Gait and/or balance disturbances associated with Alzheimer's dementia among older adults with amnestic mild cognitive impairment: A longitudinal observational study

Balance, Strength, and Risk of Dementia: Findings From the Health and Retirement Study and the English Longitudinal Study of Ageing

BACKGROUND

Slow gait speed has been consistently associated with an increased risk of dementia. This study examined whether measures of balance and lower limb strength are similarly related to the risk of developing dementia.

METHODS

Participants from the Health and Retirement Study (HRS, N = 5 658, mean age = 73.23, standard deviation [SD] = 6.22) and the English Longitudinal Study of Ageing (ELSA, N = 3667, mean age = 69.90, SD = 7.02) completed measures of gait speed, semi-tandem balance, chair stand (ELSA only), and cognitive status at baseline. Cognitive status was assessed over up to 15 years.

RESULTS

Baseline slower gait speed (hazard ratio [HR]HRS = 1.52, 95% confidence interval [CI] = 1.32-1.75, p < .001; HRELSA = 1.73, 95% CI = 1.37-2.18, p < .001); and balance impairment (HRHRS = 1.58, 95% CI = 1.26-1.96, p < .001; HRELSA = 1.97, 95% CI = 1.24-3.14, p < .01) were related to a higher risk of incident dementia, adjusting for demographic factors. The combination of slower gait and impaired balance was associated with a two-to-three times higher risk of dementia in HRS and ELSA. Worse performance on the chair stand at baseline was associated with a higher risk of dementia in ELSA (HR = 1.56, 95% CI = 1.23-1.99, p < .001). All performance measures remained significant when entered simultaneously and accounted for obesity, diabetes, blood pressure, physical activity, smoking, and depressive symptoms. There was little evidence that age, sex, or APOE ε4 moderated the association.

CONCLUSIONS

Similar to gait speed, measures of balance and strength are associated with a higher risk of incident dementia. The findings have implications for clinical practice, given that these routinely used geriatric assessment tools are similarly related to dementia risk.

Classification of Mild Cognitive Impairment and Alzheimer's Disease Using Manual Motor Measures

INTRODUCTION

Manual motor problems have been reported in mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the specific aspects that are affected, their neuropathology, and potential value for classification modeling is unknown. The current study examined if multiple measures of motor strength, dexterity, and speed are affected in MCI and AD, related to AD biomarkers, and are able to classify MCI or AD.

METHODS

Fifty-three cognitively normal (CN), 33 amnestic MCI, and 28 AD subjects completed five manual motor measures: grip force, Trail Making Test A, spiral tracing, finger tapping, and a simulated feeding task. Analyses included (1) group differences in manual performance; (2) associations between manual function and AD biomarkers (PET amyloid β, hippocampal volume, and APOE ε4 alleles); and (3) group classification accuracy of manual motor function using machine learning.

RESULTS

Amnestic MCI and AD subjects exhibited slower psychomotor speed and AD subjects had weaker dominant hand grip strength than CN subjects. Performance on these measures was related to amyloid β deposition (both) and hippocampal volume (psychomotor speed only). Support vector classification well-discriminated control and AD subjects (area under the curve of 0.73 and 0.77, respectively) but poorly discriminated MCI from controls or AD.

CONCLUSION

Grip strength and spiral tracing appear preserved, while psychomotor speed is affected in amnestic MCI and AD. The association of motor performance with amyloid β deposition and atrophy could indicate that this is due to amyloid deposition in and atrophy of motor brain regions, which generally occurs later in the disease process. The promising discriminatory abilities of manual motor measures for AD emphasize their value alongside other cognitive and motor assessment outcomes in classification and prediction models, as well as potential enrichment of outcome variables in AD clinical trials.

Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images

In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.

Exploring inertial sensor-based balance biomarkers for early detection of mild cognitive impairment

Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult since it is a transitory state that is different from total cognitive collapse. Recent clinical research studies have identified that balance impairments can be a significant indicator for predicting dementia in older adults. Accordingly, the current research focuses on finding innovative postural balance-based digital biomarkers by using wearable inertial sensors and pre-screening of MCI in home settings using machine learning techniques. For this research, sixty subjects (30 cognitively normal and 30 MCI) with waist-mounted inertial sensor performed balance tasks in four different standing postures: eyes-open, eyes-closed, right-leg-lift, and left-leg-lift. The significant balance biomarkers for MCI identification are discovered by our research, demonstrating specific characteristics in each of these four states. A robust feature selection approach is ensured by the multi-step methodology that combines the strengths of Filter techniques, Wrapper methods, and SHAP (Shapley Additive exPlanations) technique. The proposed balance biomarkers have the potential to detect MCI (with 75.8% accuracy), as evidenced by the results of machine learning algorithms for classification. This work adds to the growing body of literature targeted at enhancing understanding and proactive management of cognitive loss in older populations and lays the groundwork for future research efforts aimed at refining digital biomarkers, validating findings, and exploring longitudinal perspectives.

Predictive power of gait and gait-related cognitive measures in amnestic mild cognitive impairment: a machine learning analysis

Introduction

Gait disorders and gait-related cognitive tests were recently linked to future Alzheimer's Disease (AD) dementia diagnosis in amnestic Mild Cognitive Impairment (aMCI). This study aimed to evaluate the predictive power of gait disorders and gait-related neuropsychological performances for future AD diagnosis in aMCI through machine learning (ML).

Methods

A sample of 253 aMCI (stable, converter) individuals were included. We explored the predictive accuracy of four predictors (gait profile plus MMSE, DSST, and TMT-B) previously identified as critical for the conversion from aMCI to AD within a 36-month follow-up. Supervised ML algorithms (Support Vector Machine [SVM], Logistic Regression, and k-Nearest Neighbors) were trained on 70% of the dataset, and feature importance was evaluated for the best algorithm.

Results

The SVM algorithm achieved the best performance. The optimized training set performance achieved an accuracy of 0.67 (sensitivity = 0.72; specificity = 0.60), improving to 0.70 on the test set (sensitivity = 0.79; specificity = 0.52). Feature importance revealed MMSE as the most important predictor in both training and testing, while gait type was important in the testing phase.

Discussion

We created a predictive ML model that is capable of identifying aMCI at high risk of AD dementia within 36 months. Our ML model could be used to quickly identify individuals at higher risk of AD, facilitating secondary prevention (e.g., cognitive and/or physical training), and serving as screening for more expansive and invasive tests. Lastly, our results point toward theoretically and practically sound evidence of mind and body interaction in AD.

Association of Age with Dual-Task Objective Cognitive Indicators and Gait Parameters in Older Adults

Background

Early recognition of dementia like Alzheimer's disease is crucial for disease diagnosis and treatment, and existing objective tools for early screening of cognitive impairment are limited.

Objective

To investigate age-related behavioral indicators of dual-task cognitive performance and gait parameters and to explore potential objective markers of early cognitive decline.

Methods

The community-based cognitive screening data was analyzed. Hierarchical cluster analysis and Pearson correlation analysis were performed on the 9-item subjective cognitive decline (SCD-9) scores, walking-cognitive dual-task performance, walking speed, and gait parameters of 152 participants. The significant differences of indicators that may related to cognitive decline were statistically analyzed across six age groups. A mathematical model with age as the independent variable and motor cognition composite score as the dependent variable was established to observe the trend of motor cognition dual-task performance with age.

Results

Strong correlation was found between motor cognitive scores and SCD and age. Gait parameters like the mean value of ankle angle, the left-right difference rate of ankle angle and knee angle and the coefficient of variation of gait cycle showed an excellent correlation with age. Motor cognition scores showed a decreasing trend with age. The slope of motor cognition scores with age after 50 years (k = -1.06) was six times higher than that before 50 years (k = -0.18).

Conclusions

Cognitive performance and gait parameters in the walking-cognitive dual-task state are promising objective markers that could characterize age-related cognitive decline.