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Discrimination and Inflammation in Adolescents of Color

Open AccessPublished:May 16, 2022DOI:https://doi.org/10.1016/j.bpsgos.2022.02.008

      Abstract

      Background

      This study examined how experiences with discrimination relate to inflammation, a key biological pathway in mental and physical illnesses, and whether associations are moderated by gender across two samples of adolescents of color.

      Methods

      Study 1 was a longitudinal study of 419 African American adolescents assessed on discrimination (ages 19–20), with trajectories of biomarkers of low-grade inflammation (C-reactive protein and soluble urokinase plasminogen activator receptor) measured from ages 25 to 29. Study 2 was a cross-sectional study of 201 eighth graders of color assessed on discrimination and mechanistic indicators of a proinflammatory phenotype: 1) in vitro studies of immune cells’ inflammatory cytokine responses to stimuli; 2) in vitro studies of cells’ sensitivity to anti-inflammatory agents; 3) circulating numbers of classical monocytes, key cellular drivers of low-grade inflammation; and 4) a composite of six biomarkers of low-grade inflammation.

      Results

      Interactions of discrimination by gender were found across both studies. In study 1, African American males experiencing high discrimination showed increasing trajectories of soluble urokinase plasminogen activator receptor over time (p < .001). In study 2, adolescent boys of color experiencing greater discrimination evinced a more proinflammatory phenotype: larger cytokine responses to stimuli (p = .003), lower sensitivity to anti-inflammatory agents (p = .003), higher numbers of classical monocytes (p = .008), and more low-grade inflammation (p = .003). No such associations were found in females.

      Conclusions

      Discrimination is a pressing societal issue that will need to be addressed in efforts to promote health equity. This study suggests that adolescent males of color may be particularly vulnerable to its effects on mental health–relevant inflammatory processes.

      Keywords

      Discrimination—the treatment of a person or group of people in unfair or prejudiced ways because of background characteristics such as race/ethnicity—is a pressing social issue and commonly reported experience in U.S. society that has implications for the racial/ethnic health disparities observed in this country (
      • Williams D.R.
      • Mohammed S.A.
      Discrimination and racial disparities in health: Evidence and needed research.
      ,
      • Benner A.D.
      • Wang Y.
      • Shen Y.
      • Boyle A.E.
      • Polk R.
      • Cheng Y.P.
      Racial/ethnic discrimination and well-being during adolescence: A meta-analytic review.
      ). Discrimination has been linked to a host of poor mental health outcomes, including depression, anxiety, substance use, and psychotic disorders (
      • Benner A.D.
      • Wang Y.
      • Shen Y.
      • Boyle A.E.
      • Polk R.
      • Cheng Y.P.
      Racial/ethnic discrimination and well-being during adolescence: A meta-analytic review.
      ,
      • Pascoe E.A.
      • Smart Richman L.
      Perceived discrimination and health: A meta-analytic review.
      ,
      • Pearce J.
      • Rafiq S.
      • Simpson J.
      • Varese F.
      Perceived discrimination and psychosis: A systematic review of the literature.
      ), and various chronic physical conditions (
      • Williams D.R.
      • Mohammed S.A.
      Discrimination and racial disparities in health: Evidence and needed research.
      ,
      • Lewis T.T.
      • Cogburn C.D.
      • Williams D.R.
      Self-reported experiences of discrimination and health: Scientific advances, ongoing controversies, and emerging issues.
      ) in both adults and children (
      • Priest N.
      • Paradies Y.
      • Trenerry B.
      • Truong M.
      • Karlsen S.
      • Kelly Y.
      A systematic review of studies examining the relationship between reported racism and health and wellbeing for children and young people.
      ,
      • Cave L.
      • Cooper M.N.
      • Zubrick S.R.
      • Shepherd C.C.J.
      Racial discrimination and child and adolescent health in longitudinal studies: A systematic review.
      ,
      • Pachter L.M.
      • Coll C.G.
      Racism and child health: A review of the literature and future directions.
      ).
      One potential mechanistic pathway that could explain how discrimination is linked to mental and physical illnesses is excessive inflammation, a key contributor to psychiatric disorders such as depression, substance use, and schizophrenia (
      • Nusslock R.
      • Miller G.E.
      Early-life adversity and physical and emotional health across the lifespan: A neuroimmune network hypothesis.
      ,
      • Mac Giollabhui N.M.
      • Ng T.H.
      • Ellman L.M.
      • Alloy L.B.
      The longitudinal associations of inflammatory biomarkers and depression revisited: Systematic review, meta-analysis, and meta-regression.
      ,
      • Osimo E.F.
      • Baxter L.
      • Stochl J.
      • Perry B.I.
      • Metcalf S.A.
      • Kunutsor S.K.
      • et al.
      Longitudinal association between CRP levels and risk of psychosis: A meta-analysis of population-based cohort studies.
      ), and physical conditions such as diabetes, coronary heart disease, and neurodegenerative diseases (
      • Hotamisligil G.S.
      Inflammation and metabolic disorders.
      ,
      • Libby P.
      • Ridker P.M.
      • Hansson G.K.
      Leducq Transatlantic Network on Atherothrombosis
      Inflammation in atherosclerosis: From pathophysiology to practice.
      ). Discrimination is viewed as a stressor that modulates neuroendocrine and autonomic outflow through the negative appraisals and affective reactions it elicits (
      • Cuevas A.G.
      • Ong A.D.
      • Carvalho K.
      • Ho T.
      • Chan S.W.C.
      • Allen J.D.
      • et al.
      Discrimination and systemic inflammation: A critical review and synthesis.
      ). Changes in the hormonal end products of these systems, in turn, alter patterns of immune cell trafficking and responsivity in ways that boost inflammatory activity. Although this chain of events occurs following acute experiences of discrimination, over the long term the accumulation of repeated discrimination experiences is theorized to contribute to more chronic alterations in steady-state low-grade inflammatory activity (
      • Mays V.M.
      • Cochran S.D.
      • Barnes N.W.
      Race, race-based discrimination, and health outcomes among African Americans.
      ,
      • Myers H.F.
      Ethnicity- and socio-economic status-related stresses in context: An integrative review and conceptual model.
      ). Consistent with this scenario, discrimination has been linked to biomarkers of low-grade inflammation, such as C-reactive protein (CRP) (
      • Lewis T.T.
      • Aiello A.E.
      • Leurgans S.
      • Kelly J.
      • Barnes L.L.
      Self-reported experiences of everyday discrimination are associated with elevated C-reactive protein levels in older African-American adults.
      ,
      • Cobb R.J.
      • Parker L.J.
      • Thorpe R.J.
      Self-reported instances of major discrimination, race/ethnicity, and inflammation among older adults: Evidence from the Health and Retirement Study.
      ,
      • Ong A.D.
      • Williams D.R.
      Lifetime discrimination, global sleep quality, and inflammation burden in a multiethnic sample of middle-aged adults.
      ), which forecast an increased likelihood of chronic illnesses later in life (
      • Ridker P.M.
      • Hennekens C.H.
      • Buring J.E.
      • Rifai N.
      C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women.
      ,
      • Pradhan A.D.
      • Manson J.E.
      • Rifai N.
      • Buring J.E.
      • Ridker P.M.
      C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus.
      ). However, an understanding of the ways in which discrimination relates to inflammation earlier in life to better understand the developmental origins and prevention of disease has been lacking. The vast majority of studies on discrimination and inflammation have been conducted in adults (
      • Cuevas A.G.
      • Ong A.D.
      • Carvalho K.
      • Ho T.
      • Chan S.W.C.
      • Allen J.D.
      • et al.
      Discrimination and systemic inflammation: A critical review and synthesis.
      ), with only two studies in children (
      • Goosby B.J.
      • Malone S.
      • Richardson E.A.
      • Cheadle J.E.
      • Williams D.T.
      Perceived discrimination and markers of cardiovascular risk among low-income African American youth.
      ,
      • Priest N.
      • Truong M.
      • Chong S.
      • Paradies Y.
      • King T.L.
      • Kavanagh A.
      • et al.
      Experiences of racial discrimination and cardiometabolic risk among Australian children.
      ) and one investigating the transition to adulthood (
      • Brody G.H.
      • Yu T.
      • Miller G.E.
      • Chen E.
      Discrimination, racial identity, and cytokine levels among African-American adolescents.
      ). In addition, very few studies have taken a developmental perspective in investigating changes in inflammation over time; most studies are cross-sectional, and only a small number of studies—all in adults—have assessed biomarkers of inflammation across time (
      • Beatty Moody D.L.
      • Brown C.
      • Matthews K.A.
      • Bromberger J.T.
      Everyday discrimination prospectively predicts inflammation across 7-years in racially diverse midlife women: Study of Women’s Health across the Nation.
      ,
      • Zahodne L.B.
      • Kraal A.Z.
      • Zaheed A.
      • Farris P.
      • Sol K.
      Longitudinal effects of race, ethnicity, and psychosocial disadvantage on systemic inflammation.
      ,
      • Sims K.D.
      • Sims M.
      • Glover L.M.
      • Smit E.
      • Odden M.C.
      Perceived discrimination and trajectories of C-reactive protein: The Jackson Heart Study.
      ,
      • Cunningham T.J.
      • Seeman T.E.
      • Kawachi I.
      • Gortmaker S.L.
      • Jacobs D.R.
      • Kiefe C.I.
      • Berkman L.F.
      Racial/ethnic and gender differences in the association between self-reported experiences of racial/ethnic discrimination and inflammation in the CARDIA cohort of 4 US communities.
      ).
      In addition, nearly all previous studies in this area focus on biomarkers of inflammation. However, these biomarkers do not provide mechanistic insights about underlying immunologic processes that might allow discrimination to trigger and/or maintain inflammatory activity. Past work highlights two such underlying processes that together are hypothesized to reflect a proinflammatory phenotype (
      • Miller G.E.
      • Chen E.
      • Parker K.J.
      Psychological stress in childhood and susceptibility to the chronic diseases of aging: Moving toward a model of behavioral and biological mechanisms.
      ,
      • Miller G.E.
      • Chen E.
      • Finegood E.D.
      • Lam P.H.
      • Weissman-Tsukamoto R.
      • Leigh A.K.K.
      • et al.
      Resting-state functional connectivity of the central executive network moderates the relationship between neighborhood violence and proinflammatory phenotype in children.
      ). One is the intensity of monocyte responses to triggering stimuli, and the other is these cells’ sensitivity to inhibitory signals. Therefore, this article advances knowledge of discrimination and inflammation earlier in life by leveraging two samples of youth of color to ask: 1) how discrimination relates to 4-year trajectories of two inflammatory biomarkers, CRP and soluble urokinase plasminogen activator receptor (suPAR), during the transition to young adulthood; and 2) how discrimination during adolescence relates to multiple indicators of a proinflammatory phenotype. To bolster conclusions, these research questions were probed across two distinct study samples to determine whether consistent, replicable results would emerge.
      In addition, given that males and females report different types of, frequency of, and ways of coping with discrimination (
      • Lewis T.T.
      • Van Dyke M.E.
      Discrimination and the health of African Americans: The potential importance of intersectionalities.
      ,
      • Kwate N.O.A.
      • Goodman M.S.
      Racism at the intersections: Gender and socioeconomic differences in the experience of racism among African Americans.
      ), we tested whether associations between discrimination and inflammation might vary by gender. That is, we tested discrimination by gender interactions predicting trajectories of inflammation over time and proinflammatory phenotypes across two samples of youth of color.

      Methods and Materials: Study 1

      In this article, we report findings across two distinct study samples and designs. Study 1 presents methods and results for the Strong African American Healthy Adults Project (SHAPE) sample. See the Supplement for additional details of the methods and Table 1 for demographic information.
      Table 1Descriptives of the SHAPE and MWMH Samples
      DescriptivesPercentageMean (SD) [Range]
      SHAPE (n = 419)
       Female59%
       Intervention58%
       Family SES, ages 19–202.93 (1.59) [0 to 6]
       BMI, age 1928.08 (8.19) [15.36 to 55.03]
       Discrimination, ages 19–203.18 (3.24) [0 to 18]
       suPAR, age 253.33 (0.13) [2.96 to 4.22]
       suPAR, age 273.37 (0.13) [3.02 to 4.47]
       suPAR, age 293.36 (0.13) [2.77 to 4.10]
       CRP, age 250.54 (0.43) [0 to 1.73]
       CRP, age 270.54 (0.39) [0.08 to 1.86]
       CRP, age 290.57 (0.41) [0.04 to 2.06]
      MWMH (n = 201)
       Female64%
       Black53%
       Hispanic44%
       Age, years13.50 (0.62) [12 to 15]
       Pubertal category3.74 (0.74) [1 to 5]
       Family SES−0.22 (0.34) [−0.51 to 1.95]
       BMI24.36 (6.65) [14.61 to 53.73]
       Discrimination18.14 (5.38) [10 to 34]
       Stimulated cytokine production0.03 (0.87) [−2.00 to 2.89]
       Sensitivity to inhibitory signal−0.08 (0.98) [−3.47 to 3.01]
       Classical monocytes134.39 (58.27) [20.08 to 332.44]
       Low-grade inflammation−0.01 (0.65) [−1.13 to 4.07]
      Family SES, stimulated cytokine production, sensitivity to inhibitory signaling, and low-grade inflammation are all standardized and averaged composite variables.
      BMI, body mass index; CRP, C-reactive protein; MWMH, My World My Heart; SES, socioeconomic status; SHAPE, Strong African American Healthy Adults Project; suPAR, soluble urokinase plasminogen activator receptor.

      Participants

      Data for the first study were drawn from the longitudinal SHAPE sample (
      • Brody G.H.
      • Yu T.
      • Chen E.
      • Miller G.E.
      • Kogan S.M.
      • Beach S.R.H.
      Is resilience only skin deep?: Rural African Americans' socioeconomic status-related risk and competence in preadolescence and psychological adjustment and allostatic load at age 19.
      ). SHAPE enrolled 667 African American children in fifth grade (mean age = 11.2 years, SD = 0.3) along with primary caregivers. The University of Georgia’s Institutional Review Board approved the protocol, and written informed consent was obtained from participants and their caregivers at all assessments.

      Procedures

      When youth were 19 years old, 500 were randomly selected, due to funding constraints, to participate in a biological data collection. Psychosocial measures were assessed by youth report, and primary caregivers provided demographic information when youth were 19 and again at age 20. At age 19, participants’ height and weight were assessed. When participants were 25, 27, and 29 years old, a phlebotomist visited participants’ homes to draw fasting antecubital blood. A total of 419 participants provided blood during at least one time point, and analyses were conducted on this group.

      Measures

      See the Supplement for additional details.

      Discrimination

      Youth reported experiences with discrimination over the past year using the Schedule of Racist Events scale (
      • Landrine H.
      • Klonoff E.A.
      The Schedule of Racist Events: A measure of racial discrimination and a study of its negative physical and mental health consequences.
      ). Higher scores indicated greater discrimination, with scores averaged across ages 19 to 20. We used this time point of assessment to have a measure of discrimination that preceded our inflammation measures.

      Low-Grade Inflammation

      Two biomarkers of low-grade inflammation, CRP and suPAR, were assessed in fasting serum collected at ages 25, 27, and 29 by immunoassay (technical details in the Supplement). CRP is the most commonly assessed biomarker of inflammation (
      • Del Giudice M.
      • Gangestad S.W.
      Rethinking IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters.
      ) and has been prospectively associated with risk for diabetes, coronary heart disease, myocardial infarctions, and stroke (
      • Ridker P.M.
      • Hennekens C.H.
      • Buring J.E.
      • Rifai N.
      C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women.
      ,
      • Pradhan A.D.
      • Manson J.E.
      • Rifai N.
      • Buring J.E.
      • Ridker P.M.
      C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus.
      ,
      • Ridker P.M.
      Clinical application of C-reactive protein for cardiovascular disease detection and prevention.
      ). suPAR is a newer inflammatory biomarker that also has emerged as a predictor of cardiovascular disease, diabetes, cancer, and all-cause mortality independent of CRP (
      • Eugen-Olsen J.
      • Andersen O.
      • Linneberg A.
      • Ladelund S.
      • Hansen T.W.
      • Langkilde A.
      • et al.
      Circulating soluble urokinase plasminogen activator receptor predicts cancer, cardiovascular disease, diabetes and mortality in the general population.
      ,
      • Botha S.
      • Fourie C.M.T.
      • Schutte R.
      • Eugen-Olsen J.
      • Pretorius R.
      • Schutte A.E.
      Soluble urokinase plasminogen activator receptor as a prognostic marker of all-cause and cardiovascular mortality in a Black population.
      ) and has been speculated to reflect vascular inflammation (
      • Lyngbæk S.
      • Marott J.L.
      • Sehestedt T.
      • Hansen T.W.
      • Olsen M.H.
      • Andersen O.
      • et al.
      Cardiovascular risk prediction in the general population with use of suPAR, CRP, and Framingham risk score.
      ). Distributions were normalized with log10 transformations.

      Gender

      Gender was coded as male = 0.5 and female = −0.5.

      Covariates

      Statistical models included covariates of family socioeconomic disadvantage at ages 19 to 20 [measured using a composite of six different indicators (
      • Miller G.E.
      • Yu T.
      • Chen E.
      • Brody G.H.
      Self-control forecasts better psychosocial outcomes but faster epigenetic aging in low-SES youth.
      ,
      • Brody G.H.
      • Yu T.
      • Chen Y.F.
      • Kogan S.M.
      • Evans G.W.
      • Beach S.R.
      • et al.
      Cumulative socioeconomic status risk, allostatic load, and adjustment: A prospective latent profile analysis with contextual and genetic protective factors.
      ,
      • Brody G.H.
      • Yu T.
      • Miller G.E.
      • Ehrlich K.B.
      • Chen E.
      John Henryism coping and metabolic syndrome among young Black adults.
      )], body mass index (BMI) at age 19, and intervention status (the SHAPE cohort participated in an unrelated randomized trial at age 11 to prevent child behavior problems and substance use). Supplemental analyses included youth depressive symptoms, anxiety, and perceived stress at ages 19 to 20 as additional covariates and tested BMI as a time-varying covariate. See the Supplement for details of these measures.

      Statistical Analyses

      We conducted latent growth curve models to characterize the overall trajectories of CRP and suPAR across ages 25, 27, and 29. Each marker was analyzed separately, given that we could not incorporate a composite score into growth curve models (see the Supplement for further details). Missing data at a certain wave were handled using full information maximum likelihood estimation (
      • Muthen B.O.
      • Muthen L.K.
      Mplus Users’ Guide.
      ). The model was tested across the three waves of data and included 1) an intercept parameter with time centered at age 25 and 2) a linear slope parameter representing the average linear change in CRP and suPAR (from ages 25–29). The growth parameters were predicted from 1) the covariates of socioeconomic disadvantage and intervention status, 2) the main effects of discrimination and gender, and 3) the interaction between discrimination and gender. Follow-up analyses included BMI at age 19 as a covariate, BMI at ages 25 to 29 as time-varying covariates, and depressive symptoms, anxiety, and perceived stress at ages 19 to 20 as covariates.

      Results: Study 1

      Latent growth curve model analyses revealed that suPAR levels increased across ages 25 to 29 (slope mean = 0.027, SE = 0.003, p < .001). There was significant between-youth variation in both the intercept (variance = 0.019, SE = 0.002, p < .001) and the slope (variance = 0.003, SE = 0.001, p < .001). By contrast, CRP levels were stable across ages 25 to 29 (slope mean = 0.004, SE = 0.010, p = .717), although there was significant between-youth variation in the intercept (variance = 0.143, SE = 0.014, p < .001) and slope (variance = 0.014, SE = 0.006, p = .017). Discrimination scores did not differ by gender (t = −0.041, p = .967), and distributions of scores were equivalent across the two genders (Levene’s test for equality of variances: F = 0.009, p = .923).

      Discrimination and Gender Effects

      See Table 2 for details of analyses.
      Table 2Gender, Discrimination, and Trajectories of Inflammatory Markers: SHAPE Sample
      PredictorInterceptSlope
      bSEp ValuebSEp Value
      Growth Parameters of suPAR
       SES disadvantage, ages 19–200.0040.004.30−0.0020.002.40
       Intervention status−0.0070.013.610.0050.006.43
       Gender−0.0570.013<.01−0.0040.006.56
       Discrimination, ages 19–200.0120.006.060.0020.003.60
       Gender × discrimination−0.0050.013.700.0150.006.02
      Growth Parameters of CRP
       SES disadvantage, ages 19–200.0120.013.370.0050.007.47
       Intervention status0.0160.042.700.0010.021.95
       Gender−0.2570.040<.010.0350.021.10
       Discrimination, ages 19–20−0.0200.020.31−0.0060.010.54
       Gender × discrimination0.0320.040.420.0180.021.41
      Family SES disadvantage score and intervention status entered on step 1; gender and discrimination entered on step 2; and the interaction term entered on step 3. Gender: female = −0.5, male = 0.5. Trajectories of inflammatory markers are from ages 25, 27, and 29.
      CRP, C-reactive protein; SES, socioeconomic status; SHAPE, Strong African American Healthy Adults Project; suPAR, soluble urokinase plasminogen activator receptor.

      Soluble Urokinase Plasminogen Activator Receptor

      The latent growth curve model had borderline acceptable fit (χ2 = 56.73, comparative fit index = 0.92, Tucker Lewis index = 0.90, root mean squared error of approximation = 0.090) (
      • Hu L.
      • Bentler P.M.
      Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives.
      ,
      • Kline R.B.
      Principles and Practice of Structural Equation Modeling.
      ,
      • Browne M.W.
      • Cudeck R.
      Alternative ways of assessing model fit.
      ). After including covariates, there was a main effect of gender on the intercept, such that males (p < .01) had lower suPAR at age 25 than females. There was a significant gender × discrimination interaction (p = .02) on the slope of suPAR. To decipher this interaction, we estimated gender-specific suPAR slopes at lower and higher levels of discrimination (−1 SD and +1 SD from the sample average). Males who experienced high discrimination at ages 19 to 20 had relatively steep suPAR increases from ages 25 to 29 (slope = 0.036, SE = 0.006, p < .001). By contrast, males who experienced low discrimination had smaller increases in suPAR levels over time (slope = 0.013, SE = 0.006, p = .03). These two slopes are significantly different from each other (b = 0.012, SE = 0.005, p = .024) (Figure 1). Among females, suPAR levels increased over time, but slopes were similar in those with high (slope = 0.024, SE = 0.005, p < .001) versus low (slope = 0.031, SE = 0.005, p < .001) discrimination.
      Figure thumbnail gr1
      Figure 1Soluble urokinase plasminogen activator receptor (suPAR) (log transformed) trajectories by levels of discrimination (ages 19–20) among males in the Strong African American Healthy Adults Project sample. Low and high values of discrimination represent ±1 SD from the mean.

      C-Reactive Protein

      The model fit the data well (χ2 = 8.23, comparative fit index = 1.00, Tucker Lewis index = 1.01, root mean squared error of approximation = 0). There was a main effect of gender on the intercept, such that males (p < .01) had lower CRP than females. There were no significant main effects of discrimination or gender × discrimination interactions on the intercept or slope of CRP.

      Controlling for BMI

      Because adiposity contributes to inflammation, we tested whether adding BMI at age 19 to the models would alter the patterns reported above. It did not. The gender × discrimination interaction for the suPAR slope remained significant (b = 0.015, SE = 0.006, p = .02). Males who experienced high discrimination continued to evince increases in suPAR over time (slope = 0.036, SE = 0.006, p < .001) relative to males with low discrimination (slope = 0.013, SE = 0.006, p = .04). Discrimination remained unrelated to suPAR in females. We further examined BMI from ages 25 to 29 as a time-varying covariate. The gender × discrimination interaction for the suPAR slope remained significant (Table S1).

      Controlling for Psychological Variables

      In supplemental analyses, we tested whether adding the psychological covariates of depression, anxiety, or perceived stress would alter results. In all cases, the gender × discrimination interaction for the suPAR slope remained significant (Table S1).

      Methods and Materials: Study 2

      See the Supplement for additional details of methods and Table 1 for demographic information.

      Participants

      Eighth grade children from the Chicago area were recruited for the My World My Heart study. Each child gave written assent to participate, and parents gave written consent. Northwestern University’s Institutional Review Board approved the protocol.
      To parallel analyses in study 1, this study focused on 201 children of color enrolled (out of a total sample of 277). During a single study visit, children completed surveys (including the discrimination measure), gave fasting blood, and had height and weight assessed. Parents provided demographic information.

      Measures

      See the Supplement for details.

      Discrimination

      Children reported on the frequency of their day-to-day experiences with discrimination using the Everyday Discrimination Scale (
      • Williams D.R.
      • Yan Yu Y.
      • Jackson J.S.
      • Anderson N.B.
      Racial differences in physical and mental health: Socio-economic status, stress and discrimination.
      ), with responses ranging from “never” to “often.” Higher scores indicated greater experiences with discrimination.

      Child Gender

      Gender was coded as male = 0.5 and female = −0.5.

      Proinflammatory Phenotype

      As noted earlier, little is known about the immunologic processes that might be involved in triggering and maintaining the low-grade inflammation observed in study 1. Therefore, in study 2, we used fasting antecubital blood to conduct more in-depth assessments of inflammatory activity. These assessments included 1) in vitro studies of immune cells’ inflammatory cytokine responses to multiple stimuli; 2) in vitro studies of these cells’ sensitivity to anti-inflammatory agents; 3) circulating numbers of classical monocytes, key cellular drivers of low-grade inflammation; and 4) a composite of six circulating biomarkers of low-grade inflammation.

      Cytokine Responsivity to Stimulation and Inhibition

      With studies that rely solely on assessing biomarkers of inflammation in circulation, it can be difficult to ascertain the origins of these markers (i.e., whether they actually originate from immune cells vs. from adipose tissue or from other bodily systems) (

      Chiang JJ, Lam PH, Chen E, Miller GE (in press): Psychological stress during childhood and adolescence and its association with inflammation across the lifespan: A critical review and meta-analysis. Psychol Bull.

      ). Thus, a more direct approach is to challenge immune cells with stimuli in vitro and to quantify the production of cytokines. In these paradigms, a variety of stimuli can be used (e.g., bacterial, viral), and greater production of cytokines is interpreted as a sign of more aggressive inflammatory responding. In this study, whole blood was incubated with four distinct stimuli, representing both microbial and sterile triggers of inflammation. They were lipopolysaccharide, resiquimod, heat-shock protein 60, and advanced glycation end product from bovine serum albumin. In addition to assessing cytokine responsivity, another extension of this in vitro paradigm involves including an anti-inflammatory molecule (e.g., cortisol) to assess how sensitive immune cells are to inhibitory signals (the more sensitive they are to these signals, the more cytokine production will be curtailed). To quantify sensitivity to inhibitory signals, whole blood was co-incubated with lipopolysaccharide and compounds that provide local (interleukin [IL] 10) and systemic (glucocorticoid) anti-inflammatory feedback. In all conditions, four inflammatory cytokines were measured (IL-1β, IL-6, tumor necrosis factor alpha, and IL-8) by multiplex immunoassay. Details of the protocol are provided in the Supplement. Assays were performed in duplicate. Intra-assay coefficients of variation ranged from 1.59% to 2.28%, and interassay coefficients of variation from 2.67% to 4.03%.
      To alleviate concerns about false discovery, we decided a priori to conduct primary analyses on two composite end points. The first was a stimulated cytokine production composite, formed by averaging z-scored values across the four cytokines and across the four stimulating agents. Higher values represent larger cytokine responses to stimulating agents, which is consistent with a proinflammatory phenotype. The second was a sensitivity to anti-inflammatory signaling composite, formed by estimating a child-specific sensitivity slope for each cytokine within each condition of lipopolysaccharide + an anti-inflammatory agent, and then averaging z-scored values of cytokine slopes across conditions. Here, lower values represent less sensitivity to the anti-inflammatory properties of inhibitory compounds, which suggests a more proinflammatory phenotype. See the Supplement for further details.

      Classical Monocytes

      Although multiple populations of immune cells participate in a typical inflammatory response, classical monocytes are one of the key cellular drivers of this process. Moreover, preclinical studies indicate that following exposure to stress, these cells migrate to the brain, where they induce anxiety-like behavior; to the walls of coronary arteries, where they worsen atherosclerosis; and to adipose tissue, where they promote insulin sensitivity and fat accumulation (
      • Weber M.D.
      • Godbout J.P.
      • Sheridan J.F.
      Repeated social defeat, neuroinflammation, and behavior: Monocytes carry the signal.
      ,
      • Nahrendorf M.
      Myeloid cell contributions to cardiovascular health and disease.
      ,
      • Schloss M.J.
      • Swirski F.K.
      • Nahrendorf M.
      Modifiable cardiovascular risk, hematopoiesis, and innate immunity.
      ). Given these cells’ roles in stress-related diseases, we enumerated them here using a standardized flow cytometry protocol (
      • Heimbeck I.
      • Hofer T.P.
      • Eder C.
      • Wright A.K.
      • Frankenberger M.
      • Marei A.
      • et al.
      Standardized single-platform assay for human monocyte subpopulations: Lower CD14+CD16++ monocytes in females.
      ). See the Supplement for protocol details.

      Low-Grade Inflammation

      Six circulating (basal) biomarkers of low-grade inflammation that are implicated in cardiovascular disease (
      • Ridker P.M.
      From C-reactive protein to interleukin-6 to interleukin-1: Moving upstream to identify novel targets for atheroprotection.
      ,
      • Hodges G.W.
      • Bang C.N.
      • Wachtell K.
      • Eugen-Olsen J.
      • Jeppesen J.L.
      suPAR: A new biomarker for cardiovascular disease?.
      ) were quantified by immunoassay, including CRP, suPAR, IL-6, IL-8, IL-10, and tumor necrosis factor alpha. CRP and suPAR were measured in duplicate, and cytokines were measured in triplicate. Intra-assay coefficients of variation ranged from 1.6% to 5.0%. See the Supplement for protocol details. Biomarkers were log transformed, standardized, and averaged into a composite. Higher values reflected more inflammation.

      Covariates

      Statistical models included covariates of child race (African American: yes/no), ethnicity (Hispanic: yes/no), pubertal status (
      • Petersen A.C.
      • Crockett L.
      • Richards M.
      • Boxer A.
      A self-report measure of pubertal status: Reliability, validity, and initial norms.
      ), family socioeconomic status measured as a composite of family income and savings, and child BMI. Child age was not included as a covariate because all children were in grade 8 (supplemental analyses that replaced pubertal status with child age found similar patterns of associations; see Table S2). Supplemental analyses included child anxiety, depression, and perceived stress as covariates. See the Supplement for details about these measures.

      Statistical Analyses

      We conducted hierarchical multiple regression analyses, in which inflammatory outcomes were predicted from 1) the covariates of race, ethnicity, pubertal status, and socioeconomic status; 2) the main effects of discrimination and gender; and 3) the interaction between discrimination and gender. Separate models were then fit for males versus females. Follow-up analyses added BMI as a covariate. Supplemental analyses added child age, anxiety, depression, and perceived stress as covariates.

      Results: Study 2

      Overall, 53% of children were African American, 44% were Hispanic, and 13% identified as members of other racial/ethnic groups (multiple race/ethnicity categories were permitted). Discrimination scores did not differ by race, ethnicity, or gender (all p values > .25). Distributions of discrimination scores were also equivalent across the two genders (Levene’s test for equality of variances: F < 0.001, p = .992).

      Discrimination and Gender Effects

      See Tables 3 and 4 for details of analyses.
      Table 3Gender, Discrimination, and Proinflammatory Phenotype: MWMH Sample
      PredictorbSEp Value
      Stimulated Cytokine Production
       Puberty status−0.1130.080.16
       African American0.3740.176.04
       Hispanic−0.1970.177.26
       SES−0.2440.179.17
       Gender0.1920.149.20
       Discrimination0.0260.011.02
       Gender × discrimination0.0460.023.04
      Sensitivity to Anti-inflammatory Signaling
       Puberty status0.2890.087<.01
       African American−0.4500.191.02
       Hispanic0.3510.191.07
       SES0.1260.194.52
       Gender−0.1590.161.32
       Discrimination−0.0320.012.01
       Gender × discrimination−0.0400.025.10
      Classical Monocytes
       Puberty status3.1455.571.57
       African American24.38512.397.05
       Hispanic18.94112.389.13
       SES−12.87212.456.30
       Gender29.05610.032<.01
       Discrimination1.7420.770.02
       Gender × discrimination3.4081.554.03
      Low-Grade Inflammation
       Puberty status−0.1290.062.04
       African American0.0170.138.90
       Hispanic0.0200.138.89
       SES−0.0840.139.55
       Gender0.1870.114.10
       Discrimination0.0110.009.22
       Gender × discrimination0.0440.018.01
      Puberty status, race, ethnicity, and family SES entered on step 1; gender and discrimination entered on step 2; and the interaction term entered on step 3. Gender: female = −0.5, male = 0.5.
      MWMH, My World My Heart; SES, socioeconomic status.
      Table 4Associations of Discrimination With Proinflammatory Phenotype by Gender: MWMH Sample
      PredictorBoysGirls
      bSEp ValuebSEp Value
      Stimulated Cytokine Production
       Puberty status−0.0830.137.550.00040.144.99
       African American0.2410.314.440.4300.217.05
       Hispanic−0.2710.318.40−0.1730.215.42
       SES−0.4150.269.13−0.0950.245.70
       Discrimination0.0560.018<.010.0100.014.50
      Sensitivity to Anti-inflammatory Signaling
       Puberty status0.3960.145<.010.0730.158.64
       Hispanic0.3010.335.370.3760.235.11
       African American−0.3820.330.25−0.4550.237.06
       SES0.0320.283.910.1540.268.57
       Discrimination−0.0600.019<.01−0.0160.015.29
      Classical Monocytes
       Puberty status7.16710.090.4821.1999.122.02
       Hispanic28.54624.252.2414.78313.853.29
       African American38.90224.042.1115.30813.982.28
       SES−17.04320.246.40−9.57315.775.54
       Discrimination3.8531.399<.010.6170.909.50
      Low-Grade Inflammation
       Puberty status−0.0850.097.39−0.0310.114.78
       Hispanic−0.1650.234.480.1000.173.56
       African American−0.1120.232.630.0600.174.73
       SES−0.2050.195.30−0.0070.197.97
       Discrimination0.0410.013<.01−0.0050.011.65
      Puberty status, race, ethnicity, and SES entered on step 1; discrimination entered on step 2.
      MWMH, My World My Heart; SES, socioeconomic status.

      Stimulated Cytokine Production Composite

      There was a main effect such that higher discrimination was associated with larger cytokine responses to stimulating agents (p = .023). There was also a significant gender × discrimination interaction (p = .04). Boys showed a positive association between discrimination experiences and cytokine production (p = .003), whereas no relationship was evident in girls (p = .50).

      Sensitivity to Anti-inflammatory Signaling Composite

      There was a main effect where higher discrimination was associated with lower sensitivity to anti-inflammatory compounds (p = .009). There was a marginal gender × discrimination interaction (p = .10). Boys who experienced greater discrimination had lower sensitivity to anti-inflammatory agents (p = .003). By contrast, there was no relationship in girls (p = .29).

      Classical Monocytes

      There were main effects of both discrimination and gender, such that boys (p = .004) and those who experienced greater discrimination (p = .025) had higher numbers of proinflammatory classical monocytes. There was also a significant gender × discrimination interaction (p = .029). Boys showed a positive association between greater discrimination and classical monocyte numbers (p = .008), whereas there was no relationship among girls (p = .50).

      Low-Grade Inflammation

      There were no main effects of either discrimination or gender, but the interaction was significant (p = .014). Boys showed a positive association between greater discrimination and more low-grade inflammation (p = .003), whereas girls showed no relationship (p = .65) (Figure 2).
      Figure thumbnail gr2
      Figure 2Low-grade inflammation as a function of discrimination scores for boys (A) vs. girls (B) in the My World My Heart sample (8th graders). The inflammation composite included C-reactive protein, interleukin (IL) 6, IL-8, IL-10, tumor necrosis factor alpha, and soluble urokinase plasminogen activator receptor (log transformed, standardized, then averaged). Interaction patterns for the other three proinflammatory phenotype measures (stimulated cytokine production composite, sensitivity to anti-inflammatory signaling composite, and classical monocytes) paralleled this figure.

      Controlling for BMI

      After controlling for BMI, boys who experienced greater discrimination continued to evince a more proinflammatory phenotype, as indicated by larger cytokine responses to stimulating agents (b = 0.053, SE = 0.018, p = .005), lower sensitivity to anti-inflammatory agents (b = −0.055, SE = 0.019, p = .004), higher numbers of classical monocytes (b = 3.513, SE = 1.377, p = .013), and more low-grade inflammation (b = 0.036, SE = 0.013, p = .006). Girls showed no association of discrimination with any these measures (p values > .25).

      Controlling for Psychological Variables

      In supplemental analyses, we tested whether adding the psychological covariates of depression, anxiety, or perceived stress would alter results. In all cases, the patterns of significant gender × discrimination interactions remained the same (Table S2).

      Discussion

      The results of these two studies demonstrate that links between discrimination and a key disease-related biological process—inflammation—emerge during earlier periods of life. That is, across two studies, discrimination was associated with a proinflammatory phenotype in boys of color that worsened as they progressed into adulthood. Specifically, among adolescent boys of color, greater experiences with discrimination were associated with larger cytokine responses to multiple stimuli and lower sensitivity to local and systemic inhibitory signals that regulate inflammation. In addition, discrimination in boys of color was also associated with higher concentrations of inflammatory biomarkers and with higher numbers of classical monocytes, key cellular drivers of inflammatory activity. In a second sample of African American youth, males who experienced more discrimination showed steeper increases in suPAR across their late 20s. Girls across both samples showed no associations of discrimination with inflammation. The consistency of findings across two distinct samples supports the reliability of these effects.
      These findings provide a potential mechanistic explanation for associations documented in previous reviews between discrimination experienced during childhood and a variety of mental health, health behavior, and (to a lesser extent) physical health outcomes (
      • Benner A.D.
      • Wang Y.
      • Shen Y.
      • Boyle A.E.
      • Polk R.
      • Cheng Y.P.
      Racial/ethnic discrimination and well-being during adolescence: A meta-analytic review.
      ,
      • Priest N.
      • Paradies Y.
      • Trenerry B.
      • Truong M.
      • Karlsen S.
      • Kelly Y.
      A systematic review of studies examining the relationship between reported racism and health and wellbeing for children and young people.
      ,
      • Cave L.
      • Cooper M.N.
      • Zubrick S.R.
      • Shepherd C.C.J.
      Racial discrimination and child and adolescent health in longitudinal studies: A systematic review.
      ). Specifically, this article suggests the role of a biological mechanism– inflammation– that has been implicated in the development of many aging-related health conditions across the continuum of mental and physical illnesses (
      • Nusslock R.
      • Miller G.E.
      Early-life adversity and physical and emotional health across the lifespan: A neuroimmune network hypothesis.
      ,
      • Hotamisligil G.S.
      Inflammation and metabolic disorders.
      ,
      • Libby P.
      • Ridker P.M.
      • Hansson G.K.
      Leducq Transatlantic Network on Atherothrombosis
      Inflammation in atherosclerosis: From pathophysiology to practice.
      ,
      • Miller A.H.
      • Maletic V.
      • Raison C.L.
      Inflammation and its discontents: The role of cytokines in the pathophysiology of major depression.
      ,
      • Raison C.L.
      • Miller A.H.
      Role of inflammation in depression: Implications for phenomenology, pathophysiology and treatment.
      ).
      In addition, this study extends prior longitudinal studies on discrimination and inflammation to an earlier period of life. Few studies of discrimination with multiple time points of inflammation have been conducted; among those that have, there are findings that discrimination predicts increases in inflammatory markers over time, although these studies have either focused on older adults or included a wide age range of adults (
      • Zahodne L.B.
      • Kraal A.Z.
      • Zaheed A.
      • Farris P.
      • Sol K.
      Longitudinal effects of race, ethnicity, and psychosocial disadvantage on systemic inflammation.
      ,
      • Sims K.D.
      • Sims M.
      • Glover L.M.
      • Smit E.
      • Odden M.C.
      Perceived discrimination and trajectories of C-reactive protein: The Jackson Heart Study.
      ). This study is the first to document that discrimination experienced during the extended adolescence period forecasts subsequent trajectories of inflammation (specifically among African American males).
      Across both study 1 and study 2, discrimination findings emerged for males but not females. The gender pattern was replicated both across two distinct samples of youth of color and across multiple inflammatory measures. These patterns are consistent with the mental health literature in adolescence, which has documented that associations of discrimination with outcomes such as conduct problems and substance use are stronger in boys than girls (
      • Assari S.
      • Mistry R.
      • Lee D.B.
      • Caldwell C.H.
      • Zimmerman M.A.
      Perceived racial discrimination and marijuana use a decade later; gender differences among Black youth.
      ,
      • Wiehe S.E.
      • Aalsma M.C.
      • Liu G.C.
      • Fortenberry J.D.
      Gender differences in the association between perceived discrimination and adolescent smoking.
      ,
      • Brody G.H.
      • Chen Y.F.
      • Murry V.M.
      • Ge X.
      • Simons R.L.
      • Gibbons F.X.
      • et al.
      Perceived discrimination and the adjustment of African American youths: A five-year longitudinal analysis with contextual moderation effects.
      ,
      • Brodish A.B.
      • Cogburn C.D.
      • Fuller-Rowell T.E.
      • Peck S.
      • Malanchuk O.
      • Eccles J.S.
      Perceived racial discrimination as a predictor of health behaviors: The moderating role of gender.
      ,
      • Lee D.B.
      • Heinze J.E.
      • Neblett E.W.
      • Caldwell C.H.
      • Zimmerman M.A.
      Trajectories of racial discrimination that predict problematic alcohol use among African American emerging adults.
      ,
      • Brody G.H.
      • Kogan S.M.
      • Chen Y.F.
      Perceived discrimination and longitudinal increases in adolescent substance use: Gender differences and mediational pathways.
      ), both cross-sectionally and longitudinally. We speculate that one reason for this pattern may be that the intersection of race and gender leads to different types of discrimination experiences. For example, African American males are more likely to report being profiled or having major life discrimination experiences, such as with police brutality (
      • Lewis T.T.
      • Van Dyke M.E.
      Discrimination and the health of African Americans: The potential importance of intersectionalities.
      ,
      • Kwate N.O.A.
      • Goodman M.S.
      Racism at the intersections: Gender and socioeconomic differences in the experience of racism among African Americans.
      ,
      • Assari S.
      • Moazen-Zadeh E.
      • Caldwell C.H.
      • Zimmerman M.A.
      Racial discrimination during adolescence predicts mental health deterioration in adulthood: Gender differences among Blacks.
      ). Consistent with this notion, African American males are more likely to specifically report experiences of disrespect and being threatened than any other race/gender group, and both African American and Hispanic males are more likely than other race/gender groups to endorse that others seem afraid of them (

      Deckard F, Goosby BJ, Schwadel P, Cheadle JE (in press): Everyday discrimination: Defined, measured, and contextualized, In: Grusky DB, Dahir N, Davis C. Social Stratification: Class, Race, and Gender in Sociological Perspectives, 5th Ed., Routledge Press; Oxfordshire, UK.

      ). It is possible that these particular types of discrimination experiences that revolve around physical threats are more strongly linked to physiological responses. In addition, males of color may also have been socialized to anticipate these types of threats and to prepare for bias (
      • Hughes D.
      • Rodriguez J.
      • Smith E.P.
      • Johnson D.J.
      • Stevenson H.C.
      • Spicer P.
      Parents’ ethnic-racial socialization practices: A review of research and directions for future study.
      ), potentially resulting in greater physiological responses to certain stressors. Our pattern of findings are consistent with a study in African American youth that documented associations of race-related vigilance with poorer cardiovascular health (decreased arterial elasticity) in boys but not girls (
      • Clark R.
      • Benkert R.A.
      • Flack J.M.
      Large arterial elasticity varies as a function of gender and racism-related vigilance in Black youth.
      ) and with a previous longitudinal study in women documenting no significant main effect of discrimination on CRP over time (
      • Beatty Moody D.L.
      • Brown C.
      • Matthews K.A.
      • Bromberger J.T.
      Everyday discrimination prospectively predicts inflammation across 7-years in racially diverse midlife women: Study of Women’s Health across the Nation.
      ). Moreover, a few previous studies have tested gender by discrimination interactions on inflammatory outcomes, with some finding interaction patterns (
      • Sims K.D.
      • Sims M.
      • Glover L.M.
      • Smit E.
      • Odden M.C.
      Perceived discrimination and trajectories of C-reactive protein: The Jackson Heart Study.
      ,
      • Kershaw K.N.
      • Lewis T.T.
      • Diez Roux A.V.
      • Jenny N.S.
      • Liu K.
      • Penedo F.J.
      • Carnethon M.R.
      Self-reported experiences of discrimination and inflammation among men and women: The multi-ethnic study of atherosclerosis.
      ) but others not (
      • Lewis T.T.
      • Aiello A.E.
      • Leurgans S.
      • Kelly J.
      • Barnes L.L.
      Self-reported experiences of everyday discrimination are associated with elevated C-reactive protein levels in older African-American adults.
      ,
      • Brody G.H.
      • Yu T.
      • Miller G.E.
      • Chen E.
      Discrimination, racial identity, and cytokine levels among African-American adolescents.
      ).
      In this study, discrimination predicted trajectories of suPAR but not CRP. These findings are consistent with recent research indicating that adverse childhood experiences, such as violence and low socioeconomic status, are more strongly related to suPAR than CRP in adulthood (
      • Rasmussen L.J.H.
      • Moffitt T.E.
      • Eugen-Olsen J.
      • Belsky D.W.
      • Danese A.
      • Harrington H.
      • et al.
      Cumulative childhood risk is associated with a new measure of chronic inflammation in adulthood.
      ,
      • Rasmussen L.J.H.
      • Moffitt T.E.
      • Arseneault L.
      • Danese A.
      • Eugen-Olsen J.
      • Fisher H.L.
      • et al.
      Association of adverse experiences and exposure to violence in childhood and adolescence with inflammatory burden in young people.
      ). Some have speculated that biomarkers such as CRP can reflect both chronic and acute inflammatory states, whereas suPAR may be a more stable marker of chronic inflammation (
      • Rasmussen L.J.H.
      • Moffitt T.E.
      • Eugen-Olsen J.
      • Belsky D.W.
      • Danese A.
      • Harrington H.
      • et al.
      Cumulative childhood risk is associated with a new measure of chronic inflammation in adulthood.
      ,
      • Rasmussen L.J.H.
      • Moffitt T.E.
      • Arseneault L.
      • Danese A.
      • Eugen-Olsen J.
      • Fisher H.L.
      • et al.
      Association of adverse experiences and exposure to violence in childhood and adolescence with inflammatory burden in young people.
      ), but this premise is in need of further testing.
      Strengths of this study include the two samples of youth of color (which demonstrates both the replicability of findings and the generalizability across two different types of cohorts), the multiple in-depth inflammatory measures, and the longitudinal findings in one of the cohorts. The fact that the pattern of gender interactions was replicated across the two samples lends credence to the reliability of these findings. Limitations include the lack of clinical health outcomes tracked into adulthood. In addition, the two studies used different measures of discrimination, although there are similarities in the questions posed. In addition, we note that these studies focus on interpersonal experiences of discrimination. However, it is important to acknowledge that racism exists at multiple levels, for example, at the institutional or structural level, in addition to the interpersonal level. Future studies should investigate the effects of different types and levels of discrimination and discrimination across the life course. In addition, future studies should incorporate assessment strategies other than self-report questionnaires of discrimination (e.g., interviews) and should investigate the role of discrimination in comparison to other types of life stressors. Finally, both studies used observational designs, meaning that causality cannot be inferred; however, study 1’s longitudinal design does allow us to draw some conclusions about directionality, in demonstrating that baseline discrimination predicted trajectories of suPAR over time.
      By highlighting associations between discrimination and inflammation across two studies, this article begins to elucidate mechanistic possibilities for how discrimination may come to affect both mental and physical health outcomes over time. Among adolescent males of color, high levels of discrimination were associated with multiple indications of a proinflammatory phenotype and with low-grade inflammatory activity that worsened over time. Discrimination is a pressing societal issue that will need to be addressed as part of efforts to promote health equity, and this study contributes to a body of research establishing its relevance to adolescent and adult health.

      Acknowledgments and Disclosures

      This research was supported by the National Institutes of Health (Grant Nos. HL122328 [to GEM] , HD030588 [to GHB] , HD093718 [to EC] , and DA051361 [to GHB] ).
      The authors report no biomedical financial interests or potential conflicts of interest.

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