» Articles » PMID: 38002557

Gait Domains May Be Used As an Auxiliary Diagnostic Index for Alzheimer's Disease

Overview
Journal Brain Sci
Publisher MDPI
Date 2023 Nov 25
PMID 38002557
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction and behavioral impairment. We aimed to use principal components factor analysis to explore the association between gait domains and AD under single and dual-task gait assessments.

Methods: A total of 41 AD participants and 41 healthy control (HC) participants were enrolled in our study. Gait parameters were measured using the JiBuEn gait analysis system. The principal component method was used to conduct an orthogonal maximum variance rotation factor analysis of quantitative gait parameters. Multiple logistic regression was used to adjust for potential confounding or risk factors.

Results: Based on the factor analysis, three domains of gait performance were identified both in the free walk and counting backward assessments: "rhythm" domain, "pace" domain and "variability" domain. Compared with HC, we found that the pace factor was independently associated with AD in two gait assessments; the variability factor was independently associated with AD only in the counting backwards assessment; and a statistical difference still remained after adjusting for age, sex and education levels.

Conclusions: Our findings indicate that gait domains may be used as an auxiliary diagnostic index for Alzheimer's disease.

Citing Articles

Alzheimer's Disease: Understanding Motor Impairments.

Andrade-Guerrero J, Martinez-Orozco H, Villegas-Rojas M, Santiago-Balmaseda A, Delgado-Minjares K, Perez-Segura I Brain Sci. 2024; 14(11).

PMID: 39595817 PMC: 11592238. DOI: 10.3390/brainsci14111054.


Quantitative gait markers and the TUG time in chronic kidney disease.

Zhang X, Wang H, Lu H, Fan M, Tian W, Wang Y Heliyon. 2024; 10(15):e35292.

PMID: 39170243 PMC: 11336600. DOI: 10.1016/j.heliyon.2024.e35292.

References
1.
Xie H, Wang Y, Tao S, Huang S, Zhang C, Lv Z . Wearable Sensor-Based Daily Life Walking Assessment of Gait for Distinguishing Individuals With Amnestic Mild Cognitive Impairment. Front Aging Neurosci. 2019; 11:285. PMC: 6817674. DOI: 10.3389/fnagi.2019.00285. View

2.
Jack Jr C, Bennett D, Blennow K, Carrillo M, Dunn B, Budd Haeberlein S . NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018; 14(4):535-562. PMC: 5958625. DOI: 10.1016/j.jalz.2018.02.018. View

3.
Schober P, Vetter T . Logistic Regression in Medical Research. Anesth Analg. 2021; 132(2):365-366. PMC: 7785709. DOI: 10.1213/ANE.0000000000005247. View

4.
Hsu Y, Chung P, Wang W, Pai M, Wang C, Lin C . Gait and balance analysis for patients with Alzheimer's disease using an inertial-sensor-based wearable instrument. IEEE J Biomed Health Inform. 2014; 18(6):1822-30. DOI: 10.1109/JBHI.2014.2325413. View

5.
Auvinet B, Touzard C, Montestruc F, Delafond A, Goeb V . Gait disorders in the elderly and dual task gait analysis: a new approach for identifying motor phenotypes. J Neuroeng Rehabil. 2017; 14(1):7. PMC: 5282774. DOI: 10.1186/s12984-017-0218-1. View