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Aging and disease    2019, Vol. 10 Issue (5) : 1130-1139     DOI: 10.14336/AD.2019.0112
Review Article |
Subtyping of Parkinson’s Disease - Where Are We Up To?
Elizabeth Qian1, Yue Huang1,2,*
1School of Medical Science, Faculty of Medicine, UNSW Sydney, 2032, Australia.
2China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
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Heterogenous clinical presentations of Parkinson’s disease have aroused several attempts in its subtyping for the purpose of strategic implementation of treatment in order to maximise therapeutic effects. Apart from a priori classifications based purely on motor features, cluster analysis studies have achieved little success in receiving widespread adoption. A priori classifications demonstrate that their chosen factors, whether it be age or certain motor symptoms, do have an influence on subtypes. However, the cluster analysis approach is able to integrate these factors and other clinical features to produce subtypes. Differences in inclusion criteria from datasets, in variable selection and in methodology between cluster analysis studies have made it difficult to compare the subtypes. This has impeded such subtypes from clinical applications. This review analysed existing subtypes of Parkinson’s disease, and suggested that future research should aim to discover subtypes that are robustly replicable across multiple datasets rather than focussing on one dataset at a time. Hopefully, through clinical applicable subtyping of Parkinson’s disease would lead to translation of these subtypes into research and clinical use.

Keywords Parkinson’s disease      subtypes      translation      heterogenous     
Corresponding Authors: Huang Yue   
About author:

Present address: Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore.

Just Accepted Date: 18 February 2019   Issue Date: 27 September 2019
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Qian Elizabeth,Huang Yue. Subtyping of Parkinson’s Disease - Where Are We Up To?[J]. Aging and disease, 2019, 10(5): 1130-1139.
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Figure 1.  Summary of PD Subtyping. The flowchart demonstrated although the two subtyping approaches had different starting points, a common pathway of biological validation, prognosis evaluation, and the destinations (desired outcomes) was followed.
Cohort clinical characteristicsLiu 2011Van Rooden 2011Fereshtehnejad 2015Erro 2016Fereshtehnejad 2017Mu 2017
Number of patients138802113398421904
Inclusion criteria, in addition to PDH&Y 1-3NoneIdiopathic PD deemed as most likely causede novode novo, H&Y 1-2, age ≥ 30,Mixed cohort of drug-naïve and treated PD
Age (years)
57.47 ± 10.5860.8-66.2 (11.0-11.3)66.7 ± 8.9NS61.1 ± 9.764.28 ± 9.86
Disease duration, years (means±SD)3 (median) range: 0.5-35.09.1-12.35.7±4.2NS6.5 ± 6.58.01 ± 5.60
H&Y stageNS2-32.5 ± 0.9NSNSNS
Table 1  Comparison of sample characteristics of recent PD subtyping studies using cluster analysis.
Methodological stepsLiu 2011Van Rooden 2011Fereshtehnejad 2015Erro 2016Fereshtehnejad 2017Mu 2017
Data pre-processingStandardized scoresz scoresz scoresNSNormative valuesStandardised scores
Clustering algorithmK-meansModel-based2-stepK-meansHierarchicalK-means & Hierarchical
Basis of the determination of the number of clustersNSNSBayesian information criterionCalinski-Harabasz pseudo-F valueEstimate, Hartigan’s ruleVarious e.g. Gap Statistic and the 1-standard-error method
Cluster validation on independent sampleNoYesNoNoNoNo
Evaluation of discriminative variablesNoDiscriminant analysisNoNoPrincipal component analysisNo
Follow up period, years± meanN/AN/A4.5N/A2.73 ± 0.78No
Post hoc analysis of variables not included in the cluster analysisYes, motor phenotype consistencyNoYes, disease progressionYes, 123[I]-FP-CIT binding valuesYes, CSF and imaging biomarkers, and disease progressionNo
Table 2  Comparison of methodology of recent PD subtyping studies using cluster analysis.
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