Black Americans experience an increased risk for incident dementia compared to non-Hispanic White Americans.1-8 Data, primarily from population-based samples and meta- and systematic analyses, suggest Black adults exhibit a 64% higher rate of progression to Alzheimer's disease (AD) and related dementias (ADRD) compared to non-Hispanic Whites.9, 10 These findings emphasize the urgency to clarify factors contributing to racial inequities—factors that underlie the root causes of the differences.
Recent studies11-17 investigating AD biomarkers in Black and White cohorts offer preliminary evidence of how AD neurobiology appears to differ by racial groups. To expand our understanding of these biomarker findings we turn to two influential frameworks: the National Institute on Aging–Alzheimer's Assocation (NIA-AA) Research Framework18 and the NIA's Health Disparities Research Framework.19 By examining biomarker findings from this framework duality, mechanism-focused explanations for racial differentials in biological determinants of AD dementia emerge as critical targets for future epidemiological and translational research.
The NIA–AA Research Framework represents an effort to describe AD in vivo using biological parameters. Informally known as the AT(N) criteria, the framework defines AD based on the presence of amyloid beta plaques (A), neurofibrillary tangles (T), and neurodegeneration (N). Although the definitive diagnosis of the disease continues to require the presence of neurofibrillary tangles (NFTs) and amyloid plaques upon neuropathological examination at autopsy,20 the biological framework for AD allows investigators to more precisely define and identify the disease in vivo—a critical step toward addressing this devastating illness.
A noted disadvantage to the AT(N) framework is its reductionistic approach.21 It may be ill-suited to fully characterize a complex disease that can co-occur with other neuropathologies;22 it is also unknown how the combination of neuropathologies alters presentations of the proteinopathies of tau and amyloid, or contributes to atrophy. Finally, despite the conceptual advantage of a biological definition, its implementation is further complicated given that cut-points must be determined and used to define pathological versus non-pathological protein levels. These cut-points are highly dependent not only on how measurement tools were calibrated and standardized,23 but, crucially, on the composition of the sample from which data were collected.24 Given that the field has fallen short in efforts to recruit and retain diverse research cohorts,25 defining representative standardized cut-points that can be applied broadly could be impossible for the near future.
2 UNDERSTANDING RACE AND RACIALIZATION WITH THE HEALTH DISPARITIES FRAMEWORKRace represents a social rather than a biological construct, and questions remain about the importance of racial categories in biomedical research.26, 27 Ancestry cannot and should not be used to support or validate self-identified racial categories, nor serve as a default explanation for observed racial differentials. We propose scientists adopt a multifactorial model of socially-rooted factors in aging such as the NIA Health Disparities Research Framework19 when interpreting racial differences in AD biomarkers. Before describing the NIA Health Disparities Research Framework, we review the influence of racialization on health.
2.1 Race versus racialization—experiences altering biologyLeaders of the Human Genome Project acknowledge that genetics characterize only geographic origins of ancestors—not race.28 Still, given the preponderance of racial health disparities that are observed consistently and persistently across both chronic and infectious disease outcomes, race cannot be ignored in biomedical research. The sociological term “racialized group,”29 referring to a societally defined group status, may more accurately describe representations of race in medical research. That is, individuals are racialized as Black (i.e., societally defined on the basis of skin color, hair texture, or facial features) and that racialization influences interactions with social institutions including health care and other systems that impact human health. Importantly, even within racialized groups, there is considerable heterogeneity. Racialized groups in the United States differ in their experiences and transgenerational histories of slavery, immigration, genocide, and acculturation, among many other factors. Accordingly, population-level health outcomes are altered across generations due to exposure to racialization.30 For example, despite being racialized as Black in the United States, babies of first-generation African immigrants have birth weights similar to offspring of White US-born birth mothers. In contrast, the birth weights of babies of second- and third-generation African immigrants are lower than those of children born to White birth mothers, and over time approach the significantly lower birth weights of children born to Black mothers whose families have lived in the United States for generations.31 Altogether, across disciplines and health outcomes, robust evidence supports the relevance and influence of environmental and societal conditions associated with being racialized as Black in the United States.
A similar phenomenon at the latter end of the life course is observed in seminal work comparing dementia in Black Americans to a West African Black cohort, which found that the Yoruba people of Nigeria evidenced lower prevalence of all-cause dementia and AD clinical syndrome dementia (not informed by biomarkers) compared to Black Americans.32 Similarly, incident dementia was higher in Black Americans compared to Black Yorubas.33 Although apolipoprotein E (APOE) allele counts were similar in the two populations,32 APOE ε4 carrier status appeared to confer greater risk for AD dementia clinical syndrome in the US population than in Black Africans.34 Of note, as in many other studies that pre-date the development of biomarker technologies, it is difficult to know how much of this risk was for AD-specific versus other causes of dementia. The authors suggested that genetic variation between Africans from Nigeria and Black Americans, as well as differences in diet, may explain geographic differences in prevalence and incidence but do not mention racial disparities in upstream factors like financial resources and access to fresh food that shape dietary “choices” in the United States.35
In the future, larger samples may allow for more granular and descriptive classifications of race. However, for the purpose of the present review, we sought to be consistent with the extant literature, and cautiously use the racialized group category of Black. In the United States, the group category “Black” captures the variety of experiences of individuals who have ancestral connections to the African continent, including African Americans, Black Caribbeans, and West Africans living in the United States, among others.36 All of the aforementioned groups are classified as Black/African American according to the US census and other agencies, such as the National Institutes of Health (NIH) and US Office of Management and Budget (OMB). For these discussions, Black race refers to the confluence of African ancestry and experiences engendered by living in the United States. Racialized individuals like other minoritized groups are subject to a number of socioeconomic and health disparities, largely because racial categorizations reflect societal power structures, supporting structural racism.37
RESEARCH IN CONTEXTSystematic review: Although there are a limited number of studies examining Alzheimer's disease (AD) biomarkers in non-White groups, a preliminary body of literature exists. We identified literature using traditional (e.g., PubMed) sources. The few relevant citations are appropriately cited.
Interpretation: We interpreted the extant data using two conceptual frameworks—the National Institute on Aging–Alzheimer's Association (NIA-AA) Research Framework, and theNIA's Health Disparities Research Framework. This allowed for interpretations founded in the biological disease markers and consideration of life course exposures, including those associated with being racialized as Black.
Future directions: In addition to improving the diversity of research participants, we highlight the importance of blood-based AD biomarkers as a way to improve inclusion and retention of Black participants. We also note the importance of a more accurate consideration of race in biomedical research. All of these endeavors hold potential to reduce racial disparities in AD incidence and prevalence.
The scope of this review is narrow; however, several factors justify this focus. Although still few in number, AD biomarker studies focused on US-based Black cohorts are predominant among those examining biomarkers by race. Moreover, Black Americans are among the populations facing the most pronounced health disparities, including disparities in incidence and prevalence of AD clinical syndrome and related dementias (ADRD). Finally, the experiences linked to membership in one racialized or marginalized group—such as Black—may be useful for understanding AD biomarker data in a number of other NIA priority populations, for example, American Indians, Latinos, sexual and gender minorities, and rural communities.
2.2 NIA Health Disparities Research Framework—applied to Alzheimer's disease neuropathologyToward a fuller understanding of risk and resilience factors, we propose using a multi-level, life-course model to interpret data. Such a model permits layered and integrated approaches to understanding disparities in AD dementia and facilitates identification of intervention loci across the life course. It can also enable investigators and institutions to mitigate or eliminate many barriers to participation in ADRD cohorts and clinical trials.
The NIA Health Disparities Research Framework19 serves as a foundational model for scientists investigating health and aging across diverse populations. The Disparities Framework proposes that environmental, sociocultural, behavioral, and biological factors work in concert to influence aging not only during later decades, but throughout the life span. Specifically, risk and resilience represent the intersection and accumulation of exposures occurring across a spectrum of macro- to micro-level factors. By applying this model, race emerges as a sociocultural rather than biological phenomenon. The NIA model of disparity emphasizes the “downstream” positioning of biological disease, and illustrates how unequal distributions of “upstream” risk exposures can become systematized and cumulative when shaped by a fundamental identity marker like race—or, more accurately, by racism resulting from racialization.
The authors of the Health Disparities Research Framework emphasize that although we ultimately seek to address biological dysfunction, environmental and sociocultural exposures including urban pollutants, food deserts, historical trauma, and discrimination represent contributing factors in the causal pathway. These conditions drive downstream exposures along multiple mechanistic pathways (e.g., poor diet, chronic stress), resulting in biological disorders and disease. In the case of ADRD, resultant biological diseases such as diabetes and high blood pressure are well-established risk factors for cognitive decline and dementia, but not necessarily AD neuropathology as described by the AT(N) criteria.18 This reinforces the idea (explained further below) that the effect of health exposures on cognitive impairment and age-related change may be multi-factorial and need not act through AD pathology exclusively or at all.
Applying the Health Disparities Research Framework to ADRD, racial disparities in rates of all-cause dementia in the United States arise from multi-level and multi-factorial mechanistic origins. Notably, the role of pervasive, long-standing, and institutionalized racism should be acknowledged as a source of dementia disparities,38 which contribute to persistently elevated rates of dementia among Black Americans despite current declines in rates of dementia noted in the US White population.39, 40
At a practical level, while studies indicate that differences in genetic and cardiovascular risk inadequately account for racial disparities in dementia,1, 4, 41 a growing body of work suggests that life-course social factors account for persistent late-life cognitive health disparities in racialized and minoritized communities. For example, historical racial inequalities in education access (e.g., segregation) impact older Black populations and despite formal legal action—specifically the Civil Rights Act of 1964—de facto education disparities persist, in the form of larger student-to-teacher ratios, outdated and limited education materials, and less access to high-quality curriculum, among other factors. A large body of work demonstrates that early-life disparities in quantity and quality of education partially or completely explain racial disparities in dementia risk.42-47 Educational access creates opportunity to build cognitive reserve, and is also a powerful socioeconomic resource that subsequently associates with higher-paying occupations, financial and housing stability, and health insurance coverage. Accordingly, Chen and Zissimopolous41 have demonstrated that disparity in accumulated wealth also substantially explains disproportionate dementia burden in older Black populations. Moving downstream, racialized social disadvantage associates with a number of environmental exposures across the life course including the experience of acute and chronic stressors, and related physiological processes.
Through a health disparities lens, chronic physiological/psychological stress plausibly influences cognitive outcomes through multiple, synchronous, and likely synergistic pathways. Self-reported stress associates directly with neurotoxic processes in the brain itself, and when prolonged, chronic stress results in systemic dysregulation and accelerated biological, brain, and cognitive aging.48-50 Stress may act directly on AD pathology: in animal models, exposure to acute and chronic stressors associates with amyloid accumulation, and neurodegeneration has been observed in animal and human studies.51 However, in a multi-site study of chronic post-traumatic stress from Vietnam-era US Veterans found that posttraumatic stress disorder was associated with lower cognitive function, but was not associated with amyloid burden, hippocampal volume, or ischemic lesions.52 Crucially, stressors may contribute dementia risk via downstream behavioral pathways, constraining individual- or community-level resources to engage in and successfully sustain preventive health behaviors, such as smoking cessation53 and physical activity.54 And, universal and race-based stress associates with far-downstream biomedical risk factors for AD dementia, including hypertension55 and diabetes,56 that have provided the only explanatory context explored or hypothesized for a majority of the AD studies to be reviewed here. In recognition of such pathways, Zuelsdorff et al.57 and others58, 59 have investigated stress and cognition in racially diverse cohorts. Those studies examining late-life stress in Black elders found that disproportionately high stress exposure partially explains racial disparities in cognitive health.57, 58
A consideration of the systematized and interactive risk and resilience exposures across multiple levels facilitates an understanding of distinct pathways salient in various populations experiencing health disparities. For instance, substantial evidence suggests that social gradients of health, wherein socioeconomic status is inversely associated with morbidity and mortality, may be attenuated or “flattened” among Black populations for many health outcomes; for example, disparities in pregnancy-related mortality experienced by Black women in the United States are not mitigated by maternal educational attainment.60 The most relevant risk and protective exposures for affluent Black Americans likely vary from those impacting low-income communities, but some factors such as structural and individual-level racial discrimination play a role across socioeconomic status. In total, the Health Disparities Research Framework allows for consideration of the unique pattern of factors experienced in various groups, and importantly, the unique targets for intervention.
3 AD BIOMARKERSIncreased focus on a biological framework for AD18 intensifies the need to measure the disease biomarkers in vivo in clinical care settings. Without a biological test for the disease, diagnosis relies solely on the practical—but often imperfect—clinical assessment. Contributing to misdiagnosis, common cognitive assessment practices do not perform equally across races; inherent if unintentional test bias lowers the validity of many screeners and tests for Black patients and participants.61, 62 Clinical diagnostic and referral practices can be subject to systemic and interpersonal biases, as clinical63 and population-based64 studies have suggested. Moreover, data2, 25 revealing parallel declines with age, but worse average measured cognitive performance in Black individuals compared to White individuals suggests that cognitive tests may over-pathologize cognitive status in Black populations, and under-identify cognitive impairment in Whites. In other words, our current cognitive tests and testing environments may not perform equally across races.
Altogether, there are several advantages to applying biomarkers in conjunction with clinical examination. First, biomarkers can assist clinical assessment by increasing the reliability of diagnosis. Second, more reliable diagnosis would help identify those individuals who can benefit the most from participation in clinical trials targeting specific pathologies or symptoms. Third, work is ongoing to refine the prognostic accuracy of biomarkers for pre-clinical stages of AD, and mild cognitive impairment (MCI),65 allowing for timely detection of disease. Importantly, biomarker assessment would need to occur in combination with clinical assessment, as is typical for other health conditions including hypercholesterolemia and diabetes.
Biomarkers for AD are evolving. At present, cerebrospinal fluid (CSF) biomarkers for AD-related molecular changes and positron emission tomography (PET) tracer-dependent neuroimaging of amyloid and tau provide the most direct measurements of AD pathology. However, rates of biomarker research participation are lower for many marginalized populations including racial and ethnic minoritized communities. In addition to insights on risk and resilience mechanisms, the health disparities lens offers clarity for addressing barriers to participation in and completion of AD biomarker studies. Imaging and lumbar puncture protocols are high burden: they are time-consuming and may be intimidating to participants. Lumbar puncture can be painful and even when participants have consented and prepared to participate, research teams may not be trained to make minor procedural adaptations and accomodations needed in the presence of obesity and other health conditions more prevalent in socially disadvantaged populations. Further, lumbar puncture is associated with historical trauma for some Black participants given that the procedure was also used in the Tuskegee syphilis study. Table 1 provides a summary of currently available AD biomarker methods. In a later section, we detail findings specific to Black American cohorts for each biomarker methodology.
TABLE 1. Summary of AD biomarker methods Table 1. (A) Neuroimaging AD biomarkers PET Amyloid PET: Quantification of amyloid plaque deposition (A) Tracers Quantification Interpretation [11C]Pittsburgh compound-B (PiB) [18F]Florbetapir (Amyvid) [18F]Flutemetamol (Vizamyl) [18F]Florbetaben (Neuraceq) Amyloid quantified in comparison to a reference brain region, deriving Typical reference regions include Cerebellum (gray and/or white matter), pons, or subcortical white matter Global or regional amyloid burden Tau PET: Quantification of neurofibrillary tangles (T) Tracers Quantification Interpretation [18F]Flortaucipir (Tauvid) [18F]MK-6240 [18F]RO-948 [18F]PI-2620 [18F]GTP1 Tau quantified in comparison to a reference brain region, deriving Typical reference regions include Cerebellum (gray and/or white matter), pons or subcortical white matter Global or regional tau tangle pathology FDG-PET: Quantification of hypometabolism, synaptic dysfunction, metabolic dysfunction, neuronal cell loss (N) Tracers Quantification Interpretation [18F]Fluorodeoxyglucose (FDG) Cerebral glucose uptake/metabolism Images typically normalized to: Cerebellum, pons, whole brain/global signal Hypometabolism is typically interpreted as neurodegeneration (especially synaptic loss) MRI MRI volume: Quantification of atrophy or neurodegeneration (N) Technique Interpretation Gray matter, white matter, and CSF volumes used to index atrophy Regional atrophy assessed by examining volume at the voxel or vertex-level, or volume of segmented structures (eg, hippocampus) in reference to total brain or intracranial volume Cortical thickness/thinning Exploits Brownian motion of water molecules to assess subtle cortical and subcortical neurodegeneration MRI Measurement of cerebrovascular dysfunction. Not directly related to AT(N) criteria Technique Interpretation T2FLAIR MRI to assess WMH Arterial spin labelling perfusion MRI Marker of small vessel disease Marker of capillary flow 4D Flow Susceptibility-weighted MRI Blood flow and pulsatility within intracranial arteries Cerebral microbleeds Table 1 (B) CSF AD biomarkers CSF Aβ: Quantification of amyloid proteins in CSF (A) Proteins Detection methods Interpretation Immunoassays Mass spectrometry Lower levels of Aβ42 and reduced Aβ42/Aβ40 ratio suggest deposition in brain tissue, that is, AD neuropathology CSF tau: Quantification of tau proteins in CSF (T) Proteins Detection methods Interpretation T-tau P-tau181 P-tau217 P-tau231 Immunoassays Mass spectrometry Higher levels of tau proteins suggest Aβ pathology-induced neuronal tau phosphorylation and secretion, that is, AD-type tau pathophysiology Various other proteins measured in CSF (N) Proteins Detection methods Interpretation NfL Neurogranin (Ng) Chitinase-3-like protein 1 (YKL-40) Glial fibrillar acidic protein (GFAP) Immunoassays Mass spectrometry Axonal degeneration (NfL) Synaptic dysfunction and degeneration (Ng) Neuroinflammation/astrocytic activation (YKL-40, GFAP) Table 1 (C). Plasma AD biomarkers Plasma Aβ: Quantification of amyloid proteins in plasma (A) Proteins Detection methods Interpretation Single molecule array (SIMOA) IP-MS Lower levels of Aβ proteins suggestion deposition in brain tissue, that is, AD neuropathology Plasma tau: Quantification of tau proteins in plasma (T) Proteins Detection methods Interpretation Single molecule array (SIMOA) IP-MS Higher levels of tau proteins suggestion deposition in brain tissue, that is, AD neuropathology Various other proteins measured in plasma (N) Proteins Detection methods Interpretation NfL Glial fibrillary acidic protein (GFAP) Single molecule array (SIMOA) Axonal degeneration (NfL) Neuroinflammation/astrocytic activation (GFAP) Abbreviations: Aβ, amyloid beta; AD, Alzheimer's disease; AT(N) criteria, National Institute on Aging–Alzheimer's Association Research Framework with (A) amyloid beta deposition, (T) tau hyperphosphorylation or tangle formation, and (N) neuronal death; CSF, cerebrospinal fluid; DVR, distribution volume ratio; IP-MS, immunoprecipitation mass spectrometry; MRI, magnetic resonance imaging; NfL, neurofilament light; PET, positron emission tomography; p-tau181/217/231, tau phosphorylated at threonine 181/217/231; SUV, standardized uptake value; SUVR, standardized uptake value ratio; t-tau, total tau; WMH, white matter hyperintensities.Current methodologies for measuring biomarkers per the AT(N) criteria of the NIA-AA Research Framework18 include neuroimaging and fluid biomarkers. Apart from neuropathological evaluation, amyloid imaging with PET, using tracers that bind to fibrillar amyloid beta (Aβ) remains the gold standard for determining AD-specific pathological status and has been in use for more than 15 years.66 Amyloid PET tracers are used to determine amyloid positivity and localize regional amyloid accumulation for staging disease severity. In recent years, several compounds have been evaluated for their binding affinity to abnormal aggregates of filamentous tau protein constituting NFTs, including the Food and Drug Administration (FDA)-approved [18F]-flortaucipir (Tauvid), which preferentially binds to paired helical filament-tau containing NFTs.67 Currently, the availability of tau PET imaging data from diverse populations is very limited.
Fluorodeoxyglucose (FDG) PET and magnetic resonance imaging (MRI) have been widely used to evaluate abnormal glucose utilization and neurodegeneration in AD.68-70 Additionally, MRI can provide an estimate of the extent of cerebrovascular dysfunction or injury. Although vascular injury is not included within the AT(N) framework, determining vascular contributions to cognitive impairment is still an important focus in the study of ADRD71 (see Table 1).72-74
CSF Aβ42 and the CSF Aβ42/Aβ40 ratio are established biomarkers for the A criterion in the AT(N) framework. Recent studies indicate that the Aβ42/Aβ40 ratio is concordant with amyloid PET positivity and that the two biomarkers can be used more or less interchangeably. Originally described as markers of neurodegeneration and tangle pathology markers, CSF total tau (t-tau) and phosphorylated tau (p-tau) correlate highly75 and are remarkably AD-specific76; that is, their increase in CSF is a reaction to Aβ exposure.77 Regarding neurodegeneration, CSF neurofilament light (NfL) might be a better direct marker; it correlates with imaging evidence of neurodegeneration, and tracks age-related brain changes.78
4 AD BIOMARKER COMPARISONS BETWEEN BLACK AND NON-HISPANIC WHITE SAMPLESIn this section, we summarize the handful of studies examining AD biomarker data in US Black cohorts. In all but one case,17 Black participants’ ethnicity was not reported. Table 2 provides a summary of study characteristics and findings. In Table 3 and sections below, we organize our discussion of extant literature by the AT(N) criteria and by cohort type.
TABLE 2. Summary of published research, comparing AD biomarkers in Black and non-Hispanic White adults Authors Study design Participants AD biomarker(s) Findings Conclusions Gottesman et al. (2016)a Subset of participants recruited from population-based study. Cross-sectional analysis. Participants’ race: 141 Black; 181 White Mean age (SD) in years: 75.9 (5.4) Ethnicity not reported Compared to Whites, Black participants had lower MMSE scores, intercranial volumes, and greater proportion had HTN.Diagnostic categories included: MCI and HC
[18]Florbetapir PET; global cortical amyloid from 9 ROIs provided dichotomous outcome of Amyloid positive v. negative (SUVR ≤ 1.2); WMH measured with MRI Black participants had higher odds of beta amyloid positive status than Whites (OR = 2.08) Adjusting for vascular risk factors did not change odds ratio No racial differences in APOE ε4 associated increase odds of amyloid positive status Presence of vascular risk factors in late life does not account for racial differences in cortical amyloid. Authors considered whether sociocultural factors could account for observed differences, but dismissed as unlikely. Proposed differences in metabolomic or genetic factors may contribute to differences between. Howell et al. (2017) Convenience sample recruited from ADC, clinic and community outreach events. Cross-sectional analysis. Participants’ race: 65 Black; 70 White Mean age in years: ∼70 Ethnicity not reported Compared to Whites, greater proportion of Black participants had HTN and DM. Diagnostic categories included: AD dementia, MCI, and HC CSF levels of Aβ42, Aβ40, Aβ42/Aβ40, t-tau, p-tau181, t-tau/Aβ42, p-tau181/Aβ42 and NfL; and WMH and HV measured with MRI Racial differences in AD biomarkers only observed in HC participants CSF Aβ40, t-tau, and p-tau181 lower in Black HCs comparted to White HCs No racial differences in CSF Aβ42 or NfL or in MRI HV or WMH Although similar levels of WMH were observed, found an interaction between race and WMH effects on cognition - effect potentiated in Black individuals.
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