Lysosomal and synaptic dysfunction markers in longitudinal cerebrospinal fluid spanning from healthy subjects to prodromal and manifest Parkinson’s disease

Author:

Bartl Michael1ORCID,nilsson johanna2,Dakna Mohammed3,Weber Sandrina4ORCID,Schade Sebastian5ORCID,Xylaki Mary6ORCID,Gomes Barbara7,Ernst Marielle8,Muntean Maria-Lucia9,Sixel-Döring Friederike10,Trenkwalder Claudia11,Zetterberg Henrik12,Brinkmalm Ann13,Mollenhauer Brit3

Affiliation:

1. University Medical Center Göttingen

2. University of Gothenburg

3. Georg-August University Medical Center

4. University Goettingen

5. Paracelsus-Elena-Klinik Kassel

6. Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen

7. Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg

8. Institute of Diagnostic and Interventional Neuroradiology, University Medical Center Goettingen

9. Paracelsus-Elena-Klinik

10. Paracelsus-Elena-Klinik Kassel and Neurologische Klinik, Philipps-University

11. Georg August University Göttingen

12. Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy

13. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital

Abstract

Abstract Lysosomal and synaptic dysfunctions are hallmarks in multiple neurodegenerative diseases including Alzheimer’s disease and Parkinson’s disease (PD) and could be relevant from a biomarker perspective. Biomarker data on prodromal and early PD are not yet available. We performed targeted mass spectrometry measurements cross-sectionally and longitudinally over 10 years with an established biomarker panel, assessing autophagy and synaptic function in cerebrospinal fluid (CSF) in prodromal subjects with isolated REM sleep behavior disorder (iRBD), drug-naïve de novo PD subjects at baseline, and sex- and age-matched healthy controls. Multiple markers of autophagy, synaptic plasticity, and secretory pathways showed reduced expression in PD and iRBD compared to controls. Machine learning identified neuronal pentraxin receptor and neurosecretory protein VGF as the most relevant for discriminating between groups. CSF levels of LAMP2, neuronal pentraxins, and syntaxins correlated with clinical progression and showed predictive potential for motor- and non-motor symptoms as a valid basis for future drug trials.

Publisher

Research Square Platform LLC

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