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Introduction: The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited. Objective: This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort. Method: Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites. Results: The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage. Conclusion: These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance. [ABSTRACT FROM AUTHOR]

HIV-exposed, uninfected (HEU) children present with suboptimal growth and a greater susceptibility to infection in early life when compared to HIV-unexposed, uninfected (HUU) children. The reasons for these findings are poorly understood. We used a metabolomics approach to investigate the metabolic differences between pregnant women living with HIV (PWLWH) and their HEU infants compared to the uninfected and unexposed controls. Untargeted metabolomic profiling was performed using 1H-NMR spectroscopy on maternal plasma at 28 weeks' gestation and infant plasma at birth, 6/10 weeks, and 6 months. PWLWH were older but, apart from a larger 28 week mid-upper-arm circumference, anthropometrically similar to the controls. At all the time points, HEU infants had a significantly reduced growth compared to HUU infants. PWLWH had lower plasma 3-hydroxybutyric acid, acetoacetic acid, and acetic acid levels. In infants at birth, threonine and myo-inositol levels were lower in the HEU group while formic acid levels were higher. At 6/10 weeks, betaine and tyrosine levels were lower in the HEU group. Finally, at six months, 3-hydroxyisobutyric acid levels were lower while glycine levels were higher in the HEU infants. The NMR analysis has provided preliminary information indicating differences between HEU and HUU infants' plasma metabolites involved in energy utilization, growth, and protection from infection. [ABSTRACT FROM AUTHOR]

Introduction: Technological advancements enabled the analyses of limited sample volumes on 1H NMR. Manual spectral profiling of the data is, however, complex, and timely. Objective: To evaluate the performance of BAYESIL for automated identification and quantification of 1H NMR spectra of limited volume samples. Method: Aliquots of a pooled African elephant serum sample were analyzed using standard and reduced volumes. Performance was evaluated on confidence scores, non-detects and laboratory CV. Results: Of the 47 compounds detected, 28 had favorable performances. The approach could differentiate samples based on biological variation. Conclusions: BAYESIL is valuable for limited sample 1H NMR data analyses. [ABSTRACT FROM AUTHOR]

Objective: To better characterize the cerebrospinal fluid (CSF) metabolic profile of tuberculous meningitis (TBM) cases using a South African paediatric cohort.Methods: 1H NMR metabolomics was used to analyse the CSF of a South African paediatric cohort. Univariate and multivariate statistical analyses were performed to compare a homogeneous control group with a well-defined TBM group.Results: Twenty metabolites were identified to discriminate TBM cases from controls. As expected, reduced glucose and elevated lactate were the dominating discriminators. A closer investigation of the CSF metabolic profile yielded 18 metabolites of statistical significance. Ten metabolites (acetate, alanine, choline, citrate, creatinine, isoleucine, lysine, myo-inositol, pyruvate and valine) overlapped with two other prior investigations. Eight metabolites (2-hydroxybutyrate, carnitine, creatine, creatine phosphate, glutamate, glutamine, guanidinoacetate and proline) were unique to our paediatric TBM cohort.Conclusions: Through strict exclusion criteria, quality control checks and data filtering, eight unique CSF metabolites associated with TBM were identified for the first time and linked to: uncontrolled glucose metabolism, upregulated proline and creatine metabolism, detoxification and disrupted glutamate-glutamine cycle in the TBM samples. Associated with oxidative stress and chronic neuroinflammation, our findings collectively imply destabilization, and hence increased permeability, of the blood-brain barrier in the TBM cases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Malaria is the leading cause of death worldwide. It is urgent to assess the impact of interventions and scaled-up control efforts. Despite reported reduction in malaria prevalence in Africa, the trends in Cameroon are not yet fully understood. The aim of this study was to investigate the trends in malaria admissions among febrile patients seeking treatment over a seven-year period (2006-2012) in an endemic area in Cameroon, hypothesizing a declining trend. This period followed changes in malaria treatment policy. The objectives were to identify possible trends in malaria admissions and to evaluate the impact of changes to treatment guidelines on the prevalence. Methods: Data was collected through consultation and perusal of laboratory and prescription registers of the Mbakong Health Centre. Data analysis was conducted using SPSS and SAS Statistics. Results: Analysis revealed that 4,230 febrile patients were received from 2006-2012. Of these febrile cases, 29.30% were confirmed positive. Between 2006 and 2012 confirmed malaria positive cases of those tested fluctuated, dropping from 53.21% in 2006 to 17.20% in 2008; then rising to 35.00% in 2011 and, finally, dropping to 18.2% of those tested in 2012. The prevalence in females and males across all age groups were similar: a slightly higher risk of males to have malaria (OR = 1.08, 95% CI 0.94-1.25) were not practically significant. Of those tested, the 5 to < 15 years and the 1 to<5 years age groups were the hardest hit by malaria in the area. A practically visible and significant association was observed between the age and gender with regards to the number of malaria positive results (Pearson ×2 = 153.675, p < 0.00001, Cramer's V = 0.352). Malaria prevalence exhibited a fluctuating yet declining trend, as observed over the 28 quarters between January, 2006 and December, 2012. Conclusions: The changes to the treatment guidelines appear to result in a declining trend as was observed between 2006 and 2008. However, malaria admissions fluctuated between 2008 and 2012. There is, therefore, a need to step up control efforts of especially the vulnerable groups, such as the very young. [ABSTRACT FROM AUTHOR]

The association between hypercoagulability and use of drospirenone (DRSP) and ethinylestradiol (EE) containing combined oral contraceptives (COCs) is an important clinical concern. We have previously reported that the two formulations of DRSP combined with EE (namely, DRSP/20EE and DRSP/30EE) bring about a prothrombotic state in hemostatic traits of female users. We report here the serum metabolomic changes in the same study cohort in relation to the attendant prothrombotic state induced by COC use, thus offering new insights on the underlying biochemical mechanisms contributing to the altered coagulatory profile with COC use. A total of 78 healthy women participated in this study and were grouped as follows: control group not using oral contraceptives ( n  = 25), DRSP/20EE group ( n  = 27), and DRSP/30EE group ( n  = 26). Untargeted metabolomics revealed changes in amino acid concentrations, particularly a decrease in glycine and an increase in both cysteine and lanthionine in the serum, accompanied by variations in oxidative stress markers in the COC users compared with the controls. Of importance, this study is the first to link specific amino acid variations, serum metabolites, and the oxidative metabolic profile with DRSP/EE use. These molecular changes could be linked to specific biophysical coagulatory alterations observed in the same individuals. These new findings lend evidence on the metabolomic substrates of the prothrombotic state associated with COC use in women and informs future personalized/precision medicine research. Moreover, we underscore the importance of an interdisciplinary approach to evaluate venous thrombotic risk associated with COC use.

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.

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.