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

R Ravi Yadav S Saikat Dey R Ravichandiran Kumar A Athira P. Mohanan G Geethu T. Vasudevan M Manasi Harish N Nitish Kamble V Vikram V. Holla R Rohan R. Mahale P Pooja Mailankody M Monojit Debnath J Jitender Saini K Keshav Kumar (New Chemistry Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore 560064, Karnataka, India) A Anita Mahadevan S Sarada Subramanian P Phalguni Alladi I Indrani Datta B Binu V. Sreekumarannair P Priya Thomas A Anish Mehta A Albert Stezin M Madhura Ingalhalikar S Sweta Ramdas D Deepthi R. Bathula P Pramod Kumar Pal

Abstract

Background 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. Methods 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. Expected outcomes and conclusion 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.

Article Details

Journal PLoS ONE
Volume / Issue Vol. 20, Issue 6
Published June 26, 2025
Pages e0325624
ISSN 1932-6203
Publisher Public Library of Science

Journal Info

PLoS ONE

Public Library of Science

ISSN: 1932-6203 Open Access Health Sciences

Authors (25)

R

Ravi Yadav

S

Saikat Dey

R

Ravichandiran Kumar

A

Athira P. Mohanan

G

Geethu T. Vasudevan

M

Manasi Harish

N

Nitish Kamble

V

Vikram V. Holla

R

Rohan R. Mahale

P

Pooja Mailankody

M

Monojit Debnath

J

Jitender Saini

K

Keshav Kumar

New Chemistry Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore 560064, Karnataka, India

A

Anita Mahadevan

S

Sarada Subramanian

P

Phalguni Alladi

I

Indrani Datta

B

Binu V. Sreekumarannair

P

Priya Thomas

A

Anish Mehta

A

Albert Stezin

M

Madhura Ingalhalikar

S

Sweta Ramdas

D

Deepthi R. Bathula

P

Pramod Kumar Pal