bioRxivPathology
Early diagnosis of Parkinson's disease: A cross-species biomarker
D. Mallet, T. Dufourd, M. Decourt, C. Carcenac, P. Bossu, L. Verlin, P. O. Fernagut, M. Benoit-Marand, G. Spalletta, E. Barbier, S. Carnicella, V. Sgambato, F. Fauvelle, S. Boulet
Abstract
BackgroundCare management of Parkinsons disease (PD) patients currently remains symptomatic, especially because diagnosis relying on the expression of the cardinal motor symptoms is made too late. Detecting PD earlier therefore represents a key step for developing therapies able to delay or slow down its progression. MethodsWe investigated metabolic markers in three different animal models of PD, mimicking different phases of the disease assessed by behavioral and histological evaluation, and in 2 cohorts of de novo PD patients (n = 95). Serum and brain tissue samples were analyzed by nuclear magnetic resonance spectroscopy and data submitted to advanced multivariate statistics. ResultsOur translational strategy reveals common metabolic dysregulations in serum of the different animal models and PD patients. Some of them were mirrored in the tissue samples, possibly reflecting pathophysiological mechanisms associated with PD development. Interestingly, some metabolic dysregulations appeared before motor symptom emergence, and could represent early biomarkers of PD. Finally, we built a composite biomarker with a combination of 6 metabolites. This biomarker discriminated animals mimicking PD from controls, even from the first, non-motor signs and very interestingly, also discriminated PD patients from healthy subjects. ConclusionFrom our translational study which included three animal models and two PD patient cohorts, we propose a promising composite biomarker exhibiting a high level of predictivity for PD diagnosis in its early phase, before motor symptoms appearance. FundingsANR, DOPALCOMP, Institut National de la Sante et de la Recherche Medicale, Grenoble Alpes University.
- organism: mouse
- organism: human
- assay: metabolomics
- research focus: differential expression
- approach: statistical model
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