A map of metabolic phenotypes in patients with myalgic encephalomyelitis/chronic fatigue syndrome
Fredrik Hoel, … , Øystein Fluge, Karl J. Tronstad
Fredrik Hoel, … , Øystein Fluge, Karl J. Tronstad
Published August 23, 2021
Citation Information: JCI Insight. 2021;6(16):e149217. https://doi.org/10.1172/jci.insight.149217.
View: Text | PDF
Research Article Immunology Metabolism

A map of metabolic phenotypes in patients with myalgic encephalomyelitis/chronic fatigue syndrome

Abstract

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease usually presenting after infection. Emerging evidence supports that energy metabolism is affected in ME/CFS, but a unifying metabolic phenotype has not been firmly established. We performed global metabolomics, lipidomics, and hormone measurements, and we used exploratory data analyses to compare serum from 83 patients with ME/CFS and 35 healthy controls. Some changes were common in the patient group, and these were compatible with effects of elevated energy strain and altered utilization of fatty acids and amino acids as catabolic fuels. In addition, a set of heterogeneous effects reflected specific changes in 3 subsets of patients, and 2 of these expressed characteristic contexts of deregulated energy metabolism. The biological relevance of these metabolic phenotypes (metabotypes) was supported by clinical data and independent blood analyses. In summary, we report a map of common and context-dependent metabolic changes in ME/CFS, and some of them presented possible associations with clinical patient profiles. We suggest that elevated energy strain may result from exertion-triggered tissue hypoxia and lead to systemic metabolic adaptation and compensation. Through various mechanisms, such metabolic dysfunction represents a likely mediator of key symptoms in ME/CFS and possibly a target for supportive intervention.

Authors

Fredrik Hoel, August Hoel, Ina K.N. Pettersen, Ingrid G. Rekeland, Kristin Risa, Kine Alme, Kari Sørland, Alexander Fosså, Katarina Lien, Ingrid Herder, Hanne L. Thürmer, Merete E. Gotaas, Christoph Schäfer, Rolf K. Berge, Kristian Sommerfelt, Hans-Peter Marti, Olav Dahl, Olav Mella, Øystein Fluge, Karl J. Tronstad

×

Figure 1

Explorative data analysis (EDA) strategy.

Options: View larger image (or click on image) Download as PowerPoint
Explorative data analysis (EDA) strategy. In data preprocessing, the raw...
In data preprocessing, the raw peak data from the global metabolomics platform (Metabolon Inc., HD4) was preprocessed by first excluding variables with more than 25% missing values, metabolites classified as xenobiotics, and partially characterized molecules. Missing values were then imputed for the remaining variables. The variables were subsequently quantile normalized. In multivariate analysis, initial univariate analysis was performed on the raw normalized data using 2-tailed Welch’s test revealing 159 significant metabolites (based on P value). Fold change was also calculated. The normalized raw data were scaled using autoscaling before further multivariate analyses were performed. The 159 significant metabolites from the first univariate analyses were used for the multivariate analyses. The k-means clustering was used generate 3 patient subsets. Principal component analysis (PCA) was performed, and a loading plot was generated. The subsets discovered in the k-means clustering was used in the visualization of the PCA plot to display the variance observed between patient subsets. Additional metadata were implemented to investigate possible impacts of factors such as such as sex and fasting state. In univariate analysis, the HD4 data set was reanalyzed to separately compare the 3 ME/CFS metabolic subsets with the HC group using the HD4 data set. These data were used to create plots for visualization. The curated complex lipid data set (Metabolon Inc., CLP) was also analyzed according to the established ME/CFS subsets.