Journal
FRONTIERS IN AGING NEUROSCIENCE
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2018.00051
Keywords
biomarker; untargeted metabolomics; neurodegeneration; plasma; CSF; machinelearning
Categories
Funding
- Boehringer Ingelheim
- Lundbeck Inc.
- Novartis
- GlaxoSmithKline
- UCB/SCHWARZ PHARMA
- Merck Serono
- Johnson Johnson
- Teva Pharmaceutical Industries Ltd.
- Janssen
- Solvay Pharmaceuticals, Inc.
- Abbott
- Boehringer
- UCB
- Michael J Fox Foundation
- BMBF
- dPV (German Parkinson's disease association)
- Neuroallianz
- DZNE
- Center of Integrative Neurosciences
- European Union
- Michael J. Fox Foundation
- Robert Bosch Foundation
- Neuroalliance
- Lundbeck
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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.
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