The identification of Parkinson's disease subtypes using cluster analysis: a systematic review.
The clinical variability between patients with Parkinson's disease (PD) may point at the existence of subtypes of the disease. Identification of subtypes is important, since a focus on homogeneous groups may enhance the chance of success of research on mechanisms of disease and may also lead to tailored treatment strategies. Cluster analysis (CA) is an objective method to classify patients into subtypes. We systematically reviewed the methodology and results of CA studies in PD to gain a better understanding of the robustness of identified subtypes. We found seven studies that fulfilled the inclusion criteria. Studies were limited by incomplete reporting and methodological limitations. Differences between studies rendered comparisons of the results difficult. However, it appeared that studies which applied a comparable design identified similar subtypes. The cluster profiles "old age-at-onset and rapid disease progression" and "young age-at-onset and slow disease progression" emerged from the majority of studies. Other cluster profiles were less consistent across studies. Future studies with a rigorous study design that is standardized with respect to the included variables, data processing, and CA technique may advance the knowledge on subtypes in PD.
Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands. s.m.van_rooden@lumc.nl
Stephanie M
SM
van Rooden
Willem J
WJ
Heiser
Joost N
JN
Kok
Dagmar
D
Verbaan
Jacobus J
JJ
van Hilten
Johan
J
Marinus
D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D010300:Parkinson Disease / Q000145:classification*; D039781:PubMed / Q000706:statistics & numerical data