Elsevier

Gait & Posture

Volume 50, October 2016, Pages 1-7
Gait & Posture

Full length article
Motor performance differentiates individuals with Lewy body dementia, Parkinson’s and Alzheimer’s disease

https://doi.org/10.1016/j.gaitpost.2016.08.009Get rights and content

Highlights

  • When matched for disease severity, motor measures distinguish LBD from PD and AD.

  • The Tinetti balance scale differentiates individuals with DLB and PD with dementia.

  • The Tinetti, Berg and 9 hole peg tests differentiated LBD from both AD and PD.

  • Dual task ability and figure of 8 walk differentiates LBD from AD and PD.

  • Individuals with AD performed worse on the 9 hole peg test than those with PD.

Abstract

Introduction

Differential diagnosis of dementia with Lewy bodies (DLB), Parkinson’s disease with dementia (PDD), Parkinson’s disease (PD) and Alzheimer’s disease (AD) is challenging. Comparative motor profiles of these neurodegenerative disorders may aid in earlier diagnosis but have not been extensively studied.

Methods

Groups were rigorously matched by age, education, and sex. DLB/PDD participants were matched by Mini-Mental State Examination Score to individuals with AD and by Unified Parkinson’s Disease Rating Scale motor scores to individuals with PD. Gait, balance, dual task walking and hand dexterity measures were compared between a combined group (n = 21) of individuals with Lewy body dementia (LBD) consisting of those with DLB (n = 11) and PDD (n = 10) to individuals with PD (n = 21) or AD (n = 21).

Results

Individuals at the same disease stage with LBD walked significantly slower with shorter stride lengths (p < 0.05), demonstrated poorer balance on both the Tinetti and Berg Balance Scale, and poorer performance on dual-task and figure-of-eight walking compared to PD and AD (p < 0.05 for all) groups. Upper extremity coordination on the 9-hole peg test differentiated LBD from both PD and AD and was the only motor test in which individuals with AD performed worse than those with PD. Tinetti balance subscores were significantly lower in PDD compared to DLB participants (10.4 ± 2.3 versus 12.8 ± 2.3; p = 0.027).

Conclusions

Motor features distinguish individuals with LBD from those with AD and PD. Measures of gait, balance and finger dexterity provide an additional means of differentiating individuals with LBD from those with AD and PD.

Introduction

Gait, balance, and motor impairments are common in individuals with dementia and are associated with increased fall risk. Similarities in the clinical presentation of dementia subtypes (i.e., dementia with Lewy bodies) [1], Parkinson’s disease with dementia [1], and Alzheimer’s disease (AD) make differential diagnosis of these conditions challenging. However, evidence suggests that the pattern of motor, gait and balance deficits may differ across subtypes of dementia. Individuals with Lewy body dementia (LBD), including DLB and PDD, demonstrate greater gait impairments and worse balance on the Tinetti Mobility Test (TMT) than individuals with AD [2] and Parkinson’s disease (PD) [3]. Indeed, parkinsonian gait, a hallmark of both DLB and PDD [1], accurately identifies individuals with LBD with a sensitivity of 87% and a specificity of 84% [3] compared to other dementia subtypes. Thus, quantitative motor and gait and balance assessment may be helpful for early differential diagnosis of dementia subtypes and help in earlier interventions for motor symptoms.

Repeated falls are common to DLB, PDD, AD and PD [4]. Increased stride-to-stride variability during walking and when performing simultaneous tasks (i.e., dual-tasks) was more related to dementia than changes in mean gait parameter values [5] and was associated with higher fall risk in the elderly and individuals with PD [6]. Gait and balance assessments including dual-task assessments may assist clinicians in identifying individuals with dementia at fall risk to ensure referrals for targeted rehabilitation and fall prevention.

Substantial overlapping of clinical, neuropathologic, and metabolic features along the PD-PDD-DLB-AD disease spectrum makes accurate diagnosis difficult [7]. No studies have simultaneously examined differences in motor performance measures between individuals with LBD, AD and PD. Thus, the primary objective of this study was to compare the motor profiles of a cohort of selectively matched individuals with LBD, PD, and AD through gait, balance, and hand dexterity testing. With the prevalence of dementias increasing, a comprehensive motor examination that accurately distinguishes between individuals with these diagnoses could improve diagnosis of these dementia subtypes thereby leading to earlier intervention and more effective management of these patients.

Section snippets

Methods

This cross sectional design study complied with the Declaration of Helsinki and was approved by the Institutional Review Board.

Results

Seventy-two individuals were recruited and screened for the study. Sixty-three individuals (21 LBD matched with 21 PD and 21 AD) out of the 72 met inclusion and exclusion criteria (Table 1). No participants used assistive devices for walking. All individuals demonstrated increased variability in backward walking measures compared to forward walking conditions.

Discussion

Our study findings identify for the first time distinct motor profiles associated with individuals with dementia subtypes (DLB, PDD, AD) and PD. In general motor impairments were least severe in AD, more advanced in PD and most pronounced in those with LBD (Fig. 1).

Individuals with LBD showed greater gait, balance, and hand dexterity deficits than those with AD or PD. Performance on the TMT balance subscale differentiated between LBD, AD, and PD groups and between PDD and DLB groups. Study

Financial support

This study was funded by grants from The Mangurian Foundation, the Dr. Thomas H. and Mrs. Kelly Mallory Fund and the Robert A. Vaughan Family Fund. The funders had no involvement in the study design, data collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the paper for publication.

Acknowledgements

The authors thank Shu-Ing Chang for her contributions. We also acknowledge biostatistician Greg Young for his review of the statistics.

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