A combination of Q-TOF LC/MS and LC-MS/MS based metabolomics in pediatric-onset multiple sclerosis demonstrates potential biomarkers for unclassified patients

BACKGROUND/AIM: Metabolomics has the potential to provide putative biomarkers and insights into the pathophysiology and diagnosis of pediatric multiple sclerosis (pMS), which is an inflammatory demyelinating disorder of the central nervous system with a broad spectrum of clinical manifestations. In this study, we aimed to investigate serum metabolomics in pMS to help elucidate the pathophysiology of MS. MATERIALS AND METHODS: An untargeted approach was applied using the quadrupole time-of-flight liquid chromatography/mass spectrometry (Q-TOF LC/MS) method to study plasma metabolites in patients with pMS (n = 33), patients with unclassified central nervous system demyelinating diseases (n = 6), and age-matched healthy control subjects (n = 40). The patient and control groups were compared for metabolites and the normalized peak areas differed statistically (p < 0.05), showing at least a 1.25-fold change between groups. Bioinformatic tools combined with a clinical perspective were employed for the identification of the putative metabolites. In addition to the untargeted metabolomics approach, targeted LC-MS/MS metabolite analysis was employed to compare the pMS group with the control group. RESULTS: Significant differences between the patient and control groups were noted for tyramine, 4-hydroxyphenylacetaldehyde, sphingosine/3-dehydrosphinganine, prostaglandins/thromboxane A2, 20-hydroxy-leukotriene E4, 3α,7α,12α-trihydroxy-5β-cholestan-26-al/calcitriol, pantetheine, ketoleucine/3-methyl-2-oxovaleric acid, L-arginine/D-arginine, coproporphyrinogen III, (S)-reticuline, carnosine, cytidine, and phosphoribosyl pyrophosphate. Additional tests for sphingosine 1-phosphate, sphingophosphocholines, ceramides, oxysterols, and calcitriol levels yielded significant metabolomic differences for the pMS group compared to the control group. The metabolomic data of 3/6 patients with unclassified demyelinating disorders matched the pMS group; their follow-up verified the diagnosis of pMS. CONCLUSION: In general, plasma metabolites related to sphingolipid metabolism, myelin products, inflammatory pathways, mitochondrial dysfunction, and oxidative stress were found to be altered in cases of pMS. The method applied in this study, combining untargeted analysis with a targeted approach, can be applied to larger series of cases of pMS and other demyelinating disorders for further validation.