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. Author manuscript; available in PMC: 2026 Aug 22.
Published in final edited form as: Brain Behav Immun. 2025 Aug 22;130:106087. doi: 10.1016/j.bbi.2025.106087

Discrimination and Dendritic Cell Abundance Among Older Adults in the Health and Retirement Study

Jemar R Bather a,b, Emiko O Kranz c, Mariana Rodrigues c, Steven W Cole d, Adolfo G Cuevas a,c
PMCID: PMC12413622  NIHMSID: NIHMS2107600  PMID: 40850376

Abstract

We investigated whether peripheral blood dendritic cell (DC) abundance varies as a function of discrimination exposure in a national sample of older US adults (aged 50+ years) from the 2016 Venous Blood Study, a US Health and Retirement Study biomarker project. Density of myeloid DCs (mDC) and plasmacytoid DCs (pDC) were measured using multiparameter flow cytometry. Discrimination was assessed using the validated Everyday Discrimination Scale. Weighted linear regression models quantified associations between discrimination and natural-log transformed DC abundance, controlling for sociodemographic factors, chronic health conditions, and health behaviors. We tested whether these associations varied by race/ethnicity. For mDC, we found no significant overall association with discrimination. However, race/ethnicity significantly modified this relationship: among non-Hispanic White participants, a 1-SD increase in discrimination was associated with a non-significant 1.4% increase in mDC count (p=0.20), while non-Hispanic Black participants showed a significant 4.6% decrease (interaction p=0.021). For pDC, a 1-SD change in discrimination was significantly associated with a 2.4% increase in abundance across all participants (95% CI: 0.6%, 4.3%, p=0.010), with no significant effect modification by race/ethnicity. In this nationally representative study of older Americans, discrimination exposure was associated with altered dendritic cell abundance, with distinct patterns by cell type and race/ethnicity. Increased pDC counts across all racial/ethnic groups suggest a common immunological response to discrimination, while divergent mDC responses between non-Hispanic Black and White participants indicate race-specific immune modulation. These findings reveal complex cellular pathways through which discrimination may differentially influence immune function and contribute to health inequities.

Keywords: Psychosocial stressors, biological dysregulation, biological stress, psychoneuroimmunology, social genomics, internalizing, CTRA, epigenetics, lifestyle genomics, middle-aged

1. Introduction

Discrimination is a social determinant of health that has been linked to social inequities in mortality, morbidity, and chronic disease progression (Bailey et al., 2017; Williams et al., 2019). Research has begun to examine the biological pathways through which discrimination “gets under the skin” to affect physiological function and health, and has identified alterations in immune function as one potential mechanism of such effects (Cuevas et al., 2020; Dong et al., 2023; Lawrence et al., 2022). Recent analyses have implicated alterations in immune cell abundance and altered balance of pro- and anti-inflammatory cytokines and signaling pathways in the health effects of discrimination (Goosby & Cheadle, 2024). Such alterations may undermine the body’s capacity to respond to microbial threats and maintain homeostasis while avoiding hyper-inflammatory reactions and autoimmunity (Goosby & Cheadle, 2024). Several studies have also linked experienced discrimination to differences in leukocyte gene expression profiles involving elevated inflammatory signaling and/or altered antiviral biology in myeloid lineage innate immune cells (e.g., monocytes) (Brown et al., 2019, 2020; M. J. Li et al., 2020; Thames et al., 2019) and to altered homeostasis of lymphocyte subsets within the circulating leukocyte pool (Aronoff et al., 2022; Klopack et al., 2022). However, no research has examined the discrimination’s relationship with one key immune cell that bridges innate and adaptive immunity – the rare but crucial subpopulation of dendritic cells (DCs).

Discrimination, as a chronic psychosocial stressor, has been consistently linked to immune dysregulation (Cuevas et al., 2020; Lawrence et al., 2022). Systematic reviews have reported associations between discrimination and elevated inflammatory markers such as C-reactive protein, with emerging evidence also linking discrimination to alterations in cytokine profiles (Cuevas et al., 2020; Lawrence et al., 2022). For example, Cuevas and colleagues found that higher discrimination levels were associated with increased levels of interleukin-4 and interleukin-6 (Cuevas et al., 2020). While this literature establishes links between discrimination and immune dysfunction, it has focused on measured peripheral blood markers rather than examining specific immune cell populations that serve as the upstream regulators of these inflammatory processes (Liu et al., 2021; Marongiu et al., 2021). Understanding both the cellular mediators and molecular products of immune activation is crucial, as cellular changes may provide more proximal and sensitive indicators of stress-induced immune alterations. To our knowledge, no studies have examined whether exposure to discrimination relates to DC populations specifically, despite these cells’ central role in orchestrating immune responses (Liu et al., 2021).

As sentinel cells, DCs link innate and adaptive immune systems by detecting microbial threats and processing their antigens for presentation to T lymphocytes. Two broad classes of DCs perform distinct but complementary functions. Plasmacytoid DCs (pDCs) traffic through blood and lymphatic vessels and activate in response to viral stimuli, after which they produce large quantities of cytokines (particularly Type I interferons) and can transit to lymph nodes or sites of infection to present antigens and co-stimulatory signals to CD4+ and CD8+ T lymphocytes (Reizis, 2019). “Classical” or myeloid DCs (mDCs) transit through blood into tissues, where they can become activated by tissue injury signals, whereupon they too transit to lymph nodes to present antigens and co-stimulatory signals to CD4+ and CD8+ T cells (Chistiakov et al., 2015). DCs are distinct from all other antigen-presenting cells in the potency of their T cell stimulatory activity and their ability to elicit primary immune responses by activating naïve T lymphocytes. pDCs and mDCs both develop from myeloid lineage precursors in the bone marrow and subsequently enter the circulating leukocyte pool (either constitutively in the case of pDCs or in transit to tissues in the case of mDCs).

Previous research in animal models has shown that chronic stress and neuro-immune interactions can alter DC functionality, including cytokine production, antigen presentation, and T cell costimulatory activity, leading to dysregulation of downstream adaptive immune responses (Collado-Hidalgo et al., 2006; Powell et al., 2009, 2013). Chronic stress and sympathetic neural regulation of the bone marrow hematopoietic environment can also stimulate the production of myeloid lineage cells in general (Hanoun et al., 2015; McKim et al., 2018) and thus potentially up-regulate the production and circulating prevalence of DCs. Consistent with this general hypothesis, DC alterations have been linked to other forms of chronic social adversity in humans (e.g., loneliness and social isolation) (Cole et al., 2011, 2015), but little is known about whether DCs might contribute to the health effects of discrimination.

If DC function is affected by discrimination, its impacts may be particularly significant for older adults in the context of immune system aging and immunosenescence (where maintenance of efficient T cell responses and primary immune response capacity is of particular importance, and where DC function may be particularly affected by age-related alterations in hematopoiesis) (X. Li et al., 2023; Saavedra et al., 2023). Discrimination exposure remains a significant issue for older adults (Lu et al., 2022; Mouzon et al., 2020; Shankar & Hinds, 2017) and any consequent alterations in DC function may further exacerbate age-related declines and contribute to late life health inequities. To assess this possibility, we analyzed the relationship between experienced discrimination and circulating prevalence of pDCs and mDCs in a nationally representative sample of US older adults.

2. Materials and Methods

2.1. Study Design and Setting

We analyzed data from the Health and Retirement Study (HRS), which biennially collects health information from a nationally representative pool of adults aged 50 years or older in the United States (Sonnega et al., 2014). Sponsored by the National Institute on Aging (NIA U01AG009740), the University of Michigan conducts the HRS with cohort replenishments every six years. The HRS is based on a multistage probability research design that oversampled understudied populations (e.g., racial/ethnic minorities). A notable feature of the HRS is its mixed-mode interview approach, where study participants switch between telephone and face-to-face interviews every two years. As a result, every HRS participant engages in an in-person interview every four years. Details regarding the larger context of the HRS have been previously described (Sonnega et al., 2014).

The HRS Venous Blood Study (VBS) was initiated in 2016 to analyze the relationships between social conditions, environmental factors, and biomarkers (Crimmins et al., 2017). The VBS was constructed as a subsample of HRS participants who completed an HRS interview, did not require a proxy for their HRS interview, were not living in a nursing home, and consented to a blood draw. Nearly 80% of eligible VBS participants agreed to a blood draw, with approximately 83% of these participants completing the blood draw, receiving $50 as compensation. The New York University Institutional Review Board exempted this study from review because it used de-identified publicly available data. We report findings in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines (von Elm et al., 2007).

2.2. Analytic Sample

Of the 9,934 VBS participants, 2,334 (23.5%) were excluded because of an invalid VBS result, a zero-sampling weight, or missing discrimination data. We removed 653 (6.6%) individuals with unavailable outcome measures, resulting in a final unweighted sample of 6,947 VBS participants.

2.3. Assessment of Dendritic Cell Counts

Two months after their 2016 HRS interview, VBS participants were asked to contribute blood samples (Thyagarajan et al., 2018). These samples were drawn by a trained phlebotomist, centrifuged, and sent cold to the University of Minnesota Advanced Research and Diagnostic Laboratory. Twenty-four immune cells were assessed by a multiparameter flow cytometry assay using an LSRII or a Fortessa X20 flow cytometer (BD Biosciences, San Diego, CA). This procedure followed an adapted criterion of the Human Immunology Project (Maecker et al., 2012). Immunophenotyping employed a validated hybrid approach combining automated and manual gating strategies (Hunter-Schlichting et al., 2020). The OpenCyto R package performed automated hierarchical gating on all samples, with results reviewed using FlowAnnotator. Manual gating in FlowJo (Tree Star Inc version 10) was conducted on a subset of 931 participants to validate the automated approach, with refinements applied to the automated strategy as needed.

Dendritic cells were identified using Panel 2 of the multiparameter flow cytometry protocol (Thyagarajan et al., 2018). The antibody panel included the following markers: CD3 (Clone: UCHT1, Fluorochrome: APC), HLA-DR (G46–6, PE-CF594), CD19 (SJ25C1, PE-Cy7), CD11c (BB515), CD20 (2H7, BUV395), CD16 (3G8, BUV737), CD56 (NCAM16.2, BV421), CD14 (MOP9, BV510), CD123 (9F5, BV711), and CD45 (2D1, APC-Cy7), along with viability dye (FVS 570 [PE]). DCs were defined as CD3− CD19− CD20− CD14− HLA-DR+ cells within the peripheral blood mononuclear cell population. Within this lineage-negative, HLA-DR− positive population, mDCs were identified as CD11c+ CD123− cells, and pDCs were identified as CD11c− CD123+ cells. DC subset proportions were calculated as the proportion of total DCs (parent population), and absolute counts were derived by multiplying the DC subset proportions by the total DC count, with results expressed as 109 cells/L. Further details on the flow cytometry protocol are described elsewhere (Thyagarajan et al., 2018). The primary outcomes analyzed were natural log (ln)-transformed frequency of mDCs and pDCs (109 cells/L of circulating blood).

2.4. Daily Discrimination

The Everyday Discrimination Scale gauged how frequently participants experienced discrimination in their day-to-day life (no particular time period specified) (Williams et al., 1997). Discrimination data were collected as part of the HRS Psychosocial and Lifestyle Questionnaire, administered in either 2014 or 2016 depending on the participant’s interview schedule. As part of this questionnaire, participants were asked, “In your day-to-day life, how often have any of the following things happened to you?” The six statements were as follows: a) “You are treated with less courtesy or respect than other people,” b) “You receive poorer service than other people at restaurants or stores,” c) “People act as if they think you are not smart,” d) “People act as if they are afraid of you,” e) “You are threatened or harassed,” and f) “You receive poorer service or treatment than other people from doctors or hospitals.” Response options were scored on a 6-point Likert Scale ranging from 1 (almost every day) to 6 (never). These scores were reverse-coded (0 = never to 5 = almost every day) and then averaged. Higher mean scores indicated a higher frequency of discrimination exposure (Cronbach’s α = 0.83). For the analyses, we standardized these scores such that mean = 0 and SD = 1.

2.5. Race/Ethnicity

Race/ethnicity was operationalized as non-Hispanic White (White), non-Hispanic Black (Black), Hispanic, and non-Hispanic Other (Other). The Other category included American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander participants.

2.6. Covariates

We controlled for several key covariates that could confound the relationship between daily discrimination and DC subset abundance. Sociodemographic characteristics included age (measured continuously), sex (male versus female), educational attainment (<high school, high school diploma/GED, some college, college and above), and annual household income in USD (<$25,000; $25,000-$49,999; $50,000-$99,999; $100,000+), which are known predictors of inflammatory markers, particularly DC subset distribution (Agrawal & Gupta, 2011; Klein & Flanagan, 2016; Muscatell et al., 2020). We included educational attainment and annual household income as covariates while recognizing that socioeconomic indicators may themselves be influenced by structural racism and discrimination (Bailey et al., 2017; Williams et al., 2019). These variables help distinguish the association of interpersonal discrimination experiences from broader socioeconomic disadvantage, though we acknowledge the interconnected nature of these social determinants of health (Bailey et al., 2017; Williams et al., 2019). Chronic health conditions (hypertension, diabetes, depression) were included as covariates that independently alter dendritic cell populations (Hevia et al., 2018; Nobs et al., 2023; Powell et al., 2009). Health behaviors (smoking, alcohol use, and Body Mass Index [BMI; measured continuously]) were included given their documented effects on immune cell populations and potential to mediate discrimination-immune relationships (Molina et al., 2010; Nouri-Shirazi & Guinet, 2003; O’Shea et al., 2013).

2.7. Statistical Analysis

Descriptive statistics were generated for all variables overall and stratified by race/ethnicity, using means and standard deviations for continuous measures and counts and percentages for categorical variables. Zero-order correlation coefficients were computed to assess interrelationships among key variables and covariates.

For the primary analysis, weighted multivariable linear regression models quantified associations of daily discrimination exposure with ln(mDC count) and ln(pDC count) (Hidalgo & Goodman, 2013). We employed a sequential modeling approach: Model 1 included daily discrimination, age, sex, race/ethnicity, educational attainment, and annual household income. Model 2 additionally controlled for chronic health conditions that may influence cell abundance (hypertension, diabetes, and depression). Model 3 further controlled for health behaviors (smoking, alcohol consumption, and BMI). Finally, Model 4 tested the interaction between daily discrimination and race/ethnicity, given that discrimination may operate differently across racial/ethnic groups (Dong et al., 2023; Lehrer et al., 2020; Van Dyke et al., 2017). Missing covariate data were addressed using multivariate imputation by chained equations: multinomial logistic regression (race/ethnicity), ordinal logistic regression (educational attainment), logistic regression (depression, smoking, alcohol consumption), and predictive mean matching (BMI) (Buuren & Groothuis-Oudshoorn, 2011). Regression results were pooled across ten multiple imputed datasets using Rubin’s rules (Rubin, 1987). All multivariable models were weighted using VBS weights for generalizability.

For interpretability, we plotted predicted natural log-transformed values by discrimination level and race/ethnicity from the final models. We also tabulated attributed sources of discrimination to clarify whether observed associations were driven by specific types of discrimination experiences. Statistical significance was assessed using two-sided tests with α = 0.05. Analyses were conducted using R version 4.5.1 (R Core Team, R Foundation for Statistical Computing).

3. Results

3.1. Descriptive Statistics

The sample included 6,947 participants with a mean age of 70 years (SD = 9, Table 1). Overall, 58% were female, 25% had a college degree or higher, and 20% had an annual household income of $100,000 or more. The racial/ethnic distribution was 70% White, 15% Black, 12% Hispanic, and 3% Other. Descriptive characteristics varied by race/ethnicity. White participants were oldest (mean age 72 years) and had the highest proportions of females (57%), those with at least some college education (57%), and an annual household income of at least $100,000 (24%) compared to non-White participants. Regarding discrimination experiences, the overall sample had a mean daily discrimination score of 0.53 (SD = 0.69), indicating low discrimination levels. However, scores varied by race/ethnicity, with Black (mean = 0.71) and Other (mean = 0.72) participants reporting higher discrimination than White (mean = 0.50) and Hispanic (mean = 0.46) participants. Table 2 presents the zero-order correlations among key study variables and covariates. Examples of moderately correlated measures included: mDC and pDC (r = 0.23), BMI and diabetes (r = 0.23), hypertension and diabetes (r = 0.24), and education and annual household income (r = 0.43).

Table 1.

Descriptive statistics of the study population overall and by race/ethnicity, 2016 Venous Blood Study, Health and Retirement Study.

Characteristic Overall White Black Hispanic Other Missing race/ethnicity
N = 6,947 N = 4,851 N = 1,049 N = 834 N = 200 N = 13
Age, Mean (SD) 70.39 (9.23) 71.73 (9.33) 67.42 (8.10) 67.39 (8.30) 66.59 (8.14) 60.85 (3.83)
Sex, No. (%)
 Male 2,882 (41.5) 2,073 (42.7) 377 (35.9) 341 (40.9) 83 (41.5) 8 (61.5)
 Female 4,065 (58.5) 2,778 (57.3) 672 (64.1) 493 (59.1) 117 (58.5) 5 (38.5)
Educational attainment, No. (%)
 <High school 1,037 (14.9) 388 (8.0) 238 (22.7) 381 (45.7) 25 (12.5) 5 (38.5)
 High school diploma/GED 2,355 (33.9) 1,712 (35.3) 358 (34.1) 215 (25.8) 66 (33.0) 4 (30.8)
 Some college 1,801 (25.9) 1,305 (26.9) 282 (26.9) 167 (20.0) 45 (22.5) 2 (15.4)
 College and above 1,753 (25.2) 1,446 (29.8) 170 (16.2) 71 (8.5) 64 (32.0) 2 (15.4)
 Missing 1 (0.0) 0 (0.0) 1 (0.1) 0 (0.0) 0 (0.0) 0 (0.0)
Annual household income (USD), No. (%)
 <$25,000 2,044 (29.4) 1,028 (21.2) 509 (48.5) 436 (52.3) 65 (32.5) 6 (46.2)
 $25,000–$49,999 1,827 (26.3) 1,318 (27.2) 247 (23.5) 213 (25.5) 45 (22.5) 4 (30.8)
 $50,000–$99,999 1,724 (24.8) 1,351 (27.8) 199 (19.0) 125 (15.0) 48 (24.0) 1 (7.7)
 $100,000+ 1,352 (19.5) 1,154 (23.8) 94 (9.0) 60 (7.2) 42 (21.0) 2 (15.4)
Hypertension, No. (%)
 No 2,509 (36.1) 1,958 (40.4) 201 (19.2) 273 (32.7) 70 (35.0) 7 (53.8)
 Yes 4,438 (63.9) 2,893 (59.6) 848 (80.8) 561 (67.3) 130 (65.0) 6 (46.2)
Diabetes, No. (%)
 No 5,040 (72.5) 3,742 (77.1) 664 (63.3) 501 (60.1) 127 (63.5) 6 (46.2)
 Yes 1,907 (27.5) 1,109 (22.9) 385 (36.7) 333 (39.9) 73 (36.5) 7 (53.8)
Depression, No. (%)
 No 5,916 (85.2) 4,244 (87.5) 867 (82.7) 628 (75.3) 168 (84.0) 9 (69.2)
 Yes 1,021 (14.7) 603 (12.4) 182 (17.3) 201 (24.1) 32 (16.0) 3 (23.1)
 Missing 10 (0.1) 4 (0.1) 0 (0.0) 5 (0.6) 0 (0.0) 1 (7.7)
Ever smoker, No. (%)
 No 3,090 (44.5) 2,145 (44.2) 430 (41.0) 417 (50.0) 95 (47.5) 3 (23.1)
 Yes 3,818 (55.0) 2,679 (55.2) 613 (58.4) 411 (49.3) 105 (52.5) 10 (76.9)
 Missing 39 (0.6) 27 (0.6) 6 (0.6) 6 (0.7) 0 (0.0) 0 (0.0)
Ever drinker, No. (%)
 No 4,185 (60.2) 2,724 (56.2) 724 (69.0) 592 (71.0) 136 (68.0) 9 (69.2)
 Yes 2,747 (39.5) 2,118 (43.7) 322 (30.7) 239 (28.7) 64 (32.0) 4 (30.8)
 Missing 15 (0.2) 9 (0.2) 3 (0.3) 3 (0.4) 0 (0.0) 0 (0.0)
Body Mass Index (kg/m2), Mean (SD) 28.82 (6.06) 28.34 (5.82) 30.55 (6.90) 29.42 (5.75) 28.73 (6.50) 30.03 (6.23)
 Missing 64 31 9 23 0 1
Daily discrimination, Mean (SD) 0.53 (0.69) 0.50 (0.64) 0.71 (0.84) 0.46 (0.72) 0.72 (0.84) 0.40 (0.46)
Dendritic cells count (109 cells/L), Mean (SD) 0.05 (0.04) 0.05 (0.04) 0.06 (0.05) 0.05 (0.05) 0.05 (0.05) 0.05 (0.03)
Myeloid dendritic cells count (109 cells/L), Mean (SD) 0.04 (0.03) 0.04 (0.03) 0.04 (0.04) 0.04 (0.03) 0.04 (0.04) 0.04 (0.03)
Plasmacytoid dendritic cells count (109 cells/L), Mean (SD) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.00)
Dendritic cells (proportion of peripheral blood mononuclear cells), Mean (SD) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.00)
Myeloid dendritic cells (proportion of total dendritic cells), Mean (SD) 0.72 (0.12) 0.72 (0.12) 0.73 (0.13) 0.73 (0.14) 0.69 (0.14) 0.74 (0.11)
Plasmacytoid dendritic cells (proportion of total dendritic cells), Mean (SD) 0.16 (0.09) 0.16 (0.09) 0.14 (0.09) 0.13 (0.08) 0.15 (0.09) 0.13 (0.08)
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Other race/ethnicity includes American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander.

Table 2.

Zero-order correlations among key study variables and covariates, 2016 Venous Blood Study, Health and Retirement Study.

Term 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Age
2. Female sex −0.01
3. Black −0.14 0.05
4. Hispanic −0.12 0.00 −0.15
5. Other race/ethnicity −0.07 0.00 −0.07 −0.06
6. Education −0.10 −0.06 −0.11 −0.25 0.02
7. Household income −0.22 −0.16 −0.18 −0.19 0.00 0.43
8. Hypertension 0.17 −0.02 0.15 0.03 0.00 −0.15 −0.18
9. Diabetes 0.03 −0.04 0.09 0.10 0.04 −0.12 −0.13 0.24
10. Depression −0.08 0.11 0.03 0.10 0.01 −0.09 −0.15 0.04 0.06
11. Ever smoker −0.02 −0.16 0.03 −0.04 −0.01 −0.09 −0.04 0.01 0.01 0.03
12. Ever drinker −0.11 −0.16 −0.08 −0.07 −0.03 0.23 0.26 −0.10 −0.14 −0.10 0.11
13. Body Mass Index −0.19 0.01 0.12 0.04 0.00 −0.08 −0.05 0.21 0.23 0.07 0.00 −0.12
14. Daily discrimination −0.19 −0.08 0.10 −0.04 0.05 −0.01 −0.02 0.03 0.05 0.14 0.06 −0.03 0.09
15. Myeloid dendritic cells count 0.04 −0.04 0.07 0.01 0.00 −0.03 −0.02 0.08 0.05 0.00 0.00 0.00 0.07 0.02
16. Plasmacytoid dendritic cells count −0.01 −0.01 0.01 −0.06 0.00 0.01 0.00 −0.01 −0.05 0.01 0.03 0.03 −0.05 0.02 0.23
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Bold font indicates p<0.05.

3.2. Perceived Sources of Discrimination by Race/ethnicity

Table 3 presents data on the perceived sources of discrimination among participants who reported experiencing discrimination, stratified by race/ethnicity. White participants most reported age-based discrimination (44%) and gender-based discrimination (20%). Black participants reported the highest rates of race-based discrimination (29%) and substantial rates of ancestry/national origin discrimination (27%). Hispanic participants had the highest rates of ancestry/national origin discrimination at 34%. Across all racial/ethnic groups, very few participants reported discrimination based on religion, weight, physical disability, physical appearance, sexual orientation, or financial status, with all categories falling below 7%.

Table 3.

Perceived sources of discrimination among those who reported discrimination exposure, overall and by race/ethnicity, 2016 Venous Blood Study, Health and Retirement Study.

Attribution Overall White Black Hispanic Other
N = 3,644 N = 2,447 N = 662 N = 407 N = 128
Ancestry or national origin, No. (%)
 No 3,180 (87.3) 2,338 (95.5) 486 (73.4) 270 (66.3) 86 (67.2)
 Yes 464 (12.7) 109 (4.5) 176 (26.6) 137 (33.7) 42 (32.8)
Gender, No. (%)
 No 3,039 (83.4) 1,966 (80.3) 567 (85.6) 386 (94.8) 120 (93.8)
 Yes 605 (16.6) 481 (19.7) 95 (14.4) 21 (5.2) 8 (6.3)
Race, No. (%)
 No 3,312 (90.9) 2,384 (97.4) 468 (70.7) 344 (84.5) 116 (90.6)
 Yes 332 (9.1) 63 (2.6) 194 (29.3) 63 (15.5) 12 (9.4)
Age, No. (%)
 No 2,348 (64.4) 1,363 (55.7) 580 (87.6) 312 (76.7) 93 (72.7)
 Yes 1,296 (35.6) 1,084 (44.3) 82 (12.4) 95 (23.3) 35 (27.3)
Religion, No. (%)
 No 3,585 (98.4) 2,413 (98.6) 646 (97.6) 400 (98.3) 126 (98.4)
 Yes 59 (1.6) 34 (1.4) 16 (2.4) 7 (1.7) 2 (1.6)
Weight, No. (%)
 No 3,472 (95.3) 2,321 (94.9) 642 (97.0) 385 (94.6) 124 (96.9)
 Yes 172 (4.7) 126 (5.1) 20 (3.0) 22 (5.4) 4 (3.1)
Physical disability, No. (%)
 No 3,557 (97.6) 2,391 (97.7) 645 (97.4) 401 (98.5) 120 (93.8)
 Yes 87 (2.4) 56 (2.3) 17 (2.6) 6 (1.5) 8 (6.3)
Aspect of physical appearance, No. (%)
 No 3,519 (96.6) 2,341 (95.7) 654 (98.8) 401 (98.5) 123 (96.1)
 Yes 125 (3.4) 106 (4.3) 8 (1.2) 6 (1.5) 5 (3.9)
Sexual orientation, No. (%)
 No 3,630 (99.6) 2,437 (99.6) 659 (99.5) 406 (99.8) 128 (100.0)
 Yes 14 (0.4) 10 (0.4) 3 (0.5) 1 (0.2) 0 (0.0)
Financial status, No. (%)
 No 3,525 (96.7) 2,371 (96.9) 639 (96.5) 392 (96.3) 123 (96.1)
 Yes 119 (3.3) 76 (3.1) 23 (3.5) 15 (3.7) 5 (3.9)
Other, No. (%)
 No 3,273 (89.8) 2,145 (87.7) 634 (95.8) 373 (91.6) 121 (94.5)
 Yes 371 (10.2) 302 (12.3) 28 (4.2) 34 (8.4) 7 (5.5)
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Other race/ethnicity includes American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander.

3.3. Discrimination and Myeloid Dendritic Cells Count

Table 4 presents the adjusted associations between daily discrimination and ln(mDC count). In Models 1–3, daily discrimination was not significantly associated with mDC count. Specifically, a 1-SD increase in discrimination was associated with a 1.0% increase in mDC count in Model 1 (95% CI: −0.8%, 2.9%, p=0.27), which remained unchanged after adjusting for chronic health conditions in Model 2 (1.0% increase, 95% CI: −0.9%, 2.8%, p=0.30) and slightly attenuated after adding health behaviors in Model 3 (0.8% increase, 95% CI: −1.0%, 2.6%, p=0.40). However, Model 4 revealed significant racial/ethnic differences in the discrimination-mDC association. Among White participants, a 1-SD increase in discrimination was associated with a non-significant 1.4% increase in mDC count (p=0.20). In contrast, Black participants showed a significant opposing pattern, with a 1-SD increase in discrimination associated with a 4.6% decrease in mDC count (interaction p=0.021). These contrasting relationships are illustrated in Panel A of Figure 1.

Table 4.

Associations between daily discrimination and natural log-transformed myeloid dendritic cells count in the 2016 Venous Blood Study.

Model 1 Model 2 Model 3 Model 4
Term b (95% CI) p value b (95% CI) p value b (95% CI) p value b (95% CI) p value
Daily discrimination 0.010 (−0.008, 0.029) 0.27 0.010 (−0.009, 0.028) 0.30 0.008 (−0.010, 0.026) 0.40 0.014 (−0.007, 0.036) 0.20
Age 0.005 (0.003, 0.007) <0.001 0.004 (0.002, 0.006) <0.001 0.005 (0.003, 0.008) <0.001 0.005 (0.003, 0.008) <0.001
Sex
 Male
 Female −0.099 (−0.134, −0.063) <0.001 −0.091 (−0.126, −0.055) <0.001 −0.089 (−0.126, −0.053) <0.001 −0.090 (−0.126, −0.054) <0.001
Race/ethnicity
 White
 Black 0.164 (0.098, 0.229) <0.001 0.143 (0.077, 0.209) <0.001 0.144 (0.078, 0.210) <0.001 0.160 (0.093, 0.228) <0.001
 Hispanic 0.066 (−0.006, 0.139) 0.072 0.060 (−0.013, 0.132) 0.11 0.059 (−0.013, 0.132) 0.11 0.062 (−0.011, 0.135) 0.095
 Other 0.066 (−0.040, 0.172) 0.22 0.055 (−0.051, 0.162) 0.31 0.059 (−0.047, 0.166) 0.27 0.058 (−0.050, 0.166) 0.29
Educational attainment
 <High school
 High school diploma/GED −0.017 (−0.083, 0.048) 0.60 −0.019 (−0.085, 0.047) 0.57 −0.024 (−0.089, 0.042) 0.48 −0.025 (−0.091, 0.040) 0.45
 Some college −0.007 (−0.076, 0.061) 0.84 −0.006 (−0.075, 0.063) 0.87 −0.012 (−0.080, 0.057) 0.74 −0.014 (−0.083, 0.055) 0.69
 College and above −0.015 (−0.085, 0.056) 0.68 −0.008 (−0.078, 0.063) 0.84 −0.013 (−0.084, 0.058) 0.73 −0.015 (−0.086, 0.056) 0.68
Annual household income
 <$25,000
 $25,000–$49,999 0.070 (0.018, 0.122) 0.008 0.069 (0.017, 0.121) 0.009 0.069 (0.017, 0.121) 0.009 0.069 (0.017, 0.121) 0.009
 $50,000–$99,999 0.076 (0.023, 0.129) 0.005 0.075 (0.022, 0.128) 0.006 0.073 (0.020, 0.126) 0.007 0.072 (0.019, 0.125) 0.008
 $100,000+ 0.086 (0.030, 0.143) 0.003 0.091 (0.034, 0.148) 0.002 0.089 (0.031, 0.147) 0.002 0.089 (0.031, 0.146) 0.003
Ever had hypertension 0.074 (0.036, 0.112) <0.001 0.054 (0.016, 0.093) 0.006 0.053 (0.015, 0.092) 0.007
Ever had diabetes 0.038 (−0.005, 0.080) 0.086 0.023 (−0.020, 0.067) 0.29 0.023 (−0.021, 0.066) 0.31
Depressed in past year −0.051 (−0.103, 0.001) 0.054 −0.051 (−0.103, 0.001) 0.053 −0.051 (−0.103, 0.001) 0.054
Ever smoker −0.028 (−0.064, 0.008) 0.13 −0.029 (−0.065, 0.007) 0.11
Ever drinker 0.029 (−0.008, 0.067) 0.13 0.030 (−0.007, 0.068) 0.12
Body Mass Index 0.008 (0.004, 0.011) <0.001 0.008 (0.004, 0.011) <0.001
Daily discrimination*Black −0.061 (−0.113, −0.009) 0.021
Daily discrimination*Hispanic 0.029 (−0.035, 0.093) 0.37
Daily discrimination*Other −0.001 (−0.088, 0.087) 0.99
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Results were pooled across ten multiple imputed datasets.

Figure 1.

Figure 1.

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Predicted natural log-transformed values from pooled multiple imputation models showing the association of standardized daily discrimination scores with (A) myeloid dendritic cell counts and (B) plasmacytoid dendritic cell counts (109 cells/L) across racial/ethnic groups. Predictions were covariate-adjusted with categorical variables held at reference values and continuous variables held at sample means.

3.4. Discrimination and Plasmacytoid Dendritic Cells Count

Table 5 displays the adjusted relationships between daily discrimination and ln(pDC count). Unlike the mDC findings, discrimination was consistently associated with higher pDC counts. After accounting for sociodemographic characteristics (Model 1), each 1-SD increment in discrimination was associated with a 1.8% increase in pDC count (95% CI: 0.1%, 3.7%, p=0.044). This magnitude strengthened when chronic health conditions were included in Model 2 (2.1% increase, 95% CI: 0.2%, 4.0%, p=0.026) and after incorporating health behaviors in Model 3 (2.4% increase, 95% CI: 0.6%, 4.3%, p=0.010). Model 4 examined potential racial/ethnic moderation of the discrimination-pDC relationship, yielding non-significant interaction terms (all p≥0.14). White participants demonstrated a 2.6% elevation in pDC count per 1-SD discrimination increase, whereas Black participants exhibited a 1.3% reduction, though this racial/ethnic difference was not statistically significant (interaction p=0.14).

Table 5.

Associations between daily discrimination and natural log-transformed plasmacytoid dendritic cells count in the 2016 Venous Blood Study.

Model 1 Model 2 Model 3 Model 4
Term b (95% CI) p value b (95% CI) p value b (95% CI) p value b (95% CI) p value
Daily discrimination 0.018 (0.001, 0.036) 0.044 0.021 (0.002, 0.039) 0.026 0.024 (0.006, 0.042) 0.010 0.026 (0.005, 0.047) 0.016
Age −0.004 (−0.006, −0.001) 0.001 −0.003 (−0.005, −0.001) 0.003 −0.004 (−0.006, −0.002) <0.001 −0.004 (−0.006, −0.002) <0.001
Sex
 Male
 Female −0.077 (−0.112, −0.043) <0.001 −0.082 (−0.117, −0.047) <0.001 −0.066 (−0.102, −0.031) <0.001 −0.067 (−0.103, −0.032) <0.001
Race/ethnicity
 White
 Black −0.095 (−0.159, −0.031) 0.004 −0.081 (−0.146, −0.017) 0.013 −0.078 (−0.142, −0.013) 0.018 −0.067 (−0.133, −0.001) 0.045
 Hispanic −0.239 (−0.310, −0.169) <0.001 −0.226 (−0.297, −0.154) <0.001 −0.219 (−0.290, −0.148) <0.001 −0.217 (−0.288, −0.146) <0.001
 Other −0.084 (−0.188, 0.020) 0.12 −0.071 (−0.175, 0.033) 0.18 −0.063 (−0.167, 0.041) 0.23 −0.074 (−0.180, 0.031) 0.17
Educational attainment
 <High school
 High school diploma/GED 0.009 (−0.055, 0.074) 0.78 0.008 (−0.056, 0.073) 0.80 0.011 (−0.053, 0.075) 0.74 0.011 (−0.053, 0.075) 0.74
 Some college 0.054 (−0.013, 0.121) 0.12 0.051 (−0.016, 0.118) 0.14 0.051 (−0.016, 0.118) 0.13 0.051 (−0.016, 0.118) 0.14
 College and above 0.068 (−0.001, 0.137) 0.053 0.061 (−0.008, 0.130) 0.081 0.055 (−0.015, 0.124) 0.12 0.054 (−0.015, 0.124) 0.13
Annual household income
 <$25,000
 $25,000–$49,999 0.055 (0.004, 0.105) 0.035 0.054 (0.003, 0.105) 0.038 0.046 (−0.004, 0.097) 0.073 0.046 (−0.005, 0.097) 0.075
 $50,000–$99,999 −0.002 (−0.053, 0.050) 0.95 −0.003 (−0.055, 0.049) 0.92 −0.009 (−0.061, 0.043) 0.74 −0.010 (−0.062, 0.042) 0.71
 $100,000+ −0.030 (−0.085, 0.026) 0.30 −0.037 (−0.093, 0.019) 0.19 −0.053 (−0.109, 0.004) 0.067 −0.054 (−0.110, 0.003) 0.061
Ever had hypertension −0.019 (−0.056, 0.018) 0.32 0.005 (−0.033, 0.042) 0.80 0.004 (−0.034, 0.041) 0.85
Ever had diabetes −0.085 (−0.127, −0.043) <0.001 −0.057 (−0.099, −0.014) 0.009 −0.058 (−0.100, −0.015) 0.008
Depressed in past year 0.012 (−0.039, 0.063) 0.65 0.017 (−0.033, 0.068) 0.50 0.017 (−0.033, 0.068) 0.50
Ever smoker 0.061 (0.026, 0.096) 0.001 0.061 (0.026, 0.096) 0.001
Ever drinker 0.063 (0.026, 0.100) 0.001 0.063 (0.026, 0.100) 0.001
Body Mass Index −0.009 (−0.012, −0.005) <0.001 −0.009 (−0.012, −0.006) <0.001
Daily discrimination*Black −0.039 (−0.090, 0.012) 0.14
Daily discrimination*Hispanic 0.014 (−0.049, 0.077) 0.66
Daily discrimination*Other 0.044 (−0.041, 0.130) 0.31
Open in a new tab

Results were pooled across ten multiple imputed datasets.

4. Discussion

In this study of a nationwide sample of older US adults, those who experienced higher levels of discrimination showed a selective upregulation of pDCs in circulating blood. This association emerged above and beyond the effects of sociodemographic factors, chronic health conditions, and health behaviors. Given the key role of pDCs in mediating both innate antiviral responses (via Type I interferon production) and adaptive T cell responses to viral infections (via antigen presentation and co-stimulatory signaling), these results could potentially reflect an innate immune response to discrimination-related increases in viral activity (e.g., reactivation of chronic viral infection such as EBV, which have previously been linked to experiences of racial discrimination (Christian et al., 2012). Alternatively, these results may stem from discrimination-related stress and its effects in altering hematopoietic output of pDCs (e.g., via sympathetic nervous system enhancement of myelopoiesis (Gilliet et al., 2008; Santana-de Anda et al., 2013)). Moreover, the health significance of these results may not be restricted to viral pathogens, as pDCs also play a key role in the development of autoimmune diseases (Gilliet et al., 2008; Santana-de Anda et al., 2013). The health significance of the present observations also remains to be determined in future research (e.g., examining Type I interferon activity and T cell activation), but the present findings suggest a new dimension to the immunologic correlates of discrimination, bridging previous theories emphasizing a central role of inflammation (Cuevas et al., 2020; Goosby & Cheadle, 2024) with more recent observations documenting alterations in the lymphoid cell population dynamics within the adaptive immune system (Aronoff et al., 2022; Klopack et al., 2022).

Our findings extend the growing body of literature documenting associations between discrimination and inflammatory markers (Cuevas et al., 2020; Lawrence et al., 2022). Cuevas et al.’s (2020) systematic review identified discrimination as activating physiological stress responses that influence inflammatory pathways, including interleukin-4, interleukin-6, e-selectin, and C-reactive protein. Building on this work, Lawrence et al.’s (2022) comprehensive meta-analysis of studies using the Everyday Discrimination Scale found significant associations specifically with C-reactive protein, though not with interleukin-6.

The present study represents a novel contribution by examining DC populations specifically rather than soluble inflammatory markers. While traditional inflammatory biomarkers, such as C-reactive protein and interleukin-6, reflect the end results of immune activation (Ridker, 2016), DCs represent the cellular mediators that initiate and control these immune responses (Marongiu et al., 2021). As the primary antigen-presenting cells bridging innate and adaptive immunity, DCs play crucial roles in determining whether immune responses become inflammatory or remain regulated (Liu et al., 2021). Our finding of selective pDC upregulation suggests that discrimination may influence these fundamental immune processes at the cellular level, which, in turn, could precede and contribute to the downstream elevations of inflammatory markers documented in previous research (Cuevas et al., 2020; Lawrence et al., 2022).

The specificity of discrimination’s association with pDC prevalence but not mDC prevalence is remarkable and suggests potential directions for future research on the biological mechanisms involved. As noted above, the distinctive role of pDCs in antiviral immunity may contribute to these distinct associations. By contrast, myelopoietic up-regulation might be expected to affect both mDCs and pDCs in parallel. However, it remains possible that discrimination-related stress biology may potentially affect DC differentiation pathways downstream of DC precursor development and thereby selectively favor the production and circulation of pDCs. For example, pDC development is shaped by the IRF7 signaling pathway (Musumeci et al., 2019; Reizis, 2019), and it is possible that discrimination-related differences in stress biology might selectively enhance IRF7 activity relative to the other signaling pathways involved in mDC development (Coveney et al., 2015; Peyssonnaux et al., 2006). Mapping the specific biological pathways linking discrimination to pDC development could provide critical insights into how discrimination become biologically embedded at the level of immune regulation.

Several limitations warrant consideration. First, the cross-sectional design of this study precludes any causal inferences. Longitudinal research is needed to determine whether discrimination precedes and directly contributes to increases in pDC counts. Second, this study assessed only one dimension of discrimination using the Everyday Discrimination Scale. While this is a widely used measure, other forms of discrimination that have profound health effects, such as major discrimination and discrimination vigilance, may also play a role. Future research should incorporate multiple dimensions of discrimination to better capture their effects on DCs and DC subsets. Another limitation was the inconsistent timing of discrimination assessment relative to blood sampling in 2016. While some participants reported discrimination in the same year as blood collection, others provided discrimination data in 2014, creating a two-year gap during which exposure levels may have changed. Third, the cell types analyzed are extremely rare, resulting in small sample sizes when stratified by racial/ethnic subgroup and discrimination scores. Fourth, while this study focused on dendritic cell subsets, discrimination may influence a broader spectrum of immune cell populations. Future studies should examine the interplay between pDCs, T cells, and other immune protein or transcriptional markers to provide a more comprehensive understanding of how social stressors shape immune function.

5. Conclusion

Our findings potentially link discrimination to increased pDC abundance in circulating blood, suggesting a new cellular pathway through which this social stressor may influence immune regulation. The lack of an independent association with mDCs underscores the distinctiveness of this relationship and highlights key directions for future research on the implications of discrimination for pDC-mediated aspects of immune function (Type I interferon production, innate antiviral response, antigen presentation and the control of T cell activation and population dynamics, and autoimmunity).

Highlights.

  • Discrimination associated with elevated plasmacytoid dendritic cells in older adults

  • Novel cellular pathway connecting discrimination to immune regulation

  • Results robust after adjusting for demographics, chronic health conditions, and health behaviors

Acknowledgments

We greatly appreciate the reviewers and the editorial team for taking the time to review our manuscript and providing constructive comments.

Funding

This work was supported by the National Institute of Health (National Institute of Diabetes and Digestive and Kidney Diseases): R01DK137246; R01DK137805.

Abbreviations

DC

dendritic cell

pDC

plasmacytoid dendritic cell

mDC

myeloid dendritic cell

HRS

Health and Retirement Study

VBS

Venous Blood Study

BMI

body mass index

ln

natural log

Footnotes

Conflicts of Interest

The authors have no conflict of interest to declare.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Data availability statement

The data that support this study’s findings are publicly available on the Health and Retirement Study website (https://hrs.isr.umich.edu/about).

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Associated Data

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

Data Availability Statement

The data that support this study’s findings are publicly available on the Health and Retirement Study website (https://hrs.isr.umich.edu/about).

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