Evaluating the Alzheimer's disease data landscape

Meeting report of the fifth annual workshop on Principles and Techniques for Improving Preclinical to Clinical Translation in Alzheimer's Disease Research

The fifth annual workshop on Principles and Techniques for Improving Preclinical Translation of Alzheimer's Disease Research was held in May 2023 at The Jackson Laboratory in Bar Harbor, Maine, USA. The workshop was established in 2018 to address training gaps in preclinical translational studies for Alzheimer's disease (AD). In addition to providing fundamental knowledge and hands-on skills essential for executing rigorous in vivo studies that are designed to facilitate translation, each year the workshop aims to provide insight on state-of-the-field technological advances and new resources including novel animal models, publicly available datasets, novel biomarkers, and new medical imaging tracers. This innovative and comprehensive workshop continues to deliver training for the greater AD research community in order to provide investigators and trainees with the knowledge and skillsets essential for enabling improved preclinical to clinical translation and accelerate the process of advancing safe and effective therapeutic interventions for AD. HIGHLIGHTS: Translational research is not typically available as a course of study at academic institutions, yet there are fundamental skillsets and knowledge required to enable successful translation from preclinical experiments to clinical efficacy. It is important that there are resources and opportunities available to researchers planning preclinical translational experiments. Here we present proceedings from the fifth annual NIA-sponsored workshop focused on enabling improved preclinical to clinical translation for Alzheimer's disease research that includes didactic lectures on state-of-the-field approaches and hands-on practicums for acquiring essential translational laboratory techniques.

Irregular word reading as a marker of semantic decline in Alzheimer's disease: implications for premorbid intellectual ability measurement

BACKGROUND

Irregular word reading has been used to estimate premorbid intelligence in Alzheimer's disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities.

METHOD

Six hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer's Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory.

RESULTS

EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances.

CONCLUSIONS

While the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets

BACKGROUND

While the amyloid/tau/neurodegeneration (ATN) framework has found wide application in Alzheimer's disease research, it is unclear if thresholds obtained using distinct thresholding methods are concordant within the same dataset and interchangeable across cohorts.

OBJECTIVES

To investigate the robustness of data-driven thresholding methods and ATN profiling across cohort datasets.

DESIGN AND SETTING

We evaluated the impact of thresholding methods on ATN profiles by applying five commonly-used methodologies across cohort datasets. We assessed the generalizability of disease patterns discovered within ATN profiles by clustering individuals from different cohorts who were assigned to the same ATN profile.

PARTICIPANTS AND MEASUREMENTS

Participants with available CSF amyloid-β 1-42, phosphorylated tau, and total tau measurements were included from eleven AD cohort studies.

RESULTS

We observed high variability among obtained ATN thresholds, both across methods and datasets that impacted the resulting profile assignments of participants significantly. Clustering participants from different cohorts within the same ATN category indicated that identified disease patterns were comparable across most cohorts and biases introduced through distinct thresholding and data representations remained insignificant in most ATN profiles.

CONLUSION

Thresholding method selection is a decision of statistical relevance that will inevitably bias the resulting profiling and affect its sensitivity and specificity. Thresholds are likely not directly interchangeable between independent cohorts. To apply the ATN framework as an actionable and robust profiling scheme, a comprehensive understanding of the impact of used thresholding methods, their statistical implications, and a validation of results is crucial.

Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper

Background

Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed.

Objective

As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool.

Methods

We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model.

Results

Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables.

Conclusion

AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.

Participant completion of longitudinal assessments in an online cognitive aging registry: The role of medical conditions

INTRODUCTION

This study aimed to understand whether older adults' longitudinal completion of assessments in an online Alzheimer's disease and related dementias (ADRD)-related registry is influenced by self-reported medical conditions.

METHODS

Brain Health Registry (BHR) is an online cognitive aging and ADRD-related research registry that includes longitudinal health and cognitive assessments. Using logistic regressions, we examined associations between longitudinal registry completion outcomes and self-reported (1) number of medical conditions and (2) eight defined medical condition groups (cardiovascular, metabolic, immune system, ADRD, current psychiatric, substance use/abuse, acquired, other specified conditions) in adults aged 55+ (N = 23,888). Longitudinal registry completion outcomes were assessed by the completion of the BHR initial questionnaire (first questionnaire participants see at each visit) at least twice and completion of a cognitive assessment (Cogstate Brief Battery) at least twice. Models included ethnocultural identity, education, age, and subjective memory concern as covariates.

RESULTS

We found that the likelihood of longitudinally completing the initial questionnaire was negatively associated with reporting a diagnosis of ADRD and current psychiatric conditions but was positively associated with reporting substance use/abuse and acquired medical conditions. The likelihood of longitudinally completing the cognitive assessment task was negatively associated with number of reported medical conditions, as well as with reporting cardiovascular conditions, ADRD, and current psychiatric conditions. Previously identified associations between ethnocultural identity and longitudinal assessment completion in BHR remained after accounting for the presence of medical conditions.

DISCUSSION

This post hoc analysis provides novel, initial evidence that older adults' completion of longitudinal assessments in an online registry is associated with the number and types of participant-reported medical conditions. Our findings can inform future efforts to make online studies with longitudinal health and cognitive assessments more usable for older adults with medical conditions. The results need to be interpreted with caution due to selection biases, and the under-inclusion of minoritized communities.

Design and feasibility of an Alzheimer's disease blood test study in a diverse community-based population

INTRODUCTION

Alzheimer's disease (AD) blood tests are likely to become increasingly important in clinical practice, but they need to be evaluated in diverse groups before use in the general population.

METHODS

This study enrolled a community-based sample of older adults in the St. Louis, Missouri, USA area. Participants completed a blood draw, Eight-Item Informant Interview to Differentiate Aging and Dementia (AD8® ), Montreal Cognitive Assessment (MoCA), and survey about their perceptions of the blood test. A subset of participants completed additional blood collection, amyloid positron emission tomography (PET), magnetic resonance imaging (MRI), and Clinical Dementia Rating (CDR® ).

RESULTS

Of the 859 participants enrolled in this ongoing study, 20.6% self-identified as Black or African American. The AD8 and MoCA correlated moderately with the CDR. The blood test was well accepted by the cohort, but it was perceived more positively by White and highly educated individuals.

DISCUSSION

Studying an AD blood test in a diverse population is feasible and may accelerate accurate diagnosis and implementation of effective treatments.

HIGHLIGHTS

A diverse group of older adults was recruited to evaluate a blood amyloid test. The enrollment rate was high and the blood test was well accepted by participants. Cognitive impairment screens have moderate performance in a diverse population. Alzheimer's disease blood tests are likely to be feasible for use in real-world settings.

Alzheimer Disease Biomarkers: Moving from CSF to Plasma for Reliable Detection of Amyloid and tau Pathology

BACKGROUND

Development of validated biomarkers to detect early Alzheimer disease (AD) neuropathology is needed for therapeutic AD trials. Abnormal concentrations of "core" AD biomarkers, cerebrospinal fluid (CSF) amyloid beta1-42, total tau, and phosphorylated tau correlate well with neuroimaging biomarkers and autopsy findings. Nevertheless, given the limitations of established CSF and neuroimaging biomarkers, accelerated development of blood-based AD biomarkers is underway.

CONTENT

Here we describe the clinical significance of CSF and plasma AD biomarkers to detect disease pathology throughout the Alzheimer continuum and correlate with imaging biomarkers. Use of the AT(N) classification by CSF and imaging biomarkers provides a more objective biologically based diagnosis of AD than clinical diagnosis alone. Significant progress in measuring CSF AD biomarkers using extensively validated highly automated assay systems has facilitated their transition from research use only to approved in vitro diagnostics tests for clinical use. We summarize development of plasma AD biomarkers as screening tools for enrollment and monitoring participants in therapeutic trials and ultimately in clinical care. Finally, we discuss the challenges for AD biomarkers use in clinical trials and precision medicine, emphasizing the possible ethnocultural differences in the levels of AD biomarkers.

SUMMARY

CSF AD biomarker measurements using fully automated analytical platforms is possible. Building on this experience, validated blood-based biomarker tests are being implemented on highly automated immunoassay and mass spectrometry platforms. The progress made developing analytically and clinically validated plasma AD biomarkers within the AT(N) classification scheme can accelerate use of AD biomarkers in therapeutic trials and routine clinical practice.

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.

Irregular word reading as a marker of cognitive and semantic decline in Alzheimer's disease rather than an estimate of premorbid intellectual abilities

Background

Irregular word reading has been used to estimate premorbid intelligence in Alzheimer's disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The general aim of this study is to determine whether irregular word reading is a valid estimate of premorbid intelligence, or a marker of cognitive and semantic decline in AD.

Method

681 healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer's Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL).

Results

EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances.

Conclusions

Irregular word reading performances decline throughout the AD continuum, and therefore, premorbid intelligence estimates based on the AmNART should not be considered accurate in MCI or AD. Results are consistent with the theory of irregular word reading impairments as an indicator of disease severity and semantic decline.

An ontology-based approach for modelling and querying Alzheimer's disease data

BACKGROUND

The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained.

METHODS

Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.

RESULTS

We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease.

CONCLUSIONS

The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

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.

Understanding Online Registry Facilitators and Barriers Experienced by Black Brain Health Registry Participants: The Community Engaged Digital Alzheimer's Research (CEDAR) Study

BACKGROUND

Failure of Alzheimer's disease and related diseases (ADRD) research studies to include and engage Black participants is a major issue, which limits the impact and generalizability of research findings. Little is known about participation of Black adults in online ADRD-related research registries.

OBJECTIVES

As part of the Community Engaged Digital Alzheimer's Research (CEDAR) Study, this study aims to increase our understanding of facilitators and barriers of Black adults to participating in ADRD-related online registries, as well as to understand their preferences for communication channels.

DESIGN, SETTING, PARTICIPANTS, MEASUREMENTS

We invited all Black participants enrolled in the Brain Health Registry (BHR) to complete a cross-sectional online survey. The survey consisted of rating scales and open-text questions asking about their attitudes towards brain health research, reasons for joining and continuing to participate in BHR, difficulties with participating, and preferences for modes of contact and website usage.

RESULTS

Of all invited Black BHR participants (N=3,636), 198 (5.5%) completed the survey. The mean age was 58.4 (SD=11.3), mean years of education were 16.3 (SD=2.4), and 85.5% identified as female. Reported facilitators for joining and continuing to participate in BHR were personal interest (e.g., learning more about own brain health) and altruism (e.g., helping research). Among additional registry features which could encourage return, receiving feedback or scores about BHR tasks was rated the highest. Of those who found BHR participation difficult (21%), the most frequent reason was time burden. The most preferred way of receiving study information was via email. Participants reported that the websites that they used the most were YouTube and Facebook.

DISCUSSION

The results of our study can inform the development of culturally-responsive registry features and engagement efforts to improve inclusion and participation of Black adults in online ADRD research. Providing participants with feedback about their registry performance and reducing the number of registry tasks are among the recommended strategies.

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.

Data and sample sharing as an enabler for large-scale biomarker research and development: The EPND perspective

Biomarker discovery, development, and validation are reliant on large-scale analyses of high-quality samples and data. Currently, significant quantities of data and samples have been generated by European studies on Alzheimer's disease (AD) and other neurodegenerative diseases (NDD), representing a valuable resource for developing biomarkers to support early detection of disease, treatment monitoring, and patient stratification. However, discovery of, access to, and sharing of data and samples from AD and NDD research are hindered both by silos that limit collaboration, and by the array of complex requirements for secure, legal, and ethical sharing. In this Perspective article, we examine key challenges currently hampering large-scale biomarker research, and outline how the European Platform for Neurodegenerative Diseases (EPND) plans to address them. The first such challenge is a fragmented landscape filled with technical barriers that make it difficult to discover and access high-quality samples and data in one location. A second challenge is related to the complex array of legal and ethical requirements that must be navigated by researchers when sharing data and samples, to ensure compliance with data protection regulations and research ethics. Another challenge is the lack of broad-scale collaboration and opportunities to facilitate partnerships between data and sample contributors and researchers, in addition to a lack of regulatory engagement early in the research process to enable validation of potential biomarkers. A further challenge facing projects is the need to remain sustainable beyond initial funding periods, ensuring data and samples are shared and reused, thereby driving further research and innovation. In addressing these challenges, EPND will enable an environment of faster and more disruptive research on diagnostics and disease-modifying therapies for Alzheimer's disease and other neurodegenerative diseases.

Digital culturally tailored marketing for enrolling Latino participants in a web-based registry: Baseline metrics from the Brain Health Registry

INTRODUCTION

This culturally tailored enrollment effort aims to determine the feasibility of enrolling 5000 older Latino adults from California into the Brain Health Registries (BHR) over 2.25 years.

METHODS

This paper describes (1) the development and deployment of culturally tailored BHR websites and digital ads, in collaboration with a Latino community science partnership board and a marketing company; (2) an interim feasibility analysis of the enrollment efforts and numbers, and participant characteristics (primary aim); as well as (3) an exploration of module completion and a preliminary efficacy evaluation of the culturally tailored digital efforts compared to BHR's standard non-culturally tailored efforts (secondary aim).

RESULTS

In 12.5 months, 3603 older Latino adults were enrolled (71% of the total California Latino BHR initiative enrollment goal). Completion of all BHR modules was low (6%).

DISCUSSION

Targeted ad placement, culturally tailored enrollment messaging, and culturally tailored BHR websites increased enrollment of Latino participants in BHR, but did not translate to increased module completion.

HIGHLIGHTS

Culturally tailored social marketing and website improvements were implemented. The efforts enrolled 5662 Latino individuals in 12.5 months. The number of Latino Brain Health Registry (BHR) participants increased by 122.7%. We failed to adequately enroll female Latinos and Latinos with lower education. Future work will evaluate effects of a newly released Spanish-language BHR website.

Residual reserve index modifies the effect of amyloid pathology on fluorodeoxyglucose metabolism: Implications for efficiency and capacity in cognitive reserve

Background

The residual approach to measuring cognitive reserve (using the residual reserve index) aims to capture cognitive resilience conferred by cognitive reserve, but may be confounded by factors representing brain resilience. We sought to distinguish between brain and cognitive resilience by comparing interactions between the residual reserve index and amyloid, tau, and neurodegeneration ["AT(N)"] biomarkers when predicting executive function. We hypothesized that the residual reserve index would moderate at least one path from an AT(N) biomarker to executive function (consistent with cognitive resilience), as opposed to moderating a path between two AT(N) biomarkers (suggestive of brain resilience).

Methods

Participants (N = 332) were from the Alzheimer's Disease Neuroimaging Initiative. The residual reserve index represented the difference between observed and predicted memory performance (a positive residual reserve index suggests higher cognitive reserve). AT(N) biomarkers were: CSF β-amyloid1-42/β-amyloid1-40 (A), plasma phosphorylated tau-181 (T), and FDG metabolism in AD-specific regions ([N]). AT(N) biomarkers (measured at consecutive time points) were entered in a sequential mediation model testing the indirect effects from baseline amyloid to executive function intercept (third annual follow-up) and slope (baseline to seventh follow-up), via tau and/or FDG metabolism. The baseline residual reserve index was entered as a moderator of paths between AT(N) biomarkers (e.g., amyloid-tau), and paths between AT(N) biomarkers and executive function.

Results

The residual reserve index interacted with amyloid pathology when predicting FDG metabolism: the indirect effect of amyloid → FDG metabolism → executive function intercept and slope varied as a function of the residual reserve index. With lower amyloid pathology, executive function performance was comparable at different levels of the residual reserve index, but a higher residual reserve index was associated with lower FDG metabolism. With higher amyloid pathology, a higher residual reserve index predicted better executive function via higher FDG metabolism.

Conclusion

The effect of the residual reserve index on executive function performance via FDG metabolism was consistent with cognitive resilience. This suggests the residual reserve index captures variation in cognitive reserve; specifically, neural efficiency, and neural capacity to upregulate metabolism to enhance cognitive resilience in the face of greater amyloid pathology. Implications for future research include the potential bidirectionality between neural efficiency and amyloid accumulation.

Biopsychosocial pathways in dementia inequalities: Introduction to the Michigan Cognitive Aging Project

Racial/ethnic inequalities in dementia risk are a major public health and health justice concern. Group differences that persist despite adjustment for socioeconomic and vascular indicators suggest that known dementia risk factors exhibit differential impact across race/ethnicity and/or there are unrecognized dementia risk factors that are racially patterned. This article provides targeted examples of both of these possibilities. First, depressive symptoms and white matter hyperintensities represent two known dementia risk factors that more strongly relate to negative cognitive outcomes among Black older adults than Whites, pointing to the need to consider contextual factors. Second, racial discrimination and external perceived control predict worse brain and cognitive aging above and beyond known risk factors. These psychosocial factors warrant explicit consideration in dementia cohort studies. Several challenges appear to be particularly relevant to the study of dementia inequalities, including selective survival and recruitment. These challenges complicate not only cross-study comparisons, but also within-study causal inferences. This article provides recommendations for addressing these challenges in order to accelerate high-quality research on dementia inequalities. Stemming from these recommendations, the article introduces the design and methods of the Michigan Cognitive Aging Project, a new, racially-balanced cohort study of Black and White adults transitioning to late life. In sum, careful research with community partners is needed to more fully explore the factors and contexts that create and sustain racial/ethnic disparities, as well as those that buffer against them. The ultimate goal of this research is to facilitate the dismantling of structural barriers to health justice for diverse older people. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021

An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.