J.K. Longhurst1,2,3, J.L. Cummings4, S.E. John4, B. Poston5, J.V. Rider3,6, A.M. Salazar4, V.R. Mishra7, A. Ritter7, J.Z. Caldwell7, J.B. Miller7, J.W. Kinney4, M.R. Landers3
1. Department of Physical Therapy and Athletic Training, Saint Louis University, Saint Louis, Missouri, USA; 2. Department of Neurorehabilitation, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA; 3. Department of Physical Therapy, University of Nevada, Las Vegas, USA; 4. Department of Brain Health, University of Nevada, Las Vegas, USA; 5. Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, USA; 6. Department of Occupational Therapy, Touro University, Henderson, Nevada, USA; 7. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA
Corresponding Author: Jason K. Longhurst, PT, DPT, PHD, Department of Physical Therapy and Athletic Training, Saint Louis University, Saint Louis, Missouri, USA, 63104, firstname.lastname@example.org, tel: 314-977-8533, fax: 314-977-8513
J Prev Alz Dis 2022;
Published online January 7, 2022, http://dx.doi.org/10.14283/jpad.2022.1
Background: Preclinical Alzheimer’s disease (AD) provides an opportunity for the study and implementation of interventions and strategies aimed at delaying, mitigating, and preventing AD. While this preclinical state is an ideal target, it is difficult to identify efficiently and cost-effectively. Recent findings have suggested that cognitive-motor dual task paradigms may provide additional inference.
OBJECTIVES: Investigate the relationship between dual task performance and amyloidosis, suggestive of preclinical Alzheimer’s disease and whether dual task performance provides additional information beyond a cognitive composite, to help in the identification of amyloidosis.
SETTING: Outpatient specialty brain health clinical research institution in the United States.
PARTICIPANTS: 52 cognitively healthy adults.
MEASUREMENTS: The data included demographics, amyloid standardized uptake value ratio obtained via florbetapir-PET, neuropsychological testing, apolipoprotien E genotype, and dual task performance measures. Data were analyzed via hierarchal multiple linear regression or logistic regression, controlling for age, education, and apolipoprotien E genotype. Receiver operating characteristic curves were plotted, and sensitivity and specificity calculated via 2×2 contingency tables.
RESULTS: There was a moderate relationship (rs>.30) between motor and cognitive dual task effects and amyloid standardized uptake value ratio (ps<.042). A strong relationship (r=.58) was found between combined dual task effect, a measure of automaticity derived from dual task performance, and amyloid standardized uptake value ratio (p<.001). Additionally, combined dual task effect showed promise in its unique contributions to amyloid standardized uptake value ratio, accounting for 7.8% of amyloid standardized uptake value ratio variance beyond cognitive composite scores (p=.018). Additionally, when incorporated into the cognitive composite, combined dual task effect resulted in improved diagnostic accuracy for determining elevated amyloid standardized uptake value ratio, and increased the sensitivity and specificity of the cognitive composite.
CONCLUSSION: Dual task performance using the combined dual task effect, a measure of automaticity, was a moderate predictor of cerebral amyloidosis, which suggests that it has utility in the screening and diagnosis of individuals for preclinical AD. Additionally, when combined with the cognitive composite, the combined dual task effect improves diagnostic accuracy. Further research is warranted.
Key words: Dual-task interference, amyloid, preclinical Alzheimer’s disease, screening, motor impairments.
Early identification of preclinical Alzheimer’s disease (AD) is key in implementing disease modifying strategies against AD. As of 2021, 6.2 million Americans have Alzheimer’s dementia, with one in nine of individuals over 65 years of age having the disease, and these figures are expected to more than double by the year 2050 (1). Additionally, this preclinical phase of AD can begin decades before the onset of clinical symptoms (2, 3) and nearly eightfold as many people have preclinical AD as have symptomatic AD (4). Earlier identification may allow for changes to modifiable risk factors and delay onset or mitigate progressive decline in cognition and function (5–7).
Current diagnostic methods for preclinical AD, defined by the National Institute on Aging and the Alzheimer’s Association (NIA-AA) workgroup, require imaging biomarkers, such as abnormal amyloid or fluorodeoxyglucose (FDG) positron emission tomography (PET), hippocampal volumes on magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) biomarkers (8–10). These methods for identifying preclinical AD, while very useful, are expensive, time consuming, invasive, and are often clinically unavailable during the preclinical state (11). Behavioral markers are needed to improve the identification of individuals who are likely to have preclinical AD and to respond to this need several cognitive batteries have been developed (12–14). While useful for identifying symptomatic individuals, these cognitive batteries have modest utility for predicting amyloidosis (13), which is the earliest marker to emerge during the preclinical period (2,9).
Cognitive-motor dual task (DT) performance has been suggested as a sensitive indicator of risk for cognitive decline (11, 15). Preclinical AD-related changes in brain processes may reduce cognitive resources or limit recruitment efficiency and resource coordination with impaired capacity for performance in novel and challenging conditions, such as DT performance (16). In support of this, Nadkarni and colleagues found a moderate relationship between cerebral amyloid deposition and DT performance (17). Conversely, Åhman and colleagues found no relationship between CSF amyloid beta protein (Aβ)-42 and DT performance (18). To our knowledge, those two studies are the only ones to investigate the relationship between DT performance and amyloidosis, showing conflicting results, and both were limited by small samples. Additionally, the studies utilized only measures of single task (ST) performance during DT (i.e., they evaluated change in either motor or cognitive performance during DT). Neither study included measures of DT automaticity, defined as the ability to perform a task without directing attention to it (19). Previous work has shown that measures probing automaticity are strongly associated with levels of cognition, motor performance, brain volumes, and disease severity (20, 21).
The purpose of this study was to investigate the role of cognitive-motor automaticity in predicting levels of cerebral amyloid deposition in cognitively normal older adults. Overall, we hypothesized that cerebral amyloid deposition reduces automaticity and we predicted that individuals with preclinical AD will perform worse than their healthy counterparts. As such, the first aim of this study was to examine the relationship between DT automaticity and cerebral amyloid levels and whether this relationship extends beyond the influence of a cognitive composite used for predicting preclinical AD. We hypothesized that automaticity would predict a significant amount of variation in cerebral amyloid levels, after controlling for age, years of education, and presence of the apolipoprotien E (APOE) ε-4 genotype. Additionally, we hypothesized automaticity would explain a unique amount of variance over and above the cognitive composite. Our second aim was to determine the diagnostic accuracy of DT automaticity in identifying amyloidosis and whether the addition of DT automaticity improved the diagnostic accuracy of the cognitive composite.
A cross-sectional analysis of data was performed on data from the Center for Neurodegeneration and Translational Neuroscience (CNTN) (22), a longitudinal cohort study of older adults. Detailed methods have been described elsewhere (22). Consent and data were collected under Cleveland Clinic Institutional Review Board approval and in accordance with declaration of Helsinki.
Baseline measurements of the cognitively healthy control cohort included demographic characteristics, neuropsychological measures, functional measures, and imaging and genetic biomarkers. The inclusion criteria for this cohort were: age 55 to 90, possess adequate visual and auditory acuity for neuropsychological testing, and speak fluent English. Exclusion criteria were: significant neurologic disorders, unstable medical conditions, and evidence of cognitive impairment (operationally defined as greater than one standard deviation from age-matched normative values for more than one neuropsychological test). Additionally, participants were excluded from this study if they did not complete amyloid PET or DT assessments at baseline. Demographic data were collected at baseline and included age, sex, race, ethnicity, years of education, and the Montreal Cognitive Assessment (MoCA).
Dual Task Assessment
DT metrics were calculated from performance on a motor and cognitive task as described below. Participants were instructed to perform both the motor and cognitive tasks simultaneously as quickly and accurately as possible. These instructions were intended to encourage neutral prioritization between motor and cognitive tasks.
Participants completed the Timed Up and Go Test (TUG) (motor single task (ST)) and DT-TUG, in which participants completed the TUG while simultaneously completing a serial subtraction task. These methods have been described previously (20). Motor performance was measured in seconds to complete the TUG.
Participants completed serial subtraction by three from an arbitrary number between 80 and 100 in a seated position for 20 seconds to assess their ST cognitive performance. Following this, cognitive performance during DT-TUG was completed using the same method but beginning at a different number between 80 and 100 to minimize learning effects. The number of correct responses was recorded and correct response rate (the average number of seconds per correct response) calculated, a measure which was adapted from previous studies (23, 24).
Dual task effects
Calculation of motor (mDTE) and cognitive (cogDTE) dual task effects were completed using the following equation (25,26): DTE(%)=(-DT-ST)/ST×100%.
Calculation of combined dual task effect (cDTE), a measure of automaticity that includes both cognitive and motor aspects of dual task performance, was completed using the following equation (20,21): cDTE (%)=(-(motorDT ×Cognitive DT)-(motorST ×cognitiveST))/((motorST ×cognitiveST))×100%
A negative DTE score indicates poorer DT performance relative to single task performance, whereas a positive DTE reflects improved performance under DT conditions relative to single task performance.
Amyloid PET data acquisition and processing. All participants underwent florbetapir-PET scans, which were acquired on a Siemens Biograph mCT PET/CT scanner 50 minutes after injection of 370 MBq of florbetapir. The procedure for processing of PET imaging has been previously described (27). Standard up-take value ratios (SUVRs) were calculated and summarized SUVRs were utilized in the analyses. The summarized SUVR comprised frontal cortex, temporal cortex, parietal cortex, anterior cingulate gyrus, posterior cingulate gyrus, and the precuneus region, using the cerebellum as the reference region. Participant amyloid status (amyloid +/-) was determined using a cut-off of 1.1 for summarized SUVR (28).
Plasma samples were collected at the baseline visit. Following DNA isolation, APOE genotyping was conducted (procedures detailed in Supplemental File 1). For statistical analysis, APOE haplotypes were categorized into a two-level variable based on the presence or absence of ε4 alleles for analyses. Categorization into three groups (no ε4 alleles, one ε4 allele, and two ε4 alleles) was not possible due to the very small number of participants carrying two ε4 alleles (n=2).
Cognitive composite scores were calculated following similar methods described by Donohue and colleagues (12) in the development of the Preclinical Alzheimer’s disease Cognitive Composite (PACC). Cognitive measures were collected during the baseline neuropsychological assessment. Scores for this new cognitive composite (CNTN-CC) scores were computed from measures of episodic memory (delayed recall score on the Logical Memory IIa subtest from the Wechsler Memory Scale IV), attention (the digit symbol substitution test score), and the total MMSE score, just as the PACC (13,29). The CNTN-CC differs from the PACC with the inclusion of immediate and delayed recall scores from the Alzheimer’s Disease Assessment Scale in place of the Free and Cued Selective Reminding Test (FCSRT). This modification from the original PACC was necessitated by the lack of inclusion of the FCSRT in the CNTN neuropsychology assessment. The included measures were standardized as z-scores using the mean and standardized deviation derived from the sample. The standardized scores were summed to create a composite score that was subsequently utilized in the analyses. An additional composite variable, consisting of standardized CNTN-CC and cDTE values, was created (CC+cDTE).
Sample Size Estimation
Sample size was estimated using PASS 20.0.3 (NCSS, LLC. Kaysville, Utah, USA, www.ncss.com/software/pass) using the multiple regression module and the conditional power calculation method and was powered based on aim 1. Based on the estimate, a sample size of 38 total participants would be required to achieve 80% power to detect an R² of .20 attributable to two independent variables using an F-Test with an α of .05. The variables tested were adjusted for an additional three covariates with a combined R2 of .1 by themselves.
All analyses were conducted using SPSS 24.0 (IBM SPSS Statistics for Windows, Armonk, New York, USA: IBM Corp) with α=.05. For aim 1, hierarchal multiple linear regression (summary SUVR) and hierarchal logistic regression (Amyloid +/-) were conducted to account for potential covariates. Amyloid SUVR (linear regression) or amyloid status (logistic regression) was regressed on age, years of education, and APOE genotype in block 1, and then on DT performance (mDTE, cogDTE, or cDTE) in block 2. The same analyses were repeated with the addition of CNTN-CC to block 1. For all analyses, dummy coding was used for the coding of APOE genotype. Additionally, two hierarchal multiple linear regression models were analyzed for CNTN-CC and CC+cDTE with the intent of examining the impact of adding the cDTE to the CNTN-CC. Following these analyses, receiver operating characteristic (ROC) curves were plotted and area under the curve (AUC) analyzed for cDTE, CNTN-CC, and CC+cDTE to determine accuracy of identifying individuals with elevated cerebral amyloid deposition (SUVR cut point of 1.11). The degree of the accuracy was classified based on AUC value using the following criteria: no discrimination (0.5), poor (.5-.7), acceptable (.7-.8), excellent (.8-.9), outstanding (>0.9), and perfect test (AUC = 1) (30). Optimal cut points were determined as the closest point to the upper left corner of the ROC plot, confirmed using the Youden index (31), and sensitivity, specificity, and positive likelihood ratios (+LR) were calculated for each measure. Changes in probability were also calculated in accordance with Bayes’ Theorem.
A total of 52 cognitively normal participants (age = 70.4±6.8 years; males = 53.8%; white = 88.4%) were analyzed. The median level of education was four years of college. The average MoCA was 26.7±2.4, and there were 12 (23.1%) APOE ε4 carriers, with two of those being homozygotes. Additional descriptive characteristics can be found in Table 1.
DT performance as a predictor of cerebral amyloid deposition. No statistically significant regression equations were found in block 1 of any of the hierarchical multiple regression analyses (ps=.985) for the prediction of amyloid SUVR. Statistically significant regression equations were found in the second block for the cogDTE (F(4,48)=3.483, p=.014), and cDTE (F(4,48)=5.891, p=.001) models, such that the models accounted for 22.9% and 33.4% of the variability in amyloid SUVR, respectively (Table 2).
No statistically significant logistic regression equations were found in block 1 of any of the analyses (ps=.956) for the prediction of amyloid status (+/-). Statistically significant regression equations were found in the second block for the cogDTE (χ2(1)= 6.614, p=.010), and cDTE (χ2(1)=10.597, p=.001) models (Table 3).
DT performance as a predictor of cerebral amyloid deposition above and beyond CNTN-CC. Statistically significant regression equations were found in block 1 of all analyses (F(5,47)=6.857, p<.001) for the prediction of amyloid SUVR. Statistically significant regression equations were found after block 2 for all analyses (ps<.001). However, a statistically significant change in R2 was only identified in the cDTE analysis (F(5,47)=7.286, p<.001) with an R2 change of .074 and a p value of .018 (Table 4).
A statistically significant regression equation was found in block 1 for all analyses (χ2(4)=21.126, p<.001) for the prediction of amyloid status (+/-). However, no statistically significant improvements to model fit were found with the addition of any of the DT variables to the model (ps<.320) (Table 5).
Statistically significant regression equations were found for both models (ps<.001) for the prediction of amyloid SUVR. The CNTN-CC model explained 37.4% of the variability in amyloid SUVR, while the CC+cDTE model explained 43.7% of the variability in SUVR (Table 2).
Diagnostic accuracy. ROC curves were plotted for cDTE, CNTN-CC, and CC+cDTE (Supplemental file 2). AUCs were significant for all variables (ps<.007) with AUC values of .734, .796, and .817 for cDTE, CNTN-CC, and CC+cDTE, respectively. Using a cut point of 76.5, cDTE had a sensitivity of 70.6% and a specificity of 73.5%. At a cut point of -1.0 for CNTN-CC, sensitivity and specificity were 58.8% and 73.5%, respectively. For CC+cDTE sensitivity was 64.7% and specificity was 85.3%, utilizing a -1.0 cut point. Based on the above sensitivity and specificity values, +LR for cDTE was 2.66, CNTN-CC was 2.21, and CC+cDTE was 4.40. Pre-test probability was 32.7%. Post-test probability for cDTE was 56.4%, resulting in a shift in probability of 23.7%. For CNTN-CC the post-test probability was 51.8%, for a shift in probability of 19.1%. Lastly, post-test probability for CC+cDTE was 68.1%, for a resultant shift in probability of 35.4% .
These findings are consistent with our original hypothesis that DT performance, and specifically automaticity, would explain a significant amount of variability in amyloid SUVR. The results of this study indicate that DT performance is moderately and inversely related to cerebral amyloid deposition, consistent with the findings of Nadkarni and collegues (17). Additionally, cDTE, as a measure of automaticity, was more strongly related to amyloid SUVR (ΔR2=.331) than was either mDTE (ΔR2=.085) or cogDTE (ΔR2=.226). These findings extend our previous work showing that, among individuals with cognitive impairment, cDTE was more strongly related to brain volumes, particularly for the anterior cingulate and the entorhinal and medial orbital frontal cortices, than other measures of DT performance (20). Interpreting those findings with this study of individuals with preclinical AD, provides insight into a potential mechanism for volume loss in later disease, that of amyloidosis. These findings stand somewhat in contrast to those of Åhman and colleagues who found no relationship between CSF Aβ-42 and DT performance (18). A potential explanation for these seemingly inconsistent findings is that the samples from the two studies differed greatly. While this study included 52 individuals that were cognitively normal, the Åhman study had 88 participants, of which 80 had symptomatic AD. It has been shown that while CSF tau levels continue to progress well into the symptomatic phase of the disease, CSF Aβ and cerebral amyloid deposition begin earliest in the disease process and peak at the onset of symptomatic disease, remaining relatively stable after that point (2, 32). Unsurprisingly, the degree of amyloidosis may not be related to the magnitude of symptoms among those with symptomatic disease (33, 34), while the relationship between amyloid and subtle manifestations of disease pathology is strong among those with preclinical AD (13).
This study found that DT performance has potential as an indicator of risk for future cognitive decline, which is consistent with the literature. DT performance worsens with disease severity, even in the earliest disease states (35–41). In cognitively normal adults, DT performance has been shown to be worse among carriers of at least 1 APOE ε4 allele (16, 42). Additionally, DT performance has been shown to be a significant indicator of the progression of cognitive impairment (15, 37).
While there was much common variability between the CNTN-CC and measures of DT performance, we found that cDTE contributed uniquely to the variability in cerebral amyloid SUVR beyond the covariates and the CNTN-CC. This suggests that DT performance or automaticity relies on regions, networks, and/or processes that are different than those probed by the CNTN-CC. DT paradigms tap into multiple cognitive and motor regions and networks, which are dependent on the nature of the component tasks involved (43). Unsurprisingly, both motor control and cognitive regions have been implicated in DT performance. Specifically, the motor cortex, dorsal basal ganglia, brain stem, and cerebellum are related to motor automaticity, whereas the prefrontal cortex, cingulate, and paracingulate regions are related to executive function and attention (43, 44). The confluence of these networks has been proposed as a control pathway of locomotion, which is active when activity shifts from motor to the control path, which occurs during complex tasks, such as DT (45). Another possible explanation is that DT performance is mediated by a wide-spread gray matter network, and, thus, is associated with generalized neuronal loss (43, 45–47). In either case, DT performance, may be sensitive to early functional or structural neural changes associated with AD that are not well tested by traditional cognitive composites. Of the measures of DT performance, cDTE alone was found to be a significant predictor of amyloid deposition, which points to the utility of this novel measure and its sensitivity to subtle changes early in the disease process (20,21). It also supports the notion that cDTE taps into neural resources involved in automaticity (20), a construct not probed by the CNTN-CC or fully elucidated by the other DT measures.
Both cDTE and CNTN-CC had acceptable discrimination of amyloid positive and negative states which was improved by the inclusion of cDTE as a component of CNTN-CC (CC+cDTE). Sensitivity and specificity analyses revealed that cDTE was more sensitive than CNTN-CC to elevated amyloid SUVR (70.6% to 58.8%), while performing similarly in specificity (both at 73.5%). The addition of cDTE to the composite scoring of the CNTN-CC resulted in improved sensitivity compared to the CNTN-CC alone and improved specificity compared to cDTE and the CNTN-CC. Overall, our results point to a strong relationship between dual task performance and cerebral amyloid deposition in cognitively healthy adults.
Taken together, these results indicate that cDTE is the best measure, of those in this study, for ruling out high levels of cerebral amyloid. The cDTE and CNTN-CC were comparable in identifying elevated levels of cerebral amyloid; however; in combination (CC+cDTE), they were more effective than either measure individually. Additionally, AUCs indicate that CC+cDTE had the greatest diagnostic accuracy. Similarly, CC+cDTE had the largest +LR indicating that a positive test increases the odds of having elevated levels of cerebral amyloid by 4.4, resulting in the largest shift in probability (35.4%) of the three measures analyzed. As noted above, this is supported by the findings of Nadkarni and colleagues (17). These results implicate cDTE as a potentially useful tool in helping to identify individuals with preclinical AD. Importantly, our findings suggest that the utilization of DT in the identification of those with preclinical AD is most effective when done with a traditional cognitive composite, such as the CNTN-CC.
While the findings of this study are notable, there are limitations that should be acknowledged. The primary limitation was that this preliminary study was cross-sectional in nature, and, therefore cannot make claims about temporality, and, as such, is not a robust design for causality. The CNTN-CC, while similar in construction to the PACC, did substitute the immediate and delayed recall scores from the Alzheimer’s Disease Assessment Scale in place of the FCSRT. It is possible that this substitution decreased the sensitivity of the composite and, thus, increases the observed benefit of the addition of the cDTE. While this is possible, it is unlikely, as the FCSRT represents the construct of memory, and cDTE represents the construct of automaticity; subsequently it is unlikely that these two different constructs are highly related. Additionally, this study is limited by a small sample size. In light of these limitations, these promising findings should be interpreted cautiously. In particular, longitudinal investigation of the relationship between automaticity and amyloidosis in the development of preclinical and symptomatic AD is warranted.
DT performance was found to be a moderate to strong predictor of cerebral amyloid deposition. Additionally, cDTE, a recently developed measure of automaticity derived from DT performance, contributed uniquely to variation in amyloid SUVR beyond the influence of a cognitive composite similar in composition and construction to the PACC. DT performance appears to be sensitive to functional or structural neural changes associated with AD that are not well probed by cognitive batteries. The inclusion of cDTE improved the diagnostic accuracy of the cognitive composite, as well as its sensitivity and specificity.
Funding: This work was funded by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health: #P20GM109025. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Conflict of Interest: The authors have no conflict of interest to report.
Ethical standards: The study protocol was approved by the Institutional Review Board of Cleveland Clinic (No. 15-987). Informed consent was obtained from all participants included in the study.
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