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

• Published in: Metabolomics Vol. 15; no. 12; pp. 158 - 11
• Main Authors: Erasmus, Elardus, Mason, Shayne, van Reenen, Mari, Steffens, Francois E., Vorster, B. Chris, Reinecke, Carolus J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2019; Springer Nature B.V
• Subjects: Adult; Biochemistry; Biomarkers - urine; Biomedical and Life Sciences; Biomedicine; Biotransformation; Cell Biology; Chronic fatigue syndrome; Detoxification; Developmental Biology; Encephalomyelitis; Fatigue; Fatigue - metabolism; Fatigue - urine; Fatigue Syndrome, Chronic - metabolism; Fatigue Syndrome, Chronic - urine; Female; Humans; Life Sciences; Magnetic Resonance Spectroscopy - methods; Metabolites; Metabolomics; Metabolomics - methods; Middle Aged; Molecular Medicine; Multivariate Analysis; NMR; Nuclear magnetic resonance; Original Article; Quality of Life; Statistical analysis; Chronic fatigue; Detoxification challenge test; Phase II biotransformation; H-NMR metabolomics; Piper fatigue scale; 1H-NMR metabolomics
• ISSN: 1573-3882; 1573-3890
• DOI: 10.1007/s11306-019-1620-4

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 ( 1 H-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 1 H-NMR bins for the low (control) and high (patient) fatigue groups indicated complete separation following the detoxification experimental challenge. Important bins identified from the 1 H-NMR spectra provided quantitative metabolite information on the detoxification challenge for the fatigue groups. Conclusions Untargeted 1 H-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.