ArticleFrontiers in neurology2024
Liquid chromatography coupled to mass spectrometry metabolomic analysis of cerebrospinal fluid revealed the metabolic characteristics of moyamoya disease.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Lipidomic profiling of the cerebrospinal fluid in moyamoya angiopathy patients.Orphanet journal of rare diseases · 2025Article
- Downregulation of IL-20RA in Cerebrospinal Fluid Associated with the Risk of Moyamoya Disease: A Molecular Signatures Analysis with an Inflammation Proteomics Landscape.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Metabolomics has found extensive applications in the field of neurological diseases, significantly contributing to their diagnosis and treatment. However, there has been limited research applying metabolomics to moyamoya disease (MMD). This study aims to investigate and identify differential metabolites associated with MMD. Methods: We employed a liquid chromatography coupled with mass spectrometry (LC-MS) approach, complemented by univariate and multivariate analyses, to discern metabolic biomarkers in cerebrospinal fluid samples. We then compared these biomarkers between MMD patients and healthy controls (Ctl). Results: Sixteen patients diagnosed with MMD via cerebral angiography and eight healthy controls were enrolled in this study. Comparative analyses, including univariate and multivariate analyses, correlation studies, heatmaps, Volcano Plots, and KEGG pathway enrichment, were performed between MMD patients and controls. As a result, we identified 129 significant differential metabolites in the cerebrospinal fluid between MMD patients and controls. These metabolic biomarkers are associated with various pathways, with notable involvement in purine and pyrimidine metabolism. Conclusion: Utilizing an LC-MS-based metabolomics approach holds promise for enhancing the clinical diagnosis of MMD. The identified biomarkers offer potential avenues for the development of novel diagnostic methods for MMD and offer fresh insights into the pathogenesis of the disease.
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