Introduction

Parkinson's disease (PD) is a clinically and pathologically heterogeneous disease [1]. This heterogeneity is thought to indicate different subtypes of PD and many studies have sought to elucidate them [2], [3].

Classically, patients are divided by their motor phenotype into tremor-dominant (TD) or postural instability-gait disturbance (PIGD) subgroups, or classified by onset age as juvenile, young, or late onset PD [1]. In recent years, cluster analysis has been introduced as an objective data-driven grouping method [2], [4]. Several cluster analysis studies have shown that the age of onset [4]–[9], rate of disease progression [5]–[10], and motor phenotype [6], [9]–[10] are the major dimensions of PD subgroup classification.

The classical and data-driven approaches have focused primarily on the motor symptoms of PD. Some of the nonmotor features, such as depression [8] or cognitive decline [4], [10] were associated with previous classifications based mainly on motor phenotypes. Although recent results from two independent European cohorts have shown that the severity of nonmotor symptoms as well as motor complications are important factors in the characterization of PD subtypes [11], little attention has been paid to the clustering of nonmotor manifestations themselves [12], [13].

In the current study, we investigated the existence of different subgroups of nonmotor manifestations by using unsupervised hierarchical cluster analysis (HCA). To our knowledge, this is the first identification of nonmotor clusters using unsupervised HCA on a range of nonmotor features in early stage PD.

Methods

Results

Table 1 shows demographic and clinical characteristics of the subjects. The 56 patients included 28 men and 28 women (age range, 46–81 years). No subject had motor fluctuation or levodopa-induced dyskinesia. Twenty-seven patients were newly diagnosed de novo PD patients. The KVSS tests were well accepted by all patients. Forty-nine patients (87.5%) had hyposmia based on the reported criteria [14], [15]. For all of the PD patients, the cluster analysis of nine nonmotor features gave three clusters without a priori assumptions about relatedness. Figure 1A shows the corresponding dendrogram based on UPGMA distance. Cluster 1 included hyposmia, dream-enactment behavior, and constipation. Cluster 2 comprised memory disturbance and orthostatic dizziness. Cluster 3 contained urinary symptoms and excessive daytime sleepiness.

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Figure 1. Dendrogram of the hierarchical cluster analysis and multiscale bootstrap resampling results of nonmotor features of Parkinson's disease (PD).

The clusters with an approximately unbiased (AU, red) P-value>95% are highlighted by the red dashed rectangles. (A) all PD patients. (B) de novo PD patients.

https://doi.org/10.1371/journal.pone.0091906.g001

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Table 1. Demographics and clinical profiles of the PD patients.

https://doi.org/10.1371/journal.pone.0091906.t001

HCA in the de novo PD group revealed two main clusters (Fig. 1B). Cluster 1 was defined by hyposmia, dream-enactment behavior, and constipation. The larger cluster 2 was defined by depression, insomnia, memory disturbance, orthostatic dizziness, excessive daytime sleepiness, and urinary symptoms. The clustering stability was assessed by comparing the results of different methods of measuring similarity and different measures of intergroup dissimilarity. We obtained similar clustering results and concluded that the group structure was stable.

In cross-sectional investigation, the patients were split into two olfactory groups based on the median TDI score [20]. Gender distribution, mean current age, disease duration, LEDD, and H-Y stage did not differ between the two olfactory groups (Table 1). Dream-enactment behavior was more prevalent in patients with lower TDI scores, which imply impaired olfactory function (P = 0.029 for all PD patients; P = 0.046 for de novo PD patients; Fig. 2). There were no significant differences in other nonmotor symptoms (Figs. 2A and 2B).

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Figure 2. Group differences in nonmotor manifestations based on the KVSS II TDI score (*P = 0.029; **P = 0.046, chi-square test or Fisher's exact test, as appropriate).

(A) all PD patients. (B) de novo PD patients.

https://doi.org/10.1371/journal.pone.0091906.g002

Discussion

In this study, 87.5% of patients were hyposmic as assessed by the KVSS olfactory test, which was in line with results from previous olfactory function studies in PD patients [18], [20], [21]. Unsupervised cluster analysis suggests three nonmotor clusters for the entire group of PD patients and two clusters for the de novo PD patients. This clustering result and the cross-sectional investigation result are in agreement with results of previous studies demonstrating close relationships between olfactory dysfunction and rapid eye movement sleep behavior disorders (RBDs) [22]. Notably, our study suggests that depression is independent of olfactory dysfunction. This is a somewhat striking result, since depression, together with hyposmia, is a well-known predated nonmotor manifestation [23]. Our clustering result concurs with those of several works showing that olfactory dysfunction did not correlate with depression [20], [24], [25]. The discrepancy with the findings of Berendse et al., which addressed a potential correlation of hyposmia with depression, might partly depend on disease duration or the use of different olfactory function tests [26].

Some nonmotor manifestations, such as hyposmia, RBDs, constipation, and depression, can develop during the prodromal period of PD and precede the onset of classical PD motor symptoms [27], [28]. While patients in the premotor stage of PD exhibit no motor symptoms, they have do demonstrate various prodromal nonmotor symptoms [23], [29]. Conventional PD classification based on motor phenotype (tremor-dominant or PIDG) cannot be applied to these premotor PD; therefore, classifications that are wholly based on nonmotor symptoms are needed. The finding of this study can have implications for developing the premotor PD classification based on nonmotor features. Our research subjects, patients with newly diagnosed de novo PD and patients with relatively early stages of PD (with disease onset within 3 years), may be the closest diagnostic PD group to represent ideal premotor PD populations. Our study demonstrates the existence of different nonmotor symptom clusters in early stage PD patients and support the idea of a premotor PD subtype based on nonmotor manifestations [30]–[32].

While this cluster analysis gave interesting results, the clinical relevance of the complicated relationships among nonmotor manifestations is yet to be determined. A nonmotor feature could be caused by coexisting conditions, for example, daytime sleepiness due to nocturia. It is also possible that some symptoms are caused by PD medication, such as levodopa or dopamine agonists [33]. In addition, the patterning of nonmotor manifestations seen in our results implicates common neuropathological and neurochemical processes underlying PD [13], [34], [35]. The underlying alpha-synuclein pathology has been proposed as a shared mechanism for hyposmia, RBD, and constipation [22], [35], which define the first cluster of de novo PD patients (Fig. 1B). Other nonmotor features that comprise the second cluster of de novo PD patients, such as depression, memory disturbance and autonomic dysfunction, may be early disease manifestations that reflect underlying non-dopaminergic deficit [23], [36].

One limitation of this study is its retrospective design with a relatively small sample size, which potentially influences the generalizability of our results. Another limitation is the use of the screening questionnaire of the nonmotor features that has not been validated. The lack of replication is another caveat of this study. Although we used multiscale bootstrap resampling to validate confidence in the clusters, a larger study should be conducted to verify our results [2], [18].

This preliminary analysis examining the presentation of multiple nonmotor features suggests that it is possible to identify structures based on the profiles of nonmotor manifestations. Future data-driven replications with larger independent populations are required, and these will serve to increase our understanding of associations between various nonmotor features of PD.