Intersectional effects of race/ethnicity, sex, and APOE4 on plasma inflammatory profiles in Hispanic and non-Hispanic white adults.

Intersectional effects of race/ethnicity, sex, and APOE4 on plasma inflammatory profiles in Hispanic and non-Hispanic white adults - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Brain Behav Immun Health . 2026 May 14;54:101256. doi: 10.1016/j.bbih.2026.101256 Intersectional effects of race/ethnicity, sex, and APOE4 on plasma inflammatory profiles in Hispanic and non-Hispanic white adults JA Contreras a , NE Ortega a , CA Hayes b , J Pa a, ⁎ ; the HABS-HD Study Team 1 ; HABS-HD MPIs: Sid E O'Bryant ; the HABS-HD Investigators: Meredith Braskie Author information Article notes Copyright and License information a Department of Neurosciences, Alzheimer's Disease Cooperative Study (ADCS), University of California, San Diego, CA, USA b Department of Epidemiology and Population Health, Stanford University School of Medicine, CA, USA ⁎ Corresponding author. Alzheimer's Disease Cooperative Study (ADCS), Department of Neurosciences. University of California, San Diego, 9500 Gilman Drive, MC 0949, La Jolla, CA, 92093-0949, USA. jpa@ucsd.edu 1 The Health and Aging Brain Study: Health Disparities (HABS-HD) Investigative Team: HABS-HD MPIs: Sid E O'Bryant, Kristine Yaffe Arthur Toga, Robert Rissman, & Leigh Johnson; and the HABS-HD Investigators: Meredith Braskie, Kevin King, James R Hall, Melissa Petersen, Raymond Palmer, Robert Barber Yonggang Shi, Fan Zhang, Rajesh Nandy Roderick McColl, David Mason, Bradley Christian, Nicole Phillips, Stephanie Large, Joe Lee, Badri Vardarajan, Mónica Rivera Mindt Amrita Cheema, Lisa Barnes, Mark Mapstone Annie Cohen, Amy Kind, Ozioma Okonkwo Raul Vintimilla, Zhengyang Zhou, Michael Donohue, Matthew Borzage Michelle Mielke, Beau Ances, Ganesh Babulal, Jorge Llibre-Guerra, Carl Hill, and Rocky Vig. Received 2025 Nov 26; Revised 2026 May 2; Accepted 2026 May 10; Collection date 2026 Jul. © 2026 The Authors This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13227229  PMID: 42238522 Abstract Background Despite known links between inflammation and Alzheimer's disease (AD), little is understood about how inflammatory processes vary across racial/ethnic and sex groups, particularly in relation to the major genetic risk factor apolipoprotein E ε4 (APOE4), whether these differences contribute to AD disparities among understudied Hispanics. Objective This study investigated the interactive effects of race/ethnicity, sex, and APOE4 status on plasma inflammatory markers (IL-5, IL-6, IL-10, TNF-α). Secondary analyses examined whether these inflammatory markers were associated with cognitive performance and plasma AD-related biomarkers. Methods Cross-sectional baseline data from the Health & Aging Brain Study – Health Disparities (HABS-HD) were analyzed in a multiethnic cohort of 1348 non-Hispanic white (NHW) (56.9% women, 27.4% APOE4 carriers) and 1420 Hispanics (66% women, 17.6% APOE4 carriers). Linear regression models examined main and interaction effects between race/ethnicity, sex, and APOE4 on plasma inflammatory markers, adjusting for age, education, BMI, and vascular comorbidities. Follow-up models tested associations with plasma AD biomarkers (Aβ42/40, pTau181) and cognition. Results Significant three-way interactions were observed for IL-10 (p = 0.012) and at trend-level for IL-5 (p = 0.056), with the highest inflammatory levels in Hispanic men who are APOE4 carriers. No consistent associations emerged between inflammatory markers and global cognition or plasma AD biomarkers. An IL-10 × race/ethnicity interaction was associated with language performance (p = 0.035), though this did not survive correction for multiple comparisons. Conclusion Inflammatory responses to APOE4 differ by race/ethnicity and sex, with Hispanic men showing elevated IL-10 and IL-5. While associations with cognition and AD biomarkers were limited, these findings highlight intersectional heterogeneity in inflammatory profiles that may contribute to understanding biological pathways underlying AD disparities. Longitudinal studies are needed to determine whether these cytokines predict future cognitive decline or AD pathology. Keywords: Inflammation, APOE4, Sex differences, Hispanic, Race/ethnicity, Alzheimer's disease Highlights • How raceand ethnicity, sex, and APOE4 jointly influence Alzheimer's disease risk remains poorly understood.f • APOE4 influences AD-related inflammation, yet its role among hispanics remains poorly understood. • Three-way interactions linked sex, race and ethnicity, and APOE4 status to IL-10 and IL-5 levels. • Examining race and ethnicity, sex, APOE4, and inflammation may clarify differences in Alzhiemer's disease risk. 1. Introduction Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by neuronal damage and the accumulation of amyloid-β (Aβ) plaques and hyperphosphorylated tau tangles in the brain, which ultimately disrupts learning, memory and overall cognitive function. The majority of AD cases (∼90%) are late-onset, sporadic, multifactorial, and genetically complex, with heritability estimates ranging between 60% and 80% ( Harman, 2006 ; Van Cauwenberghe et al., 2016 ). Despite extensive research on AD, significant gaps remain in understanding the race/ethnicity- and sex-specific risks for AD development and progression. Racial and ethnic factors strongly influence AD biology. While non-Hispanic whites (NHWs) comprise the largest number of individuals living with AD in the United States, Hispanics are disproportionately more likely to develop the disease. By 2050, the number of aging Hispanics is expected to triple, with AD rates projected to increase six-fold among this population (Colby and Ortman, 2014; Calsolaro and Edison, 2016 ). Sex disparities are also evident ( Aggarwal and Mielke, 2023 ), with data showing that women have a higher prevalence of AD than men among middle-aged and older adults (2024 Alzheimer's disease facts and figures). Importantly, AD pathophysiology appears to differ between Hispanics and NHWs ( Contreras et al., 2024 ). For instance, the apolipoprotein E (APOE) ε4 allele, which poses the greatest risk for AD in NHWs, is less significant in Hispanics ( Haan et al., 2003 ), possibly due to its lower frequency and smaller effect size ( O'Bryant et al., 2021 , 2022a ). A recent study found that APOE4 carrier status did not increase the risk of mild cognitive impairment (MCI) among Hispanics ( O'Bryant et al., 2022b ). Additionally, Hispanics face significant AD health disparities compared to NHWs, including earlier onset of cognitive impairment by about ten years ( O'Bryant et al., 2007 ), higher rates of missed and later diagnoses, and increased prevalence of modifiable risk factors ( Sundquist and Winkleby, 1999 ). One possible explanation is APOE4's role in inflammation. APOE regulates lipid metabolism and immune function, with the ε4 allele linked to heightened innate immune responses and systemic inflammation ( Gale et al., 2014 ; Parhizkar and Holtzman, 2022 ). Importantly, neuroinflammatory processes, while protective in moderation, can accelerate AD pathology by promoting Aβ aggregation, neuronal loss, and synaptic dysfunction resulting in faster trajectories of cognitive decline. ( Kinney et al., 2018 ; Michaud and Rivest, 2015 ). Therefore, while mild neuroinflammation is a feature of normal aging, AD brains exhibit exaggerated activation, underscoring the importance of balanced pro- and anti-inflammatory signaling. Yet, studies of inflammatory biomarkers in underrepresented groups—particularly Hispanics—remain scarce. Limited evidence suggests Hispanics may experience higher levels of chronic inflammation ( Schmeer and Tarrence, 2018 ), but little is known about its role in aging among this population and how the inflammatory processes interact with APOE4, sex, or cognition within Hispanics. This gap is especially urgent given the rise in AD among Hispanics. The present study addresses this need by examining how inflammatory profiles vary by race/ethnicity, sex, and APOE4 status in a large, harmonized cohort of cognitively healthy NHW and Hispanic adults. We further investigate whether systemic inflammation relates to AD-related pathology and major cognitive domains, and whether these relationships differ by sex or race/ethnicity. By focusing on an underrepresented population, this study aims to clarify biological mechanisms that may contribute to Alzheimer's disease risk in diverse populations. To address these gaps, the primary aim of this study was to examine whether race/ethnicity, sex, and APOE4 carrier status interact to influence plasma inflammatory marker levels in a large, community-based cohort of Hispanic and non-Hispanic White adults. We selected IL-6 and TNF-α as canonical pro-inflammatory cytokines consistently implicated in aging and AD-related neuroinflammation, and IL-10 and IL-5 as immunoregulatory cytokines with context-dependent roles that may reflect compensatory or dysregulated immune responses. Given evidence that APOE4 modulates innate immune signaling and that inflammatory processes vary across sex and racial/ethnic groups, we tested two- and three-way interaction models to capture potential intersectional biological effects that may not be evident in main-effect analyses. As secondary aims, we explored whether these inflammatory markers were associated with plasma AD-related biomarkers (Aβ42/40 and pTau181) and cognitive performance, and whether these associations differed by race/ethnicity or sex. We hypothesized that (1) APOE4 carrier status would be associated with elevated inflammatory marker levels; (2) these associations would vary by sex and race/ethnicity, with stronger inflammatory elevations anticipated among Hispanic APOE4 carriers; and (3) inflammatory markers would show modest or subgroup-specific associations with AD-related biomarkers and cognition. By explicitly examining intersectional immune heterogeneity, this work seeks to advance understanding of biological mechanisms that may contribute to Alzheimer's disease disparities. 2. Methods 2.1. Participants Participants were chosen based on available blood biomarkers and APOE genotype data from the Health & Aging Brain Study – Health Disparities (HABS-HD; formally the Health & Aging Brain study among Latino Elders, HABLE study) resulting in baseline data from 1420 Hispanics and 1348 NHW participants. HABS-HD study is an ongoing, longitudinal, community-based project examining health disparities in MCI and AD among Hispanics as compared to NHW participants. HABS-HD methods have been published elsewhere ( O'Bryant et al., 2021 ) and are briefly outlined below. Inclusion criteria for the study includes (1) self-reported ethnicity of Hispanic (1313 Mexican American; 15 Puerto Rican; 5 Cuban; 87 other) or non-Hispanic White (NH White) participants (2) willingness to provide blood samples, (3) capable of undergoing neuroimaging studies, (4) age 50 and above, and (5) fluent in English or Spanish. Sex was analyzed as a binary variable (female vs. male), as recorded in the HABS-HD dataset; gender identity was not assessed in the present study. Exclusion criteria includes (1) Type 1 diabetes, (2) presence of active infection, (3) current/recent (12 month) cancer (other than skin cancer), (4) current severe mental illness that could impact cognition (other than depression), (5) recent (12 months) traumatic brain injury with loss of consciousness, (6) current/recent alcohol/substance abuse, and (7) active severe medical condition that could impact cognition (e.g., end stage renal failure, chronic heart failure, chronic obstructive pulmonary disease). All aspects of the study protocol have been conducted in Spanish or English. The HABS-HD study is conducted under IRB approved protocols and each participant (or his/her legal representative) signs written informed consent. All HABS-HD data are available upon request through the Laboratory of Neuro Imaging (LONI) data portal. 2.2. Diagnostic classification APOE4 genotyping was performed using commercially available TaqMan Genotyping Kits for rs429158 and rs7412 using TaqMan GTXpress Master Mix (ThermoFisher). Target amplification and detection was performed using the 7500 RealTime PCR System (Applied Biosystems). Genotypes were called according to combined allele amplification results at the two SNPs as follows (rs429358, rs7412): APOE - 2/2; 2/3; 2/4; 3/3; 3/4; 4/4. Positive controls (individuals of known, independently typed APOE genotypes) and negative controls were included on all runs. APOE genotypes frequencies were confirmed to be in Hardy-Weinberg equilibrium ( O'Bryant et al., 2022a ). 2.3. Blood collection and processing procedures for biomarkers Fasting blood samples were collected and processed according to international guidelines for AD biomarker studies, including standardized processing within 2 h from phlebotomy to freezer storage (O’Bryant et al., 2014). Assay preparation was completed using a custom automated StarPlus system (Hamilton Robotics, Reno, NV, USA). Plasma AD-related biomarkers were quantified using single molecule array (Simoa) technology (Quanterix, Lexington, MA, USA) on the HD-X Automated Immunoassay Analyzer. Plasma amyloid-β 40 (Aβ40) and amyloid-β 42 (Aβ42) were measured to derive the Aβ42/40 ratio, and phosphorylated tau (pTau181) was assayed using validated Simoa-based immunoassays. Plasma inflammatory markers (IL-5, IL-6, IL-10, and TNF-α) were measured using the same Simoa HD-X platform. The HD-X system quantifies target proteins using a two-step immunoassay to generate a fluorescent signal and fits concentrations using a four-parameter logistic curve with 1/y 2 weighting. Coefficients of variation for all assays were ≤5%. Detailed assay procedures and validation metrics for these biomarkers within the HABS-HD cohort have been previously published ( O’Bryant et al., 2019 , 2022c ; Petersen et al., 2025 ). 2.4. Cognitive data Individual cognitive variables were screened and cleaned prior to the creation of harmonized domain scores. Because raw cognitive scores are reported on different scales, each variable was transformed to a standardized z -score (mean = 0, SD = 1) using the study sample as the reference. Higher z -scores therefore correspond to better performance except for timed tests in which larger raw values denote slower performance. For the Trail-Making Test parts A and B, the standardized scores were multiplied by −1 so that higher values consistently indicated faster (better) processing speed. Domain-level composites were created by averaging the relevant standardized scores: 1. episodic memory composite was calculated as the mean of Spanish-English Verbal Learning Test (SEVLT) immediate recall, SEVLT delayed recall, and Wechsler Logical Memory I and II z -scores, 2. executive function/working memory composite was calculated as the mean of Digit Span total and Digit Symbol Substitution z -scores, 3. processing speed composite was calculated as mean of the (sign-reversed) Trail-Making A and B z -scores and, 4. language composite was calculated as mean of Animal Fluency and F-A-S verbal fluency z -scores. All cleaned and derived cognitive variables were carried forward to subsequent statistical analyses. 2.5. Biomarkers Plasma concentrations of interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-5 (IL-5) were screened for data-entry sentinel values indicating missingness. Any occurrence of these codes was recoded as missing (NA). Missing cytokine values were not imputed or estimated through model inference. Participants with missing biomarker data were excluded from analyses involving that specific marker. The number of available observations for each biomarker is reported in Table 1 . Table 1. Demographic and clinical characteristics of participants by race/ethnicity in the HABS-HD cohort. Group differences were evaluated using independent two-sample t-tests for continuous variables and chi-square tests for categorical variables; Fisher's exact test was used when expected cell counts were small. Statistical significance was defined as p < 0.05. Available sample sizes are reported for variables with missing data. NHW (N = 1348) Hispanic (N = 1420) p Overall (N = 2768) Age (years), Mean (SD) 68.80 (8.73) 63.3 (8.05) <0.00 66.0 (8.82) Female, n (%) 767 (56.90) 937 (66.0) <0.00 1704 (61.60) Education (years), Mean (SD) 15.50 (2.56) 10.3 (4.63) <0.00 12.8 (4.60) Hypertension, n (%) 784 (58.20) 903 (63.6) <0.05 1687 (60.90) Diabetes, n (%) 188 (13.90) 497 (35.0) <0.00 685 (24.70) BMI, Mean (SD) 29.10 (6.23) 31.0 (6.08) <0.00 30.1 (6.22) Missing, n (%) 9 (0.70) 6 (0.4) 15 (0.50) APOE4, n (%) 369 (27.40) 250 (17.6) <0.00 619 (22.40) Missing, n (%) 114 (8.50) 145 (10.2) 259 (9.40) Plasma IL-5 0.77 Mean (SD) 0.14 (0.131) 0.13 (0.23) 0.133 (0.190) Median [Min, Max] 0.10 [0.01, 1.69] 0.08 [0.01, 3.42] 0.09 [0.01, 3.42] Missing, n (%) 422 (31.30) 481 (33.90) 903 (32.6) Plasma IL-6 0.09 Mean (SD) 1.26 (2.22) 1.64 (6.70) 1.45 (4.98) Median [Min, Max] 0.86 [0.14, 40.70] 1.03 [0.17, 198] 0.94 [0.14, 198] Missing, n (%) 386 (28.6) 475 (33.5) 861 (31.10) Plasma IL-10 0.92 Mean (SD) 0.49 (1.05) 0.494 (1.96) 0.49 (1.57) Median [Min, Max] 0.34 [0.06, 24.70] 0.347 [0.11, 58.20] 0.35 [0.06, 58.20] Missing, n (%) 392 (29.10) 469 (33.00) 861 (31.10) Plasma TNF-α 0.88 Mean (SD) 1.91 (7.56) 1.87 (3.92) 1.89 (6.02) Median [Min, Max] 1.53 [0.54, 231] 1.57 [0.58, 118] 1.55 [0.54, 231] Missing, n (%) 420 (31.20) 494 (34.80) 914 (33.00) Memory (z), Mean (SD) 0.25 (0.91) −0.25 (0.80) <0.00 −0.00 (0.89) Missing, n (%) 1.00 (0.10) 3.00 (0.20) 4.00 (0.100) Executive Function (z), Mean (SD) 0.44 (0.73) −0.42 (0.81) <0.00 −0.01 (0.88) Missing, n (%) 0.00 (0.00) 1.00 (0.10) 1 (0.0) Processing Speed (z), Mean (SD) 0.30 (0.70) −0.33 (1.07) <0.00 −0.02 (0.96) Missing, n (%) 5 (0.40) 10 (0.70) 15 (0.50) Language (z), Mean (SD) 0.30 (0.87) −0.28 (0.81) <0.00 −0.00 (0.89) CDR Sum of Boxes, Mean (SD) 0.43 (1.33) 0.55 (1.47) 0.02 0.50 (1.41) Open in a new tab To minimize the influence of extreme values, biomarker distributions were inspected and observations exceeding ±5 standard deviations from the mean of each biomarker distribution were classified as extreme values and set to missing. All remaining values were retained as biologically plausible observations. After this cleaning step, each analyte was further inspected using frequency histograms and bivariate scatterplots against chronological age, overlaid with an ordinary least squares trend line and 95% confidence band, to provide a preliminary view of age-related trends and distributional properties. Because cytokine concentrations displayed pronounced right skew, values were then log-transformed. A base-10 logarithm with a constant offset of 1 was applied so that samples with zero or undetectable concentrations were retained while stabilizing variance across the range. Post-transformation distributions were re-evaluated using histograms and repeated skewness estimates; in every case, the transformation substantially reduced skewness. All subsequent primary statistical analyses therefore used the cleaned, log-transformed biomarker variables. As an additional sensitivity analysis requested during peer review, we repeated the primary IL-5 and IL-10 interaction models after reapplying the same threshold to the log-transformed IL-5 and IL-10 distributions, excluding values exceeding ±5 standard deviations from the mean before rerunning the models. 2.6. Linear regression modeling Analyses were conducted in R (version 4.4.3). Group differences in demographic and baseline characteristics by race/ethnicity, sex, and APOE4 status were assessed using independent samples t-tests and chi-square (χ 2 ) tests. Linear regression models were used to examine the associations between inflammatory markers (IL-5, IL-6, IL-10, and TNF-α) and predictors of interest, including race/ethnicity, sex, and APOE4 carrier status. Models included covariates selected based on known relevance to inflammation, including age, education, body mass index (BMI), and hypertension diagnosis. We tested for two-way and three-way interaction effects between race/ethnicity, sex, and APOE4 status. To reduce the likelihood of Type II error in detecting interaction effects, we used a liberal screening threshold (p < 0.20) to determine whether stratified analyses were warranted. This approach has been used in prior work examining sex × APOE interactions ( Pa et al., 2022 ). Statistical significance for interaction and stratified effects was interpreted using conventional α levels (p < 0.05). Because cytokine concentrations displayed pronounced right-skew, values were log10-transformed prior to analysis. Accordingly, effect estimates may appear numerically small but reflect differences in log-transformed biomarker levels rather than raw concentration values. Model assumptions were evaluated by examining residual distributions, normality of residuals, homoscedasticity, and influential observations. Log transformation reduced skewness and improved model fit. All models met standard assumptions for linear regression. To assess associations between inflammatory markers and Alzheimer's disease (AD) biomarkers (Aβ42/40 ratio and pTau181), we ran separate linear regression models adjusted for age, education, BMI, diabetes, hypertension. We also tested for two-way and three-way interactions between IL-5 or IL-10 and race/ethnicity, sex, and APOE4 status. Finally, we tested whether inflammatory markers were associated with global and domain-specific cognition (MMSE, episodic memory, language, executive function, and processing speed). Linear regression models included inflammatory markers (IL-5, IL-6, IL-10 or ΤNF-α), race/ethnicity, sex, and APOE4 as predictors, as well as the same covariates. Interaction terms (e.g., IL-10 × race/ethnicity, IL-10 × sex) were included to examine subgroup-specific effects. Multiple comparisons were addressed using Bonferroni. 3. Results 3.1. Sample characteristics The study included 1420 Hispanic participants (66% women; 17.6% APOE4 carriers; mean age ± SD = 63.3 ± 8.05) and 1348 non-Hispanic White (NHW) participants (56.9% women; 27.4% APOE4 carriers; mean age ± SD = 68.8 ± 8.73). Demographic characteristics are presented in Table 1 . As shown in Table 1 , Hispanic participants were younger and had fewer years of education than non-Hispanic White participants (both p < 0.001), and a higher proportion were female (p = 0.02). These baseline differences were accounted for in subsequent adjusted regression models. 3.2. Race/ethnicity × sex × APOE4 interactions on inflammatory markers We first examined whether race/ethnicity, sex, and APOE4 carrier status interacted to predict plasma inflammatory marker levels using adjusted linear regression models. For IL-10 as the outcome, there was a significant three-way interaction between race/ethnicity × sex × APOE4 status (F(12, 1872) = 3.198, p = 0.012; Fig. 1 B; Table 2 ). For IL-5 as the outcome, a three-way interaction between race/ethnicity × sex × APOE4 status was observed at trend-level significance (F(12, 1830) = 7.666, p = 0.056; Fig. 1 A; Table 2 ). No significant three-way interactions were observed for IL-6 or TNF-α. Individual data points are shown in Supplementary Fig. S1 to illustrate the distribution of IL-5 and IL-10 values across subgroups. In additional sensitivity analyses excluding extreme observations from the log-transformed IL-5 and IL-10 distributions, the three-way race/ethnicity × sex × APOE4 interactions were attenuated and no longer statistically significant (Supplementary Fig. S2; Supplementary eTable 1). Fig. 1. Open in a new tab Estimated marginal means of IL-5 and -10 levels by sex, race/ethnicity, and APOE4 status. Panel A shows estimated marginal means (±95% confidence intervals) of plasma IL-5 levels, and Panel B displays those for IL-10, stratified by sex (Men, Women), race/ethnicity (Non-Hispanic White [NHW], Hispanics), and APOE4 carrier status (Non-carrier, Carrier). Values are derived from linear models adjusted for age, education, BMI, diabetes and hypertension. In both panels, sex- and APOE4-specific differences were more prominent among Hispanic participants, with Hispanic men APOE4 carriers showing the highest levels of IL-10. These findings suggest potential inflammatory pathway differences by sex, ancestry, and genetic risk profile. Table 2. Three-way interaction effects of race/ethnicity, sex, and APOE4 carrier status on plasma inflammatory markers (IL-5 and IL-10). Estimates represent beta coefficients with 95% confidence intervals from adjusted linear regression models including race/ethnicity × sex × APOE4 interaction terms and covariates (age, education, BMI, diabetes, and hypertension). P-values are uncorrected. E4 × Race × Sex Interactions Predicting Biomarker without Correction Predictors log il 5 plasma log il 6 plasma log il 10 plasma log TNFα plasma Estimates p Estimates p Estimates p Estimates p Age 0.00 (0.00, 0.00) <0.001 0.00 (0.00, 0.00) <0.001 0.00 (0.00, 0.00) 0.006 0.00 (0.00, 0.00) <0.001 Education −0.00 (−0.00, 0.00) 0.978 −0.00 (−0.00, 0.00) 0.199 0.00 (−0.00, 0.00) 0.243 −0.00 (−0.00, 0.00) 0.766 BMI 0.00 (−0.00, 0.00) 0.266 0.01 (0.01, 0.01) <0.001 0.00 (−0.00, 0.00) 0.378 0.00 (0.00, 0.00) <0.001 Diabetes 0.00 (−0.00, 0.00) 0.987 −0.02 (−0.03, −0.01) <0.001 −0.01 (−0.01, −0.00) 0.021 −0.02 (−0.02, −0.01) <0.001 Hypertension −0.00 (−0.00, 0.00) 0.370 −0.01 (−0.01, 0.00) 0.171 −0.00 (−0.01, 0.00) 0.341 −0.00 (−0.01, 0.00) 0.827 APOE4 −0.00 (−0.01, −0.00) 0.003 0.01 (−0.00, 0.01) 0.217 −0.00 (−0.01, 0.00) 0.171 0.00 (−0.00, 0.01) 0.241 Hispanic (vs NHW) −0.00 (−0.01, −0.00) 0.018 −0.01 (−0.02, 0.00) 0.120 −0.00 (−0.01, 0.00) 0.221 −0.00 (−0.01, 0.01) 0.668 Female (vs Male) 0.01 (0.01, 0.01) <0.001 −0.00 (−0.01, 0.01) 0.526 0.01 (0.00, 0.01) 0.003 −0.00 (−0.01, 0.01) 0.843 APOE4∗Hispanic 0.01 (0.00, 0.01) <0.001 −0.00 (−0.01, 0.01) 0.685 0.00 (−0.00, 0.01) 0.090 −0.01 (−0.01, 0.00) 0.094 APOE4∗Female −0.01 (−0.01, −0.00) <0.001 0.00 (−0.01, 0.01) 0.634 −0.00 (−0.01, 0.00) 0.368 −0.00 (−0.01, 0.01) 0.847 APOE4∗Female∗Hispanic 0.00 (−0.00, 0.01) 0.056 0.01 (−0.00, 0.01) 0.210 0.01 (0.00, 0.01) 0.012 0.00 (−0.00, 0.01) 0.650 Observations 1843 1887 1885 1835 R 2 /R 2 adjusted 0.05/0.04 0.14/0.13 0.20/0.01 0.08/0.07 Open in a new tab 3.3. Stratified analyses of significant interactions To clarify the observed interaction patterns, we conducted stratified analyses. Among men, there was a significant APOE4 × race/ethnicity interaction for both IL-10 (p = 0.014) and IL-5 (p = 0.001), indicating that Hispanic male APOE4 carriers had higher inflammatory levels compared to NHW male APOE4 carriers ( Table 3 , Table 4 ). These interactions were not observed among women. Table 3. APOE4 × Race/ethnicity interaction effects on inflammatory markers, stratified by sex. ( Significant associations for IL-5 and IL-10 observed among men only.) MEN ONLY Predictors Log IL-5 plasma Log IL-10 plasma estimates p-value estimates p-value Intercept 0.01(-0.04 – 0.06) 0.66 0.11(0.01 – 0.21) 0.03 APOE4 status 0.00(-0.01 – 0.01) 0.72 −0.01(-0.04 – 0.01) 0.33 Race/ethnicity −0.00(-0.01 – 0.01) 0.82 −0.01(-0.03 – 0.02) 0.55 Age 0.00(0.00 – 0.00) 0.04 0(-0.00 – 0.00) 0.52 Education 0(-0.00 – 0.00) 0.98 0.00(-0.00 – 0.00) 0.46 BMI 0(-0.00 – 0.00) 0.44 0(-0.00 – 0.00) 0.65 Diabetes −0.01(-0.02 – 0.01) 0.36 0.01(-0.01 – 0.03) 0.29 Hypertension 0(-0.01 – 0.01) 0.93 0.00(-0.02 – 0.02) 0.80 APOE4∗Race/ethnicity 0.04(0.02 – 0.06) <0.00 0.05(0.01 – 0.09) 0.01 Observations 733 743 R 2 /R 2 adjusted 0.04/0.03 0.01/0.00 WOMEN ONLY Predictors Log IL-5 plasma Log IL-10 plasma estimates p-value estimates p-value Intercept 0.01(-0.03 – 0.04) 0.73 0.04(-0.02 – 0.11) 0.19 APOE4 status −0.01(-0.02 to −0.00) 0.02 0.01(-0.01 – 0.03) 0.38 Race/ethnicity −0.01 (−0.01 – 0.00) 0.10 0.00(-0.01 – 0.02) 0.59 Age 0.00(0.00 – 0.00) 0.00 0.00(0.00 – 0.00) 0.00 Education 0(-0.00 – 0.00) 0.95 0.00(-0.00 – 0.00) 0.34 BMI 0(-0.00 – 0.00) 0.42 0(-0.00 – 0.00) 0.48 Diabetes 0.00(-0.00 – 0.01) 0.34 0.02(0.00 – 0.03) 0.03 Hypertension 0.00(-0.00 – 0.01) 0.24 0.01(-0.01 – 0.02) 0.33 APOE4∗Race/ethnicity 0.01(0.00 – 0.03) 0.03 −0.01(-0.04 – 0.02) 0.46 Observations 1110 1114 R 2 /R 2 adjusted 0.03/0.02 0.02/0.01 Open in a new tab Table 4. APOE4 × sex interaction effects on inflammatory markers, stratified by race/ethnicity. (Comparisons within Hispanics and non-Hispanic Whites.) NH Whites Predictors Log IL-5 plasma Log IL-10 plasma estimates p-value estimates p-value Intercept 0(-0.03 – 0.04) 0.92 0.08(-0.01 – 0.16) 0.07 APOE4 status 0(-0.01 – 0.01) 0.61 −0.01(-0.03 – 0.01) 0.33 Sex −0.01(-0.01 – 0.00) 0.05 −0.02(-0.03 – 0.00) 0.06 Age 0(0.00 – 0.00) <0.00 0(0.00 – 0.00) 0.01 Education 0(-0.00 – 0.00) 0.77 0(-0.00 – 0.00) 0.97 BMI 0(-0.00 – 0.00) 0.25 0(-0.00 – 0.00) 0.76 Diabetes 0(-0.01 – 0.00) 0.33 0.02(-0.00 – 0.04) 0.13 Hypertension 0 (−0.00 – 0.01) 0.41 0.01(-0.01 – 0.02) 0.37 APOE4∗Sex −0.01(-0.02 to −0.00) 0.05 0.02(-0.01 – 0.05) 0.22 Observations 915 945 R 2 /R 2 adjusted 0.04/0.04 0.02/0.01 Hispanics Predictors Log IL-5 plasma Log IL-10 plasma estimates p-value estimates p-value Intercept 0.01 (−0.02 – 0.05) 0.46 0.09(0.02 – 0.15) 0.01 APOE4 status 0.04(0.02 – 0.05) <0.00 0.04(0.01 – 0.06) 0.01 Sex −0.01(-0.02 to −0.00) 0.02 −0.01(-0.02 – 0.01) 0.28 Age 0(-0.00 – 0.00) 0.06 0(-0.00 – 0.00) 0.21 Education 0(-0.00 – 0.00) 0.88 0(-0.00 – 0.00) 0.24 BMI 0.00(-0.00 – 0.00) 0.51 0(-0.00 – 0.00) 0.39 Diabetes 0(-0.01 – 0.01) 0.62 0.01(-0.00 – 0.03) 0.06 Hypertension 0(-0.01 – 0.01) 0.57 0(-0.02-0.01) 0.64 APOE4∗Sex −0.03(-0.05 to −0.01) 0.00 −0.04(-0.07 to −0.01) 0.02 Observations 928 940 R 2 /R 2 adjusted 0.05/0.04 0.02/0.01 Open in a new tab When stratified by race/ethnicity, significant APOE4 × sex interactions were observed among Hispanic participants for IL-5 (p = 0.001) and IL-10 (p = 0.022), whereas no significant APOE4 × sex effects were observed among NHW participants. Collectively, these findings indicate that Hispanic men who are APOE4 carriers exhibited the highest IL-10 and IL-5 levels across demographic subgroups. 3.4. Associations between inflammatory markers and plasma AD biomarkers We next examined whether IL-5 and IL-10 were associated with plasma AD-related biomarkers (Aβ42/40 ratio and pTau181) using adjusted linear regression models. Across all models, there were no significant main effects or interaction effects between IL-5 or IL-10 and race/ethnicity or sex on Aβ42/40 or pTau181 (Supplementary eTable 2). 3.5. Associations between inflammatory markers and cognition Finally, we examined whether IL-5 and IL-10 were associated with global and domain-specific cognitive outcomes (MMSE, episodic memory, executive function, processing speed, and language). Using adjusted linear regression models including interaction terms, there was a significant IL-10 × race/ethnicity interaction predicting language performance (p = 0.035). However, this association did not survive correction for multiple comparisons ( Table 5 ). Table 5. Associations between inflammatory marker IL-10 and cognitive outcomes. ( Includes global cognition and domain-specific scores; results adjusted for demographics and vascular covariates.)-no significant associations were found with IL-5 . Predictors Memory Executive Function Processing Speed Language estimates p-value estimates p-value estimates p-value estimates p-value Intercept 0.65(0.20 – 1.09) 0.01 0.65(0.28 – 1.01) 0.00 0.38(-0.07 – 0.82) 0.10 0.61(0.16 – 1.05) 0.01 IL-10 plasma −0.18(-0.69 – 0.33) 0.49 0.15(-0.27 – 0.57) 0.49 0.14(-0.37 – 0.66) 0.59 −0.15(-0.66 – 0.36) 0.56 Age −0.02(-0.03 to −0.02) <0.00 −0.02(-0.03 to −0.02) <0.00 −0.03(-0.03 to −0.03) <0.00 −0.02(-0.02 to −0.01) <0.00 Education 0.07(0.06 – 0.08) <0.00 0.11(0.10 – 0.12) <0.00 0.12(0.11 – 0.13) <0.00 0.09(0.08 – 0.10) <0.00 Race −0.51(-0.78 to −0.23) <0.00 −0.66(-0.89 to −0.43) <0.00 −0.21(-0.49 – 0.07) 0.15 −0.48(-0.75 to −0.20) 0.00 Sex 0.42(0.35 – 0.49) <0.00 0.16(0.10 – 0.22) <0.00 0.08(0.01 – 0.15) 0.03 0.09(0.02 – 0.16) 0.01 APOE4 status −0.18(-0.26 to −0.10) <0.00 −0.06(-0.12 – 0.01) 0.10 −0.05(-0.13 – 0.03) 0.22 −0.06(-0.15 – 0.02) 0.14 BMI 0.01(0.00 – 0.02) 0.00 −0.00(-0.01 – 0.00) 0.46 0.01(0.00 – 0.01) 0.03 −0.002(-0.01 – 0.01) 0.61 Diabetes 0.01(-0.08 – 0.10) 0.81 −0.03(-0.10 – 0.04) 0.44 −0.11(-0.20 to −0.02) 0.02 −0.02(-0.107 – 0.068) 0.66 Hypertension 0.04(-0.12 – 0.04) 0.29 −0.09(-0.15 to −0.02) 0.01 −0.08(-0.16 to −0.00) 0.04 −0.09(-0.17 to −0.02) 0.02 IL-10∗Race/ethnicity 0.07(-0.66 – 0.80) 0.85 −0.07(-0.67 – 0.53) 0.82 −0.16(-0.89 – 0.57) 0.68 0.79(0.06 – 1.52) 0.04 Observations 1688 1689 1680 1990 R 2 /R 2 adjusted 0.33/0.32 0.56/0.56 0.45/0.44 0.33/0.32 Open in a new tab No significant associations were observed between IL-5 or IL-10 and global cognition or other cognitive domains. 4. Discussion Understanding who is most at risk for Alzheimer's disease (AD) requires examining how genetic and biological factors intersect, yet much of the current literature lacks adequate representation of diverse populations. Of equal importance, there is a sex disparity between men and women's risk for AD, with women having a greater lifetime risk of developing AD dementia compared to men ( Aggarwal and Mielke, 2023 ; Mielke, 2018 ) with even less known about this disparity in a Hispanic population. Most research examining AD markers such as inflammation and APOE4 has been conducted in predominantly White cohorts, leaving a dearth of knowledge regarding how APOE4 and inflammatory processes operate in Hispanic adults and whether these relationships differ by sex. In this study, we examined how race/ethnicity, sex, and APOE4 status jointly influence levels of plasma inflammatory markers among Hispanic and non-Hispanic adults enrolled in the HABS-HD cohort. In the primary analyses, we observed significant race/ethnicity × sex × APOE4 interactions for IL-10 and trend-level interactions for IL-5, with the highest levels observed among Hispanic men who were APOE4 carriers. In contrast, no significant three-way interaction effects were observed for IL-6 or TNF-α, and inflammatory markers were not consistently associated with plasma AD-related biomarkers or global cognition. These findings suggest selective rather than global immune differences across demographic subgroups. Stratified regression models clarified these interaction patterns, showing that Hispanic men who are APOE4 carriers exhibited the highest levels of both IL-10 and IL-5, compared to all other race/ethnicity × sex × APOE4 subgroups. These interactions were not observed in NH White participants. Furthermore, sex-stratified models showed that the APOE4 × race/ethnicity interaction was significant among men only, reinforcing the possibility that Hispanic men who are APOE4 carriers may represent a subgroup with distinct inflammatory profiles ( Davidson-Turner et al., 2024 ). Race/ethnicity-stratified models similarly confirmed significant APOE4 × sex interaction among Hispanic participants, but not among NH White participants. These findings align with prior evidence suggesting that different racial/ethnicity groups may experience distinct immunological aging trajectories compared to non-Hispanic White populations ( Duchowny et al., 2025 ; Kapse et al., 2024 ). IL-10 is generally considered an anti-inflammatory cytokine( Iyer and Cheng, 2012 ; Porro et al., 2020 ) while the role of IL-5 is more pro-inflammatory ( Moțăț;ăianu et al., 2024 ). There is evidence to suggesting that upregulation of these markers, particularly in the context of APOE4, may reflect a compensatory response to underlying immune dysregulation ( Fleeman et al., 2023 ). IL-5 has also been implicated in reducing tau hyperphosphorylation ( Zhou et al., 2017 ), while IL-10 has been shown to suppress pro-inflammatory cascades ( Carlini et al., 2023 ). Moreover, IL-10 may regulate IL-5-mediated immune activity, including limiting eosinophil recruitment ( Lee et al., 2024 ), suggesting a complex immunoregulatory relationship. One study showed that the ratio of IL-10 to IL-5 producing cells differs in patients with AD depending on the disease state( D'Anna et al., 2017 ; Weston et al., 2021 ; Zhang et al., 2025 ). So, while both IL-5 and IL-10 may be involved in AD, with IL-5 promoting eosinophil-driven inflammation and IL-10 generally acting as an anti-inflammatory and immunoregulatory cytokine, their relationship is multifaceted. IL-10 may be acting to counterbalance some of IL-5's pro-inflammatory effects. Thus, the elevated levels observed in Hispanic men who are APOE4 carriers may represent either neuroprotective compensation or an early sign of immune system imbalance that contributes to downstream AD risk. Importantly, these findings contribute to our understanding of Alzheimer's disease disparities by suggesting that APOE4-related inflammatory responses may not operate uniformly across race/ethnicity and sex. Rather, genetic risk may manifest within distinct biological and social contexts. Prior evidence indicates that race/ethnicity groups may experience different immunological aging trajectories ( Duchowny et al., 2025 ; Kapse et al., 2024 ), potentially reflecting cumulative life-course exposures, cardiometabolic burden, and structural stressors that shape immune regulation. In this context, the elevated IL-10 and IL-5 levels observed among Hispanic male APOE4 carriers may reflect an interaction between genetic susceptibility and environmental or social determinants that differentially influence inflammatory pathways. Notably, we did not observe similar APOE4-related inflammatory elevations among women. Although women have a higher lifetime risk of AD and often exhibit heightened immune responsivity, the absence of significant interaction effects among women in this study suggests that IL-10 and IL-5 may represent sex-specific immune pathways that are differentially regulated. It is possible that hormonal, metabolic, or immune regulatory mechanisms modify APOE4-associated inflammatory responses in women, resulting in distinct inflammatory trajectories compared to men. These findings underscore the importance of examining sex-specific immune processes rather than assuming uniform inflammatory effects across sexes. Although we did not find consistent associations between inflammatory markers and AD pathology markers or global cognition, we did observe an IL-10 × race/ethnicity interaction for language performance. This association did not remain significant after correction for multiple comparisons and should therefore be interpreted cautiously. Interpretation of this finding also warrants caution because cognitive testing in HABS-HD was conducted in English or Spanish according to participant preference, and differences related to language of test administration could contribute to performance on language measures. At the same time, these procedures were designed to improve cultural and linguistic appropriateness, and HABS-HD normative approaches account for key demographic factors including primary language, education, and age. As such, this finding should be viewed as preliminary and in need of further evaluation in longitudinal and linguistically stratified analyses. IL-10 has traditionally been characterized as an anti-inflammatory cytokine ( Porro et al., 2020 ), although its effects on the brain may be context-dependent and influenced by chronic systemic conditions, social determinants of health, or genetic background ( Carlini et al., 2023 ). Notably, this association did not remain significant after correcting for multiple comparisons and should therefore be interpreted with caution. However, this finding may point to a potential race/ethnicity-specific vulnerability in the relationship between inflammation and cognitive functioning (particularly within language domains that are sensitive to both sociocultural and neurological factors). Further investigation in larger and more diverse cohorts is needed to determine whether IL-10 plays a differential role in cognitive aging and Alzheimer's disease risk across racial/ethnic groups. Furthermore, the modest effect sizes and heterogeneity of inflammatory responses across individuals may also limit detection of robust associations in cross-sectional analyses. Finally, our cohort was composed of largely cognitively intact adults, many of whom may not yet exhibit sufficient cognitive or pathological burden for peripheral inflammation to show strong behavioral correlates. Longitudinal follow-up will be critical to determine whether elevated IL-10 and IL-5 levels predict future decline or track with emerging AD pathology over time. Strengths of this study include the large, community-based, multi-ethnic cohort; rigorous biomarker quantification; and attention to intersectional factors including sex, race/ethnicity, and APOE4. However, several limitations should be noted. First, this is a cross-sectional analysis, limiting inferences about causality or temporal dynamics. Second, while IL-10 and IL-5 are informative, they represent only a subset of the broader inflammatory landscape. Third, cognitive data were collected in a sample of individuals without dementia, which may limit detection of inflammation-cognition associations. Finally, although extreme observations were addressed during preprocessing and biomarkers were log-transformed prior to primary analyses, additional sensitivity analyses attenuated the three-way interaction findings, indicating that these results should be interpreted cautiously. 5. Conclusion Our findings suggest that inflammatory responses associated with APOE4 differ by race/ethnicity and sex, with Hispanic men demonstrating the greatest elevations in IL-10 and IL-5. These results highlight intersectional heterogeneity in immune profiles that may contribute to understanding biological pathways underlying Alzheimer's disease disparities. Future longitudinal studies are needed to determine whether elevated IL-10 and IL-5 levels among Hispanic male APOE4 carriers predict subsequent cognitive decline or accumulation of AD pathology. Expanding biomarker panels and integrating neuroimaging and genetic ancestry data will further clarify mechanisms linking systemic inflammation to neurodegenerative risk across diverse populations. CRediT authorship contribution statement J.A. Contreras: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. N.E. Ortega: Formal analysis, Investigation, Writing – review & editing. C.A. Hayes: Formal analysis, Methodology, Writing – review & editing. J. Pa: Funding acquisition, Project administration, Supervision, Writing – review & editing. Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Joey A Contreras reports financial support was provided by National Institutes of Health. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the Health Enhancement Scientific Program (HESP) U19AG078109 as well as by AD-RCMAR (P30AG059299). Additionally, we were supported by theNational Institute on Aging (NIA(RF1AG088811, PI: Pa)) , the Alzheimer's Association (SAGA23-1141238, PI: Pa) and Wellcome Leap CARE (PI:Pa). . The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We acknowledge the full Health and Aging Brain Study – Health Disparities (HABS-HD) Study Team U19AG078109 and thank all study participants, their families, and the Fort Worth community for their invaluable contributions. Individuals who provided significant non-authorship contributions are recognized for their specific roles. Footnotes Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbih.2026.101256 . Contributor Information J. Pa, Email: jpa@ucsd.edu. HABS-HD MPIs: Sid E O'Bryant: Kristine Yaffe , Arthur Toga , Robert Rissman , and Leigh Johnson the HABS-HD Investigators: Meredith Braskie: Kevin King , James R. Hall , Melissa Petersen , Raymond Palmer , Robert Barber , Yonggang Shi , Fan Zhang , Rajesh Nandy , Roderick McColl , David Mason , Bradley Christian , Nicole Phillips , Stephanie Large , Joe Lee , Badri Vardarajan , Mónica Rivera Mindt , Amrita Cheema , Lisa Barnes , Mark Mapstone , Annie Cohen , Amy Kind , Ozioma Okonkwo , Raul Vintimilla , Zhengyang Zhou , Michael Donohue , Matthew Borzage , Michelle Mielke , Beau Ances , Ganesh Babulal , Jorge Llibre-Guerra , Carl Hill , and Rocky Vig Appendix A. Supplementary data The following are the Supplementary data to this article. Multimedia component 1 mmc1.docx (24.8KB, docx) Multimedia component 2 mmc2.docx (235.2KB, docx) Multimedia component 3 mmc3.docx (468.1KB, docx) figs1. Open in a new tab Data availability The data used in this study are publicly available through the Health & Aging Brain Study – Health Disparities (HABS-HD) and can be accessed by submitting a request online. References Aggarwal N.T., Mielke M.M. 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Supplementary Materials Multimedia component 1 mmc1.docx (24.8KB, docx) Multimedia component 2 mmc2.docx (235.2KB, docx) Multimedia component 3 mmc3.docx (468.1KB, docx) Data Availability Statement The data used in this study are publicly available through the Health & Aging Brain Study – Health Disparities (HABS-HD) and can be accessed by submitting a request online. Articles from Brain, Behavior, & Immunity - Health are provided here courtesy of Elsevier ACTIONS View on publisher site PDF (1.4 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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