Cerebral Amyloid-β Deposition Is Associated with Impaired Gait Speed and Lower Extremity Function

Abstract

Background:

Impaired physical function (i.e., slowing of gait, muscle weakness, and poor mobility) is common in older adults with cognitive impairment and dementia. Evidence suggests that cerebral small vessel disease, specifically white matter lesions (WMLs), is associated with impaired physical function, but little research has been conducted to understand the specific role of Alzheimer’s disease pathology in physical outcomes.

Objective:

The objective of this study was to examine the association between cerebral amyloid-β (Aβ) deposition and physical function in people with cognitive impairment.

Methods:

Thirty participants completed an ¹¹C Pittsburgh compound B (PIB) position emission tomography (PET) scan to quantify global Aβ deposition using standardized uptake value ratio (SUVR). We assessed usual gait speed, muscle strength of the lower extremities, balance, and functional mobility using the Short Physical Performance Battery (SPPB) and the Timed Up and Go Test (TUGT). Multiple linear regression analyses examined the association between Aβ and each measure of physical function, adjusting for age, body mass index, and WML load.

Results:

Global PIB SUVR was significantly associated with usual gait speed (β= –0.52, p = 0.01) and SPPB performance (β= –0.47, p = 0.02), such that increased Aβ deposition was associated with reduced performance on both measures. Global PIB SUVR was not significantly associated with TUGT performance (β= 0.32, p = 0.08).

Conclusions:

Cerebral Aβ deposition is associated with reduced gait speed, muscle strength, and balance in older adults with cognitive impairment independent of WML load. However, Aβ deposition was not associated with functional mobility.

Keywords

Alzheimer’s disease amyloid-β mild cognitive impairment mobility physical function

INTRODUCTION

Impaired physical function (i.e., slow gait, muscle weakness, and poor mobility) is common in older adults with cognitive impairment and dementia [1, 2]. Notably, physical impairments appear to precede and may predict the onset of both mild cognitive impairment (MCI) [3, 4] and Alzheimer’s disease (AD) [3, 4]. Though reduced physical function is a known clinical manifestation of AD, little research has been conducted to understand the role of AD pathology, specifically amyloid-β (Aβ) plaques, on physical outcomes.

To date, research has mainly focused on the effects of cerebral small vessel ischemic disease, particularly white matter lesions (WMLs), on physical function. Several published studies provide consistent evidence that WMLs negatively impact physical performance. For example, the Leukoaraiosis And DISability (LADIS) study found that moderate to severe levels of age related white matter changes were associated with slowed gait speed and poor balance [5]. Similarly, the Cardiovascular Health Study found that greater baseline WML load was associated with greater decline in gait speed and chair stand time both cross-sectionally and longitudinally [6]. In addition, a systematic review reported that increased WML load was associated with reduced balance control and gait speed, and increased falls risk [7]. This research has also been important for understanding physical impairments in AD, as WMLs and AD pathology often co-occur; autopsy studies report incidental WMLs in over 60% of people with AD [8, 9]. It is unclear whether vascular lesions are independent or causal in the pathogenic process of AD. Neuroimaging studies predominantly indicate that WMLs and Aβ deposition are not correlated, which suggest that they may arise from independent pathogenic processes [10]. However, epidemiological studies indicate that AD and cerebrovascular pathology share common cardiovascular risk factors [11]. Furthermore, the co-occurrence of Aβ and WMLs have an additive effect; the presence of WMLs among those who have high levels of Aβ show lower cognitive scores and experience more rapid cognitive decline and progression to MCI and AD [12, 13]. These findings suggest that there is a link between AD and cerebrovascular pathology, such that the co-occurrence of WMLs and Aβ may exacerbate clinical symptoms.

Currently, it is unclear whether Aβ deposition may impact physical outcomes, independent of WML pathology. One autopsy study reported that AD pathology, including both Aβ deposition and neurofibrillary tangles, were associated with declining gait speed over an average follow-up time of 6.4 years prior to death [14]. Researchers have only recently begun to assess the effects of cerebral Aβ in vivo on physical function. A cross-sectional analysis of the Mayo Clinic Study of Aging found that higher global Aβ deposition was associated with slower gait speed, lower cadence, longer double support time, and greater stance time variability in cognitively normal individuals [15]. This analysis controlled for AD associated neurodegeneration, as measured by cerebral glucose uptake, hippocampal volume, and cortical thickness. A longitudinal analysis of the same study found that higher Aβ deposition was associated with declining gait speed, decreasing cadence, and increasing double support time over a median 15.6 months [16]. However, neither study controlled for WML burden. One study that accounted for the effects of WML volume, found that regional Aβ deposition was independently associated with slower gait speed in cognitively normal and MCI participants [17]. These study results indicate that slow gait speed may be an early marker of AD pathology.

The goal of our study was to further investigate the relationship between Aβ deposition and physical function, which is defined as the ability to perform the basic actions that are essential for maintaining independence and carrying out more complex activities [18]. The majority of studies to date assessing the effect of Aβ on physical function have included gait speed [15–17 , 19], with few studies assessing other subdomains of physical function, such as muscle strength, balance, and mobility [20]. Thus, we currently do not know how broadly Aβ deposition may impact physical function in older adults with cognitive impairment. We address these knowledge gaps by quantifying gait speed, muscle strength, balance, and functional mobility using the Short Physical Performance Battery (SPPB) and the Timed Up and Go Test (TUGT). We hypothesize that Aβ deposition has a broad and negative impact on measures of physical function in people with cognitive impairment.

METHODS

Ethical approval was obtained from the Vancouver Coastal Health Research Institute (V07-01160 and V13-01573) and the University of British Columbia’s Clinical Research Ethics Board (H07-01160 and H13-01573). All subjects gave written informed consent in accordance with the Declaration of Helsinki.

Participants and study design

This study included participants from two studies. Nineteen participants were included from the Promotion of the Mind Through Exercise (PROMoTE) study [21, 22], which was a proof of concept randomized controlled trial assessing the effect of an aerobic training program on cognitive function in older adults with subcortical ischemic vascular cognitive impairment (SIVCI) [21]. For these 19 participants, their baseline data were used in this study. Another 11 participants were included from a cross-sectional study aimed at characterizing AD, SIVCI, and mixed AD-SIVCI. All participants were recruited from either the University of British Columbia Hospital Clinic for AD and Related Disorders, the Vancouver General Hospital Stroke Prevention Clinic, or specialized geriatric clinics in Metro Vancouver, British Columbia. The diagnosis of cognitive impairment stemming from AD, SIVCI, or mixed AD-SIVCI pathology was confirmed in each participant by a neurologist. Cognitive impairment primarily due to AD was based on the core clinical criteria of possible or probable AD based on recommendations from the National Institute on Aging and the Alzheimer’s Association guidelines [23]. Cognitive impairment primarily due to SIVCI was based on the presence of cerebral small vessel ischemic disease and cognitive impairment [24, 25]. Cerebral small vessel disease was based on the presence of periventricular and deep WMLs and at least one lacunar infarct and the absence of non-lacunar territorial (cortical and/or cortico-subcortical) strokes or other specific causes of WMLs (i.e., multiple sclerosis, leukodystrophies, sarcoidosis, brain irradiation) on clinical magnetic resonance imaging (MRI) scans. Cognitive impairment was defined as a Montreal Cognitive Assessment (MOCA) score <26/30 at baseline [26]. Mixed AD-SIVCI required a diagnosis of possible AD [23] and the criteria for SIVCI [25]. Participants within our study will be referred to as cognitive impairment plus (CI+), to reflect the inclusion of AD, SIVCI, and mixed AD-SIVCI.

Individuals were eligible for study entry if they met the following criteria: 1) subjects fulfilling the criteria for cognitive impairment due to AD, SIVCI, or mixed AD-SIVCI; 2) being ≥55 years of age; 3) Mini-Mental State Examination (MMSE) ≥20 at screening [27]; and 4) provide informed consent. Exclusion criteria included: 1) being diagnosed with moderate or severe dementia (MMSE <20)— only participants with milder impairment were included; 2) being diagnosed with another type of dementia other than AD, SIVCI, or mixed AD-SIVCI (e.g., Lewy body or frontal temporal dementia); 3) being diagnosed with other neurological conditions (e.g., multiple sclerosis or Parkinson’s disease); and 4) participation in a clinical drug trial concurrent to this study.

Descriptive variables

Information regarding age, biological sex, and body mass index (BMI) were collected at baseline. Depression was measured using the Geriatric Depression Scale (GDS) [26]. In addition, information regarding WML load was rated by a neurologist (GYRH or WAK) on either clinical (5 participants) or research (25 participants) MRI scans using the Fazekas rating scale [27]. The grading score of periventricular WML was defined as: grade 0 = absence; grade 1 = ‘caps’ or pencil-thin lining; grade 2 = smooth ‘halo’ and; grade 3 = irregular periventricular hyperintensities extending into the deep white matter. The grading score of deep WML was defined as: grade 0 = absence; grade 1 = punctate foci; grade 2 = beginning confluence of foci and; grade 3 = large confluent.

Dependent variables: gait speed and lower extremity function

Usual gait speed

This measure was extracted from the SPPB 4-meter walk test, in which participants were asked to walk at their usual pace along a 4-meter path. Gait speed (m/s) was calculated from the best of two trials. The test-retest reliability (interclass correlation coefficient) of gait speed in our laboratory is 0.95 [28].

Short Physical Performance Battery (SPPB)

Participants were assessed on performances of standing balance (i.e., side-by-side stand, semi-tandem stand, and tandem stand), 4-meter walk test, and repeated chair stands. Each component was rated from 0 (inability to perform the task) to 4 (optimum performance), for a maximum of 12 points; a score of <9/12 predicts subsequent disability [29]. Specifically, this test assesses balance, usual gait speed, and strength [30], and has excellent test-retest reliability [31].

Timed Up and Go Test (TUGT)

Participants were instructed to rise from a standard chair without using armrests, walk a distance of 3 meters at usual pace, turn, walk back to the chair, and sit down again [32]. A stopwatch was used to measure the time to complete the TUGT and the mean of two trials was calculated and used for statistical analysis. Specifically, this test assesses functional mobility.

Independent variable: amyloid-β deposition

Positron emission tomography (PET) scans were performed using ¹¹C-Pittsburgh Compound-B (PIB) produced at UBC TRIUMF. Scans were performed in 3-D mode using the GE Advance tomograph (General Electric, Canada/USA). A 90 min dynamic acquisition started at tracer injection and data were framed into a 18×300 s imaging sequence prior reconstruction.

After image reconstruction, data were first frame-to-frame realigned using AIR [33] to minimize the impact of head motion. Using SPM 8 (Wellcome Department of Cognitive Neurology, Institute of Neurology, University College London) a mean of all the PET images was obtained, i.e., radiotracer concentration averaged over the entire scan duration. In the next step, each subject’s mean PIB-PET image was normalized to a mean PIB-PET image template in MNI space. Normalization was performed using non-linear regularization, 16 nonlinear iterations, 8 mm smoothing and affine regularization into an average sized template. The corresponding transformation parameters were then applied to all the PIB-PET frames for that subject to bring all of them to the MNI space. The PIB-PET template was created by averaging PIB-PET scans for a cohort of healthy controls that had all been warped with their own MRI to the SPM MNI305 template. To quantify PIB uptake we used standardized uptake value ratio (SUVR) from 40 to 90 min after injection. SUVR is calculated by normalizing SUV (tracer concentration/(injected dose/body weight)) images to the cerebellar cortex SUV.

Regions of interest (ROIs) analysis

A custom set of ROIs was defined on the coronal view of the MNI305 template. These ROIs were transposed to each subject’s warped MRI and mean-PET images (normalized to MNI space) and adjusted as necessary. The modified set of ROIs was then applied to the PIB-PET image for minutes 40 to 90 and the average SUVR within each ROI was extracted. Global SUVR was determined by averaging values in bilateral frontal (orbitofrontal and medial prefrontal cortex), parietal (angular gyrus, superior parietal, precuneus, and supramarginal gyrus), temporal (lateral temporal gyrus, medial temporal gyrus, and temporal pole), sensory-motor cortex, occipital cortex, posterior and anterior cingulate gyrus, caudate, and putamen.

Statistical analysis

All statistical analyses were performed using Statistical Package for the Social Sciences 22.0. A multiple linear regression analysis was conducted to obtain estimates for the unique contribution of global PIB SUVR, i.e., age, BMI (previous studies indicate that BMI is associated with both physical function [34, 35] and Aβ deposition [36]), and Fazekas score were entered in the first step as covariates, and global PIB SUVR was entered in the second step to determine the unique contribution of global Aβ deposition on physical function. To determine region specific effects of Aβ deposition, we conducted the same analyses with the ROI in the second step. We report standardized betas and not p-values, as multiple significance testing (i.e., 9 ROIs×3 dependent variables for a total of 27 statistical tests) would be inappropriate for our sample size. Given the large number of tests, our sample size would not have sufficient power for Type I error adjustments. For each regression model, we computed collinearity statistics (tolerance and variance inflation factor), histograms of the residuals, and scatterplots of the predicted versus residual values to ensure that the assumptions of linear regression were met. In all models, multicollinearity was not an issue among predictor variables, and the residuals were normally distributed and homoscedastic.

RESULTS

Participant characteristics

Thirty participants (8 females, 22 males) were included in this study. Clinical diagnosis included: 4 participants with AD; 22 with SIVCI; and 4 with mixed AD-SIVCI. The mean age was 72 years with an average MMSE score of 25.93 and MOCA score of 22.30. Of the 30 participants, 12 were PIB-positive (based on a threshold cutoff of SUVR 1.5 [37]) including 3 AD, 6 SIVCI, and 3 mixed AD-SIVCI participants. The global PIB SUVR was 1.44. Detailed demographic characteristics are presented in Table 1.

Table 1

Descriptive characteristic

Variable	Mean or No.	SD or %
Age	72.40	8.09
Female Sex, No. (%)	8	27
Clinical diagnosis, No. (%)
AD	4	13
SIVCI	22	73
Mixed AD-SIVCI	4	13
MMSE (max. score 30)	25.93	2.79
MOCA (max. score 30)	22.30	3.05
BMI	26.83	4.89
GDS	2.37	2.22
WML Load, No. (%)
Fazekas 0	1	3
Fazekas 1	13	43
Fazekas 2	8	27
Fazekas 3	8	27
PIB-positive, No. (%)	12	40
Global PIB SUVR	1.44	0.45
Mobility Assessments
TUGT	8.66	2.39
SPPB	10.43	1.43
Usual Gait Speed	1.08	0.27

Cerebral Aβ and physical function

Global PIB SUVR values were significantly inversely associated with gait speed (β= –0.52, p = 0.01); the total adjusted variance accounted by the final model was 39.4% (Table 2). Global PIB SUVR values were also significantly inversely associated with SPPB performance (β= –0.47, p = 0.02); the total adjusted variance accounted by the final model was 34.9% (Table 3). Global PIB SUVR values were not significantly associated with TUGT performance (β= 0.32, p = 0.08) (Table 4). Please refer to Table 5 for standardized betas of each ROI.

Table 2

Multiple linear regression assessing the contribution of PIB SUVR on Usual Gait Speed

Independent Variables	R²	Adjusted R²	R² Change	Unstandardized B (Standard Error)	Standardized β	p
Step 1	0.20	0.11	0.20
Age				–0.01 (0.01)	–0.42	0.05
BMI				–0.01 (0.01)	–0.21	0.25
Fazekas Score				–0.00 (0.06)	–0.01	0.95
Step 2	0.39	0.30	0.19 *
Age				–0.01 (0.01)	–0.38	0.05
BMI				–0.03 (0.01)	–0.47	0.02
Fazekas score				0.02 (0.06)	0.06	0.75
PIB SUVR				–0.31 (0.11)	–0.52	0.01

^*significant at p≤0.05.

Table 3

Multiple linear regression assessing the contribution of PIB SUVR on SPPB

Independent Variables	R²	Adjusted R²	R² Change	Unstandardized B (Standard Error)	Standardized β	p
Step 1	0.19	0.10	0.19
Age				–0.06 (0.04)	–0.36	0.10
BMI				–0.07 (0.05)	–0.23	0.20
Fazekas Score				–0.10 (0.33)	–0.07	0.76
Step 2	0.35	0.24	0.16 *
Age				–0.06 (0.03)	–0.33	0.10
BMI				–0.14 (0.06)	–0.47	0.02
Fazekas Score				0.00 (0.30)	0.00	0.99
PIB SUVR				–1.50 (0.61)	–0.47	0.02

^*significant at p≤0.05.

Table 4

Multiple linear regression assessing the contribution of PIB SUVR on TUGT

Independent Variables	R²	Adjusted R²	R² Change	Unstandardized B (Standard Error)	Standardized β	p

Step 1	0.38	0.31	0.38
Age				0.02 (0.05)	0.05	0.77
BMI				0.28 (0.08)	0.57	<0.01
Fazekas Score				0.64 (0.48)	0.24	0.19
Step 2	0.46	0.37	0.07
Age				0.01 (0.05)	0.03	0.85
BMI				0.36 (0.08)	0.73	<0.01
Fazekas Score				0.53 (0.46)	0.20	0.27
PIB SUVR				1.70 (0.93)	0.32	0.08

^*significant at p≤0.05.

Table 5

Standardized betas for regions of interest

Regions of Interest	Gait Speed	SPPB	TUGT
Frontal cortex	–0.41	–0.40	0.25
Parietal cortex	–0.45	–0.42	0.25
Temporal cortex	–0.50	–0.48	0.40
Sensory-motor cortex	–0.53	–0.46	0.31
Occipital cortex	–0.05	0.11	0.13
Posterior cingulate gyrus	–0.25	–0.17	0.02
Anterior cingulate gyrus	–0.52	–0.49	0.34
Caudate	–0.60	–0.51	0.35
Putamen	–0.63	–0.55	0.39

DISCUSSION

Previous studies assessing the effect of cerebral Aβ deposition on physical function have largely focused on healthy older adults [15 , 20] and few investigations have been conducted in people with cognitive impairment [17, 38]. Among those with CI+, we found that cerebral Aβ deposition was associated with reduced gait speed, balance, and muscle strength. However, cerebral Aβ deposition was not associated with functional mobility. The results of our study corroborate existing literature implicating Aβ pathology in impaired physical function [15–17 , 38].

Several studies have linked increased Aβ with reduced gait speed [15, 16] and increased gait variability [15 , 19] in healthy older adults. Similar results have been reported in a combined cohort of healthy older adults and older adults with MCI. Del Campo and colleagues [38] found that increased Aβ deposition in motor related regions (i.e., posterior and anterior putamen, occipital cortex, precuneus, and anterior cingulate) was associated with decreased gait speed; however, this study was not able to control for the potential effects of WML load. In another study with cognitively normal and MCI participants, regional Aβ deposition was associated with slower gait speed independent of demographics, cardiac risk, hippocampal volume, and WML volume [17]. In extension of these findings, the results of our study further affirm the relationship between Aβ pathology and reduced gait speed in people with CI+.

Furthermore, we found an association between increased Aβ deposition and reduced SPPB performance. To our knowledge, few studies have assessed the relationship between Aβ deposition and physical function of the lower extremities in people with cognitive impairment. In a study with healthy older adults, the Baltimore Longitudinal Study of Aging neuroimaging (BLSA-NI) substudy found that higher baseline Aβ deposition was associated with a decline in lower extremity performance, as measured by the Health ABC Physical Performance Battery (i.e. timed performance on five repeated chair stands, timed standing balance, timed 6 m walk at a usual pace, and timed narrow 6 m walk test) [20]. In contrast, we did not find a significant association between Aβ deposition and functional mobility, as measured by the TUGT. Perhaps a more comprehensive measure of lower extremity function, such as the SPPB, is more sensitive to amyloid pathology. The SPPB includes measures of gait speed, balance, and muscle strength, whereas the TUGT is a basic functional mobility test. Notably, the SPPB is a highly relevant measure in geriatric medicine as it is predictive of declines in activities of daily living, disability, hospitalization, and mortality [30 , 40]. Overall, our findings expand previous research by highlighting the deleterious effects of Aβ deposition on lower extremity function in people with CI+.

The exact mechanisms by which AD pathology can lead to impaired physical function remains unknown. One hypothesis postulates that Aβ pathology may cause physical impairments by disrupting striatal circuits [38]. The striatum is anatomically composed of the caudate nucleus and putamen and is responsible for proper motor function. Disruptions to these circuits are implicated in neurodegenerative diseases characterized by motor symptoms [41]. Though the striatum is not conventionally associated with AD pathology, striatal Aβ deposition has been observed in AD and in people without dementia [38, 42]. Moreover, a study assessing the effect of Aβ deposition in motor related regions found that Aβ in the putamen, occipital cortex, precuneus, and anterior cingulate were significantly associated with slowed gait speed, with the putamen and precuneus showing the strongest effects (i.e., highest beta coefficients) [38]. Within the current study, we also found that Aβ deposition in the putamen and caudate had the strongest effect on gait speed. The putamen also had the strongest effect on lower extremity function (i.e., SPPB and TUGT). These results are consistent with our understanding of motor pathways; the striatal region is uniquely interlinked with primary motor, premotor, supplementary motor, and primary somatosensory cortex and, thus, plays a pivotal role in modulating motor circuits [38, 43].

The results of our study should be evaluated within its limitations. First, our sample size of participants with cognitive impairment was modest; thus, these analyses need to be replicated in future studies with larger sample sizes. In addition, the association between Aβ deposition and gait speed may be stronger in women [15, 16], but we are unable to conduct sex-stratified analyses due to our small sample of females. Second, this work focuses on the effect of cerebral Aβ deposition without considering other brain pathologies such as tauopathies [44], cerebral atrophy [45], or cortical thinning [46], but evidence from other studies suggest that measures of neurodegeneration such as hippocampal volume, cortical thickness, and glucose metabolism do not attenuate the relationship between greater Aβ deposition and impaired physical function [15, 17]. Third, we did not find an association between WML load and physical function. This may be due to the limited range or accuracy of the Fazekas scale, though we note that previous studies such as the LADIS [5] and the Cardiovascular Health Study [6] were able to detect an association between impaired physical function and WML load using a graded rating scale. However, these studies included a much larger sample size and, thus, our study may not have had sufficient power to detect an association between WML load and physical function. Perhaps a more accurate measure such as WML volume would be more sensitive. Lastly, this was a cross-sectional analysis and does not allow us to draw conclusions about directionality or the prognostic value of Aβ deposition on future physical performance. Thus, future studies with a longitudinal design are needed to fully elucidate the effect of Aβ on physical function. To date, dementia research has primarily focused on cognitive outcomes; however, it is critical that we continue to highlight the effect of AD pathology on physical function as physical function is a reliable marker of current and future health [47] and is associated with increased risk for developing dementia [48].

Footnotes

ACKNOWLEDGMENTS

This study was jointly funded by the Canadian Stroke Network, Heart and Stroke Foundation of Canada, Jack Brown & Family Alzheimer’s Research Foundation, and Alzheimer Society of Canada. T.L.A. is a Canada Research Chair (Tier II) in Physical Activity, Mobility, and Cognitive Neuroscience. G.Y.R.H has received research support from the Canadian Institutes of Health Research (CIHR) and the Alzheimer Society of British Columbia. E.D. has received a doctoral training award from CIHR. TRIUMF is gratefully acknowledged for PET tracer production.

Authors’ disclosures available online .

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