Pioneering early diagnosis of α-synucleinopathies: innovative peripheral biomarkers analysis in olfactory mucosa, urine, and blood

Using innovative technologies, we aim to identify peripheral biomarkers for Parkinson’s disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB) by analyzing olfactory mucosa (OM), blood, and urine samples. This includes exploring disease-associated α-synuclein (αSynD) distribution and examining key proteins and molecules involved in these conditions with the aim of generating disease-specific biological fingerprints. Patients with PD (n=20), MSA (n=20), DLB (n=15), other neurodegenerative diseases (OND, n=10), and age-matched healthy subjects (HS, n=10) underwent olfactory function testing before OM, urine and blood sampling. An integrated analytical approach included: Seed Amplification Assay -SAA- (to assess αSynD distribution), Simoa SR-X (to measure NfL, p-Tau231, and p-Tau181 levels), ELISA (to investigate α-synuclein and synapsin-3 levels), and NTA (to analyze extracellular vesicles -EVs-, contained in urine and blood). Innovative recombinant α-synuclein proteins were produced for protein-NMR studies of SAA reaction products. Patients with PD and DLB showed the most severe olfactory impairment. Preliminary SAA results confirmed the presence of αSynD in OM of PD, MSA, and DLB patients, with higher sensitivity observed in PD and MSA compared to DLB. Simoa analysis showed a significant increase in plasma NfL levels in MSA patients compared to PD and HS, while no differences were found in p-Tau-181 and p-Tau-231 levels. NTA analysis of EVs from blood indicated a trend of higher concentration (particles/mL) and a greater ratio (concentration/size) in the PD, DLB, MSA, and OND groups compared to HS. Our preliminary findings highlight the potential of OM, blood and urine as sources of biomarkers that can be easily and periodically collected. These biomarkers are promising for advancing the clinical understanding of PD, MSA and DLB. With ongoing analysis and data integration of machine-learning model we will verify whether our approach can generate specific biological fingerprints for these diseases.