1. Academic Validation
  2. Promising Metabolite Profiles in the Plasma and CSF of Early Clinical Parkinson's Disease

Promising Metabolite Profiles in the Plasma and CSF of Early Clinical Parkinson's Disease

  • Front Aging Neurosci. 2018 Mar 5;10:51. doi: 10.3389/fnagi.2018.00051.
Daniel Stoessel 1 2 3 Claudia Schulte 4 Marcia C Teixeira Dos Santos 5 Dieter Scheller 6 Irene Rebollo-Mesa 7 Christian Deuschle 4 Dirk Walther 2 3 Nicolas Schauer 1 Daniela Berg 4 8 Andre Nogueira da Costa 5 Walter Maetzler 4 8
Affiliations

Affiliations

  • 1 Metabolomic Discoveries GmbH, Potsdam, Germany.
  • 2 Department of Biochemistry and Biology, Universität Potsdam, Potsdam, Germany.
  • 3 Max Planck Institute für Molekulare Pflanzenphysiologie, Potsdam, Germany.
  • 4 Department of Neurodegeneration, German Center for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany.
  • 5 Experimental Medicine and Diagnostics, Global Exploratory Development, UCB Biopharma SPRL, Brussels, Belgium.
  • 6 Consultancy Neuropharm, Neuss, Germany.
  • 7 Exploratory Statistics, Global Exploratory Development, UCB Pharma SA, Slough, United Kingdom.
  • 8 Department of Neurology, Christian-Albrechts-University Kiel, Kiel, Germany.
Abstract

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. These metabolites 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.

Keywords

CSF; biomarker; machine learning; neurodegeneration; plasma; untargeted metabolomics.

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