Titolo della tesi: Salivary biomarkers in de novo Parkinson’s disease: a combined approach through ELISA and RT-QuIC assays.
Background: Among neurological disorders, Parkinson’s disease (PD) is the fastest growing in prevalence, disability, and deaths. To reduce the burden in PD cases, biomarkers studies have been highly encouraged, in order to develop primary intervention strategies based on the pathological mechanisms underlying PD. Neurodegeneration in PD encompasses a complex pathogenesis involving alpha-synuclein (a-syn) aggregation but also tau aggregation, autophagy dysfunction and neuroinflammation. Involvement of these molecular pathways might be variable in individual patients thus contributing to PD clinical heterogeneity.
Objectives: To investigate potential salivary diagnostic and prognostic biomarkers in PD using a combined methodologic approach based on quantitative ELISA and Real-Time Quaking Induced Conversion (RT-QuIC) assays.
Methods: ELISA analysis was applied on saliva samples of 80 de novo PD patients and 62 healthy controls (HC) to measure total and oligomeric a-syn (disease biomarker), total-tau and phosphorylated-tau (axonal damage biomarkers), MAP-LC3 (autophagy biomarker) and TNFa (neuroinflammation biomarker). RT-QuIC analysis was applied on saliva samples of 41 de novo PD patients and 26 HC for the detection of seeding competent a-syn oligomers.
Results: Increased levels of oligomeric a-syn, total-tau, MAP-LC3beta and TNFa were found in saliva samples of de novo PD patients in comparison to HC. Phosphorylated-tau directly correlates with MAP-LC3, whereas it inversely correlates with TNFa in PD. Salivary MAP-LC3 levels showed an inverse correlation with non-motor symptoms severity in our PD cohort. Principal Component Analysis showed that molecular and clinical data clusterize separately in the de novo PD patients. ROC analysis revealed that salivary oligomeric a-syn and MAP-LC3beta levels have a diagnostic accuracy of about 100% in distinguishing de novo PD patients from HC. The diagnostic accuracy of total a-syn raised when it was combined with the other salivary biomarkers. Salivary RT-QuIC demonstrated a good diagnostic accuracy in distinguishing de novo PD patients from HC. Hierarchical clustering and linear regression showed a modest association between increased disease severity (evaluated with motor and non-motor scores) and a greater response in salivary RT-QuIC assay.
Conclusions: ELISA analysis of biomarkers targeting different molecular pathways and RT-QuIC analysis of a-syn seeding components performed in saliva, an easily accessible biofluid, can differentiate PD patients from HC since the early stage of the disease and may help in determining clinical PD subtypes.