Screening and enrollment of underrepresented ethnocultural and educational populations in the Alzheimer's Disease Neuroimaging Initiative (ADNI)

Neuropsychiatric Symptom Profile in Alzheimer's Disease and Their Relationship With Functional Decline

OBJECTIVE

Understanding the course of individual neuropsychiatric symptoms (NPS) and their relationship with function is important for planning targeted interventions for preventing and delaying functional decline. This study aims to disentangle relative contributions of individual NPS on functional decline.

METHODS

Longitudinal study of 9,358 well-characterized participants with baseline diagnoses of Mild Cognitive Impairment or AD in the National Alzheimer's Coordinating Center Uniform Data Set. Function was measured using the Functional Assessment Questionnaire (FAQ). Clinician judgment of seven common behavioral symptoms were examined simultaneously: apathy-withdrawal, depressed mood, visual or auditory hallucinations, delusions, disinhibition, irritability, and agitation.

RESULTS

Apathy was the most common NPS at baseline (33.7%) and throughout follow-up, endorsed by clinicians in 63.7% of visits. Apathy was the most persistent with 36.7% of participants having clinician-endorsed apathy in ≥50% of their visits. Apathy strongly correlated with faster rate of functional decline. Compared to those who never had apathy, baseline FAQ was worse in those with intermittent or persistent/always apathy (intermittent: estimated coefficient ±SE=1.228±0.210, 95% CI=[0.817, 1.639]; persistent/always: 2.354±0.244 (95% CI=[1.876, 2.832], both p <0.001). Over time, rate of functional decline was faster in those with intermittent and persistent/always apathy (intermittent: 0.454±0.091, 95% CI=[0.276, 0.632]; persistent/always: 0.635±0.102, 95% CI=[0.436, 0.835], both p <0.001). Worse agitation, delusions, and hallucinations also correlated with functional decline, but magnitudes of the estimates were smaller.

CONCLUSION

Individual NPS may be sensitive targets for tracking longitudinal change in function. The study raises awareness of the need for more comprehensive assessment of functional decline in AD patients with noncognitive symptoms.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

A Pragmatic, Investigator-Driven Process for Disclosure of Amyloid PET Scan Results to ADNI-4 Research Participants

BACKGROUND

Prior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples.

METHODS

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included.

RESULTS

Data collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators.

CONCLUSION

The pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.

Brain health registry updates: An online longitudinal neuroscience platform

INTRODUCTION

Remote, internet-based methods for recruitment, screening, and longitudinally assessing older adults have the potential to facilitate Alzheimer's disease (AD) clinical trials and observational studies.

METHODS

The Brain Health Registry (BHR) is an online registry that includes longitudinal assessments including self- and study partner-report questionnaires and neuropsychological tests. New initiatives aim to increase inclusion and engagement of commonly underincluded communities using digital, community-engaged research strategies. New features include multilingual support and biofluid collection capabilities.

RESULTS

BHR includes > 100,000 participants. BHR has made over 259,000 referrals resulting in 25,997 participants enrolled in 30 aging and AD studies. In addition, 28,278 participants are coenrolled in BHR and other studies with data linkage among studies. Data have been shared with 28 investigators. Recent efforts have facilitated the enrollment and engagement of underincluded ethnocultural communities.

DISCUSSION

The major advantages of the BHR approach are scalability and accessibility. Challenges include compliance, retention, cohort diversity, and generalizability.

HIGHLIGHTS

Brain Health Registry (BHR) is an online, longitudinal platform of > 100,000 members. BHR made > 259,000 referrals, which enrolled 25,997 participants in 32 studies. New efforts increased enrollment and engagement of underincluded communities in BHR. The major advantages of the BHR approach are scalability and accessibility. BHR provides a unique adjunct for clinical neuroscience research.

Quantification of race/ethnicity representation in Alzheimer's disease neuroimaging research in the USA: a systematic review

BACKGROUND

Racial and ethnic minoritized groups are disproportionately at risk for Alzheimer's Disease (AD), but are not sufficiently recruited in AD neuroimaging research in the United States. This is important as sample composition impacts generalizability of findings, biomarker cutoffs, and treatment effects. No studies have quantified the breadth of race/ethnicity representation in the AD literature.

METHODS

This review identified median race/ethnicity composition of AD neuroimaging US-based research samples available as free full-text articles on PubMed. Two types of published studies were analyzed: studies that directly report race/ethnicity data (i.e., direct studies), and studies that do not report race/ethnicity but used data from a cohort study/database that does report this information (i.e., indirect studies).

RESULTS

Direct studies (n = 719) have median representation of 88.9% white or 87.4% Non-Hispanic white, 7.3% Black/African American, and 3.4% Hispanic/Latino ethnicity, with 0% Asian American, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native, Multiracial, and Other Race participants. Cohort studies/databases (n = 44) from which indirect studies (n = 1745) derived are more diverse, with median representation of 84.2% white, 83.7% Non-Hispanic white, 11.6% Black/African American, 4.7% Hispanic/Latino, and 1.75% Asian American participants. Notably, 94% of indirect studies derive from just 10 cohort studies/databases. Comparisons of two time periods using a median split for publication year, 1994-2017 and 2018-2022, indicate that sample diversity has improved recently, particularly for Black/African American participants (3.39% from 1994-2017 and 8.29% from 2018-2022).

CONCLUSIONS

There is still underrepresentation of all minoritized groups relative to Census data, especially for Hispanic/Latino and Asian American individuals. The AD neuroimaging literature will benefit from increased representative recruitment of ethnic/racial minorities. More transparent reporting of race/ethnicity data is needed.

Personalising Alzheimer's Disease progression using brain atrophy markers

INTRODUCTION

Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD.

METHODS

Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC).

RESULTS

Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change.

CONCLUSIONS

Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.

Improving generalizability and study design of Alzheimer's disease cohort studies in the United States by including under-represented populations

The poor generalizability of clinical research data due to the enrollment of highly educated, non-Latinx White participants hampers the development of therapies for Alzheimer's disease (AD). Black and Latinx older adults have a greater risk for dementia, yet it is unclear how health-care disparities and sociocultural factors influence potential AD therapies and prognosis. Low enrollment of under-represented populations may be attributable to several factors including greater exclusion due to higher rates of comorbidities, lower access to AD clinics, and the legacy of unethical treatment in medical research. This perspective outlines solutions tested in the Brain Health Registry (BHR) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), including culturally-informed digital research methods, community-engaged research strategies, leadership from under-represented communities, and the reduction of exclusion criteria based on comorbidities. Our successes demonstrate that it is possible to increase the inclusion and engagement of under-represented populations into US-based clinical studies, thereby increasing the generalizability of their results.

John J, Kono N, Henderson VW, Mack WJ. Generalizability of cognitive results from clinical trial participants to an older adult population: Addressing external validity

Introduction

Study inclusion criteria and recruitment practices limit the generalizability of randomized-controlled trial (RCT) results. Statistical modeling could enhance generalizability of outcomes. To illustrate this, the cognition-depression relationship was assessed with and without adjustment relative to the target population of older women.

Methods

Randomized participants from four RCTs and non-randomized participants from two cohorts were included in this study. Prediction models estimated probability of being randomized into trials from target populations. These probabilities were used for inverse odds weighting relative to target populations. Weighted linear regression was used to assess the depression-cognition relationship.

Results

There was no depression-cognition relationship in the combined randomized sample. After applying weights relative to a representative cohort, negative relationships were observed. After applying weights relative to a non-representative cohort, bias of estimates increased.

Discussion

Quantitative approaches to transportability using representative samples may explain the absence of a-priori established relationships in RCTs.

Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community-engaged approach in the Alzheimer's Disease Neuroimaging Initiative 4

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50-60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022.

METHODS

ADNI4 will recruit URPs using community-engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50-60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in-clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials.

RESULTS AND DISCUSSION

ADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.

The Community Engaged Digital Alzheimer's Research (CEDAR) Study: A Digital Intervention to Increase Research Participation of Black American Participants in the Brain Health Registry

BACKGROUND

Although Black/African American older adults bear significant inequities in prevalence, incidence, and outcomes of Alzheimer's disease and related dementias, they are profoundly under-included in Alzheimer's Disease research. Community-Engaged Research (e.g., equitable community/science partnerships) is an evidence-based approach for improving engagement of underrepresented populations into Alzheimer's Disease research, but has lacked scalability to the national level. As internet use among older adults from underrepresented populations continues to grow, internet-based research shows promise as a feasible, valid approach to engagement and longitudinal assessment. The Community Engaged Digital Alzheimer's Research (CEDAR) study utilizes a community-engaged research approach to increase the engagement and research participation of Black/African American adults in the Brain Health Registry (BHR) and Alzheimer Disease clinical research.

OBJECTIVES

To describe the methods and evaluate the feasibility of the CEDAR culturally-informed digital platform within BHR.

DESIGN

All Black/African American participants in BHR were invited to enroll in CEDAR and to consider serving on a newly convened Community-Scientific Partnership Board to guide the study. The community board guided the development a culturally-informed cadre of engagement materials and strategies to increase research participation. Engagement strategies included incentives for study task completion, culturally-informed communications (e.g., landing page, emails and social media), resources about brain health, and video and written testimonials by CEDAR participants.

SETTING

BHR, an Internet-based registry and cohort.

PARTICIPANTS

BHR participants self-identifying as Black/African American were invited to enroll. All participants who signed an online informed consent document were enrolled.

MEASUREMENTS

We report the number of participants invited, enrolled, completed tasks, and volunteered to join the community board. We compared the demographics, cognitive profile, and baseline BHR task completion rates between CEDAR participants and all those invited to join the study.

RESULTS

Of 3738 invited, 349 (9.34%) enrolled in CEDAR. 134 (37% of CEDAR participants) volunteered to join the community board, of which 19 were selected for the community board. Compared to those invited, the CEDAR cohort had a higher percentage of female participants (84.5%) and a lower percentage of participants who identify as belonging to more than one ethnocultural group (21.8%). Compared to those did not enroll in CEDAR, those enrolled in CEDAR had a higher percentage of participants completing all BHR tasks (22%) and a higher percentage of participants completing at least one cognitive test (76%). Those enrolled in CEDAR also had a higher percentage of participants having an enrolled study partner (18%).

CONCLUSIONS

A culturally-informed Community-Engaged Research approach, including a remotely-convened community board, to engagement of Black/African American participants in an online research registry is feasible. This approach can be adapted for use in various clinical studies and other settings. Future studies will evaluate the effectiveness of the engagement strategies.

Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment

Background

In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning–based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype ¹⁸F-FDG PET, age, and sex.

Methods

Three hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4 non-carriers (APOE4-nc; majority class: amyloid negative (Aβ-)) and carriers (APOE4-c; majority class: amyloid positive (Aβ +)) from ¹⁸F-FDG-PET, age, and sex. Classifiers were tested on two different datasets. Finally, frequencies of progression to dementia were compared between gold standard and predicted A-status.

Results

Aβ- in APOE4-nc and Aβ + in APOE4-c were predicted with a precision of 87% and a recall of 79% and 51%, respectively. Predicted A-status and gold standard A-status were at least equally indicative of risk of progression to dementia.

Conclusion

We developed an algorithm allowing approximation of A-status in MCI with good reliability using APOE-genotype, ¹⁸F-FDG PET, age, and sex information. The algorithm could enable better estimation of individual risk for developing AD based on existing biomarker information, and support efficient selection of patients who would benefit most from further etiological clarification. Further potential utility in clinical routine and clinical trials is discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05879-6.