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MENTAL COMPONENT SCORE (MCS) FROM HEALTH-RELATED QUALITY OF LIFE PREDICTS INCIDENCE OF DEMENTIA IN U.S. MALES

 

X. Ding1, E.L. Abner3,5,6, F.A. Schmitt3,4, J. Crowley7, P. Goodman8, R.J. Kryscio2,3,5
 

1. Western Kentucky University, Department of Public Health, Bowling Green, Kentucky, USA; 2. University of Kentucky, Department of Statistics, Lexington, Kentucky, USA; 3. University of Kentucky, Sanders-Brown Center on Aging, Lexington, Kentucky, USA; 4. University of Kentucky, Department of Neurology, Lexington, Kentucky, USA; 5. University of Kentucky, Department of Biostatistics, Lexington, Kentucky, USA; 6. University of Kentucky, Department of Epidemiology, Lexington, Kentucky, USA; 7. SWOG Cancer Research and Biostatistics, Seattle, WA, USA; 8. SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Corresponding Author: Xiuhua Ding, M.D., Ph.D., Department of Public Health, Western Kentucky University, 1906 College Heights Blvd, Bowling Green, KY 42101, USA, Email: xiuhua.ding@wku.edu, phone: 270-745-3618, Fax: 270-745-6950

J Prev Alz Dis 2020;
Published online September 18, 2020, http://dx.doi.org/10.14283/jpad.2020.50

 


Abstract

Background: The Medical Outcomes Study Questionnaire Short Form 36 health survey (SF-36) measures health-related quality of life (HRQoL) from the individual’s point of view and is an indicator of overall health status.
Objective: To examine whether HRQoL shows differential changes over time prior to dementia onset and investigate whether HRQoL predicts incidence of dementia.
Design: Prevention of Alzheimer’s Disease (AD) by Vitamin E and Selenium (PREADViSE) trial, which recruited 7,547 non-demented men between 2002 and 2009. A subset of 2,746 PREADViSE participants who completed up to five SF-36 assessments at annual visits was included in the current analysis
Setting: Secondary data analysis of PREADViSE data.
Participants: A subset of 2,746 PREADViSE participants who completed up to five SF-36 assessments at annual visits was included in the current analysis.
Measurements: Two summary T scores were generated for analysis: physical component score (PCS) and mental component score (MCS), each with a mean of 50 (standard deviation of 10); higher scores are better. Linear mixed models (LMM) were applied to determine if mean component scores varied over time or by eventual dementia status. Cox proportional hazards regression was used to determine if the baseline component scores were associated with dementia incidence, adjusting for baseline age, race, APOE-4 carrier status, sleep apnea, and self-reported memory complaint at baseline.
Results: The mean baseline MCS score for participants who later developed dementia (mean± SD: 53.9±9.5) was significantly lower than for those participants who did not develop dementia during the study (mean±SD: 56.4±6.5; p = 0.005). Mean PCS scores at baseline (dementia: 49.3±7.9 vs. non-dementia: 49.8±7.8) were not significantly different (p = 0.5) but LMM analysis showed a significant time effect. For MCS, the indicator for eventual dementia diagnosis was significantly associated with poorer scores after adjusting for baseline age, race, and memory complaint. Adjusted for other baseline risk factors, the Cox model showed that a 10-unit increase in MCS was associated with a 44% decrease in the hazard of a future dementia diagnosis (95% CI: 32%-55%).
Conclusion: The SF-36 MCS summary score may serve as a predictor for future dementia and could be prognostic in longitudinal dementia research.

Key words: Health-related Quality of Life, dementia, outcome measures.


 

Introduction

Dementia is not a specific disease but a syndrome defined by its symptoms (1). Alzheimer’s disease (AD) is the most common form of dementia, and worldwide around 50 million people live with AD and other dementias; over 5 million with dementia live in the U.S. (2). These numbers are expected to grow as the population ages, but the estimated annual costs of dementia exceeded $600 billion (U.S. dollars) already by 2010 (3). With increasing prevalence, these costs are expected to increase by 85% by 2030 (3). Therefore, AD and related dementias are important public health issues. With no cure currently available and with anticipated increases in both prevalence and costs, early diagnosis and intervention are keys to maintaining health and independence for as long as possible. .
Health-related quality of life (HRQoL) describes one’s physical, mental, emotional, and social functioning (4-6). The Medical Outcomes Study 36-item Short Form (SF-36) survey is one of most commonly used instruments to measure HRQoL (7). Although many previous studies used SF-36 to examine HRQoL for participants with chronic diseases (8-10), SF-36 HRQoL scores are also associated with mortality risk and incidence of disease: Nilsson et al. used the SF-36 to predict coronary heart disease incidence in a middle-age Swedish population (11). Drageset et al. found that HRQoL predicted cancer mortality for nursing home residents without cognitive impairment (12).
The SF-36 is a general health measure with eight domains: Physical Functioning (PF), Role Limitations Due to Physical Problems (RP), Bodily Pain (BP), General Health Perceptions (GH), Vitality (VT), Social Functioning (SF), Role Limitations Due to Emotional Problems (RE), and General Mental Health (MH). Heterogeneous methods have been used to analyze and interpret SF-36 scores. Several studies have examined SF-36 measurements by comparing a single SF-36 domain score between different groups (13, 14), while others have created a total score. However, Lins et al. suggest that using a single SF-36 total score, which necessarily ignores potential differences in physical and mental HRQoL, may lead to poor validity (15).
The SF-36’s eight domains can be clustered into two component summary scores: a physical component summary score (PCS) and a mental component summary score (MCS). The SF-36 user’s manual introduced two ways to develop these summary scores: psychometric-based summary measures and standardized scoring (norm-based summary measures). For the latter method, the manual (16) provides the necessary documentation to score and interpret the PCS and MCS. Studies report that SF-36 summary scores account for 80-85% of variance in the eight SF-36 domain scales, and thus their use in place of the eight domains can reduce the number of statistical comparisons needed (17). The PCS and MCS summary scores are expected to perform reliably in both cross-sectional and longitudinal settings (17, 18) and have been used in several studies (11, 12, 14, 19).
The aims of this study were to investigate whether SF-36 PCS and MCS scores change over a five-year interval and to determine whether the baseline SF-36 PCS and MCS scores are associated with risk of dementia in a large sample of older adult men who were enrolled in a randomized, double-blind clinical trial to prevent dementia.

 

Methods

Study population and data sources

This secondary data analysis is based on a subset of participants from the Prevention of Alzheimer’s Disease by Vitamin E and Selenium (PREADViSE) trial (20, 21). PREADViSE was an ancillary study to the Selenium and Vitamin E Cancer Prevention Trial (SELECT) (NCT00006392) and recruited 7,547 men without dementia age 60 and older from 128 participating SELECT (a large prostate cancer prevention RCT) sites to assess the effectiveness of antioxidant supplements in preventing incident Alzheimer’s Disease (AD). SELECT randomized participants to treatments (22): 400 International Units (IU) Vitamin E, 200 micrograms(mg) of Selenium, 400 IU Vitamin E plus 200 mg of Selenium, or Placebo. Since the use of antioxidant supplements did not affect the risk of dementia, the effects of the antioxidant supplements will not be considered further in this report (23).
PREADViSE investigators were blind to SELECT treatment assignment during follow-up. The eligibility criteria for participating in the PREADViSE trial included active SELECT enrollment at a participating site and absence of dementia and other active conditions that affect cognition, such as major psychiatric disorders, including depression. In 2008, the SELECT Data Safety Monitoring Committee discontinued study supplements after a futility analysis for the prostate cancer outcome. PREADViSE then continued as an observational study until 2015 to ascertain incident dementia cases (21). The University of Kentucky Institutional Review Board (IRB) and the IRBs at each SELECT study site approved PREADViSE research activities. All participants provided written informed consent.

Analytic Sample

Participants were included in this retrospective analysis if they had completed the SF-36 at least once at their annual in-person visits during the randomized clinical trial (RCT) phase of the study (between 2002 and 2009). In total, 2,748 participants out of 7,547 completed up to five SF-36 assessments at annual visits and were included in the current analysis.

Case Ascertainment

The Memory Impairment Screen (MIS) was the primary dementia screening instrument in both the RCT and the observational period of PREADViSE (24, 25). If participants failed the MIS (scored ≤5/8 on the immediate or delayed recall portion of the MIS), a second-tier screen was administered. An expanded Consortium to Establish a Registry in Alzheimer’s Disease battery (CERAD-e) was used during the RCT period (26), and the modified Telephone Interview for Cognitive Status (TICS-m) was used during the observational period (27, 28). The CERAD-e and the TICS-m assessed participants’ global cognitive function. Failure on the second screen (CERAD-e T score ≤ 35, TICS-m total score ≤ 35) led to a recommendation for a clinic visit with their local physician. Three to five expert clinicians, including two neurologists and at least one neuropsychologist, reviewed records from the clinic visit for a consensus diagnosis. In cases in which the neurologists disagreed in their diagnoses, the study primary investigator made the final determination (23). Annual screenings were completed in May 2014, and a small number of participants were followed for medical records through August 2015.
Incident dementia cases were identified using two methods. First, as described above, a medical records-based consensus diagnosis was conducted with date of diagnosis assigned as the date of the failed screen. Second, because many participants were reluctant to obtain medical examination for their memory, additional measures including the AD8 Dementia Screening Interview (29) were employed. In addition to the AD8, dementia determination was based on self-reported medical history; self-reported diagnosis of dementia; use of memory-enhancing prescription drugs; and cognitive scores on the MIS, CERAD-e T score, New York University Paragraph Delayed Recall, and TICS-m. The diagnostic criteria for the second method were AD8 total of 1 or greater (at any time during follow-up) to indicate functional impairment plus one or more of the following: self-reported diagnosis of dementia, use of a memory-enhancing prescription drug (donepezil, rivastigmine, galantamine, memantine), or a cognitive score below the cutoff for intact cognition on any test (e.g., 1.5 standard deviations below expected performance based on age and education normative data). Date of diagnosis was assigned to the earliest event (29).

Health Related Quality of Life

Scoring was performed according to the SF-36 Health Survey Manual and Interpretation Guide (16). First, a raw score for each of the eight domains was calculated by summing the item responses within each domain. Raw summary scores were then transformed to scale scores that ranged from 0 to 100 (16). Each SF-36 domain scale score was standardized to the general U.S. older adult male population ages 65 years and older by computing a z score for each domain (16). Means and standard deviations used to generate the Z scores are given in Supplemental Table 1.

Table 1. Characteristics Study Population by incident dementia status*

*All PREADViSE participants are male; †SD=standard deviation.

The PCS and MCS were then generated using a weighted sum of the domain Z scores. Aggregate physical and mental component scores were then calculated using the formulas below (16):
PCS = (PF_Z * 0.42402) + (RP_Z * 0.35119) + (BP_Z * 0.31754) + (GH_Z * 0.24954) + (VT_Z * 0.02877) + (SF_Z * -0.00753) + (RE_Z * -0.19206) + (MH_Z * -0.22069)
MCS = (PF_Z * -0.22999) + (RP_Z * -0.12329) +(BP_Z * -0.09731) + (GH_Z * -0.01571) + (VT_Z * 0.23534) + (SF_Z * 0.26876) + (RE_Z * 0.43407) + (MH_Z * 0.48581)
Finally, the PCS and MCS scores were transformed to T scores by multiplying the PCS and MCS sum scores by 10 and adding 50. Then these two component T scores (PCS and MCS) were compared to national normative data based on the manual (16) and were used in statistical modelling.

Covariates

Data were also collected on age at baseline, race, ethnicity, years of education, APOE ε4 allele carrier status, and self-reports on the presence of comorbidities including diabetes mellitus, hypertension, sleep apnea, memory complaint (measured as “Have you noticed any changes in your memory?”), and family history of dementia in a first degree relative. These covariates were selected based on results from the descriptive analysis and findings from previous studies (20).

Statistical analysis

Chi-square and t-test statistics were used compare categorical and continuous variables between participants who did not and did not develop dementia during follow-up. And general characteristics between PREADViSE participants who completed SF-36 and PREADViSE participants who did not complete SF-36. Linear mixed models (LMM) were constructed for SF-36 PCS and MCS summary scores, with time-dependent between-subjects factor cognitive status at the year of assessment (dementia versus non-dementia) and within-subjects factor year of assessment; a cognitive status*year interaction term was also included. Covariates were age at baseline, years of education, Black race (Yes vs. No), Hispanic ethnicity (Yes vs. No), APOE (presence vs absence of at least one ε4 allele), self-reported baseline indicators for diabetes mellitus, hypertension, memory problem, sleep apnea, and family history of dementia.
To determine if the baseline SF-36 PCS and MCS, which were defined as the PCS and MCS from the first SF-36 measurement in PREADViSE, affected the hazard of dementia, a series of Cox proportional hazards regression models with SF-36 PCS or MCS as the independent variable and survival time to diagnosis of dementia as the dependent variable, were applied to a multivariable survival analysis. The follow-up time was defined as years between date of PREADViSE enrollment and date of dementia diagnosis or, in the absence of dementia, date of last assessment[23]. The multivariable Cox models included age at baseline, Black race (Yes vs. No), APOE (presence vs absence of at least one ε4 allele), sleep apnea at baseline, and self-reported memory complaint and PCS or MCS. Hypertension, diabetes and family history of dementia, years of education were excluded from the model due to insignificance. Since the PCS was not included in the final model due to insignificance, the proportional hazard assumption was tested for MCS only through maximum residual method and it was met.
All data were analyzed using PC-SAS version 9.4, and 0.05 was set as the significance level.

 

Results

Demographic characteristics of study participants with and without SF-36 data are given in Supplemental Table 2. PREADViSE participants who completed the SF-36 were similar to participants who never completed the SF-36 in terms of the proportion of APOE ε4 carriers, but were slightly younger, less educated, more likely to report Black race or Hispanic ethnicity, and less likely to report family history of dementia (Supplemental Table 2).
Men who developed dementia (n=128) were significantly older at baseline (p <0.001), more likely to report Black race (p=0.01and memory change (p <0.001), but less likely to report Hispanic ethnicity (p<0.001) (Table 1).They were also more likely to carry at least one APOE ε4 allele (p = 0.01) (Table 1). Compared to the population of U.S. males ages 65 and over (16), in this sample mean PCS (mean±SD: 49.8 ± 7.8) was significantly higher than the general population (mean±SD: 42.0 ± 11.4; p<0.001), and mean MCS (mean±SD: 56.3 ± 6.7) in the study sample was also significantly higher than the general population (mean±SD: 52.5 ± 9.8; p<0.001)[16]. These differences are expected in a population of healthy men who would be motivated to enroll in a prevention trial. On average, men were followed up 5.9 ± 2.7 years. Men who developed dementia were followed up longer than the men who did not develop dementia (p<0.001) (Table 1).
Men who developed dementia had baseline MCS scores (mean±SD: 53.9 ± 9.5) that were significantly lower than the men who did not develop dementia (mean±SD: 56.4 ± 6.5; p=0.005), while there was not a significant difference in SF-36 PCS baseline scores (mean±SD for dementia: 49.3 ± 7.9 vs. mean±SD for non-dementia: 49.8 ± 7.8; p=0.54). Means for PCS and MCS by cognitive status at each visit are depicted in Figure 1a and Figure 1b, respectively. Mean PCS significantly decreased over time, but no significant difference was observed in the rate of decline between men who developed dementia and men who did not. Lower mean MCS scores were significantly associated with incidence (risk) of dementia, but there was not a significant change over time. These associations remained in our adjusted LMM. The LMM analysis showed no interaction effect between dementia status and year at assessment. There were significant effects of time for PCS and dementia group for MCS, respectively after adjusting for covariates. PCS declined linearly by 0.46 (SE: 0.03) each year (p<0.001). Participants with an eventual dementia diagnosis had lower overall estimated MCS 2.86 (SE: 0.8) than participants who did not develop dementia (p<0.001) (Supplemental Table 3).
Table 2 displays adjusted hazard ratios (HRs) for dementia diagnosis from multivariable Cox models. Baseline SF-36 MCS was significantly associated with risk of dementia in the adjusted model (HR = 0.6 for a 10-unit difference, 95% CI=0.5-0.7), while baseline SF-36 PCS was not significant.

Table 2. Association between SF36 MCS and Risk of Dementia Based on Adjusted Cox Model

*Variables included in the adjusted models were MCS, Baseline age, Black, APOE ε4 carrier, Memory complaint, Sleep Apnea.
 

Figure 1. Comparison of PCS and MCS scores by visits and cognitive status at each visit

 

Discussion

This study investigated whether SF-36 PCS and MCS summary scores were changed over time, and whether they were associated with the incidence/risk of dementia, in a U.S. older adult male population. We found that higher baseline SF-36 MCS was associated with a significantly lower risk of dementia. Baseline PCS was not significantly associated with risk of dementia. Over 5 years of follow-up, PCS decreased significantly but slowly, while MCS did not change significantly.
Although previous studies on the SF-36 as a predictor of incident dementia diagnosis are rare, the result that MCS can predict incidence of dementia is not surprising. There are several possible explanations to describe the associations between SF-36 summary scores and risk of dementia. First, lower MCS score may be a preclinical manifestation for cognitive impairment and/or dementia. The MCS score may capture concerns over early cognitive changes. Second, three scales—Mental Health, Role-Emotional, and Social Functioning—are the most heavily weighted domains that contribute to the MCS composite measure. Lower MCS scores potentially indicate presence of psychological and/or psychosocial risk factors such as social support and networks, exposure to discrimination, satisfaction with the life, which may be highly associated with risk of dementia. Peitsch et al showed that general life satisfaction predicted risk of dementia in older adults (30). Gulpers et al showed through a meta-analysis that anxiety is associated with increased risk for dementia (31). Furthermore, the relationship between lower MCS and increased risk of dementia can be traced back to self-reported health. Several studies reported that self-reported health associates with dementia risk. Montlahuc et al. showed that increased risk of dementia was associated with poor (adjusted HR = 1.7, 95% CI: 1.2-2.4) or fair self-rated health (adjusted HR = 1.5, 95% CI: 1.00-2.2) compared to those with good self-related health (32). Abner et al. found that subjective memory complaints are associated with increased risk of dementia (33). Certainly, cognitively impaired participants may not rate the same set of health conditions in the same way as participants who are not cognitively unimpaired. The lower MCS score may also represent unmeasured or undiagnosed diseases or other unmeasured confounding factors. We would hypothesize that a brain disease may manifest earlier in mental symptoms than physical ones, particularly in the way they are measured by SF-36. Similar to Sabia et al’s study that physical activity was not found as a risk factor of dementia, our study showed that PCS did not predict risk of dementia (34). Also, Shimada et al found that physical frailty was not significantly associated with risk of dementia (35).
This study also has limitations as previously described (20). Only 35.8% PREADiVSE participants completed the SF-36. Participants who completed the SF-36 were similar to participants who never completed the SF-36 on the proportion of APOE 4 carriers, but different on age, education, race, ethnicity, and family history of dementia. So the numbers of cases developed from the participants in this study may be disproportional to the numbers of cases from non-participants, which may lead to biased result. Also, clinical trial participants are often different than the trial’s target population. Here, our sample was highly educated and reported better overall health than the general population of U.S. men age 65 and over, so our results may have limited generalizability to other populations. We plan to replicate this analysis in independent datasets.
Strengths for the current study include a large, well characterized sample with over five years of average follow-up. The use of normative methods to estimate the MCS and PCS SF-36 summary scores, which are easily interpretable, is also a strength. These SF-36 summary scores are also reliable (17). Finally, using the two summary scores reduced the number of statistical comparisons but still allowed for mental and physical HRQoL to differ.

 

Conclusion

Our study provides evidence that MCS from HRQoL may predict incidence of dementia in older men. Although the exact mechanism remains unclear, this may occur because of unmeasured factors, or underlying neuropsychological factors. In summary, SF-36 MCS may predict future dementia, and thus may have utility either as a modifiable risk factor or early warning sign of impending cognitive decline. Further studies with more diverse populations and longer follow-up are needed.
 

Funding: Funding Source: PREADViSE (NCT00040378) is supported by NIA R01 AG019421. Additional support for the current study comes from NIA R01 AG038651 and NIA P30 AG028383. SELECT was supported by NCI grants CA37429 and UM1 CA182883. 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 except that NCI was involved in the design of SELECT.

Acknowledgments: We sincerely acknowledge all PREADVISE study participants for their participation and thank all of the PREADVISE support staff and Biostatistics group for assistance with study procedures and data management.

Conflict of interest: Drs. Erin Abner and Richard Kryscio report grants from NIA during the conduct of the study. The other authors have no conflict interests.

Ethical Standards: The University of Kentucky Institutional Review Board (IRB) and the IRBs at each SELECT study site approved PREADViSE research activities. All participants provided written informed consent.

 
SUPPLEMENTARY MATERIAL
 

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DETECTING TREATMENT GROUP DIFFERENCES IN ALZHEIMER’S DISEASE CLINICAL TRIALS: A COMPARISON OF ALZHEIMER’S DISEASE ASSESSMENT SCALE – COGNITIVE SUBSCALE (ADAS-COG) AND THE CLINICAL DEMENTIA RATING – SUM OF BOXES (CDR-SB)

 

A.M Wessels, S.A. Dowsett, J.R. Sims

 

Eli Lilly and Company, Indianapolis, IN, USA

Corresponding Author: Alette Wessels, Eli Lilly and Company, Indianapolis, IN 46285, USA, E-mail: wessels_alette_maria@lilly.com, Tel: 317-2769502

J Prev Alz Dis 2018;5(1):15-20
Published online January 11, 2018, http://dx.doi.org/10.14283/jpad.2018.2

 


Abstract

The Alzheimer’s Disease Assessment Scale’s cognitive subscale (ADAS-Cog) has been widely used as an outcome measure in Alzheimer’s Disease (AD) clinical trials. In its original form (ADAS-Cog11), the scale has been used successfully in mild-to-moderate AD dementia populations, but its use is more limited in the study of earlier disease (mild cognitive impairment [MCI] or mild dementia due to AD) owing to lack of appropriate sensitivity of some items. With recent focus on earlier treatment, efforts have focused on the development of more sensitive tools, including the Clinical Dementia Rating-Sum of Boxes (CDR-SB), a global assessment tool to evaluate both cognition and function. The ability of the ADAS-Cog and CDR-SB to detect treatment group differences in the clinical trial environment has not been systematically studied. The aim of this analysis was to compare the utility of these tools in detecting treatment group differences, by reviewing study findings identified through advanced searches of clinicaltrials.gov and Ovid, and press releases and scientific presentations. Findings from placebo-controlled studies of ≥ 6m duration and enrolling >100 participants were included; reporting of both the ADAS-Cog and CDR-SB at endpoint was also a requirement. Of the >300 records identified, 34 studies fulfilled the criteria. There were significant placebo versus active drug group differences based on findings from at least one measure for 14 studies. The ADAS-Cog detected treatment differences more frequently than the CDR-SB. Based on these and previously published findings, the ADAS-Cog appears more useful than the CDR-SB in detecting treatment group differences..

Key words: CDR-SB, ADAS-Cog, clinical trial, outcome measures, Alzheimer’s disease.


 

Introduction

The Alzheimer’s Disease Assessment Scale’s cognitive subscale (ADAS-Cog) (1) was developed in the 1980’s in response to the perceived shortage of specific instruments to assess efficacy of drug treatments in Alzheimer’s Disease (AD) clinical trials. The scale was designed for evaluation of the severity of cognitive and non-cognitive behavioral dysfunctions characteristic of individuals with AD and has been widely used as an outcome measure in AD clinical trials. In its original form (ADAS-Cog11), the scale comprised of a composite of 11 items (0-70 point score) to assess memory, orientation, language, and praxis. Later, modifications were made to include additional relevant tests and to increase sensitivity to earlier stages of the disease- Delayed Word Recall (ADAS Cog12, 0-80 points) plus Number Cancellation (ADAS-Cog13, 0-85 total points) plus a Maze test (ADAS-Cog14, 0-90 points) (2). ADAS-Cog is scored based on both assessment of the ability of an individual to perform selected tasks and rater’s subjective assessment of the affected individual’s current functional performance during testing. In addition, there are alternative test versions available to facilitate multiple testing of the same individual.
Although the ADAS-Cog11 has been used successfully in symptomatic treatment trials of mild-to-moderate AD dementia populations, its use is more limited in the study of AD earlier in the clinical continuum (mild cognitive impairment [MCI] or mild dementia due to AD) due to lack of appropriate sensitivity of some of the items. Specifically, in individuals with mild or moderate AD dementia, a number of the ADAS-Cog subscales demonstrate ceiling effects (individuals score maximally) (3-5), such that these items are likely to be uninformative in the study of populations at earlier stages of the disease. This could potentially be a contributing factor in the failure of some clinical trials of AD treatments.
With recent focus on treatment of MCI and mild AD dementia, and the recognized limitations of using the ADAS-Cog in these populations, research efforts have focused on the development of more sensitive tools that can provide clinical meaningful findings. The Clinical Dementia Rating-Sum of Boxes (CDR-SB) (6, 7) is a global assessment tool that can be used to effectively evaluate both cognition and function, with few floor or ceiling effects in a mild to moderate AD dementia population (8). The tool was initially developed to measure dementia severity and covers six categories or “boxes” – Memory, Orientation, Judgment and Problem Solving, Community Affairs, Home and Hobbies, and Personal Care. CDR global ratings, calculated using an algorithm, range from 0 (no dementia) to 3 (severe dementia) while CDR-SB scores, calculated by adding the box scores, range from 0 to 18 (with higher scores indicative of more impairment). Scoring is determined by a clinician through a semi structured and in-depth interview with both the affected individual and their caregiver, rather than through direct testing. This scale demonstrates acceptable psychometric characteristics (8, 9) and has been shown to be sensitive enough to detect disease progression, even in populations with less advanced clinical disease (10, 11). Because the CDR-SB is an assessment tool that measures both cognition and function, it is suggested for use as a single primary endpoint for trials studying individuals with early clinical signs of AD (MCI due to AD) (12, 13).
While ADAS-Cog and CDR-SB have been well studied with regard to detecting disease progression, the ability of each tool to detect treatment group differences in the clinical trial environment has not been systematically studied. The aim of this analysis was to compare the utility of a tool designed to measure cognitive ability (ADAS-Cog) versus a global assessment tool (CDR-SB) in detecting treatment group differences in placebo-controlled trials, through a review of the published literature.

 

Methods

Published findings from double-blind, placebo-controlled Phase 2 and Phase 3 clinical trials of 6 or more months duration, enrolling 100 or more participants with AD (defined at the individual study level), and including both ADAS-Cog and CDR-SB as endpoints were identified through advanced searches of clinicaltrials.gov and Ovid® (Ovid Technologies, Inc.) in the August/September 2017 timeframe, and through press releases or presentation at scientific meetings.  Exploratory and post-hoc analyses were excluded.
In clinicaltrials.gov, we searched closed Phase 2 and Phase 3 studies of Alzheimer’s Disease, and used additional search words of CDR or clinical dementia rating. The Ovid advanced search was ADAS plus Alzheimer’s Disease OR Alzheimer and the results of the search were subsequently limited to [clinical study or clinical trial, all or clinical trial or multicenter study or randomized controlled trial].  “ADAS” rather than “CDR” was chosen for the Ovid search since, even if both measures were used in the study, ADAS was more likely to be a primary endpoint and thus mentioned in the abstract.
Findings were reviewed to ensure that they fulfilled the outlined criteria and p values for active versus placebo treatment group difference (in change from baseline) were presented for ADAS-Cog and CDR-SB. Only the trial endpoint was considered in the analysis. Where multiple doses of active drug were studied, all were considered in the analysis. Findings from pre-specified subgroup analyses based on baseline Mini-Mental State Examination (MMSE) were also included. While the main focus of this analysis was ADAS-Cog versus CDR-SB, findings from the MMSE and the Alzheimer’s Disease Cooperative Study Group-Activities of Daily Living (ADCS-ADL) are also presented when available.

 

Results

Our search of clinicaltrials.gov yielded 92 records for further review, while the Ovid search yielded 232 (some of which were repetitions of those found through clinicaltrials.gov). We located 19 studies through clinicaltrials.gov that fulfilled the criteria for inclusion in the analysis and for which results had been published, either on clinicaltrials.gov or in a scientific journal. Ovid provided findings from an additional 12 studies. Findings from a further 7 studies were identified through press releases and/or scientific meeting presentations. Overall, results from 34 studies were considered in the final analysis (Table 1). Including findings from the various doses and subgroup analyses by baseline MMSE score resulted in a total of 61 records.

Table 1. Studies identified as fulfilling the criteria to be included in the analysis

Table 1. Studies identified as fulfilling the criteria to be included in the analysis

Abbreviations:  ACHEI, acetylcholinesterase inhibitor; ADAS-Cog, Alzheimer’s Disease Assessment Scale’s cognitive subscale; ADCS-MCI, Alzheimer’s Disease Cooperative Study- mild cognitive impairment; DHA, docosahexaenoic acid; MMSE, Mini-Mental State Examination  ; NA, not available; RAGE, receptor for advanced glycation end products; *Number of active treatment groups reported. Equals 1 in following cases –  i) if more than one dose studied but results reported are those for the active doses combined; ii) if more than one dose studied but results reported are for one dose only; and iii) if one active treatment arm but titrated dose; †Number randomized in all treatment groups; ‡MMSE inclusion criteria reported differently across studies (e.g., 14 to 26, 14-26, 14-26 inclusive). This range was inferred from what was reported; §Subgroup analysis by baseline MMSE performed; ||This was a 36-month study but p values only reported at 12 months; {Two active treatments included in study; #Post futility analysis; **Subgroup analysis of population with mild AD dementia in EXPEDITION and EXPEDITION2

 

Of these 61 records, 44 did not show a significant treatment group difference for any of the four measures (ADAS-Cog, CDR-SB, MMSE or ADCS-ADL).  There were significant treatment group differences (placebo versus active drug) based on findings from at least one measure for 13 studies (17 records) (Table 2). For 6 studies (8 records), ADAS-Cog but not CDR-SB findings showed significant treatment group differences; for 3 studies (3 records), CDR-SB but not ADAS-Cog findings showed significant treatment group differences; for 4 studies (5 records), both ADAS-Cog and CDR-SB findings showed significant treatment group differences. In the latter case, the level of significance was greater for ADAS-Cog than CDR-SB in most all cases. MMSE and ADCS-ADL findings showed significant treatment group differences for 6 and 4 records, respectively, but in most cases either ADAS-Cog or CDR-SB also showed significant treatment group differences.

Table 2. Studies for which statistically different active-placebo treatment groups differences reported for at least one measure

Table 2. Studies for which statistically different active-placebo treatment groups differences reported for at least one measure

Abbreviations:  ADAS-Cog, Alzheimer’s Disease Assessment Scale’s cognitive subscale; ADCS-MCI, Alzheimer’s Disease Cooperative Study- mild cognitive impairment; MMSE, Mini-Mental State Examination  ; NA, not available; ns, not significant (p≥0.05); Bolded p values highlights those records in which placebo-active treatment group difference is p<0.05 (bold italicized, p<0.005).

 

Discussion

The aim of this study was to assess the sensitivity of the ADAS-Cog versus the CDR-SB based on their ability to detect treatment group differences in placebo-controlled trials, identified through a review of clinicaltrials.gov and the published literature. Based on the data review, the ADAS-Cog detected treatment differences more frequently than CDR-SB. When both scales detected active drug-placebo differences, p values were generally favorable for the ADAS-Cog.
A reason for the superior performance of ADAS-Cog in detecting treatment group differences is likely the result of differences in how the tool is administered, the nature of the questions, and scoring methodology. In the case of the ADAS-Cog, items are generally specific tasks and are administered in a structured manner, so that assessment is more objective than in the case of the CDR-SB, where items are administered in a semi-structured fashion and scoring is based on the clinician’s view of the affected individual. In addition, the scoring of the CDR-SB also relies upon input from the caregiver to the rater and this can be expected to result in an additional level of subjectivity to hinder accurate evaluation.
In order to be successful in identifying treatment group differences in a multi-center trial setting, a tool should exhibit high inter-rater agreement. ADAS-Cog has been shown to exhibit acceptable inter-rater reliability (3, 46). In the case of CDR-SB, the inter-rater reliability has generally been found to be acceptable (47-51) though some have reported low inter-rater agreement in populations with mild AD dementia (49, 50). These studies of CDR-SB were all conducted in North America. While both ADAS-Cog and CDR-SB have been used globally, there is potential for greater variability where tools include items to assess functional impairment, as is the case for CDR-SB; functional impairment is often perceived differently across countries as a result of cultural and societal differences (52, 53).
There are no published studies in which ADAS-Cog and CDR-SB have been directly compared in terms of inter-rater reliability.  Khan et al (46), in a study of the psychometric properties of ADAS-Cog in double-blind, placebo-controlled, multicenter trials found that the three clinician-rated (subjective) items in the ADAS-Cog (spoken language ability, word finding difficulty and comprehension) generally showed more rater variability than the performance (objective) items. As a result, one can expect that inter-rater reliability might be lower for the CDR-SB, where the scoring relies more heavily on clinical judgement.
While ADAS-Cog was superior in this analysis, the key question is whether these findings can be generalized to studies going forward. The AD clinical trial landscape is changing rapidly, with increasing focus on the early AD population (mild AD dementia, MCI) and on use of disease-modifying therapies over symptomatic treatments.  The studies included in the current analysis covered populations along the continuum, from MCI to advanced AD dementia but only 9 of the 34 studies included were focused on mild AD dementia or MCI (MMSE ≥ 20). Since CDR-SB measures both cognitive and functional impairment, it is predicted that it may be less useful in populations with MCI/mild AD dementia where functional impairment is less or not apparent. In addition, in individuals with “very mild AD” (defined as those with a CDR-SB rating of 0.5), the CDR-SB was shown to be unable to detect separate cognitive and functional factors [8], putting into question its benefit over ADAS-Cog in detecting both cognitive and functional impairment. In the earliest stages of clinically apparent disease, there is potential that retesting over the course of a study could result in study subjects learning some of the simple memory tests, like those included in the CDR-SB, thus obscuring decline. The availability of alternate version for retesting can alleviate this. Currently, alternate versions are available in the case of ADAS-Cog, but not for CDR-SB.
The ADAS-Cog is also likely to have shortcomings in study of populations earlier in the clinical continuum, due to the substantial ceiling effects apparent with some of the scale components. A significant ceiling effect has not been observed with the Delayed Word Recall and Number Cancellation items, additional components of the modified versions of the ADAS-Cog (12-, 13-versions)  (5, 10). It can therefore be predicted that these versions might be more sensitive at detecting treatment group differences in the earlier stages of disease. In the current study, while we did collect data on ADAS-Cog version used, there were insufficient data to identify an effect of ADAS-Cog version, if any, on findings. One potential challenge with using ADAS-Cog as a primary endpoint of a clinical trial is in relation to regulatory implications. The FDA has stipulated that the primary endpoint(s) should assess function as well as cognition (13). While the ADAS-Cog does encompass function to some degree, this is not seen as adequate and, as a result, studies employing the ADAS-Cog as the primary endpoint may be required to include a co-primary endpoint to measure function. This has implications for statistical testing and sample size.
There are limitations to this analysis. While every attempt was made to be inclusive of available study findings through use of multiple search methodologies, there are potentially findings that have not been located. With the recognized publication bias towards publishing positive study findings (i.e., significant active versus placebo group outcome differences), it is likely that there are trials that have been performed but which were not subsequently published. This was evident from the clinicaltrials.gov findings where there were studies located but no published findings. This is not expected to have skewed the main findings of this paper which focused on studies with at least one positive finding. This analysis is exploratory and no formal statistical methodology was used to compare the findings. Conclusions were drawn based on findings from 13 (17 records) studies.
In conclusion, based on findings from this analysis, the ADAS-Cog appears to be more sensitive than CDR-SB in the detection of treatment group differences in the clinical trial environment. These findings, together with the recognized benefits of ADAS-Cog, including its direct measure of cognition, standardized instructions and scoring methodology, and the availability of different versions, support its use in the clinical trial setting as a primary endpoint and this may even more applicable in the study of earlier clinical disease.

 

Acknowledgements: None

Funding: This study was conducted and the manuscript prepared by full-time employees at Eli Lilly and Company.

Conflict of interest: Wessels, Dowsett and Sims are full time employees and minor stock holders of Eli Lilly and Company.

Ethical standards: Ethical review board approval and participant informed consent are reported in the primary publications of the studies from which data were used in the current analyses.

 

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