jpad journal

AND option

OR option



A.R. Shaw1, J. Perales-Puchalt1, E. Johnson1, P. Espinoza-Kissell1, M. Acosta-Rullan1, S. Frederick1, A. Lewis1, H. Chang2, J. Mahnken2, E.D. Vidoni1


1. University of Kansas Alzheimer’s Disease Center, University of Kansas Medical Center, Kansas City, USA; 2. Department of Biostatistics, University of Kansas, Medical Center, Kansas City, USA

Corresponding Author: Eric Vidoni, 4350 Shawnee Mission Parkway, Fairway, KS, 66205, USA, Email:; Phone: 913-588-5312; Fax: 913-945-5035

J Prev Alz Dis 2021;
Published online August 26, 2021,



Despite older racial and ethnic minorities (REMs) being more likely to develop dementia they are underrepresented in clinical trials focused on neurological disorders. Inclusion of REMs in dementia prevention studies is vital to reducing the impact of disparities in dementia risk. We conducted a systematic review to characterize the number of REM enrolled in brain health and prevention randomized controlled trials (RCTs). RTCs published from January 1, 2004 to April 21, 2020 were included. Participants were normal cognitive adults aged 45 years and older who participated in a Phase II or Phase III U.S. based preventative trial. Analyses were performed to examine differences in trial characteristics between RCTs that did and those that did not report race/ethnicity and to calculate the pooled proportion of each racial/ethnic group in randomized brain healthy prevention trials. A total of 42 studies consisting of 100,748 participants were included in the final analyses. A total of 26 (62%) reported some racial/ethnic identity data. The pooled proportion of REM participants was 0.256 (95% CI, 0.191, 0.326). There is a lack of racial/ethnic reporting of participants and REMs remain underrepresented in brain health prevention RCTs.

Key words: Alzheimer’s disease, dementia, underrepresented minorities, aging, prevention.



In 2021 it is estimated that 6.2 million older Americans are living with dementia (1). The population aged 65 years and older is projected to increase from 56 million to 94.7 million by 2060, with much of the growth being affiliated with the aging of Baby boomers (2). Additionally, the proportion of racial and ethnic minority (REM) older adults is expected to increase among African Americans (9% to 13%) (3) and Hispanics (8% to 21%) (4) by 2060. As the older population continues to rapidly grow it is projected that the number people living with dementia will also increase. Among people 65 years and older African Americans and Hispanics are disproportionally impacted. Previous research has indicated that older African Americans are twice as likely and older Hispanics are 1.5 times as likely to develop dementia compared to non-Hispanic Whites (1). Despite the growing number of racial/ethnic minorities in the United States and the current disproportionate impact of dementia among these populations, there is a poor representation of these groups in randomized control trials (RCTs) focused on neurological disorders (5-7). Furthermore, it is estimated that in dementia RCTs, minority participation rates are lower than 5% (8).
There are several possible reasons why most racial and ethnic minorities are underrepresented in RCTs, including historical unethical practices and cultural barriers. Unethical practices such as the Tuskegee syphilis study in which researchers infected and withheld treatment for syphilis to Black men (9) and more recently the Havasupai Tribe case, in which DNA samples initially collected for genetic markers of type 2 diabetes had been used in several unrelated studies such as schizophrenia, and migration without consent from tribal members (10) have led to mistrust in research field among REMs. Cultural barriers in RCTs have been documented as lack of tailoring to diverse communities, implicit bias, and investigators; limiting participation of REM within studies (11). Yet, previous research has indicated that REMs are willing to participate in clinical trials when presented with the opportunity and when trial objectives can be translated in a culturally relevant manner (12), which demonstrates that REMs are not necessarily hard to reach but are rarely reached.
Race is a socially constructed category and a proxy for unique psychosocial factors strongly related to dementia (13) that needs to be considered when designing dementia prevention interventions. Because dementia prevention trials have primarily focused on non-Hispanic Whites, progress in research related to characteristics of dementia among REM has been limited. Dementia prevention is likely to be a critical aspect in reducing racial and ethnic disparities (14). However, disease prevention is not a one-size fits all model and it is imperative that preventative approaches aimed at mitigating risk factors of dementia among REM incorporate culture.
Inclusion of REMs in dementia prevention studies is vital to reducing the impact of disparities and critical for addressing imperative gaps in knowledge. Therefore, we conducted a systematic review to characterize the number of REMs enrolled in brain health and prevention trials.



We searched Ovid MEDLINE, Embase, CINAHL Complete, and clinical published in English from January 1, 2004 to April 21, 2020. Minimum race and ethnicity reporting standards were adopted by the National Institutes of Health in January 2002 (NOT-OD-01-053). Our search window allows for the completion and reporting of smaller clinical trial projects initiated following this directive. Additionally, we screened references from eligible studies to determine additional eligible articles to include in the review. A detailed search strategy of this review is available in the supplementary materials.
We included published RCTs that met the following eligibility criteria 1) enrollees with normal cognition ages 45 years and older, 2) Phase II or Phase III randomized controlled trials, 3) at least one explicitly identified cognitive outcome measure, and 4) United States-based trials. We excluded investigational medication trials seeking FDA approval, trials aimed at treatment of existing cognitive impairment, psychiatric-related cognitive trials (i.e. major depression as primary diagnosis), protocol related articles, clinical trials that did not provide results, RCTs with no cognitive outcome examined, retrospective, and secondary articles.
Eight reviewers (ARS, EDV, JPP, EDJ, SIF, PEK, MDA, AL) independently screened all titles and abstracts. Two reviewers (ARS and EDV) cross checked all titles, abstracts, and completed full text-review of all eligible studies following screening. Information was abstracted from all eligible studies: publication information (first author, title, journal, PubMed ID [PMID], year of publication); funding source; demographics of enrollees (total number of participants, average age, number of females, number and type of race or ethnicity as defined by the study); study design data (type of intervention, intervention components, primary language of intervention delivery, cognitive tests used); Percentages of ethno-racial groups in the eligible studies were only included in this review if it was specifically mentioned in the manuscript.
We examined the differences in trial characteristics between RCTs that did and those that did not report race/ethnicity of trial participants using Student’s t-test for continuous variables and X2 tests for categorical variables. Study characteristics examined included average proportion female, average age, sample size (mean), type of intervention (i.e. diet/supplement vs exercise vs. cognitive training vs. multi-domain), funding source, non-English language delivery (yes vs. no).
We conducted a meta-regression analysis to calculate the pooled proportion of each racial/ethnic group in randomized brain healthy prevention trials. Our primary outcome was non-White/non-Hispanic which included a composite of the following racial/ethnic groups African American, Hispanic or Latinx (hereafter referred to as Hispanic), Asian, American Indian or Alaskan Native, Native Hawaiian or Pacific Islander, and Other Race, following standard NIH reporting guidance. We conducted a subgroup analysis to assess the heterogeneity across different study factors including type of intervention (diet/supplement, exercise, cognitive training, multi-domain), cognitive tests in intervention (MMSE, Rey Auditory Verbal, other) and funding (public, private, or mixed). The pooled estimate of proportion and 95% confidence (15) interval were calculated using random effects meta-analyses with inverse variance weighting. The Freeman-Tukey double arsine transformation of the proportion was used in the estimation (16). Analyses were performed using SAS 9.4 and R 4.0.2. We used the metaprop function from the “meta” package in R to calculate the pooled estimate and confidence intervals (17).



A total of 4,600 articles were screened: 4385 non-duplicate abstracts identified via our search strategy, and 215 articles manually added. After review of titles and abstracts, 49 underwent a full text review. Of the 49 articles reviewed; 7 were excluded for not being RCTs or being derivative of the primary report (n=6), or including participants younger than 45 years old (n=1). A total of 42 articles were eligible for inclusion. The study selection flow diagram is shown in Figure 1.

Figure 1. PRISMA flow diagram of reviewed publications and results


Of the 42 studies, 26 (62%) reported some ethno-racial identity data. The 42 eligible studies included a total of 100,748 participants, including the 76365 participants in studies that reported ethno-racial information. White race was reported in 23 trials, Black or African American in 15, Asian in 6, American Indian or Alaska Native in 3, Hawaii Native or Pacific Islander in 1. No studies reported bi-racial identity. In 9 of these trials, White race was explicitly centered and either no other race categories were listed, or all other races were combined into an “Other Race” category. In one instance, only the African American proportion of the sample was reported and all other races including White were captured under “Other Race.” Hispanic ethnicity was reported in 10 trials, including two trials for only Hispanic individuals. In all studies, Hispanic ethnicity appeared to be included as a separate ethno-racial category with no intersection with an identified racial identity. No studies reported enrolling exclusively White, non-Hispanic participants.
Table 1 shows the characteristics of the eligible studies stratified by whether the studies reported ethno-racial information or not. Studies that reported ethno-racial information did not have different sample sizes ( p=0.48, CI [-5459.7, 2633.4]), average age (p=0.75, CI [-5.4, 3.9]), or have a different percentage of women (p=0.24, CI [-13.9, 3.5]). Studies that reported ethno-racial information did not employ different intervention types (X2 = 8.0, p=0.33), or have significantly different funding (X2 = 9.8, p=0.08) but in general were more often funded by the National Institutes of Health (62% vs 19%). Only two studies were explicitly delivered in Spanish, both of which exclusively enrolled individuals who identified as Hispanic.

Table 1. Characteristics of Eligible Studies

Some combination of race and/or ethnicity was reported in 26 studies. Number or percentage of individuals who were white were reported in 23 trials, Black or African American in 15 trials, Asian in 6 trials, American Indian or Alaska Native in 3 trials, Hawaii Native or Pacific Islander in 1 trial. Hispanic ethnicity was reported in 10 trials and appeared to be included as a separate ethno-racial category with no overlap with race. Funding identification based on sources listed in text.


The overall and subgroup frequency of each level and the pooled estimated proportions of participants from ethno-racial minority groups are illustrated using forest plotting, Figure 2. The plot displays the estimate and confidence intervals for both overall and subgroup analyses. For studies that reported racial or ethnic identity of participants, the estimated pooled percentage of REM participation was 25.6%. Only the type of intervention demonstrated differences in the proportion of ethno-racial minorities in the sample, (p<0.003). Specifically, diet studies had a lower estimated pooled proportion of REM enrollees. There was insufficient evidence to conclude the proportion of minority enrollees different by funding source or cognitive measure employed.

Figure 2. Pooled Proportion of REM in brain healthy prevention trials between 2004 and 2020 using meta-analyses, and subgroup analyses to assess heterogeneity



In the current study, we conducted a systematic review to characterize the number of REMs enrolled in brain health and prevention trials. We conducted this research because inclusion of REMs in dementia prevention studies is vital to reducing the impact of disparities in dementia risk and critical for addressing imperative gaps in knowledge. Our findings suggest that, between 2004 and 2020, one third of studies failed to report ethno-racial information. Of the studies that did report this information, we found the estimated pooled percentage of REM participation was 25.6%.
This estimate is higher than the representation of REMs in dementia pharmacological treatment RCTs as reported in a 2007 review(18), 10% in NIH and 3.2% in industry-funded RCTs. However, it is important to note that one third of studies in the current review did not report race or ethnicity, which suggests that the numbers of minorities are likely low. Also, 25.6% is lower than the 2019 the U.S. Census Bureau estimate of 30.4% people 45 years and older that identified as a REM, demonstrating that REM remain under-represented in preventative RCTs (19). Our study findings of the lack of representation of REM in brain health and prevention trials, aligns with current evidence demonstrating the underrepresentation of culturally and linguistical populations in clinical trials (20-22). Lack of racial/ethnic representation in clinical trials is problematic for generalizability of study findings and equity for REM in obtaining the benefits of participating in clinical trials (23). It is also troubling given that at a minimum, we know that individuals identifying as African American, Hispanic, and American Indians are at greater risk of developing cognitive change (24, 25).
Barriers to REM participation in clinical trials are well-documented (26, 27) due to past unethical research practices that have had a significant influence on the community resulting in mistrust of research, medical, and academic institutions as a whole (28, 29). Additionally, cultural and institutional barriers have impacted REM participation including primary use of passive recruitment strategies (e.g. flyers) that are not culturally tailored to REM communities. Study designs and inclusion criteria for clinical trials also present significant challenges for REM participation (e.g. medical/health eligibility criteria, time/duration of study, transportation requirements to go to study sites) (11, 13, 30).
The prevalence of dementia is high among REM, especially among older African Americans (13.8%) and Hispanics (12.2%) compared to Whites (10.3%) (31). Although evidence consistently indicates that disparities in dementia disproportionately impact REM, especially in terms of incidence, prevalence, diagnosis, and disease burden, the same populations have been historically underrepresented and nearly absent in dementia research (14, 18, 25, 32), in which less than 4% of ADRD prevention brain health trials are focused on REM communities.(33) However, this review found that an estimated 25.6% enrollment of REM in dementia prevention trials, which indicates there have been improvements made within research to enhance participation and inclusion of REM in clinical trials.
Effective approaches to increase recruitment among REM in research have been made, which emphasize the importance of forming sustainable partnerships with REM communities (e.g. community centers, churches, and trusted community leaders). Centering the community partnerships at all phases of the research in useful to acquiring buy-in from the community as a whole.(34) For example, a church-based HIV intervention used community based participatory research approach to increase HIV testing among African Americans resulting in increased HIV testing (59% vs. 42%, p = 0.008) and church-based testing (54% vs. 15%, p < 0.001) within 12 months (35). In another in trial, Promotoras de Salud delivered a diabetes prevention program for prediabetic Latina adults in Spanish. Participants reported overall high satisfaction with the culturally tailored program and results indicated significant reduction in weight loss (5.6% of initial body weight) and cardiovascular related risk factors (e.g. diastolic blood pressure, insulin, and LDL cholesterol) (36). These trials demonstrate that community driven trials that are culturally tailored result in feasibility, acceptability, and effectiveness in addressing health disparities among REM. Therefore, it is imperative that dementia prevention trials involve community partners to enhance recruitment and acceptability among REM. In addition, resources through the NIA’s Office of Special Populations are available to support recruitment and retention efforts for REM (37). Incorporating technology to support recruitment and participation has shown to be effective in reaching REM and reducing barriers to participation such as time. The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease program enhanced timely recruitment into trials by leveraging a cost-effective information technology infrastructure (38). Also, the internet-based platform Alzheimer’s Prevention Registry raised awareness about AD prevention trials and served a recruitment tool to connect members in the community to current enrolling trials (39). These studies have demonstrated that using technology can serve as an effective tool to support recruitment of REM who have traditionally been underrepresented in trials. Policy-related strategies might also help increase the representation of REMs in dementia prevention studies. The NIA is considering a funding strategy for Practice Based Research Networks, which have been reported to increase access to a more diverse population (40). Another policy may be ensuring accountability by means of contingencies upon achieving the quotas of REM participation proposed in the research design. The NIA Clinical Research Operations & Management System (CROMS), currently being piloted, may assist track recruitment and retention to make it more equitable (41). Providing incentives has also been shown to enhance participation in dementia clinical trials. African Americans reported that receiving financial compensation, cognitive and genetic tests results would make them more likely to enroll in dementia focused clinical trials (28), demonstrating that cultural alignment and the use of incentives can support strides towards achieving representation in dementia prevention trials. It is important to note that diversifying samples will lead to greater generalizability of prevention recommendations. Tailored and targeted interventions can address specific social, environmental, and in limited cases possible biological differences between REM groups in the US. These benefits vastly outweigh any challenges to efficacy assessment that may be experienced in inclusive trials due to attrition.
This study incorporated a comprehensive systematic methodological search using both electronic databases and gray literature. This study had a few limitations which merit discussion. First, we applied a U.S. based study limit to our review to focus on U.S. based REMs which limits generalization of findings to REMs outside of U.S. Second, we limited our search to studies reported in English, which has been argued to result in systematic bias (42). Third, we included only studies that reported change in cognitive scores, therefore excluding those that only reported dementia or MCI incidence as an outcome. Future research should explore those studies, although there are likely few, due to the required long time periods to assess those outcomes.
In conclusion, this systematic review highlights the lack of ethno-racial reported among participants in brain health prevention RCT trials. Representation of REM is dementia prevention trials is critical to reducing the disproportionate burden dementia has among these populations. Reporting of ethno-racial within dementia prevention trials is encouraged and use of effective recruitment including collaboration with community partners is suggested to enhance recruitment for future dementia prevention trials.


Funding: This work was supported by the National Institute of Aging grant number P30AG035982.

Acknowledgements: Not applicable.

Disclosures/Competing interests: The authors declare that they have no competing interests.



1. Association As. Facts and Figures 2021 [Available from:
2. Vespa J, Armstrong DM, Medina L. Demographic turning points for the United States: Population projections for 2020 to 2060: US Department of Commerce, Economics and Statistics Administration, US …; 2018.
3. Living AfC. 2018 Profile of African Americans Age 65 and Over. Washington DC: U.S. Department of Health and Human Services; 2018.
4. Living AfC. 2018 Profile of Hispanic Americans Age 65 and Over. Washington DC: U.S. Department of Health and Human Services; 2018.
5. Burneo JG, Martin R. Reporting race/ethnicity in epilepsy clinical trials. Epilepsy & Behavior. 2004;5(5):743-5.
6. Zhang Y, Ornelas IJ, Do HH, Magarati M, Jackson JC, Taylor VM. Provider perspectives on promoting cervical cancer screening among refugee women. J Community Health. 2017;42(3):583-90.
7. Robbins NM, Bernat JL. Minority representation in migraine treatment trials. Headache: The Journal of Head and Face Pain. 2017;57(3):525-33.
8. George S, Duran N, Norris K. A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am J Public Health. 2014;104(2):e16-31.
9. Scharff DP, Mathews KJ, Jackson P, Hoffsuemmer J, Martin E, Edwards D. More than Tuskegee: understanding mistrust about research participation. J Health Care Poor Underserved. 2010;21(3):879-97.
10. Garrison NA. Genomic Justice for Native Americans: Impact of the Havasupai Case on Genetic Research. Sci Technol Human Values. 2013;38(2):201-23.
11. Green-Harris G, Coley SL, Koscik RL, Norris NC, Houston SL, Sager MA, et al. Addressing Disparities in Alzheimer’s Disease and African-American Participation in Research: An Asset-Based Community Development Approach. Frontiers in Aging Neuroscience. 2019;11:125.
12. Forrester S, Jacobs D, Zmora R, Schreiner P, Roger V, Kiefe CI. Racial differences in weathering and its associations with psychosocial stress: The CARDIA study. SSM – Population Health. 2019;7:100319.
13. Konkel L. Racial and Ethnic Disparities in Research Studies: The Challenge of Creating More Diverse Cohorts. Environmental Health Perspectives. 2015;123(12):A297-A302.
14. Barnes LL, Bennett DA. Alzheimer’s disease in African Americans: risk factors and challenges for the future. Health Aff (Millwood). 2014;33(4):580-6.
15. Woodward M. Epidemiology: study design and data analysis: CRC press; 2013.
16. Lin L, Xu C. Arcsine-based transformations for meta-analysis of proportions: Pros, cons, and alternatives. Health Sci Rep. 2020;3(3):e178.
17. Schwarzer G. meta: An R package for meta-analysis. R news. 2007;7(3):40-5.
18. Faison WE, Schultz SK, Aerssens J, Alvidrez J, Anand R, Farrer LA, et al. Potential ethnic modifiers in the assessment and treatment of Alzheimer’s disease: challenges for the future. Int Psychogeriatr. 2007;19(3):539-58.
19. Bureau USC. Census estimates 2019 2019 [Available from:
20. McDougall GJ, Jr., Simpson G, Friend ML. Strategies for research recruitment and retention of older adults of racial and ethnic minorities. J Gerontol Nurs. 2015;41(5):14-23; quiz 4-5.
21. Stahl SM, Vasquez L. Approaches to improving recruitment and retention of minority elders participating in research: examples from selected research groups including the National Institute on Aging’s Resource Centers for Minority Aging Research. J Aging Health. 2004;16(5 Suppl):9s-17s.
22. Mody L, Miller DK, McGloin JM, Freeman M, Marcantonio ER, Magaziner J, et al. Recruitment and retention of older adults in aging research. J Am Geriatr Soc. 2008;56(12):2340-8.
23. Hughson J-a, Woodward-Kron R, Parker A, Hajek J, Bresin A, Knoch U, et al. A review of approaches to improve participation of culturally and linguistically diverse populations in clinical trials. Trials. 2016;17(1):263.
24. Perales-Puchalt J, Gauthreaux K, Shaw A, McGee JL, Teylan MA, Chan KCG, et al. Risk of mild cognitive impairment among older adults in the United States by ethnoracial group. Int Psychogeriatr. 2021;33(1):51-62.
25. Mayeda ER, Glymour MM, Quesenberry CP, Whitmer RA. Inequalities in dementia incidence between six racial and ethnic groups over 14 years. Alzheimer’s & Dementia. 2016;12(3):216-24.
26. Williams MM, Scharff DP, Mathews KJ, Hoffsuemmer JS, Jackson P, Morris JC, et al. Barriers and facilitators of African American participation in Alzheimer disease biomarker research. Alzheimer Dis Assoc Disord. 2010;24 Suppl(Suppl):S24-9.
27. Ballard EL, Gwyther LP, Edmonds HL. Challenges and opportunities: recruitment and retention of African Americans for Alzheimer disease research: lessons learned. Alzheimer Dis Assoc Disord. 2010;24 Suppl(0):S19-S23.
28. Zhou Y, Elashoff D, Kremen S, Teng E, Karlawish J, Grill JD. African Americans are less likely to enroll in preclinical Alzheimer’s disease clinical trials. Alzheimer’s & Dementia: Translational Research & Clinical Interventions. 2017;3(1):57-64.
29. Corbie-Smith G, Thomas SB, Williams MV, Moody-Ayers S. Attitudes and beliefs of African Americans toward participation in medical research. J Gen Intern Med. 1999;14(9):537-46.
30. Wendler D, Kington R, Madans J, Wye GV, Christ-Schmidt H, Pratt LA, et al. Are Racial and Ethnic Minorities Less Willing to Participate in Health Research? PLOS Medicine. 2005;3(2):e19.
31. Prevention CfDCa. U.S. burden of Alzheimer’s disease, related dementias to double by 2060 2018 [Available from:,Pacific%20Islanders%20(8.4%20percent).
32. Gilmore-Bykovskyi AL, Jin Y, Gleason C, Flowers-Benton S, Block LM, Dilworth-Anderson P, et al. Recruitment and retention of underrepresented populations in Alzheimer’s disease research: A systematic review. Alzheimer’s & Dementia: Translational Research & Clinical Interventions. 2019;5:751-70.
33. Rayhan R, Rochelle, P., Holzaphel, D., Vradenburg, G. Non-Pharmacological Therapies in Alzheimer’s disease: A Systematic Review. 2019.
34. Samus QM, Amjad H, Johnston D, Black BS, Bartels SJ, Lyketsos CG. A Multipronged, Adaptive Approach for the Recruitment of Diverse Community-Residing Elders with Memory Impairment: The MIND at Home Experience. Am J Geriatr Psychiatry. 2015;23(7):698-708.
35. Berkley-Patton JY, Thompson CB, Moore E, Hawes S, Berman M, Allsworth J, et al. Feasibility and Outcomes of an HIV Testing Intervention in African American Churches. AIDS Behav. 2019;23(1):76-90.
36. O’Brien MJ, Perez A, Alos VA, Whitaker RC, Ciolino JD, Mohr DC, et al. The Feasibility, Acceptability, and Preliminary Effectiveness of a Promotora-Led Diabetes Prevention Program (PL-DPP) in Latinas:A Pilot Study. The Diabetes Educator. 2015;41(4):485-94.
37. Health NIo. Office of Special Populations [Available from:
38. Jimenez-Maggiora GA, Bruschi S, Raman R, Langford O, Donohue M, Rafii MS, et al. TRC-PAD: Accelerating Recruitment of AD Clinical Trials through Innovative Information Technology. J Prev Alzheimers Dis. 2020;7(4):226-33.
39. Langbaum JB, High N, Nichols J, Kettenhoven C, Reiman EM, Tariot PN. The Alzheimer’s Prevention Registry: A Large Internet-Based Participant Recruitment Registry to Accelerate Referrals to Alzheimer’s-Focused Studies. The journal of prevention of Alzheimer’s disease. 2020;7(4):242-50.
40. VideoCasting NIoH. Development of an NIA Practice-Based Research Network to Conduct Alzheimer’s and Related Dementias Clinical Research. 2021.
41. Aging NIo. NIA’s Clinical Research Operations & Management System (CROMS) 2021 [Available from:
42. Morrison A, Polisena J, Husereau D, Moulton K, Clark M, Fiander M, et al. THE EFFECT OF ENGLISH-LANGUAGE RESTRICTION ON SYSTEMATIC REVIEW-BASED META-ANALYSES: A SYSTEMATIC REVIEW OF EMPIRICAL STUDIES. International Journal of Technology Assessment in Health Care. 2012;28(2):138-44.



N. Costa1,2,3, M. Mounié1,2, A. Pagès2,4,5, H. Derumeaux1, T. Rapp6, S. Guyonnet2,3,5, N. Coley2,3,7, C. Cantet2,3,5, I. Carrié5, S. Andrieu2,3,7, L. Molinier1,2,3 and on behalf of the MAPT/DSA Group*


1. Health Economic Unit of the University Hospital of Toulouse, Toulouse 31059, France; 2. INSERM-UMR 1027, Toulouse 31000, France; 3. University of Toulouse III, Toulouse 31330, France; 4. Department of Pharmacy, Toulouse University Hospital, Toulouse, 31000, France; 5. Gérontopôle, Department of Geriatrics, University Hospital of Toulouse, Toulouse 31059, France; 6. Université de Paris (LIRAES EA4470), Paris 75006, Paris, France; 7. Department of Epidemiology and Public Health, university Hospital of Toulouse, Toulouse 31059, France

Corresponding Author: Nadège Costa, Health economist, PhD, Health Economic Unit of the University Hospital of Toulouse, Hôtel Dieu Saint-Jacques, 2, rue viguerie, 31059 Toulouse Cedex 9, France, Email :, Tel: +335 61 77 83 72

J Prev Alz Dis 2021;4(8):425-435
Published online August 2, 2021,



BACKGROUND: To date, no curative treatment is available for Alzheimer’s disease (AD). Therefore, efforts should focus on prevention strategies to improve the efficiency of healthcare systems.
Objective: Our aim was to assess the cost-effectiveness of three preventive strategies for AD compared to a placebo.
Design: The Multidomain Alzheimer Preventive Trial (MAPT) study was a multicenter, randomized, placebo-controlled superiority trial with four parallel groups, including three intervention groups (one group with Multidomain Intervention (MI) plus a placebo, one group with Polyunsaturated Fatty Acids (PFA), one group with a combination of PFA and MI) and one placebo group.
Setting: Participants were recruited and included in 13 memory centers in France and Monaco.
Participants: Community-dwelling subject aged 70 years and older were followed during 3 years.
Interventions: We used data from the MAPT study which aims to test the efficacy of a MI along PFA, the MI plus a placebo, PFA alone, or a placebo alone.
Measurement: Direct medical and non-medical costs were calculated from a payer’s perspective during the 3 years of follow-up. The base case incremental Cost-Effectiveness Ratio (ICER) represents the cost per improved cognitive Z-score point. Sensitivity analyses were performed using different interpretation of the effectiveness criteria.
Results: Analyses were conducted on 1,525 participants. The ICER at year 3 that compares the MI + PFA and the MI alone to the placebo amounted to €21,443 and €21,543 respectively, per improved Z score point. PFA alone amounted to €111,720 per improved Z score point.
Conclusion: Our study shows that ICERS of PFA combined with MI and MI alone amounted to €21,443 and €21,543 respectively per improved Z score point compared to the placebo and are below the WTP of €50,000 while the ICER of PFA alone amounted to €111,720 per improved Z score point. This information may help decision makers and serve as a basis for the implementation of a lifetime decision analytic model.

Key words: Cost-effectiveness, economics, Alzheimer disease, prevention.



According to the 2019 World Alzheimer report, 50 million people worldwide and 1.2 millions in France suffer from Alzheimer’s disease (AD) (1). Associated costs of care are consistent and vary from €24,140 for mild and moderate stages to €44,171 for the severe stage at 18 months (2).
According to the latest meta-analyses, specific drugs in the treatment of AD have a low and time-limited efficacy on symptoms, quality of life, institutionalization, mortality and the burden of caregivers (3, 4). In 2016, the French High Authority for Health (HAS) considered that the benefit of these medicines was insufficient to justify reimbursement by the French National Health Insurance (FNHI) (5, 6).
As no curative treatment is available, efforts should focus on prevention strategies. Current evidence suggests that nutrition, physical exercise, cognitive activity and social stimulation may improve cognitive health (7). Results from the Multidomain Alzheimer Preventive Trial (MAPT), which test the effect of Multidomain Intervention (MI) and supplementation using omega 3 polyunsaturated fatty acids (PFA) alone or in combination on cognitive decline alongside a large randomized controlled trial show no significant differences in 3-year cognitive decline between any of the three intervention groups and the placebo group (8). Nevertheless, this trial shows a trend in z-score differences in favor of MI + PFA and MI alone groups.
Published cost-effectiveness analyses of primary prevention strategies for AD show cost-saving results. Nevertheless, these studies are only based on simulated models and hypothetical interventions indicating potential cost-effectiveness results (9). Interventions tested were physical activity, management of cardiovascular risk factors, vitamin supplementation, and multidomain cardiovascular disease prevention programs (10-13). Currently in France, these interventions are not reimbursed specifically for the prevention of Alzheimer’s disease but they can be offered to the patient for the maintenance of their overall health. More randomized control trials (RCT) are required to reinforce the results cost-effectiveness study of prevention programs for AD.
In the framework of the large MAPT study, we aim to assess the cost-effectiveness of PFA supplementation alone, MI (nutritional counseling, physical exercise, and cognitive stimulation) alone or a combination of both interventions compared to a placebo.



Design, setting and participants

The MAPT study was a multicenter, randomized, placebo-controlled superiority trial with four parallel groups, including three intervention groups (one group with MI plus a placebo, one group with PFA, one group with a combination of PFA and MI) and one placebo group. Community-dwelling subjects, followed during 3 years, aged 70 years and older were recruited at 13 memory centers in France and Monaco. In France, memory centers are outpatient structures that performed diagnostic workup and follow-up of elderly subjects. Participants met at least one of three criteria: spontaneous memory complaint, limitation in one instrumental Activity of Daily Living (ADL), or slow gait were eligible to be included in the study. Participants with a Mini Mental State Examination (MMSE) score below 24, those who were diagnosed with dementia, those with any difficulty in basic ADL and those taking PFA supplements at baseline were excluded. Full methods have been previously described elsewhere (8, 14). The trial protocol was approved by the French Ethics Committee in Toulouse (CPP SOOM II) and was authorized by the French Health Authority (8).


Participants were randomly assigned to one of the following four groups:
– Multidomain Intervention: consisted of 2 h group sessions focusing on three domains (cognitive stimulation, physical activity, and nutrition) and a preventive consultation (at baseline, 12 months, and 24 months). MI was done twice a week during the first month, once a week during the second month, and one per month for the remainder of the three years study,
– Omega 3 Polyunsaturated Fatty Acids: two capsules per day with 400 mg docosahexaenoic acid (DHA) and no more than 112·5 mg eicosapentaenoic acid (EPA),
– Combined intervention: Multidomain intervention and Omega-3 PFA,
– Placebo: two capsules per day containing flavoured paraffin oil.

More details are given elsewhere (8).


All costs were recorded throughout the MAPT trial at 6, 12, 18, 24, 30 and 36 months using a Case Report Form and were analysed from the FNHI perspective. All monetary values are in 2018 Euros. Costs taken into account were direct medical (hospitalizations, consultations, medical and paramedical procedures and drugs) and non-medical (transportation) costs.
Valuation was based on several sources of unit costs (Appendix 1. Table A1). Hospital stays were valued using the French Disease Related Groups (DRG) including extra charges if applicable (e.g. the cost of days of intensive care) (15). We used mean DRG rates calculated from the national hospitalization database for patients aged 70 or over, according to the medical unit to which the participant was admitted. Rehabilitation and psychiatric hospitalizations were valued using per diem costs. Consultations were valued using the General French Nomenclature for Medical Procedures according to the specialization (16). Medical procedures were valued using the Medical Classification of Clinical Procedures (CCAM) (Version 54.10) and the Nomenclature of Clinical Biological Procedures (NABM) according to the type of medical procedure (imaging, biology, other) (17, 18). Each consultation and medical procedure was valued using the appropriate FNHI reimbursement rate.
No details, except the frequency, were available in the database on transportation and paramedical procedures, therefore valuation was based on means estimate from a sample of the FNHI database, the General Sample of Beneficiaries database (EGB) (19, 20). The gamma distribution shape and scale parameters were derived from the mean and variance observed in the 2018 EGB database for each cost component for the population aged 70 years or older.
For drugs reimbursed by the FNHI, we assumed that the daily dosage was equal to the Daily Defined Dose (DDD) (21). If there was no recommended DDD, we calculated an average daily dose according to the Summary of Product Characteristics (SmPC) (22). We then multiplied the reimbursement price per unit by the daily dosage and the treatment duration (23). For hospital drugs, only the costs of very expensive drugs were taken into account because the others were included in the DRG rate (24).
MI was valued by the mean wage rate for a psychologist, dietician and physical activity facilitator (40€) multiplied by the intervention period (2.30 hours) and the number of prescribed sessions (46) during three years. PFA was valued using the retail price per capsule (€0.50 cents) multiplied by the number of prescribed capsules taken per participant per year (365.5/year), multiplied by 3 years.
The primary efficacy outcome used to determine the ICER consisted of the change from baseline to 36 months in a composite Z score (8). It combines four cognitive tests (free and total recall of the Free and Cued Selective Reminding Test, ten MMSE orientation items, the Digit Symbol Substitution Test score from the Revised Wechsler Adult Intelligence Scale, and the Category Naming Test [2 min category fluency in animals]) (8). Z-scores represent the number of standard deviations above or below the mean. Coley et al estimated that a 0.3-point decrease in Z score is the minimum clinically significant difference, which predicts dementia (25). We used this cut-off, in addition to the Z-score, to define whether a participant presented an aggravation in memory function in order to make the ICER more comprehensive for clinicians and decision makers. Other variables (age, gender, comorbidities, Fried frailty phenotype, educational level and Z score) were collected at baseline.

Statistical analyses

Description and comparison of baseline characteristics were made using mean and standard deviation and occurrences and percentages for continuous and qualitative variables on one hand and using Kruskal Wallis or Chi-squared on the other hand.
Cost components for participants who had a complete follow-up were summarized for each group. Three-year cumulative costs were expressed in terms of mean costs per participant and their bias-corrected and accelerated (BCa) bootstrap 95% confidence intervals (CI). Cost differences between groups where tested using a global non-parametric Kruskal-Wallis test.
Missing data on total cumulative cost at 3 years were accounted for by multiple imputation and predictive mean matching methods (26). Age group tercile, gender, intervention groups, initial frailty score tercile and pooled occurrences of medical history tercile were used in the imputation. We assumed that missing cost data are “Missing At Random” and we used Hausman test to verify whether our results were subject to attrition bias issues (27). Efficacy data used in our analysis were smoothed by a mixed model as described elsewhere (8). The fixed effects used in this model were intervention group, time, and interactions between intervention groups and each time. The random effects used were center-specific and participant-specific variables. In order to include adjusted outcomes in both the numerator and denominator of the ICER, we used a Generalized Linear Model (GLM) with a gamma distribution and a log link that allowed the use of fitted cost data (28). The same variables used for imputation were also used for adjustment. Fitted and imputed costs as well as fitted Z scores were then described using mean and BCA bootstrap CI.
We used non-parametric bootstrap outputs to graphically determined the 95% confidence ellipses and illustrate the uncertainty around the ICER (29, 30). ICERs with a positive value were compared to a Willingness-To-Pay (WTP) threshold set up at 50,000€ per Quality Adjusted Life Years (31-33). Additionally, the cost-effectiveness acceptability curve (CEAC) showed the probability that an intervention was cost-effective compared with the alternative according to a range of WTP thresholds (34). Moreover, sensitivity analysis was conducted using the data for patient with a complete follow-up.



Patient’s characteristics

Between 30 May 2008 and 24 February 2011, 1,680 participants were enrolled and randomly assigned to four arms. Participants were excluded from the modified intention-to-treat efficacy analysis because no cognitive assessment was available after baseline for 154 participants, and one participant in the PFA group withdrew their consent. One thousand two hundred and eighty-six participants completed the final visit and economic data were available for 1,320 participants (Figure 1). Missing economic data accounted for 12% to 15% in each group. A full description of the population was provided in prior work (14). The baseline characteristics of our sample are summarized in Table 1. No substantial difference was noted in any demographic or clinical characteristics between the arms.

Figure 1. Flow chart for patient’s selection

*The intention-to-treat analysis included assigned participants with a composite score at baseline who had at least one post-baseline visit.

Table 1. Participants’ characteristics at baseline

*p-value of khi2 or Kruskal Wallis test according to variables; † Gastrointestinal; ‡Ear Nose and Throat; §Polyunsaturated Fatty Acids; || Multidomain intervention

Three years costs description

The observed costs for the three-year follow-up period for 1,320 participants with complete economic data are presented in table 2. Total costs without intervention amounted to €7,702; €7,951; €7,845 and €7,106 for PFA + MI, PFA, MI and the placebo group, respectively (p=0.77). When the intervention cost was included in the total costs, they were significantly different and amounted to €9,171; €8,500; €8,765 and €7,106, respectively (p=0.001). The main cost driver in each group was inpatient stays which accounted for approximately 50% of the total cost in all groups. The second cost driver was medication, which accounted for 24% to 30% of the total cost depending on the group.
At 3 years, the placebo group had the lowest inpatient costs of the three groups, and particularly for psychiatric hospitalizations that were higher in the three other groups. The PFA group had significantly higher GP, cardiologist and lab test costs than others groups (p= 0.026, p= 0.018 and p=0.090, respectively). Finally, cardiovascular medication costs were higher in the PFA + MI group (p=0.018).

Table 2. Total costs over the three-year follow-up period

*Confidence Interval; †p-value of Kruskal-Wallis rank sum test; ‡Medical Surgical and Obstetrics; §Polyunsaturated Fatty Acids; ||Multidomain Intervention


Three years costs analysis

Detailed costs for every 6 months show a substantial increase in total costs for each group between 24 and 36 months of follow-up, which was mainly due to a substantial increase in hospital costs (Appendix 2. Table A2).
Table 3 presents the results of the GLM for the whole population (1,525) and show that total costs including intervention costs increased with age, number of medical conditions and the type of intervention. It was significantly higher in the PFA + MI group and the MI group. The GLM regression of total costs without intervention costs shows that only age and the number of medical conditions increased healthcare costs significantly.

Table 3. Multivariate analysis of total cost with and without intervention over the 3-year follow-up period (N= 1,525)

*Relative Risk; †Confidence Interval; ‡p-value; §Polyunsaturated Fatty Acids; ||Multidomain Intervention


Cost-effectiveness analysis

Differences in total costs including intervention costs between the intervention groups and the placebo group were €1,237, €1,705 and €1,986 for the PFA, MI and PFA + MI groups, respectively ( Appendix 3. Table A3). Changes in Z scores between the intervention groups and the control group were 0.011 for PFA, 0.079 for MI and 0.093 for PFA + MI, respectively (Appendix 3. Table A3).
As presented in the base case ICER scatter plot (Figure 2-a), the ICER comparing combined intervention and MI alone with placebo amounted to €21,443 and €21,543 per improved Z score point, respectively. The confidence ellipses of the ICERs comparing the PFA + MI and MI strategies overlap. All dots that represent the results of the 1,000 replications of ICERs for the PFA + MI and the MI strategies alone vs. placebo are concentrated in the northeast quadrant. As presented in the CEAC (Appendix 4. Figure A4.a), PFA + MI and MI alone have a probability of 95% to be cost-effective at a €50,000 WTP threshold. When the percentage of patients with no aggravation of cognitive functions between baseline and year 3 (Figure 2.b) is used, it can be noted that all the bootstrapped ICERs of the PFA + MI strategy vs. placebo are located in the northeast quadrant. The probability that PFA + MI and MI alone are cost-effective at a €50,000 threshold is 90% and 65%, respectively. (Appendix 4. Figure A4.b).
Results for the sensitivity analysis using the complete data set show an ICER amounting to €19,638 and €20,595 per improved Z score point for combined intervention and MI alone compared to placebo. All dots that represent the results of the 1,000 replications of ICERs for the PFA + MI and the MI strategies alone vs. placebo are concentrated in the northeast quadrant (Appendix 5).

Figure 2. Confidence ellipses of intervention strategies versus placebo



This study provides first time evidence on the cost-effectiveness of preventive interventions for AD. Our study showed that PFA + MI and MI alone have an ICER of €21,443 and €21,543 respectively per improved Z score point compared to the placebo and are below the WTP of €50,000. Clinical results from the MAPT study showed that in the modified intention-to-treat population (n=1525), there were no significant differences in 3-year cognitive decline between any of the three intervention groups and the placebo group, explaining the impossibility to conclude that an intervention was most efficient than another (8). Between-group differences compared with the placebo were 0.093 (95% CI 0.001 to 0.184; adjusted p=0.142) for the combined intervention group, 0.079 (-0.012 to 0.170; adjusted p=0.179) for the MI plus placebo group, and 0.011 (-0.081 to 0.103; adjusted p=0.812) for the PFA group. Although the clinical results do not show any significant differences in efficacy between the different interventions studied, we can note a trend regarding the increase in efficacy for the combined intervention and MI groups in comparison with placebo, with a p value less than 0.2. In this context, the implementation of a cost-minimisation analysis was not appropriate, because interventions effectiveness were not strictly equivalent, that is why we choose to implement cost-effectiveness analyses. Additionally, clinical efficacy and efficiency are different measurement tools that have different aims. Efficiency measurement provides information on whether healthcare resources are used to get the best value for money while efficacy measurement determines whether an intervention produces the expected result under ideal circumstances (35).
Two efficiency studies with interventions to reduce risk factors for dementia showed cost-saving results (11, 13). The Lin et al. study used a cohort-based simultaneous equation system in United States with a lifetime time horizon. The intervention (disease management of overweight, diabetes, hypertension and other cardiovascular diseases) was cost saving (-9,259 US$ for a gain of 0.03 LY without dementia) (11). The Zhang et al. study used a Markov model in Sweden and Finland with a 20-year time horizon. The intervention (health promotion program combined with pharmacological treatment of cardiovascular risk factors) was cost saving (-21,974 SEK for a gain of 0.0511 QALY) (13). In another study on physical activity, van Baal and colleagues used a Markov model in United Kingdom with a lifetime time horizon. They calculated incremental costs of -4600 GBP to 1500 GBP depending on the scenarios (physical activity levels and adherence to recommendations), the interventions were cost saving or cost-effective depending on the context, and the maximum ICER was £2,777/LY (10). Finally, an economic evaluation of nutritional supplementation (B-vitamins) in the prevention of dementia based on stochastic decision model in United Kingdom with a lifetime time horizon was tested. Contrary to our study, the intervention was cost saving (-502 GBP for 0.008 QALY gained) (12). However, this supplementation was based on B-vitamins and not PFA. Caution should be exercised in comparing because all these studies were based on hypothetical interventions in decision models and were not RCT like our study (9).
Three years total costs amounted from €7,106 for the placebo group to €7,951 for the PFA group. Costs differences between groups were not statistically significant when interventions costs are not included and becomes significant after the inclusion of these costs. This results show that intervention costs is the main cost component, which explain the difference in total costs. Nevertheless, we can note a cost difference of at least €596 between placebo group and the other three groups. This difference is mainly lead by psychiatric hospitalizations (p=0.012). We can explain this difference by the fact that few patients are hospitalized for psychiatric reasons in each group. In the placebo group, only two psychiatric hospitalizations were found during the three years follow-up period while between four and eight psychiatric hospitalizations were found for the other groups. Moreover, we can note a significant cost difference between groups for consultation costs. This is led by the cost of general practitioner cardiologist, which were higher for PFA group compared with other groups. However, this cost difference from a clinical perspective, correspond between 0.5 to 1.5 consultations in terms of frequency during the three years follow-up period. Annualized costs amounted to €2,567; €2,650; €2,618 and €2,369, for PFA + MI, PFA alone, MI alone and placebo groups, respectively. As shown in the original clinical paper, 45% of the participants included in the MAPT study had at least one Fried frailty criterion and the other participants had none of those criteria. The sample of participants included in the MAPT study was considered as pre-frail or robust [8]. A meta-analysis published in 2019 showed that annual healthcare costs for the elderly varied from €1,217 to €2,056 for a Spanish study and from €9,193 and to €18,525 for a study performed in the USA, for robust and pre-frail older adults, respectively [36]. All the studies included in this meta-analyse took into account inpatient stays, ER and outpatient care. Total costs for Mexican and German studies, which also included formal and informal care costs, varied from €1,248 to €1,775 and from €2,568 to €3,284 for robust and pre-frail older adults, respectively (36). In a French study, the authors demonstrated that annual costs for outpatient care were €1,254 for a robust population of older adults (37). This cost was higher for participants 70-74 years of age and amounted to €1,432. In our study, the mean annualized outpatient care costs amounted to €1,315. A comparison with other studies shows that our cost results are consistent with results in published papers.
The efficacy of MI and/or PFA supplementation was estimated using a Z score. Some countries, such as the UK (NICE), recommend the use of QALY to inform decision-makers for resource allocation. We chose not to use QALY in our study because it is very limited for the elderly. Health related quality of life instruments such as EQ-5D-5L measures do not capture the maintenance of independence or the social effects of interventions, which are particularly important dimensions for the elderly [38]. The QALY metric has also been criticized for being insufficiently sensitive to measure small but clinically meaningful changes in health status or utility (39, 40). In order to provide an ICER that can be informative for clinicians and decision makers, sensitivity analyses were performed on different interpretation of effectiveness using the 0.3-point Z-score as a cut-off (25). ICERs were €434/percent of participants with no aggravation of cognitive functions between baseline and 3 years for PFA + MI compared to the placebo. The use of different interpretation of effectiveness did not change the results and confirmed that the PFA + MI strategy present an ICER under the WTP threshold.
Informal and formal care costs were not included in our analysis. Unlike for demented people for which formal and informal care costs can constitute more than 50% of total costs, these type of costs are very low in older people without dementia (41, 42). As stated in the Panaponaris et al study, only 10% of the older people without dementia needed assistance with ADL and less than 25% needed assistance with IADL (42).
Healthcare consumption was measured using ad hoc questionnaires and was based on declarative data. In order to take into account the uncertainty, we implemented probabilistic sensitivity analyses. We used Probabilistic Sensitive Analysis (PSA) instead of Determinist Sensitive Analysis (DSA) because in DSA analysis the analyst himself chooses parameters and their variation (which leads to selection bias); it only allows the simultaneous variation of a few parameters and cannot take into account the interaction between parameters (43-46). Moreover, the Missing at Random characteristics of our data were verified and the issue of missing data was addressed through multiple imputation. In addition, the sensitivity analysis of the ICERs calculated with the 1,320 participants for whom economic data were available was performed and confirmed our results. Nevertheless, results are based on individual data recorded alongside an RCT, which provided us with robust data analysed using adapted statistical methods.



Results show ICERS of PFA combined with MI and MI alone amounted to €21,443 and €21,543 respectively per improved Z score point compared to the placebo and are below the WTP of €50,000 while the ICER of PFA alone amounted to €111,720 per improved Z score point. These results are consolidated through the sensitivity analyses performed on different effectiveness criteria and ICER calculated using observed data on 1320 participants. The article provides additional information to strictly medical data and may serve as a basis for decision making for the FNHI and more widely for other relevant policy makers. Further results, using lifetime horizon analytical model, are necessary to complete information provided as part of this RCT based study. This study was the first to collect economic and clinical data of older people probably at risk of developing AD during a three years follow-up period. This study may help the scientific community to access additional economic data in the field of AD prevention which can be used on the one hand to build lifetime model and on the other hand, to compare results between different countries and finally contribute to improve the economic research on AD.


* MAPT/DSA Group: Principal investigator: Bruno Vellas (Toulouse); Coordination: Sophie Guyonnet; Project leader: Isabelle Carrié; CRA: Lauréane Brigitte; Investigators: Catherine Faisant, Françoise Lala, Julien Delrieu, Hélène Villars; Psychologists: Emeline Combrouze, Carole Badufle, Audrey Zueras; Methodology, statistical analysis and data management: Sandrine Andrieu, Christelle Cantet, Christophe Morin; Multidomain group: Gabor Abellan Van Kan, Charlotte Dupuy, Yves Rolland (physical and nutritional components), Céline Caillaud, Pierre-Jean Ousset (cognitive component), Françoise Lala (preventive consultation) (Toulouse). The cognitive component was designed in collaboration with Sherry Willis from the University of Seattle, and Sylvie Belleville, Brigitte Gilbert and Francine Fontaine from the University of Montreal. Co-Investigators in associated centres: Jean-François Dartigues, Isabelle Marcet, Fleur Delva, Alexandra Foubert, Sandrine Cerda (Bordeaux); Marie-Noëlle-Cuffi, Corinne Costes (Castres); Olivier Rouaud, Patrick Manckoundia, Valérie Quipourt, Sophie Marilier, Evelyne Franon (Dijon); Lawrence Bories, Marie-Laure Pader, Marie-France Basset, Bruno Lapoujade, Valérie Faure, Michael Li Yung Tong, Christine Malick-Loiseau, Evelyne Cazaban-Campistron (Foix); Françoise Desclaux, Colette Blatge (Lavaur); Thierry Dantoine, Cécile Laubarie-Mouret, Isabelle Saulnier, Jean-Pierre Clément, Marie-Agnès Picat, Laurence Bernard-Bourzeix, Stéphanie Willebois, Iléana Désormais, Noëlle Cardinaud (Limoges); Marc Bonnefoy, Pierre Livet, Pascale Rebaudet, Claire Gédéon, Catherine Burdet, Flavien Terracol (Lyon), Alain Pesce, Stéphanie Roth, Sylvie Chaillou, Sandrine Louchart (Monaco); Kristelle Sudres, Nicolas Lebrun, Nadège Barro-Belaygues (Montauban); Jacques Touchon, Karim Bennys, Audrey Gabelle, Aurélia Romano, Lynda Touati, Cécilia Marelli, Cécile Pays (Montpellier); Philippe Robert, Franck Le Duff, Claire Gervais, Sébastien Gonfrier (Nice); Yannick Gasnier and Serge Bordes, Danièle Begorre, Christian Carpuat, Khaled Khales, Jean-François Lefebvre, Samira Misbah El Idrissi, Pierre Skolil, Jean-Pierre Salles (Tarbes). MRI group: Carole Dufouil (Bordeaux), Stéphane Lehéricy, Marie Chupin, Jean-François Mangin, Ali Bouhayia (Paris); Michèle Allard (Bordeaux); Frédéric Ricolfi (Dijon); Dominique Dubois (Foix); Marie Paule Bonceour Martel (Limoges); François Cotton (Lyon); Alain Bonafé (Montpellier); Stéphane Chanalet (Nice); Françoise Hugon (Tarbes); Fabrice Bonneville, Christophe Cognard, François Chollet (Toulouse). PET scans group: Pierre Payoux, Thierry Voisin, Julien Delrieu, Sophie Peiffer, Anne Hitzel, (Toulouse); Michèle Allard (Bordeaux); Michel Zanca (Montpellier); Jacques Monteil (Limoges); Jacques Darcourt (Nice). Medico-economics group: Laurent Molinier, Hélène Derumeaux, Nadège Costa (Toulouse). Biological sample collection: Bertrand Perret, Claire Vinel, Sylvie Caspar-Bauguil (Toulouse). Safety management: Pascale Olivier-Abbal. DSA Group: Sandrine Andrieu, Christelle Cantet, Nicola Coley.

Acknowledgments: The present study called ECO-MAPT study was supported by grants from the French Ministry of Health (PHRC 2008) and the France Alzheimer Association (Doctoral Scholarship 2010). The MAPT study was supported by grants from the Gérontopôle of Toulouse, the French Ministry of Health (PHRC 2008, 2009), the Pierre Fabre Research Institute (manufacturer of the polyunsaturated fatty acid supplement), Exhonit Therapeutics, and Avid Radiopharmaceuticals. No sponsor placed any restriction on this study or had any role in the design of the study, data collection, data analyses or interpretation, or in the preparation, review, or approval of the manuscript. The promotion of this study was supported by the University Hospital Center of Toulouse. We are indebted to the investigators from CHU de Toulouse, Centre Hospitalier Lyon-Sud, Hôpital de Tarbes, Hôpital de Foix, Hôpital de Castres, CHU de Limoges, CHU de Bordeaux, Hôpital de Lavaur, CHU de Montpellier, Hôpital Princesse Grace, Hôpital de Montauban, CHU de Nice, and CHU de Dijon for their participation in this study.

Funding: The present study called ECO-MAPT study was supported by grants from the French Ministry of Health (PHRC 2008) and the France Alzheimer Association (Doctoral Scholarship 2010). The original MAPT study was supported by grants from the Toulouse Geriatric Center, the French Ministry of Health (PHRC 2008, 2009), Pierre Fabre Research Institute (manufacturer of the omega-3 supplement), ExonHit Therapeutics SA, and Avid Radiopharmaceuticals Inc. Trial registration number: NCT00672685, first registered in May 6, 2008

All authors meet criteria for authorship as stated in the COI form, as well as their contributions to the manuscript. All authors’ specific areas of contributions is listed, using categories below: – Study concept and design: Nadège Costa, Hélène Derumeaux, Sophie Guyonnet, Isabelle Carrié, Sandrine Andrieu, Laurent Molinier; – Acquisition of data: Nadège Costa, Hélène Derumeaux, Sophie Guyonnet, Isabelle Carrié, Sandrine Andrieu, Laurent Molinier; – Analysis and interpretation of data: Nadège Costa, Michael Mounié, Arnaud Pagès, Hélène Derumeaux, Nicola Coley, Chrsitelle Cantet, Sandrine Andrieu, Laurent Molinier; – Drafting of the manuscript: Nadège Costa, Michael Mounié, Arnaud Pagès, Hélène Derumeaux, Laurent Molinier; – Critical revision of the manuscript for important intellectual content: Thomas Rapp, Nicola Coley, Sandrine Andrieu.

Ethics approval and consent to participate: The investigating physicians, who verified inclusion and exclusion criteria and obtained written informed consent, recruited all participants. The trial protocol was approved by the French Ethical Committee located in Toulouse (CPP SOOM II) and was authorised by the French Health Authority.

Conflict of interest: Authors report no conflict of interest.

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.









1. International Alzheimer’s Disease. World Alzheimer Report 2019: Attitudes to dementia. Accessed on March 13, 2020.
2. Rapp T, Andrieu S, Chartier F, et al. Resource Use and Cost of Alzheimer’s Disease in France: 18-Month Results from the GERAS Observational. Value in Health 2018;21(3):295-303.
3. Bond M, Rogers G, Peters J, et al. The effectiveness and cost-effectiveness of donepezil, galantamine, rivastigmine and memantine for the treatment of Alzheimer’s disease (review of Technology Appraisal No. 111): a systematic review and economic model. Health Technol Assess Winch Engl 2012;25(1):470.
4. Birks JS, Harvey RJ. Donepezil for dementia due to Alzheimer’s disease. Cochrane Database Syst Rev 2018;6:CD001190.
5. French National Authority for Health (HAS) – Transparency Committee. Reassessment of the Medical Benefit of acetylcholinesterase inhibitors and non-competitive NMDA-receptor antagonist in the treatment of Alzheimer’s disease. s.l. : French National Authority for Health, 2016.
6. Pouryamout L, Dams J, Wasem J, et al. Economic evaluation of treatment options in patients with Alzheimer’s disease: a systematic review of cost-effectiveness analyses. Drugs 2012;72:789-802.
7. Andrieu S, Coley N, Lovestone S, et al. Prevention of sporadic Alzheimer’s disease: lessons learned from clinical trials and future directions. Lancet Neurol 2015;14:926-44.
8. Andrieu S, Guyonnet S, Coley N, et al. Effect of long term omega 3 polyinsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol 2017;16(5):377-389.
9. Handels R, Wimo A. Challenges and recommendations for the health-economic evaluation of primary prevention programmes for dementia. Aging Ment Health 2019;23(1):53-59.
10. Baal PHM van, Hoogendoorn M, Fischer A. Preventing dementia by promoting physical activity and the long-term impact on health and social care expenditures. Prev Med 2016;85:78-83.
11. Lin P-J, Yang Z, Fillit HM, et al. Unintended Benefits: The Potential Economic Impact Of Addressing Risk Factors To Prevent Alzheimer’s Disease. Health Aff (Millwood) 2014;33(4):547-554.
12. Tsiachristas A, Smith AD. B-vitamins are potentially a cost-effective population health strategy to tackle dementia: Too good to be true? Alzheimers Dement Transl Res Clin Interv 2016;2(3):156-161.
13. Zhang Y, Kivipelto M, Solomon A, Wimo A. Cost-Effectiveness of a Health Intervention Program with Risk Reductions for Getting Demented: Results of a Markov Model in a Swedish/Finnish Setting. J Alzheimers Dis 2011 ;26(4) :735-744.
14. Vellas B, Carrie I, Gillette-Guyonnet S, et al. Mapt study: a multidomain approach for preventing Alzheimer’s disease: design and baseline data. J Prevent Alzheimer 2014;(1):13-22.
15. Boudemaghe T, Belhadj I . Data Resource Profile: The French National Uniform Hospital Discharge Data Set Database (PMSI). International Journal of Epidemiology 2017 ;46(2) :392-392d.
16. French Health Insurance (Assurance Maladie). The general nomenclature of professional acts (La nomenclature générale des actes professionnels (NGAP)). Accessed on January 17, 2019.
17. French Health Insurance (Assurance Maladie). Common Classification of Medical Acts (Classification Commune des Actes Médicaux (CCAM)). 3A%221.1%22%3B%7D&add=1.1.13#chapitre_1.1.13.Ameli. Accessed on January 17, 2019.
18. French Health Insurance (Assurance Maladie). National Biology Coding Table (Table Nationale de codage de Biologie). Accessed on January 17, 2019.
19. Bezin J , Duong M, Lassalle R, et al. The national healthcare system claims databases in France, SNIIRAM and EGB: Powerful tools for pharmacoepidemiology. Pharmacoepidemiol Drug Saf 2017;26(8):954-962.
20. Moulis G, Lapeyre-Mestre M, Palmaro A, et al. French health insurance databases: What interest for medical research? Rev Med Interne 2015;36(6):411-417.
21. World Health Organisation (WHO). Collaborating Centre for Drug Statistics Methodology. Anatomical Therapeutic Chemical (ATC) classification system and the Defined Daily Dose (DDD) [Internet]. World Health Organisation (WHO). Accessed on June 28, 2020.
22. European Medicine Agency. Committee for Medicinal Products for Human Use (CHMP). Accessed June 28, 2020.
23. French Health Insurance (Assurance Maladie). Drug database and pricing information (Base des médicaments et informations tarifaires). presentation.php?p_site=AMELI. Accessed on June 10, 2020.
24. Gridchyna I, Aulois-Griot M, Maurain C, Bégaud B. How innovative are pharmaceutical innovations?: the case of medicines financed through add-on payments outside of the French DRG-based hospital payment system. Health Policy 2012;104(1):69-75.
25. Coley N, Gallini A, Ousset JP, et al. Evaluating the Clinical Relevance of a Cognitive Composite Outcome Measure: An Analysis of 1414 Participants From the 5-year GuidAge Alzheimer’s Prevention Trial. Alzheimers Dement 2016;12(12):1216-1225.
26. Burton A, Billingham LJ, Bryan S. Cost-effectiveness in clinical trials: using multiple imputation to deal with incomplete cost data. Clinical Trials 2007;4(2):154-161.
27. Contoyannis P, Jones A, Rice N. The Dynamics of Health in the British Household Panel Survey. Journal of Applied Econometrics 2004;19:473-503.
28. Nixon RM, Thompson SG. Parametric modelling of cost data in medical studies. Statist Med. 2004, Vol. 23, pp. 1311-1331.
29. Briggs AH, Gray AM. Handling uncertainty when performing economic evaluation of healthcare interventions. Health Technol Assess 1999;3(2):1-134.
30. Briggs AH, Wonderling DE, Mooney CZ. Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation. Health Econ 1997;6(4):327-340.
31. Anderson JL, Heidenreich PA, Barnett PG, et al. ACC/AHA statement on cost/value methodology in clinical practice guidelines and performance measures: a report of the American College of Cardiology/American Heart Association Task Force on Performance Measures and Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63(21):2304-2322.
32. Claxton K, Martin S, Soares M, et al. Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Health Technol Assess 2015 ;19(14):1-503.
33. French National Authority for Health (HAS). Reference values for health economic evaluation (Valeurs de réference pour l’évaluation économique en santé). Paris : s.n., 2014. Accessed on July 23, 2020.
34. Fenwick E, Byford S. A guide to cost-effectiveness acceptability curves. Br J Psychiatry 2005;187:106-8.
35. lo Storto C, Goncharuk AG. Efficiency vs Effectiveness: a Benchmarking Study on European Healthcare Systems. Economics and Sociology 2017;10(3):102-115.
36. Gotaro Kojima. Increased healthcare costs associated with frailty among community-dwelling older people: A systematic review and meta-analysis. Arch Gerontol Geriatr 2019:82:103898.
37. Sirven N, Rapp T. The cost of frailty in France. Eur J Health Econ 2017;18(2):243-253.
38. Huter K, Kocot E,Kissimova-Skarbek K,Dubas-Jakóbczyk K, Rothgang H. Economic evaluation of health promotion for older people-methodological problems and challenges. BMC Health Serv Res 2016;16(S5):328.
39. Knapp M, Mangalore R. The trouble with QALYs…. Epidemiol Psichiatr Soc 2007 ;16 : 289-293.
40. Dennis A Revicki. Measuring health outcomes for cost-effectiveness studies: Are all quality adjusted life years created equal? Drug information journal 1995;29:1459-1467.
41. Costa N, Ferlicoq L, Derumeaux-Burel H, Rapp T, Garnault V, Gillette-Guyonnet S, Andrieu S, Vellas B, Lamure M, Grand A, Molinier L. Comparison of informal care time and costs in different age-related dementias: a review. Biomed Res Int. 2013;2013:852368. doi: 10.1155/2013/852368. Epub 2012 Dec 5. PMID: 23509789; PMCID: PMC3591240.
42. Paraponaris A, Davin B. Economics of the Iceberg: Informal Care Provided to French Elderly with Dementia. Value Health 2015;18(4):368-375.
43. Doubilet P, Begg CB, Weinstein M.C, Braun P, McNeil BJ. Probabilistic Sensitivity Analysis Using Monte Carlo Simulation: A Practical Approach. Medical Decision Making 1985;5(2):157-177.
44. Baltussen RM, Hutubessy RC, Evans DB, Murray CJ . Uncertainty in cost-effectiveness analysis. Probabilistic uncertainty analysis and stochastic league tables. Int J Technol Assess Health Care 2002;18(1):112-119.
45. Adalsteinsson E, Toumi M. Benefits of probabilistic sensitivity analysis – a review of NICE decisions. J Mark Access Health Policy 2013;1:10.
46. Claxton K, Sculpher M, McCabe C, et al. Probabilistic sensitivity analysis for NICE technology assessment: Not an optional extra. Health Econ 2005;14(4):339-349.



I. Choi1, H. La Monica1, S.L. Naismith2, A. Rahmanovic2, L. Mowszowski2, N. Glozier1


1. Central Clinical School, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Australia; 2. Charles Perkins Centre, Faculty of Science, School of Psychology and the Brain and Mind Centre, University of Sydney, Australia

Corresponding Author: Dr Isabella Choi, 94 Mallett Street, Camperdown, NSW 2050, Australia,, +612 8627 7240.

J Prev Alz Dis 2021;
Published online June 23, 2021,



Communicating personal Alzheimer’s disease risk profiles based on validated risk algorithms may improve public knowledge about risk reduction, and initiate action. This proof of concept pilot trial aimed to test whether this is feasible and potentially effective and/or harmful. Older at-risk adults (N=24) were provided with their personal Alzheimer’s disease risk profile online, which contained information on their personal risk level, scores and tailored recommendations to manage modifiable risk factors. After receiving the risk profile, participants were significantly more accurate in identifying risk and protective factors, and revised their perceived risk to be lower than their initial estimate. There was no apparent harm seen in psychological distress or dementia-related worry. This shows preliminary support for the feasibility of delivering personal dementia risk profiles to low risk, help-seeking older adults in an online format. A definitive trial examining behavioural outcomes and testing in groups with higher risk profiles is now warranted.

Key words: Risk communication, health literacy, psychological distress, prevention, Alzheimer’s disease.



Up to a third of Alzheimer’s disease cases can be prevented through improved education and reduction of modifiable risk factors (1). Growing evidence from multi-domain interventions shows that targeting modifiable risk factors can reduce risk of Alzheimer’s disease (AD) and improve cognition (2, 3). However, lack of knowledge about dementia and its risk factors among the public is a major barrier to individuals implementing behavioural and lifestyle change and, in turn, to dementia risk reduction (4).
Having an accurate understanding of one’s personal risk of future disease is considered essential for engaging in behaviours for risk reduction. Most health behaviour change models, including the Health Belief Model, identify four major constructs that surround health behaviour: health literacy, perceived susceptibility, motivation to change, and perceived barriers to change (5). However, there is poor health literacy of dementia risk, risk factors, and prevention strategies among the public. A systematic review found that almost half of the general public agreed that dementia was a normal and non-preventable part of ageing (6). Mental activity, healthy diet, physical exercise and social engagement were the most commonly nominated ways to reduce risk, but other well established risk factors such as vascular risk (including smoking, high blood pressure, high cholesterol, obesity), low education, poor mental health, brain trauma, and environmental toxins were rarely mentioned (6-8). Only around 25% of Australians were confident they could reduce risk (8). However, there is support that improving dementia health literacy has a positive impact on risk reduction. People who had a strong belief that dementia risk could be reduced, had moderate to high knowledge of risk-reduction behaviours, or had high confidence that risk reduction can be achieved were almost twice as likely to take action to reduce risk (9).
Communicating personal dementia risk level and risk factors to at-risk adults, based on validated AD risk algorithms, may be one way to improve dementia risk knowledge and engage people in risk reduction behaviours. A number of dementia risk algorithms have been validated for the general population and can identify those with high risk with acceptable predictive ability (10). For instance, the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI) includes various modifiable risk factors that have been validated for middle-age and older adults that are easily assessed via self-report (11, 12). These evidence-based algorithms, along with personalised risk factor feedback and recommendations to reduce risk, can be delivered online for wide access, allowing people to screen for their risk without having had to first consult with a physician. Older adults are able to use technology proficiently and over 90% of help-seeking older adults with some degree of cognitive impairment use the Internet at home, most commonly for emails (13). Such online dementia risk algorithms already exist and are made freely available to the public, for instance, the CAIDE Risk Score App allows users to detect their dementia risk, obtain information on modifiable risk factors, and receive suggestions on how to modify their risk (14).
Surveys have found that there is high interest among older adults in knowing their risk of AD (15, 16) While there may be potential benefits to disclosing risk, there are also concerns that this could cause negative psychological outcomes. For instance, about 30% of older adults who were interested in knowing their risk also actively worried about developing AD (17). Evidence from a systematic review suggests that disclosure of increased AD risk was not associated with anxiety or depression, but did lead to heightened test-related distress, long-term care insurance uptake and health-related behaviour changes (18).
This pilot trial explores the feasibility and acceptability of communicating online personal dementia risk profiles to at-risk older adults and the impact on dementia health literacy, motivation to engage in behaviour change, and potential harmful psychological effects.




This is an uncontrolled proof of concept pilot trial. The study was approved by the University of Sydney Human Research Ethics Committee (protocol number: 2019/669) and registered with the Australian New Zealand Clinical Trials Registry (ANZCTRN12619001242112p).


People attending the Healthy Brain Ageing Clinic, a specialised memory research clinic for people aged 50 years or older in Sydney, Australia, who were clinically diagnosed with mild cognitive impairment (MCI) or subjective cognitive decline (SCD) between October 2019 to May 2020 were recruited. Those who had dementia or pre-existing severe cognitive impairment due to neurological conditions, psychosis, intellectual disability, substance misuse, stroke, or acquired brain injury were excluded.
As part of standard clinic procedure, all clinic attendees completed self-report measures and were assessed by a geriatrician or neurologist, a psychologist, and a neuropsychologist for a review of medical and psychiatric history, mood, and cognitive functioning. Diagnoses were rated according to consensus, including at least two neuropsychologists and one specialist, and were used to exclude those with a dementia diagnosis and other exclusion criteria. Within two weeks of the clinic assessment, attendees received neuropsychological test result feedback over the phone from a neuropsychologist. The clinic does not provide treatment but refers attendees to suitable clinical investigations, e.g. sleep studies, if warranted, as well as clinical trials for which they are deemed eligible.
After receiving neuropsychological test feedback from a neuropsychologist, eligible attendees were invited via a telephone call to participate in the current study. Interested people had to have an email account and were emailed a survey link via REDCap (Research Electronic Data Capture), a web-based research management platform, with the Participant Information Statement and Consent Form. Participants gave consent to extract relevant data collected from their recent clinic assessment to populate their personal dementia risk profile.


Participants completed self-report baseline measures online via REDCap. Participants’ demographic and risk information were extracted from their standard clinic assessment to compile their personal dementia risk profile using the ANU-ADRI (11). Risk factors in the model included: age, gender, highest level and total number of years of education completed, body mass index, diabetes, depression, high cholesterol, traumatic brain injury, smoking status, alcohol intake, social engagement, physical activity, cognitive activity, and diet.
Within two weeks of completing the baseline self-report measures, participants were emailed a pdf document with their personal dementia risk profile. The risk profile contained standard information about dementia, an explanation of their personal dementia risk profile, and information about the ANU-ADRI risk model. Participants were presented with a visual representation of their risk level in the form of a thermometer showing their risk from 0 to 100, along with an explainer “Your risk of developing dementia is low/ moderate/ high. It is estimated that XX out of 100 people with your risk factors will develop dementia in their lifetime” (Figure 1). They were reminded that this is an estimate based on their risk factors rather than a definitive guarantee, and that there are some risk factors they cannot change but some they could potentially work on to reduce their risk. Participants also received a summary of the dementia risk factors included in the risk model and their scores on each risk factor. They were told why the risk factor was important for brain functioning and were given tailored recommendations to manage each risk factor based on their risk factor score, as well as links for more information.
One week after receiving their risk profile, participants received an email asking them to complete the online post-intervention measures in REDCap. After completing all study measures, participants were reimbursed with a $20 gift card in return for their time.

Figure 1. Example of the risk level and risk factor feedback provided in the personal risk profile




Primary outcome: Dementia health literacy

Participants were asked “How likely do you think that you will get Alzheimer’s disease in your lifetime?” to assess perceived risk on a scale, where 0%=certain not to happen and 100%=certain to happen. To examine accuracy of perceived risk, the participant’s perceived risk was subtracted from their ANU-ADRI risk estimate. We recoded the difference (D) into a categorical variable, with <−10% indicating overestimation, >10% indicating underestimation, and accurate if −10% ≤ D ≤ 10%, in accordance with previous studies (19). Similarly, to examine change in perceived risk, participants’ perceived risk at post-intervention was subtracted from their perceived risk at baseline (d), and <-10% indicates increased perceived risk, >10% indicates reduced perceived risk, and −10% ≤ d ≤ 10% indicates no change.
We adapted the dementia risk and protective factors questionnaire in the MijnBreincoach survey (20) to assess for knowledge of dementia risk factors. We included five additional modifiable risk and protective factors that were identified in the ANU-ADRI (11) and the Lancet Commission for dementia prevention (21) (i.e. traumatic brain injury, social activity, sleep, education, and age), totalling 19 risk factors. Additional questions asked about barriers to improving brain health, confidence in risk reduction (8), and worry about getting dementia (7).

Secondary outcome: Motivation to Change Lifestyle and Health Behaviours for Dementia

The Motivation to Change Lifestyle and Health Behaviours for Dementia Risk Reduction (MCLHB-DRR) Scale is designed to assess beliefs and attitudes about lifestyle and health behavioural changes for dementia risk reduction among middle-aged and older adults (22). The scale includes (27) items matched onto seven subscales that reflect the seven concepts of the Health Behaviour Model. All items are rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The scale has moderate to high internal consistencies for the seven subscales, and moderate test-retest reliability (18). Cronbach’s alpha for each of the subscales in this study were: perceived susceptibility (.916), perceived severity (.331), perceived benefits (.715), perceived barriers (.878), cues to action (.656), general health motivation (.638), and self-efficacy (.615).

Secondary outcome: Psychological distress

The K10 is a commonly used screening scale for non-specific psychological distress validated for use in Australia (23). The K10 has also been demonstrated as having moderate sensitivity to symptom change in an Australian sample (24). Scores on the K10 range from 10 to 50, and a score of 30 or more indicates a severe level of distress. Cronbach’s alpha in this study was 0.841.

Secondary outcome: Dementia-related worry

The Dementia Worry Scale was used to assess dementia-related worry (25). It has strong internal consistency and test-retest reliability. It consists of 12 items with scores ranging from 15 to 60. Cronbach’s alpha in this study was 0.908.

User evaluation

We adapted a five-point scale (from 1= not at all to 5= completely) previously used to assess user experience of a dementia information website (26). Participants were asked whether the information provided was engaging and easy to understand as well as how helpful they found the risk profile and how much they felt they had learned (from 1=nothing at all to 5=a great deal). Additionally, participants were asked if they required more information about their dementia risk profile and were given the option to discuss their experience of using the risk profile with a researcher in a telephone interview.

Data analysis

Data was analysed using SPSS version 23.0. Descriptive statistics regarding participant and baseline characteristics were analysed. Fischer’s exact tests and paired samples t-tests were used to test for differences between outcome measures pre- and post- receiving the dementia risk profile. All p-values were two-sided with an alpha of 0.05 to test for significance.



Demographics and baseline characteristics

Overall, 24 eligible participants participated in the trial (Figure 2). Participants’ ages ranged from 53 to 87, with a mean age of 69.54 years (SD 7.69). Over half of the participants (54%) were female and majority spoke English as a first language (83%). Majority were tertiary educated (75%), 21% had completed a trade certificate, and 4% had completed high school. Majority were retired (58%), 29% were employed, and 13% were unemployed. Over half were married or in a de facto relationship (58%), 25% were widowed or divorced, and 17% had never married. The majority (75%) of participants had MCI and 25% had SCD.
Almost all participants (96%; 23/24) were considered to have Low Risk of developing AD (ANU-ADRI score of less than 17), and one participant (4%) was considered as having High Risk (ANU-ADRI score of greater than 27). Participants’ perceived risk of developing AD ranged from 10-99 (M=51.63, SD=23.85), with the majority of participants overestimating their personal risk (87.5%; 21/24).

Figure 2. Participant flow

Pre-post change on dementia health literacy

All participants completed the post-intervention questionnaires. After receiving their personal dementia risk profile, participants’ perceived risk of developing AD ranged from 3-81 (M=38.83, SD=25.02). There was a significant decrease in perceived risk among the group (p = .010). A total of 18 participants (75%) still overestimated their level of risk whereas the remaining 6 correctly identified their level of risk (25%). Eleven participants (45.8%) reported a reduction in their perceived risk, eleven (45.8%) reported no change, and two participants’ (8.3%) perceived risk had increased.
The average number of correctly identified risk and protective factors increased from a mean of 11.42 items (SD= 4.50) at baseline to 13.96 items (SD= 3.98) (t1,23= -3.839, p=.001) at follow-up.

Pre-post change on motivation to engage in behaviour change and psychological effects

There was a significant reduction on the perceived susceptibility subscale of the MCLHB (t1,23=4.416, p<.001) from baseline (M=12.86; SD=3.30) to 1-week follow up (M=10.29; SD=4.21), but no change on the other subscales. There was no change on the K10 or Dementia Worry Scale.
Participants’ self-reported worry about getting dementia was significantly reduced at follow-up, from 2.75 (SD=.85) to 2.29 (SD=.91) (t1,23= 3.412, p= .002). The most common barriers to reducing risk factors at baseline were lack of knowledge (45.8%; 11/24), followed by health problems (25%; 6/24). At 1-week follow up, the most common barriers were lack of motivation (29.2%; 7/24), health problems (29.2%; 7/24), and difficulty with organisation (25%; 6/24). There was no change in confidence to take action to change risk.

User evaluation

Overall, 70.9% (17/24) of participants agreed that the information in the personal risk profile was engaging, 79.2% (19/24) agreed the information was easy to understand, 79.2% (19/24) agreed it was helpful, and 79.2% (19/24) reported they learned a good deal from their personal risk profile.
Two participants participated in the optional telephone interview with a researcher. Both expressed surprise at their lower than expected AD risk feedback, and identified difficulty addressing some of their risk factors (e.g. getting motivated to exercise). One participant agreed that seeking guidance from a health professional may support them to work on their risk factors.



This pilot study aimed to explore the feasibility, acceptability and potential impact of providing an online personal dementia risk profile to help-seeking older adults at risk of developing AD on risk knowledge, motivation to change health-related behaviours, and psychological effects. To our knowledge, this is the first study focusing on the effects of communicating personal risk profile using risk algorithms based on epidemiological risk factors. Communication of the personal dementia risk profile led to more accurate knowledge of AD risk factors and improved understanding of perceived susceptibility among patients with MCI and SCD. Importantly, there was no negative effect of communicating the personal risk profile online on psychological distress or dementia-related worry among our participants. Participants mostly had a low risk of developing AD, but still reported reduced worry about getting dementia after receiving their risk profile. These findings support the feasibility and acceptability of using dementia risk algorithms to deliver personal risk profiles to low risk older adults in an online format, and indicate that providing this information can improve AD health literacy without a negative impact on psychological wellbeing.
However, there was little evidence in this study that providing personal risk profiles as a standalone intervention was sufficient to motivate change in behaviours to address AD risk factors. Although providing the dementia risk profile addressed one main barrier for risk reduction at baseline (i.e. lack of knowledge of dementia risk factors), participants reported that lack of motivation, health problems, and difficulty with organisation became the main barriers after receiving their risk profile. This suggests that older adults need extra support to effect behavioural change. The personal dementia risk profile could potentially be used as part of a collaborative, shared decision-making approach to address these barriers by guiding and engaging users, carers and clinicians to choose several high impact or easy-to-change risk factors to focus on, and by providing feedback on the change in risk level if risk factors are modified. Trials are underway to test the impact of a tailor-made online lifestyle programme targeting modifiable risk factors on risk score and health behaviours and compliance to health advice (27). There may also be a role for clinicians to follow up with specific guidance on addressing health problems and to assist the older adult to develop a personalised risk reduction plan. A recent rapid review on approaches to healthy ageing interventions for older adults demonstrated that optimal interventions are those that incorporate collaborative approaches with shared decision-making and behavioural change techniques (28). In this regard, the personal dementia risk profile represents a useful tool that clinicians can draw on to present evidence-based, tailored, health and risk information, which in turn can stimulate a collaborative decision-making process around which health/lifestyle factors to target and how best to achieve long-lasting behaviour change.
An interesting finding was that most participants overestimated their risk of developing AD, even after receiving their personal risk profile. This is possibly reflective of our cohort which was composed of help-seeking older adults seeking an evaluation at a memory clinic and were concerned with developing dementia. The continuing high levels of perceived risk at follow up is unsurprising given that previous research has found that even among individuals who accurately recalled their communicated AD risk, over 50% did not fully adjust their perceived risk to match the communicated risk, and that high baseline AD risk perception was the strongest predictor of overestimation of risk (29). It is also possible that participants may not have readily accepted the communicated risk after receiving lower than expected risk feedback, as seen by interview participants expressing surprise at their communicated risk.
Nonetheless, this has implications for supporting clinicians to communicate AD risk information to people with MCI. A survey found that 90% of neurologists said they counselled MCI patients on their risk of AD in general terms but only 60% communicated AD risk in numeric terms (30). Our findings provide preliminary support that patients with MCI or SCD understood numeric AD risk information and risk factors even when it is communicated online without support, and that the multifaceted approach with a clear visual representation and accompanying explanatory text may have facilitated understanding. Clinicians are encouraged to discuss numeric risk estimates with patients with visual aids, explain how these are estimated from risk models, and explore reasons for discord to improve risk appraisal.
There are several important considerations in interpreting the findings. The study sample was a well-educated inner-city cohort who have expressed concern about their memory and were highly motivated to seek help. Participants already knew that they did not have dementia. Their relatively positive reactions to their personal risk profile may reflect their personal interest in managing their brain health or because they were reassured of having low risk. It is unclear whether a general population or primary care sample, who had subjective memory concerns and not assessed for AD, would have similar reactions. It should be noted that a number of older adults approached to take part in the trial declined due to not wanting to know their risk or because they felt overwhelmed. Further research is needed to explore these concerns about knowing one’s personal risk.
The majority of our sample had low risk of developing AD, and it is unknown how moderate or high risk adults would respond to their personal dementia risk profile. There is some indication that high risk individuals, such as those who screen positive to genetic biomarkers, have heightened test-related distress (18). In order for dementia risk profiles to be widely and safely distributed to older adults in public health programs, particularly if they are to be delivered online in the absence of immediate clinical support, it is important to understand how moderate or high risk older adults react to their personal dementia risk profile and to monitor any potential adverse reactions. Finally, this was a pilot trial with a small sample size and short-term follow up. Longer-term follow up and randomised controlled trials to examine effects of communication of personal risk of developing AD are required.
The application of dementia risk algorithms to identify those at risk and to promote and encourage risk reduction behaviour is still in its early stages. This study provides preliminary support for the utility of using risk models that incorporate accessible and potentially modifiable risk factors to communicate personal dementia risk profiles to at-risk older adults.


Acknowledgements: The authors would like to acknowledge Professor Kaarin J. Anstey and Dr Sarang Kim for permission to use the ANU Alzheimer’s Disease Risk Index and their advice on adapting it to the Healthy Brain Ageing clinic measures. We thank the participants who have helped make this research possible.

Funding: This study was supported by a Dementia Australia Research Foundation project grant award. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Ethical standards: The study was approved by the University of Sydney Human Research Ethics Committee (protocol number: 2019/669) and registered with the Australian New Zealand Clinical Trials Registry (ANZCTRN12619001242112p).

Conflict of interest: The authors confirm that there are no known conflicts of interest associated with this publication.



1. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. The Lancet Neurology. 2014;13(8):788-794
2. Isaacson RS, Hristov H, Saif N, et al. Individualized clinical management of patients at risk for Alzheimer’s dementia. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2019;15(12):1588-1602
3. Ngandu T, Lehtisalo J, Solomon A, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. The Lancet. 2015;385(9984):2255-2263 S0140-6736(15)60461-5.
4. Kim S, Sargent-Cox KA, Anstey KJ. A qualitative study of older and middle-aged adults’ perception and attitudes towards dementia and dementia risk reduction. J Adv Nurs. 2015;71(7):1694-1703
5. Janz NK, Becker MH. The Health Belief Model: A Decade Later. Health Educ Q. 1984;11:1-47
6. Cations M, Radisic G, Crotty M, Laver KE. What does the general public understand about prevention and treatment of dementia? A systematic review of population-based surveys. PLoS One. 2018;13(4):e0196085
7. Low L-F, Anstey KJ. Dementia literacy: Recognition and beliefs on dementia of the Australian public. Alzheimer’s & Dementia. 2009;5(1):43-49
8. Smith BJ, Ali S, Quach H. Public knowledge and beliefs about dementia risk reduction: a national survey of Australians. BMC Public Health. 2014;14(1):661
9. Smith BJ, Ali S, Quach H. The motivation and actions of Australians concerning brain health and dementia risk reduction. Health Promot J Austr. 2015;26(2):115-121
10. Hou X-H, Feng L, Zhang C, Cao X-P, Tan L, Yu J-T. Models for predicting risk of dementia: a systematic review. J Neurol Neurosurg Psychiatry. 2018:jnnp-2018-318212
11. Anstey KJ, Cherbuin N, Herath PM. Development of a New Method for Assessing Global Risk of Alzheimer’s Disease for Use in Population Health Approaches to Prevention. Prevention Science. 2013;14(4):411-421
12. Anstey KJ, Cherbuin N, Herath PM, et al. A Self-Report Risk Index to Predict Occurrence of Dementia in Three Independent Cohorts of Older Adults: The ANU-ADRI. PLoS One. 2014;9(1):e86141
13. LaMonica HM, English A, Hickie IB, et al. Examining Internet and eHealth Practices and Preferences: Survey Study of Australian Older Adults With Subjective Memory Complaints, Mild Cognitive Impairment, or Dementia. J Med Internet Res. 2017;19(10):e358
14. Sindi S, Calov E, Fokkens J, et al. The CAIDE Dementia Risk Score App: The development of an evidence-based mobile application to predict the risk of dementia. Alzheimer’s & Dementia : Diagnosis, Assessment & Disease Monitoring. 2015;1(3):328-333
15. Sheffrin M, Stijacic Cenzer I, Steinman MA. Desire for predictive testing for Alzheimer’s disease and impact on advance care planning: a cross-sectional study. Alzheimers Res Ther. 2016;8(1):55.
16. Wikler EM, Blendon RJ, Benson JM. Would you want to know? Public attitudes on early diagnostic testing for Alzheimer’s disease. Alzheimers Res Ther. 2013;5(5):43
17. Roberts JS, McLaughlin SJ, Connell CM. Public beliefs and knowledge about risk and protective factors for Alzheimer’s disease. Alzheimer’s & Dementia. 2014;10(5):S381-S389
18. Bemelmans SASA, Tromp K, Bunnik EM, et al. Psychological, behavioral and social effects of disclosing Alzheimer’s disease biomarkers to research participants: a systematic review. Alzheimers Res Ther. 2016;8(1):46
19. Rimer BK, Halabi S, Sugg Skinner C, et al. Effects of a mammography decision-making intervention at 12 and 24 months. Am J Prev Med. 2002;22:247-257
20. Heger I, Deckers K, van Boxtel M, et al. Dementia awareness and risk perception in middle-aged and older individuals: baseline results of the MijnBreincoach survey on the association between lifestyle and brain health. BMC Public Health. 2019;19(1):678
21. Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. The Lancet. 2017;390(10113):2673-2734
22. Kim S, Sargent-Cox K, Cherbuin N, Anstey KJ. Development of the motivation to change lifestyle and health Behaviours for Dementia Risk Reduction Scale. Dement Geriatr Cogn Dis Extra. 2014;4(2):172-183
23. Kessler R, Andrews G, Colpe L, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(06):959-976
24. Perini SJ, Slade T, Andrews G. Generic effectiveness measures: Sensitivity to symptom change in anxiety disorders. J Affect Disord. 2006;90:123-130
25. Kinzer A, Suhr JA. Dementia worry and its relationship to dementia exposure, psychological factors, and subjective memory concerns. Applied Neuropsychology: Adult. 2016;23(3):196-204
26. Farrow M. User perceptions of a dementia risk reduction website and its promotion of behavior change. JMIR research protocols. 2013;2(1):e15-e15
27. Vrijsen J, Abu-Hanna A, Maeckelberghe ELM, et al. Uptake and effectiveness of a tailor-made online lifestyle programme targeting modifiable risk factors for dementia among middle-aged descendants of people with recently diagnosed dementia: study protocol of a cluster randomised controlled trial (Demin study). BMJ Open. 2020;10(10):e039439
28. Owusu-Addo E, Ofori-Asenso R, Batchelor F, Mahtani K, Brijnath B. Effective implementation approaches for healthy ageing interventions for older people: A rapid review. Arch Gerontol Geriatr. 2021;92:104263
29. Linnenbringer E, Roberts JS, Hiraki S, Cupples LA, Green RC. “I know what you told me, but this is what I think:” Perceived risk of Alzheimer disease among individuals who accurately recall their genetics-based risk estimate. Genet Med. 2010;12(4):219-227
30. Roberts JS, Karlawish JH, Uhlmann WR, Petersen RC, Green RC. Mild cognitive impairment in clinical care: A survey of American Academy of Neurology members. Neurology. 2010;75(5):425-431



S. Walter1, O.G. Langford1, T.B. Clanton1, G.A. Jimenez-Maggiora1, R. Raman1, M.S. Rafii1, E.J. Shaffer1, R.A. Sperling2, J.L. Cummings3, P.S. Aisen1 and the TRC-PAD Investigators*


1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Department of Brain Health, School of Integrated Health Sciences, University of Las Vegas, Nevada; Cleveland Clinic Lou Ruvo Center for Brain Health, USA; * TRC-PAD investigators are listed at

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

J Prev Alz Dis 2020;4(7):234-241
Published online August 13, 2020,


BACKGROUND: The Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s disease (TRC-PAD) aims to accelerate enrollment for Alzheimer’s disease (AD) clinical trials by remotely identifying and tracking individuals who are at high risk for developing symptoms of AD, and referring these individuals to in-person cognitive and biomarker evaluation with the purpose of engaging them in clinical trials. A risk algorithm using statistical modeling to predict brain amyloidosis will be refined as TRC-PAD advances with a maturing data set.
Objectives: To provide a summary of the steps taken to build this Trial-Ready cohort (TRC) and share results of the first 3 years of enrollment into the program.
Design: Participants are remotely enrolled in the Alzheimer Prevention Trials (APT) Webstudy with quarterly assessments, and through an algorithm identified as potentially at high risk, referred to clinical sites for biomarker confirmation, and enrolled into the TRC.
Setting: Both an online study and in-clinic non-interventional cohort study.
Participants: APT Webstudy participants are aged 50 or older, with an interest in participation in AD therapeutic trials. TRC participants must have a study partner, stable medical condition, and elevated brain amyloid, as measured by amyloid positron emission tomography or cerebrospinal fluid analysis. Additional risk assessments include apolipoprotein E genotyping.
Measurements: In the APT Webstudy, participants complete the Cognitive Function Index and Cogstate Brief Battery. The TRC includes the Preclinical Alzheimer’s Cognitive Composite, comprised of the Free and Cued Selective Reminding Test, the Delayed Paragraph Recall score on the Logical Memory IIa test from the Wechsler Memory Scale, the Digit-Symbol Substitution test from the Wechsler Adult Intelligence Scale-Revised, and the Mini Mental State Examination total score (1).
Results: During the first 3 years of this program, the APT Webstudy has 30,650 consented participants, with 23 sites approved for in person screening, 112 participants have been referred for in-clinic screening visits with eighteen enrolled to the TRC. The majority of participants consented to APT Webstudy have a family history of AD (62%), identify as Caucasian (92.5%), have over twelve years of formal education (85%), and are women (73%). Follow up rates for the first quarterly assessment were 38.2% with 29.5% completing the follow up Cogstate Battery.
Conclusions: After successfully designing and implementing this program, the study team’s priority is to improve diversity of participants both in the APT Webstudy and TRC, to continue enrollment into the TRC to our target of 2,000, and to improve longitudinal retention, while beginning the process of referring TRC participants into clinical trials.

Key words: Alzheimer’s disease, prevention, webstudy, remote study.



The Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s disease (TRC-PAD) program aims to accelerate enrollment into clinical trials for AD by building a cohort of biomarker-confirmed eligible participants. The first stage of the program is remote recruitment of participants to the Alzheimer Prevention Trials (APT) Webstudy (2). Participants are followed with quarterly assessments, and through an algorithm identified as potentially at high risk, and referred to clinical sites. Participants are screened for Trial-ready cohort (TRC) eligibility, involving cognitive testing, genotyping and amyloid biomarker measures, and then if eligible, enrolled and followed longitudinally until an appropriate clinical trial becomes available. Separate papers summarize the program design and implementation considerations (3), the complex informatics infrastructure (4), the algorithm to predict brain amyloidosis and risk for AD (5), and recruitment strategies (2). Here we expand on the experience of the TRC-PAD program during its initial three years.

Study network and infrastructure

The TRC-PAD program is the result of extensive collaboration between multiple principal investigators (PIs), online National Registries, the Coordinating Center, and the network of clinical trial sites. Registries that referred participants to the APT Webstudy were the Alzheimer’s Prevention Registry (APR), The Alzheimer’s Association TrialMatch, Brain Health Registry (BHR), and Healthy Brains, as well as registries managed by clinical trial sites. The study was coordinated by the Alzheimer’s Therapeutic Research Institute (ATRI) at the University of Southern California (USC). Periodic updates were provided to the clinical trial sites involved in the program during the development phase. A small group of “vanguard sites” were selected first, with their study teams providing feedback on the referral process, before expanding to the total sites. Each clinical site participating in the TRC receives modest financial support for local recruitment and referral efforts, separate from their reimbursement for TRC participant visits. In 2019, the TRC-PAD program became affiliated with the Alzheimer’s Clinical Trials Consortium (ACTC) with scientific guidance provided by the ACTC Steering Committee.

Regulatory oversight

The APT Webstudy is overseen by the University of Southern California (USC) Institutional Review Board (IRB), which reviews and approves all participant-facing content, including the informed consent documents, web pages, emails, newsletters, and quarterly testing reminders. The IRB provided initial approval for the APT Webstudy in November 2017 and the Webstudy launched four weeks later (Figure 1). IRBs overseeing Registries also reviewed recruitment materials. The protocol describing in-person visits, screening, and enrollment in the TRC is overseen by Advarra IRB, the central IRB. In some cases, the local IRBs that oversee the clinical trial sites also required review of materials.

APT Webstudy Participant support

Support is provided in-house by the APT Webstudy team at the USC Alzheimer’s Therapeutic Research Institute (ATRI). Participants may telephone or email the study team with their questions. Using a ticketing and tagging system, each issue is tracked centrally, which allows the support team to identify patterns and trends. Questions are triaged to subject matter experts when needed; for example, to a clinician or technical team member. Issues are reviewed centrally at regular intervals and used to improve the website and study communications.

Retention tools

Retention of study participants and capturing longitudinal assessments, particularly cognitive testing, are critical to the program aims. The APT Webstudy team developed a participant engagement platform to optimize the Webstudy experience. Each participant is provided results of their clinical and cognitive testing over the course of the study. Reminder emails alert participants when the next quarterly assessment is due. In addition, a quarterly newsletter called “Alzheimer’s Research Today” is emailed to all participants, including updates from the field of AD research, describing upcoming studies, and providing information on new features of the Webstudy.

APT Webstudy experience

In order to register for the APT Webstudy, participants are asked to log in using either their existing social login credentials, or to create an account by providing a username, email address and password. Once logged on, participants are considered ‘registered.’ The Webstudy is designed as a ‘walk through’ experience, with each new section opening after completion of the former section.

Step 1

Personal profile. Participants provide basic information including age, race and ethnicity, education, zip code, whether they have been diagnosed with Alzheimer’s disease, and whether they are interested in participating in future AD clinical trials and are willing to share information with clinical sites near them.

Step 2

Consent. Each participant is asked to indicate whether they agree to participate or do not agree to participate. The consent form is displayed online and may be downloaded. Consent is required to move forward and may be revoked at any time.

Step 3

Lifestyle. Participants are asked brief questions about diet and lifestyle. Questions about prior genetic and amyloid testing were added in January 2019, 12 months after the APT Webstudy launched. Participants enrolled prior to this question being included are prompted to respond to these questions the next time they sign on.

Step 4

Remote Cognitive and Functional Assessments. The Cognitive Function Instrument (CFI) is a 15-item participant-reported questionnaire (6, 7). This assessment captures the participant’s perceived ability to perform high level functional tasks in daily life, as well as their sense of overall cognitive functional ability. The participant self-reported CFI has been validated in prior studies to provide early indication of future cognitive decline (7). The Cogstate Brief Battery (CBB), comprised of four simple playing card tasks measuring psychomotor speed and recent memory (8), is used to assess cognition and memory function. The One-Card Learning Test has shown particular sensitivity to amyloid-related decline in preclinical and prodromal AD (9).

Step 5: Review Scores

After completing the remote assessments, the participant can review their CFI scores in a ‘Dashboard’ view. There is a description below the score of the test, explaining what the scores might mean, (e.g. “An increasing score over time might mean cognitive decline”). CBB scores are processed within 2-5 days, and participants are notified by email when scores are available. The website description of the CBB emphasizes that the tool is used for research, and that a change in score between -10 to +10 is considered normal. After completion, the cognitive test questions are no longer available to the participant, and the next available testing date is displayed (3 months from previous test date).

Clinical Site Referrals

Data from the APT Webstudy are evaluated monthly using an adaptive algorithm. This algorithm uses statistical models to assess each participant’s risk of AD amyloidosis (5). In order to be referred, participants must have consented to the APT Webstudy, agreed to share information with researchers, and provided a valid zip code. Participants are ranked by their predicted risk, and those with the highest risk are referred to the nearest TRC-PAD site based on their zip code. Site referrals are provided via a secure web-based tool, the Site Referral System (SRS), with the flow of participants customized to meet the site’s capacity. Site staff reach out to participants using their preferred method of contact, conduct prescreening, and if the participants are interested and appears to be eligible, invites them for an in-person screening visit to confirm eligibility for the Trial-Ready Cohort (TRC).

Trial Ready Cohort (TRC)

The eligibility criteria for TRC-PAD broadly encompass both current and upcoming clinical trials in prodromal and preclinical AD, with the aim of enrolling 2,000 participants; approximately 1,000 preclinical and 1,000 prodromal. Screening is conducted in multiple phases, first confirming clinical and cognitive eligibility and performing apolipoprotein E (APOE) genetic testing. Using this additional information, the participant’s risk assessment is updated and reviewed centrally before screening proceeds to amyloid testing, either by positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) collection by lumbar puncture. Following procedures that were designed and refined for the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study (10, 11), participants are told whether they are eligible for the TRC. A 21 CFR Part 11 compliant electronic data capture system was developed by the TRC-PAD study team to manage participant data (4). Broad data-sharing in the TRC consent allow the data to be potentially used as run-in data for downstream clinical trials, minimizing participant burden. Once enrolled in the TRC, participants are followed with clinical and cognitive assessments every 6 months until a clinical trial becomes available at their site. The decision to screen for a clinical trial is entirely that of the participants, with appropriate guidance from their clinician. The protocol is designed to allow participants to re-enter the cohort after participation in another study or a break for any other reason.



APT Webstudy Enrollment

The first major increase in APT Webstudy enrollment followed an article in a San Diego newspaper in February 2018, which resulted in over 2,000 consented participants. Gradually other recruitment initiatives were rolled out, resulting in 10,000 participants in January 2019, doubling to 20,000 participants in August 2019. As of the data cut for this manuscript (April 20, 2020) there are 30,650 participants consented to the APT Webstudy. More details on APT Webstudy recruitment methods and metrics are described in another paper in this series (2).

Table 1. APT and TRC Demographics (April 20, 2020)


APT Webstudy Demographics

Participants consenting to the APT Webstudy range in age from 17 to 94 with the mean age of 64.5, and 98.8% of participants are over the age of 50. A majority (73.0%) of Webstudy participants are female. 62.2% have a parent or sibling diagnosed with AD, and 4.6% report a diagnosis of AD. 85% have post-secondary education, with 14% reporting high school or equivalent education. Participants are 92.5% Caucasian, with 2.3% Hispanic/Latino, 1.5% African American, 1.4% Asian, and 0.2% American Indian and 0.1% Pacific Islander. 53.2% of participants are retired or not working, 30.6% are working full time, and 14.7% part-time. Within the US, geographic distribution of participants is broad, with participants in 59.1% of US counties (2). About half of APT Webstudy participants report no medical concerns, with the other half most commonly reporting high blood pressure (30%), diabetes (8%), or vascular disease (4%). In terms of lifestyle, 74% exercise 1 or more hours per week, and 81% do not drink alcohol regularly (defined as 2 drinks per day or more). Most participants prefer being contacted by email (78.7%) over a phone call (3.1%).

TRC Enrollment

The first referral from SRS to a TRC-PAD Site was in July 2019 with the first screening visit conducted one month later (Figure 1). As additional sites were approved to enroll, screening activity increased to 20 screens per month in late 2019 and into early 2020. As of April 20, 2020, 1,178 participants have been referred to SRS, and 171 (14%) (Figure 2) have been subsequently referred to be screened for TRC. 112 TRC screening visits have been conducted at 9 Sites, with 54 TRC participants completing the amyloid testing, resulting in 25 participants eligible for enrollment into the TRC. 18 participants have completed a baseline visit (Figure 2).

Figure 1. TRC-PAD Program Timeline

igure 2. TRC-PAD Program Funnel


TRC Demographics

Of the 112 participants with an in-person screening visit, participants are aged 60-79, (mean 71.1 SD 10.6), 49.5% are women, and 93% identify as Caucasian.

APT Webstudy Retention and Drop-outs

Participants were most likely to drop from the Webstudy at the point of consent, with 3,307 (9.7%) registering for the Webstudy but not completing the consent and 8,850 (28.9% of consented participants) not completing the initial CBB. Based on feedback from participants through the user support desk, the missed cognitive assessments are due to technical challenges and lack of compatibility of the CBB with smart phones. Retention is a challenge in the Webstudy with only 10,393 (38.2%) returning for their 2nd visit and 7,220 (28.8%) returning for visit 3. 538 participants have completed up to 8 follow up visits. The CBB retention has been lower, with 8,025 (29.5%) completing testing for the 2nd visit, and 5,777 (23%) for the 3rd visit. 461 participants have completed the CBB for up to the 8th follow up visit.

User Support

Since launching the APT Webstudy, over 1,900 inquiries have been received from users, with a majority (78%) received by email. The most frequent reason for support (38%) is regarding the Cogstate testing. 19% of support requests are related to logging into the Webstudy, 7% are questions related to the scores for CFI or Cogstate, 7% are for non Cogstate-related technical support, and the remainder are miscellaneous support needs. Most inquiries require more than one response and took more than 2 days to resolve. Phone inquiries require an average of 20 minutes of staff time to resolve.

Self-report of prior testing

13.03% of the APT Webstudy participants report undergoing prior APOE testing. Of these 3,989 participants with prior testing, 28% report not carrying the APOE-4 risk gene, 33% report one copy of the APOE-4 allele, and 9% reported having 2 copies. 5% reported that they carry the risk gene but do not know the details, and 23% didn’t know the results. In contrast, only 4.03% of participants had prior amyloid testing, with 2.86% having a prior PET Scan, and 1.17% a prior lumbar puncture.

APT clinical and cognitive assessment

Nearly every participant that signed consent completed the CFI (97%), with a majority scoring in normal ranges (Figure 3). 65% of Webstudy participants completed the initial Cogstate testing (Figure 4).

Figure 3. APT Webstudy Cognitive Function Instrument (CFI)

Figure 4. APT Webstudy Cogstate One card learning



We have demonstrated that it is feasible to build a cohort of remotely-consented and enrolled participants with normal cognition, with broad geographic distribution using an unsupervised cognitive assessment battery to evaluate for increased risk for future cognitive decline. This first stage of the TRC-PAD program represents the best in what collaborative science can achieve. The partnership between the PI’s, an experienced Coordinating Center, the network of sites, academic partners, and the valuable experience and advice of investigators overseeing the APR, TrialMatch, HealthyBrains, and BHR have been critical to this success.
In general, the group of individuals enrolled in the APT Webstudy are similar to those enrolled in clinical trials, with most being highly educated and Caucasian, and a majority reporting a family history of AD. We were intentional in designing the APT assessments to be as brief as possible, and believe that low drop-out rates during initial visit is due to this. The most commonly reported problem leading to missing information on the CBB was incompatibility with smart phones; we expect that compatibility will be improved in the future.
Retention to the APT Webstudy is comparable to what has been reported by online Registry studies (12) and remains a significant challenge. Capturing longitudinal information is an important goal of TRC-PAD. More work is needed to understand why participants are not returning, in order to improve content, language, and presentation.
The APT Webstudy and TRC have both recruited a mostly white and highly educated group, which limits the representativeness of clinical trial participants using this program to the general population. We hope to improve accessibility of the APT Webstudy with the recently released Spanish translation and Spanish-language user support.
Providing consistent and knowledgeable user support for a remote Webstudy has been critical to success. We have found great value in using a centralized ticketing system, which consolidates multiple communication channels (e.g. email, telephone) and allows the study team to identify trends and prioritize development and refinement of procedures.
Ultimately, the success of TRC-PAD will be measured by efficient referral of representative participants from TRC-PAD into clinical trials. Can we predict brain amyloid elevation using Webstudy data augmented by in-person assessment, APOE genotyping and eventually plasma amyloid peptide testing (3) to reduce screening amyloid PET expenses? Can we reduce the long recruitment and screening timelines seen in studies like A4 and early symptomatic-stage AD trials? Can we minimize participant and site burden through efficient design and data-sharing between TRC-PAD and clinical trials? How do we enroll an inclusive group of individuals who are representative of the population at greatest risk for cognitive decline due to AD? TRC-PAD remains a work in progress. Continuing adjustments to its design are essential to optimizing its value.


Funding: The study was supported by a grant from NIA/NIH (R01AG053798). 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.

Acknowledgments: We would like to acknowledge and thank our participants, the teams at each of the clinical sites, and the USC Alzheimer’s Therapeutic Research Institute (ATRI) Coordinating Center team members whose work made this study possible. In particular, Devon Gessert, Yuliana Cabrera, Emily Voeller, Stefani Bruschi, Jia-Shing So, Marian Wong, Rosio Gonzalez-Beristain, and Godfrey Coker. A full list of TRC-PAD investigators is at:

Ethical standard: Institutional Review Boards (IRBs) approved these studies, and all participants gave informed consent before participating.

Conflict of interest: The authors report grants from National Institute on Aging, during the conduct of the study. None of the authors have additional financial interests, relationships or affiliations relevant to the subject of this manuscript.

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.



1. Mormino EC, Papp KV, Rentz DM, et al. Early and late change on the preclinical Alzheimer’s cognitive composite in clinically normal older individuals with elevated amyloid β. Alzheimers Dement 2017;13(9):1004-1012
2. Walter S, Clanton TB, Langford OG, Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD). J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.46
3. Aisen PS, Sperling R., Cummings J, et al. The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) Project: An Overview. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.45
4. Jimenez-Maggiora GA , Bruschi S., Raman R, et al. TRC-PAD: Accelerating Recruitment of AD Clinical Trials through Innovative Information Technology. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.48
5. Langford O, Raman R, Sperling RA, et al. Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.44
6. Walsh SP, Raman R, Jones KB, Aisen PS. ADCS Prevention Instrument Project: The Mail-In Cognitive Function Screening Instrument (MCFSI). Alzheimer Dis Assoc Disord 2006;20(4 Suppl 3):S170-8
7. Amariglio RE, Donohue MC, Marshall GA, et al. Tracking Early Decline in Cognitive Function in Older Individuals at Risk for Alzheimer Disease Dementia. JAMA Neurol 2015;72(4):446-454
8. Crook TH, Kay GG, Larrabee GJ, et al. Computer-based cognitive testing. Neuropsychol assess of Neuropsychiatr. and Neuromedical Disord 2009:84-100
9. Darby DG, Brodtmann A, Pietrzak RH, et al. Episodic Memory Decline Predicts Cortical Amyloid Status in Community-Dwelling Older Adults. J Alzheimers Dis 2011;27(3):627-637
10. Sperling RA, Rentz, DM, Johnson KA, et al. The A4 Study: Stopping AD before Symptoms Begin? Sci Transl Med 2014 Mar 19; 6(228): 228fs13
11. Sperling, RA, Donohue, MC, Raman, R, Sun, et al. Association of Factors with Elevated Amyloid burden in Clinically Normal Older Individuals. JAMA Neurol 2020 Apr 6;e200387. Doi: 10.1001/jamaneurol.2020.0387
12. Weiner MW, Nosheny R, Camacho M, et al. The Brain Health Registry: An internet-based platform for recruitment, assessment, and longitudinal monitoring of participants for neuroscience studies. Alzheimers Dement 2018;14(8):1063-1076

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail


S. Walter1, T.B. Clanton1, O.G. Langford1, M.S. Rafii1, E.J. Shaffer1, J.D. Grill3, G.A. Jimenez-Maggiora1, R.A. Sperling2, J.L. Cummings4, P.S. Aisen1 and the TRC-PAD Investigators*

1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Institute for Memory Impairments and Neurological Disorders, University of California, Irvine;
4. Department of Brain Health, School of Integrated Health Sciences, University of Las Vegas, Nevada; Cleveland Clinic Lou Ruvo Center for Brain Health, USA;* TRC-PAD Investigators are listed at

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

J Prev Alz Dis 2020;4(7):219-225
Published online August 11, 2020,



BACKGROUND: The Alzheimer Prevention Trials (APT) Webstudy is the first stage in establishing a Trial-ready Cohort for Preclinical and Prodromal Alzheimer’s disease (TRC-PAD). This paper describes recruitment approaches for the APT Webstudy.
Objectives: To remotely enroll a cohort of individuals into a web-based longitudinal observational study. Participants are followed quarterly with brief cognitive and functional assessments, and referred to Sites for in-clinic testing and biomarker confirmation prior to enrolling in the Trial-ready Cohort (TRC).
Design: Participants are referred to the APT Webstudy from existing registries of individuals interested in brain health and Alzheimer’s disease research, as well as through central and site recruitment efforts. The study team utilizes Urchin Tracking Modules (UTM) codes to better understand the impact of electronic recruitment methods.
Setting: A remotely enrolled online study.
Participants: Volunteers who are at least 50 years old and interested in Alzheimer’s research.
Measurements: Demographics and recruitment source of participant where measured by UTM.
Results: 30,650 participants consented to the APT Webstudy as of April 2020, with 69.7% resulting from referrals from online registries. Emails sent by the registry to participants were the most effective means of recruitment. Participants are distributed across the US, and the demographics of the APT Webstudy reflect the referral registries, with 73.1% female, 85.0% highly educated, and 92.5% Caucasian.
Conclusions: We have demonstrated the feasibility of enrolling a remote web-based study utilizing existing registries as a primary referral source. The next priority of the study team is to engage in recruitment initiatives that will improve the diversity of the cohort, towards the goal of clinical trials that better represent the US population.

Key words: Trial-ready cohort, online registry, remote recruitment, web-based, preclinical, Alzheimer’s disease, prevention.



Identifying eligible participants for early intervention Alzheimer’s disease (AD) clinical trials continues to be a significant challenge in the field (1, 2). The overarching aim of the Trial-Ready Cohort in Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD) program is to accelerate enrollment for early stage AD clinical trials (3). This will be accomplished by identifying and screening participants to confirm eligibility for these trials, including amyloid biomarker confirmation, and then monitoring and maintaining engagement with these participants through longitudinal visits until an appropriate trial is available. The considerations behind the design of TRC-PAD are described by Aisen et al. (4). The first step in establishing the Trial-ready Cohort (TRC) was to recruit participants into the Alzheimer Prevention Trials (APT) Webstudy, an online assessment tool designed to serve as a feeder to the in-person TRC-PAD cohort. We projected the APT Webstudy would require between 25,000 and 50,000 participants, with at least 20% participants from under-represented communities, in order to identify enough eligible participants for a planned TRC of n=2,000. The APT Webstudy program requires secure and scalable informatics infrastructure (5), as well as an algorithm to identify participants and rank them by risk of brain amyloidosis and development of AD dementia (6). These elements of the program are described in separate papers in this series.
The APT Webstudy was launched as clinical trials have increasingly utilized web-based tools, including registries, to improve efficiency in screening (7-9). Although leveraging registries to recruit for clinical trials is not a new concept, the establishment of online registries has broadened access to participants who are interested and eligible for studies (10-13). Going further than remote recruitment, Orri et al (14) conducted the first entirely web-based clinical trial run under an Investigational New Drug (IND) application. Digital tools allow researchers to optimize the use of mobile technologies in clinical trials, respond to the preferences of participants (15), and measure and fine-tune communication methods (16). To our knowledge, TRC-PAD is the first program inviting participants from various existing registries to a join a longitudinal Webstudy with identification and referral of high-risk individuals to an in-person TRC. In this article, we describe the preliminary experience of efforts to recruit to APT Webstudy, including from national and local registries, as a unifying path to enrollment in TRC-PAD.



APT Webstudy Experience

Participants log in using either their existing social login credentials or by creating an account and providing a username, email address and password. Once logged on, participants are considered ‘registered.’ The Webstudy is designed as a ‘walk through’ experience, with each new section opening after completion of the former. The sections are: Step 1: Personal Profile; Step 2: Consent; Step 3 Lifestyle; Step 4: Remote cognitive and functional assessments; Step 5: Review scores. Sections are described in more detail in a separate paper in this series (17). The questionnaires and assessments were designed to be brief with a target duration of 15 minutes.


APT Webstudy participants are recruited from multiple sources. For the purposes of this paper, the term registry refers to a online registry, study, or service matching individuals interested in participating in studies or clinical trials to prevent or delay AD dementia. Early in its development, the TRC-PAD study team established partnerships with each of the largest “Feeder” registries, and in collaboration with the managing team or investigators, developed a referral strategy based on the registry’s unique population and existing communication pathways. Each strategy began small and was expanded when we were able to ensure the stability of the Webstudy infrastructure, as well as our capacity to provide user support. Outreach took the form of direct email campaigns highlighting the APT Webstudy on the registry website, e-newsletters, and social media posts. In addition to referrals from registries, both central and site-based strategies were employed.

UTM Codes

Urchin Tracking Modules (UTM) were generated to track participants that registered for the APT Webstudy in response to digital outreach, and were embedded in emails, webpages, and social media advertisements. For some registries, although various outreach activities were utilized, all responses linked back through the registry website, requiring use of a single UTM, and limiting our ability to understand the response rates to different digital communications. Recruitment strategies that did not utilize a UTM included printed materials (i.e., brochures, newsletters and magazines) and earned media (i.e., online and print newspaper articles).

The Alzheimer’s Prevention Registry (APR) (

APR was launched in October 2012 by the Banner Alzheimer’s Institute with the aim of providing a shared resource to the AD scientific community to facilitate enrollment in studies to prevent AD. In 2015, APR began offering an optional APOE genotyping program (GeneMatch) to members ages 55-75 to help match individuals to research studies. As of August 2018, APR enrolled a total 320,000 participants with 75,351 agreeing to the GeneMatch program, and approximately 75,000 agreeing to be contacted by researchers (18). APR participants are primarily women (65.6%) and Caucasian (45.5%); 1.8% are Hispanic/Latino and less than 1% are from other underrepresented groups. It should be noted that these percentages are a reflection of only the 60.8% of APR participants who provided their Race or Ethnicity (Table 1) (19). 14% of APR participants are age 50-59, 35% age 60-69, and 23% age 70-79 (Table 1). The APT Webstudy recruitment strategy began with a pilot phase in April 2018, with batches of emails sent from APR to 7,293 individuals (Figure 1). This was followed by an article in the APR quarterly newsletter introducing the APT Webstudy and posts on APR’s social media accounts. In January 2019, emails were sent in batches to 75,000 registrants inviting them to join the APT Webstudy. In March and April 2020, follow up emails were sent to participants who had not opened the email or clicked the link for the APT Webstudy, with additional reminders scheduled for May 2020.

Alzheimer’s Association TrialMatch (

Alzheimer’s Association TrialMatch ( is a free online matching service that utilizes user’s information to generate a custom report of clinical trials for which they may be a good fit. TrialMatch has a large pool of 322,997 users, with 134,148 providing contact and personal information. Individuals enrolled in TrialMatch indicate whether they are a healthy volunteer (52.8%), a caregiver looking for clinical trials for someone else such as a family member with AD (31.7%), or a person living with the disease looking for trials (13.3%). A small percent (2.2%) of users are entered into TrialMatch by a physician or researcher. Individuals under 50 comprise 35% of the Healthy Volunteers and 20% of all TrialMatch participants. 69% of TrialMatch are over the age of 50. Participants are 73.4% Caucasian, 4.5% Hispanic/Latino, and 65% are women. Women comprise 78% of the healthy controls and 54% of caregivers looking for trials for someone else. 22% of TrialMatch users either care for someone with a diagnosis of AD or have a diagnosis of AD. The first APT Webstudy recruitment campaign began in March 2019, with direct emails targeting 48,000 TrialMatch users living within 200 miles of potential TRC-PAD clinical sites. An additional 33,000 users were invited to join APT Webstudy beginning in December 2019. Emails were sent in batches of 5,000 twice a week, and is ongoing at the time of this manuscript.

The Brain Health Registry (BHR) (

The Brain Health Registry (BHR) ( collects longitudinal health, cognitive, and lifestyle data through detailed self-report questionnaires and online cognitive tests (Cogstate, Lumosity, and MemTrax) (16). BHR was launched in 2014 and currently has baseline data on 56,982 participants. BHR participants are 80.9% Caucasian, 5.3% Hispanic/Latino, 73.9% women, with 73% of participants over the age of 50 (20) (Table 1). The BHR team sent emails to 18,240 participants inviting them to register for the APT Webstudy beginning in March 2019 (Figure 1). Emails were sent in batches of 500 every week. If participants do not respond, two follow-up emails are sent, with a second set of reminder emails 231 and 238 days from their initial email contact. The BHR team also featured the APT Webstudy in their e-newsletter.

Table 1. Feeder Registries and APT Demographics

The Cleveland Clinic Healthy Brains Registry (


The Cleveland Clinic Healthy Brains Registry ( is a longitudinal, web-based symptomatic and lifestyle assessment (21), with over 13,000 registrants, and over half expressing interest in enrolling into clinical trials. HealthyBrains has registrants and newsletter subscribers from across the nation. The highest number of registrants in the US states of Ohio, Nevada, California and Florida. Registrants were invited to join the APT Webstudy through an article on the HealthyBrains website in May 2018, followed by features in two newsletters, sent by email (Figure 1).

Figure 1. Alzheimer Prevention Trials (APT) Webstudy: Feeder Registry Recruitment Campaign Timeline


UCI Consent-to-Contact (C2C) Registry (

UCI Consent-to-Contact (C2C) Registry ( is a confidential online tool to help match local volunteers in Orange County, CA, with research studies at the University of California, Irvine (22). Registrants enroll by providing an email address or by phoning the research site, remotely completing a series of questions regarding medical history and research interests. Beginning in July 2019, 7,300 C2C participants were invited by email to join the APT Webstudy (Figure 1).

Other sources

Anticipating that the registry-based approach would have limitations, especially in identifying eligible participants from under-represented groups, the APT Webstudy team developed recruitment strategies utilizing the TRC-PAD site network as well as other central activities. Sites participating in the TRC-PAD cohort study were identified early in the development of the program, with some agreeing to work locally to recruit participants to the APT Webstudy. Each of the TRC sites were invited to utilize their own databases of individuals interested in clinical research and email information about the APT Webstudy. The TRC-PAD study team provided flyers, postcards, newsletter and email template language, social media content and leaflets describing the APT Webstudy. Language for these materials was approved by the Institutional Review Board (IRB) and UTM codes were generated where appropriate. Sites also held community outreach events, partnered with other local community organizations to share information about the study, advertised on social media, and posted information about the Webstudy on their own webpages. Central recruitment efforts included generating earned media including newspaper and online and print edition magazine articles, local TV interviews, and posting the study on websites for clinical trials and AD. The earned media stories included an article in the San Diego Union Tribune in January 2018, two letters to the editor in May 2019, in local papers that have circulations of 80,000 (Charleston, SC) and 150,000 (Lexington, KY) respectively. Grand Magazine published an online piece about the APT Webstudy on August 12, 2019, generating 54,000 impressions. The Saturday Evening Post, with a circulation of 302,000 and majority of readers over the age of 45, included APT in its January/February 2020 print edition. So far, the only paid advertising was in the form of Facebook advertisements. Facebook ads ran in eight markets for two weeks in November 2018 for a cost of $12,000, and six markets for 5 weeks in August-September 2019 for a cost of $3,000. The ads were targeted geographically and to the largest minority population in each location, based around the location of TRC sites.



APT Webstudy Enrollment: At the time of preparing this mansuscript, there are 30,650 participants consented to the APT Webstudy. Recruitment strategies for the first year were a mix of central and local efforts (Figure 1). The first notable increase was in January 2018 following local newspaper coverage. In March 2018, email referrals were piloted for APR Registry. In April 2018, APR and HealthyBrains introduced the Webstudy in their newsletters. In the first year, 388 participants per month consented to the APT Webstudy on an average. The APR email referrals began in earnest in January 2019, leading to a dramatic increase in consented participants, with 5,196 consenting in January 2019 (Figure 1). This was followed by email referrals from TrialMatch and BHR. In the second year, participants consented to the APT Webstudy on an average of 1,514 per month.


Participants in the APT Webstudy have a mean age of 64.56 with a majority of participants ages 50-59 (28.9%) and 60-69 (44.1%) (Table 1). Most participants identify as women (73.0%), white (92.5%) and more than high school level education (85.0%). 2.3% of APT Webstudy participants self describe as Hispanic/Latino. Although most participants are retired or not working (53.2%), a significant percentage are employed either full (30.6%) or part-time (14.7%) (Table 2). A majority of participants have a family history of AD (62.6%) and do not have a personal diagnosis of AD (94.6%). Further details on lifestyle and medical history are provided on Tables 2 and 3.

Table 2. APT Webstudy Health and Lifestyle

Table 3. APT Webstudy Recruitment by Referral Sourc


Enrollment by Referral sources

At this point in the recruitment to the APT Webstudy, registries were the primary source of participants, with referrals resulting in 69.69% of consented individuals, according to UTM codes. APR was by far the biggest contributer with 38.98% of all APT Webstudy consented participants, followed by 25.40% referred by TrialMatch. Those referred by APR were also slightly more likely to both register and consent to APT (Table 3). All together 15.9% of the APR participants that were contacted consented to APT, compared to 9.8% or less for other registries. Email (32.92%) and websites (40.78%) were the most common mode of referral, however website visits were largely driven by email campaigns. Central media efforts that could be tracked with UTM resulted in 234 participants. The central Facebook ads accounted for 7,800 and 3,000 clicks which translated to 0.15% of consenting participants.

Geographic Distribution

APT Webstudy participants reside in each of the 50 United States (US), the District of Columbia, and Puerto Rico. States with the highest number of consented participants include California (16.63%), Florida (5.65%), New York (4.67%), Texas (4.66%), and Virginia (4.38%). International location is not currently collected. Participants consented to the APT Webstudy reside in 1931 (or 60%) of US counties. The top ten counties with participants consented to APT are San Diego County, CA (n=1621); Orange County, CA (n=861) Maricopa County, AZ (n=764), Los Angeles County, CA (n=612), Cook County, IL (n=443) Charleston County, SC (n=384), Fayette County, KY (n=279), King County, WA (n=270) Pima County, AZ (n=239) and Middlesex County, MA (n=238) (Figure 2).

Figure 2. APT Webstudy Enrollment: Heatmap of US Counties



We have demonstrated that online registries are not only feasible but they are an excellent method to identify and recruit participants for a Webstudy. Participants in a registry have already demonstrated an interest in research and willingness to provide information about themselves. In addition, registries have communication infrastructure and digital platforms designed to engage individuals through educational materials, newsletters and other outreach, which may lead to higher rates of referral. UTM codes were shown to be an effective method to track the referral source in this study. The strategy that yielded highest rates of responses was to first feature the APT Webstudy in the registry’s newsletter, followed by direct email communication to registrants. Although not tracked with separate UTM codes, the consistent increase of participants demonstrates that sending second and third emails to non-responders produces additional participants. Although central media efforts and social media advertising were piloted in this first stage of recruitment, this strategy has not been fully explored as a potential source for remotely enrolled participants.
The registries used in this study had a contact-to-consent rate ranging from 1.8%-15.9%, despite having very similar composition of registrants. This brings up several questions as to best practices. Was the higher rate of consent from APR compared to BHR due to the fact that APR directly targets individuals interested in clinical trials? Could the observed rate of consent to contacted participant be influenced by the level of engagement utilized by the respective registries?
It is not surprising that the demographics of participants in the APT Webstudy are similar in demographics to the registries that referred the majority of participants. However, understanding why such a large majority of participants are women is important. Further research may reveal both barriers to in-person research and preferences for online studies. The low rate of Hispanic/Latino involvement in APT Webstudy can likely be attributed to 2 factors, (1) the low rates of Hispanic/Latino participants in the referral registries and (2) the APT Webstudy and recruitment materials had not been translated into Spanish.
We acknowledge that the APT Webstudy has an inherent selection bias, in that participants must have access to the internet in order to participate. This disproportionately excludes many people from under-represented communities, where according to recent Pew reports, only 57% of Hispanic and African American adults own a laptop or a tablet (23), compared to 82% of Caucasians. Although those over 65 years of age are more likely to use a desktop or tablet to access the internet, lower income Americans, those with less than college education, and black and Hispanic populations, are all more likely to use a cell phone to access the internet (24). Although the APT Webstudy is mobile-friendly, the cognitive testing at present requires use of a tablet or computer. The study team is considering changes to cognitive testing that will allow for the use of smart phones and expand accessibility to all communities. Other researchers (25) have demonstrated that text messages can be an effective communication channel with research participants. Would people be more responsive to a text message inviting them to return for a study visit?
The Spanish language version of the APT Webstudy was launched early in 2020, with efforts underway to optimize the cultural sensitivity of the Webstudy and all participant-facing content. A key aim of the study is to engage in recruitment initiatives that will improve the diversity of the cohort, towards the goal of clinical trials that better represent the US population. For the African-American community in particular, recruitment campaigns will highlight disparities in Alzheimer’s disease risk and care, and the role research and clinical trials can play in effecting change.
This study has several limitations. The feeder registries differ in numerous ways, including sample sizes, aims or purpose, geographic distribution, length of time from when participants were first engaged with, and frequency of participant engagement. The current analyses did not account for these differences. Similarly, varying levels of data were available for participants in feeder registries, preventing combination of data streams for more sophisticated analyses of recruitment efficiency. Recruitment from feeder registries was peformed over multiple years, introducing potential confounding by time. Quantification of site level efforts toward recruitment was minimal, limiting our ability to understand the efficacy of site level efforts relative to using central efforts or these feeder registries.
In conclusion, this study demonstrates the feasibility of recruiting from feeder registries into a common platform for identifying potentially eligible participants for a Trial-ready cohort. A robust sample was assembled in a relatively short period of time that is anticipated to play a key role in the national AD clinical trial agenda.


Acknowledgements: From the Alzheimer’s Assocation, our thanks to Keith Fargo, Stephen Hall, and Martha Tierney. From APR: Jessica Langbaum, Cassandra Kettenhoven, and Nellie High. From Brain Health Registry: Rachel Nosheny, and Joseph Eichenbaum. From University California Irvine Registry we’d like to thank Meagan Witbracht. Coordinating Center staff providing support to APT Webstudy participants are Godfrey Coker and Rocio Gonzalez-Beristain. The informatics development team is Stefania Burschi, Jia-Shing So, and Marian Wong.

Funding: The study was supported by R01AG053798 from NIA/NIH. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Ethical standard: Institutional Review Boards (IRBs) approved these studies, and all participants gave informed consent before participating.

Conflict of interest: The authors report grants from National Institute on Aging, during the conduct of the study. None of the authors have additional financial interests, relationships or affiliations relevant to the subject of this manuscript.

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.



1. Fargo KN, Carrillo MC, Weiner MW, Potter WZ, Khachaturian Z. The crisis in recruitment for clinical trials in Alzheimers and dementia: An action plan for solutions. Alzheimers Dement 2016;12(11):1113-1115.
2. Sperling RA, Jack CR Jr, Aisen PS. Testing the right target and the right drug at the right stage. Sci Transl Med. 2011 Nov 30; 3(111):111cm33.
3. Sperling R, Cummings J, Donohue M, Aisen P. Global Alzheimer’s Platform Trial Ready Cohorts for the Prevention of Alzheimer’s Dementia. J Prev Alzheimers Dis 2016;3:185-7.
4. Aisen PS, Sperling R., Cummings J, et al. The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) Project: An Overview. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.45.
5. Jimenez-Maggiora GA , Bruschi S., Raman R, et al. TRC-PAD: Accelerating Recruitment of AD Clinical Trials through Innovative Information Technology. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.48.
6. Langford O, Raman R, Sperling RA, et al. Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.44.
7. Mohebati A, Knutson A, Zhou XK, et al. A web-based screening and accrual strategy for a cancer prevention clinical trial in healthy smokers. Contemp Clin Trials 2012;33(5):942-948.
8. Parker G, Fletcher K, Blanch B, Greenfield L. The ‘real world’ utility of a web-based bipolar disorder screening measure. Acta Psychiatr Scand 2013;127(5):373-380.
9. Thibault-Halman G, Rivers CS, Bailey CS, et al. Predicting Recruitment Feasibility for Acute Spinal Cord Injury Clinical Trials in Canada Using National Registry Data. JNeurotrauma 2017;34(3):599-606.
10. Wessel J, Gupta J, Groot MD. Factors Motivating Individuals to Consider Genetic Testing for Type 2 Diabetes Risk Prediction. Plos One 2016;11(1).
11. Russo R, Coultas D, Ashmore J, et al. Chronic obstructive pulmonary disease self-management activation research trial (COPD–SMART): Results of recruitment and baseline patient characteristics. Contemp Clin Trials 2015;41:192-201.
12. Andersen MR, Schroeder T, Gaul M, Moinpour C, Urban N. Using a Population-Based Cancer Registry for Recruitment of Newly Diagnosed Patients With Ovarian Cancer. Am J Clin Oncol 2005;28(1):17-20.
13. Hein A, Gass P, Walter CB, et al. Computerized patient identification for the EMBRACA clinical trial using real-time data from the PRAEGNANT network for metastatic breast cancer patients. Breast Cancer Res Treat 2016;158(1):59-65.
14. Orri M, Lipset CH, Jacobs BP, Costello AJ, Cummings SR. Web-based trial to evaluate the efficacy and safety of tolterodine ER 4mg in participants with overactive bladder: REMOTE trial. Contemp Clin Trials 2014;38(2):190-197.
15. Perry B, Geoghegan C, Lin L, et al. Patient preferences for using mobile technologies in clinical trials. Contemp Clin Trials Commun 2019;15:100399.
16. Baca-Motes K, Edwards AM, Waalen J, et al. Digital recruitment and enrollment in a remote nationwide trial of screening for undiagnosed atrial fibrillation: Lessons from the randomized, controlled mSToPS trial. Contemp Clin Trials Commun 2019;14:100318.
17. Walter S, Langford OG, Clanton TB, et al. The Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD): Experience from the First 3 Years. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.47.
18. Langbaum JB, Karlawish J, Roberts JS, et al. GeneMatch: A novel recruitment registry using at-home APOE genotyping to enhance referrals to Alzheimer’s prevention studies. Alzheimers Dement 2019;15:515-24.
19. Langbaum JB, High N, Nichols J, Kettenhoven C, Reiman EM, Tariot PN. The Alzheimer’s Prevention Registry: a large internet-based participant recruitment registry to accelerate referrals to Alzheimer’s-focused studies. J Prev Alz Dis 2020; DOI:10.14283/jpad.2020.31.
20. Weiner MW, Nosheny R, Camacho M, et al. The Brain Health Registry: An internet-based platform for recruitment, assessment, and longitudinal monitoring of participants for neuroscience studies. Alzheimers Dement 2018;14(8):1063-1076.
21. Zhong K, Cummings J. From Registry to Randomization. J Prev Alzheimers Dis 2016;3(3):123-126. doi:10.14283/jpad.2016.100
22. Grill, JD, Hoang, D, Gillen, DL et al. Constructing a Local Potential Participant Registry to Improve Alzheimer’s Disease Clinical Research Recruitment. J Alzheimers Dis 2018;63(3):1055-1063.
23. Perrin A, Turner E. Smartphones help blacks, Hispanics bridge some – but not all – digital gaps with whites. Pew Research Study. 2019 Accessed April 5, 2020.
24. Anderson, M. Mobile Technology and home Broadband 2019. Pew Research Study 2019. Accessed May 14, 2020
25. Lincoln KD, Chow TW, Gaines BF BrainWorks: A Comparative Effectiveness Trial to Examine Alzheimer’s Disease Education for Community-Dwelling African Americans. Am J Geriatr Psychiatr. 2019 Jan;27(1):53-61.

What do you want to do ?

New mail



L.M.P. Wesselman1, A.K. Schild2, A.M. Hooghiemstra1,3, D. Meiberth2, A.J. Drijver4, M.v. Leeuwenstijn-Koopman1, N.D. Prins1, S. Brennan5, P. Scheltens1, F. Jessen2,6, W.M. van der Flier1,7, S.A.M. Sikkes1,8


1. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; 2. Department of Psychiatry, University Hospital Cologne, Medical Faculty, Cologne, Germany; 3. Department of Medical Humanities, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1089a, 1081 HV Amsterdam, The Netherlands; 4. Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, The Netherlands; 5. The Adapt Centre, & The Institute of Neuroscience, Trinity College Dublin; 6. German Center for Neurodegenerative Disorders (DZNE), Bonn-Cologne, Germany; 7. Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; 8. Clinical Developmental Psychology & Clinical Neuropsychology, Faculty of Behavioural and Movement Sciences (FGB), Vrije University Amsterdam, Amsterdam, the Netherlands.

Corresponding Author: Linda M.P. Wesselman, Alzheimer Center Amsterdam and Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, P.O. Box 7057, 1007 MB Amsterdam, the Netherlands, Telephone: +31-204440816; Fax: +31-204448529; E-mail:

J Prev Alz Dis 2020;3(7):184-194
Published online March 2, 2020,



Background: Online programs targeting lifestyle have the potential to benefit brain health. We aimed to develop such a program for individuals with subjective cognitive decline (SCD). These individuals were reported to be at increased risk for dementia, and report both an intrinsic need for brain health information and motivation to participate in prevention strategies. Co-creation and user-evaluation benefits the adherence to and acceptance of online programs. Previously, we developed a prototype of the online program in co-creation with the users .
Objectives: We now aimed to evaluate the user-experiences of our online lifestyle program for brain health.
Design: 30-day user test; multi-method.
Setting: Participants were recruited in a memory clinic and (online) research registries in the Netherlands (Alzheimer Center Amsterdam) and Germany (Center for memory disorders, Cologne).
Participants: Individuals with SCD (N=137, 65±9y, 57% female).
Measurements: We assessed user-experiences quantitatively with rating daily advices and usefulness, satisfaction and ease of use questionnaires as well as qualitatively using telephone interviews.
Results: Quantitative data showed that daily advices were rated moderately useful (3.5 ±1.5, range 1-5 points). Participants (n=101, 78%) gave moderate ratings on the programs’ usability (3.7±1.3, max 7), ease of learning (3.6±1.9) and satisfaction (4.0±1.5), and marginal ratings on the overall usability (63.7±19.0, max 100). Qualitative data collected during telephone interviews showed that participants highly appreciated the content of the program. They elaborated that lower ratings of the program were mainly due to technical issues that hindered a smooth walk through. Participants reported that the program increased awareness of lifestyle factors related to brain health.
Conclusions: Overall user-experience of the online lifestyle program was moderate to positive. Qualitative data showed that content was appreciated and that flawless, easy access technique is essential. The heterogeneity in ratings of program content and in program use highlights the need for personalization. These findings support the use of online self-applied lifestyle programs when aiming to reach large groups of motivated at-risk individuals for brain health promotion.

Key words: Lifestyle, dementia, subjective cognitive decline, eHealth, prevention.

Abbreviations: SCD: subjective cognitive decline; MCI: mild cognitive impairment; SUS: System Usability Scale; USE: User Satisfaction and Ease of use.



The World Health Organization (WHO) Global Action Plan on Dementia emphasized the need for campaigns to increase public awareness and understanding of dementia (1). Recent studies found that knowledge about prevention and treatment of dementia remains poor and that there is a need for adequate dementia prevention education (2, 3).
The body of evidence on the association between a healthy lifestyle and brain health keeps growing (4). Risk factors for dementia due to Alzheimer’s disease (AD), such as lifestyle factors, are suggested to be partly modifiable (5). A healthy lifestyle may therefore decrease the risk for AD dementia. Since the etiology of AD is complex and multifactorial, recommendations are made to target several risk factors simultaneously (6, 7). Indeed, a multifactorial intervention has been found to improve or maintain cognitive functioning in people at risk for dementia (8). However, this intervention was offered face-to-face, which is beneficial for program use because of personal contact, but is relatively expensive and limits possibilities to reach a larger group of individuals. Offering intervention programs online has an important advantage because it offers the opportunity to reach many users, in particular in remote areas (9).
Our international EuroSCD-project aimed to develop an online lifestyle program for brain health. Individuals with subjective cognitive decline (SCD) experience cognitive decline in absence of objective cognitive impairments. SCD has previously been reported to be a risk factor for dementia and AD (10, 11). Therefore, individuals with SCD might be an ideal target group for online interventions. This at-risk group might present at memory clinics, their GP or research registries, and was found to be motivated to participate in prevention strategies (12). Individuals at-risk might benefit most from prevention strategies aimed at optimizing brain health or preventing cognitive decline (13, 14).
Our recent review and meta-analysis on online lifestyle programs for brain health suggested that these programs could indeed benefit brain health (15). However, the programs that we reviewed were heterogeneous in content and set-up. Further, characteristics and the methods and results of evaluations of the programs were often not described consistently. More specifically, it was often unclear how user-participation was operationalized and thus how users were involved during the development of the programs (15). This is an important aspect during the development of online programs, because it is essential to involve future users during development. With the users’ input, a program will better fit the users’ needs, which benefits acceptance and adherence, and thereby the implementation of sustainable innovations (16). Previously, we investigated barriers and facilitators for the use of an online lifestyle program in individuals with SCD (12). We found that both program characteristics and personal factors need to be considered, with trustworthiness, user-friendliness, and personalization being important facilitators. We implemented these results during the development of an online lifestyle program for brain health. In co-creation with the users, we developed and adapted the program in multiple iterations. We now aimed to evaluate user-experiences of our online lifestyle program in Dutch and German individuals with SCD, using both quantitative and qualitative methods.



Project and study design

This study is part of the European ‘Subjective cognitive decline in preclinical Alzheimer’s Disease: European initiative on harmonization and on a lifestyle-based prevention strategy’ project (Euro-SCD; JPND_PS_FP-689-019), which aims to develop an online lifestyle program for individuals with SCD. The Euro-SCD project is a collaboration between the Alzheimer Center Amsterdam, the Netherlands (17), Hospital Clinic Barcelona, Spain, and the Center for memory disorders, University Hospital Cologne, Germany. The study was conducted in accordance with Good Clinical Practice (GCP) Guidelines, applicable national guidelines, and to the Declaration of Helsinki. The local ethical committees approved this study and all participants provided informed consent.
The current study was conducted in the Netherlands and Germany (Figure 1: study overview). First, we conducted a feasibility study in the Netherlands to evaluate practicalities and study procedures. This allowed us to improve the online program and optimize the planned study procedures. Subsequently, we performed a 30-day online user test in both the Netherlands and Germany to evaluate user-experiences.

Figure 1. Study overview

Figure 1. Study overview

NOTE: This Figure illustrates the study overview. During the feasibility study, using an iterative process, the program was adapted and study procedures were optimized. The 30-day online user test was quantitatively evaluated with questionnaires, rating of daily advices and data log, and qualitatively by follow-up telephone interviews in a subsample of participants. USE: User Satisfaction and Ease of use questionnaire; SUS: System Usability Scale. a: conducted in the Netherlands, b: recruited via Dutch Brain Health Registry, c: recruited via Cologne Alzheimer dementia prevention registry.



Individuals with SCD were recruited through either a memory clinic or research registry:
1) memory clinic: we included individuals that visited the Alzheimer Center Amsterdam because of cognitive complaints. They underwent clinical work-up including clinical evaluation, neuropsychological assessment, and MRI scan. Although not mandatory, an informant was present in most cases during consults and assessments. When all clinical investigations were normal, and no cognitive disorder could be objectified, patients were labelled as having SCD ((17) i.e. clinical criteria for MCI, dementia or psychiatric disorder not fulfilled, no neurological diseases known to cause memory complaints (e.g. Parkinson’s disease, epilepsy), HIV, abuse of alcohol or other substances). Individuals were invited for study participation based on the following criteria: I) diagnosis of SCD II) age 50 years or older, and III) owning a smartphone, tablet or computer.
2) research registries: we included individuals that signed up for research registries, a) the Dutch Brain Health Registry (online register; which facilitates participant recruitment for neuroscience studies and is open for individuals of any age; b) the Cologne Alzheimer dementia prevention registry [Kölner Alzheimer Präventionsregister (KAP)], which is open for individuals of any age interested in the field of dementia. Through newsletters individuals receive information on research and are asked to participate in scientific studies. Individuals were invited for study participation based on the following criteria: I) self-reported experience of memory loss as assessed by either the question “Do you have memory complaints?” (Dutch registry) or the SCD interview (18) (German registry), II) age 50 years or older, III) no diagnosis of Alzheimer’s disease, another type of dementia or mild cognitive impairment, as assessed through self-report, and VI) owning a smartphone, tablet or computer to access the online lifestyle program. No informant information was available for the participants from the research registries.

Online lifestyle program

Hello Brain is a European Project (FP7 grant no 304867) led by Trinity College Dublin. Hello Brain comprises a website and app which are available in English French and German. The website shares information and videos about the brain, brain health and brain research. The App aims to support users to live a brain healthy life by giving daily suggestions called ‘brain buffs’. There are five brain buff categories: physical activity, social activity, mental activity, lifestyle (nutrition, smoking, alcohol) and attitude (referring to stress management and positive thinking; 30 brain buffs per category). Participants are instructed to read the brain buff and are encouraged to engage in the described activity. If the user cannot or does not want to conduct a specific brain buff, a new brain buff can be requested.
For the current project, a collaboration was started between the EuroSCD team and Trinity College Dublin. We first investigated the preferences and wishes for an online lifestyle program in an international group of users (12). Then, in collaboration with users and a technical party, we adapted the program HelloBrain (Dutch: HalloHersenen, German: HalloGehirn; Appendix 1: details and screenshots). The scientific content was translated and the modules were adapted in order for the interactive module to become the main module. Additional brain buffs were created by a team of brain researchers and added to the program (15 per category) in order to allow tailoring based on a personal profile. The overall lay-out of HelloBrain was changed to a calmer look-and-feel by applying the grey background, that was included in some of the original HelloBrain screens, to all screens while keeping the colorful details.

Feasibility study

After the above mentioned adaptations, we performed a feasibility study to evaluate accessibility and the study procedures, to collect qualitative feedback and optimize study procedures for the online user test. We used 4 iterations of user input and adaptations to create a version of the program that was ready to evaluate user-experiences in a 30-day user test.

Focus groups

In 4 focus groups (memory clinic + Dutch Brain Health Registry, total N=17: 67±6y, 65% female) the language and structure of the program was evaluated. Specific topics were hierarchy of screens (wireframe), language, lay-out, and the wording of reminders and instructions. Feedback was translated into technical and content-related adaptations, and passed on to the developers. We used an iterative process, meaning that after each focus group the program was adapted. In the next focus group the adapted version of the program was evaluated.

Technical pilot

We conducted a technical pilot to evaluate accessibility of the program. Accessibility was defined as the ability to log in to the website or the app independently, with devices at home. Participants (memory clinic, N=5: 61±8y, 80% female) received access to the program through the website or the app for 2 weeks. All technical issues raised by the participants were collected and adaptations were made.

Pilot test phase

To evaluate feasibility of the planned study procedures, we conducted a pilot test phase in which we included 43 SCD subjects (Dutch Brain Health Registry, 65±8y, 66% female). Participants received account information by email and were instructed to use the program for 30 days. Users were able to email the researchers and if necessary, we initiated contact by telephone. At the end of the test-period, participants received digital questionnaires by email to evaluate the procedure of sending online questionnaires, having participants filling out the questionnaires and adequate data collection.

30-day user test: user-experiences


Finally, we conducted a 30-day user test to evaluate user-experiences. Individuals from the Dutch Brain Health Registry and the Cologne Alzheimer dementia prevention registry were approached. These individuals were not involved in previous phases of the program development.


Participants received account information and could access the program for 30 days. Participants were instructed to use the program on a daily basis and complete one brain buff each day. Besides the daily brain buff, participants could access the information on brain health as they liked. After the 30-day user test the participants received self-report questionnaires to evaluate the program. In addition, participants were asked whether they were willing to share their experiences during a telephone interview. Study procedures slightly differed between centers, because of characteristics of the research registers (online in the Netherlands, on paper after an in-person information session in Germany) and requirements of the Cologne ethical committee to send information via post instead of email.


Data log

During the online user test, log data regarding the usage of the program were collected. Log data entailed number of log ins, log outs, brain buffs completed, brain buffs passed, and page visits during the test period.

Usefulness of daily advices

After indicating that a brain buff was completed, participants were asked to provide a rating of the usefulness of the brain buff. This rating was illustrated with 1 to 5 stars. Participants were invited to leave a comment.

Usability, ease of learning and satisfaction

We used the User Satisfaction and Ease of use (USE) (19) and the System Usability Scale (SUS) questionnaire (20) to assess perceived user-experiences of the online program. The USE questionnaire includes items on usefulness (e.g. is the program perceived as useful, does it have value to the user), ease of learning (e.g. is it easy to learn how the program works) and satisfaction (e.g. does it fulfill the wishes and expectations of the user), with scores ranging from 1 to 7. We used the domain scores for usefulness, ease of learning and satisfaction, by averaging the scores of items per domain. The SUS questionnaire includes 10 items on usability (scores ranging from 1-5; e.g. degree of convenience when using the program). The SUS questionnaire includes both positive and negative items. Total SUS score (range 0-100) was calculated by subtracting 1 from positive items and inversing negative items (5 – item score), summing these scores and multiplying with 2.5 (20). For both questionnaires higher scores indicate better ratings.

Qualitative exploration of user-experiences

We held semi-structured telephone interviews to gain more insight in the questionnaire results and to discuss additional topics. We chose a random sample (N=30) from participants that indicated to be willing to participate in the telephone interview. Aspects that were deemed most important to improve, good and useful aspects of the program and communication during the user test were discussed. In case the questionnaire results needed clarification, the interviewer posed specific questions.

Frequency of Internet use

In the Dutch subsample, a question regarding frequency of internet use was included in the usability questionnaire. In a German subsample frequency of internet use was discussed during the in-depth interview.

Data analysis

Analyses of quantitative data were conducted using SPSS version 22. Descriptive methods were used to describe demographics, average ratings of daily advices per category, use of the program (data log) and user-experience scores (questionnaires) in means and standard deviation, or percentages. Analysis of variance was used to compare questionnaire scores of Dutch and German participants, and to compare the ratings between brain buff categories. P-values of ≤0.05 were considered significant. Qualitative data was collected during the telephone interviews. Every interview was summarized in a short report. All comments were summarized independently by two researchers (LW, AKS). Data was then structured by these researchers upon consensus, in order to identify themes that were of importance to the participants when using the program of when participating in this study.



Feasibility study

Focus groups

We let the participants discuss terminology within the program. At first, we kept some English terms in the program. The participants proposed to use Dutch language only. We discussed which terms should be incorporated to replace the English terms. ‘Brain buff’ became ‘Oppepper’ (Dutch for ‘Boost’), and although the category name ‘Attitude’ also translates to the Dutch ‘Attitude’, participants preferred a different wording (‘Houding’; Dutch synonym for ‘Attitude’). Participants agreed with the order and hierarchy of the screens (the wireframe). Upon their input the button for instructions was enlarged and placed more prominently, and we added ‘Uitleg’ (Dutch for ‘Explanation’) underneath this circled question mark symbol. Participants mentioned that back-and-forth buttons needed to be more prominent, which we adapted accordingly, and the hierarchy of the current location should be visible. Therefore, so called ‘Breadcrumbs’ were added to the page. Breadcrumbs are a simple display of the current location in the program, and easy way to click to a location with higher hierarchy (e.g. Start page / Brain Health / Neuroplasticity). Participants mentioned that they would prefer more instructions when entering the main screen. Together with the technical party and participants we came up with the solution to add a highlighting instruction, which highlights and explains all parts of the screen one by one.

Technical pilot

Of the 5 participants that evaluated the accessibility of the program, nearly all (4/5) reported a smooth download and log in without any assistance. One participant was not able to log in, as a result of a problem with the internet browser. Together with the technical party, the issue was resolved. After log in, 2 participants reported several technical bugs, such as wrong linking between pages or not enough variation in the daily advices, which was caused by an algorithm error. These issues were solved by the technical party.

Pilot test phase

Sending and receiving the questionnaires digitally went well. Participants did not report problems filling out the digital questionnaires. Almost all communication was done via email and online questionnaires. Some participants liked the efficient communication and felt that they were skilled enough to work online, while others would have preferred personal contact throughout the test-phase and provide feedback by telephone. Some participants mentioned that they would have liked an ‘emergency hotline’ in order to have personal contact by telephone in case they would have needed help when using in the program. Based on participants’ suggestions, we made the instructions for the online user test more detailed.

30-day user test: user-experiences


In total, 137 SCD subjects (55 Netherlands, 82 Germany) were included in the online user test. Participants were on average 65.1±8.6 years of age, 57% female and participants completed 11.3±1.9 years of education. German participants had on average more years of education (12.6±1.4) compared to Dutch participants (10.2±1.9, p<.01). The majority of the participants reported to use the internet on a daily basis (>90% of a subsample; Dutch N=55, German N=15).

Data log

In total, 120 (88%) participants used the online lifestyle program during the 30-day test period, whereas 17 (12%) participants did not log in. On average, participants reported to have completed 31±31 daily advices and requested a different brain buff 23±40times during these 30 days. Participants switched between pages on average 117 times (from brain buff screen to informative module and back, or within the informative module).

Usefulness of brain buffs

In total, participants rated 3266 brain buffs with a mean score of 3.5 (±1.5, max 5). The mean ratings differed between categories (F(4,3261)=5,725, p=.000). In general, buffs in the Attitude (3.6±1.4) and Physical activity (3.6±1.4) categories were higher appreciated than Lifestyle advices (3.3±1.6, resp. p<.001 and p=.001). Brain buffs of all categories received scores ranging from 1 to 5 stars and rankings were accompanied by both positive and negative comments. While some participants really liked a brain buff (“It would be very easy and fun to do this every day”) others disliked the same brain buffs (“I have never liked this and I will not do this today”). This diversity in appreciation of the categories, is presented in Figure 2.

Figure 2. Variety in reported usefulness of brain buffs

Figure 2. Variety in reported usefulness of brain buffs

NOTE: This figure illustrates the percentages of brain buffs that received 1 to 5 stars ratings per category, and presents a negative (1 star, left) and a positive (5 stars, right) quotes for each category for illustrative purposes


Usability, ease of learning and satisfaction

The questionnaire was completed by 101 participants (response rate 74%; 37 Netherlands, 64 Germany). Participants gave on average moderate scores on items of the USE questionnaire (max 7): usefulness 3.7±1.3, ease of learning 3.6±1.9 and satisfaction 4.0±1.5 points. Dutch participants rated the program higher on these 3 domains compared to German participants (Dutch: 4.1±1.3, 4.8±1.5, 4.4±1.4 vs. German: 3.4±1.3, 2.4±1.5, 3.7±1.6; p<.05). The average score for usability on the SUS questionnaire was 63.7±19 out of 100, which translates to ‘OK to good’ (21) and did not differ between Dutch and German participants. Figure 3 presents the heterogeneity of user-experience scores within the total group.

Figure 3. Heterogeneity in user-experiences

Figure 3. Heterogeneity in user-experiences


Qualitative exploration of user-experiences

Table 1 gives a summary of the qualitative feedback illustrated by quotes. Participants mentioned that they would prefer a personalized program, meaning that it would fit their specific preferences. For example, with content based on their current lifestyle and preferred lifestyle category. Some of the participants used the program mainly for information, while others mainly liked the interactive part. When asked what was most important to improve, participants mostly mentioned to optimize technical aspects of the program to ensure a smooth walk-through.
Most participants mentioned to highly appreciate the content of the program. They liked to have a platform available to read about the brain and brain health, and to have access to a trustworthy source of information. When specifically asked what they liked most about the program, participants reported that the program induced awareness of lifestyle factors that are related to brain health. While most participants knew that physical exercise is related to brain health, they were often not aware of the relation between nutrition or social activities and brain health. Some participants mentioned that the program was positive and induced motivation to live healthier. Others were stimulated to look at their current lifestyle, felt confirmation that they have a healthy lifestyle or were motivated to continue with current lifestyle changes.

Table 1. Summary of in-depth exploration of user-experiences, collected during telephone interviews

Table 1. Summary of in-depth exploration of user-experiences, collected during telephone interviews

NOTE: This Table presents qualitative feedback, which was provided by 30 participants during the telephone interviews.



We developed an online lifestyle program for brain health and found that its’ overall user-experience was moderate to positive. Qualitatively, participants reported to appreciate the content of the program and having a trustworthy source of information on lifestyle and brain health. Quantitative scores on usefulness and ease of learning showed room for improvement. We observed high heterogeneity in the preference of specific lifestyle topics, which emphasizes the need for personalization.
Content on the brain and brain health of the online program, as offered in the brain buffs and the information pages, was highly appreciated by the participants. Both the brain buffs and the information pages were reported to be interesting and useful. Many participants reported to have learned new things. Often they were not aware that all the lifestyle factors that were included in the program were associated to brain health. Previous studies into the attitudes towards prevention of AD and related dementias highlighted the need to improve the beliefs and attitudes towards dementia prevention (1, 3, 22). Our study showed that a tool with information on lifestyle and brain health can contribute to the awareness on modifiable risk factors of dementia.
Involving the users throughout the process of development of an online program is expected to benefit usability and thereby adherence to the program. Our recent review, however, showed that for online lifestyle programs aimed at brain health it was often unclear whether and how users were involved during development and evaluation of the program (15). For example, a study on adherence to lifestyle interventions for dementia prevention found that adherence was lowest for the unsupervised computer-based cognitive training compared to other supervised trainings (23). However, user-involvement during development and evaluation was not described and therefore it remains unclear whether this could have benefitted adherence rates. In this study we aimed to evaluate and optimize user-experiences. When a program will be implemented internationally, it is important to explore cultural differences. Our multinational participatory research design increases the quality of output and sustainability, but also ensures culturally appropriate research, which is of importance when developing an international application (24). As a next step, additional options to increase the impact of the program should be explored. It might be worthwhile to evaluate integration of persuasive technologies that aim to influence behavior and attitudes. If such technologies are used the right way, it is more likely that users reach health-related goals (25).
The heterogeneity in the ratings of brain buffs, the frequent requests for different brain buffs and the qualitative feedback emphasize the need for personalization. Personalization has also been identified as one of the principles to increase appreciation and overall adherence to an online intervention (26, 27). In the current version of the program, part of the content was personalized, since users could request a different brain buff and could access information as they wished. Participants mentioned that they would prefer to receive brain buffs based on their current lifestyle behavior. Further evaluation and integration of personalization options, such as adapting lifestyle advices based on current lifestyle habits, could improve user-experience and thereby adherence to the program.
Lessons learned from the qualitative input of the users, mainly entail the preference for tailoring based on current lifestyle behavior. In addition, participants mentioned different possible effects of the program. Therefore, it might be interesting to rethink the most appropriate outcome measures of future lifestyle-based interventions in SCD. While changes in lifestyle or brain health might seem obvious, effects on psychological well-being or fear for dementia could also be worth consideration.
The quantitative ratings evaluating user-experience were moderate, which was lower than we expected. We identified room for improvement, particularly in ease of learning. Meaning that additional adaptations are necessary to improve instructions and clarity within the program. Differences in ratings could have several reasons, such as education, cultural differences, differences in reporting and differences in digital skills – which we did not assess systematically. Regarding the 30-day user test, German participants reported difficulties when learning to use the program. Some of the technical difficulties occurred only in the German back-end. Although we fixed these technical issues and thoroughly tested the program, we could not rule out remaining minor issues, possibly contributing to the differences in these scores. In our previous study (12), we found that German individuals with SCD used the Internet less often compared to the Dutch participants. However, information on the current Dutch sample and a subset of the German sample showed that over 90% of the participants uses the internet on a daily basis and therefore frequency of internet use is unlikely to influence perceived difficulties. Further, we did not match participants from the feasibility study and 30-day user test. Therefore, we cannot rule out the influence of demographical differences on perceived usability and satisfaction during development and the actual test phase.
In summary, qualitative feedback on the programs’ content was positive, while quantitative feedback on program characteristics showed room for improvement. This discrepancy between the positive qualitative feedback and the moderate quantitative ratings emphasizes the importance of combining methods when evaluation usability of eHealth applications, which was also emphasized in a recent scoping review on methods of usability testing in the development of eHealth applications (28).
This study had some limitations. First, the development and feasibility study took place in the Netherlands, and not in Germany. However, we believe it is promising that participants with different nationalities appreciated the same program, strengthening feasibility to offer one program in multiple countries. Second, a selection bias might have occurred, a study on an online program could have attracted individuals with better digital skills. However, the digital literacy of the participants varied from limited to very skilled, which was also reflected in the variety of feedback regarding ‘ease-of-learning’. Since an online program will only be used by those able and willing to access a program online, the current participants seem representative for the actual target group. Third, we did not have detailed information on drop-outs and therefore cannot describe their characteristics. We did however encourage all individuals to complete the questionnaires and we interviewed individuals independent of their attitude towards the program. Fourth, the participants were recruited based on different SCD criteria between the memory clinic and the research registries. However, we deem the population representative for the heterogeneous populations that can be recruited via memory clinics and brain health registries, and results are generalizable as such. Finally, based on the data log we cannot make a distinction between merely clicking through the program and attentively reading pages. Therefore, we could not take frequency or duration of active participation into account. In the future, this information could be considered when evaluating user-experiences and lifestyle effects of the program.
The strengths of the study include the study design. This was a multicenter study conducted in Germany and the Netherlands. This international character contributes to the generalizability of the findings to other European populations. Results also suggest that although some differences were found, one online tool for multiple European countries would be feasible. Second, we involved the target population throughout the process of development and evaluation. Co-creation is expected to increase the extent to which the tool fits the users’ preferences and digital skills and thereby acceptation and impact of the innovation in further stages (29, 30). Therefore, the users’ input was crucial and resulted in an online lifestyle program fitting the needs and preferences of individuals with SCD. Third, we combined methods to assess user-experiences of the online lifestyle program. The quantitative and qualitative methods were found to complement each other. Finally, we focused on individuals with SCD, who do not show cognitive deficits but at group level are at increased risk for cognitive decline. Therefore, this group is of clinical relevance in the context of dementia prevention. Individuals with SCD also report a need for brain health information, which has not yet been fulfilled since trustworthy sources are still lacking. This group is willing to participate in prevention strategies, which was also observed during recruitment and led to a higher inclusion number than planned.
In conclusion, in this study we developed and evaluated an online lifestyle program for brain health in individuals with SCD. We found that the overall user-experience of our program was moderate to positive. Participants appreciated content on lifestyle and brain health. The variety in preferences for different categories highlighted the need for personalization. It was feasible to offer this online lifestyle program in an at-risk population with SCD. Online self-applied lifestyle programs seem useful when aiming to reach large groups of motivated at-risk individuals for brain health promotion.


Conflicts of interest: The authors state no conflicts of interest. Ms. Wesselman reports grants from JPND/ZonMw, grants from Stichting Equilibrio, during the conduct of the study; Dr. Schild reports grants from Bundesministerium für Bildung und Forschung, during the conduct of the study; Dr. Hooghiemstra has nothing to disclose; Mr. Meiberth reports grants from Bundesministerium für Bildung und Forschung, during the conduct of the study; Ms. Drijver has nothing to disclose; Ms. v Leeuwenstijn-Koopman has nothing to disclose; Dr. Prins has nothing to disclose; Dr. Brennan has nothing to disclose; Dr. Scheltens has nothing to disclose; Dr. Jessen reports grants from Bundesministerium für Bildung und Forschung, during the conduct of the study; Dr. van der Flier reports grants from JPND/ZonMw, during the conduct of the study; Dr. Sikkes reports grants from JPND/ZonMw, grants from Stichting Equilibrio, during the conduct of the study.

Funding: The project is supported through the following funding organizations under the aegis of JPND (; JPND_PS_FP-689-019): Germany, Bundesministerium für Bildung und Forschung (BMBF grant number: 01ED1508), the Netherlands, ZonMw grant no. 733051043. It was additionally supported by a research grant from Stichting Equilibrio. W.M. van der Flier is recipient of a grant by Gieskes-Strijbis fonds. S. Sikkes is recipient of a ZonMw Off Road grant (grant no. 451001010).

Acknowledgements: We thank all participants for their contribution to this research project. We thank Roxelane BV. and specifically Rudolf Wolterbeek, Brian Fa Si Oen and Max Hasenaar for their contribution to this project representing the technical party within the collaboration. We thank Mark Dubbelman for data visualization. We thank the founders of (Trinity College Dublin, supported by European Union’s Seventh Framework Program for research, grant no. 304867) for the fruitful collaboration. The website shares easy-to-understand information and animations about the brain, brain health and brain research. The freely available interactive app, Hello Brain Health, aims to support users to live a brain healthy life by giving daily suggestions called ‘brain buffs’. The app is available on the project website, the App Store and Google Play. The Alzheimer Center Amsterdam is supported by Alzheimer Nederland and Stichting VUmc fonds. Research of the Alzheimer Center Amsterdam is part of the neurodegeneration research program of Neuroscience Amsterdam. Wiesje van der Flier holds the Pasman chair. is funded by ZonMw-Memorabel (project no 73305095003), a project in the context of the Dutch Deltaplan Dementie, the Alzheimer’s Society in the Netherlands and the Brain Foundation Netherlands.

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.





1. Petersen R C, Smith GE, Waring SC, et al. Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology 1999;56:303-308.
2. Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine 2004;256:240-246.
3. Baumgart M, Snyder HM, Carrillo MC, et al. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimer’s & Dementia 2015;11:718-726.
4. Bourre J & Paquotte P. Seafood (wild and farmed) for the elderly: Contribution to the dietary intakes of iodine, selenium, DHA and vitamins B12 and D. The Journal of Nutrition, Health & Aging 2008;12:186-192.
5. Dangour A, Allen E, Elbourne D, et al. Fish consumption and cognitive function among older people in the UK: Baseline data from the opal study. The Journal of Nutrition, Health & Aging 2009;13:198-202.
6. Morris MC, Evans DA, Tangney CC, et al. Fish consumption and cognitive decline with age in a large community study. Archives of Neurology 2005;62:1849-1853.
7. Innis SM. Dietary (n-3) fatty acids and brain development. The Journal of Nutrition 2007;137:855-859.
8. Valenzuela A. Docosahexaenoic acid (DHA), an essential fatty acid for the proper functioning of neuronal cells: their role in mood disorders. International Journal of Fats and Oils 2009;60:203-212.
9. van de Rest O, Geleijnse JM, Kok FJ, et al. Effect of fish oil on cognitive performance in older subjects: a randomized, controlled trial. Neurology 2008;71:430-438.
10. Dangour AD, Allen E, Elbourne D, et al. Effect of 2-y n−3 long-chain polyunsaturated fatty acid supplementation on cognitive function in older people: a randomized, double-blind, controlled trial. The American Journal of Clinical Nutrition 2010;91:1725-1732.
11. Danthiir V, Hosking DE, Nettelbeck T, et al. An 18-mo randomized, double-blind, placebo-controlled trial of DHA-rich fish oil to prevent age-related cognitive decline in cognitively normal older adults. The American Journal of Clinical Nutrition 2018;107:754-762.
12. Moran C, di Palumbo AS, Bramham J, et al. Effects of a Six-Month Multi-Ingredient Nutrition Supplement Intervention of Omega-3 Polyunsaturated Fatty Acids, vitamin D, Resveratrol, and Whey Protein on Cognitive Function in Older Adults: A Randomised, Double-Blind, Controlled Trial. The Journal of Prevention of Alzheimer’s disease 2018;5:175-183.
13. Nilsson A, Radeborg K, Salo I, et al. Effects of supplementation with n-3 polyunsaturated fatty acids on cognitive performance and cardiometabolic risk markers in healthy 51 to 72 years old subjects: a randomized controlled cross-over study. Nutrition Journal 2012;11:99.
14. Witte AV, Kerti L, Hermannstädter HM, et al. Long-chain omega-3 fatty acids improve brain function and structure in older adults. Cerebral Cortex 2013;24:3059-3068.
15. Külzow N, Witte AV, Kerti L, et al. Impact of Omega-3 Fatty Acid Supplementation on Memory Functions in Healthy Older Adults. Journal of Alzheimer’s Disease 2016;51:713-725.
16. Quinn JF, Raman R, Thomas RG, et al. Docosahexaenoic acid supplementation and cognitive decline in Alzheimer disease: a randomized trial. JAMA 2010;304:1903-1911.
17. Freund-Levi Y, Eriksdotter-Jönhagen M, Cederholm T, et al. ω-3 fatty acid treatment in 174 patients with mild to moderate Alzheimer disease: OmegAD study: a randomized double-blind trial. Archives of Neurology 2006;63:1402-1408.
18. Kotani S, Sakaguchi E, Warashina S, et al. Dietary supplementation of arachidonic and docosahexaenoic acids improves cognitive dysfunction. Neuroscience Research 2006;56:159-164.
19. Chiu C-C, Su K-P, Cheng T-C, et al. The effects of omega-3 fatty acids monotherapy in Alzheimer’s disease and mild cognitive impairment: a preliminary randomized double-blind placebo-controlled study. Progress in Neuropsychopharmacology and Biological Psychiatry 2008;32:1538-1544.
20. Yurko-Mauro K, McCarthy D, Rom D, et al. Beneficial effects of docosahexaenoic acid on cognition in age-related cognitive decline. Alzheimer’s & Dementia 2010;6:456-464.
21. Vakhapova V, Cohen T, Richter Y, et al. Phosphatidylserine containing ω–3 fatty acids may improve memory abilities in non-demented elderly with memory complaints: A double-blind placebo-controlled trial. Dementia and Geriatric Cognitive Disorders 2010;29:467-474.
22. Mazereeuw G, Lanctot K, Chau S, et al. Effects of omega-3 fatty acids on cognitive performance: a meta-analysis. Neurobiology of Aging 2012;33:1482.e17-1482.e29.
23. Sinn N, Milte CM, Street SJ, et al. Effects of n-3 fatty acids, EPA v. DHA, on depressive symptoms, quality of life, memory and executive function in older adults with mild cognitive impairment: a 6-month randomised controlled trial. The British Journal of Nutrition 2012;107:1682-1693.
24. Lee L, Shahar S, Chin AV, et al. Docosahexaenoic acid-concentrated fish oil supplementation in subjects with mild cognitive impairment (MCI): a 12-month randomised, double-blind, placebo-controlled trial. Psychopharmacology 2013;225:605-612.
25. Rondanelli M, Opizzi A, Faliva M, et al. Effects of a diet integration with an oily emulsion of DHA-phospholipids containing melatonin and tryptophan in elderly patients suffering from mild cognitive impairment. Nutritional Neuroscience 2012;15:46-54.
26. Yacong B, Xueyuan Z, Youli W, et al. The n-3 Polyunsaturated Fatty Acids Supplementation Improved the Cognitive Function in the Chinese Elderly with Mild Cognitive Impairment: A Double-Blind Randomized Controlled Trial. Nutrients 2017;9:54.
27. Barberger-Gateau P, Samieri C, Feart C, et al. Dietary omega 3 polyunsaturated fatty acids and Alzheimer’s disease: interaction with Apolipoprotein E genotype. Current Alzheimer Research 2011;8:479-491.
28. Fjell AM & Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Reviews in the Neurosciences 2010;21:187-222.
29. Chhetri JK, de Souto Barreto P, Cantet C, et al. Trajectory of the MAPT-PACC-preclinical Alzheimer cognitive composite in the Placebo Group of a Randomized Control Trial: results from the MAPT Study: lessons for Further Trials. The Journal of Prevention of Alzheimer’s disease 2018;5:31-35.
30. Berlau DJ, Corrada MM, Head E, et al. APOE ε2 is associated with intact cognition but increased Alzheimer pathology in the oldest old. Neurology 2009;72:829-834.
31. Randolph C. Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). San Antonio: Psychological Corporation, 1998;.
32. Nelson H. The Nelson adult reading test (NART) manual. Windsor, England. NFER-Nelson 1982;530:531.
33. Mendoza JE, Apostolos GT, Humphreys JD, et al. Coin rotation task (CRT): a new test of motor dexterity. Archives of Clinical Neuropsychology 2009;24:287-292.
34. Pachana NA, Thompson, LW, Marcopulos BA, et al. California older adult Stroop test (COAST) development of a Stroop test adapted for geriatric populations. Clinical Gerontologist 2004;27:3-22.
35. Army US. Army individual test battery. Manual of directions and scoring. Washington, DC: War Department, Adjutant General’s Office, 1944.
36. Wechsler D. Wechsler Adult Intelligence Scale, 3rd ed. San Antonio, TX: Psychological Corporation, 1997.
37. Thornton A & Leathem, JM. Coin rotation task. The development of norms for New Zealand and the United States. Presented at 6th International and 11th National Congress of Clinical Psychology. Santiago De Compostela, Spain, (2013, June).
38. Sheikh JI & Yesavage JA. Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontologist: The Journal of Aging and Mental Health 1986;5:165-173.
39. Pachana NA, Byrne GJ, Siddle H, et al. Development and validation of the Geriatric Anxiety Inventory. International Psychogeriatrics 2007;19:103-114.
40. Ware Jr JE & Gandek B. The SF-36 Health Survey: Development and use in mental health research and the IQOLA Project. International Journal of Mental Health 1994;23:49-73.
41. Bartels C, Wegrzyn M, Wiedl A, et al. Practice effects in healthy adults: a longitudinal study on frequent repetitive cognitive testing. BMC Neuroscience 2010;11:118.
42. Ahl RE, Beiser A, Seshadri S, et al. Defining MCI in the Framingham Heart Study Offspring: Education vs. WRAT-based norms. Alzheimer Disease and Associated Disorders 2013;27:1-15.
43. Das UN. Nutritional factors in the pathobiology of human essential hypertension. Nutrition 2001;17:337-346.
44. Morris MC, Sacks F & Rosner B. Does fish oil lower blood pressure? A meta-analysis of controlled trials. Circulation 1993;88:523-533.
45. Sullivan PF, Neale MC & Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. American Journal of Psychiatry 2000;157:1552-1562.
46. Deckers K, Boxtel MP, Schiepers OJ, et al. Target risk factors for dementia prevention: a systematic review and Delphi consensus study on the evidence from observational studies. International Journal of Geriatric Psychiatry 2015;30:234-246.


C.W. Ritchie1, G. Muniz-Terrera1, M. Kivipelto2, A. Solomon2, B. Tom3, J.L. Molinuevo4 On Behalf of the EPAD Consortium

1. University of Edinburgh, United Kingdom; 2. Karolinska Institute, Sweden; 3. University of Cambridge, United Kingdom; 4. Barcelona Beta Research Centre, Spain

Corresponding Author: Craig William Ritchie, University of Edinburgh, United Kingdom,

J Prev Alz Dis 2020;(7) in press
Published online November 26, 2019,



Background: The European Prevention of Alzheimer’s Dementia (EPAD) Programme is a pan-European project whose objective is to deliver a platform, adaptive, Phase 2 proof of concept (PoC) trial for the secondary prevention of Alzheimer’s dementia. A component of this platform is the Longitudinal Cohort Study (LCS) which acts as a readiness cohort for the PoC Trial as well as generating data for disease modelling work in the preclinical and prodromal phases of Alzheimer’s dementia.
Objectives: The first data wave has been collected, quality checked, released and now available for analysis to answer numerous research questions. Here we describe the results from key variables in the EPAD LCS with the objective of using these results to compliment analyses of these data in the future.
Design: EPAD LCS is a cohort study whose primary objective is as a readiness cohort for the EPAD PoC Trial. As such recruitment is not capped at any particular number but will continue to facilitate delivery of the EPAD PoC Trial. Research Participants are seen annually (with an additional 6 month visit in the first year).
Setting: The EPAD Trial Delivery Network comprises currently 21 centres across Europe.
Participants: Research participants are included if they are over 50 years old and do not have a diagnosis of dementia.
Measurements: All research participants undergo multiple assessments to fully characterise the biology of Alzheimer’s disease and relate this to risk factors (both fixed and modifiable) and biomarker expression of disease through brain imaging, fluid samples (CSF, blood, urine and saliva), cognitive performance, functional abilities and neuropsychiatric symptomatology.
Results: V500.0 represents the first 500 research participants baselined into EPAD LCS. The mean age was 66.4 (SD=6.7) and 47.8% were male. The data was split for presentation into 4 groups: [1] CDR=0 and Amyloid + (preclinical AD), [2] CDR=0 and Amyloid –, [3] CDR=0.5 and Amyloid + (prodromal AD) and [4] CDR=0.5 and Amyloid -.
Conclusions: The EPAD LCS is achieving its primary objective of trial readiness and the structured approach to data release as manifest by this first data release of V500.0 will assist researchers to describe and compare their findings as well as in systematic reviews and meta-analyses. It is anticipated given current recruitment rates that V1500.0 data release will take place in Autumn 2019. V500.1 (when the 1 year follow up is completed on the V500.0 (sub)cohort will be in Autumn 2019 also.

Key words: EPAD, Cohort, Alzheimer’s disease, Prevention, Disease modelling.



The European Prevention of Alzheimer’s Dementia (EPAD) project was initiated in January 2015 and is funded by the Innovative Medicines Initiative. The overall project background and objectives are described elsewhere (1). In summary EPAD has a singular objective and that is to develop an entire infrastructure for the delivery of the EPAD Proof of Concept Trial. The EPAD PoC Trial is a platform trial, which employs a single master protocol with multiple appendices (representing each intervention) that uses Bayesian Adaptive Designs to develop interventions for the secondary prevention of Alzheimer’s dementia through Phase 2. The EPAD Infrastructure has many components including a virtual register of people in partnering parent cohorts across Europe, the Trial Delivery Centre (TDC) network, the PoC trial platform of TDCs, vendors and Clinical Research Organisations and the Longitudinal Cohort Study (LCS). The primary objective of the EPAD LCS is as a readiness cohort for the PoC to minimise screen failure by way of detailed characterisation of research participants within it and to provide run-in data to be used to compare with post-randomisation data from the PoC itself. In accumulating vast amounts of data from a very large and highly characterised cohort, the EPAD LCS will be able to deliver data (eventually on an open data access platform) to the entire research community to assist with disease modelling and knowledge generation regarding the interplay between risk factors, brain disease, expression of brain disease (through biomarkers, cognition, function and neuropsychiatric symptoms) and how these change over time. Aware of the potential power of these data in the understanding of preclinical and prodromal Alzheimer’s disease, the management of data releases to the research community had to be measured, transparent and organised. Moreover, all data and sample collection and sample analysis has been conducted to the highest GLP and GCP standards.
The V500.0 data release represents the first formal data release from the EPAD project for use by multiple researchers. This paper describes this data in detail to assist current and future researchers with their analysis and also to facilitate between project comparisons of data in systematic reviews and meta-analysis.



The EPAD LCS Protocol and Methodology is provided in detail elsewhere (2). The EPAD LCS has as its primary objective to be a readiness cohort for the EPAD PoC Trial. Therefore, recruitment into LCS is not capped and will continue ad infinitum to provide the necessary number of suitable research participants for the EPAD PoC Trial. The secondary objective of the EPAD LCS is to use the data generated for disease modelling. After consent, research participants complete a comprehensive series of assessments.
Research Participants are eligible for inclusion if they are over the age of 50 and do not have a diagnosis of dementia. They must also be deemed suitable in principal for later inclusion in a clinical trial and therefore should not have any medical or psychiatric disorders which would normally exclude people from such trials.
Research Participants are seen every year where the entire protocol of assessments is completed. There is also a 6-month visit where only cognition is assessed. The domains of assessment are [1] cognition, [2] neuroimaging, [3] fluid biomarkers, [4] genetics, [5] lifestyle, [6] clinical and psychiatric assessment, [7] neuropsychiatric symptoms, [8] function and [9] basic demography. The decisions on which outcome measures to use were subject to intense deliberation and review of the extant scientific literature by four EPAD Scientific Advisory Groups on Cognition and Clinical Outcomes, Biomarkers, Neuroimaging and Genetics.
Data releases from the EPAD LCS will be highly systematised. In our chosen nomenclature (V500.0): V=version, 500 is the number of sequentially recruited research participants and ‘.0’ refers to the data including only the baseline (visit 0) data. This subcohort will be followed over time so that the next release from this cohort V500.1 will take place in approximately 12 months. This will include all the baseline, 6 month and 12 month data from these 500 research participants. The ‘.1’ refers to the data including all data up until the 1 year visit, V500.2 will be when all data up until the 2 year visit is released.

Cognition and Clinical Outcomes

The Cognition and Clinical Scientific Advisory Group advised the LCS protocol authors on the construction of the EPAD Cognitive Examination (ECE) (3, 4) as well as on functional outcomes and the capturing of key neuropsychiatric features namely sleep, anxiety and depression. The cognitive outcomes captured are: RBANS (5, 6) (Primary Outcome Measure for EPAD PoC Trial), CDR (7), MMSE (8), NIH Toolbox tests (Dot Counting, Flanker) (9, 10), UCSF Brain Health Assessment (Favorites) (11), Supermarket Trolley Test (12) and Four Mountains Test (13). Function is assessed using the Amsterdam Instrumental Activity of Daily Living Assessment (14, 15). Sleep is assessed using the Pittsburgh Sleep Questionnaire (16); Anxiety is measured using the State/Trait Anxiety Inventory (17) and Depression using the Geriatric Depression Scale (18, 19).
All cognitive and clinical data is captured on tablets (either on the Medavante Virgil Platform or the UCSF Tabcat System). These data are then uploaded to the EPAD LCS Master Database held by the EPAD LCS Clinical Research Organisation IQVIA for conciliation with other data sources e.g. imaging and eCRF data before being quality controlled and then pushed to the Analytical Database hosted by the EPAD Partner Aridhia.

Neuroimaging Outcomes

The Neuroimaging Scientific Advisory Group advised the LCS protocol authors on structural and functional MRI based evaluations optimised for understanding brain changes in preclinical and prodromal Alzheimer’s disease (20). The structural sequence captured in the protocol were Cortical thickness, deep grey matter volumes, fractional anisotropy of temporal lobe, diffusion kurtosis (multi b-valueDTI) and network alterations. The functional MRI outcomes were global & parietal CBF and changes within the default-mode network & relation with hippocampal activity(rsfMRI), Bolus arrival time (multi-delay arterial spin labelling) and network analysis (rsfMRI) though not all of these data have been analysed as yet in V500.0 and therefore not presented in this paper.
All brain-imaging facilities are accredited by the EPAD LCS Imaging CRO IXICO. Imaging files from the site are transferred to IXICO for central reading and safety evaluation. Key outcomes are then transferred to the Master Database for conciliation with other data feeds before these data are transferred to Aridhia and the EPAD Analytical Database. MRI scanners are a minimum of 1.5T.

Biomarker Outcomes

The Biomarker Scientific Advisory Group after review of the existing evidence around neuropathological changes in preclinical and prodromal AD had to decide which biomarkers were either fully validated as markers of disease or remained at the discovery phase of development. The former were to be incorporated in the protocol whilst the potential to explore the others would be reserved to a future date from EPAD LCS samples collected, shipped and stored under optimal conditions. The Biomarker SAG therefore also oversaw the creation of the laboratory manual (available on line at
The only biomarkers in the protocol are CSF ABeta, Tau and Phosphorylated Tau. All samples are shipped from sites and stored centrally at the EPAD BioBank at the University of Edinburgh before CSF samples taken in Sarstedt tubes are shipped to the University of Gothenburg, Sweden for analysis using the Roche Diagnostics Elecsys Platform. Results are then forwarded to the IQVIA Master Database and then transferred to the Aridhia Analytical Database. Using this system a threshold of 1,000pg/ml of ABeta42 was agreed upon to define amyloid positivity.
Saliva (drooling sample and salivette), urine and plasma are also stored in the EPAD BioBank for future use. To date none of these samples have been analysed.

Genetics Outcomes

The Genetics Scientific Advisory Group had a similar remit to that of the Biomarker SAG in so much as they agreed on recommendations for outcomes to be done in all samples and within the protocol and advise on optimal storage for future use. They recommended that all samples should be tested for ApoE status of the research participants. Sampling preparation and storage details can be found in the EPAD lab manual (available on line at
Taqman Genotyping was carried out in a single laboratory on QuantStudio12K Flex to establish ApoE variants. Genomic DNA was isolated from whole blood and genotyping was performed in 384 well-plates, using the TaqMan polymerase chain reaction-based method. The final volume PCR reaction was 5 μl using 20 ng of genomic DNA, 2.5 μl of Taqman Master Mix and 0.125μl of 40x Assay By design Genotyping Assay Mix, or 0.25µl of 20x Assay On Demand Genotyping Assay. The cycling parameters were 95° for 10 minutes, followed by forty cycles of denaturation at 92° for 15 seconds and annealing/extension at 60° for 1 minute. PCR plates were then read on ThermoFisher QuantStudio 12K Flex Real Time PCR System instrument with QuantStudio 12K Flex Software or Taqman Genotyper Software v1.3.

Demographic, lifestyle, clinical and other outcomes

The EPAD LCS Protocol authors decided upon all outcomes following advice from the four listed Scientific Advisory Groups. Other outcomes were decided solely by the protocol authors. Lifestyle factors captured were self reported physical activity, diet, smoking/alcohol/drug behaviour. Demography included gender, age, years of education and race (where allowable by regional authorities to be captured). Clinical history and physical examination was captured in standardised source documents to cover all medical and psychiatric domains including medication use. Specific assessment of head injury was undertaken using the Brain Injuries Screening Questionnaire.


All results are presented similarly as the total sample and by amyloid and CDR status. Some data on variables presented are missing and then within that grouping amyloid status may be missing too due to analysis problems.
Lifestyle variables and medical history of research participants are not presented here but available on line on the EPAD website.

Table 1. Demographics and ApoE Status

Table 1. Demographics and ApoE Status

Table 2. Cognition

Table 2. Cognition

Table 3. Neuroimaging

Table 3. Neuroimaging

Table 4. CSF Biomarkers

Table 4. CSF Biomarkers

Table 5. Other Key Outcomes

Table 5. Other Key Outcomes



The objective of this paper was to convey the structure of the EPAD LCS using the first data release from the project. With a large cohort that has open ended and perpetual recruitment, it was considered crucial that the academic and broader research community could have clarity on which data sets are being used from EPAD that form the basis of secondary data analysis. It is not in the scope of this paper to draw any conclusions from the data as no research question is being proposed here or hypothesis being tested. However, in terms of readiness for the EPAD PoC trial, just under 35% of the cohort are amyloid positive with the majority of these being CDR=0 which probably reflects the initial source of recruitment to EPAD LCS from population based parent cohorts (21). To date 26.6% of the sample have preclinical Alzheimer’s disease and 8.3% prodromal Alzheimer’s disease. However with an increasing drive in recent months to significantly increase the proportion of people in the cohort with CDR=0.5 (in V500.0 = 14.8%) to nearer 30%, we expect confidently that by 2020 and commencement of the PoC trial, the LCS will have the necessary level of readiness. V500.0 also has a broad range of research participants in terms of many key outcomes that will assist in disease modelling work and other hypothesis testing.
The quality of the data, sample preparation, storage and analysis was of paramount concern to the EPAD Consortium and much effort has gone into this with lab and imaging manuals available on the EPAD website to give researchers both assurance and clarity on the processes undertaken in EPAD to deliver high quality data.
The value of the EPAD LCS will clearly increase as the sample size increases and greater length of follow up is achieved in each sub-cohort. Description of each data release from EPAD is planned and will follow this initial formatting.



The EPAD Programme is a very large and ambitious programme that is ultimately set to deliver a platform, multi-arm PoC trial to test interventions targeting the secondary prevention of Alzheimer’s dementia. Whilst the project has numerous key components, the EPAD LCS is central to all the efforts. Data from this cohort will be of great value to the research community so managed, orderly, structured and well described data releases are critical as a reflection of the importance of this database and the time and effort all our research participants have committed and made to help understand Alzheimer’s disease before dementia develops and in doing so move a step closer to its prevention.


Conflict of interest: No conflict of interest.

Funding: The European Prevention of Alzheimer’s Dementia project has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking EPAD grant agreement number 115736.

Ethical Standards: The EPAD LCS protocol and materials are submitted to the Independent Ethics Committee or other relevant ethical review board for written approval as required by local laws and regulations. A copy of approval is required by the University of Edinburgh as Sponsor before the study commences at each site. The study is designed and conducted in accordance with the guidelines for Good Clinical Practice (GCP), and with the ethical principles as proclaimed in the Declaration of Helsinki. All participants are required to provide written informed consent prior to participation in any research activities laid out in the EPAD LCS protocol.

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.



1. Ritchie CW, Molinuevo JL, Satlin A, Van Der Geyten, Lovestone S and Truyen L. The European Prevention of Alzheimer’s Dementia (EPAD) Consortium: A platform to enable the secondary prevention of Alzheimer’s Dementia through improved Proof of Concept Trials. Lancet Psychiatry 2016;3(2):179-86
2. Solomon A et al., The European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study (EPAD LCS): study protocol. BMJ Open. 2018;8:e021017
3. Mortamais M, Ash JA, Harrison J, Kaye J, Kramer J, Randolph C, Pose C, Albala B, Ropacki M, Ritchie CW, Ritchie K. Detecting cognitive changes in preclinical Alzheimer’s disease: A review of its feasibility. Alzheimers Dement. 2017;13(4):468-92.
4. Ritchie K, Ropacki M, Albala B, Harrison J, Kaye J, Kramer J, Randolph C and Ritchie CW. Recommended cognitive outcomes in preclinical Alzheimer’s disease: Consensus statement from the European Prevention of Alzheimer’s Dementia project. Alzheimers Dement. 2017;13(2):186-195
5. Karantzoulis S, Novitski J, Gold M, Randolph C. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Utility in detection and characterization of mild cognitive impairment due to Alzheimer’s disease. Arch Clin Neuropsychol. 2013;28(8):837-44.
6. England HB, Gillis MM, Hampstead BM. RBANS memory indices are related to medial temporal lobe volumetrics in healthy older adults and those with mild cognitive impairment. Arch Clin Neuropsychol. 2014;29(4):322-8.
7. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurol. 1993;43:2412-2414
8. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189-98
9. Kramer JH, Mungas D, Possin KL, Rankin KP, Boxer AL, Rosen HJ, Bostrom A, Sinha L, Berhel A, Widmeyer M. NIH EXAMINER: conceptualization and development of an executive function battery. J Int Neuropsychol Soc. 2014;20(1):11-9.
10. Weintraub S, Dikmen SS, Heaton RK, Tulsky DS, Zelazo PD, Bauer PJ, Carlozzi NE, Slotkin J, Blitz D, Wallner-Allen K, Fox NA. Cognition assessment using the NIH Toolbox. Neurol. 2013;80(11 Supplement 3):S54-64.
11. Possin KL, Moskowitz T, Erlhoff SJ, Rogers KM, Johnson ET, Steele NZ, Higgins JJ, Stiver J, Alioto AG, Farias ST, Miller BL. The Brain Health Assessment for Detecting and Diagnosing Neurocognitive Disorders. J Am Geriatr Soc. 2018;66(1):150-6.
12. Tu S, Wong S, Hodges JR, Irish M, Piguet O, Hornberger M. Lost in spatial translation–A novel tool to objectively assess spatial disorientation in Alzheimer’s disease and frontotemporal dementia. Cortex. 2015;67:83-94.
13. Moodley K, Minati L, Contarino V, Prioni S, Wood R, Cooper R, D’incerti L, Tagliavini F, Chan D. Diagnostic differentiation of mild cognitive impairment due to Alzheimer’s disease using a hippocampus-dependent test of spatial memory. Hippocampus. 2015;25(8):939-51.
14. Sikkes SA, Knol DL, Pijnenburg YA, de Lange-de Klerk ES, Uitdehaag BM, Scheltens P. Validation of the Amsterdam IADL Questionnaire©, a new tool to measure instrumental activities of daily living in dementia. Neuroepidemiology. 2013;41(1): 35-41
15. Sikkes SA, de Lange-de Klerk ES, Pijnenburg YA, Gillissen F, Romkes R, Knol DL, Uitdehaag BM, Scheltens P. A new informant-based questionnaire for instrumental activities of daily living in dementia. Alzheimers Dement. 2012;8(6):536-43.
16. Buysse DJ, Reynolds III CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213.
17. Julian LJ. Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res. 2011;63(S11):S467-72.
18. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, Leirer VO. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982;17(1):37-49.
19. Debruyne H, Van Buggenhout M, Le Bastard N, Aries M, Audenaert K, De Deyn PP, Engelborghs S. Is the geriatric depression scale a reliable screening tool for depressive symptoms in elderly patients with cognitive impairment? Int J Geriatr Psychiatry. 2009;24(6):556-62.
20. Ingala S, ten Kate M, Schwarz A, Fox N, Chetelat G, van Berckel B, Ewers M, Foley C, Gispert JD, Hill D, Irizarry M, Lammertsma A, Molinuevo JL, Ritchie CW, Scheltens P, Schmidt M, Visser PJ, Waldman A, Wardlaw J, Haller S, Barkhof F. Secondary prevention of Alzheimer’s dementia – neuroimaging contributions. J Prev Alzheimers Dis. 2018;10(1):112
21. Vermunt L, Veal CD, ter Meulen L, Chrysostomou C, van der Flier W, Frisoni GB, Guessous I, Kivipelto M, Marizzoni M, Martinez-Lage P, Molinuevo JL, Porteus D, Ritchie K, Scheltens P, Ousset PJ, Ritchie CW, Luscan G, Brookes AJ, Visser PJ. European Prevention of Alzheimer’s Dementia (EPAD) Registry: recruitment and pre-screening approach for a longitudinal cohort and prevention trials. Alzheimers Dement. 2018;14(6): 837-842



A. Rosenberg1, F. Mangialasche2,3, T. Ngandu4, A. Solomon1,2, M. Kivipelto2,5,6,7,8


1.  Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland; 2. Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; 3. Aging Research Center, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden; 4. Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; 5. Theme Aging, Karolinska University Hospital, Stockholm, Sweden; 6. Stockholms Sjukhem, Research & Development Unit, Stockholm, Sweden; 7. The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom;
8. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

Corresponding Author: Miia Kivipelto,  Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Karolinska Universitetssjukhuset, Karolinska Vägen 37 A, QA32, 171 64 Solna, Sweden, Phone: +46 (0)73 99 40 922,

J Prev Alz Dis 2019;
Published online October 10, 2019,



Alzheimer’s disease (AD) and dementia are a global public health priority, and prevention has been highlighted as a pivotal component in managing the dementia epidemic. Modifiable risk factors of dementia and AD include lifestyle-related factors, vascular and metabolic disorders, and psychosocial factors. Randomized controlled clinical trials (RCTs) are needed to clarify whether modifying such factors can prevent or postpone cognitive impairment and dementia in older adults. Given the complex, multifactorial, and heterogeneous nature of late-onset AD and dementia, interventions targeting several risk factors and mechanisms simultaneously may be required for optimal preventive effects. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) is the first large, long-term RCT to demonstrate that a multidomain lifestyle-based intervention ameliorating vascular and lifestyle-related risk factors can preserve cognitive functioning and reduce the risk of cognitive decline among older adults at increased risk of dementia. To investigate the multidomain intervention in other populations and diverse cultural and geographical settings, the World-Wide FINGERS (WW-FINGERS) network was recently launched ( Within this network, new FINGER-type trials with shared core methodology, but local culture and context-specific adaptations, will be conducted in several countries. The WW-FINGERS initiative facilitates international collaborations, provides a platform for testing multidomain strategies to prevent cognitive impairment and dementia, and aims at generating high-quality scientific evidence to support public health and clinical decision-making. Furthermore, the WW-FINGERS network can support the implementation of preventive strategies and translation of research findings into practice.

Key words: Alzheimer’s disease, cognitive impairment, dementia, multidomain, prevention, randomized controlled trial.


Dementia is the main cause of disability among older adults, affecting around 50 million people worldwide (1). Driven by population aging, this number is expected to increase rapidly to over 150 million by 2050, creating a major public health and social challenge (2). Alzheimer’s disease (AD) underlies the majority of dementia cases, often in association with vascular neuropathology. Disease-modifying therapies are not yet available for AD, and despite the recent positive signals in some of the ongoing randomized controlled trials (RCTs) testing anti-amyloid compounds, drug trials have mostly reported disappointing results (3,4). Given the evidence emerging from longitudinal observational studies, indicating that late-life cognitive impairment, AD, and dementia are heterogeneous and multifactorial conditions driven by a combination of genetic, vascular, metabolic, and lifestyle-related factors, the potential of dementia prevention through risk factor modification and management has gained increasing attention.
Findings from observational studies need to be substantiated by RCTs, which are considered the gold standard to verify the effect of an intervention. Results of the earlier, smaller, shorter-term prevention RCTs focusing on individual lifestyle components and risk factors have been mostly modest, and evidence from large, long-term trials is only beginning to emerge (5). Importantly, in light of the current knowledge about the complex and multifactorial etiology of late-onset AD and dementia, targeting several risk factors and mechanisms simultaneously, as well as tailoring interventions to individual risk profiles, may be necessary to obtain optimal preventive effects. So far, three large European multidomain lifestyle-based prevention trials have been completed: the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) (6), the French Multidomain Alzheimer Preventive Trial (MAPT) (7), and the Dutch Prevention of Dementia by Intensive Vascular Care (PreDIVA) (8). The FINGER trial reported significant beneficial intervention effects on the primary outcome, namely change in global cognitive performance, among older ‘at risk’ adults from the general population (6). Notably, exploratory subgroup analyses of MAPT and PreDIVA also suggested cognitive benefits in subpopulations of participants with increased risk of dementia (7-9). Taken together, these studies indicate that administering multidomain lifestyle-based interventions to older at-risk adults may be feasible and effective. However, to fully understand the potential and impact of multidomain preventive interventions, their efficacy and feasibility needs to be explored in diverse populations and contexts worldwide. The FINGER model is now tested and adapted in several new preventive trials globally, and the World-Wide-FINGERS (WW-FINGERS) network ( was launched to support these joint initiatives aiming to reduce the burden of cognitive impairment and dementia. This article will provide an up-to-date overview of the multidomain intervention concept, lessons learned from the recent multidomain RCTs, and future directions in the field.


Modifiable risk and protective factors of cognitive impairment, Alzheimer’s disease, and dementia: a window of opportunity for prevention

Increasing evidence from long-term prospective cohort studies linking several modifiable risk and protective factors with late-onset dementia and AD has accumulated during the past decades (10, 11). These include vascular and metabolic risk factors and disorders, lifestyle-related, and psychosocial factors. It is also common that neurodegenerative and vascular pathology co-occur, particularly in older adults with dementia, and mixed dementia has been reported to be the most common type of dementia among individuals older than 80 years (12, 13). With regard to several vascular risk factors, the association with dementia risk is modified by age, and different risk factors may be relevant at different time points in life (14). For example, hypertension, obesity, and hypercholesterolemia in mid-life are risk factors for late-onset dementia and AD (15), but the opposite association has been reported later in life and in studies with shorter follow-up times, possibly reflecting reverse-causality (i.e., those factors decrease in the early, asymptomatic stages of dementia most likely as a consequence of the disease) (16, 17). In addition to vascular and metabolic factors (high blood pressure and cholesterol, obesity, diabetes, impaired glucose metabolism), smoking (18), excessive use of alcohol (19), depression (20), as well as other psychosocial factors, such as work-related stress, feelings of hopelessness or loneliness, and infrequent social contacts (21-23), are associated with an increased dementia risk. Protective factors for cognitive impairment, dementia and AD include regular physical activity (24), having a higher formal education (25) and an intellectually demanding and stimulating work (occupational complexity) (26), as well as engaging in cognitively and mentally stimulating leisure activities (27). Social engagement and having a rich social network have also been associated with a reduced risk of dementia and AD (28, 29).
Among pharmacological treatments, both observational studies and RCTs have indicated that antihypertensive drugs may be associated with a reduced risk of AD and dementia (30). The recent large, long-term Systolic Blood Pressure Intervention Trial (SPRINT) Memory and Cognition IN Decreased Hypertension (MIND) RCT reported that intensive blood pressure control (goal <120 mmHg) can be more effective in reducing the risk of cognitive impairment than standard blood pressure control (goal <140 mmHg), although the question of the optimal therapeutic target for systolic blood pressure among oldest old individuals (85+ years) still remains (31). Findings regarding other medications, such as statins, hormone replacement therapy, and non-steroidal anti-inflammatory drugs, are conflicting, as the beneficial effects suggested by observational studies have not been confirmed in RCTs (10).
In relation to diet, some individual nutrients, including omega-3 polyunsaturated fatty acids, vitamins B6 and B12, folate, vitamin D, and vitamins A, C, E (antioxidants), have been associated with a reduced risk of dementia in observational studies (32), although no conclusive evidence has so far emerged from trials testing nutraceutical supplements. Furthermore, regular intake of fish, fruits, vegetables, and nuts have been linked with a reduced risk of cognitive impairment and dementia. Among dietary patterns, cognitive benefits have been reported for different diets which are based on frequent consumption of fruits and vegetables, unsaturated fats, whole grain products, and fish: the Mediterranean Diet, the DASH (Dietary Approaches to Stop Hypertension), the hybrid MIND (Mediterranean-DASH Intervention for Neurodegenerative Delay) diet, and the healthy Nordic diet (33-37). As opposed to single nutrients, the role of healthy and balanced dietary patterns may be more relevant, because nutrients have cumulative and synergistic effects.
Overall, it has been estimated that approximately 35% of dementia cases worldwide could be attributable to nine modifiable risk factors: low educational attainment in early life, midlife hypertension and obesity, diabetes mellitus, smoking, physical inactivity, depression, social isolation and hearing loss over the entire adult life course (38). This indicates clearly a prevention potential across the lifespan. In line with these findings, secular trend studies have indicated that the age-specific incidence and prevalence of dementia may have declined in some Western countries (39), potentially as a result of improved treatment of cardiovascular disease and vascular risk factors, reduction in smoking, increased educational attainment, and an overall improvement in lifestyle. However, the prevalence of dementia has been shown to increase faster than expected in countries like China and Japan (40,41), and with increasing prevalence of some risk factors, such as obesity and type 2 diabetes (42), there is a great need for global efforts to manage risk factors and reduce the burden of dementia.
A key issue to consider in preventive interventions is the fact that multiple risk and protective factors for dementia and AD usually co-occur and interact across the lifespan to determine the individual’s overall risk of dementia. For instance, in the context of interactions between genetic and environmental factors, it has been reported that the harmful effects of unhealthy lifestyle (i.e., unhealthy diet, alcohol misuse, smoking, physical inactivity) may be more pronounced among carriers of apolipoprotein E (APOE) ε4 allele, which is the most well-known genetic risk factor of late-onset AD (43). Furthermore, vascular factors can have additive effects (10). Overall, co-occurrence of risk factors, as well as their time- and age-dependent effects, underline the complexity of dementia prevention and imply that a ”one-size-fits-all” preventive approach might not be effective. Instead, a tailored, life-course approach targeting multiple risk factors is likely needed for effective prevention of cognitive impairment and dementia. This means that middle-aged and older adults, as well as individuals with heterogeneous risk profiles, may benefit from somewhat different multidomain preventive strategies in order to change their risk profiles.


From observational studies to clinical trials: large multidomain lifestyle-based interventions

Three pioneering, large, long-term multidomain lifestyle prevention trials have been recently conducted in Europe: the Finnish FINGER trial; the French MAPT trial, and the Dutch PreDIVA trial.
The two-year FINGER trial (NCT01041989) is the first large, long-term, multicenter RCT showing  a significant effect of the multidomain lifestyle intervention against cognitive decline among older adults who had increased risk of dementia (6, 44). The FINGER trial enrolled 1260 older adults aged 60-77 years, recruited from previous population-based surveys. Inclusion criteria were as follows: increased risk of dementia based on the CAIDE (Cardiovascular Risk Factors, Aging and Dementia) Dementia Risk Score (≥6 points) (45); and cognitive performance at the mean level or slightly lower than expected for age. Participants were randomized into the multidomain intervention or control group. The multidomain intervention was delivered by trained professionals through both individual sessions and group activities, and it consisted of dietary counseling, exercise, cognitive training, social activities, and monitoring and management of vascular and metabolic risk factors. The control group was offered regular health advice.
The primary outcome of the trial was change in cognitive performance measured by a neuropsychological test battery (NTB) composite score, and secondary cognitive outcomes included domain-specific NTB scores. After two years, the intervention showed significant beneficial effects on the NTB composite score (25% more improvement compared to control), as well as on executive functioning (83% more improvement), processing speed (150% more improvement), and complex memory tasks (40% more improvement). Furthermore, the intervention group had a lower risk of cognitive decline. Follow-ups at 5 and 7 years have been recently completed to determine long-term effects (data analysis is ongoing). The multidomain intervention was safe and well accepted, with high adherence and a low drop-out rate (12%), supporting the feasibility of lifestyle interventions in older at-risk adults. Importantly, the intervention benefits were not limited to cognition: additional favorable effects included body mass index (BMI) reduction (6), improved adherence to dietary guidelines and recommendations (46), and increase in physical activity (6) and health-related quality of life (47). The intervention also improved physical performance and supported daily functioning (48) and lowered the risk of multimorbidity as well as risk of developing new chronic diseases (49). Notably, pre-specified subgroup analyses indicated that the intervention was beneficial regardless of age, sex, education, vascular risk profile and baseline cognitive performance, indicating that the beneficial effects were not limited to a subset of participants, but findings may be generalized to a large population of older adults at increased risk of dementia (50). APOE ε4 carriers got clear benefit from the intervention (51).
The three-year MAPT trial (NCT00672685) is a large, long-term RCT combining lifestyle-based intervention with a nutraceutical compound (7). MAPT enrolled 1680 community dwellers aged 70 years or older who had either subjective memory complaints, limitation in one instrumental activity of daily living, or slow gait speed. In the four parallel arms of the RCT, two intervention groups received a multidomain lifestyle intervention consisting of cognitive training and counseling on nutrition and physical activity, either alone or in combination with omega-3 fatty acid supplementation. One intervention group received only the omega-3 fatty acid supplementation, and one arm was assigned to placebo. The primary outcome was change in a cognitive composite score, and secondary outcomes included the individual components of the composite score, other cognitive test scores (e.g. Mini-Mental State Examination MMSE), and the Short Physical Performance Battery and Alzheimer’s Disease Cooperative Study-Activities of Daily Living (ADCS-ADL) Prevention Instrument scores. Although the trial failed to meet its primary outcome, beneficial intervention effects were observed when both groups receiving the multidomain lifestyle intervention were combined. Also, the combined multidomain lifestyle plus omega-3 fatty acid intervention had beneficial effects on some secondary outcomes (ten MMSE orientation items). Moreover, exploratory analyses indicated beneficial effects in specific subgroups of at-risk participants: those with brain amyloid pathology or a CAIDE risk score of ≥6 points (7, 9), which was the same cut-off used in FINGER to select participants.
The PreDIVA (ISRCTN29711771) is a six-year study targeting 3526 older adults aged 70-78 years, recruited via general practices (8). Compared to the FINGER and MAPT participants, the PreDIVA population was rather unselected. The intervention group received a nurse-led multidomain intervention consisting of advice concerning healthy lifestyle and intensive vascular care and risk factor management, including initiation or optimization of antithrombotics and pharmacological treatments for hypertension, dyslipidemia, or diabetes, when necessary. The control group was offered regular care. The main results of the trial did not show any difference in dementia incidence, which was the primary outcome, between the intervention and control groups. However, in the exploratory analyses, a reduction in the incidence of dementia was observed among individuals with untreated hypertension who adhered to the treatment during the trial.
Several important lessons can be learned from these large multidomain prevention trials. First, selecting the right target population at the right time is crucial. Targeting at-risk individuals (as opposed to an unselected population) is likely the most feasible strategy. Second, the FINGER trial demonstrated the importance of starting early enough: the prevention potential of a multidomain lifestyle intervention, especially if not combined with pharmacological treatments, may be highest among relatively healthy and younger old adults. Finally, the content of the intervention is crucial. The intervention may need to be intensive enough and preferably include also active counseling and coaching delivered in different ways (not only advice). Based on the content and duration of the intervention sessions and study visits, the FINGER intervention was the most intensive, and the participants attended both group and individual sessions. Despite the relatively intensive nature of the intervention, adherence was high, as approximately 72% of the participants reported at least some engagement in all intervention components. Thus, the FINGER multidomain intervention seemed feasible, pragmatic, and not too strenuous. Designing and adapting the content of the intervention for various target populations is essential to optimize the effect. Finally, the choice of an appropriate outcome measure to assess intervention effects is also important. Incidence of dementia is a robust outcome, and trials with such outcome would require a large sample size and long-term follow-up, especially when targeting cognitively healthy older adults. For this population, there is currently no gold standard measure to detect cognitive changes predictive of future dementia. However, composite cognitive scores capturing several cognitive domains may be useful (52), not only to detect early changes typical for AD, but also for vascular cognitive impairment, since both disorders often co-occur in advanced age.


Other innovative multidomain preventive strategies

Building upon the experiences of the preventive RCTs conducted so far, the next generation of multidomain prevention trials has started to incorporate and utilize novel technologies and tools, such as eHealth and mHealth, to optimize the delivery of multidomain interventions. One example of an Internet-based eHealth study is the Healthy Aging Through Internet Counselling in the Elderly (HATICE, ISRCTN48151589), which is a European 18-month RCT testing the efficacy of an Internet platform in improving self-management of cardiovascular risk factors for prevention of cardiovascular disease and cognitive decline (53). The trial enrolled 2724 non-demented, computer literate community-dwellers aged 65+ from Finland, France, and the Netherlands. Participants were required to have at least two cardiovascular risk factors and/or history of cardiovascular disease or diabetes. Participants were randomized 1:1 to intervention and control groups. The intervention group had access to an interactive Internet platform, designed to encourage lifestyle changes with the remote support of a lifestyle coach, according to national and European guidelines for cardiovascular risk factor management (54). The control platform included only basic health information and no interactive features or coach support. The trial has been completed, and data analysis is ongoing. If the delivery of preventive interventions through Internet or e.g. via mobile applications proved to be feasible and effective and induced sustained behavioral changes, it could support self-management and be a cost-effective way to reach and involve a large population across the world.
While the FINGER, MAPT, and PreDIVA trials targeted older adults from the general population, some new multidomain prevention studies focus on at-risk populations in clinical settings. One particularly relevant target population for multidomain prevention are individuals with prodromal AD. For this more advanced and symptomatic state of AD dementia risk, lifestyle and vascular changes alone may not be sufficient. Rather, a combination of lifestyle and pharmacological approaches may be necessary to prevent or delay the onset of dementia. There are currently no proven therapeutic options available for such individuals, but in the multinational European LipiDiDiet trial (NTR1705) (55), the effects of the medical food product Fortasyn Connect (Souvenaid) were investigated in 311 memory clinic patients with prodromal AD, as defined by the International Working Group (IWG)-1 research criteria (56). Fortasyn Connect is a mixture of multiple nutrients, such as vitamins, polyunsaturated fatty acids, and phospholipids, which improves the formation and function of synapses (57). The primary outcome of the LipiDiDiet trial was change in cognitive performance measured with an NTB composite score. Secondary outcomes included change in e.g. memory scores, Clinical Dementia Rating-Sum of Boxes (CDR-SB), and brain volume. The two-year core trial was completed in 2015. Despite no significant effect on the primary outcome, group differences in favor of the treatment group were observed for cognitive and functional outcomes (45% less worsening in the CDR-SB in the intervention group), and hippocampal atrophy (26% less deterioration in the intervention group) (55). Notably, the observed decline in the NTB in the control group was smaller than expected. Analyses of the intervention effects at 36 months will be completed soon.

Going global: from FINGER to World-Wide FINGERS

Following the encouraging results of the FINGER trial, the World-Wide FINGERS (WW-FINGERS, network was launched in July 2017 in connection to the Alzheimer’s Association International Conference in London (founder and scientific lead: Professor Miia Kivipelto; hosted by Alzheimer Association). By collectively convening international research teams under the WW-FINGERS leadership of Prof. Miia Kivipelto and Dr. Maria Carrillo, WW-FINGERS will facilitate data sharing and joint analysis across studies, establish opportunities for joint initiatives across country borders, and strengthen the potential evidence-base for multidomain lifestyle interventions.
This initiative supports and coordinates other trials worldwide in testing the feasibility and efficacy of FINGER-type preventive interventions in different at-risk populations, across diverse geographical and cultural settings. All WW-FINGERS trials share the same key concept of a pragmatic multidomain approach, i.e. targeting several modifiable risk factors simultaneously. WW-FINGERS will facilitate data sharing and joint analysis across studies, and to ensure comparability of the results and to facilitate pooling of accumulating data, the trials aim to use common core outcome measures. At the same time, local and cultural adaptations will be applied in relation to the content and delivery method of the intervention. For example, dietary counseling will follow national recommendations while taking into account country- or region-specific habits, and pharmacological vascular risk factor management, when applicable, will be based on national care guidelines. This is essential to improve engagement and adherence, and subsequently, to facilitate the effective and sustainable implementation of preventive strategies. Several countries worldwide have joined the WW-FINGERS network and are currently at different stages of planning and conducting their FINGER-type prevention trials (Figure 1). Recruitment is already ongoing in several trials.

Figure 1. World map with countries which are involved in the WW-FINGERS network. Blue indicates involvement in ongoing WW-FINGERS studies. Studies are currently planned in countries marked with purple

Figure 1. World map with countries which are involved in the WW-FINGERS network. Blue indicates involvement in ongoing WW-FINGERS studies. Studies are currently planned in countries marked with purple



The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER), supported by the Alzheimer’s Association, aims to test the FINGER intervention in a more diverse US population. It is a two-year trial targeting 2000 older adults aged 60-79 years with normal cognition but increased risk for future cognitive decline. The trial will compare two lifestyle-based interventions (structured vs self-guided lifestyle intervention), which vary in their intensity and structure. Another ongoing trial testing the FINGER-based model is the randomized controlled Multimodal INtervention to delay Dementia and disability in rural China (MIND-CHINA), aiming at recruiting up to 3500 older adults aged 60-79 years who are living in rural areas of the Shandong province. The MIND-CHINA trial uses cluster randomization by village and includes two intervention arms and a control arm. Due to high prevalence of untreated vascular risk factors in this population, the trial will focus on the management and treatment of these factors. Participants in the vascular intervention group will be provided with pharmacological control and management of three major vascular risk factors (hypertension, dyslipidemia, diabetes); the multidomain intervention group will have both the management of the vascular risk factors and a multidomain lifestyle intervention. In the lifestyle intervention, special emphasis will be placed on reducing salt intake, which is a key dietary challenge in China. In Singapore, the six-month feasibility study SINGapore GERiatric intervention study to reduce physical frailty and cognitive decline (SINGER) targeting 70 participants with mild/moderate frailty and/or cognitive impairment is ongoing. The two-year study AUstralian-Multidomain Approach to Reduce Dementia Risk by PrOtecting Brain Health with Lifestyle intervention (AU-ARROW) is currently planned in Australia. Another Australian multidomain prevention trial, the ongoing three-year Maintain Your Brain (MYB) trial, is associated with WW-FINGERS (study design and outcomes not fully harmonized with other WW-FINGERS studies). The MYB RCT randomized 6236 non-demented community-dwellers aged 55-77 years. Assessments and interventions are conducted online, and the multidomain eHealth intervention consists of exercise, cognitive training, dietary advice, guidance to stop smoking and reduce alcohol consumption, blood pressure and cholesterol management, and cognitive behavior therapy to manage depressive symptoms and to facilitate social interaction.
Another initiative within the WW-FINGERS network is the multinational European collaboration project MIND-AD – Multimodal preventive trials for Alzheimer’s Disease: towards multinational strategies, which is based on the promising results of the FINGER and LipiDiDiet trials. In the ongoing six-month Multimodal Preventive Trial for Alzheimer’s Disease (MIND-ADmini)(NCT03249688) pilot trial (extended six-month follow-up in some countries), a multidomain lifestyle intervention derived from the FINGER trial is tested both alone and in combination with Souvenaid among individuals with prodromal AD and vascular or lifestyle-related risk factors. The control group receives usual care and regular health advice. Trial participants were recruited from Finland, France, Germany, and Sweden, and the main objective is to assess the feasibility of the multidomain intervention in this population. MIND-AD can serve as a model and platform for future trials combining non-pharmacological and pharmacological approaches to prevent or delay the onset of dementia. A master protocol for combination therapy is currently under development.
In addition to the abovementioned trials, WW-FINGERS interventions are also planned in several other countries including Japan, Canada, UK, the Netherlands, Spain, Italy, India, South Korea, Malaysia, and several Latin American countries (LATAM FINGER project) (Figure 1).



Prevention has been recognized as pivotal in halting the expected worldwide increase of AD and dementia cases. Successful preventive approaches should be feasible, accessible, cost-effective, and sustainable for populations in different geographical, economic, and cultural settings. Several modifiable risk factors, which can be managed to promote brain health and reduce the risk of late-life AD and dementia, have been identified. Yet, the majority of the observational studies on risk and protective factors have been conducted in high-income countries, with few findings available from low- and middle-income countries, which are facing the highest rise in dementia prevalence and incidence. In fact, by 2050, 68 % of all people with dementia worldwide are expected to live in low- and middle-income countries (1). The World Health Organization has invited experts worldwide to produce a global action plan and guidelines for cognitive decline and dementia risk reduction (58), and studies documenting prevalence and time-trends of risk factors in low- and middle-income countries can help develop preventive models for these areas.
The multidomain preventive approach has already proven its efficacy in other age-related chronic conditions (diabetes mellitus, cardiovascular disease (59,60)), and can facilitate also the reduction of dementia risk by addressing the multifactorial, complex, and heterogeneous nature of late-life cognitive impairment, AD, and dementia. Importantly, it offers prevention potential on a large scale, with possibilities for worldwide implementation. Country-specific adaptations will be crucial to ensure effective implementation of multidomain preventive interventions in different cultural, geographical, and economical settings, as well as public health care systems. Introduction of innovative eHealth and mHealth tools can facilitate implementation and monitoring of the interventions, while reducing costs and reaching larger regions and populations. The proportion of older adults using Internet is increasing, supporting the use of eHealth-based approaches, but feasibility is a key issue and is actively investigated. For instance, in the HATICE RCT older adults were involved in the development of the Internet platform that was used to deliver the multidomain intervention, in order to optimize its acceptability and use (61). Similar efforts may be needed also in future trials investigating eHealth or mHealth tools.
The complex nature of late-life cognitive impairment, AD, and dementia translates into a need to identify different risk profiles in order to develop tailored preventive strategies, within the framework of preventive precision medicine. WW-FINGERS is a landmark initiative, which will facilitate identification of efficacious preventive approaches for specific risk profiles and cost-effective implementation of such approaches in different settings. The WW-FINGERS model can be further developed to integrate pharmacological treatments, as the AD drug development field advances and succeeds in identifying effective disease-modifying compounds. Multidomain schemes combining pharmacological and non-pharmacological interventions can be developed and tested to define secondary and tertiary preventive strategies across the full spectrum of AD.
The WW-FINGERS network facilitates international collaboration in dementia prevention and provides an opportunity to harmonize prevention studies, as well as share experiences and data to obtain maximum scientific impact. Furthermore, the network aims at generating high-quality scientific evidence to support public health and clinical decision-making. This global joint effort can also have a key role in promoting rapid and effective dissemination and implementation of research findings.


Funding: This work was supported by the Academy of Finland grants (278457, 287490, 305810, 317465, 319318); Joint Program of Neurodegenerative Disorders – prevention (MIND-AD) grant through the following funding organisations under the aegis of JPND – Finland, Suomen Akatemia (Academy of Finland, 291803); Sweden, Vetenskapsrådet (VR) (Swedish Research Council, 529-2014-7503); Swedish Research Council grant 2017-06105; Juho Vainio Foundation, Finnish Medical Foundation; Finnish Social Insurance Institution; Ministry of Education and Culture Research Grant; Finnish Cultural Foundation North Savo regional fund, Finnish Brain Foundation; Knut and Alice Wallenberg Foundation Sweden; Center for Innovative Medicine (CIMED) at Karolinska Institutet Sweden; Stiftelsen Stockholms sjukhem Sweden; Konung Gustaf V:s och Drottning Victorias Frimurarstiftelse Sweden; af Jochnick Foundation Sweden; the European Research Council Starting Grant (ERC-804371), Alzheimer Fonden Sweden. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Conflict of interest: The authors have no conflicts of interest to declare.

Ethical standards: All studies presented in this article are conducted according to the principles of the Declaration of Helsinki and following the guidelines for Good Clinical Practice. Studies are approved by local ethics committees and all participants provided written informed consent.

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.



1.    Alzheimer’s Disease International. World Alzheimer Report 2015: The Global Impact of Dementia. ADI, 2015.
2.     WHO. Dementia key facts. Available at: Accessed 21 March 2019.
3.     Cummings JL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther 2014;6(4):37.
4.     Cummings J, Lee G, Ritter A, Zhong K. Alzheimer’s disease drug development pipeline: 2018. Alzheimers Dement (N Y) 2018;4:195-214.
5.     Andrieu S, Coley N, Lovestone S, Aisen PS, Vellas B. Prevention of sporadic Alzheimer’s disease: lessons learned from clinical trials and future directions. Lancet Neurol 2015;14(9):926-944.
6.     Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet 2015;385:2255-2263.
7.     Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol 2017;16:377-389.
8.     Moll van Charante, E P, Richard E, Eurelings LS, van Dalen JW, Ligthart SA, van Bussel EF, et al. Effectiveness of a 6-year multidomain vascular care intervention to prevent dementia (preDIVA): a cluster-randomised controlled trial. Lancet 2016;388:797-805.
9.     Chhetri JK, de Souto Barreto P, Cantet C, Pothier K, Cesari M, Andrieu S, et al. Effects of a 3-Year Multi-Domain Intervention with or without Omega-3 Supplementation on Cognitive Functions in Older Subjects with Increased CAIDE Dementia Scores. J Alzheimers Dis 2018;64:71-78.
10.     Solomon A, Mangialasche F, Richard E, Andrieu S, Bennett DA, Breteler M, et al. Advances in the prevention of Alzheimer’s disease and dementia. J Intern Med 2014;275:229-250.
11.     Winblad B, Amouyel P, Andrieu S, Ballard C, Brayne C, Brodaty H, et al. Defeating Alzheimer’s disease and other dementias: a priority for European science and society. Lancet Neurol 2016;15:455-532.
12.     Jellinger KA, Attems J. Prevalence of dementia disorders in the oldest-old: an autopsy study. Acta Neuropathol 2010;119:421-433.
13.     Schneider JA, Wilson RS, Bienias JL, Evans DA, Bennett DA. Cerebral infarctions and the likelihood of dementia from Alzheimer disease pathology. Neurology 2004;62:1148-1155.
14.     Legdeur N, Heymans MW, Comijs HC, Huisman M, Maier AB, Visser PJ. Age dependency of risk factors for cognitive decline. BMC Geriatr 2018;18:2.
15.     Kivipelto M, Ngandu T, Fratiglioni L, Viitanen M, Kareholt I, Winblad B, et al. Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch Neurol 2005;62:1556-1560.
16.     Tolppanen AM, Solomon A, Soininen H, Kivipelto M. Midlife vascular risk factors and Alzheimer’s disease: evidence from epidemiological studies. J Alzheimers Dis 2012;32:531-540.
17.     Kivimaki M, Luukkonen R, Batty GD, Ferrie JE, Pentti J, Nyberg ST, et al. Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals. Alzheimers Dement 2018;14:601-609.
18.     Cataldo JK, Prochaska JJ, Glantz SA. Cigarette smoking is a risk factor for Alzheimer’s Disease: an analysis controlling for tobacco industry affiliation. J Alzheimers Dis 2010;19:465-480.
19.     Anttila T, Helkala EL, Viitanen M, Kareholt I, Fratiglioni L, Winblad B, et al. Alcohol drinking in middle age and subsequent risk of mild cognitive impairment and dementia in old age: a prospective population-based study. BMJ 2004;329:539.
20.     Gao Y, Huang C, Zhao K, Ma L, Qiu X, Zhang L, et al. Depression as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Int J Geriatr Psychiatry 2013;28:441-449.
21.     Hakansson K, Soininen H, Winblad B, Kivipelto M. Feelings of Hopelessness in Midlife and Cognitive Health in Later Life: A Prospective Population-Based Cohort Study. PLoS One 2015;10:e0140261.
22.     Sindi S, Hagman G, Hakansson K, Kulmala J, Nilsen C, Kareholt I, et al. Midlife Work-Related Stress Increases Dementia Risk in Later Life: The CAIDE 30-Year Study. J Gerontol B Psychol Sci Soc Sci 2017;72:1044-1053.
23.     Kuiper JS, Zuidersma M, Oude Voshaar RC, Zuidema SU, van den Heuvel, E R, Stolk RP, et al. Social relationships and risk of dementia: A systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev 2015;22:39-57.
24.     Blondell SJ, Hammersley-Mather R, Veerman JL. Does physical activity prevent cognitive decline and dementia?: A systematic review and meta-analysis of longitudinal studies. BMC Public Health 2014;14:510.
25.     Ngandu T, von Strauss E, Helkala EL, Winblad B, Nissinen A, Tuomilehto J, et al. Education and dementia: what lies behind the association? Neurology 2007;69:1442-1450.
26.     Andel R, Crowe M, Pedersen NL, Mortimer J, Crimmins E, Johansson B, et al. Complexity of work and risk of Alzheimer’s disease: a population-based study of Swedish twins. J Gerontol B Psychol Sci Soc Sci 2005;60:251.
27.     Wilson RS, Segawa E, Boyle PA, Bennett DA. Influence of late-life cognitive activity on cognitive health. Neurology 2012;78:1123-1129.
28.     Fratiglioni L, Paillard-Borg S, Winblad B. An active and socially integrated lifestyle in late life might protect against dementia. Lancet Neurol 2004;3:343-353.
29.     Marioni RE, Proust-Lima C, Amieva H, Brayne C, Matthews FE, Dartigues JF, et al. Social activity, cognitive decline and dementia risk: a 20-year prospective cohort study. BMC Public Health 2015;15:6.
30.     Rouch L, Cestac P, Hanon O, Cool C, Helmer C, Bouhanick B, et al. Antihypertensive drugs, prevention of cognitive decline and dementia: a systematic review of observational studies, randomized controlled trials and meta-analyses, with discussion of potential mechanisms. CNS Drugs 2015;29:113-130.
31.     SPRINT MIND Investigators for the SPRINT Research Group, Williamson JD, Pajewski NM, Auchus AP, Bryan RN, Chelune G, et al. Effect of Intensive vs Standard Blood Pressure Control on Probable Dementia: A Randomized Clinical Trial. JAMA 2019;321:553-561.
32.     Coley N, Vaurs C, Andrieu S. Nutrition and Cognition in Aging Adults. Clin Geriatr Med 2015;31:453-464.
33.     Lourida I, Soni M, Thompson-Coon J, Purandare N, Lang IA, Ukoumunne OC, et al. Mediterranean diet, cognitive function, and dementia: a systematic review. Epidemiology 2013;24:479-489.
34.     Morris MC, Tangney CC, Wang Y, Sacks FM, Bennett DA, Aggarwal NT. MIND diet associated with reduced incidence of Alzheimer’s disease. Alzheimers Dement 2015;11:1007-1014.
35.     Martinez-Lapiscina EH, Clavero P, Toledo E, Estruch R, Salas-Salvado J, San Julian B, et al. Mediterranean diet improves cognition: the PREDIMED-NAVARRA randomised trial. J Neurol Neurosurg Psychiatry 2013;84:1318-1325.
36.     Smith PJ, Blumenthal JA, Babyak MA, Craighead L, Welsh-Bohmer KA, Browndyke JN, et al. Effects of the dietary approaches to stop hypertension diet, exercise, and caloric restriction on neurocognition in overweight adults with high blood pressure. Hypertension 2010;55:1331-1338.
37.     Mannikko R, Komulainen P, Schwab U, Heikkila HM, Savonen K, Hassinen M, et al. The Nordic diet and cognition–The DR’s EXTRA Study. Br J Nutr 2015;114:231-239.
38.     Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, et al. Dementia prevention, intervention, and care. Lancet 2017;390:2673-2734.
39.     Wu YT, Beiser AS, Breteler MMB, Fratiglioni L, Helmer C, Hendrie HC, et al. The changing prevalence and incidence of dementia over time – current evidence. Nat Rev Neurol 2017;13:327-339.
40.     Chan KY, Wang W, Wu JJ, Liu L, Theodoratou E, Car J, et al. Epidemiology of Alzheimer’s disease and other forms of dementia in China, 1990-2010: a systematic review and analysis. Lancet 2013;381:2016-2023.
41.     Dodge HH, Buracchio TJ, Fisher GG, Kiyohara Y, Meguro K, Tanizaki Y, et al. Trends in the prevalence of dementia in Japan. Int J Alzheimers Dis 2012;2012:956354.
42.     Loef M, Walach H. Midlife obesity and dementia: meta-analysis and adjusted forecast of dementia prevalence in the United States and China. Obesity (Silver Spring) 2013;21:51.
43.     Kivipelto M, Rovio S, Ngandu T, Kareholt I, Eskelinen M, Winblad B, et al. Apolipoprotein E epsilon4 magnifies lifestyle risks for dementia: a population-based study. J Cell Mol Med 2008;12:2762-2771.
44.     Kivipelto M, Solomon A, Ahtiluoto S, Ngandu T, Lehtisalo J, Antikainen R, et al. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER): study design and progress. Alzheimers Dement 2013;9:657-665.
45.     Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol 2006;5:735-741.
46.     Lehtisalo J, Ngandu T, Valve P, Antikainen R, Laatikainen T, Strandberg T, et al. Nutrient intake and dietary changes during a 2-year multi-domain lifestyle intervention among older adults: secondary analysis of the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) randomised controlled trial. Br J Nutr 2017;118:291-302.
47.     Strandberg T, Levalahti E, Ngandu T, Solomon A, Kivipelto M, Lehtisalo J, et al. Health-related quality of life in a multidomain intervention trial to prevent cognitive decline (FINGER). European Geriatric Medicine 2017;8:164-167.
48.     Kulmala J, Ngandu T, Havulinna S, Levalahti E, Lehtisalo J, Solomon A, et al. The Effect of Multidomain Lifestyle Intervention on Daily Functioning in Older People. J Am Geriatr Soc 2019 February 26.
49.     Marengoni A, Rizzuto D, Fratiglioni L, Antikainen R, Laatikainen T, Lehtisalo J, et al. The Effect of a 2-Year Intervention Consisting of Diet, Physical Exercise, Cognitive Training, and Monitoring of Vascular Risk on Chronic Morbidity-the FINGER Randomized Controlled Trial. J Am Med Dir Assoc 2018;19:360.e1.
50.     Rosenberg A, Ngandu T, Rusanen M, Antikainen R, Backman L, Havulinna S, et al. Multidomain lifestyle intervention benefits a large elderly population at risk for cognitive decline and dementia regardless of baseline characteristics: The FINGER trial. Alzheimers Dement 2018;14:263-270.
51.     Solomon A, Turunen H, Ngandu T, Peltonen M, Levalahti E, Helisalmi S, et al. Effect of the Apolipoprotein E Genotype on Cognitive Change During a Multidomain Lifestyle Intervention: A Subgroup Analysis of a Randomized Clinical Trial. JAMA Neurol 2018;75:462-470.
52.     Vellas B, Andrieu S, Sampaio C, Coley N, Wilcock G, European Task Force Group. Endpoints for trials in Alzheimer’s disease: a European task force consensus. Lancet Neurol 2008;7:436-450.
53.     Richard E, Jongstra S, Soininen H, Brayne C, Moll van Charante, E P, Meiller Y, et al. Healthy Ageing Through Internet Counselling in the Elderly: the HATICE randomised controlled trial for the prevention of cardiovascular disease and cognitive impairment. BMJ Open 2016;6:010806.
54.     Barbera M, Mangialasche F, Jongstra S, Guillemont J, Ngandu T, Beishuizen C, et al. Designing an Internet-Based Multidomain Intervention for the Prevention of Cardiovascular Disease and Cognitive Impairment in Older Adults: The HATICE Trial. J Alzheimers Dis 2018;62:649-663.
55.     Soininen H, Solomon A, Visser PJ, Hendrix SB, Blennow K, Kivipelto M, et al. 24-month intervention with a specific multinutrient in people with prodromal Alzheimer’s disease (LipiDiDiet): a randomised, double-blind, controlled trial. Lancet Neurol 2017;16:965-975.
56.     Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6:734-746.
57.     van Wijk N, Broersen LM, de Wilde MC, Hageman RJ, Groenendijk M, Sijben JW, et al. Targeting synaptic dysfunction in Alzheimer’s disease by administering a specific nutrient combination. J Alzheimers Dis 2014;38:459-479.
58.     WHO guidelines on risk reduction of cognitive decline and dementia. Available at Accessed 20 March 2019.
59.     Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;344:1343-1350.
60.     Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med 2008;358:580-591.
61.     Jongstra S, Beishuizen C, Andrieu S, Barbera M, van Dorp M, van de Groep B, et al. Development and Validation of an Interactive Internet Platform for Older People: The Healthy Ageing Through Internet Counselling in the Elderly Study. Telemed J E Health 2017;23:96-104.



M. Rochoy1,2, V. Rivas1, E. Chazard1,3, E. Decarpentry1, G. Saudemont1, P.-A. Hazard1, F. Puisieux1, S. Gautier1,2, R. Bordet1,2


1. Univ. Lille, F-59000 Lille, France; 2. INSERM, U1171-Degenerative and Vascular Cognitive Disorders, F-59000 Lille, France; 3. EA2694, Public Health Department, Univ Lille, CHU Lille, F-59000 Lille, France

Corresponding Author: Michaël Rochoy, 20 rue André Pantigny, 62230 Outreau, France. +33667576735,

J Prev Alz Dis 2019;2(6):121-134
Published online February 11, 2019,



Alzheimer’s disease (AD) is a frequent pathology, with a poor prognosis, for which no curative treatment is available in 2018. AD prevention is an important issue, and is an important research topic.
In this manuscript, we have synthesized the literature reviews and meta-analyses relating to modifiable risk factors associated with AD. Smoking, diabetes, high blood pressure, obesity, hypercholesterolemia, physical inactivity, depression, head trauma, heart failure, bleeding and ischemic strokes, sleep apnea syndrome appeared to be associated with an increased risk of AD. In addition to these well-known associations, we highlight here the existence of associated factors less described: hyperhomocysteinemia, hearing loss, essential tremor, occupational exposure to magnetic fields.
On the contrary, some oral antidiabetic drugs, education and intellectual activity, a Mediterranean-type diet or using Healthy Diet Indicator, consumption of unsaturated fatty acids seemed to have a protective effect.
Better knowledge of risk factors for AD allows for better identification of patients at risk. This may contribute to the emergence of prevention policies to delay or prevent the onset of AD.

Key words: Alzheimer’s disease, prevention, risk factors, early intervention.

List of abbreviations: AD: Alzheimer’s disease; OR: Odds-Ratio; RR: Relative Ratio



The prevalence of dementia is estimated to be over 45 million people and could reach 115 million by 2050 (1). Alzheimer’s disease (AD) accounts for 60-70% of dementias (2). The prevalence of dementia is increasing as the population ages (3). Nevertheless, several studies showed a decrease in the prevalence rate of dementia or severe cognitive impairment after the age of 65 over the last 10 to 30 years in the United States (4–7) and Europe (8–13). The decrease in the prevalence rate could be explained by prevention and better management of risk factors (14).
The main risk factor for AD is age. Another known risk factor is heredity; thus, many genetic determinants have been studied, notably ApoE4, ApoE3 or presenilin S1 and S2 (15, 16).
In 2018, AD remains incurable and prevention is essential: it is based on the management of modifiable risk factors.
Many studies focused on AD “risk factors”. In response to the large number of articles, systematic literature reviews focused on classes of risk factors (genetic, environmental, infectious, etc.). For clinicians, it seems important to synthesize these numerous studies and reviews, and provide an overview of literature reviews.
Our aim was to summarize the literature reviews conducted on modifiable risk factors for AD.



This overview of literature reviews was conducted using the PubMed search engine (MEDLINE database), with the equation: “Alzheimer disease”[MeSH] AND “risk factor” [All Fields] AND (Meta-Analysis[ptyp] OR Review[ptyp]). The research was carried out in February 2017 and checked in November 2017.
The inclusion criteria were literature review or meta-analysis of epidemiological articles.
There was no time limit on the reviews and meta-analyses included.
The exclusion criteria were:
–     Descriptive literature reviews
–     Literature reviews that do not detail the populations studied
–    Pathophysiological literature reviews
–     Animal model studies
–     Articles not accessible in full
–     Articles in a language other than English or French

An additional search was performed on the UpToDate® site. The literature reviews cited, not found via the PubMed query, have been added.
A second additional search was carried out on the French Health Scientific Literature search engine (LiSSa) to look for other risk factors that were not noticed during the initial searches, via literature reviews published in journals not indexed on MEDLINE.
When the same association was covered by several reviews, we excluded the oldest ones.
As the purpose of this work is to provide an up-to-date synthesis of the literature reviews, most of the main and/or relevant results of the studies have been extracted.



We identified 668 literature reviews and included 86 from PubMed. We included 5 articles with additional research (UpToDate® and LiSSa). Reviews and meta-analysis included are summarized in Table 1.

Table 1. Summary of reviews and meta-analysis

Table 1. Summary of reviews and meta-analysis

There are many modifiable risk factors for AD, summarized in Table 2. Levels of evidence are resumed in Table 3.

Table 2. Modifiable factors associated with AD

Table 2. Modifiable factors associated with AD


Table 3. Summary of evidences concerning associated factors for Alzheimer’s dementia

Table 3. Summary of evidences concerning associated factors for Alzheimer’s dementia



Several literature reviews established a link between hypertension at different life stages and an increased risk of AD (17–21).
A systematic review summarized the findings from population-based observational studies and randomized clinical trials addressing the relations of blood pressure to cognitive function and dementia (20). Concerning “late-life” hypertension, seven longitudinal studies reported an association with AD, three longitudinal studies and two cross-sectional studies found no association, five cross-sectional studies reported an inverse association (potentially protective) (20). Concerning “mid-life” hypertension, an association with AD has been reported in four of five longitudinal studies (20).
Barnes and Yaffe associated «mid-life» hypertension and AD with OR = 1.61 (CI95 [1.16 – 2.24]). «Late-life» hypertension was not associated with an increased risk of AD in 8 of the 13 studies included (22). In another literature review, patients with the highest coefficient of variation in blood pressure were more likely to have an increased risk of cognitive impairment or dementia (17).
In 2006, another review highlighted a link between low diastolic BP (between 65 and 80 mmHg) and increased risk of AD (18).
Most published studies demonstrate associations between atrial fibrillation and impaired cognition, but no atrial fibrillation treatment has yet been associated with a reduced incidence of cognitive decline or dementia (23).
Heart failure was associated with 60% increased dementia risk (RR = 1.60 ; CI95 [1.19-2.13]) (24). Heart failure is associated with increased radiologic brain damage, particularly in the limbic system (which includes the hippocampus), similar to objective damage in AD patients (25).
A meta-analysis o associated hyperhomocysteinemia (> 15 µmol/L) and AD: OR = 3.37 (CI95% [1.90 – 5.95]) (26). This association is suggested in other reviews MA (27, 28). The link between vitamin deficiency (B9 or B12) and hyperhomocysteinemia is known and this could constitute a confounding bias.
Kivipelto et al. associated risk of AD and «mid-life» hypercholesterolemia (not in «late life») (29). In another review, 5 prospective studies were studied: 3 showed a significant association between AD and hypercholesterolemia; on the other hand, 1 study showed no significant association, while the last showed, conversely, a protective effect with a RR estimated at 0.4 (CI95 [0.2 – 0.8]) (18).
Hemorrhagic and ischemic stroke was also considered as risk factors for dementia: an increase in the risk of dementia by 10% after a first episode, and up to more than 20% after several episodes (30). Subclinical brain microbleeds (4 or more) were associated with an increase in cognitive impairment (HR = 2.10 ; CI95 [1.21 – 3.64]) (31).
One review analyzed different types of antihypertensive agents and their possible association with AD: there was no difference between control group, diuretic group or angiotensin 2 antagonist. In contrast, the authors report a decrease in RR dementia in patients using a calcium channel blocker (RR = 0.55; CI95 [0.24 – 0.73]) (32). For ACE inhibitors, the authors reported, at the conclusion of their review, a significant difference between the treatment and control groups in the incidence of cognitive impairment, but no significant difference in the occurrence of dementia (32).
In the review by Miida et al., the included cross-sectional studies showed a significant decrease in the risk of AD in patients using statins; but 3 out of 4 prospective studies did not show a significant decrease in the risk of AD, as did the 8 randomized controlled trials (RCTs) (33).

Habitus, social contact and educational level

Physical activity was associated with a decreased risk of cognitive impairment in 21 of 24 cohorts included (87.5%) and 100% of cross-sectional studies; a meta-analysis of 8 studies reported RR at 0.58 (CI95 [0.49 – 0.70]) for high vs low physical activity (34). In 2011, Barnes and Yaffe concluded that there is an association between physical inactivity and increased risk of dementia in the various reviews and meta-analyses included in their study (19). Wheeler et al. suggest that reducing and replacing sedentary behavior with intermittent light-intensity physical activity may protect against cognitive decline by reducing glycemic variability (35).
Active smoking (versus never smoking) was associated with an increased risk of dementia: RR = 1.79 (CI95 [1.43 – 2.23]) (22). In 2014, Beydoun et al. published a literature review with somewhat more nuanced results on this relationship between smoking and cognitive impairment: 16 of the 29 cohort epidemiological studies included (55.2%) found an association between smoking and increased risk of cognitive impairment, as 2 of the 7 cross-sectional studies included. In a meta-analysis of 9 of 29 studies, the authors calculated a RR of AD for current and former smokers compared to never smokers at 1.37 (CI95 [1.23 – 1.52]) (28).
Concerning alcohol, Beydoun et al. noted an association between alcohol and increased cognitive impairment in 44% of cohorts included and 75% of cross-sectional studies (28).
In 2009, Anstey et al. published a meta-analysis of 15 cohorts whose results suggest that alcohol consumption is associated with a decreased risk of AD: RR = 0.66 (CI95 [0.47 – 0.94]) (36). Confounding factors were possible, particularly due to alcohol-related comorbidities.
There was a moderate association between caffeine consumption and decreased risk of cognitive problems: in one review, 4 of the 7 cross-sectional studies and 3 of the 11 cohorts included found this association. Five other cohorts among the 11 identified this link in a partial way in the subgroup analyses (for example, only among women) (28).
Frequent social contact and cognitive stimulation would be protective factors (37).
A lower educational level was associated with an increased risk of AD, with an RR estimated at 1.80 (CI95 [1.43 – 2.27]) (38) or 1.99 (CI95 [1.30 – 3.04]) in Beydoun’s meta-analysis (knowing that low educational level here means less than 8 years of education) (28).


A meta-analysis of 6 cohort studies (19,940 patients) associated sleep apnea syndrome and dementia (RR = 1.69; CI95 [1.34 – 2.13]). The association is also found in subgroup analyses, with or without polysomnography, adjusted or not on ApoE4 (39).


One of 3 case-control studies and 2 epidemiological studies showed a possible link between Chlamydia pneumoniae infection and AD possibly through chronic neuronal inflammation (40).
The prevalence of Helicobacter pylori was increased in patients with AD in case-control studies; in cohorts patients with H. pylori often have poorer cognitive performance (confounding bias). However, the authors conclude their narrative review with a lack of longitudinal studies to support the association between infection and AD, to explain more precisely the mechanism by which H. pylori would actually intervene in pathogenesis, and to determine the utility of eradication of the bacterium in patients with AD or cognitive disorders (41).
A review of 12 case-control studies did not associated Herpes Simplex Virus 1 infection and AD (40). In the same review, it is suggested that HHV6 is not an independent risk factor for AD. Nevertheless, its presence could increase the neuronal damage caused by HSV1 in patients with ApoE4.

Endocrinology and metabolism

The risk of dementia (especially AD) increased in cases of «mid-life» underweight (BMI < 18.5) or «mid-life» obesity (between 45 and 64 years of age according to the authors); this association is not present after 64 years of age, in late life (42). «Mid-life» obesity was associated with an increased risk of dementia : RR = 1.59 (CI95 [1.02 – 2.48]) (22).
Diabetes is also a risk factor for AD in most studies, with an estimated RR of 1.53 (CI95 [1.42 – 1.63]), or slightly higher in so-called «eastern» populations, with an RR of 1.62 (CI95 [1.49 – 1.75]) (43). Mid-life and late-life diabetes were associated with AD (42); interaction is possible with cerebrovascular risk (18).
The impact of metformin use on the occurrence of cognitive impairment is unclear: protective role in a cohort, risk factor in a case-control study. In one studiy, metformin + hypoglycemic sulfonamide combination therapy is associated with a decrease in AD compared to untreated diabetic patients (HR = 0.65; CI95 [0.56 – 0.74]) (44).
Hypotestosteronemia in elderly men would be associated with an increase in AD (RR = 1.48; CI95 [1.12 – 1.96]). However, the authors do not detail their definition of «elderly male» and report that the studies included in their meta-analysis have different definitions for hypotestosteronemia (45)

Psychiatry, neurology and anesthesia

A meta-analysis associated history of depression and increased risk of AD: RR = 1.90 (CI95 [1.55 – 2.33]) in cohort studies (46). Late-life depression is associated with increased risk of AD (37).
In 2015, Harrington et al. studied the relationship between depression and Aβ plaques in a healthy, older adult population. The majority of included studies found a significant increase in Aβ levels in depressed patients. However, the authors mentioned many biases in the 19 cross-sectional studies included (47).
Long-term benzodiazepine users had an increased risk of dementia compared with never users: RR = 1.49 (CI95 [1.30 – 1.72]). The risk of dementia increased by 22% for every additional 20 defined daily dose per year (RR = 1.22 ; CI95 [1.18 – 1.25]) (48).
Peripheral and central hearing impairment were associated with a risk of AD (49). The RR was estimated at 2.82 (CI95 [1.47 – 5.42]) between hearing impairment and risk of cognitive impairment (50).
Head injury with loss of consciousness could also be a risk factor for AD, according to several studies, with an estimated RR of 1.82 (CI95 [1.26 – 2.67]) (51). In a subgroup analysis, OR is significant only for men (OR = 2.29 ; CI95 [1.47 – 2.06]), not for women (52).
A review of 6 epidemiological studies reports that an essential tremor would be associated with an increased risk of AD (53).
A meta-analysis of 15 case-control studies did not associated general anesthesia and AD (OR = 1.05; CI95 [0.93 – 1.19]) (54).


Several literature reviews have highlighted a possible link between AD and exposure to extremely low frequency electromagnetic fields, particularly in a professional context (electrician, electronics technician, welder…) (55–57). The latest meta-analysis (20 studies) highlighted the numerous biases (particularly publication bias) and heterogeneity of the populations being compared, without a dose-response relationship. They suggested a higher risk for train drivers (RR = 2.94; CI95 [1.15 – 7.51]) than for welders (RR = 1.54; CI95 [1.00 – 2.38]) or electricians (RR = 1.18; CI95 [1.01 – 1.37]) (58).
A positive association was observed between pesticide exposure and AD (OR = 1.34; CI95 [1.08 – 1.67]) (59).

Diet, nutrients

Regular use of anti-acids (with aluminium) has no relationship with increased risk of AD: a meta-analysis estimated OR for case-control studies at 1.0 (CI95 [0.8 – 1.2]) and for prospective studies at 0.8 (CI95 [0.4 – 1.8]) (60). A review highlighted a possible relationship between aluminium in drinking water and AD, but noted inconsistencies (61).
A review showed no association or inconsistent associations between vitamin B12 intake and cognitive function (62).
A literature review of 57 studies concluded that there is no evidence to support a possible recommendation for the preventive use of zinc for AD (63).
A decrease in manganese plasma levels may also be associated with an increased risk of AD (64).
In one review, magnesium was not associated with AD, but a lowered level of magnesium within the cerebrospinal fluid increased the risk of AD (65).
A combined meta-analysis of 3 meta-analyses estimates an HR of 0.92 (CI95 [0.88 – 0.97]) in favour of an inverse (protective) relationship between mediterranean diet and risk of AD (66). In 2017, Yusufov et al. published a systematic review of the literature in which 10 of the 12 studies included found an association between Mediterranean diet and reduction in the risk of AD (67).
Van de Rest et al. studied the impact of the Healthy Diet Indicator diet, based on World Health Organization recommendations: 6 of the 6 cross-sectional studies and 6 of the 8 longitudinal studies included found an association between diet adequate to HDI recommendations and decreased risk of cognitive impairment (66).
The intake of unsaturated fatty acids (notably via fish consumption) is associated with a reduction in the risk of AD and dementia (68). This association is mainly found in cross-sectional studies (5/5), less in cohorts (7/18); a meta-analysis of 5 studies estimated RR at 0.67 (CI95 [0.47 – 0.95]) (28). A role of the intestinal flora has been mentioned in the pathogenesis of Alzheimer’s disease (69).
In the review by Yusufov et al. 7 of the 9 studies included found that dietary intake of vitamin E was associated with a decreased risk of AD (67). Beydoun et al. report a similar association, but in 9 of 21 cohort studies and 2 of 6 cross-sectional studies included in their review (34).
An overview of systematic review suggested that Ginkgo biloba extract has potentially beneficial effects for people with dementia when it is administered at doses greater than 200mg/day for at least 5 months (70).
Yusufov et al. note that 4 of the 5 included studies found an association between folate (vitamin B9) intake and decreased risk of AD (67).
Plasma vitamin D concentration greater than 560 ng/mL is associated with minimal gain at the MMSE level estimated at 1.16 points (CI95 [0.46 – 1.85]) in a meta-analysis (71). Several confounding biases are possible, including better sun exposure of non-dementia patients.
An other meta-analysis showed significantly lower plasma levels of folate, vitamin A, vitamin B12, vitamin C, and vitamin E (P < .001), non-significantly lower levels of zinc (P = .050) and vitamin D (P = .075) in AD patients, and non-significant differences for plasma levels of copper and iron; this lower plasma nutrient levels could indicate that patients with AD have impaired systemic availability of several nutrients (72).



Principal results

Cardiovascular risk factors are an important part of the reviews selected in this research work. Thus, most of the journals included report an association between the different cardiovascular risk factors and the occurrence of AD.
Medically, several reviews suggest that a history of depressive syndrome is associated with an increased risk of AD. Hearing, central or peripheral impairment also seems to increase the risk of AD. However, the studies do not allow us to conclude whether the risk is corrected with the use of hearing aids.
On the environmental level, intellectual inactivity and low educational level (less than 8 years of study) seem to be the most associated factors in this research with an increased risk of AD.
Unlike intellectual inactivity and low educational level, the authors of the reviews hypothesize that a higher level of education would be associated with a lower risk of AD.
On the drug side, it would appear that the use of calcium channel blockers is associated with a decrease in dementia (but not in AD in particular). For IECs, the results point to a decrease in cognitive disorders, but not in the occurrence of dementia.
In terms of diet, the Mediterranean diet is the most studied, and seems to be associated with a decrease in the risk of AD. The results of the various reviews also seem to point towards a protective role for a diet rich in unsaturated fatty acids ω.
Dietwise, no relationship has been found between plasma or cerebrospinal zinc levels and the incidence of dementia or AD. Similarly, there is no reported link between zinc supplementation and the prevention of AD.
The same applies to vitamin B or vitamin E supplementation. Plasma magnesium levels also do not appear to be associated with a risk of AD.
The absorption of aluminum, through drinking water or medication, does not appear to be associated with the development of AD or dementia.
Medically, the use of ARB2 or diuretics does not appear to affect the onset of dementia. More anecdotally, general anesthesia did not show an association with the occurrence of AD either.
From an environmental perspective, it appears that HSV1 and HHV6 infections are not associated with the occurrence of AD.
Alcohol consumption presents contradictory results. Some studies tend to show a protective effect of alcohol on the occurrence of dementia and AD, while others suggest the opposite relationship.

Strengths and limitations

Alzheimer’s disease is a frequent pathology, with a poor prognosis, without curative treatment available in 2018. Knowing how to prevent it better is an important issue, and a lot of research is being carried out in this direction. The large number of literature reviews and meta-analyses carried out on the subject can make a global approach difficult. Our synthesis is intended to be an overview of the various literature reviews in 2018, in order to take stock of what seems likely, what seems doubtful and what is not yet well studied. This is a substantial work based on 90 literature reviews and meta-analyses. Our results are consistent with the previous syntheses carried out on the same subject.
It is likely that the initial research equation may have caused a selection bias in the results presented by the PubMed database.
A publication bias is to be mentioned; in order to limit it, we completed our research with a search in the encyclopedia «UpToDate» and in French-speaking journals not indexed via LiSSa.
An «interpretation» bias is possible in the inclusion of the different reviews.
This study assumes that all included reviews and meta-analyses are of equal quality and value, which is not the case. The various reviews may be sources of bias, which may have influenced the results presented. Many confounding biases can exist in studies and be amplified by literature reviews.
Moreover, the subject matter is vast and complicated, it is not easy to approach it in its entirety, through very heterogeneous publications, which highlight different pathophysiological hypotheses in order to explain in a rational and scientific way the possible suspected association.


We have only studied literature reviews; risk or protective factors may exist, have been studied in retrospective or prospective studies that have not yet been reviewed.
As studies and reviews progress, some clearly identified risk factors can be modified, particularly in the cardiovascular and environmental fields. It may be interesting to study the impact of prevention on targeted modifiable risk factors in order to assess the impact on the incidence of AD and dementia.
On the other hand, some of the factors studied appear to be «doubtful», and it seems appropriate to carry out additional studies. For example, it may be relevant to study the impact of H. pylori eradication on the occurrence of AD, in order to determine whether systematic treatment can be an axis of AD prevention.



Specifying the risk factors for AD is a major issue to better prevent or delay its appearance. Current studies identify many modifiable risk factors. The impact of these modifiable factors appears to be greater and more reliable than genetic factors. Risk factors can induce AD, anticipate it or aggravate it; protective factors can have a specific effect or an effect limiting the impact of a pathology (antidepressant, anti-hypertensive…). To our knowledge, the cumulative effect of the various risk factors has not been studied.
Identifying patients at risk is important in order to prevent or delay the onset of AD, and also to limit high-risk behaviors (driving, treatment management, gas handling), anticipate dependency, limit financial risks (legal protection) or integrate a research protocol.
Synthesizing the literature reviews also highlights doubtful risk factors and unstudied risk factors. In addition, some risk factors have emerged in recent articles, but have not yet been studied in literature reviews. Studying these factors also makes it possible to make physiopathological assumptions that will lead to a better understanding of the mechanisms involved in the development of AD.


Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and material: Not applicable / all articles used in this overview are cited in the text.

Competing interests: The authors declare that they have no competing interests.

Funding: Not applicable.

Authors’ contributions: MR, VR reviewed the literature and were involved in manuscript preparation and revision. EC, ED, GS, PAH, FP, SG, RB were involved in manuscrit revision. All authors read and approved the final manuscript.

Acknowledgements: The authors acknowledge fellow colleagues for the discussions. We apologize to authors whose studies were not cited due to space constraints.



1.    Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: A systematic review and metaanalysis. Alzheimers Dement 2013;9:63-75.e2. doi:10.1016/j.jalz.2012.11.007.
2.    Garre-Olmo J. [Epidemiology of Alzheimer’s disease and other dementias]. Rev Neurol 2018;66:377–86.
3.    2018 Alzheimer’s disease facts and figures. Alzheimers Dement J Alzheimers Assoc 2018;14:367–429. doi:10.1016/j.jalz.2018.02.001.
4.    Manton KC, Gu XL, Ukraintseva SV. Declining prevalence of dementia in the U.S. elderly population. Adv Gerontol Uspekhi Gerontol 2005;16:30–7.
5.    Langa KM, Larson EB, Karlawish JH, Cutler DM, Kabeto MU, Kim SY, et al. Trends in the prevalence and mortality of cognitive impairment in the United States: is there evidence of a compression of cognitive morbidity? Alzheimers Dement J Alzheimers Assoc 2008;4:134–44. doi:10.1016/j.jalz.2008.01.001.
6.    Rocca WA, Petersen RC, Knopman DS, Hebert LE, Evans DA, Hall KS, et al. Trends in the incidence and prevalence of Alzheimer’s disease, dementia, and cognitive impairment in the United States. Alzheimers Dement J Alzheimers Assoc 2011;7:80–93. doi:10.1016/j.jalz.2010.11.002.
7.    Satizabal CL, Beiser AS, Chouraki V, Chêne G, Dufouil C, Seshadri S. Incidence of Dementia over Three Decades in the Framingham Heart Study. N Engl J Med 2016;374:523–32. doi:10.1056/NEJMoa1504327.
8.    Grasset L, Brayne C, Joly P, Jacqmin-Gadda H, Peres K, Foubert-Samier A, et al. Trends in dementia incidence: Evolution over a 10-year period in france. Alzheimers Dement 2015;11:P219–20. doi:10.1016/j.jalz.2015.07.243.
9.    Matthews FE, Arthur A, Barnes LE, Bond J, Jagger C, Robinson L, et al. A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet Lond Engl 2013;382:1405–12. doi:10.1016/S0140-6736(13)61570-6.
10.    Lobo A, Saz P, Marcos G, Dia JL, De-la-Camara C, Ventura T, et al. Prevalence of dementia in a southern European population in two different time periods: the ZARADEMP Project. Acta Psychiatr Scand 2007;116:299–307. doi:10.1111/j.1600-0447.2007.01006.x.
11.    Schrijvers EMC, Verhaaren BFJ, Koudstaal PJ, Hofman A, Ikram MA, Breteler MMB. Is dementia incidence declining?: Trends in dementia incidence since 1990 in the Rotterdam Study. Neurology 2012;78:1456–63. doi:10.1212/WNL.0b013e3182553be6.
12.    Qiu C, von Strauss E, Bäckman L, Winblad B, Fratiglioni L. Twenty-year changes in dementia occurrence suggest decreasing incidence in central Stockholm, Sweden. Neurology 2013;80:1888–94. doi:10.1212/WNL.0b013e318292a2f9.
13.    Christensen K, Thinggaard M, Oksuzyan A, Steenstrup T, Andersen-Ranberg K, Jeune B, et al. Physical and cognitive functioning of people older than 90 years: a comparison of two Danish cohorts born 10 years apart. Lancet Lond Engl 2013;382:1507–13. doi:10.1016/S0140-6736(13)60777-1.
14.    Chibnik LB, Wolters FJ, Bäckman K, Beiser A, Berr C, Bis JC, et al. Trends in the incidence of dementia: design and methods in the Alzheimer Cohorts Consortium. Eur J Epidemiol 2017;32:931–8. doi:10.1007/s10654-017-0320-5.
15.    Rosenberg RN, Lambracht-Washington D, Yu G, Xia W. Genomics of Alzheimer Disease: A Review. JAMA Neurol 2016;73:867–74. doi:10.1001/jamaneurol.2016.0301.
16.    Giri M, Zhang M, Lü Y. Genes associated with Alzheimer’s disease: an overview and current status. Clin Interv Aging 2016;11:665–81. doi:10.2147/CIA.S105769.
17.    Nagai M, Hoshide S, Dote K, Kario K. Visit-to-visit blood pressure variability and dementia: Blood pressure variability and dementia. Geriatr Gerontol Int 2015;15:26–33. doi:10.1111/ggi.12660.
18.    Cowppli-Bony P, Dartigues J-F, Orgogozo J-M. Facteurs de risque vasculaire et risque de maladie d’Alzheimer : revue d’études épidémiologiques. Psychol Neuropsychiatr Vieil 2006;4:47–60.
19.    Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol 2011;10:819–28. doi:10.1016/S1474-4422(11)70072-2.
20.    Qiu C, Winblad B, Fratiglioni L. The age-dependent relation of blood pressure to cognitive function and dementia. Lancet Neurol 2005;4:487–99. doi:10.1016/S1474-4422(05)70141-1.
21.    Iadecola C, Yaffe K, Biller J, Bratzke LC, Faraci FM, Gorelick PB, et al. Impact of Hypertension on Cognitive Function: A Scientific Statement From the American Heart Association. Hypertens Dallas Tex 1979 2016;68:e67–94. doi:10.1161/HYP.0000000000000053.
22.    Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol 2011;10:819–28. doi:10.1016/S1474-4422(11)70072-2.
23.    Aldrugh S, Sardana M, Henninger N, Saczynski JS, McManus DD. Atrial fibrillation, cognition and dementia: A review. J Cardiovasc Electrophysiol 2017;28:958–65. doi:10.1111/jce.13261.
24.    Wolters FJ, Segufa RA, Darweesh SKL, Bos D, Ikram MA, Sabayan B, et al. Coronary heart disease, heart failure, and the risk of dementia: A systematic review and meta-analysis. Alzheimers Dement J Alzheimers Assoc 2018. doi:10.1016/j.jalz.2018.01.007.
25.    Alosco ML, Hayes SM. Structural brain alterations in heart failure: a review of the literature and implications for risk of Alzheimer’s disease. Heart Fail Rev 2015;20:561–71. doi:10.1007/s10741-015-9488-5.
26.    Hu Q, Teng W, Li J, Hao F, Wang N. Homocysteine and Alzheimer’s Disease: Evidence for a Causal Link from Mendelian Randomization. J Alzheimers Dis 2016;52:747–56. doi:10.3233/JAD-150977.
27.    Morris MS. Homocysteine and Alzheimer’s disease. Lancet Neurol 2003;2:425–428.
28.    Beydoun MA, Beydoun HA, Gamaldo AA, Teel A, Zonderman AB, Wang Y. Epidemiologic studies of modifiable factors associated with cognition and dementia: systematic review and meta-analysis. BMC Public Health 2014;14:643. doi:10.1186/1471-2458-14-643.
29.    Kivipelto M, Solomon A. Cholesterol as a risk factor for Alzheimer’s disease–epidemiological evidence. V 2006;114:50–57.
30.    Pendlebury ST, Rothwell PM. Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia: a systematic review and meta-analysis. Lancet Neurol 2009;8:1006–1018.
31.    Akoudad S, Wolters FJ, Viswanathan A, de Bruijn RF, van der Lugt A, Hofman A, et al. Association of Cerebral Microbleeds With Cognitive Decline and Dementia. JAMA Neurol 2016;73:934. doi:10.1001/jamaneurol.2016.1017.
32.    Nagai M, Hoshide S, Kario K. Hypertension and Dementia. Am J Hypertens 2010;23:116–24. doi:10.1038/ajh.2009.212.
33.    Miida T. Can statin therapy really reduce the risk of Alzheimer’s disease and slow its progression? 2005.
34.    Beydoun MA, Beydoun HA, Gamaldo AA, Teel A, Zonderman AB, Wang Y. Epidemiologic studies of modifiable factors associated with cognition and dementia: systematic review and meta-analysis. BMC Public Health 2014;14.
35.    Wheeler MJ, Dempsey PC, Grace MS, Ellis KA, Gardiner PA, Green DJ, et al. Sedentary behavior as a risk factor for cognitive decline? A focus on the influence of glycemic control in brain health. Alzheimers Dement N Y N 2017;3:291–300. doi:10.1016/j.trci.2017.04.001.
36.    Anstey KJ, Mack HA, Cherbuin N. Alcohol consumption as a risk factor for dementia and cognitive decline: meta-analysis of prospective studies. Am J Geriatr Psychiatry 2009;17:542–555.
37.    Bellou V, Belbasis L, Tzoulaki I, Middleton LT, Ioannidis JPA, Evangelou E. Systematic evaluation of the associations between environmental risk factors and dementia: an umbrella review of systematic reviews and meta-analyses. Alzheimers Dement 2017;13:406–18. doi:10.1016/j.jalz.2016.07.152.
38.    Caamaño-Isorna F, Corral M, Montes-Martínez A, Takkouche B. Education and Dementia: A Meta-Analytic Study. Neuroepidemiology 2006;26:226–32. doi:10.1159/000093378.
39.    Zhu X, Zhao Y. Sleep-disordered breathing and the risk of cognitive decline: a meta-analysis of 19,940 participants. Sleep Breath 2018;22:165–73. doi:10.1007/s11325-017-1562-x.
40.    Honjo K, van Reekum R, Verhoeff NPLG. Alzheimer’s disease and infection: Do infectious agents contribute to progression of Alzheimer’s disease? Alzheimers Dement 2009;5:348–60. doi:10.1016/j.jalz.2008.12.001.
41.    Doulberis M, Kotronis G, Thomann R, Polyzos SA, Boziki M, Gialamprinou D, et al. Review: Impact of Helicobacter pylori on Alzheimer’s disease: What do we know so far? Helicobacter 2018;23:e12454. doi:10.1111/hel.12454.
42.    Tolppanen A-M, Solomon A, Soininen H, Kivipelto M. Midlife vascular risk factors and Alzheimer’s disease: evidence from epidemiological studies. J Alzheimers Dis 2012;32:531–540.
43.    Zhang J, Chen C, Hua S, Liao H, Wang M, Xiong Y, et al. An updated meta-analysis of cohort studies: Diabetes and risk of Alzheimer’s disease. Diabetes Res Clin Pract 2017;124:41–7. doi:10.1016/j.diabres.2016.10.024.
44.    Alagiakrishnan K, Sankaralingam S, Ghosh M, Mereu L, Senior P. Antidiabetic Drugs and Their Potential Role in Treating Mild Cognitive Impairment and Alzheimer’s Disease. Discov Med 2013;16:277–86.
45.    Wenshan L, Du N, Fan X, Wang Y, Jia X, Hou X, et al. Low Testosterone Level and Risk of Alzheimer’s Disease in the Elderly Men: a Systematic Review and Meta-Analysis. Mol Neurobiol 2016;53:2679–84. doi:10.1007/s12035-015-9315-y.
46.    Ownby RL, Crocco E, Acevedo A, John V, Loewenstein D. Depression and Risk for Alzheimer Disease: Systematic Review, Meta-analysis, and Metaregression Analysis. Arch Gen Psychiatry 2006;63:530. doi:10.1001/archpsyc.63.5.530.
47.    Harrington KD, Lim YY, Gould E, Maruff P. Amyloid-beta and depression in healthy older adults: a systematic review. Aust N Z J Psychiatry 2015;49:36–46.
48.    Billioti de Gage S, Pariente A, Bégaud B. Is there really a link between benzodiazepine use and the risk of dementia? Expert Opin Drug Saf 2015;14:733–47. doi:10.1517/14740338.2015.1014796.
49.    Panza F, Solfrizzi V, Logroscino G. Age-related hearing impairment – a risk factor and frailty marker for dementia and AD. Nat Rev Neurol 2015;11:166–175.
50.    Zheng Y, Fan S, Liao W, Fang W, Xiao S, Liu J. Hearing impairment and risk of Alzheimer’s disease: a meta-analysis of prospective cohort studies. Neurol Sci 2017;38:233–9. doi:10.1007/s10072-016-2779-3.
51.    Mortimer JA, Van Duijn CM, Chandra V, Fratiglioni L, Graves AB, Heyman A, et al. Head trauma as a risk factor for Alzheimer’s disease: a collaborative re-analysis of case-control studies. Int J Epidemiol 1991;20:S28–S35.
52.    Fleminger S, Oliver DL, Lovestone S, Rabe-Hesketh S, Giora A. Head injury as a risk factor for Alzheimer’s disease: the evidence 10 years on; a partial replication. V 2003;74:857–862.
53.    LaRoia H, Louis ED. Association between Essential Tremor and Other Neurodegenerative Diseases: What Is the Epidemiological Evidence? Neuroepidemiology 2011;37:1–10. doi:10.1159/000328866.
54.    Seitz DP, Shah PS, Herrmann N, Beyene J, Siddiqui N. Exposure to general anesthesia and risk of Alzheimer’s disease: a systematic review and meta-analysis. BMC Geriatr 2011;11:83.
55.    Hug K, Röösli M, Rapp R. Magnetic field exposure and neurodegenerative diseases – recent epidemiological studies. Soz- Präventivmedizin SPM 2006;51:210–20. doi:10.1007/s00038-006-5096-4.
56.    Garcia AM, Sisternas A, Hoyos SP. Occupational exposure to extremely low frequency electric and magnetic fields and Alzheimer disease: a meta-analysis. Int J Epidemiol 2008;37:329–40. doi:10.1093/ije/dym295.
57.    Maes A, Verschaeve L. Can cytogenetics explain the possible association between exposure to extreme low-frequency magnetic fields and Alzheimer’s disease?: Electromagnetic fields and Alzheimer’s disease ? J Appl Toxicol 2012;32:81–7. doi:10.1002/jat.1724.
58.    Jalilian H, Teshnizi SH, Röösli M, Neghab M. Occupational exposure to extremely low frequency magnetic fields and risk of Alzheimer disease: A systematic review and meta-analysis. NeuroToxicology 2017. doi:10.1016/j.neuro.2017.12.005.
59.    Yan D, Zhang Y, Liu L, Yan H. Pesticide exposure and risk of Alzheimer’s disease: a systematic review and meta-analysis. Sci Rep 2016;6:32222. doi:10.1038/srep32222.
60.    Virk SA, Eslick GD. Brief Report: Meta-analysis of Antacid Use and Alzheimer’s Disease. Epidemiology 2015;26:769–73. doi:10.1097/EDE.0000000000000326.
61.    Flaten TP. Aluminium as a risk factor in Alzheimer’s disease, with emphasis on drinking water. Brain Res Bull 2001;55:187–196.
62.    Doets EL, van Wijngaarden JP, Szczecińska A, Dullemeijer C, Souverein OW, Dhonukshe-Rutten RAM, et al. Vitamin B12 intake and status and cognitive function in elderly people. Epidemiol Rev 2013;35:2–21. doi:10.1093/epirev/mxs003.
63.    Loef M, von Stillfried N, Walach H. Zinc diet and Alzheimer’s disease: a systematic review. Nutr Neurosci 2012;15:2–12. doi:10.1179/1476830512Y.0000000010.
64.    Du K, Liu M, Pan Y, Zhong X, Wei M. Association of Serum Manganese Levels with Alzheimer’s Disease and Mild Cognitive Impairment: A Systematic Review and Meta-Analysis. Nutrients 2017;9:231. doi:10.3390/nu9030231.
65.    Veronese N, Zurlo A, Solmi M, Luchini C, Trevisan C, Bano G, et al. Magnesium Status in Alzheimer’s Disease: A Systematic Review. Am J Alzheimers Dis Dementias® 2016;31:208–13. doi:10.1177/1533317515602674.
66.    van de Rest O, Berendsen AA, Haveman-Nies A, de Groot LC. Dietary Patterns, Cognitive Decline, and Dementia: A Systematic Review. Adv Nutr Int Rev J 2015;6:154–68. doi:10.3945/an.114.007617.
67.    Yusufov M, Weyandt LL, Piryatinsky I. Alzheimer’s disease and diet: a systematic review. Int J Neurosci 2017;127:161–75. doi:10.3109/00207454.2016.1155572.
68.    Cederholm T, Salem N, Palmblad J. -3 Fatty Acids in the Prevention of Cognitive Decline in Humans. Adv Nutr Int Rev J 2013;4:672–6. doi:10.3945/an.113.004556.
69.    Jiang C, Li G, Huang P, Liu Z, Zhao B. The Gut Microbiota and Alzheimer’s Disease. J Alzheimers Dis JAD 2017;58:1–15. doi:10.3233/JAD-161141.
70.    Yuan Q, Wang C-W, Shi J, Lin Z-X. Effects of Ginkgo biloba on dementia: An overview of systematic reviews. J Ethnopharmacol 2017;195:1–9. doi:10.1016/j.jep.2016.12.005.
71.    Balion C, Griffith LE, Strifler L, Henderson M, Patterson C, Heckman G, et al. Vitamin D, cognition, and dementia A systematic review and meta-analysis. Neurology 2012;79:1397–1405.
72.    Lopes da Silva S, Vellas B, Elemans S, Luchsinger J, Kamphuis P, Yaffe K, et al. Plasma nutrient status of patients with Alzheimer’s disease: Systematic review and meta-analysis. Alzheimers Dement J Alzheimers Assoc 2014;10:485–502. doi:10.1016/j.jalz.2013.05.1771.



A.C. Nutaitis1, S.D. Tharwani1, M.C. Serra2, F.C. Goldstein1, L. Zhao3, S.S. Sher4, D.D. Verble1, W. Wharton1


1. Emory University, Department of Neurology; 2. Atlanta VA Medical Center & Emory University Department of Medicine; 3. Emory University, Department of Biostatistics and Bioinformatics; 4. Emory University, Department of Internal Medicine

Corresponding Author: Whitney Wharton, PhD, Assistant Professor, Neurology, Emory University,

J Prev Alz Dis 2018
Published online November 30, 2018,



Background: African Americans (AA) are more likely to develop Alzheimer’s disease (AD) than Caucasians (CC). Dietary modification may have the potential to reduce the risk of developing AD.
Objective: The objective of this study is to investigate the relationship between Southern and Prudent diet patterns and cognitive performance in individuals at risk for developing AD.
Design: Cross-sectional observational study.
Participants: Sixty-six cognitively normal AA and CC individuals aged 46-77 years with a parental history of AD were enrolled.
Measurements: Participants completed a Food Frequency questionnaire, cognitive function testing, which consisted of 8 neuropsychological tests, and cardiovascular risk factor assessments, including evaluation of microvascular and macrovascular function and ambulatory blood pressure monitoring.
Results:  Results revealed a relationship between the Southern diet and worse cognitive performance among AAs. AAs who consumed pies, mashed potatoes, tea, and sugar drinks showed worse cognitive performance (p<0.05) compared with CCs. In addition, gravy (p=0.06) and cooking oil/fat (p=0.06) showed negative trends with cognitive performance in AAs. In both CC and AA adults, greater adherence to a Prudent dietary pattern was associated with better cognitive outcomes. Cardiovascular results show that participants are overall healthy. AAs and CCs did not differ on any vascular measure including BP, arterial stiffness and endothelial function.
Conclusion: Research shows that dietary factors can associate with cognitive outcomes. This preliminary cross-sectional study suggests that foods characteristic of the Southern and Prudent diets may have differential effects on cognitive function in middle-aged individuals at high risk for AD. Results suggest that diet could be a non-pharmaceutical tool to reduce cognitive decline in racially diverse populations. It is possible that the increased prevalence of AD in AA could be partially reduced via diet modification.

Key words: Alzheimer’s disease, Diet, African-American, Prevention, Nutrition, Race, Cognition, Vascular.

Abbreviations:  AA: African Americans; AD: Alzheimer’s disease; CCs: Caucasians.



Over five million people in the U.S. are living with Alzheimer’s disease (AD), and in the next thirty years, the prevalence will increase to over sixteen million (1). Individuals at high risk of AD include African Americans (AAs), who have a 64% higher chance of developing AD than Caucasians (CCs) (2), and individuals with a parental history of AD, who are ten times more likely to become afflicted themselves (3). In the absence of a disease-modifying treatment, it is critical that we identify modifiable risk factors to promote cognitive health and reduce AD risk. Current preventative efforts focusing on lifestyle interventions including diet, exercise, and cognitive training (4, 5). Importantly, midlife (40-65 years of age) is when the neuropathological AD related changes begin and when the impact of vascular risk factors begin to have lasting effects. Thus, middle age is the optimal time to implement an AD focused lifestyle intervention.
Research suggests that adherence to a healthy diet confers cognitive benefits in older populations (6-8). Such diets include the Prudent, Dietary Approaches to Stop Hypertension (DASH) and Mediterranean diets, characterized by fruit, vegetables, legumes, fish and olive oil. While these studies are encouraging, few studies have examined the potential influence of diet on cognition in middle-aged, ethnically diverse populations, who are at high risk for AD.
In addition to genetic contributions, the increased prevalence of AD in AAs may be a result of modifiable risk factors including dietary intake (9-12). In a study examining the association between the Mediterranean diet and cognitive decline, AA participants who had higher adherence with the Mediterranean diet had slower cognitive decline compared to participants with less Mediterranean diet adherence (13). Furthermore, current literature suggests that geographic and racial differences in cardiovascular disease risk are associated with the Southern dietary pattern (characterized by fried foods, fats, eggs, organ and processed meats and sugar-sweetened beverages) and thus it is possible that this Southern dietary pattern may contribute to cognitive decline (14). These findings stress the need for prospective studies addressing the relationships between diet and cognitive function in racially diverse populations in the U.S (15).
The goal of this study was to assess the relationship between dietary patterns, vascular function, and cognitive decline, in a middle aged, diverse cohort at high risk for AD due to a parental history of AD. We hypothesize that a higher intake of a  Southern dietary pattern and lower intake of a  Prudent (healthy) in dietary pattern increases the risk for vascular dysfunction and cognitive impairment, especially among AA, compared to CC, adults.


Subjects and Methods

Study Sample

Sixty-six subjects enrolled in an ongoing NIH/NIA funded study (ASCEND PI: Wharton) and with a parental history of AD took part in this cross-sectional pilot observational cohort study. Parental history was confirmed via autopsy or probable AD as defined by NINDS-ADRDA criteria and the Dementia Questionnaire (16). Subjects received vascular and cognitive assessments under the IRB approved protocol.

Demographic Information

Age, gender, level of education, income, exercise, smoking status, and depression was acquired via a self-reported survey. Exercise was reported as mean days per week of cardiovascular exercise (17).
Dietary Pattern Assessment: Diet was assessed via the Jackson Heart Study’s shortened version of the Lower Mississippi Delta Nutrition Intervention Research Initiative Food Frequency Questionnaire (FFQ) (18). The questionnaire consists of 160 items and takes 20 minutes to complete. Participants self-reported quantity and frequency of food and drink consumption on an online survey at home via a secure, individual web link. Subjects were given a $15.00 gift card for completing the survey.
Food items from the FFQ were classified into the Southern or Prudent diets in accordance Reasons for Geographic and Racial Differences in Stroke (REGARDS) study guidelines (14). Food items including fried foods, fats, eggs, organ and processed meats and sugar-sweetened beverages were classified as characteristic of the Southern diet (14). Healthy foods including fruits, vegetables, whole grains, and fish were classified as Prudent diet related items (19).

Cardiovascular Risk Factor Assessment

Vascular measures were selected based on prior research with vascular function in individuals at risk for AD (20, 21). Participants underwent a one-hour fasting assessment including microvascular vasodilatory function, using digital pulse amplitude tonometry (EndoPAT) and macrovascular vascular function (assessed by flow mediated vasodilation (FMD)). In addition, a blood pressure (BP) assessment was obtained via 24-hour ambulatory BP monitoring (Spacelabs Healthcare©). We examined 24-hour average systolic and diastolic blood pressure and nocturnal dipping patterns, all of which have been linked to cognition and AD (22).

Neuropsychological testing

Cognitive function was evaluated by a one-hour battery of eight neuropsychological tests in domains reportedly affected in early AD and susceptible to the effects of hypertension (23). The tests included: Montreal Cognitive Assessment (MOCA), Benson visuospatial memory task, Buschke Delay Memory Test, Trails A and B, Digit Span Backwards, Mental Rotation Test (MRT), and Multilingual Naming Test (MINT). These tests targeted specific AD related cognitive domains including: working memory, executive function (Trail-Making Test B) (24, 25), language (MINT) (26), verbal memory (Buschke) (27), visuospatial ability (MRT) (28) and global cognition (MOCA) (29).

Data Analysis

Researchers utilized IBM SPSS Statistics Version 22 to test for group differences between AAs and CCs in demographics, vascular risk factors, and cognitive performance. We conducted independent two-sample t-test for continuous variables and chi-square test for characteristic variables, controlling for age, gender and education. As there is not sufficient power to detect an interaction of diet and race, we examined the association between diet and cognition in each racial group separately. Correlations between cognitive performance and foods were assessed using Pearson’s r partial correlations controlling for education and age on the cognitive tests in which we found racial differences at p=0.10. Because eight cognitive tests were included in the analyses, the threshold of significance level using a false discovery rate approximation was adjusted such that a threshold p-value of 0.03 was used.



Table 1 shows the demographic characteristics for 21 AAs and 45 CCs. Participants were middle aged (M=58.6 years), mostly female (67.6%), and highly educated (83.8% graduate or postgraduate education). While AAs and CCs did not differ on demographics including age, education, exercise, smoking status, or self-reported depression, significant racial differences were present for gender and income, such that a larger percent of AA females than CC females participated in the study, and AAs reported significantly less income compared to CCs. Participants were generally very healthy and AAs and CCs did not differ on any vascular measure including BP, arterial stiffness and endothelial function.

Table 1. Demographic Characteristics and Cardiovascular Data for African Americans and Caucasians. (AA= African American, CC=Caucasian)

Table 1. Demographic Characteristics and Cardiovascular Data for African Americans and Caucasians. (AA= African American, CC=Caucasian)

*P < 0.05; ** P < 0.01; RHI=reactive hyperemia index; AIx= augmentation index; FMD= flow mediated vasodilation


Table 2 shows cognitive test results by race. Results show that CCs significantly outperformed AAs on global cognition (MOCA), naming (MINT), and executive function (Trails B) tests (all p values <0.05). In addition, results revealed a trend for CCs to outperform AAs in verbal memory (Buschke Delay) (p= 0.073).
Table 3 shows Pearson’s r partial correlations between foods and cognitive performance, by race. Five of six southern foods show moderate to strong correlations with cognitive tests in AAs. In AAs, pies, mashed potatoes, and sugar drinks were correlated with cognitive performance (all p values <0.01) and trends were found with tea (p=0.04), gravy (p=0.06) and cooking oil/fat (p=0.06), such that AAs performed worse on cognitive tests with consumption of these foods. Results show that AAs were more negatively impacted than CCs by foods characteristic of the Southern diet. Conversely, CCs who consumed mashed potatoes (p=0.01) and sugar drinks (p<0.10) performed better on cognitive assessments. Foods characteristic of the Prudent diet, such as whole grain breads (p=0.04), baked fish (p=0.03), and grape juice (p<0.01), were positively associated with cognitive performance in CCs. In addition, 100% orange juice (OJ) showed a trend (p<0.10) of better performance on cognitive assessment in CC. The most pronounced relationship was seen with 100% grape juice, such that AAs consuming 100% grape juice performed significantly better on the MINT (p<0.01). Results suggest a stronger relationship between the Prudent diet and cognitive performance in CCs vs. AAs.


Table 2. Means and standard deviations on cognitive tests in African Americans and Caucasians. (AA= African American, CC=Caucasian)

Table 2. Means and standard deviations on cognitive tests in African Americans and Caucasians. (AA= African American, CC=Caucasian

†P<0.1; *P < 0.05

Table 3. Pearson’s r correlations between cognition and foods by race for individuals who completed Food Frequency Questionnaire. (AA= African American, CC=Caucasian; 1-6=Southern Diet, 7-10=Prudent Diet)

Table 3. Pearson’s r correlations between cognition and foods by race for individuals who completed Food Frequency Questionnaire. (AA= African American, CC=Caucasian; 1-6=Southern Diet, 7-10=Prudent Diet)

†P<0.10; *P<0.05; **P<0.01



To our knowledge, this is the first study to report a relationship between diet and cognitive performance in healthy, racially diverse middle-aged adults with a parental history of AD. CCs outperformed AAs on cognitive tests of global cognition, language, and executive function. Racial differences on cognitive tests could not be explained by age, education, vascular risk factors, exercise, smoking, or depression. However, our results suggest that these differences may be partially attributed to dietary patterns specific to the Southern and Prudent diets.
A positive relationship between cognition and the Prudent (healthy) diet and a negative relationship between cognition and the Southern (less healthy) diet was observed. Similarly, Shakersain et al. recently identified a relationship between lower adherence to a Prudent diet and greater rates of cognitive decline [6]. Further, Seetharaman et al. reported that elevated diabetes risk, which is higher in AAs than CCs, is related to poorer performance on perceptual speed, verbal ability, spatial ability, and overall cognition (30).  Foods in our study characteristic of the Southern diet, such as pies, tea, and sugar drinks, were negatively associated with cognitive performance and thus it is possible that this may be a result of the higher glycemic index of these foods. Our results also align with studies showing that a diet high in gravy or butter is associated with poor cognition in older adults (31). Further, we show that racial differences in diet such that AAs reported stronger alliance with the Southern diet than CCs. This finding is not unique to our study, as previous studies show that AAs are less likely to adhere to the DASH diet compared with CCs (32). Our study highlights the need for culturally sensitive dietary interventions to combat cognitive decline in high-risk populations.
Only one Prudent item (100% grape juice) was correlated to cognitive performance in AAs, in contrast to five Prudent items (whole grain breads, mashed potatoes, baked fish, 100% grape juice and 100% OJ). The Prudent diet is nutrient dense, containing numerous nutrients with anti-inflammatory and antioxidant properties, including fiber, poly-unsaturated fatty acids, vitamins, minerals, carotenoids, and polyphenols, among others (6). Therefore, it is possible that the negative effects of elevated inflammation and oxidative stress, which is more prevalent among AAs, on cognitive health may be dampened by the effects of the Prudent diet (34, 35). The association between beverages and cognitive performance should also be noted. Individuals may be more consistent with their beverage choices, (i.e. coffee or OJ), than food choices, and thus beverages may associate more strongly with cognitive function due to a higher intake.
The need for advancements in preventative and treatment strategies in high-risk groups, including AAs is great (36). Results showed racial differences in the relationship between diet and cognitive performance. It is possible that dietary intake may be contributing to early cognitive decline in AAs, or preservation of cognitive functioning in CCs. This finding is important, as the current literature suggests that even though late-life positive dietary patterns may result in notable health improvements (19, 37), mid-life is the optimal time to incorporate these changes, before the irreversible AD cascade begins (38). Thus diet modification may hold promise as a modifiable risk factor for AD.
Strengths of this study include a comprehensive battery of neuropsychology testing and vascular measures, and a middle aged, racially diverse cohort at high risk for AD. Also the FFQ is both racially and geographically sensitive (18). Limitations of this pilot project include the small sample size and the overall health of the cohort. It is possible that diet may have a more pronounced impact in individuals with preexisting health complications. Next the FFQ does not include information regarding longitudinal food choices, and these data should be collected in future studies (39).
In summary, our results stress the need for further research investigating the potential of dietary intake as a non-pharmaceutical intervention in individuals at risk for AD. Because AAs have an increased incidence and prevalence of AD (2, 40), investigation of modifiable risk factors that target this high-risk group is essential. Specifically, nutritional education and dietary interventions designed to shift individuals, particularly AAs, from Southern diets to healthier, Prudent – like diets, may be a cost efficient way to preserve cognitive function in otherwise healthy individuals.


Funding: This project was funded by the National Institute of Health (NIH) and in part by the Scholarly Independent Research at Emory (SIRE) Research grant for undergraduate students. The NIH and SIRE had no role in study design, collection, analysis or interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Acknowledgments: All persons who have made substantial contributions to this manuscript are listed as authors. Their contributions are listed below: Alexandra C. Nutaitis, BS: Designed research, Conducted research, Analyzed data, Wrote paper, Had primary responsibility for final content . Sonum D. Tharwani: Conducted research, Wrote paper. Monica C. Serra, PhD: Provided essential reagents or materials, Analyzed data, Wrote paper. Felicia C. Goldstein, PhD: Designed research, Wrote paper. Liping Zhao, MSPH: Provided essential reagents or materials, Analyzed data, Wrote paper
Salman S. Sher, MD: Conducted research, Provided essential reagents or materials, Analyzed data, Wrote paper
Danielle D. Verble, MA: Conducted research, Wrote paper
Whitney Wharton, PhD: Designed research, Conducted research, Analyzed data, Wrote paper Had primary responsibility for final content

Sources of Support: NIH-NIA under grants: NIH-NIA 5 P50 AG025688, K01AG042498, and U01 AG016976. Independent funding for the present pilot study was obtained through Emory University’s Scholarly Inquiry Research Grant for undergraduate students (PI: Nutaitis).

Conflict of interest: No author has a conflict of interest to report.

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.



1.    Alzheimer’s Association. Alzheimer’s Disease Facts and Figures [cited 2016; Available from:
2.    Steenland, K., et al. A Meta-Analysis of Alzheimer’s Disease Incidence and Prevalence Comparing African-Americans and Caucasians. J Alzheimers Dis 2016; 50(1):71-6.
3.    Alzheimer’s Association. African-Americans and Alzheimer’s 2016  [cited 2016; Available from:
4.    Rege, S.D., et al. Can Diet and Physical Activity Limit Alzheimer&#39;s Disease Risk? Curr Alzheimer Res 2017; 14(1):76-93.
5.    Baumgart, M., et al. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimers Dement 2015; 11(6):718-26.
6.    Shakersain, B., et al. The Nordic Prudent Diet Reduces Risk of Cognitive Decline in the Swedish Older Adults: A Population-Based Cohort Study. Nutrients 2018; 10(2).
7.    Tangney, C.C., et al. Relation of DASH- and Mediterranean-like dietary patterns to cognitive decline in older persons. Neurology 2014; 83(16):1410-6.
8.    van de Rest, O., et al. Dietary patterns, cognitive decline, and dementia: a systematic review. Adv Nutr 2015; 6(2):154-68.
9.    Chin, A.L., S. Negash, and R. Hamilton. Diversity and disparity in dementia: the impact of ethnoracial differences in Alzheimer disease. Alzheimer Dis Assoc Disord 2011; 25(3):187-95.
10.    Norton, S., et al. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol 2014; 13(8):788-94.
11.    Biessels, G.J. Capitalising on modifiable risk factors for Alzheimer’s disease. Lancet Neurol 2014; 13(8):752-3.
12.    Sofi, F., et al. Accruing evidence on benefits of adherence to the Mediterranean diet on health: an updated systematic review and meta-analysis. Am J Clin Nutr 2010; 92(5):1189-96.
13.    Koyama, A., et al. Association between the Mediterranean diet and cognitive decline in a biracial population. J Gerontol A Biol Sci Med Sci 2015; 70(3):354-9.
14.    Shikany, J.M., et al. Southern Dietary Pattern is Associated With Hazard of Acute Coronary Heart Disease in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Circulation 2015; 132(9):804-14.
15.    Harmon, B.E., et al. Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. Am J Clin Nutr 2015; 101(3):587-97.
16.    Kawas, C., et al. A validation study of the Dementia Questionnaire. Arch Neurol 1994; 51(9):901-6.
17.    Jacobs, D.R., Jr., et al. Validity and Reliability of Short Physical Activity History: Cardia and the Minnesota Heart Health Program. J Cardiopulm Rehabil 1989; 9(11):448-459.
18.    Carithers, T.C., et al. Validity and calibration of food frequency questionnaires used with African-American adults in the Jackson Heart Study. J Am Diet Assoc 2009; 109(7):1184-1193.
19.    Shakersain, B., et al. Prudent diet may attenuate the adverse effects of Western diet on cognitive decline. Alzheimers Dement 2016; 12(2):100-109.
20.    Zhong, W., et al. Pulse wave velocity and cognitive function in older adults. Alzheimer Dis Assoc Disord 2014; 28(1):44-9.
21.    Hajjar, I., et al. Roles of Arterial Stiffness and Blood Pressure in Hypertension-Associated Cognitive Decline in Healthy Adults. Hypertension 2016; 67(1):171-5.
22.    Tarumi, T., et al. Amyloid burden and sleep blood pressure in amnestic mild cognitive impairment. Neurology 2015; 85(22):1922-9.
23.    Asthana, S., et al. Cognitive and neuroendocrine response to transdermal estrogen in postmenopausal women with Alzheimer’s disease: results of a placebo-controlled, double-blind, pilot study. Psychoneuroendocrinology 1999; 24(6):657-77.
24.    Dodrill, C.B. A neuropsychological battery for epilepsy. Epilepsia 1978; 19(6):611-23.
25.    Stroop, J. Studies of interference in serial verbal reactions. J Exp Psychol 1935; 18:643-662.
26.    Spreen, O. and E. Strauss, A compendium of neuropsychological tests. 2nd ed. 1998, New York, New York: Oxford Press.
27.    Buschke, H. Selective reminding for analysis of memory and learning. J Verb Learn Verb Behav 1973; 12:543-550.
28.    Vandenberg, S.G. and A.R. Kuse. Mental rotations, a group test of three-dimensional spatial visualization. Percept Mot Skills 1978; 47(2):599-604.
29.    Nasreddine, Z.S., et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53(4):695-9.
30.    Seetharaman, S., et al. Blood glucose, diet-based glycemic load and cognitive aging among dementia-free older adults. J Gerontol A Biol Sci Med Sci 2015; 70(4):471-9.
31.    Granic, A., et al. Dietary Patterns High in Red Meat, Potato, Gravy, and Butter Are Associated with Poor Cognitive Functioning but Not with Rate of Cognitive Decline in Very Old Adults. J Nutr 2016; 146(2):265-74.
32.    Epstein, D.E., et al. Determinants and consequences of adherence to the dietary approaches to stop hypertension diet in African-American and white adults with high blood pressure: results from the ENCORE trial. J Acad Nutr Diet 2012; 112(11):1763-73.
33.    Feairheller, D.L., et al. Racial differences in oxidative stress and inflammation: in vitro and in vivo. Clin Transl Sci 2011; 4(1):32-7.
34.    Morris, A.A., et al. Differences in systemic oxidative stress based on race and the metabolic syndrome: the Morehouse and Emory Team up to Eliminate Health Disparities (META-Health) study. Metab Syndr Relat Disord 2012; 10(4):252-9.
35.    Froehlich, T.E., S.T. Bogardus, Jr., and S.K. Inouye. Dementia and race: are there differences between African Americans and Caucasians? J Am Geriatr Soc 2001; 49(4):477-84.
36.    Bardach, S.H., N.E. Schoenberg, and B.M. Howell. What Motivates Older Adults to Improve Diet and Exercise Patterns? J Community Health 2016; 41(1):22-9.
37.    Barage, S.H. and K.D. Sonawane. Amyloid cascade hypothesis: Pathogenesis and therapeutic strategies in Alzheimer’s disease. Neuropeptides 2015; 52:1-18.
38.    Montero, P., et al. Lifetime dietary change and its relation to increase in weight in Spanish women. Int J Obes Relat Metab Disord 2000; 24(1):14-9.
39.    Dilworth-Anderson, P., et al. Diagnosis and assessment of Alzheimer’s disease in diverse populations. Alzheimers Dement 2008; 4(4):305-9.