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. Author manuscript; available in PMC: 2026 May 16.
Published in final edited form as: J Alzheimers Dis. 2026 Mar 25;110(4):1766–1777. doi: 10.1177/13872877261426582

Generalizability of Blood-Based Biomarkers of Alzheimer’s Disease and Related Dementias (ADRD) in a Multicultural Cohort of Older Adults: The effect of adjustment for Kidney Function

Ángel García de la Garza 1,2, Cuiling Wang 1,2, Carol A Derby 2, Qi Gao 1, Kenny Q Ye 1, Henrik Zetterberg 3,4,5,6,7,8, Christopher G Engeland 9, Richard B Lipton 2, Mindy J Katz 2
PMCID: PMC13178941  NIHMSID: NIHMS2172865  PMID: 41883006

Abstract

BACKGROUND:

Interpreting blood-based biomarkers of Alzheimer’s Disease and Related Dementias (ADRD) in a Multicultural Cohort is complicated by inconsistent evidence on racial differences. Kidney function, which varies by race and influences biomarker levels, is often overlooked, potentially contributing to these inconsistencies.

OBJECTIVE:

To characterize racial differences in plasma levels of ADRD biomarkers after adjusting for comorbidities, and assessed the impact of eGFR adjustment using either race-specific or race-neutral equations

METHODS:

Data from the Einstein Aging Study, a multi-cultural cohort of older adults, included plasma biomarkers (Aβ40, Aβ42, p-tau181, NfL, GFAP). Linear regression models evaluated racial differences in AD plasma biomarkers, adjusting for age, sex, body mass index, kidney function assessed via race-adjusted (eGFR-ASR) and race-neutral (eGFR-AS) equations, comorbidities (e.g., diabetes, hypertension, cardiovascular disease), and APOE ε4 carrier status.

RESULTS:

Among 269 participants, Black participants had lower plasma levels of Aβ40 (p=0.004), Aβ42 (p=0.002), and NfL (p = 0.022) compared to White participants. We observed modest variation in the magnitude of racial differences depending on the method used to adjust for kidney function. However, race differences remained after adjusting for comorbidities or APOE ε4 carrier status.

CONCLUSION:

Observed racial differences in ADRD biomarkers remain unexplained by kidney function, comorbidities or APOE ε4 carrier status. Future research focusing on associations between blood-based biomarkers and gold standards of brain pathology in multi-cultural cohorts are essential to advance the usability of blood biomarkers.

Keywords: Blood-based Biomarkers, Alzheimer’s Disease, Kidney Function, Comorbidities, Multicultural Cohort

Introduction

While PET and CSF biomarkers of β amyloid (A), tau (T), and neurodegeneration (N) are effective for diagnosing AD,1,2 their use is limited by availability, high costs, and the invasiveness of lumbar punctures.3,4 Blood biomarkers offer a practical, cost-effective alternative, addressing accessibility issues and revolutionizing AD/ADRD diagnosis across disease stages,5 enabling tracking the conversion from normal cognition to mild cognitive impairment (MCI),6–10 the progression from MCI to AD11–13 and evolution of symptomatic AD.14 Blood biomarkers have been used to pre-screening for clinical trials, as pharmacodynamic markers, as surrogate endpoints, or as alternatives to PET or CSF examination in community-based aging research.3,15 Studies such as Washington Heights-Inwood Columbia Aging Project (WHICAP),16 the Health & Aging Brain Study – Health Disparities (HABS-HD),17 and the Mayo Clinic Study of Aging (MCSA)18 highlight the potential of blood biomarkers for risk prediction and disease tracking in community-based aging research.

The distribution and effectiveness of blood-based biomarkers for ADRD as indicators of risk and prognosis vary by demographic factors such as age,19,20 sex,21–23 and race.24 Although Black individuals are at higher risk of ADRD compared to White individuals,17,25,26 several studies have reported lower plasma levels of AD biomarkers in Black individuals. Specifically, lower concentrations in plasma levels of amyloid beta 40 (Aβ40),27,28 amyloid beta 42 (Aβ42),27 amyloid ratio (Aβ42/ Aβ40),29 phosphorylated tau 181 (p-tau181),27 neurofilament light chain (NfL),30 and GFAP30 in black individuals compared to white individuals have been observed. However, other studies exploring racial differences in plasma AD biomarkers have found no racial differences in AD biomarkers. For example, analyses from WHICAP16,31 and Mayo Clinic Alzheimer’s Disease Research Center (ADRC),32 found no racial differences in the Aβ42/Aβ40 ratio, NfL, GFAP, or p-tau181. These inconsistencies across the literature complicate efforts to interpret biomarker levels across multicultural populations. Developing clinical guidelines that incorporate demographic variability (e.g., sex, race) is essential for refining interpretation, generalizability, and establishing normative reference ranges for clinical and research applications.33 Further investigation is needed to clarify the basis for these inconsistent findings and the extent to which observed differences reflect biological variation, measurement bias, or other sources of confounding.

Kidney function has often been overlooked in the literature examining blood-based AD biomarkers in multicultural cohorts,34 despite reduced kidney function being strongly associated with higher concentrations in plasma AD biomarkers15,35–38 and known demographics differences (age, sex, race) in kidney function.39,40 In the majority of research examining blood-based biomarkers of ADRD in multicultural populations have not considered measures of kidney function when examining racial differences.41 Kidney function is typically assessed using estimated glomerular filtration rate (GFR) equations based on creatinine or cystatin-C and demographic variables (age, sex, race).42 Several equations have been developed to estimate GFR, including both race-specific and race-neutral formulations. However, these equations were developed in samples in which GFR was higher in Black individuals than in non-Black individuals,43–45 despite Black individuals having a higher risk of develop kidney disease than non-Black individuals.46,47 While recent guidance encourages the use of race-neutral equations because race-adjusted equations overestimate GFR in black individuals,48–50 previous studies examining associations between kidney function and blood-based biomarkers of ADRD have relied on either direct creatinine measures,51,52 or estimating GFR based on race-specific equations.53–55 Hence, better understanding how the method of adjusting for kidney function impacts observed racial differences in AD biomarkers is crucial for interpreting data across studies.

Racial differences in prevalence of medical comorbidities and APOE genotype may also explain some of the previously observed racial differences in plasma AD biomarkers levels.56 Previous research has demonstrated associations between AD biomarkers and the presence of several chronic diseases.34 For example, elevated levels of plasma Aβ40, Aβ42, total tau are found in individuals with diabetes, hypertension, cardiovascular disease (CVD), chronic kidney disease (CKD), or history of non-skin cancer.57 Further, ε4 status is associated with increased AD pathology and AD risk.58 Additionally, Black individuals are more likely to be ε4 carriers than non-Black individuals.32,59 Studies vary in whether they adjust for comorbidities and APOE status, and observed racial differences after adjustment only persist in a subset of those analyses.27,32

To clarify the role of kidney function and racial differences in AD biomarkers, we analyzed data from black and white participants from the Einstein Aging Study (EAS), a multicultural, community-based cohort comprising Bronx residents aged 70 and older. We evaluated plasma Aβ40, Aβ42, glial fibrillary acidic protein (GFAP), NfL, p-tau181, and the Aβ42/Aβ40 ratio, measured from stored plasma samples. Our main goals are to 1) characterize race differences in these biomarkers after adjusting for demographics (age, sex, years of education) and clinical characteristics, including body mass index (BMI), and comorbidities (diabetes, hypertension, cardiovascular disease, history of non-skin cancer) and 2) to assess the impact of eGFR adjustment using either race-specific or race-neutral equations. In secondary analyses, we assess whether adjusting for APOE status in addition to eGFR impacts racial differences in a subset of participants with available APOE status.

Methods

Participants

Participants were recruited from the EAS, an ongoing longitudinal cohort study of older adults in Bronx County, NY.60 Participants are identified through systematic sampling from the registered voter lists of the Bronx, followed by preliminary screening via mailings and phone to determine eligibility. Eligibility criteria include being at least 70 years old, ambulatory, a Bronx resident, non-institutionalized, English-speaking, and without visual or auditory impairments that would hinder neuropsychological testing. Additionally, participants must not have active psychiatric symptoms that interfere with assessment completion,61 or prevalent dementia, as determined by the telephone version of the Memory Impairment Screen (MIS).62 Participants engage in annual in-person study visits, which encompass assessments of comorbidities, clinical neurological exams, neuropsychological assessment, anthropometric measures, and fasting blood draws. Our cross-sectional analyses include baseline data collected between May 2017 and January 2020. Participants were classified as either having MCI or being cognitively / neuropsychologically unimpaired (CU) based on the Jak/Bondi criteria63,64 utilizing data from neuropsychological test performance at the in-person EAS clinic visit.61 Additional details on the MCI classification criteria can be found on the supplement. All participants in the Einstein Aging Study provided informed consent before participating in our study. Informed consent was obtained in accordance with protocols approved by the local institutional review board.

Blood Biomarkers

Fasting blood samples were collected in EDTA-coated collection tubes during the in-clinic visit. Following centrifugation, plasma was separated from the cell pellet, aliquoted with a minimum of 0.5 mL of plasma and stored at −80°C pending biomarker measurement. Plasma Aβ40, Aβ42, NfL and GFAP concentrations were measured by Single molecule array (Simoa) technology using the NEUROLOGY 4-PLEX E assay on an HD-X instrument as described by the manufacturer (Quanterix, Billerica, MA). Plasma p-tau181 concentration was measured using an in-house Simoa assay as previously described in detail.5 All measurements were performed in singlicates in one round of experiments using one batch of reagents. Intra-assay coefficients of variation, monitored using duplicate quality control samples in the beginning and end of each plate, were <10%. We analyzed biomarkers in samples collected at the first study visit where participants completed the overall study protocol. Elevated plasma levels of phosphorylated tau (p-tau181), NfL, and GFAP are associated with cognitive decline and neurodegeneration, whereas lower concentrations of amyloid-β (Aβ42), and Aβ42/Aβ40 ratios reflect greater amyloid deposition and are associated with greater cognitive decline risk.13,65–67 Plasma measures of Amyloid-β (Aβ40) alone are generally not a reliable standalone indicator of cognitive decline risk, and are not commonly used for prediction.68,69 Nonetheless, our manuscript present results of Aβ40 to facilitate interpretation of analyses pertaining to the Aβ42/Aβ40 amyloid ratio.

Race Definition

Self-reported race was categorized based on the U.S. Census Bureau’s 1994 designations: White, Black, Asian, Other, and Multiracial. Our main analyses focused on racial differences without stratifying by ethnicity. Due to the small number of participants in the Other, Asian, and Multiracial groups, our analyses here were restricted to White and Black participants. Ethnicity was not considered in the present study.

History of Comorbidities

Lifetime medical history was ascertained by self-report during in-person assessments. Participants were first asked, “Has a doctor, nurse practitioner, or health care provider told you that you had any of the following conditions or treated you for them?” followed by a list that included diabetes, hypertension, cardiovascular conditions (heart attack, congestive heart failure, stroke, revascularization procedures, bypass surgery, coronary angioplasty, and stent placement), and cancer (including cancer type). Cardiovascular disease was defined as having any of the listed cardiovascular conditions. History of non-skin cancer was defined as history of cancer excluding melanoma, squamous cell carcinoma, and basal cell carcinoma.

Kidney Function as Measured by Estimated Glomerular Filtration Rate (eGFR)

Participants’ blood creatinine levels were estimated from blood collected during the in-clinic visit. To examine how different adjustments for kidney function influence observed racial differences in AD blood biomarkers, we calculated eGFR using two methods: 1) based on the widely adopted Chronic Kidney Epidemiology Collaboration70 equation that adjusts for race (referred to as eGFR-ASR), and 2) based on the race-free equation42 referred to as eGFR-AS in this manuscript. eGFR formulas can be found in the supplement. We conducted separate analyses using eGFR adjusted for either age and sex (eGFR-AS), or eGFR adjusted for age, sex, and race (eGFR-ASR) as we hypothesized that the type of formula used to adjust for kidney function might confound race effects. We further conducted a separate set of analyses using creatinine directly, rather than eGFR. Lower values of eGFR and higher values of creatinine indicate worse kidney function. Equations used to calculate eGFR in this manuscript were developed exclusively on race and were derived from samples that were entirely White or Black.

APOE Genotype

EAS participants were APOE-genotyped two times. APOE genotyping was conducted at the Albert Einstein College of Medicine Genetics Core, where haplotypes were determined by genotyping SNPs rs7412 and rs429358. Additionally, genome-wide genotyping conducted by the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) was used to derive APOE haplotypes as well. Results from both methods were compared for accuracy and yielded identical genotypes. We assess whether APOE status accounts for observed racial differences and/or influences the effect of renal function (eGFR) on AD biomarker levels in the subset of EAS participants for which APOE genotype is available. Analyses binarized APOE status as either ε4 carriers (ε2/ε4, ε3/ε4, and ε4/ε4) or non-carriers (ε2/ε2, ε2/ε3, ε3/ε3).

Statistical Analysis

Baseline demographic (age, sex, race, years of education) and clinical characteristics, including body mass index (BMI), MCI status, comorbidities (diabetes, hypertension, cardiovascular disease, history of non-skin cancer, estimated glomerular filtration rate (eGFR-ASR and eGFR-AS), creatinine, and Alzheimer’s Disease blood biomarker levels were summarized by race and compared using two-sample t-test for continuous variables and the Chi-square test for categorical variables. A series of linear regression models were conducted separately for each biomarker outcome —Aβ40, Aβ42, the Aβ42/Aβ40 ratio, ptau-181, NfL, and GFAP—to investigate potential racial differences in AD blood biomarkers levels. All biomarkers were standardized for easier interpretation, and extreme outlier observations (N = 2) were removed as values appeared unrealistic (both were 10 SD above the mean). All results reported include a point estimate as well as the corresponding 95% confidence interval. To address objective 1, we conducted linear regressions to identify racial differences in the biomarker outcome after adjusting for age, sex, and BMI, comorbidities (e.g., diabetes, hypertension, coronary heart disease, and history of non-skin cancer). For objective 2, we further adjusted these models for eGFR (using either eGFR-AS or eGFR-ASR), or creatinine. To address objective 3, we performed an analysis in the subsample of participants with available APOE genotype data. First, we examined whether the analysis performed in objectives 1 and 2 results held within this subset. Then, we tested whether racial differences in AD biomarker levels persisted after adjustment for APOE ε4 carrier status. At last, we conducted additional sensitivity analyses to assess whether any observed racial differences in plasma AD biomarker concentrations persisted after adjusting for cognitive status. All models were conducted using R version 4.3.

Results

Overview

The final sample consisted of 269 participants with data for AD blood biomarkers and covariates at baseline. Participant ages ranged from 70 to 94 years, with a mean age of 77.53 years (SD = 4.95). The sample was 68% female, with an average education of 14.98 ± 3.63 years. Among the participants, 55% identified as White and 45% as Black. A total of 79 participants met the criteria for MCI based on the Jak/Bondi actuarial standards. Compared to the Black group, the White group had a significantly lower proportion of females (60% vs. 78%), lower average BMI (28.80 vs. 29.86), higher average educational attainment (15.53 vs. 14.30), lower prevalence of MCI (23% vs. 38%), and lower prevalence of hypertension (57% vs. 83%) (see Table 1). Significant race differences were observed with eGFR-AS but not in eGFR-ASR. No differences were found in creatinine levels. In terms of AD blood-based biomarkers, Black participants exhibited lower plasma levels of Aβ40, Aβ42, and NfL compared to White participants.

Table 1:

Summary of Demographics, Comorbidities, and Biomarker Levels Across the Entire Sample and by Race.

Broken down by Race
Characteristic Overall, N = 2691 White, N = 1491 Black, N = 1201 p-val2
Age 77.53 (4.95) 77.24 (4.91) 77.89 (5.00) 0.26
Sex 0.002
 Female 182 (68%) 89 (60%) 93 (78%)
 Male 87(32%) 60 (40%) 27 (23%)
Body Mass Index 29.27 (5.94) 28.80 (6.41) 29.86 (5.25) 0.023
Years of Education 14.98 (3.63) 15.53 (3.82) 14.30 (3.27) 0.005
Mild Cognitive Impairment 79 (29%) 34 (23%) 45 (38%) 0.009
Diabetes 68 (25%) 31 (21%) 37 (31%) 0.060
Hypertension 184 (68%) 85 (57%) 99 (83%) <0.001
Cardiovascular Disease 58 (22%) 38 (26%) 20 (17%) 0.080
Non-Skin Cancers 62 (23%) 32 (21%) 30 (25%) 0.50
Aβ40 114.22 (38.27) 120.29 (39.38) 106.68 (35.57) 0.002
Aβ42 7.18 (2.16) 7.51 (2.11) 6.78 (2.17) <0.001
Aβ42/Aβ40 Ratio 0.06 (0.01) 0.06 (0.01) 0.07 (0.01) 0.71
NfL 23.08 (12.75) 24.48 (13.73) 21.35 (11.24) 0.011
GFAP 152.80 (77.71) 157.23 (81.76) 147.30 (72.31) 0.43
ptau-181 15.67 (6.65) 15.61 (6.50) 15.75 (6.87) 0.62
eGFR-AS3 (Age-Sex Adjusted) 72.90 (17.33) 75.48 (16.75) 69.70 (17.57) 0.004
eGFR-ASR3 (Age-Sex-Race Adjusted) 72.56 (17.98) 70.38 (16.10) 75.27 (19.80) 0.072
Creatinine 0.93 (0.28) 0.92 (0.28) 0.95 (0.29) 0.26
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1

Mean (SD); n (%);

2

T-Test; Pearson’s Chi-squared test;

3

Estimated Glomerular Filtration Rate

Racial differences in Blood-Based Biomarker Levels

The results of separate linear regressions examining racial differences for each biomarker, adjusted for age, sex, and BMI, are summarized in Table 2. Significant racial differences were observed in plasma levels of Aβ40 (p = 0.004), Aβ42 (p = 0.002), and NfL (p = 0.022), with Black participants exhibiting lower levels of these biomarkers compared to White participants. Across all six biomarkers, age was significantly positively associated with AD biomarker plasma levels (Aβ40: p < 0.001; Aβ42: p = 0.009; GFAP: p < 0.001; NfL: p = 0.002; ptau-181: p = 0.024) and negatively associated with Aβ42/Aβ40 (p = 0.007). Men demonstrated higher plasma levels of ptau-181 (p = 0.012), along with lower Aβ42/Aβ40 ratios (p = 0.007) than women. BMI showed a negative association with plasma levels of GFAP (p < 0.001) and NfL (p < 0.001). We further found higher levels of Aβ40 (p = 0.046) in participants with diabetes, and higher plasma levels of NfL (p = 0.039) in participants with hypertension. No other comorbidities tested showed significant associations with any of the biomarkers.

Table 2:

Results from Separate Linear Regressions for each of Biomarker, Adjusting for Age, Sex, BMI, Comorbidities, and Race.

Aβ40 Aβ42 GFAP NfL ptau-181 Aβ42 / Aβ40
Characteristic Beta1 p Beta1 p Beta1 p Beta1 p Beta1 p Beta1 p
Age 0.05 (0.03–0.07) <0.001 0.03 (0.01–0.06) 0.009 0.05 (0.03–0.07) <0.001 0.04 (0.02–0.06) 0.002 0.03 (0.00–0.05) 0.024 −0.03 (−0.06–−0.01) 0.007
Sex (Male) 0.17 (−0.09–0.43) 0.208 −0.05 (−0.32–0.22) 0.716 −0.10 (−0.36–0.16) 0.443 0.08 (−0.18–0.35) 0.546 0.35 (0.08–0.63) 0.012 −0.38 (−0.65–−0.10) 0.007
Race (Black) −0.36 (−0.61–−0.12) 0.004 −0.40 (−0.65–−0.15) 0.002 −0.11 (−0.35–0.13) 0.368 −0.29 (−0.53–−0.04) 0.022 0.02 (−0.23–0.28) 0.854 −0.03 (−0.28–0.23) 0.844
Body Mass Index 0.00 (−0.02–0.02) 0.785 −0.01 (−0.04–0.01) 0.2 −0.04 (−0.06–−0.02) <0.001 −0.04 (−0.06–−0.02) <0.001 −0.01 (−0.03–0.01) 0.471 −0.01 (−0.03–0.01) 0.253
Diabetes 0.28 (0.00–0.57) 0.05 0.27 (−0.02–0.56) 0.07 −0.13 (−0.41–0.15) 0.356 0.16 (−0.12–0.45) 0.266 −0.05 (−0.35–0.24) 0.723 −0.09 (−0.38–0.21) 0.561
Hypertension 0.01 (−0.25–0.27) 0.935 0.11 (−0.16–0.38) 0.415 0.08 (−0.18–0.34) 0.528 0.28 (0.01–0.54) 0.039 0.21 (−0.07–0.48) 0.138 0.17 (−0.10–0.44) 0.218
Heart Disease 0.28 (−0.01–0.58) 0.063 0.16 (−0.15–0.46) 0.32 0.28 (−0.01–0.57) 0.058 0.15 (−0.15–0.45) 0.324 0.05 (−0.26–0.36) 0.763 −0.19 (−0.50–0.12) 0.222
Non-Skin Cancer 0.02 (−0.26–0.30) 0.884 0.05 (−0.23–0.34) 0.707 −0.23 (−0.50–0.04) 0.1 −0.09 (−0.37–0.19) 0.514 0.18 (−0.11–0.47) 0.222 0.00 (−0.29–0.28) 0.996
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1

Coefficient (95% Confidence Interval)

Adjustment for Kidney Function

Results with further adjustments to the initial models by covarying for kidney function (eGFR-ASR, eGFR-AS or creatinine) are presented in Table 3. As expected, eGFR-ASR, eGFR-AS, and creatinine are highly correlated. In our sample, the correlation between eGFR-ASR and eGFR-AS is ρ = 0.94 (p < 0.001); between eGFR-ASR and creatinine, ρ = −0.85 (p < 0.001); and between eGFR-AS and creatinine, ρ = −0.86 (p < 0.001).

Table 3:

Results from Separate Linear Regressions for each of Biomarker, Adjusting for Age, Sex, BMI, Comorbidities, Race, and Kidney Function (eGFR-ASR / eGFR-AS / Creatinine)

Aβ40 Aβ42 GFAP NfL Ptau-181 Aβ42 / Aβ40
Group Characteristic Beta1 p Beta1 p Beta1 p Beta1 p Beta1 p Beta1 p
eGFR ASR2 Age 0.03 (0.01–0.06) 0.005 0.02 (−0.360.01–0.04) 0.134 0.04 (0.02–0.07) <0.001 0.02 (0.00–0.04) 0.099 0.02 (−0.01–0.05) 0.13 −0.03 (−0.05–0.00) 0.035
Sex 0.13 (−0.12–0.38) 0.315 −0.08 (−0.35–0.18) 0.536 −0.12 (−0.38–0.13) 0.343 0.04 (−0.21–0.29) 0.776 0.33 (0.05–0.60) 0.019 −0.36 (−0.63–−0.09) 0.01
Race −0.26 (−0.50–−0.03) 0.027 −0.32 (−0.57–−0.07) 0.012 −0.06 (−0.29–0.18) 0.649 −0.18 (−0.41–0.06) 0.136 0.07 (−0.18–0.33) 0.574 −0.06 (−0.32–0.19) 0.633
Body Mass Index −0.01 (−0.03–0.01) 0.559 −0.02 (−0.04–0.00) 0.122 −0.04 (−0.06–−0.02) <0.001 −0.04 (−0.06–−0.02) <0.001 −0.01 (−0.03–0.01) 0.369 −0.01 (−0.03–0.01) 0.297
eGFR ASR 2 −0.02 (−0.02–−0.01) <0.001 −0.01 (−0.02–−0.01) <0.001 −0.01 (−0.02–0.00) 0.005 −0.02 (−0.03–−0.01) <0.001 −0.01 (−0.02–0.00) 0.019 0.01 (0.00–0.01) 0.072
Diabetes 0.16 (−0.11–0.44) 0.238 0.17 (−0.11–0.46) 0.239 −0.20 (−0.47–0.08) 0.165 0.03 (−0.24–0.30) 0.841 −0.11 (−0.40–0.19) 0.487 −0.04 (−0.34–0.25) 0.779
Hypertension 0.00 (−0.25–0.25) 0.975 0.11 (−0.16–0.37) 0.426 0.08 (−0.18–0.33) 0.541 0.27 (0.02–0.52) 0.033 0.21 (−0.07–0.48) 0.138 0.17 (−0.10–0.45) 0.209
Cardiovascular Disease 0.18 (−0.11–0.46) 0.229 0.07 (−0.23–0.37) 0.657 0.22 (−0.07–0.51) 0.133 0.03 (−0.25–0.32) 0.833 0.00 (−0.32–0.31) 0.977 −0.15 (−0.46–0.16) 0.336
Non-Skin Cancers −0.01 (−0.27–0.26) 0.965 0.03 (−0.24–0.31) 0.815 −0.24 (−0.51–0.03) 0.077 −0.12 (−0.38–0.14) 0.361 0.17 (−0.12–0.46) 0.239 0.01 (−0.28–0.29) 0.95
eGFR AS3 Age 0.03 (0.01–0.06) 0.004 0.02 (0.00–0.04) 0.114 0.04 (0.02–0.07) <0.001 0.02 (0.00–0.04) 0.086 0.02 (−0.01–0.05) 0.127 −0.03 (−0.05–0.00) 0.033
Sex 0.14 (−0.11–0.39) 0.278 −0.07 (−0.34–0.19) 0.579 −0.12 (−0.37–0.14) 0.363 0.05 (−0.20–0.30) 0.711 0.33 (0.06–0.61) 0.017 −0.36 (−0.64–−0.09) 0.009
Race −0.45 (−0.69–−0.22) <0.001 −0.47 (−0.72–−0.22) <0.001 −0.16 (−0.40–0.08) 0.187 −0.39 (−0.62–−0.16) 0.001 −0.02 (−0.28–0.23) 0.864 0.01 (−0.24–0.26) 0.94
Body Mass Index −0.01 (−0.03–0.01) 0.553 −0.02 (−0.04–0.00) 0.122 −0.04 (−0.06–−0.02) <0.001 −0.04 (−0.06–−0.02) <0.001 −0.01 (−0.03–0.01) 0.362 −0.01 (−0.03–0.01) 0.299
eGFR AS3 −0.02 (−0.03–−0.01) <0.001 −0.01 (−0.02–−0.01) <0.001 −0.01 (−0.02–0.00) 0.004 −0.02 (−0.03–−0.01) <0.001 −0.01 (−0.02–0.00) 0.012 0.01 (0.00–0.01) 0.061
Diabetes 0.16 (−0.11–0.43) 0.244 0.17 (−0.12–0.46) 0.24 −0.20 (−0.47–0.08) 0.161 0.02 (−0.25–0.29) 0.866 −0.11 (−0.41–0.19) 0.47 −0.04 (−0.33–0.26) 0.791
Hypertension −0.01 (−0.26–0.25) 0.968 0.10 (−0.16–0.36) 0.458 0.07 (−0.18–0.33) 0.568 0.26 (0.01–0.51) 0.04 0.20 (−0.07–0.47) 0.147 0.18 (−0.09–0.45) 0.199
Cardiovascular Disease 0.17 (−0.12–0.46) 0.248 0.06 (−0.24–0.37) 0.678 0.22 (−0.07–0.51) 0.142 0.02 (−0.26–0.31) 0.886 −0.01 (−0.32–0.30) 0.946 −0.15 (−0.46–0.16) 0.349
Non-Skin Cancers −0.01 (−0.27–0.26) 0.959 0.03 (−0.24–0.31) 0.818 −0.24 (−0.51–0.02) 0.075 −0.12 (−0.38–0.14) 0.353 0.17 (−0.12–0.46) 0.242 0.01 (−0.27–0.29) 0.946
Creatinine Age 0.04 (0.02–0.06) <0.001 0.03 (0.00–0.05) 0.036 0.05 (0.02–0.07) <0.001 0.03 (0.01–0.05) 0.011 0.02 (0.00–0.05) 0.058 −0.03 (−0.06–−0.01) 0.014
Sex −0.15 (−0.42–0.12) 0.273 −0.33 (−0.61–−0.04) 0.024 −0.27 (−0.55–0.01) 0.056 −0.29 (−0.55–−0.02) 0.035 0.19 (−0.11–0.49) 0.213 −0.28 (−0.57–0.02) 0.066
Race −0.44 (−0.67–−0.20) <0.001 −0.46 (−0.70–−0.22) <0.001 −0.15 (−0.38–0.09) 0.219 −0.37 (−0.60–−0.14) 0.001 −0.01 (−0.26–0.24) 0.931 0.00 (−0.26–0.25) 0.983
Body Mass Index −0.01 (−0.02–0.01) 0.58 −0.02 (−0.04–0.00) 0.121 −0.04 (−0.06–−0.02) <0.001 −0.04 (−0.06–−0.02) <0.001 −0.01 (−0.03–0.01) 0.379 −0.01 (−0.03–0.01) 0.284
Creatinine 1.3 (0.86–1.7) <0.001 1.1 (0.67–1.6) <0.001 0.69 (0.24–1.1) 0.003 1.5 (1.1–1.9) <0.001 0.65 (0.16–1.1) 0.009 −0.40 (−0.88–0.08) 0.104
Diabetes 0.14 (−0.13–0.41) 0.324 0.14 (−0.14–0.43) 0.33 −0.21 (−0.49–0.07) 0.141 −0.01 (−0.28–0.26) 0.948 −0.12 (−0.42–0.18) 0.43 −0.04 (−0.34–0.26) 0.784
Hypertension −0.02 (−0.27–0.22) 0.847 0.08 (−0.18–0.34) 0.538 0.06 (−0.19–0.32) 0.62 0.24 (−0.01–0.48) 0.056 0.19 (−0.08–0.46) 0.168 0.18 (−0.09–0.45) 0.189
Cardiovascular Disease 0.20 (−0.08–0.48) 0.158 0.09 (−0.21–0.38) 0.57 0.24 (−0.05–0.53) 0.104 0.06 (−0.22–0.34) 0.682 0.01 (−0.30–0.32) 0.962 −0.17 (−0.48–0.14) 0.287
Non-Skin Cancers −0.01 (−0.27–0.25) 0.915 0.02 (−0.25–0.30) 0.861 −0.24 (−0.51–0.02) 0.071 −0.13 (−0.39–0.12) 0.312 0.17 (−0.12–0.46) 0.246 0.01 (−0.27–0.29) 0.945
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1

Coefficient (95% Confidence Interval);

2

Estimated Glomerular Filtration Rate Age, Sex and Race Adjusted;

3

Estimated Glomerular Filtration Rate Age, Sex Adjusted

First, we present the results when controlling for the eGFR estimate calculated with age, sex, and race (eGFR-ASR). A significant negative association was found between eGFR-ASR and AD biomarker plasma levels (Aβ40: p < 0.001; Aβ42: p < 0.001; GFAP: p = 0.005; NfL: p < 0.001; ptau-181: p = 0.019). No associations were found between eGFR-ASR and the Aβ42/Aβ40 ratio. We observed significant racial differences as in the previous section (in models that do not adjust for eGFR-ASR), although the differences were smaller for plasma levels of Aβ40 (p = 0.027) and Aβ42 (p = 0.012), with Black participants showing lower levels of these biomarkers compared to White participants. However, the significant racial differences in NfL are no longer significant (p = 0.136).

When using eGFR calculated with age and sex (eGFR-AS), similar significant associations were found between eGFR-AS and AD biomarker plasma levels (p < 0.001; Aβ42: p < 0.001; GFAP: p = 0.004; NfL: p < 0.001; ptau-181: p = 0.012) where higher eGFR values were again associated with lower biomarker levels. Racial differences remained significant for Aβ40 (p < 0.001), Aβ42 (p < 0.001), and NfL (p = 0.001) with Black participants exhibiting lower levels compared to White participants.

Similar significant associations were found between creatinine and AD biomarker plasma levels when adjusting for creatinine rather than eGFR (Aβ40: p < 0.001; Aβ42: p < 0.001; GFAP: p = 0.003; NfL: p < 0.001; ptau-181: p = 0.009) where higher creatinine values were associated with higher biomarker levels. Similar to the eGFR-AS model, racial differences remained significant for Aβ40 (p < 0.001), Aβ42 (p < 0.001), and NfL (p = 0.001) with Black participants exhibiting lower levels compared to White participants.

Adjustment for APOE ε4 carrier status

APOE genotype was available for 220 participants in the analysis sample (81%). Of these, 58 participants were either ε2/ε4, ε3/ε4 or ε4/ε4, while 162 participants were ε2/ε2, ε2/ε3, or ε3/ε3. We found no significant differences in race or age between APOE ε4 carriers and non-carriers. Our results in this subsample of participants yielded similar results to those in the main analyses, with point estimates and trends observed being similar to those found in the overall sample (Tables S.1.1–S.1.2). We found that APOE ε4 carriers had lower levels of Aβ42 (p = 0.005), higher levels of p-tau181 (p = 0.013), and lower Aβ42/Aβ40 ratios (p < 0.001). Furthermore, we found that adjustment for APOE ε4 carrier status did not impact the observed racial differences or the results regarding the effects of adjustment of eGFR and comorbidities (Tables S.1.3–S.1.4).

Sensitivity Analyses

We conducted sensitivity analyses to assess whether the observed racial differences in plasma AD biomarker levels persisted after adjustment for cognitive status. In these analyses, we adjusted all previously reported models for cognitive status (MCI vs. cognitively unimpaired) and found that (1) cognitive status was not significantly associated with biomarker levels, and (2) results across all prior subsections remained consistent after this adjustment (Tables S.2.1–S.2.2).

Discussion

Our findings raise important questions about the generalizability of blood-based AD biomarkers across multi-cultural populations. Black participants had lower levels of Aβ40, Aβ42, and NfL, but similar Aβ42/Aβ40 ratios compared to White participants. These differences persisted after adjusting for eGFR, comorbidities, and APOE ε4 status. Given the well-documented higher risk of dementia among Black individuals,71 these results suggest that AD plasma biomarker thresholds may not be universally applicable and underscore the importance of validating group-biomarker thresholds across all populations to ensure appropriate clinical interpretation.

Our findings are consistent with previous studies that have reported lower plasma levels of Aβ40, Aβ42 in Black individuals.27,28 Because the magnitude of the race differences in Aβ40, Aβ42 was similarly (approximately −0.3 SD for both), we observed no racial differences in the Aβ42/Aβ40 ratio, consistent with previous reports.16,31,32 We further observed lower levels of NfL in Black individuals compared to White individuals. Although NfL is often elevated in individuals with AD and is considered a marker of neuroaxonal injury, prior work has also reported lower NfL levels in African American individuals compared with White individuals.27 These findings, together with our own results, suggest that racial differences in NfL may not directly indicate underlying racial differences in Alzheimer’s pathology, but instead reflect differences in other neurodegenerative diseases, cerebrovascular disease, and inflammatory conditions in which NfL is elevated. Further, they may reflect group differences in biomarker production, release, or peripheral clearance that are unrelated to AD stage. Overall, these patterns highlight that population-level ADRD risk does not necessarily translate into uniformly higher biomarker concentrations, and that multiple biological, environmental, and sociocultural processes may shape plasma biomarker levels.

We observed variation in the magnitude of racial differences depending on the method used to adjust for kidney function. The race-neutral eGFR-AS formula yielded higher estimates in race differences, while the race-adjusted eGFR-ASR formula attenuated them. This likely reflects known biases: eGFR-AS underestimates kidney function in Black individuals and overestimates it in non-Black individuals,42 potentially exaggerating group differences due to the inverse relationship between eGFR and biomarker levels. Conversely, eGFR-ASR may overestimate GFR in Black individuals, biasing differences toward the null. Using eGFR-ASR in models that also adjust for race may result in double adjustment, as the equation itself incorporates race, complicating interpretation and limiting conclusions about which formula should be used in practice. Analyses using creatinine directly yielded similar results to those using eGFR-AS.

Our comorbidity-adjusted models further support the influence of health conditions on biomarker levels. Hypertension was associated with higher NfL levels, and history of non-skin cancer with lower GFAP levels. Given that the distribution of comorbidities and kidney function differs across population groups, failure to adjust for these variables may contribute to inconsistent findings in the literature. The persistence of racial differences after adjustment for kidney function, comorbidities, and APOE suggests that other unmeasured factors may contribute. These may include sociocultural and structural determinants of health, chronic stress exposure, environmental factors, and differences in healthcare access or quality. Future studies incorporating more detailed measures of these domains will be important for clarifying why racial differences in plasma biomarker concentrations arise.

Our results help contextualize the inconsistent race difference findings across the literature and prior studies that did not observe racial differences by demonstrating how methodological decisions, particularly the choice of kidney function adjustment, can meaningfully influence the magnitude of observed biomarker differences and potentially contribute to divergent conclusions across cohorts. Importantly, even though our results suggest that estimated size of the racial differences shifts depending on whether race-specific or race-neutral eGFR equations are used, the overall direction of the associations found remains stable and aligns with studies showing lower biomarker concentrations in Black adults. These findings raise important questions about the clinical use of blood-based biomarkers for Alzheimer’s disease, particularly whether race-specific cut-scores are needed to optimize diagnostic validity for AD and AD-related MCI. Addressing this will require evaluation of diagnostic and predictive performance in racially diverse cohorts using gold-standard reference measures, including clinical adjudication and ATN biomarkers assessed by PET and/or CSF. Although these questions are beyond the scope of the present study, our findings highlight the need for diverse validation cohorts and harmonized analytic approaches including consistent renal function adjustment. Future multi-cultural studies should include larger samples to enhance the generalizability of findings and better capture biomarker variation across groups.

Despite observed differences in biomarker levels between racial groups, it remains unclear whether the relationship between plasma biomarkers and AD pathology is consistent across demographic groups. If biomarker-pathology associations vary by subgroup, then applying uniform cut-points may reduce predictive accuracy. Future studies should examine whether the associations between plasma biomarkers and imaging-confirmed AD pathology differ across racial and ethnic groups, and whether biomarker cut-points indicating disease should be calibrated accordingly. Kidney function plays a central role in biomarker clearance, but it is not yet known whether impaired kidney function contributes directly to AD pathology or simply leads to higher circulating biomarker levels. Future studies should examine whether kidney function correlates with imaging markers of AD pathology and whether these associations differ by demographic characteristics. These efforts are essential for improving risk stratification and tailoring interventions.

The generalizability of current eGFR equations is also a concern. Most were developed using data from younger populations (mean age ~47), raising questions about their validity in older adults. While current guidelines favor the race-free eGFR-AS formula, our findings suggest that both eGFR-AS and eGFR-ASR may introduce bias when used in multi-cultural aging populations. Cystatin-based equations have shown improved accuracy and should be prioritized in future studies; however, we did not collect cystatin data in this analysis. Future biomarker studies should incorporate both creatinine and cystatin-C to improve kidney function estimation and control for its impact on biomarker clearance. New eGFR equations should be developed using data from older, multi-cultural cohorts to enhance their accuracy and applicability. Hispanic individuals, for example, were underrepresented in the datasets used to develop current formulas. Addressing these gaps is essential for improving the interpretability of plasma biomarkers across multi-cultural groups.

This study has several strengths. First, the study analyzes a racially diverse, community-dwelling aging cohort, which is an underrepresented population in biomarker validation research that is typically clinic-based and majority white. Second, our systematic comparison of race-specific and race-neutral eGFR equations provides one of the few direct evaluations of how kidney function adjustment affects observed racial differences in plasma biomarkers. Third, The availability of detailed comorbidity data and APOE genotype allowed for rigorous assessment of potential confounders, and the inclusion of multiple biomarkers spanning amyloid, tau, neurodegeneration, and glial pathways enabled us to evaluate whether racial differences were consistent across AD-related processes. Last, a series of sensitivity analyses further supported the robustness of our findings.

Our study has a number of limitations: First, our analyses rely on self-reported race, a sociocultural construct that reflects lived experience and structural context, not genetic ancestry. Incorporating genetic ancestry or polygenic risk in future work may help clarify how genetic susceptibility and sociocultural factors jointly shape biomarker levels. Second, our sample consists solely of dementia-free individuals, which may also explain the lack of cross-sectional associations between plasma AD biomarkers and cognitive status. Race-related differences in biomarkers such as p-tau, NfL, and GFAP may emerge in participants further along the AD continuum, which could explain why we primarily observed differences in Aβ40 and Aβ42. This may also explain the lack of differences across cognitive groups. Third, our sample includes only adults aged 70 and older, in whom biomarker levels may reflect age-related co-pathologies; future studies in younger cohorts will be important to assess whether these racial differences persist. Last, the panel examined does not include the most sensitive blood biomarkers and assay platforms currently in use (e.g., p-tau217); however, it is reasonable to hypothesize that these results remain generalizable.

In conclusion, Black older adults in this community-based cohort had lower plasma levels of Aβ40, Aβ42, and NfL than White adults, and these differences persisted after adjusting for comorbidities, kidney function, and APOE ε4 status. Although the magnitude of racial differences varied depending on whether race-specific or race-neutral eGFR equations were used, the direction of these effects was consistent across all approaches. These findings show that methodological decisions related to estimating kidney function can influence biomarker estimates and should be carefully considered when interpreting group differences. Overall, our results highlight the need for diverse validation cohorts, harmonized analytic strategies that include consistent approaches to renal function adjustment, and deeper evaluation of sociocultural, environmental, and biological contributors to biomarker variability. These efforts will be essential for optimizing the use of blood-based AD biomarkers in clinical settings across multi-cultural populations.

Supplementary Material

Supplement

Acknowledgements

The authors would like to thank the dedicated EAS participants for their time and effort in support of this research. This research was made possible through the hard work of EAS research assistants: we thank Diane Sparracio and April Russo for assistance with participant recruitment; Betty Forro, Maria Luisa Giraldi, and Sylvia Alcala for assistance with clinical and neuropsychological assessments; and Michael Potenza for assistance with data management.

Funding

This work was supported by the National Institute on Aging at the National Institutes of Health (grant numbers P01 AG03949), the Leonard and Sylvia Marx Foundation, and the Czap Foundation. HZ is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018 and #2019-02397), the European Union’s Horizon Europe research and innovation programme under grant agreement No 101053962, Swedish State Support for Clinical Research (#ALFGBG-71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund and the Alzheimer’s Association (#ADSF-21-831376-C, #ADSF-21-831381-C, #ADSF-21-831377-C, and #ADSF-24-1284328-C), the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States (NEuroBioStand, #22HLT07), the Bluefield Project, Cure Alzheimer’s Fund, the Olav Thon Foundation, the Erling-Persson Family Foundation, Familjen Rönströms Stiftelse, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2022-0270), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme - Neurodegenerative Disease Research (JPND2021-00694), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, the UK Dementia Research Institute at UCL (UKDRI-1003), and an anonymous donor.

Footnotes

Ethical Considerations

All research presented in the following manuscript was conducted in accordance with the ethical standards outlined in the 1964 Declaration of Helsinki and its later amendments.

Consent to Participate

All participants in the Einstein Aging Study provided informed consent before participating in our study. Informed consent was obtained in accordance with protocols approved by the Albert Einstein College of Medicine institutional review board.

Declaration of Conflicting Interests

HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZpath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Enigma, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Quanterix, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures sponsored by Alzecure, BioArctic, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, Roche, and WebMD, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). RL receives research support from the National Institutes of Health (NIH): 2PO1 AG003949 (Program Director), S&L Marx Foundation, and Czap Foundation, the US Food and Drug Administration (FDA). He serves as consultant, advisory board member, or has received honoraria from Abbvie (Allergan), American Academy of Neurology, Amgen, Biohaven, Eli Lilly, GlaxoSmithKline, Grifols, Lundbeck (Alder), Merck, Pfizer, Teva. He holds stock options in Biohaven and Manistee. None of the other authors have disclosures to declare.

Data Availability Statement

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

Code Availability

Code will be made publicly available on Github after acceptance of this article.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement
NIHMS2172865-supplement-Supplement.pdf (282.9KB, pdf)

Data Availability Statement

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

Code will be made publicly available on Github after acceptance of this article.

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