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. Author manuscript; available in PMC: 2023 Jul 1.
Published in final edited form as: Brain Behav Immun. 2022 Apr 2;103:28–36. doi: 10.1016/j.bbi.2022.03.017

Associations between perceived discrimination and immune cell composition in the Jackson Heart Study

Jacob E Aronoff a, Edward B Quinn b, Allana T Forde c, Láshauntá M Glover d, Alexander Reiner e, Thomas W McDade a,f, Mario Sims g
PMCID: PMC9149129  NIHMSID: NIHMS1797322  PMID: 35381348

Abstract

African American adults suffer disproportionately from several non-communicable and infectious diseases. Among numerous contributing factors, perceived discrimination is considered a stressor for members of historically marginalized groups that contributes to health risk, although biological pathways are incompletely understood. Previous studies have reported associations between stress and both an up-regulation of non-specific (innate) inflammation and down-regulation of specific (adaptive) immunity. While associations between perceived discrimination and markers of inflammation have been explored, it is unclear if this is part of an overall shift that also includes down-regulated adaptive immunity. Relying on a large cross-section of African American adults (n = 3,319) from the Jackson Heart Study (JHS) in Jackson, Mississippi, we tested whether perceived everyday and lifetime discrimination as well as perceived burden from lifetime discrimination were associated with counts of neutrophils (innate), monocytes (innate), lymphocytes (adaptive), and the neutrophil-to-lymphocyte ratio (NLR), derived from complete white blood cell counts with differential. In addition, DNA methylation (DNAm) was measured on the EPIC array in a sub-sample (n = 1,023) of participants, allowing estimation of CD4T, CD8T and B lymphocyte proportions. Unexpectedly, high lifetime discrimination compared to low was significantly associated with lower neutrophils (b : −0.14, [95% CI: −0.24, −0.04]) and a lower NLR (b : −0.15, [95% CI: −0.25, −0.05]) after controlling for confounders. However, high perceived burden from lifetime discrimination was significantly associated with higher neutrophils (b : 0.17, [95% CI: 0.05, 0.30]) and a higher NLR (b : 0.16, [95% CI: 0.03, 0.29]). High perceived burden was also associated with lower lymphocytes among older men, which our analysis suggested might have been attributable to differences in CD4T cells. These findings highlight immune function as a potentially important pathway linking perceived discrimination to health outcomes.

1. Introduction

Black-White health disparities in the United States are well-documented across numerous health outcomes, particularly chronic non-communicable diseases such as cardiovascular disease (CVD) (Gravlee, 2009; Hunt & Whitman, 2015; Orsi, Margellos-Anast, & Whitman, 2010). However, COVID-19 has also heightened awareness of disparities in infectious diseases, which have existed long before the pandemic (Evans, 2020; Feigenbaum, Muller, & Wrigley-Field, 2019; McLaren, 2021; Tirupathi et al., 2020). While there are numerous contributors to these health disparities, a growing body of evidence has indicated the importance of psychosocial stress experienced by members of historically marginalized groups (Boen, 2020; Cardel et al., 2021; Forde et al., 2020; Gravlee, 2009; McLaren, 2021; M. Sims et al., 2012; M. Sims et al., 2016). For example, reported experiences of discrimination predict morbidity and mortality risk independent of other life stressors such as socioeconomic status (SES) (Barnes et al., 2008; Boen, 2020; Cardel et al., 2021; Forde et al., 2020; M. Sims et al., 2012; Thoits, 2010). The biological pathways through which discrimination-related stress can become embedded and impact health are not well understood; however, research has increasingly highlighted the immune system (Baune, Rothermundt, Ladwig, Meisinger, & Berger, 2011; Black & Garbutt, 2002; Y.-Z. Liu, Wang, & Jiang, 2017; Simons et al., 2017; Wirtz & von Känel, 2017).

Recent research on stress and immune function has focused primarily on chronic inflammation, which is linked to stress and implicated in a host of chronic non-communicable diseases, including CVD, type II diabetes and Alzheimer’s (Black & Garbutt, 2002; Y.-Z. Liu et al., 2017; Wirtz & von Känel, 2017). Studies focusing specifically on discrimination-related stress have reported associations with elevated markers of chronic inflammation, such as C-reactive protein (CRP) and interleukin (IL)-6 (Brody, Yu, Miller, & Chen, 2015; Cuevas et al., 2020; Lewis, Aiello, Leurgans, Kelly, & Barnes, 2010; McClendon, Chang, Boudreaux, Oltmanns, & Bogdan, 2021; Saban et al., 2018; K. D. Sims, Sims, Glover, Smit, & Odden, 2020). However, the recent pandemic has highlighted the importance of considering how discrimination-related stress might be related to immune function more broadly, potentially influencing vulnerability to infectious disease (Evans, 2020; Feigenbaum et al., 2019).

In contrast to the up-regulated inflammatory activity that has been the recent focus in stress research, earlier studies focused on immunosuppressive effects (Herbert & Cohen, 1993; Y.-Z. Liu et al., 2017; Segerstrom & Miller, 2004). Such seemingly contradictory findings of both suppression and heightened activity can be explained by considering the separate arms of the immune system, which include non-specific (often called innate) defense and specific (often called adaptive/acquired) defense. The innate arm is the faster acting first line of defense, responding to a host of bodily disturbances by initiating an inflammatory response (Kindt, Goldsby, Osborne, & Kuby, 2007; McDade, Georgiev, & Kuzawa, 2016; Paul, 2013). Commonly used markers of innate inflammatory activity include CRP and IL-6, measured either in an unstimulated condition or following a laboratory-induced response (Cohen et al., 2012; Gouin, Glaser, Malarkey, Beversdorf, & Kiecolt-Glaser, 2012; Kiecolt-Glaser et al., 2003; Miller et al., 2014; Saban et al., 2018). In contrast, the adaptive arm is the slower acting second line of defense, targeting specific pathogens and developing memory for more efficient future protection (Kindt et al., 2007; McDade et al., 2016; Paul, 2013). Measures of adaptive immune function include lymphocyte counts and proliferation following challenge (Herbert & Cohen, 1993; Segerstrom & Miller, 2004). Collectively, research findings suggest that stress is associated with an overall shift in immune function toward reduced adaptive and heightened innate activity, which is thought to increase morbidity and mortality risk across both non-communicable and infectious diseases (Fu et al., 2020; Herbert & Cohen, 1993; Y.-Z. Liu et al., 2017; Segerstrom & Miller, 2004; Simons et al., 2017). While perceived discrimination has been associated with elevated markers of innate inflammation (Brody et al., 2015; Cuevas et al., 2020; Saban et al., 2018), it is unclear if this is also part of a larger shift in immune function that includes the down-regulation of adaptive immunity.

This study examines associations between perceived discrimination and measures of immune function across both innate and adaptive immunity among African American men and women in the Jackson Heart Study (JHS) (Sempos, Bild, & Manolio, 1999; M. Sims, Wyatt, Gutierrez, Taylor, & Williams, 2009). Perceived everyday and lifetime discrimination, as well as perceived burden of lifetime discrimination, were measured along with the prevalence of innate and adaptive cells, including innate neutrophils and monocytes and adaptive lymphocytes. Further, DNA methylation (DNAm) was measured in peripheral blood mononuclear cells (PBMC’s) for a subsample of participants, which allowed estimation of proportions of different types of lymphocytes in circulation, including CD4T, CD8T, and B cells (Salas et al., 2018). DNAm also allowed estimation of accelerated aging of the immune system (Horvath, 2013). Up-regulation of the innate arm and down-regulation of the adaptive arm occurs during normal aging (Fülöp et al., 2018), while perceived discrimination has been associated with accelerated biological aging (Brody, Miller, Yu, Beach, & Chen, 2016; Lee, Kim, & Neblett Jr, 2017; S. Y. Liu & Kawachi, 2017). Thus, we wanted to clarify this indirect pathway from perceived discrimination to immune function (Figure 1). Using these data, we hypothesized that perceived discrimination and burden would be associated with a greater proportion of innate cells and lower proportion of adaptive cells in circulation. In addition, we hypothesized that perceived discrimination and burden would be associated with markers of accelerated immune system aging, and that accelerated aging would mediate the association between perceived discrimination and immune cell counts.

Figure 1.

Figure 1.

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Direct and indirect pathways tested

2. Methods

2.1. Sample

The JHS is a single-site, longitudinal, community-based cohort study of the determinants of CVD among African American adults in the tri-county area of Jackson, Mississippi (Sempos et al., 1999). The present study focused on cross-sectional data from the first visit (2000–2004), for which data on perceived discrimination, immune cell counts and DNAm were available. The analysis sample included individuals with complete data across all study variables of interest, including perceived discrimination, white blood cell count with differential and important demographic and health information to adjust for potential confounding effects. Further, individuals with a white blood cell (WBC) count above 20,000 cells (K)/μL (n = 2) or high sensitivity (hs)-CRP above 10 (mg/L) were treated as having active infections and excluded from the analysis (n = 4). This resulted in a final analysis sample of 3,319 individuals. Among this sample, a subsample of 1,023 participants also had DNAm measures available that passed quality control steps described below. The JHS was approved by the institutional review boards of University of Mississippi Medical Center, Jackson State University, and Tougaloo College. All participants provided informed consent, and JHS data are publicly available upon request (https://www.jacksonheartstudy.org).

2.2. Predictors: Perceived Discrimination

The JHS Discrimination Instrument (JHSDIS) is a multidimensional measure of perceived discrimination, developed specifically with the baseline JHS sample (M. Sims et al., 2009). The JHSDIS consists of three perceived discrimination measures, including everyday discrimination, lifetime discrimination and burden of lifetime discrimination. For everyday discrimination, individuals were asked 9 questions regarding day-to-day experiences of discrimination, with values ranging from never (1) to several times a day (7). Mean values across the 9 questions were taken to obtain a single score. For lifetime discrimination, individuals were asked 9 questions regarding perceived discrimination during their lifetime, including the areas of school, medical care, housing, employment, and any other context not listed. Responses across all questions were summed to provide a single value ranging from 0–9. For burden from lifetime discrimination, individuals were asked 3 questions regarding stress, interference with a productive life, and life difficulty. Responses ranged from 1–4, with 1 indicating low burden and 4 high burden. Mean values were calculated to provide a single burden score. Psychometric testing of these measures showed good internal consistency, with Cronbach’s α = 0.78, 0.84 and 0.77 for everyday, lifetime and burden respectively (M. Sims et al., 2009).

2.3. Outcomes: Innate and adaptive cell prevalence and markers of immune system aging

A complete WBC count (K/μL) with differential was obtained using flow cytometry on the Coulter GenS (BeckmanCoulter, Hialeah, Florida) at the University of Mississippi Medical Center (UMMC) laboratory in Jackson, Mississippi (Carpenter et al., 2004), providing percentages of neutrophils, lymphocytes, and monocytes. DNAm was measured in peripheral blood mononuclear cells (PBMC’s) using the Illumina 850k EPIC array. DNAm refers to methyl groups that bind to DNA and regulate the functioning of genes. Methylation is involved in the development and maintenance of cell lines, and different cell types have distinct DNAm patterns that can be used to estimate their prevalence in a biological sample (Houseman et al., 2012; Ji et al., 2010). Standard quality control procedures were used to filter out poor samples prior to cell type prevalence estimation, including mismatches between DNAm-predicting sex and sample label, an indicator of sample swaps, as well as contaminated samples and those with over 10% of probes with a bead count below 3 and/or a detection p-value above 0.01. These procedures were implemented using the R packages “ewastools” and “Meffil” (Heiss & Just, 2018; Min, Hemani, Davey Smith, Relton, & Suderman, 2018).

Percentages of CD4T, CD8T and B cells were estimated from DNAm measures using the deconvolution method developed by Salas et al. (2018) and implemented using the R packages “FlowSorted.Blood.EPIC”, “IlluminaHumanMethylationEPICmanifest”, and “minfi” (Aryee et al., 2014; Hansen & Aryee, 2016). This deconvolution method has been shown to perform well in predicting cell type prevalence and was developed specifically for EPIC array data, with 69% of the 450 CpG’s used being unique to the EPIC array (Salas et al., 2018). Pre-processing for this deconvolution method included Noob-normalization (Triche Jr, Weisenberger, Van Den Berg, Laird, & Siegmund, 2013).

Prevalence of age-adjusted naïve CD4T and CD8T cells, CD8+CD28-CD45RA− T cells, and plasmablasts were also estimated to assess aging of the immune system. Naïve CD4T and CD8T cells decline during normal aging, while CD8+CD28-CD45RA− T cells and plasmablasts increase. Thus, age-adjusted estimates of these cell types were used to indicate accelerated immune system aging. We obtained these estimates using Horvath and colleagues’ clock estimator (https://dnamage.genetics.ucla.edu) (Horvath, 2013; Horvath et al., 2016; Horvath & Levine, 2015).

2.4. Analysis

Percentages of lymphocytes, neutrophils, and monocytes were multiplied by the total WBC count to provide absolute counts that would not be confounded by individual variation in total WBC in regression analysis. Neutrophil-to-lymphocyte ratios (NLR) were also calculated and added to the analysis, since a higher ratio is thought to predict numerous future health risks, including CVD, Alzheimer’s and certain cancers (Angkananard, Anothaisintawee, McEvoy, Attia, & Thakkinstian, 2018; Kuyumcu et al., 2012; Templeton et al., 2014). The NLR has also been found to prospectively predict coronary heart disease, heart failure, and all-cause mortality specifically in the JHS sample (Kim, Eliot, Koestler, Wu, & Kelsey, 2018).

Ordinary least squares (OLS) regression models were used to test associations with lymphocytes, neutrophils, monocytes, the NLR, and immune aging measures. These measures were standardized (mean = 0, SD = 1) prior to modeling so that coefficients reflect changes in SD. Associations with proportions of CD4T, CD8T and B cells were tested using beta regression models, since percentages follow a beta distribution that is inappropriately modeled using OLS regression (Cribari-Neto & Zeileis, 2010). Lymphocyte proportions were not standardized to enhance interpretability, with model coefficients representing change in percentages of the cell type.

To consider potential non-linear effects of perceived discrimination, as well as make results more easily interpretable, a dummy variable approach was taken. Following the methods in Sims et al. (2020), frequency of everyday discrimination was categorized as “Never” (1), “Less frequent” (>1–3) and “More frequent” (>3–7), with “never” serving as the reference in models. Similarly, lifetime discrimination was categorized as “Lowest” (0–2), “Middle” (3–4) and “Highest” (5–8), with “Lowest” serving as the reference. For perceived burden from lifetime discrimination, individuals reporting no perceived lifetime discrimination were categorized as “None” (0) and served as the reference for “Some” (1–2.5) and “High” (>2.5–4) burden.

We added potential confounding factors to the models based on previous research, including age in years (Fülöp et al., 2018), biological sex (male=0; female=1) (Foo, Nakagawa, Rhodes, & Simmons, 2017), income (adjusted for family size; 1 = lower, 2 = lower-middle, 3 = higher-middle, 4 = affluent), education (0 = less than high school, 1 = high school diploma or general equivalency diploma (GED), 2 = attended trade school, vocational school, or college) (Jousilahti, Salomaa, Rasi, Vahtera, & Palosuo, 2003), weekly alcohol consumption (number of drinks) (Imhof et al., 2001), current smoker (1=yes, 0=no) (Gonçalves et al., 2011), statin and antiarrhythmic medication use (1=yes, 0=no) (Hanna et al., 2006), CVD history (1=yes, 0=no) and continuously measured diastolic blood pressure (mmHg) (C. U. Chae, Lee, Rifai, & Ridker, 2001), low-density lipoprotein (LDL; mg/dL) (Rhoads & Major, 2018), high-density lipoprotein (HDL; mg/dL), triglycerides (mg/dL) (Welty, 2013), glomerular filtration rate (ml/min/1.73m2) (Mielniczuk et al., 2008), and waist circumference (cm) (Berg & Scherer, 2005). While here we report our analysis using waist circumference to account for body composition, results were also consistent using BMI. Outliers for all continuous variables, excluding the bounded lymphocyte proportions, were winsorized (Dixon, 1960) to 3 standard deviations from the mean. Following previous studies, we considered effect modifications by sex and age using interaction terms (Cardel et al., 2021; Forde et al., 2020; Hickson et al., 2012; K. D. Sims et al., 2020). Statistical significance was determined at p < 0.05.

All analyses were conducted in R version 4.1.0 (R Core Team, 2021). Other packages used in the analysis included the “Tidyverse” suite of packages to set up the analysis (Wickham et al., 2019), “betareg” to implement beta regression models (Cribari-Neto & Zeileis, 2010), “car” to calculate variance inflation factors (VIF) that assess collinearity among model covariates (Fox et al., 2012), “Stargazer” to output results into tables (Hlavac, 2018), and “jtools” to generate figures of the results (Long, 2020). The analysis code including bioinformatics is available at: github.com/jakearonoff/JHS_discrimination_immunefunction.

3. Results

Table 1 shows descriptive statistics for the full analysis sample alongside the methylation sub-sample, indicating no major systematic differences. There were slightly more women than men in the sample. The most prevalent cell type in circulation on average was neutrophils, followed by lymphocytes and monocytes. CD4T cells were the most prevalent type of lymphocyte in the methylation sub-sample, followed by CD8T and B cells. In addition, all discrimination measures were positively correlated with each other (all r > 0.4, Table S1). Demographic, SES, health behavior and body composition measures are shown stratified across discrimination values in Tables S2–4. Perceived discrimination tended to be more common among individuals who were younger, men, had higher income and education, were current smokers, consumed more alcohol, and had higher waist circumference and BMI.

Table 1.

Select Descriptive Statistics

Analysis Sample (n = 3,319) Methylation Subsample (n = 1,023)
Statistic Mean SD Mean SD
Age (years) 53.65 12.89 54.55 12.40
Female (%) 0.64 0.61
No Daily Discrimination (%) 0.17 0.19
Less Frequent Everyday Discrimination (%) 0.67 0.65
More Frequent Everyday Discrimination (%) 0.16 0.16
Lowest Lifetime Discrimination (%) 0.44 0.44
Middle Lifetime Discrimination (%) 0.31 0.32
Highest Lifetime Discrimination (%) 0.25 0.24
No Lifetime Discrimination Burden (%) 0.15 0.16
Some Lifetime Discrimination Burden (%) 0.53 0.53
High Lifetime Discrimination Burden (%) 0.32 0.31
Attended trade school, vocational school, or some college (%) 0.66 0.66
Current Smoker (%) 0.12 0.13
Lymphocytes (K/μL) 1.92 0.62 1.94 0.63
Neutrophils (K/μL) 3.06 1.29 3.14 1.26
Monocytes (K/μL) 0.39 0.14 0.39 0.14
Complete White Blood Cell Count (K/μL) 5.55 1.68 5.65 1.62
CD4T % 0.18 0.06
CD8T % 0.11 0.04
B cell % 0.08 0.03
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3.1. Cell counts and NLR

Table 2 shows both unadjusted and fully adjusted model results for lymphocytes and neutrophils. There were no significant associations between discrimination measures and lymphocytes after adjusting for covariates. Less frequent everyday discrimination (compared to none) and highest lifetime discrimination (compared to lowest) were both significantly associated with lower neutrophils after covariate adjustment (β = −0.12, [95% CI: −0.22, −0.02] and β = −0.14, [95% CI: −0.24, −0.04] respectively). Some burden from lifetime discrimination (compared to none) and high burden (compared to none) were significantly associated with higher neutrophils (β = 0.16, [95% CI: 0.05, 0.27] and β = 0.17, [95% CI: 0.05, 0.30] respectively). The similarity in effect sizes suggests a non-linear, threshold association between perceived burden from lifetime discrimination and neutrophils (also shown in Figure 2). Associations between discrimination measures and neutrophils were also independent of CRP, a commonly used marker in studies testing associations between perceived discrimination and chronic inflammation (Table S5) (Cuevas et al., 2020; K. D. Sims et al., 2020).

Table 2.

Regression Models Predicting Lymphocytes and Neutrophils (n = 3,319)

Coefficients (95% CI a)
Lymphocytes (K/μL) Neutrophils (K/μL)
Unadjusted Fully adjustedb Unadjusted Fully adjustedb
Less Frequent Everyday Discriminationc 0.14** 0.06 −0.10* −0.12*
(0.04, 0.24) (−0.03, 0.16) (−0.20, −0.004) (−0.22, −0.02)
More Frequent Everyday Discriminationc 0.26** 0.10 −0.02 −0.07
(0.12, 0.39) (−0.03, 0.22) (−0.15, 0.11) (−0.20, 0.07)
Middle Lifetime Discriminationd −0.05 −0.03 −0.07 −0.07
(−0.14, 0.04) (−0.11, 0.05) (−0.16, 0.02) (−0.16, 0.02)
Highest Lifetime Discriminationd −0.03 0.04 −0.16** −0.14**
(−0.12, 0.08) (−0.06, 0.13) (−0.26, −0.06) (−0.24, −0.04)
Some Lifetime Discrimination Burdene 0.12* 0.06 0.18** 0.16**
(0.01, 0.23) (−0.05, 0.16) (0.06, 0.29) (0.05, 0.27)
High Lifetime Discrimination Burdene 0.09 0.02 0.21** 0.17**
(−0.04, 0.22) (−0.10, 0.14) (0.09, 0.34) (0.05, 0.30)
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a

Coefficients reflect change in SD.

b

Adjusted for age, sex, income, education, current smoker, weekly alcohol consumption, waist circumference, diastolic blood pressure, LDL, HDL, triglycerides, GFR, CVD history, statin and anti-arrhythmic medication use.

c

Reference category is no everyday discrimination

d

Reference category is lowest lifetime discrimination

e

Reference category is no lifetime discrimination burden

*

p < 0.05

**

p < 0.01

Figure 2.

Figure 2.

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Fully adjusted model results predicting cell counts and NLR from Tables 2–3.

Models predicting lymphocytes with sex and age interaction terms are presented in Table S6. There were significant interactions between sex and more frequent everyday discrimination (more frequent everyday × men β = 0.32, [95% CI: 0.07, 0.57]), some burden from lifetime discrimination (some burden × men β = −0.30, [95% CI: −0.52, −0.08]), and high burden (high burden × men β = −0.26, [95% CI: −0.50, −0.01]). There was also a significant interaction between age and high burden from lifetime discrimination (high burden × age β = −0.01, [95% CI: −0.02, −0.001]). For interpretability of sex and age effect modifications, we present associations stratified by sex and the median age (54 years) in Figure 3 and Table S7. There was a clear divergence for older men, in which both some burden from lifetime discrimination (compared to none) and high burden (compared to none) were significantly associated with lower lymphocytes (β = −0.31, [95% CI: −0.52, −0.11] and β = −0.28, [95% CI: −0.52, −0.05] respectively). There were no significant interactions in models predicting neutrophils (models not shown).

Figure 3.

Figure 3.

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Fully adjusted models predicting lymphocytes, stratified by sex and median age (54 years). Estimates reflect changes in SD (also presented in Table S7).

Table 3 shows unadjusted and fully adjusted models predicting monocytes and the NLR. There were no significant associations between perceived discrimination measures and monocytes. Similar to the lymphocyte and neutrophil results, both less frequent everyday discrimination (compared to none) and highest lifetime discrimination (compared to lowest) were significantly associated with a lower NLR (β = −0.16, [95% CI: −0.26, −0.06] and β = −0.15, [95% CI: −0.25, −0.05] respectively). Both some burden from lifetime discrimination and high burden (compared to none) were significantly associated with a higher NLR (β = 0.11, [95% CI: 0.003, 0.23] and β = 0.16, [95% CI: 0.03, 0.29] respectively). There were significant interactions between sex and everyday discrimination in predicting the NLR (Table S8, less frequent everyday × men β = −0.29, [95% CI: −0.50, −0.09] and more frequent everyday × men β = −0.34, [95% CI: −0.61, −0.08]) as well as burden from lifetime discrimination (some burden × men β = 0.25, [95% CI: 0.02, 0.49] and high burden × men β = 0.31, [95% CI: 0.05, 0.57]).

Table 3.

Regression Models Predicting Monocytes and the NLR (n = 3,319)

Coefficients (95% CI a)
Monocytes (K/μL) NLR
Unadjusted Fully adjustedb Unadjusted Fully adjustedb
Less Frequent Everyday Discriminationc −0.06 −0.02 −0.20** −0.16**
(−0.16, 0.04) (−0.12, 0.08) (−0.30, −0.10) (−0.26, −0.06)
More Frequent Everyday Discriminationc −0.001 0.02 −0.20** −0.13
(−0.13, 0.13) (−0.12, 0.15) (−0.33, −0.07) (−0.26, 0.005)
Middle Lifetime Discriminationd −0.02 −0.03 −0.02 −0.03
(−0.11, 0.07) (−0.11, 0.06) (−0.11, 0.07) (−0.12, 0.06)
Highest Lifetime Discriminationd −0.06 −0.05 −0.13* −0.15**
(−0.16, 0.04) (−0.15, 0.05) (−0.23, −0.03) (−0.25, −0.05)
Some Lifetime Discrimination Burdene 0.06 0.06 0.08 0.11*
(−0.05, 0.17) (−0.05, 0.17) (−0.03, 0.20) (0.003, 0.23)
High Lifetime Discrimination Burdene 0.12 0.08 0.15* 0.16*
(−0.01, 0.25) (−0.04, 0.21) (0.02, 0.28) (0.03, 0.29)
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a

Coefficients reflect change in SD.

b

Adjusted for age, sex, income, education, current smoker, weekly alcohol consumption, waist circumference, diastolic blood pressure, LDL, HDL, triglycerides, GFR, CVD history, statin and anti-arrhythmic medication use.

c

Reference category is no everyday discrimination

d

Reference category is lowest lifetime discrimination

e

Reference category is no lifetime discrimination burden

*

p < 0.05

**

p < 0.01

3.2. CD4T, CD8T, and B cell proportions

Table 4 shows both unadjusted and fully adjusted beta regression models predicting CD4T and CD8T proportions. Highest lifetime discrimination compared to lowest was significantly associated with higher CD4T proportions, while there were no significant associations between perceived discrimination measures and CD8T proportions. There were significant interactions between sex and less frequent everyday discrimination (compared to none) in both CD4T and CD8T (Table S9, less frequent everyday × men β = 0.14, [95% CI: 0.01, 0.27] and 0.18, [95% CI: 0.03, 0.34] respectively). In addition, the interaction between sex and high burden from lifetime discrimination (compared to none) approached significance in predicting CD4T proportions (β = −0.15, [95% CI: −0.32, 0.03], p = 0.096), possibly indicating this cell type was the main driver of the association between discrimination burden and overall lymphocytes among older men. There were no significant interactions with age for either CD4T or CD8T proportions. Beta regression models predicting B cell proportions are shown in Table S10. After covariate adjustment, there were no significant main effects between perceived discrimination measures and B cells. There was a significant interaction between sex and less frequent everyday discrimination (less frequent everyday × men β = 0.14, [95% CI: 0.01, 0.27]) in predicting B cells, but no significant age interactions.

Table 4.

Beta regression models predicting CD4T and CD8T proportions (n = 1,023)

Coefficients (95% CI a)
CD4T CD8T
Unadjusted Fully adjustedb Unadjusted Fully adjustedb
Less Frequent Everyday Discriminationc 0.02 −0.03 0.02 −0.04
(−0.05, 0.08) (−0.09, 0.04) (−0.05, 0.10) (−0.11, 0.04)
More Frequent Everyday Discriminationc −0.003 −0.06 0.02 −0.07
(−0.09, 0.08) (−0.15, 0.03) (−0.08, 0.12) (−0.17, 0.03)
Middle Lifetime Discriminationd 0.01 0.01 −0.02 −0.02
(−0.05, 0.07) (−0.05, 0.07) (−0.09, 0.05) (−0.09, 0.05)
Highest Lifetime Discriminationd 0.07 0.09* −0.0002 0.02
(−0.003, 0.14) (0.02, 0.15) (−0.08, 0.08) (−0.06, 0.10)
Some Lifetime Discrimination Burdene −0.03 −0.04 0.05 0.03
(−0.11, 0.04) (−0.11, 0.04) (−0.04, 0.13) (−0.05, 0.12)
High Lifetime Discrimination Burdene −0.06 −0.04 0.03 0.04
(−0.15, 0.03) (−0.13, 0.04) (−0.07, 0.13) (−0.06, 0.13)
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a

Coefficients reflect change in %.

b

Adjusted for age, sex, income, education, current smoker, weekly alcohol consumption, waist circumference, diastolic blood pressure, LDL, HDL, triglycerides, GFR, CVD history, statin and anti-arrhythmic medication use.

c

Reference category is no everyday discrimination

d

Reference category is lowest lifetime discrimination

e

Reference category is no lifetime discrimination burden

*

p < 0.05

**

p < 0.01

3.3. Markers of immune system aging

Associations between perceived discrimination and markers of accelerated immune system aging are shown in Tables S11–13. There were no significant associations between perceived discrimination measures and age-adjusted naïve CD4 and CD8T cells (Table S11) and no significant interactions. While there were no significant main effects of perceived discrimination on age-adjusted plasmablasts, there was a significant interaction between sex and less frequent everyday discrimination (Table S12, less frequent everyday × men β = −0.38, [95% CI: −0.73, −0.03]). Highest lifetime discrimination compared to lowest was significantly associated with lower age-adjusted CD8+CD28-CD45RA− T cells after adjusting for covariates (Table S13, β = −0.25, [95% CI: −0.43, −0.07]). There was also a significant interaction between more frequent everyday discrimination and age (more frequent everyday × age β = −0.02, [95% CI: −0.04, −0.003]). Since we found no evidence that greater perceived discrimination was associated with accelerated immune system aging, we did not proceed with a formal mediation analysis.

4. Discussion

It is well documented that African American adults suffer disproportionately from both chronic non-communicable and infectious diseases, and while previous research has highlighted experiences of discrimination as a possible contributor, biological pathways are not fully understood. Therefore, we tested whether perceived discrimination was associated with immune profiles thought to increase vulnerability to both chronic non-communicable and infectious diseases, including higher prevalence of innate inflammatory cells and lower prevalence of adaptive/acquired lymphocytes. We found complex associations that depended on the perceived discrimination measure, immune cell type, age, and sex. Measures of perceived everyday and lifetime discrimination tended to provide unexpected results, for example highest compared to lowest lifetime discrimination was associated with lower neutrophils, lower NLR, higher CD4T cell proportions, and lower age-adjusted CD8+CD28-CD45RA− T cells. However, the measures assessing the extent to which individuals perceived lifetime discriminatory experiences to be a burden in their lives provided results consistent with our expectations. Reporting any perceived burden from lifetime discrimination was associated with higher neutrophils and a higher NLR, while interaction terms indicated that perceived burden was also associated with lower lymphocytes among older men.

4.1. Health implications for African American adults

These complex findings contribute to a better understanding of the biological pathways linking discriminatory experiences to the health of African American adults. Chronic inflammation is thought to increase risk for numerous non-communicable diseases for which African American adults show higher rates than non-Hispanic White adults, including CVD, type II diabetes, Alzheimer’s, and certain cancers (Angkananard et al., 2018; Hunt & Whitman, 2015; Kuyumcu et al., 2012; Lines, Sherif, & Wiener, 2014; Y.-Z. Liu et al., 2017; Templeton et al., 2014). Further, a higher NLR prospectively predicts coronary heart disease, heart failure, and all-cause mortality in this sample (Kim et al., 2018). Lower lymphocyte counts can also increase vulnerability to infectious diseases, including COVID-19 (Bal, Dogan, Cabalak, & Dirican, 2021; Fu et al., 2020; Mahmoudi, Rezaei, Mansouri, Marjani, & Mansouri, 2020; Zou et al., 2020). It is noteworthy that we found an association between perceived burden from lifetime discrimination and lower lymphocytes among older men, as risk for hospitalization and death from COVID-19 has been higher for individuals who are older, men, and African American (Carethers, 2021). We also found evidence that this association might have been attributable to differences in CD4T counts, while lower CD4T counts are predictive of worse prognosis in HIV-positive individuals (Fahey et al., 1990) and higher HIV mortality rates have been documented among African American adults compared to non-Hispanic Whites (Hunt & Whitman, 2015).

4.2. Comparisons with other studies

The mix of expected and unexpected findings in this study are consistent with previous JHS studies. Greater lifetime discrimination has been associated with prevalent and incident hypertension (Forde et al., 2020; M. Sims et al., 2012), and both greater everyday and lifetime discrimination have been associated with greater metabolic syndrome severity (Cardel et al., 2021). However, greater everyday discrimination has also been associated with reduced all-cause mortality risk (Dunlay et al., 2017). Further, a recent study testing associations between perceived discrimination and CRP, a marker of chronic innate inflammation, reported negative associations with daily and lifetime discrimination cross-sectionally but positive associations longitudinally (K. D. Sims et al., 2020), highlighting the complex findings in the JHS sample.

The contrasting associations for the discrimination measures are partly in contradiction to previous study findings among other study populations, which have largely reported positive associations between perceived discrimination and markers of innate inflammation (Brody et al., 2015; Cuevas et al., 2020; Lewis et al., 2010). We examined how general perceived stress was related to our measures to potentially clarify the associations, and while perceived stress was positively correlated with all perceived discrimination measures (Table S14), it did not predict any of the immune markers (analysis not shown). One possible explanation for our unexpected findings is stress habituation. While some studies have reported an overall increase in inflammatory responses with repeated stress exposure (Bennett, Rohleder, & Sturmberg, 2018; von Känel, Kudielka, Preckel, Hanebuth, & Fischer, 2006), a recent study found that greater stress response habituation, measured by lower cortisol production in response to a repeated experimental stressor, was associated with lower IL-6 production (Thoma et al., 2017). This finding suggests that at least some individuals show habituation and resulting attenuation of innate inflammatory responses to stressors. In our sample, perceiving the least frequent discriminatory experiences but considering them burdensome predicted the highest neutrophil counts and NLR. Thus, our contrasting findings might be driven by a combination of (1) individuals who perceived a small number of discriminatory experiences but considered them burdensome due to lack of habituation and (2) individuals who have perceived more frequent discriminatory experiences but consider them less burdensome due to habituation. Another possible explanation is “attributional protection”, which refers to the potential protective effect of attributing negative social experiences to racial discrimination versus self-blame (Crocker & Major, 1989; Crocker, Voelkl, Testa, & Major, 1991; Major, Quinton, & Schmader, 2003; Schmitt & Branscombe, 2002; Schmitt, Branscombe, Postmes, & Garcia, 2014). Not all studies assessing attributional protection have found supportive evidence (Schmitt et al., 2014); however, we cannot rule this out as an explanation for our unexpected findings.

4.3. Perceived discrimination and accelerated immune system aging

Associations between perceived discrimination and indicators of accelerated immune system aging were weak, suggesting this indirect pathway was less important than the direct pathway of perceived discrimination to immune function (Figure 1). While we did not exhaustively consider all potential markers of accelerated immune aging, other studies have failed to find main effects of perceived discrimination on leukocyte telomere lengths, another marker of accelerated aging of immune cells (D. H. Chae et al., 2016; Coimbra et al., 2020; Glover et al., 2021). These findings suggest that the relationship between perceived discrimination and aging might be more complex and difficult to detect compared to direct effects on immune function.

4.4. Limitations

This study is not without limitations. Our analysis focused on African American adults in the tri-county area of Jackson, Mississippi, and our findings might not be generalizable to other racial or ethnic groups or other geographic locations. Data on perceived discrimination and proportions of innate and adaptive cell types were only available at one time point, which does not allow us to make causal inferences. In addition, DNAm measures were only available in a sub-sample of participants, which could have limited our ability to detect associations with indicators of immune system aging. The cell types used to assess aging, including CD8+CD28-CD45RA− T cells, plasmablasts, and naïve CD4T and CD8T cells, were estimates rather than direct measures, which could have also limited our ability to detect associations. We also did not have measures of lymphocyte sub-populations, which could have helped further clarify the relationship between perceived discrimination and immune function. For example, previous studies have reported associations between stressful experiences and up-regulation of inflammatory Th1 and Th17 lymphocytes, which are implicated in inflammatory diseases such as CVD (do Prado, Grassi-Oliveira, Daruy-Filho, Wieck, & Bauer, 2017; Schmidt et al., 2010; Wenzel et al., 2016; Zhu et al., 2016). Finally, the prevalence of different cell types is only one way to assess immune function. Previous studies among African American adults have found associations between greater perceived everyday discrimination and stronger inflammatory responses during a laboratory-induced stressor (Lucas et al., 2017; Saban et al., 2018), showing the importance of considering immune response measures as well.

4.5. Conclusion

Findings from this study revealed both expected and unexpected associations between perceived discrimination and immune function, highlighting the importance for future studies to consider both perceived discrimination frequency and perceived burden, as only the burden measures showed expected associations with cell counts. These findings contribute to understandings of the biological pathways through which discriminatory experiences might impact risk and vulnerability to both non-communicable and infectious disease in African American adults, implicating immune function as one possible pathway.

Supplementary Material

1

Highlights:

  • Poor health outcomes among African American adults are well-documented

  • The impact of discriminatory experiences might be a contributing factor

  • Biological pathways incompletely understood, but might involve immune function

  • Associations were tested between perceived discrimination and leukocyte composition

  • Perceived discrimination burden predicted a higher neutrophil-to-lymphocyte ratio

Acknowledgements.

The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The authors also wish to thank the staffs and participants of the JHS. Allana T. Forde is supported by the Division of Intramural Research of the National Institute on Minority Health and Health Disparities, National Institutes of Health. Láshauntá M. Glover is supported by the NHLBI under award number F31HL159910. Thomas W. McDade is a CIFAR Fellow in the Child and Brain Development program. Finally, this research was supported in part through the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology.

Footnotes

Competing Interests. The authors declare no competing interests.

Disclaimer. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.

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.

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