Clinical diagnosis of Parkinson's disease (PD), multiple system atrophy (MSA) and dementia with Lewy bodies (DLB) is challenging, especially in the early stages. Each disease is associated with distinct conformers of misfolded alpha-synuclein (maS) which form typical protein aggregates in the brain and represent key disease biomarkers. Thus, detection and characterization of intracerebral maS aggregates allow a definite diagnosis. The recent development of ultrasensitive assays enabled the detection of maS and other potential new biomarkers in peripheral tissues, although with several limitations. Here, the investigators propose to combine the expertise of leading and young researchers in the field of neurology, structural and molecular biology, biophysics and machine learning to perform ultrasensitive and multi-omics analyses of olfactory mucosa (OM), blood and urine of PD, MSA and DLB patients for detecting and characterizing key peripheral biomarkers allowing accurate disease recognition.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Identify a predictive model for the diagnosis of PD, MSA and DLB
Timeframe: 3 years