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Related Experiment Video

Updated: Jun 3, 2025

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
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A predictive model for longitudinal cognitive subtypes in Parkinson's disease.

Meng-Yun Wang1, Ran Xin2, Jing-Yu Shao3

  • 1Department of Neurology, Henan University People's Hospital, Zhengzhou, 450003, Henan, China.

Neurological Sciences : Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
|January 8, 2025
PubMed
Summary

Predicting cognitive decline in Parkinson's disease (PD) is crucial. This study identified two patient subtypes, stable and deteriorating, and developed a model to predict cognitive decline in early PD.

Keywords:
Cognitive subtypesLatent class mixed modelParkinson’s diseasePredictive model

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Area of Science:

  • Neuroscience
  • Neurology
  • Clinical Research

Background:

  • Longitudinal cognitive changes in Parkinson's disease (PD) are highly variable.
  • Predicting cognitive trajectories in early PD is essential for patient counseling and clinical trials.

Purpose of the Study:

  • To identify distinct longitudinal cognitive subtypes in early Parkinson's disease.
  • To develop a predictive model for cognitive deterioration in early PD patients.

Main Methods:

  • Utilized data from 337 early PD patients in the Parkinson's Progression Markers Initiative (PPMI) database with a 6-year follow-up.
  • Employed Latent Class Mixed Models (LCMM) to identify cognitive trajectories and constructed a nomogram using baseline clinical variables.
  • Assessed cognitive function using the Montreal Cognitive Assessment (MoCA).

Main Results:

  • Identified two cognitive subtypes: cognitive stable (81.9%) and cognitive deteriorating (18.1%).
  • The deteriorating subtype showed poorer baseline cognition, older age, more severe motor/non-motor symptoms, higher serum NFL, and lower striatal DAT uptake.
  • A nomogram predictive model using six baseline variables achieved an AUC of 0.92, demonstrating high predictive accuracy.

Conclusions:

  • Two longitudinal cognitive subtypes were identified in early PD patients over 6 years.
  • Eighteen percent of early PD patients fall into a cognitive deterioration subtype.
  • A validated predictive model using six baseline variables can estimate the risk of cognitive deterioration in PD.