Ethnic and racial influences on blood biomarkers for Alzheimer's disease: A systematic review

Abstract

Background

Little is known about confounding factors influencing Alzheimer's disease (AD) blood biomarker concentrations.

Objective

The objective of this systematic review was to explore the available evidence for the influences of ethnicity and race on AD blood biomarker concentrations.

Methods

We conducted a comprehensive systematic search in PubMed and Web of Science databases spanning from inception until 15 June 2023. We included studies that utilized plasma or serum biomarkers (amyloid-β [Aβ], total tau [t-tau], phosphorylated tau [p-tau], neurofilament light [NfL], and glial fibrillary acidic protein [GFAP]), compared individuals with AD to healthy controls, and included a minimum of two ethnic or racial groups for comparison. A total of 10 studies were included in the qualitative synthesis. All studies were conducted in the US.

Results

Seven studies reported differences in blood biomarker concentrations between ethnic or racial groups. However, after adjusting for medical conditions and social determinants of health, the differences became non-significant in two of the studies. The included studies differed in their included covariates and their statistical approaches, which complicated the interpretation of the observed differences.

Conclusions

The available evidence suggests that ethnicity and race may influence blood biomarker concentrations. However, it remains unclear to what extent these differences are mediated by differences in social determinants of health and medical conditions. Future studies are needed to explore ethnic and racial differences in blood biomarkers, including studies in diverse samples outside the US.

Keywords

Alzheimer's disease biomarkers blood-based biomarkers dementia diversity ethnicity plasma racial groups review serum

Introduction

Blood biomarkers may serve as novel tools contributing to early and accurate detection of Alzheimer's disease (AD). Blood biomarkers have demonstrated their relevance from extensive evidence of their diagnostic- and prognostic value.^1–8 However, this evidence is almost exclusively based on studies in Western White individuals, and little is known about how the results generalize to ethnically and racially diverse individuals. Under-representation of diverse populations is a general issue in dementia research⁹ that also applies to research on AD blood biomarkers. This underrepresentation may be related to barriers for ethnic and racial minority populations, including language barriers in migrants, difficulty accessing the healthcare system, financial or logistic barriers to participate in research, and insufficient community engagement. Similar challenges have been described in including ethnically and racially diverse populations in AD drug trials.¹⁰ Research on ethnic and racial influences on blood biomarker concentrations, and related diagnostic and prognostic accuracy, is crucial before wider clinical implementation.^2,11

Biomarker concentrations have previously been found to differ between ethnic or racial groups in cerebrospinal fluid (CSF)^12–15 and in positron emission tomography (PET) measuring amyloid deposition.¹⁴ The reasons behind these differences are unclear but may in part be due to genetic differences. It is well-established that genetic variation between individuals belonging to different ethnic or racial groups can lead to different disease expressions or disease trajectories, for instance in the apolipoprotein E (APOE) ε4 gene^16,17 and the tau H2 haplotype associated with the microtubule-associated protein tau (MAPT)¹⁸ that has a direct impact on CSF tau concentrations.¹⁹ However, in some studies, racial or ethnic differences in CSF concentrations persist even after adjusting for APOE ε4.^20,21

Biomarker concentrations may also differ based on medical conditions and social determinants of health. At the present stage, studies have found some evidence that age, sex, kidney function, cholesterol metabolism, and body mass index (BMI) may influence blood biomarker concentrations such as neurofilament light (NfL) and glial fibrillary acidic protein (GFAP).^22–24 Concurrently, there are well-established differences between ethnic and racial groups in the prevalence of these medical conditions and AD, with higher prevalence typically being reported for minority and racialized groups.^25–35 Therefore, it is imperative to understand if blood biomarker concentrations vary by ethnicity and race and if these potential differences reflect genetic differences or differences in medical conditions and/or social determinants of health.^36,37

The objective of this systematic review was to investigate ethnic and racial influences on blood biomarker concentrations and diagnostic and prognostic accuracy for AD. Ethnicity is understood as a term referring to shared cultural attributes, including geographic region, language, and social identity. Ethnicity in research studies typically focus on geographic regions, such as country of origin. Meanwhile, race typically refers to physical characteristics such as skin color. As the reviewed studies generally, did not consistently differentiate between race and ethnicity, we will generally refer to ethnicity/race for clarity.

Methods

Search strategy

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.³⁸ Studies were identified from PubMed and Web of Science databases from inception until 15 June 2023. The search strategy for identification of studies was: (blood OR plasma OR serum) AND (alzhei* OR dement*) AND (race OR ethnic* OR black OR Hispanic OR Latin* OR Asia*) AND (amyloid OR tau OR NfL OR neurofilament OR GFAP OR “Glial fibril*” OR β). After the removal of duplicates, all studies were assessed for eligibility by title/abstract screening. Potentially relevant studies proceeded to full-text screening and were evaluated for eligibility by two independent researchers (DK and TRN). Any disagreements were settled by consensus.

Selection criteria

Eligibility criteria were established in accordance with the PRISMA guidelines.³⁸ The criteria were: (1) Studies that included either participants with clinical AD or AD by CSF/PET confirmation of amyloid pathology and included participants from more than one ethnic/racial group. For longitudinal studies, a minimum follow-up period of 2 years was required, and participants were required to be diagnosed with clinical AD at follow-up; (2) studies that examined plasma or serum amyloid-β (Aβ), total tau (t-tau), phosphorylated tau (p-tau), NfL, or GFAP; (3) studies that compared a cognitively unimpaired healthy control group to the AD group and compared the included ethnic/racial groups; (4) studies with any measurement of the influence of ethnic/racial group on plasma or serum biomarker concentrations, diagnostic accuracy, and/or prognostic value; and (5) studies with an observational study design.

In vitro studies, intervention studies, case reports, editorials, commentaries, reviews, studies in non-human subjects, studies not published in English, longitudinal studies with less than 2 years of follow-up, and studies in participants aged less than 18 years were excluded.

Data analysis

A qualitative synthesis of the included studies was performed, focusing on the influence of ethnicity/race on blood biomarker concentrations as well as differences in sociodemographic factors and medical conditions. The reviewed studies used various ethnic and racial categories, hence for the sake of clarity the terms ‘Black’, ‘Hispanic’, ‘White’ are used throughout the review.

Quality assessment

Each study was assessed for risk of bias and applicability concerns using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.³⁹ Each study was evaluated independently on participant selection, index test, reference standard, and flow and timing. Risk of bias and applicability was independently evaluated as high risk, low risk, or unclear risk by two researchers (DK and TRN), and any disagreement was settled by consensus. The individual assessments can be found in Supplemental Table 2.

Results

Study selection

After the removal of duplicates, 683 studies were screened. Of these, 619 were excluded following title/abstract screening with reasons noted in Figure 1. Full-text screening of the remaining 64 studies resulted in the identification of 12 studies fulfilling the inclusion criteria. Careful examination of each study resulted in the exclusion of another two studies as no comparisons or analyses of ethnic/racial groups were reported.^40,41 Thus, a total of 10 studies were included in the qualitative synthesis in the present systematic review.^42–51

Figure 1.

PRISMA flowchart. Flowchart created with Lucidchart.com.

Study characteristics

Study characteristics can be found in Supplemental Table 1. Importantly, all studies originated from the United States (US). Of which two studies included the same participant samples from The Washington Height-Hamilton Heights-Inwood Community Aging Project (WHICAP) cohort,^43,44 and three included participants from the Health and Aging Brain among Latino Elders (HABLE)/Health & Aging Brain Study-Health Disparities (HABS-HD) cohort.^45,47,51 Of these, two studies included the same participant sample.^47,51

Regarding ethnic/racial groups, White participants were included in all studies, Hispanic participants in eight studies,^{42–47,50,51} and Black participants in seven studies.^{42–46,48,49} Ethnicity/race was self-reported in all included studies except for one study⁴² in which the Health Care Financing Administration records were used. Six studies reported sociodemographic characteristics and/or medical covariates stratified by ethnic/racial group (see Supplemental Table 1).^45,47–51 White participants were older in five of the six studies, had more years of education compared to Hispanic participants, but not the Black participants, and had lower proportions of medical conditions compared to Black and Hispanic participants except for one study in which Hispanic participants had a lower proportion of diabetes.⁴⁷ Nine studies included patients with clinically diagnosed AD,^{42–47,49–51} and five studies included patients with amyloid-PET or CSF-confirmed AD.^{43,46–48,51} NfL was the most examined biomarker (n = 9), followed by t-tau (n = 6), Aβ_42/40 (n = 5), Aβ₄₂ (n = 3), Aβ₄₀ (n = 3), p-tau181 (n = 3), p-tau217 (n = 2), GFAP (n = 2), and p-tau231 (n = 1).

Risk of bias and applicability

QUADAS-2 risk of bias and applicability concerns are presented in Supplemental Table 2. Overall risk of bias was low across the included studies. However, in several studies, it was unclear if the index test and reference standard were interpreted independently as this was not explicitly reported. In one study,⁴⁴ there was applicability concern regarding participant selection as the division of participant groups and comparison of ethnic/racial groups did not fully match our research question.

Influence of ethnicity/race on blood biomarkers

Biomarker concentrations. Seven of the 10 included studies reported differences in blood biomarker concentrations between ethnic/racial groups (see Table 1).^{42,45,47–51} However, in two of five studies that adjusted for covariates,^45,47–50 these differences became non-significant.^47,50

Table 1.

Blood biomarker concentrations and ethnicity/race

Study	Influence of ethnic/racial group on biomarker concentration	Biomarker platform	Authors’ interpretation(s)
Luchsinger et al. ⁴²	Plasma concentration (across HC and clinical AD dementia) Aβ₄₀ (pg/mL) White (N=69) 134.6 Hispanic (N=141) 136.0 Black (N=117) 153.0 Difference * (unadjusted)	Plasma (ELISA) Aβ₄₀ Aβ₄₂	Aβ₄₀ concentration varied by Black race compared to White and Hispanic participants.
Barker et al. ⁴⁶	Plasma concentration (across HC, MCI, and clinical AD dementia) NfL No significant differences between Hispanics and non-Hispanics in NfL for either HC or AD adjusted for age (data not reported) (Total N=309).	Plasma (SIMOA) NfL	NfL concentration was not related to Hispanic ethnicity when adjusting for age.
Brickman et al. ⁴³	Plasma concentration (across HC and clinical AD dementia) Aβ_42/40, t-tau, p-tau181, p-tau217, and NfL No significant differences for any biomarker between the White (N=100), Black (N=100), and Hispanic group (N=100). Difference: p range 0.11 - 0.54 (unadjusted)	Plasma (SIMOA) Aβ_42/40 T-tau NfL Plasma (MSD) P-tau181 P-tau217	Biomarker concentrations were similar across race/ethnicity
Gonzales et al. ⁵⁰	Serum concentration (across HC, MCI, and clinical AD dementia) t-tau (pg/mL) NfL (pg/mL) GFAP (pg/mL) White (N=650) 0.3 (0.2; 0.6) 28 (20; 39) 344 (203; 488) Hispanic (N=1193) 0.3 (0.1; 0.4) 17 (12; 26) 155 (110; 224) Difference NS NS NS	Serum (SIMOA) t-tau NfL GFAP	Biomarker concentrations were not different across ethnicity when matching on demographics.
Brickman et al. ⁴⁴	Distribution of ethnic/racial groups in plasma / symptomatic defined groups No differences in the proportions of ethnic/racial groups across p-tau positive/negative and symptomatic/asymptomatic groups (Total N = 300). p range: 0.43 - 0.47.	Plasma (SIMOA) Aβ42/40 T-tau NfL Plasma (MSD) P-tau181 P-tau217	Biomarker concentrations were not different across ethnic/racial groups.
Hall et al. ⁴⁵	Plasma concentration HC Aβ₄₀ (pg/mL) Aβ₄₂(pg/mL) Aβ_42/40 (ratio) t-tau (pg/mL) NfL (pg/mL) White (N=644) 266.01±62.66 12.24±3.12 0.047±0.013 2.31±1.07 20.10±12.69 Hispanic (N=642) 237.73±66.41 11.86±3.35 0.051±0.022 2.56±1.07 16.75±12.97 Black (N=142) 163.38±41.83 8.87±3.03 0.065±0.029 1.71±0.64 12.58±10.45 Difference * * * * *** Clinical AD Aβ₄₀ (pg/mL) Aβ₄₂ (pg/mL) Aβ_42/40 (ratio) t-tau (pg/mL) NfL(pg/mL) White (N=45) 262.64±62.11 11.92±3.10 0.0457±0.011 2.63±0.71 25.98±1.87 Hispanic (N=64) 245.04±67.46 11.28±3.40 0.0491±0.018 2.76±0.71 26.11±1.65 Black (N=19) 174.79±49.56 10.13±3.49 0.0580±0.028 2.03±0.74 21.85±2.41 Difference *** NS * * ***	Plasma (SIMOA) Aβ₄₀ Aβ₄₂ Aβ₄₂/Aβ₄₀ T-tau NfL	Biomarker concentrations vary across racial/ethnic groups.
O’Bryant et al. ⁴⁷	Plasma concentration (across HC, MCI, and clinical AD dementia) NfL (pg/mL) White (N=813) 20.87±11.29 Hispanic (N=890) 17.43±11.97 Difference NS	Plasma (SIMOA) NfL	NfL concentrations were not different across ethnic groups when adjusting for covariates.
Schindler et al. ⁴⁸	Plasma concentration (across HC and biomarker defined AD) Aβ₄₀ Aβ₄₂ Aβ_42/40 p-tau181 p-tau231 NfL (pg/mL) (pg/mL) (ratio) (pg/mL) (pg/mL) (pg/mL) White (N=76) 425 40.9 0.096 14.2 9.1 11.8 (390; 482) (37.8; 46.3) (0.090; 0.103) (10.6; 19.3) (6.6; 13.1) (8.9; 16.7) Black (N=76) 409 41.9 0.105 12.3 8.2 11.1 (380; 470) (39.3; 49.6) (0.099; 0.110) (10.2; 16.2) (4.4; 11.3) (7.6; 15.5) Difference NS * *** NS NS NS	Plasma (IP/MS) Aβ₄₀ Aβ₄₂ Aβ₄₂/Aβ₄₀ Plasma (SIMOA) P-tau181 P-tau231 NfL	Some biomarker concentrations vary across racial group, but these differences more likely reflect differences in comorbidities, social determinants of health, or effects of systematic racism.
Rajan et al. ⁴⁹	Serum concentration (across HC and clinical AD dementia) t-tau (pg/mL) NfL(pg/mL) GFAP (pg/mL) White (N=516) 0.83±3.04 40.2±64.2 288.7±251.9 Black (N=811) 0.76±2.67 33.4±38.6 271.9±278.6 Difference NS * NS	Serum (SIMOA) t-tau NfL GFAP	Baseline NfL concentrations differed between racial groups, but not t-tau and GFAP, when adjusting for covariates.

Aβ: amyloid-beta; AD: Alzheimer's disease; ELISA: enzyme-linked immunosorbent assay; GFAP: glial fibrillary acidic protein; HC: healthy controls; IP/MS: immunoprecipitation mass spectrometry assay; MSD: Meso Scale Discovery; MCI: mild cognitive impairment; NfL: neurofilament light; NS: non-significant; p-tau: phosphorylated tau; SIMOA: Single Molecule Array; t-tau: total tau.

*p<0.05; **p<0.01; ***p<0.001.

O’Bryant and colleagues⁵¹ is not reported in the table as they do not report on their biomarker concentrations.

Among the studies in which differences in biomarker concentrations were found, Luchsinger and colleagues found significantly higher plasma Aβ₄₀ concentrations in Black participants compared to Hispanic and White participants in unadjusted comparisons.⁴² While Hall and colleagues also found differences in plasma Aβ₄₀, Aβ₄₂, Aβ_42/40, t-tau, and NfL concentrations across the White, Hispanic, and Black participants after adjusting for age and sex differences.⁴⁵ Differences were also found on concentrations of plasma Aβ_42/40 and Aβ₄₂, but not on Aβ₄₀, p-tau181, p-tau231, and NfL concentrations by Schindler and colleagues when matching Black and White participants one-to-one on APOE ε4 status, cognitive status, and time of plasma extraction.⁴⁸ Rajan and colleagues found differences in NfL, GFAP, and t-tau concentrations between Black and White participants in unadjusted comparisons.⁴⁹ However, after adjusting for age, only differences in NfL remained significant. Meanwhile, O’Bryant and colleagues found that concentrations of plasma Aβ₄₂ were related to the Geriatric Depression Scale (GDS) for Hispanic, but not White, participants in analyses adjusting for age, education, and cognitive diagnosis (Table 1).⁵¹

When adjusting for covariates, the influence of Hispanic ethnicity disappeared in two studies. Gonzales and colleagues found higher concentrations of serum GFAP, NfL, and t-tau among White compared to Hispanic participants in unadjusted comparisons.⁵⁰ However, when adjusting for all included covariates (age, sex, APOE ε4 status, education, and diagnostic group), these differences became non-significant. The same pattern was reported by O’Bryant and colleagues who found higher plasma NfL concentrations in White compared to Hispanic participants in an unadjusted comparison.⁴⁷ But, after adjusting for the covariates age, sex, education, and ethnicity, only age remained a statistically significant predictor of NfL concentrations.

Three studies did not find ethnic/racial differences in biomarker concentrations.^43,44,46 Brickman and colleagues found no significant biomarker concentration differences between White, Hispanic, and Black participants.⁴³ Another study by Brickman and colleagues found that the proportions of White, Hispanic, and Black participants did not significantly differ when comparing plasma p-tau positive/negative and symptomatic/non-symptomatic participants.⁴⁴ In a comparison adjusted for age, Barker and colleagues found no differences in plasma NfL concentrations between Hispanic and non-Hispanic groups.⁴⁶

Diagnostic accuracy. Three studies examined the influence of ethnicity/race on diagnostic accuracy as presented in Table 2.^43,48,50 Brickman and colleagues found a lower area under the receiver operating characteristic curves (AUCs) for separating clinical AD from controls in Hispanic compared to White and Black participants across most of the included blood biomarkers (Table 2).⁴³ Schindler and colleagues reported that the precision of the included plasma biomarkers to predict CSF Aβ_42/40 positivity increased when including the covariates race, sex, age, APOE ε4 status, and cognitive status.⁴⁸ Of these covariates, race was a significant covariate for plasma p-tau181, p-tau23, and NfL, but not Aβ_42/40. Gonzales and colleagues explored the risk of dementia or mild cognitive impairment (MCI) and found that serum t-tau concentration, but not NfL and GFAP concentrations, significantly interacted with ethnicity/race.⁵⁰ However, this interaction became non-significant after matching the ethnic/racial groups on age, sex, APOE ε4 status, education, and diagnostic group.

Table 2.

Diagnostic accuracy

Study	Diagnostic accuracy	Authors’ interpretation(s)
Brickman et al. ⁴³	AUC from plasma (clinical AD versus HC) ^‡ Aβ_42/40 t-tau p-tau181 p-tau217 NfL White 0.50 0.53 0.69 0.71 0.65 Hispanic 0.45 0.58 0.51 0.52 0.48 Black 0.50 0.56 0.63 0.68 0.64	There was generally lower accuracy for the biomarkers in Hispanic participants, while the biomarkers were comparable among White and Black participants.
Gonzales et al. ⁵⁰	Risk of AD/MCI (OR) with interaction from serum t-tau NfL GFAP White 1.59 1.57 1.83 Hispanic 1.22 1.43 1.42 Difference * NS NS	No interactions were present for NfL and GFAP, while significant interactions in t-tau odds-ratios were found. Interactions based on ethnicity generally dissipated in a subset of demographically matched participants.
Schindler et al. ⁴⁸	AUC of CSF confirmed AD with significance of race as covariate from plasma Aβ_42/40 p-tau181 p-tau231 NfL AUC 0.90 0.85 0.85 0.81 Race as covariate NS **	Accuracy based on plasma Aβ₄₂/Aβ₄₀ was not affected by self-identified race. However, differences across p-tau181, p-tau231, and NfL were found, suggesting that applying cut-offs based on White participants would be problematic.

Aβ: amyloid-beta; AD: Alzheimer's disease; AUC: area under the ROC curve; GFAP: glial fibrillary acidic protein; HC: healthy controls; MCI: mild cognitive impairment: NfL: neurofilament light; NS: non-significant; OR: odds-ratio; p-tau: phosphorylated tau; t-tau: total tau.

*p < 0.05; **p < 0.01; ***p < 0.001.

‡

No statistical comparison of AUCs were performed.

Prognostic accuracy. Two studies examined the influence of ethnicity/race on prognostic accuracy of blood biomarkers as presented in Table 3.^43,49 Brickman and colleagues found no effect of including race as a covariate in the prediction of developing clinical AD during a four-year period (using p-tau181 and p-tau217).⁴³ Rajan and colleagues found that GFAP, not NfL and t-tau, concentrations had a significantly greater annual increase among Black compared to White participants after adjusting for age, sex, education, and APOE ε4.⁴⁹

Table 3.

Prognostic accuracy

Study	Prognostic accuracy	Authors’ interpretation(s)
Brickman et al. ⁴³	Risk of development of clinical AD ∼4 years later from plasma (White participants as reference) p-tau181 OR (95%CI) Hispanic 1.09 (0.58-2.04) Black 0.82 (0.43-1.59) p-tau217 OR (95%CI) Hispanic 0.66 (0.34-1.29) Black 0.80 (0.42-1.54)	The results support the use of blood biomarkers in diverse cohorts.
Rajan et al. ⁴⁹	Annual change in serum concentration t-tau NfL GFAP White 5.4% 5.6% 3.5% Black 4.0% 6.1% 4.4% Difference - - *	The annual change may not differ between Black and White patients for NfL and t-tau. A difference in the annual change for GFAP between Black and White patients may suggest higher amount of progressive atrophy in Black patients.

AD: Alzheimer's disease; GFAP: glial fibrillary acidic protein; NfL: neurofilament light; p-tau: phosphorylated tau; t-tau: total tau.

*p < 0.05; **p < 0.01; ***p < 0.001.

Discussion

To our knowledge, this is the first systematic review exploring the influence of ethnicity/race on AD blood biomarker concentrations. Although the review found some evidence for ethnic/racial differences in blood biomarker concentrations, it remains unclear whether these differences are mediated by differences in social determinants of health and medical conditions rather than ethnicity/race per se.

Regarding blood biomarker concentrations, seven of the ten reviewed studies reported ethnic/racial differences.^{42,45,47–51} After adjusting for sociodemographic covariates, including age, sex, and education, differences were no longer significant in two of the studies,^47,50 whereas differences persisted in three studies after adjusting for age and sex,^45,49 or matching on APOE ε4, cognitive status, and time of plasma extraction.⁴⁸ These results generally align with previous studies reporting racial/ethnic differences in CSF AD biomarker concentrations.^12–15 However, the reason behind the differences remains unresolved. For instance, it is unclear if differences in medical conditions or genetic variation in APOE ε4^16,17 and MAPT^18,19 are sufficient to explain ethnic/racial differences as such deep phenotyping was not included in the reviewed studies. Importantly, the term ‘race’ is socially defined and it can be argued that ‘race’ does not necessarily refer to any specific genetic characteristics or geographic origin but rather to the skin color of people who may have lived in the country of examination for generations. Therefore, operating with such racialized groups, differences in blood biomarker concentrations may reflect individuals’ socio-economic status and access to healthcare, which in turn affect the prevalence of medical conditions. Considering medical conditions, especially the ones affecting kidney function and thus blood filtration, may be crucial for our understanding of blood biomarkers. Therefore, it is surprising that measures of kidney function were not considered more in the included studies. However, the current evidence does not provide any conclusive answers to the reasons behind the observed ethnic/racial differences in blood biomarker concentrations, hence further research is needed.

For clinical practice, however, it may be more relevant to understand if the observed differences in blood biomarker concentrations affect diagnostic and prognostic accuracy. Three of the included studies did report an influence of ethnicity/race on accuracy measures.^43,48,49 However, in two other studies the effect of ethnicity/race either disappeared when matching on age, sex, APOE ε4 status, education, and diagnostic group,⁵⁰ or no effect of ethnicity/race was found for prognostic accuracy.⁴³ Thus, it remains uncertain whether race/ethnicity influences the diagnostic and prognostic accuracy of AD blood biomarkers. However, as several studies do report an influence, it seems paramount to explore this further before widespread implementation. Nevertheless, it has been suggested to use different cut-off values for CSF based on race.⁵² However, applying different cut-off values of blood, CSF, or amyloid-PET based on ethnicity/race seems premature. Firstly, the number of studies including diverse samples remains sparse. This is problematic as it may lead to misinterpretations and poorer diagnostic outcomes if biomarkers are applied in populations in which they have not been sufficiently examined. Secondly, the reasons behind ethnic/racial differences in AD blood biomarker concentrations remain unknown. Therefore, identification and quantification of important influencing factors on blood biomarker concentrations need to be identified first.

No definite proof of influencing factors on blood biomarker concentrations or accuracy measures is currently available, hence unmeasured covariates may affect the results from blood biomarkers. Furthermore, when identifying effects, it is challenging to distinguish between confounding, mediating, or indirect effects of covariates. This challenge becomes clear as studies disagree on whether ethnicity/race, certain medical conditions, BMI, education, or other variables should be considered when analyzing or interpreting the results of blood biomarkers.

Interestingly, across the included studies White participants were generally older, had more years of education, and had lower prevalence of medical conditions compared with Black and Hispanic participants, especially concerning diabetes, hypertension, and dyslipidemia (Supplemental Table 1). This is in line with the Centers for Disease Control and Prevention (CDC) which reports higher rates of diabetes and obesity among Black and Hispanic compared to White Americans as well as higher rates of hypertension in Black compared to White Americans.³⁵ Three of the included studies demonstrated possible effects of hypertension, diabetes, dyslipidemia,⁴⁷ BMI,⁴³ and depression⁵¹ on blood biomarkers. Other studies in primarily White populations have also shown effects of BMI, kidney function, and diabetes.^22–24,53 Potentially, higher BMI leads to higher blood volume diluting protein concentrations, while kidney function, measured through creatinine or estimated glomerular filtration rate, affects blood filtration causing protein build-up.²³ For example, a study by Simonsen and colleagues recommends accompanying serum NfL with data on age and kidney function as the influence on NfL concentrations was significant.²⁴ As social determinants of health and medical conditions are known to differ across ethnic/racial groups possible interactions between ethnicity/race and other factors require further exploration.³²

Based on the available evidence it is not possible to rule out that a lack of adjustment for important covariates may have influenced reported results. Importantly, the ethnic/racial groups included in the current review differ in medical conditions that have been demonstrated to impact blood biomarker concentrations and differ in known risk factors of AD. Therefore, ethnic/racial differences in blood biomarker concentrations may reflect that ethnicity/race is a mediating factor for medical conditions which have a direct influence on blood biomarker concentrations through for example kidney function (Figure 2).

Figure 2.

Hypothetical model visualizing race/ethnicity as a possible mediating factor for medical conditions and social determinants of health influencing blood biomarkers. Figure created with Adobe Illustrator 2024.

Limitations

This systematic review has several limitations that should be considered. First, all included studies originated from the US and adopted US-specific racialized categories, limiting the generalizability of the findings to other world regions and contexts. These categories are not readily applicable to other parts of the world, as the migration histories, languages, and geographic origins of people with minority ethnic backgrounds vary significantly between countries and world regions.⁵⁴ Moreover, the same cohorts were used across several studies, which further limits the generalizability of the findings. Second, the included studies examined different covariates, possibly leading to different results regarding which social determinants of health and medical conditions influence blood biomarkers. Third, only studies published in English were included, with the risk of overlooking relevant studies published in other languages. Fourth, the only social determinant of health included in the reviewed studies was education. However, many other variables such as socio-economic status and access to healthcare could be relevant to examine. The results of this systematic review should be interpreted with these limitations in mind.

Conclusions

As blood biomarkers may change the landscape for diagnosing AD, it is paramount to identify factors that need to be taken into consideration in the interpretation of the biomarker results. This review provides some evidence for ethnic/racial differences in blood biomarker concentrations. Importantly, when working with diverse populations in terms of medical conditions and social determinants of health, previously rejected factors may become relevant since the prevalence of medical conditions may differ. Before wider clinical implementation of AD blood biomarkers, there is a need to further explore confounding or mediating influences on blood biomarker concentrations and their diagnostic and prognostic accuracy. As the identified studies in this review exclusively originated from the US, it also seems relevant to conduct studies in other cultural contexts and ethnic/racial groups. For instance, to better understand associations between ethnicity and blood-based biomarkers, it would be valuable to conduct studies using geographic region of origin as a basis for defining ethnic groups, rather than relying on socially constructed racialized categories. This approach would enhance the global generalizability of studies, whether they are conducted within or outside the US. Blood biomarkers may revolutionize the way neurodegenerative disorders are diagnosed, nonetheless, they are still in the early stages of exploration.

Supplemental Material

sj-docx-1-alz-10.1177_13872877241299047 - Supplemental material for Ethnic and racial influences on blood biomarkers for Alzheimer's disease: A systematic review

Supplemental material, sj-docx-1-alz-10.1177_13872877241299047 for Ethnic and racial influences on blood biomarkers for Alzheimer's disease: A systematic review by Daniel Kjaergaard, Anja Hviid Simonsen, Gunhild Waldemar and T Rune Nielsen in Journal of Alzheimer's Disease

Footnotes

Acknowledgments

We would like to thank visual designer Amalie Frimann Friis-Andersen for her valuable contribution in designing for this article.

ORCID iD

Daniel Kjaergaard

Author contributions

Daniel Kjaergaard (Conceptualization; Formal analysis; Methodology; Writing – original draft); Anja Hviid Simonsen (Conceptualization; Supervision; Writing – review & editing); Gunhild Waldemar (Conceptualization; Methodology; Writing – review & editing); T. Rune Nielsen (Conceptualization; Methodology; Project administration; Supervision; Writing – review & editing).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Innovation Fund Denmark [grant number 1099-00022B], under the frame of ERA PerMed [reference number ERAPERMED2021-184]. AHS was supported by the Absalon Foundation founded 1^st of May 1978. These funding sources had no involvement in the formulation of this review.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data supporting the findings of this study are available on reasonable request from the corresponding author.

Supplemental material

Supplemental material for this article is available online.

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