Parkinson's disease (PD) presents a complex challenge due to its progressive neurodegenerative nature, affecting various bodily systems. Despite decades of research, understanding its onset and progression remains unclear, complicating early diagnosis and treatment. Recent advances in PD pathophysiology suggest promising treatments to slow disease progression, yet reversing cellular degeneration remains elusive. With novel therapies emerging, the need for early detection tools is urgent. However, validated biomarkers for PD diagnosis are lacking, relying on subjective scales like Hoehn and Yahr or costly medical imaging techniques. The accumulation of misfolded α-Synuclein (α-Syn) proteins in PD pathology has sparked interest, but defining diagnostic roles requires further investigation. Recent findings of α-Syn in neuronal-derived extracellular vesicles (NDEVs) from PD patients suggest a potential for novel diagnostic methods. Our proposed project, VαMPiRE, aims to conduct a longitudinal study involving 600 PD and 600 non-PD participants using a cluster-adjusted case-control methodology, to explore α-Syn isoforms and related biomarkers in NDEVs for early PD detection. We plan to develop and validate an innovative in-vitro diagnostic (IVD) test capable of detecting PD's earliest stages and estimating disease prognosis and progression. Utilizing AI models to generate data analysis algorithms and collaboration with leading analytical laboratories and IVD manufacturers, we aim to ensure the reliability and feasibility of the developed prototype. Through consortium efforts, we envision licensing the generated intellectual property to drive the commercialization of our results. Two round of blood sample extractions will be performed within a 24-month gap to PD participants and a single baseline for non-PD controls. All participants will be regularly followed up during this 24-month period to monitor disease evolution and treatment, and non-PD controls developing the disease will be part of a third cohort (expected to be around 24 subjects according to 4% incidence) that will confirm the sensitivity of the test in asymptomatic subjects. The unique aspect of the project is that we anticipate being able to detect theses 4% of non-PD participants that will go on to develop the disease, therefore demonstrating the value of these biomarkers to identify PD early. The prototype will be validated for its discriminative capacity, using the first baseline set of PD and non-PD samples, and for its ability to detect the PD-progression comparing baseline and 24-months data plus blood samples. Improved early screening could allow for 270,000 new cases of PD to be detected earlier, improve the disease management of 9.4 M people currently diagnosed of PD and avoid losing a total of 5.8 million disability adjusted life years (DALYs) by 2028 leading also the development of better treatments.
The VαMPiRE study (Validation of α-synuclein Modifications in Parkinson's dIsoRder Evolution) is a multicenter, longitudinal observational study in the context of European Grant Horizon Europe (101156370-2) and is designed to validate specific α-synuclein (α-Syn) isoforms and their post-translational modifications as biomarkers for the early detection and progression monitoring of Parkinson's disease (PD). The study addresses the critical unmet need for a cost-effective, non-invasive diagnostic method by combining innovative biochemical analyses, artificial intelligence (AI)-driven data models, and comprehensive clinical assessments. This effort aligns with emerging global priorities to enhance the early diagnosis and personalized treatment of neurodegenerative diseases.
Study Type
OBSERVATIONAL
Enrollment
1,200
Participants' clinical histories will be reviewed at T0 (baseline) and T1 (24 months). They will also undergo a clinical assessment at T0 and T1. The assessments will be tailored based on whether the participant belongs to the PD or non-PD group, as outlined below: • MDS-UPDRS + H\&Y: Used to evaluate the neurological domain, applied to the PD group. • BERG: Used to assess balance and performance, applied to both PD and non-PD groups. • CIRS-G: Used to evaluate comorbidities, applied to both PD and non-PD groups. • PD-CRS: Used to assess cognitive function, applied to the PD group. • MMSE (temporal and spatial orientation only): Used to assess cognitive function, applied to both PD and non-PD groups. • PDQ-8: Used to evaluate quality of life, applied to the PD group. • PD-CFRS: Used for functional assessment, applied to the PD group. • GDS: Used to assess depression, applied to both PD and non-PD groups. Blood samples will be collected at T0 and T1, with 20 mL drawn per participant.
Aristotle University of Thessaloniki
Thessaloniki, Greece
NOT_YET_RECRUITINGCasa di Cura Igea
Milan, Mi, Italy
RECRUITINGInstytut Psychiatrii I Neurologii
Warsaw, Poland
NOT_YET_RECRUITINGAsociacion Parkinson Madrid
Madrid, Spain
NOT_YET_RECRUITINGQuantification of α-Synuclein Isoforms (-140, -126, -112, -98) and Their SUMOylated Forms in NDEVs from Blood Samples
The primary outcome measure involves the quantification of α-synuclein (α-Syn) isoforms (-140, -126, -112, -98) and their SUMOylated forms in neuronal-derived extracellular vesicles (NDEVs) isolated from blood samples. Advanced biochemical techniques, including lateral flow immunoassay coupled with digital readers, are employed for precise quantification. AI-driven data analysis evaluates correlations between biomarker levels and Parkinson's Disease (PD) progression. Clinical Implications: This outcome aims to validate α-Syn biomarkers for early PD diagnosis, offering a non-invasive, cost-effective alternative to current methods like DaT imaging. Reliable identification of α-Syn isoforms could improve early intervention strategies, slow disease progression, and support personalized treatment plans. Additionally, it could provide insights into disease mechanisms, enhancing future therapeutic developments and enabling scalable, accessible diagnostic solutions globally.
Time frame: Baseline (T0) and 24 months post-enrollment (T1).
Validation of AI-Generated predictive score for PD diagnosis
Development and validation of artificial intelligence (AI)-powered diagnostic algorithms utilizing multivariate biomarker data, clinical assessments, and demographic variables. The AI model generates a predictive score correlating α-Syn biomarker levels with disease onset, progression, and treatment response. The validation process evaluates the model's sensitivity, specificity, and predictive accuracy for Parkinson's Disease diagnosis.
Time frame: Baseline (T0), every 3 months through periodic surveys, and 24 months post-enrollment (T1).
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