TY - JOUR A1 - Stoessel, Daniel A1 - Schulte, Claudia A1 - dos Santos, Marcia C. Teixeira A1 - Scheller, Dieter A1 - Rebollo-Mesa, Irene A1 - Deuschle, Christian A1 - Walther, Dirk A1 - Schauer, Nicolas A1 - Berg, Daniela A1 - da Costa, Andre Nogueira A1 - Maetzler, Walter T1 - Promising Metabolite Profiles in the Plasma and CSF of Early Clinical JF - Frontiers in Aging Neuroscience N2 - Parkinson's disease (PD) shows high heterogeneity with regard to the underlying molecular pathogenesis involving multiple pathways and mechanisms. Diagnosis is still challenging and rests entirely on clinical features. Thus, there is an urgent need for robust diagnostic biofluid markers. Untargeted metabolomics allows establishing low-molecular compound biomarkers in a wide range of complex diseases by the measurement of various molecular classes in biofluids such as blood plasma, serum, and cerebrospinal fluid (CSF). Here, we applied untargeted high-resolution mass spectrometry to determine plasma and CSF metabolite profiles. We semiquantitatively determined small-molecule levels (<= 1.5 kDa) in the plasma and CSF from early PD patients (disease duration 0-4 years; n = 80 and 40, respectively), and sex-and age-matched controls (n = 76 and 38, respectively). We performed statistical analyses utilizing partial least square and random forest analysis with a 70/30 training and testing split approach, leading to the identification of 20 promising plasma and 14 CSF metabolites. The semetabolites differentiated the test set with an AUC of 0.8 (plasma) and 0.9 (CSF). Characteristics of the metabolites indicate perturbations in the glycerophospholipid, sphingolipid, and amino acid metabolism in PD, which underscores the high power of metabolomic approaches. Further studies will enable to develop a potential metabolite-based biomarker panel specific for PD KW - biomarker KW - untargeted metabolomics KW - neurodegeneration KW - plasma KW - CSF KW - machinelearning Y1 - 2018 U6 - https://doi.org/10.3389/fnagi.2018.00051 SN - 1663-4365 VL - 10 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Stoessel, Daniel A1 - Stellmann, Jan-Patrick A1 - Willing, Anne A1 - Behrens, Birte A1 - Rosenkranz, Sina C. A1 - Hodecker, Sibylle C. A1 - Stuerner, 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 JF - Frontiers in human neuroscienc 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. KW - untargeted metabolomics KW - biomarker KW - PPMS KW - MS neurodegeneration KW - LysoPC(20:0) Y1 - 2018 U6 - https://doi.org/10.3389/fnhum.2018.00226 SN - 1662-5161 VL - 12 PB - Frontiers Research Foundation CY - Lausanne ER - 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426307 SN - 1866-8372 IS - 694 ER -