Daniel Stoessel, Jan-Patrick Stellmann, Anne Willing, Birte Behrens, Sina C. Rosenkranz, Sibylle C. Hodecker, Klarissa H. Stuerner, Stefanie Reinhardt, Sabine Fleischer, Christian Deuschle, Walter Maetzler, Daniela Berg, Christoph Heesen, Dirk Walther, Nicolas Schauer, Manuel A. Friese, Ole Pless
- Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing remitting subtype (RRMS), primary neurodegenerative disease (Parkinson’s disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC.Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing remitting subtype (RRMS), primary neurodegenerative disease (Parkinson’s disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC. Finally, we investigated these metabolites in a longitudinal cohort of PPMS patients over a 24-month period. PLS-DA yielded predictive models for classification along with a set of 20 PPMS-specific informative metabolite markers. These metabolites suggest disease-specific alterations in glycerophospholipid and linoleic acid pathways. Notably, the glycerophospholipid LysoPC(20:0) significantly decreased during the observation period. These findings show potential for diagnosis and disease course monitoring, and might serve as biomarkers to assess treatment efficacy in future clinical trials for neuroprotective MS therapies.…
MetadatenAuthor details: | Daniel StoesselORCiDGND, Jan-Patrick Stellmann, Anne Willing, Birte Behrens, Sina C. Rosenkranz, Sibylle C. Hodecker, Klarissa H. Stuerner, Stefanie Reinhardt, Sabine Fleischer, Christian Deuschle, Walter MaetzlerORCiD, Daniela BergORCiD, Christoph Heesen, Dirk WaltherORCiDGND, Nicolas Schauer, Manuel A. FrieseORCiD, Ole PlessORCiD |
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DOI: | https://doi.org/10.3389/fnhum.2018.00226 |
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ISSN: | 1662-5161 |
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Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/29915533 |
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Title of parent work (English): | Frontiers in human neuroscienc |
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Publisher: | Frontiers Research Foundation |
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Place of publishing: | Lausanne |
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Publication type: | Article |
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Language: | English |
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Date of first publication: | 2018/06/04 |
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Publication year: | 2018 |
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Release date: | 2021/11/22 |
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Tag: | LysoPC(20:0); MS neurodegeneration; PPMS; biomarker; untargeted metabolomics |
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Volume: | 12 |
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Number of pages: | 13 |
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Funding institution: | Bundesministerium fur Bildung und Forschung (BMBF)Federal Ministry of Education & Research (BMBF) [16GW0082, 16GW0103K, 16GW0053, 16GW0054]; Hertie-Institut fur klinische Hirnforschung (HIH); Deutsches Zentrum fur Neurodegenerative Erkrankungen (DZNE) |
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Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät |
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DDC classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
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License (German): | CC-BY - Namensnennung 4.0 International |
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External remark: | Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 694 |
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