Untargeted Metabolomics Analysis Reveals Aberrant Long-Chain Fatty Acids Metabolism in Relapsing-Remitting Multiple Sclerosisublication title

ABSTRACT:

Background: Growing evidence indicates significant alterations in fatty acid metabolism in patients with relapsing-remitting multiple sclerosis (RRMS). However, the metabolic status of long-chain fatty acids (LCFAs), including mono-unsaturated fatty acids (MUFAs) and poly-unsaturated fatty acids (PUFAs), and their potential link to immune-inflammatory responses during RRMS relapses, remain unclear. This study aims to uncover the aberrant metabolic signatures of LCFAs, potential LCFA biomarkers during RRMS relapses, and their interactive network with peripheral inflammatory responses.

Methods: In this study, plasma samples from 20 RRMS patients and 22 age- and sex-matched healthy controls (HCs) were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics method.

Results: Metabolomics analysis revealed marked changes in the LCFA metabolic profile of RRMS patients. Compared to HCs, 26 differentially abundant metabolites (DAMs) belonging to amino acids, fatty acids, and their derivatives were identified in RRMS samples, including significantly upregulated LCFA palmitic acid (FA 16:0) (Padj < 0.001), MUFA oleic acid (FA 18:1) (Padj < 0.05), PUFA arachidonic acid (FA 20:4) (Padj < 0.01), and significantly downregulated dodecanoic acid (FA 12:0) (Padj < 0.01). These DAMs were mainly enriched in amino acid, fatty acid, and lipid synthesis/metabolism pathways. Additionally, the circulating levels of pro-inflammatory factors TNF-α and IL17A were significantly elevated (Padj < 0.001), while the concentrations of chemokines such as IL1RA, CCL2, CCL3, CCL4, CCL5, PDGFB, IL7, CXCL8, IL9, and IL12A were significantly reduced (Padj < 0.01) in RRMS compared to HC samples. Linear regression analysis showed significant positive correlations between FA 20:4 and IL17A (r = 0.370, Padj < 0.05), and significant negative correlations between FA 16:0 and PDGFB (r = -0.339, Padj < 0.05). Receiver operating characteristic (ROC) curve analysis indicated that individual fatty acids (e.g., FA 12:0 [AUC=0.881], FA 18:1 [AUC=0.833], FA 16:0 [AUC=0.881]) have high potential for predicting RRMS, with higher accuracy, specificity, and sensitivity when combining two (FA 12:0 and FA 18:1 [AUC=0.929]) or four (FA 12:0, FA 18:1, FA 16:0, and FA 20:4 [AUC=0.952]) fatty acids.

Conclusion: Our results uncover the aberrant metabolic features of LCFAs and potential biomarkers in RRMS patients, and the interactive network and key molecular nodes between LCFAs and peripheral immune-inflammatory responses. The interplay between LCFAs and immuno-inflammation may drive the migration of inflammatory events from the periphery to the CNS, reigniting CNS neuroinflammation and causing RRMS relapses. These findings offer valuable insights for RRMS diagnosis and novel therapeutic development.

INSTRUMENT(S): Liquid Chromatography MS -

PROVIDER: MTBLS13949 | MetaboLights | 2026-03-23

REPOSITORIES: MetaboLights