Although complex in nature, a number of metabolites have been implicated in the onset of exercise-induced fatigue. The purpose of this study was to identify changes in the plasma metabolome and specifically, to identify candidate metabolites associated with the onset of fatigue during prolonged cycling. Eighteen healthy and recreationally active men (mean ± SD; age: 24.7 ± 4.8 yr; mass 67.1 ± 6.1 kg; body mass index: 22.8 ± 2.2; peak oxygen uptake: 40.9 ± 6.1 ml·kg-1·min-1) were recruited to this study. Participants performed a prolonged cycling time-to-exhaustion (TTE) test at an intensity corresponding to a fixed blood lactate concentration (3 mmol/l). Plasma samples collected at 10 min of exercise, before fatigue (last sample before fatigue <10 min before fatigue), immediately after fatigue (point of exhaustion), and 20 min after fatigue were assessed using a liquid chromatography-mass spectrometry-based metabolomic approach. Eighty metabolites were putatively identified, with 68 metabolites demonstrating a significant change during the cycling task (duration: ~80.9 ± 13.6 min). A clear multivariate structure in the data was revealed, with the first principal component (36% total variance) describing a continuous increase in metabolite concentration throughout the TTE trial and recovery, whereas the second principal component (14% total variance) showed an increase in metabolite concentration followed by a recovery trajectory, peaking at the point of fatigue. Six clusters of correlated metabolites demonstrating unique metabolite trajectories were identified, including significant separation in the metabolome between prefatigue and postfatigue time points. In accordance with our hypothesis, free-fatty acids and tryptophan contributed to differences in the plasma metabolome at fatigue.NEW & NOTEWORTHY Metabolites have long been implicated in the onset of fatigue. This study applied a metabolomic approach to track 80 plasma-borne metabolites during a cycle to fatigue task. Of these, 68 metabolites demonstrated significant change, with the plasma metabolome at fatigue being clearly distinguishable from other time points. Six unique clusters of metabolites were identified, and free fatty acids were strongly associated with fatigue onset therein lending support to the central fatigue hypothesis.
We performed quantitative metabolic phenotyping of blood plasma in parallel with cytokine/chemokine analysis from participants who were either SARS-CoV-2 (+) (n = 10) or SARS-CoV-2 (-) (n = 49). SARS-CoV-2 positivity was associated with a unique metabolic phenotype and demonstrated a complex systemic response to infection, including severe perturbations in amino acid and kynurenine metabolic pathways. Nine metabolites were elevated in plasma and strongly associated with infection (quinolinic acid, glutamic acid, nicotinic acid, aspartic acid, neopterin, kynurenine, phenylalanine, 3-hydroxykynurenine, and taurine; p < 0.05), while four metabolites were lower in infection (tryptophan, histidine, indole-3-acetic acid, and citrulline; p < 0.05). This signature supports a systemic metabolic phenoconversion following infection, indicating possible neurotoxicity and neurological disruption (elevations of 3-hydroxykynurenine and quinolinic acid) and liver dysfunction (reduction in Fischer's ratio and elevation of taurine). Finally, we report correlations between the key metabolite changes observed in the disease with concentrations of proinflammatory cytokines and chemokines showing strong immunometabolic disorder in response to SARS-CoV-2 infection.
We present a multivariate metabotyping approach to assess the functional recovery of nonhospitalized COVID-19 patients and the possible biochemical sequelae of "Post-Acute COVID-19 Syndrome", colloquially known as long-COVID. Blood samples were taken from patients ca. 3 months after acute COVID-19 infection with further assessment of symptoms at 6 months. Some 57% of the patients had one or more persistent symptoms including respiratory-related symptoms like cough, dyspnea, and rhinorrhea or other nonrespiratory symptoms including chronic fatigue, anosmia, myalgia, or joint pain. Plasma samples were quantitatively analyzed for lipoproteins, glycoproteins, amino acids, biogenic amines, and tryptophan pathway intermediates using Nuclear Magnetic Resonance (NMR) spectroscopy and mass spectrometry. Metabolic data for the follow-up patients (n = 27) were compared with controls (n = 41) and hospitalized severe acute respiratory syndrome SARS-CoV-2 positive patients (n = 18, with multiple time-points). Univariate and multivariate statistics revealed variable patterns of functional recovery with many patients exhibiting residual COVID-19 biomarker signatures. Several parameters were persistently perturbed, e.g., elevated taurine (p = 3.6 × 10-3 versus controls) and reduced glutamine/glutamate ratio (p = 6.95 × 10-8 versus controls), indicative of possible liver and muscle damage and a high energy demand linked to more generalized tissue repair or immune function. Some parameters showed near-complete normalization, e.g., the plasma apolipoprotein B100/A1 ratio was similar to that of healthy controls but significantly lower (p = 4.2 × 10-3) than post-acute COVID-19 patients, reflecting partial reversion of the metabolic phenotype (phenoreversion) toward the healthy metabolic state. Plasma neopterin was normalized in all follow-up patients, indicative of a reduction in the adaptive immune activity that has been previously detected in active SARS-CoV-2 infection. Other systemic inflammatory biomarkers such as GlycA and the kynurenine/tryptophan ratio remained elevated in some, but not all, patients. Correlation analysis, principal component analysis (PCA), and orthogonal-partial least-squares discriminant analysis (O-PLS-DA) showed that the follow-up patients were, as a group, metabolically distinct from controls and partially comapped with the acute-phase patients. Significant systematic metabolic differences between asymptomatic and symptomatic follow-up patients were also observed for multiple metabolites. The overall metabolic variance of the symptomatic patients was significantly greater than that of nonsymptomatic patients for multiple parameters (χ2p = 0.014). Thus, asymptomatic follow-up patients including those with post-acute COVID-19 Syndrome displayed a spectrum of multiple persistent biochemical pathophysiology, suggesting that the metabolic phenotyping approach may be deployed for multisystem functional assessment of individual post-acute COVID-19 patients.