Nosheny, Rachel L.

AbstractBackgroundEthnoculturally diverse individuals from underrepresented populations (URPs), including but not limited to Black/African American and Hispanic/Latinx adults, are disproportionally affected by Alzheimer’s disease (AD) compared to non‐Hispanic/Latinx white (NLW) adults. However, URPs have historically been under‐included in AD studies. The objective of this study is to describe efforts, and the results of those efforts, to increase enrollment and engagement of ethnoculturally diverse individuals from URPs in the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).MethodsEfforts undertaken by ADNI to increase enrollment and engagement of URPs included (1) launching of a Diversity Taskforce (DVTF) lead by two health disparities experts in July 2020, charged with overseeing ADNI’s mission in this space. Specific DVTF activities included: (2) the establishment, funding, and instrumental support (e.g., training, consultation, resources) of diversity at 13 ADNI3 sites; (3) changing ADNI protocol to facilitate research participation of URPs (e.g., optional lumbar puncture and sharing of amyloid PET results); (4) hiring a marketing agency to launch culturally‐informed digital/social media campaigns for the 13 DVTF Sites, with local websites; (5) development of a database to capture performance metrics of inclusion efforts at the 13 DVTF Sites; and (6) formation of a six‐member External Advisory Board, comprised of leaders in health disparities research, to guide DVTF efforts.ResultURP enrollment at these 13 DVTF sites improved from 1.1 URPs per month previously to >4 URPs per month, a 264% increase. Across all 59 ADNI sites, overall enrollment of ethnoculturally diverse individuals from URPs went from 89 (16.8%) to 126 (21.1%), representing a 25% increase in enrollment between May 2020 and September 2021. Moreover, since February 1st, 2021, ADNI enrolled 43 new participants of which 33 (77%) were from URPs.ConclusionIt is possible to improve the enrollment and retention of URPs in AD research using a multi‐pronged community engaged approach that also provides human capital, infrastructure, and financial resources. The success of the DVTF has led to the formation of a new Engagement Core in ADNI4, tasked with implementing a scaled‐up culturally‐informed, community‐engaged research approach to URP inclusion and retention across all 59 ADNI sites.

AbstractBackgroundThe Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a longitudinal observational study of individuals across the dementia spectrum that validates biomarkers for clinical trials. The newest phase of the study, ADNI4, aims to substantially increase the inclusion of underrepresented populations to improve generalizability of ADNI results. Novel biomarkers, including a speech‐based cognitive assessment and blood‐based plasma biomarkers, are integrated into the study design as key participant screening measures.MethodTo help achieve recruitment goals, ADNI4 uses a new, multi‐phase approach to remotely enroll and screen participants. A large study cohort (aiming for 20,000+ participants) provide initial screening data via an online study platform including demographics, medical history related to study exclusionary criteria, and results from a speech‐based cognitive memory assessment (Novoic Ltd. Storyteller test) and a measure of subjective cognitive/functional decline (Everyday Cognition (ECog) 12‐item). Based on screening results, a subset of online study participants is referred to a Quest Diagnostics location to provide a blood sample for plasma biomarker analysis (aiming for 4,000+ participants). Finally, select participants are referred to ADNI clinical sites for in‐depth characterization (goal of up to 1,500 in‐clinic participants).ResultWe report ADNI’s initial approach for selecting participants from the online screening cohort to undergo plasma biomarker phenotyping, and subsequent selection to a clinical site. Participants from diverse demographic backgrounds (especially for race, ethnicity, and/or education) and those with a higher estimated likelihood of cognitive impairment are given priority in the referral selection process. Likelihood of cognitive impairment is assessed using results from subjective memory complaint questions, responses to the ECog 12‐item questionnaire, and the Novoic Storyteller results. Individuals with results showing high concordance across these measures are prioritized, as well as those who have subsequent plasma results indicating biomarker positivity.ConclusionWeighing the various study requirements and inclusivity goals increases the complexity of participant selection decision making, and is a time‐intensive process. As the ADNI study progresses we aim to automate the participant selection/referral process by developing algorithms that account and encode for changing study prioritizations and dynamics across numerous dimensions. Learnings from ADNI4’s new remote processes will help inform future clinical trial design.

AbstractBackgroundSmartphone‐based cognitive and motor tests enable frequent remote assessments in observational research studies and clinical trials, limiting travel burden and increasing access to research for underrepresented groups. One such smartphone tool is the ALLFTD Mobile App, which was designed to capture the clinical features of neurodegenerative diseases via a battery of cognitive, speech, and motor tasks. In this study, we focus on recruiting a demographically diverse normative sample. We have partnered with the UCSF Brain Health Registry (BHR) to recruit 1,000 cognitively healthy participants across the lifespan. Here we describe the study design and initial feasibility results.MethodsWe designed an automated workflow in which participants never interact with the study team except for troubleshooting. BHR participants without reported cognitive impairment were contacted by email to participate in this study (n = 668). Interested participants downloaded the app onto their smartphone and completed in‐app electronic consent. They were asked to complete three ∼30‐minute sessions of cognitive, motor and speech measures over two weeks. Adherence was determined by evaluating the completion rate of app tasks. In a subset who completed a user experience questionnaire (n = 116), responses were quantified using descriptive statistics.ResultsThe current enrollment rate is 23.20% (155/668) of contacted participants. Average age is 62 (SD = 8.2, range = 25‐84). Average adherence is 76%. Participants reported a range of perceived task difficulty: 38% indicated tasks were “very easy” or “somewhat easy”, 26% indicated they were “neutral,” 36% found the tasks “somewhat difficult,” and none reported them to be “very difficult.” The time commitment was reported to be acceptable by 97% of participants, and 20% were willing to complete additional tasks. Over half (59%) of participants indicated that they preferred using a smartphone to complete cognitive assessments over traditional pen‐and‐paper measures.ConclusionsOur preliminary results demonstrate the feasibility and acceptability of recruiting participants, including older adults, through a registry and conducting remote smartphone testing via a fully automated workflow that is highly scalable. Future analysis will study the effects of demographic factors on feasibility and investigate the reliability and validity of the app measures in this sample.

ImportanceIncreased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors.ObjectiveTo estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes.Design, Setting, and ParticipantsThis cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers—the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer’s Disease Neuroimaging Initiative (ADNI; July 2007 to September 2021), and the Harvard Aging Brain Study (HABS; September 2010 to December 2019).Main Outcome and MeasuresThe main outcomes were the independent associations of neurodegeneration (decreases in gray matter volume), parenchymal amyloidosis (assessed by amyloid positron emission tomography), and vessel amyloidosis (evidenced by cerebral microbleeds [CMBs]) with cross-sectional and longitudinal WMH.ResultsData from 3960 MRI sessions among 1141 participants were included: 252 pathogenic variant carriers from DIAN (mean [SD] age, 38.4 [11.2] years; 137 [54%] female), 571 older adults from ADNI (mean [SD] age, 72.8 [7.3] years; 274 [48%] female), and 318 older adults from HABS (mean [SD] age, 72.4 [7.6] years; 194 [61%] female). Longitudinal increases in WMH volume were greater in individuals with CMBs compared with those without (DIAN: t = 3.2 [P = .001]; ADNI: t = 2.7 [P = .008]), associated with longitudinal decreases in gray matter volume (DIAN: t = −3.1 [P = .002]; ADNI: t = −5.6 [P < .001]; HABS: t = −2.2 [P = .03]), greater in older individuals (DIAN: t = 6.8 [P < .001]; ADNI: t = 9.1 [P < .001]; HABS: t = 5.4 [P < .001]), and not associated with systemic vascular risk (DIAN: t = 0.7 [P = .40]; ADNI: t = 0.6 [P = .50]; HABS: t = 1.8 [P = .06]) in individuals with ADAD and LOAD after accounting for age, gray matter volume, CMB presence, and amyloid burden. In older adults without CMBs at baseline, greater WMH volume was associated with CMB development during longitudinal follow-up (Cox proportional hazards regression model hazard ratio, 2.63; 95% CI, 1.72-4.03; P < .001).Conclusions and RelevanceThe findings suggest that increased WMH volume in AD is associated with neurodegeneration and parenchymal and vessel amyloidosis but not with elevated systemic vascular risk. Additionally, increased WMH volume may represent an early sign of vessel amyloidosis preceding the emergence of CMBs.

Importance The Clinical Dementia Rating (CDR) is a well-validated instrument widely used to detect and stage dementia due to Alzheimer disease. The digital Electronic Clinical Dementia Rating (eCDR) can be remotely self-administered and automatically scored, with potential to facilitate efficient dementia screening and staging. Objective To evaluate the association of the eCDR with the CDR and other in-clinic assessments for screening older adults for cognitive impairment. Design, Setting, and Participants This multisite, cross-sectional study used baseline data from a longitudinal, observational study from 2020 to 2023, including up to 3 years of follow-up. Participants were enrolled from 3 Alzheimer Disease Research Centers and the Brain Health Registry. Participants (aged ≥55 years, with a study partner, and no acute or unstable major medical conditions) were recruited during in-clinic visits or by automated emails. Exposures Participants completed the Uniform Data Set Version 3 (UDS; including the CDR) in supervised clinical research settings, and then completed the eCDR remotely, online and unsupervised, using their own device. Main Outcomes and Measures The primary outcomes were eCDR scores (item; categorical box and global; continuous box and global), CDR scores (item; categorical box and global), and UDS assessment scores. Associations were evaluated using linear and logistic regressions. Results A total of 3565 participants were contacted, and 288 were enrolled. Among 173 participants with item-level data (mean [SD] age, 70.84 [7.65] years; 76 women [43.9%]), eCDR to CDR concordance was 90% or higher for 33 items (63%) and 70% to 89% for 13 items (25%). Box (domain) level concordance ranged from 80% (memory) to 99% (personal care). The global score concordance rate was 81%. κ statistics were fair to moderate. Among 206 participants with box and global scores (mean [SD] age, 71.34 [7.68] years; 95 women [46.1%]), eCDR continuous global score was associated with CDR global (categorical) score with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.70-0.87). Correlations between eCDR and in-clinic UDS assessments were similar to those between CDR sum of box scores and the same in-clinic assessments. Conclusions and Relevance These findings suggest that the eCDR is valid and has potential use for screening and assessment of older adults for cognitive and functional decline related to Alzheimer disease. Instrument optimization and validation in diverse cohorts in remote settings are crucial for evaluating scalability and eCDR utility in clinical research, trials, and health care settings.

AbstractThe 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.

AbstractBackgroundDyadic (self and study partner) subjective reports of an individual’s recent cognitive and functional decline are an efficient approach to facilitate identification of older adults with, and at risk for, Mild Cognitive Impairment (MCI). Such methods can be easily adapted for remote administration, but few studies have investigated the ability of remotely‐collected, unsupervised dyadic subjective reports to accurately detect MCI and cognitive decline.MethodsIn 289 older adults enrolled in the Brain health Registry (BHR), with clinically‐confirmed diagnoses of Cognitively‐unimpaired (CU) or MCI, we identified the optimal combination of remote measures to accurately distinguish CU from MCI. We then externally validated our predictive model in 217 participants enrolled in a separate cohort, Validation of Online Methods to Predict Cognitive Decline (eVAL). Predictors included Cogstate Brief Battery One Card Learning (OCL) accuracy; self‐ and study partner‐reported memory concern (SMCs); performance on online adaptations of Everyday Cognition Scale (ECog, self‐ and study partner‐report versions), Geriatric Depression Scale Short Form (GDS); family history of AD, age, gender, and education. We calculated 10‐fold cross validated area under the ROC curves (cvAUC), 95% confidence intervals (CI), and the percentage of participants correctly classified (accuracy) for all models. Likelihood ratio (LR) tests were used to compare model fits.ResultsIn the BHR cohort, models including dyad‐report ECog, family history of AD, GDS, and Cogstate OCL scores distinguished CU from MCI with the highest accuracy (cvAUC = 0.90; 95% CI = 0.83, 0.92; accuracy = 85.5%). Models excluding study partner‐report ECog distinguished diagnostic groups with moderate accuracy (cvAUC = 0.83; 95% CI = 0.78, 0.85; accuracy = 75.9%) When applied to the validation cohort, the optimal training model including dyadic report performed with moderate accuracy ( Table 2 ). In the validation cohort, inclusion of study partner (but not self) report SMC, in addition to dyadic‐report ECog, improved model fit (LR test p<0.01).ConclusionsRemotely‐collected dyadic subjective measures, along with other remote measures, accurately distinguish CU from MCI. Subjective cognitive and functional decline and concerns about memory problems independently contribute to diagnostic discrimination. Results support future utilization of remote dyadic subjective measures to efficiently identify older adults with MCI for prodromal AD clinical trials, and in clinical care settings.

AbstractThe 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.

AbstractIntroductionThe 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.MethodsADNI4 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 DiscussionADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.

The brain changes of Alzheimer’s disease and other degenerative dementias begin long before cognitive dysfunction develops, and in people with subtle cognitive complaints, clinicians often struggle to predict who will develop dementia. The public increasingly sees benefits to accessing dementia risk evidence (DRE) such as biomarkers, predictive algorithms, and genetic information, particularly as this information moves from research to demonstrated usefulness in guiding diagnosis and clinical management. For example, the knowledge that one has high levels of amyloid in the brain may lead one to seek amyloid reducing medications, plan for disability, or engage in health promoting behaviors to fight cognitive decline. Researchers often hesitate to share DRE data, either because they are insufficiently validated or reliable for use in individuals, or there are concerns about assuring responsible use and ensuring adequate understanding of potential problems when one’s biomarker status is known. Concerns include warning people receiving DRE about situations in which they might be compelled to disclose their risk status potentially leading to discrimination or stigma. The Advisory Group on Risk Evidence Education for Dementia (AGREEDementia) welcomes all concerned with how best to share and use DRE. Supporting understanding in clinicians, stakeholders, and people with or at risk for dementia and clearly delineating risks, benefits, and gaps in knowledge is vital. This brief overview describes elements that made this group effective as a model for other health conditions where there is interest in unfettered collaboration to discuss diagnostic uncertainty and the appropriate use and communication of health-related risk information.