Opportunities and challenges of alpha-synuclein as a potential biomarker for Parkinson’s disease and other synucleinopathies

Abstract

AbstractParkinson’s disease (PD), the second most common progressive neurodegenerative disease, develops and progresses for 10–15 years before the clinical diagnostic symptoms of the disease are manifested. Furthermore, several aspects of PD pathology overlap with other neurodegenerative diseases (NDDs) linked to alpha-synuclein (aSyn) aggregation, also called synucleinopathies. Therefore, there is an urgent need to discover and validate early diagnostic and prognostic markers that reflect disease pathophysiology, progression, severity, and potential differences in disease mechanisms between PD and other NDDs. The close association between aSyn and the development of pathology in synucleinopathies, along with the identification of aSyn species in biological fluids, has led to increasing interest in aSyn species as potential biomarkers for early diagnosis of PD and differentiate it from other synucleinopathies. In this review, we (1) provide an overview of the progress toward mapping the distribution of aSyn species in the brain, peripheral tissues, and biological fluids; (2) present comparative and critical analysis of previous studies that measured total aSyn as well as other species such as modified and aggregated forms of aSyn in different biological fluids; and (3) highlight conceptual and technical gaps and challenges that could hinder the development and validation of reliable aSyn biomarkers; and (4) outline a series of recommendations to address these challenges. Finally, we propose a combined biomarker approach based on integrating biochemical, aggregation and structure features of aSyn, in addition to other biomarkers of neurodegeneration. We believe that capturing the diversity of aSyn species is essential to develop robust assays and diagnostics for early detection, patient stratification, monitoring of disease progression, and differentiation between synucleinopathies. This could transform clinical trial design and implementation, accelerate the development of new therapies, and improve clinical decisions and treatment strategies.