Abstract

Background: We recently identified three distinct Parkinson's disease subtypes: “motor only” (predominant motor deficits with intact cognition and psychiatric function); “psychiatric & motor” (prominent psychiatric symptoms and moderate motor deficits); “cognitive & motor” (cognitive and motor deficits). Objective: We used an independent cohort to replicate and assess reliability of these Parkinson's disease subtypes. Methods: We tested our original subtype classification with an independent cohort (N = 100) of Parkinson's disease participants without dementia and the same comprehensive evaluations assessing motor, cognitive, and psychiatric function. Next, we combined the original (N = 162) and replication (N = 100) datasets to test the classification model with the full combined dataset (N = 262). We also generated 10 random split-half samples of the combined dataset to establish the reliability of the subtype classifications. Latent class analyses were applied to the replication, combined, and split-half samples to determine subtype classification. Results: First, LCA supported the three-class solution – Motor Only, Psychiatric & Motor, and Cognitive & Motor– in the replication sample. Next, using the larger, combined sample, LCA again supported the three subtype groups, with the emergence of a potential fourth group defined by more severe motor deficits. Finally, split-half analyses showed that the three-class model also had the best fit in 13/20 (65%) split-half samples; two-class and four-class solutions provided the best model fit in five (25%) and two (10%) split-half replications, respectively. Conclusions: These results support the reproducibility and reliability of the Parkinson's disease behavioral subtypes of motor only, psychiatric & motor, and cognitive & motor groups.

Original languageEnglish
Article number107016
JournalParkinsonism and Related Disorders
Volume124
DOIs
StatePublished - Jul 2024

Keywords

  • Classification
  • Latent class analysis
  • Parkinson disease
  • Psychometrics

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