# Progression and natural history of Atypical Parkinsonism (ATPARK): Protocol for a longitudinal follow-up study from an underrepresented population

## TL;DR

This study tracks the progression of atypical Parkinsonian disorders in an underrepresented population to better understand their causes and improve diagnosis and treatment.

## Contribution

The study introduces a longitudinal dataset from an underrepresented population to explore molecular mechanisms and disease progression in atypical Parkinsonism.

## Key findings

- The study will collect comprehensive molecular and clinical data from 400 APS patients over time.
- Machine learning models will be developed to aid in early diagnosis and differentiation of APS types.
- Longitudinal data will enhance understanding of symptom progression and management in APS.

## Abstract

Atypical Parkinsonian Syndromes (APS) form the third largest group of neurodegenerative disorders including Progressive Supranuclear Palsy (PSP), Multiple System Atrophy (MSA), and Corticobasal Syndrome (CBS). These conditions are characterized by rapid progression, poor prognosis, low survival rates, and limited treatment options. Few studies have suggested that genetic, environmental factors and inflammation contribute to the pathobiology of these complex disorders, however, the etiology of disease and progression remains unclear.

A multicenter prospective longitudinal (3-time point) study will be conducted with a total sample size of 400 across all the groups (PSP, MSA, CBS). Patients with APS will be recruited after a detailed evaluation by movement disorder specialists and obtaining valid informed consent. The socio-demographic data and whole exome sequencing will be performed only at the baseline. Non-invasive procedures such as neurological and cognitive assessments, sleep quality assessments including polysomnography, brain imaging, and retinal imaging will be conducted at each time point. In addition, gene expressions, methylation patterns, inflammatory cytokines, disease-associated pathological proteins (Tau, pTau-181, α-synuclein and β-amyloid), non-targeted proteomics, skin biopsy, and iPSC will be performed at each time point eventually. The statistical analysis will be performed, followed by the developing of machine learning (ML) models.

This unique native dataset in APS will enhance our understanding of the molecular mechanisms driving pathological protein aggregation and disease progression. Furthermore, the longitudinal design of the study enables a detailed examination of symptom development, progression, and management. The ML models combined with advanced imaging techniques will aid in early diagnosis, differentiation among APS types, and the development of future clinical trials and treatment strategies.