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.

Impact of CYP2D6 genotype on amitriptyline efficacy for the treatment of diabetic peripheral neuropathy: a pilot study.

AIM: Therapy with low-dose amitriptyline is commonly used to treat painful diabetic peripheral neuropathy. There is a knowledge gap, however, regarding the role of variable CYP2D6-mediated drug metabolism and side effects (SEs). We aimed to generate pilot data to demonstrate that SEs are more frequent in patients with variant CYP2D6 alleles. METHOD: To that end, 31 randomly recruited participants were treated with low-dose amitriptyline for painful diabetic peripheral neuropathy and their CYP2D6 gene sequenced. RESULTS: Patients with predicted normal or ultra-rapid metabolizer phenotypes presented with less SEs compared with individuals with decreased CYP2D6 activity. CONCLUSION: Hence, CYP2D6 genotype contributes to treatment outcome and may be useful for guiding drug therapy. Future investigations in a larger patient population are planned to support these preliminary findings.

Evaluation of predictive CYP2C19 genotyping assays relative to measured phenotype in a South African cohort.

AIM: To align predicted and measured CYP2C19 phenotype in a South African cohort. MATERIALS & METHODS: Genotyping of CYP2C19*2, *3, *9, *15, *17, *27 and *28 was performed using PCR-RFLP, and an activity score (AS) system was used to predict phenotype. True phenotype was measured using plasma concentrations of omeprazole and its metabolite 5'-hydroxyomperazole. RESULTS: Partial genotype-phenotype discrepancies were reported, and an adapted AS system was developed, which showed a marked improvement in phenotype prediction. Results highlight the need for a more comprehensive CYP2C19 genotyping approach to improve prediction of omeprazole metabolism. CONCLUSION: Evidence for the utility of a CYP2C19 AS system is provided, for which the accuracy can be further improved by means of comprehensive genotyping and substrate-specific modification.

Predicting the influence of multiple components on microbial inhibition using a logistic response model--a novel approach.

BACKGROUND: There are several synergistic methods available. However, there is a vast discrepancy in the interpretation of the synergistic results. Also, these synergistic methods do not assess the influence the tested components (drugs, plant and natural extracts), have upon one another, when more than two components are combined. METHODS: A modified checkerboard method was used to evaluate the synergistic potential of Heteropyxis natalensis, Melaleuca alternifolia, Mentha piperita and the green tea extract known as TEAVIGO™. The synergistic combination was tested against the oral pathogens, Streptococcus mutans, Prevotella intermedia and Candida albicans. Inhibition data obtained from the checkerboard method, in the form of binary code, was used to compute a logistic response model with statistically significant results (p < 0.05). This information was used to construct a novel predictive inhibition model. RESULTS: Based on the predictive inhibition model for each microorganism, the oral pathogens tested were successfully inhibited (at 100% probability) with their respective synergistic combinations. The predictive inhibition model also provided information on the influence that different components have upon one another, and on the overall probability of inhibition. CONCLUSIONS: Using the logistic response model negates the need to 'calculate' synergism as the results are statistically significant. In successfully determining the influence multiple components have upon one another and their effect on microbial inhibition, a novel predictive model was established. This ability to screen multiple components may have far reaching effects in ethnopharmacology, agriculture and pharmaceuticals.

Multi-element analysis of South African wines by ICP-MS and their classification according to geographical origin.

Wines from three important wine-producing regions, Stellenbosch, Robertson, and Swartland, in the Western Cape Province of South Africa, were analyzed by ICP-MS and the elemental composition used in multivariate statistical analysis to classify the wines according to geographical origin. The method is based on the assumption that the provenance soil is an important contributor to the trace element composition of a wine. A total of 40 elements were determined in 40 wines. Of these, 20 elements: Li, B, Mg, Al, Si, Cl, Sc, Mn, Ni, Ga, Se, Rb, Sr, Nb, Cs, Ba, La, W, Tl, and U showed differences in their means across the three areas. In a stepwise discriminant analysis procedure, functions based on linear combinations of the log-transformed element concentrations of Al, Mn, Rb, Ba, W, and Tl were generated to correctly classify wines from each region. In an alternative approach, a pairwise discriminant analysis procedure, not previously used in wine provenance studies, was tested. In this procedure, the classification was done in three steps, with each step classifying a wine as coming from a certain region or not. The combination of elements characterizing wines from a particular region was different in each region. The discriminant functions were based on the following elements: Al, Mn, Rb, Ba, and W for Stellenbosch; Se, Rb, Cs, and Tl for Robertson; and Al, Mn, Rb, Sr, Ba, and Tl for Swartland. After this procedure, the classification of the wines into one of the groups was 100% successful.