiring any knowledge of the detection limit of the metabolomics platform. XERp is able to identify variables that discriminate between two groups by simultaneously extracting information from the difference in the proportion of zeros and shifts in the distributions of the non-zero observations. XERp uses simple rules to classify new subjects and a weight pair to adjust for unequal sample sizes or sensitivity and specificity requirements.

Contribution towards a Metabolite Profile of the Detoxification of Benzoic Acid through Glycine Conjugation: An Intervention Study.

Benzoic acid is widely used as a preservative in food products and is detoxified in humans through glycine conjugation. Different viewpoints prevail on the physiological significance of the glycine conjugation reaction and concerns have been raised on potential public health consequences following uncontrolled benzoic acid ingestion. We performed a metabolomics study which used commercial benzoic acid containing flavored water as vehicle for designed interventions, and report here on the controlled consumption of the benzoic acid by 21 cases across 6 time points for a total of 126 time points. Metabolomics data from urinary samples analyzed by nuclear magnetic resonance spectroscopy were generated in a time-dependent cross-over study. We used ANOVA-simultaneous component analysis (ASCA), repeated measures analysis of variance (RM-ANOVA) and unfolded principal component analysis (unfolded PCA) to supplement conventional statistical methods to uncover fully the metabolic perturbations due to the xenobiotic intervention, encapsulated in the metabolomics tensor (three-dimensional matrices having cases, spectral areas and time as axes). Identification of the biologically important metabolites by the novel combination of statistical methods proved the power of this approach for metabolomics studies having complex data structures in general. The study disclosed a high degree of inter-individual variation in detoxification of the xenobiotic and revealed metabolic information, indicating that detoxification of benzoic acid through glycine conjugation to hippuric acid does not indicate glycine depletion, but is supplemented by ample glycine regeneration. The observations lend support to the view of maintenance of glycine homeostasis during detoxification. The study indicates also that time-dependent metabolomics investigations, using designed interventions, provide a way of interpreting the variation induced by the different factors of a designed experiment-an approach with potential to advance significantly our understanding of normal and pathophysiological perturbations of endogenous or exogenous origin.

Cerebrospinal fluid in tuberculous meningitis exhibits only the L-enantiomer of lactic acid.

BACKGROUND: The defining feature of the cerebrospinal fluid (CSF) collected from infants and children with tuberculous meningitis (TBM), derived from an earlier untargeted nuclear magnetic resonance (NMR) metabolomics study, was highly elevated lactic acid. Undetermined was the contribution from host response (L-lactic acid) or of microbial origin (D-lactic acid), which was set out to be determined in this study. METHODS: In this follow-up study, we used targeted ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) to determine the ratio of the L and D enantiomers of lactic acid in these CSF samples. RESULTS: Here we report for the first time that the lactic acid observed in the CSF of confirmed TBM cases was in the L-form and solely a response from the host to the infection, with no contribution from any bacteria. The significance of elevated lactic acid in TBM appears to be that it is a crucial energy substrate, used preferentially over glucose by microglia, and exhibits neuroprotective capabilities. CONCLUSION: These results provide experimental evidence to support our conceptual astrocyte-microglia lactate shuttle model formulated from our previous NMR-based metabolomics study - highlighting the fact that lactic acid plays an important role in neuroinflammatory diseases such as TBM. Furthermore, this study reinforces our belief that the determination of enantiomers of metabolites corresponding to infectious diseases is of critical importance in substantiating the clinical significance of disease markers.

Variable selection for binary classification using error rate p-values applied to metabolomics data.

BACKGROUND: Metabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups. Two tasks need to be performed: (i) finding variables which differ between the two groups; and (ii) determining how the selected variables can be used to classify new subjects. We introduce an approach using minimum classification error rates as test statistics to find discriminatory and therefore informative variables. The thresholds resulting in the minimum error rates can be used to classify new subjects. This approach transforms error rates into p-values and is referred to as ERp. RESULTS: We show that non-parametric hypothesis testing, based on minimum classification error rates as test statistics, can find statistically significantly shifted variables. The discriminatory ability of variables becomes more apparent when error rates are evaluated based on their corresponding p-values, as relatively high error rates can still be statistically significant. ERp can handle unequal and small group sizes, as well as account for the cost of misclassification. ERp retains (if known) or reveals (if unknown) the shift direction, aiding in biological interpretation. The threshold resulting in the minimum error rate can immediately be used to classify new subjects. We use NMR generated metabolomics data to illustrate how ERp is able to discriminate subjects diagnosed with Mycobacterium tuberculosis infected meningitis from a control group. The list of discriminatory variables produced by ERp contains all biologically relevant variables with appropriate shift directions discussed in the original paper from which this data is taken. CONCLUSIONS: ERp performs variable selection and classification, is non-parametric and aids biological interpretation while handling unequal group sizes and misclassification costs. All this is achieved by a single approach which is easy to perform and interpret. ERp has the potential to address many other characteristics of metabolomics data. Future research aims to extend ERp to account for a large proportion of observations below the detection limit, as well as expand on interactions between variables.

A hypothetical astrocyte-microglia lactate shuttle derived from a 1H NMR metabolomics analysis of cerebrospinal fluid from a cohort of South African children with tuberculous meningitis.

Tuberculosis meningitis (TBM) is the most severe form of extra-pulmonary tuberculosis and is particularly intense in small children; there is no universally accepted algorithm for the diagnosis and substantiation of TB infection, which can lead to delayed intervention, a high risk factor for morbidity and mortality. In this study a proton magnetic resonance (1H NMR)-based metabolomics analysis and several chemometric methods were applied to data generated from lumber cerebrospinal fluid (CSF) samples from three experimental groups: (1) South African infants and children with confirmed TBM, (2) non-meningitis South African infants and children as controls, and (3) neurological controls from the Netherlands. A total of 16 NMR-derived CSF metabolites were identified, which clearly differentiated between the controls and TBM cases under investigation. The defining metabolites were the combination of perturbed glucose and highly elevated lactate, common to some other neurological disorders. The remaining 14 metabolites of the host's response to TBM were likewise mainly energy-associated indicators. We subsequently generated a hypothesis expressed as an "astrocyte-microglia lactate shuttle" (AMLS) based on the host's response, which emerged from the NMR-metabolomics information. Activation of microglia, as implied by the AMLS hypothesis, does not, however, present a uniform process and involves intricate interactions and feedback loops between the microglia, astrocytes and neurons that hamper attempts to construct basic and linear cascades of cause and effect; TBM involves a complex integration of the responses from the various cell types present within the CNS, with microglia and the astrocytes as main players.

3-Methylglutaconic aciduria--lessons from 50 genes and 977 patients.

Elevated urinary excretion of 3-methylglutaconic acid is considered rare in patients suspected of a metabolic disorder. In 3-methylglutaconyl-CoA hydratase deficiency (mutations in AUH), it derives from leucine degradation. In all other disorders with 3-methylglutaconic aciduria the origin is unknown, yet mitochondrial dysfunction is thought to be the common denominator. We investigate the biochemical, clinical and genetic data of 388 patients referred to our centre under suspicion of a metabolic disorder showing 3-methylglutaconic aciduria in routine metabolic screening. Furthermore, we investigate 591 patients with 50 different, genetically proven, mitochondrial disorders for the presence of 3-methylglutaconic aciduria. Three percent of all urine samples of the patients referred showed 3-methylglutaconic aciduria, often in correlation with disorders not reported earlier in association with 3-methylglutaconic aciduria (e.g. organic acidurias, urea cycle disorders, haematological and neuromuscular disorders). In the patient cohort with genetically proven mitochondrial disorders 11% presented 3-methylglutaconic aciduria. It was more frequently seen in ATPase related disorders, with mitochondrial DNA depletion or deletion, but not in patients with single respiratory chain complex deficiencies. Besides, it was a consistent feature of patients with mutations in TAZ, SERAC1, OPA3, DNAJC19 and TMEM70 accounting for mitochondrial membrane related pathology. 3-methylglutaconic aciduria is found quite frequently in patients suspected of a metabolic disorder, and mitochondrial dysfunction is indeed a common denominator. It is only a discriminative feature of patients with mutations in AUH, TAZ, SERAC1, OPA3, DNAJC19 TMEM70. These conditions should therefore be referred to as inborn errors of metabolism with 3-methylglutaconic aciduria as discriminative feature.