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

生物标志物 疾病 脑脊液 帕金森病 神经炎症 氧化应激 医学 神经黑素 α-突触核蛋白 生物信息学 神经学 病理 内科学 精神科 生物 黑质 生物化学
作者
Andreas Dammann Andersen,Michael Binzer,Egon Stenager,Jan Bert Gramsbergen
出处
期刊:Acta Neurologica Scandinavica [Wiley]
卷期号:135 (1): 34-56 被引量:98
标识
DOI:10.1111/ane.12590
摘要

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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