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Cerebrospinal fluid biomarkers for Parkinson's disease - a systematic review

Journal

ACTA NEUROLOGICA SCANDINAVICA
Volume 135, Issue 1, Pages 34-56

Publisher

WILEY
DOI: 10.1111/ane.12590

Keywords

biomarker; cerebrospinal fluid; Parkinson's disease; Alpha-synuclein; metabolomics; Amyloid-beta; Tau-protein; neurofilament light chain

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Diagnosis of Parkinson's disease (PD) relies on clinical history and physical examination, but misdiagnosis is common in early stages. Identification of biomarkers for PD may allow early and more precise diagnosis and monitoring of dopamine replacement strategies and disease modifying treatments. Developments in analytical chemistry allow the detection of large numbers of molecules in plasma or cerebrospinal fluid, associated with the pathophysiology or pathogenesis of PD. This systematic review includes cerebrospinal fluid biomarker studies focusing on different disease pathways: oxidative stress, neuroinflammation, lysosomal dysfunction and proteins involved in PD and other neurodegenerative disorders, focusing on four clinical domains: their ability to (1) distinguish PD from healthy subjects and other neurodegenerative disorders as well as their relation to (2) disease duration after initial diagnosis, (3) severity of disease (motor symptoms) and (4) cognitive dysfunction. Oligomeric alpha-synuclein might be helpful in the separation of PD from controls. Through metabolomics, changes in purine and tryptophan metabolism have been discovered in patients with PD. Neurofilament light chain (NfL) has a significant role in distinguishing PD from other neurodegenerative diseases. Several oxidative stress markers are related to disease severity, with the antioxidant urate also having a prognostic value in terms of disease severity. Increased levels of amyloid and tau-proteins correlate with cognitive decline and may have prognostic value for cognitive deficits in PD. In the future, larger longitudinal studies, corroborating previous research on viable biomarker candidates or using metabolomics identifying a vast amount of potential biomarkers, could be a good approach.

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