Progression of gait dysfunction in incident Parkinson's disease: Impact of medication and phenotype

Galna, Brook; Lord, Sue; Burn, David J.; Rochester, Lynn

doi:10.1002/mds.26110

Cited by 177 publications

(194 citation statements)

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“…22 Gait was quantified according to an a priori model developed for older adults 23 and validated in PD 24 that describes 16 discrete gait characteristics. 25 We examined change in each gait characteristic over 36 months and characteristics that exhibited substantial change (despite optimal medication) were defined as doparesistant. Quantification of CSF biomarkers.…”

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confidence: 99%

Decrease in Aβ42 predicts dopa-resistant gait progression in early Parkinson disease

et al. 2017

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Objective: This prospective observational study investigates the role of CSF biomarkers in predicting progression of dopa-resistant gait impairments in Parkinson disease (PD) in the first 36 months from diagnosis.Methods: Quantitative gait analysis was carried out longitudinally using an instrumented walkway (GAITRite) in 108 people with PD and 130 age-matched controls. A subgroup of 44 people with PD underwent lumbar puncture from which a battery of CSF biomarkers was measured: b-amyloid 1-42 and 1-40 (Ab42 and Ab40), total and phosphorylated tau protein (t-tau/p-tau 181 ), and a-synuclein (aSyn). Linear mixed models examined the association between CSF and doparesistant gait characteristics (defined as substantial progression despite optimal medication).Results: Low baseline CSF Ab42, and to a lesser extend Ab40, predicted decline in gait characteristics in the first 3 years following diagnosis, independently explaining up to 12% of progression of step time variability (single task) and step length variability (dual-task). Interestingly, these findings were independent of age and cognition.Conclusions: These findings implicate underlying amyloid pathology in neural networks involved in locomotor control. Results suggest that disturbed Ab metabolism may be a biomarker for doparesistant gait impairments in early PD. Our findings raise interesting questions regarding therapeutic interventions such as compounds or molecules aimed at reducing amyloid burden to mitigate gait disturbance in early PD and potentially falls risk. Finally, progression of discrete gait characteristics suggests they may have potential as clinical biomarkers of pathology and disease progression. Parkinson disease (PD) is a common neurodegenerative disorder, second to Alzheimer disease. 1Gait impairments are significant in very early disease, and even at this stage dominate as risk factors for falls.2 While some aspects of gait are well-controlled by dopaminergic therapies in the early stages, resistance to levodopa makes clinical management challenging.Recent work highlights the significant contribution of cholinergic disturbance to gait, 3,4 and recent trials targeting the cholinergic system have met with moderate success.5 CSF proteins (e. g., b-amyloid [Ab] 40 and Ab42; total and p-tau 181 ), traditionally biomarkers of dementia and dementia risk, 6-9 have also been implicated in motor impairment, highlighting a role for pathologic protein accumulation other than Lewy body and PD-specific a-synuclein (aSyn). Crosssectional studies in early and advanced PD show an association between CSF biomarkers and postural instability and gait (PIGD) phenotype. 10,11 However, lack of quantitative gait analysisFrom the Institute of Neuroscience

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confidence: 99%

Decrease in Aβ42 predicts dopa-resistant gait progression in early Parkinson disease

et al. 2017

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“…27,28 PIGD subtype is also associated with faster gait deterioration 30 and increased fall frequency. 26,31 It appeared that most individuals with PIGD in our study were already in the high fall risk trajectory.…”

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confidence: 98%

Two-Year Trajectory of Fall Risk in People With Parkinson Disease: A Latent Class Analysis

Paul

Thackeray

Duncan

et al. 2016

Archives of Physical Medicine and Rehabilitation

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Objective To examine fall risk trajectories occurring naturally in a sample of individuals with early to middle stage Parkinson’s disease (PD). Design Latent class analysis, specifically growth mixture modeling (GMM) of longitudinal fall risk trajectories. Setting Not applicable. Participants 230 community-dwelling PD participants of a longitudinal cohort study who attended at least two of five assessments over a two year period. Interventions Not applicable. Main Outcome Measures Fall risk trajectory (low, medium or high risk) and stability of fall risk trajectory (stable or fluctuating). Fall risk was determined at 6-monthly intervals using a simple clinical tool based on fall history, freezing of gait, and gait speed. Results The GMM optimally grouped participants into three fall risk trajectories that closely mirrored baseline fall risk status (p=.001). The high fall risk trajectory was most common (42.6%) and included participants with longer and more severe disease and with higher postural instability and gait disability (PIGD) scores than the low and medium risk trajectories (p<.001). Fluctuating fall risk (posterior probability <0.8 of belonging to any trajectory) was found in only 22.6% of the sample, most commonly among individuals who were transitioning to PIGD predominance. Conclusions Regardless of their baseline characteristics, most participants had clear and stable fall risk trajectories over two years. Further investigation is required to determine whether interventions to improve gait and balance may improve fall risk trajectories in people with PD.

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“…The addition of a gyroscope sensor within the BWM would allow the complete quantification of the 16 gait characteristics identified as important [27,28]. Additionally, the extra sensor would allow the correct identification and allocation of left/right steps.…”

Section: Study Limitations and Future Workmentioning

confidence: 99%

“…Whilst multiple characteristics can describe gait, studies have helped to provide a simplified framework for selection of important gait characteristics [5,26,27]. In this study characteristics were selected based on our previous work [27,28] which identified a comprehensive range of 16 core gait characteristics (using GaitRite) recognised as indicative of healthy ageing and pathology (Parkinson's disease, PD). Here, we used a BWM and adopted a novel combination of two algorithms to quantify gait grouped by the properties they measure such as summary mean values, variability and asymmetry and including phases of the gait cycle.…”

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Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson's Disease: Toward Clinical and at Home Use

Din

Godfrey

Rochester

2016

IEEE J. Biomed. Health Inform.

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Measurement of gait is becoming important as a tool to identify disease and disease progression, yet to date its application is limited largely to specialist centers. Wearable devices enables gait to be measured in naturalistic environments, however questions remain regarding validity. Previous research suggests that when compared with a laboratory reference, measurement accuracy is acceptable for mean but not variability or asymmetry gait characteristics. Some fundamental reasons for this have been presented, (e.g., synchronization, different sampling frequencies) but to date this has not been systematically examined. The aims of this study were to: 1) quantify a comprehensive range of gait characteristics measured using a single triaxial accelerometer-based monitor; 2) examine outcomes and monitor performance in measuring gait in older adults and those with Parkinson's disease (PD); and 3) carry out a detailed comparison with those derived from an instrumented walkway to account for any discrepancies. Fourteen gait characteristics were quantified in 30 people with incident PD and 30 healthy age-matched controls. Of the 14 gait characteristics compared, agreement between instruments was excellent for four (ICCs 0.913-0.983); moderate for four (ICCs 0.508-0.766); and poor for six characteristics (ICCs 0.637-0.370). Further analysis revealed that differences reflect an increased sensitivity of accelerometry to detect motion, rather than measurement error. This is most likely because accelerometry measures gait as a continuous activity rather than discrete footfall events, per instrumented tools. The increased sensitivity shown for these characteristics will be of particular interest to researchers keen to interpret "real-world" gait data. In conclusion, use of a body-worn monitor is recommended for the measurement of gait but is likely to yield more sensitive data for asymmetry and variability features.

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Progression of gait dysfunction in incident Parkinson's disease: Impact of medication and phenotype

Cited by 177 publications

References 45 publications

Decrease in Aβ42 predicts dopa-resistant gait progression in early Parkinson disease

Decrease in Aβ42 predicts dopa-resistant gait progression in early Parkinson disease

Two-Year Trajectory of Fall Risk in People With Parkinson Disease: A Latent Class Analysis

Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson's Disease: Toward Clinical and at Home Use

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