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K. Wang1, H. Liu1


1. Center of Medical Reproduction, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China. Kanran Wang’s ORCID ID is 0000-0002-6958-7677

Corresponding Author: Hong Liu, MD, PhD, Center of Medical Reproduction, the First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Street, Yuzhong District, Chongqing, China, 400016,

J Prev Alz Dis 2021;
Published online June 24, 2021,



BACKGROUND: This study aimed to assess the relation of early-onset type 2 diabetes (age<55years) versus later in life to the risk of dementia, Alzheimer Disease (AD) dementia and stroke.
Methods: This study was based on the Framingham Heart Study Offspring cohort (FHS-OS) which is a community-based prospective cohort. Glycemic status was ascertained at serial examinations over six decades among participants who initially did not have diabetes. Surveillance for incident events including dementia and stroke has been continued for approximately 30 years.
Results: At baseline, there were 142 (5%) subjects with onset of diabetes prior to age 55 years, 172 (6%) subjects with 55-64 years, 349 (11%) subjects over 65 years and 2389 (78%) subjects without diabetes. The risk of dementia, AD and stroke increased with decreasing age of diabetes onset (P<0.05, for trend). Compared with never developing diabetes, early-onset diabetes conferred a higher risk of all dementia, AD dementia and stroke [HR 2.86(1.16-5.51) for dementia; HR 2.42(1.63-4.33) for AD; HR 2.85(1.37-3.98) for stroke]. Whereas later-onset diabetes was only associated with greater risk for stroke, neither dementia nor AD.
Conclusion: Early-onset diabetes was stronger associated with an increased risk of all dementia, AD dementia and stroke than later-onset.

Key words: Early-onset diabetes, dementia, Alzheimer disease, risk factor, Framingham Heart Study.



Dementia is a major public health concern posing substantial burden on patients, their proxies, and national health-care systems (1-3). It comprises Alzheimer disease (AD), which contributes to 50–70% of dementia cases, vascular dementia (VD), which contributes to ~25%, and other forms of dementia[4]. The causes of dementia, especially Alzheimer’s disease (AD), remain unclear, and there are no disease-modifying therapies (5). Thus, there is an urgent need to identify factors that can prevent development of dementia to decrease the burden of this disease.
Type 2 diabetes (herein referred to as “diabetes”) is highly prevalent and manifests frequently at a younger age as well as at older age (6). Although it is known that diabetes confers substantial risk for dementia and AD, it remains unclear whether this risk significantly varies by age of diabetes onset (7). On the one hand, dementia or AD risk may be more pronounced in earlier-onset diabetes, among persons with longer durations of exposure and more likely poor glycemic control; on the other hand, the risk may be greater in later-onset diabetes, among persons in whom age-related risk factors for dementia tend to aggregate (8). Besides, earlier-onset diabetes may represent a more aggressive form of disease, characterized by a much more rapid deterioration of the β-cell function with a more frequent need for insulin therapy and rapidly raised the incidence of macrovascular and microvascular complications (9, 10). Furthermore, plenty of large-scale epidemiological studies have confirmed that early-onset diabetes is associated with greater risk for adverse outcomes including mortality, cardiovascular disease, metabolic disease and psychological disease (11). However, to our knowledge, few number of epidemiological studies have assessed the relationship between early-onset diabetes and dementia and AD. At the same time, we intended to explore the relationship between early-onset diabetes and the risk for stroke in the analysis, as both stroke and dementia share common risk factors and etiologies.
Accordingly, using large-scale data from community based Framingham Heart Study Offspring cohort (FHS-OS) with detailed review of all medical records and a nearly 30-year follow-up, we aimed to examine the long-term dementia, AD and stroke risk associated with developing diabetes early versus late in the adult life course.



Study Design

This study was carried out as a secondary analysis of data from the population-based Framingham Heart Study Offspring cohort (FHS-OS) (12). The FHS-OS is a longitudinal community-based study established in 1971 and includes 5124 men and women who were children and spouses of children of the original Framingham Heart Study (FHS). And the participants of FHS-OS were reassessed 8 years after the baseline examination (in 1971) and every 4 years thereafter including standardized interviews, physician examinations, and laboratory testing. The details of the study design of FHS-OS have previously been described elsewhere (13). This study complied with the Declaration of Helsinki. The Boston Medical Center’s institutional review board approved all study protocols, and all participants provided informed consent. The National Heart, Lung, and Blood Institute (NHLBI) of National Institutes of Health (NIH) has approved this study protocol as well.

Type 2 Diabetes Assessment

Non fasting blood glucose was assessed at the first two examinations; in the latter cohort, fasting blood glucose was assessed at all subsequent examinations. Diabetes status was defined as presence of fasting blood glucose>126mg/dL or non-fasting blood glucose >200 mg/dL, or self-reported use of insulin or oral hypoglycemic agents, at two consecutive examinations (to ensure the stability over time for a given glycemic phenotype) (14).We defined disease onset as the first examination at which the criteria for diabetes was met, which was ascertained with use of all available plasma glucose data collected at serial examinations attended by FHS-OS participants. What’s more, we assumed that diagnosed diabetes was type 2 diabetes based on extremely low rates of type 1 diabetes in our cohort and the high prevalence of type 2 diabetes in the U.S (15). The “early-onset” diabetes was defined as diabetes diagnosed prior to age 55 years, given epidemiological data suggesting characteristics including clinical feature, morbidity, mortality and healthcare expenditure among persons with diabetes similar to characteristics among this age-group (16, 17). We also selected age 55 years as the threshold for defining early-onset diabetes to optimize the number of individuals and, in turn, statistical power for analyzing individuals in our study who had suffered the dementia, AD dementia or stroke prior to reaching this age threshold.

Ascertainment of Dementia and AD

The surveillance methods and dementia tracking for the FHS-OS have been published (see supplement eMethods for details). Cognitive screening is performed at each FHS-OS examination cycle using the Mini-Mental State Examination (MMSE) supplemented with extensive neuropsychological testing at selected examination cycles. A diagnosis of dementia was made in accordance with the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (18). And Alzheimer’s disease (AD) based on the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s disease and Related Disorders Association (NINCDS-ADRDA) for definite, probable, or possible AD (19).

Ascertainment of stroke

Stroke incidence was assessed through the continuous monitoring of hospital admissions in Framingham and by reviewing all available medical records and results. Stroke was defined as focal neurological symptoms of rapid onset and presumed vascular origin, lasting >24 hours or resulting in death within 24 hours. A committee comprising of least 3 FHS investigators, including at least 2 neurologists, adjudicated stroke diagnosis. The committee considered all available medical records, brain imaging, cerebrovascular imaging, and the assessment of the study neurologist who visited the participant (20).


The clinical covariates were drawn from the first examination cycle at which data were available. BMI was calculated as kg/m2. Waist circumference (in inches) was measured at the level of the umbilicus. Current smokers were defined as participants who smoked regularly in the year preceding the examination cycle. The educational level and use of antihypertensive medications were assessed by medical interview. Total cholesterol, low-density lipoprotein cholesterol and fasting blood glucose were measured after an overnight (>10 hours) fast.

Statistical Analysis

Descriptive statistics was performed for the 4 subgroups: participants with age at onset of diabetes less than 55 years, 55-64 years and over 65 years, and persons without ever developing diabetes serving as the referent group. For continuous variables mean, standard deviation and range were calculated for approximately normally distributed data, otherwise median and range were used. For discrete data, absolute and relative frequencies were computed. Follow-up for dementia and stroke was from the baseline examination to the time of incident event. And for persons with no incident events, follow-up was censored at the time of death or the date the participant was last known to be dementia or stroke free. For survival analysis, we used multivariable Cox regression to relate case-versus-control status to age-group at onset of diabetes with adjustment for age at dementia, sex, smoking status, body mass index (BMI), systolic blood pressure (SBP), education levels, serum total cholesterol, use of antihypertensive therapy, MMSE and duration of diabetes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. The same analysis was carried out for Alzheimer’s disease and stroke. All analyses were performed with SAS software, version 9.4 (SAS Institute, Cary, NC). A 2-sided with P<0.05 was considered statistically significant.




Of the 5142 participants of the FHS-OS, 3261 subjects attend the last examination for outcome ascertainment. Wherein, 209 subjects were excluded because 94 died at age <55 years without diabetes, 11 had diabetes and was aged >55 years at baseline, 47 had no assessment about dementia or stroke, 8 suffered from dementia and 6 stroke before the diabetes and 43 had no follow-up for dementia or stroke (Figure 1). Thus, 3052 subjects could be included in the final analyses, in which 142 (5%) subjects with onset of diabetes prior to age 55 years, 172 (6%) subjects with 55-64 years, 349 (11%) subjects over 65 years and 2389 (78%) subjects without diabetes.
The characteristics of our study participants were shown in Table 1. Participants with early onset of diabetes had greater prevalence of hypertension and tended to smoke more. In addition, they were less likely to be women and more likely to have a high blood glucose, total cholesterol, SBP, WC and BMI. See Table 1.And the disease duration between different subgroup were shown in the supplement eTable 1.

Table 1. Characteristics of the study sample of the Framingham Heart Study by diabetes onset group

Abbreviations: BMI, body mass index; WC, waist circumference. LDL, low-density lipoprotein, TC: total cholesterol, MMSE, mini-mental state examination; Values displayed are from the first examination cycle where these were available, except for the age at ascertainment of dementia and the age of diabetes onset

Figure 1. Study profile from the Framingham Heart Study offspring cohort


Early onset of Type 2 Diabetes and Risk for Dementia and Stroke

In the final research sample, 282 (9%) developed dementia, including 181 (6%) with AD dementia over a median follow-up of 12 years (interquartile range, 9 to 14 years), with an overall incidence rate of 8.30 per 1000 person-years. In addition, 206 (7%) cases of incident stroke were identified over a median follow-up of 12 years (interquartile range, 6 to 15 years) with an overall incidence rate of 6.06 per 1000 person-years.
The trends of decreasing age of onset of diabetes in relation to increasing risk of dementia ,AD and stroke were statistically significant (P<0.05 for all, trend).When compared with individuals who never developed diabetes, people with onset of diabetes prior to age 55 years were associated with 2.86-fold higher risks (HR 2.86, 95%CI 1.16-5.51) of dementia and 2.42-fold higher (HR 2.42, 95%CI 1.63-4.33) of AD independent of potential confounders including sex, age at events, smoking status, SBP, BMI, total cholesterol, use of antihypertensive, education levels , MMSE and duration of diabetes. In the contrast, onset of diabetes at age over 65 years did not confer a statistically significant higher risk of dementia or AD (HR 1.00, 95%CI 0.47-2.10 for dementia, HR 0.96, 95%CI 0.41-1.78 for AD). And the results were similar when limiting to the risk of stroke, early onset of diabetes was associated with a greater risk of stroke than individual with late-onset diabetes (HR 2.85, 95%CI 1.37-3.98 with onset before age 55 years, HR 1.63, 95%CI 1.23-3.56 for 55-64 year, HR 1.46, 95%CI 1.06-3.16 for over 65 years), compared with no diabetes after multivariate adjustment (Table 2). Figure 2 showed the cumulative incidence curves for dementia, AD and stroke stratified by groups with different onset age of diabetes after full adjustment.

Table 2. Cumulative hazards based on diabetes age of onset

Model 1 Sex and age at events; Model 2 in addition for smoking status, SBP, BMI, TC, use of antihypertensive, education levels and duration of diabetes. All dementia and AD dementia were additionally adjusted for MMSE; Abbreviations: HR, hazard ratio; AD, Alzheimer disease; SBP: systolic blood pressure; BMI: body mass index; TC: total cholesterol; MMSE, mini-mental state examination; All the covariates were based on data from first examination at which measures and assessments were available.

Figure 2. Adjusted cumulative incidence of dementia and stroke based on diabetes age of onset

Abbreviations: AD, Alzheimer disease. Data are for cumulative incidence of (A) all dementia, (B) Alzheimer disease dementia and (C) stroke among participants based on diabetes age of onset in the Framingham Heart Study. Adjustments were made for sex, age at events, smoking status, SBP, BMI, total cholesterol, use of antihypertensive, education levels and duration of diabetes.



We conducted a longitudinal study of diabetes and dementia, AD and stroke risk in the FHS-OS and observed that a potentially important subset of diabetes may be defined based on the age of onset of diabetes. Specifically, it was found that when diabetes occurs prior to age 55 years as early-onset diabetes, there is a significantly greater life-time risk for dementia, AD and stroke compared with diabetes that manifests at a later age after adjustment for conventional risk factors.
Previous studies have confirmed that diabetes increases the risk of dementia, with HR values ranging from 1.5 to 2.0. To the end, a meta-analysis of 144 prospective studies showed a significant association between diabetes and increased risk of all-cause dementia (RR: 1.43, 95%CI: 1.33–1.53, I2 =79%) and AD (RR: 1.43, 95% CI: 1.25-1.62, I2 =81%) (7). However, it is well-known that diabetes is a heterogeneous disorder in terms of its natural history (21). Individuals with diabetes can vary widely in their disease course and complications-and this variation poses ongoing clinical challenges for diagnosing and managing affected persons (22). Thus, as part of efforts to identify higher-risk disease subgroups amid heterogeneity, only a few prior studies have assessed the effects of earlier versus later onset of diabetes on the dementia risk posed, the results have varied greatly and most were limited in the type 1 diabetes (T1D) in adolescents and the cognitive function instead of the clinical outcomes. On the one hand, a cross-sectional study recruiting 50 subjects with T1D with 30 healthy controls (ages between 7 and 16 years) suggested that subjects with early-onset T1D had significantly poorer performance than controls on most subtests of memory, intelligence and executive functioning as measured by the Benton Visual Retention Test (BVRT), Wechsler Intelligence Scale for Children (WISC), and Wisconsin Card Sorting Test (WCST) (23). A meta-analysis including 2,144 children consisted of 1,393 study subjects with type 1 diabetes and 751 control subjects from 19 studies demonstrated that cognitive effects are most pronounced and pervasive for children with early-onset diabetes with moderately lower performance across most cognitive domains compared with control subjects (24). On the other hand, although there was few study focusing on the relationship between early-onset diabetes and cognitive function. There were studies showed that early-onset diabetes suggested no burden of adverse outcomes risk factors (25). For instance, some studies have founded either a lower risk or no difference in risk for macrovascular complications among persons with earlier-versus later-onset diabetes after accounting for diabetes duration (26).
Given this situation, the early- versus late-onset diabetes were defined using objective clinical and biochemical data collected from serial examinations in a community-based cohort with over 30 years of prospective follow-up which allowed for a comprehensive assessment of long-term dementia risk in relation to age of diabetes onset. To our knowledge, our study is the first to demonstrate accelerated probability of developing all dementia, AD and stroke year-on-year at a population level in early-onset diabetes compared with late-onset based on much more accurate assessments for age of diabetes onset. And it is not a cross-sectional observations, but a long-term implications after full adjustment. Although diabetes onset in older age is of often associated with excess dementia or stroke risk factors that can further increase their risks and a longer time window to develop the events, diabetes onset at a younger age may represent a more aggressive subgroup that confers greater risks even after the disease duration and other confounders are accounted for.
There are several possible explanations for our main findings. Firstly, the metabolic disease followed the early-onset diabetes may harm cognitive function and lead to stroke. It is found in several studies that individuals with earlier- versus later-onset diabetes appear to have more pronounced clinical features of metabolic disease such as greater obesity, adiposity, dyslipidemia, hyperglycemia and hyperuricemia (27-29). And all of the metabolic disease are related to the development of dementia and stroke (30-33). Secondly, early-onset diabetes may be a phenotype distinct from late-onset diabetes with increased risk for dementia conferred by certain genetic variants besides APOE4 gene, rather than the same disease simply manifesting at a different point in life (34-37). Hence, the age of onset of diabetes can identify an especially high-risk subgroup of diabetes that confers a higher risk of dementia and stroke, although the mechanisms and certain genetic variants are incompletely understood.
The main strength of our study was the use of a community-based sample with detailed characteristics, accurate definition of dementia, AD and stroke and precise assessment for age of diabetes onset. Limitations of our study include the observational nature of the study and a small number of incident events. Second, we also did not account for the changes over time in the pharmacological approach to the primary and secondary prevention of dementia and stroke which may have affected the outcomes in our study. Thirdly, as our sample was of Caucasian decent, it is unclear how our results generalize to other ethnic groups. Fourthly, although, many sociodemographic variable were adjusted, there may be residual confounding by variables that cannot be measured with precision or were not available in the public dataset.



To sum up, the findings of this research demonstrate that relatively early-onset diabetes was associated with an increased risk of all dementia, AD dementia and stroke independent from multiple demographic factors. These findings have implications for prioritizing efforts to reduce dementia, AD and stroke risk in persons with prevalent diabetes-particularly younger individuals and the age of diabetes onset could represent a potentially indicator for prevention measures. It is upon future research to confirm our findings and to determine the targeted interventions and mechanism.

Data Availability

Data described in the manuscript, code book, and analytic code will not be made available because the authors are prohibited from distributing or transferring the data and codebooks on which their research was based to any other individual or entity under the terms of an approved NHLBI Framingham Heart Study Research Proposal and Data and Materials Distribution Agreement through which the authors obtained these data.

What this study adds to the literature

Relatively early-onset diabetes (age<55years) was associated with an increased risk of all dementia, AD dementia and stroke. Prioritizing efforts should be made in persons with prevalent diabetes-particularly younger individuals and the age of diabetes onset could represent a potentially indicator for prevention measures.


Conflicts of Interest: The authors declare that they have no conflicts of interest.

Acknowledgments: The authors thank the National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA and Chongqing Medical University, Chongqing, China.

Ethical Standards: The study procedures followed were in accordance with the ethical standards of the Institutional Review Board and the Principles of the Declaration of Helsinki.

Funding: Chongqing Medical University Scholarship Fund for Development of Young Talents (No. XRJH201901).





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X.-T. Wang1, Z.-T. Wang2, H.-Y. Hu1, Y. Qu1, M. Wang1, X.-N. Shen3, W. Xu1, Q. Dong3, L. Tan1,2,*, J.-T. Yu3,*


1. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China; 2. College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, China; 3. Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

Corresponding Author: Prof. Jin-Tai Yu, MD, PhD, Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai 200040, China; Or Prof. Lan Tan, MD, PhD, Qingdao Municipal Hospital, Qingdao University, China. E-mail address: (J.T. Yu); (L. Tan).

J Prev Alz Dis 2021;3(8):277-285
Published online May 28, 2021,



Background: Subjective cognitive decline (SCD) as an early pathological manifestation of brain aging has become more prevalent among older adults.
Objectives: We aimed to investigate the associations of subjective cognitive decline (SCD) with the combined risk of cognitive impairment and dementia.
Design: We performed a systematic review and meta-analysis via searching Embase, PubMed and Cochrane electronic databases from January 1 st 1970 to June 4th, 2020.
Setting: Prospective cohort studies
Participants: Healthy individuals were recruited from community, clinics and population.
Measurements: Healthy individuals with SCD were classified into exposure groups, while those without were considered as the reference group. Adjusted relative risks (RR) were estimated in a random-effects model. Both primary and subgroup analyses were conducted.
Results: Of 28,895 identified studies, 21 studies containing 22 cohorts were eligible for inclusion in the meta-analysis. SCD increased the risk of subsequent cognitive disorders (RR=2.12, 95% confidence intervals [CI] =1.75-2.58, I2=87%, P<0.01). To be specific, SCD conferred a 2.29-fold excess risk for cognitive impairment (RR=2.29, 95% CI=1.66-3.17, I2=83%, P<0.01) and a 2.16-fold excess risk for dementia (RR=2.16, 95% CI=1.63-2.86, I2=81%, P<0.01). In subgroup analyses, participants with SCD in the subgroup of 65-75 years old, long-education (>15 years) subgroup and subgroup of clinics showed a higher risk of developing objective cognitive disorders.
Conclusions: SCD is associated with an increased combined risk of cognitive impairment and incident dementia and should be considered a risk factor for objective cognitive disorders.

Key words: Subjective cognitive decline, cognitive impairment, dementia, systematic review, meta-analysis.



Longer life expectancy has led to the growth of the older population, and older adults might account for nearly 16% of the world’s population by 2050 (1). Disorders of aging, especially neurodegenerative changes, which eventually result in dementia, has become an increasing concern, in recent years (2). With a lack of curative treatments for cognitive impairment and dementia, many studies have focused on identifying risk factors at the prodromal and preclinical stages of Alzheimer’s disease (AD) (3). As an early pathological manifestation of brain aging, subjective cognitive decline (SCD), has become a research hotspot (4).
An international working group called the Subjective Cognitive Decline Initiative (SCD-I) focusing on advances in related research has been established (5). SCD could be defined as a self-experienced persistent decline in cognitive capacity in comparison with a previously normal status, which is unrelated to an acute event. Moreover, normal age-, sex- and education-adjusted performance on standardized cognitive tests is used to classify mild cognitive impairment (MCI) (6, 7). SCD has several alternative names, including subjective cognitive complaints (SCC) (8, 9), subjective memory decline (SMD) (10) and subjective memory complaints (SMC) (11). A previous systematic analysis provided evidence for the prognostic validity of memory complaints to predict the risk for subsequent dementia and cognitive impairment (12), while it might ignore the baseline cognitive status of included individuals. Besides, healthy controls without memory complaints should be taken into the consideration as the reference group to ensure the preciseness of analysis. Therefore, we conducted this meta-analysis in healthy population with more strict inclusion criteria. Our aim was to explore the association of SCD with the combined risk of cognitive impairment and dementia in longitudinal studies.



Search Strategy

This meta-analysis was conducted following the guidelines of the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) (13) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (14). PubMed, Embase and Cochrane databases were searched with the same strategy ‘(subjective memory decline OR concern* OR complaint* OR SCD OR SMC OR SMD OR SCC) AND (risk OR association) AND (dementia OR cogniti* OR alzheimer* OR MCI OR mild cognitive impairment)’ from January 1st, 1970 to June 4, 2020. Conference abstracts and unpublished studies were also reviewed. Additional studies were identified by screening related reviews and reference lists of studies. If full texts were unavailable, we contacted corresponding authors.

Study Selection

The study selection process was described in Fig.1. There were 28,895 studies from three databases, after deleting duplicates in the EndNote. Studies which met the following criteria were eligible: (1) studies investigating the association of subjective memory complaints with cognitive impairment or dementia (all-cause dementia [ACD] or vascular dementia [VaD] or AD); (2) prospective longitudinal studies with a follow-up of at least 6 months; (3) studies including cognitively normal participants at baseline who were divided into an exposure group with subjective cognitive concerns (assessed by various questionnaires) and a reference group without complaints; (4) studies using recognized diagnostic criteria for objective cognitive performance (including cognitive impairment or dementia) as an end point of the study, such as the criteria made by National Institute on Aging-Alzheimer’s Association (NIA-AA). We did not place language restrictions upon the eligibility criteria of included studies. Randomized clinical trials were excluded, as therapies, psychological suggestions and interventions provided may influence the associations of subjective memory complaints with cognitive impairment and incident dementia. Moreover, people with psychoactive medication use, neurological disease (e.g. Parkinson’s disease, epilepsy, and multiple sclerosis), history of brain lesion (e.g. infection and infarction), head trauma or other systematic diseases of sufficient severity to adversely affect cognition were also excluded. First, after screening the titles and abstracts, we excluded the articles unconcerned with our topic, and only included topic related ones (n=165) for further selection. We then read full texts of those potential eligible articles, searched bibliographies of relevant reviews or meta-analyses, and finally selected 21 articles based on the criteria mentioned above.

Data Extraction

We extracted authors, year of publication, study period, country, language, sample size, inclusion or exclusion criteria, source of participants, age, numbers of male and female individuals, education, follow-up time, methods of diagnosis, count data, unadjusted and adjusted estimates of odds ratio (OR), relative risk (RR), hazard risk (HR) and their 95% confidence intervals (CI) for cognitive impairment or incident dementia. As for we encountered some studies from same cohorts, we chose the study with the largest number of included participants at the baseline. Among effective values reported in the studies, we chose the maximally adjusted estimates. If effective values were not available directly, we used RR calculated by the ratio comparing the of incident rates of cognitive impairment or dementia between exposed and reference groups. Information was first extracted by one investigator, and then checked independently by another two authors. Discrepancies were resolved by discussion. When the data we required were not available in the article, we contacted the corresponding authors for original information.

Quality Assessment

The Newcastle-Ottawa Scale (NOS) has been used to assess the quality of published non-randomized studies in meta-analyses (15, 16). The NOS contains eight items which can be categorized into three dimensions (selection, comparability and outcome). A star system is employed to allow a semi-quantitative assessment of study quality, with a maximum of one star for each item except the comparability item which allows the assignment of two stars (17). The highest quality studies could be awarded a maximum of nine stars.

Statistical Analysis

We mainly analyzed the pooled RR, showing whether individuals with SCD at baseline were more likely than those without to develop cognitive impairment and dementia during follow-up in our study. Given that ORs tend to overestimate the effect sizes compared with RRs/HRs particularly when the incidence is not low, we transformed ORs into RRs using the following algorithm:(18)

RRadjusted = ORadjusted /[(1 − P0) + (P0 × ORadjusted]

P0 indicates the incidence of endpoint (dementia or cognitive decline) in the non-exposed group of the cohort. When P0 is not available, the incidence rate of total sample was used as a proxy.(18) HR, compared with RR, additionally considering the factor of time, might be approximately equal to RR at a point in time. Effective values across studies were combined to provide overall estimates and their 95% CIs using random-effects DerSimonian-Laird models (19). Participants with cognitive disorders were additionally stratified into cognitive impairment and dementia groups. Further subgroup analyses (stratified by different age, sex, year of education, follow-up time and source) were also conducted to investigate whether other factors would change the results using the same models. When calculating RR and 95%CI in subgroup analyses, participants without SCD in each subgroup were considered as the reference group. Each subgroup of basic characteristics might include at least three studies to ensure the reliability of subgroup analyses.
Heterogeneity between studies was assessed by I2 test statistics for each analysis. An I2 of less than 25% is considered as no statistical heterogeneity, 25% to 50% as low statistical heterogeneity, 50% to 75% as medium statistical heterogeneity, and more than 75% as high statistical heterogeneity (20). Meta-regression analyses (n≥10) were also conducted with robust variance estimation, assessing the potentially important covariates that might exert a substantial impact on between-study heterogeneity. Sensitivity analyses were additionally carried out to explore the source of heterogeneity by excluding one study at a time.
We also evaluated the potential publication bias with funnel plots for the outcomes, the symmetry of which was detected by Egger’s test. Egger’s test, also known as linear regression method, uses standard normal deviate and precision of included studies to establish regression equation (21). Moreover, if statistically significant publication bias was detected, the trim-and fill method was used to adjust for bias. A two-tailed P values <0.05 is considered as statistically significant. Statistical analyses were conducted in R (R programming).



Basic characteristics of included studies

A total of 21 studies (22-42) were selected for our meta-analyses (Fig.1). In a study conducted by Snitz (36), both the individuals from communities and clinics were divided into SCD and non-SCD groups. We consider this study as two independent cohorts to include in our meta-analysis. Therefore, 22 cohorts were ultimately included in our meta-analyses. The basic characteristics of included studies are presented in Table 1. A total of 47,805 individuals from the 22 studies were included at baseline. The median of the mean age was 72.70 years old (ranging from 62.8 to 83.31 years old), except one study (25) which did not report its mean age. The female proportion of included studies ranged from 46.46% to 100% and the average years of education was more than 9 years. The average length of follow-up across studies ranged from 2 to 18 years (mean: 5.4; standard deviation [SD]: 3.6), showing an obvious difference among studies, which might contribute to higher heterogeneity. Effective values are also presented in Table 1, ranging from 0.20 to 70.10.

Figure 1. The flowchart of study selection

*: A study contains two cohorts, &: two studies with specific data on both cognitive impairment and dementia; #: Only three of five studies with specific data of both Alzheimer’s disease and non-Alzheimer’s disease.


Table 1. Basic characteristics of included studies

SD: standard deviation; EDU: education; FU: follow-up; RR: relative ratio; NA: not accessible; AD: Alzheimer’s disease; 95%CI: 95% confidence interval; GP: general practitioner; AgeCoDe: German Study on Ageing, Cognition, and Dementia in Primary Care Patients; preDIVA: Prevention of Dementia by Intensive Vascular Care trial; ADRC: University of Pittsburgh Alzheimer Disease Research Center; MYHAT: the Monongahela-Youghiogheny Health Aging Team study; OSHPE: the Obu Study of Health Promotion for the Elderly; ISAAC: the Intelligent Systems for Assessing Aging Changes study; MADRC: Massachusetts Alzheimer’s Disease Research Center longitudinal cohort; NACC: the National Alzheimer’s Coordinating Center; LADIS: Leukoaraiosis and Disability; NA: not accessible; MAAS: the Maastricht Aging Study; ACT: the Adult Changes in Thought study; MSHA: the Manitoba Study of Health and Aging;


Methods for assessing SCD (eg. “Do you feel like your memory is becoming worse?”) and criteria of diagnosing cognitive impairment or dementia, like NIA-AA, are presented in eTable 1 and eTable 2, respectively (Supplementary materials). Bias assessment based on the Newcastle-Ottawa Scale is provided in Supplementary eTable 3. All the included studies were of high quality, as they all got 7 or more than 7 stars (a maximum of 9 stars) (43).

Results of primary analyses

In the primary analyses, SCD showed an increased risk of developing subsequent cognitive impairment or dementia in Fig.2 (RR=2.12, 95%CI=1.75-2.58, I2=87%, P<0.01). Among the 22 cohorts included in our study, 11 cohorts with cognitive impairment as the outcome showed that 1,481 out of the total 8,346 individuals progressed into cognitive impairment at the last follow-up visit, demonstrating a significant association between SCD and cognitive impairment (RR=2.29, 95%CI=1.66-3.17, I2=83%, P<0.01) (Fig.3). And among the participants with SCD, the risk of developing dementia (RR=2.16, 95%CI=1.63-2.86, I2=81%, P<0.01) was similar to that of developing cognitive impairment (Fig.3). Individuals in four studies (24, 26, 29, 34) progressed to either cognitive impairment or dementia. Moreover, two (24, 34) of the four studies showed separate incidence rates of cognitive impairment and dementia. The other two studies showed incidence rate ratios of mixed cognitive disorders, which made it difficult for us to get the numbers of individuals who progressed to different types of cognitive disorders.

Figure 2. SCD shows a significant association with the risk of developing objective cognitive disorders

RR: relative risk, CI: confidence interval, SCD: subjective cognitive decline.


Figure 3. SCD shows significant associations with cognitive impairment and dementia

RR: relative risk, CI: confidence interval, SCD: subjective cognitive decline


Results of subgroup analyses

For further analysis, all included studies were stratified into subgroups based on their demographic characteristics, including age, female proportion, years of education, follow-up time and source of participants (Supplementary eTable 4). We observed that SCD conferred an excess risk of subsequent cognitive impairment in the individuals aged 65-75 years old (RR=2.29, 95%CI=1.83-2.88, I2=87%, P<0.01) (Supplementary eFig 1). SCD showed similar risks for cognitive disorders in the two subgroups stratified by female proportion (Female>50%: RR=2.18, 95%CI=1.26-3.75, I2=75%, P<0.01; Female≤50%: RR=2.11, 95%CI=1.69-2.64, I2=89%, P<0.01) (Supplementary eFig.2). There was a trend for well-educated individuals (>15 years) to be more strongly influenced by SCD (RR=3.71, 95%CI=2.10-6.56, I2=79%, P<0.01) (Supplementary eFig.3). In the subgroup with longer follow-up, individuals with SCD had a nearly doubled risk of progression to cognitive disorders (cognitive impairment and dementia) compared to those without (RR=1.98, 95%CI=1.61-2.44, I2=89%, P<0.01) (Supplementary eFig.4). In the subgroup of different settings, individuals with SCD showed approximately twice higher risks for cognitive disorders in community (RR=2.08, 95%CI=1.58-2.75, I2=88%, P<0.01) and population (mixed settings) groups (RR=1.93, 95%CI=1.37-2.72, I2=89%, P<0.01), as well as a four times higher risk for cognitive disorders in clinics (RR=4.25, 95%CI=1.08-16.77, I2=85%, P<0.01), compared with those without SCD (Supplementary eFig.5). The influence of SCD on the risks of cognitive disorders in various subgroups were summarized in eFig.6 (Supplementary materials).

When we further divided cognitive disorders into cognitive impairment and dementia, subgroup analyses were also conducted and the results were shown in eTable 5 and eTable 6 (Supplementary materials). In the subgroup analyses of the 11 cohorts focused on cognitive impairment, individuals with SCD had a higher risk of subsequent cognitive impairment in the subgroup of female proportion>50% (RR=2.64, 95%CI=1.61-4.33, I2=87%, P<0.01) (eFig.7) and in the subgroup of > 15 years of education (RR=2.64, 95%CI=1.61-4.33, I2=87%, P<0.01) (eFig.8). Additionally, the influence of SCD on cognitive impairment showed nearly no marked difference between individuals with and without the APOE ε4 allelic gene (APOE ε4+, RR=1.67, 95%CI=1.07-2.61, I2=58%, P=0.07; APOE ε4-, RR=1.89, 95%CI=1.17-3.03, I2=85%, P<0.01) (eFig.9). Individuals with SCD also showed higher risks of cognitive impairment in subgroup of 65-75 years old (RR=2.69, 95%CI=1.79-4.04, I2=83%, P<0.01), subgroup of shorter follow-up (RR=3.49, 95%CI=2.14-5.69, I2=0%, P<0.01) and subgroup of individuals from clinics (RR=8.06, 95%CI=1.68-38.67, I2=66%, P=0.09). Results on the influence of SCD on the progression into cognitive impairment were summarized in eFig.10 (Supplementary materials).

In the subgroup analysis of the cohorts which progressed into dementia, SCD individuals in the subgroup of short follow-up time showed a higher incidence rate of dementia (RR=3.40, 95%CI=1.46-7.89, I2=34%, P=0.22) (eFig.11). Moreover, when we classified dementia into AD and Non-AD groups, SCD showed a significant association with AD (RR=2.39, 95%CI=1.00-5.74, I2=76%, P<0.01), while it had a non-significant association with non-AD dementia (RR=1.37, 95%CI=0.93-2.03, I2=0%, P=0.73) (eFig.12). Results in subgroups of 75-85 years old (RR=1.75, 95%CI=1.43-2.14, I2=0%, P=0.93), female proportion more than 50% (RR=2.10, 95%CI=1.44-3.05, I2=85%, P<0.01) and individuals from clinics (RR=1.77, 95%CI=1.41-2.22, I2=0%, P=0.62) might need further investigation, since they were limited by the numbers of included studies in the subgroups. Results on the influence of SCD on the progression into dementia were summarized in Supplementary eFig.13.

Meta-Regression Analysis, Sensitivity Analysis and Publication Bias

Based on the results of meta-regression analysis (Supplementary eTable 7), the influence of the covariates on heterogeneity, such as participant’s mean age (p=0.163; 95%CI, -0.100238 to 0.0181451; τ2=0.1979), female proportion (p=0.271; 95%CI, -3.221842 to 0.9562178; τ2=0.2041), years of education (p=0.329; 95%CI, -0.068722 to 0.1892374; τ2=0.3314) and length of follow-up (p=0.231; 95%CI, -0.0968754 to 0.0248457; τ2=0.1917) were not statistically significant, as the two-tailed P values were all greater than 0.05, ranging from 0.072 to 0.928.
The sensitivity analysis showed two studies (36, 38) significantly influenced the heterogeneity (Supplementary eFig.14). When each of the studies was excluded separately, the heterogeneities still remained at 84%. The funnel plot showed relatively bilateral symmetry and the p value was 0.4557 (Supplementary eFig.15), indicating no publication bias.



SCD was associated with a higher risk of subsequent cognitive disorders, which increased that SCD would possibly elevate the risk. Individuals with SCD showed higher risks of subsequent cognitive impairment and dementia both of which were more than two-fold compared with those without. When data were stratified by their basic characteristics, participants with SCD in subgroup of 65-75 years old, subgroup of female proportion more than 50%, long education subgroup, short follow-up subgroup and subgroup of individuals from clinics had higher risks of objective cognitive disorders. SCD participants showed significant higher risk of developing cognitive impairment compared to non-SCD participants, but there was nearly no marked difference in the rate of progression to cognitive impairment between individuals with/without the APOE ε4 allelic gene in SCD participants. Moreover, SCD participants also showed a significantly higher risk of developing AD dementia rather than non-AD dementia compared with those without SCD.
Biological alternations induced by SCD could occur before objective cognitive decline, such as gray matter volume reduction (44). Individuals with SCD have also been reported to have larger white matter hyperintensity (WMH) volumes, hippocampal atrophy (45) and increased β-amyloid (Aβ) deposition (46, 47), which are typical characteristics of AD. Furthermore, some studies illustrated that SCD was a subjective symptom reflecting anxiety or depression about senility and health rather than neurodegenerative causes (48, 49) and was just a risk factor rather than a mechanism underlying preclinical AD or other neurodegenerative dementias (6), as many participants with SCD might not develop subsequent cognitive impairment or even dementia (50). Hence, SCD was more likely to be a risk factor for cognitive disorders. Previous studies also suggested that cognitively unimpaired individuals with SCD were at a significantly increased risk of future objective cognitive disorders and clinical progression to symptomatic disease stages (12, 36, 51) which was in accordance with our results that SCD conferred excess risks of subsequent cognitive impairment and dementia. Furthermore, individuals with SCD were considered as high-risk individuals and they need necessary interventions during stages at which objective cognitive impairment remains clinically unapparent.(52)
What was more, Wang found that age modified the association between SCD and future cognitive disorders, with HR decreasing from 6.0 at age 70 to 1.6 at age 80 (42). Though previous studies have proven that older elderly are more likely to develop cognitive disorders than younger elderly (12, 35, 53, 54), older elderly may have a casual attitude towards their cognitive conditions. Older elderly are less likely to worry about themselves, so subjective complaints from younger elderly are likely to be more predictive than those from older elderly. Therefore, this might explain our result that the influence of SCD was more obvious in the subgroup of older elderly. In the subgroup analysis by female proportion, individuals with SCD showed a nearly 2.5 times risk of developing cognitive impairment than those without in the subgroup of female proportion more than 50%, which was consistent with the previous conclusion that women were prone to cognitive impairment (27). A previous study reported that education affected the process of memory decline (55). Well-educated people usually seem knowledgeable, and more concerned about their health, suggesting their self-reported of SCD is more accurate. For this possible reason, longer education may contribute to an increased risk of progression from SCD to cognitive disorders, which was in accordance with the results of our subgroup analysis stratified by education including the one of all 22 studies with the outcome of cognitive disorders and the one of the 11 studies with the outcome of cognitive impairment.
Individuals with SCD in the subgroup of follow up>3years showed lower risks of developing cognitive disorders, especially dementia, compared with the subgroup of not more than 3 years, which might be explained by the increased drop-out rate or increased mortality of participants during longer follow-up. Several studies (5, 36) clearly showed that settings might affect the influence of SCD. In our study, SCD showed the strongest association with cognitive disorders in individuals chosen from clinics, as people might be classified explicitly and diagnosed in clinical settings, using available and easily measurable criteria and standard definitions of cognitive impairment or/and dementia. Moreover, previous studies also illustrated that patients in clinics were more likely to experience the first sign or the preclinical stage of a neurodegenerative disease (47, 56). A study found a significant effect of APOEε4 on memory (57). And our result suggested that SCD was a risk factor for cognitive impairment independent of the APOEε4 gene, which was likely to be limited by insufficient samples. Additionally, some individuals with SCD showed gray matter volume reduction (44) and greater similarity to an AD gray matter pattern (58) compared with subjects without SCD, which was consistent with our subgroup analyses.
There was considerable heterogeneity, which might be due to the different characteristics of individuals. Therefore, we conducted specific analyses, such as subgroup analyses based on different characteristics of studies and sensitivity analysis to find out cohorts which were significantly different from others. Apart from basic characteristics, measurements of SCD have also been reported to influence the risk of developing cognitive impairment (51). Cohorts included in our study used different assessments of SCD, which might be one of the factors leading to a bit higher heterogeneity. Recruiting larger samples, comparing important characteristics of participants, unifying the assessment of SCD and searching for methods to lower drop-out rates are necessary in future well-designed longitudinal studies.
The primary strength of our meta-analysis lies in the unity in design of studies (prospective longitudinal studies). The prospective longitudinal study minimized the potential influence of recall and selection bias, which might be inevitable in retrospective design. Besides, our retrieval was comprehensive, since we screened the three databases involving almost all available assays. Also, our search term contained, as more as possible, expressions of the same meaning we focus on (including SCD and dementia), and used “OR” as conjunctions for expressions of the same meanings, which could expand our retrieval range. Furthermore, our studies had independent blind assessments or reliable diagnostic criteria of outcomes (cognitive impairment and dementia), which were reflected in the Newcastle-Ottawa Scale questionnaire (Supplementary eTable 3). Studies included are all of high quality (Newcastle-Ottawa Scale ≥ 6 stars) (43), without having publication bias. Overall, the result that SCD increases the risk of subsequent cognitive impairment and dementia is reliable.


There are some limitations in our meta-analysis. First, various questionnaires had different criteria for identifying SCD, which might contribute to a lack of uniformity in diagnosis of SCD. In addition, due to the association of patients and informants, the accuracy of SCD detection could be easily influenced by informants’ expectations of being normal. Second, during the follow-up, as time went on, more and more participants dropped out. Those who are lost to follow-up usually tend to be older, sicker, and have lower socioeconomic status, which might lead to attrition bias. Finally, the reliability of our subgroup analyses might be oppugned owing to our insufficient studies in certain subgroups and the possibility of type I error. Future studies are required to reduce these limitations and make more reliable inferences.



In conclusion, SCD is associated with an increased risk of objective cognitive disorders, including cognitive impairment and incident dementia. Individuals with SCD in subgroup of 65-75 years old, subgroup of female proportion more than 50%, longer education subgroup and subgroup of individuals from clinics showed higher risks of cognitive disorders. SCD deserve more attention, as it could serve as a potential target for early intervention trials in cognitive disorders.


Acknowledgements: None.

Funding: This study was supported by grants from the National Key R&D Program of China (2018YFC1314700), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

Conflicting interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author’s Contributions: JTY, QD and LT conceptualized and designed the study. XTW, ZTW, HYH, YQ, MW, XNS and WX conducted the study. XTW, ZTW, HYH, YQ and MW analyzed and extracted data. XTW, ZTW and JTY wrote the first draft of the manuscript. All authors reviewed the manuscript.

Ethical Standards: None





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V. Bloniecki1,2, G. Hagman1,3, M. Ryden3, M. Kivipelto1,3,4,5,6


1. Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden; 2. Dermato-Venereology Clinic, Karolinska University Hospital, Stockholm, Sweden; 3. Theme Aging, Karolinska University Hospital, Stockholm, Sweden;
4. Ageing and Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK; 5. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland, Kuopio, Finland; 6. Research and Development Unit, Stockholms Sjukhem, Stockholm, Sweden.

Corresponding Author: Victor Bloniecki, Karolinska Institute, Karolinska Uinversity Hospital, Eugeniavägen 3, SE-17176, Stockholm, Sweden. Tel.: +46 70-726 82 20; Email:

J Prev Alz Dis 2021;
Published online January 18, 2021,



Background: Due to an ageing demographic and rapid increase of cognitive impairment and dementia, combined with potential disease-modifying drugs and other interventions in the pipeline, there is a need for the development of accurate, accessible and efficient cognitive screening instruments, focused on early-stage detection of neurodegenerative disorders.
Objective: In this proof of concept report, we examine the validity of a newly developed digital cognitive test, the Geras Solutions Cognitive Test (GCST) and compare its accuracy against the Montreal Cognitive Assessment (MoCA).
Methods: 106 patients, referred to the memory clinic, Karolinska University Hospital, due to memory complaints were included. All patients were assessed for presence of neurodegenerative disorder in accordance with standard investigative procedures. 66% were diagnosed with subjective cognitive impairment (SCI), 25% with mild cognitive impairment (MCI) and 9% fulfilled criteria for dementia. All patients were administered both MoCA and GSCT. Descriptive statistics and specificity, sensitivity and ROC curves were established for both test.
Results: Mean score differed significantly between all diagnostic subgroups for both GSCT and MoCA (p<0.05). GSCT total test time differed significantly between all diagnostic subgroups (p<0.05). Overall, MoCA showed a sensitivity of 0.88 and specificity of 0.54 at a cut-off of <=26 while GSCT displayed 0.91 and 0.55 in sensitivity and specificity respectively at a cut-off of <=45.
Conclusion: This report suggests that GSCT is a viable cognitive screening instrument for both MCI and dementia.

Key words: Dementia, MCI, cognitive test, MoCA, e-medicine.


Dementia is currently a global driver of health care costs, and with an ageing demographic, the disease burden of neurodegenerative disorders will increase exponentially in the future. The prevalence is estimated to double every two decades, reaching approximately 80 million affected patients worldwide in 2030 (1). In 2016, the global costs associated with dementia were 948 billion US dollars and are currently projected to increase to 2 trillion US dollars by 2030, corresponding to roughly 2% of the world’s total current gross domestic product (GDP) (2, 3)..
Dementia, or major neurocognitive disorder (MCD), is an umbrella term for neurodegenerative disorders typically characterized by memory dysfunction with Alzheimer’s disease (AD) constituting approximately 60% of all cases. Other common forms of dementia include vascular dementia, Lewy-Body dementia and Frontotemporal dementia. Modern diagnostic tools, such as various imaging modalities and cerebrospinal fluid biomarkers (4, 5), have improved our diagnostic accuracy substantially. These methods have also provided key insights into the pathological mechanisms associated with neurodegenerative and contributed to the development of concepts such as mild cognitive impairment (MCI) and “preclinical AD” (6, 7). Preclinical AD is defined by the presence of cerebral amyloid or tau pathology, identified by positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) biomarkers, before the onset of clinical symptoms (8).
Nevertheless, assessment of cognitive functions, the primary clinical outcome of interest, still largely relies on analogue “pen and paper” based tests administered to patients by health care providers (9). Although some regional differences exist, two of the most known and used cognitive tests include the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) (10, 11). Both tests assess various cognitive domains, with some inter-test differences, including for example; orientation, memory, concentration, executive functions, language, and visuospatial abilities (9) with scores ranging from 0 to 30 points. MoCA, as compared to MMSE which is mostly focused on memory deficits, includes assessment of more cognitive domains thus increasing its diagnostic accuracy. Although optimal cut-off points vary somewhat between different studies, a score lower than 26 on MoCA and 24 on MMSE are considered indicative of dementia (12–15). MoCA has in a previous meta-analysis shown to have a sensitivity and specificity of 0.94 and 0.60 respectively, at a cut-off of 26 points (16). This indicates a good ability to detect dementia, but at the cost of a high amount of false positives. MMSE has, in a meta-analysis, demonstrated a sensitivity of 0.85 and specificity of 0.9 (14). However, MMSE has limited value in detecting MCI and prodromal AD patients from healthy controls (17). Albeit, in the setting of cognitive screening tests a trade-off between sensitivity and specificity is necessary and screening instruments should favor sensitivity over specificity.
Given the current scientific consensus that potential future disease-modifying drugs for AD need to be administered early on in the disease continuum, there is a clear need to develop accurate and widely available cognitive screening tests in order to facilitate early diagnosis of MCI patients in the future. In the European Union, there are currently approximately 20 million individuals over the age of 55 with MCI, most of whom have not undergone screening for cognitive impairment (18). A previous study investigating the treatment and diagnostic capacity of six European countries (France, Germany, Italy, Spain, Sweden, United Kingdom) estimated that over 1 million patients would progress from MCI to AD due to capacity constraints within current health care systems if a disease modifying treatment were to be available in 2020 (18). As such, digital cognitive screening instruments are likely to be a part of the diagnostic process in the future, especially when considering the advancement of digitalized health care in multiple facets of modern medicine (19).
Cognitive assessment instruments are available in different settings including clinic based and at home testing (20, 21). Current cognitive evaluation methods include both pen-and-paper screening tools, which is the conventional method administrated by a clinical neuropsychologist, and computerized cognitive tests (20, 21). Increasing advances in technology has led clinical trials to move away from the conventional methods and adopt validated digital cognitive tools that are sensitive to capturing cognitive changes in early prevention stages (20, 22). Computerized cognitive assessment tools offer several benefits over the traditional instruments, enabling recording of accuracy and speed of response precisely, minimizing floor and ceiling effects and eliminating the examiner bias by offering a standardized format (20–22). Computerized cognitive assessments may also generate potential time and cost savings as the test can be administrated by the patient or other healthcare professionals than neuropsychologist, as long as appropriate professional will be responsible for the test interpretation and diagnosis (20, 22). Thus, unmonitored digital tools provide practical advantages of reduced need for trained professionals, self-administration, automated test scoring and reporting and ease of repeat adjustments, which enable administration for large-scale screening (22, 23). On the other hand, cognitive assessment tools are typically administrated to elderly population who might lack familiarity with digital tools, which can negatively affect their performance (22, 24). However, the attitude and perception of patients using a computerized cognitive assessment have been investigated in the elderly population, and individuals expressed a growing acceptance of using computerized cognitive assessments and rated them as understandable, easy to use and more acceptable than pen and paper tests (20, 22). They also perceived them as having the potential to improve patient care quality and the relationship between the patient and clinician when human intervention is involved (20).
Currently, there are a number of computerized screening instruments available, and they are either a digital version of the existing standardized tests or new computerized tests and batteries for cognitive function assessment (25). The pen-and-paper version of the MoCA test was recently transformed to an electronic version (eMoCA) (24). eMoCA was tested on a group of adults to compare its performance to MoCA, and most of the subjects performed comparably (24). For the detection of MCI, eMoCA (24, 25) and CogState (26) showed promising psychometric properties (25). Computer test of Inoue (27), CogState (26) and CANS-MCI (28) showed a good sensitivity in detecting AD (25). Unlike the other computerized cognitive screening tools, Geras Solutions is a comprehensive tool that provides, besides the cognitive test, a medical history questionnaire that is administrated by the patient, and a symptom survey that is administrated by the patient’s relatives. Thus, it has the potential to save more time and cost compared to the other digital assessment instruments by providing a more complete clinical evaluation.
The primary objective of this study is to investigate the accuracy and validity of a newly developed digital cognitive test (Geras Solutions Cognitive Test [GSCT]). The GSCT is a self-administered cognitive screening test provided by Geras Solutions predominantly based on MoCA. In this study, we intend to investigate the validity of GSCT, including psychometric properties, agreement with MoCA and diagnostic accuracy by establishing sensitivity, specificity, receiver operating characteristics (ROC), area under the curve values (AUC) and optimal cut-off levels, as well as compare performance with MoCA.


Materials and methods

Geras Solutions cognitive test

The GSCT, is a newly developed digital screening tool for cognitive impairment and is included in the Geras Solutions APP (GSA). Development of the screening tool was done in collaboration with the research and clinical team at Theme Aging, Karolinska University Hospital, Solna memory clinic and Karolinska Institutet. GSCT is developed on existing cognitive assessment methods (MoCA and MMSE) and includes additional proprietary tests developed at the memory clinic, Karolinska University Hospital Stockholm, Sweden. The test is suitable for digital administration through devices supporting iOS and Android.
The test is composed of 16 different items assessing various aspects of cognition, developed in order to screen for cognitive deterioration in the setting of dementia and to ensure suitability for administration via mobile devices. The GSCT is scored between 0-59 points in total and has six main subdomains including; memory (0-10 points), visuospatial abilities (0-11 points), executive functions (0-13 points), working memory (0-19 points), language (0-1 point) and orientation (0-5 points). Additionally, the time needed to complete the individual tasks is registered and presented as total test time and subdomain test time. The GSCT is automatically scored using a computer algorithm and results are presented as the total score as well as subdomain scores. A detailed description of the GSCT test items and scoring is provided in supplement 1.


The included study population consisted of 106 patients referred to the memory clinic at Karolinska University Hospital, Solna, predominantly by primary care practitioners due to memory complaints and suspicion of cognitive decline. All patients referred to the clinic between January 2019 and January 2020 were asked to participate in the study. No exclusion criteria were established a priori. If a patient fulfilled the criteria for inclusion (i.e. referred for investigation of suspect dementia at the memory clinic and provided informed consent) they were included in the study. A total of 106 patients accepted participation in the study. Five patients did not complete GSCT (two with MCI, two with subjective cognitive impatient [SCI] and one with dementia) and three patients displayed test scores with evident irregularities (one with MCI, one with SCI and one with dementia) and were excluded from the final analysis, thus leaving 98 complete cases. Irregularities included two patients whom started the test multiple times and one patient with a congenital cognitive deficiency resulting in test scores below 2 SD from the mean on both MoCA and GSCT.
All patients included in the study underwent the standard investigative procedure for dementia assessment as conducted at Karolinska University Hospital Memory Clinic. The investigative process is completed in its entirety in one week and includes; brain imaging, lumbar punctures for analysis of CSF biomarkers and neuropsychological assessment including administration of different cognitive rating scales, including MoCA. Patients received a dementia or MCI diagnosis according to the ICD-10 classification and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) criteria were used as clinical support (29). If no evidence of neurodegeneration was observed patients were provided with an SCI diagnosis based on the ICD-10 classification (30). Final diagnosis was determined by specialist in geriatric medicine. In parallel, patients who accepted inclusion in the study completed the self-administered GSCT during the investigative process. GSCT was, in all cases, administered after MoCA, but not during the same day. Patients were provided a tablet and conducted GSCT alone with a health care provider adjacent if any technical difficulties would arise. The GSCT is a self-administered and all test instructions are provided by the platform. The test is intended to be performed in a home environment without any assistance. Information regarding patients GSCT scores, MoCA scores, age, gender and final diagnosis were collected for statistical analysis. All included patients provided informed consent, and the study was approved by the Regional Ethics Committee of Karolinska Institute, Stockholm, Sweden. Registration number: 2018/998-31/1.
The mean age for the whole included population (n=98) was 58 years. 5 patients were below 50 years of age (5%), 58 patients were between 50 and 60 years of age (59%), 34 patients were between 61 and 70 years of age (35%) and one patient over 70 years (1%) Altogether, 67% (n=65) of the patients were assessed without any signs of neurodegenerative disorder and diagnosed with SCI. 24% were diagnosed with MCI, and 9% received a dementia diagnosis. The dementia group consisted of 8 patients with AD and 1 patient with vascular dementia.


All statistical analyses were done using Statistica software (version 13). Baseline descriptive characteristics were calculated and are provided in Table 1. The rating scales (GSCT and MoCA) were treated as both continuous and dichotomous variables when identifying optimal cut-off levels based on sensitivity and specificity analysis. Both parametric and non-parametric tests were used for the analysis to validate findings and are reported if discrepancies were seen. Agreement between test measures were analyzed using standardized concordance correlation coefficient and analysis of Bland-Altman plot. Association between GSCT and MoCA was assessed using Pearson correlation. The internal consistency of GSCT was analyzed using Cronbach’s alpha index.
ANOVA was used to assess the differences in cognitive test scores categorized by diagnostic subgroups. Post-hoc analysis was conducted using Fisher’s Least Significant Difference (LSD) method. Logistic regression of total test scores was done in order to compare odds ratio between the tests.
Validation of GSCT total score against MoCA required the following to be established or calculated: ROC curves, the area under the curve (AUC) values with 95 % confidence intervals and sensitivity/specificity levels. Analyses were performed to estimate optimal cutoff values based on the best-compiled outcome from a range of sensitivity and specificity levels when testing the continuous scale against a dichotomous test of reference (SCI vs dementia and SCI vs MCI). Adjustment for multiple comparisons was done using the FDR-method. The presented p-values are adjusted values with the FDR-method. An adjusted p-level of <0.05 was defined as statistically significant.



Baseline data, psychometric properties and normative data

Baseline patient characteristics, including cognitive test scores, are provided in Table 1. The mean score for GSCT was 45 points in the SCI group, 36 points in the MCI group and 28 points in patients with dementia.

Table 1. Descriptive statistics

Descriptive data and test scores. Values are shown as means, standard deviations and minimum/maximum; A. p<0.05 compared to SCI. B. p<0.05 compared to MCI. C. p<0.05 compared to dementia; SCI = Subjective cognitive impairment. MCI = Mild cognitive impairment. GSCT = Geras Solutions cognitive test. MoCA = Montreal Cognitive Assessment.

Figure 1. Bland-Altman plot of standardized test scores

X-axis= mean of MoCA and GSCT. Y-axis = Difference in MoCA and GSCT

The correlation between GSCT and MoCA was (r(96) = 0.82, p <0.01). Standardized concordance correlation coefficient between GSCT and MoCA was 0.82, indicating a high level of agreement. Agreement between the GSCT and MoCA was also analyzed using a Bland-Altman plot with standardized values showing that 97 % of data points lie within ±2SD of the mean difference, see figure 1. Estimation of the internal consistency of GSCT showed a standardized Cronbach’s alpha index of α = 0.87.
Age was not significantly correlated with GSCT scores (r =-0.16, p=0.1). Diagnostic subgroup was significantly associated with age (F(2, 95) = 4,8 = 0.02), with post hoc test showing a significant difference between dementia and SCI patients (mean 63 vs 57 years, p=0.01) but not between SCI and MCI (mean 57 vs 60 years, p=0.08) or MCI and dementia patients (mean 60 vs 63 years, p=0.2). No differences in GSCT scores were observed depending on gender (t (96) =-0.3, p= 0.74) with males having a mean score of 41 points and females 40.4. Finally, both age, gender and education were included in an ANCOVA showing that education (F(1, 93) = 5.4, p= 0.03) was significantly associated with GSCT scores in contrast to age (F(1, 93) = 2.9, p = 0.1) and gender (F(1, 93) = 0.74, p = 0.4). Patients with more than 12 years of education showed higher mean test scores as compared to patients with 12 years or less (mean 42.2 vs 37.6 points, p = 0.05). GSCT total test time differed significantly depending on diagnostic subgroup (F(2, 95) = 36.4, p < 0.01) (Figure 2). Post-hoc tests showed that the differences in mean scores were significant between all three subgroups with SCI patients showing a mean test time of 1057 seconds compared to 1296 and 2065 seconds for MCI and dementia patients respectively (SCI vs MCI, p < 0.01) (SCI vs dementia, p < 0.01) (MCI vs dementia, p < 0.01).

Figure 2. Differences in GSCT test time depending on diagnosis

Mean GSCT total test time and a 95% confidence interval for patients with SCI, MCI and Dementia. p<0.05 between all subgroups.


Between-group differences in GSCT and MoCA

Average GSCT scores differed significantly depending on diagnostic subgroup (F(2, 95) = 20.3, p < 0.01). Post-hoc tests showed that the differences in mean scores were significant between all three subgroups (SCI vs MCI, p < 0.01) (SCI vs dementia, p < 0.01) (MCI vs dementia, p = 0.02).
Mean MoCA scores were also significantly different depending on diagnosis (F(2, 95) = 29.5, p < 0.01) and the mean scores were significantly different for all three subgroups (SCI vs MCI, p < 0.01) (SCI vs dementia, p < 0.01) (MCI vs dementia, p < 0.01) (Table 1).

Box plots for test scores for both GSCT and MoCA categorized by diagnosis can be seen in Figure 3. Odds ratios were calculated showing a one unit increase on the GSCT increased the odds of being healthy by 1.15 (CI 95% 1.07 – 1.22) while MoCA was associated with a 1.47 increase in odds (CI 95% 1.22-1.76).

Figure 3. Boxplots showing differences in test scores depending on diagnosis

Median GSCT and MoCA scores are represented by small squares. Larger squares represent interquartile range while whiskers show non-outlier range.


Accuracy and comparison with MoCA

GSCT showed very good to excellent discriminative properties at a wide range of cut-off values. When including all patients, thus coding both MCI and dementia patients into a binary classification of healthy/cognitively impaired, GSCT total score displayed an AUC value of 0.80 with 95% CI [0.70-0.90], whereas MoCA showed an AUC value of 0.80 with CI [0.70-0.90]. MoCA showed a sensitivity of 0.88 and specificity of 0.54 at a cut-off of <=26 while GSCT total score displayed 0.91 and 0.55 in sensitivity and specificity respectively at a cut-off of <=45. Figure 4 shows respective AUC curves and Table 2 presents the respective summary statistics.

Figure 4. Comparison of ROC curves between cognitive tests

Receiver operating characteristics curves for GSCT and MoCA in; top left SCI vs (MCI + dementia); Top right SCI vs MCI. Bottom left SCI vs Dementia.

When assessing the accuracy in discriminating between SCI and MCI patients GSCT showed an AUC value of 0.74 with 95% CI [0.62-0.85] whereas MoCA showed an AUC value of 0.74 with 95% CI [0.61-0.85]. Sensitivity and specificity at a cut-off level of <=45 was 0.88 and 0.55, respectively for GSCT total score. Whereas MoCA, at the traditional cut-off of <=26, displayed a sensitivity of 0.83 and specificity of 0.54 (Table 2). Both tests were excellent at discriminating dementia patients from SCI. GSCT showed an AUC score of 0.96 with 95% CI [0.92-0.1] while MoCA had an AUC score of 0.98 with 95% CI [0.95-0.1]. At the traditional MoCA cut-off of <= 26, sensitivity and specificity scores were 1 and 0.54, respectively whereas GSCT using a cut-off of <=35.5 showed a sensitivity of 1 and specificity of 0.9. As seen in Figure 5, both tests show good capabilities in discriminating between different diagnostic subgroups in this material, although some overlap between MCI and SCI patients existed for both tests. GSCT was marginally better at discriminating MCI from SCI patients as compared to MoCA. No patients with dementia scored within the normal range for either test.

Figure 5. Scatterplot of cognitive test scores depending on diagnosis

Scatter plot of GSCT and MoCA categorized by diagnosis. Marked lines represent cut-off points.

Table 2. Summary of accuracy for both tests

Summary statistics ROC



In this study, we present the first results on a newly developed digital cognitive test provided by Geras Solutions. GSCT displayed good agreement with MoCA based on concordance correlation analysis and Bland-Altman plot indicating that both tests measure similar cognitive domains. Additionally, normative data regarding the influence of age, gender and education was analyzed showing that education, but not age and gender, affected test scores. Individuals with more than 12 years of education had higher mean GSCT scores as compared to individuals with 12 years or less of education providing valuable information regarding scoring analysis in different demographic groups. GSCT showed equally good discriminative properties compared to the MoCA test. Both tests were excellent at discriminating dementia patients from SCI patients with a sensitivity of 1 for both GSCT and MoCA while showing a specificity of 0.9 and 0.56, respectively. This result is similar to the differential capabilities of other digital cognitive test showing sensitivity and specificity scores ranging from 0.85-1 and 0.81-1 respectively (31–33). Both tests also showed similar capabilities when discriminating between SCI and MCI patients with AUC scores of 0.74. GSCT was in this study slightly better in correctly identifying cognitive deterioration in MCI patients with a sensitivity of 0.88 compared to 0.83 for MoCA while both tests showed similar specificity of 0.55 and 0.54 receptively. The GSCT showed somewhat better sensitivity in detecting MCI patients compared to other digital screening tools, such as CogState, which previously reported sensitivity scores ranging between 0.63 and 0.84, albeit those test demonstrated higher specificity (31, 33, 34). Since GSCT is intended as a screening tool used early in the diagnostic process we believe that focus on high sensitivity is of more importance and must come at the cost of lower specificity.
Both tests demonstrated significant differences in mean test scores between all diagnostic subgroups. Additionally, the total GSCT time was also significantly different between all subgroups providing further valuable clinical information as compared to current paper and pen based cognitive screening instruments. GSCT showed very good internal consistency (α = 0.87). Based on this study, we suggest a cut-off level of <=45 for detection of MCI while values <=35.5 indicate manifest dementia.

Overall, GSCT performed at least as well as compared to currently available screening tools for dementia disorders (MoCA) while simultaneously providing several advantages. First, the test is administered via a digital device, thus eliminating the time-consuming need for testing provided by health care practitioners while also increasing the availability of cognitive screening. Given the earlier reported estimated increase in dementia prevalence combined with possible disease-modifying drugs, there is an urgent need for increased accessibility. Additionally, the digital set up of the test eliminates administration bias from health care providers and creates a more homogenous diagnostic tool. Albeit, future studies are needed to test the device in a setting without health care providers nearby. Furthermore, the possibility to register total and domain-specific test time may provide valuable clinical information potentially increasing the diagnostic capabilities, a hypothesis needing further testing in future research. Due to current trends, the development of an effective and accurate digital screening tool for cognitive impairment is of utter importance. Given a sufficiently accurate test, patients scoring in the normal range would not need to undergo further examination in the hospital setting. Instead, this digital screening instrument could identify the proper individuals in need of expanded testing e.g. MRI, CSF analysis and detailed neuropsychological testing, thus saving resources for the health care system and allocating interventions for those in need.


In this initial study we were not able to include healthy subjects. Instead, SCI patients were used as “healthy controls”. Although these patients have a self-reported presence of cognitive dysfunction, no objective findings for the presence of an ongoing neurodegenerative process could be identified. Future studies should include healthy patients without any subjective symptoms. Additionally, future larger normative studies are required to investigate how factors such as age, gender and education affect GSCT performance in order to increase validity and diagnostic accuracy. Another limitation of the test is the lack of information regarding test-retest reliability. In this preliminary trial, we were unable to obtain longitudinal data thus hindering such analysis. Future studies must include longitudinal measurements in order to determine the test-retest reliability of GSCT.
Another limitation of this study is the small sample size, especially in the MCI and dementia subgroups. These findings should be interpreted with caution and future studies, including more patients with MCI and dementia disorders, are necessary to improve the accuracy of the test. Albeit the low sample size increases the risk of type 2 errors, we found significant differences for all groups in mean GSCT scores, further supporting the robustness of the findings. Continuous collection of data from new individuals will improve test performance and provide normative information. Another limitation is the fact that patients were administered GSCT during the same week as MoCA, which could potentially generate practice effects. Furthermore, all testing in the study was conducted in Swedish and all included patients were living in close proximity to Stockholm, Sweden. Thus, there may be a potential bias in the selection of the study population and future studies should investigate whether GSCT scores are affected by regional differences as well as examine the suitability of different language versions in order to improve accessibility.



Overall, the Geras Solutions Cognitive Test performed very well with diagnostic capabilities equal to MoCA when tested on this study population.
This report suggests that GSCT could be a viable cognitive screening instrument for both MCI and dementia. Continued testing and the collection of normative data and test-retest reliability analysis is needed to improve the validity and diagnostic accuracy of the test. Additionally, future studies should explore the diagnostic value of total test time as well as item specific test time.

Funding: Theme Aging Research Unit had research collaboration with Geras Solutions during the study and a grant from Geras Solutions was provided to support conducting the study. The study was conducted independently at the memory clinic, Karolinska University Hospital, and the funding organizations had not been involved in analyses and writing. Other research support: Joint Program of Neurodegenerative Disorders, IMI, Knut and Alice Wallenberg Foundation, Center for Innovative Medicine (CIMED) Stiftelsen Stockholms sjukhem, Konung Gustaf V:s och Drottning Victorias Frimurarstiftelse, Alzheimer’s Research and Prevention Foundation, Alzheimerfonden, Region Stockholm (ALF and NSV grants). Advisory board (MK): Geras Solutions, Combinostics, Roche. GH: Advisory board: Gears Solutions. VB: Consultant for Geras Solutions.

Conflict of Interest: MK: Advisory board: Combinostics, Roche; GH: Advisory board: Gears Solutions; VB: Consultant for Geras Solutions.

Ethical Standards: The study was approved by the Regional Ethics Committee of Karolinska Institute, Stockholm, Sweden. Registration number: 2018/998-31/1.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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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I. McRae1, L. Zheng2,4, S. Bourke3, N. Cherbuin1, K.J. Anstey2,4

1. Centre for Research on Ageing Health and Wellbeing, Research School of Population Health, The Australian National University, Canberra, ACT, Australia; 2. Neuroscience Research Australia, Margarete Ainsworth Building, Barker Street, Randwick, Sydney NSW, Australia; 3..Department of Health Services Research and Policy, Research School of Population Health, The Australian National University, Canberra, ACT, Australia; 4. Ageing Futures Institute, School of Psychology, University of New South Wales, Sydney, NSW, Australia

Corresponding Author: Dr Ian McRae, Centre for Research on Ageing Health and Wellbeing, Research School of Population Health, The Australian National University, Canberra, ACT 2600, Australia, Email:, Ph: +61 431 929 750

J Prev Alz Dis 2020;
Published online December 15, 2020,



Background: Assessment of cost-effectiveness of interventions to address modifiable risk factors associated with dementia requires estimates of long-term impacts of these interventions which are rarely directly available and must be estimated using a range of assumptions.
OBJECTIVES: To test the cost-effectiveness of dementia prevention measures using a methodology which transparently addresses the many assumptions required to use data from short-term studies, and which readily incorporates sensitivity analyses.
DESIGN: We explore an approach to estimating cost-effective prices which uses aggregate data including estimated lifetime costs of dementia, both financial and quality of life, and incorporates a range of assumptions regarding sustainability of short- term gains and other parameters.
SETTING: The approach is addressed in the context of the theoretical reduction in a range of risk factors, and in the context of a specific small-scale trial of an internet-based intervention augmented with diet and physical activity consultations.
MEASUREMENTS: The principal outcomes were prices per unit of interventions at which interventions were cost-effective or cost-saving.
RESULTS: Taking a societal perspective, a notional intervention reducing a range of dementia risk-factors by 5% was cost-effective at $A460 per person with higher risk groups at $2,148 per person. The on-line program costing $825 per person was cost-effective at $1,850 per person even if program effect diminished by 75% over time.
CONCLUSIONS: Interventions to address risk factors for dementia are likely to be cost-effective if appropriately designed, but confirmation of this conclusion requires longer term follow-up of trials to measure the impact and sustainability of short-term gains.

Key words: Dementia, risk factors, cost-effectiveness, interventions, sustainability.



While many studies have addressed the association of lifestyle and vascular factors with dementia, few have addressed whether interventions designed to reduce risk factors are cost-effective (1). This is in part because dementia risk reduction programs are implemented well before the usual age of dementia onset. This means that economic evaluation using simulation modelling requires parameters relevant to a long-term time frame. As most intervention studies to date have 5 years or less of follow-up (1) (exceptions include the planned trials of multi-domain interventions (2)), cost-effectiveness studies require model parameters to be extrapolated well beyond the data observed. Reviews of model-based economic evaluations of dementia interventions (1, 3) have identified very few methods which assess prevention strategies. The types of non-pharmaceutical interventions identified in these reviews mainly focused on early assessment of dementia, screening or diagnosis rather than reduction in risk factors (3).
Short-term cost-effectiveness studies (4) and methodologies have been published which address cost-effectiveness of transitions from mild cognitive impairment (MCI) to dementia (5). However, assessing cost-effectiveness of programs which reduce or treat risk factors (many of which occur in mid-life) requires modelling the impact of interventions over longer time frames (1, 3, 5-9) and requires assumptions on how trial results are sustained over the longer term. In the absence of robust estimates of many of the parameters needed for full Markov or other simulation models, we suggest an alternate approach to estimating the price at which programs are cost-effective. This approach provides transparency in estimating the sensitivity of these prices with highly uncertain parameter estimates.
A 2019 review of health economic evaluations of primary prevention programs for dementia (1) identified three analyses of prevention strategies (6, 8, 9) which modelled dementia progress and costs over the long-term. Noting the range of uncertainties, the review recommended that “extensive sensitivity analysis to examine the impact of assumptions” be implemented. This included assumptions regarding long-term vs short-term outcomes of interventions, the impact of optimal program targeting, and discounting (1). Two of the analyses were partial evaluations which addressed potential cost savings from reductions in dementia levels, but did not address health benefits (usually measured by Quality Adjusted Life Years (QALYs)), so cost-effectiveness was not testable(6, 8). While including an extensive sensitivity analysis, the study which addressed cost-effectiveness (9) required a range of assumptions to estimate parameters including annual risk rates, mortality rates for those with and without dementia and QALY levels by age for people with dementia (9).
Estimated age/gender specific incidence rates(10) for dementia are available for Australia, but the impact of interventions on incidence of dementia at each age is not known, nor are age-specific costs or QALY estimates. Hence, there is value in exploring non-simulation approaches to estimate the cost-effectiveness of interventions which address dementia risk factors using aggregate data. We use an approach based on average lifetime costs of dementia and losses in quality of life per individual who develops dementia. Until long-term parameters can be obtained with confidence, this approach avoids the need for transition probabilities and cost and QALY measures by age. It also gives a direct means of linking costs and benefits and provides a transparent means of undertaking sensitivity analyses of all factors, including parameters reflecting the sustainability of improvements in risk factors, program targeting and discounting.
To demonstrate the proposed approach we draw on two examples (11, 12): (1) a study that estimated the effects of risk reduction through population attributable risk(PAR) and (2) a recent randomized control trial (RCT) which assessed the impact of an on-line dementia prevention program. The RCT has a relatively short follow-up (15 months), so to estimate the long term cost-effective and cost-saving prices we provide a range of different assumptions including the degree to which gains in risk reduction are sustained and how well the program is targeted to people with high likelihoods of progressing to dementia.


Methods and Data


We used available estimates of the proportion of adults aged 65 and over who are expected to develop dementia and then estimated the reduction in prevalence of dementia for a target population from the two example interventions. Savings in costs and QALYs per person generated by the interventions were estimated using the average per person life-time costs of dementia and loss of QALY due to dementia. This enabled us to estimate the maximum price per person for an intervention to be cost saving or cost effective.
The standard measure of cost-effectiveness (technically cost-utility) is the incremental cost per QALY gained (i.e. the Incremental Cost-Effectiveness Ratio or ICER). For the purposes of this study, an intervention with an ICER below $50,000 is considered cost-effective. While Australia has no formal ICER thresholds this is the level most commonly quoted and is consistent with UK, Australian (13) and American(14) literature.
Apart from sensitivity analysis for uncertain parameters, we examined: (1) the impact of program targeting, as an intervention targeted at the highest risk groups has a greater opportunity to reduce dementia prevalence, (2) the impact of “decay” which reflects reduction in the gains from an intervention over time, and (3) the impact of different levels of discounting. Discounting is a means of “valuing down” (1) future financial and health costs as people may prefer to save money (or gain health benefits) now rather than in the future.

Lifetime Costs of Developing Dementia

Lifetime costs for people with dementia are the product of average annual costs of treatment/care and the duration of care. While estimates of duration of dementia vary widely depending mainly on age at diagnosis, international evidence and reviews suggest that a mean of 5 years is appropriate for the duration of care for dementia (15, 16) (noting this may not be the same as the actual duration of dementia) (17)).
The available estimates of costs of dementia take several perspectives. An American study (18) including direct healthcare costs and costs of informal care estimated $260,000 per person in 2015 (all costs in Australian Dollars); while a 2016 Australian analysis found average annual costs of $35,550 per person including indirect costs such as loss of productivity of both patients and carers (10). With 5 years life with dementia, this becomes $177,750 lifetime cost per person. A later Australian study (19) of people with dementia in residential care with a markedly different methodology estimated higher annual costs of $88,000 per year for residential care compared to $55,000 from the earlier study(10). Given the varying results from these studies, we used a figure of $200,000 as baseline, with a range from $150,000 to $300,000 used for sensitivity analyses.

Loss of Quality Adjusted Life Years by People Developing Dementia

The lifetime loss of QALYs for people with dementia includes the loss due to poorer quality of life and the loss due to premature mortality. A conservative median estimated years of life lost to dementia used here as a baseline is 5 years. This is consistent with previous studies (20) and an Australian systematic review (15). Some estimates as high as 9 years of life have been found (7, 21); we use this as the upper limit for sensitivity analysis purposes.
Few generally applicable estimates of QALY values for people with dementia are available (22). Most studies deriving QALYs in a dementia context relate to specific RCTs with specific populations rather than comparing average people with and without dementia. We draw on estimates of average QALYs for people with dementia and the wider aged population (7, 23). With 5 years of life with dementia, and 5 years loss of life due to dementia there is an average loss 1.5 QALYs while alive and 4.2 QALYS due to premature mortality giving a lifetime loss of 5.7 QALYs from the dementia. A previous estimate (7) based on 6 years with dementia and 9 years loss of life led to an estimated 9.4 QALYs lost which we use as an upper level for sensitivity testing.

Prevalence of dementia

Population prevalence data is not required for Example 1 as the predicted outcomes are explicitly in prevalence terms, although it is required for Example 2. While “Australian data on dementia prevalence are lacking” (AIHW 2018 p138 (24)), we use an estimate of 10% for people aged 65 or over from a study using Australian data (10), which is marginally above estimates combining Australian and international data (10, 24). For Example 2, we assume that any reduction in risk will lead to an equivalent reduction in prevalence when the cohort reaches age 65 or over, and that this reduction will apply to the estimated 10% prevalence of dementia in this age group.


People generally value future costs and effects less than current costs and effects and the value diminishes the further into the future they are expected to occur (25). Hence, economic evaluations adjust the value of costs and benefits for the time at which they occur, using discounting (25). Discounting over long periods has major impacts on results of cost-effectiveness studies (1), particularly when comparing program costs at midlife to medical and other savings in later life (26). A range of discount levels are used by different organisations including: a) the use of 3% for both costs and QALYs (9), b) discount rates of 4% for costs and 1.5% for QALYs( 1), c) the use of 5% for both costs and QALYs in Australia by the Medicare Services Advisory Committee (25), and d) a UK recommendation that 3.5% be applied to both costs and QALYs (25).
In the light of extremely low interest rates in Australia and many other countries at present, and the long durations of discounting in this study, we use baseline discount rates of 3% for both costs and QALYs. For sensitivity analysis we include the Australian standard of 5% for both costs and QALYs, and the 4%/1.5% applied in Holland (1).
Simulation approaches apply discounting each year. However, assuming on average no differences between treated and untreated groups before onset of dementia, the discounting will have no material impact on the differences between treatment groups prior to diagnosis (note that while in principle costs change at onset, they are only measured from diagnosis). We, therefore, discount from the average age of commencement of the intervention to approximately the mid-point of the dementia period. To establish the period of discounting we take an average age of diagnosis as being in the early 80s (27-29). Most studies addressing average age at diagnosis show averages from the high 70s to mid 80s, but most commence with aged populations which may lead to some upward bias. We, therefore, include some alternate discounting periods for sensitivity analysis.

Example 1 – Estimates of Dementia Prevention using Population Attributable Risk

Ashby-Mitchell et al.(2017) (11) explored the aggregate Population Attributable Risk (PAR) from a set of known correlates of dementia (midlife obesity, physical inactivity, smoking, low educational attainment, diabetes mellitus, midlife hypertension, depression). They used PAR values to estimate the impact of uniform reductions in these correlates on dementia prevalence. They concluded that a uniform 5% improvement across all risks would, over 20 years, lead to a reduction in the prevalence of dementia of 3.2% or 17,454 people in Australia.
Any intervention which aimed to reduce the risk factors addressed in Example 1 would need to improve obesity levels and hypertension in mid-life so we assume an intervention targeted at the population aged 45 years and over with an average age of around 65 years. Consistent with the modelling in Example 1 this gives a 20-year period from average age at intervention to average age of dementia diagnosis (early 80s) which we use for discounting (15 years used for sensitivity testing).

Example 2 – BBL-GP Intervention

The Body-Brain-Life in General Practice program (BBL-GP) aims to reduce known dementia risk factors using a mixture of on-line training and face-to-face consultations with dietitians and exercise physiologists (12). Results are assessed using an aggregate measure combining a range of known risk factors (the ANU-ADRI (30)) with program participants compared to an active control group. After 62 weeks the BBL-GP participants showed a decline in ANU-ADRI scores of 4.62 units more than the active controls (12). For a population of Australians aged 60-64 years at baseline, a difference in baseline values of 1 point of ANU-ADRI is associated with a difference of 8% in people developing mild cognitive decline (MCI) or dementia after 12 years (31). This suggests a BBL-GP effect of 37% if the 4.62 units improvement is sustained.
This is an upper limit. Firstly, it is unlikely all the gains in risk factors will be sustained (e.g. maintaining weight loss). Secondly, the evidence of the impact of one point of ANU-ADRI on MCI and dementia may be the same as the long-term impact on dementia, but need not be, as there is likely to be a bias towards reducing MCI in those who are least likely to go forward to dementia. In this case the 8% impact of one ADRI point would be an overstatement. Finally, it is not clear if differences in the index obtained from an intervention have the same effect as differences brought about by lifetime experiences. The size of “decay” for any particular intervention is, therefore, driven by a range of factors including the time period between the intervention and the age at which dementia diagnosis is likely. For sensitivity analysis we test a range of different levels of reduction in impact of the BBL-GP program on actual dementia risk, beginning with a 50% reduction and increasing to a 95% reduction. We term this “decay” to reflect both the difficulty in sustaining the intervention’s short-term gains and the other issues described.
The trial population in Example 2 had an average age of 51 years (12), so for discounting purposes there is approximately 30 years to the average age of dementia diagnosis (20 years used for sensitivity testing). The average cost per participant in the BBL-GP trial relative to an active control was $2,700 including set-up costs. The number of participants in this trial was small, and while there are fixed costs of around $200 per person, other expenditures was almost independent of participant numbers. If more fully implemented the program would be expected to be at least quadrupled in size and costs would become $825 per person. We use this figure to assess cost-effectiveness. With a larger implementation, average costs would be further reduced.



Table 1 shows baseline estimates for Example 1 with a target population of all people aged 45 years or over. This suggests that, ignoring program costs and discounting, over the lifetimes of the people protected from dementia by the lifestyle changes there would be savings of $3.5b and 99,488 QALYs. While these savings are large, with a targeting across the whole population, the savings per targeted person are only $342. After allowing for discounting, the maximum cost per targeted person which could lead to a cost saving program is $189, while a cost less than $460 would achieve a cost-effective incremental cost per QALY gained (the ICER) of less than $50,000.

Table 1. Example 1 – PAR – Baseline costing

1. (10)= ((4) + (9)*(5))/(6)
Table 2 provides estimates of maximum costs per person for a program to be cost saving or cost-effective under different assumptions on target size, lifetime costs, QALY losses and discount rates. Tests 1-3 show relatively little sensitivity in cost-effective or cost-saving prices to changes in estimated lifetime costs and lifetime QALY losses to dementia, with greater effects of QALY increases than cost increases on the cost-effective price. Test 4 assumes the intervention targets only half the population aged 45 and over and assumes the targeting is so well focused on those at higher risk that the number of people avoiding dementia is unchanged. This generates a much greater change in the maximum acceptable costs than shown in Tests 1-3. Test 5 assumes an intervention targeted at a population of only 10,000 who are at very high risk of developing dementia (25% prevalence rate), and again with 3.2% of the anticipated cases “saved” from dementia (11). The cost-effective price increases to $2,069 (after discounting), more than 4 times the baseline estimate. With such precise targeting the percentage saved would probably be greater than 3.2%, and any increase in this parameter would increase the cost-effective prices proportionately. Table 2 also shows the impact of different discounting rates, with the 4%/1.5% levels having broadly similar results to baseline, but the 5%/5% showing acceptable prices around 60% of 3%/3% meaning interventions are considerably less likely to be cost-effective. Should the duration of discounting (the period from the intervention to average age of diagnosis) be reduced, for the 3%/3% calculation the maximum cost-effective price would increase by 15% meaning more expensive interventions would be cost-effective.

Table 2. Example 1 – PAR Costings – Sensitivity analyses

NOTE: * shows variation from baseline
Table 3 provides baseline estimates for Example 2. For presentation purposes the assumed population is 10,000 but results are independent of this number. The discounted program prices at baseline of $3,052 per person to be cost saving and $7,401 per person for the program to be cost-effective are well above the average price per participant of $825 relative to the active control.

Table 3. Example 2- BBL-GP – Baseline Costing

Table 4 provides sensitivity testing which in addition to the factors tested for Example 1 tests levels of “decay”, and shows that the targeting, decay and discounting assumptions have the greatest impact on the overall outcomes. The targeting level of 60% was chosen as the trial participants were mainly people with obesity, with the relative risk of developing dementia of 1.6 (11). With an average price of $825, results discounted at 3% and all other factors at baseline level, a decay of up to 88% would be cost-effective, although not 95% (Test 3). With a 60% loading for targeting and the maximum levels of cost savings from preventing dementia and QALY lost to dementia, the intervention would be cost-effective at 95% decay (Test 7). Test 8 shows that with the 60% loading for targeting and other factors at baseline, even at 93% decay from the short term results the program would be cost-effective.

Table 4. Example 2 – BBL-GP Costing – Sensitivity Analyses

NOTE: * shows variation from baseline
The patterns in these tables show that results are linear with respect to both targeting and “decay”, and less than linear with respect to estimated lifetime costs and QALYs lost to dementia. As for Example 1, discounting has a major impact on the results, although even with relatively high levels of “decay” (80% with all other factors at baseline) the intervention is likely to remain cost-effective with 5%/5% discounting. Should the duration of discounting be reduced, the maximum cost-effective price would increase by 34% for the 3%/3% discounting, although this does not lift any of these prices above $825 for the examples in Table 4.



Our results suggest that multi-domain programs such as the BBL-GP in Example 2 are likely to be cost-effective (unless program impacts decay almost completely over time), while the more generic approach of Example 1 requires tight targeting to at-risk populations to be cost-effective. These results are consistent with prior studies (1, 32) in showing the importance of targeting and sustainability of observed results beyond the period of study follow-up.
The estimated cost of $825 per person in Example 2 would be reduced with wider implementation. Previous studies have estimated cost of dementia risk reduction programs of $200 to $500 per person (9, 33). If Example 2 could be conducted at these lower costs it is more likely to be cost-effective even at high levels of “decay”. Should the duration from intervention to diagnosis of dementia be less than the assumed levels, the effect of discounting would be reduced, and maximum cost-effective program prices increased.
Recalling that “decay” includes other factors as well as the need for participants to maintain lifestyle changes over many years, high levels of decay are possible. Studies with long follow-up are needed to assess actual program effects. Programs which continue to interact with the participants continuously over time are likely to improve effects but increase costs. We also note that improving dementia risk factors would improve a range of other health outcomes (e.g. cardiovascular health, diabetes, mild cognitive impairment), in addition to dementia related outcomes. If the total benefits of risk reduction programs were included, they would be even more likely to be cost-effective.


The main limitation in this and any other analysis of cost-effectiveness of dementia prevention interventions is the uncertainty in many parameters, which has required extensive sensitivity analysis to assess a reasonable range of outcomes. However, the approach taken here integrates sensitivity analysis and facilitates estimation of outcomes under varied assumptions.
The study assumed binary outcomes of dementia against no dementia and did not address the benefit of delay in onset of dementia, which also reduced the likelihood of finding cost-effective outcomes. Dementia related QALY losses prior to diagnosis were not included in the study, leading to a further conservative bias in estimates.
Like all approaches to cost-effectiveness modelling for dementia prevention interventions this study is limited by having only short-term program outcomes (1). The baseline calculations assume (1) in the case of Example 1, that well-established associations between risk factors and dementia are causative; (2) for both examples, changes in risk factors driven by interventions have the same effect as if the level of the risk factor was achieved ”naturally” (e.g. reversing midlife obesity with an intervention has the same effect as achieving a normal weight at midlife without intervention) and; (3) changes in risk from a short-term intervention are sustained over time(e.g. weight does not revert to previous levels). The approach used here however provides a simple and transparent way to test the impact of these ongoing concerns.



To explore the cost-effectiveness of interventions aimed at dementia risk reduction requires a means of extrapolating outcomes from what, to date, have been relatively short-term trials. We examined lifetime costs (in both dollar and QALY terms) of dementia and applied these to projected changes in risks of dementia from two example studies. The results suggest that the multi-domain approach of BBL-GP is highly likely to be cost-effective.
The approach shows further the importance of targeting programs to “at risk” portions of the population and the sensitivity to the sustainability or otherwise of trial results. While these factors are well-known, the approach provides a means of estimating the orders of magnitude of program impacts and reinforces the need for longer-term studies to measure all relevant factors to enable assessment of cost-effectiveness with greater confidence.


Funding Sources: This research was undertaken as part of the Centre for Research Excellence in Cognitive Health, which was funded by the National Health and Medical Research Council grant #1100579. Anstey is funded by NHMRC Fellowship #1102694, Zheng is part supported by the NHMRC Dementia Centre for Research Collaboration. The funders had no role in the design and conduct of this study; in the analysis and interpretation of the data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Acknowledgements: We acknowledge the ARC Centre of Excellence in Population Ageing Research.

Conflict of Interest: Dr McRae, Dr Zheng, Dr Bourke, and Professor Cherbuin declare that they have no conflict of interest. Professor Anstey reports personal fees from StaySharp, outside the submitted work.

Ethical standards: The authors followed the ethical guidelines of the Journal for this manuscript.



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A. De Mauleon1, J. Delrieu1, C. Cantet1, B. Vellas1, S. Andrieu1, P.B. Rosenberg2, C.G. Lyketsos2, M. Soto Martin1

1. Gérontopole, INSERM U 1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France; 2. Department of Psychiatry and Behavioral Sciences, Johns Hopkins Bayview, Johns Hopkins University, Baltimore, United States; On behalf of the A3C study group: L. BORIES, A. ROUSTAN, Y. GASNIER, S. BORDES, M.N. CUFI, F. DESCLAUX, Y. GASNIER, V. FELICELLI, N. GAITS, T. UGUEN, P. TESTE, M. PERE-SAUN, J.F. PUCHEU, S. BORDES, J.P. SALLES.

Corresponding Authors: Adelaide de Mauleon, MD, Gerontopôle de Toulouse, Department of Geriatric Medecine, Toulouse University Hospital, 224, avenue de Casselardit, 31059 TOULOUSE Cedex 9, France, Phone : +, Fax : +, E-mail:

J Prev Alz Dis 2020;
Published online November 26, 2020,



BACKGROUND: To present methodology, baseline results and longitudinal course of the Agitation and Aggression in patients with Alzheimer’s Disease Cohort (A3C) study.
Objectives: The central objective of A3C was to study the course, over 12 months of clinically significant Agitation and Aggression symptoms based on validated measures, and to assess relationships between symptoms and clinical significance based on global ratings.
Design: A3C is a longitudinal, prospective, multicenter observational cohort study performed at eight memory clinics in France, and their associated long-term care facilities.
Setting: Clinical visits were scheduled at baseline, monthly during the first 3 months, at 6 months, at 9 months and at 12 months. The first three months intended to simulate a classic randomized control trial 12-week treatment design.
Participants: Alzheimer’s Disease patients with clinically significant Agitation and Aggression symptoms lived at home or in long-term care facilities.
Measurements: Clinically significant Agitation and Aggression symptoms were rated on Neuropsychiatric Inventory (NPI), NPI-Clinician rating (NPI-C) Agitation and Aggression domains, and Cohen Mansfield Agitation Inventory. Global rating of agitation over time was based on the modified Alzheimer’s Disease Cooperative Study-Clinical Global Impression of Change. International Psychogeriatric Association “Provisional Diagnostic Criteria for Agitation”, socio-demographics, non-pharmacological approaches, psychotropic medication use, resource utilization, quality of life, cognitive and physical status were assessed.
Results: A3C enrolled 262 AD patients with a mean age of 82.4 years (SD ±7.2 years), 58.4% women, 69.9% at home. At baseline, mean MMSE score was 10.0 (SD±8.0), Cohen Mansfield Agitation Inventory score was 62.0 (SD±15.8) and NPI-C Agitation and Aggression clinician severity score was 15.8 (SD±10.8). According to the International Psychogeriatric Association agitation definition, more than 70% of participants showed excessive motor activity (n=199, 76.3%) and/or a verbal aggression (n=199, 76.3%) while 115 (44.1%) displayed physical aggression. The change of the CMAI score and the NPI-C Agitation and Aggression at 1-year follow-up period was respectively -11.36 (Standard Error (SE)=1.32; p<0.001) and -6.72 (SE=0.77; p<0.001).
Conclusion: Little is known about the longitudinal course of clinically significant agitation symptoms in Alzheimer’s Disease about the variability in different outcome measures over time, or the definition of a clinically meaningful improvement. A3C may provide useful data to optimize future clinical trials and guide treatment development for Agitation and Aggression in Alzheimer’s Disease.

Key words: Agitation/aggression, dementia, cohort, validated measurements, trials.



The syndrome of agitation and aggression (A/A) in Alzheimer’s disease (AD) encompasses a range of affective, verbal or motor disturbances such as restlessness, cursing, aggression, hyperactivity, combativeness, wandering, repetitive calling out, irritability and disinhibition (1). A/A occurs in as many as 29% (2,3) of people with AD living at home, and up to 40-60% of those living in long term care facilities (LTCF) (4). A/A is among the most prevalent, persistent and disturbing neuropsychiatric syndrome (NPS). Its severity and frequency increase with disease progression (4). Moreover, A/A has major adverse consequences for patients, families and health care systems including worse quality of life for patients and their caregivers (5), greater disability with earlier institutionalization (6), accelerated transition from prodromal AD to dementia (7), accelerated transition from mild dementia to severe dementia or death (8) and higher health care costs (9). Thus, the management of A/A is a major priority in caring for patients with AD.
Currently the management of A/A remains a challenge for clinicians and caregivers due to the lack of safe and efficient medications as well as due to the difficulty of implementing best evidence non-pharmacological approaches in “real life” clinical setting (10). Although, medication development for treatment of A/A has seen advances in recent years, major methodological questions remain (11, 12). In part, this stems from limited natural history data about generalizable A/A cohorts: most data come from research whose main objective was to study cognitive and functional parameters (e.g., 13), in which patients with significant NPS were excluded or where NPS were inadequately quantified (2). Hence, for the most part these studies have been inadequate to describe the natural evolution of A/A over time or to determine associated clinical characteristics of NPS. Further, none investigated the variability of A/A measures, variances inherent in these measures, or factors influencing this variability, which are issues crucial to trial design and interpretation of this results.
We hypothesized that A/A in AD has a predictable course and associated factors, and that a longitudinal prospective observational survey specifically assessing A/A in patients with AD would provide useful data for treatment development. The overarching aim of the Agitation and Aggression in patients with Alzheimer’s Disease a Cohort (A3C) study was to assess the evolution and longitudinal course of A/A in patients with AD.
The central objective was to study the natural course of symptoms in patients with clinically significant A/A over 12 months of follow-up, with special attention to the first three which is a commonly used duration for NPS trials.
Secondary objectives included estimating the minimal clinically important differences (MCID) in outcomes and assessing the variance of A/A symptoms over time.




A3C is a longitudinal prospective multicenter observational cohort study performed at eight memory clinics from southwest France and their associated LTCF: Castres, Foix, Lannemezan, Lavaur, Lourdes, Montauban, Tarbes and Toulouse. Toulouse University Hospital was the coordinating center. Clinical visits (V) were scheduled at baseline (V1), monthly during the first 3 months of follow-up (V2 to V4), at 6 months (V5), at 9 months (V6) and at 12 months (V7) during a 1-year follow-up period. The first three months of A3C were designed to simulate a classic randomized controlled trial 12-week treatment design. Participants were recruited between December 2014 and August 2017. The last follow up visit took place in June 2018.


Participants were men and women, aged 60 years and older, with possible Alzheimer’s dementia according to NIA-AA’s criteria (14), with or without cerebrovascular components, and regardless of Mini Mental State Examination (MMSE) score. Participants had clinically significant agitation defined broadly by the presence of significant symptoms on at least one of the following NPS as rated on the Neuropsychiatric Inventory (NPI): A/A, disinhibition, aberrant motor behavior and/or irritability (15). Clinically significant was defined as NPI agitation/aggression domain score ≥ 4 with NPI frequency score ≥ 2 at entry. Participants also met the International Psychogeriatric Association (IPA) provisional definition of agitation in cognitive disorders (16).
1. Patients could live at home or in a LTCF. To be included, community dwelling patients had to have an identified primary caregiver, who visited at least three times a week for several hours and supervised patient’s care, and was available to accompany the patient to study visits and to participate in the study. Patients living in a LTCF had lived in the facility for at least two months before inclusion. Patients were excluded if: they had other brain diseases (e.g., extensive brain vascular disease, Parkinson’s disease, other dementias or traumatic brain injury), major depressive episode according to DSM-IV(TR) criteria (, or serious illness that would impair their ability to perform study assessments; the agitation or aggression was attributable to concomitant medications, active medical or psychiatric conditions; had clinically significant psychosis with a NPI domain’s score (hallucinations or delusions) ≥ 4 or were participating in a clinical trial.
Participants and their caregivers took part in the study voluntarily: written informed consent was obtained from all patients (or legal representatives) and caregivers (for the community dwelling population). Each participant’s capacity to give consent was assessed in clinical interviews by clinicians experienced in dementia research. Consent was personally provided if the participant was found to be capable. If the participant was not fully capable of consent, then it was obtained from an authorized legal representative. A3C had ethical approval and oversight from the local Institutional Review Board (Toulouse University Hospital).

Institutional long-term care facilities

In this study, an LTCF was defined as a place of communal living where care and accommodation are provided as a package by a public agency, nonprofit company or private company. LTCF included assisting living, nursing home and other long-term care facilities.

Data collection

At baseline and at every follow-up visit, data collection was performed by trained professionals with clinical experience, during to face-to-face interviews, and recorded on standardized case record form. All raters were standardized trained to perform the scales used in the study. A special standardized training was performed in all clinicians’ raters for primary outcomes: mADCS-CGIC, CMAI and NPI-C A/A. Visits were performed in outpatient memory clinics for community dwelling patients and their caregivers. For institutionalized patients, data were collected from the LTCF staff, in the majority of cases the same each patient’s “referent staff” was interviewed each rating. Table 1 shows the investigation schedule for participants and their primary caregivers, if applicable.

Table 1. A3C investigation schedule for participants and their primary caregivers if applicable

Abbreviations: V=Visit, M0=baseline, M1=1 month, M2=2 months, M3=3 months, M6=6 months, M9=9 months, M12=12 months, NPI=neuropsychiatric inventory, IPA=International Psychogeriatric Association, CGI-S=Clinical Global Impression of Severity, NPI-C=neuropsychiatric inventory clinician rating scale, CMAI=Cohen Mansfield agitation inventory, ADSC-CGIC=Alzheimer disease cooperative study clinical global impression of change, MMSE=mini mental state examination, ADL=activities daily living, QoL-AD=quality of life of patient with Alzheimer’s disease (Logsdon scale), RUD=resource utilization in dementia instrument. *if patient living at home with an identified primary caregiver



Participant age, gender, education, living arrangement and community care services were recorded using a structured questionnaire directed to patients and/or their caregivers as appropriated at baseline. The socio-demographic characteristics were recorded from the primary caregiver for patients living at home by an identified primary caregiver. Changes in living arrangement and community care services were noted at each visit. Whether the patient lived in a LTCF and a dementia special care unit were both recorded.

Medical characteristics

Medical history of past and current conditions was recorded with a focus on cardio-vascular conditions, fractures, cancers, renal failure, sensory disabilities, gastro-intestinal, neurologic and psychiatric diseases. At baseline, caregiver current medical history and ongoing treatments was collected when appropriate. At each visit, clinical examination of participants was performed; changes concerning pharmacological treatments with focus on anti-dementia treatments (Donepezil, Rivastigmine, Galantamine, Memantine), other psychotropic drugs and intercurrent events (hospitalizations, falls, undernutrition) since the last visit were collected.

NPS assessment

Agitation and aggression symptoms

Agitation severity was rated by validated measures such as the A/A domain from the Neuropsychiatric Inventory (NPI) (17), the Neuropsychiatric Inventory Clinician rating (NPI-C) (18) and the Cohen Mansfield Agitation Inventory (CMAI) (1).
The NPI A/A domain measures frequency and severity of A/A symptoms. The identified caregiver rated the A/A NPI domain for symptoms frequency (in a 1-4 scale: occasionally [less than once per week], often [about once per week], frequently [several times per week but less than every day] or very frequently [more than once per day], respectively) and severity (in a 1-3 scale: mild, moderate and marked, respectively). The NPI’s scoring yields a composite (frequency x severity) score of 1-12 for the domain. The NPI A/A domain also quantifies caregiver distress on a scale 0-5: none, minimal, mild, moderate, marked or extremely marked.
NPI-C (18) measures the severity of A/A based on a combined domain score of distinct agitation (13 items) and aggression (8 items) domains (NPI-C-A/A). Each NPI-C domain measures: (1) item frequency on a 1-4 scale: less than once per week, about once a week, several times per week but less than every day or more than once per day respectively, (2) item severity domain on a 1-3 scale: minimal, mild, moderate and marked respectively , (3) caregiver distress on a 0-5 scale: none, minimal, mild, moderate, marked or extremely marked, and (4) item clinician severity on a 0-3 scale: none, mild, moderate and marked, based on clinician judgement. The combined clinician severity score of both domains (agitation and aggression) ranges from 0 to 63 and is the NPI-C rating of interest in A3C. NPI-C is a clinician-rated questionnaire.
The Cohen Mansfield Agitation Inventory (CMAI) (1) is a caregiver-rated questionnaire. It quantifies the frequency of 29 behaviors exhibited by the patient on a 7-point scale from never (1), less than once a week (2), once or twice a week (3), several times a week (4), once or twice a day (5), several times in a day (6) to several times in an hour (7) throughout the preceding 2 weeks. Total score ranged from 29 to 203. A higher score indicated more severe NPS.
The Alzheimer Disease Cooperative Study-Clinical Global Impression of Change (ADCS-CGIC) (19) is a global rating of change and was developed to assess clinically significant change in symptoms over time in AD clinical trials by experienced clinicians. The modified ADCS-CGIC agitation domain version (20) rates agitation five areas globally: mood lability, emotional distress, physical agitation, verbal aggression and physical aggression. It defines the severity of agitation from absent, not at all ill (1), to borderline ill (2), mildly ill (3), moderately ill (4), markedly ill (5), severely ill (6), or among the most extremely ill patients (7) at baseline. During follow-up, the mADCS-CGIC agitation domain rated global clinical change in agitation as: very much improved (1), much improved (2), minimally improved (3), no change (4), minimally worse (5), much worse (6), very much worse (7) compared to baseline symptoms.
The Clinical Global Impression of Severity (CGI-S) is a clinician-rated, 7-point scale that is designed to rate the severity of the subject’s agitation symptoms at baseline using the investigator’s judgment and past experience with the subjects who have the same symptoms (21).
The International Psychogeriatric Association (IPA) “Provisional Diagnostic Criteria for Agitation” (16), identifies three groups of agitation symptoms:
– Excessive motor activity (moving continuously, swinging, gesturing, pointing, repetitive mannerism, restless).
– Verbal aggression (screaming, speaking aloud in an excessive way, coarseness, yelling, shouting, voice bursts).
– Physical aggression (tearing, pushing, resisting, hitting, kicking people or objects, scratching, biting, throwing objects, hitting oneself, slamming doors, tearing things apart, destroying property).

Other neuropsychiatric symptoms

The NPI-C measures a total of 12 individual domains besides agitation and aggression: delusions, hallucinations, depression/dysphoria, anxiety, elation/euphoria, apathy/indifference, disinhibition, irritability/lability, aberrant motor behavior, sleep, appetite and eating disorders, aberrant vocalizations. Each NPI-C domain is rated as with the agitation and aggression NPI-C domains. Each NPI-C domain is included in the NPI except for aberrant vocalizations.
Psychotropic medication
Psychotropic medications were differentiated according to ACT coding as antipsychotic, antidepressant, hypnotic, anxiolytic and other drugs. All medications were recorded at each visit based on the patient’s prescription drug that was verified by the physician from the memory clinic or by the nurse form the LTCF.

Non-pharmacological approaches

The study team was documenting non-pharmacologic treatments and/or approaches for A/A and were classified into three groups according their targets: (1) targeting the patient, (2) targeting the informal or professional caregiver and (3) targeting the environment. For example, caregiver supportive interventions were collected as binary variables (yes/no) such as caregiver training in education about dementia, communication skills, improving caregiver mismatch of her expectations and dementia severity and, finally, assessment or informal caregiver’s burden or mood disorders. Concerning the environment, the following interventions were collected as binary variables (yes/no): improving excess/lack of stimulation, patient isolation, establishing an everyday structured routine, proposing meaningful activities adapted to the patient’s abilities and tastes (10). Intervention by different health professionals was also recorded in both settings.

Non behavioral and psychological assessment

Cognitive assessment

Time since diagnosis of AD was recorded. Cognitive impairment was rated on Mini Mental State Examination (MMSE) to evaluate orientation, memory, attention, concentration, denomination, repetition, comprehension, ability to formulate a whole sentence and to copy polygons (22). Disease severity at entry was defined as mild (≥21), moderate (20-15), moderately severe (14-10), or severe (<10). If AD biomarkers in cerebrospinal fluid were measured to help the diagnosis was noted.

Functional evaluation

Physical impairment was based on Katz’s activities of daily living (ADL) scale (23). This is a 6-item scale with a total score ranging from 0 from 6. A higher score indicates less functional impairment. One leg balance test was performed to evaluate risk of falls. The risk of fall increases if the one leg balance test is less than or equal to 5 seconds (24).

Quality of life

Quality of life of was based on QoL-AD (25). This scale evaluates 13 items self- or caregiver-report: physical health, energy, mood, living situation, memory, family, marriage, friends, self, ability to do chores, ability to do things for fun, money and life as a whole. Each item is rated on a 1-4 score scale: poor, fair, good, excellent respectively. Total score ranges from 13 to 52. A higher score indicated a better quality of life.

Resource Utilization in Dementia

Health care resources consumed by patients with AD were assessed with the Resource Utilization in Dementia (RUD) instrument (26). This questionnaire collected data about medical resources (inpatient stays, outpatient visits and medication), community care services (district nurse, home help, day care, transportation, meals on wheels), and time spent by the caregiver on ADL and instrumental ADL.

Statistical analysis

To describe the characteristics at baseline, we presented frequencies and percentages for the qualitive variables, and the mean ±Standard Deviation (SD) for the quantitative variables. To compare the characteristics of participants between patients in LTCF vs patients at home at baseline we used the Chi-square test or the Fisher’s exact test (if theorical frequency<5) for the qualitative variables. For the quantitative variables, we used the Student test for Gaussian distributions and the Kruskal-Wallis non parametric test for non-Gaussian distributions. To estimate the change from baseline of CMAI and NPI-C-A/A, we used a linear mixed model with time in continuous. Mixed models included all available data (M0, M1, M2, M3, M6, M9 and M12). We included subject-specific random effects to take into account the intra-subject correlation: a random intercept to take into account the heterogeneity of the CMAI and NPI-C-A/A at baseline and a random slope to take into account the heterogeneity of the slopes between subjects. The centre-specific random intercept was not included because this term was not significant. All statistical analyses were performed using SAS software version 9.4 (SAS Institute Inc, Cary, NC).



Baseline characteristics of the A3C cohort

Table 2 shows the baseline characteristics of the participants. Study patients were very elderly with mean age above 80 years and the majority were women. The greatest majority lived at home alone or with an informal caregiver. Almost two-thirds had a cardio-vascular risk factor but only a minority had a psychiatric history. Based on mean MMSE most had moderate or more severe dementia of several years’ duration. Fewer than half received an AD specific medication, mostly anticholinesterases, but ~80% were receiving a psychotropic. The mean duration of follow-up for patients was 9.5 months (Standard Deviation (SD) ± 4.3).

Table 2. Baseline characteristics of the A3C cohort (N=262)

*mean (standard deviation); **if patient living at home with an identified primary caregiver; Abbreviations: MMSE = Mini Mental State Examination; AD = Alzheimer’s disease; ADL = Katz’s activities of daily living scale; A/A = agitation/aggression.

Figure 1 presents subject disposition at follow-up in detail. Of the 262 patients enrolled 86 (32.8%) subjects dropped out over the 1-year follow-up. Attrition during the three first months, the critical period of A3C study, was 13.0% (n=34).

Figure 1. Flow chart of the A3C study

Abbreviations: V = Visit.

At baseline, study patients in LTCF were older (p=0.0003), more physically (p<0.0001) and cognitively (p<0.0001) disabled than the home-based subgroup. All types of psychotropic medications were more frequent among patients living in LTCF: antipsychotics (p=0.004), antidepressants (p=0.0006), anxiolytics (p<0.0001) and hypnotics (p<0.0001). However, the total score of NPI and the total score of CMAI were not significantly different between both subgroups at baseline (p=0.31 and p=0.25, respectively).

Baseline characteristics of neuropsychiatric symptoms (NPS)

By the IPA agitation definition, most participants had excessive motor activity (76.3%) and/or a verbal aggression (76.3%), while 115 (44.1%) displayed physical aggression. Table 3 shows the characteristics of A/A ratings and other NPS in study participants.

Table 3. Baseline characteristics of neuropsychiatric symptoms of the A3C cohort (N=262)

*mean (standard deviation) ; Abbreviations: CGI-S = Clinician’s Global Impression of Severity; NPI = Neuropsychiatric Inventory; NPI-C = Neuropsychiatric Inventory Clinician rating scale; CMAI = Cohen Mansfield Agitation Inventory; AMB = aberrant motor behavior; A/A = agitation/aggression; IPA = International Psychogeriatric Association.


Longitudinal courses of agitation symptoms over 12 months of follow-up

The CMAI score decreased significantly between the baseline (mean = 61.5; [Standard Error SE±1.0]), the 6 months of follow-up (V5) (mean = 50.5; [SE±1.0]) and the 12 months (V7) (mean = 50.1; [SE±1.2]). The mean of the NPI-C-A/A clinician severity score was 15.5 (SE±0.7) at baseline, 8.8 (SE±0.6) at 6 months (V5) and 8.8 (SE±0.7) at 12 months (V7). The change of the CMAI score and the NPI-C A/A at 1-year follow-up period was respectively -11.36 (SE=1.32; p<0.001) and -6.72 (SE=0.77; p<0.001). The figure 2 presents the change of the CMAI score and the change of the NPI-C-A/A score during the 1-year follow-up period. The figure 3 shows the variation of the modified ADCS-CGIC agitation domain version at each visit of follow-up (V2 to V7) with the modified ADCS-CGIS at baseline.

Figure 2. Change of the total CMAI and the total NPI-C-A/A during the follow-up (results from mixed linear models)

Figure 3. Comparison of the modified ADCS-CGIC agitation domain version at each visit of follow-up (V2 to V7) with the baseline (V1)

Abbreviations: V=Visit, mADSC-CGIC= modified Alzheimer disease cooperative study clinical global impression of change agitation domain.



Two hundred sixty-two patients with clinically significant A/A were enrolled in A3C study, most living at home, with moderate to severe dementia. At baseline, more than 70% showed excessive motor activity and/or a verbal aggression while fewer than half displayed physical aggression. At baseline, psychotropic medication was prescribed to 80%. Agitation symptoms experienced the greatest decreases during the first three months of follow-up, and A/A continued to improve through 12 months.
Concerning the specific study design of the A3C study, a monthly visit schedule during the first 3 months was chosen to address the primary aim of the A3C study. The first three months of A3C intends to simulate a classic randomized control trial 12-week treatment design. Evolution, variability and associations between different outcome measures will be specifically studied during this time. Subsequently, visits every three months were chosen until the end of a year for the purposes of detecting changes in NPS in shorter periods of time. In fact, since NPS are characterized by frequent fluctuations as well as by differences in the concurrent presentation of different symptoms, shorter intervals between assessments is needed in order to better and more precisely describe their course. Further data of the A3C will help to identify different A/A trajectories based on variations in change over the time in the frequency and the severity of symptoms and their associated factors, to study the coexistence of other clinically significant NPS, and to analyze patterns, and impact of pharmacological and other non-pharmacological approaches in the management of clinically significant A/A.
A recent systematic review estimated the incidence of clinically significant agitation in nursing home patients to be 18.8% over 12 months and 36% over 4-years (27). Several studies evaluated disease progression of agitation in AD: six studies reported an increase in severity/frequency of agitation over time, two studies presented mixed results and one showed a decrease (27). To our knowledge, to date no study describes the course of A/A overtime in community-dwelling AD patients. Interestingly, in A3C study, a major decrease of A/A symptoms was observed during the first 3 months of follow-up which slowly continued to decrease over the course until the end of follow-up. Certainly, the A3C cohort benefits from a management for the treatment of NPS that may include medications and/or non-pharmacological approaches that may be considered as “usual care” since no intervention was implemented in A3C, and as consequence, A3C still studies natural history of the agitation syndrome in usual care settings. However, the specific design of A3C with a follow up with short periods of time between visits, could be considered as a way of intervention similar to clinical trials. This could explain the continued decrease of agitation symptoms over time during the entire follow-up.
Several organizations, including the Food and Drug Administration and the European Alzheimer’s Disease Consortium, have expressed interest in better characterizing NPS, such as psychosis or depression in AD, which would be highly relevant for treating NPS in AD (28). Consensus diagnosis criteria for Agitation in AD were recently proposed (16). To our knowledge, the A3C study is the first cohort study using these criteria in a longitudinal observational study design. The whole A3C population met the criteria for A/A syndrome based on IPA criteria definition: three quarters showed excessive motor activity and/or verbal aggression and physical aggression occurred in a lesser patient. Gaining clarity about the clinical entity of A/A is of great importance, since its different phenotypes may delineate underlying disruptions in specific neuronal regions and/or circuitry and shed light on etio-pathogenesis enhancing the development of pharmacologic or non-pharmacologic treatments for specific A/A phenotypes. Aggressive behavior may respond to a drug differently than excessive motor activity behavior. Moreover, improving knowledge about pathogenesis pathways of A/A may lead to the study of biomarkers and to the increase of the use of biomarkers to maximize the productivity of clinical trials for NPS (12).
Of the common NPS, the natural history of A/A (phenomenology, course and associated factors) in AD is least well understood resulting in the lack of a “gold standard” efficacy outcome in therapeutics research development. In fact, little is known about the natural course of clinically significant A/A in AD patients, about factors influencing this course or about the variability of different outcome measures over time, such as the NPI or CMAI. This is even more evident for new scales such as the NPI-C. Findings from A3C will provide a better estimation of placebo group variability in trials, thus allowing for, more precise power estimates. Moreover, knowledge of the impact of demographic and other variables, such as vascular diseases or other NPS, on the trajectory of A/A overtime might improve the homogeneity of the sample population. In order to assess agitation response to treatment, three approaches have been used in clinical trials: 1) structured caregiver interviews (NPI-A/A, CMAI), 2) expert clinician scale ratings (NPI-C A/A), and 3) structured global ratings (modified ADCS-CGIC, CGI-C) based on judgment of experienced clinicians (11, 29). In order to complement NPS ratings based on caregiver report, clinical global ratings are used, since their strength is their being derived from experienced clinicians. A recent EU-US-CTAD Task Force (12) highlighted that choosing the best outcome measure for clinical trials was the key to treatment development for A/A and proposed to use a combining clinician- and caregiver-derived outcome as primary efficacy outcome measure in absence of gold standard (12). In the meantime, this Task Force encouraged using existing datasets to construct an evidence-based single novel measure of agitation by selecting items subsets of existing scales. Data from the A3C study will help in answering this question and in moving forward the field.
The main limitation of A3C is attrition of more than 30 % during one year of follow-up. Attrition is common in cohorts of older adults, especially when patients are affected by a severe and progressive chronic disease such as AD (30). The AD patients from A3C were notably and medically frail, and present a particularly severe complex form of disease with major complications such as distressing NPS and as consequence, with a higher risk of adverse outcomes that may explain this higher attrition compared with previous cohorts of AD patients. However, attrition during the three first months, the critical period of A3C study, was much lower (<20%). The data of the attrition will also help for calculating sample size in future trials. The second limitation is that the diagnosis of AD was based on clinical criteria and there was no requirement for biomarker confirmation. Therefore, our population has a possible AD. The lumbar puncture was only performed in 23 patients (8.9%); neither physio pathological biomarkers nor neuro-imaging biomarkers were performed in a standardized protocol since A3C cohort was a usual care survey, and in general it is not clinical standard of care to assess AD biomarkers in a cohort with such advanced dementia as A3C.
The A3C study addresses a clinically important population, AD older patients with disruptive NPS, which are often under assessed and excluded from the research field. Thus, this study gives the opportunity if developing research in this vulnerable population. In addition, data from A3C study may improve clinical practice by better defining and measuring agitation and, consequently, by better targeting pharmacological and non-pharmacological treatments.
Little is known about the longitudinal course of clinically significant A/A in AD patients, about factors influencing this course or about the variability of different outcome measures over time, such as the NPI or CMAI, or the definition of a clinically meaningful improvement in these scales. This is even more evident for new scales such as the NPI-clinician rating. A3C study may provide useful data in order to improve clinical practice and to optimize future clinical trials of treatments for agitation symptoms in AD.


Acknowledgments: L. Bories, A. Roustan, Y. Gasnier, S. Bordes, M.N. Cufi, F. Desclaux, Y. Gasnier, N. Gaits, M. Péré-Saun, S. Bordes.

Funding: The A3C cohort was supported by Ethypharm and Toulouse University Hospital.

Conflict of Interest: C. Lyketsos declares: 1) Grant support (research or CME) from NIH, Functional Neuromodulation, Bright Focus Foundation and 2) Payment as consultant or advisor from Avanir, Astellas, Roche, Karuna, SVB Leerink, Maplight, Axsome, Global Institute on Addictions. None conflict for the others authors.

Ethical Standards: A3C had ethical approval and oversight from the local Institutional Review Board (Toulouse University Hospital).

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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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H. Miao1,*, K. Chen2,*, X. Yan3, F. Chen4

1. Department of Endocrinology, Jiangjin Central Hospital of Chongqing, Chongqing, China; 2. Out-Patient Department, Jinzi Mountain Hospital of Chongqing Mental Health Center, Chongqing, 401147, China; 3. Department of Pediatrics, Jiangjin Central Hospital of Chongqing, Chongqing, China; 4. Department of Geriatrics, Chongqing Mental Health Center, Chongqing, China; * Hongyun Miao and Ken Chen are co-first author

Corresponding Author: Fei Chen, First Psychogeriatric Ward, Jinzi Mountain Hospital of Chongqing Mental Health Center, No102, Jinzi Mountain, Jiangbei, District, Chongqing, 401147, China. Phone:+86-023-67511695, Fax: +86-023-67511695, e-mail:; Xiaoyong Yan, Department of Pediatrics, Jiangjin Central Hospital of Chongqing , No.725 Jiangzhou Street, Jiangjin District, Chongqing,402260,China. Phone: +86-023-475213

J Prev Alz Dis 2020;
Published online November 9, 2020,




Background: This study aimed to investigate the association between sugar in beverage and dementia, Alzheimer Disease (AD) dementia and stroke.
Methods: This prospective cohort study were based on the US community-based Framingham Heart Study (FHS). Sugar in beverage was assessed between 1991 and 1995 (5th exam). Surveillance for incident events including dementia and stroke commenced at examination 9 through 2014 and continued for 15-20 years.
Results: At baseline, a total of 1865 (63%) subjects consumed no sugar in beverage, whereas 525 (18%) subjects consumed it in 1-7 servings/week and 593 (29%) in over 7 servings/week. Over an average follow-up of 19 years in 1384 participants, there were 275 dementia events of which 73 were AD dementia. And 103 of 1831 participants occurred stroke during the follow-up nearly 16 years. After multivariate adjustments, individuals with the highest intakes of sugar in beverage had a higher risk of all dementia, AD dementia and stroke relative to individuals with no intakes, with HRs of 2.80(95%CI 2.24-3.50) for all dementia, 2.55(95%CI 1.55-4.18) for AD dementia, and 2.11(95%CI 1.48-3.00) for stroke. And the same results were shown in the subgroup for individuals with median intakes of sugar in beverage.
Conclusion: Higher consumption of sugar in beverage was associated with an increased risk of all dementia, AD dementia and stroke.

Key words: Sugar in beverage, dementia, Alzheimer disease, stroke, Framingham Heart Study.



Dementia is a major cause of disability among the elderly and its medical and economic burdens on society have been increasing worldwide (1). Recent population-based studies have reported that lifestyle-related diseases such as hypertension (2), diabetes as well as lifestyle factors such as smoking habits (3, 4), alcohol consumption and dietary patterns are associated with the risk of incidence of dementia and AD (5, 6). However, the influence of particular individual dietary components at habitual intake amounts on dementia, especially Alzheimer disease (AD) dementia and stroke which share common risk factors and etiologies with dementia, remains incompletely understood (5).
Sugar consumption is excessive worldwide nowadays, which is widely recognized as key dietary risk factors for obesity and poor cardiometabolic health (7, 8). And longitudinal studies indicate that frequent sugar-sweetened beverages consumption is associated with a higher risk of type 2 diabetes and cardiovascular disease and raises the possibility that higher concentrations of ceramide which is associated with adverse metabolic health (9). Moreover, to our knowledge, studies yet showed that higher intake of sugary beverages was associated cross-sectionally with markers of preclinical AD and the risk of incident dementia (10, 11). Generalizability of these findings was limited due to the less accurate for sugar in beverage and less comprehensive outcomes only including dementia or preclinical AD without stroke.
Accordingly, the primary aim of this study was to examine whether sugar in beverage were associated with the 15-year risks of incident dementia and stroke in the community-based Framingham Heart Study (FHS) with detailed review of all medical records and autopsies. And we aimed to further investigate the underlying mechanisms by taking into account socio-demographic characteristics, medical history, and lifestyle factors.



Study Design

This study was carried out as a secondary analysis of data from the population-based FHS. The FHS is a longitudinal multi-generational study conducted in Framingham, MA, USA (12). The original cohort started in 1949 and consisted initially of 5209 respondents of a random sample of two-thirds of the adult population characterized by absence of atherosclerotic cardiovascular disease. The ‘Framingham Offspring Study’ was initiated in 1971 as a sample (n=5 5124, age = 12–58 years) consisting of the surviving descendants of the original cohort participants and spouses of those descendants.[13] Since their recruitment, participants from the Offspring cohort have had 9 serial examinations including standardized interviews, physician examinations, and laboratory testing. The characteristics and study protocol of both cohorts have been published elsewhere. For the present investigation, we included participants of the offspring cohort who were examined between 1990 and 1994 and for whom data of a detailed pain examination were available. This study complied with the Declaration of Helsinki; written informed consent was obtained from all study participants. The National Heart, Lung, and Blood Institute (NHLBI) of National Institutes of Health (NIH) has approved the study protocol.

Sugar in beverage assessment

Over the study course, Participants completed the Harvard semi quantitative FFQ at examination 5. For further details, see This FFQ is designed to measure dietary intake over the past year and has been validated extensively. Participants were asked how often they consumed one glass, bottle, or can of each sugary beverage item, on average, across the previous year. Each item was scored according to nine responses spanning from “never or almost never” to “61 per day.” Participants were presented with three items on sugar-sweetened soft drink (“Coke, Pepsi, or other cola with sugar,” “caffeine-free Coke, Pepsi, or other cola with sugar, “and “other carbonated beverages with sugar”), four items on fruit juice (“apple juice,” “orange juice,” “grapefruit juice,” and “other juice”), one item on sugar-sweetened fruit drinks and three items on diet soft drink (“low-calorie cola with caffeine,” “low-calorie caffeine-free cola,” and “other low-calorie beverages”). The individual items were summed to create exposure variables reflecting intake of (I) total sugary beverages (excluding diet soft drinks), (II) fruit juice, (III) sugar-sweetened soft drinks (all sugary beverages excluding diet soft drink, fruit juice, and fruit drinks), and (IV) diet soft drinks. Total sugary in beverage consumption was examined as 0-140 servings per week and was further categorized into a three-level variable: none intake (0 serving/week), 1-7 servings/week and >7 servings/week. Moreover, intake of sugary in beverages ascertained via the FFQ is reliable when measurements are repeated after 12 months, with correlation coefficients ranging from 0.85 for cola and 0.86 for fruit juice. Details were further described in FHS coding manual (http: // manuals/vr_ffreq_ex05_1_0575d_coding_manual.pdf).

Ascertainment of dementia and AD

All FHS participants were under ongoing continuous surveillance for onset of cognitive impairment and clinical dementia. We related beverage consumption to the 15-year risk of dementia. Surveillance commenced from examination cycle 5 to the time of incident event over a maximum of 41 years or until last known contact with the participant. A diagnosis of dementia was made in line with the Diagnostic and Statistical Manual of Mental Disorders, 4th edition. A diagnosis of Alzheimer’s disease (AD) dementia was based on the criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the AD and Related Disorders Association for definite, probable, or possible AD. See the online-only data supplement for complete details on our methods of surveillance, diagnosis, and case ascertainment on dementia and AD (14, 15). (eMethods in the Supplement).

Ascertainment of stroke

Stroke incidence was assessed through the continuous monitoring of hospital admissions in Framingham and by reviewing all available medical records and results (16). Stroke was defined as focal neurological symptoms of rapid onset and presumed vascular origin, lasting >24 hours or resulting in death within 24 hours. A committee comprising of least 3 FHS investigators, including at least 2 neurologists, adjudicated stroke diagnosis. The committee considered all available medical records, brain imaging, cerebrovascular imaging, and the assessment of the study neurologist who visited the participant. See more details on the FHS code manual (http: //


The most recent covariate information during the period of sugar in beverage assessment (Exam 5). We calculated nutrient, energy intake and alcohol consumption from the aforementioned FFQ. Hypertension was defined as systolic blood pressure>140 mm Hg, diastolic blood pressure>90 mm Hg, or use of antihypertensive medications. Individuals with fasting plasma glucose concentrations≥7 mmol/L or who self-reported use of antidiabetic medications were considered diabetic. BMI was calculated as kg/m2. Waist circumference (in inches) was measured at the level of the umbilicus. Current smokers were defined as participants who smoked regularly in the year preceding examination cycle 5.The educational level were assessed by medical interview. Total cholesterol, low-density lipoprotein cholesterol and glucose were measured after an overnight (>10 hours) fast.

Statistical Analysis

Descriptive statistics was performed for the 3 subgroups: participants with no intake for sugar in beverage, 1-7 servings/week and >7 servings/week. For continuous variables mean, standard deviation and range were calculated for approximately normally distributed data, otherwise median and range were used. For discrete data, absolute and relative frequencies were computed. Follow-up for dementia and stroke was from the baseline examination to the time of incident event. And for persons with no incident events, follow-up was censored at the time of death or the date the participant was last known to be dementia free. For survival analysis, Cox proportional hazards modelling was applied and the following covariates were included: crude analysis; Model 1 adjusted for age and sex; Model 2 adjusted for age, sex, and the following dementia risk factors: hypertension, smoking, diabetes and body mass index. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. The same analysis was carried out for Alzheimer’s disease and stroke. Analyses were performed using SAS software, version 9.4 (SAS Institute Inc, Cary, North Carolina) and Stata statistical software, version 15 (Stata Corporation, College Station, Texas, USA). A 2-sided with P<0.05 was considered statistically significant.




Of the 3702 participants of the FHS Offspring Cohort attending the 5th exam, 2983 subjects underwent the sugar in beverage examination and thus were included in this study. Thereof, 770 subjects were excluded because 730 subjects lacked assessment on dementia, 4 subjects were diagnosed with dementia at baseline and 36 subjects were lost follow-up. Thus, 2664 subjects could be included in the final analyses for dementia, with 1865(63%) subjects with no intake for sugar in beverage, 525(18%) subjects with 1-7 servings/week and 593(29%) subjects with over 7 servings/week. And 2916 subjects were available for incident stroke analysis, since 40 lacked assessment on dementia and 13 were lost follow-up. See Figure 1.

Figure 1. Selection of Study Participants in the Framingham Heart Study

Descriptive data are presented in Table 1. The initial health status was comparable between both groups. The three groups descriptively differed with regard to age, sex, BMI, total caloric intake, saturated fat, dietary fiber, smoking status and history of diabetes. See Table 1.

Table 1. Characteristics of the study sample of the Framingham Heart Study at baseline sugar in the beverage assessment

Abbreviations: BMI, body mass index; WC, waist circumference. LDL, low-density lipoprotein, TC: total cholesterol.


Sugar in beverage and Risk for Dementia and stroke

The mean follow-up time for dementia was 19 years (interquartile range, 18-20years) and for stroke was 16 years (interquartile range, 15-18 years).The absolute number of all dementia events over the follow-up was 553 including 142 for AD dementia. And 203 stroke occurred during the follow-up.
When adjusting for age, sex, diabetes, hypertension, smoking and BMI using Cox regression analysis, subjects with more than 7 servings/week of sugar in beverage showed a higher risk of all dementia, AD dementia and stroke [HR 2.80 (2.24-3.50) for all dementia; 2.55(1.55-4.18) for AD dementia; 2.11(1.48-3.00) for stroke] than subjects with no intake for sugar in beverage. And the results were comparable for participants with 1-7 servings/week of sugar in beverage [HR 2.65(2.14-3.30) for all dementia; 2.49(1.55-3.99) for AD dementia; 1.94(1.38-2.72) for stroke] (Table 2.).And the results were comparable when limiting the age to over 65 years for incidence of dementia and AD dementia, but not for the stroke which may be resulted from the rare stroke events occurred (eTable 1 in supplement). Figure 2 shows the cumulative incidence curves for all dementia, AD dementia and stroke stratified by groups with different levels of sugar in beverage intake after adjusting for age and sex.

Table 2. Cumulative hazards based on sugar in beverage intake

Abbreviations: AD, Alzheimer’s disease; HR, hazard ratio; CI, confidence interval; Model 1 Sex and Age; Model 2 in addition for hypertension, smoking, diabetes and body mass index.

Figure 2. Adjusted cumulative incidence of dementia and stroke based on sugar in beverage intake



In our longitudinal analysis of a large community-based sample, it is found that higher consumption of sugar in beverage was associated with an increased risk of all dementia, AD dementia and stroke at the same time. These findings were striking given that they were evident in a middle-aged sample and were observed even after statistical adjustment for numerous confounders such as sex, age, hypertension, diabetes and BMI.
Excess intake of sugar in beverage is known to be associated with cardiovascular disease and metabolic disease, which, in turn, is associated with vascular brain injury (17, 18). Although this suggests a possible link between sugary beverage consumption and vascular brain injury, we observed more striking associations between sugar in beverage and incidence of dementia and AD dementia. The Nurses’ Health Study and Health Professionals Follow-Up Study reported that higher consumption of sugar and artificially sweetened soft drinks was each independently associated with a higher risk of incident stroke over 28 years of follow-up for women and 22 years of follow-up for men (19). On the contrast, a prospective cohort study including 2888 participants reported an association between daily intake of artificially sweetened soft drink and an increased risk of both all-cause dementia and dementia because of AD, but not sugar-sweetened (10). Stephan and colleagues speculated that high fructose intake is a risk factor for dementia and that increasing consumption of fructose in the U.S. population could lead to greater dementia risk, but there is little persuasive evidence in humans at typical intake levels (20). In a cross-sectional observations in a large community-based sample, higher sugary beverage intake was associated with markers of preclinical AD, including brain atrophy and poorer episodic memory (11). To our knowledge, our study is the first to demonstrate accelerated probability of developing all dementia, AD dementia and stroke year-on-year at a population level and much more accurate assessments for sugar in beverage. And it is not a cross-sectional observations, but a long-term implications, especially for more than 5-year follow-up. In detail, it is like a fraud that excessive consumption of sugar in beverage is safe in the short term like less than 5-year follow-up, but would increase the risk of dementia, AD dementia and stroke in the long term. And only in a prospective study the truth can be told.
First, the metabolic changes followed by intake of sugar in beverage may harm cognitive function and lead to stroke, although the mechanisms are incompletely understood, and inconsistent findings have been reported. Sugar in beverage leading to a rapid rise in blood glucose and insulin, providing a plausible mechanism to link the sugar in beverage consumption to the development of dementia and stroke risk factors (21). A study shown that long term consumption of sucrose-sweetened water led to increased body weight, glucose intolerance, insulin resistance, and hypercholesterolemia and were associated with exacerbation of spatial learning and memory impairment and cerebral Aβ deposition in transgenic mouse model of AD (22). Also in animal models, high fat refined sugar diets increase Aβ aggregation and plasma total tau level, hippocampal atrophy, and reduce levels of brain-derived neurotropic factor (BDNF) which could lead to impaired memory performance and reduced synaptic plasticity within the hippocampus (11, 23, 24). Secondly, the association may be mediated by lifestyle factors related to high intake of sugar in beverage. A study recruiting 640 adolescents suggested that sugar-sweetened beverage intake may attenuate the beneficial effects of physical activity on skeletal muscle mass and lead to obesity (25). A survey demonstrated that individuals who consumed diet beverages were simply more likely to smoke regularly like what is showed in our baseline characters (26). And it is confirmed that low physical activity and muscle mass, obesity and smoking regularly were risky for incident dementia (4, 27-29). In a large, dementia-free community-dwelling cohort, our results provide further evidence that sugar in beverage consumption is associated with increased risk of all dementia, AD dementia and stroke after adjustment for confounders including sex, age, BMI, diabetes and hypertension. These findings are highly relevant given that dementia and stroke are two of the largest global public health challenge interfering with a person’s daily living activities facing our aging population.
The main strength of our study was the use of a large and well-characterized community-based sample free of clinical stroke and dementia based on FHS which collected detailed dietary, lifestyle, clinical data and accurate assessment for sugar in beverage. Limitations of our study include the observational nature of the study, which precludes conclusions about causality and the temporal associations between sugar in beverage intake and dementia, AD dementia and stroke. Second, the use of a self-report FFQ to quantify sugar in beverage intake data may be subject to recall bias. Third, there is multifactor affecting the relationship between sugar in beverage and cognitive decline. Although we addressed confounding in numerous ways, we cannot exclude the possibility of residual confounding.



In conclusion, the findings of this research demonstrate that sugar in beverage consumption was associated with an increased risk of all dementia, AD dementia and stroke independent from multiple demographic factors. These finding may help to further advise people consume less sugar in beverage to keep better health. Future research is needed to replicate our findings and to investigate the mechanisms underlying the reported associations.


Data Availability: Data described in the manuscript, code book, and analytic code will not be made available because the authors are prohibited from distributing or transferring the data and codebooks on which their research was based to any other individual or entity under the terms of an approved NHLBI Framingham Heart Study Research Proposal and Data and Materials Distribution Agreement through which the authors obtained these data.

Conflicts of Interest: The authors declare that they have no conflicts of interest.

Acknowledgments: The authors thank the National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, Chongqing Mental Health Center and Jiangjin Central Hospital of Chongqing.

Grant Support: None. All authors have read the journal’s authorship agreement and that the manuscript has been reviewed by and approved by all named authors.

Ethical Standards: The study procedures followed were in accordance with the ethical standards of the Institutional Review Board and the Principles of the Declaration of Helsinki.



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P.A. Amofa1, D.E.C. Locke2, M. Chandler3, J.E. Crook4, C.T. Ball4, V. Phatak5, G.E. Smith1


1. Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA; 2. Department of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ, USA; 3. Department of Psychiatry and Psychology, Mayo Clinic Florida, Jacksonville, FL, USA; 4. Division of Biomedical Statistics and Informatics, Mayo Clinic Florida, Jacksonville, FL, USA; 5. Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA; 6. Department of Psychiatry and Psychology, Mayo Clinic Minnesota, Rochester, MN, USA.

Corresponding Author: Dona E.C. Locke, Division of Psychology, Mayo Clinic, 13400 E. Shea Blvd., Scottsdale, AZ 85259; Ph: 480-301-8297; Fax: 480-301-6258; Email:

J Prev Alz Dis 2021;1(8):33-40
Published online October 26, 2020,



Background/Objective: Various behavioral interventions are recommended to combat the distress experienced by caregivers of those with cognitive decline, but their comparative effectiveness is poorly understood.
Design/Setting: Caregivers in a comparative intervention study randomly had 1 of 5 possible interventions suppressed while receiving the other four. Caregivers in a full clinical program received all 5 intervention components. Care partner outcomes in the study group were compared to participants enrolled in a full clinical program.
Participants: Two hundred and seventy-two dyads of persons with amnestic mild cognitive impairment (pwMCI) and care partners enrolled in the comparative intervention study. 265 dyads participated in the full clinical program.
Intervention: Behavioral intervention components included: memory compensation training, computerized cognitive training, yoga, support group, and wellness education. Each was administered for 10 sessions over 2 weeks.
Measurements: A longitudinal mixed-effect regression model was used to analyze the effects of the interventions on partner burden, quality of life (QoL), mood, anxiety, and self-efficacy at 12 months follow-up.
Results: At 12 months, withholding wellness education or yoga had a significantly negative impact on partner anxiety compared to partners in the clinical program (ES=0.55 and 0.44, respectively). Although not statistically significant, withholding yoga had a negative impact on partner burden and mood compared to partners in the full clinical program (ES=0.32 and 0.36, respectively).
Conclusion: Our results support the benefits of wellness education and yoga for improving partner’s burden, mood, and anxiety at one year. Our findings are the first to provide an exploration of the impact of multicomponent interventions in care partners of pwMCI.

Key words: Non-pharmacological interventions, MCI, dementia, caregiver, patient preferences.




As a medical community, we are increasingly able to identify dementia at an early stage, including the Mild Cognitive Impairment (MCI) stage. Amnestic MCI is defined as memory abnormality beyond normal age-related decline with relatively retained functional capacity (1). However, it is acknowledged in this definition that persons with MCI often have some mild problems with complex tasks they previously performed (such as paying bills) such that they may take longer or make more errors than in the past (2). Therefore, some care partner support is often needed, even if it is only minor reminders and supervision. Rates of depression and other psychological comorbidities (e.g., burden, anxiety, decreased quality of life) are elevated in family members of people with MCI as compared to the general population (but not as pronounced as in caregivers of persons with dementia) (3). The neurobehavioral symptoms, psychological wellbeing, cognitive and functional decline, executive functioning difficulties, and dependency present in persons with MCI (pwMCI) are common predictors of the psychological symptoms experienced by care partners (4–6). Thus, interventions to improve outcomes in patients with MCI could also potentially impact outcomes in care partners of those with MCI.
Mayo Clinic developed the HABIT (Healthy Action to Benefit Independence & Thinking ®) program, which is a 50-hour behavioral intervention treatment program with 5 components. The 5 components include physical exercise via yoga, computerized cognitive training (CCT), wellness education, patient and partner support groups, and cognitive rehabilitation with a compensatory memory support system (MSS). Each of these behavioral interventions has support in the literature for effectiveness for pwMCI across a variety of outcomes (e.g., cognitive functioning, quality of life, mood, partner burden) in comparison to no treatment (7–12). However, there is a dearth of literature comparing the effectiveness of behavioral interventions for those with MCI or their loved ones who participate as support partners. In a pilot study, we compared the outcome of wellness education plus compensation based cognitive rehabilitation to wellness education plus cognitive exercise, and we compared each combination to no treatment. We found that patient memory-related activities of daily living (ADLs) were improved over no treatment in the cognitive rehabilitation condition while they were not in the cognitive exercise condition (13). We also found that partners in both treatment groups showed stable to improved mood and anxiety symptoms while partners in the untreated group showed worsening depression and anxiety symptoms over 6 months (14). There were no statistically significant differences between the impact of cognitive rehabilitation or cognitive exercise on patient self-efficacy or other partner outcomes (quality of life, burden); however, effect size estimates suggested the possibility of greater impact of the cognitive rehabilitation intervention on several of these outcomes that this small pilot study was underpowered to detect (Cohen’s d range .37 to .73) (13, 14).
The current study sought to compare the effectiveness of the five behavioral interventions that comprise HABIT®. The full details of our rationale, design, and initial enrollment of the comparative effectiveness study are outlined elsewhere (15). Briefly, we utilized a subtraction model, randomizing groups of dyads to have one of the five interventions withheld while receiving the other four. These groups were compared to a clinical dataset of dyads who received the full clinical HABIT ® program. Patient-related outcomes of the comparative effectiveness study are outlined in a separate report (16). As delivery of the HABIT interventions requires a care partner to participant in the sessions, the interventions are as much geared towards the care partner as the pwMCI themselves. We hypothesize that the intervention components that promote self-care and resilience (as opposed to improve cognitive function) will encourage care partners to develop skills which will impact their burden, mood, and overall quality of life directly. This report outlines outcomes for partners one-year post intervention. Patient/partner-advocated outcomes were determined by surveying alumni who had previously completed HABIT®. This survey asked patients and partners to identify their preference of outcomes they were seeking in a behavioral intervention for MCI. Focusing just on partner-related outcomes, burden was ranked as most important of the partner outcomes, followed by partner quality of life, partner self-efficacy, partner anxiety, and partner mood, respectively (17). These are the outcomes that are the focus of this analysis.




272 dyads were recruited through clinical services at Mayo Clinic in Minnesota, Arizona, and Florida as well as University of Washington to take part in the comparative effectiveness intervention study. Consecutive candidates with diagnoses of amnestic MCI (single or multi-domain) were approached for the study, underwent further evaluation for study inclusion/exclusion criteria, and enrolled in the trial. Inclusion criteria included a Clinical Dementia Rating Scale (18) score of <0.5, a cognitively normal (Mini Mental Status Exam, MMSE (19) (>24)) care partner who has at least twice-weekly contact with the pwMCI, either not taking or stable on nootropic medication for at least 3 months, and fluent in English. Exclusion criteria included current participation in another treatment-related clinical trial or significant auditory, visual, or motor impairment impacting ability to participate in the program.

HABIT Intervention and Randomization

The clinical HABIT program involves 10 days of intervention over the course of two weeks. All components are designed to help pwMCI and their care partners initiate new health behavior habits with the aim of sustaining these behaviors post-HABIT®. In the comparative effectiveness study, block randomization was utilized to suppress one of the five components from groups of 10-20 couples in each session. Interventions for both the clinical HABIT program and the comparative effectiveness study were run by PhD clinical neuropsychologists, master’s trained counselors, or cognitive rehabilitation and dementia education specialists. Certified yoginis conducted the yoga sessions. Components of HABIT® include:
1. Yoga: Partners and patients engaged in daily 45 to 60-minute sessions of physical exercise and relaxation/mindfulness training via yoga. They were provided a customized DVD to encourage continued practice post-HABIT
2. Computerized Cognitive Training (CCT): Partners and patients completed 45- to 60-minute sessions of cognitive training via the commercially available Brain-HQ™ program (Posit Science; San Francisco CA). They were provided a one-year subscription to the program to encourage continued use post-HABIT.
3. Wellness: Partners and patients attended daily 45- to 60-minute lectures covering a range of health topics such as Living with MCI, Changes in Roles and Relationships, Sleep Hygiene, MCI and Depression, Nutrition, and Assistive Technologies. Dyads were given resources and written information to help engage behavioral changes post-HABIT.
4. Support Groups: pwMCI and care partners met separately in support groups 45-60 minutes each day. The pwMCI support group focused on reminiscence-focused group sessions with the opportunity for psychotherapeutic discussion of MCI-related concerns as desired by patients. The partner support group focused on building resources for coping with the change in their loved one. Dyads were encouraged to seek out community-based support groups (e.g. Alzheimer’s Association groups) for continued support post-HABIT.
5. Memory Support System (MSS): The patient received cognitive rehabilitation daily focused on compensatory-focused MSS development. This involved training using a structured curriculum in use of a two page-per-day written memory book to develop compensatory written reminders for important appointments, tasks, or reminders. Patients and partners were provided the paper MSS materials in an ongoing manner to enable continued use of the system post-HABIT.

The final sample for the comparative effectiveness study included 56 dyads who had the yoga component suppressed, 54 dyads who had the CCT suppressed, 52 dyads who had the wellness component suppressed, 53 dyads who had the support group suppressed, and 57 dyads who had the MSS suppressed.
To assess the impact of individual intervention components on partner outcomes, we compared data from the comparative effectiveness study to a clinical HABIT sample. The clinical HABIT sample was similar in makeup to the participants in the comparative effectiveness study except that the clinical HABIT sample patients: received all five interventions, completed their sessions prior to the PCORI trial, and did not include patients from the University of Washington. Data used in these analyses came from only those clinical HABIT program participants who had provided informed consent for the use of their data for research purposes, which included follow-up for 5 years post-HABIT. The final clinical HABIT sample included 265 dyads recruited through the clinical HABIT programs at Mayo Arizona, Mayo Florida, and Mayo Minnesota.

Measures: Partner

The care partner completed measures at baseline, end of treatment, 6-month follow-up, and 12-month follow-up.

Partner burden

Care partner burden at 12-month follow-up was our primary outcome measure. This was assessed with the short form of the Caregiver Burden Inventory (20). Scores range from 0-48 with higher scores suggesting more burden.

Partner mood and anxiety

Partners completed the Center for Epidemiological Studies Depression Scale (CES-D) (21) for measurement of depression-related symptoms and the Resources for Enhancing Alzheimer’s Caregiver Health (REACH) (22) scale for anxiety. The CES-D scores range from 0-60 with higher scores suggestive of more symptoms of depression. Reach total scores range from 10-40 with higher scores suggestive of more symptoms of anxiety.

Partner quality of life

Quality of life (QoL) was measured using the Quality of Life AD (QOL-AD) scale (23). Scores range from 13-52 with higher scores representing better QOL.

Partner self-efficacy

Partners completed the Caregiving Competence and Mastery of the Pearlin (24) scales. Scores range from 7-18 with higher scores indicating higher self-efficacy.


We compared baseline outcome measures of partners in the experimental groups to those in the clinical HABIT group using a mixed-effects regression model with a fixed effect for clinical HABIT group and random effects for site-dependent patient/partner group. For the primary analysis, a longitudinal mixed-effects regression model was used to compare primary partner outcome measures using data from four time points (baseline, end of treatment, 6-month, and 12-month) among the six groups – the five comparative intervention study groups and one clinical HABIT group. The clinical HABIT group also had outcome measures collected at 3 months, which were accounted for in the model. The primary analysis focused on changes in the measures from baseline to 12 months with burden as the primary outcome and QOL, mood, anxiety, and self-efficacy as secondary outcomes. Specifically, each outcome measure at baseline was modeled with fixed effects for age, sex, site, and group (clinical vs. experimental). The mean change in each outcome measure from baseline to follow-up time point was also modeled with fixed effects for group (clinical HABIT and each of the 5 experimental groups), age, and sex. We included random effects for partner to account for the multiple measurements over time. Comparisons of each experimental group to the clinical HABIT group on partner burden, QOL, mood, anxiety, and self-efficacy at 12 months were of primary interest. Furthermore, we obtained fitted trajectories over time for all experimental groups and the clinical HABIT group for each of the 5 outcome measures. We created 95% confidence intervals (CI) using the profile likelihood method and performed testing using corresponding likelihood ratio tests. Effect sizes (ES) were calculated as the fitted mean change from baseline for a hypothetical average partner divided by the standard deviation (SD) of the baseline measures of partners in the experimental groups. The Holm method was used to adjust for multiple comparisons. Data missing at random were accounted for by using longitudinal mixed models for our primary analysis. Analyses were performed using R statistical software, version 3.6.2 (R Foundation for Statistical Computing, Vienna, Austria).




Study partner baseline characteristics and outcome measures at baseline and 12 month follow-up per study arm are shown in Table 1. Two hundred seventy-two dyads entered the comparative effectiveness intervention study and 228 dyads (83.8%) completed the study through the 12-month follow-up. The most common reasons for withdrawal from the study included the presence of other significant medical concerns and changes in living situation. The clinical group included 265 dyads at baseline and data from all 265 dyads were included in 12-month outcome analysis . Table 1 shows the number of missing observations for each group. Mood, anxiety, and self-efficacy were worse at baseline for the clinical HABIT group compared to the patients in the experimental groups (p<0.001). This difference was adjusted for in the comparison analysis at 12 months outlined below. There was no evidence of baseline differences in burden or QoL between the clinical HABIT group and the experimental groups (p=0.21 and p=0.18, respectively).

Table 1. Partner Baseline and 12 Month Follow-up Characteristics

Abbreviations: CCT, computerized cognitive training; MSS, memory support system; HABIT, Healthy Action to Benefit Independence and Thinking; QOL-AD, Quality of Life in Alzheimer Disease; Resources for Enhancing Alzheimer Caregiver Health; Note. For Burden, higher scores indicate greater burden; for QOL, higher scores indicate greater QOL; for CES-D, higher scores indicate more symptoms of depression; for REACH, higher scores indicate more symptoms of anxiety; for Self-efficacy, higher scores indicate higher self-efficacy. Superscripts represent the number of missing observations. Asterisk (*) represents significant (p<0.001) baseline difference in outcome measure between clinical HABIT group and experimental group based on a likelihood ratio test from a mixed effects regression model with fixed effect for group (clinical vs. experimental) and a random effect for patient/partner session.


Outcome Measures

At the end of treatment (see Figure 1), the clinical HABIT group saw an improvement from baseline in QOL (ES= 0.20; 95% CI 0.09 to 0.30), mood (ES=0.16, 95% CI 0.04 to 0.29), and anxiety (ES=0.12, 95% CI 0.00 to 0.24), but not in burden or self-efficacy. All the experimental groups showed evidence of improvement in QOL from baseline to end of treatment except the no yoga group (ES, -.07, 95% CI –0.06 to 0.32) and the no wellness group (ES, -.04, 95% CI -0.24 to 0.17). However, the no wellness condition was the only experimental condition that was significantly lower than clinical HABIT (ES, -0.23, 95% CU -0.46 to -0.01). For the other 4 intervention groups, the difference in the change in QOL from baseline to end of treatment compared to clinical HABIT was not significantly different. There were no other notable differences between clinical HABIT and intervention groups with respect to change from baseline to end of treatment on mood, anxiety, burden, or self-efficacy.
In the comparison analysis at 12 months (Table 2), there was a trend toward the no yoga group having significantly worsened outcome in our primary measure (partner burden) compared to the clinical HABIT sample (ES=-0.32, 95% CI, -0.58 to -0.06, adjusted p=.080), although these were non-significant after adjusting p-values for multiple comparisons.

Table 2. Differences in Caregiver Outcomes at 12 Months Compared to Clinical HABIT Program Participants

Abbreviations: HABIT, Healthy Action to Benefit Independence and Thinking; CI, confidence interval; WE, wellness education; CCT, computerized cognitive training; SG, support group; MSS, memory support system. Differences in effect sizes are interpreted such that experimental groups with a negative difference had worse partner outcomes at 12 months compared to the clinical HABIT group. Effect sizes were estimated from longitudinal mixed effects regression models, in which a 1-unit increase in the effect size corresponds to a 1 standard deviation (SD) improvement in partner outcome from baseline. Baseline SDs from the 5 experimental groups were used for the effect sizes: 6.77 for burden, 5.45 for QOL (quality of life), 6.10 for mood, 4.88 for anxiety, and 3.21 for self-efficacy. Adjusted p values were computed using the Holm method for multiple comparisons based on 5 tests.


In analysis at 12 months of the other secondary outcome measures (Table 2), withholding wellness (ES=-0.55, 95% CI, -0.84 to -0.25, adjusted p=0.013) and withholding yoga (ES=-0.44, 95% CI, -0.73 to -0.16, adjusted p=0.013) each had a significant negative impact on partner anxiety compared to the clinical HABIT sample. There was a trend toward withholding yoga having a negative impact on mood compared to clinical HABIT (ES=-0.36, 95% CI, -0.66 to -0.06, adjusted p=0.10), however this was not statistically significant after adjusting for multiple comparisons. There were no significant differences in QOL or self-efficacy at 12 months when intervention components were withheld in comparison to the full HABIT program. The course of change over time for each of the 5 outcome measures and each study group are illustrated in Figure 1.

Figure 1. Effect Sizes

Effect sizes were estimated from longitudinal mixed effects regression models, in which a 1-unit increase in the effect size corresponds to a 1 standard deviation (SD) improvement in caregiver outcome. Baseline SDs from the 5 study arms with one HABIT component removed were 6.77 for burden (A), 5.45 for QOL (quality of life) (B), 6.10 for mood (C), 4.88 for anxiety (D), and 3.21 for self-efficacy (E). Abbreviations: EOT, end of treatment; CCT, computerized cognitive training; MSS, memory support system. Error bars represent 95% confidence intervals for the effect sizes.



Psychological distress and reduced quality of life among care partners of those with MCI are related to neuropsychiatric symptoms, executive functioning deficits, and memory dysfunction in their loved one with MCI (3, 5, 25). The HABIT program for pwMCI aims to impact these care partner symptoms as well as create healthy lifestyle and coping habits in pwMCI. We have reported on primary patient outcomes elsewhere (13). Our aim with this report is to compare the impact on care partners by providing five behavioral treatments (MSS, CCT, yoga, wellness, and support group) in a multicomponent program for pwMCI and their care partners.
Priority care partner outcomes measured in the study were determined by previous participants as burden (primary), quality of life, mood, anxiety, and self-efficacy (17, 26). Among all study arms, partner burden, mood, anxiety, quality of life, and self-efficacy remained stable or improved by end of treatment but varied by group at 12 months after the intervention (Figure 1). Across measures, partner outcomes were stable (or improved for anxiety) at 12 months in the full clinical program (after adjustment to baseline difference between the intervention groups and the clinical sample) but showed variable worsening in the arms with an intervention withheld. For anxiety specifically, worsening was significant if either yoga or wellness was withheld. Partner burden and mood also trended toward worsening in the group with yoga withheld.
Our findings were partially supportive of our hypothesis. When compared to a clinical HABIT program inclusive of all five components, partners who did not received wellness or yoga showed worsened anxiety (and a trend toward greater burden and worsened mood) at 12 months. However, there was no difference in partner QoL across interventions. The two intervention components that impacted anxiety, and to a lesser extent burden, provide knowledge and skills for ongoing self-care and resiliency directly to the care partner in addition to the pwMCI. Cognitive exercise and cognitive rehabilitation components of the program were mainly aimed to help the pwMCI initiate new behavioral habits that promote independence and maintain cognitive functioning; while support group intervention component was to encourage pursuing of emotional support and community building.
The concepts of self-care and resilience, which are broadly applied in HABIT, are perceived as the core aspects of yoga and wellness (27). Our yoga component, while teaching physical exercise with yoga poses, also taught relaxation and mindfulness practices. Wellness encouraged self-care practices such as eating well, exercise, getting enough sleep, monitoring moods, and staying connected socially. This helps explain why these interventions may have more impact on care partner sense of burden and anxiety as time went on. Recent studies have shown yoga intervention to be associated with greater improvements in mood and wellbeing compared to other exercise regimens (27, 28). Likewise, engagement in meaningful activities combined with psychoeducational materials (covering information about MCI and what to expect, and healthy lifestyle materials) improve mood and decrease burden among caregivers of pwMCI (29, 30). The impact of both yoga and wellness activities on outcomes in both the pwMCI (16) and their care partner is promising, considering their easy accessibility. From a patient-centered behavioral intervention prospective, this is reassuring.
Baseline levels of anxiety and mood symptoms were higher, and self-efficacy was lower in our clinical sample than in our experimental sample. It is possible that the clinical care partner sample had a higher distress level as a result of a worse cognitive function level among their persons with MCI. Regardless, this difference could not account entirely for the observed results as our statistical method factored these baseline differences into the study model.


Due to our innovative study design, interpretation of our results in comparison to other studies requires caution. The subtraction approach employed in the intervention study approximates the cost of not receiving an intervention. By comparison with the clinical sample data, we infer that intervention components make a significant contribution to the outcomes of interest given the impact on outcomes when that intervention is absent. Although studies have shown that support group and compensatory cognitive rehabilitation reduce burden, improve quality of life, and reduce psychological distress among care partners in the short term (12, 29, 30), it is possible that these interventions do not yield the same results on longer term outcomes. This could be in part due to the increased demands of caring for a family member with progressive cognitive impairment. Additionally, our study is from a predominantly non-Hispanic white population with high educational level. Also, all interested outcomes were of subjective reports; no objective performance-based reports were added to support our findings.
While our effect sizes are modest and our findings may not be extended to other forms of behavioral interventions (e.g. different versions of wellness, physical activity, and support group therapy), we offer our results to encourage multimodal trials among care partners of pwMCI. Enhancing knowledge and skills early in the course of a progressive process may not only impact trajectory but also increase hope. Further research can examine the effect of different modalities of physical activity (e.g. resistance training or a different form of yoga) combined with the different types of group therapy and wellness interventions.


Support/Funding: Research reported in this manuscript was primarily funded through a Patient-Centered Outcomes Research Institute (PCORI) Award (CER-1306-01897). The statements in this publication are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee. Additional support for DECL: NIA P30AG19610, NIA R01 AG031581, the Arizona Alzheimer’s Research Consortium, the Ralph J. Wilson Foundation Development Gift to Mayo Clinic. Additional support for GES: NIA P50AG47266, State of Florida Ed and Ethel Moore program.

Sponsor’s Role: The sponsor 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.

Disclosure statement: No conflict of interest was reported by all authors.

Author Contribution: Study concept and design: D.E.C.L., M.C., J.E.C., C.T.B., V.P., G.E.S. Data acquisition and interpretation: All authors; Statistical analysis: C.T.B., J.E.C. Manuscript draft: P.A.A., D.E.C.L. Critical revision of manuscript: P.A.A., D.E.C.L., M.C., J.E.C., C.T.B., G.E.S.

Approval of final manuscript: All authors.

Data availability statement: The data that support the findings of this study are available from the corresponding author, D.E.C.L., upon reasonable request.

Trial registration: Identifier: NCT02265757.

IRB: Institutional Review Boards at the Mayo Clinic (14-000885) and University of Washington (49235)



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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:, phone: 270-745-3618, Fax: 270-745-6950

J Prev Alz Dis 2020;
Published online September 18, 2020,



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.



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.



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.


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.



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



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.



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.



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P.S. Aisen1, J. Cummings2, R. Doody3, L. Kramer4, S. Salloway5, D.J. Selkoe6, J. Sims7, R.A. Sperling6, B. Vellas8 and the EU/US CTAD 2019 Task Force*


* EU/US/CTAD TASK FORCE: Susan Abushakra (Framingham) ; Paul Aisen (San Diego) ; John Alam (Boston) ; Sandrine Andrieu (Toulouse) ; Anu Bansal (Simsbury) ; Monika Baudler (Basel) ; Joanne Bell (Wilmington) ; Mickaël Beraud (Zaventem); Tobias Bittner (Basel); Samantha Budd Haeberlein (Cambridge) ; Szofia Bullain (Basel) ; Marc Cantillon (Gilbert) ; Maria Carrillo (Chicago) ; Carmen Castrillo-Viguera (Cambridge) ; Ivan Cheung (Woodcliff Lake) ; Julia Coelho (San Francisco) ; Jeffrey Cummings (Las Vegas) ; Michael Detke (San Francisco) ; Daniel Di Giusto (Basel) ; Rachelle Doody (South San Francisco) ; John Dwyer (Washington) ; Michael Egan (North Wales) ; Colin Ewen (Slough) ; Charles Fisher (San Francisco) ; Serge Gauthier (Montreal) ; Michael Gold (North Chicago) ; Harald Hampel (Woodcliff Lake) ; Ping He (Cambridge) ; Suzanne Hendrix (Salt Lake City) ; David Henley (Titusville) ; Michael Irizarry (Woodcliff Lake) ; Atsushi Iwata (Tokyo) ; Takeshi Iwatsubo (Tokyo) ; Michael Keeley (South San Francisco) ; Geoffrey Kerchner (South San Francisco) ; Gene Kinney (San Francisco) ; Hartmuth Kolb (Titusville) ; Marie Kosco-Vilbois (Lausanne) ; Lynn Kramer (Westport) ; Ricky Kurzman (Woodcliff Lake) ; Lars Lannfelt (Uppsala) ; John Lawson (Malvern) ; Jinhe Li (Gilbert) ; Frank Longo (Stanford) ; Mark Mintun (Philadelphia) ; Vaidrius Navikas (Valby) ; Gerald Novak (Titusville) ; Gunilla Osswald (Stockholm) ; Susanne Ostrowitzki (South San Francisco) ; Anton Porsteinsson (Rochester) ; Rema Raman (San Diego) ; Ivana Rubino (Cambridge) ; Marwan Sabbagh (Las Vegas) ; Stephen Salloway (Providence) ; Rachel Schindler (New York) ; Lon Schneider (Los Angeles) ; Hiroshi Sekiya (Malvern) ; Dennis Selkoe (Boston) ; Eric Siemers (Zionsville) ; John Sims (Indianapolis) ; Lisa Sipe (San Marcos) ; Olivier Sol (Lausanne) ; Reisa Sperling (Boston) ; Andrew Stephens (Berlin) ; Johannes Streffer (Braine-l’Alleud) ; Joyce Suhy (Newark) ; Chad Swanson (Woodcliff Lake) ; Gilles Tamagnan (New Haven) ; Rudolph Tanzi (Boston) ; Pierre Tariot (Phoenix); Edmond Teng (South San Francisco) ; Martin Tolar (Framingham) ; Jacques Touchon (Montpellier) ; Martin Traber (Basel) ; Bruno Vellas (Toulouse) ; Andrea Vergallo (Woodcliff Lake) ; Christian Von Hehn (Cambridge) ; George Vradenburg (Washington) ; Judy Walker (Singapore) ; Michael Weiner (San Francisco) ; Glen Wunderlich (Ridgefield) ; Jennifer Ann Zimmeri (Indianapolis) ; Haichen Yang (North Wales) ; Wagner Zago (San Francisco) ; Thomas Zoda (Austin)

1. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA; 2. Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, and Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA; 3. Genentech/Roche, Basel, Switzerland; 4. Eisai Co., Ltd., Eisai, Inc., Woodcliff Lake, NJ, USA; 5. The Warren Alpert Medical School of Brown University, Providence RI, USA; 6. Brigham and Women’s Hospital, Boston MA, USA; 7. Eli Lilly and Company, Indianapolis, IN, USA; 8. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France

Corresponding Author: P.S. Aisen, University of Southern California Alzheimer’s Therapeutic Research Institute, San Diego, CA, USA,

J Prev Alz Dis 2020;3(7):146-151
Published online April 17, 2020,



The termination of many clinical trials of amyloid-targeting therapies for the treatment of Alzheimer’s disease (AD) has had a major impact on the AD clinical research enterprise. However, positive signals in recent studies have reinvigorated support for the amyloid hypothesis and amyloid-targeting strategies. In December 2019, the EU-US Clinical Trials on Alzheimer’s Disease (CTAD) Task Force met to share learnings from these studies in order to inform future trials and promote the development of effective AD treatments. Critical factors that have emerged in studies of anti-amyloid monoclonal antibody therapies include developing a better understanding of the specific amyloid species targeted by different antibodies, advancing our insight into the mechanism by which those antibodies may reduce pathology, implementing more comprehensive repertoires of biomarkers into trials, and identifying appropriate doses. Studies suggest that Amyloid-Related Imaging Abnormalities – effusion type (ARIA-E) are a manageable safety concern and that caution should be exercised before terminating studies based on interim analyses. The Task Force concluded that opportunities for developing effective treatments include developing new biomarkers, intervening in early stages of disease, and use of combination therapies.

Key words: Alzheimer’s disease, dementia, amyloid hypothesis, monoclonal antibody treatment, BACE inhibitors, combination therapy.



Despite encouraging results from the aducanumab Phase 1 and BAN2401 Phase 2 anti-amyloid antibody clinical trials, amyloid-beta protein (Aß)-based strategies for the treatment of Alzheimer’s disease (AD) appeared to take a crippling blow in March 2019 when Biogen announced it was terminating two clinical trials (EMERGE and ENGAGE) of the anti-Aß monoclonal antibody aducanumab based on the results of an interim analysis demonstrating a lack of benefit or ‘futility.’ The field had another major challenge in July when Novartis, Amgen, and the Banner Alzheimer’s Institute announced termination of pivotal trials of the beta-site amyloid precursor protein cleaving enzyme (BACE) inhibitor umibecestat after an interim analysis identified cognitive worsening in trial participants. This marked the fifth failed BACE inhibitor in less than two years, two with trials stopped because of adverse events (Merck’s verubecestat and Janssen’s atabecestat) and two trials stopped for lack of efficacy (Astra Zeneca and Eli Lilly’s lanabecestat and Eli Lilly’s LY3202626) (1–3). A fifth BACE inhibitor trial of Eisai and Biogen’s elenbecestat was halted in September 2019 due to an unfavorable risk/benefit profile (4). Trials for another anti- Aß monoclonal antibody, Genentech and Roche’s crenezumab, were terminated in 2019 for futility (5).
Then, in October, a stunning reversal: Biogen announced that the futility analysis in the aducanumab trial was misleading. Analysis of a larger data set indicated that aducanumab did indeed slow cognitive decline in trial participants who received a higher dose of the drug for longer periods of time in one of the two studies. Following this announcement, Biogen indicated they planned to submit aducanumab to the U.S. Food and Drug Administration (FDA) for regulatory approval. Any form of approval for aducanumab has the potential to transform the AD field, providing hope for patients and researchers alike. Regulatory success could also reinvigorate support for the amyloid cascade hypothesis, which posits that deposition of Aβ in the brain leads to the neurodegeneration and dementia that characterize AD. This hypothesis has driven the development of AD therapeutics for decades. Secretase inhibitors block production of Aβ, while anti-Aβ antibodies are designed to clear Aβ and prevent the formation of amyloid plaques as well as neutralize soluble Aß oligomers. Prior to the announcement of aducanumab’s potential beneficial effects, no secretase inhibitor and only two monoclonal antibodies — BAN2401 and gantenerumab — had preliminary evidence of possible efficacy against Aß, and there was much speculation in the field that the amyloid hypothesis was dead or at least unhelpful in guiding development of AD therapeutics. However substantial emerging evidence supports the amyloid cascade hypothesis (6).
To better understand the implications of these clinical trial results and the future of amyloid-based therapies, the European Union and United States Clinical Trials on Alzheimer’s Disease Task Force (EU/US CTAD-TF) convened a meeting in San Diego on December 4, 2019, bringing together industry scientists involved in clinical trials of anti- Aß and other AD therapies along with representatives from pharmaceutical, biotechnology, diagnostics, and medical device companies, academic researchers, clinicians, and non-profit organizations. Their goal was to articulate lessons learned from these trials with the hope of enabling future successful trials that will lead to the approval of effective treatments for AD.


Learnings from trials of anti-amyloid monoclonal antibody trials

The Task Force discussed five anti-amyloid monoclonal antibody therapies currently in clinical development: aducanumab (7), BAN2401 (6), gantenerumab (8), solanezumab (9–13) , and donanemab. Other anti-amyloid monoclonal antibodies (e.g., crenezumab) are also in development (5, 14). As summarized in Table 1, these antibodies target different forms of amyloid, may have different mechanisms of action, and are being tested for efficacy at different stages of disease.


Table 1. Anti-amyloid monoclonal antibody therapies


The importance of dose

The futility analysis in the ENGAGE and EMERGE aducanumab trials – two identically designed Phase 3 studies — was based on a pooled interim dataset of approximately 50% of enrolled participants using a probability calculation that assumed non-heterogeneity between the two studies. A subsequent analysis of a larger dataset, however, revealed that protocol amendments allowing increased dosing in apolipoprotein E epsilon 4 (APOE4) carriers had differential effects on the two studies due to the relative timing of enrollment. This analysis demonstrated a statistically significant reduction in clinical decline across multiple clinical endpoints among early AD patients in EMERGE, likely due to high dose exposure to the drug. Participants in the ENGAGE trial who had received higher doses (10 mg/kg) for at least 10 doses had clinical effects similar to those of the EMERGE participants. Amyloid positron emission tomography (PET) studies demonstrated dose-dependent reduction of brain amyloid deposition across both trials.
Other trials have also demonstrated substantial dose-related amyloid lowering. Study 201 of BAN2401 used an adaptive randomization design with six arms to understand the impact of dose and minimize the number of participants treated with ineffective doses. The highest dose (10 mg/kg biweekly) produced the greatest slowing of disease progression and most robust reduction in brain amyloid levels compared to placebo and is used in the recently-launched Phase 3 Clarity AD study.
Open-label extensions of two early Phase 3 gantenerumab trials, in which study participants were assigned one of five titration schemes, also showed that five times higher dose of ganternerumab than was used in the earlier phase 3 studies drove increased amyloid reduction assessed with amyloid PET imaging (15). These findings prompted the initiation of a new Phase 3 program using this five-fold higher doses.

Mechanism matters

Amyloid is not a monolithic target but a family of monomers, oligomers, protofibrils, and fibrils; and different anti-Aβ antibodies target partially different species. The molecular dynamics by which targeting different species results in variable effects on plaque burden and brain volume loss are not well understood; however, these differential mechanisms may help explain the different trial effects observed.
Solanezumab was hypothesized to remove brain amyloid through what is called the “peripheral sink hypothesis,” i.e., by increasing the clearance of soluble Aβ via the formation of antibody-Aβ complexes in the plasma. However, pharmacodynamic studies showed that a reduction of Aβ in the peripheral compartment failed to shift the equilibrium between Aβ species enough to cause a substantial reduction of fibrillary Aβ in the brain (16); the possible beneficial effect of solanezumab on cognitive decline may nonetheless be mediated by its binding to smaller, diffusible forms. Other possible mechanisms of anti-Aβ antibodies include direct targeting of Aβ plaques or other toxic species of Aβ for removal or activating phagocytosis of Aβ by microglia (17). Clinical trials of solanezumab in mild-moderate AD and in prodromal/mild AD failed to show a drug-placebo difference and no effects on biomarkers were observed. Solanezumab continues in the Anti-Amyloid treatment of Asymptomatic Alzheimer’s disease (A4) study of cognitively asymptomatic participants with positive amyloid imaging.
The effects of anti-Aβ antibodies on brain volume loss is poorly understood. In the EXPEDITION trials, treatment with solanezumab showed a modest but statistically insignificant slowing of brain atrophy (13). Gantenerumab produced no such effects on the measures collected (8). One theory suggests that driving down amyloid may itself be reflected as a reduction in brain volume. The effects on brain volume, however, could differ depending on which form of amyloid the antibody targets (e.g. plaques versus oligomeric forms). Further analysis of data from anti-Aβ antibody trials may help clarify this issue. The correlation of treatment-related brain volume loss and disease progression is also unclear.

ARIA appears to be a manageable safety concern

The incidence of amyloid-related imaging abnormalities – effusion type (ARIA-E) associated with anti-Aβ antibody treatment has been a substantial concern in the development of these therapies (18). For example, in the aducanumab trials, ARIA-E was seen in more than one-third of participants, although these episodes were typically asymptomatic and resolved within 4-16 weeks without long-term sequelae. ARIA-E was also observed in about 10% of participants in the BAN2401 Phase 2 study, occurring primarily in the first three months of treatment.
Recent studies suggest that ARIA-E can be safely managed by titrating drug to the target dose. For example, in the gantenerumab studies, titrating to the target dose reduced ARIA-E incidence in both APOE4 carriers and non-carriers and the majority of episodes were asymptomatic. Other studies have suggested that APOE4 carriers are at higher risk for ARIA-E. While ARIA-E appears to be manageable, uncertainty remains about whether even a minimal risk could be problematic for preclinical AD patients, or whether ARIA-E occurs less frequently in earlier stages of disease or in individuals with lower levels of vascular amyloid.
Although it may be challenging, it will be necessary to develop criteria that could be used in primary care settings for safely beginning treatment and monitoring for ARIA-E should an anti-Aβ monoclonal antibody treatment be approved for AD,. A better understanding of the mechanisms involved could relieve concerns among primary care physicians once these therapies become available.

Interim and futility analyses are useful only if appropriately designed

Futility analyses are designed to protect participants from unnecessary exposure to drugs that have little chance of providing benefits, but if they result in premature termination of a trial, participants and sponsors alike – indeed, the entire field – may suffer adverse consequences from a failure to identify efficacious treatments and the failure to collect a complete dataset from the trial (19). The aducanumab Phase 3 program is not the only example in the field in which interim analysis wrongly predicted futility, raising questions about the design and appropriateness of futility analyses.
Among the fundamental tenets of futility analyses is that participants included in the analysis are representative of those in the full dataset and that drop-outs are equally distributed across all treatment groups. Protocol amendments made in the course of the aducanumab study, however, resulted in non-identical interim and final populations and in cohorts that had received different doses for different periods of time
All futility analyses come with a price: loss of statistical power to demonstrate efficacy. This cost must be carefully weighed against any benefits from early termination. While there are clear advantages to stopping early when failure is inevitable, the possibility of misleading futility analyses suggests that criteria for defining failure versus success need to be very carefully specified. To implement criteria for interim analyses requires a better understanding of the clinical-biological trajectories of disease progression in stratified patient populations (19,20). Interim analyses could also benefit from looking at the totality of evidence and by aggregating signals to reduce noise.

Responder analyses could help identify subgroup differences

To determine the disease stage at which a treatment may be efficacious, the optimal duration of treatment, and other patient characteristics that may affect efficacy, responder analyses of trial data and data from open-label extension studies can be valuable. Post-hoc exploratory data analyses may yield improved understanding of study results and inform the design of future studies. For example, in the SCarlet RoAD study of gantenerumab, an exploratory analysis that classified participants according to whether they were slow or fast progressors suggested that fast progressors showed a greater exposure-dependent slowing of clinical and cognitive decline with treatment (8). While not a classic responder analysis, the exploration of the faster progressing subset allowed modeling related to a drug-placebo difference and helped to define inclusion criteria for the ongoing Phase 3 GRADUATE program with higher dose of gantenerumab.


Moving forward with amyloid-based therapies

Genetic, neuropathologic, biochemical, and now clinical trials support the amyloid hypothesis of AD while recognizing that downstream pathological processes contribute importantly to the development of the disease (6). Many questions remain to be answered in order to translate the amyloid hypothesis into efficacious therapies. For example, further research is needed to determine which Aβ species are most important to target, whether relevant Aβ species change over the course of disease, if there is an optimal time for targeting a particular Aβ species, and whether at some point amyloid becomes less relevant or irrelevant. Developing a larger repertoire of biomarkers to predict disease onset and progression, e.g. microglial activation biomarkers, may help clarify the role of amyloid-related mechanisms as well as other mechanisms in disease progression (21). Preliminary data from the monoclonal antibody trials suggest there are “downstream” effects on cerebrospinal fluid levels of neurofilament light, neurogranin, and tau. These may be crucial measures of the biological effects of interventions and that can eventually be compared across trials.
An effective treatment may also require an Aß-targeting drug in combination with a drug targeting another mechanism (e.g. neuroinflammation) or two drugs that target different amyloid mechanisms (e.g. production and clearance of Aβ) (22). Investigators have explored targeting Aß in combination with tau, the protein found in the neurofibrillary tangles that along with amyloid plaques represent the major pathological hallmarks of AD. Moving this approach forward, however, will require a better understanding of the value of various tau-related targets, the relationship of amyloid to the level of tau burden as well as the time lag between amyloid deposition, tau deposition, and cognitive impairment (23). Employing tau PET studies in clinical trials may help define these aspects of the role of tau in AD (24,25). Other tau biomarkers are in development. For example, Walsh and colleagues have shown that an N-terminal fragment of tau (NT1) and p-tau in plasma are significantly increased in AD and mild cognitive impairment (MCI) (26).
Analysis of data from several failed clinical trials of amyloid-targeting drugs suggest that to slow or prevent disease progression, it may be necessary to intervene at very early, pre-symptomatic stages of the disease (27,28). Studies currently underway to test this include the A4 study in clinically normal older individuals with elevated amyloid levels on screening PET; and the AHEAD 3-45 study in clinically normal individuals with elevated or intermediate amyloid. Other prevention trials are underway in clinically normal participants at increased genetic risk of developing AD, including the Alzheimer’s Prevention Initiative (API) Colombia Trial (20). [The DIAN-TU studies involving both clinically normal and symptomatic autosomal dominant mutation carriers recently reported negative topline results.] The challenges inherent in these prevention trials include the difficulty of detecting a slowing of progression in cognitively normal individuals and the resulting large sample size and long trial durations required; the hope of preventing AD has motivated many individuals around the world to volunteer for these studies.
Very early intervention, including primary prevention, may be more feasible with active vaccination or oral therapy rather than passive immunotherapy requiring repeated intravenous or subcutaneous administration. Active vaccination against Aß remains a plausible strategy (e.g. CAD-106; UB-311). Orally bioavailable BACE inhibitor programs have been halted with concern about observations of cognitive worsening in trials; however, evidence that this cognitive toxicity is dose-related and reversible raises hope that viable regimens may eventually move forward.



The termination of multiple clinical trials for futility or adverse events has had a major impact on the AD clinical research enterprise. However, evidence strongly supports amyloid as a viable target although not the only important target. Given the complexity of AD pathology, combination treatment will likely be needed. If antibody trials are sufficiently positive, they could represent a good first step towards combination treatment and lead to financial coverage and use of amyloid PET, which would be a major advance for the clinical care of AD.
To optimize the potential benefits and reduce the potential risks to participants as much as possible, methodological improvements in the design and conduct of clinical trials are needed. For example, adaptive dose finding studies may result in more patients assigned to an effective dose and avoid exposure of patients to ineffective doses. In addition, since disease modification depends on protecting neurons from the pathology, a better understanding of neuroprotection, the relationship of the biological underpinnings of the aging process (30), and the development of intermediate biomarkers of neuroprotection are needed. Advancing understanding of the complexity underlying the development of AD and potential interventions that could slow or halt the disease pathophysiological progression will require more discovery science as well as increased use of platform trials. Public-private partnerships with strong collaborations and data sharing will be necessary to accelerate these efforts, along with broad public engagement.


Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.

Conflicts of interest: The Task Force was partially funded by registration fees from industrial participants. These corporations placed no restrictions on this work. Dr. Aisen reports grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, grants from Eisai, personal fees from Merck, personal fees from Biogen, personal fees from Roche, personal fees from Lundbeck, personal fees from Proclara, personal fees from Immunobrain Checkpoint, outside the submitted work; Dr Cummings is a consultant for Acadia, Actinogen, AgeneBio, Alkahest, Alzheon, Annovis, Avanir, Axsome, Biogen, Cassava, Cerecin, Cerevel, Cognoptix, Cortexyme, EIP Pharma, Eisai, Foresight, Gemvax, Green Valley, Grifols, Karuna, Nutricia, Orion, Otsuka, Probiodrug, ReMYND, Resverlogix, Roche, Samumed, Samus Therapeutics, Third Rock, Signant Health, Sunovion, Suven, United Neuroscience pharmaceutical and assessment companies, and the Alzheimer Drug Discovery Foundation; and owns stock in ADAMAS, BioAsis, MedAvante, QR Pharma, and United Neuroscience. Dr. Doody is an employee of Genentech/F Hoffman-LaRoche and holds stock in the company; Dr. Kramer is an employee of Eisai Company, Ltd. Dr. Kramer is an employee of Eisai Company Ltd; Dr. S. Salloway: NC; Dr. Selkoe: NC; Dr. Sims reports other from Employee of Eli Lilly and Company, outside the submitted work; Dr. Sperling reports personal fees from AC Immune, personal fees from Biogen, personal fees from Janssen, personal fees from Neurocentria , personal fees from Eisai, personal fees from GE Healthcare, personal fees from Roche, personal fees from InSightec, personal fees from Takeda Pharmaceuticals, grants from Eli Lilly , grants from Janssen, grants from Digital Cognition Technologies, grants from Eisai, grants from NIA , grants from Alzheimer’s Association, personal fees and other from Novartis, personal fees and other from AC Immune, personal fees and other from Janssen, outside the submitted work; Dr. Vellas reports grants from Lilly, Merck, Roche, Lundbeck, Biogen, grants from Alzheimer’s Association, European Commission, personal fees from Lilly, Merck, Roche, Biogen, outside the submitted work.

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30. Sierra F. Geroscience and the role of aging in the etiology and management of alzheimer’s disease. Journal of Prevention of Alzheimer’s Disease [Internet]. 2019 Mar 1 [cited 2020 Jan 30]; Available from:

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S. Gauthier1, P.S. Aisen2, J. Cummings3, M.J. Detke5, F.M. Longo6, R. Raman2, M. Sabbagh4, L. Schneider7, R. Tanzi8, P. Tariot9, M. Weiner10, J. Touchon11, B.Vellas12 and the EU/US CTAD Task Force*


* EU/US/CTAD TASK FORCE: Susan Abushakra (Framingham); John Alam (Boston); Sandrine Andrieu (Toulouse); Anu Bansal (Simsbury); Monika Baudler (Basel); Joanne Bell (Wilmington); Mickaël Beraud (Zaventem); Tobias Bittner (Basel); Samantha Budd Haeberlein (Cambridge); Szofia Bullain (Basel); Marc Cantillon (Gilbert); Maria Carrillo (Chicago); Carmen Castrillo-Viguera (Cambridge); Ivan Cheung (Woodcliff Lake); Julia Coelho (San Francisco); Daniel Di Giusto (Basel); Rachelle Doody (South San Francisco); John Dwyer (Washington); Michael Egan (North Wales); Colin Ewen (Slough); Charles Fisher (San Francisco); Michael Gold (North Chicago); Harald Hampel (Woodcliff Lake) ; Ping He (Cambridge) ; Suzanne Hendrix (Salt Lake City) ; David Henley (Titusville) ; Michael Irizarry (Woodcliff Lake); Atsushi Iwata (Tokyo); Takeshi Iwatsubo (Tokyo); Michael Keeley (South San Francisco); Geoffrey Kerchner (South San Francisco); Gene Kinney (San Francisco); Hartmuth Kolb (Titusville); Marie Kosco-Vilbois (Lausanne); Lynn Kramer (Westport); Ricky Kurzman (Woodcliff Lake); Lars Lannfelt (Uppsala); John Lawson (Malvern); Jinhe Li (Gilbert); Mark Mintun (Philadelphia); Vaidrius Navikas (Valby); Gerald Novak (Titusville); Gunilla Osswald (Stockholm); Susanne Ostrowitzki (South San Francisco); Anton Porsteinsson (Rochester); Ivana Rubino (Cambridge); Stephen Salloway (Providence); Rachel Schindler (New York); Hiroshi Sekiya (Malvern); Dennis Selkoe (Boston); Eric Siemers (Zionsville); John Sims (Indianapolis); Lisa Sipe (San Marcos); Olivier Sol (Lausanne); Reisa Sperling (Boston); Andrew Stephens (Berlin); Johannes Streffer (Braine-l’Alleud); Joyce Suhy (Newark); Chad Swanson (Woodcliff Lake); Gilles Tamagnan (New Haven); Edmond Teng (South San Francisco); Martin Tolar (Framingham); Martin Traber (Basel); Andrea Vergallo (Woodcliff Lake); Christian Von Hehn (Cambridge); George Vradenburg (Washington); Judy Walker (Singapore) ; Glen Wunderlich (Ridgefield); Roy Yaari (Indianapolis); Haichen Yang (North Wales); Wagner Zago (San Francisco); Thomas Zoda (Austin)

1. McGill Center for Studies in Aging, Verdun, QC, Canada; 2. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA; 3. Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), USA; 4. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 5. Cortexyme, South San Francisco, CA, USA; 6. Stanford University School of Medicine, Stanford CA USA; 7. University of Southern California Keck School of Medicine, Los Angeles, CA USA; 8. Harvard University, Boston, MA USA; 9. Banner Alzheimer’s Institute, Phoenix AZ USA; 10. University of California, San Francisco, CA USA; 11. Montpellier University, INSERM 1061, Montpellier, France; 12. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France

Corresponding Author: Serge Gauthier, McGill Center for Studies in Aging, Verdun QC, Canada,

J Prev Alz Dis 2020;3(7):152-157
Published online April 6, 2020,



While amyloid-targeting therapies continue to predominate in the Alzheimer’s disease (AD) drug development pipeline, there is increasing recognition that to effectively treat the disease it may be necessary to target other mechanisms and pathways as well. In December 2019, The EU/US CTAD Task Force discussed these alternative approaches to disease modification in AD, focusing on tau-targeting therapies, neurotrophin receptor modulation, anti-microbial strategies, and the innate immune response; as well as vascular approaches, aging, and non-pharmacological approaches such as lifestyle intervention strategies, photobiomodulation and neurostimulation. The Task Force proposed a general strategy to accelerate the development of alternative treatment approaches, which would include increased partnerships and collaborations, improved trial designs, and further exploration of combination therapy strategies.

Key words: Alzheimer’s disease, dementia, tau, tauopathy, neurotrophins, neuroinflammation, lifestyle intervention, photobiomodulation, neurostimulation, geroscience.



Following a discussion on lessons learned from clinical trials of amyloid-based therapies for Alzheimer’s disease (AD) (1), on December 4, 2019, the EU/US CTAD Task Force turned their attention to alternative approaches for disease modification. These strategies do not negate the validity of the amyloid hypothesis; indeed, recently discovered genetic evidence continues to support the centrality of amyloid in the neurodegenerative processes that lead to AD (2–4). However, genetic and other studies point to additional mechanisms and pathways both upstream and downstream of amyloidogenesis, which may provide druggable therapeutic targets with potential for disease modification.
Neuropathological and imaging studies confirm the complexity and heterogeneity of AD (5) Mixed pathologies are evident in most individuals with a clinical diagnosis of AD (6), and in early clinical studies of amyloid-targeting drugs, a significant proportion of trial participants were shown to have no detectable amyloid. Nonetheless, among putative disease-modifying AD drugs in clinical trials, 40% target amyloid either with small molecules or immunotherapies. Another 18% target tau. Other mechanisms targeted for disease modification include neuroprotection, anti-inflammatory effects, growth factor promotion, and/or metabolic effects (7). Additional trials are underway assessing non-pharmacological approaches to treat AD, including lifestyle interventions and neurostimulation.


Anti-tau therapies

The microtubule-associated protein tau (MAPT, commonly referred to as tau) is the main constituent of the neurofibrillary tangles that are one of the two primary pathological hallmarks of AD. Its normal function is to stabilize microtubules and thus regulate intracellular trafficking, but in AD and other tauopathies, the protein undergoes post-translational modifications that lead to the development of a variety of oligomeric species, tangles, and neuropil threads that may be deposited as aggregates in specific brain regions, disrupting normal cytoskeletal function and protein degradation pathways (8). In the human brain, six isoforms of tau are present, which are classified as either 3R or 4R tau based on the number of repeat domains. Approximately equal levels of 3R and 4R tau are expressed in the normal brain; however, 3R:4R tau imbalances are seen in brains of individuals with tauopathies. In AD, isoform imbalances vary across brain regions and disease progression.
Unlike levels of amyloid beta protein (Aβ), which correlate poorly with cognition, tau levels are associated with both neurodegeneration and cognitive deficits (9). Tau pathology has been shown to follow a characteristic progression pathway in the brain, starting in areas responsible for learning and memory before spreading to cortical areas involved in other cognitive functions (10).
The complex progression of tau pathological events provides multiple potential opportunities for intervention. Anti-tau drugs in development target tau expression, aggregation, degradation, protein modifications (e.g. phosphatase modifiers, kinase inhibitors), microtubule stabilization, and extracellular tau inter-neuronal spread (8). As of February 2019, clinical trials were underway for 17 tau-targeting drugs – seven small molecules and 10 biologics (7). Only one drug, LMTX (TRx0237) – a reduced form of methylene blue, and a tau protein aggregation inhibitor — is currently being tested in a Phase 3 trial in early AD at 8 – 16 mg/day doses versus placebo (NCT03446001). This trial follows two Phase 3 trials in mild and mild to moderate AD (NCT01689246, NCT01689233) and a trial in behavioral variant FTD (NCT01626378) with higher doses, which showed negative results in the primary analysis of clinical efficacy. Biogen has a Phase 2 study underway of the anti-tau agent BIIB092 (gosuranemab) in participants with MCI due to AD or mild AD (NCT03352557). Phase 2 studies in biologically defined populations are also being conducted. For example, Roche/Genentech is conducting two Phase 2 studies of the anti-tau monoclonal antibody semorinemab in participants with prodromal or probable AD confirmed by amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) testing (NCT03828747). Clinical trials of anti-tau therapeutics have been conducted in other tauopathies, although two recent Phase 2 studies of anti-tau monoclonal antibody therapies (Abbvie’s AABV-8E12 and Biogen’s gosuranemab) in participants with progressive supranuclear palsy (PSP) were recently terminated for lack of efficacy (NCT2985879 and NCT03068468, respectively). Non-clinical studies of innovative anti-tau therapies are underway, such as a study that uses engineered tau-degrading intrabodies to target intracellular tau (11).
It is also theoretically possible that early anti-amyloid intervention may attenuate or even preclude downstream effects on tau. That is, non-tau-based treatments could have implications for tau and tangles.
Several challenges face developers of tau-based therapeutics. For tau reduction approaches, it is not known how much reduction is needed, how quickly and safely it can be accomplished, when different interventions might be effective during the course of the disease, and how long drug levels must be maintained to get an effect. Tau biology is complicated with numerous fragments and post-translational modifications associated with tauopathies, yet it remains unclear which tau species are toxic. Moreover, the targets, mechanisms and cellular locations through which such tau species promote degeneration remain to be identified. These issues make the design of clinical trials especially complicated and highlight the need for better tau biomarkers. Recent progress made in the development of tau ligands for PET may improve the efficiency of clinical trials, since tau-PET enables early diagnosis and tracking of disease progression, identifying individuals at risk for faster cognitive decline, and rapidly assessing pharmacodynamic effects of treatments (12). Plasma levels of total tau (t-tau) and neurofilament light (NfL) have been developed as biomarkers of neurodegeneration (13). Still needed are biomarkers that distinguish 3R from 4R tau and that quantify the many different tau species.


Neurotrophic strategies

The neurodegeneration that occurs in AD results from a complicated molecular and biochemical signaling network, likely triggered by Aβ and eventually leading to synaptic dysfunction, loss of dendritic spines, and neurite degeneration (14). Growth factors called neurotrophins regulate neuronal survival, development, and function by binding to cell surface receptors. The signaling networks regulated by these receptors have extensive overlap with those associated with neurodegeneration and modulation of neurotrophin receptors has thus been proposed as a potential therapeutic strategy (15). The Longo lab and others have zeroed in on the p75 neurotrophin receptor (p75NTR) as a therapeutic target for AD. Their working hypothesis, supported by human genomic and proteomic data, along with animal studies is that the p75NTR modulates the complex AD degenerative signaling network and that downregulating its signaling renders oligomeric Aβ unable to promote degeneration (16, 17).
Longo and colleagues have developed small molecule ligands that bind to p75NTR, activate survival-promoting signaling, and prevent Aβ-induced neurodegeneration and synaptic impairment (18). One molecule in particular, LM11A-31, has been shown to block Aβ-induced tau phosphorylation, misfolding, oligomerization and mislocalization; reverse late-stage spine degeneration; reverse synaptic impairment; prevent microglial dysfunction; and in wildtype mice suppress age-related basal forebrain cholinergic neuron degeneration (18–20). There is evidence that dendritic spine preservation is associated with cognitive resilience (21).
A Phase 2a pilot study sponsored by PharmatrophiX Inc. and funded in part by the National institute on Aging (NIA) and the Alzheimer Drug Discovery Foundation is underway, testing oral LM11A-31 in participants with mild-to-moderate AD and amyloid positivity assessed by CSF Aβ screening (NCT03069014). With an expected completion in the third quarter of 2020, the trial will assess safety and tolerability as well as cognitive, clinical, biomarker, and imaging exploratory endpoints. LM11A-31 may be effective in other disorders such as Huntington’s disease (22), diabetes-induced macular oedema (23), and traumatic brain injury (24).


Anti-microbial and anti-inflammatory strategies

Neuropathological studies of the AD brain show not only amyloid plaques and tau-based tangles but neuroinflammation as well. Indeed, according to the innate immune hypothesis, plaques, tangles, and neuroinflammation orchestrate an innate immune response that has evolved to protect the brain against microbial infection, with Aβ itself acting as an antimicrobial peptide (AMP) in the brain (25, 26). This hypothesis suggests that subclinical microbial infections in the brain rapidly ‘seed’ Aβ to trap microbes, and that this process drives Aβ neurotoxicity and opsonization (i.e, an ‘eat me’ signal for microglia to remove axons and synapses) (25). Tangles form in response to microbe invasion to block neurotropic microbe spread. AD risk genes are implicated in the innate immune protection hypothesis, which posits that AD-associated genetic risk variants were evolutionarily conserved to keep Aβ deposition, tangle formation, and gliosis/neuroinflammation on a ‘hair trigger’ as a means of protecting a subset of the human species in the advent of a major epidemic of brain infection.
The molecular pathways involved in these processes provide multiple potential therapeutic targets, including the use of anti-viral drugs, antibiotics, blockade of toxic microbial products, and immunization for prevention of subclinical infections; secretase inhibitors and immunotherapies to prevent Aβ seeding; kinase or phosphatase inhibitors to prevent the development of pathological forms of tau, and anti-inflammatories to suppress neuroinflammation. Gut microbiota may also play a role in AD pathogenesis by disrupting neuroinflammation and metabolic homeostasis, thus representing another potential intervention target (27).
One example of a bacterial hypothesis and associated strategy is based on the discovery of the bacterium Porphyromonas gingivalis (Pg), most commonly associated with periodontitis, in the brains of AD patients. Toxic virulence factors from the bacterium, proteases called gingipains, have been identified in AD brains, and gingipain levels correlated with tau and ubiquitin pathology. Oral infection of mice with Pg resulted in brain colonization, increased Aβ1-42, and loss of hippocampal neurons, effects that were blocked by COR388, a small-molecule irreversible lysine- gingipain inhibitor. COR388 significantly lowered markers of inflammation in plasma as well as AD-associated APOE fragments in CSF in a small Phase 1b study in mild-moderate AD patients (28), and a large Phase 2/3 study is underway with an interim readout expected in Q4 2020 and topline data in Q4 2021 (NCT03823404).
A retrospective cohort study showed that Herpes simplex virus (HSV)-infected subjects had a nearly 3-fold increased risk of AD but that treatment with anti-viral drugs such as acyclovir brought risk to non-infected levels (29). There is an ongoing phase 2 trial of valacyclovir for patients with mild AD and positive titers for HSV1 and HSV2 (NCT03282916). Trials in AD using doxycycline and minocycline did not show efficacy (30).
Anti-inflammatory strategies are also being pursued. A Phase 2 study underway in participants with late mild cognitive impairment (MCI) or early AD aims to protect neurons against oxidative stress using two small molecule drugs — tauroursodexycholic acid (TUDCA) and sodium phenylbutyrate — repurposed by Amylyx Pharmaceutical as AMX0035 (NCT03533257). Yet another Phase 3 study sponsored by AZTherapies, Inc. aims to reduce neuroinflammation by converting microglia from a proinflammatory to phagocytic state to promote clearance of Aβ by using a combination of two marketed drugs, cromolyn and ibuprofen, known as ALZT-OP1 (NCT02547818) (31).


Lifestyle intervention strategies and other non-pharmacological approaches

Multiple epidemiological studies in Europe, the United States, and Canada investigating an observed decline in the prevalence of dementia in recent years have suggested that dementia may be preventable by targeting lifestyle risk factors such as diabetes, hypertension, obesity, physical inactivity, smoking, depression, low education, and social isolation (32). Clinical studies are now beginning to support this assertion. The Systolic Blood Pressure Intervention Trial –Memory and Cognition in Decreased Hypertension (SPRINT MIND) study suggested that intensive blood pressure control may reduce the risk of probable dementia and mild cognitive impairment (MCI), although the results were not statistically significant, in part because the SPRINT trial was terminated early based on the significant benefits of blood pressure control on cardiovascular outcomes. The study may have been underpowered for cognitive endpoints (33). Further study is warranted given that a 10-year study in France showed that hypertension was associated with poorer cognition in middle-aged individuals (34).
Multi-domain strategies have focused on lifestyle factors. For example, the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) trial demonstrated improved or stabilized cognitive function in participants that adhered to an intervention combining diet, physical exercise, cognitive training, and vascular risk monitoring (35). The Multidomain Alzheimer Prevention Trial (MAPT) tested an intervention combining cognitive and physical intervention along with omega-3 polyunsaturated fatty acid supplementation in frail, non-demented, community dwelling adults (36, 37). While MAPT failed to demonstrate significant slowing of cognitive decline, subgroup analyses suggested that individuals with low plasma levels of docosahexaenoic acid (DHA, an omega-3 fatty acid) have more cognitive decline, which appeared to be normalized with omega-3 supplementation(38). The benefits of omega-3 supplementation appeared to be greater in amyloid-positive individuals and in those with increased cardiovascular risk scores (39, 40). Based on the results from FINGER, MAPT, and other multidomain intervention studies, many additional studies are planned, including worldwide FINGERS studies (WW-FINGERS), a network of studies throughout the world that are adapting the multidomain strategies of the FINGER trial to different populations (41).
In addition to physical and cognitive activity, other non-pharmacological strategies are being investigated for their potential to slow cognitive decline and prevent dementia. For example, photobiomodulation (PBM) has been shown to be neuroprotective. In animal models PBM improved memory and normalized markers of AD, oxidative stress and neuroinflammation (42). A pilot study is now underway in participants with probable AD (NCT03405662).
Non-invasive neurostimulation with techniques such as repetitive transcranial magnetic stimulation (rTMS) has been proposed as a treatment for AD (43). Other technological approaches including assistive technologies, smart technologies, and telemedicine may improve the treatment and care of people with AD.



Given that aging is the major risk factor for AD, therapeutic strategies aimed at the diseases of aging (e.g., frailty) may slow cognitive decline and the development of dementia (44) Considerable research is underway to investigate the relationship between biological aging and neurodegenerative disease. These efforts have coalesced in the emerging field of geroscience (44), which explores whether the physiological hallmarks of aging such as mitochondrial dysfunction, loss of proteostasis, increased cellular senescence, and stem cell exhaustion may contribute to the development of AD pathology and neurodegeneration (45). Identification of biomarkers of aging and elucidation of how the molecular pathways of aging and AD intersect could advance the identification of novel therapeutic targets and next-generation therapies, such as the use of mesenchymal stem cells (46). The links between aging and AD are being explored as one element of the INSPIRE Research Initiative (Barreto JFA in press).


Conclusions/moving forward

While the AD drug development pipeline continues to be dominated by Aβ-targeting therapies, there is increasing recognition that addressing the complexity of AD may require multiple agents and may need to start in early disease stage before pathology becomes irreversible. A “deep biology” view, such as that proposed by advocates of p75NTR modulation, posits that key ‘hub’ targets may enable modulation of multiple mechanisms (e.g. resilience to both Aβ and tau) and that key components of pathology could be reversible (e.g. spines, synaptic function). A single treatment could thus promote synaptic function and slow progression and prevent upstream tau aggregation and oligomer formation.
Given the importance of tau in the development of AD, and reflecting the recently proposed Research Framework (47), CTAD Task Force members advocated assessment of both Aβ and tau levels in all clinical trials. The A-T+N+ AD phenotype is common and should be targeted for anti-tau trials. A suggestion was made to name this phenotype Dementia Associated and Neurofibrillary tangle Neuroimaging Abnormality (DANNA). Tau imaging may provide a biological outcome, at least in Phase 2 studies, although the Task Force recognized that amyloid and/or tau PET imaging adds substantial subject and trial burden and cost. Other suggestions that could accelerate the development of anti-tau therapies include using basket designs that include participants with other tauopathies such as frontotemporal degeneration (FTD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). While such trials would include participants with heterogeneous presentations, an outcome assessment such as Goal Attainment Scaling (GAS) could enable capture of clinically meaningful outcomes from diverse participants. This tool enables patients, caregivers, and clinicians, to set goals for treatment using a standardized guided interview, followed by an assessment of whether those goals have been attained (48, 49).
The Task Force suggested that combination therapy may be required to tackle such a complex disease as AD (50). They also advocated employing other innovative clinical trial methodologies to accelerate development of alternative approaches.
The Task Force proposed a general strategy to accelerate the development of alternative treatment approaches, which would include:
• Increased partnerships in the pre-competitive space with increased sharing of granular level data, shared biomarkers, statistical approaches, information on site performance
• Innovative trial design
• More collaborative approaches to recruitment and retention of participants for clinical trials with a focus on participation of representative populations.


Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.

Conflicts of interest: The Task Force was partially funded by registration fees from industrial participants. These corporations placed no restrictions on this work. Dr. Gauthier is a member of scientific advisory boards for Biogen, Boehringer-Ingelheim, and TauRx; and a member of the DSMB for ADCS, ATRI, and Banner Health; Dr. Aisen reports grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, grants from Eisai, personal fees from Merck, personal fees from Biogen, personal fees from Roche, personal fees from Lundbeck, personal fees from Proclara, personal fees from Immunobrain Checkpoint, outside the submitted work; Dr. Cummings is a consultant for Acadia, Actinogen, AgeneBio, Alkahest, Alzheon, Annovis, Avanir, Axsome, Biogen, Cassava, Cerecin, Cerevel, Cognoptix, Cortexyme, EIP Pharma, Eisai, Foresight, Gemvax, Green Valley, Grifols, Karuna, Nutricia, Orion, Otsuka, Probiodrug, ReMYND, Resverlogix, Roche, Samumed, Samus Therapeutics, Third Rock, Signant Health, Sunovion, Suven, United Neuroscience pharmaceutical and assessment companies, and the Alzheimer Drug Discovery Foundation; and owns stock in ADAMAS, BioAsis, MedAvante, QR Pharma, and United Neuroscience. Dr. Detke reports personal fees, non-financial support and other from Cortexyme, during the conduct of the study; personal fees and other from Embera, personal fees and other from Evecxia, personal fees from NIH, outside the submitted work; Dr Kramer is an employee of Eisai Company, Ltd; Dr Longo has equity in and consults for PharmatrophiX, a company focused on the development of small molecule modulators for neurotrophin receptors. He is also a co-inventor on related patent applications. Dr. Raman reports grants from NIH, grants from Eli Lilly, grants from Eisai, outside the submitted work; Dr Sabbagh reports personal fees from Allergan, personal fees from Biogen, personnal fees from Grifols, personal fees from vTV Therapeutics, personal fees from Sanofi, personal fees from Neurotrope, personal fees from Cortexyme, other from Neurotrope, other from uMethod, other from Brain Health Inc, other from Versanum Inc, other from Optimal Cognitive Health Company, outside the submitted work; Dr. Schneider reports grants and personal fees from Eli Lilly, personal fees from Avraham, Ltd, personal fees from Boehringer Ingelheim, grants and personal fees from Merck, personal fees from Neurim, Ltd, personal fees from Neuronix, Ltd, personal fees from Cognition, personal fees from Eisai, personal fees from Takeda, personal fees from vTv, grants and personal fees from Roche/Genentech, grants from Biogen, grants from Novartis, personal fees from Abbott, grants from Biohaven, grants from Washington Univ/ NIA DIAN-TU, personal fees from Samus, outside the submitted work; Dr. Tanzi is a consultant and shareholder in AZTherapies, Amylyx, Promis, Neurogenetic Pharmaceuticals, Cerevance, and DRADS Capital; Dr. Tariot reports personal fees from Acadia , personal fees from AC Immune, personal fees from Axsome, personal fees from BioXcel, personal fees from Boehringer-Ingelheim, personal fees from Brain Test Inc., personal fees from Eisai, personal fees from eNOVA, personal fees from Gerontological Society of America, personal fees from Otuska & Astex, personal fees from Syneos, grants and personal fees from Abbvie, grants and personal fees from Avanir, grants and personal fees from Biogen, grants and personal fees from Cortexyme, grants and personal fees from Genentech, grants and personal fees from Lilly, grants and personal fees from Merck & Co, grants and personal fees from Roche, grants from Novartis, grants from Arizona Department of Health Services, grants from National Institute on Aging, other from Adamas, outside the submitted work; In addition, Dr. Tariot has a patent U.S. Patent # 11/632,747, “Biomarkers of Neurodegenerative disease.” issued; Dr. Weiner is the PI of The Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Brain Health Registry. I am a Professor at University of California San Francisco; Dr. Touchon has received personnal fees from Regenlife and is JPAD associated Editor and part of the CTAD organizing committee; Dr. Vellas reports grants from Lilly, Merck, Roche, Lundbeck, Biogen, grants from Alzheimer’s Association, European Commission, personal fees from Lilly, Merck, Roche, Biogen, outside the submitted work

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