4.7 Article

Identifying subtypes of mild cognitive impairment in Parkinson's disease using cluster analysis

Journal

JOURNAL OF NEUROLOGY
Volume 267, Issue 11, Pages 3213-3222

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s00415-020-09977-z

Keywords

Parkinson's disease; Mild cognitive impairment; Cluster analysis

Funding

  1. Royal Brisbane and Woman's Hospital Foundation
  2. Lions Medical Research Foundation

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Introduction The concept of Mild Cognitive Impairment (MCI) in Parkinson's disease (PD) has shown the potential for identifying at-risk dementia patients. Identifying subtypes of MCI is likely to assist therapeutic discoveries and better clinical management of patients with PD (PWP). Recent cluster-based approaches have demonstrated dominance in memory and executive impairment in PD. The present study will further explore the role of memory and executive impairment and associated clinical features in non-demented PWP. Method A K-means cluster analysis was performed on ten frontal and posterior cognitive variables derived from a dataset of 85 non-demented PWP. The resulting cluster structure was chosen based on quantitative, qualitative, theoretical, and clinical validity. Cluster profiles were then created through statistical analysis of cognitive and clinical/demographic variables. A descriptive analysis of each cluster's performance on a comprehensive PD-MCI diagnostic battery was also explored. Results The resulting cluster structure revealed four distinct cognitive phenotypes: (1) frontal-dominant impairment; (2) posterior-cortical-dominant impairment; (3) global impairment, and (4) cognitively intact. Demographic profiling revealed significant differences in the age, gender split, global cognitive ability, and motor symptoms between these clusters. However, there were no significant differences between the clusters on measures of depression, apathy, and anxiety. Conclusion These results validate the existence of distinct cognitive phenotypes within PD-MCI and encourage future research into their clinical trajectory and neuroimaging correlates.

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