Movement disorders : official journal of the Movement Disorder Society
September 18, 2026 · via Europe PMC
Serum Metabolomic Profiles Unveil Biomarkers and Core Metabolic Pathways in Multiple System Atrophy Patients.
Wan L, Chen Z, Wan N, +16 more, Jiang H.
AI summary — for doctors, generated from the abstract only
Serum metabolomics identified a riboflavin-xanthine panel that distinguished multiple system atrophy from controls in internal and external cohorts.
What was studied
An untargeted serum metabolomics study of 85 MSA patients and 85 controls, with targeted validation in internal (37 MSA, 36 controls) and external (44 MSA, 44 PD, 42 controls) cohorts and cell experiments.
Key findings
- 182 differential metabolites across six pathways, including arginine/proline, purine and riboflavin metabolism
- Riboflavin-xanthine panel AUC 0.876 internal and 0.943 external for MSA vs controls
- Adding xanthine to NfL raised MSA vs PD AUC from 0.821 to 0.890
- Riboflavin improved viability in alpha-synuclein-overexpressing cell models
Clinical relevance
Identifies candidate MSA biomarkers and pathways; requires further validation before diagnostic use.
Who should care
Limitations
Modest cohort sizes and cross-sectional sampling; cell findings are preliminary.
Serum metabolites such as riboflavin and xanthine may aid MSA diagnosis research.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Multiple system atrophy (MSA) is an adult-onset, fatal, neurodegenerative disease lacking mechanistic understanding, early diagnosis, and specific treatments. Metabolomics has been widely used in neurodegenerative diseases for biomarker identification and pathophysiology exploration; however its application in MSA is extremely limited.
Objectives
To investigate the metabolomic landscape, core metabolic pathways, and novel biomarkers for MSA.
Methods
Untargeted metabolomics (ultra-high-performance liquid chromatography-Q-Exactive HF mass spectrometry [UHPLC-QE-MS]) was performed on serum samples from 85 MSA patients and 85 healthy controls (HCs). Candidate metabolites were validated via targeted metabolomics in an internal cohort (37 MSA, 36 HCs) and an external cohort (44 MSA, 44 Parkinson's disease [PD], 42 HCs). In vitro validation was performed using MO3.13 cells and SH-SY5Y cells overexpressing hSNCA.
Results
We identified 112 and 70 differential metabolites (DMs) in positive and negative modes, respectively. Pathway analysis revealed six significant pathways, including arginine and proline metabolism; pentose phosphate pathway; valine, leucine, and isoleucine biosynthesis; purine metabolism; arginine biosynthesis; and riboflavin metabolism. A riboflavin-xanthine panel effectively distinguished MSA from HCs (internal validation: [area under the curve [AUC] = 0.876), and this performance was confirmed in the external cohort (AUC = 0.943). Reduced xanthine levels in MSA patients compared with HCs and PD patients slightly enhanced the diagnostic power of neurofilament light chain for MSA-PD differentiation, boosting the AUC from 0.821 to 0.890. Both riboflavin and xanthine supplementation enhanced viability and reduced apoptosis in hSNCA-overexpressing MO3.13 cells, while only riboflavin exerted therapeutic effects in hSNCA-overexpressing SH-SY5Y cells.
Conclusions
This study defined the serum metabolomic signature of MSA and highlighted novel biomarkers, pathological mechanisms, and therapeutic targets deserving further validation. © 2026 International Parkinson and Movement Disorder Society.
DOI
10.1002/mds.70541
PMID
42760624
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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