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.
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.
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 characteristics
Van Rooden 2011
Number of patients
Inclusion criteria, in addition to PD
Idiopathic PD deemed as most likely cause
de novo, H&Y 1-2, age ≥ 30,
Mixed cohort of drug-naïve and treated PD
Age (years) (means±SD)
57.47 ± 10.58
66.7 ± 8.9
61.1 ± 9.7
64.28 ± 9.86
Disease duration, years (means±SD)
3 (median) range: 0.5-35.0
6.5 ± 6.5
8.01 ± 5.60
2.5 ± 0.9
Table 1 Comparison of sample characteristics of recent PD subtyping studies using cluster analysis.
Van Rooden 2011
K-means & Hierarchical
Basis of the determination of the number of clusters
Bayesian information criterion
Calinski-Harabasz pseudo-F value
Estimate, Hartigan’s rule
Various e.g. Gap Statistic and the 1-standard-error method
Cluster validation on independent sample
Evaluation of discriminative variables
Principal component analysis
Follow up period, years± mean
2.73 ± 0.78
Post hoc analysis of variables not included in the cluster analysis
Yes, motor phenotype consistency
Yes, disease progression
Yes, 123[I]-FP-CIT binding values
Yes, CSF and imaging biomarkers, and disease progression
Table 2 Comparison of methodology of recent PD subtyping studies using cluster analysis.
Mizuno Y (2007). Where do we stand in the treatment of Parkinson's disease? J Neurol, 254:13-18.
Vos T, Allen C, Arora M, Barber RM, Bhutta ZA, Brown A, et al. (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet, 388:1545-1602.
Williams-Gray CH, Barker RA (2017). Parkinson disease: Defining PD subtypes - a step toward personalized management? Nat Rev Neurol, 13:454-455.
von Coelln R, Shulman LM (2016). Clinical subtypes and genetic heterogeneity: of lumping and splitting in Parkinson disease. Curr Opin Neurol, 29:727-734.
Foltynie T, Brayne C, Barker RA (2002). The heterogeneity of idiopathic parkinson's disease. J Neurol, 249:138-145.
Marras C (2015). Subtypes of Parkinson's disease: state of the field and future directions. Curr Opin Neurol, 28:382-386.
Fereshtehnejad SM, Postuma RB (2017). Subtypes of Parkinson's Disease: What Do They Tell Us About Disease Progression? Curr Neurol Neurosci Rep, 17:34.
Prikrylova Vranova H, Mares J, Hlustik P, Nevrly M, Stejskal D, Zapletalova J, et al. (2012). Tau protein and beta-amyloid (1-42) CSF levels in different phenotypes of Parkinson's disease. J Neural Transm (Vienna), 119:353-362.
Xiang Y, Gong T, Wu J, Li J, Chen Y, Wang Y, et al. (2017). Subtypes evaluation of motor dysfunction in Parkinson's disease using neuromelanin-sensitive magnetic resonance imaging. Neurosci Lett, 638:145-150.
Wang G, Huang Y, Chen W, Chen S, Wang Y, Xiao Q, et al. (2016). Variants in the SNCA gene associate with motor progression while variants in the MAPT gene associate with the severity of Parkinson's disease. Parkinsonism Relat Disord, 24:89-94.
Huang Y, Wang G, Rowe D, Wang Y, Kwok JB, Xiao Q, et al. (2015). SNCA Gene, but Not MAPT, Influences Onset Age of Parkinson's Disease in Chinese and Australians. Biomed Res Int, 2015:135674.
Marder K, Wang Y, Alcalay RN, Mejia-Santana H, Tang M-X, Lee A, et al. (2015). Age-specific penetrance of LRRK2 G2019S in the michael J. fox ashkenazi jewish LRRK2 consortium. Neurol, 85:89-95.
Huang Y, Halliday GM, Vandebona H, Mellick GD, Mastaglia F, Stevens J, et al. (2007). Prevalence and clinical features of common LRRK2 mutations in australians with parkinson's disease. Mov Disord, 22:982-989.
Kestenbaum M, Alcalay RN2017. Clinical features of LRRK2 carriers with parkinson’s disease. In Leucine-Rich Repeat Kinase 2 (LRRK2). RideoutHJ, editor. New York, United States of America: Springer New York LLC. 31-48.
Lücking CB, Dürr A, Bonifati V, Vaughan J, De Michele G, Gasser T, et al. (2000). Association between early-onset parkinson's disease and mutations in the parkin gene. N Engl J Med, 342:1560-1567.
Ma LY, Chan P, Gu ZQ, Li FF, Feng T (2015). Heterogeneity among patients with Parkinson's disease: cluster analysis and genetic association. J Neurol Sci, 351:41-45.
Alves G, Wentzel-Larsen T, Aarsland D, Larsen JP (2005). Progression of motor impairment and disability in parkinson disease: A population-based study. Neurol, 65:1436-1441.
Jellinger KA (2018). Very old onset parkinsonism: A clinical-pathological study. Parkinsonism Relat Disord, 57:39-43.
Kempster PA, O'Sullivan SS, Holton JL, Revesz T, Lees AJ (2010). Relationships between age and late progression of Parkinson's disease: a clinico-pathological study. Brain, 133:1755-1762.
Salawu FK (2012). Patient considerations in early management of Parkinson's disease: focus on extended-release pramipexole. PPA, 6:49-54.
Kostic V, Przedborski S, Flaster E, Sternic N (1991). Early development of levodopa-induced dyskinesias and response fluctuations in young-onset parkinson's disease. Neurol, 41:202-205.
Paviour DC, Surtees RA, Lees AJ (2004). Diagnostic considerations in juvenile parkinsonism. Mov Disord, 19:123-135.
Bertucci Filho D, Teive HAG, Werneck LC (2007). Early-onset parkinson's disease and depression. Arq Neuropsiquiatria, 65:5-10.
Wickremaratchi MM, Knipe MDW, Sastry BD, Morgan E, Jones A, Salmon R, et al. (2011). The motor phenotype of parkinson's disease in relation to age at onset. Mov Disord, 26:457-463.
Zetusky WJ, Jankovic J, Pirozzolo FJ (1985). The heterogeneity of parkinson’s disease: Clinical and prognostic implications. Neurol, 35:522-526.
Jankovic J, McDermott M, Carter J, Gauthier S, Goetz C, Golbe L, et al. (1990). Variable expression of parkinson’s disease: A base-line analysis of the datatop cohort. Neurol, 40:1529-1534.
Eggers C, Kahraman D, Fink GR, Schmidt M, Timmermann L (2011). Akinetic-rigid and tremor-dominant Parkinson's disease patients show different patterns of FP-CIT single photon emission computed tomography. Mov Disord, 26:416-423.
Poewe W, Gerstenbrand F (1986). Clinical subtypes in Parkinson's disease. Wiener Medizinische Wochenschrift, 136:384-387.
Aleksovski D, Miljkovic D, Bravi D, Antonini A (2018). Disease progression in Parkinson subtypes: the PPMI dataset. Neurol Sci, 39:1971-1976.
Xu C, Zhuang P, Hallett M, Zhang Y, Li J, Li Y (2018). Parkinson's Disease Motor Subtypes Show Different Responses to Long-Term Subthalamic Nucleus Stimulation. Front Hum Neurosci, 12:365.
Huang X, Ng SY, Chia NS, Acharyya S, Setiawan F, Lu ZH, et al. (2018). Serum uric acid level and its association with motor subtypes and non-motor symptoms in early Parkinson's disease: PALS study. Parkinsonism Relat Disord, 55:50-54.
Jankovic J, Kapadia AS (2001). Functional decline in parkinson disease. Arch Neurol, 58:1611-1615.
Burn DJ, Rowan EN, Minett T, Sanders J, Myint P, Richardson J, et al. (2003). Extrapyramidal features in parkinson's disease with and without dementia and dementia with lewy bodies: A cross-sectional comparative study. Mov Disord, 18:884-889.
Alves G, Larsen JP, Emre M, Wentzel-Larsen T, Aarsland D (2006). Changes in motor subtype and risk for incident dementia in Parkinson's disease. Mov Disord, 21:1123-1130.
Johnson AR, Bucks RS, Kane RT, Thomas MG, Gasson N, Loftus AM (2016). Motor Subtype as a Predictor of Future Working Memory Performance in Idiopathic Parkinson's Disease. PLoS One, 11:e0152534.
Reijnders JS, Ehrt U, Lousberg R, Aarsland D, Leentjens AF (2009). The association between motor subtypes and psychopathology in Parkinson's disease. Parkinsonism Relat Disord, 15:379-382.
Burn DJ, Landau S, Hindle JV, Samuel M, Wilson KC, Hurt CS, et al. (2012). Parkinson's disease motor subtypes and mood. Mov Disord, 27:379-386.
Ba F, Obaid M, Wieler M, Camicioli R, Martin WR (2016). Parkinson Disease: The Relationship Between Non-motor Symptoms and Motor Phenotype. Can J Neurol Sci, 43:261-267.
Lewis SJ, Foltynie T, Blackwell AD, Robbins TW, Owen AM, Barker RA (2005). Heterogeneity of Parkinson's disease in the early clinical stages using a data driven approach. J Neurol Neurosurg Psychiatry, 76:343-348.
Rosenberg-Katz K, Herman T, Jacob Y, Giladi N, Hendler T, Hausdorff JM (2013). Gray matter atrophy distinguishes between parkinson disease motor subtypes. Neurol, 80:1476-1484.
Benninger DH, Thees S, Kollias SS, Bassetti CL, Waldvogel D (2009). Morphological differences in Parkinson's disease with and without rest tremor. J Neurol, 256:256-263.
Kang JH, Irwin DJ, Chen-Plotkin AS, Siderowf A, Caspell C, Coffey CS, et al. (2013). Association of cerebrospinal fluid beta-amyloid 1-42, T-tau, P-tau181, and alpha-synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA Neurol, 70:1277-1287.
Simuni T, Caspell-Garcia C, Coffey C, Lasch S, Tanner C, Marek K, et al. (2016). How stable are Parkinson's disease subtypes in de novo patients: Analysis of the PPMI cohort? Parkinsonism Relat Disord, 28:62-67.
Erro R, Picillo M, Vitale C, Palladino R, Amboni M, Moccia M, et al. (2016). Clinical clusters and dopaminergic dysfunction in de-novo Parkinson disease. Parkinsonism Relat Disord, 28:137-140.
Fereshtehnejad SM, Zeighami Y, Dagher A, Postuma RB (2017). Clinical criteria for subtyping Parkinson's disease: biomarkers and longitudinal progression. Brain, 140:1959-1976.
Elliott P, Hawthorne G (2005). Imputing missing repeated measures data: how should we proceed? ANZ J Psychiat, 39:575-582.
Shiffler RE (1988). Maximum z scores and outliers. Am Sta, 42:79-80.
Iglewicz B, Hoaglin D 1993. How to detect and handle outliers. Milwaukee, United States of America: ASQC Quality Press.
Saleem A, Asif KH, Ali A, Awan SM, Alghamdi MA 2014. Pre-processing methods of data mining. In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing. London, United Kingdom: UCC 2014. 451-456.
Collins LM, Williams-Gray CH (2016). The Genetic Basis of Cognitive Impairment and Dementia in Parkinson's Disease. Front Psychiat, 7:89.
Hernan MA, Takkouche B, Caamano-Isorna F, Gestal-Otero JJ (2002). A meta-analysis of coffee drinking, cigarette smoking, and the risk of Parkinson's disease. Ann Neurol, 52:276-284.
Svensson E, Horvath-Puho E, Stokholm MG, Sorensen HT, Henderson VW, Borghammer P (2016). Appendectomy and risk of Parkinson's disease: A nationwide cohort study with more than 10 years of follow-up. Mov Disord, 31:1918-1922.
Svensson E, Horvath-Puho E, Thomsen RW, Djurhuus JC, Pedersen L, Borghammer P, et al. (2015). Vagotomy and subsequent risk of Parkinson's disease. Ann Neurol, 78:522-529.
van Rooden SM, Heiser WJ, Kok JN, Verbaan D, van Hilten JJ, Marinus J (2010). The identification of Parkinson's disease subtypes using cluster analysis: a systematic review. Mov Disord, 25:969-978.
de Lau LM, Verbaan D, van Rooden SM, Marinus J, van Hilten JJ (2014). Relation of clinical subtypes in Parkinson's disease with survival. Mov Disord, 29:150-151.
Fereshtehnejad SM, Romenets SR, Anang JB, Latreille V, Gagnon JF, Postuma RB (2015). New Clinical Subtypes of Parkinson Disease and Their Longitudinal Progression: A Prospective Cohort Comparison With Other Phenotypes. JAMA Neurol, 72:863-873.
Vavougios GD, Doskas T, Kormas C, Krogfelt KA, Zarogiannis SG, Stefanis L (2018). Identification of a prospective early motor progression cluster of Parkinson's disease: Data from the PPMI study. J Neurol Sci, 387:103-108.
Mu J, Chaudhuri KR, Bielza C, de Pedro-Cuesta J, Larranaga P, Martinez-Martin P (2017). Parkinson's Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms. Front Aging Neurosci, 9:301.
Mestre TA, Eberly S, Tanner C, Grimes D, Lang AE, Oakes D, et al. (2018). Reproducibility of data-driven Parkinson's disease subtypes for clinical research. Parkinsonism Relat Disord.
Wooten GF (2004). Are men at greater risk for Parkinson's disease than women? Journal of Neurology, Neurosurgery & Psychiatry, 75:637-639.
Song Y, Gu Z, An J, Chan P, Chinese Parkinson Study G (2014). Gender differences on motor and non-motor symptoms of de novo patients with early Parkinson's disease. Neurol Sci, 35:1991-1996.
Perrin AJ, Nosova E, Co K, Book A, Iu O, Silva V, et al. (2017). Gender differences in Parkinson's disease depression. Parkinsonism Relat Disord, 36:93-97.
Sriram TVS, Venkateswara Rao M, Satya Narayana GV, Kaladhar DSVGK2014. Diagnosis of parkinson disease using machine learning and data mining systems from voice dataset. In Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). S.S, B.B, S.U, and J.M, editors. Cham: Springer.
Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M (2018). A hybrid intelligent system for the prediction of parkinson's disease progression using machine learning techniques. Biocybern and Biomed Eng, 38:1-15.
Ström F, Koker R (2011). A parallel neural network approach to prediction of parkinson's disease. Expert Sys Appl, 38:12470-12474.
Planey CR, Gevaert O (2016). CoINcIDE: A framework for discovery of patient subtypes across multiple datasets. Genome Med, 8:27.