Yuting Zhang, Hao Zhang, Hao Zhang, Weiguo Liu, Weiguo Liu, Ya-Ting Chen, Hui-Ying Ren, Maoqin He, Yan-Xi Zhang, Yan-Xi Zhang, Yuping Fan, Wei Liu, Menghan Li, Ya-Xin Shi, Qiu-Yi Tang, Yi Liu, Sheng-Hua Zong, Anke Wesselius, Maurice P. Zeegers, Yun Xu, Cheng-Xiao Ma, Yang Zhang, Yang Zhang, Evan Y. Yu
Parkinson's disease (PD) remains insidious and clinically elusive at early stages due to the lack of precise, non-invasive biomarkers. Given their ability to cross the blood-brain barrier, extracellular vesicles (EVs) offer a promising platform for biomarker discovery in neurodegeneration. Using an affinity-based EV isolation method, we profile EV proteomes and lipidomes from plasma across life stages, followed by targeted validation via parallel reaction monitoring (PRM). We identify both age-independent and age-dependent EV biomarkers predictive of prodromal PD and capable of distinguishing PD from multiple system atrophy (MSA). Functional analyses reveal protein-lipid interactions contributing to PD pathophysiology, including lipid-mediated modulation of APOE signaling. Machine learning models integrating putative EV proteins achieve robust classification across age groups. Our findings demonstrate the diagnostic potential of EV-derived molecules for early PD detection and mechanistic insights, advancing a clinically applicable, non-invasive strategy for risk stratification and disease intervention.