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Metabolomic profiles for primary progressive multiple sclerosis stratification and disease course monitoring

  • 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.show moreshow less

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Author details:Daniel StoesselORCiDGND, Jan-Patrick Stellmann, Anne Willing, Birte Behrens, Sina C. Rosenkranz, Sibylle C. Hodecker, Klarissa H. Stürner, Stefanie Reinhardt, Sabine Fleischer, Christian Deuschle, Walter Maetzler, Daniela Berg, Christoph Heesen, Dirk WaltherORCiDGND, Nicolas Schauer, Manuel A. Friese, Ole PlessORCiD
URN:urn:nbn:de:kobv:517-opus4-426307
DOI:https://doi.org/10.25932/publishup-42630
ISSN:1866-8372
Title of parent work (English):Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
Publication series (Volume number):Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (694)
Publication type:Postprint
Language:English
Date of first publication:2019/04/05
Publication year:2018
Publishing institution:Universität Potsdam
Release date:2019/04/05
Tag:LysoPC(20:0); MS neurodegeneration; PPMS; biomarker; untargeted metabolomics
Issue:694
Number of pages:13
Source:Frontiers in Human Neuroscience 12 (2018) Art. 226 DOI: 10.3389/fnhum.2018.00226
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät
DDC classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer review:Referiert
Publishing method:Open Access
Grantor:Frontiers
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Bibliographieeintrag der Originalveröffentlichung/Quelle
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