Fatty Acid and Cytokine Profiles in Multiple Sclerosis Patients

Plasma metabolomics data from 20 relapsing-remitting multiple sclerosis (RRMS) patients and 22 healthy controls. The study identifies 26 differentially abundant metabolites, including long-chain fatty acids like palmitic acid (FA 16:0) and oleic acid (FA 18:1), and measures levels of inflammatory cytokines such as TNF-α and IL17A.

Use Cases

• Analyze correlations between specific fatty acids (e.g., FA 20:4) and cytokine levels (e.g., IL17A) to model immune-metabolic interactions in RRMS.
• Train a classifier using fatty acid biomarkers like FA 12:0 and FA 18:1 to predict RRMS diagnosis, leveraging reported AUC values.
• Investigate the enrichment of differentially abundant metabolites in amino acid and fatty acid synthesis pathways to understand metabolic dysregulation.
• Compare the circulating levels of pro-inflammatory factors (TNF-α, IL17A) and chemokines (CCL2, CCL3, PDGFB) between patient and control groups.

Strengths

• Includes data from a controlled study with 20 RRMS patients and 22 age- and sex-matched healthy controls.
• Identifies 26 differentially abundant metabolites with statistical significance (P_adj values reported).
• Reports specific correlation coefficients (e.g., r=0.370 between FA 20:4 and IL17A) and diagnostic AUC values (up to 0.952).

Limitations

• Small sample size of 42 total subjects limits statistical power and generalizability of findings.
• Data is presented in a document format (DOCX), not a structured, machine-readable table, requiring extraction for analysis.
• Single time-point measurement from plasma samples provides a cross-sectional view without longitudinal relapse tracking.

Provenance

Source

Figshare, authored by Xiao-xi Zhu.

Collection Method

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics on plasma samples.

Time Range

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