TY  - GEN
A1  - Stoessel, Daniel
A1  - Stellmann, Jan-Patrick
A1  - Willing, Anne
A1  - Behrens, Birte
A1  - Rosenkranz, Sina C.
A1  - Hodecker, Sibylle C.
A1  - Stürner, Klarissa H.
A1  - Reinhardt, Stefanie
A1  - Fleischer, Sabine
A1  - Deuschle, Christian
A1  - Maetzler, Walter
A1  - Berg, Daniela
A1  - Heesen, Christoph
A1  - Walther, Dirk
A1  - Schauer, Nicolas
A1  - Friese, Manuel A.
A1  - Pless, Ole
T1  - Metabolomic profiles for primary progressive multiple sclerosis stratification and disease course monitoring
T2  - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
N2  - 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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 694 
KW  - untargeted metabolomics
KW  - biomarker
KW  - PPMS
KW  - MS neurodegeneration
KW  - LysoPC(20:0)
Y1  - 2019
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/42630
UR  - https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-426307
SN  - 1866-8372
IS  - 694
ER  -