Behavioural Brain Research 287 (2015) 1–7
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Research report
Distal motor deficit contributions to postural instability and gait disorder in Parkinson’s disease Griet Vervoort a,∗ , Aniek Bengevoord a,b , Evelien Nackaerts a , Elke Heremans a , Wim Vandenberghe c , Alice Nieuwboer a a
KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium Radboud Universiteit Nijmegen, Medical Centre, Nijmegen, The Netherlands c University Hospitals Leuven, Department of Neurology, Leuven, Belgium b
h i g h l i g h t s • • • •
In early to mid-disease, a clinical balance test distinguishes between PIGD and TD subtypes. Objective gait analysis shows limited gait deficits in PIGD compared to TD. PIGD has pronounced distal coordination impairment compared to TD. PIGD shows more widespread motor dysfunction than hitherto reported.
a r t i c l e
i n f o
Article history: Received 5 February 2015 Received in revised form 11 March 2015 Accepted 12 March 2015 Available online 21 March 2015 Keywords: Parkinson’s disease Clinical subtype Motor control
a b s t r a c t Clinical subtypes in Parkinson’s disease (PD) are often based on the presence of clustered motor symptoms. In contrast to the tremor dominant (TD) subtype, the postural instability and gait disorder (PIGD) subtype is characterized by predominantly axial motor involvement and increased cognitive impairment. It is, however, unclear if subtypes represent distinct underlying neuropathological mechanisms or reflect more severe disease progression. We aimed to clarify the validity of PD subtypes by investigating behavioral outcomes at multiple levels. Therefore, spatiotemporal kinematics of gait, upper and lower limb repetitive movements in combination with a balance and cognitive assessment were recorded in 73 patients with PD. We classified patients as PIGD (n = 43), TD (n = 22) or indeterminate (n = 8) while ‘off’ medication and recruited 20 age-matched controls. Surprisingly, differences between PIGD and TD were more prominent during repetitive distal motor tasks than during gait. Gait impairment in PIGD was only shown by reduced step length and gait speed. However, motor scaling and coordination of distal movements were more affected in PIGD than in TD patients. PIGD patients also had impaired postural control compared to TD patients as shown by lower mini-BESTest scores. There were no cognitive differences between patient subgroups. Distal movement was not significantly different in TD patients from controls, except for greater movement asymmetry. The results indicate a widespread impairment within PIGD with more pronounced distal than axial motor deficits. This suggests involvement of different neurotransmitter systems in the neuropathology of PD subtypes, which are at least partially independent of disease progression. © 2015 Elsevier B.V. All rights reserved.
1. Introduction
∗ Corresponding author. Tel.: +32 16 37 65 75. E-mail addresses: [email protected] (G. Vervoort), [email protected] (A. Bengevoord), [email protected] (E. Nackaerts), [email protected] (E. Heremans), [email protected] (W. Vandenberghe), [email protected] (A. Nieuwboer). http://dx.doi.org/10.1016/j.bbr.2015.03.026 0166-4328/© 2015 Elsevier B.V. All rights reserved.
The clinical heterogeneity in Parkinson’s disease (PD) has inspired the delineation of clinical subtypes. Clinical differences between tremor dominant (TD) and postural instability and gait disorder (PIGD) patients were first described by Jankovic et al. (1990) [1]. Both subtypes are mainly defined by their clustered motor symptoms. While TD is characterized by a predominant presence of tremor, PIGD is associated with bradykinesia, rigidity,
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falling and freezing of gait (FOG) [1]. Different empirical and data-driven studies have established clear associations between the PIGD subtype, rapid disease progression and old age of onset and between the TD subtype, slow disease progression and young age of onset [2,3]. Auyeung et al. (2012) even showed that PIGD at disease onset is associated with a 2.1 times higher mortality, although this could not be confirmed by other studies [4,5]. However, questions have been raised about the significance of the distinction between the PIGD and TD phenotypes. Studies that looked into non-motor differences between both subtypes showed that PIGD is associated with cognitive decline and depression [6–8] and suggested this to reflect more advanced disease [9]. In a longitudinal study, PIGD patients had increased disease duration and severity, lower MMSE scores and higher depression scores compared to the TD group at baseline. After 8 years, 57.1% of non-PIGD patients changed to the PIGD subtype while only 1.9% changed in the opposite direction. Moreover, the switch to the PIGD subgroup was associated with a severely increased risk of developing dementia. This led the authors to conclude that transition between TD and PIGD is unidirectional, irreversible and strongly related to cognitive decline [6]. This suggests that PIGD may simply represent a more advanced disease stage of PD than the TD profile. By contrast, different imaging studies compared groups of TD and PIGD patients who were well-matched for age and disease severity, and showed structural and functional differences, providing support for the existence of distinct subtypes [2,10–13]. Bohnen et al. (2011) showed increased white matter hyperintensities (WMHs) in the PIGD compared to TD group independent of dopaminergic denervation [13]. However, these results could not be replicated [14]. Although the above-mentioned studies provided a first indication for true clinical phenotypes in PD, it is difficult to draw strong conclusions due to the diversity of methodologies, methods of classification and sometimes conflicting results of the studies performed [15]. In addition, symptom mapping usually occurred based on Unified Parkinson’s Disease Rating Scales (UPDRS) scores only without precise measurements of behavioral correlates. Therefore, this study aimed to clarify the validity of PIGD and TD subgroups matched for age and disease severity (Unified Parkinson’s Disease Rating Scale (UPDRS), part III), by investigating behavioral outcomes at multiple levels in early to mid-stage PD. We expected to find clear gait-related deficits in the PIGD compared to TD group as well as subtle postural instability and cognitive impairment. In addition, we tested a wide range of spatiotemporal outcomes for each task category, as recommended for gait by Lord et al. (2013), to capture the nature of underlying motion deficits and their neural basis [16]. Due to the previously established cognitive and balance deterioration in the PIGD group, we hypothesized that this group would show greater abnormality in the gait variability and postural control domain [15–19]. Recent studies revealed that freezing-related movement abnormalities were not only episodic and gait-related, but also occurred outside freezing episodes and in other movement effectors [17,20]. Due to the suggested relationship between FOG and PIGD, we also hypothesized that lower limb (LL) and upper limb (UL) repetitive movements would be able to distinguish between PIGD and TD [21]. To our knowledge, this is the first study comparing PIGD and TD based on quantitative evaluation of cognition, balance and spatiotemporal motor tasks in different movement effectors. 2. Methods 2.1. Participants Seventy-three patients with PD and 20 healthy controls were included. Patients with PD were classified as PIGD (n = 43), TD
(n = 22) or indeterminate (n = 8) based on subscores of the MDSUPDRS while ‘off’ medication using an adapted method for the revised version of the UPDRS [22], based on the one developed by Jankovic et al. (1990) [1]. Patients were recruited at the Movement Disorders Clinic of the University Hospital Leuven according to the UK Brain Bank criteria. Inclusion criteria were: Hoehn and Yahr (H&Y) stage 1–3 while ‘off’ medication, ability to walk independently for 10 min and a Mini-Mental State Examination (MMSE) score ≥ 24. Exclusion criteria were presence of motor or neurological comorbidities affecting test performance. Patients with an indeterminate subtype were excluded from further analysis. Healthy controls, PIGD and TD subgroups were a priori matched for age and gender and post-hoc for disease severity (MDS-UPDRS III ‘off’ medication). The disease dominant side was the side of disease onset. The study was approved by the Ethical Committee of the University Hospitals Leuven. All subjects gave written informed consent according to the Declaration of Helsinki. 2.2. Behavioral tests To maximize between group behavioral differences, all tests were performed in a semi-randomized order while patients were in a practically defined ‘off’ state, i.e. at least 12 h after last medication intake. Disease severity was assessed using the MDS-UPDRS. Prevalence and severity of FOG were assessed using the New Freezing of Gait Questionnaire (NFOG-Q). All tasks involving the LL were recorded using a 10-camera VICON 3D motion analysis system (©Vicon Motion Systems Ltd. UK) at a sample frequency of 100 Hz. Sixteen markers were placed on anatomical landmarks according to the pelvic and LL part of the Full Plugin Gait model. Marker trajectories were processed using Nexus software (version 1.8.5). UL movement was recorded using a Micro 1401 acquisition unit (Cambridge Electronic Device, UK) with Spike 2 software with temporal and spatial resolutions of 2000 Hz and 0.0001◦ through analogue encoders placed on the rotation axis of a device connected to each index finger [23]. 2.2.1. Gait Subjects were instructed to perform a continuous overground gait task at a self-preferred pace until at least 40 steps were recorded in 94.6% of subjects. The remaining 5.4% had only 30–40 identifiable steps due to marker visibility issues. Position and timing of heel strikes and toe-offs were processed in a customized Matlab program to calculate within-limb mean and variability and between-limb asymmetry for step length and time, stance time and swing time. For step width and gait speed, the mean and variability were calculated [24]. Coefficients of variability (CV) were used to express variability outcomes. Asymmetry was calculated between the disease dominant and non-dominant side for patients and between right and left side for controls. A conceptual approach was applied to reduce the number of parameters according to the gait model described by Lord et al. (2013) [16,25]. Spatiotemporal gait parameters were converted to z-scores and summed within the 5 gait domains: pace, rhythm, variability, asymmetry and postural control. 2.2.2. Foot and finger tapping task Subjects performed a bilateral anti-phase (alternating) tapping movement with their feet and index fingers while visual feedback of the movements was prevented as previously described for the UL by Vercruysse et al. (2012) [23]. An auditory cue was provided for the first 8 movement cycles at a frequency of 89 beats per minute (bpm) for the LL and 1.63 Hz for the UL task [23]. After the cue, subjects were instructed to internally generate a similar movement for the following 20–25 s. Each task was repeated 3 times unless the patients indicated this was not feasible. Outcome
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measures for both LL and UL were calculated for the uncued conditions and consisted of the within limb mean and variability and between limb asymmetry of amplitude and frequency. For the phase difference, the mean and variability were calculated. CV and asymmetry were calculated as explained for gait. Analogous to gait, spatiotemporal parameters were converted to z-scores and summed within 4 conceptual domains: amplitude, frequency, variability (amplitude and frequency CV) and coordination: amplitude and frequency asymmetry, phase difference (mean and variability). 2.2.3. Balance and cognitive assessment The total and subdomain scores of the Mini-BESTest were used to evaluate anticipatory balance, reactive postural control and sensory orientation of balance and dynamic gait. All subjects underwent a cognitive test battery including the Montreal Cognitive Assessment (MoCa), Trail Making Test (TMT) parts A and B and Frontal Assessment Battery (FAB) to assess executive functions and task switching. 2.3. Statistical analyses Outliers were removed from analyses if values deviated more than 2 standard deviations from the mean (0.47% of all data). In case of a normal distribution and homogeneity of variances, we used unpaired t-tests or Chi2 tests for between patient group comparisons. Due to the expected dependency between spatiotemporal outcome measures within a particular motor task, comparisons between patient groups and controls were performed using parametric MANOVAs with post-hoc Tukey HSD tests. Starting from a model including all outcome measures, we applied a stepwise reduction of the model until the largest part of the variance was explained with the least amount of variables. In case residuals of the data were not normally distributed or there was no homogeneity of variances as tested by the Levene’s test, we used a non-parametric Kruskal-Wallis ANOVAs with post-hoc Mann-Whitney U tests. A Bonferroni correction for the number of comparisons within each task and for the number of between group comparisons was used for the non-parametric post-hoc tests. Tests were repeated with age-correction, but this did not change the results substantially. Therefore, results without age-correction were reported. In addition, the results section only reports the post-hoc results. The level of statistical significance was set at ˛ = 0.05. Cohen’s d effect sizes between the PIGD and TD group were calculated for all outcome measures. Pearson correlations were calculated between the significantly different outcome measures and PIGD and TD scores within
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the entire patient group. In case of abnormal distribution, Spearman correlations were calculated. 3. Results 3.1. Subject characteristics Subject characteristic are described in Table 1. PIGD patients had significantly higher MDS-UPDRS II and PIGD scores and lower TD scores compared to TD patients. In addition, the PIGD subgroup had longer disease duration (from date of first motor symptom onset), higher LED values and lower MMSE scores compared to TD patients. Table 1 does not include values from the indeterminate group. 3.2. Gait Post-hoc tests indicated slower gait speed (p = 0.037) and shorter step lengths at the dominant (p = 0.035) and non-dominant side in the PIGD compared to TD group (p = 0.031). Surprisingly, there were no other group differences when comparing the individual kinematics (Table 2), which was also reflected in the small standardized effect sizes. When normalized gait parameters (zscores) within the different gait domains were compared, there were lower scores in the postural control domain in PIGD compared to controls (p = 0.032). 3.3. Foot and finger tapping task Differences between PIGD and TD were more prominent during repetitive motor tasks than in gait. This was reflected by the higher and remarkably strong Cohen’s d values than those reported in Table 2. During foot tapping, PIGD patients had decreased amplitudes of the dominant (TD: p = 0.014; CTRL: p = 0.0032) and non-dominant side (TD: p = 0.00048; CTRL: p = 0.0020) and increased phase difference variability (TD: p = 0.034; CTRL: p = 0.013) compared to TD patients and controls. The phase difference was increased in PIGD patients compared to controls (p = 0.032). Interestingly, amplitude asymmetry was increased in TD compared to PIGD patients (p = 0.0019) and controls (p = 0.030) (Table 3). When combining the parameters in the various domains, PIGD patients had more impairment in the LL amplitude domain compared to TD patients (p = 0.0035) and controls (p = 0.0018). During finger tapping, PIGD patients showed a higher phase difference compared to controls (p = 0.000027). Phase difference
Table 1 Subject characteristics.
Age (years) Sex (M/F) Education (years) Disease dominance (L/R) Disease duration (years) MDS-UPDRS I (0-48) MDS-UPDRS II (0-52) MDS-UPDRS III (0-132) MDS-UPDRS IV (0-24) PIGD score (0-20) TD score (0-44) LED (mg/day) N-FOGQ (0-29) MMSE (0-30)
PIGD (n = 43)
TD (n = 22)
CTRL (n = 20)
p-value
61.4 (± 10.9) 29/14 13.6 (± 3.0) 17/26 7.2 (± 4.3) 10.4 (± 5.3) 12.7 (± 8.0) 30.5 (± 13.9) 2.1 (± 2.6) 4.3 (± 2.8) 4.0 (± 4.1) 535.1 (± 285.8) 4.2 (± 7.7) 28.0 (± 1.7)
57.3 (± 8.8) 12/10 15.5 (± 3.9) 8/14 4.8 (± 2.7) 9.0 (± 5.0) 7.4 (± 4.0) 26.2 (± 10.9) 1.8 (± 2.4) 1.5 (± 0.9) 8.0 (± 3.5) 286.4 (± 263.9) 0.0 (± 0.0) 28.9 (± 1.2)
57.7 (± 8.8) 14/6 14.4 (± 2.5) NA NA NA NA NA NA NA NA NA NA 29.3 (± 0.8)
0.25a 0.31a 0.99a 0.80 0.03 0.32 0.005 0.21 0.57 0.00004 0.0002 0.001 0.07 0.04a
0.34b 0.84b 0.89b
0.99c 0.30c 0.95c
0.003b
0.38c
Means and standard deviations (SD) are shown. P-values are the result of comparisons between 2 or 3 groups with ␣-level of significance set at 0.05. indicate posthoc differences between the PIGD and TD group, PIGD and CTRL group and TD and CTRL group respectively. PIGD: Postural Instability and Gait Disorder; TD: Tremor Dominant; CTRL: Controls; MDS-UPDRS: Movement Disorder Society–Unified Parkinson’s Disease Rating Scale; LED: Levodopa Equivalent Dose; N-FOGQ: New- Freezing Of Gait Questionnaire; MMSE: Mini Mental State Examination; NA: Not Applicable. a,b,c
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Table 2 Spatiotemporal gait parameters within 5 gait domains for patient groups and controls. PIGD Pace Domain** Gait speed (m/s)* Step length D (cm)* Step length ND (cm)* Swing time CV D (%)* Swing time CV ND (%)* Rhythm Domain** Step time D (ms)* Step time ND (ms)* Swing time D (ms)* Swing time ND (ms)* Stance time D (ms)* Stance time ND (ms)* Variability Domain* Gait speed CV (%)* Step length CV D (%)* Step length CV ND (%)* Step time CV D (%)* Step time CV ND (%)* Stance time CV D (%)* Stance time CV ND (%)* Asymmetry Domain** Swing time asymmetry (ms)* Step time asymmetry (ms)* Stance time asymmetry (ms)* Postural Control Domain** Step length asymmetry (mm)* Step width D (cm)** Step width ND (cm)** Step width CV D (%)** Step width CV ND (%)**
−0.20 (± 3.05) 0.99 (±0.10) 53.7 (± 5.9) 53.7 (± 5.5) 9.4 (± 3.5) 9.8 (± 3.7) -0.38 (± 6.02) 548.9 (± 32.4) 551.4 (± 34.5) 449.1 (± 23.5) 449.2 (± 25.6) 648.3 (± 47.1) 648.7 (± 41.9) 0.091 (± 6.39) 16 (±5.2) 14 (± 5.6) 14 (±5.7) 10 (± 3.9) 9.9 (± 3.7) 12 (± 4.5) 11 (± 4.7) -0.26 (± 2.32) 5.4 (± 5.5) 7.3 (± 11.4) 5.1 (± 6.5) 0.28 (± 1.52) 13.2 (± 13.7) 6.8 (± 1.6) 6.7 (± 1.9) 5.6 (±1.3) 5.7 (± 1.2)
TD 0.80 (± 2.70) 1.04 (±0.10) 57.0 (± 4.9) 56.4 (± 5.9) 9.1 (3.7) 9.6 (± 3.6) -0.36 (± 5.02) 547.0 (± 25.7) 550.2 (± 26.3) 449.6 (± 22.7) 451.0 (± 15.8) 647.1 (± 36.2) 643.7 (± 38.8) 0.22 (±6.22) 16 (± 4.6) 13 (± 5.8) 15 (± 5.9) 10 (± 3.6) 9.8 (± 3.8) 12 (± 4.3) 13 (± 5.7) -0.29 (±2.33) 5.8 (± 7.8) 7.4 (± 11.6) 4.8 (± 5.4) -0.27 (± 1.08) 12.6 (± 18.6) 6.8 (± 1.3) 6.9 (± 1.2) 5.5 (± 7.5) 5.3 (± 8.4)
CTRL
Overall p
0.76 (± 1.88) 1.03 (±0.088) 56.5 (± 3.0) 56.3 (± 3.5) 9.6 (± 3.5) 9.6 (± 3.3) 0.46 (2.93) 551.9 (± 15.0) 552.0 (± 15.4) 456.3 (± 10.6) 454.0 (± 12.2) 648.1 (± 22.4) 647.8 (± 20.8) 0.54 (± 5.91) 17 (±3.8) 14 (± 4.8) 15 (±5.5) 11 (± 3.6) 10 (± 3.4) 12 (± 4.1) 12 (± 4.2) -0.71 (± 0.83) 4.0 (± 2.0) 4.8 (± 4.1) 3.8 (± 2.4) -0.64 (± 0.55) 6.3 (± 4.2) 6.6 (± 4.0) 6.6 (± 0.67) 5.5 (±0.33) 5.7 (± 0.42)
0.38 0.037 0.026 0.025 0.94 0.97 0.91 0.57 0.82 0.26 0.58 0.82 0.79 0.93 0.92 0.90 0.83 0.77 0.77 0.83 0.79 0.62 0.52 0.54 0.73 0.032 0.20 0.42 0.63 0.86 0.27
Post-hoc p-values PIGD-CTRL PIGD-TD
TD-CTRL
NA 0.037 0.035 0.031 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.23 NA NA NA NA NA
NA 0.69 0.85 0.80 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.23 NA NA NA NA NA
NA 0.28 0.16 0.17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0096 NA NA NA NA NA
Cohen’s d −0.35 −0.5 −0.61 −0.47 0.09 0.04 −0.003 0.07 0.04 −0.02 −0.08 0.03 0.12 −0.05 0 0.18 −0.17 −0.03 0.03 −0.02 −0.28 0.01 −0.06 −0.009 0.05 0.42 0.04 0 −0.13 0.09 0.39
Means and standard deviations are shown. Overall p-values indicate results from overall between group analyses. Cohen’s d values indicate effect sizes for the differences between the PIGD and TD group. Parametric analyses and non-parametric analyses are indicated by * and ** respectively. The ␣-level of significance was set at 0.05. PIGD: Postural Instability and Gait Disorder; TD: Tremor Dominant; CTRL: Controls; CV: Coefficient of Variability; D: disease dominant; ND: disease non-dominant; NA: Not Applicable.
variability (PIGD: p = 0.00011; TD: p = 0.0034) and frequency asymmetry (PIGD: p = 0.00021; TD: p = 0.0029) were increased in both patient groups compared to controls. There was a trend for increased phase difference (p = 0.059) in the PIGD compared to TD group (Table 3). Grouping the parameters into domains, led to more impairment in the UL coordination domain in both patient groups (PIGD: p = 0; TD: p = 0.0032) compared to controls.
3.4. Balance PIGD patients also had lower Mini-BESTest scores (22.2 ± 4.7) compared to TD patients (25 ± 2.2, p = 0.010) and controls (26.4 ± 1.7; p = 0.000014), indicating poorer balance control (Fig. 1a). The difference between TD patients and controls (p = 0.020) did not remain significant after Bonferroni correction. Using the 4 subdomains, PIGD showed worse anticipatory control than the TD group (p = 0.0067) and controls (p = 0.00014) and had worse dynamic gait compared to the control group (p = 0.00023).
3.5. Cognition In addition, PIGD patients had higher TMTB completion times (94.5 ± 54.6s; p = 0.00010) and lower FAB scores (15.5 ± 1.9; p = 0.0024) compared to controls (TMTB: 54.1 ± 12.8 s; FAB: 17.2 ± 1.0). There were no significant differences between both patient groups and between TD patients (TMTB: 73.5 ± 36.3 s; FAB: 16.1 ± 1.6) and controls (Fig. 1b–d).
3.6. Correlations The PIGD score was negatively correlated with the LL amplitude at the non-dominant side (r: −0.38; p = 0.00087), the overall LL amplitude domain (r = −0.29; p = 0.016), the mini-BESTest total score (r = −0.60; p = 0) and the anticipatory control (r = -0.50; p = 0.000005) and the dynamic gait score (r = −0.48; p = 0.00002) of the Mini-BESTest. On the other hand, there was a positive correlation between the PIGD score and UL phase difference variability (r = 0.46; p = 0.0001), the UL coordination domain (r = 0.30; p = 0.0097) and the TMT B score (r = 0.25; p = 0.032). These correlations were absent with the TD-score. 4. Discussion This study aimed to characterize the magnitude and distribution of behavioral impairment within clinical subtypes of PD in an early to mid-stage disease cohort and to evaluate performance on a repetitive movement task as a possible simple test to discriminate between TD and PIGD. The main and novel finding of this study was that patients with PIGD had more severe and generic motor abnormalities that were not restricted to gait and posture. In contrast, TD patients’ motor performance was very similar to controls except for a greater amplitude asymmetry in LL repetitive movements, which could be due to a lateralized kinetic tremor which was shown to be common in PD patients, even when absent during clinical testing [26]. Although we expected to find more extensive gait abnormalities, we could only show decreased gait speed and step length in the PIGD compared to TD group. Both patient groups had no significant variability, pace, rhythm and postural gait deficits compared to
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Table 3 Foot and finger tapping spatiotemporal movement parameters for patient groups and controls. PIGD Foot tapping amplitude domain Amplitude D (mm)** Amplitude ND (mm)** Foot tapping frequency domain Frequency D (Hz)* Frequency ND (Hz)* Foot tapping variability domain Amplitude CV D (%)* Amplitude CV ND (%)* Frequency CV D (%)* Frequency CV ND (%)* Foot tapping coordination domain Amplitude asymmetry (mm)** Frequency asymmetry (mm)* Phase difference (◦ )* Phase difference CV (%)* Finger tapping amplitude domain Amplitude D (mm)* Amplitude ND (mm)* Finger tapping frequency domain Frequency D (Hz)* Frequency ND (Hz)* Finger tapping variability domain Amplitude CV D (%)* Amplitude CV ND (%)* Frequency CV D (%)** Frequency CV ND (%)** Finger tapping coordination domain Amplitude asymmetry (mm)* Frequency asymmetry (mm)** Phase difference (◦ )** Phase difference CV (%)*
-0.83 (±1.32) 16.9 (± 7.5) 18.6 (± 8.1) −0.015 (±1.95) 2.89 (± 0.77) 3.12 (± 0.89) 0.29 (±2.42) 3.8 (± 1.8) 3.9 (± 2.3) 3.1 (± 1.0) 3.0 (± 1.5) 0.35 (±2.03) 3.61 (± 3.00) 0.43 (± 0.37) 12.77 (± 10.51) 37.89 (± 11.93) -0.39 (±1.60) 16.2 (± 10.4) 18.3 (± 12.0) 0.23 (± 2.24) 2.00 (± 0.49) 1.97 (± 0.42) 0.59 (±2.73) 1.9 (± 1.3) 1.5 (± 1.2) 0.87 (± 0.60) 0.85 (± 0.68) 0.83 (±2.39) 6.70 (± 6.57) 0.10 (± 0.13) 33.72 (± 28.00) 46.50 (± 13.47)
TD 0.50 (±1.61) 21.6 (± 7.2) 30.4 (± 14.1) 0.015 (±1.89) 2.97 (± 0.84) 3.03 (± 0.74) 0.12 (±3.80) 3.8 (±2.0) 3.3 (± 1.0) 3.0 (± 1.9) 2.9 (± 1.9) 0.18 (±2.35) 9.01 (± 8.21) 0.41 (± 0.45) 11.57 (± 8.74) 30.02 (±6.82) 0.13 (±2.04) 16.4 (± 12.1) 25.1 (± 15.8) −0.0060 (±1.76) 1.99 (± 0.51) 1.90 (± 0.30) −0.59 (±2.62) 1.5 (± 1.2) 1.4 (± 1.0) 0.70 (± 0.67) 0.60 (±0.64) 0.57 (±3.35) 9.30 (± 9.96) 0.18 (± 0.39) 22.11 (± 19.30) 40.41 (± 14.63)
CTRL
Overall p
1.12 (±2.48) 27.70 (± 14.30) 30.01 (± 14.78) 0.13 (±1.76) 3.08 (± 0.62) 3.04 (± 0.82) 0.15 (±2.56) 3.9 (± 1.7) 4.3 (± 2.5) 2.7 (± 0.86) 3.0 (± 1.3) -1.14 (±2.75) 4.83 (± 5.41) 0.32 (± 0.37) 6.31 (± 5.19) 28.92 (± 12.89) 0.62 (±2.20) 22.8 (±16.7) 24.6 (± 11.8) 0.26 (±1.46) 1.89 (± 0.30) 1.89 (± 0.28) 0.45 (±2.07) 1.4 (± 1.1) 1.4 (± 1.0) 0.70 (±0.37) 0.70 (± 0.37) -1.94 (±0.98) 6.25 (± 3.96) 0.0096 (± 0.012) 10.48 (± 9.79) 27.24 (± 9.07)
0.0011 0.0031 0.0004 0.80 0.56 0.99 0.19 0.95 0.29 0.47 0.91 0.062 0.0088 0.63 0.039 0.0050 0.13 0.13 0.092 0.65 0.71 0.97 0.18 0.33 0.91 0.34 0.15 0 0.33 0.0010 0.0002 0.000005
Post-hoc p PIGD-TD
PIGD-CTRL
TD-CTRL
Cohen’s d PIGD-TD
0.0035 0.014 0.00048 NA NA NA NA NA NA NA NA NA 0.0019 NA 0.87 0.034 NA NA NA NA NA NA NA NA NA NA NA 0.33 NA 0.96 0.059 0.27
0.0018 0.0032 0.0020 NA NA NA NA NA NA NA NA NA 0.80 NA 0.032 0.013 NA NA NA NA NA NA NA NA NA NA NA 0 NA 0.00021 0.000027 0.00011
0.53 0.21 0.74 NA NA NA NA NA NA NA NA NA 0.030 NA 0.16 0.90 NA NA NA NA NA NA NA NA NA NA NA 0.0032 NA 0.0029 0.062 0.0034
−0.90 −0.64 −1.03 0 0.1 0.11 0.13 0 0.34 0.07 0.06 0.08 −0.87 0.05 0.12 0.81 −0.28 −0.02 −0.48 0.12 0.02 0.19 0.44 -0.69 0.18 0.32 0.09 0.089 0.27 0.38 -0.31 -0.28
Means and standard deviations are shown. Overall p-values indicate results of overall group comparisons. Post-hoc p-values indicate results of post-hoc tests between groups. Cohen’s d values indicate effect sizes for differences between the PIGD and TD group. Parametric and non-parametric tests are indicated by * and ** respectively. ␣-level of significance was set at 0.05. PIGD: Postural Instability and Gait Disorder; TD: Tremor Dominant; CTRL: Controls; CV: Coefficient of Variability; D: Disease Dominant; ND: Disease Non-Dominant; NA: Not Applicable.
controls. These results are in line with recent work from Herman et al. (2014) who could also not detect subgroup differences with regards to objective gait and balance measures, except when using a more stringent subgroup classification method [15]. They argued that a more rigorous classification, especially in advanced disease stages, would lead to mixed phenotypes in more than 66% of patients. However, we had less than 11% of patients with mixed phenotypes, indicating that the original PIGD-TD classification method adapted by Stebbins et al. worked well in our cohort of early to mid-stage PD [22]. Lord et al. (2014) did find greater gait disorder in de novo PIGD versus TD when tested in ON [27]. These discrepant results are likely due to patient characteristics and medication factors, although age correction led to the same results in the present study and therefore is unlikely to play a role. When we clustered gait parameters across different domains, impairment in the postural control domain became evident in PIGD and not in TD. Our results also indicated postural control deficits in the PIGD compared to the TD group as shown by decreased Mini-BESTest scores. This highlights that a clinical balance test, which can be performed within 10 minutes, was able to distinguish between clinical subtypes. The more affected anticipatory control and dynamic gait in the PIGD compared to TD group indicated that the mini-BESTest was also able to characterize the specific components of postural deficits. Whereas gait impairment was limited in our cohort, we did find clear differences in performance on UL and LL repetitive movement tasks with considerably larger effect sizes than the gait outcomes. The PIGD group showed amplitude scaling difficulties during foot tapping, but, in contrast to gait, also had decreased coordination stability during this task. In addition, a PIGD-specific temporal
coordination deficit was present during finger tapping. Previous work showed that impaired bilateral coordination and anti-phase high frequency bimanual tasks could provide a useful tool for early diagnosis of PD [28,29]. Our results confirm that such movements, at least in the foot, were sensitive enough to distinguish between patients and controls in early to mid-stage PD and between PD subtypes and could thus serve as an easy and quick screening tool to determine subtypes. Earlier work from our group showed that a bilateral anti-phase finger tapping task was also able to discriminate between freezers and non-freezers [30]. Moreover, a recent neuroimaging study found decreased basal ganglia and supplementary motor area activity in PD compared to controls during a bimanual anti-phase motion. Increased cerebellar and motor cortical activity was suggested to compensate for this deficit [31]. Although this should be confirmed with neuroimaging data, our data could suggest a similar compensation mechanisms in TD compared to PIGD patients. There are only few quantitative studies investigating repetitive LL movements in PD. Nantel et al. (2011) found increased asymmetry and rhythmic variability in freezers compared to non-freezers and controls during a stepping in place task [32]. We found strikingly impaired amplitude scaling and coordination stability in our PIGD versus TD cohort, which were of greater magnitude during repetitive distal LL motor tasks than during gait. Previous research mainly performed in patients with and without FOG using different paradigms did not confirm these findings, although a recent study associated lower limb bradykinesia with amyloid- levels in the cerebrospinal fluid [32–34]. Our results possibly indicate a greater sensitivity of distal movements to pick-up amplitude and rhythmic control deficits than gait and more so in the LL compared to the UL.
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Fig. 1. Balance and cognition scores for patient groups and controls. Bars and whiskers represent means and standard deviations respectively within each subject group. * shows significant differences between groups with p < 0.05 ** and light gray lines show significant differences between group.
In addition, we found that UL deficits were greater in the coordination domain and LL deficits in the amplitude domain, suggesting different underlying neural pathways. This was confirmed by Barbe et al. (2014) who suggested that UL and LL sequential movement tasks might involve different subareas of central motor processing [35]. In line with earlier research, we found general cognitive impairment in the PIGD compared to TD group, as well as specifically decreased set-shifting abilities in PIGD compared to healthy controls that were not present in the TD group [6,7,9]. The ICICLE-PD study group recently showed that 42.1% of newly diagnosed patients presented with MCI illustrating the importance of cognitive impairment in early disease stages [36]. The fact that patients were cognitively impaired compared to controls and yet did not show any evidence of impaired gait variability is in contrast with the work of Hausdorff and co-workers [18]. Our findings of combined impairments of gait, postural control and distal motor control in PIGD patients, confirmed by correlation analysis, connects well with a recent body of work showing involvement of cholinergic neural pathways in the symptomatology even in early PD[37,38]. Bohnen et al. (2009) suggested that clinical heterogeneity in PD may be explained by the variable involvement of different neurotransmitter systems and that the PIGD phenotype could be associated with a greater degree of cholinergic denervation, mediating cognitive control of motor function [38,39]. In contrast, amplitude scaling during gait and performance of rapid anti-phase movements were shown to be predominantly dopaminergic in nature [40,41]. Our results showed that the PIGD group presented a profile of dopamine- as well as acetylcholine-mediated symptoms compared to the TD group. TD-patients did not display
such deficits, except for motor asymmetry, which is probably indicative of increased disease lateralization. As our groups were early to mid-stage and generally well-matched for MDS-UPDRS III scores, we hypothesize that the more complex symptomatology in the PIGD compared to TD subtype is not solely a representation of disease progression but of more severe and widespread dysfunction from the outset in more than one neurotransmitter system [13,39]. Interestingly, an 18-month follow-up study on an incident cohort showed more gait deterioration in TD than in PIGD [42]. However, disease progression cannot be ruled out as a factor mediating this pattern, as our PIGD group reported to have a longer duration of motor symptoms, higher UPDRS II and LED than the TD patients, being a limitation of our study. Longitudinal studies, combining behavioural and neuroimaging data are necessary to address this more definitively. Moreover, the relationship between empirical subtyping and data-driven methods is not straightforward as the PIGD and TD motor phenotypes did not seem to overlap with datadriven clusters in a study from Liu et al. (2011) [43]. Therefore, datadriven methods in large patient cohorts could provide a useful complementary tool to explore heterogeneity in Parkinson’s disease as it provides an objective and robust method to identify subtypes. 5. Conclusion Our results confirmed increased balance problems in PIGD patients and suggest the mini-BESTest as well as a bilateral repetitive movement task to be sensitive markers for PIGD in early to mid-stage PD. A novel finding is that motor scaling and coordination of repetitive UL and LL movement tasks is worse in PIGD compared to TD patients and counter-intuitively is more strongly
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related to the PIGD subtype than gait. This suggests a need to refine the PIGD subtype towards a more complex motor profile, which could be more accurately labeled as a non-TD rather than PIGD. To clarify the contribution of different neurotransmitter systems and the existence of true clinical subtypes rather than different stages of disease progression, longitudinal investigation including neuroimaging is indicated. Acknowledgements We would like to thank all subjects for their motivated and enthusiastic participation in this study. Special thanks go to C. Nielsen and A. Vanneste for their assistance in data collection. Support of this study was provided through a grant from the Special Research Fund of KU Leuven, Belgium (contract OT/11/091). Conflict of Interest: All authors report no conflict of interest. References [1] Jankovic J, McDermott M, Carter J, Gauthier S, Goetz C, Golbe L, et al. Variable expression of Parkinson’s disease: a base-line analysis of the DATATOP cohort. The Parkinson Study Group. Neurology 1990;40:1529–34. [2] Eggers C, Pedrosa DJ, Kahraman D, Maier F, Lewis CJ, Fink GR, et al. Parkinson subtypes progress differently in clinical course and imaging pattern. PloS One 2012;7:e46813–20. [3] van Rooden SM, Heiser WJ, Kok JN, Verbaan D, van Hilten JJ, Marinus J. The identification of Parkinson’s disease subtypes using cluster analysis: a systematic review. Mov Disord 2010;25:969–78. [4] Selikhova M, Williams DR, Kempster Pa, Holton JL, Revesz T, Lees aJ. A clinicopathological study of subtypes in Parkinson’s disease. Brain 2009;132:2947–57. [5] Auyeung M, Tsoi TH, Mok V, Cheung CM, Lee CN, Li R, et al. Ten year survival and outcomes in a prospective cohort of new onset Chinese Parkinson’s disease patients. J Neurol Neurosurg Psychiatry 2012;83:607–11. [6] Alves G, Larsen JP, Emre M, Wentzel-Larsen T, Aarsland D. Changes in motor subtype and risk for incident dementia in Parkinson’s disease. Mov Disord 2006;21:1123–30. [7] Burn DJ, Rowan EN, Allan LM, Molloy S, O’Brien JT, McKeith IG. Motor subtype and cognitive decline in Parkinson’s disease, Parkinson’s disease with dementia, and dementia with Lewy bodies. J Neurol Neurosurg Psychiatry 2006;77:585–9. [8] Taylor JP, Rowan EN, Lett D, O’Brien JT, McKeith IG, Burn DJ. Poor attentional function predicts cognitive decline in patients with non-demented Parkinson’s disease independent of motor phenotype. J Neurol Neurosurg Psychiatry 2008;79:1318–23. [9] Verbaan D, Marinus J, Visser M, van Rooden SM, Stiggelbout AM, Middelkoop HA, et al. Cognitive impairment in Parkinson’s disease. J Neurol Neurosurg Psychiatry 2007;78:1182–7. [10] Rossi C, Frosini D, Volterrani D, De Feo P, Unti E, Nicoletti V, et al. Differences in nigro-striatal impairment in clinical variants of early Parkinson’s disease: evidence from a FP-CIT SPECT study. Eur J Neurol 2010;17:626–30. [11] Prodoehl J, Planetta PJ, Kurani AS, Comella CL, Corcos DM, Vaillancourt DE. Differences in brain activation between tremor- and nontremor-dominant Parkinson disease. JAMA Neurol 2013;70:100–6. [12] Rosenberg-Katz K, Herman T, Jacob Y, Giladi N, Hendler T, Hausdorff JM. Gray matter atrophy distinguishes between Parkinson disease motor subtypes. Neurology 2013;80:1476–84. [13] Bohnen NI, Müller MLTM, Zarzhevsky N, Koeppe Ra Bogan CW, Kilbourn MR, et al. Leucoaraiosis, nigrostriatal denervation and motor symptoms in Parkinson’s disease. Brain 2011;134:2358–65. [14] Herman T, Rosenberg-Katz K, Jacob Y, Auriel E, Gurevich T, Giladi N, et al. White matter hyperintensities in Parkinson’s disease: do they explain the disparity between the postural instability gait difficulty and tremor dominant subtypes. PloS One 2013;8:e55193–200. [15] Herman T, Weiss A, Brozgol M, Giladi N, Hausdorff JM. Gait and balance in Parkinson’s disease subtypes: objective measures and classification considerations. J Neurol 2014;261:2401–10. [16] Lord S, Galna B, Rochester L. Moving forward on gait measurement: toward a more refined approach. Mov Disord 2013;28:1534–43. [17] Hausdorff JM, Schaafsma JD, Balash Y, Bartels AL, Gurevich T, Giladi N. Impaired regulation of stride variability in Parkinson’s disease subjects with freezing of gait. Exp Brain Res 2003;149:187–94.
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[18] Plotnik M, Giladi N, Hausdorff JM. Bilateral coordination of gait and Parkinson’s disease: the effects of dual tasking. J Neurol Neurosurg Psychiatry 2009;80:347–50. [19] Baltadjieva R, Giladi N, Gruendlinger L, Peretz C, Hausdorff JM. Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson’s disease. Eur J Neurosci 2006;24:1815–20. [20] Vercruysse S, Gilat M, Shine JM, Heremans E, Lewis S, Nieuwboer A. Freezing beyond gait in Parkinson’s disease: a review of current neurobehavioral evidence. Neurosci Biobehav Rev 2014;43:213–27. [21] Factor Sa, Steenland NK, Higgins DS, Molho ES, Kay DM, Montimurro J, et al. Postural instability/gait disturbance in Parkinson’s disease has distinct subtypes: an exploratory analysis. J Neurol Neurosurg Psychiatry 2011;82:564–8. [22] Stebbins GT, Goetz CG, Burn DJ, Jankovic J, Khoo TK, Tilley BC. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson’s disease rating scale: comparison with the unified Parkinson’s disease rating scale. Mov Disord 2013;28:668–70. [23] Vercruysse S, Spildooren J, Heremans E, Vandenbossche J, Levin O, Wenderoth N, et al. Freezing in Parkinson’s disease: a spatiotemporal motor disorder beyond gait. Mov Disord 2012;27:254–63. [24] Pijnappels M, Bobbert MF, van Dieën JH. Changes in walking pattern caused by the possibility of a tripping reaction. Gait Posture 2001;14:11–8. [25] Lord S, Galna B, Verghese J, Coleman S, Burn D, Rochester L. Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. J Gerontol A Biol Sci Med Sci 2013;68:820–7. [26] Kraus PH, Lemke MR, Reichmann H. Kinetic tremor in Parkinson’s disease–an underrated symptom. J Neural Transm 2006;113:845–53. [27] Lord S, Galna B, Coleman S, Yarnall A, Burn D, Rochester L. Cognition and gait show a selective pattern of association dominated by phenotype in incident Parkinson’s disease. Front Aging Neurosci 2014;6:249. [28] Almeida QJ, Wishart LR, Lee TD. Bimanual coordination deficits with Parkinson’s disease: the influence of movement speed and external cueing. Mov Disord 2002;17:30–7. [29] Ponsen MM, Daffertshofer A, van den Heuvel E, Wolters EC, Beek PJ, Berendse HW. Bimanual coordination dysfunction in early, untreated Parkinson’s disease. Parkinsonism Relat Disord 2006;12:246–52. [30] Vercruysse S, Spildooren J, Heremans E, Vandenbossche J, Wenderoth N, Swinnen SP, et al. Abnormalities and cue dependence of rhythmical upper-limb movements in Parkinson patients with freezing of gait. Neurorehabil Neural Repair 2012;26:636–45. [31] Wu T, Wang L, Hallett M, Li K, Chan P. Neural correlates of bimanual anti-phase and in-phase movements in Parkinson’s disease. Brain 2010;133:2394–409. [32] Nantel J, de Solages C, Bronte-Stewart H. Repetitive stepping in place identifies and measures freezing episodes in subjects with Parkinson’s disease. Gait Posture 2011;34:329–33. [33] Shine JM, Matar E, Bolitho SJ, Dilda V, Morris TR, Naismith SL, et al. Modeling freezing of gait in Parkinson’s disease with a virtual reality paradigm. Gait Posture 2013;38:104–8. [34] Alves G, Pedersen KF, Bloem BR, Blennow K, Zetterberg H, Borm GF, et al. Cerebrospinal fluid amyloid- and phenotypic heterogeneity in de novo Parkinson’s disease. J Neurol Neurosurg Psychiatry 2013;84:537–43. [35] Barbe MT, Amarell M, Snijders AH, Florin E, Quatuor E-L, Schönau E, et al. Gait and upper limb variability in Parkinson’s disease patients with and without freezing of gait. J Neurol 2014;261:330–42. [36] Yarnall AJ, Breen DP, Duncan GW, Khoo TK, Coleman SY, Firbank MJ, et al. Characterizing mild cognitive impairment in incident Parkinson disease: the ICICLE-PD study. Neurology 2014;82:308–16. [37] Rochester L, Yarnall AJ, Baker MR, David RV, Lord S, Galna B, et al. Cholinergic dysfunction contributes to gait disturbance in early Parkinson’s disease. Brain 2012;135:2779–88. [38] Bohnen NI, Frey Ka Studenski S, Kotagal V, Koeppe Ra Scott PJH, et al. Gait speed in Parkinson disease correlates with cholinergic degeneration. Neurology 2013;81:1611–6. [39] Bohnen NI, Albin RL. Cholinergic denervation occurs early in Parkinson disease. Neurology 2009;73:256–7. [40] O’Sullivan JD, Said CM, Dillon LC, Hoffman M, Hughes AJ. Gait analysis in patients with Parkinson’s disease and motor fluctuations: influence of levodopa and comparison with other measures of motor function. Mov Disord 1998;13:900–6. [41] Blin O, Ferrandez AM, Pailhous J, Serratrice G. Dopa-sensitive and doparesistant gait parameters in Parkinson’s disease. J Neurol Sci 1991;103:51–4. [42] Galna B, Lord S, Burn DJ, Rochester L. Progression of gait dysfunction in incident Parkinson’s disease: Impact of medication and phenotype. Mov Disord 2014;30:359–67. [43] Liu P, Feng T, Wang YJ, Zhang X, Chen B. Clinical heterogeneity in patients with early-stage Parkinson’s disease: a cluster analysis. J Zhejiang Univ Sci B 2011;12:694–703.
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Research report
Distal motor deficit contributions to postural instability and gait disorder in Parkinson’s disease Griet Vervoort a,∗ , Aniek Bengevoord a,b , Evelien Nackaerts a , Elke Heremans a , Wim Vandenberghe c , Alice Nieuwboer a a
KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium Radboud Universiteit Nijmegen, Medical Centre, Nijmegen, The Netherlands c University Hospitals Leuven, Department of Neurology, Leuven, Belgium b
h i g h l i g h t s • • • •
In early to mid-disease, a clinical balance test distinguishes between PIGD and TD subtypes. Objective gait analysis shows limited gait deficits in PIGD compared to TD. PIGD has pronounced distal coordination impairment compared to TD. PIGD shows more widespread motor dysfunction than hitherto reported.
a r t i c l e
i n f o
Article history: Received 5 February 2015 Received in revised form 11 March 2015 Accepted 12 March 2015 Available online 21 March 2015 Keywords: Parkinson’s disease Clinical subtype Motor control
a b s t r a c t Clinical subtypes in Parkinson’s disease (PD) are often based on the presence of clustered motor symptoms. In contrast to the tremor dominant (TD) subtype, the postural instability and gait disorder (PIGD) subtype is characterized by predominantly axial motor involvement and increased cognitive impairment. It is, however, unclear if subtypes represent distinct underlying neuropathological mechanisms or reflect more severe disease progression. We aimed to clarify the validity of PD subtypes by investigating behavioral outcomes at multiple levels. Therefore, spatiotemporal kinematics of gait, upper and lower limb repetitive movements in combination with a balance and cognitive assessment were recorded in 73 patients with PD. We classified patients as PIGD (n = 43), TD (n = 22) or indeterminate (n = 8) while ‘off’ medication and recruited 20 age-matched controls. Surprisingly, differences between PIGD and TD were more prominent during repetitive distal motor tasks than during gait. Gait impairment in PIGD was only shown by reduced step length and gait speed. However, motor scaling and coordination of distal movements were more affected in PIGD than in TD patients. PIGD patients also had impaired postural control compared to TD patients as shown by lower mini-BESTest scores. There were no cognitive differences between patient subgroups. Distal movement was not significantly different in TD patients from controls, except for greater movement asymmetry. The results indicate a widespread impairment within PIGD with more pronounced distal than axial motor deficits. This suggests involvement of different neurotransmitter systems in the neuropathology of PD subtypes, which are at least partially independent of disease progression. © 2015 Elsevier B.V. All rights reserved.
1. Introduction
∗ Corresponding author. Tel.: +32 16 37 65 75. E-mail addresses: [email protected] (G. Vervoort), [email protected] (A. Bengevoord), [email protected] (E. Nackaerts), [email protected] (E. Heremans), [email protected] (W. Vandenberghe), [email protected] (A. Nieuwboer). http://dx.doi.org/10.1016/j.bbr.2015.03.026 0166-4328/© 2015 Elsevier B.V. All rights reserved.
The clinical heterogeneity in Parkinson’s disease (PD) has inspired the delineation of clinical subtypes. Clinical differences between tremor dominant (TD) and postural instability and gait disorder (PIGD) patients were first described by Jankovic et al. (1990) [1]. Both subtypes are mainly defined by their clustered motor symptoms. While TD is characterized by a predominant presence of tremor, PIGD is associated with bradykinesia, rigidity,
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falling and freezing of gait (FOG) [1]. Different empirical and data-driven studies have established clear associations between the PIGD subtype, rapid disease progression and old age of onset and between the TD subtype, slow disease progression and young age of onset [2,3]. Auyeung et al. (2012) even showed that PIGD at disease onset is associated with a 2.1 times higher mortality, although this could not be confirmed by other studies [4,5]. However, questions have been raised about the significance of the distinction between the PIGD and TD phenotypes. Studies that looked into non-motor differences between both subtypes showed that PIGD is associated with cognitive decline and depression [6–8] and suggested this to reflect more advanced disease [9]. In a longitudinal study, PIGD patients had increased disease duration and severity, lower MMSE scores and higher depression scores compared to the TD group at baseline. After 8 years, 57.1% of non-PIGD patients changed to the PIGD subtype while only 1.9% changed in the opposite direction. Moreover, the switch to the PIGD subgroup was associated with a severely increased risk of developing dementia. This led the authors to conclude that transition between TD and PIGD is unidirectional, irreversible and strongly related to cognitive decline [6]. This suggests that PIGD may simply represent a more advanced disease stage of PD than the TD profile. By contrast, different imaging studies compared groups of TD and PIGD patients who were well-matched for age and disease severity, and showed structural and functional differences, providing support for the existence of distinct subtypes [2,10–13]. Bohnen et al. (2011) showed increased white matter hyperintensities (WMHs) in the PIGD compared to TD group independent of dopaminergic denervation [13]. However, these results could not be replicated [14]. Although the above-mentioned studies provided a first indication for true clinical phenotypes in PD, it is difficult to draw strong conclusions due to the diversity of methodologies, methods of classification and sometimes conflicting results of the studies performed [15]. In addition, symptom mapping usually occurred based on Unified Parkinson’s Disease Rating Scales (UPDRS) scores only without precise measurements of behavioral correlates. Therefore, this study aimed to clarify the validity of PIGD and TD subgroups matched for age and disease severity (Unified Parkinson’s Disease Rating Scale (UPDRS), part III), by investigating behavioral outcomes at multiple levels in early to mid-stage PD. We expected to find clear gait-related deficits in the PIGD compared to TD group as well as subtle postural instability and cognitive impairment. In addition, we tested a wide range of spatiotemporal outcomes for each task category, as recommended for gait by Lord et al. (2013), to capture the nature of underlying motion deficits and their neural basis [16]. Due to the previously established cognitive and balance deterioration in the PIGD group, we hypothesized that this group would show greater abnormality in the gait variability and postural control domain [15–19]. Recent studies revealed that freezing-related movement abnormalities were not only episodic and gait-related, but also occurred outside freezing episodes and in other movement effectors [17,20]. Due to the suggested relationship between FOG and PIGD, we also hypothesized that lower limb (LL) and upper limb (UL) repetitive movements would be able to distinguish between PIGD and TD [21]. To our knowledge, this is the first study comparing PIGD and TD based on quantitative evaluation of cognition, balance and spatiotemporal motor tasks in different movement effectors. 2. Methods 2.1. Participants Seventy-three patients with PD and 20 healthy controls were included. Patients with PD were classified as PIGD (n = 43), TD
(n = 22) or indeterminate (n = 8) based on subscores of the MDSUPDRS while ‘off’ medication using an adapted method for the revised version of the UPDRS [22], based on the one developed by Jankovic et al. (1990) [1]. Patients were recruited at the Movement Disorders Clinic of the University Hospital Leuven according to the UK Brain Bank criteria. Inclusion criteria were: Hoehn and Yahr (H&Y) stage 1–3 while ‘off’ medication, ability to walk independently for 10 min and a Mini-Mental State Examination (MMSE) score ≥ 24. Exclusion criteria were presence of motor or neurological comorbidities affecting test performance. Patients with an indeterminate subtype were excluded from further analysis. Healthy controls, PIGD and TD subgroups were a priori matched for age and gender and post-hoc for disease severity (MDS-UPDRS III ‘off’ medication). The disease dominant side was the side of disease onset. The study was approved by the Ethical Committee of the University Hospitals Leuven. All subjects gave written informed consent according to the Declaration of Helsinki. 2.2. Behavioral tests To maximize between group behavioral differences, all tests were performed in a semi-randomized order while patients were in a practically defined ‘off’ state, i.e. at least 12 h after last medication intake. Disease severity was assessed using the MDS-UPDRS. Prevalence and severity of FOG were assessed using the New Freezing of Gait Questionnaire (NFOG-Q). All tasks involving the LL were recorded using a 10-camera VICON 3D motion analysis system (©Vicon Motion Systems Ltd. UK) at a sample frequency of 100 Hz. Sixteen markers were placed on anatomical landmarks according to the pelvic and LL part of the Full Plugin Gait model. Marker trajectories were processed using Nexus software (version 1.8.5). UL movement was recorded using a Micro 1401 acquisition unit (Cambridge Electronic Device, UK) with Spike 2 software with temporal and spatial resolutions of 2000 Hz and 0.0001◦ through analogue encoders placed on the rotation axis of a device connected to each index finger [23]. 2.2.1. Gait Subjects were instructed to perform a continuous overground gait task at a self-preferred pace until at least 40 steps were recorded in 94.6% of subjects. The remaining 5.4% had only 30–40 identifiable steps due to marker visibility issues. Position and timing of heel strikes and toe-offs were processed in a customized Matlab program to calculate within-limb mean and variability and between-limb asymmetry for step length and time, stance time and swing time. For step width and gait speed, the mean and variability were calculated [24]. Coefficients of variability (CV) were used to express variability outcomes. Asymmetry was calculated between the disease dominant and non-dominant side for patients and between right and left side for controls. A conceptual approach was applied to reduce the number of parameters according to the gait model described by Lord et al. (2013) [16,25]. Spatiotemporal gait parameters were converted to z-scores and summed within the 5 gait domains: pace, rhythm, variability, asymmetry and postural control. 2.2.2. Foot and finger tapping task Subjects performed a bilateral anti-phase (alternating) tapping movement with their feet and index fingers while visual feedback of the movements was prevented as previously described for the UL by Vercruysse et al. (2012) [23]. An auditory cue was provided for the first 8 movement cycles at a frequency of 89 beats per minute (bpm) for the LL and 1.63 Hz for the UL task [23]. After the cue, subjects were instructed to internally generate a similar movement for the following 20–25 s. Each task was repeated 3 times unless the patients indicated this was not feasible. Outcome
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measures for both LL and UL were calculated for the uncued conditions and consisted of the within limb mean and variability and between limb asymmetry of amplitude and frequency. For the phase difference, the mean and variability were calculated. CV and asymmetry were calculated as explained for gait. Analogous to gait, spatiotemporal parameters were converted to z-scores and summed within 4 conceptual domains: amplitude, frequency, variability (amplitude and frequency CV) and coordination: amplitude and frequency asymmetry, phase difference (mean and variability). 2.2.3. Balance and cognitive assessment The total and subdomain scores of the Mini-BESTest were used to evaluate anticipatory balance, reactive postural control and sensory orientation of balance and dynamic gait. All subjects underwent a cognitive test battery including the Montreal Cognitive Assessment (MoCa), Trail Making Test (TMT) parts A and B and Frontal Assessment Battery (FAB) to assess executive functions and task switching. 2.3. Statistical analyses Outliers were removed from analyses if values deviated more than 2 standard deviations from the mean (0.47% of all data). In case of a normal distribution and homogeneity of variances, we used unpaired t-tests or Chi2 tests for between patient group comparisons. Due to the expected dependency between spatiotemporal outcome measures within a particular motor task, comparisons between patient groups and controls were performed using parametric MANOVAs with post-hoc Tukey HSD tests. Starting from a model including all outcome measures, we applied a stepwise reduction of the model until the largest part of the variance was explained with the least amount of variables. In case residuals of the data were not normally distributed or there was no homogeneity of variances as tested by the Levene’s test, we used a non-parametric Kruskal-Wallis ANOVAs with post-hoc Mann-Whitney U tests. A Bonferroni correction for the number of comparisons within each task and for the number of between group comparisons was used for the non-parametric post-hoc tests. Tests were repeated with age-correction, but this did not change the results substantially. Therefore, results without age-correction were reported. In addition, the results section only reports the post-hoc results. The level of statistical significance was set at ˛ = 0.05. Cohen’s d effect sizes between the PIGD and TD group were calculated for all outcome measures. Pearson correlations were calculated between the significantly different outcome measures and PIGD and TD scores within
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the entire patient group. In case of abnormal distribution, Spearman correlations were calculated. 3. Results 3.1. Subject characteristics Subject characteristic are described in Table 1. PIGD patients had significantly higher MDS-UPDRS II and PIGD scores and lower TD scores compared to TD patients. In addition, the PIGD subgroup had longer disease duration (from date of first motor symptom onset), higher LED values and lower MMSE scores compared to TD patients. Table 1 does not include values from the indeterminate group. 3.2. Gait Post-hoc tests indicated slower gait speed (p = 0.037) and shorter step lengths at the dominant (p = 0.035) and non-dominant side in the PIGD compared to TD group (p = 0.031). Surprisingly, there were no other group differences when comparing the individual kinematics (Table 2), which was also reflected in the small standardized effect sizes. When normalized gait parameters (zscores) within the different gait domains were compared, there were lower scores in the postural control domain in PIGD compared to controls (p = 0.032). 3.3. Foot and finger tapping task Differences between PIGD and TD were more prominent during repetitive motor tasks than in gait. This was reflected by the higher and remarkably strong Cohen’s d values than those reported in Table 2. During foot tapping, PIGD patients had decreased amplitudes of the dominant (TD: p = 0.014; CTRL: p = 0.0032) and non-dominant side (TD: p = 0.00048; CTRL: p = 0.0020) and increased phase difference variability (TD: p = 0.034; CTRL: p = 0.013) compared to TD patients and controls. The phase difference was increased in PIGD patients compared to controls (p = 0.032). Interestingly, amplitude asymmetry was increased in TD compared to PIGD patients (p = 0.0019) and controls (p = 0.030) (Table 3). When combining the parameters in the various domains, PIGD patients had more impairment in the LL amplitude domain compared to TD patients (p = 0.0035) and controls (p = 0.0018). During finger tapping, PIGD patients showed a higher phase difference compared to controls (p = 0.000027). Phase difference
Table 1 Subject characteristics.
Age (years) Sex (M/F) Education (years) Disease dominance (L/R) Disease duration (years) MDS-UPDRS I (0-48) MDS-UPDRS II (0-52) MDS-UPDRS III (0-132) MDS-UPDRS IV (0-24) PIGD score (0-20) TD score (0-44) LED (mg/day) N-FOGQ (0-29) MMSE (0-30)
PIGD (n = 43)
TD (n = 22)
CTRL (n = 20)
p-value
61.4 (± 10.9) 29/14 13.6 (± 3.0) 17/26 7.2 (± 4.3) 10.4 (± 5.3) 12.7 (± 8.0) 30.5 (± 13.9) 2.1 (± 2.6) 4.3 (± 2.8) 4.0 (± 4.1) 535.1 (± 285.8) 4.2 (± 7.7) 28.0 (± 1.7)
57.3 (± 8.8) 12/10 15.5 (± 3.9) 8/14 4.8 (± 2.7) 9.0 (± 5.0) 7.4 (± 4.0) 26.2 (± 10.9) 1.8 (± 2.4) 1.5 (± 0.9) 8.0 (± 3.5) 286.4 (± 263.9) 0.0 (± 0.0) 28.9 (± 1.2)
57.7 (± 8.8) 14/6 14.4 (± 2.5) NA NA NA NA NA NA NA NA NA NA 29.3 (± 0.8)
0.25a 0.31a 0.99a 0.80 0.03 0.32 0.005 0.21 0.57 0.00004 0.0002 0.001 0.07 0.04a
0.34b 0.84b 0.89b
0.99c 0.30c 0.95c
0.003b
0.38c
Means and standard deviations (SD) are shown. P-values are the result of comparisons between 2 or 3 groups with ␣-level of significance set at 0.05. indicate posthoc differences between the PIGD and TD group, PIGD and CTRL group and TD and CTRL group respectively. PIGD: Postural Instability and Gait Disorder; TD: Tremor Dominant; CTRL: Controls; MDS-UPDRS: Movement Disorder Society–Unified Parkinson’s Disease Rating Scale; LED: Levodopa Equivalent Dose; N-FOGQ: New- Freezing Of Gait Questionnaire; MMSE: Mini Mental State Examination; NA: Not Applicable. a,b,c
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Table 2 Spatiotemporal gait parameters within 5 gait domains for patient groups and controls. PIGD Pace Domain** Gait speed (m/s)* Step length D (cm)* Step length ND (cm)* Swing time CV D (%)* Swing time CV ND (%)* Rhythm Domain** Step time D (ms)* Step time ND (ms)* Swing time D (ms)* Swing time ND (ms)* Stance time D (ms)* Stance time ND (ms)* Variability Domain* Gait speed CV (%)* Step length CV D (%)* Step length CV ND (%)* Step time CV D (%)* Step time CV ND (%)* Stance time CV D (%)* Stance time CV ND (%)* Asymmetry Domain** Swing time asymmetry (ms)* Step time asymmetry (ms)* Stance time asymmetry (ms)* Postural Control Domain** Step length asymmetry (mm)* Step width D (cm)** Step width ND (cm)** Step width CV D (%)** Step width CV ND (%)**
−0.20 (± 3.05) 0.99 (±0.10) 53.7 (± 5.9) 53.7 (± 5.5) 9.4 (± 3.5) 9.8 (± 3.7) -0.38 (± 6.02) 548.9 (± 32.4) 551.4 (± 34.5) 449.1 (± 23.5) 449.2 (± 25.6) 648.3 (± 47.1) 648.7 (± 41.9) 0.091 (± 6.39) 16 (±5.2) 14 (± 5.6) 14 (±5.7) 10 (± 3.9) 9.9 (± 3.7) 12 (± 4.5) 11 (± 4.7) -0.26 (± 2.32) 5.4 (± 5.5) 7.3 (± 11.4) 5.1 (± 6.5) 0.28 (± 1.52) 13.2 (± 13.7) 6.8 (± 1.6) 6.7 (± 1.9) 5.6 (±1.3) 5.7 (± 1.2)
TD 0.80 (± 2.70) 1.04 (±0.10) 57.0 (± 4.9) 56.4 (± 5.9) 9.1 (3.7) 9.6 (± 3.6) -0.36 (± 5.02) 547.0 (± 25.7) 550.2 (± 26.3) 449.6 (± 22.7) 451.0 (± 15.8) 647.1 (± 36.2) 643.7 (± 38.8) 0.22 (±6.22) 16 (± 4.6) 13 (± 5.8) 15 (± 5.9) 10 (± 3.6) 9.8 (± 3.8) 12 (± 4.3) 13 (± 5.7) -0.29 (±2.33) 5.8 (± 7.8) 7.4 (± 11.6) 4.8 (± 5.4) -0.27 (± 1.08) 12.6 (± 18.6) 6.8 (± 1.3) 6.9 (± 1.2) 5.5 (± 7.5) 5.3 (± 8.4)
CTRL
Overall p
0.76 (± 1.88) 1.03 (±0.088) 56.5 (± 3.0) 56.3 (± 3.5) 9.6 (± 3.5) 9.6 (± 3.3) 0.46 (2.93) 551.9 (± 15.0) 552.0 (± 15.4) 456.3 (± 10.6) 454.0 (± 12.2) 648.1 (± 22.4) 647.8 (± 20.8) 0.54 (± 5.91) 17 (±3.8) 14 (± 4.8) 15 (±5.5) 11 (± 3.6) 10 (± 3.4) 12 (± 4.1) 12 (± 4.2) -0.71 (± 0.83) 4.0 (± 2.0) 4.8 (± 4.1) 3.8 (± 2.4) -0.64 (± 0.55) 6.3 (± 4.2) 6.6 (± 4.0) 6.6 (± 0.67) 5.5 (±0.33) 5.7 (± 0.42)
0.38 0.037 0.026 0.025 0.94 0.97 0.91 0.57 0.82 0.26 0.58 0.82 0.79 0.93 0.92 0.90 0.83 0.77 0.77 0.83 0.79 0.62 0.52 0.54 0.73 0.032 0.20 0.42 0.63 0.86 0.27
Post-hoc p-values PIGD-CTRL PIGD-TD
TD-CTRL
NA 0.037 0.035 0.031 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.23 NA NA NA NA NA
NA 0.69 0.85 0.80 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.23 NA NA NA NA NA
NA 0.28 0.16 0.17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0096 NA NA NA NA NA
Cohen’s d −0.35 −0.5 −0.61 −0.47 0.09 0.04 −0.003 0.07 0.04 −0.02 −0.08 0.03 0.12 −0.05 0 0.18 −0.17 −0.03 0.03 −0.02 −0.28 0.01 −0.06 −0.009 0.05 0.42 0.04 0 −0.13 0.09 0.39
Means and standard deviations are shown. Overall p-values indicate results from overall between group analyses. Cohen’s d values indicate effect sizes for the differences between the PIGD and TD group. Parametric analyses and non-parametric analyses are indicated by * and ** respectively. The ␣-level of significance was set at 0.05. PIGD: Postural Instability and Gait Disorder; TD: Tremor Dominant; CTRL: Controls; CV: Coefficient of Variability; D: disease dominant; ND: disease non-dominant; NA: Not Applicable.
variability (PIGD: p = 0.00011; TD: p = 0.0034) and frequency asymmetry (PIGD: p = 0.00021; TD: p = 0.0029) were increased in both patient groups compared to controls. There was a trend for increased phase difference (p = 0.059) in the PIGD compared to TD group (Table 3). Grouping the parameters into domains, led to more impairment in the UL coordination domain in both patient groups (PIGD: p = 0; TD: p = 0.0032) compared to controls.
3.4. Balance PIGD patients also had lower Mini-BESTest scores (22.2 ± 4.7) compared to TD patients (25 ± 2.2, p = 0.010) and controls (26.4 ± 1.7; p = 0.000014), indicating poorer balance control (Fig. 1a). The difference between TD patients and controls (p = 0.020) did not remain significant after Bonferroni correction. Using the 4 subdomains, PIGD showed worse anticipatory control than the TD group (p = 0.0067) and controls (p = 0.00014) and had worse dynamic gait compared to the control group (p = 0.00023).
3.5. Cognition In addition, PIGD patients had higher TMTB completion times (94.5 ± 54.6s; p = 0.00010) and lower FAB scores (15.5 ± 1.9; p = 0.0024) compared to controls (TMTB: 54.1 ± 12.8 s; FAB: 17.2 ± 1.0). There were no significant differences between both patient groups and between TD patients (TMTB: 73.5 ± 36.3 s; FAB: 16.1 ± 1.6) and controls (Fig. 1b–d).
3.6. Correlations The PIGD score was negatively correlated with the LL amplitude at the non-dominant side (r: −0.38; p = 0.00087), the overall LL amplitude domain (r = −0.29; p = 0.016), the mini-BESTest total score (r = −0.60; p = 0) and the anticipatory control (r = -0.50; p = 0.000005) and the dynamic gait score (r = −0.48; p = 0.00002) of the Mini-BESTest. On the other hand, there was a positive correlation between the PIGD score and UL phase difference variability (r = 0.46; p = 0.0001), the UL coordination domain (r = 0.30; p = 0.0097) and the TMT B score (r = 0.25; p = 0.032). These correlations were absent with the TD-score. 4. Discussion This study aimed to characterize the magnitude and distribution of behavioral impairment within clinical subtypes of PD in an early to mid-stage disease cohort and to evaluate performance on a repetitive movement task as a possible simple test to discriminate between TD and PIGD. The main and novel finding of this study was that patients with PIGD had more severe and generic motor abnormalities that were not restricted to gait and posture. In contrast, TD patients’ motor performance was very similar to controls except for a greater amplitude asymmetry in LL repetitive movements, which could be due to a lateralized kinetic tremor which was shown to be common in PD patients, even when absent during clinical testing [26]. Although we expected to find more extensive gait abnormalities, we could only show decreased gait speed and step length in the PIGD compared to TD group. Both patient groups had no significant variability, pace, rhythm and postural gait deficits compared to
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Table 3 Foot and finger tapping spatiotemporal movement parameters for patient groups and controls. PIGD Foot tapping amplitude domain Amplitude D (mm)** Amplitude ND (mm)** Foot tapping frequency domain Frequency D (Hz)* Frequency ND (Hz)* Foot tapping variability domain Amplitude CV D (%)* Amplitude CV ND (%)* Frequency CV D (%)* Frequency CV ND (%)* Foot tapping coordination domain Amplitude asymmetry (mm)** Frequency asymmetry (mm)* Phase difference (◦ )* Phase difference CV (%)* Finger tapping amplitude domain Amplitude D (mm)* Amplitude ND (mm)* Finger tapping frequency domain Frequency D (Hz)* Frequency ND (Hz)* Finger tapping variability domain Amplitude CV D (%)* Amplitude CV ND (%)* Frequency CV D (%)** Frequency CV ND (%)** Finger tapping coordination domain Amplitude asymmetry (mm)* Frequency asymmetry (mm)** Phase difference (◦ )** Phase difference CV (%)*
-0.83 (±1.32) 16.9 (± 7.5) 18.6 (± 8.1) −0.015 (±1.95) 2.89 (± 0.77) 3.12 (± 0.89) 0.29 (±2.42) 3.8 (± 1.8) 3.9 (± 2.3) 3.1 (± 1.0) 3.0 (± 1.5) 0.35 (±2.03) 3.61 (± 3.00) 0.43 (± 0.37) 12.77 (± 10.51) 37.89 (± 11.93) -0.39 (±1.60) 16.2 (± 10.4) 18.3 (± 12.0) 0.23 (± 2.24) 2.00 (± 0.49) 1.97 (± 0.42) 0.59 (±2.73) 1.9 (± 1.3) 1.5 (± 1.2) 0.87 (± 0.60) 0.85 (± 0.68) 0.83 (±2.39) 6.70 (± 6.57) 0.10 (± 0.13) 33.72 (± 28.00) 46.50 (± 13.47)
TD 0.50 (±1.61) 21.6 (± 7.2) 30.4 (± 14.1) 0.015 (±1.89) 2.97 (± 0.84) 3.03 (± 0.74) 0.12 (±3.80) 3.8 (±2.0) 3.3 (± 1.0) 3.0 (± 1.9) 2.9 (± 1.9) 0.18 (±2.35) 9.01 (± 8.21) 0.41 (± 0.45) 11.57 (± 8.74) 30.02 (±6.82) 0.13 (±2.04) 16.4 (± 12.1) 25.1 (± 15.8) −0.0060 (±1.76) 1.99 (± 0.51) 1.90 (± 0.30) −0.59 (±2.62) 1.5 (± 1.2) 1.4 (± 1.0) 0.70 (± 0.67) 0.60 (±0.64) 0.57 (±3.35) 9.30 (± 9.96) 0.18 (± 0.39) 22.11 (± 19.30) 40.41 (± 14.63)
CTRL
Overall p
1.12 (±2.48) 27.70 (± 14.30) 30.01 (± 14.78) 0.13 (±1.76) 3.08 (± 0.62) 3.04 (± 0.82) 0.15 (±2.56) 3.9 (± 1.7) 4.3 (± 2.5) 2.7 (± 0.86) 3.0 (± 1.3) -1.14 (±2.75) 4.83 (± 5.41) 0.32 (± 0.37) 6.31 (± 5.19) 28.92 (± 12.89) 0.62 (±2.20) 22.8 (±16.7) 24.6 (± 11.8) 0.26 (±1.46) 1.89 (± 0.30) 1.89 (± 0.28) 0.45 (±2.07) 1.4 (± 1.1) 1.4 (± 1.0) 0.70 (±0.37) 0.70 (± 0.37) -1.94 (±0.98) 6.25 (± 3.96) 0.0096 (± 0.012) 10.48 (± 9.79) 27.24 (± 9.07)
0.0011 0.0031 0.0004 0.80 0.56 0.99 0.19 0.95 0.29 0.47 0.91 0.062 0.0088 0.63 0.039 0.0050 0.13 0.13 0.092 0.65 0.71 0.97 0.18 0.33 0.91 0.34 0.15 0 0.33 0.0010 0.0002 0.000005
Post-hoc p PIGD-TD
PIGD-CTRL
TD-CTRL
Cohen’s d PIGD-TD
0.0035 0.014 0.00048 NA NA NA NA NA NA NA NA NA 0.0019 NA 0.87 0.034 NA NA NA NA NA NA NA NA NA NA NA 0.33 NA 0.96 0.059 0.27
0.0018 0.0032 0.0020 NA NA NA NA NA NA NA NA NA 0.80 NA 0.032 0.013 NA NA NA NA NA NA NA NA NA NA NA 0 NA 0.00021 0.000027 0.00011
0.53 0.21 0.74 NA NA NA NA NA NA NA NA NA 0.030 NA 0.16 0.90 NA NA NA NA NA NA NA NA NA NA NA 0.0032 NA 0.0029 0.062 0.0034
−0.90 −0.64 −1.03 0 0.1 0.11 0.13 0 0.34 0.07 0.06 0.08 −0.87 0.05 0.12 0.81 −0.28 −0.02 −0.48 0.12 0.02 0.19 0.44 -0.69 0.18 0.32 0.09 0.089 0.27 0.38 -0.31 -0.28
Means and standard deviations are shown. Overall p-values indicate results of overall group comparisons. Post-hoc p-values indicate results of post-hoc tests between groups. Cohen’s d values indicate effect sizes for differences between the PIGD and TD group. Parametric and non-parametric tests are indicated by * and ** respectively. ␣-level of significance was set at 0.05. PIGD: Postural Instability and Gait Disorder; TD: Tremor Dominant; CTRL: Controls; CV: Coefficient of Variability; D: Disease Dominant; ND: Disease Non-Dominant; NA: Not Applicable.
controls. These results are in line with recent work from Herman et al. (2014) who could also not detect subgroup differences with regards to objective gait and balance measures, except when using a more stringent subgroup classification method [15]. They argued that a more rigorous classification, especially in advanced disease stages, would lead to mixed phenotypes in more than 66% of patients. However, we had less than 11% of patients with mixed phenotypes, indicating that the original PIGD-TD classification method adapted by Stebbins et al. worked well in our cohort of early to mid-stage PD [22]. Lord et al. (2014) did find greater gait disorder in de novo PIGD versus TD when tested in ON [27]. These discrepant results are likely due to patient characteristics and medication factors, although age correction led to the same results in the present study and therefore is unlikely to play a role. When we clustered gait parameters across different domains, impairment in the postural control domain became evident in PIGD and not in TD. Our results also indicated postural control deficits in the PIGD compared to the TD group as shown by decreased Mini-BESTest scores. This highlights that a clinical balance test, which can be performed within 10 minutes, was able to distinguish between clinical subtypes. The more affected anticipatory control and dynamic gait in the PIGD compared to TD group indicated that the mini-BESTest was also able to characterize the specific components of postural deficits. Whereas gait impairment was limited in our cohort, we did find clear differences in performance on UL and LL repetitive movement tasks with considerably larger effect sizes than the gait outcomes. The PIGD group showed amplitude scaling difficulties during foot tapping, but, in contrast to gait, also had decreased coordination stability during this task. In addition, a PIGD-specific temporal
coordination deficit was present during finger tapping. Previous work showed that impaired bilateral coordination and anti-phase high frequency bimanual tasks could provide a useful tool for early diagnosis of PD [28,29]. Our results confirm that such movements, at least in the foot, were sensitive enough to distinguish between patients and controls in early to mid-stage PD and between PD subtypes and could thus serve as an easy and quick screening tool to determine subtypes. Earlier work from our group showed that a bilateral anti-phase finger tapping task was also able to discriminate between freezers and non-freezers [30]. Moreover, a recent neuroimaging study found decreased basal ganglia and supplementary motor area activity in PD compared to controls during a bimanual anti-phase motion. Increased cerebellar and motor cortical activity was suggested to compensate for this deficit [31]. Although this should be confirmed with neuroimaging data, our data could suggest a similar compensation mechanisms in TD compared to PIGD patients. There are only few quantitative studies investigating repetitive LL movements in PD. Nantel et al. (2011) found increased asymmetry and rhythmic variability in freezers compared to non-freezers and controls during a stepping in place task [32]. We found strikingly impaired amplitude scaling and coordination stability in our PIGD versus TD cohort, which were of greater magnitude during repetitive distal LL motor tasks than during gait. Previous research mainly performed in patients with and without FOG using different paradigms did not confirm these findings, although a recent study associated lower limb bradykinesia with amyloid- levels in the cerebrospinal fluid [32–34]. Our results possibly indicate a greater sensitivity of distal movements to pick-up amplitude and rhythmic control deficits than gait and more so in the LL compared to the UL.
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Fig. 1. Balance and cognition scores for patient groups and controls. Bars and whiskers represent means and standard deviations respectively within each subject group. * shows significant differences between groups with p < 0.05 ** and light gray lines show significant differences between group.
In addition, we found that UL deficits were greater in the coordination domain and LL deficits in the amplitude domain, suggesting different underlying neural pathways. This was confirmed by Barbe et al. (2014) who suggested that UL and LL sequential movement tasks might involve different subareas of central motor processing [35]. In line with earlier research, we found general cognitive impairment in the PIGD compared to TD group, as well as specifically decreased set-shifting abilities in PIGD compared to healthy controls that were not present in the TD group [6,7,9]. The ICICLE-PD study group recently showed that 42.1% of newly diagnosed patients presented with MCI illustrating the importance of cognitive impairment in early disease stages [36]. The fact that patients were cognitively impaired compared to controls and yet did not show any evidence of impaired gait variability is in contrast with the work of Hausdorff and co-workers [18]. Our findings of combined impairments of gait, postural control and distal motor control in PIGD patients, confirmed by correlation analysis, connects well with a recent body of work showing involvement of cholinergic neural pathways in the symptomatology even in early PD[37,38]. Bohnen et al. (2009) suggested that clinical heterogeneity in PD may be explained by the variable involvement of different neurotransmitter systems and that the PIGD phenotype could be associated with a greater degree of cholinergic denervation, mediating cognitive control of motor function [38,39]. In contrast, amplitude scaling during gait and performance of rapid anti-phase movements were shown to be predominantly dopaminergic in nature [40,41]. Our results showed that the PIGD group presented a profile of dopamine- as well as acetylcholine-mediated symptoms compared to the TD group. TD-patients did not display
such deficits, except for motor asymmetry, which is probably indicative of increased disease lateralization. As our groups were early to mid-stage and generally well-matched for MDS-UPDRS III scores, we hypothesize that the more complex symptomatology in the PIGD compared to TD subtype is not solely a representation of disease progression but of more severe and widespread dysfunction from the outset in more than one neurotransmitter system [13,39]. Interestingly, an 18-month follow-up study on an incident cohort showed more gait deterioration in TD than in PIGD [42]. However, disease progression cannot be ruled out as a factor mediating this pattern, as our PIGD group reported to have a longer duration of motor symptoms, higher UPDRS II and LED than the TD patients, being a limitation of our study. Longitudinal studies, combining behavioural and neuroimaging data are necessary to address this more definitively. Moreover, the relationship between empirical subtyping and data-driven methods is not straightforward as the PIGD and TD motor phenotypes did not seem to overlap with datadriven clusters in a study from Liu et al. (2011) [43]. Therefore, datadriven methods in large patient cohorts could provide a useful complementary tool to explore heterogeneity in Parkinson’s disease as it provides an objective and robust method to identify subtypes. 5. Conclusion Our results confirmed increased balance problems in PIGD patients and suggest the mini-BESTest as well as a bilateral repetitive movement task to be sensitive markers for PIGD in early to mid-stage PD. A novel finding is that motor scaling and coordination of repetitive UL and LL movement tasks is worse in PIGD compared to TD patients and counter-intuitively is more strongly
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related to the PIGD subtype than gait. This suggests a need to refine the PIGD subtype towards a more complex motor profile, which could be more accurately labeled as a non-TD rather than PIGD. To clarify the contribution of different neurotransmitter systems and the existence of true clinical subtypes rather than different stages of disease progression, longitudinal investigation including neuroimaging is indicated. Acknowledgements We would like to thank all subjects for their motivated and enthusiastic participation in this study. Special thanks go to C. Nielsen and A. Vanneste for their assistance in data collection. Support of this study was provided through a grant from the Special Research Fund of KU Leuven, Belgium (contract OT/11/091). Conflict of Interest: All authors report no conflict of interest. References [1] Jankovic J, McDermott M, Carter J, Gauthier S, Goetz C, Golbe L, et al. Variable expression of Parkinson’s disease: a base-line analysis of the DATATOP cohort. The Parkinson Study Group. Neurology 1990;40:1529–34. [2] Eggers C, Pedrosa DJ, Kahraman D, Maier F, Lewis CJ, Fink GR, et al. Parkinson subtypes progress differently in clinical course and imaging pattern. PloS One 2012;7:e46813–20. [3] van Rooden SM, Heiser WJ, Kok JN, Verbaan D, van Hilten JJ, Marinus J. The identification of Parkinson’s disease subtypes using cluster analysis: a systematic review. Mov Disord 2010;25:969–78. [4] Selikhova M, Williams DR, Kempster Pa, Holton JL, Revesz T, Lees aJ. A clinicopathological study of subtypes in Parkinson’s disease. Brain 2009;132:2947–57. [5] Auyeung M, Tsoi TH, Mok V, Cheung CM, Lee CN, Li R, et al. Ten year survival and outcomes in a prospective cohort of new onset Chinese Parkinson’s disease patients. J Neurol Neurosurg Psychiatry 2012;83:607–11. [6] Alves G, Larsen JP, Emre M, Wentzel-Larsen T, Aarsland D. Changes in motor subtype and risk for incident dementia in Parkinson’s disease. Mov Disord 2006;21:1123–30. [7] Burn DJ, Rowan EN, Allan LM, Molloy S, O’Brien JT, McKeith IG. Motor subtype and cognitive decline in Parkinson’s disease, Parkinson’s disease with dementia, and dementia with Lewy bodies. J Neurol Neurosurg Psychiatry 2006;77:585–9. [8] Taylor JP, Rowan EN, Lett D, O’Brien JT, McKeith IG, Burn DJ. Poor attentional function predicts cognitive decline in patients with non-demented Parkinson’s disease independent of motor phenotype. J Neurol Neurosurg Psychiatry 2008;79:1318–23. [9] Verbaan D, Marinus J, Visser M, van Rooden SM, Stiggelbout AM, Middelkoop HA, et al. Cognitive impairment in Parkinson’s disease. J Neurol Neurosurg Psychiatry 2007;78:1182–7. [10] Rossi C, Frosini D, Volterrani D, De Feo P, Unti E, Nicoletti V, et al. Differences in nigro-striatal impairment in clinical variants of early Parkinson’s disease: evidence from a FP-CIT SPECT study. Eur J Neurol 2010;17:626–30. [11] Prodoehl J, Planetta PJ, Kurani AS, Comella CL, Corcos DM, Vaillancourt DE. Differences in brain activation between tremor- and nontremor-dominant Parkinson disease. JAMA Neurol 2013;70:100–6. [12] Rosenberg-Katz K, Herman T, Jacob Y, Giladi N, Hendler T, Hausdorff JM. Gray matter atrophy distinguishes between Parkinson disease motor subtypes. Neurology 2013;80:1476–84. [13] Bohnen NI, Müller MLTM, Zarzhevsky N, Koeppe Ra Bogan CW, Kilbourn MR, et al. Leucoaraiosis, nigrostriatal denervation and motor symptoms in Parkinson’s disease. Brain 2011;134:2358–65. [14] Herman T, Rosenberg-Katz K, Jacob Y, Auriel E, Gurevich T, Giladi N, et al. White matter hyperintensities in Parkinson’s disease: do they explain the disparity between the postural instability gait difficulty and tremor dominant subtypes. PloS One 2013;8:e55193–200. [15] Herman T, Weiss A, Brozgol M, Giladi N, Hausdorff JM. Gait and balance in Parkinson’s disease subtypes: objective measures and classification considerations. J Neurol 2014;261:2401–10. [16] Lord S, Galna B, Rochester L. Moving forward on gait measurement: toward a more refined approach. Mov Disord 2013;28:1534–43. [17] Hausdorff JM, Schaafsma JD, Balash Y, Bartels AL, Gurevich T, Giladi N. Impaired regulation of stride variability in Parkinson’s disease subjects with freezing of gait. Exp Brain Res 2003;149:187–94.
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[18] Plotnik M, Giladi N, Hausdorff JM. Bilateral coordination of gait and Parkinson’s disease: the effects of dual tasking. J Neurol Neurosurg Psychiatry 2009;80:347–50. [19] Baltadjieva R, Giladi N, Gruendlinger L, Peretz C, Hausdorff JM. Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson’s disease. Eur J Neurosci 2006;24:1815–20. [20] Vercruysse S, Gilat M, Shine JM, Heremans E, Lewis S, Nieuwboer A. Freezing beyond gait in Parkinson’s disease: a review of current neurobehavioral evidence. Neurosci Biobehav Rev 2014;43:213–27. [21] Factor Sa, Steenland NK, Higgins DS, Molho ES, Kay DM, Montimurro J, et al. Postural instability/gait disturbance in Parkinson’s disease has distinct subtypes: an exploratory analysis. J Neurol Neurosurg Psychiatry 2011;82:564–8. [22] Stebbins GT, Goetz CG, Burn DJ, Jankovic J, Khoo TK, Tilley BC. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson’s disease rating scale: comparison with the unified Parkinson’s disease rating scale. Mov Disord 2013;28:668–70. [23] Vercruysse S, Spildooren J, Heremans E, Vandenbossche J, Levin O, Wenderoth N, et al. Freezing in Parkinson’s disease: a spatiotemporal motor disorder beyond gait. Mov Disord 2012;27:254–63. [24] Pijnappels M, Bobbert MF, van Dieën JH. Changes in walking pattern caused by the possibility of a tripping reaction. Gait Posture 2001;14:11–8. [25] Lord S, Galna B, Verghese J, Coleman S, Burn D, Rochester L. Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. J Gerontol A Biol Sci Med Sci 2013;68:820–7. [26] Kraus PH, Lemke MR, Reichmann H. Kinetic tremor in Parkinson’s disease–an underrated symptom. J Neural Transm 2006;113:845–53. [27] Lord S, Galna B, Coleman S, Yarnall A, Burn D, Rochester L. Cognition and gait show a selective pattern of association dominated by phenotype in incident Parkinson’s disease. Front Aging Neurosci 2014;6:249. [28] Almeida QJ, Wishart LR, Lee TD. Bimanual coordination deficits with Parkinson’s disease: the influence of movement speed and external cueing. Mov Disord 2002;17:30–7. [29] Ponsen MM, Daffertshofer A, van den Heuvel E, Wolters EC, Beek PJ, Berendse HW. Bimanual coordination dysfunction in early, untreated Parkinson’s disease. Parkinsonism Relat Disord 2006;12:246–52. [30] Vercruysse S, Spildooren J, Heremans E, Vandenbossche J, Wenderoth N, Swinnen SP, et al. Abnormalities and cue dependence of rhythmical upper-limb movements in Parkinson patients with freezing of gait. Neurorehabil Neural Repair 2012;26:636–45. [31] Wu T, Wang L, Hallett M, Li K, Chan P. Neural correlates of bimanual anti-phase and in-phase movements in Parkinson’s disease. Brain 2010;133:2394–409. [32] Nantel J, de Solages C, Bronte-Stewart H. Repetitive stepping in place identifies and measures freezing episodes in subjects with Parkinson’s disease. Gait Posture 2011;34:329–33. [33] Shine JM, Matar E, Bolitho SJ, Dilda V, Morris TR, Naismith SL, et al. Modeling freezing of gait in Parkinson’s disease with a virtual reality paradigm. Gait Posture 2013;38:104–8. [34] Alves G, Pedersen KF, Bloem BR, Blennow K, Zetterberg H, Borm GF, et al. Cerebrospinal fluid amyloid- and phenotypic heterogeneity in de novo Parkinson’s disease. J Neurol Neurosurg Psychiatry 2013;84:537–43. [35] Barbe MT, Amarell M, Snijders AH, Florin E, Quatuor E-L, Schönau E, et al. Gait and upper limb variability in Parkinson’s disease patients with and without freezing of gait. J Neurol 2014;261:330–42. [36] Yarnall AJ, Breen DP, Duncan GW, Khoo TK, Coleman SY, Firbank MJ, et al. Characterizing mild cognitive impairment in incident Parkinson disease: the ICICLE-PD study. Neurology 2014;82:308–16. [37] Rochester L, Yarnall AJ, Baker MR, David RV, Lord S, Galna B, et al. Cholinergic dysfunction contributes to gait disturbance in early Parkinson’s disease. Brain 2012;135:2779–88. [38] Bohnen NI, Frey Ka Studenski S, Kotagal V, Koeppe Ra Scott PJH, et al. Gait speed in Parkinson disease correlates with cholinergic degeneration. Neurology 2013;81:1611–6. [39] Bohnen NI, Albin RL. Cholinergic denervation occurs early in Parkinson disease. Neurology 2009;73:256–7. [40] O’Sullivan JD, Said CM, Dillon LC, Hoffman M, Hughes AJ. Gait analysis in patients with Parkinson’s disease and motor fluctuations: influence of levodopa and comparison with other measures of motor function. Mov Disord 1998;13:900–6. [41] Blin O, Ferrandez AM, Pailhous J, Serratrice G. Dopa-sensitive and doparesistant gait parameters in Parkinson’s disease. J Neurol Sci 1991;103:51–4. [42] Galna B, Lord S, Burn DJ, Rochester L. Progression of gait dysfunction in incident Parkinson’s disease: Impact of medication and phenotype. Mov Disord 2014;30:359–67. [43] Liu P, Feng T, Wang YJ, Zhang X, Chen B. Clinical heterogeneity in patients with early-stage Parkinson’s disease: a cluster analysis. J Zhejiang Univ Sci B 2011;12:694–703.
