Metabolic profiling of a myalgic encephalomyelitis/chronic fatigue syndrome discovery cohort reveals disturbances in fatty acid and lipid metabolism

Abstract

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) remains a continuum spectrum disease without biomarkers or simple objective tests, and therefore relies on a diagnosis from a set of symptoms to link the assortment of brain and body disorders to ME/CFS. Although recent studies show various affected pathways, the underlying basis of ME/CFS has yet to be established. In this pilot study, we compare plasma metabolic signatures in a discovery cohort, 17 patients and 15 matched controls, and explore potential metabolic perturbations as the aftermath of the complex interactions between genes, transcripts and proteins. This approach to examine the complex array of symptoms and underlying foundation of ME/CFS revealed 74 differentially accumulating metabolites, out of 361 (P < 0.05), and 35 significantly altered after statistical correction (Q < 0.15). The latter list includes several essential energy-related compounds which could theoretically be linked to the general lack of energy observed in ME/CFS patients. Pathway analysis points to a few pathways with high impact and therefore potential disturbances in patients, mainly taurine metabolism and glycerophospholipid metabolism, combined with primary bile acid metabolism, as well as glyoxylate and dicarboxylate metabolism and a few other pathways, all involved broadly in fatty acid metabolism. Purines, including ADP and ATP, pyrimidines and several amino acid metabolic pathways were found to be significantly disturbed. Finally, glucose and oxaloacetate were two main metabolites affected that have a major effect on sugar and energy levels. Our work provides a prospective path for diagnosis and understanding of the underlying mechanisms of ME/CFS.

Fig. 1

Distribution of logged metabolic scores for metabolites significantly different between controls and patients (A) Contains the 35 metabolites with Q<0.15 by the Kruskal-Wallis test (B) Contains the 74 metabolites with P<0.05 by the t-test or the Kruskal-Wallis test. The identity of the numbered metabolites can be found in Table 2.

Fig. 2

Dendrograms derived from the heat map analysis, with decreasing number of metabolites used for analysis as statistical criteria are applied to the dataset.

Fig. 3

Relative importance of metabolites. (A) Variable importance plots. All 361 metabolites were used but only the 20 most important are shown. The identity of the numbered metabolites can be found in Table 2. (B) Partial dependence plots. Each plot shows the log-odds for being a patient given the concentration of a single metabolite adjusted for the other 360 metabolites.