ReviewJournal of neural transmission (Vienna, Austria : 1996)2026
Detecting the seeds of Parkinson's disease: the evolution of α‑synuclein seed amplification assay as a clinical biomarker.
Review in Journal of neural transmission (Vienna, Austria : 1996), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by α-synuclein (α-syn) aggregation, but diagnosis is still largely clinical and often delayed. α-syn seed amplification assays (α-syn SAAs), including real-time quaking-induced conversion (RT-QuIC) and protein misfolding cyclic amplification (PMCA), have enabled sensitive detection of misfolded α-syn, offering a promising biomarker for early and objective diagnosis. This narrative review evaluates the development and diagnostic performance of α-syn SAA across different biological specimens in PD and related synucleinopathies. Literature from 2016 to 2026 was reviewed across major databases, focusing on studies addressing assay methodology, diagnostic accuracy, and clinical application, with narrative synthesis. α-syn SAAs demonstrate consistently high diagnostic accuracy, with pooled sensitivity ranging from approximately 0.86-0.88 and specificity from 0.92 to 0.96. Cerebrospinal fluid remains the most validated specimen; however, peripheral tissues such as skin, blood, and olfactory or gastrointestinal samples show increasing diagnostic utility with comparable specificity in several studies. Large cohorts confirm strong performance in both symptomatic and prodromal PD. Kinetic and strain differences also support disease stratification and differentiation among synucleinopathies. α-syn SAA represents a major advancement in the biomarker landscape of PD, enabling direct detection of misfolded protein pathology across multiple biospecimens with high diagnostic accuracy. Key limitations include lack of standardization, but ongoing advances support its clinical translation and integration into molecular classification frameworks.
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