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AMNESTIC MILD COGNITIVE IMPAIRMENT IS CHARACTERIZED BY THE INABILITY TO RECOVER FROM PROACTIVE SEMANTIC INTERFERENCE ACROSS MULTIPLE LEARNING TRIALS

 

D.A. Loewenstein, R.E. Curiel Cid, M. Kitaigorodsky, E.A. Crocco, D.D. Zheng, K.L. Gorman

 

Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Miami, Florida,. U.S.A.

Corresponding Authors: David A. Loewenstein, PhD, ABPP-CN; Director, Center for Cognitive Neuroscience and Aging; Professor of Psychiatry and Behavioral Sciences; Professor of Neurology; University of Miami, 1695 NW 9th Ave, Suite 3202, Miami, FL 33136; dloewenstein@med.miami.edu; Phone: (305) 355-7016; Fax: (305) 255-9076

J Prev Alz Dis 2021;
Published online January 18, 2021, http://dx.doi.org/10.14283/jpad.2021.3

 


Abstract

Background: Difficulties in inhibition and self-monitoring are early features of incipient Alzheimer’s disease and may manifest as susceptibility to proactive semantic interference. However, due to limitations of traditional memory assessment paradigms, recovery from interference effects following repeated learning opportunities has not been explored.
Objective: This study employed a novel computerized list learning test consisting of repeated learning trials to assess recovery from proactive and retroactive semantic interference.
Design: The design was cross-sectional.
Setting: Participants were recruited from the community as part of a longitudinal study on normal and abnormal aging.
Participants: The sample consisted of 46 cognitively normal individuals and 30 participants with amnestic mild cognitive impairment.
Measurements: Participants were administered the Cognitive Stress Test and traditional neuropsychological measures. Step-wise logistic regression was applied to determine which Cognitive Stress Test measures best discriminated between diagnostic groups. This was followed by receiver operating characteristic analyses.
Results: Cued A3 recall, Cued B3 recall and Cued B2 intrusions were all independent predictors of diagnostic status. The overall predictive utility of the model yielded 75.9% sensitivity, 91.1% specificity, and an overall correct classification rate of 85.1%. When these variables were jointly entered into receiver operating characteristic analyses, the area under the curve was .923 (p<.001).
Conclusions: This novel paradigm’s use of repeated learning trials offers a unique opportunity to assess recovery from proactive and retroactive semantic interference. Participants with mild cognitive impairment exhibited a continued failure to recover from proactive interference that could not be explained by mere learning deficits.

Key words: Proactive semantic interference, retroactive semantic interference, prodromal Alzheimer’s disease, mild cognitive impairment, intrusions.


 

Introduction

Hasher and Zacks (1988) first described age-related changes in inhibitory processes that diminished the ability to ignore distracting information (1). This was confirmed in subsequent studies (2-4). Difficulties in inhibitory processes and self-monitoring have also been seen as early features of incipient Alzheimer’s disease (AD; 5-8). Loewenstein and colleagues (2004) posited that learning deficits are related to deficiencies in the semantic network and found that proactive interference of competing to-be-remembered lists of semantically related targets were especially sensitive to the mild cognitive impairment (MCI) stages of AD (6). Curiel et al (2013) employed a novel paradigm (9), the Loewenstein and Acevedo Scales for Semantic Interference and Learning (LASSI-LTM) that required learning a list of 15 target items representing three semantic categories (fruits, musical instruments, and articles of clothing). Maximal learning was facilitated by category cues at both acquisition and recall. Proactive semantic interference (PSI) and the failure to recover from PSI (frPSI) were assessed by having the examinee attempt to learn 15 new targets on List B (representing the identical semantic categories used for List A targets) over two additional learning trials while using these identical category cues during both acquisition and retrieval.
Subsequent studies conducted in independent cohorts in the United States and other countries have found that performance deficits on the LASSI-L were superior to several traditionally used memory tests (e.g., list learning measures, delayed paragraph recall) in distinguishing between cognitively normal older adults and those with preclinical AD or early and late stage MCI. Various studies on the LASSI-L have related these early cognitive changes to biological markers of AD such as in-vivo amyloid imaging (10-12) and neurodegeneration measured by magnetic resonance imaging (MRI; 13-14), functional MRI (15), and fluorodeoxyglucose positron emission tomography (PET/CT; 16). In a majority of these studies, AD pathology was more associated with deficits in frPSI than impairments in initial PSI. Using Receiver Operator Characteristic Curve (ROC) analyses, Matias-Guiu and colleagues found that the LASSI-L was superior to the Free and Cued Selective Reminding Test (FCSRT), in detecting MCI patients with suspected AD (16) and in differentiating both early and late stage MCI individuals from cognitively normal older adults.
It has been proposed that both PSI and frPSI can be assessed in different manners (11). These include the number of correct responses on List B relative to List A or the number of semantic intrusions rendered on List B recall trials. In one recent study, MCI patients that were amyloid positive and had presumptive AD evidenced significantly more intrusion errors than MCI participants who had a clinical history consistent with AD but were amyloid negative, or MCI participants diagnosed with other neurological and neuropsychiatric conditions who were also amyloid negative (11).
The finding that frPSI is particularly sensitive to incipient AD raises an interesting theoretical as well as empirical question. Will deficits in frPSI continue in the presence of additional opportunities to learn two competing semantic word lists? That is, could extending additional opportunities to learn both List A and List B provide deeper insights into initial learning deficits in aMCI participants at risk for AD, as well as their ability to completely recover from PSI deficits over time? An additional question is whether the failure to recover from retroactive interference (frRSI) is an issue in persons with aMCI. These issues have not been addressed by the LASSI-L and other paradigms.
To test the abovementioned potential limitations of this novel assessment paradigm, we employed the Cognitive Stress Test (CST). The CST required learning of 18 targets words, all of which belonged to one of three semantic categories: occupations, household items and types of transportation. Identical category cues were provided during each of the three learning trials as well as during each of the three cued recall trials for each list. This provided a unique opportunity to directly assess the immediate and persistent effects of semantic interference over multiple trials. In addition, we assessed the ability to recover from retroactive semantic interference, which has not been previously examined in aMCI and AD research. We hypothesized that failure to recover from proactive semantic interference would continue to be problematic for individuals with aMCI despite multiple trials that would allow the recovery from these deficits.

 

Methods

Participants were part of an NIH-funded longitudinal study on normal and abnormal aging. All participants provided informed consent for this IRB-approved study. In this investigation, we carefully selected 46 individuals classified as cognitively normal (CN) and 30 participants with amnestic mild cognitive impairment (aMCI). Inclusion and exclusion criteria are as follows.

Cognitively normal group (n=46)

Participants were classified as CN if there were: a) no subjective cognitive complaints made by the participant and/or a collateral informant; b) no evidence of memory or other cognitive decline after an extensive interview with the participant and an informant; c) Global Clinical Dementia Rating (CDR) scale score of 0 (17); and d) all memory (e.g.: Hopkins Verbal Learning Test, Revised (HVLT-R; 18) or delayed paragraph recall from the National Alzheimer’s Coordinating Center Uniform Data Set (NACC UDS; 19) and non-memory measures (e.g., Category Fluency (20), Trails A and B (21), WAIS-IV Block Design subtest (22)) were less than 1.0 standard deviation below normal limits for age, education, and language group.

Amnestic MCI group (n=30)

Participants were classified as aMCI if: (a) they fulfilled Petersen’s criteria (23) for MCI, b) subjective cognitive complaints were reported by the participant and/or collateral informant; c) Global CDR scale score was 0.5; and d) delayed recall was impaired (i.e., 1.5 standard deviations or more below the mean, accounting for age, education, and language of testing) on either the HVLT-R or delayed paragraph recall from the NACC UDS.

Exclusion Criteria for all study groups

Exclusion criteria included significant sensory or motor deficits (e.g., visual or hearing impairment, paralysis) or literacy lower than the 6th grade level on the WRAT-4 (24) evidenced during the clinical evaluation by Drs. Loewenstein or Curiel and judged to preclude completion of the study measures; 2) DSM-5 diagnosis of major depressive disorder, bipolar disorder, current psychotic disorder, substance use disorder or any DSM-5 Axis 1 diagnosis after an extensive interview by the study clinicians using the SCID (25). Individuals with major depressive disorder were excluded from the study given that this condition often results in attention and/or concentration difficulties and psychomotor slowing that may adversely affect test performance on neuropsychological measures. Individuals with major neurocognitive disorder were not included in this sample.

Cognitive Stress Test (CST)

We employed a novel computerized measure called the Cognitive Stress Test (CST) that expands upon our previous work with the widely-studied Loewenstein-Acevedo Scale for Semantic Interference and Learning (LASSI-L), including the computerized version of the LASSI-L which has evidenced high test-retest reliability and discriminative validity (Curiel et al, in press). The CST employs the following: 1) semantic cuing at both acquisition and retrieval of 18 List A targets representing three semantic categories (occupations, household items, or types of transportation) over three initial learning trials, 2) three consecutive presentations of a second list of 18 new targets (List B) representing the same categories as the first list to examine PSI and frPSI, and 3) use of category cues to elicit recall of List A targets to assess retroactive semantic interference (RSI), with an additional learning trial to examine failure to recover from retroactive semantic interference (frRSI). The CST represents an exciting approach to preclinical AD assessment in that it builds upon our previous work and is a fully computer-administered web-based task, which facilitates remote deliverability, reduces the need for a skilled psychometrist, and allows for automatically recording of correct responses, intrusions and other errors.

Statistical Analyses

Statistical analyses were conducted using SPSS Version 26. First, age, gender, education, and language of testing and then global cognitive function were evaluated between diagnostic groups using one-way ANOVAs and Chi-square analyses with Yate’s Correction for Discontinuity. CST cued recall and intrusion scores were compared using ANOVA while adjusting for factors that were distributed differently between diagnostic groups. The alpha cutoff value was adjusted using the Bonferroni correction for multiple comparisons. Step-wise logistic regression models were employed to determine the best independent classification using CST variables. These were followed by a ROC analysis examining significant independent predictors with regards to area explained under the ROC curve.

 

Results

As depicted in Table 1, there were no statistically significant differences between aMCI and CN groups with regards to mean age, education and language of testing. Participants in the aMCI group had lower mean Mini-Mental State Examination (MMSE) scores (26) and there were more males in the aMCI group than the CN group.
Table 2 indicates that individuals with aMCI had lower scores on all CST trials . After adjusting for baseline differences in MMSE scores and using sex as a covariate, aMCI participants scored lower on all three List A initial learning trials and all three List B trials susceptible to PSI and frPSI. After covariate adjustment, there were no aMCI and CN differences on recall trials susceptible to retroactive semantic interference (RSI) or the ability to recover from RSI (frRSI). Table 3 denotes intrusion errors across the different CST trials. With and without adjustment for covariates, the only measures that differentiated groups were semantic intrusions on List B1 (which measures PSI), List B2 (which measures frPSI) and List B3 (which measures persistent frPSI after repeated learning trials).

Table 1. Demographics by Diagnostic Group

Table 2. CST Cued Recall Scores by Diagnostic Group

*Values survived Bonferroni Correction at 0.05/8=0.00625

Table 3. CST Intrusion Errors by Diagnostic Group

*Values survived Bonferroni Correction at p<.05

We calculated PSI, the initial failure to recover from proactive semantic interference after 1 additional learning trial (frPSI1), and the persistence of proactive semantic interference after 2 additional learning trials (frPSI2). PSI was calculated using the ratio of Cued B1 Recall to Cued A1 Recall. FrPSI1 was calculated using the ratio of Cued B2 Recall to Cued A2 Recall. FrPSI2 was calculated using the ratio of Cued B3 Recall to Cued A3 Recall.
There were no aMCI versus CN differences in the Cued B1/ Cued A1 ratio (F(1.74)= 1,59; p=.211). However, aMCI participants demonstrated more frPSI1 (F(1.74)= 8,25; p=.005) and frPSI2 (F(1.74)=19,45; p<.001). As depicted in Table 4, on the Cued B3 recall trial, which followed two additional learning trials of List B items, CN participants were able to recover so that they could recall an average of 88.6% of the that they recalled during Cued A3 recall. In contrast, participants with aMCI were only able to recover an average of 67.4% of the items that they recalled during Cued A3 recall.
Step-wise logistic regression was employed to determine which of the initial learning and PSI measures best discriminated between aMCI and CN groups. As indicated in Table 5, Cued A3 recall, Cued B3 recall and Cued B2 intrusions were predictors of diagnostic status. The overall predictive utility of the model yielded 75.9% sensitivity and 91.1% specificity, and overall correct classification rate of 85.1%. When these variables were jointly entered into ROC analyses, the area under the ROC curve was .923 (p<.001).

Table 4. Proactive Interference and Failure to Recover from Proactive Interference Ratios

Table 5. Step-Wise Logistic Regression Using Measures of Initial Learning and Susceptibility to Proactive Interference to Distinguish Amnestic Mild Cognitive Impairment and Cognitively Normal Groups

*Model at step 3 yielded 75.9%. sensitivity and 91.1% specificity (overall classification 85.1%)

Discussion

The current investigation used a novel computerized paradigm with semantically competing target word lists, the Cognitive Stress Test, to investigate whether the effects of proactive semantic interference (PSI) and the initial failure to recover from PSI (frPSI) would persist or diminish with additional learning trials. The obtained pattern of results indicated that, despite repeated administrations of the second list, participants with amnestic MCI had a persistent failure to recover from proactive semantic interference (frPSI). This cannot be explained by mere learning deficits alone since proactive semantic interference deficit ratios adjusted for initial learning on the corresponding trial of List A targets. The unique nature of proactive semantic interference deficits was also evidenced by increased intrusion errors, which were produced by aMCI participants on Cued B1, Cued B2 and Cued B3 trials but not on additional trials of List A susceptible to retroactive interference. In fact, no measure of retroactive interference reached statistical significance, which is consistent with the notion that PSI is uniquely related to early cognitive function in older adults with aMCI at risk for AD (17, 27). Previous studies have suggested that PSI effects may be more associated with MCI and early AD than RSI (27-28). In contrast, in 2012 Ricci and colleagues (29) found RSI but lack of PSI effects using the Rey Auditory Verbal learning Test (RAVLT). It should be noted, however, that the RAVLT list-learning task did not specifically elicit semantic interference, which is the focus of the current investigation.
Unlike previous studies, the current investigation incorporated multiple trials of two sets of 18 different targets, each belonging to one of three semantic categories. The current findings suggest that even after repeated learning trials, aMCI participants are not able to overcome the effects of semantic interference. Our finding of a combined area under the ROC curve exceeding .92 for Cued A3 Recall, Cued B3 recall and Cued B2 intrusions indicates that aMCI participants have deficits in initial learning as well as a failure to recover from proactive interference. The latter is evidenced by increasing deficits in recall of List B relative to List A targets over time (percentage of correct responses), as well as intrusion errors on measures susceptible to proactive interference and the failure to recover from proactive interference. This suggests that different measures of failure to recover from proactive semantic interference may have different biological underpinnings. Indeed, using the LASSI-L, which only affords one opportunity to recover from proactive semantic interference, Cued B2 recall was correlated with atrophy in AD prone regions (13-14). In contrast, Loewenstein et al., (2018) showed that it was not Cued B2 recall but Cued B2 semantic intrusions that could differentiate between MCI groups who were amyloid positive versus other MCI groups who were amyloid negative (11), suggesting the potential specificity of intrusion errors as a cognitive breakdown associated with AD brain pathology. Similarly, Sanchez and colleagues (2017) found that among clinically asymptomatic middle-age offspring of AD parents, Cued B2 intrusions were highly related to abnormal limbic connectivity issues on fMRI (15).
Torres et al. (2019) conducted a qualitative analysis on List B intrusion errors and found that the vast majority were incorrect recall of List B targets followed by semantic errors related to the List B target but not explicitly derived from List A (30). This indicates potential disruptions in cortical-limbic difficulty observed by others (13) and suggests that semantic intrusions represent potentially greater deficits in executive inhibitory processes that allow the individual to access source memory and inhibit previously learned responses.
Strengths of the current paradigm include computerized and uniform administration of three learning trials of 18 targets (representing three different categories) to assess maximum learning using cues at both the encoding and retrieval stages. When applied to three additional trials of 18 different targets (representing identical semantic categories), there was a unique opportunity to study proactive interference and failure to recover from proactive interference (as assessed by the ratio of correct recall on List B to correct recall of List A on the same trial) and semantic intrusions. Participants were comprehensively assessed by both clinical and neuropsychological assessment and compared to older adults of equivalent age with similar educational attainment. There did not appear to be any issues with ceiling or floor effects using 18 to-be-remembered targets, which may have been related to adequate category cues provided at acquisition and retrieval. Finally, the CST was not used in diagnostic formulation to avoid potential issues with circularity.
Potential limitations of the study involve relatively modest numbers of participants and lack of longitudinal follow-up. We intend to keep recruiting and following these participants and obtaining both structural MRI as well as amyloid and tau PET. Future work with fMRI may further elucidate the mechanisms underlying the inability of aMCI participants to break free from the effects of semantic interference when provided with additional learning opportunities. Normal controls appear to be able to increasingly recover from proactive semantic interference effects over time, but this does not hold true with individuals with aMCI. Further exploration into this phenomenon has significant theoretical and clinical implications.

 

Funding: R01AG061106-02 Loewenstein, David, PI; Florida Department of Health Ed and Ethel Moore Grant #8AZ23. 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: This study was. supported by the National Institute on Aging (NIA). The CST measure was developed by and is intellectual property held by Drs. Loewenstein and Curiel at the University of Miami.

Ethical standards:This study was IRB approved and met all national and international standards for the protection of human subjects.

 

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A NOVEL COMPUTERIZED COGNITIVE STRESS TEST TO DETECT MILD COGNITIVE IMPAIRMENT

 
R.E. Curiel Cid1, E.A. Crocco1, M. Kitaigorodsky1, L. Beaufils2, P.A. Peña2, G. Grau1, U. Visser2, D.A. Loewenstein1
 

1. Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Miami, Florida, 33136. U.S.A; 2. Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, Florida 33146, U.S.A.

Corresponding Author: Rosie E. Curiel, Psy.D., Associate Professor of Psychiatry & Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Suite 3202, Miami, FL 33136. RCuriel2@miami.edu

J Prev Alz Dis 2021;
Published online January 19, 2021, http://dx.doi.org/10.14283/jpad.2021.1

 


Abstract

BACKGROUND: The Loewenstein Acevedo Scales of Semantic Interference and Learning (LASSI-L) is a novel and increasingly employed instrument that has outperformed widely used cognitive measures as an early correlate of elevated brain amyloid and neurodegeneration in prodromal Alzheimer’s Disease (AD). The LASSI-L has distinguished those with amnestic mild cognitive impairment (aMCI) and high amyloid load from aMCI attributable to other non-AD conditions. The authors designed and implemented a web-based brief computerized version of the instrument, the LASSI-BC, to improve standardized administration, facilitate scoring accuracy, real-time data entry, and increase the accessibility of the measure.
Objective: The psychometric properties and clinical utility of the brief computerized version of the LASSI-L was evaluated, together with its ability to differentiate older adults who are cognitively normal (CN) from those with amnestic Mild Cognitive Impairment (aMCI).
Methods: After undergoing a comprehensive uniform clinical and neuropsychological evaluation using traditional measures, older adults were classified as cognitively normal or diagnosed with aMCI. All participants were administered the LASSI-BC, a computerized version of the LASSI-L. Test-retest and discriminant validity was assessed for each LASSI-BC subscale.
Results: LASSI-BC subscales demonstrated high test-retest reliability, and discriminant validity was attained.
Conclusions: The LASSI-BC, a brief computerized version of the LASSI-L is a valid and useful cognitive tool for the detection of aMCI among older adults.

Key words: Computerized test, mild cognitive impairment, Alzheimer’s disease, semantic intrusion errors, semantic interference, clinical trials.


 

Introduction

Alzheimer’s disease (AD) is a devastating condition that is expected to significantly impact the rapidly aging population. Important advancements have been made to identify novel candidate biomarkers of AD, and a research framework to stage the disease from its preclinical stage onward has been proposed, with the aim of establishing a biological definition of the disease (1). Despite these formidable advances, neuropsychological assessment remains an essential component of the evaluative process because cognitive impairment is a fundamental defining symptom of AD that emerges early, at a certain point in the transition from the preclinical to clinically symptomatic stages of the disease. Further, cognitive changes are used to detect and track disease progression over time and a measurable change in cognitive ability represents a potentially meaningful clinical outcome (2). Thus, the identification of cognitive markers that are sensitive to detecting early disease states and converge with biological markers of AD pathology, have become increasingly necessary in terms of identifying individuals at risk, monitoring disease progression, and ascertaining treatment efficacy (3).
Traditional paper-and-pencil cognitive measures employed for the detection of AD-related Mild Cognitive Impairment (MCI) are often insensitive to detecting subtle cognitive changes that occur during preclinical or prodromal disease states (5, 6). There is a developing body of literature, however, that cognitive stress paradigms can measure subtle deficiencies that are highly implicated in early AD disease states among older adults. One such paradigm that measures semantic interference in memory, the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), was sensitive enough to differentiate older adults who are cognitively unimpaired from those with subjective memory complaints, and early amnestic MCI (7, 8). On this memory measure, proactive semantic interference (PSI) deficits and particularly, the inability to recover from PSI (frPSI) was also highly associated with brain amyloid load in older adults with otherwise normal performance on a traditional battery of cognitive tests (9). The LASSI-L has outperformed other widely used memory measures in detecting prodromal AD in both English and Spanish (10, 11), and has been found to be useful in different cultural/language groups (7, 11, 12). In addition to measuring the total number of correct targets recalled on individual LASSI-L subscales, there is evidence that semantic intrusion errors may have specific utility in the assessment of prodromal AD. Loewenstein and colleagues (4) found that semantic intrusion errors sensitive to PSI and frPSI on the LASSI-L could differentiate amyloid positive aMCI groups from amyloid negative aMCI groups with non-AD diagnoses.
While it is recognized that intrusion errors represent early manifestations of neurodegenerative brain disease, a potential limitation of previous approaches is that the number of intrusion errors are often highly dependent on an individual’s total responses on a particular trial. Thus, even a seemingly modest number of intrusion errors may actually represent an at-risk cognitive profile, depending on the total number of responses that are correct. For example, an individual may make a minimal number of intrusion errors on a given trial, which may appear to be clinically insignificant. However, if the number of total responses is low, even a modest number of intrusion errors may indicate impaired inhibitory processes and underlying brain pathology. As a result, we recently developed a novel method to evaluate semantic intrusion errors utilizing the percentage of intrusion errors (PIE) in relation to total correct responses (13). This method takes into account the observation that the number of intrusion errors a person makes is often highly dependent on their total responses on a particular trial. Thus, even a seemingly modest number of intrusion errors may represent an at-risk cognitive profile. PIE demonstrated high levels of sensitivity and specificity in differentiating CN from amyloid positive persons with preclinical AD and preliminary work suggests that it is a novel and sensitive index of early memory dysfunction (11, 13).
Traditional paper and pencil neuropsychological assessments are lengthy, require a skilled examiner, are vulnerable to human error in administration and scoring, and associated with practice effects. Moreover, some of these measures have been found to be biased among diverse ethnic/cultural and language groups. To address some of these concerns, computerized testing batteries have been developed to explore a more suitable option to mitigate some of the abovementioned limitations (14-17). However, these too have limitations in early detection of AD-associated cognitive impairment. For example, many of these computerized batteries are relatively successful at distinguishing between older adults with normal cognition and those with dementia or late stage MCI, but lack the predictive power needed to move the field forward, which is to correctly classify individuals with MCI and/or earlier on the disease continuum, and do so in a manner that is validated for use among different ethnic/cultural and language groups. This highlights a major problem with many traditional computerized batteries; they are automated versions of traditional paper-and-pencil cognitive assessment paradigms that lack sensitivity to detect AD-associated cognitive decline, and employ the same paradigms originally developed for the assessment of dementia or traumatic brain injury (17).
Recent work by Curiel and associates (5-12) led to the development of a brief computerized version of the LASSI-L, the LASSI-BC, which incorporates all the elements of this well-established cognitive stress test. The LASSI-BC is currently being studied extensively in a longitudinal study of at-risk aging adults. This novel computerized version of the instrument does not require a skilled examiner, is web-based and can remotely run on most browser-capable devices. Moreover, it is intuitive and appropriate for use among older adults that are either predominantly English or Spanish-speaking and who have varying ethnic/cultural backgrounds including Hispanics and African Americans.
In this first validation study, we examine the psychometric properties of the LASSI-BC. We also evaluate the clinical utility several LASSI-BC subscales as it relates to their ability to differentiate older adults with normal cognition from those with aMCI on measures of: i) proactive semantic interference, ii) the failure to recover from proactive semantic interference, iii) retroactive semantic interference and iv) the percentage of intrusion errors in relation to total cued recall responses by the participant. Performance on these specific subscales were selected a priori because, as noted above, our previous work using the paper-and-pencil LASSI-L has robustly demonstrated that these particular subscales are the most sensitive to cognitive breakdowns associated with MCI due to preclinical and prodromal AD.

 

Methods

This study included 64 older adults that were evaluated as part of an IRB-approved longitudinal investigation funded by the National Institute on Aging. An experienced clinician administered a standard clinical assessment protocol, which included the Clinical Dementia Rating Scale (CDR) (18), and the Mini-Mental State Examination (MMSE) (19). Subsequently, a neuropsychological battery was independently administered in either Spanish or English dependent on the participant’s dominant and preferred language. Spanish language evaluations were completed with equivalent standardized neuropsychological tests and appropriate age, education, and cultural/language normative data (20-23). Proficient bilingual (Spanish/English) psychometricians performed all the testing.
Diagnostic groups were classified using the following criteria:

Amnestic MCI group (aMCI) (n=25)

Participants met Petersen’s criteria (24)) for MCI and evidenced all of the following: a) subjective cognitive complaints by the participant and/or collateral informant; b) evidence by clinical evaluation or history of memory or other cognitive decline; c) Global Clinical Dementia Rating scale of 0.5 (18); d) below expected performance on delayed recall of the HVLT-R (23) or delayed paragraph recall from the National Alzheimer’s Coordinating Center -Unified Data Set (NACC-UDS) (25) as measured by a score that is 1.5 SD or more below the mean using age, education, and language-related norms.

Cognitively Normal Group (n=39)

Participants were classified as cognitively normal if all of the following criteria were met: a) no subjective cognitive complaints made by the participant and a collateral informant; b) no evidence by clinical evaluation or history of memory or other cognitive decline after an extensive interview with the participant and an informant; c) Global CDR score of 0; d) performance on all traditional neuropsychological tests (e.g.: Category Fluency (26), Trails A and B (27), WAIS-IV Block Design subtest (28) was not more than 1.0 SD below normal limits for age, education, and language group.

Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief Computerized Version (LASSI-BC)

The LASSI-BC was not used for diagnostic determination in this study. This computerized cognitive stress test is a novel computerized measure that is briefer than the paper-and-pencil LASSI-L, taking approximately 10 to 12 minutes to complete. The LASSI-BC contains the elements of the original LASSI-L which demonstrated the greatest differentiation between aMCI, PreMCI and CN older adults in previous studies. For example, free recall preceding the cued recall trials of the LASSI-L added time to the administration but was never as effective as cued recall in distinguishing among diagnostic groups. Developed in collaboration with the University of Miami Department of Computer Science, the LASSI-BC is a remotely accessible test available in both English and Spanish. As a web application, it can be run on devices that can run Google Chrome, including desktop computers, laptops, tablets, or even smartphones. While the LASSI-BC is a fully self-administered test with all verbal responses recorded and scored by the computer, for the purposes of this validation study, a trained study team member was present for each administration to systematically record responses, which provided a double check on the accuracy of data. The LASSI-BC utilizes Google Cloud Speech API , which has been successfully implemented for use with older adults. The test leverages Google Cloud’s Speech to Text software in conjunction with a backup lexicon for understanding the participants’ spoken words. The lexicon is designed to account for variations in participant’s pronunciation by allowing for words that the computer “mishears” to serve as alternatives to the actual word being spoken. Lexicons were chosen based on observations from participants during the test.
Upon initiating the examination, the participant is instructed in both audio and visual formats. They will see 15 words belonging to one of three semantic categories: fruits, musical instruments, or articles of clothing (five words per category). The words are then individually presented on the screen and audio for a 6-second interval. This presentation facilitates optimal encoding and storage of the to-be-remembered information. Further, this instruction style has been easily understood and accepted by older adults during pilot studies in the course of developing the LASSI-BC. After the computer presents all 15 words, participants are presented with each category cue (e.g., fruits) and asked to recall the words that belonged to that category. Participants are then presented with the same target stimuli for a second learning trial with subsequent cued recall to strengthen the acquisition and recall of the List A targets. The exposure to the semantically related list (i.e., List B) is then conducted in the same manner as exposure to List A. List B consists of 15 words different from List A, all of which belong to each of the three categories used in List A (i.e., fruits, musical instruments, and articles of clothing). Following the presentation of the List B words, the person is asked to recall each of the List B words that belonged to each of the categories. List B words are presented again, followed by a second category-cued recall trial. Finally, to assess retroactive semantic interference, participants are asked to free recall the original List A words. Primary measures used in this study are the second cued recall score for List A (maximum learning), first cued recall score for List B (susceptibility to proactive semantic interference), second cued recall of List B (failure to recover from proactive semantic interference), and the third cued recall of List A (retroactive semantic interference). In addition, we evaluated the novel ratio used with the LASSI-L, that takes into account the percentage of intrusion errors (PIE) as a function of total responses on subscales that measure proactive semantic interference and the failure to recover from proactive semantic interference. Specifically, the ratio is denoted as follows: Total Intrusion Errors/ (Total Intrusion Errors + Total Correct Responses) for LASSI-BC Cued B1 (a measure of susceptibility to proactive semantic interference) and LASSI-BC Cued B2 recall (a measure of recovery from proactive semantic interference).

 

Results

The computerized version of the LASSI-BC had psychometric properties that compared favorably to the test-retest reliabilities obtained on the original paper-and-pencil LASSI-L (7). As depicted in Table 1, CN (n=39) and aMCI (n=25) groups did not differ in terms of age, sex, or language of evaluation. Individuals diagnosed as aMCI, although well educated (Mean =14.26; SD=3.5), had less educational attainment relative to their cognitively normal counterparts (Mean =16.32; SD=2.3). As expected, aMCI participants also had lower mean MMSE scores (Mean =26.04; SD=2.3).

Table 1. Demographic Characteristics and Computerized LASSI-BC Scores among Participants who are Cognitively Normal and with Amnestic Mild Cognitive Impairment

 

Test-retest reliability

Test-retest reliability data was obtained on a subset of 15 older adults diagnosed with aMCI using Petersen’s criteria (24) for each of the LASSI-BC subscales. The mean age was 73.4 (SD=6.3); education 15.4 (SD=3.6); and the mean MMSE score for this group was 26.6 (SD=2.2). These individuals (60% primary English-speakers and 60% female) were administered the LASSI-BC on two occasions, within a 4 to 39-week interval (Mean =13.9.; SD=10.6 weeks). In our pilot work, we found robust test-retest correlations ranging from 0.55 to 0.721 on the subscales that have shown to be the most sensitive measures of cognitive decline in the original paper-and-pencil version. In this study, test-retest comparisons were conducted for Cued Recall A2 (measures maximum learning), Cued Recall B1 (measures proactive semantic interference), and Cued Recall B2 (measures the failure to recover from proactive semantic interference). One-tailed Pearson Product Moment Correlation Coefficients were obtained given the directional hypotheses concerning test-retest relationships. High, statistically significant test-retest reliabilities were obtained for Cued A2 Recall (r=.726; p<.001); Cued Recall B1 (r=.529; p=0.021); Cued Recall B2 (r=.555; p=0.016).

Discriminant validity

As depicted in Table 1, LASSI-BC scales sensitive to maximum learning (Cued A2), vulnerability to proactive semantic interference (Cued B1) and the failure to recover from proactive semantic interference (Cued B2) were statistically significant in discriminating between older adults with amnestic MCI and cognitively normal counterparts. These results were identical when demographic variables such as education were entered in the model as covariates
We then calculated areas under the Receiver Operating Characteristic (ROC) curve for LASSI-BC correct responses as well as the PIE indices for Cued B1 and Cued B2 subscales. We selected these measures a priori given that performance on these specific subscales have traditionally been the most discriminant measures on the paper-and-pencil form of the LASSI-L.
As shown in Table 2, an optimal cut-point of 5 by Youden’s criteria on correct responses for Cued Recall B1, yielded a sensitivity of 84.6% and a specificity of 86.8%. An optimal cut-point of 9 by Youden’s criteria on correct responses provided on Cued Recall B2, yielded statistically significant areas under the ROC curve of .868 (SE=0.88) and .824 (SE=.051), respectively.

Table 2. Classification of aMCI versus Cognitively Normal Participants on the LASSI-BC

 

We subsequently examined an optimal cut-point for PIE on the Cued Recall B1 and the Cued Recall B2 subscales. For PIE on Cued Recall B1, the area under the ROC was .879 (SE=.06) with a sensitivity of 92.9% and specificity of 80%, respectively using an optimal cut-point of .2540. For PIE on Cued Recall B2, the area under the ROC was .801 (SE=.07), using an optimal cut-point of .2159, which yielded a sensitivity of 78.6% and specificity of 68.0%. We selected these specific subscales because they have shown to be the strongest predictors of aMCI in the paper-and-pencil form of the LASSI-L.
We subsequently entered the statistically significant LASSI-BC subscales (Cued Recall B1 and Cued Recall B2) into a stepwise logistic regression. As seen in Table 3, the first variable to enter the logistic regression model was PIE on Cued B1 [B=6.86 (SE=1.67) Wald=17.07, p<0.001)]. On the second step of the logistic regression model, correct responses on Cued Recall B2 entered the model [B=-.34 (SE=.128), Wald= 17.1 (p=.008)]. Combining PIE Cued Recall B1 and correct responses on Cued Recall B2, yielded an overall sensitivity of 80% and specificity of 89.7%. It should be noted logistic regression weighs overall classification in a manner that favors the largest diagnostic group (in this case CN participants). Nonetheless, ROC and stepwise regression models yielded similar results indicating excellent discriminative ability.
In sum, our findings support that the LASSI-BC has equal or better psychometric properties than the original paper-and-pencil LASSI-L and demonstrates that computerized administration is both feasible, well accepted, and has excellent discriminant properties.

Table 3. Step-wise Logistic Regression Using Proactive Semantic Interference Measures on the Computerized LASSI-BC

 

Discussion

The present study was designed to examine the psychometric properties of the LASSI-BC, the brief computerized version of the LASSI-L, a cognitive stress test that utilizes a novel cognitive assessment paradigm based on semantic interference in memory. In studies conducted in the United States and abroad, the LASSI-L has shown great utility in detecting cognitive changes among individuals during the preclinical and prodromal stages of AD (4, 29) and has been found to be appropriate for use among diverse ethnic/cultural and language groups (11, 30, 12). The paradigm that this measure employs is unique in that it explicitly and from the outset organizes the examinee’s learning around specific semantic categories, which promotes active encoding, reduces the use of individualized learning strategies that can help or hinder performance, increases depth of initial learning, and is designed to tap an individual’s vulnerability to semantic interference.
The current investigation examined all salient subscales of the LASSI-BC, which were selected based on previous work with the paper-and-pencil versions. The computerized version evidenced good test-retest reliability for participants diagnosed with aMCI. Scores on all LASSI-BC subscales were higher for cognitively normal older adults, relative to aMCI participants. In addition, high levels of discriminant validity were obtained in differentiating aMCI from cognitively normal groups based on ROC analyses and logistic regression.
A potential limitation of this first validation study is that we employed modest numbers of participants who were tested in either English or Spanish on the LASSI-BC. Although, our overall findings were highly significant and the paper-and-pencil LASSI-L has been validated in different languages (i.e.- Spanish speakers in Argentina, Spanish speakers in Spain, Spanish speakers from Mexico, etc.) and with different ethnic/cultural groups (European Americans, Hispanics and African Americans), such future comparisons should be made with the LASSI-BC. Further, additional studies with the LASSI-BC will include evaluating the diagnostic utility of this computerized cognitive stress test to differentiate older adults earlier on the preclinical continuum of AD, and relate performance to biomarkers of AD pathology, as well as compare it to other traditional and widely used cognitive measures in the field.
There has been an increase in the number of computerized tests developed including the CogState (31) and the Cognition Battery from the NIH Toolbox (16), but limitations exist. For example, one of the most widely-used computerized cognitive batteries for the assessment of MCI is the CogState. As part of the Mayo Clinic Study on Aging, Mielke and associates (32) administered the CogState to eighty-six participants diagnosed with MCI who were found to have worse performance than cognitively healthy individuals; however, it is likely that individuals classified as MCI ranged from early states of MCI to late MCI, the latter of which is more cognitively similar to early dementia in terms of neuropsychological test performance, limiting evidence that this measure in sensitive to preclinical cognitive change. Further, the authors noted that their results are not generalizable to other ethnicities due to the demographic makeup of the region (Minnesota, USA). Another study conducted by Mielke and colleagues (33) aimed to examine performance on the CogState with neuroimaging biomarkers (MRI, FDG PET, and amyloid PET) among cognitively normal participants aged 51-71; however, only weak associations were found between CogState subtests and biomarkers of neurodegeneration.
With the rapidly aging population, early detection of cognitive decline in individuals at risk for AD and related disorders has become a global priority. Accurately identifying at risk individuals through the detection and monitoring of subtle, albeit sensitive cognitive changes that transpire early in the disease course is an important initiative and computerized cognitive outcome measures have the potential to greatly reduce burden for participants, clinical researchers and clinicians.
The development of computerized cognitive tests for older adults has significantly increased during the past decade. In fact, available systematic reviews have identified more than a dozen computerized measures designed to detect dementia or MCI (34, 35, 36). Moreover, the use of computerized assessments with older adults has been found to be feasible and reliable (37, 38). A recent meta-analysis has shown relatively good diagnostic accuracy, and authors further concluded that their performance distinguishing individuals with MCI and dementia is comparable with traditional paper-pencil neuropsychological measures (35). It is anticipated that as technology advances, clinical trials will include validated computerized testing to sensitively capture cognitive performance, particularly in large-scale secondary prevention efforts (39). The impact of this technological advancement in computerized, web-based cognitive testing has the potential to facilitate remote deliverability, allow for real-time data entry, improves standardization, and reduces administration and scoring errors. Moreover, computerized assessment can more readily monitor longitudinal cognitive changes for each individual, facilitating a precision-based approach. It is critical; however, that emerging cognitive tests move beyond simply computerizing outdated, insensitive cognitive paradigms and instead invest in the development and validation of cognitive paradigms that are sensitive and specific to early cognitive breakdowns that occur during the preclinical stages of AD. These too should exhibit sensitivity to biomarkers of AD (e.g., amyloid load, tau deposition, and neurodegeneration in AD-prone regions). Doing so may address some of the most critical challenges facing clinical trials including proper selection of at-risk participants, and monitoring meaningful cognitive change over time.

Funding: This research was funded by the National Institute of Aging Grant 1 R01 AG047649-01A1 (David Loewenstein, PI), 1 R01 AG047649-01A1 (Rosie Curiel Cid, PI) 5 P50 AG047726602 1Florida Alzheimer’s Disease Research Center (Todd Golde, PI), 8AZ. 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.

Ethical standard: This research study was conducted in alignment with the Declaration of Helsinki and through the approval of the University of Miami Institutional Review Board.
Conflict of interest: Drs. Curiel and Loewenstein have intellectual property used in this study.

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EXECUTIVE FUNCTION PREDICTS THE VALIDITY OF SUBJECTIVE MEMORY COMPLAINTS IN OLDER ADULTS BEYOND DEMOGRAPHIC, EMOTIONAL, AND CLINICAL FACTORS

 

R.-Y. Chao1, T.-F. Chen2, Y.-L. Chang1,2,3,4

 

1. Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan (R.O.C.); 2. Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan; 3. Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan; 4. Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan

Corresponding Author: Yu-Ling Chang, PhD (ORCID: 0000-0003-2851-3652), Department of Psychology, College of Science, National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan. Tel/Fax: +886-2-33663105/ +886-2-23629909; E-mail address: ychang@ntu.edu.tw

J Prev Alz Dis 2020;
Published online October 28, 2020, http://dx.doi.org/10.14283/jpad.2020.61

 


Abstract

Background: Although evidence suggests that subjective memory complaints (SMCs) could be a risk factor for dementia, the relationship between SMCs and objective memory performance remains controversial. Old adults with or without mild cognitive impairment (MCI) may represent a highly heterogeneous group, based partly on the demonstrated variability in the level of executive function among those individuals. It is reasonable to speculate that the accuracy of the memory-monitoring ability could be affected by the level of executive function in old adults.
Objective: This study investigated the effects of executive function level on the consistency between SMCs and objective memory performance while simultaneously considering demographic and clinical variables in nondemented older adults.
Setting: Participants were recruited from both the memory clinics and local communities.
Participants: Participants comprised 65 cognitively normal (CN) older adults and 54 patients with MCI.
Measurements: Discrepancy scores between subjective memory evaluation and objective memory performance were calculated to determine the degree and directionality of the concordance between subjective and objective measures. Demographic, emotional, genetic, and clinical information as well as several executive function measurements were collected.
Results: The CN and MCI groups exhibited similar degrees of SMC; however, the patients with MCI were more likely to overestimate their objective memory ability, whereas the CN adults were more likely to underestimate their objective memory ability. The results also revealed that symptoms of depression, group membership, and the executive function level together predicted the discrepancy between the subjective and objective measures of memory function; however, the executive function level retained its unique predictive ability even after the symptoms of depression, group membership, and other factors were controlled for.
Conclusion: Although both noncognitive and cognitive factors were necessary for consideration, the level of executive function may play a unique role in understanding the equivocal relationship of the concurrence between subjective complaints and objective function measures. Through a comprehensive evaluation, high-risk individuals (i.e., CN individuals heightened self-awareness of memory changes) may possibly be identified or provided with the necessary intervention during stages at which objective cognitive impairment remains clinically unapparent.

Key words: Aging, awareness, mild cognitive impairment, memory complaints.


 

Introduction

Subjective memory complaints (SMCs), commonly observed in older adults, refer to the self-perception of memory decline that does not require confirmation by cognitive tests. Recent studies on SMCs have revealed that they are associated with underlying brain morphometric changes (1) or increased β-amyloid deposition (2), which is consonant with dementia pathology (3).
Although evidence suggests that SMCs could be a risk factor for dementia (4, 5), the relationship between SMCs and objective memory performance remains controversial. Some studies have reported SMCs to be associated with a decline in objective memory performance (6), whereas other studies have not reported such an association (7, 8). Noncognitive factors, such as old age (9, 10), female gender (11), appearance of health conditions (e.g., hypertension and diabetes mellitus) (9, 12), low education level (9), apolipoprotein E ε4 (ApoE ε4) allele (10, 13), and depression (8-10), could also contribute to the appearance of SMCs and confound the association between SMCs and objective memory performance.
In addition to the noncognitive factors, one aspect that is crucial but has often been overlooked in studies examining the concurrence between subjective and objective memory changes is individual differences in metamemory ability. Metamemory, an aspect of high executive function level (14), is an individual’s self-awareness of his or her own memory contents and capacities. Furthermore, it is the ability of an individual to monitor or judge his or her own learning and memory efficiency. Although an age-related decline in metamemory function has been observed (15, 16), evidence suggests that aging populations, including cognitively normal (CN) older adults and individuals with mild cognitive impairment (MCI), may represent a highly heterogeneous group, based partly on the demonstrated variability in the level of executive function among those individuals (17). Accordingly, it is reasonable to speculate that the accuracy of the memory-monitoring ability could be affected by the level of executive function in both CN older adults and patients with MCI.
Thus, the aim of the present study was to examine the relationship between the level of executive function and the accuracy of SMCs while simultaneously considering noncognitive factors (including demographic variables, mood, ApoE ε4 status, and health conditions) in CN older adults and patients with MCI. To evaluate the accuracy of SMCs, we calculated the discrepancy scores between the self-reported memory concerns using a memory questionnaire and objective memory performance based on standardized neuropsychological tests, which enabled us to evaluate the accuracy of SMCs in two directions (i.e., overestimation or underestimation of objective memory performance). A lower level of executive function was hypothesized to be associated with a higher discrepancy between the subjective report of memory function and objective measurement using standardized memory tests in both CN older adults and patients with MCI, even after controlling for noncognitive (e.g., demographic variables) or clinical (e.g., health conditions and ApoE ε4 status) factors.

 

Materials and methods

Participants

The present study included 119 older adults, of whom 65 and 54 were classified as CN older adults and patients with MCI, respectively. Among the participants, 89 participants (48 CN and 41 MCI) were recruited from memory clinics, and 30 (17 CN and 13 MCI) were through community advertising. Individuals with any current evidence of major neurological diseases that may affect central nervous system function, psychiatric disorders, or a history of substance abuse were excluded.
Participants received a diagnosis of MCI according to the criteria recommended by the International Working Group (18). Specifically, the criteria for MCI were as follows: (1) absence of dementia, (2) defective performance on objective neuropsychological tests, and (3) generally preserved basic daily activities or the slightest impairment in instrumental activities. The objective cognitive decline was determined using the directive suggested by Jak et al. (19): the presence of at least two test scores within a cognitive domain (i.e., memory or executive function) on available neuropsychological tests (Table 1) that were one or more standard deviations less than the age-appropriate norms. Different MCI subtypes could be classified according to the aforementioned guideline. The present sample consisted of 23 patients with amnestic MCI single domain, 29 with amnestic MCI multiple domains, and 2 with nonamnestic MCI single domain. The present study was approved by the Ethics Committee and Institutional Review Board at the National Taiwan University Hospital according to the Declaration of Helsinki. Written informed consent was obtained from all participants.

Neuropsychological and Clinical Measures

A neuropsychological battery was administered to all participants. The measures included five executive function tests, namely the Matrix Reasoning and Similarities subtests of the Wechsler Adult Intelligence Scale-III (WAIS-III), category fluency test (animal and fruit), Modified Card Sorting Test (MCST), and Color Trails Test (CTT-1 and -2). A composite z-score was computed to represent each participant’s relative executive function level; the greater the positive number, the better performance it represented. Specifically, the raw score of participants’ performance on each executive function measure was first transformed into a z-score based on the norms obtained from the entire participant pool in the present study. Because lower scores (indicating that less time was required to complete the task) on the CTT reflect higher performance, the z-score of the CTT was inverted to ensure unidirectionality prior to averaging the z-scores of the five tests.
Four episodic memory tests were administered in the present study, namely the logical memory (LM) test, the visual reproduction (VR) test, the visual paired associates (VP) subtests of the Wechsler Memory Scale-III (WMS-III) (20), and the California Verbal Learning Test-II (CVLT-II) (21). A z-transforming memory composite score representing the relative performance on the episodic memory test was computed for each participant by using the method previously described; thus, a positive number represented higher memory performance. Notably, although all the four memory tests were used to classify participants’ group membership (i.e., CN versus MCI), to match the SMC subscales selected in the present study, only the two verbal episodic memory tests, namely the LM and CVLT-II, were used to compute episodic memory composite scores and for following analyses, which included both immediate (LM I, and CVLT-II List A 1-5 total recall) and delayed (LM II, and CVLT-II long-delayed free recall) recall scores.
SMCs were assessed using the Memory Complaints Inventory (22), which consisted of nine subscales designed to tap different types of reported memory problems: General Memory Problems, Verbal Memory Problems, Numeric Information Problems, Visuospatial Memory Problems, Pain Interferes with Memory, Memory Interferes with Work, Impairment of Remote Memory, Amnesia for Complex Behavior, and Amnesia for Antisocial Behavior. The first six subscales of the inventory included plausible memory complaints, and the last three subscales were intentionally designed to detect individuals with a tendency to exaggerated or feigned memory complaints. In the present study, we included scores from two subscales, namely the General Memory Problems and Verbal Memory Problems, for further analysis to sufficiently match the nature of the objective memory tests used in the present study. Lower scores on the self-evaluated questionnaire reflect a lower endorsement of memory problems by an individual. A z-transforming SMC composite score was calculated to indicate the level of endorsement of memory problems for each individual; to maintain consistency with the direction of objective memory test results, the z-scores of the SMC scores based on the questionnaire were inverted before calculating discrepancy scores. Additionally, the Framingham Stroke Risk Profile (FSRP) (23) and the Geriatric Depression Scale-Short Form (GDS-S) (24) were included to survey the participants’ cerebrovascular burden and depression status, respectively. The ApoE genotyping was conducted based on the method previously published (25), and participants were classified as ApoE ε4 carriers or non-carriers based on the appearance of at least on ε4 allele or not.

Discrepancy between Subjective and Objective Memory Evaluation

We used a modified discrepancy measure based on Miskowiak et al. (26) Specificially, the discrepancy between SMCs and objective memory performance was calculated for each participant by subtracting the standardized objective memory composite z-scores from the inversed z-transforming SMC scores. A positive value of the discrepancy score was considered to indicate that the participants’ rank ordering for their subjective evaluation was higher than their objective performance; that is, they overestimated their objective memory functioning. By contrast, a negative value of the discrepancy score was considered to indicate an underestimation of their objective memory function. Scores near zero were considered to indicate relatively high concordance between self-evaluated memory function and objective memory performance.

Statistical Analysis

Group differences were compared using analysis of variance, t-test, analysis of covariance, or chi-square tests. Statistical significance for demographic and clinical variables were set at an alpha level of 0.05, whereas the significance level for neuropsychological measures was set at p < 0.003 based on Bonferroni correction to avoid inflated type I errors. The discrepancy scores were checked for normal distribution using the Kolmogorov–Smirnov test, and the result indicated that it did not violate the null hypothesis (p > 0.20, with a mean score of 0.01, standard deviation [SD] = 1.53).
Hierarchical regression analyses were conducted to examine the predictive ability of the level of executive function for determining the discrepancy between subjective and objective memory evaluations; the corresponding alpha level was set at 0.05. Specifically, demographic variables including age, sex, and education were considered simultaneously in the first step. Subsequently, clinical variables, including FSRP, depressive state, ApoE ε4 status, and group membership (i.e., CN versus MCI), were considered in the second step. Finally, the composite z-score of executive function level was considered in the third step. All statistical analyses were conducted using SPSS (version 22.0. IBM Corp, Armonk, NY, USA).

 

Results

Demographics, Clinical Data, and Neuropsychological Performance

The two groups differed in age (F(1, 117) = 10.49, p = 0.002), education (F(1, 117) = 9,32, p = 0.003), and FSRP (F(1, 117) = 6.02, p = 0.016,Table 1), but they did not differ in the distribution of sex (χ2(2, N = 119) = 0.44, p > 0.05), frequency of ApoE ε4 carriers (χ2(2, N = 119) = 0.20, p > 0.05), scores on the depression measures (F(1, 117) = 1.39, p > 0.05), or distribution of recruitment source by the diagnostic group (χ2(2, N = 119) = 0.07, p > 0.05).

Table 1. Demographic, clinical, and cognitive characteristics with means (SDs) in the groups comprising cognitively normal older adults and patients with mild cognitive impairment

Abbreviations: CN, cognitively normal; CVLT-II, California Verbal Learning Test; FSRP, the Framingham Stroke Risk Profile; GDS-S, Geriatric Depression Scale-Short Form; GMCI, Green’s Memory Complaints Inventory; LM, Logical Memory; MCI, mild cognitive impairment; MCST, Modified Card Sorting Test; EF z-score, executive function composite z-score; VP, Visual Paired Associate; VR, Visual Reproduction Associate; η2, effect size of analysis of variance or analysis of covariance; SD, standard deviation; * p < 0.05. ** p < 0.003 (Bonferroni correction); †Group difference controlling for age, education, and FSRP. ‡ Time difference was calculated by subtracting CTT-1 from CTT-2.

 

After the effects of age, education, and FSRP were controlled for, the performance of the CN group was higher than that of the MCI group on all executive function measures (see Table 1), including the WAIS-III Matrix Reasoning subtest (F(1, 113) = 18.19, p < 0.001), VF (F(1, 113) = 14.08, p < 0.001), MCST (F(1, 114) = 26.40, p < 0.001), and executive function composite z-score (F(1, 114) = 30.68, p < 0.001), except for the WAIS-III Similarities subtest (F(1, 113) = 6.60, p > 0.003) and CTT measure (i.e., CTT-2 − CTT-1; F(1, 114) = 6.08, p > 0.003). Furthermore, the performance of the CN group was higher than that of the MCI group on all episodic memory measures, including the immediate recall (F(1, 114) = 33.31, p < 0.001), delayed recall (F(1, 114) = 55.19, p < 0.001), and delayed recognition (F(1, 114) = 43.51, p < 0.001) of the WMS-III VR subtests; immediate (F(1, 114) = 39.61, p < 0.001) and delayed recall (F(1, 114) = 26.01, p < 0.001) of the VP subtests; immediate (F(1, 114) = 44.21, p < 0.001) and delayed recall (F(1, 114) = 57.90, p < 0.001) of the LM subtest; immediate List A 1-5 total recall (F(1, 112) = 99.68, p < 0.001) and long-delayed free recall (F(1, 112) = 106.55, p < 0.001) of the CVLT-II; and episodic memory composite z-score (F(1, 114) = 105.36, p < 0.001).

Subjective and Objective Memory Discrepancy Measures

No differences in the SMC scores were observed between the groups (F(1, 114) = 1.12, p > 0.05) after age, education, and FSRP were controlled for. In addition, no differences in the SMC scores were observed by recruitment source of participants in the CN group (T(63) = -1.03, p > 0.05) or in the MCI group (T(52) = -1.38, p > 0.05). However, the absolute discrepancy score values differed between the two groups (F(1,114) = 14.60, p < 0.001, eta square = 0.11) after the effects of age, education, and FHS-stroke risk were controlled for; in which the CN group exhibited a relatively higher accuracy (i.e., values trended toward zero regardless of the directionality) than the MCI group in estimating objective memory. Furthermore, the two groups demonstrated significant differences in discrepancy scores (F(1, 114) = 16.71, p < 0.001) when directionality (i.e., overestimation versus underestimation) was considered and age, education, and FSRP were controlled for. We observed that this differential pattern of discrepancy scores remained significant even after further controlling for the level of executive function (F(1, 113) = 7.50, p = 0.007, η2 = 0.06); this finding suggests that the CN group, despite its relatively high objective memory performance, tended to endorse more memory complaints than the MCI group did, but the MCI group exhibited an opposite pattern.
We also compared the frequencies of overestimation and underestimation of memory function between the two groups by dichotomizing the discrepancy scores (i.e., ≥0 and <0). The results showed that the two groups exhibited significant differences in the frequency distribution of the two discrepancy categories (χ2(2, N = 119) = 19.13, p < 0.001). The number of participants underestimating their objective memory ability was higher in the CN group than in the MCI group, whereas that of participants overestimating their objective memory ability was higher in the MCI group than in the CN group (Figure 1). We further analyzed the demographic and clinical characteristics of the two subgroups (i.e., underestimation versus overestimation of objective memory ability) in each of the CN and MCI groups. Within the CN group, the underestimation and the overestimation subgroups did not differ in age (T(63) = -1.16, p = > 0.05), education(T(63) = 0.43, p = > 0.05), distribution of sex (χ2(1, N = 65) = 1.87, p > 0.05), FSRP (T(63) = -1.03, p = > 0.05), frequency of ApoE ε4 carriers (χ2(1, N = 65) = 3.11, p > 0.05), depression status(T(63) = 0.83, p > 0.05), or executive function (T(63) = 1.61, p > 0.05). Notably, a trend of higher executive function was observed among CN participants who underestimated their memory ability (executive function z-score = 0.44 ± 0.53) compared with those who overestimated their memory ability (executive function z-score = 0.22 ± 0.54). Similarly, within the MCI group, the underestimation and the overestimation subgroups did not differ in age (T(52) = -1.90, p = > 0.05), education (T(52) = 0.58, p = > 0.05), distribution of sex (χ2(1, N =54) = 0.02, p > 0.05), frequency of ApoE ε4 carriers (χ2(1, N =54) = 0.89, p > 0.05), or depression status(T(52) = 0.19, p = > 0.05), but the subgroup with overestimation of objective memory ability demonstrated a higher FSRP score(T(52) = -2.43, p = 0.001, FSRP score = 16.6% ±12.15) and marginally lower executive function (T(52) = 1.87, p = 0.06, executive function z-score = -0.54 ± 0.70) compared to the underestimation subgroup (FSRP score = 8.5% ± 4.38; executive function z-score = -0.16 ± 0.57).

Figure 1. Pie charts depicting a comparison of frequency distribution of underestimation (negative discrepancy z-scores) and overestimation (positive discrepancy z-scores) of objective memory function between the groups comprising cognitively normal older adults and patients with mild cognitive impairment

 

Notably, when participants were classified into small (i.e., z-scores within the range between +1 to −1) versus large discrepancy scores (z-scores > + 1 or < −1) without considering the directionality of the scores, significantly more MCI patients (55.6%) obtained large discrepancy scores, indicating a larger misjudgment for their memory ability compared than for the CN group (26.2%) (χ2(1, N = 119) = 10.67, p = 0.001). We further analyzed the demographic and clinical characteristics of the two subgroups (i.e., small versus large discrepancy) in each of the CN and MCI groups. Within the CN group, the two subgroups did not differ in age (T(63) = 0.24, p = > 0.05), education(T(63) = -0.99, p = > 0.05), distribution of sex (χ2(1, N = 65) = 0.80, p > 0.05), FSRP (T(63) = -0.68, p = > 0.05), frequency of ApoE ε4 carriers (χ2(1, N = 65) = 1.42, p > 0.05), or depression score (T(63) = -0.97, p > 0.05). The two subgroups did not differ in executive function (T(63) = −0.96, p > 0.05). However, a trend toward higher executive function was observed among those with a greater degree of misjudgment (executive function z-score = 0.48 ± 0.51) compared with patients with mild misjudgment (executive function z-score = 0.33 ± 0.55). Within the MCI group, the two subgroups did not differ in age (T(52) = -1.22, p = > 0.05), education (T(52) = -0.72, p = > 0.05), distribution of sex (χ2(1, N =54) = 0.84, p > 0.05), FSRP score (T(52) = -0.31, p > 0.05), frequency of ApoE ε4 carriers (χ2(1, N =54) = 0.73, p > 0.05), depression score (T(52) = 1.37, p = > 0.05), or executive function (T(52) = 0.17, p > 0.05).

Relationships between Executive Function Level and Discrepancy Score

Hierarchical multiple regression analysis (Table 2) demonstrated that increased endorsement of depressive symptoms predicted negative discrepancy scores (i.e., increase in self-reporting of memory concerns and an underestimation of objective memory ability) (β = −0.17, p = 0.046), and diagnosis of MCI predicted positive discrepancy scores (i.e., overestimation of objective memory ability) (β = 0.26, p = 0.008). Moreover, the level of executive function (β = −0.24, ΔR2 = 0.03, p = 0.027) explained the unique variances in the discrepancy scores in addition to the demographic and clinical variables; a higher level of executive function was associated with underestimation of objective memory ability (Figure 2).

Table 2. Hierarchical regression models with predictive ability of demographic, clinical, and executive function level for discrepancy between subjective and objective memory evaluations

Abbreviations: FSRP, the Framingham Stroke Risk Profile; GDS-S, Geriatric Depression Scale-Short Form; Group, participants were classified as cognitively normal older adults or patients with mild cognitive impairment; ApoE ε4: participants were classified as ApoEε4 carriers or noncarriers; EF z-score, executive function composite z-score. * p < 0.05; ** p < 0.01.

Figure 2. Scatter plot of the relationship between executive function level and discrepancy scores between subjective memory complaints and objective memory performance. A low level of executive function was associated with positive discrepancy scores (i.e., overestimation of objective memory functioning).* p < 0.05; ** p < 0.01

 

Discussion

The primary objective of this study was to investigate the effect of the level of executive function on the consistency between SMCs and performance on objective memory function measures while considering demographic (e.g., age, education, and sex), emotional (i.e., symptoms of depression), and clinical (e.g., ApoEε4 status, and health conditions related to cardiovascular risk factors) variables in a sample comprising CN older adults and patients with MCI. An analysis of the discrepancy scores between the subjective and objective measures revealed that although the symptoms of depression, group membership, and level of executive function together predicted the discrepancy between the subjective and objective measures of memory performance, the level of executive function retained its predictive ability even after the symptoms of depression, group membership, or other factors were controlled for.
In this study, we used five executive function measures to assess the relationships between the level of executive function and the consistency between the subjective and objective measures of memory functioning; these five measures were thought to involve prefrontal function (27) and were essential for successful self-monitoring (28), such as reasoning, ability to use external feedback to modify thinking or behavior, and shifting and updating information. As predicted, we found that a lower level of executive function was associated with a greater degree of discrepancy between subjective and objective measures of memory function in our sample of elderly participants. This result is consistent with the emerging literature that has demonstrated a relationship between reduced awareness of memory loss and frontal lobe dysfunction in patients with Alzheimer disease (29) and MCI (30).
Another critical finding in this study was that the CN group generally had higher accuracy in estimating their memory ability compared with the MCI group, despite the CN and MCI groups exhibiting similar degrees of memory complaints. Furthermore, group membership predicted the discrepancy scores. Notably, the two groups exhibited different patterns of discrepancy scores: The participants in the CN group were more likely to underestimate their objective memory ability. However, 63% (27 out of 43) of participants in the underestimation subgroup underestimated their memory ability within a relatively mild range (i.e., discrepancy z-scores > −1). By contrast, the participants in the MCI group were more likely to overestimate their objective memory ability. Such findings appear to be consistent with those of a recent study by Fragkiadaki et al. (31), who used an “in-session” cognitive efficiency measure and found that CN older adults underestimated their performance, whereas patients with MCI overestimated their performance on a task. This inclination to underestimate actual performance was also reported by Vannini et al. (30) in CN older people with β-amyloid deposition; the authors introduced the term “hypernosognosia” to indicate that heightened memory self-awareness may be the first stage of progression toward Alzheimer disease in a hypothetical memory awareness model. Notably, the subset of CN participants in the present study with a larger degree of underestimation (i.e., discrepancy z-scores < −1) of their objective memory ability exhibited a trend of higher executive function compared with participants with mild underestimation of their memory ability. Despite its counterintuitive nature, our findings may suggest that individuals with hypernosognosia may have relatively high self-monitoring ability and may have experienced some memory loss relative to a previously higher baseline memory function, which could not be captured by the neuropsychological tests employed here. Although we attempted to include multiple measures of memory in the present study to ensure the reliability and sensitivity of the measurements, these measures might still not be sufficiently sensitive to detect subtle within-person memory declines, because the calculations of the objective memory index are completely based on comparisons with group norms. Consequently, the CN older adults with hypernosognosia may represent a group of people who are at risk of dementia in the future, particularly when factors such as symptoms of depression or healthy conditions, which could potentially confound the interpretations of their “worries,” were considered. Following up CN older adults with hypernosognosia longitudinally to further examine such a hypothesis is crucial.
Consistent with accumulating studies that have regarded depression as a crucial factor accounting for SMCs (32), in the present study, we also observed that an increase in the symptoms of depression was predictive of an increase in self-reporting of memory concerns and an underestimation of memory ability. Previous studies have indicated that a higher frequency of SMCs were strongly associated with a higher number of depressive symptoms, regardless of the objective cognitive performance (8). By contrast, other evidence indicates late-life depression to be a risk factor for progression to dementia (32). Although the exact relationships among depressive symptoms, SMC, and objective cognitive function warrant further investigation, the present study extends previous findings by demonstrating that the contribution of the level of executive function is crucial because its contribution to the concurrence between subjective and objective measures is unique and is separate from that of the symptoms of depression per se.
Despite the potential clinical value of our findings, our study has limitations. First, we included only depressive symptoms in the analyses, and we did not consider other affective (e.g., anxiety) or personality factors, which might also affect the concurrence between the objective and subjective memory measures as reported by previous studies (32, 33). Second, the cross-sectional design of the present study precluded us from investigating the relationship among the level of executive function, discrepancy scores, and subsequent function declines. This design also limited our ability to examine the linear continuum versus nonlinear evolution from SCD to MCI. In addition, the sample size of the current study was relatively small, particularly when considering the number of predictive variables used for the regression model. The small sample size also prevented us from clarifying the heterogeneity among the older adults, particularly among the patients with MCI. Despite the aforementioned limitations, the present study is the first to consider various factors in investigating the directionality of the concurrence between subjective and objective memory function. We also extend previous studies by using a more ecologically relevant self-report measure to survey the individuals’ subjective memory concerns and objectively measure memory function through standardized cognitive tests.
In conclusion, the present study reveals the complexity involved in understanding the meaning of SMCs. Although both noncognitive and cognitive factors were necessary for consideration, the level of executive function may play a unique role in understanding the equivocal relationship of the concurrence between subjective complaints and objective function measures in the literature. Through a comprehensive evaluation, high-risk individuals (i.e., CN individuals with hypernosognosia) may possibly be identified or provided with the necessary intervention during stages at which objective cognitive impairment remains clinically unapparent. Inclusion of biomarkers and longitudinal follow-up data can provide additional information on the neural mechanism underlying the discordance.

 

Funding: This work was supported by the Taiwan Ministry of Science and Technology (grant numbers 107-2314-B-182A-065 and108-2410-H-002-106-MY2 to Y.L.C.). 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.

Acknowledgements: The authors thank Yen-Shiang Chiu, Chia-Hua Lin, and Yi-Yuan Zhuo for assistance in data collection, and Dr. Jung-Lung Hsu for constructive feedbacks.

Conflict of interest: The authors have no conflict of interest to report.

Ethical Standards: The institutional Review Board(IRB) approved this study, and all participants gave informed consent before participating.

 

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EARLY DETECTION OF MILD COGNITIVE IMPAIRMENT (MCI) IN AN AT-HOME SETTING

 

M.N. Sabbagh1, M. Boada1, S. Borson3, P. Murali Doraiswamy4, B. Dubois5, J. Ingram6, A. Iwata7, A.P. Porsteinsson8, K.L. Possin9, G.D. Rabinovici9, B. Vellas10, S. Chao11, A. Vergallo12, H. Hampel12

 

1. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 2. Memory Clinic and Research Center of Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya (UIC), Barcelona, Spain; 3. University of Washington School of Medicine, Seattle, Washington, and Dementia Care Research and Consulting, Santa Ana, CA, USA; 4. Departments of Psychiatry and Medicine, Duke University School of Medicine, USA; 5. Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Center of excellence of neurodegenerative disease (CoEN) and National Reference Center for Rare or Early Dementias Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l’hôpital, Paris, France; 6. Seniors Lead Physician, Central East Region, Ontario and Founder and Medical Director of Kawartha Centre, Peterborough, Ontario, Canada; 7. Department of Neurology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan; 8. Department of Psychiatry, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA; 9. Department of Neurology, University of California San Francisco, Memory and Aging Center, San Francisco, USA; 10. Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse, France; UMR INSERM, 1027 University of Toulouse III, Toulouse, France Faculté de médecine, Toulouse, France; 11. ClearView Healthcare Partners – Newton, MA, USA; 12.Global Medical Affairs, Neurology Business Group, Eisai Inc., Woodcliff Lake, New Jersey, USA

Corresponding Author: Marwan N. Sabbagh, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA, sabbagm@ccf.org; Tel.: (702) 483-6029; Fax: (702) 722-6584

J Prev Alz Dis 2020;3(7):171-178
Published online April 6, 2020, http://dx.doi.org/10.14283/jpad.2020.22

 


Abstract

Emerging digital tools have the potential to enable a new generation of qualitative and quantitative assessment of cognitive performance. Moreover, the ubiquity of consumer electronics, such as smartphones and tablets, can be harnessed to support large-scale self-assessed cognitive screening with benefit to healthcare systems and consumers. A wide variety of apps, wearables, and new digital technologies are either available or in development for the detection of mild cognitive impairment (MCI), a risk factor for dementia. Two categories of novel methodologies may be considered: passive technologies (which monitor a user’s behavior without active user input) and interactive assessments (which require active user input). Such examinations can be self-administered, supervised by a caregiver, or conducted by an informant at home or outside of a clinical setting. These direct-to-consumer tools have the potential to sidestep barriers associated with cognitive evaluation in primary care, thus improving access to cognitive assessments. Although direct-to-consumer cognitive assessment is associated with its own barriers, including test validation, user experience, and technological concerns, it is conceivable that these issues can be addressed so that a large-scale, self-assessed cognitive evaluation that would represent an initial cognitive screen may be feasible in the future.

Key words: Alzheimer’s disease, mild cognitive impairment, cognitive screening, digital consumer.


 

Introduction

The incidence of age-related diseases such as Alzheimer’s disease (AD) will increase dramatically as the expected life-expectancy does. The increasing incidence of AD will fundamentally overburden healthcare institutions and services worldwide. The exponential growth of AD represents a major impact with severe medical, financial, ethical, emotional, and physical implications at both the individual and societal level. However, reasonable hope exists that disease-modifying therapies currently in late-stage clinical development can be approved in the near future. These candidate treatments target patients in early clinical stages of AD (i.e., mild cognitive impairment [MCI]) before dementia symptoms manifest and have the potential to significantly delay disease progression. However, healthcare systems need structural and functional innovation towards early detection and diagnosis of AD from as early as the preclinical/prodromal stages, in order to implement disease-modifying treatments once they will be available.
This article represents the third part of a three-part series of an expert consensus perspective on the screening, identification, and management of MCI, the barriers and frictions that prevent large-scale cognitive evaluation, and recommendations for test-makers going forward. These recommendations were delivered by a global panel of clinical and research experts focused on MCI and AD. The views and recommendations presented here represent the consensus opinion of this group based on meetings of the expert group in April 2019. The first article of this series of three publications covers more detail on the need for and value of testing for MCI as well as a review of existing consensus statements and recommendations in the literature on MCI clinical identification. The second article examines the current state of MCI testing in the primary care setting, key hurdles, and recommendations for future improvements. As described in the prior articles, we utilize the term “MCI” to refer to the broad definition of intermediate cognitive impairment due to a variety of etiologies and use the term “MCI due to AD” or “MCI-AD” to refer specifically to the MCI clinical syndrome associated with positive biomarkers of AD pathophysiology (1, 2). This article will focus on direct-to-consumer detection of cognitive impairment, intended to be conducted by an individual or their family member, outside of a physician’s office, such as at home or a retail pharmacy, without direct supervision by healthcare providers.
While evaluating cognitive performance outside of a clinical setting is less straightforward than traditional neuropsychometric evaluation in a clinical setting, the working group believes that the potential to leverage technology to monitor and understand cognitive function offers exciting possibilities. MCI screening has historically been considered strictly as a healthcare provider-administered assessment conducted in a clinical setting. Several companies have developed products and tools with an office-based clinical and commercial model in mind, incorporating per-use licensing fees with the expectation that the test could be reimbursed by insurance plans such as Medicare (3). However, for several reasons articulated in the second article of this series, these existing tools have not been widely adopted, prompting the need to consider alternative clinical and commercial approaches to not only office-based tools and assessments, but also innovative direct-to-consumer approaches focused on at-home use by individuals and informants. Technology-driven evaluation of cognitive performance is of great interest, given its potential to improve patient care, empower patients, and identify patients who are currently undetected through traditional healthcare avenues. At present, cognitive decline often remains undetected for a long time, ultimately forcing patients and families to cope with cognitive impairment and ensuing dementia and AD with little preparation or support. However, new digital technologies may rectify these gaps. For example, in the future, adults may be instructed to complete an online or telephone questionnaire at home prior to visiting their doctor. Two categories of novel methodologies may be considered: passive technologies that monitor user behavior without active user input and interactive assessments that require the adult user of these technologies to actively engage in the evaluation. For those individuals who proactively seek tools to monitor their own cognitive performance, smartphones use could be “passively” monitored for subtle changes suggestive of cognitive decline. Such functionality could even be incorporated into smart homes, such that changes in an individual’s activities of daily living can be detected and analyzed, alerting the patient or their physician that their cognitive or emotional state may have changed. Moreover, smart homes may offer the possibility of multi-dimensional and dynamic monitoring of cognitive performances, from simple to complex tasks.
Though home-based evaluation for MCI is associated with meaningful barriers, it will be critical to work toward making at-home testing accessible and scalable across a broad population to allow individuals with MCI-AD to present to a healthcare provider to initiate the diagnostic process, to eventually receive adequate care. It is important to note that the goal of at-home testing is not to replace current neuropsychometric testing and clinical evaluation by trained healthcare providers in the primary care setting, but to enable large-scale cognitive screening and to optimize access to cognitive-oriented care through a potentially more accurate and comprehensive evaluation pathway. Within this context, we have outlined current barriers that limit use and/or effectiveness of home-based cognitive evaluation and recommended potential solutions that may help overcome these parameters. We have also outlined parameters of an ideal home-based evaluation tool and provided an initial perspective on how home-based testing may be integrated with testing in a clinical setting, as a starting point for future investigation of the optimal care pathway for MCI individuals. Finally, we have briefly characterized recent developments in the field of direct-to-consumer or at-home cognitive performance evaluation and highlighted potentially disruptive (i.e., transformative for the current management paradigm) technologies in development.

 

Current landscape

Barriers Related to Test Validation

Currently, the quality of clinical data generated by at-home direct-to-consumer tests conducted by individuals and informants lags behind tests administered by healthcare providers in an office setting, some of which have decades of development and validation behind them (e.g., MMSE, MoCA, ADAS-Cog). Most direct-to-consumer tests and tools lack the robust clinical validation and data needed to create confidence that a test is accurate and repeatable across a heterogeneous test population. As with other clinical tests, at-home tools are unlikely to be approved by regulatory agencies for clinical use without validation in a controlled, clinical trial setting that includes study subjects representative of the diverse demographics of the real-world population. Lack of regulatory approval will result in difficulties with loss of credibility and reimbursement with patients and clinicians, further limiting the reach, availability, and impact of at-home tests. Investing in initial validation studies, robust implementation studies, and large-scale studies with long-term follow-up and a large sample size will be essential to legitimize the clinical value of at-home testing to physicians and, subsequently, to patients.

Barriers Related to User Experience

Potential users of direct-to-consumer cognitive tools may face a wide variety of potential barriers that prevent at-home cognitive evaluation. Test users may not be aware of any deficit in their cognitive function, particularly given that early dementia can be characterized by impaired self-awareness, or patients may be in denial that a cognitive concern exists for fear of the stigma associated with cognitive decline. In either case, the result is that many individuals will have little motivation to seek out and utilize at-home testing. Even if an individual or informant is concerned about a potential loss of cognitive function and motivated to act, they are unlikely to be aware of home-based assessments at their disposal. Additionally, even if a user completes a home-based test, these tests rarely provide actionable information about where to seek medical care given the lack of established care and referral pathways and lack of effective treatments for MCI due to AD. Another critical factor is that in today’s current healthcare setting, at-home tests designed to detect and identify potential MCI are unlikely to be reimbursed by individual and employer health insurance plans if they are neither approved by regulatory agencies, robustly validated, nor directly associated with informing clinical diagnosis and treatment decisions. Given that reality, if a test intended for use at home is associated with a financial cost to either purchase the test or to take the test, widespread use is unlikely. Additionally, insufficient engagement with community memory screening resources (e.g., at senior centers or pharmacies) can limit access to early detection outside of the primary care physician’s (PCP) office.

Barriers Related to Emerging Technological Approaches

Several technological barriers contribute to the challenge of integrating at-home testing into the MCI screening and identification paradigm. Most direct-to-consumer at-home testing options will require a minimum baseline of technological fluency that many older adults may struggle with, which may limit both the use and accuracy of at-home testing. Even patients with the ability to use technology may prefer face-to-face testing with a healthcare provider due to the sensitive nature and potential implications of cognitive testing. While this challenge is likely to decrease in relevance for future generations given the ubiquity of consumer electronics, many older adults today have little familiarity with technology (for example, only using a computer for email, or not at all) and may struggle with an at-home digital test. This challenge may be compounded in patients with comorbid behavioral and psychological symptoms (e.g., apathy), which have been associated with more rapid cognitive decline, higher caregiver burden, and a higher risk of conversion to dementia (4–7). An additional consideration for at-home testing is data privacy concerns both in terms of users being concerned about family members or others seeing them taking a cognitive performance assessment, or the concern that their test results may not stay private. Data security will also become a more daunting issue for at-home tests as more digital technologies gain regulatory approval in the future and are therefore subject to more regulation. Finally, self-administered digital tools introduce variability in testing conditions: in addition to variation in testing environment (e.g., level of background noise, environmental distractions), variation associated with the digital device itself (e.g., type of computer or tablet, operating system and version, recency of software updates) creates uncertainty when considering the accuracy of a cognitive performance assessment.

 

Parameters of an Ideal Tool

After identifying the most critical barriers to direct-to-consumer assessment of cognitive performance, the working group aligned on potential features of an “ideal” tool to help guide creation or refinement of novel tools.

Test methodology

While multiple direct-to-consumer cognitive evaluations are available, only a minority of assessments explore functional abilities or symptoms associated with cognitive decline (e.g., behavioral symptoms, sleep disorders). The potential for evaluation of functional decline remotely through direct observation by clinicians is certainly possible. As noted in the second publication of this series, an ideal cognitive performance assessment would evaluate across all of these categories. We propose that current cognitive tests would benefit from inclusion of a functional measure and/or questions about cognitive change over time, directed towards a patient and/or an informant. Notably, this robust test methodology is not intended to create an at-home cognitive performance assessment to replace evaluation in a clinical setting. Instead, a well-designed at-home assessment will empower individuals to begin discussing cognitive performance with their PCPs and may help address anxiety about potential cognitive decline among adults with high cognitive performance.

Logistics

For logistics of at-home testing, flexibility is highly favorable, given the variable aptitude and digital fluency of the current generation of older adults. First, the option of having a family member administer a digital or pen-and-paper questionnaire or test to an individual who may not be familiar with consumer electronics may be ideal to maximize the reach and accessibility of at-home testing. We identified relatively few currently-available tests that allow this option; however, the COGSelfTest is designed such that a family member or caregiver can input the user’s answers without impacting the assessment (8, 9). Second, early detection tools should ideally be self-administered or able to be administered by an informant on a range of digital devices, with results either directly forwarded electronically to the PCP office or easily shown to the physician at the next regular appointment. Third, the assessment should be brief, ideally less than 10 minutes, to minimize the perception of the test as a chore or disruptive to daily activities and to reduce attrition of individuals abandoning the test before completion. Many tools meet this criteria, including the BrainCheck assessment, which incorporates multiple established cognitive tests (e.g., Stroop interference test, immediate and delayed recall tasks) into a single 5 – 10 minute session (10).

Score Reporting

Test results should provide the user with information on next steps and available resources if a cognitive impairment is detected. Additionally, an ideal test and score report should allow the user’s test results to be tracked over time – for example, a score report may include a comparison to previous results, when available. Ideally, an at-home resources will be integrated with primary care testing and evaluation. For example, an ideal tool may provide the option for a patient’s score to be sent to their PCP to promote a discussion about cognitive performance at their next routine health visit and to increase the ease with which patients can manage their cognitive performance. Importantly, regardless of performance on the test, the output should include a directive to patients to discuss any cognitive concerns with a PCP.

Validation

Validation of tests and tools in large, diverse populations reflective of the demographics of a real-world population would be ideal. Additionally, at-home cognitive performance assessments should seek to replicate real-world testing environments (i.e., asking users to complete the test at home to replicate distractions, variation in technical ability, and variation in equipment) to understand the accuracy of each assessment in realistic circumstances. At minimum, validation studies should be designed to meet regulatory requirements for marketing authorization.

 

Optimal care pathway

Integrating direct-to-consumer and/or at-home tests within a clinical care pathway that includes primary care providers is an important step toward establishing the clinical utility of these assessments. Given the nascent stage of development of at-home testing and the range of potential options for its integration with the healthcare system, a multiplicity of viable options to ensure appropriate crosstalk between patients and providers may exist. Importantly, establishing standardized care and referral pathways will be vital, even in the absence of disease-modifying pharmacotherapies, to motivate users, informants, and physicians alike to initiate testing in a manner that allows for appropriate, effective care while also remaining sensitive to the emotional and social impact of MCI and AD. Initiation of at-home testing might occur when an individual or their family members or caregivers have an ongoing concern about cognitive performance and reaches out to their physician. At that point, the physician’s office might provide a recommendation to conduct an at-home test as a first step to see if the concern is warranted without the potentially alarming suggestion to seek an immediate medical opinion. Importantly, the healthcare provider may remind the patient to undergo cognitive testing on a regular basis at home so that a decline can be noted early and can be appropriately captured and communicated to the healthcare provider. As smart home technology offerings expand, they may provide an avenue for monitoring that can be communicated to physicians. Automatic transmission of user results to a healthcare provider would be the optimal strategy to integrate at-home testing with the primary care office. Additionally, artificial intelligence-based technologies may immediately provide scores and clinical labels without requiring transfer of data from the home device to a general server (11). However, privacy concerns associated with these approaches may decrease patient willingness to undergo testing, in addition to the significant infrastructural challenges associated with electronic medical records that remains a persistent challenge for healthcare systems globally. While the pace of development suggests that these challenges can be addressed with time, we encourage test creators to balance potential integration into a clinical care pathway and patient privacy when developing direct-to-consumer or home-based cognitive evaluations.

 

Recent trends and potential disruptors

In recent years, interest in methods to evaluate cognitive performance outside a clinical setting has steadily increased, with countless cognitive performance assessments in development across academia and industry. At-home cognitive assessments, including phone-based and online batteries, have shown promise in identifying individuals who are most likely to have MCI identified through outpatient neuropsychometric testing (12). A variety of assessments are currently available to consumers for at-home use, though levels of clinical validation and interactivity vary (Table 1). Importantly, patient registries like the Brain Health Registry have begun incorporating at-home cognitive tests (13–16), demonstrating that validation of at-home cognitive assessments is an area of ongoing research. Furthermore, a comprehensive set of additional modalities are under active investigation, suggesting that at-home evaluation may ultimately expand from traditional cognitive testing to novel methodologies (Figure 1). Two categories of novel methodologies may be considered: passive technologies that monitor user behavior without active user input and interactive assessments.

Figure 1. Multiple technologies are under active investigation as methods to detect mild cognitive impairment (MCI) and ultimately improve access to care. Smart devices (e.g., smartphone, fitness tracker, smartwatch, smart-home devices) provide an ideal platform for longitudinal passive data capture on users’ habits and patterns. These data sets may then be analyzed by mobile or online applications to detect subtle changes that may be indicative of decline in cognitive performance. In parallel, active assessments (e.g., virtual reality tools, consumer genetic testing) may help empower users to understand and monitor their own cognitive performance

Table 1. Digital at-home cognitive performance assessments currently available (non-exhaustive)

 

Passive technologies are of particular interest to the working group given that they could ultimately provide a low-effort, easy-to-use solution for widespread cognitive performance monitoring. Recent academic studies have suggested that subtle changes in behavior, motor function, and cognitive ability are often predictive of future MCI or MCI due to AD (17–20). Indeed, actigraphy-based measurements of behavioral symptoms, including apathy and sleep disorders, may precede cognitive decline (6, 7, 21, 22). Passive monitoring of daily activity via smartwatches, fitness trackers, and smart-home devices could provide a means of tracking behavioral changes over time to detect cognitive decline (23, 24). Select examples of such technologies include the Mindstrong Health application, NeuraMetrix software, and the TATC algorithm. Mindstrong Health has developed a smartphone application that monitors smartphone use to detect behavioral changes associated with mood disorders, with potential to expand to cognitive decline associated with neurodegeneration (25). Similarly, NeuraMetrix has developed software that passively monitors user typing habits to detect changes (e.g., reduced typing speed) associated with cognitive decline (26). The TATC algorithm uses actigraphy to detect behavioral changes associated with abnormal aging and AD (27). These technologies are promising, given the convenient nature of passive monitoring, which does not disrupt a user’s daily routine. Notably, this working group agreed that passive analysis might disrupt the cognitive performance paradigm, resulting in large-scale changes in how we measure cognitive performance, how we monitor patients, and how we understand cognitive performance and aging. In the future, we foresee multiple passive data streams (e.g., smartphone habits, behavioral monitoring, activity monitoring) being integrated into a single application to strengthen early identification of MCI as well as monitor disease progression over time.
In parallel, interactive assessments have undergone extensive investigation in recent years and offer great promise. For example, multiple investigators and companies have created assessments that utilize virtual reality to test a user’s memory, executive functioning, and visuospatial functioning. One notable example is the assessment created by Altoida, in which users navigate a virtual building through a mobile application. Individuals’ performance in this augmented reality environment has been closely correlated with activities of daily living and clinical evaluation of MCI (28, 29). Similarly, many groups have created speech analysis tools that prompt users to perform a verbal task (e.g., describing a photograph) and then utilizes an AI-based algorithm to predict whether users are suffering from cognitive decline. The speech analysis software developed by Winterlight Labs is an example of this approach, which has demonstrated compelling accuracy in detecting MCI and AD in small-scale studies (30, 31). While we perceive these options as less disruptive than passive analysis, we acknowledge that these technologies represent a meaningful step forward with the potential to meaningfully impact patient care.
While we have primarily focused on at-home assessments, we also acknowledge that significant shifts around evaluation of cognitive performance in primary care could dramatically shift the interest in and need for at-home assessments. Blood-based biomarkers of AD are undergoing rapid development and may ultimately be disruptive if an efficient, scalable, and cost-effective technology can be identified and incorporated into primary care. Indeed, there is growing optimism regarding the potential for blood-based biomarkers to detect distinctive AD pathophysiological mechanisms, supported by increasing evidence that core biomarkers and proteins associated with inflammatory and neurodegenerative pathways can be detected in blood (32, 33). Blood-based biomarkers are minimally invasive and time- and resource-effective, thus allowing a decentralized and globally accessible in-vivo biological investigation of AD. While cognitive and functional testing is likely to remain critical even if blood-based biomarkers become widely available, we expect that blood-based biomarker panels will play an increasing central role in future diagnosis and management of AD where disease-modifying therapies agnostic of the clinical stage may be started on a purely biological basis.

 

Summary

In light of future disease modifying treatments the early detection of MCI in an At-home setting will be mandatory. Significant potential and promising early data exists for at-home detection of MCI. However, the development of digital tools for cognitive evaluation is ultimately still in its infancy. Many tests available today have not yet been validated in large, controlled study settings, thereby preventing widespread adoption and use by concerned adults and physicians. Appropriate regulation of these tools will require updated input from regulatory bodies in this ever-shifting era of digital health. Additionally, the field must carefully consider ethical implications of any algorithm that assesses a user’s personal data, and test creators must strive to respect the privacy and autonomy of any individual user. Test creators must also seek to demonstrate the clinical utility of any at-home test to potentially skeptical physicians and healthcare systems, as integration with current healthcare infrastructure is a critical step toward achieving broad population-level screening and detection, particularly in light of future disease-modifying therapies. Importantly, enhancing holistic patient care and management, irrespective of the availability of a disease-modifying pharmacotherapy, will be equally as important as improving accuracy and timing of the detection of MCI to equip PCPs, patients, and their family members and caregivers with appropriate resources and guidance to cope with MCI.
Despite select barriers that must be addressed, electronic point-of-contact testing holds great promise and will be a critical method to support large-scale cognitive screening for the early detection of MCI, particularly MCI due to AD. Supplementing in-clinic evaluation with at-home assessment may help identify individuals with MCI, allowing physicians to intervene and ultimately to monitor progression, potentially without requiring the individual to present to the physician’s office. When combined with potential improvements to testing in the primary care setting outlined in the second publication of this series, significant potential remains for improving large-scale cognitive screening for the timely and accurate identification of MCI due to AD in a responsible and scalable manner that can be absorbed by healthcare systems.

 

Acknowledgements and funding: Medical writing support, under the direction of the authors, was provided by ClearView Healthcare Partners, LLC, funded by Eisai Inc., in accordance with Good Publication Practice (GPP3) guidelines.

Disclosures: MNS Royalty: HarperCollins, Stock: uMethod Health, Brain Health, Inc, Optimal Cognitive Health Company, M3 Biosciences, Versanum, NeuroReserveAlzheon and Athira, Speakers Bureau: Peerview and Medscape and HWP, Consultant: Biogen, Bracket/Signant, Neurotrope, Cortexyme, Roche, Grifols, Sanofi, Regeneron, Eisai, Neuronix, Acadia. MB is affiliated with the Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain; and with the Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Spain. Private research funding sources include Grifols SA; Caixabank S.A.; Life Molecular Imaging; Araclon Biotech; Laboratorios Echevarne; Festival Castell Paralada; Bonpreu/Esclat; and Famila Carbó. Public grants include those from Instituto de Salud Carlos III. Ministerio de Salud. Gobierno de España; Dirección General de Farmacia. Ministerio de Salud. Gobierno de España; and European Commission:H2020 program, Innovative Medicine Initiative (IMI-2); and ERA-NET NEURON program, European Marie Sklodowska Curie. Advisory work includes that for Araclon Biotech, Biogen, Bioibérica, Eisai, Grifols, Lilly, Merck, Nutricia, Roche, Oryzon, Schwabe Farma, Servier, and Kyowa Kirin. PMD has received research grants (through Duke University) from Avid, Lilly, Neuronetrix, Avanir, Bauch, Alzheimer’s Drug Discovery Foundation, Cure Alzheimer’s Fund, Wrenn Trust, DOD, ONR, and NIH. PMD has received speaking or advisory fees from Anthrotronix, Neuroptix, Genomind, Clearview, Cognicity, Nutricia, Living Media, Verily, RBC, Brain Canada, and CEOs Against Alzheimers. PMD owns shares in Muses Labs, Anthrotronix, Evidation Health, Turtle Shell Technologies and Advera Health Analytics whose products are not discussed here. He has received travel support from World Economic Forum, CCABH and Canaan Ventures. PMD served/serves on the board of Baycrest, AHEL, TLLF and TGHF. PMD is a co-inventor (through Duke) on patents relating to dementia biomarkers and therapies. BD is affiliated with the Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Salpêtrière Hospital, AP-HP, Sorbonne-Université, Paris, France. JI is affiliated as an Assistant Professor-Adjunct (Group1) with the Department of Family Practice at Queen’s University. She was also a panelist for Hoffman-La Roche Limited, Ottawa, on an Alzheimer’s Disease Panel. She is involved with the Canadian Consortium on Neurodegeneration in Aging (CCNA), Extension Study as a Co- Investigator and Research Coordinator. AI received lecture fees from Eisai, Janssen, Otsuka, Eli Lilly, MSD, Chugai-Roche, Daiichi-Sankyo, Alnylam, Takeda, UCB, Ono, Integra Japan, IQVIA, Fuji Rebio, Biogen, and advisory fees from Janssen during the past three years. AP reports personal fees from Acadia Pharmaceuticals, Functional Neuromodulation, Neurim Pharmaceuticals, Grifols, Eisai, BioXcel, Tetra Discovery Partners, and Merck; grants from AstraZeneca, Avanir, Biogen, Biohaven, Eisai, Eli Lilly, Janssen, Genentech/Roche, Novartis, Merck, as well as funding from NIA, NIMH, DOD. KLP receives grant funding from the National Institute on Aging, the National Institute of Neurological Disorders and Stroke, the Global Brain Health Institute, and Quest Diagnostics. BV has consultancy and research grants from Roche, Biogen, EISAI, Nestle, Lilly, Cerecin, and Merck. HH is an employee of Eisai Inc. and serves as Senior Associate Editor for the Journal Alzheimer’s & Dementia; during the past three years he had received lecture fees from Servier, Biogen and Roche, research grants from Pfizer, Avid, and MSD Avenir (paid to the institution), travel funding from Eisai, Functional Neuromodulation, Axovant, Eli Lilly and company, Takeda and Zinfandel, GE-Healthcare and Oryzon Genomics, consultancy fees from Qynapse, Jung Diagnostics, Cytox Ltd., Axovant, Anavex, Takeda and Zinfandel, GE Healthcare, Oryzon Genomics, and Functional Neuromodulation, and participated in scientific advisory boards of Functional Neuromodulation, Axovant, Eisai, Eli Lilly and company, Cytox Ltd., GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics. He is co-inventor in the following patents as a scientific expert and has received no royalties: • In Vitro Multiparameter Determination Method for the Diagnosis and Early Diagnosis of Neurodegenerative Disorders Patent Number: 8916388; • In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases Patent Number: 8298784; • Neurodegenerative Markers for Psychiatric Conditions Publication Number: 20120196300; • In Vitro Multiparameter Determination Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders Publication Number: 20100062463; • In Vitro Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders Publication Number: 20100035286; • In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases Publication Number: 20090263822; • In Vitro Method for the Diagnosis of Neurodegenerative Diseases Patent Number: 7547553; • CSF Diagnostic in Vitro Method for Diagnosis of Dementias and Neuroinflammatory Diseases Publication Number: 20080206797; • In Vitro Method for The Diagnosis of Neurodegenerative Diseases Publication Number: 20080199966; • Neurodegenerative Markers for Psychiatric Conditions Publication Number: 20080131921. AV is an employee of Eisai Inc. and received lecture honoraria from Roche, MagQu LLC, and Servier.

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EARLY DETECTION OF MILD COGNITIVE IMPAIRMENT (MCI) IN PRIMARY CARE

 

M.N. Sabbagh1, M. Boada2, S. Borson3, M. Chilukuri4, B. Dubois5, J. Ingram6, A. Iwata7, A.P. Porsteinsson8, K.L. Possin9, G.D. Rabinovici9, B. Vellas10, S. Chao11, A. Vergallo12, H. Hampel12

 

1. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 2. Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain; and Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Spain; 3. University of Washington School of Medicine, Seattle, Washington, and Dementia Care Research and Consulting, Santa Ana, CA, USA; 4. Durham Family Medicine, Durham, North Carolina, USA; 5. Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Center of excellence of neurodegenerative disease (CoEN) and National Reference Center for Rare or Early Dementias Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l’hôpital, Paris, France; 6. Seniors Lead Physician, Central East Region, Ontario and Founder and Medical Director of Kawartha Centre, Peterborough, Ontario, Canada; 7. Department of Neurology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan; 8. Department of Psychiatry, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA; 9. Memory & Aging Center, Departments of Neurology, Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA; 10.Gerontopole, Toulouse University Hospital, UMR 1027, University of Toulouse; 11. ClearView Healthcare Partners – Newton, MA, USA; 12. Global Medical Affairs, Neurology Business Group, Eisai Inc., Woodcliff Lake, New Jersey, USA

Corresponding Author: Marwan N. Sabbagh, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA, sabbagm@ccf.org; Tel.: (702) 483-6029; Fax: (702) 722-6584

J Prev Alz Dis 2020;3(7):165-170
Published online April 6, 2020, http://dx.doi.org/10.14283/jpad.2020.21

 


Abstract

Mild cognitive impairment (MCI) is significantly misdiagnosed in the primary care setting due to multi-dimensional frictions and barriers associated with evaluating individuals’ cognitive performance. To move toward large-scale cognitive screening, a global panel of clinicians and cognitive neuroscientists convened to elaborate on current challenges that hamper widespread cognitive performance assessment. This report summarizes a conceptual framework and provides guidance to clinical researchers and test developers and suppliers to inform ongoing refinement of cognitive evaluation. This perspective builds upon a previous article in this series, which outlined the rationale for and potentially against efforts to promote widespread detection of MCI. This working group acknowledges that cognitive screening by default is not recommended and proposes large-scale evaluation of individuals with a concern or interest in their cognitive performance. Such a strategy can increase the likelihood to timely and effective identification and management of MCI. The rising global incidence of AD demands innovation that will help alleviate the burden to healthcare systems when coupled with the potentially near-term approval of disease-modifying therapies. Additionally, we argue that adequate infrastructure, equipment, and resources urgently should be integrated in the primary care setting to optimize the patient journey and accommodate widespread cognitive evaluation.

Key words: Alzheimer’s disease, mild cognitive impairment, cognitive screening, disease-modifying.


 

Introduction

Mild Cognitive Impairment (MCI) is a syndrome defined by clinical, cognitive, and functional criteria and is characterized by an objective cognitive decline in one or more cognitive domains without any significant impairment in daily-life activities. MCI may be associated with a variety of underlying causes, including Alzheimer’s pathophysiology (1, 2).
Late-stage clinical development of drugs with a disease-modifying effect represents unprecedented hope for individuals suffering from Alzheimer’s disease (AD), particularly at preclinical or prodromal stages (i.e., MCI due to AD [MCI-AD]). In addition, the expanding knowledge on non-pharmacological approaches to cognitive decline (e.g., lifestyle-oriented treatments, non-invasive brain stimulation) suggests the possibility to treat secondary causes of MCI. This report represents the second part of a three-part consensus perspective on testing for MCI and is focused on the primary care setting. The suggestions and opinions within these publications represent the consensus opinion of a working group comprised of international experts on MCI and AD that was convened in April 2019 to discuss the challenges of detecting MCI at a large-scale and the potential solutions to overcoming these barriers.
Recommendations described here focus on improvements to MCI detection that may be feasible and ready for widespread use in the near-term (i.e., within approximately three years). The implementation of a system of healthcare delivery focused on dementia screening and large-scale cognitive screening is necessary to accommodate the global rising incidence of AD, and to prepare the public and healthcare providers for the availability of disease-modifying therapies for AD. Blood-based and biologic biomarkers are expected to play a key role in this paradigm shift. Indeed, blood-based biomarker panels are widely accessible, minimally invasive, and less time- and cost-consuming than cerebral spinal fluid (CSF) and neuroimaging assessments. To that end, we have outlined current barriers to the timely and accurate detection of MCI and MCI-AD, provided potential solutions, identified methods and emerging technologies to improve cognitive evaluation, and estimated potential timelines for accomplishing an optimal care pathway for managing MCI and MCI-AD at a large-scale.

 

Current landscape

Barriers Related to Physician Training and Support

The expert panel identified a wide range of barriers, including expertise, schedule, and available assessment tools, that often prevent primary care physicians (PCPs) from evaluating cognition.. The short duration of most primary care visits (frequently less than twenty minutes) represents one of the key logistical barriers to the establishment of cognitive evaluation in a primary care setting. The high prevalence of comorbidities among older adult individuals intensifies this challenge. Cognitive pathways require access to collateral informants, usually family members. Physicians may lack sufficient access to these collateral sources who are close enough to the individual to provide accurate longitudinal insight into his or her cognitive performance and functional abilities. Given these logistical barriers, PCPs may not consider assessing an individuals’ cognitive performance within the context of a standard appointment feasible.
Separate from logistical concerns, PCPs are also likely to encounter barriers around their comfort with cognitive assessment and/or motivation to assess cognition (3). Importantly, many PCPs have reported limited confidence in cognitive assessment. Training programs for Primary Care providers incorporate limited exposure to these skills. As a result, many PCPs are left feeling poorly equipped, inexperienced, or uncomfortable about monitoring cognitive performance (4, 5). In addition, if cognitive impairment is detected, PCPs may face uncertainty about what next steps to pursue (e.g., how to appropriately explain any test results, whether or not to refer to a specialist). Finally, PCPs likely face low motivation to evaluate an individual’s cognitive status, given uncertainty around whether or not identifying MCI provides a clear benefit to the individual. Without effective treatments for MCI, detecting MCI may be perceived only to be detrimental to individuals and their family members. While emergence of a novel disease-modifying therapy may ultimately address physician motivation, challenges to confidence and familiarity with cognitive assessment may require large-scale training efforts.

Barriers Related to Healthcare Systems

Barriers associated with healthcare systems also significantly limit widespread early detection of MCI, as exploring all cognitive domains and quantifying overall cognitive performance is currently a time-consuming process. In the context of an individual physician’s office, current medical practice can limit the use of MCI in many different ways. Poor integration of cognitive assessments with EMR systems creates a significant administrative burden, as substantial clerical work is needed to document the output of a cognitive performance assessment. In addition, lack of proper integration with the EMR system also limits the ability to track an individual’s cognition over time, which in turn limits the utility of cognitive evaluation. In some circumstances, testing tools are poorly designed and/or unintuitive for users.
This increases system burden due to cost (e.g. administration time, training, clerical burden etc.), which decreases the frequency of cognitive performance assessment. On the macro scale, inadequate reimbursement of costs associated with assessing cognitive function and providing post-diagnostic care, including physician time, significantly decreases the incentive for wide adoption of MCI detection. Consistent, reliable reimbursement of comprehensive assessment and cognitive testing by payers is therefore required to support extensive evaluation of cognition in the primary care setting.

Barriers Related to Test Design and Validation

The limited length of time of the average PCP visit requires tests to be conducted in 10 minutes or less. This constraint introduces a major limitation, as cognition is multifaceted, and many different cognitive domains can be impacted by MCI. Testing all domains of cognition in a short test is likely not feasible, so tools must strike a proper balance between time and depth of testing to maximize their utility.
Additionally, many cognitive tests have demonstrated limited value when deployed in a heterogeneous patient population. This limitation results from initial development and validation with highly homogeneous populations in mind – specifically, highly-educated English-speakers. Effective tests must be usable in a broader community that includes individuals across multiple levels of educational attainment, various races and ethnicities, and multiple languages (including varying familiarity with English). Validation in homogeneous populations can lead to the development and use of tools that are significantly less accurate than expected when used in a diverse patient population; for example, a patient with fewer years of education may score artificially low on a screening tool developed and validated in college graduates. Many tests also lack validation in multiple languages, which prevents standardization across communities and countries.

Barriers Related to MCI

Early detection of MCI is also inherently challenging due to barriers associated with the disease itself. Symptoms related to the initial onset of MCI can vary significantly between individuals, depending on etiology, cognitive reserve, and variable demands of day-to-day living, among other factors. Additionally, MCI can be less relevant than other medical comorbidities, contributing to a different medical prioritization ahead of monitoring cognition. Furthermore, care partners and patients are likely to be particularly sensitive about cognitive performance in comparison to other health concerns. Patient concerns may result in a scenario where a physician is hesitant to discuss the cognitive performance assessment or the implications these have on other skills (such driving) with patients due to concern about compromising the physician-patient relationship. Similarly, individuals may actively avoid discussing cognitive performance with their physician due to concerns about the implications of cognitive assessment and/or perceived stigma associated with cognitive impairment. All of these issues can limit the utility of even clinically useful tests due to lack of use and compliance.

 

Parameters of an ideal tool

To help guide the refinement of existing cognitive performance evaluations or development of novel tools, we have outlined the parameters of an “ideal” MCI detection tool. This guidance is intended to offer potential solutions to the barriers currently facing MCI detection. While this working group does not recognize a single assessment that meets all of these criteria, multiple cognitive performance assessments include components of an “ideal” tool, suggesting that these promising tools may approach the “ideal” profile with minor refinement and/or additional validation.

Test Methodology

Similar to previous recommendations (6), this panel agreed that a tool for the detection of MCI would ideally incorporate three critical components: cognitive assessment, functional questionnaires, and clinical history-taking. First, cognitive assessment refers to directly assessing cognitive function through objectively evaluated tasks, such as a word recall task, clock-drawing task, etc. To meet the criteria for defining MCI versus dementia, a cognitive tool should also encompass multiple cognitive domains; our working group recommends that, at minimum, memory and executive function be assessed. Ideally, measures of visuospatial and language skills would also be included. Many currently-available cognitive tests encompass multiple cognitive domains, including Cognigram (offered by CogState) (7), CogniSense (offered by Quest Diagnostics) (8), and CANS-MCI (offered by Screen Inc.) (9). Emerging computer-based neuropsychological approaches may also be considered. The Toronto Cognitive Assessment (TorCA) is an example of a computer-based platform integrated across multiple sites, providing consistent analysis and interpretation (10).
Second, functional questionnaires refer to tools that ask the individual or a family member about activities of daily living, which by definition must be returned to diagnose MCI but must be impacted by cognitive challenges to permit the diagnosis of dementia (e.g., ability to carry out financial tasks, driving, shopping). A long-standing example is the Functional Activities Questionnaire (FAQ), a 10-question form with questions around ability to go shopping or prepare a meal (11).
Third, clinical history-taking aims to identify comorbidities and their impact on function and to understand whether the individual himself or a family member has noticed a change in cognitive function over time. Questionnaires such as the AD8 or IQCODE can be utilized to facilitate clinical history-taking (12–14).
Importantly, clinical history-taking will help identify individuals with MCI but may also help identify individuals with subjective cognitive decline (SCD), which is often a precursor to MCI and can be considered a preclinical phase of AD (15, 16). An ideal cognitive assessment would encompass all three of these components; notably, a single questionnaire could incorporate both a functional component as well as questions for clinical history taking. An ideal tool would include a core assessment based on assessment of the individual him/herself, with an optional module that could incorporate feedback from a family member when possible. Furthermore, an ideal tool would allow the family member to complete a survey remotely (e.g., through an online form linked to the assessment). A tool that incorporates these components and features is likely to achieve compelling accuracy, even in individuals with subtle cognitive decline.

Logistics

The working group recommends several logistical characteristics that may optimize ease of use and minimize the time burden associated with detection of MCI. Tests should 1) be administered digitally on a laptop, tablet, or smartphone to facilitate widespread use and allow testing to scale, 2) require less than ten minutes, 3) not require a physician (i.e., should be self-administered or conducted by a technician or nurse). Upon completion, the assessment should automatically create a report that outlines next steps in care specific to each healthcare system and/or region. The automated report should be integrated with EMR systems and should be available to the PCP instantly so that they can easily discuss the results with the individual at the beginning of the patient visit. The cost of administering a test also can be a significant logistical consideration. While this panel recognizes that a highly accurate, validated, and well-designed test will command a higher price than other options, an “ideal” assessment would be offered at a low price point and/or would be reimbursed by payers to maximize access and use of the assessment. Multiple currently available tools align with one or more of these criteria given the recent increase in creation of digitally administered tests. CogniSense, offered by Quest Diagnostics, meets the above mentioned criteria and can be automatically integrated into EMR (17). This panel recommends that creators of assessment tools seek to incorporate features that will optimize functionality and minimize administrative burden associated with detection of MCI.

Validation

An ideal tool would be validated in a diverse population (i.e., varied cultural and educational backgrounds) and validated (not merely translated) in multiple languages. Validating studies should be conducted in populations representative of the distribution of mild cognitive impairment, dementia, and normal cognition in a primary care setting, not in populations enriched for subjects with cognitive impairment. Recently, creators of the Brain Health Assessment (BHA; developed at UCSF) utilized this approach to validate the accuracy of the BHA in a clinically-representative patient population (~55% healthy controls, ~30% MCI individuals, ~10% dementia patients, and ~5% with subjective cognitive concerns) [18]. If a tool is to be utilized broadly in a primary care setting, moderate specificity may be acceptable, given that additional downstream assessment will occur.
High sensitivity will ensure that a high proportion of suitable individuals receives follow-up assessment. The members of this working group acknowledge that validation of a cognitive performance assessment can be challenging. In the absence of an accepted “gold standard” test (or set of tests), multiple tests may be considered suitable. This working group recommends that novel tests continue to be validated in comparison to diagnosis based on a detailed clinical examination supported by multiple long-standing assessment tools.

 

Optimal care pathway

In light of these barriers to MCI detection, the expert panel agreed that it will be necessary to clarify the “optimal care pathway” for the detection of early cognitive impairment. While further work will be needed to understand how cognitive performance assessments can best be integrated across healthcare systems, this panel discussed select characteristics of an “ideal” care pathway. First, given the enduring uncertainty around whether universal screening is beneficial, this group recognized that the most valuable early detection pathway would begin with individuals who already have a cognitive performance concern (initiated either by the individual themselves, a family member, or the healthcare provider) or individuals who actively opt-in to cognitive assessment. Indeed, it is likely that individuals with a concern about their own cognitive performance are most likely to benefit from cognitive assessment given that subjective memory complaint (i.e., a self-reported loss of memory performance without objective cognitive decline) represents a condition at-risk for developing MCI in general. Moreover, SCD may underlie the beginning of the AD clinical continuum (1, 19, 20). Therefore, large-scale cognitive screening may also involve subjects with SCD in light of initiating therapeutic interventions targeting preclinical AD. The assessment of the SCD condition starts from a self-reported dysfunction and requires an assessment of the whole cognitive battery test employed for investigating if an objective cognitive decline exists (and per definition as to be negative). Therefore, the screening of potential preclinical stages of AD would be included in the same protocol to identify MCI.
Individuals identified with MCI then must be efficiently and thoroughly evaluated and guided toward appropriate next steps. We discussed two potential pathways: in one pathway, individuals undergo a brief cognitive assessment in parallel with a standard primary care appointment (e.g., a Medicare Annual Wellness Visit in the U.S. or MINT Clinic in Canada). In a second pathway, the individual may schedule a separate optional cognitive assessment appointment with their physician. In both pathways, the brief cognitive assessment should be either self-administered or administered by trained medical personnel (potentially a medical technician or nurse).
In some medical systems, creation of embedded nursing personnel trained to carry out cognitive evaluations on an as-needed basis have been well received. Creation of primary care clinicians with special training and expertise in this area, coupled with specific memory teams in primary care, has received wide-spread endorsement (https://www.hqontario.ca/Quality-Improvement/Quality-Improvement-in-Action/ARTIC/ARTIC-Projects/Primary-Care-Collaborative-Memory-Clinics). System-based changes in healthcare delivery are required to achieve the desired impact. Ultimately, individual choice of cognitive screening tools should decrease as pathways, including assessments, aligned with prior parameters become operationalized.
Importantly, an optimal care pathway must effectively support individuals and their caregivers following the cognitive assessment. After the assessment, the physician must allow sufficient time to help the individual understand the results and to provide guidance on next steps. Depending on the healthcare system and the capabilities of each primary care practice, this pathway may include further assessment in the primary care office, referral to a specialist, or simple monitoring of cognitively intact individuals to potentially detect a decline in subsequent years. To ensure maximal compliance with testing practices, we recommend that the next steps be clearly outlined by each healthcare system, in EMR systems, and/or by evaluative tools themselves.

 

Potential Future Blood-based Testing

If blood-based biomarkers become available earlier than anticipated, this is expected to significantly accelerate the diagnosis of AD and improve global accessibility of diagnostic tools. However, despite the promise of blood-based testing, our panel agreed that cognitive performance assessments will remain critical for distinguishing MCI and MCI-AD in the future, even after blood-based biomarkers are implemented into primary care practice. Cognitive testing and functional evaluation will remain necessary to understand the individual’s current cognitive performance, monitor changes in cognitive function, and identify cognitive changes not associated with a distinct biological signature (e.g., secondary causes of MCI).
Blood-based biomarkers are expected to facilitate critical clinical solutions catalyzed by the global threat of the evolving AD epidemic. The negative predictive value of blood-based biomarkers will support early screening and identification of individuals with a very low probability of developing AD-related pathophysiology and increase the probability that individuals with AD pathophysiology are being selected for further investigation by using more specific, expensive, and/or more invasive methods with reduced accessibility (e.g., PET imaging or CSF assessment). Blood-based biomarkers have excellent potential to be routinely and rapidly assessed in all healthcare settings and in asymptomatic individuals due to minimal invasiveness, cost-efficiency, accessibility (i.e., blood can even be withdrawn in an individual’s home), and reduced time and resource utilization compared with neuroimaging- and CSF-based techniques used for AD.
Indeed, growing optimism exists regarding blood-based biomarkers reflecting distinctive AD pathophysiological mechanisms, supported by increasing evidence that core biomarkers and proteins associated with inflammatory and neurodegenerative pathways can be detected in blood (21, 22). While the sensitivity of conventional immunoassays may be insufficient to detect changes in the blood in individuals with MCI and MCI-AD, promising assays using novel technologies are in development. Multiplex digital ELISA platforms (e.g., Quanterix® Simoa®) have been used by multiple groups to distinguish MCI and MCI-AD using blood levels of various proteins, including neurofilaments, Aβ, and tau (23–25). Another approach that has improved sensitivity of immunoassays is immunomagnetic reduction (IMR) via the use of superconducting quantum interference devices (SQUIDs). In one study, use of an IMR-SQUID assay combining analysis of Aβ42 and tau, enabled detection of AD with an AUC of 0.98 (26). Promising results also have been achieved in the academic setting using mass spectrometry. Multiple publications have distinguished between AD, MCI, and normal controls using ratios of Aβ and APP isoforms (27), or by using composite protein profiles detected via mass spectrometry (28). While further research is needed, development of an accurate, cost-effective, scalable blood-based biomarker for cognitive decline will shift the clinical paradigm dramatically, increasing diagnostic confidence and comfort of physicians, and integrating the novel biomarker test into the diagnostic paradigm will be critical.

 

Discussion

As outlined above and in the first article of this series, anticipated societal and medical changes (e.g., aging populations and potential advancements in management of MCI, respectively) will necessitate a significant improvement in the early detection of MCI. As PCP’s are the initial point of contact, especially for stoutly progressive chronic illness with subtle initial presentations, the PCP is likely the central player in initial identification and management of MCI. Importantly, PCPs are often inadequately supported to allow widespread evaluation of cognitive and functional performance, and cognitive assessment tools themselves are not optimally designed to support widespread use in the primary care setting. While tools evolve, health care developments and spending should focus on improving training on identification and management of MCI at the PCP’s office. Care pathways and staffing at the primary care physician’s office to support PCP management of cognition are needed (e.g., ensuring visits are long enough to allow for a cognitive evaluation). A critical need exists to refine cognitive performance assessments and to validate tools in diverse, representative populations, ideally in multiple languages. Test makers should also be aware of the barriers that limit early detection of MCI, including barriers associated with the primary care setting and with broader healthcare systems, so that tests can be designed to counteract or limit these barriers
This working group recommends that key stakeholders representing PCPs, regulatory stakeholders, test makers, and patient advocates collectively take action to improve the use and quality of tests for the detection of MCI. In the next and final article of this series, we shall explore the role and value of direct-to-consumer cognitive testing options.

 

Acknowledgements and funding: Medical writing support, under the direction of the authors, was provided by ClearView Healthcare Partners, LLC, funded by Eisai Inc., in accordance with Good Publication Practice (GPP3) guidelines.

Disclosures: MB is affiliated with the Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain; and with the Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Spain. Private research funding sources include Grifols SA; Caixabank S.A.; Life Molecular Imaging; Araclon Biotech; Laboratorios Echevarne; Festival Castell Paralada; Bonpreu/Esclat; and Famila Carbó. Public grants include those from Instituto de Salud Carlos III. Ministerio de Salud. Gobierno de España; Dirección General de Farmacia. Ministerio de Salud. Gobierno de España; and European Commission:H2020 program, Innovative Medicine Initiative (IMI-2); and ERA-NET NEURON program, European Marie Sklodowska Curie. Advisory work includes that for Araclon Biotech, Biogen, Bioibérica, Eisai, Grifols, Lilly, Merck, Nutricia, Roche, Oryzon, Schwabe Farma, Servier, and Kyowa Kirin. BD is affiliated with the Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Salpêtrière Hospital, AP-HP, Sorbonne-Université, Paris, France. JI is affiliated as an Assistant Professor-Adjunct (Group1) with the Department of Family Practice at Queen’s University. She was also a panelist for Hoffman-La Roche Limited, Ottawa, on an Alzheimer’s Disease Panel. She is involved with the Canadian Consortium on Neurodegeneration in Aging (CCNA), Extension Study as a Co- Investigator and Research Coordinator. AI received lecture fees from Eisai, Janssen, Otsuka, Eli Lilly, MSD, Chugai-Roche, Daiichi-Sankyo, Alnylam, Takeda, UCB, Ono, Integra Japan, IQVIA, Fuji Rebio, Biogen, and advisory fees from Janssen during the past three years. AP reports personal fees from Acadia Pharmaceuticals, Functional Neuromodulation, Neurim Pharmaceuticals, Grifols, Eisai, BioXcel, Tetra Discovery Partners, and Merck; grants from AstraZeneca, Avanir, Biogen, Biohaven, Eisai, Eli Lilly, Janssen, Genentech/Roche, Novartis, Merck, as well as funding from NIA, NIMH, DOD. KLP receives grant funding from the National Institute on Aging, the National Institute of Neurological Disorders and Stroke, the Global Brain Health Institute, and Quest Diagnostics. BV has consultancy and research grants from Roche, Biogen, EISAI, Nestle, Lilly, Cerecin, and Merck. HH is an employee of Eisai Inc. and serves as Senior Associate Editor for the Journal Alzheimer’s & Dementia; during the past three years he had received lecture fees from Servier, Biogen and Roche, research grants from Pfizer, Avid, and MSD Avenir (paid to the institution), travel funding from Eisai, Functional Neuromodulation, Axovant, Eli Lilly and company, Takeda and Zinfandel, GE-Healthcare and Oryzon Genomics, consultancy fees from Qynapse, Jung Diagnostics, Cytox Ltd., Axovant, Anavex, Takeda and Zinfandel, GE Healthcare, Oryzon Genomics, and Functional Neuromodulation, and participated in scientific advisory boards of Functional Neuromodulation, Axovant, Eisai, Eli Lilly and company, Cytox Ltd., GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics. He is co-inventor in the following patents as a scientific expert and has received no royalties: • In Vitro Multiparameter Determination Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders Patent Number: 8916388; • In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases Patent Number: 8298784; • Neurodegenerative Markers for Psychiatric Conditions Publication Number: 20120196300; • In Vitro Multiparameter Determination Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders Publication Number: 20100062463; • In Vitro Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders Publication Number: 20100035286; • In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases Publication Number: 20090263822; • In Vitro Method for The Diagnosis of Neurodegenerative Diseases Patent Number: 7547553; • CSF Diagnostic in Vitro Method for Diagnosis of Dementias and Neuroinflammatory Diseases Publication Number: 20080206797; • In Vitro Method for The Diagnosis of Neurodegenerative Diseases Publication Number: 20080199966; • Neurodegenerative Markers for Psychiatric Conditions Publication Number: 20080131921. AV is an employee of Eisai Inc. and received lecture honoraria from Roche, MagQu LLC, and Servier.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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RATIONALE FOR EARLY DIAGNOSIS OF MILD COGNITIVE IMPAIRMENT (MCI) SUPPORTED BY EMERGING DIGITAL TECHNOLOGIES

 

M.N. Sabbagh1, M. Boada2, S. Borson3, M. Chilukuri4, P.M. Doraiswamy5, B. Dubois6, J. Ingram7, A. Iwata8, A.P. Porsteinsson9, K.L. Possin10, G.D. Rabinovici10, B. Vellas11, S. Chao12, A. Vergallo13, H. Hampel13

 

1. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 2. Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain; and Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Spain; 3. University of Washington School of Medicine, Seattle, Washington, and Dementia Care Research and Consulting, Santa Ana, CA, USA; 4. Durham Family Medicine, Durham, North Carolina, USA; 5. Departments of Psychiatry and Medicine, Duke University School of Medicine, USA; 6. Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Center of excellence of neurodegenerative disease (CoEN) and National Reference Center for Rare or Early Dementias Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l’hôpital, Paris, France; 7. Seniors Lead Physician, Central East Region, Ontario and Founder and Medical Director of Kawartha Centre, Peterborough, Ontario, Canada; 8. Department of Neurology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan; 9. Department of Psychiatry, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA; 10. Memory & Aging Center, Departments of Neurology, Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA; 11. Gerontopole, Toulouse University Hospital, UMR 1027, University of Toulouse, France; 12. ClearView Healthcare Partners – Newton, MA, USA; 13. Global Medical Affairs, Neurology Business Group, Eisai Inc., Woodcliff Lake, New Jersey, USA

Corresponding Author: Marwan N. Sabbagh, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA, sabbagm@ccf.org; Tel.: (702) 483-6029; Fax: (702) 722-6584

J Prev Alz Dis 2020;3(7):158-164
Published online March 6, 2020, http://dx.doi.org/10.14283/jpad.2020.19

 


Abstract

Disease-modifying pharmacotherapies for Alzheimer’s Disease (AD) are currently in late-stage clinical development; once approved, new healthcare infrastructures and services, including primary healthcare, will be necessary to accommodate a huge demand for early and large-scale detection of AD. The increasing global accessibility of digital consumer electronics has opened up new prospects for early diagnosis and management of mild cognitive impairment (MCI) with particular regard to AD. This new wave of innovation has spurred research in both academia and industry, aimed at developing and validating a new “digital generation” of tools for the assessment of the cognitive performance. In light of this paradigm shift, an international working group (the Global Advisory Group on Future MCI Care Pathways) convened to elaborate on how digital tools may be optimally integrated in screening-diagnostic pathways of AD The working group developed consensus perspectives on new algorithms for large-scale screening, detection, and diagnosis of individuals with MCI within primary medical care delivery. In addition, the expert panel addressed operational aspects concerning the implementation of unsupervised at-home testing of cognitive performance. The ultimate intent of the working group’s consensus perspectives is to provide guidance to developers of cognitive tests and tools to facilitate the transition toward globally accessible cognitive screening aimed at the early detection, diagnosis, and management of MCI due to AD.

Key words: Alzheimer’s disease, mild cognitive impairment, cognitive screening, disease modifying, digital, healthcare.


 

Introduction

The late-stage clinical development and potential near-approval of drugs with modifying effectd on Alzheimer’s disease (AD) calls for a substantial paradigm shift in the diagnosis and management of the disease, including the Mild Cognitive Impairment (MCI) stage (also called MCI due to AD or prodromal AD). The availability of disease-modifying therapies will result in unprecedented demand for cognitive performance assessments (i.e., large-scale cognitive screening). Moreover, the progressive rise of lifespan in populous developed countries such as the U.S., EU5, China, and Japan (1, 2) will bring about an exponential increase in the incidence of age-related diseases, including AD,. It is expected that widespread demand for cognitive evaluation will likely overwhelm existing healthcare infrastructures and services at both primary care and specialist levels.
Early detection, of MCI or preclinical AD stages, coupled with timely initiation of disease-modifying treatments,has become the clear path to successfully facing the social and medical threat of AD. Gaps on both sides,particularly a paucity of detection tools suitable for practical use in patient populations and the absence of approved disease-modifying therapies, impede progress toward finding effective therapeutics. We focus here on MCI, a syndrome defined by clinical, cognitive, and functional criteria and characterized by objective cognitive decline in one or more cognitive domains with no significant impairment in daily-life activities (3). MCI may result from a variety of underlying causes, including Alzheimer’s pathophysiology (4, 5). As a result, watchful monitoring of adults with MCI is a crucial step within the work-up for early identification of AD and will be a critical stage of treatment monitoring as novel disease-modifying AD therapies enter the marketplace. In this publication series, we use the term “MCI” to refer to non-dementia cognitive impairment due to any cause and the terms “MCI due to AD” or “MCI-AD” to refer specifically to MCI associated with positive biomarkers of AD pathophysiology, as established in current research diagnostic criteria (3-5).
If disease modifying-therapies enter clinical practice, several issues must be overcome to achieve large-scale cognitive and biological screening of AD.
First, MCI is heterogeneous in its clinical spectrum and has historically been challenging to define, identify, and monitor in clinical practice. In addition, the currently qualified biomarkers for AD are assessed through invasive, expensive, and time- and resource-consuming investigations such as cerebrospinal fluid (CSF) analysis and positron-emission tomography (PET) imaging. The progressive establishment of blood-based biomarkers (6) and the validation of multi-dimensional diagnostic techniques have the potential to make the diagnosis and management of MCI-AD feasible in primary care, as is necessary for early screening and detection.
Primary care physicians (PCPs) currently lack technical support, infrastructure, training, and experience to efficiently detect and manage AD along its clinical continuum, from preclinical phases to MCI and dementia. A 2019 survey conducted among U.S. PCPs reported that short cognitive evaluations are assessed in only half of individuals 65 years of age and older and that cognitive evaluations are frequently omitted due to: i) subtle cognitive impairment, ii) lack of time, and iii) patient resistance to testing (7). In a parallel patient survey, only 16% of Americans aged 65 years and older reported receiving regular cognitive assessments during routine health visits (7). Less than half of older adults report ever having discussed their cognitive performance with a physician, and less than a third have ever been assessed for cognitive impairment. Additionally, a majority of surveyed PCPs reported uncertainty around which cognitive assessment to deploy, how to perform a brief cognitive assessment, and, importantly, what to do after assessing cognition [7]. Referral to specialists (e.g., neurologists, psychiatrists, geriatricians, and neuropsychologists) is the default for evaluating cognition and diagnosing MCI-AD. However, with the expected AD (and other neurodegenerative disease) epidemic burden, access to specialists has been and will become even more challenging. PCPs will need to be fully involved in the process and equipped with the proper means to ensure timely and efficient detection and care.
In recognition of current challenges around the detection of MCI, a working group composed of international experts on MCI and AD convened in April 2019 to elaborate on existing frictions and barriers, in both clinical and non-clinical settings, that prevent widespread cognitive screening for early detection of MCI and particularly MCI-AD. We summarize potential solutions to overcoming those barriers in a series of three manuscripts, of which this is the first. The first manuscript focuses on advantages and disadvantages of early detection of MCI, the current MCI detection landscape, and data-driven hypothetical models on how MCI-AD diagnosis and management may change in the future given recent technological advances and potential approval of disease-modifying therapies. In the second manuscript of this series, we offer recommendations around ways to meaningfully and rapidly implement MCI detection in primary care settings. The third manuscript of this series will focus on the potential for direct-to-consumer cognitive testing intended for use by adults or informants in an at-home setting without direct supervision by a healthcare provider. Given the critical importance of these topics, the recommendations outlined across this suite of manuscripts reflect careful consideration by this group of cognitive neuroscientists and physicians and extensive iteration and discussion from April 2019 through the present.
From the outset of this endeavor, the primary objective of this working group has been to identify actionable methods to improve detection of MCI, thereby providing developers and researchers of novel tools and tests with guidance and tangible recommendations to maximize their potential usability. To that end, the group agreed that the most feasible strategy to optimize early detection of MCI in the near-term will be to boost primary care capacity for detection by providing infrastructure and equipment that improve the accuracy and efficiency of tools for cognitive assessment without substantially increasing workload for primary care clinicians. Recommendations and strategies to facilitate this paradigm shift are described in detail in the second manuscript of this series.
Our group recognized that detection tools intended for use outside of clinical settings present unique challenges, yet they deserve critical future development, because they offer the potential to dramatically improve the scale of MCI detection. With dramatic increases in the use of consumer electronics by aging adults, digital approaches that leverage the capacities of mobile devices and Internet connectivity are a promising avenue for detection of MCI in non-clinical settings, if these consumer-directed resources can be suitably validated and linked to healthcare systems. The third manuscript in this series summarizes the challenges and opportunities relating to the detection of MCI in non-clinical settings, i.e., at an individual’s home or in everyday settings such as pharmacies or community screening events, as has already been accomplished with brief paper and pencil tests (8, 9).
In this initial manuscript, we summarize existing guidelines and consensus statements to provide context around this set of recommendations. We also weigh the advantages and disadvantages of early detection of MCI, and we ultimately support the idea that detection of MCI is an important component of whole person care. Finally, we summarize our expectations of how the MCI detection landscape may continue to shift in the next 3 – 5 years to highlight the need for proactive changes and ongoing research and development.

 

Advantages and drawbacks of MCI detection

Consistent with previous guidelines and consensus statements (10), this panel recognized that early detection of MCI is associated with both advantages and disadvantages, and we acknowledge that the decision to assess an individual’s cognitive function should be made on a case-by-case basis with each individual’s best interests in mind. To support large-scale cognitive screening, the field would requirea clinical consensus on the appropriate course of action for PCPs when an individual is identified as cognitively impaired. Additionally, large-scale cognitive screening algorithms were acknowledged as unlikely to become standard practice in the near-term, given the infrastructural challenges inherent to existing healthcare systems around the world. In this context, we have summarized the most critical benefits and drawbacks of early detection of MCI identified by this working group.

Benefits of Early Detection of MCI

If disease-modifying therapies for delaying or even halting AD at its MCI stage (also called prodromal stage) become available, the necessity of detecting MCI accurately, extensively, and in a timely manner is obvious. Moreover, the inability to robustly identify patients at prodromal stages remains a substantial limitation for developing AD therapies and may, at least in part, contribute to the series of drug failures. Accordingly, early MCI detection may optimize identification of patients eligible for future clinical trials and maximize the likelihood of successfully developing novel AD therapies.
However, even in the absence of a disease-modifying therapy, and regardless of the underlying etiology, multiple advantages remain associated with early detection of MCI. Individuals and healthcare systems can only benefit from an efficient algorithm for investigating MCI at a large-scale.
An early identification of MCI also increases the possibility of a timely diagnosis of the medical condition that may underlie a cognitive impairment (i.e., secondary cause of MCI), which are all potentially treatable or even reversible (e.g., metabolic and endocrine diseases, mood and sleep disorders, iatrogenicity) (11). In addition, growing evidence demonstrates that specific lifestyle habits and activities may slow down or even prevent cognitive decline. Early detection of MCI may provide subjects with greater motivation to implement lifestyle modifications and, at a minimum, will provide physicians with an additional opportunity to counsel individuals on lifestyle changes. In the current screening and diagnostic paradigm, the identification of MCI is likely to escape the therapeutic window where individuals may benefit from these non-pharmacological interventions to slow cognitive decline. Early identification of cognitive impairment also can help individuals and their families better prepare for future care needs and address financial planning considerations, for example. In the absence of a disease-modifying therapy in the immediate future, early detection also can identify potential candidates for research and clinical trials for therapies in development that target individuals in the earlier stages of their cognitive decline and disease.
Emerging evidence also suggests that early detection of MCI may provide an economic benefit to healthcare systems. Although this has been investigated less extensively in individuals with MCI than in individuals with dementia, published literature suggests that the financial burden associated with caring for MCI patients is significant and that routine cognitive assessment may be cost effective (12-16). Tong et al. investigated screening for MCI and dementia by PCPs in England and reported that PCP use of the Mini-Mental State Examination (MMSE), 6-Item Cognitive Impairment Test and the General Practitioner Assessment of Cognition (GPCOG) led to more quality-adjusted life-years (QALYs) than informal PCP assessment alone (i.e., observing individual cognitive ability) (12). While additional research is needed to further understand the economic benefits of early detection of MCI, existing literature suggests that healthcare systems may derive significant benefits from implementing early detection practices.

Drawbacks of Early Detection of MCI

While early detection of MCI offers many positive benefits, even in the absence of a disease-modifying therapy, we acknowledge that early detection efforts may not be universally beneficial. For example, false negatives may provide subjects with false reassurance that their cognitive function has not declined, thereby preventing them from seeking further care. Similarly, false positives may create undue stress for impacted individuals and their families. These potential drawbacks underscore the urgent need for an accurate assessment, with sensitivity and specificity sufficient to minimize the detrimental impact of an incorrect result. In the context of an accurate identification, MCI individuals and their families likely will experience distress upon learning of cognitive impairment. Anecdotally, this panel noted that MCI individuals might react to this distress by distancing themselves from their physician and/or the healthcare system in response to the societal stigma that exists for individuals with a known cognitive impairment.
Importantly, implementing widespread evaluation of MCI in the primary care setting may require significant time and resources, representing a burden that may be untenable for all PCPs. Similarly, as routine wellness exams tend to last fewer than twenty minutes, devoting time to cognitive assessment may limit time spent addressing other health concerns. Limited time in PCP visits is likely to be a particularly pressing issue for the care of geriatric individuals, who are more likely to have cognitive performance issues but also often have more morbidities and preventative health needs that must be addressed during PCP visits. Additionally, expanding cognitive testing may create an administrative burden for medical personnel, although a digital tool can help minimize this impact. Finally, widespread cognitive assessment is likely to increase the burden on specialists, as greater identification of primary care patients with MCI likely will translate to more referrals to specialists for confirmatory diagnosis. However, if the quality of cognitive assessment in a primary care setting can be improved, this may help identify patients in whom a referral is appropriate, limiting referrals for patients with only a subjective memory complaint (i.e., a self-reported loss of memory performance without objective cognitive decline). Subjective memory complaint (SMC) represents a condition at-risk for AD (5, 17-21). Moreover, SMC may underlie the beginning of the AD clinical continuum (5, 17-21), Therefore, it will be even more imperative to provide physicians with the proper tools for timely detection of AD to allow initiation of appropriate care pathways.

 

Summary of previous guidelines and consensus statements

Numerous consensus statements and clinical guidelines have been published in the past to provide perspective and expert guidance on defining and detecting MCI through cognitive testing (3, 22). Previous guidelines have summarized the circumstances when cognitive testing becomes appropriate, examined the current testing landscape for MCI detection, and identified the challenges and uncertainties around the detection of MCI. However, an expert consensus with an updated view on the field of cognitive neuroscience in light of novel testing modalities that are now practical due to consumer digital technology, such as smartphone applications, online games and questionnaires, etc., does not currently exist in the literature. Increasing adoption of consumer digital technology and digital fluency, even among older adults, will allow for novel testing modalities that will improve whole patient care and pave the way for potential novel therapies for AD.
A controversy remains in the field of cognitive neuroscience on the appropriate frequency of cognitive testing, as advocates see the benefit of widespread use, while others are proponents of limited and targeted use of cognitive testing. In the U.S., the 2009 Affordable Care Act (ACA) federal legislature mandated an annual cognitive assessment to be conducted during Medicare Annual Wellness visits for seniors (7, 23). However, the ACA did not mandate or provide any guidance on what type of testing should be used to meet the ACA requirements, leaving this decision to the discretion of the clinical community. In the wake of the ACA mandate, the controversy on the appropriateness of cognitive testing has continued. In 2014, the United States Preventive Services Task Force concluded that the available evidence was insufficient to assess the benefits (e.g., potential lifestyle interventions and better patient management) and drawbacks of screening for cognitive impairment (e.g., false positives and negatives, patient suspicion and alienation from physicians, etc.) and therefore, could not recommend universal screening (24). However, due to the ACA mandate, there was still a clear need for a consensus from the field on best practices for the development, validation, and use of cognitive testing. This view has been validated by the National Institute on Aging and the Alzheimer’s Association AD Framework and the 2015 working group of the International Association of Gerontology and Geriatrics (IAGG) which concluded that early identification of MCI is essential to improving cognitive performance in older adults (25). The IAGG working group found that benefits can be derived from better management of the treatable components of cognitive impairment and lifestyle interventions that may slow cognitive decline (25). Finally, a 2017 Edinburgh consensus group focused on the implications of disease-modifying treatments for AD emphasized the crucial importance of identifying early cognitive impairment, given that therapies will likely be most efficacious early in cognitive decline (26).
In addition to assessing the scenarios in which testing is appropriate, select organizations have published recommendations and advisories on preferred methods for MCI detection. As noted above, the ACA declined to recommend a specific methodology for cognitive assessment in the U.S. given that noconsensus has been reached on a universally accepted screening methodology, and formal guidance has yet to be issued on this topic by federal health authorities (e.g., Centers for Medicare & Medicaid Services, (CMS)).
In response to the “call to arms” that the ACA mandate represents, an Alzheimer’s Association working group outlined specific recommendations for the detection of cognitive impairment in the primary care setting. The expert consensus highlighted that in other countries, such as Canada, the national consensus guidelines have detailed primary care as preferred site for evaluation (https://alzheimer.ca/sites/default/files/files/national/for-hcp/for_hcp_recos_cccdtd4_en.pdf). Moreover, the group recommended that both structured (e.g., use of a formal cognitive test) and unstructured (e.g., informal physician questions about memory) cognitive assessments should be utilized for testing and tracking cognitive function by PCPs (23). A 2018 clinical practices guideline from the American Academy of Neurology noted that relying on subjective cognitive complaints alone is an insufficient assessment criterion for MCI because of the significant potential for over or under-identification (27). The guideline instead recommended that physicians use a validated tool for cognitive assessment and solicit patient history along with informant input (27). The 2015 IAGG working group also recommended utilizing both patient and informant assessments of cognitive function along with physician testing to evaluate subjects for early cognitive impairment (25). The group urged the use of validated tests that take only three to seven minutes to conduct, limiting the time burden for patients and providers. Similarly, the Gerontological Society of America workgroup recommended screening tests that take five or fewer minutes to administer, are free of charge, assess multiple cognitive domains, and are validated in a community-based sample (25). Unfortunately, despite this past guidance, specific and up-to-date recommendations grounded in currently available tools do not yet exist, potentially resulting in uncertainty among PCPs about how best to detect MCI.
In addition to considering when testing is appropriate and what battery of tests is most appropriate, previous groups have also elaborated on how an optimal care pathway may be achieved for early detection of cognitive impairment. This is a critical unmet need in the field, as the uncertainty toward the methodology of assessing for cognitive impairment is compounded by the lack of consensus on what physicians should do in the event of a positive result. Uncertainty about an assessment returns a positive result may de-motivate PCPs from broaching the topic of cognitive impairment or testing with their patients in the first place. In contrast to this PCP-directed recommendation, the 2015 Edinburgh Consensus working group noted that the UK healthcare system would be unable to accommodate the strain on PCPs and specialists if a disease-modifying therapy becomes available, noting that the current role of the PCP in controlling patient access to specialists is unclear (26). This group emphasized that restructuring cognitive healthcare to allow patients to receive care across disciplines may optimize efficiency and improve patient care (26).
Similarly, despite published insight on select aspects of an “ideal” cognitive assessment (25), it remains unclear which specific tools, particularly digital assessments, are best suited for widespread use and how new assessments could be improved to allow higher detection rates of MCI. This panel sought to provide further clarity on technologies that can improve early detection of MCI, including noting how potential modification or validation of existing tools could contribute to enhanced patient care.

 

Anticipated changes to the MCI landscape

The late-stage development of some compounds with a putative disease-modifying effect support optimism that a disease-modifying therapy may become available within the next 3 – 5 years (28). Among these Phase 3 agents, monoclonal antibodies targeting amyloid-β have recently gaining momentum. Several clinical studies indicate that targeting the early phases of AD, including MCI or even preclinical populations, can increase the likelihood of clinical success (28). This recent focus on early intervention in cognitive decline underscores the value of identification of MCI before the onset of more severe cognitive decline to maximize the potential for intervention and minimize the personal, clinical, and economic costs of cognitive decline.
Approval of a novel therapy for MCI-AD is expected to dramatically increase both patient and physician involvement in cognitive screening. In the absence of proactive preparation, demand for cognitive assessment will likely present a significant strain on global healthcare systems around the world, with PCPs shouldering a significant portion of the burden. Specialists are also likely to be strained by a large volume of referrals, likely including a minority of patients with MCI-AD and a majority of individuals with MCI due to other etiologies, or even intact cognition (e.g., misdiagnosed or “worried well” individuals with an SMC). It is in the best interest of patients, physicians, and healthcare systems to implement large-scale cognitive screening and to develop and refine technological solutions that can optimize early and accurate detection of MCI and MCI-AD. Going hand in hand with improved detection will also be better patient-physician alignment on the appropriate care pathways once MCI-AD is observed. For example, prior studies evaluating the impact of cholinesterase inhibitors on cognitive symptoms in patients with mild – moderate AD suggest rivastigmine and galantamine may provide statistically significant symptomatic benefits in patients treated earlier in the disease trajectory, which were not achieved for patients in whom treatment was initiated later in the disease course (29, 31). Though clinical data to support early initiation of cholinesterase inhibitors in individuals with MCI at risk of developing AD is mixed (27, 29, 32), data from clinical trials investigating anti amyloid-β drugs have further supported the importance of early diagnosis and treatment in AD.

 

Conclusion

Current neuroscientific discoveries point toward a compelling need to optimize and harmonize clinical protocols for the timely and accurate detection and diagnosis of MCI-AD, also in light of late-stage clinical development of disease-modifying therapies. In subsequent manuscripts, we will outline potential methods to enhance MCI detection in individuals at risk and across clinical and non-clinical settings, focusing on cognitive, functional, and interview-based approaches. However, we acknowledge the potential for the detection of AD pathology, regardless to the clinical stage, to undergo a substantial change due to technological advances in the future, including blood-based biomarker assays to detect AD. In this case, it may become necessary to compare the utility of various screening modalities in parallel through subsequent clinical trials.
The present working group has identified actionable methods to improve cognitive and functional assessment tools, recognizing thatreassessing the optimal care pathway upon the availability of a blood-based biomarkers test with high clinical utility will be necessary for screening and diagnostic purposes. Consequently, blood-based biomarkers should be integrated into the design of studies evaluating the accuracy of cognitive and functional algorithms to detect MCI-AD. Additionally, both biomarkers and cognitive algorithms remain critical components of inclusion criteria and endpoints in clinical trials designed to evaluate MCI-AD therapies.
In this suite of publications, we hope to promote thoughtful but dramatic changes to the existing management plan for adults with MCI, inclusive of cognitive screening for the early detection of MCI. In subsequent articles, we will provide guidance for designing and validating cognitive assessment tools to enhance their real-world utility, which represents a “call to action” for our colleagues in the cognitive evaluation field.
In summary, this task force seeks to support MCI diagnosis and detection of underlying pathophysiology as a public health imperative, in parallel with other major organizations such as the World Health Organization (WHO) and the Alzheimer’s Association, in order to improve clinical outcomes for aging individuals – not just in preparation for a novel AD therapy but also in the current context of this field.

 

Acknowledgements and funding: Medical writing support, under the direction of the authors, was provided by ClearView Healthcare Partners, LLC, funded by Eisai Inc., in accordance with Good Publication Practice (GPP3) guidelines..

Disclosures: MB is affiliated with the Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain; and with the Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Spain. Private research funding sources include Grifols SA; Caixabank S.A.; Life Molecular Imaging; Araclon Biotech; Laboratorios Echevarne; Festival Castell Paralada; Bonpreu/Esclat; and Famila Carbó. Public grants include those from Instituto de Salud Carlos III. Ministerio de Salud. Gobierno de España; Dirección General de Farmacia. Ministerio de Salud. Gobierno de España; and European Commission:H2020 program, Innovative Medicine Initiative (IMI-2); and ERA-NET NEURON program, European Marie Sklodowska Curie. Advisory work includes that for Araclon Biotech, Biogen, Bioibérica, Eisai, Grifols, Lilly, Merck, Nutricia, Roche, Oryzon, Schwabe Farma, Servier, and Kyowa Kirin. PMD has received research grants (through Duke University) from Avid, Lilly, Neuronetrix, Avanir, Bauch, Alzheimer’s Drug Discovery Foundation, Cure Alzheimer’s Fund, Wrenn Trust, DOD, ONR, and NIH. PMD has received speaking or advisory fees from Anthrotronix, Neuroptix, Genomind, Clearview, Cognicity, Nutricia, Living Media, Verily, RBC, Brain Canada, and CEOs Against Alzheimers. PMD owns shares in Muses Labs, Anthrotronix, Evidation Health, Turtle Shell Technologies and Advera Health Analytics whose products are not discussed here. He has received travel support from World Economic Forum, CCABH and Canaan Ventures. PMD served/serves on the board of Baycrest, AHEL, TLLF and TGHF. PMD is a co-inventor (through Duke) on patents relating to dementia biomarkers and therapies. BD is affiliated with the Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Salpêtrière Hospital, AP-HP, Sorbonne-Université, Paris, France. JI is affiliated as an Assistant Professor-Adjunct (Group1) with the Department of Family Practice at Queen’s University. She was also a panelist for Hoffman-La Roche Limited, Ottawa, on an Alzheimer’s Disease Panel. She is involved with the Canadian Consortium on Neurodegeneration in Aging (CCNA), Extension Study as a Co- Investigator and Research Coordinator. AI received lecture fees from Eisai, Janssen, Otsuka, Eli Lilly, MSD, Chugai-Roche, Daiichi-Sankyo, Alnylam, Takeda, UCB, Ono, Integra Japan, IQVIA, Fuji Rebio, Biogen, and advisory fees from Janssen during the past three years. AP reports personal fees from Acadia Pharmaceuticals, Functional Neuromodulation, Neurim Pharmaceuticals, Grifols, Eisai, BioXcel, Tetra Discovery Partners, and Merck; grants from AstraZeneca, Avanir, Biogen, Biohaven, Eisai, Eli Lilly, Janssen, Genentech/Roche, Novartis, Merck, as well as funding from NIA, NIMH, DOD. KLP receives grant funding from the National Institute on Aging, the National Institute of Neurological Disorders and Stroke, the Global Brain Health Institute, and Quest Diagnostics. BV has consultancy and research grants from Roche, Biogen, EISAI, Nestle, Lilly, Cerecin, and Merck. HH is an employee of Eisai Inc. and serves as Senior Associate Editor for the Journal Alzheimer’s & Dementia; during the past three years he had received lecture fees from Servier, Biogen and Roche, research grants from Pfizer, Avid, and MSD Avenir (paid to the institution), travel funding from Eisai, Functional Neuromodulation, Axovant, Eli Lilly and company, Takeda and Zinfandel, GE-Healthcare and Oryzon Genomics, consultancy fees from Qynapse, Jung Diagnostics, Cytox Ltd., Axovant, Anavex, Takeda and Zinfandel, GE Healthcare, Oryzon Genomics, and Functional Neuromodulation, and participated in scientific advisory boards of Functional Neuromodulation, Axovant, Eisai, Eli Lilly and company, Cytox Ltd., GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics. He is co-inventor in the following patents as a scientific expert and has received no royalties: • In Vitro Multiparameter Determination Method for the Diagnosis and Early Diagnosis of Neurodegenerative Disorders Patent Number: 8916388; • In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases Patent Number: 8298784; • Neurodegenerative Markers for Psychiatric Conditions Publication Number: 20120196300; • In Vitro Multiparameter Determination Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders Publication Number: 20100062463; • In Vitro Method for The Diagnosis and Early Diagnosis of Neurodegenerative Disorders Publication Number: 20100035286; • In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases Publication Number: 20090263822; • In Vitro Method for The Diagnosis of Neurodegenerative Diseases Patent Number: 7547553; • CSF Diagnostic in Vitro Method for Diagnosis of Dementias and Neuroinflammatory Diseases Publication Number: 20080206797; • In Vitro Method for The Diagnosis of Neurodegenerative Diseases Publication Number: 20080199966; • Neurodegenerative Markers for Psychiatric Conditions Publication Number: 20080131921. AV is an employee of Eisai Inc. and received lecture honoraria from Roche, MagQu LLC, and Servier.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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6. Hampel H, O’Bryant SE, Molinuevo JL, et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol 2018;14:639–652
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8. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695–699
9. De Roeck EE, De Deyn PP, Dierckx E, Engelborghs S. Brief cognitive screening instruments for early detection of Alzheimer’s disease: a systematic review. Alzheimers Res Ther 2019;11:21
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12. Tong T, Thokala P, McMillan B, Ghosh R, Brazier J. Cost effectiveness of using cognitive screening tests for detecting dementia and mild cognitive impairment in primary care: Cost effectiveness of cognitive screening tests in primary care. Int J Geriatr Psychiatry 2017;32:1392–1400
13. Ton T, DeLeire T, May S, et al. Assessing the Financial Burden Associated with Mild Cognitive Impairment. 2015
14. Ton TGN, DeLeire T, May SG, et al. The financial burden and health care utilization patterns associated with amnestic mild cognitive impairment. Alzheimers Dement J Alzheimers Assoc 2017;13:217–224
15. Pennington M, Gomes M, Chrysanthaki T, et al. The cost of diagnosis and early support in patients with cognitive decline. Int J Geriatr Psychiatry 2018;33:5–13
16. Gomes M, Pennington M, Black N, Smith S. Cost-effectiveness analysis of English memory assessment services 2 years after first consultation for patients with dementia. Int J Geriatr Psychiatry 2019;34:439–446
17. Choe YM, Byun MS, Lee JH, Sohn BK, Lee DY, Kim JW. Subjective memory complaint as a useful tool for the early detection of Alzheimer’s disease. Neuropsychiatr Dis Treat 2018;14:2451–2460
18. Brailean A, Steptoe A, Batty GD, Zaninotto P, Llewellyn DJ. Are subjective memory complaints indicative of objective cognitive decline or depressive symptoms? Findings from the English Longitudinal Study of Ageing. J Psychiatr Res 2019;110:143–151
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EFFECTIVENESS OF THE OPEN SCREENING PROGRAMS IN RECRUITING SUBJECTS TO PRODROMAL AND MILD ALZHEIMER’S DISEASE CLINICAL TRIALS

 

D. Wójcik1,2, K. Szczechowiak1, M. Zboch1, M. Pikala3

 

1. Wroclaw Alzheimer’s Center – Wroclaw, Poland; 2. Division of Quality Services, Procedures and Medical Standards, Medical University in Lodz, Poland; 3. Department of Epidemiology and Biostatistics, the Chair of Social and Preventive Medicine of the Medical University of Lodz, Poland.

Corresponding Author: Daniel Wójcik, Wroclaw Alzheimer’s Center, Poland, danielwojcik82@gmail.com

J Prev Alz Dis 2020;4(7):251-255
Published online March 2, 2020, http://dx.doi.org/10.14283/jpad.2020.15

 


Abstract

BACKGROUND AND OBJECTIVES: Due to the lack of scientific data comparing the success and cost-effectiveness of trial recruiting strategies, the main goal of this paper is to present our results and experiences in recruiting participants to prodromal and mild AD clinical trials from an open-access screening program.
DESIGN: The screening procedure includes the interview, and combined tests administration conducted by experienced neuropsychologist: Mini-Mental State Examination (MMSE) and Auditory-Verbal Learning Test (AVLT). The clinical evaluation was based on test scores, patient and/or caregiver interview, and the health questionnaire.
SETTINGS AND PARTICIPANTS: The open-access screening program was conducted in Wroclaw Alzheimer’s Center for 18 months (2018-2019). We invited individuals age 50 or older with the caregivers. The total number of subjects was 730 (N=730).
MEASUREMENTS AND RESULTS: Due to our research, the detection rates in the screened population were 0,7% for severe dementia, 4,1% for moderate dementia, 18,6% for mild dementia, and 28,9% for mild cognitive impairment (MCI). From 347 individuals classified in our open-access screening programs as MCI or mild dementia patients, as many as 248 patients were screened in Alzheimer’s disease clinical trials, which is 71,47%. Moreover, 63 from 347 individuals selected from our program as MCI or mild dementia patients were randomized into the clinical trials, which is 18,16%. Furthermore, 63 from total 730 (8,6%) patients were randomized in clinical trials.
CONCLUSIONS: Open-access screening programs can improve detection of MCI and dementia in society, help to distinguish demented from non-demented elderly, and improve recruitment of prodromal AD patients who would probably not have come to the memory clinic otherwise.

Key words: Alzheimer’s disease, mild cognitive impairment, dementia, screening, randomized controlled trial.


 

Introduction

Due to worldwide elderly population growth and lifespan extension, the number of patients with dementia, most often caused by Alzheimer’s disease (AD), will probably increase exponentially. The 2016 World Alzheimer Report estimates that the number of people with dementia worldwide (46.8 million) will almost double every 20 years, and it is expected to rise to 131,5 million by 2050. Furthermore, the total estimated worldwide cost of dementia in 2016 was US$818 billion, and it was predicted to rise to a trillion-dollar disease by 2018 (1).
It is well known that pathophysiological changes in Alzheimer’s disease begin many years prior to clinical manifestations of the disease. Therefore, in 2011 the National Institute of Aging and Alzheimer’s Association (NIA-AA) created separate diagnostic guidelines for the clinical stages of AD including mild cognitive impairment (MCI) and dementia (2). The definition of MCI provided by NIA-AA characterized mild cognitive impairment (MCI) as a cognitive performance below the expected range for that individual based on all available information, including clinical judgment and/or on cognitive test performance (with or without adjustments for age, education, occupation, sex, etc.). In the diagnostic procedure, the evidence of decline in cognitive performance from baseline must also be present (reported by the individual, study partner, or observed by change on longitudinal cognitive testing/behavioral assessments or by a combination of these). It is noteworthy that cognitive presentations that are not primarily amnestic can also occur, and the subject performs daily life activities independently, but cognitive problems can impact more complex activities of daily life (2). On the other hand, dementia is defined as a substantial progressive cognitive impairment that affects several domains and/or neurobehavioral symptoms. The problems may be reported by an individual, observer (study partner) or observed by change on longitudinal cognitive testing. The primary feature differentiating dementia from MCI is clearly evident functional impact on daily life, the patient needs assistance with daily living activities. Dementia can be divided into mild, moderate, and severe (2).
Furthermore, the latest research framework (2018) from the NIA-AA points to the importance of early detection of the disease and the clinical trial enrollment of participants in prodromal (MCI) or preclinical (“asymptomatic”) stages of AD, when the treatment could be more effective (2). Interestingly, subjective experience of cognitive decline in the elderly, without objective impairment on cognitive assessment, called subjective cognitive decline (SCD), has been suggested to be a possible first symptom of preclinical AD (3). Compared to healthy individuals, the elderly with SCD have an increased risk of progression to MCI and dementia (4). Therefore, focusing on AD as a continuum, the crucial need is to recognize the disease as soon as possible, based on both clinical and biomarker findings.
Unfortunately, despite the science and industry efforts, there is still no cure for Alzheimer’s disease, and the available treatment strategies bring only symptomatic benefits. Therefore, Alzheimer’s disease remains the leading cause of death with no effective treatment available (5). During the last decades, considerable effort has been focused on research and clinical trials of new drugs and promising therapies in AD. The NIA-AA counted more than 150 open studies calling for more than 70 000 patients which could require screening of more than 700 000 potential participants (6). Moreover, following the data, a combination of restrictive inclusion and exclusion criteria determine a small proportion of AD patients who are eligible for trials, 27% of patients from the research, and 10-13% from the clinical settings (1, 6).
The key challenge in AD research is low participation in clinical trials (7). A review of 24. phase II and III AD clinical trials reveals that only a third recruited a sufficient number of participants within a year (1). Scientific data indicates that the majority of all AD patients are older than 75 years, which increases the risk of exclusion for age-related reasons such as comorbidities and use of prohibited drugs (1). Clement et al. (2019) distinguished several barriers and facilitators of AD trials recruitment related to three themes: systemic factors (AD diagnostic pathway, patient records, embedding research in patient care, and the national research database), healthcare professionals, and patients and their companions. Authors showed that current diagnostic pathways and data systems made screening process difficult. Moreover, they indicate that challenges such as gatekeeping and restricted access for potentially eligible patients are often caused by, preferred by clinicians, recruiting subjects only from their own clinics, and recommended the use of a wide range of the new approaches to identify and recruit patients (7). The open-access screening programs are one of the most effective methods to improve trial recruitment. Increasing the pool of potential participants by enhancing awareness and facilitates attitudes towards research via advertising, education, and community outreach campaigns are also one of the remedial strategies in AD research suggested by Boada et al. (2018) (6).
Due to the challenges faced by Alzheimer’s disease research to enroll the specified number of participants and the problems with slow recruitment to AD clinical trials, it is noteworthy that open-access screening programs could be an effective method to improve AD trial recruitment. It is especially valuable in recruiting prodromal AD patients who would probably not have come to the doctor or to the memory clinic otherwise. People with MCI belong to the group of the high risk of developing dementia or AD when compared with similarly aged individuals in the general population. The data mentioned by Diniz et al. showed that patients with MCI convert to dementia at rates of approximately 10% per year and that subjects with MCI had a 6.7 higher risk to progress to dementia (8). Therefore, creating screening programs for the elderly including early diagnosis of MCI and dementia is highly recommended. There is a pressing demand for testing new treatments and interventions which can slow or halt the progression of AD, and increase the pool of potential participants.
Our program was aimed at citizens of Wroclaw (the Lower Silesia province, Poland). Due to the data collected by the Central Statistical Office (Poland), the 60+ population was 21,9% of the number of Lower Silesia province citizens. In Wroclaw, in 2017 the number of people aged 60+ was about 152 thousand, including 15,2 thousand dementia patients, and approximately 23 – 45,7 thousand people with MCI which was a large group of individuals potentially interested in participating in the open-access screening program targeting cognitive decline (9).

 

Methods

In this report, we will focus on the open-access screening program conducted in Wroclaw Alzheimer’s Center for 18 months (2018-2019) as a valuable trial recruitment strategy. The program was implemented to increase awareness in the local society of early diagnosis of cognitive disorders and memory problems, and to improve the prevention of cognitive decline and dementia. The interest in screening tests of individuals with memory problems was gained due to the advertising campaign and frequent appearance in the local media (newspapers, television, and radio interviews), as well as on the internet (social media and websites targeting the elderly and their families). The main goal of our program was to create an efficient, cost-effective, and relatively quick method of screening for cognitive impairment, and recruiting for Alzheimer’s disease clinical trials. It is noteworthy that the applied screening process as a part of the evaluation for dementia and cognitive impairment was quickly administered and relatively cheap – approximately 30 USD per individual, which includes site personnel time and advertising. The screening procedures were conducted during the site working time, so it does not cause additional costs. The internet-based advertisement, e.g. social media, the website was for free. Due to the importance and meaning of the initiative of the open-access screening program for the elderly, the local media (newspapers and television) were willing to provide the information and invite our health care specialists for the interviews free of expense, which increased the number of people who received information about our program

Program eligibility criteria

The main reasons for creating the program were that the majority of patients with early dementia and MCI are undiagnosed in primary care practices and the memory complaints or other cognitive symptoms are often minimized by the patients and/or their families, as well as there are underestimated by health professionals. We assume that a brief combined screen can detect mild cognitive impairment (MCI) and dementia with reasonable accuracy. We invited to cognitive screening the citizens of Wroclaw, age 50 or older who have memory complaints with their caregivers (family members or friends). The patient’s motivation to participate in the program varies from the concerns about own cognitive health to the family worries due to the cognitive problems of the subject. The participation in the program was free for the patients which was an important factor influencing the decision to get tested.

Screening procedure

The clinical evaluation conducted by the experienced neuropsychologist was based on test scores, patient and informant interviews, and the health questionnaire – self-reported health information such as age, family history, medical history, and medication information. The caregiver interview was crucial for diagnosing the functional impairment, and cognitive decline. The screening program was created to diagnose cognitive functioning of the screened subjects. Therefore, the experienced geriatric neuropsychologist evaluated only cognitive functions and the diagnosis includes normal cognitive functioning, MCI or dementia. Obviously, it is impossible to find the cause of dementia or MCI only due to cognitive tests and interview. Moreover, the important aspect of the screening process was to differentiate between MCI, dementia and depression which was based on clinical evaluation, observation during the tests, and interview with the patient and informant, conducted by the experienced neuropsychologist. The screening procedure includes combined tests administration: Mini-Mental State Examination (MMSE) and Rey Auditory-Verbal Learning Test (AVLT) which were the first step in the screening process.
The MMSE is the most widely used, quick (10 minutes), and valuable instrument for grading cognitive impairment in the elderly. It measures several cognitive domains such as orientation to time and place, immediate recall, short-term memory, calculation, language, and constructive ability. The items of the MMSE include tests of orientation, registration, recall, calculation and attention, naming, repetition, comprehension, reading, writing and drawing (10). The scores of MMSE are reliable between tests and raters. Moreover, the MMSE correlates significantly with other mental tests and batteries (such as the cognitive subscale of the Alzheimer’s Disease Assessment Scale – ADAS-Cog), electroencephalography, computerized tomography, magnetic resonance imaging, single photon emission computed tomography scan, cerebrospinal fluid proteins and enzymes, and brain biopsy synapse numbers (11). The maximum MMSE score is 30 and the following cut-off levels classify the severity of cognitive impairment: no cognitive impairment 27-30, mild cognitive impairment (MCI) 24-26, mild dementia 19-23, moderate dementia 11-18; and severe dementia ≤10. Cognitive performance as measured by the MMSE varies within the population by age and education level, therefore we used Mungases et al. (12) scoring system to evaluate the cognitive performance of patients. The correction of the raw score was made by adding specific numerical value due to the age and educational level of the subject.
It is noteworthy, due to the data provided by Benson et al. (13), that the MMSE is a crucial and effective instrument in screening dementia, but it is relatively ineffective in separating the MCI patients from those with depression. The authors recommended the use of other than the MMSE or additional method to evaluate mental status more effectively. Furthermore, Mitchell (14) shows a very limited value of MMSE in making diagnosis of MCI against healthy controls and modest rule-out accuracy. He also pointed at the necessity of combining MMSE with other methods to diagnose MCI. On the other hand, Diniz et al. highlight that the qualitative analysis of the cognitive performance of MCI patients in the subitems of the MMSE may help distinguish the MCI subtypes in clinical practice. Accordingly, the subjects with MCI presented worse performance than controls on the verbal memory task and “pentagon drawing” task. Moreover, amnestic MCI patients performed worse only on the “three-word recall” task; non-amnestic MCI subjects performed worse on the “three-stage command” task, and multiple-domain MCI patients performed worse on the “drawing a pentagon” task (15).
Due to the increasing number of prodromal AD clinical trials and the global efforts to slow\stop the progression of the disease in the early stages, one of the main goals of our program was to improve the detection of MCI and recruitment of prodromal AD patients. According to the scientific data showing that the use of only one neuropsychological test often over-estimates abnormality, resulting in sub-optimal specificity, we utilized the combination of the MMSE and the AVLT supported by the interview with patient and informant as an effective method of detecting Alzheimer’s dementia and MCI due to AD. The data collected by Lachner and Engel [16] reveal that memory task that uses delayed retrieval with distraction may differentiate best the demented from depressed patients. Moreover, also Vuoksimaa et al. (16) pointed at there is a potential for improving the detection of MCI by requiring more than one episodic memory measure and AVLT seems to be quite practical and cost-effective in both clinical and research settings. Interestingly, the results of their study in prodromal AD patients show a significantly higher risk of conversion to AD of AVLT- individuals at the 3-year follow-up than AVLT+ individuals. Accordingly, conversion rates were 50.9% for the AVLT- group, but only 16.5% for the AVLT+ group (17). The AVLT is a useful tool in detection of MCI and prediction of its progression to dementia. It is also optimal in balancing sensitivity and specificity in clinical settings (18).
The Rey‘s AVLT is widely spread, brief and easy to use tool for evaluating verbal learning and memory, including proactive inhibition, retroactive inhibition, retention, encoding versus retrieval, and subjective organization (18). It requires the subject to learn a 15-item word list which an examiner reads aloud at the rate of one per second over five trials (List A, Trials 1-5), then to recall that list after a short period (Trial 6) during which another 15-item word list (List B) is presented once for recall; again recall List A after 20-30 minutes of additional testing (Delayed Recall). Eventually, the patient identifies as many of the 15 words as possible when presented with them in the context of a longer list of words (Recognition) (19, 20). It is approximately 10 to 15 minutes required for the test procedure (not including 30 minutes interval). The AVLT has been proven helpful and effective neuropsychological marker of AD dementia and MCI due to AD, and valuable tool for differentiating between the preclinical phase of Alzheimer’s disease, mild cognitive impairment and normal aging (21). Furthermore, it is noteworthy that patients with probable AD and probable subcortical ischemic vascular dementia (SIVD) can be also distinguished with a high degree of accuracy by recognition memory subtest of the Rey’s AVLT (21). Unfortunately, there is a lack of population-based norms for AVLT. In our program we use norms created by Ivnic et al. (20) as support for qualitative evaluation of the patient’s performance conducted by our experienced geriatric neuropsychologist, focusing especially on the recognition subtest of the AVLT as a crucial factor in cognitive decline due to the AD. We are aware of the limitations of this method but we are also certain that it could be a very useful tool for the psychologist specialized in neurodegenerative diseases in clinical evaluation of cognitive function.

 

Results

The total number of subjects examined in the open screening program conducted in 2018-2019 in Wroclaw Alzheimer’s Center was 730 (N=730). The data were collected from January 1, 2018, to May 20, 2019. The mean age of the participants was 71,7 years. Due to our research, the detection rates in the screened population were 0,7% for severe dementia, 4,1% for moderate dementia, 18,6% for mild dementia, and 28,9% for MCI. Less than half of the screened population – 47,7% – were evaluated as cognitively normal. We investigated the proportion of people with MCI and dementia who were eligible for clinical trials. The number of individuals with mild dementia and MCI screened in prodromal and mild AD clinical trials was 248 (34% of all individuals). Furthermore, 63 from 730 (8,6%) patients were randomized in clinical trials, which includes 4,9% MCI and 3,7% mild dementia cases screened in our program, with a 74,6% screen failure (SF) rate, which is typical result for AD clinical trials. Moreover, 19,9% of patients with mild dementia, and 17,1 % of individuals with MCI detected during the program were randomized in prodromal and mild AD clinical trials.
More interestingly, from 347 patients classified in our open-access screening programs as those with MCI or mild dementia, as many as 248 individuals were screened in AD clinical trials, which is 71,47%. Moreover, 63 from 347 individuals selected from our program as MCI or mild dementia patients were randomized into the clinical trials, which is 18,16% of the selected group which means that as much as over 18% individuals selected in open-access screening program as cognitively impaired (MCI or mild dementia) were randomized into prodromal and mild AD clinical trials, and as much as 71,47% were eligible for screening for clinical trials. This numbers shows how important and effective in improving recruitment of the participants to AD clinical trial the open screening programs could be.

Figure 1. The detection rates of cognitive impairment in the studied population (n=730)

Figure 2. The number and percentage of patients screened and randomized into prodromal and mild Alzheimer’s disease clinical trials selected from the open-access screening program

 

Discussion

Summarizing, open-access screening programs can improve detection of MCI and dementia in society, help to distinguish demented from non-demented elderly, and improve recruitment of prodromal AD patients who would probably not have come to the memory clinic otherwise. The weakness of the study is the lack of measure collecting the functional impairment of the patient. The information gathred were based on the caregiver interview which were our choice because of the cost and time reasons. Likewise, we tried to limit the number of tools used in our open screening program to create a cost-effective and relatively quick method of screening patients which prevented us from using more complex and valuable methods such as CDR (Clinical Dementia Rating), FAQ (Functional Activities Questionnaire), ADCS-ADL (Alzheimer’s Disease Cooperative Study Activities of Daily Living), ADAS-Cog (Alzheimer’s Disease Assessment Scale–Cognitive Subscale), and RBANS (Repeatable Battery for the Assessment of Neuropsychological Status). Moreover, it is important to point at the lack of the population-based norms for AVLT. Nonetheless, it still can be usefull tool for experienced geriatric neuropsychologist in qualitative evaluation. Our findings may help elucidate the role and importance of the screening process in detecting cognitive impairment in the elderly as an effective and relatively cheap recruitment method in AD clinical trials. There is an urgent need for research focusing on the cost-effectiveness, applicability, and barriers of different recruitment strategies. It is noteworthy that the improvement of clinical trial recruitment strategies, including open screening programs can result in more rapid drug development.

 

Funding: No sources of funding were used to assist in the preparation of this review.

Acknowledgments: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.

Conflicts of interest: There are no conflict of interest.

Ethical standards: This work was conducted in accordance with the principles set fourth by the Declaration of Helsinki. All volunteers gave written iformed consent before participating.

 

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DEVELOPMENT OF AN UPSA SHORT FORM FOR USE IN LONGITUDINAL STUDIES IN THE EARLY ALZHEIMER’S DISEASE SPECTRUM

T.E. Goldberg1, P.D. Harvey2, D.P. Devanand1, R.S.E. Keefe3, J.J. Gomar4

1. Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; 2. Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, FL, USA; 3. Department of Psychiatry, Duke University School of Medicine, Durham NC, USA; 4. Litwin-Zucker Alzheimer’s Disease Center, Feinstein Institute, Manhassett, NY, USA

Corresponding Author: Dr. Jesus J. Gomar, Litwin-Zucker Alzheimer’s Disease Center, Feinstein Institute, Manhassett, NY, USA, Email: jgomar@northwell.edu, Telephone: 516-562-0420, Fax: 516-562-0401

J Prev Alz Dis 2020;3(7):179-183
Published online October 28, 2019, http://dx.doi.org/10.14283/jpad.2019.51

 


Abstract

BACKGROUND: In individuals with only mild or very mild cognitive attenuations (i.e., so-called pre-clinical AD), performance-based measures of function may be superior to informant-based measures because of increased sensitivity, greater reliability, and fewer ceiling effects.
Objective: We sought to determine if a performance-based measure of everyday function would demonstrate adequate psychometric properties and validity in the context of serial assessment over a one-year period in patients with Mild Cognitive Impairment (MCI) and early stage Alzheimer’s disease (AD).
Design: Participants were assessed with the performance-based measure at baseline, six weeks, and one year.
Setting: A specialized center for the assessment and treatment of AD.
Participants: Three groups of subjects participated: a healthy subjects (HS) older cognitively intact group (N=43), an MCI group (N=20), and an AD group (N=26).
Measurements: A three subtest short form of the UCSD Performance-Based Skills Assessment (UPSA) (called the UPSA-3) was the measure of interest. It consisted of the Communication, Planning, and Finance subtests.
Results: Mixed model repeated measures were used to assess performance over time. Large group effects were present (HS>MCI>AD). Additionally, the AD and MCI groups demonstrated declines over one year, while the HS group remained stable (group x time interaction p=.11). The MCI/AD group demonstrated adequate test-retest reliability and did not demonstrate ceiling or floor effects.
Conclusion: Our data indicate that the UPSA-3 is suitable for clinical trials in that it has adequate ecological coverage and reasonable psychometric properties, and perhaps most importantly, demonstrates validity in serial assessments.

Key words: UPSA, everyday function, Mild Cognitive Impairment, Alzheimer’s disease.


 

Introduction

The UCSD Performance Skills Assessment (UPSA) (1) is a performance-based measure of higher level instrumental functional abilities and originally designed for use in psychiatric populations. It has been accepted for use by the Food and Drug Administration (FDA) as a co-primary endpoint in clinical trials involving cognitive enhancing medications for psychiatric disorders such as schizophrenia and major depression. It has been deployed in several versions in addition to its original 5-test assessment. For instance, Mausbach et al (2) and colleagues developed a short form with adequate psychometric characteristics, consisting of the Finance and Comprehension/Planning subtests (UPSA Brief, UPSA-B). Goldberg and colleagues were the first to deploy it in Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). They reasoned that in MCI, findings of preserved everyday function (a diagnostic criterion for amnestic MCI) were due to insensitivity of informant-based measures designed for use in AD. They found that the UPSA-B was significantly more sensitive than the Alzheimer’s Disease Cooperative Study – Activities of Daily Living (ADCS-ADL), an informant-based measure, to functional competence compromises in MCI, and that the UPSA-B demonstrated adequate psychometric properties (e.g., lack of ceiling and floor effects, near normal distribution) (3). Based on the results from this study and data from an independent older healthy control group, another short form was developed (UPSA-Geriatric or UPSA-G; (4) and used in a variety of follow up studies (5, 6). It consisted of Comprehension/Planning and Communication subtests. The original studies in older populations of individuals with MCI, and AD and healthy controls used single cross-sectional observations with no longitudinal data collected.
Two factors provided the impetus for the present study, which was aimed at further exploring the validity of performance-based assessments of functional capacity in the early AD spectrum. One was the need for a short form that considered important, ecologically relevant activities that could increase generalization across cohorts in early AD. As a result, we added the standard UPSA Finances subscale to the UPSA-G form of the UPSA because of considerable evidence that financial functioning is often compromised in the early stages of AD (7-9). The second reason for the study was the need for longitudinal data in a disorder that demonstrates progressive decline. Both of these were emphasized as critical in a C-Path panel convened to examine functional outcomes for use in AD clinical trials. In the present study, we thus assessed older healthy subjects (HSs), MCI, and early AD patients at three timepoints over one year on a 3-test short-form version of the UPSA (UPSA-3) that was comprised of Communication, Comprehension and Planning, and Finance subtests.

 

Methods

Subjects

Three different groups were examined in this study.
1. A sample of 43 healthy subjects (HS) were included and they were free of preexisting psychiatric illness, including schizophrenia, bipolar disorder, major depression, and substance abuse. Those requiring use of psychoactive agents, with a history of significant medical problems, or with a history of neurological disease (e.g., epilepsy, movement disorders) or significant head trauma were excluded.
2. A total of 20 individuals with MCI defined by Petersen criteria (10) were included.
3. Twenty-six «probable» AD patients diagnosed according to National Institutes of Neurological and Communicative Disorders and Stroke– Alzheimer’s Disease and Related Disorders Association criteria (11)participated.
Participants were identified for inclusion in these analyses from the Litwin-Zucker Alzheimer’s Disease Center by diagnostic consensus conferences (comprised of neurologists, neuropsychologists, and psychiatrists). MCI participants had Mini Mental State Examination (MMSE) scores>23 and Clinical Dementia Rating (CDR) global scores of 0.5. AD participants had MMSE scores<24 and Clinical Dementia Rating scores of 1 or 2. All of the AD participants had MMSE scores of 18 or above, which would be considered mild. Table 1 describes key demographic characteristics of the groups.
HS participants came from different recruitment sources including spouses and community centers. As subjective memory complaints (SMC) were among the exclusion criteria for otherwise cognitively healthy subjects in the overall program of research being conducted, none of the healthy subjects included in this study had SMC.  All subjects provided written informed consent after receiving a complete description of the study. Detailed inclusion and exclusion criteria are in the Supplement.
The aim of this study was to acquire longitudinal data on the UPSA-3 in early stage AD. We administered the UPSA-3 serially at baseline, six weeks, and twelve months in a. HSs b. MCI; and c. AD groups. We predicted that the UPSA-3 would demonstrate adequate psychometric properties, discriminate among the groups (MCI and AD), and demonstrate greater decline in the impaired groups over 12 months.

Table 1. Key Demographic Characteristics and Cognitive Screening Scores of the Study Samples

Table 1. Key Demographic Characteristics and Cognitive Screening Scores of the Study Samples

 

UPSA-3

We examined a three-subtest version of the UPSA that included Comprehension and Planning, Communication, and Finance subtests (UPSA-3). The Comprehension/Planning domain measures the participant’s ability to comprehend written material describing recreational outings and then plan the activities and list appropriate items necessary to bring to the outings. The Communication domain involves a series of role play situations that require the participant to make emergency calls, call directory assistance to request a telephone number, call the number, and then reschedule a medical appointment and remember to bring specific records.  In the Financial skills domain, the participant must count out given amounts from real currency, make change, and fill out a check to pay a utility bill. For each subtest, we first derived a percentage based on correct raw/maximum raw score and then computed the mean of the three.

Statistical Approaches

Repeated Measures Analysis

In our mixed model repeated measures (MMRM) we used LSMEANS to determine performances over time.  In the critical MMRM analyses we adjusted for age, sex, and education. Variance was unstructured. Subjects were treated as random effects, while diagnostic group (HS, MCI, and AD) and time (baseline, three months, twelve months) were fixed effects. We also conducted identical analyses for the three individual subtests of the UPSA- 3. We also sought to determine if the aggregated subtests performed “better” than individual subtests.

Reliability

Test-retest reliability was based on baseline to 6-week correlations. A ceiling effect was defined as having an UPSA-3 score as greater than 95% percentile; a floor effect was defined as a score less than 5% percentile. Alternatively 90% ceiling and 10% floor scores were also counted.

Practice Effects

Practice effects were examined by Cohen’s d (12) derived from baseline-week 6 and baseline-one year contrasts within each group.

Results

Diagnostic Groups

We found a significant diagnostic group effect such that HSs performed better than MCIs on UPSA-3, who in turn performed better than the AD group (F2, 84 = 59.78, p<.0001). The time effect was significant, due in part to an overall decline from baseline to 1 year (F2, 84 = 7.54, p=.001). Last, we found a near trend effect (F4, 84 =1.92, p=.11) for a group x time interaction, driven by stability from baseline to 1 year in the HS group and declines in the MCI and AD groups of .039 and .067 respectively. See Figure 1A for the longitudinal course of UPSA-3 performance and Figure 1B for the effect size differences among the groups at baseline and endpoint. When we examined the HS group and an aggregated MCI/AD group separately, only the latter group declined significantly over one year (t=2.10, p=.05).

Figure 1. A. Longitudinal Performance of the HS, MCI, and AD Groups on the UPSA over a One-Year Time Interval. Error bars are in SEM units. B. The Magnitude of Differences Between Each Group at Each of the Three Timepoints Expressed as Cohen’s d Effect Sizes

Figure 1. A. Longitudinal Performance of the HS, MCI, and AD Groups on the UPSA over a One-Year Time Interval. Error bars are in SEM units. B. The Magnitude of Differences Between Each Group at Each of the Three Timepoints Expressed as Cohen’s d Effect Sizes

We next examined the individual subtests over time in the three groups. For each, a main effect of group was present such that HS>MCI>AD (all ps<.01). A main effect of time was present for only two of the subtests (p<.05), namely Comprehension/Planning and Communication.  Additionally, and perhaps most importantly, no statistically significant or trend level group x time interactions were present (all ps>.34) for the subtests when examined individually.

Psychometrics

Ceiling/Floor

At baseline 5 HSs performed above the 95th%ile and 5 more above the 90th%ile. No HS performed below the 10th%ile. For the MCI group no participant was at ceiling or at floor. For the AD group 5 participants performed below the 5th %ile and 5 more below the 10th %ile. None approached ceiling.

Skewness and Kurtosis

Skewness and kurtosis results are listed in Table 2 for baseline. All groups demonstrated leftward skewed distributions; the two impaired groups had longer tails of poor performers with respect to the group mean. For kurtosis, the HS group had “heavier” tails than did the other two groups. Thus, the MCI and AD groups were bunched around their respective means. Nevertheless, a score within -1 to 1 is considered adequate for psychometric purposes (13).

Table 2. Skewness and Kurtosis Values for the HS, MCI, and AD Groups Skewness Kurtosis

Table 2. Skewness and Kurtosis Values for the HS, MCI, and AD Groups
Skewness Kurtosis

 

Table 3. TEST-RETEST UPSA3 Score

Table 3. TEST-RETEST UPSA3 Score

 

Test-Retest Reliability

For the total group test retest reliability coefficient was r= .90 as listed in Table 3. For the MCI/AD group reliabilities were adequate, between .88 and .72. Healthy control reliabilities were lower, between .27 and .45, perhaps because of restriction of range.

Practice Effects

Within-group practice effects are shown in the baseline vs. week 6 and baseline vs. year 1 contrasts  using Cohen’s d effect size metrics in Figure 2. Effect sizes were medium for the HS and MCI groups in the baseline to week 6 contrast. In the baseline-one year contrast, AD and MCI individuals did not demonstrate a practice effect, while the practice effect in the HS group was small.

Figure 2. Within-subject Practice Effects on the UPSA Expressed as Cohens d Effect Size for Each Group. The time contrasts were baseline-6 weeks and baseline-one year

Figure 2. Within-subject Practice Effects on the UPSA Expressed as Cohens d Effect Size for Each Group. The time contrasts were baseline-6 weeks and baseline-one year

 

Discussion

Performance based measures of everyday functional competence have certain potential advantages over informant-based measures. These include sensitivity to subtle compromise, reduced ceiling effects in healthier populations, and elimination of informant requirements or potential biases. The UPSA was developed as a performance-based measure by Patterson and was designed to make full use of potential advantages while minimizing test sampling restriction with careful selection of real world proxies (1).  Theoretically this version of the UPSA (the UPSA-3) assays many types of real world competencies, tasks and scenarios that might be frequently encountered by cohorts in the 60-90 year old age range. These include planning trips, remembering documents, and performing basic financial tasks such as check writing. Our analysis of individual subtests suggest that it is advantageous to combine the subtests into a single short form. In our main analysis the short form demonstrated a strong trend for a group x time interaction, while the individual subtests showed no such trend. The combined score reduced variance and increased sensitivity to time as well.
Our results also suggest that this three-subtest version of the UPSA has adequate validity and psychometric properties such that it can be used in clinical trials with three assessments over one year (or more). First, it was able to differentiate performances among the HSs, MCI and AD groups (HS>MCI>AD). Additionally, the latter groups demonstrated steeper declines over one year. Indeed, the HSs remained stable. Psychometrically, ceiling effects were not common in the HS group. Floor effects in the AD and MCI groups were highly infrequent. Practice effects were evident in the HS group at 6 weeks, but at one year they were minimal. Thus, UPSA-3 may be better suited for longer-term studies and information about 3-month or longer reassessments may be of interest.  In a previous study of the UPSA-B in HS samples (n=51), retested at 18 and 40 months after baseline, the effect size for change across the three assessments was d=.08 (14).
Later research with the UPSA-3 has been focused on subjective cognitive decline. In a recent study (15), a computerized functional capacity measure was quite sensitive to performance of people with subjective memory complaints as compared to older individuals without complaints. The effect sizes for functional capacity differences and cognitive performance were quite similar, d=0.65 for functional capacity and mean of d=.54 for neuropsychological tests.
In sum, this study examined a three subtest version of the UPSA in a longitudinal design in which healthy subjects and MCI and AD participants were serially assessed three times in a one year interval. The results demonstrate that the three subtest short form for the UPSA has adequate psychometric properties (test-retest reliability, lack of ceiling and floor effects, and rather small practice effects at one year). We have suggested elsewhere that performance -based measures like the UPSA-3 may have both validity and psychometric advantages over informant based measures (e.g., reporter bias, sensitivity, range), and may be less unwieldy in terms of time or props than the full version of the UPSA and such otherwise interesting and well thought-out performance measures as the Naturalistic Action Test [16]. Most important, from a validity standpoint the UPSA-3 was sensitive: 1) to group differences among MCI, AD, and HS participants, such that UPSA-3 scores showed that HS>MCI>AD at all timepoints; and 2.) a combined MCI/AD group demonstrated significant decline, while the HS group did not decline on the UPSA. We acknowledge however that our sample was not neurobiologically characterized.
Overall, the data from this study suggest that the UPSA-3 may be suitable for clinical trials in that it has adequate ecological coverage, has reasonable, albeit imperfect, psychometric properties, and perhaps most importantly, demonstrates validity.

 

Funding: Drs. Goldberg and Devanand are supported by grant 1R01AG052440 (Goldberg TE, PI) from the National Institutes of Health (NIH). Dr.Gomar is supported by a fellowship grant from the Alzheimer’s Association (AACFD-16-438886).  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.

Disclosures: Dr. Goldberg has received funding from Neurocog Trials for consulting on functional competence measures for Alzheimer’s disease and royalties for the use of the Brief Assessment of Cognition in Schizophrenia (BACS) in clinical trials. Dr. Devanand is a consultant for Acadia, Eisai, Avanir, Genentech, Neuronix, and Grifols. He receives Research Support from National Institute of Aging. Dr. Harvey has received consulting fees or travel reimbursements from Alkermes, Boehringer Ingelheim,  Intra-Cellular Therapies, Jazz Pharma, Minerva Pharma, Otsuka America, Sanofi Pharma, Sunovion Pharma, Takeda Pharma, and Teva during the past year. He receives royalties from the Brief Assessment of Cognition in Schizophrenia. He is chief scientific officer of i-Function, Inc. He has a research grant from Takeda and from the Stanley Medical Research Foundation.  Dr. Richard Keefe currently or in the past 3+ years has received honoraria, served as a consultant, speaker, or advisory board member for Abbott Labs, Abbvie, Abide Therapeutics, Acadia, Aeglea Bio Therapeutics, Akebia, Akili Interactive Labs, Alkermes, Allergan, Amgen, Aptinyx, Armagen, Astellas, Asubio, Avanir, AviNeuro/ChemRar, Axovant Sciences, Biogen Idec, BiolineRx, Biomarin, Biomimetix, Boehringer-Ingelheim, Braincells, Bristol-Myers Squibb, Cerecor, CHDI, Composite Type, Critical Path Institute, Eli Lilly Laboratories, FORUM, Gammon, Howard & Zeszotarski, Global Medical Education, GW Pharmaceuticals, Helicon, Idorsia, Intra-Cellular Therapies, Janssen, JCR, Karuna, Kempharm (DCRI), LSK Global, Lundbeck, Lysogen, MedScape, Memory Pharmaceuticals, Mentis Cura, Merck, Merrakris Therapeutics, Minerva, Mitsubishi, Montana State University, Moscow Research Institute of Psychiatry, Neuralstem, Neuronix, NeuroSearch, New York State Office of Mental Health, Novartis, Orion, Orygen, Otsuka, Paradigm Testing, Parexel, Percept Solutions, Pfizer, Pharm-Olam, PsychoGenics, Regenxbio, Renuron, Reviva, Roche, Sangamo, Sanofi, Science 37, Shire, Six Degrees Medical, SOBI, Solvay, Sunovion, Takeda, Targacept, Teague, Rotenstreich, Stanaland, Fox & Holt, Thrombosis Research Institute, University of Moscow, University of Southern California, University of Texas Southwest Medical Center, WebMD, Wilson Therapeutics, and Wyeth. Dr. Keefe has currently or in the past 3+ years received research funding from Allon, Astra Zeneca, Boehringer-Ingelheim, Department of Veteran’s Affairs, Feinstein Institute for Medical Research, GlaxoSmithKline, National Institute of Mental Health, Novartis, Psychogenics, Research Foundation for Mental Hygiene, Inc., and Singapore Medical Research Council. Dr. Keefe receives royalties from versions of the BAC testing battery, the MATRICS Battery (BACS Symbol Coding), and the Virtual Reality Functional Capacity Assessment Tool (VRFCAT). Dr. Keefe is a shareholder in VeraSci and Sengenix.

Ethical standards: This work was conducted in accordance with the principles set forth by the Declaration of Helsinki. The institutional ethics committees of Northwell Health Inc. and its Institutional Review Board (IRB) approved this study, and all participants gave written informed consent before participating.

 

SUPPLEMENTARY MATERIAL

 

References

1.     Patterson TL, Goldman S, McKibbin CL, et al. UCSD Performance-Based Skills Assessment: development of a new measure of everyday functioning for severely mentally ill adults. Schizophr Bull 2001;27:235-245.
2.     Mausbach BT, Depp CA, Bowie CR, et al. Sensitivity and specificity of the UCSD Performance-based Skills Assessment (UPSA-B) for identifying functional milestones in schizophrenia. Schizophr Res 2011;132:165-170.
3.     Goldberg TE, Koppel J, Keehlisen L, et al. Performance-based measures of everyday function in mild cognitive impairment. Am J Psychiatry 2010;167:845-853.
4.     Gomar JJ, Harvey PD, Bobes-Bascaran MT, et al.  Development and cross-validation of the UPSA short form for the performance-based functional assessment of participants with mild cognitive impairment and Alzheimer disease. Am J Geriatr Psychiatry 2011;19:915-922.
5.     Kirchberg BC, Cohen JR, Adelsky MB, et al. Semantic distance abnormalities in mild cognitive impairment: their nature and relationship to function. Am J Psychiatry 2012;169:1275-1283.
6.     Sousa A, Gomar JJ, Ragland JD, et al.The Relational and Item-Specific Encoding task in Mild Cognitive Impairment and Alzheimer Disease. Dement Geriatr Cogn Disord 2016;42:265-277.
7.     Tabert MH, Albert SM, Borukhova-Milov L, et al.  Functional deficits in patients with mild cognitive impairment: prediction of AD.  Neurology 2002;58:758-764.
8.     Marson, D.   Investigating functional impairment in preclinical Alzheimer’s disease: Potential measure characteristics and methodology.  J Prevention Alzheimer’s Disease 2015;2:4-6.
9.     Devanand DP, Liu X, Brown PJ.  Impact of functional deficits in instrumental activities of daily living in Mild Cognitive Impairment: A clinical algorithm to predict progression to dementia.  Alzheimer Dis Assoc Disord 2017;31:55-61.
10.     Petersen RC, Knopman DS, Boeve BF, et al. Mild Cognitive Impairment: Ten Years Later. Arch Neurol 2009;66:1447–1455.
11.     McKhann G, Knopman D, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011;7:263–269.
12.     Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd Edition). 1988. Lawrence Erlbaum Associates, New York, NY.
13.     George D, Mallery M. SPSS for Windows: Step-by-step. 2010. Pearson, Boston.
14.     Harvey PD, Reichenberg A, Bowie CR, et al. The course of neuropsychological performance and functional capacity in older patients with schizophrenia: influences of previous history of long-term institutional stay. Biol Psychiatry 2010;67:933-939.
15.     Atkins AS, Khan A, Ulsen D, et al. Assessment of Instrumental Activities of Daily Living in Older Adults with Subjective Cognitive Decline Using the Virtual Reality Functional Capacity Assessment Tool (VRFCAT). J Prev Alzheim Dis 2009; 5:216-234.
16.     Giovannetti T, Libon DJ, Hart T. Awareness of naturalistic action errors in dementia. J Intl Neuropsychol Soc 2002;8:633-644.

HEALTH LITERACY IN INDIVIDUALS AT RISK FOR ALZHEIMER’S DEMENTIA: A SYSTEMATIC REVIEW

 

A. Rostamzadeh1, J. Stapels1, A. Genske2, T. Haidl1, S. Jünger2, M. Seves1, C. Woopen2,3, F. Jessen1,4

 

1. Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, Cologne, Germany; 2. Cologne Center for Ethics, Rights, Economics, and Social Sciences of Health (CERES), University of Cologne, Köln, Germany; 3. Institute for the History of Medicine and Medical Ethics, Research Unit Ethics, University of Cologne,   Faculty of Medicine and University Hospital Cologne, Cologne, Germany; 4. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

Corresponding Author: Ayda Rostamzadeh, MD, Department of Psychiatry and Psychotherapy, University of Cologne, Kerpener Straße 62, 50937 Cologne, Germany, Phone: +49 (0)221 – 478 3870, Fax: +49 (0)221 – 478 6030, E-Mail: ayda.rostamzadeh@uk-koeln.de

J Prev Alz Dis 2019;
Published online September 16, 2019, http://dx.doi.org/10.14283/jpad.2019.34

 


Abstract

Background: Health literacy (HL) refers to the capacity to access, understand, appraise and apply information for decision-making and acting in health-related matters. In the field of Alzheimer’s disease (AD), expanding technologies of early disease detection, disease course prediction and eventually personalized prevention confront individuals at-risk with increasingly complex information, which demand substantial HL skills. Here we report current findings of HL research in at-risk groups.
Methods: Search strings, referring to HL, AD, amyloid and risk, were developed. A systematic review was conducted in PUBMED, Cochrane Library, PsycINFO, and Web of Science to summarize the state of evidence on HL in at-risk individuals for Alzheimer’s dementia. Eligible articles needed to employ a validated tool for HL, mention the concept or one dimension (access, understand, appraise and apply information for decision-making and acting).
Results: 26 quantitative and 9 qualitative studies addressing at least one dimension of HL were included. Overall, there is evidence for a wish to gain knowledge about the own brain status and risk of dementia. Psychological distress may occur and the subjective benefit-risk estimation may be modified after risk disclosure. Effects on lifestyle and planning may occur. Overall understanding and appraisal of information related to AD risk seem variable with several impacting factors. In mild cognitive impairment (MCI) basic HL skill seem to be affected by cognitive dysfunction.
Conclusions: Systematic assessment of HL in at-risk population for AD is sparse. Findings indicate the paramount importance of adequate communication with persons at risk, being sensitive to individual needs and preferences. Substantial research needs were identified.

Key words: Health literacy, individuals at risk, access, understanding and evaluating health information, decision making in health, mild cognitive impairment, Alzheimer’s disease.


 

Introduction

Current state of research in early detection of Alzheimer’s disease

Alzheimer’s disease (AD), as the underlying cause of Alzheimer’s dementia, has become a major public health challenge. The pathophysiological processes of AD start decades before its symptom onset and can be identified early in the course of the disease by biomarkers of amyloid and tau deposition as well as of neurodegeneration (1). Current research criteria allow biomarker-based diagnosis in the prodromal and even in the asymptomatic stage, long before functional disability of dementia becomes apparent (1–3). This has stimulated extensive research on early disease identification, dementia risk-prediction and prevention strategies in AD with the ultimate aim of slowing the disease course by impacting on modifiable risk factors in lifestyle-based interventions (4) and targeting molecular pathways of AD by pharmacological approaches (5–7). In all such cases, interventions start at a pre-dementia stage and focus on selected groups of at-risk individuals.

Health Literacy in at-risk groups for Alzheimer’s dementia

Health literacy (HL) is a new concept which can be described as the specific knowledge, competency and skills with respect to health-related matters (8). Sørensen et al. (9) integrated existing concepts and describe HL as a person’s ability to (1) access, (2) understand and (3) appraise medical or clinical issues and (4) apply health information. As HL reflects these multidimensional abilities and skills, it is believed to play an essential role in the design and eventually success of selective and targeted prevention programs. In the case of AD, selective prevention addresses healthy individuals with an increased risk for AD, such as healthy carriers of the risk-enhancing Apolipoprotein E4 (APOE4) genotype, while targeted prevention in AD aims at symptomatic individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) (2, 3). To date, knowledge about HL in individuals with very early AD and increased risk of dementia is very limited, which significantly limits the development of adequate communication approaches for research and clinical practice. Taking into account that the longitudinal deterioration of cognitive functioning in these patients will affect HL skills progressively, there are unique challenges concerning counselling and informed consent procedures, but also public health campaigns. In addition, there is little knowledge on the overall engagement of such individuals in the health-care system.
This is, to the best of our knowledge, the first review systematically investigating the current state of knowledge about HL in at-risk individuals for Alzheimer’s dementia. The goal of this review is to provide a summary of the evidence on how at-risk groups for Alzheimer’s dementia gain access to, understand, appraise and apply risk-related health information in decision-making and acting. We focus specifically on studies which employed biomarkers of amyloid pathology or genetic risk factors.

 

Methods

Conceptual framework

This review is based on the integrative model of HL developed by the HLS-EU Consortium in 2012, and on the Australian HL concept outlined in the Health Literacy National Statement (AHLNS) (10).
Using the definition by Sørensen et al. (9) we developed a search strategy that covers HL as an umbrella term as well as its four subdomains (access, understand, appraise, apply). For the purpose of this review, we decided to split the step “apply” into two sub categories: decision-making and action; this allows us to better understand gaps between the phases of intention (decision-making) and actual health behavior change (action).

Search strategy

Search strings consisted of three sections that were combined using the Boolean Operator “AND”. One section was referring to HL, one was the term “Alzheimer’s disease” and “amyloid” and one was “risk” and “risk factors” (see appendix 1 for the detailed search strings). The final search was carried out in PUBMED, Cochrane Library, PsycINFO and Web of Science. The period of literature search was between March 2017 and March 2018 (last read out 21.03.2018). Furthermore, the reference lists of all publications included in this review were hand searched for additional studies. Search strategy, screening and data selection were carried out in accordance with the PRISMA criteria (11). This review is registered in the international prospective register of systematic reviews (PROSPERO) with the  registration number: CRD42016052345 (http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42016052345). The date of registration was 12.12.2016.

Paper selection/Inclusion criteria

We included studies that investigated individuals at risk of developing Alzheimer’s dementia. These were individuals with SCD or MCI and those with a family history of AD or with biological risk, such as a genetic predisposition (i.e. healthy siblings of individuals with monogenic early-onset AD (EOAD) or carriers of the APOE4 allele) or individuals with evidence for amyloid-pathology measured by biomarkers. To be included in this review, articles had to be published in a peer-reviewed journal and written in English or German. No starting time point regarding the publication date was applied. This review considered all types of study designs including quantitative (such as observational, prospective and retrospective cohort studies, clinical trials), qualitative and mixed methods designs. To be included, studies had to use a validated tool to measure HL or examine any of its dimensions (access, understanding, appraisal, decision-making or action) as a primary or secondary outcome. Alternatively, studies had to mention the concept of HL plus at least one of the four dimensions in the title or abstract.

Screening and Assessment

Articles were screened for eligibility based on title and abstract by two independent reviewers and then checked independently according to the inclusion criteria. Any discrepancies in rating were resolved through discussion, and when necessary, a third reviewer judged the respective publication. If eligibility was unclear, but studies deemed potentially relevant by title or abstract, a full text review was performed as in all articles selected for full text review.

Data extraction

Due to the heterogeneity of study designs and outcomes,  we decided to conduct a narrative review. The complex nature of the research question made it necessary to include a variety of study types including different quantitative formats as well as qualitative interview studies. Therefore, we applied the methodology of a mixed-methods review. For data extraction and synthesis, we followed recommendations for appraising evidence from different study types within one review (12, 13). In order to ensure adequate data extraction, an evaluation matrix for data analyses was designed based on the inclusion criteria and our research question (see appendix 2). Along this form, study characteristics (author, title, journal, year of publication and country of origin) were extracted. The next steps included extraction of additional information on study design, characteristics and population and regarding the main outcome measures. We applied a segregated methodology (14), where we first analyzed and synthesized data from quantitative studies, before we examined qualitative findings in a separate step. In the final step, all findings were combined in a narrative synthesis. The narrative includes the target population characteristics, the HL process, the methodology, the study setting, and the type of outcome. Thematic categories were predefined based on the research question and were further refined during the data analysis process. Data analyses was performed by two independent reviewers and in case of any discrepancies, a third reviewer judged re-evaluated.

Quality assessment (Risk of bias)

The quality of included studies was evaluated by two independent reviewers using a standardized set of criteria proposed by Hawker et al. in 2002 for mixed-methods reviews (see appendix 3) (15). Discrepancies between raters were resolved by discussion and where necessary re-assessed by a third reviewer.

 

Results

Included Studies

The initial search yielded 7804 papers. 112 articles were identified through reference check. 43 articles were selected for full text review. After full text review, 35 studies fulfilled the inclusion criteria for analysis. The detailed selection process is depicted in figure 1.

igure 1. PRISMA Flow-Chart of paper selection

igure 1. PRISMA Flow-Chart of paper selection

 

The 35 papers meeting the inclusion criteria are summarized in appendix 4. A total of 27 studies were carried out in the USA (16, 17, 26–35, 18, 36–42, 19–25), two in Austria (43, 44) and one in each of the following countries: Cuba (45), the Netherlands (46), Germany (47), United Kingdom (48), Belgium (49) and Sweden (50). We neither found studies that considered HL as a basic concept for the particular investigation, nor studies using established assessment tools for HL, such as HLS-EU, HLQ, REALM or TOFLHA. However, we identified studies where at least one aspect of HL was actively applied. The majority of identified studies used customized tools for their individual study purposes to measure single domains of HL. Four studies used established measures for single domains of HL, such as the health numeracy scale (24, 43) and the tool for Capacity to Consent to Treatment, an instrument to evaluate medical-decision making capacity (30, 31). Regarding the target populations, we identified reports with symptomatic individuals such as MCI patients (9 studies) and patients with SCD (1 study). Furthermore, we identified studies on healthy adults with first-degree relatives suffering from Alzheimer’s dementia (21 studies) and healthy first-degree relatives of individuals with monogenetic early onset AD EOAD (4 studies). Three studies investigated disclosure on brain amyloid-status in individuals with MCI and SCD (40, 41, 49). We included one qualitative study addressing mild Alzheimer’s dementia patients and their relatives, because we considered the results of the interviews important regarding the patients’ decision-making capacities, self-determination and autonomy (47). A total of 20 studies were carried out as part of the REVEAL-program (Risk Evaluation and Education for AD), which is a series of multi-site randomized controlled clinical trials with the objective to evaluate the impact of genetic risk assessment and disclosure in healthy adults with first-degree relatives with Alzheimer’s dementia (35, 38).
According to the quality assessment a total of 11 publications were rated as “good,” 21 as “fair” and 3 papers as “poor” (see appendix 3). The articles rated as “poor” were not excluded from the study, because the findings were considered as relevant for our research question, but need to be evaluated with consideration due to the quality rating.

Findings for the different HL domains

As highlighted above, individuals at risk for Alzheimer’s dementia can be grouped into asymptomatic individuals, such as healthy adults with first-degree relatives with Alzheimer’s dementia, and into symptomatic individuals, including MCI and SCD patients. Therefore, we decided to present the results of the two target populations separately. The thematic fields of the findings within the different HL domains are summarized in table 1.

Table 1. Content analysis within the different HL domains, clustered in cognitively impaired and unimpaired individuals

Table 1. Content analysis within the different HL domains, clustered in cognitively impaired and unimpaired individuals

 

Access to health-related information on AD

Access in cognitively impaired individuals

Access to health-related information in cognitively impaired individuals has not been investigated in the included studies.

Access in cognitively unimpaired individuals

Two studies reported that first-degree relatives of AD patients obtained and accessed health-related information about AD mainly informally through spouses, friends, literature, patient organizations and mass media and less likely obtained disease-related information through health care providers (20, 50).

Understanding

Understanding in cognitively impaired individuals

Three studies investigated understanding, in terms of medical decision-making capacity in individuals with MCI and very mild dementia, using the validated Capacity to Consent to Treatment Instrument (CCTI) (30, 31). In addition, health numeracy, i.e. the ability to understand and use health-related numerical information, was studied in healthy and cognitively impaired individuals between the age of 50 – 95 years (43). The studies showed that even MCI is associated with reduced competencies such as “understanding”, “appreciation”, and “reasoning”. This is most likely related to impairment in basic cognitive domains including executive function and calculation abilities.
In contrast, two other studies that analyzed the impact of AD risk communication on MCI patients and their caregivers (26), as well as the effects of standardized counselling and risk disclosure of biomarker-based AD diagnosis (41), revealed that study participants generally comprehended the medical information.

Understanding in cognitively unimpaired individuals

Six studies investigated the ability to understand health and dementia-risk-related issues in healthy individuals with AD-affected relatives (16, 22, 27, 33, 34, 50). At one-year post-disclosure only 48% of the participants were able to recall their lifetime risk estimate correctly, whereas 76% were able to report correctly the number of copies of the (“risk-enhancing”) APOE4 allele they carried. Overall the studies indicated, that participants more likely remembered general information provided during the counselling and disclosure sessions, such as dichotomous information as being a risk gene carrier or not, than more complex information as specific genotypes or lifetime risk estimates. Furthermore, the studies indicated knowledge differences between ethnic groups, which were related to cultural factors and to inequalities in healthcare system access.

Appraisal

Appraisal in cognitively impaired individuals

Studies on appraisal of risk and AD-related health information in cognitively impaired individuals covered a wide range of aspects such as framing effects (44) and experiences as well as coping mechanisms when receiving diagnostic information (40, 46, 48, 49).
Framing effects refer to a bias that can be generated by the setting and the specific wording when communicating information. One study indicated that MCI and mild AD patients showed stronger proneness to framing effects as compared to controls. Such framing effects correlated inversely with performance in the domains of verbal and figural memory, attention span, executive functions and mental complex calculation (44).
One research group reported that MCI patients were highly interested in their brain amyloid status (49) and were aware of possible emotional effects after disclosure. Two studies in individuals with SCD and MCI reported effects of disclosure of amyloid status in real (40) and in hypothetical scenarios (48). Both concluded that psychological well-being was not negatively affected, when counselling and disclosure were performed at a specialized center and psychoeducational materials were offered.
Contrary, one study that used a qualitative approach to explore the experiences and coping mechanisms of MCI patients, indicated that the discussion of cognitive decline as a consequence of the amyloid status  provoked negative emotions and reactions as well as problematic interaction with the family and the social environment (46). It was not investigated how these negative effects influence appraisal of health-related information, decision-making or health behavior, especially the decision to receive testing for AD.

Appraisal in cognitively unimpaired individuals

The REVEAL-study group analyzed participants’ perceptions of benefits and advantages (pros) and risks and limitations (cons) of genetic susceptibility testing (21). Initially, participants appraised the benefits as being more important than the limitations and risks. After performing genetic testing and disclosure of the test results, however, participants tended to have a less positive attitude and concerns increased. The result that benefits are initially prevailing in the decision-making process was confirmed by a Swedish study (50), where relatives of AD-patients stated, that, if available, they would go through a pre-symptomatic AD test. These results were confirmed in a Cuban study (45) that investigated the attitudes and knowledge of healthy relatives from a large Cuban family with monogenic EOAD patients.
The authors of the REVEAL-study found that risk disclosure of the APOE genotype had an impact on the individual participants’ perceived risk of developing AD (22, 25, 29), however, it did not have a clinically relevant psychological impact on the participants (28).
Two qualitative interview studies investigated experiences and coping mechanisms of cognitively healthy at-risk individuals for Alzheimer’s dementia (27, 39). During the interviews some of the participants expressed feelings of helplessness and appraised their life and health as uncontrollable when reflecting about their risk of developing Alzheimer’s dementia. Reported concerns referred to personal health and life perspective as well as to effects on other family members (27).
The REVEAL Qualitative Research Initiative (REVEAL-QRI) explored appraisal and coping preferences (39). The appraisal of potential emotional reactions to the APOE-genotype disclosure played a crucial role in the decision whether to proceed with genetic susceptibility testing or not. After APOE-genotype disclosure individuals developed problem-focused coping mechanisms such as financial planning and creating advance directives, but also search for more information and usage of biomedical tests to further clarify the individual risk. These findings were confirmed by quantitative research within the REVEAL-study group (16, 33).

Apply: Decision-Making

Decision-Making in cognitively impaired individuals

Semi-structured interviews with MCI patients to capture the motivation to participate in real (49) and hypothetical (48) AD biomarker disclosure revealed that the most common reasons for choosing biomarker result disclosure were better understanding of their own brain condition and being able to make better decisions about future life planning. The need for a definite diagnosis preoccupied most of the participants. Possible negative social or legal consequences of the knowledge of one’s biomarker status were not put forward. The authors found that MCI was associated with poorer financial and healthcare decision-making capacity, which in turn was related to worse global cognitive functioning (24).

Decision-Making in cognitively unimpaired individuals

One study examined the intentions for APOE genotyping within six hypothetical scenarios (32) and revealed that the overall interest in genetic testing was high and that greater interest in testing was associated with male gender and with scenarios describing high test accuracy, detailed information on risk and most importantly available treatment options. Seemingly, the appraisal of possible emotional reactions to the APOE-genotype disclosure are decisive whether to proceed with genetic susceptibility testing or not (39).
Intentions of changing health-related behavior were examined in a secondary analysis from data of the REVEAL-study (20, 37). The authors reported that APOE4-positive participants were more likely to think about insurance changes than APOE4-negative participants, especially regarding long-term care coverage.

Apply: Effects on health behavior

Behavior change in cognitively impaired individuals

One study in patients with mild AD reported that access to specialized health services, such as medical specialists or memory clinics, is mainly initiated by spouses or primary care physicians, but not by the patients themselves (47).
Within a clinical trial, individuals with SCD and a positive first-degree family history for AD completed amyloid positron-emission tomography (PET) scans and were engaged in a psychoeducational intervention with regard to AD (40) Participants who learned about their positive PET result reported that they had changed their lifestyle in terms of more physical exercise, healthier diet and planning ahead.

Behavior change in cognitively unimpaired individuals

The REVEAL-study group examined motivational aspects for participation in the APOE-disclosure study. The participants rated the following reasons as most important: contribution to research (94%), arrangement of personal affairs (87%), hope that effective treatment will be developed (87%), arrangement of long-term care (81%), preparation of family for the possibility of illness (78%) and doing things sooner than planned (75%) (35, 36). Regarding participation in clinical trials with APOE-genotyping the study group identified higher household income, age below 60 years, Caucasian ethnicity and college graduate education status as predictors for seeking of genetic testing (20, 35, 38).
Health behavior changes were examined in secondary analyses from data of the REVEAL-study. Five studies found an association between health behavior changes and APOE genotype (19, 20, 23, 35, 42). APOE4-positive participants were more likely to use additional medication or dietary supplements, mostly vitamins and/or botanical supplements and more likely to show changes in diet and exercise than APOE4-negative participants. The rates of health behavior change were similar in the APOE4-negative participants and in the control group.

 

Discussion

This systematic review shows that research on HL in at-risk individuals for Alzheimer’s dementia is currently very limited. Existing results are fragmented and based on mostly small and often non-representative samples. The current literature shows that single aspects of HL have been addressed in research projects, but the concept of HL as a whole has not been embedded in study designs or outcome evaluations.
Nutbeam (8) has conceptualized three levels of HL, the functional level, the interactive level and the critical level. Current research in subjects at risk of Alzheimer’s dementia focuses on individual domains of HL, including knowledge, attitudes, risk perception, lifestyle changes etc., which corresponds to the functional and the interactive level of HL (8). A core competence of functional literacy is sufficient cognitive capacity, which constitutes the basis for any further HL skill. This is of significant importance in HL related to AD, since a fraction of the at-risk population (MCI) is cognitively impaired and others will become impaired in the course of the disease. At the same time, these individuals are faced with increasingly complex medical information due to rapid technical advances in the field early AD diagnosis, risk prediction and future treatments. This review highlights that reduced mental flexibility and amnestic and executive deficits severely impact on basic functional HL skills, such as comprehension (24, 30, 31, 43, 48). The literature indicates that complex risk information is challenging for lay persons, and that information overload and the degree of information complexity may lead to misunderstanding and misperception of the presented information (16, 22, 34). This could be indicated by either deficits in understanding risk-related information correctly, or an overestimation of benefits of genetic testing for AD, which may be due to several potential causes such as framing of the risk information, the need for certainty or the belief that there might be treatment for AD in the near future.
Individuals with higher levels of HL, such as the interactive level, are more likely to communicate their diagnosis and test results with family, friends, and their social environment. Discussing health-related topics such as positive and negative effects of early AD detection within the social environment can have a positive impact on psychological well-being and may result in a more reasonable and informed opinion in the individual but also in the social environment. Findings suggest that the role of the caregivers is of major importance, since their active presence in the disclosure session can positively contribute to the cognitive processing of information by the MCI patient and hence facilitate the comprehension of risk information (26). Interactive HL was only addressed by one study, where individuals discussed health-related topics with their social environment (17). The authors concluded that beliefs about AD risks and causes, genetic testing and development of treatment impact on the interaction pattern of the individuals with their social environment. Conversely, this process may influence the way medical information is appraised and may lead to a more reasonable and informed decision-making.
According to Nutbeam’s HL concept, critical HL may deserve stronger attention in the context of AD. Critical HL is defined as a combination of advanced literacy skills and social skills, enabling an individual to critically analyze information and to use it to exert greater control over life events and situations. Findings from our review, in line with other research, show the strong wish for clarity concerning one’s health risks and the need for clarification and a definite diagnosis, respectively (28, 40, 41, 49). Participants are willing to accept invasive diagnostics in order to attain certainty and control. In some cases, however, test outcomes may lead to the necessity of further assessments, and uncertainty may even increase. In addition, research on risk perception consistently suggests that people translate the statistical probability of a risk into a dichotomous information. When individuals are informed to be at risk, this may subjectively be equaled as being ill, including related negative psychosocial consequences. This indicates a potential misconception of risk states as opposed to disease diagnosis. Critical HL may therefore play an important role in the context AD, since it may enable individuals to engage in decision-making in better accordance with their personal values and preferences.
Overall our findings highlight the paramount importance of adequate communication with persons at risk of AD, which is sensitive to individual needs, skills, and preferences. Health care professionals are faced with unique complexities in the communication process when consulting MCI patients, as these patients have difficulties in handling and remembering abstract and complex risk information. Finally, HL skills are affected progressively in an individual with ongoing cognitive decline. In order to meet the individual requirements and needs of persons at risk for AD, there is a clear need to be flexible and responsive to patients’ preferences for more basic or more detailed information. This is in line with the abovementioned conceptual framework underlying this review, i.e. that in order to promote individual HL, it needs to be embedded in a health literate environment (9).
A limitation of our review is, that most identified reports are based on data from the REVEAL-study group which provides a more comprehensive understanding of HL among offsprings of individuals with AD, who are considering genetic susceptibility testing for AD. However, this is a rare case since genetic susceptibility testing is not an established method for risk prediction in clinical practice. In fact, at present, APOE genotyping is not recommended in most guidelines, not even in the diagnostic process of patients with dementia (35). The current approaches of early AD detection and risk prediction of Alzheimer’s dementia encompass biomarker-based diagnostics with cerebrospinal fluid (CSF) analysis or positron-emission tomography (PET). Regarding these technologies, studies on HL in at-risk groups are very limited. Furthermore, the findings of our review are limited in terms of evidence on HL in individuals at-risk for Alzheimer’s dementia as an outcome and particularly regarding the assessment of validated tools for HL or its sub-dimensions.

 

Conclusion

This systematic review highlights the current research on HL of at-risk individuals for Alzheimer’s dementia and reflects the need for more systematic research in this field. The results show that studies concentrate on single domains of HL, but do not comprehensively investigate all steps required for reliable decision-making. A particular challenge in the field of AD is that cognitively impaired individuals are faced with a number of disease-immanent cognitive barriers which hinder them to apply HL skills the way cognitively “normal” individuals would do. Therefore, it will be of utmost importance to analyze their needs, and to provide essential information to facilitate their informed decision-making in the context of specific medical situations (e.g. biomarker-based predictive diagnosis of AD). Herein, it will be critical to adjust to patients’ individual skills, needs, and preferences with respect to the level of detail of the information provided. One future direction for research is to identify the necessary requirements for an informed decision-making and for improved counselling of individuals at risk.

 

Contributors: Ayda Rostamzadeh and Julia Stapels conducted the literature review and were responsible for data analysis, data interpretation and writing the article. Mauro Seves and Theresa Haidl functioned as third reviewer to resolve discrepancies in the inclusion process. Anna Genske and Saskia Jünger developed the research methodology and assisted in writing the paper. All authors were engaged in the conceptualization, reviewed and commented on the manuscript. Frank Jessen and Christiane Woopen contributed to the conceptualization, writing, review of drafts of the article and obtained funding.

Funding: This project is funded by the Robert Bosch Foundation („Health literacy of persons at risk – from information to action (RisKomp)”; grant number 11.5.A402.0002.0) and by the Federal Ministry of Education and Research – BMBF as part of the Network of European Funding for Neuroscience Research – ERA-NET NEURON („Ethical and Legal Framework for Predictive Diagnosis of Alzheimer’s Disease: Quality of Life of Individuals at Risk and their Close Others” (PreDADQoL); funding number: 01GP1624). Both joint projects are conducted under the leadership of the Cologne Center for Ethics, Rights, Economics, and Social Sciences of Health (ceres). The sponsors did not have any influence on study initiation, conducting and reporting. 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.

Acknowledgement: We are grateful to thank Christian Albus, Kristina Enders, Marc Hellstern, Samia Peltzer, Kerstin Rhiem, Stephan Ruhrmann and Rita Schmutzler for their valuable input and fruitful discussions during project meetings and scientific workshops. Moreover, we acknowledge Sophie Heseler for her support doing test searches that helped us refine our search strategy. Finally, we wish to thank Nicole Skoetz for her excellent methodological advice.

Declaration of conflicting interests: The authors declared no potential conflicts of this article.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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DIETARY FAT INTAKE AND COGNITIVE FUNCTION AMONG OLDER POPULATIONS: A SYSTEMATIC REVIEW AND META-ANALYSIS

 

G.-Y. Cao1, M. Li1, L. Han2, F. Tayie3, S.-S. Yao1, Z. Huang1, P. Ai1, Y.-Z. Liu4, Y.-H. Hu1,5, B. Xu5

 

1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; 2. Department of Medicine, Yale School of Medicine, New Haven, Connecticut, US; 3. Department of Food, Nutrition and Dietetics; Human Environmental Studies; Southeast Missouri State University; Cape Girardeau, MO, US; 4. Department of Toxicology, School of Public Health, Tianjin Medical University, Tianjin, China; 5. Peking University Medical Informatics Center, Beijing, China

Corresponding Author: Beibei Xu. Peking University Medical Informatics Center, Beijing, China. Phone: 010-82805904. E-mail: xubeibei@bjmu.edu.cn

J Prev Alz Dis 2019;
Published online March 22, 2019, http://dx.doi.org/10.14283/jpad.2019.9

 


Abstract

Objective: The associations between dietary fat intake and cognitive function are inconsistent and inconclusive. This study aimed to provide a quantitative synthesis of prospective cohort studies on the relationship between dietary fat intake and cognitive function among older adults.
Methods: PubMed, EMBASE, PsycINFO and Web of Science databases were searched for prospective cohort studies published in English before March 2018 reporting cognitive outcomes in relation to dietary fat intake. Four binary incident outcomes included were mild cognitive impairment (MCI), dementia, Alzheimer disease (AD) and cognitive impairment. The categories of dietary fat intake were based on fat consumption or the percentage of energy from fat consumption, including dichotomies, tertiles, quartiles and quintiles. The relative risk (RR) with the corresponding 95% confidence intervals (CIs) was pooled using a random effects model.
Results: Nine studies covering a total of 23,402 participants were included. Compared with the lowest category of consumption, the highest category of saturated fat intake was associated with an increased risk of cognitive impairment (RR = 1.40; 95% CI: 1.02-1.91) and AD (RR: 1.87, 95% CI: 1.09-3.20). The total and unsaturated fat intake was not statistically associated with cognitive outcomes with significant between-study heterogeneity.
Conclusion: This study reported a detrimental association between saturated fat intake and cognitive impairment and mixed results between unsaturated fat intake and selected cognitive outcomes. Given the substantial heterogeneity in the sample size and methodology used across studies, the evidence presented here should be interpreted with caution.

Key words: High-fat diet, cognitive function, mild cognitive impairment, dementia, Alzheimer’s disease.


 

 

Introduction

Cognitive decline has been estimated to appear among approximately 25% to 50% of the community-dwelling older population (1). The burden of cognitive impairment as well as the associated financial costs could be unbearable for individuals, families, and public health services (2, 3). Previous studies have reported that dietary fat intake served as a risk factor for cognitive decline among older populations (4-6). Dietary habits of older adults have undergone significant changes during a period of nutrition transition, such as an increasing trend in percentage of energy from total fat, from about 25% in 1991 to 32% in 2009 (7). Recently, the Prospective Urban Rural Epidemiology (PURE) study, which was conducted in 18 countries located on 5 continents, showed that higher total fat and saturated fat (SFA) intake were associated with reduced total mortality (8). Inconsistent findings were reported regarding the associations between different types of dietary fat intake and a variety of health outcomes, including cognitive decline, diabetes, cancer, stroke, myocardial infarction and mortality (9-13). A review by nutrition scientists reported that replacement of SFA with naturally occurring unsaturated fats (UFA) provided health benefits for the general population (14).
In the past decade, an increasing number of population-based studies have been conducted on the associations between dietary fat intake and cognitive impairment (15-20). However, results from these studies were inconsistent and inconclusive. Owing to different study designs and methods for assessments of cognitive outcomes or dietary fat, it is difficult to draw conclusions on the consistency of the associations.  In addition, previous reviews and meta-analyses have mostly focused on the associations between polyunsaturated fatty acids (PUFAs) and cognitive function (21-23). They reported that higher docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) intakes were associated with better cognitive performance. To our knowledge, no previously published meta-analysis has examined cognitive outcomes in relation to the intake of different types of dietary fat among older adults. Therefore, this study aimed to examine the associations between different types of dietary fat intake and cognitive outcomes among older populations.

 

Methods

Search strategy

Two reviewers (C.G.Y. and L.M.) searched for articles published before March 2018 using electronic databases, including PubMed, EMBASE, PsycINFO, and Web of Science. Studies were identified using the search terms “(fat intake OR high-fat diet OR dietary fat) AND (cognition OR cognitive function OR cognitive decline OR cognitive impairment OR dementia OR Alzheimer’s)”. The language was restricted to English. The complete search strategy is presented in Supplementary Table 1.

Eligibility criteria

Original studies were included in this meta-analysis if they met all of the following criteria: (1) investigated the association between dietary total fat, SFA, MUFA, or PUFA intake and cognitive outcomes in population-based samples; (2) used a prospective cohort design; (3) included a population aged ≥ 55 years; (4) incorporated the cognitive outcomes of mild cognitive impairment (MCI), dementia, and Alzheimer’s disease (AD) as defined by validated cognitive tests; and (5) reported the relative risks (RRs), odds ratios (ORs), or hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) of cognitive outcomes in relation to dietary fat intake.

Outcome measures

Four binary incident outcomes, including MCI, dementia, AD and cognitive impairment, were based on standard tests or diagnosis. In this study, cognitive impairment was the overall composite estimate of cognitive function, including any cognitive outcome, either MCI or dementia or AD (24, 25). Table 1 shows cognitive outcomes and their assessment tools.

Table 1. Summary of studies on the associations between fat intake and cognitive outcomes

Table 1. Summary of studies on the associations between fat intake and cognitive outcomes

*Statistically significant difference (P < 0.05); SFA = saturated fat; MUFA = monounsaturated fat; PUFA = polyunsaturated fat; FFQ= Food Frequency Questionnaire.CDR = Clinical Dementia Rating, MMSE = Mini Mental State Examination, TICS = Telephone Interview for Cognitive Status, EBMT = East Boston Memory Test, IADL = Instrumental Activities of Daily Living, DECO = Observed Cognitive Deterioration (“DEtérioration Cognitive Observée”); DSM-IV = Statistical Manual of Mental Disorders, 4th edition; CSI-D = Community Screening Instrument for Dementia; DSM-III-R = Diagnostic and Statistical Manual of Mental Disorders, third edition; NINDS-AIREN = National Institute of Neurological Disorders and Stroke and Association Internationale pour la Recherche et l’Enseignement en Neurosciences; NINCDS-ADRDA = National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association.

 

Definition of dietary fat intake

The dietary fat intake, including total, SFA, MUFA and PUFA, were assessed by using the semiquantitative Food Frequency Questionnaire (FFQ) in each study included. The categories of dietary fat intake were based on fat consumption or the percentage of energy from fat consumption, including dichotomies, tertiles, quartiles and quintiles. Table 1 shows the types and categories of dietary fat.

Data extraction

Two reviewers (L.M. and C.G.Y.) reviewed the articles and identified all relevant studies independently. Differences in study selection were resolved by consensus discussion and consultation with a third reviewer (Y.S.S.). Data from the selected articles were extracted for this study. If multiple articles were published using the same cohort and the same cognitive outcomes, we included only the article with the mostly complete details. If multiple articles from the same cohort reported different cognitive outcomes, we included each of these articles separately in the analysis. The extracted data included the first author’s last name, publication year, length of follow-up, country where the study was conducted, sample size, participants’ ages at baseline, dietary assessment, cognitive outcomes and their assessment tools, categories of fat intake, covariates in the final model, and crude or adjusted RRs, ORs, or HRs with 95% CIs (Table 1). The cutoff value for each category of fat intake and the RR of cognitive impairment in relation to the individual type of fat intake are shown in Supplementary Table 2.

Risk of bias/study quality

Publication bias was estimated with Egger’s regression asymmetry test (if the number of studies was ≥ 3) or Begg’s adjusted rank correlation test (if the number of studies was < 3) (26, 27) which was conducted by two investigators (C.G.Y. and L.M.) independently. The quality of the included studies was evaluated independently by two investigators (L.M. and C.G.Y.) using the Newcastle-Ottawa Scale (NOS) (score range 1-9) (28). Our study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (29) and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist (30).

Statistical analysis

If the incidence of an outcome of interest in the study population was low, the ORs and HRs were considered as RRs (31). We calculated the summarized risk estimates of the highest vs. lowest fat intake categories to analyze the relationship between dietary fat intake and cognitive outcomes, including cognitive impairment, MCI, dementia, and AD. Statistical heterogeneity among the studies was estimated using the I2 statistic, and very low, low, moderate, and high degrees of heterogeneity were defined as ≤ 25%, 25% to ≤ 50%, 50% to ≤ 75%, and ≥ 75%, respectively (32). All effect estimates were then pooled using a weighted random-effects model. A two-sided P value < 0.05 was considered statistically significant (27). All analyses were performed using Stata software (version 14.0; Stata SE Corporation LP, College Station, TX, USA).

 

Results

Literature research and characteristic of studies

A total of 6,080 articles were identified based on the initial search (Figure 1). After applying the inclusion criteria, nine studies covering 23,402 participants from five countries, including the United States (5, 17, 33, 34), the Netherlands (35), France (18), Finland (36, 37) and Italy (38), met the inclusion criteria and were included in the meta-analysis. Table 1 summarizes the characteristics and NOS scores of the nine included studies. The sample size of the included studies ranged from 278 to 6,183 participants, and the follow-up period ranged from 2.1 to 21 years. Seven studies included both men and women and two studies included only women (5, 18). The study quality was assessed using the NOS scale, with a score ≥ 7 considered high quality (overall mean NOS score = 8.8, SD = 0.6, range = 7-9). The included studies varied in terms of the covariates that were adjusted; however, the majority of the studies were adjusted for age, gender and education.

Figure 1. Flow diagram of the systematic review process

Figure 1. Flow diagram of the systematic review process

 

Total fat and cognitive outcomes

This meta-analysis of seven studies suggested that compared with the lowest category of total fat intake, the highest category was not significantly associated with the risk of cognitive impairment (RR = 1.11; 95% CI: 0.84-1.47), with no evidence of publication bias (P = 0.76) but significant between-study heterogeneity (I2 = 54.2%, P = 0.03) (Figure 2A).

The meta-analysis of two studies found that compared with the lowest category of total fat intake, the highest category of total fat intake was not associated with the risk of MCI (RR = 0.97; 95% CI: 0.33-2.86), with no evidence of publication bias (P = 0.32) but significant between-study heterogeneity (I2 = 88.9%, P < 0.01) (Figure 2B).
This meta-analysis of three studies did not find a significant association between the highest category of total fat intake and the risk of dementia (RR = 1.19; 95% CI: 0.69-2.04) when compared with the lowest category, with no evidence of publication bias (P = 0.74) but moderate heterogeneity among the studies (I2 = 59.8%, P = 0.08) (Figure 2C).
Regarding AD, the summarized results of four studies showed that compared with the lowest category of total fat intake, the highest category was not associated with the risk of AD (RR = 1.24; 95% CI: 0.90-1.71) (Figure 2D). No evidence of publication bias (P = 0.43) or heterogeneity (I2 = 0%, P = 0.54) was observed

Figure 2. Forest plots of associations between total fat intake and cognitive outcomes, including cognitive impairment (A), (B) MCI (B), dementia (C) and AD (D)

Figure 2. Forest plots of associations between total fat intake and cognitive outcomes, including cognitive impairment (A), (B) MCI (B), dementia (C) and AD (D)

 

SFAs and cognitive outcomes

The meta-analysis of eight studies suggested that compared with the lowest category, the highest SFA category was associated with an increased risk of cognitive impairment (RR = 1.40; 95% CI: 1.02-1.91), with no evidence of publication bias (P = 0.12) but significant between-study heterogeneity (I2 = 55.3%, P = 0.02) (Figure 3A).

Figure 3. Forest plots of associations between SFA intake and cognitive outcomes, including cognitive impairment (A), MCI (B), dementia (C) and AD (D)

Figure 3. Forest plots of associations between SFA intake and cognitive outcomes, including cognitive impairment (A), MCI (B), dementia (C) and AD (D)

 

The meta-analysis of four studies found that compared with the lowest category, the highest SFA category was not associated with the risk of MCI (RR = 1.24; 95% CI: 0.65-2.38), with no evidence of publication bias (P = 0.95) but significant between-study heterogeneity (I2 = 74.6%, P < 0.01) (Figure 3B).
The summarized estimate of three studies indicated that the highest SFA category was not associated with an increased risk of dementia (RR = 1.39; 95% CI: 0.79-2.42), with no evidence of publication bias (P = 0.12) but significant between-study heterogeneity (I2 = 50.9%, P = 0.13) compared with the lowest SFA category (Figure 3C).
The summarized results of three studies indicated an increased risk of AD (RR = 1.87; 95% CI: 1.09-3.20) for the highest versus the lowest SFA intake categories. No evidence of publication bias (P = 0.97) or heterogeneity (I2 = 0%, P = 0.66) was observed (Figure 3D).

MUFAs and cognitive outcomes

The meta-analysis of seven studies suggested that compared with the lowest category, the highest MUFA intake category was not significantly associated with the risk of cognitive impairment (RR = 0.90; 95% CI: 0.66-1.23), with no evidence of publication bias (P = 0.35) and low heterogeneity (I2 = 46.2%, P = 0.07) (Figure 4A).

Figure 4. Forest plots of associations between MUFA intake and cognitive outcomes, including cognitive impairment (A), MCI (B), dementia (C) and AD (D)

Figure 4. Forest plots of associations between MUFA intake and cognitive outcomes, including cognitive impairment (A), MCI (B), dementia (C) and AD (D)

 

The summarized estimate of three studies indicated no significant association between the highest MUFA intake category and a decreased risk of MCI (RR = 0.79; 95% CI: 0.45-1.39), with no evidence of publication bias (P = 0.91) but significant between-study heterogeneity (I2 = 62.6%, P = 0.05) (Figure 4B).
Regarding dementia, the summarized estimate of two studies indicated that compared to the lowest MUFAs category, the highest MUFA category was not associated with the risk of dementia (RR = 1.16; 95% CI: 0.93-1.43) (Figure 4C). No evidence of publication bias (P = 0.32) or heterogeneity (I2 = 0%, P = 0.83) was observed.
For AD, the summarized results of two studies indicated no statistically significant association between MUFAs and AD (RR = 0.85; 95% CI: 0.44-1.64) (Figure 4D). No evidence of publication bias (P = 0.32) or heterogeneity (I2 = 0%, P = 0.76) was observed.

PUFAs and cognitive outcomes

Five studies reported associations between the PUFA intake and cognitive function (Figure 5A). The summarized estimate suggested that compared with the lowest category, the highest PUFA intake category was not associated with the risk of cognitive impairment (RR = 0.88; 95% CI: 0.65-1.20), with no evidence of publication bias (P = 0.91) but significant between-study heterogeneity (I2 = 51.5%, P = 0.05).
Four studies reported an association between PUFAs and MCI. The summarized results suggested that compared with the lowest category, the highest PUFA intake category was not associated with the risk of MCI (RR = 0.83; 95% CI: 0.48-1.45), with no evidence of publication bias (P = 0.41); however, significant between-study heterogeneity (I2 = 70.2%, P = 0.02) was observed (Figure 5B).
The summarized estimate of two studies indicated no association between the highest PUFA intake category and the risk of dementia (RR = 0.85; 95% CI: 0.44-1.65), when compared with the lowest category, with low heterogeneity (I2 = 47.4%, P = 0.17) (Figure 5C). No evidence of publication bias (P = 0.32) was observed.

Figure 5. Forest plots of associations between PUFA and cognitive outcomes, including cognitive impairment (A), MCI (B), dementia (C) and AD (D)

Figure 5. Forest plots of associations between PUFA and cognitive outcomes, including cognitive impairment (A), MCI (B), dementia (C) and AD (D)

 

Discussion

Total Fat and cognitive outcomes

It should be noted that the relationship between total fat intake and cognitive function depending upon not only the quantity but also the quality of fat intake. One previous review of human epidemiological and animal studies reported both adverse and protective effects of the dietary total fat intake depending upon the quantity and quality of fat consumed (39). Moreover, one previous study has also suggested that the inconsistent associations between total fat intake and cognitive function reported in different studies may largely depend on the dietary fat composition (40).

SFAs and cognitive outcomes

Similar to our findings, a previous systematic review of three studies reported that old adults consuming a diet high in SFAs had an increased risk of dementia (41). Similarly, one cohort studies not included in this meta-analysis showed that compared with the lowest category of SFA intake, the highest category of SFA intake was associated with adverse changes in cognitive scores over different follow-up periods (6). However, one cohort study with relatively younger participants with an average age of 55.3 years reported compared with the lowest category of SFA intake, the highest category of SFA intake was not statistically significantly associated with cognitive decline assessed by four different tests (the 15 Words Verbal Learning Test, the Stroop Color Word Test, the Word Fluency test, and the Letter Digit Substitution Test) (19). Similarly, two cohort studies reported that compared with the lowest category of SFA intake, the highest category of SFA intake was not statistically significantly associated with cognitive decline among relatively healthy older women after a 3-year follow-up (16) or older women with high vascular risk after a 5-year follow-up (4).
A previous review of animal studies showed that chronic ingestion of SFAs at mid to high levels could adversely affect cognitive performance (42). A higher SFA intake was associated with an increased risk of CVD and cerebrovascular disease (43-45), which were subsequently related to cognitive impairment (46) and dementia, especially vascular dementia (47, 48). Dietary SFAs exert an effect on CVD through plasma lipoproteins that elevate low-density lipoprotein (LDL) cholesterol concentrations, which is considered the most important CVD risk factor (49, 50). Previous reviews (39, 51, 52)of human or rodent studies have suggested that a high-fat diet (HFD) rich in SFAs may result in neuronal cell dysfunction by inducing insulin resistance and impaired glucose regulation and finally lead to cognitive dysfunction. Other reviews also suggested that oxidative stress generated by fat metabolism and inflammation might explain the association between a HFD, especially high SFA intake, and aging-associated cognitive disorders (53, 54). Several animal studies have shown that mice fed a HFD rich in SFAs had inflammation induced by an increase in the Firmicutes-to-Bacteroidetes ratio (55-57).

UFAs and cognitive outcomes

Several cohort studies not included in this meta-analysis reported that compared with the lowest category of MUFA or PUFA intake, the highest category of MUFA or PUFA intake was not associated with the cognitive decline assessed by scores among older women with type 2 diabetes mellitus (58), as well as an old biracial community population aged ≥ 65 years (6) or a middle and old population aged between 43 to 70 years (19). However, one cohort study not included in this meta-analysis showed that compared with the lowest category of MUFA intake, the highest category of MUFA intake was significantly associated with better cognitive function during 3-year (16). Similar results were observed in another one cohort study reported that compared with the lowest category of MUFA or PUFA intake, the highest category of MUFA or PUFA intake was inversely related to risk of cognitive decline among the oldest women with high cardiovascular risk (4).
PUFAs and MUFAs can both exert potentially beneficial effects on cognition via antioxidant effects (59, 60), anti-inflammatory effects (61, 62), and vascular protection through reducing macrophage uptake of plasma oxidized LDL (63, 64) and reducing triglycerides and apolipoprotein B (65-67). Other studies have shown that UFAs, especially PUFAs, might maintain cognitive function by reducing the risk of CVD and stroke with improved insulin sensitivity and glucose metabolism (68-72). The beneficial effect of PUFA on cognitive function may also be related to neuroprotection by maintaining the structural integrity of neuronal membranes (73) and enhancing synaptosomal membrane fluidity, thereby regulating neuronal transmission (74).

Limitations

Several factors limited the interpretation of our results. First, the limited number of studies as well as the considerable between-study heterogeneity may have reduced the precision of our pooled effect size estimates. Second, assessments of dietary fat intake were conducted using different items on FFQs across different settings, making a comparison of the results difficult in this meta-analysis study. Third, the definition and assessment of cognitive outcomes as well as the categories of dietary fat intake vary across studies which may result in misclassification bias. Participants were classified to each outcome using different formal diagnostic criteria, and different methods might group the same participant into different outcome categories. Furthermore, to avoid undue complexity, we simplified our meta-analysis by comparing only the highest versus the lowest fat intake categories due to varied cutoff points used to define the intermediate levels across studies. Finally, unmeasured or residual confounding in the source studies could not be addressed in this meta-analysis using only published data. Because there is insufficient number of studies reporting the dose-response relationship, the information provided was inadequate for further dose-response analyses.

 

Conclusion

In summary, this systematic review and meta-analysis found a detrimental association between SFA intake and the risk of cognitive function decline, whereas mixed results regarding the associations between UFA intake and the risk of selected cognitive function outcomes. However, given the substantial heterogeneity in the sample size and methodology used across studies, the evidence presented here should be interpreted with caution. Since a randomized clinical trial investigating dietary fat intake and cognitive function may not be feasible, due to practical considerations, future well-designed, prospective, cohort studies involving different populations are needed to confirm the associations between fat intake and cognitive outcomes and determine the age-, gender- and population-specific cutoff values for fat intake to provide evidence for more personalized dietary fat recommendations.

 

Acknowledgement: The authors appreciate the assistance of Drs. Beibei Yuan and Guang Ren in contributing to the theoretical background and providing their professional comments in this study. All authors approved the final version of the manuscript for publication.

Conflict of interest: The authors declare that they have no competing interests.

Funding: This work was supported by National Natural Science Foundation of China (No.81703304 and No.81602850).

 

SUPPLEMENTARY TABLE 2

 

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