Protecting cognitive function in older adults with age-related hearing loss: Insights from The Irish Longitudinal Study on Ageing (TILDA) and the role of hearing aids

BACKGROUND

Age-related hearing loss (ARHL) is prevalent in adults over 70, impairing hearing sensitivity and speech perception. ARHL has been linked to an increased risk of cognitive decline and dementia. However, most affected adults are not receiving adequate treatment, including hearing aids.

OBJECTIVE

This study aimed to evaluate the impact of ARHL on cognitive decline in older adults participating in the Irish Longitudinal Study on Aging (TILDA).

DESIGN

METHODS: Data from four TILDA waves, a 6-year follow-up, was collected and analyzed using zero-inflated Poisson regression. The primary outcome, cognitive function, was assessed using Mini-Mental State Examination (MMSE) total score and error counts.

RESULTS

Our analysis revealed that age, education, use of aids to help with hearing, and history of stroke were significantly associated with error counts at baseline. Additionally, poor hearing was associated with a negative change in MMSE score from wave 4, indicating the potential role of ARHL in cognitive decline. When further adjusted for age, sex, history of stroke, hypertension, any emotional, nervous, or psychiatric problem, polypharmacy, and hearing aids, the zero-inflated Poisson model indicated that poor hearing, use of hearing aids, stroke, hypertension, and polypharmacy all predicted MMSE error counts in follow-up assessments. Moreover, the use of hearing aids was associated with a decreased likelihood of cognitive decline.

CONCLUSION

ARHL was independently associated with cognitive decline, underscoring the importance of addressing hearing loss in older adults. Future research should explore the potential of hearing aids to protect cognitive functioning in older adults.

Brain connectivity in frailty: Insights from The Irish Longitudinal Study on Ageing (TILDA)

Frailty in older adults is associated with greater risk of cognitive decline. Brain connectivity insights could help understand the association, but studies are lacking. We applied connectome-based predictive modeling to a 32-item self-reported Frailty Index (FI) using resting state functional MRI data from The Irish Longitudinal Study on Ageing. A total of 347 participants were included (48.9% male, mean age 68.2 years). From connectome-based predictive modeling, we obtained 204 edges that positively correlated with the FI and composed the "frailty network" characterised by connectivity of the visual network (right); and 188 edges that negatively correlated with the FI and formed the "robustness network" characterized by connectivity in the basal ganglia. Both networks' highest degree node was the caudate but with different patterns: from caudate to visual network in the frailty network; and to default mode network in the robustness network. The FI was correlated with walking speed but not with metrics of global cognition, reinforcing the matching between the FI and the brain connectivity pattern found (main predicted connectivity in basal ganglia).

66 GRIP STRENGTH, ONLY A PHYSICAL TASK? ASSOCIATIONS BETWEEN RESTING-STATE BRAIN CONNECTIVITY AND GRIP STRENGTH

Background

Grip strength (GS) is a simple measure used in the assessment of frailty and sarcopenia. Despite being physical, studies show that it is also related with depression and cognition. Our aim was to explore if brain connectivity is implicated in GS.

Methods

Participants from Wave 3 of TILDA with adequate resting-state functional-MRI data were included. Connectome-based predictive modelling was used on Shen parcellated brain connectivity matrices to find connections that are positively and negatively associated with grip strength: positive and negative networks (PN and NN). On each iteration of a randomized 10-fold cross-validation, the training folds were utilized to perform partial Spearman correlations (age-, sex-adjusted) between each connection and GS. Significant (p<0.001) connections were selected. PN and NN strengths were computed by summing across all significant correlations. Linear models regressing GS on network strengths were built on training data and used to predict GS in the test fold. After 10 folds were complete the performance was measured via Pearson R between predicted and true GS values. This 10-fold process was repeated 1000 times and a mean R obtained. Permutation significance testing was employed where the cross-validation procedure was repeated 1000 times, with GS randomly shuffled each time. The P-value was the proportion of permuted R values >= the mean R.

Results

317 participants were included (mean(SD) age 67.3(7.2) and 49.8% female). For PN: R[95% intervals]=0.29[0.26, 0.32], P<0.001 and for NN: R=0.24[0.20, 0.28], P<0.001. In both, the default mode, dorsal attention, and cerebellum networks were highly involved but with differing patterns: most notable was the presence of high connectivity between both cerebellar hemispheres in the PN but not in the NN.

Conclusion

Grip strength is related with different brain connectivity patterns suggesting the involvement of networks beyond motor areas. Further studies are required to disentangle the neuroscience behind this clinically relevant physio-cognitive task.

A New Software for Quantifying Motor Deficit After Stroke: A Case-Control Feasibility Pilot Study

Introduction: The degree of disability after stroke needs to be objectively measured to implement adequate rehabilitation programs. Here, we evaluate the feasibility of a custom-built software to assess motor status after stroke.

Methods: This is a prospective, case–control pilot study comparing stroke patients with healthy volunteers. A workout evaluation that included trunk and upper limb movement was captured with Kinect® and kinematic metrics were extracted with Akira®. Trunk and joint angles were analyzed and compared between cases and controls. Patients were evaluated within the first week from stroke onset using the National Institutes of Health Stroke Scale (NIHSS), Fulg-Meyer Assessment (FMA), and modified Rankin Scale (mRS) scales; the relationship with kinematic measurements was explored.

Results: Thirty-seven patients and 33 controls were evaluated. Median (IQR) NIHSS of cases was 2 (0–4). The kinematic metrics that showed better discriminatory capacity were body sway during walking (less in cases than in controls, p = 0.01) and the drift in the forearm–trunk angle during shoulder abduction in supination (greater in cases than in controls, p = 0.01). The body sway during walking was moderately correlated with NIHSS score (Rho = −0.39; p = 0.01) but better correlated with mRS score (Rho = −0.52; p < 0.001) and was associated with the absence of disability (mRS 0–1) (OR = 0.64; p = 0.02). The drift in the forearm–trunk angle in supination was associated with the presence of disability (mRS >1) (OR = 1.27; p = 0.04).

Conclusion: We present a new software that detects even mild motor impairment in stroke patients underestimated by clinical scales but with an impact on patient functionality.

Abstract WP143: Computational Analysis of Movement for Evaluation of Motor Functional Impairment After Stroke

Motion capture systems (MCS) are used in neurological rehabilitation and for diagnosis of movement disorders. We aimed to explore the usefulness of MCS to obtain an objective measurement of functional status after stroke, especially in patients with minor or without deficit when measured by current motor clinical scales.

Methods: Prospective observational case-control pilot study using Microsoft Kinect® analyzed with the software Akira®. Patients after acute stroke were included. All subjects performed the same exercises (standing, walking, sitting position, abduction of the upper limb in pronation and supination during 5 seconds each position and flexion of the upper limbs) that were recorded in a three-dimensional space. Joint angles and time of execution were registered and analyzed. The differences in execution between both sides of the body were compared between cases and controls. The relationship with the NIHSS score was analyzed with linear regression analysis and the mRS score with Pearson’s correlation coefficient (PCC).

Results: 50 controls and 33 stroke patients were analyzed. The median NIHSS score was 2 (rank 0-12) and the median mRS was 0 (rank 0-4). The measurements that showed better discrimination capacity were those obtained from abduction of the upper limb: shift of the joint angles were different between cases and controls in the frontal plane for the elbow in pronation (p=0.01) and in supination (p< 0.001); and for the shoulder both in pronation and supination (p=0.01). These differences were independent of the NIHSS score, but were moderately correlated to the mRS score at the moment of the evaluation: elbow in pronation (PCC=0.48 IC=0.051-0.12) and in supination (PCC=0.61 IC=0.16-0.29); and shoulder in pronation (PCC=0.39 IC=0.02-0.08) and in supination (PCC=0.63 IC=0.13-0.25).

Conclusion: Computational analysis of movement could be a useful tool for evaluation of upper limb function in stroke patients with slight deficit underestimated using current motor clinical scales and this is correlated with mRS. Further studies are needed to determine the better exercises to be considered and the relationship with functional outcome.