Literature DB >> 32535681

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

Dana Pourzinal1,2, Ji Hyun J Yang1, Gerard J Byrne1,3, John D O'Sullivan1,4, Leander Mitchell2, Katie L McMahon5, David A Copland1,6, Nadeeka N Dissanayaka7,8,9.   

Abstract

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.

Entities:  

Keywords:  Cluster analysis; Mild cognitive impairment; Parkinson’s disease

Mesh:

Year:  2020        PMID: 32535681     DOI: 10.1007/s00415-020-09977-z

Source DB:  PubMed          Journal:  J Neurol        ISSN: 0340-5354            Impact factor:   4.849


  3 in total

1.  Early Alert of Elderly Cognitive Impairment using Temporal Streaming Clustering.

Authors:  Omar A Ibrahim; Sunyang Fu; Maria Vassilaki; Ronald C Petersen; Michelle M Mielke; Jennifer St Sauver; Sunghwan Sohn
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2021-12

2.  Anxiety disorders are associated with verbal memory impairment in patients with Parkinson's disease without dementia.

Authors:  Nadeeka N Dissanayaka; Elana J Forbes; Ji Hyun J Yang; Dana Pourzinal; John D O'Sullivan; Leander K Mitchell; David A Copland; Katie L McMahon; Gerard J Byrne
Journal:  J Neurol       Date:  2021-08-04       Impact factor: 4.849

3.  Mapping Actuarial Criteria for Parkinson's Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes.

Authors:  Lauren E Kenney; Adrianna M Ratajska; Francesca V Lopez; Catherine C Price; Melissa J Armstrong; Dawn Bowers
Journal:  Brain Sci       Date:  2021-12-30
  3 in total

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