racial/ethnic disparities in cortisol diurnal patterns and affect in...
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Racial/ethnic disparities in cortisol diurnal patterns and affectin adolescence
LILLYBELLE K. DEER,a GRANT S. SHIELDS,a SUSANNAH L. IVORY,b CAMELIA E. HOSTINAR,a AND
EVA H. TELZERb
aUniversity of California, Davis; and bUniversity of North Carolina, Chapel Hill
Abstract
Racial/ethnic minorities are more vulnerable to mental and physical health problems, but we know little about the psychobiological underpinnings of thesedisparities. In this study, we examined racial/ethnic differences in cortisol diurnal patterns and affect as initial steps toward elucidating long-term healthdisparities. A racially/ethnically diverse (39.5% White, 60.5% minority) sample of 370 adolescents (57.3% female) between the ages of 11.9 and 18 years(M ¼ 14.65 years, SD ¼ 1.39) participated in this study. These adolescents provided 16 cortisol samples (4 samples per day across 4 days), allowing thecomputation of diurnal cortisol slopes, the cortisol awakening response, and diurnal cortisol output (area under the curve), as well as daily diary ratings of high-arousal and low-arousal positive and negative affect. Consistent with prior research, we found that racial/ethnic minorities (particularly African American andLatino youth) exhibited flatter diurnal cortisol slopes compared to White youth, F (1, 344.7) ¼ 5.26, p ¼ .02, effect size g ¼ 0.25. Furthermore, AfricanAmerican and Asian American youth reported lower levels of positive affect (both high arousal and low arousal) compared to White youth. Racial/ethnicdifferences in affect did not explain differences in cortisol patterns, suggesting a need to refine our models of relations between affect and hypothalamic–pituitary–adrenocortical activity. We conclude by proposing that a deeper understanding of cultural development may help elucidate the complex associationsbetween affect and hypothalamic–pituitary–adrenocortical functioning and how they explain racial/ethnic differences in both affect and stress biology.
American youth belonging to racial/ethnic minority groups(e.g., African Americans, Latinos, and Asian Americans;henceforth, minorities) are more vulnerable to mental andphysical health problems (Alegria, Vallas, & Pumariega,2010; Braveman, Cubbin, Egerter, Williams, & Pamuk,2010; Merikangas et al., 2010), including mood and anxietydisorders (Merikangas et al., 2010), chronic fatigue (Dinoset al., 2009), and obesity (Ogden et al., 2006). These healthdisparities are potentially the result of differential exposureto stressful life experiences, which in turn result in differencesin the daily patterning of affective, cognitive, and biologicalprocesses that accumulate over time to foster psychopathol-ogy or physical illness (DeSantis et al., 2007; Levy, Heissel,Richeson, & Adam, 2016; Mays, Cochran, & Barnes, 2007;Shields, Moons, & Slavich, 2017; Susman, 2007). These
physical and mental health disparities can be thought of asthe products of developmental cascades, defined as the cumu-lative outcomes of interactions within and among developingsystems that can spread effects across domains of functioningand across generations (Masten & Cicchetti, 2010). The fieldof cultural development and psychopathology has begun ex-amining how cultural processes within the individual andwithin society can act within developmental cascades andset the stage for normal or abnormal behavior, risk, or resili-ence (for review, see Causadias, 2013). However, there is ascarcity of research testing the links between culturally bounddaily psychological experiences and biological processes thatmay help explain racial inequalities in long-term health out-comes. The current study aimed to address this gap by exam-ining associations between racial/ethnic1a differences in dailyaffective states and diurnal variation in the functioning of thehypothalamic–pituitary–adrenocortical (HPA) axis, one ofthe body’s primary stress-response systems. We focus onthe HPA axis given evidence linking HPA dysregulationwith concurrent and future symptoms of numerous mentaland physical health conditions across the life span (Adam
Address correspondence and reprint requests to: Eva H. Telzer, DavieHall Psychology Building, 235 E. Cameron Avenue, Chapel Hill, NC27599-3270; E-mail: [email protected]; or Camelia E. Hostinar, Universityof California-Davis, 202 Cousteau Place, Davis, CA 95618; E-mail: [email protected].
This manuscript was prepared with support from the Center for Poverty Re-search at University of California, Davis and National Science FoundationGrant 1327768 (to L.K.D. and C.E.H.), the National Institutes of HealthGrant R01DA039923 and National Science Foundation Grant SES1459719 (to E.H.T.), the Department of Psychology at the University of Illi-nois, and the Department of Psychology and Neuroscience at the Universityof North Carolina at Chapel Hill. We greatly appreciate Lynda Lin and Mi-chelle Miernicki for assistance with collecting and preparing the data. 1. We consider race and ethnicity to be sociocultural categories based on
physical appearance or cultural affiliation that people self-identify with(e.g., see definitions by Garcıa Coll et al., 1996). As both categories reflectsocially constructed groups, we do not differentiate one from the other inthis manuscript and instead use race/ethnicity to refer to self-identified be-longing to a social group.
Development and Psychopathology, 2018, page 1 of 17# Cambridge University Press 2018doi:10.1017/S0954579418001098
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et al., 2017; Shields & Slavich, 2017). In adolescence, dysre-gulated cortisol patterns have been associated with greaterrisk of current and future psychopathology such as depressionand anxiety (Adam, 2006; Adam et al., 2010; Carnegie et al.,2014; Doane et al., 2013; Van den Bergh & Van Calster,2009), as well as higher odds of common physical health con-ditions such as obesity (Ruttle et al., 2013). We focused onaffective states and their daily association with cortisol be-cause there is increasing evidence that approximately 50%of variability in diurnal HPA functioning is state dependent,as opposed to showing traitlike continuity (Ross, Murphy,Adam, Chen, & Miller, 2013). Given the high responsivityof this system to daily fluctuations based on experience, itis important to understand its associations with daily subjec-tive perceptions of affective states to better understand howthe system may be calibrated by experience during develop-ment. Even though we do not examine health disparities di-rectly in this study, our goal is to begin elucidating ethnic/ra-cial differences in affective and biological processes that mayset the stage for long-term health disparities.
Brief Overview of the HPA Axis
When confronted with physical or psychological challengesthat tax or overwhelm the organism’s capacity to cope, thebody initiates a stress response consisting of physiologicaland behavioral responses mediated by the nervous, endo-crine, and immune systems (Smith & Vale, 2006). TheHPA axis plays an integral role in these processes by mobiliz-ing energy for coping with stressors and modifying the indi-vidual’s responses to similar stressors in the future (Gunnar,Doom, & Esposito, 2015; Sapolsky, Romero, & Munck,2000). The activity of the HPA axis can be characterizedalong two basic dimensions, basal functioning and reactivityto stressors (Spencer & Deak, 2016). Basal HPA functioningfollows a diurnal rhythm whereby cortisol, one of the mainproducts of the HPA axis, is secreted in a pulsatile fashionacross the day, reaching peak levels in the morning approxi-mately 30 min after awakening, and declining graduallyacross the day to reach minimum levels at night (Gunnaret al., 2015). Superimposed on this basal rhythm is the reac-tivity of the HPA axis to physical or psychological threats towell-being (i.e., stressors). The HPA axis is powerfully acti-vated by social threats (Dickerson & Kemeny, 2004; Gunnar& Adam, 2012), as well as by physical threats more typicallystudied in nonhuman animals, such as immobilization (Smith& Vale, 2006).
There are three indices of basal HPA functioning that aremost commonly examined and that we focused on in thisstudy. The diurnal slope is a negative slope from morningto evening cortisol levels, and deviations from this typicalpattern such as flatter (i.e., less negative) slopes have beenlinked to deleterious emotional and physical health problems,particularly immune-related and metabolic outcomes (Adamet al., 2017). The cortisol awakening response is the rise incortisol production from wake-up to approximately 30 min
later and is thought to reflect distinct processes related to an-ticipated demands for energy mobilization for the day (Fries,Dettenborn, & Kirschbaum, 2009). General life stress and de-pression have been associated with an elevated cortisol awa-kening response, whereas posttraumatic stress, fatigue, burn-out, and exhaustion have been linked to an atypically lowcortisol awakening response (Boggero, Hostinar, Haak, Mur-phy, & Segerstrom, 2017; Chida & Steptoe, 2009). Finally,the area under the curve represents an integrated measureof total daily cortisol output, which is calculated based on re-peated measurements throughout the day and the spacing be-tween them (Pruessner, Kirschbaum, Meinlschmid, & Hell-hammer, 2003). Chronic stress has been associated withgreater total daily cortisol output, combined with a flatterslope; that is, this pattern results from lower than expectedmorning levels but higher afternoon and evening production(Miller, Chen, & Zhou, 2007).
Racial/Ethnic Differences in HPA Activity
The prior literature on racial/ethnic differences in HPA activ-ity is somewhat limited, but these differences in physiologyare important to explore as they may help us explain racial/ethnic health disparities. Some intriguing patterns have begunto emerge in studies with children (Bush, Obradovic, Adler,& Boyce, 2011; Martin, Bruce, & Fisher, 2012), adolescents(DeSantis et al., 2007; Hostinar, McQuillan, Mirous, Grant,& Adam, 2014; Zeiders, Causadias, & White, 2017), andadults (Bennett, Merritt, & Wolin, 2004; Cohen et al.,2006; Hajat et al., 2010; McCallum, Sorocco, & Fritsch,2006; Suglia et al., 2010). In particular, flatter cortisol slopeshave been observed in African American compared to Whiteyouth (DeSantis et al., 2007; Martin et al., 2012). Results aremore inconsistent when comparing other minorities (e.g., La-tino or multiracial youth) with White youth, with one studyfinding that Latino adolescents exhibited flatter slopes thannon-Latino White adolescents (DeSantis et al., 2007),whereas another did not detect a statistically significant dif-ference (Martin et al., 2012). In adults, studies have consis-tently found flatter slopes in both African American and La-tino compared to White adults (Cohen et al., 2006; Hajatet al., 2010; McCallum et al., 2006). In terms of daily cortisoloutput (area under the curve), ethnic minority kindergartenchildren tend to exhibit higher area under the curve comparedto White children (Bush et al., 2011), whereas Latino andAfrican American adults show lower area under the curvecompared to White adults (Hajat et al., 2010). Finally, forthe cortisol awakening response, African American adults ex-hibit a lower cortisol awakening response compared to Whiteadults, an effect that is even more pronounced in low- socio-economic status African Americans (Bennett et al., 2004).Overall, these patterns suggest that ethnic minorities tend toshow flatter diurnal cortisol slopes and lower cortisol awaken-ing response levels, with differences most consistently ob-served when comparing African Americans with Whitesand results being more mixed when comparing Latino with
L. K. Deer et al.2
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non-Latino Whites. Racial/ethnic differences in total cortisoloutput (area under the curve) have been more rarely investi-gated, as only two studies to date, one examining children(Bush et al., 2011) and one adults (Hajat et al., 2010), haveexplored these differences.
Current theory suggests that these ethnic/racial differencesare related to experiences of chronic stress in general andexperiences of discrimination more specifically, which aremore prevalent among African American and Latino groups(Lewis, Cogburn, & Williams, 2015). For instance, studieshave found that both recent and long-term exposure to per-ceived discrimination is associated with flatter cortisol slopes(Adam et al., 2015; Zeiders, Hoyt, & Adam, 2014) or greaterdaily cortisol output (Zeiders, Doane, & Roosa, 2012). How-ever, some studies have found that discrimination does notexplain the associations between race/ethnicity and diurnalcortisol (Cohen et al., 2006; Martin et al., 2012). A recentmeta-analysis of associations between racial discriminationand cortisol parameters sheds some light on these discrepantfindings (Korous, Causadias, & Casper, 2017). This meta-analysis reported that the average effect size for this associa-tion is small (r ¼ .04, though effects are slightly larger whenexperimental protocols are used, such as exposing partici-pants to instances of discrimination in the laboratory). Thesmall effect size suggests that more research is needed to ex-plore other factors that might further explain racial/ethnic dis-parities in cortisol output (Korous et al., 2017). Furthermore,researchers propose that specific experiences such as discri-mination may not fully or consistently account for racial/eth-nic differences in diurnal cortisol rhythms because they donot take into account individual differences in processingand coping with such experiences. Instead, considering par-ticipants’ affective states as aggregate measures of their re-sponses to multiple streams of experiences might be a moreproximal predictor of HPA patterns (Cohen et al., 2006).For this reason, the current study aimed to examine the extentto which racial/ethnic differences in affect may explain racial/ethnic differences in diurnal cortisol patterns.
Another possible explanation for racial/ethnic differencesin HPA activity is that they might be due to differences in so-cioeconomic status, which is closely intertwined with race/ethnicity in the United States. Previous studies have reportedassociations between socioeconomic status and cortisol pa-rameters, with minorities and low-socioeconomic status par-ticipants exhibiting similar profiles. However, this researchhas indicated that racial/ethnic differences in cortisol param-eters persist after accounting for socioeconomic status and in-dependently predict cortisol outcomes (Bush et al., 2011; Co-hen et al., 2006; DeSantis et al., 2007; Hajat et al., 2010;Martin et al., 2012). Moreover, ethnic minorities appear tobenefit less from high socioeconomic status compared toWhites (Bennett et al., 2004). Overall, these patterns suggestthat race/ethnicity and socioeconomic status may have uniqueimpacts on cortisol output. As such, the current study will ex-amine socioeconomic status as a covariate in the relationshipbetween race/ethnicity and cortisol parameters.
Associations Between HPA Functioning and Affect
Because the HPA axis reacts powerfully to social and physi-cal threats to well-being, most prior literature has focused onassociations between HPA activity and negative affect such asfear, sadness, or worry. For instance, one naturalistic study ofdiurnal cortisol rhythms in adolescents found that momentaryincreases in state negative affect (worry, stress, anger, or frus-tration) were significantly associated with higher basal corti-sol levels (Adam, 2006). In another study of a diverse sampleof adolescents, higher levels of negative affect were associ-ated with flatter diurnal cortisol slopes (DeSantis et al., 2007).
Research on associations between positive affect andHPA functioning has begun to emerge. For instance, excitingevents can elevate cortisol, such as competitive events amongadults (Bateup, Booth, Shirtcliff, & Granger, 2002; Carre,Muir, Belanger, & Putnam, 2006) and Christmas Eve forchildren (Flinn, 2006). Furthermore, the “cortisol boosthypothesis” suggests that cortisol elevations provide a boostof energy and may promote positive, alert states as well asbe protective against subsequent elevations in negativemood (Hoyt, Zeiders, Ehrlich, & Adam, 2016). Thus, the ex-tent to which cortisol levels are differentially associated withnegative versus positive affective states remains to be fullycharacterized. Furthermore, we know little about the extentto which differences in experiences of both positive andnegative affect may explain racial/ethnic disparities in HPAactivity. Nevertheless, we predicted that racial/ethnic minori-ties would report higher levels of negative affect and lowerlevels of positive affect compared to White youth based onprior evidence that they are at higher risk of developingmood and anxiety disorders (Merikangas et al., 2010), aswell as their greater exposure to life stressors, perceived dis-crimination, stereotype threat, and sleep difficulties (Levyet al., 2016), all of which can predispose toward negativeaffect and reduce positive affect.
The Present Study
Biological processes have historically been assumed to be hard-wired and universal, but recent research has challenged thisview and begun exploring the interplay between biology andculture, including racial/ethnic differences in life experiencesand psychopathology (Causadias, 2013; Causadias, Telzer, &Gonzales, 2018; Causadias, Telzer, & Lee, 2017). Many ques-tions remain unexplored in this area of inquiry, particularly re-garding the psychological interface between cultural and bio-logical variation. The current study aims to address this gapby examining the role of affective processes as possible media-tors of racial/ethnic differences in biological processes. Specif-ically, we aim to address the following questions:
Question 1. What is the magnitude of racial/ethnic differencesin diurnal cortisol patterns in adolescence?Hypothesis 1.1. We hypothesize that minority participantswill exhibit flatter cortisol slopes than White participants.
Racial/ethnic disparities in cortisol and affect 3
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Hypothesis 1.2. We hypothesize that minority participantswill exhibit a lower cortisol awakening response than Whiteparticipants.Hypothesis 1.3. We hypothesize that minority participantswill exhibit higher total cortisol output (area under the curve)than White participants.Question 2. What is the magnitude of racial/ethnic differencesin low-arousal and high-arousal positive and negative affectstates in adolescence?Hypothesis 2. We hypothesize that racial/ethnic minoritieswill report on average greater levels of negative affect andlower levels of positive affect.Question 3. Do differences in affective patterns explain (i.e.,statistically mediate) racial/ethnic differences observed indiurnal cortisol?Hypothesis 3. We hypothesize that affective patterns willstatistically mediate racial/ethnic differences observed indiurnal cortisol.
We focused on adolescence because this is a period of mas-sive changes in both the activity of the HPA axis and affectiveprocesses, which may explain the onset of psychopathologyduring this developmental window for many youth (Dahl &Gunnar, 2009). We used linear mixed modeling to capturedaily within-person variations in cortisol slopes, cortisol awa-kening response, and area under the curve. We focused onmultiple HPA indices given that they are influenced by differ-ent factors (Fries et al., 2009) and may point to different pa-thophysiological mechanisms toward later dysfunction. Westudied a diverse group of adolescents, with sizable subsam-ples for multiple racial/ethnic backgrounds including AfricanAmerican, Latino, Asian American, White, and multiracialyouth. This allowed us to capture racial/ethnic differencesacross multiple groups. In addition, we utilized a daily diaryapproach for assessing both positive and negative mood.Daily diary reports are the measurement method of choice
for assessing affect because they are more accurate thanmethods that can be subject to recall biases (Bolger, Davis,& Rafaeli, 2003).
Method
Participants
Participants included 370 adolescents (57.3% female) be-tween the ages of 11.9 and 18 years (M age ¼ 14.65 years,SD¼ 1.39 years; see Table 1 for detailed demographic infor-mation). Participants were recruited from the communityusing convenience sampling, including posting flyers atschools, posting on listservs serving ethnic minority families,recruiting participants from other studies who agreed to becontacted for other research studies, and word of mouth.Due to this method of recruitment, we do not have informa-tion on the percentage of the sample approached who partic-ipated. The sample was diverse in terms of race/ethnicity:39.46% non-Latino White (from here on referred to as White,N ¼ 146), 25.4% Asian American (N ¼ 94), 17.8% Latino(N ¼ 66), 10.8% African American (N ¼ 40), and 6.5%mixed race or other race (N¼ 24). The sample covered a fairlybroad range of the socioeconomic spectrum. When consider-ing maternal education as an index of socioeconomic status,9.73% of mothers had less than an eighth-grade education,2.43% completed junior high school, 11.35% attendedsome high school, 24.05% completed high school, 4.59% at-tended trade or vocational school, 21.89% completed college,and 22.97% completed graduate school (2.97% declined toanswer). Adolescents and parents completed written assentand consent in accordance with the institutional review board.
Procedure
Participants received diary checklists for 14 days and a salivacollection kit to complete on days 2 through 5. They had the
Table 1. Sample demographic information and descriptive statistics for primary constructs
Variable Mean SD Range N
Area under the curve 160.73 68.3 2185.58–492.92 358Cortisol awakening response 4.45 9.56 227.48–41.60 369Cortisol slope 21.27 0.74 24.52–1.18 360High-arousal positive affect 3.08 0.91 1.00–5.00 368Low-arousal positive affect 3.40 0.79 1.08–5.00 368High-arousal negative affect 1.66 0.69 1.00–4.06 368Low-arousal negative affect 1.77 0.64 1.00–4.61 368Age 14.65 1.39 11.91–18.02 370Sex
Male 158Female 212
RaceAfrican American 40Asian American 94Latino 66Mixed/other 24White 146
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option to complete the diaries with paper and pencil or via asecure website. For those completing with paper/pencil, wemonitored completion of the checklists by providing partici-pants with 14 manila envelopes and an electronic time stam-per. The time stamper is a small, handheld device that im-prints the current date and time and is programmed with asecurity code so that the correct date and time cannot be al-tered. Participants were instructed to place their completedchecklists into a sealed envelope each night and to stampthe seal of the envelope with the time stamper. For those com-pleting the surveys on the secure website, an e-mail with thelink to each daily survey was sent, and the time and date ofcompletion were recorded via the website.
Measures
Diurnal cortisol. Adolescents provided saliva samples acrossdays 2 through 5 of the daily diaries at four time points eachday (i.e., 16 total samples): (a) wake-up time, (b) 30 min afterwaking up, (c) 5 p.m. (or before dinner), and (d) 8 p.m. (orbefore bed). Participants were instructed to take their samplesbefore or at least 30 min after brushing their teeth, drinking,eating, or using tobacco. Participants were provided with acard to log the times of each sample using an electronictime stamper (Dymo Corporation, Stamford, CT), which im-printed the current date and time and was programmed with asecurity code such that adolescents could not alter the correcttime and date. Adolescents were instructed to stamp the cardbeside the appropriate heading for each sample and to placethe sample in their freezer. At the end of the saliva collectionperiod, research staff returned to the home or participantsbrought the samples to the lab, which were immediatelystored at 280 8C until shipment to the Laboratory ofBiological Psychology at the Technical University ofDresden, Germany, where they were assayed using high-sensitivity chemiluminescence-immunoassays (IBL Interna-tional, Hamburg, Germany). The interassay coefficient ofvariation was below 8%. A subsample of 251 participantsprovided information regarding medication they were taking,and among them, n¼ 6 reported using hormonal medications(corticosteroids, estradiol, etc.). Results were largely identical(within rounding error) when excluding these 6 participants,and thus we report our results on the full sample on which weconducted our analyses.
Participants received $10 for completing the diaries and$10 for completing the saliva samples. In addition, adoles-cents were told that they would receive a $20 bonus if inspec-tion of the data indicated that they had completed all the dia-ries and saliva samples correctly and on time.
We computed cortisol slopes, the cortisol awakening re-sponse, and cortisol area under the curve using standard for-mulas. Cortisol slopes were computed as the difference be-tween the fourth (bedtime) cortisol sample and the firstmorning sample, divided by the time elapsed between thesetwo samples. We computed the cortisol awakening responseas the increase in cortisol from wake to 30 min postwake. The
total cortisol area under the curve was computed from thefirst, third, and fourth cortisol concentrations (i.e., excludingthe second sample, which reflects the cortisol awakening re-sponse) using the standard trapezoid method (Pruessner et al.,2003).
Affect. Daily mood diary data from the 4 days when salivarycortisol was collected were used for our analyses in order tobest capture potential associations between daily affect andcortisol. Daily mood was assessed with items taken fromthe Profile of Mood States (McNair, Lorr, & Droppelman,1971). Adolescents used a 5-point scale ranging from 1(not at all) to 5 (extremely) to indicate the extent to whichthey felt a number of affective states each day: enthusiastic,excited, interested, and joyful (these were the high-arousalpositive valence states); calm, cheerful, and happy (the low-arousal positive valence states); angry, nervous, on edge,mad, uneasy, worried, embarrassed, and stressed (the high-arousal negative valence states); and discouraged, exhausted,fatigued, hopeless, sad, lonely, and bored (the low-arousalnegative valence states). The scores for the affective stateswithin each of these four superordinate categories were aver-aged together to create one score for the category. Confirmingour grouping of these emotions in the four superordinate ca-tegories, the internal consistencies for these subscales werehigh for high-arousal positive affect (a ¼ 0.88), low-arousalpositive affect (a ¼ 0.79), high-arousal negative affect (a ¼0.89), and low-arousal negative affect (a ¼ 0.82) states.
Data analysis plan
All analyses performed were linear mixed models that nesteddays (Level 1) within participants (Level 2). Fixed effectswere tested at the level of participants (i.e., Level 2). This sta-tistical approach accounts for dependency within participantsand introduces less bias related to missing data compared totraditional statistical analyses, such as repeated-measuresanalysis of variance (Finch, Bolin, & Kelley, 2014; Rauden-bush & Bryk, 2002). All analyses were conducted using the Rstatistical programming language, version 3.4.0 (R CoreTeam, 2017). Linear mixed models were estimated usingthe lmerTest package in R, version 2.0-33 (Kuznetsova,Brockhoff, & Christensen, 2017); estimated marginal meansand standard errors were derived using the lsmeans packagein R, version 2.26-3 (Lenth, 2016). All degrees of freedomwere estimated using the Satterthwaite approximation, whichmakes the analyses more robust to outliers and violations ofnormality but entails that the degrees of freedom containnumbers that are not integers (Keselman, Algina, Kowalchuk,& Wolfinger, 1999; Kuznetsova et al., 2017).
To address our first question, we conducted linear mixedmodels that included race/ethnicity as a Level 2 predictor ofcortisol parameters (slope, cortisol awakening response, andarea under the curve) examined independently in separateanalyses. We first treated race/ethnicity as a binary variable(0 ¼ “White,” 1 ¼ “racial/ethnic minority”), which was fur-
Racial/ethnic disparities in cortisol and affect 5
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ther probed in follow-up analyses examining specific con-trasts between five racial/ethnic groups: White, Asian Amer-ican, Latino, African American, and mixed/other. For thesefollow-up contrasts, we report which findings remain signif-icant after applying a Bonferroni correction (p , .005) tominimize risk of Type I error given the number of pairwisecomparisons conducted. We report estimated marginal meansand standard errors derived from our models for each racial/ethnic group, as these best represent our results. Age andsex were included as covariates in all analyses, though the re-sults were essentially identical without these covariates in-cluded. For our second research question, we conducted lin-ear mixed models that included race/ethnicity as a Level 2predictor of four affect variables (low-arousal and high-arousal positive affect, and low-arousal and high-arousalnegative affect). Similar to cortisol analyses, we first treatedrace/ethnicity as a binary variable, and then examined spe-cific contrasts between all five racial/ethnic groups. For ourthird question, we examined whether racial/ethnic differencesin affect explain cortisol differences. We entered the affectvariables into linear mixed models as predictors of cortisolparameters to test cortisol–affect associations and to explorewhether racial/ethnic differences in cortisol patterns persistafter accounting for differences in affect. Finally, we con-ducted sensitivity analyses to test the extent to which the re-sults of these analyses change when accounting for time ofwake-up and maternal education (a proxy for socioeconomicstatus), both of which have been linked to cortisol functioningin past studies (e.g., Kudielka, Hellhammer, & Wust, 2009).We report confidence intervals and effect sizes (Hedge’s g)for all primary results.
Results
Descriptive statistics
Table 1 displays sample characteristics on major constructs.Table 2 displays bivariate correlations among the major con-structs of interest in the entire sample, whereas Table 3 showsthe same correlations separately for the minority group andthe White group. As can be seen in Table 2, cortisol indices
showed expected correlations with each other, and affect vari-ables also showed significant correlations with each other.The only cortisol measure showing significant associationswith affect was the cortisol awakening response, which waspositively correlated with high-arousal negative affect and in-versely correlated with low-arousal positive affect. The slopeand area under the curve did not show any strong or signifi-cant bivariate associations with affect variables.
Question 1. What is the magnitude of racial/ethnicdifferences in diurnal cortisol patterns in adolescence?
Effects of minority status. A comparison of White andminority participants’ cortisol slopes controlling for ageand sex indicated that there was a significant main effect ofrace/ethnicity, F (1, 344.7) ¼ 5.26, p ¼ .02, v2
partial ¼ .012,such that minority participants had flatter (i.e., less negative)slopes (M ¼ –1.19, SE ¼ 0.05) compared to White partici-pants (M ¼ –1.36, SE ¼ 0.06), 95% confidence intervaldifference (CIdiff ) [0.02, 0.31], g ¼ 0.25. This effect can beobserved in Figure 1, which displays average cortisol diurnalslopes for minority and White youth. There was also a signif-icant main effect of age, F (1, 350.0) ¼ 28.67, p , .001,v2
partial ¼ .073, indicating that slopes were flatter for older par-ticipants than younger (B ¼ 0.14, SE ¼ 0.03, b ¼ 0.19). Theeffect of sex was not significant (p¼ .13; g¼ –0.16). Similaranalyses of participants’ cortisol awakening responseindicated that there was not a significant main effect of race(p ¼ .055, v2
partial ¼ .008), age (p ¼ .06, v2partial ¼ .008), or
sex (p ¼ .23, g ¼ 0.13).Analyses of participants’ area under the curve cortisol out-
put indicated that there was not a significant main effect ofrace (p ¼ .21, v2
partial ¼ .002). There were, however, signifi-cant effects for both covariates. The significant main effect ofage, F (1, 351.2)¼ 33.64, p , .001, v2
partial ¼ .085, indicatedthat cortisol area under the curve was lower for olderparticipants compared to younger ones (B ¼ –14.06, SE ¼2.42, b ¼ –0.20). The significant main effect of sex, F(1, 352.7) ¼ 6.09, p ¼ .01, v2
partial ¼ .014, indicated thatfemale participants exhibited larger cortisol areas under thecurve (M ¼ 168.02, SE ¼ 5.23) than male participants (M ¼151.30, SE ¼ 4.40), 95% CIdiff [3.39, 30.04], g ¼ 0.26.
Table 2. Bivariate correlations among major constructs in the entire sample
Variable 1. 2. 3. 4. 5. 6. 7. 8.
1. Area under the curve 12. Cortisol awakening response –.06 13. Cortisol slope –.62** .26** 14. High-arousal positive affect –.10 –.08 .10 15. Low-arousal positive affect –.05 –.10* .05 .79** 16. High-arousal negative affect .00 .13* .06 –.11* –.32** 17. Low-arousal negative affect .01 .10 .06 –.22** –.38** .84** 18. Age –.31** –.08 .30** .02 –.05 .08 .09 1
*p , .05. **p , .001.
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Follow-up by specific racial/ethnic categories. To furtherprobe the effects of minority status, we next consideredrace/ethnicity as a five-level category and examined pairwisecontrasts between specific groups. For cortisol slopes, therewas again a significant main effect of race, F (4, 348.5) ¼2.91, p ¼ .02, v2
partial ¼ .021, and age, F (1, 344.8) ¼16.65, p , .001, v2
partial ¼ .043, with the effect of sex beingnonsignificant (p ¼ .12, v2
partial ¼ .004). As illustrated inFigure 2, contrasts indicated that the cortisol slopes of African
American participants (M ¼ –1.02, SE ¼ 0.11) were signifi-cantly flatter than the slopes of the White group (M ¼ –1.36,SE ¼ 0.06), t (339.8) ¼ –2.76, p ¼ .006, 95% CIdiff [0.10,0.57], g ¼ 0.30, and the Asian American group (M ¼
–1.31, SE ¼ 0.07), t (344.8) ¼ 2.21, p ¼ .03, 95% CIdiff
[0.03, 0.54], g ¼ 0.24. Furthermore, Latino youth alsoshowed flatter slopes than the White group (M ¼ –1.08, SE¼ 0.10), t (361.8) ¼ –2.38, p ¼ .02, 95% CIdiff [0.05,0.51], g ¼ 0.25. However, when the Bonferroni correction
Table 3. Bivariate correlations among major constructs within minority group (top panel) and White participants (bottompanel)
Minority participants 1. 2. 3. 4. 5. 6. 7. 8.
1. Area under the curve 12. Cortisol awakening response –.07 13. Cortisol slope –.61*** .17* 14. High-arousal positive affect –.20** –.11 .19** 15. Low-arousal positive affect –.13 –.10 .14* .76*** 16. High-arousal negative affect .03 .09 .03 –.06 –.26*** 17. Low-arousal negative affect .08 .09 .03 –.19** –.33*** .84*** 18. Age –.31*** –.07 .26*** .04 –.08 .08 .06 1
White participants 1. 2. 3. 4. 5. 6. 7. 8.1. Area under the curve 12. Cortisol awakening response –.08 13. Cortisol slope –.63*** .41*** 14. High-arousal positive affect .02 –.06 .04 15. Low-arousal positive affect .04 –.14 –.02 .83*** 16. High-arousal negative affect –.04 .17* .10 –.19* –.43*** 17. Low-arousal negative affect –.09 .10 .11 –.27** –.47*** .85*** 18. Age –.27*** –.08 .33*** .02 .04 .10 .15 1
*p , .05. **p , .01. ***p , .001.
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Figure 1. Average diurnal cortisol patterns by race/ethnicity coded in a binary fashion as minority versus White.
Racial/ethnic disparities in cortisol and affect 7
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was applied using a cutoff of p , .005, these pairwise differ-ences were no longer statistically significant. As before, thesignificant main effect of age indicated that older participantshad flatter slopes than younger participants (B ¼ 0.12, SE ¼0.03, b ¼ 0.16).
For the cortisol awakening response, analyses indicatedthat there was not a significant effect of race (p ¼ .08,v2
partial ¼ .013), age (p ¼ .33, v2partial , .001), or sex (p ¼
.32, v2partial , .001). Contrasts indicated that there was a sig-
nificant difference between White and Latino participants,t (332.3) ¼ 2.35, p ¼ .02, 95% CIdiff [–6.34, –0.56], g ¼–0.26, such that Latino participants exhibited a lower cortisolawakening response (M ¼ 2.11, SE ¼ 1.23) compared toWhite participants (M¼ 5.56, SE¼ 0.74; see Figure 3). How-ever, this was not a significant difference when applying theBonferroni correction (p , .005). There were no other signif-icant pairwise differences, ps . .05, jgjs , .17.
For cortisol area under the curve, analyses revealed a sig-nificant main effect of race, F (4, 354.5) ¼ 5.52, p , .001,v2
partial ¼ .048, age, F (1, 351.9) ¼ 11.41, p , .001, v2partial
¼ .029, and sex F (1, 353.6) ¼ 5.38, p ¼ .02, v2partial ¼
.012. The significant main effect of race was driven by Latinoparticipants exhibiting significantly lower area under thecurve values than each of the other groups and mixed/otherparticipants showing significantly higher values than eachof the other groups (see Figure 4). Latino participants (M ¼125.20, SE ¼ 9.13) showed lower area under the curve corti-sol than White participants (M ¼ 165.19, SE ¼ 5.10),t (357.4) ¼ 3.74, p , .001, 95% CIdiff [–61.03, –18.96], g¼ –0.39, African American participants (M ¼ 152.83, SE¼ 10.13), t (362.4) ¼ 1.99, p ¼ .05, 95% CIdiff [0.35,
54.91], g ¼ 0.21, Asian American participants (M ¼
164.55, SE ¼ 6.58), t (364.0) ¼ 3.36, p , .001, 95% CIdiff
[16.29, 62.40], g ¼ 0.35, and mixed/other participants (M¼ 192.73, SE ¼ 12.74), t (347.7) ¼ –4.26, p , .001, 95%CIdiff [–98.69, –36.38], g ¼ –0.46. Mixed/other participantsexhibited higher area under the curves than White partici-pants, t (340.2) ¼ –2.02, p ¼ .04, 95% CIdiff [0.68, 54.40],g ¼ 0.22, African American participants, t (349.5) ¼–2.47, p ¼ .01, 95% CIdiff [–71.73, –8.08], g¼ –0.26, AsianAmerican participants, t (344.8)¼ –1.98, p¼ .05, 95% CIdiff
[–56.21, –0.16], g ¼ –0.21, and, as noted above, Latinoparticipants. When employing the Bonferroni correction(p , .005), the remaining significant contrasts were betweenLatino and White participants, Latino and Asian Americanparticipants, and Latino and mixed/other participants. As be-fore, the significant main effect of age indicated that olderparticipants had lower cortisol area under the curve thanyounger ones (B ¼ –9.14, SE ¼ 2.71, b ¼ –0.13), and thatfemale participants (M ¼ 167.77, SE ¼ 4.79) had larger cor-tisol area under the curve than males (M ¼ 152.43, SE ¼5.59), 95% CIdiff [2.33, 28.35], g ¼ 0.25.
Question 2. What is the magnitude of racial/ethnicdifferences in low-arousal and high-arousal positive andnegative affect states in adolescence?
Effects of minority status. In analyses controlling for age andsex, minority participants reported lower levels of positive af-fect (both low-arousal and high-arousal positive affect) com-pared to White participants, with no significant differences innegative affect (see Figure 5). Specifically, a comparison of
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Figure 2. Average cortisol slopes by race/ethnicity (all five groups shown).
L. K. Deer et al.8
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White and minority participants’ levels of high-arousal posi-tive affect indicated a significant main effect of race, F(1, 387.9)¼ 6.43, p¼ .01, v2
partial ¼ .014, such that minorityyouth reported lower levels of high-arousal positive affect(M ¼ 2.94, SE ¼ 0.06) compared to White participants(M ¼ 3.17, SE ¼ 0.07), 95% CIdiff [–0.40, –0.05], g ¼–0.26. Similarly, there was a significant main effect of race
for low-arousal positive affect, F (1, 387.2) ¼ 6.59, p ¼.01, v2
partial ¼ .014, such that minority participants reportedsignificantly lower levels of low-arousal positive affect(M ¼ 3.26, SE ¼ 0.05) compared to White participants(M ¼ 3.45, SE ¼ 0.06), 95% CIdiff [–0.35, –0.05], g ¼–0.26. The main effect of race was much weaker and didnot reach statistical significance for either high-arousal or
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Figure 3. Average cortisol awakening response by specific racial/ethnic group. *p , .05.
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Figure 4. Average area under the curve (total daily cortisol output) by specific racial/ethnic group. Significant pairwise contrasts shown.*p , .05. **p , .01. ***p , .001.
Racial/ethnic disparities in cortisol and affect 9
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low-arousal negative affect, p¼ .50, 95% CIdiff [–0.17, 0.09],g ¼ –0.07, and p ¼ .31, 95% CIdiff [–0.19, 0.06], g ¼ 0.10,respectively.
In terms of sex and age differences, for high-arousalnegative affect there was a significant main effect of sex,F (1, 394.9) ¼ 5.48, p ¼ .02, such that female participants(M¼ 1.72, SE¼ 0.04) reported higher levels of high-arousalnegative affect than male participants (M¼ 1.57, SE¼ 0.05),95% CIdiff [0.02, 0.28], g¼ 0.24. In addition, there was a sig-nificant main effect of both age and sex for low-arousalnegative affect. The significant main effect of age, F(1, 402.2)¼ 4.14, p¼ .04, indicated that older participants ex-perienced higher levels of low-arousal negative affect thanyounger ones (B¼ 0.05, SE¼ 0.02,b¼ 0.08). The significantmain effect of sex, F (1, 396.8)¼ 6.37, p¼ .01, indicated thatfemale participants (M ¼ 1.84, SE ¼ 0.04) reported higherlevels of low-arousal negative affect than male participants(M ¼ 1.68, SE ¼ 0.05), 95% CIdiff [0.03, 0.28], g ¼ 0.25.
Follow-up by specific racial/ethnic categories. Next,we probed racial/ethnic differences in affect using the five-category race/ethnicity variable and each of the four affect
variables as outcomes: high-arousal positive affect, low-arousal positive affect, high-arousal negative affect, andlow-arousal negative affect. All models controlled for ageand sex. Analyses predicting high-arousal positive affect re-vealed a significant main effect of race, F (4, 399.8) ¼5.81, p , .001, v2
partial ¼ .045. Specifically, White youth(M¼ 3.16, SE¼ 0.07) reported higher levels of high-arousalpositive affect than African American youth (M¼ 2.71, SE¼0.13), t (398.3) ¼ 3.07, p ¼ .002, 95% CIdiff [–0.73, –0.16],g¼ –0.31, and Asian American youth (M¼ 2.81 SE¼ 0.08),t (381.8) ¼ 3.25, p ¼ .001, 95% CIdiff [–0.56, –0.41], g ¼–0.33. Furthermore, Latino youth (M ¼ 3.37, SE ¼ 0.12)also reported higher levels of high-arousal positive affectthan both African American, t (415.8) ¼ –3.63, p , .001,95% CIdiff [–1.01, –0.30], g ¼ –0.36, and Asian Americanyouth, t (417.8) ¼ –3.63, p , .001, 95% CIdiff [–0.86,–0.25], g ¼ –0.35. All of these differences remained signifi-cant after Bonferroni correction (p , .005). White and Latinoyouth did not differ from each other, p ¼ .15, 95% CIdiff
[–0.49, 0.07], g ¼ –0.14 (see Figure 6).Similarly, analyses of racial differences in low-arousal
positive affect revealed a significant main effect of race, F
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Figure 5. Self-reported affect by race/ethnicity (binary-coded as minority vs. White). *p , .05.
L. K. Deer et al.10
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(4, 398.7)¼ 2.99, p¼ .02, v2partial ¼ .019. Contrasts indicated
that White participants (M¼ 3.45, SE¼ 0.06) reported higherlevels of low-arousal positive affect than both African Amer-ican (M ¼ 3.12, SE ¼ 0.11), t (396.5) ¼ 2.59, p ¼ .01, 95%CIdiff [0.08, 0.57], g¼ 0.26, and Asian American participants(M¼ 3.19, SE¼ 0.07), t (378.0)¼ 2.76, p¼ .006, 95% CIdiff
[0.07, 0.44], g ¼ 0.28, while Latino participants (M ¼ 3.44,SE¼ 0.11) reported higher levels than African American par-ticipants, t (417.2)¼ –2.00, p¼ .05, 95% CIdiff [–0.00, 0.62],g¼ 0.20. However, none of these comparisons held up to theBonferroni correction (p , .005).
There was not a significant main effect of race forhigh-arousal negative affect, p ¼ .66, v2
partial , .001, butthere were significant main effects of age, F (1, 390.2) ¼3.95, p ¼ .05, v2
partial ¼ .007, and sex, F (1, 395.0) ¼ 5.14,p ¼ .02, v2
partial ¼ .010. Older participants experienced morehigh-arousal negative affect than younger participants (B ¼0.053, SE ¼ 0.026, b ¼ 0.09). Females (M ¼ 1.70, SE ¼0.05) experienced more high-arousal negative affect than males(M ¼ 1.56, SE¼ 0.05), 95% CIdiff [0.02, 0.28], g ¼ 0.23.
Finally, analyses of racial differences for low-arousalnegative affect revealed significant main effects of race,F (4, 399.9) ¼ 2.70, p ¼ .03, v2
partial ¼ .017, age,F (1, 392.1) ¼ 7.33, p ¼ .01, v2
partial ¼ .016, and sex, F
(1, 396.7) ¼ 5.06, p ¼ .02, v2partial ¼ .010. The main effect
of race appeared to be driven by African American partici-pants reporting higher levels of low-arousal negative affect(M ¼ 1.94, SE ¼ 0.09) than both Asian American (M ¼1.70, SE ¼ 0.06), t (395.8) ¼ 2.24, p ¼ .03, 95% CIdiff
[0.03, 0.45], g ¼ 0.22, and Latino participants (M ¼ 1.57,SE ¼ 0.09), t (414.7) ¼ 2.94, p ¼ .003, 95% CIdiff [0.12,0.62], g ¼ 0.29. However, only the difference betweenAfrican American and Latino participants held up to theBonferroni correction (p , .005). Older participants alsoexperienced more low-arousal negative affect than youngerparticipants (B ¼ 0.067, SE¼ 0.024, b ¼ 0.12), and females(M ¼ 1.83, SE ¼ 0.04) experienced more low-arousalnegative affect than males (M ¼ 1.70, SE ¼ 0.05), 95%CIdiff [0.02, 0.26], g ¼ 0.23.
Question 3. Do differences in affective patterns explainracial/ethnic differences in diurnal cortisol?
We next attempted to determine if racial/ethnic differences inaffect explained differences in diurnal rhythms of cortisol.However, none of the affect variables we considered were sig-nificantly related to cortisol area under the curve or the corti-sol awakening response (ps . .05) in models including race/
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Figure 6. Self-reported affect by specific racial/ethnic categories. *p , .05. **p , .01. ***p , .001.
Racial/ethnic disparities in cortisol and affect 11
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ethnicity, age, and sex, entailing that racial/ethnic differencesin affect did not mediate differences in diurnal rhythms ofcortisol as measured by cortisol area under the curve or thecortisol awakening response. As for cortisol slopes, high-arousal positive affect was related to more positive (i.e.,steeper) cortisol slopes, B ¼ 0.09, t (1174.3) ¼ 2.21, p ¼.03, as was low-arousal negative affect, B ¼ 0.16,t (1103.4)¼ 2.28, p¼ .02, in models including race/ethnicity,age, and sex. However, these affect variables did not mediateracial differences in cortisol slopes (ps . .05), which re-mained largely identical after controlling for affect variables.
Sensitivity analyses
We conducted follow-up analyses to examine whether resultswere robust when controlling for time of wake-up, an impor-tant variable when considering diurnal variation in cortisolpatterns, and when taking into account socioeconomic status.Results were unchanged when controlling for time of wake-up. When considering the binary race/ethnicity variable, con-trolling for time of wake-up did not change any of the results:in these analyses, race/ethnicity still did not significantlymoderate the cortisol awakening response, F (1, 331.8) ¼3.06, p ¼ .081, v2
partial ¼ .006, or area under the curve, F(1, 328.8) ¼ 2.42, p ¼ .121, v2
partial ¼ .004, while still mod-erating cortisol slopes, F (1, 332.9) ¼ 5.97, p ¼ .015, v2
partial¼ .015. When the race/ethnicity variable including multiplecategories was used, race/ethnicity was still not a significantmoderator of the cortisol awakening response, F (4, 342.3)¼1.71, p ¼ .146, v2
partial ¼ .008, but consistent with prior anal-yses remained a significant moderator of cortisol slopes, F(4, 341.0) ¼ 2.55, p ¼ .039, v2
partial ¼ .018, and area underthe curve, F (4, 341.8) ¼ 5.74, p , .001, v2
partial ¼ .052.We then added maternal education (a proxy for socioeco-
nomic status) to models including race/ethnicity, age, andsex. After controlling for maternal education, the previous re-sults regarding cortisol patterns, affect patterns, and media-tion of cortisol differences by affect were unchanged, andmany of the significant results identified above were strength-ened (data available upon request). This is despite a signifi-cant role of maternal education in some of these models,which indicated that higher maternal education was associ-ated with steeper diurnal cortisol slopes, F (1, 325.7) ¼9.77, B¼ 0.072, SE¼ 0.023, b¼ 0.129, p¼ .002, and lowercortisol area under the curve, F (1, 333.5)¼ 4.63, B¼ –4.63,SE¼ 2.15, b¼ –0.089, p¼ .032. Maternal education was notsignificantly associated with the cortisol awakening response,F (1, 337.0)¼ 0.14, B¼ 0.11, SE¼ 0.31, b¼ 0.014, p¼ .71.
Discussion
The present study had three primary goals: (a) to examine ra-cial/ethnic differences in diurnal cortisol patterns in adoles-cence, (b) to investigate how adolescents’ low-arousal andhigh-arousal positive and negative affect states differ byrace/ethnicity, and (c) to assess whether differences in daily
affective patterns explain racial/ethnic differences in diurnalcortisol.
Racial/ethnic differences in HPA activity
We first examined racial/ethnic differences in diurnal cortisolproduction. These analyses indicated that minority adoles-cents exhibited flatter cortisol slopes than White adolescents,with a small overall effect size for race/ethnicity (g ¼ 0.25).Follow-up analyses indicated that these overall patterns weredue to both African American and Latino youth exhibitingflatter slopes than White youth. The pairwise contrasts didnot survive the Bonferroni correction, indicating that the ef-fect sizes were small. Nevertheless, these results are consis-tent with prior research reporting flatter cortisol slopes inAfrican American children and adolescents (DeSantis et al.,2007; Martin et al., 2012), adults (Cohen et al., 2006; Hajatet al., 2010), and older adults (McCallum et al., 2006) thanin Whites. Replicating results from another study with adoles-cents, we also found flatter cortisol slopes among Latinoyouth compared to White youth (DeSantis et al., 2007).The finding that Asian American youth had steeper slopesthan African American youth and slopes that were similarto White youth is a novel finding. The cortisol slope patternsare noteworthy because flatter slopes have been linked tomultiple emotional and physical health problems, includingimmune-related and metabolic conditions (Adam et al.,2017). Of note, these conditions (e.g., diabetes) are moreprevalent among African American and Latino individualscompared to White and Asian American (Centers for DiseaseControl and Prevention, 2017). Together, these results sug-gest that flattened diurnal cortisol slopes may play a role inbiological processes that contribute to racial/ethnic health dis-parities.
In contrast, the cortisol awakening response appeared to bea weaker candidate for explaining racial/ethnic health dispari-ties, given only marginal differences in the cortisol awaken-ing response by race/ethnicity irrespective of whether we con-sidered it as a binary outcome (minority vs. White) or as afive-level categorical variable. The only significant pairwisedifference we identified was that of a lower cortisol awaken-ing response in Latino compared to White youth, which mir-rors findings from another study with adults (Hajat et al.,2010). However, the effect size was small and this findingdid not survive the Bonferroni correction. It is possible thatthis pattern may signal a specific but small vulnerability ofLatino groups to conditions linked in prior meta-analyses toa low cortisol awakening response, such as posttraumaticstress, fatigue, burnout, and exhaustion (Boggero et al.,2017; Chida & Steptoe, 2009).
Racial/ethnic comparisons regarding the cortisol area un-der the curve revealed that Latino youth exhibited the lowestarea under the curve, whereas youth belonging to mixed/othergroups showed the highest area under the curve of all groups.Even with the Bonferroni correction, Latino youth continuedto show significantly lower area under the curve compared to
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White, Asian American, and mixed/other youth. However,the pattern showing higher area under the curve for themixed/other group was less robust, did not survive Bonferronicorrection, and should be replicated given lack of prior litera-ture regarding this heterogeneous group. Again, we foundthat Asian American youth had area under the curve levelsthat were similar to White youth, which is a novel findingand suggests that similar biological processes may be inplay for these two groups. Prior studies have revealed a poten-tial developmental shift, such that racial/ethnic minority chil-dren display lower area under the curve compared to Whitechildren (Bush et al., 2011), whereas in adults, Latino andAfrican American groups show lower area under the curvecompared to Whites (Hajat et al., 2010). It is possible thatearly in development chronic stress associated with racial/eth-nic minority status leads to increased daily cortisol output(Bush et al., 2011), but over time this results in downregula-tion of the HPA axis and lower area under the curve, cortisolawakening response, and flatter slopes, the effects noted inLatino and African American participants in our sample.These complex patterns of hypocortisolism may be explainedby a meta-analysis of studies on chronic stress and HPA activ-ity (Miller et al., 2007), which revealed that HPA activity in-creases acutely after stressor onset but reduces over time asstressors become more chronic.
The psychological mechanisms that explain the HPA ra-cial/ethnic differences we observed are not well understood.Experiences of discrimination in particular and life stress ingeneral may influence patterns of cortisol production, as theHPA axis responds strongly to social stressors (Dickerson& Kemeny, 2004; Gunnar & Adam, 2012). However, somerecent research has indicated that cortisol rhythms are not as-sociated with or persist after accounting for exposure to dis-crimination (Cohen et al., 2006; Martin et al., 2012). This in-dicates that other factors need to be explored in futureresearch, as these may help to explain the racial/ethnic differ-ences observed. We considered affect differences as a possi-ble explanation.
Racial/ethnic differences in affect
The second goal of the study was to explore racial/ethnic dif-ferences in affect. These analyses indicated that racial/ethnicminority participants endorsed lower levels of positive affect(both high and low arousal) compared to White youth,whereas the differences between the two groups on negativeaffect (either high- or low-arousal negative affect) were small,close to zero. The most pronounced differences were thatAfrican American and Asian American youth reported lowerlevels of positive affect (both high arousal and low arousal)compared to White youth. Effect sizes were larger for high-arousal positive affect, where all the contrasts remained sig-nificant even after the Bonferroni correction. These resultsare consistent with the greater risk of mood disorders in ethnicminorities compared to White youth (Merikangas et al.,2010), and findings that indicate lower positive affect among
minority samples are also consistent with some previous re-search that has noted differences in emotion expression andvalence between Asian American and White children (Lewis,Takai-Kawakami, Kawakami, & Sullivan, 2009) and adults(Gross, Richards, & John, 2006). Research on cultural differ-ences in ideal affect, or affect that people would like to feel,has also indicated that Asian Americans are less likely to en-dorse high arousal positive states and more likely to endorselow arousal positive states than European Americans (for re-view, see Tsai, 2007).
Positive affect and greater neural responsivity to rewardsprotect against the development of adolescent depression(Forbes & Dahl, 2005, 2012). Furthermore, positive affectis associated with better health, lower morbidity, and greaterlongevity (Pressman & Cohen, 2005). Thus, our findingssuggest that African American and Asian American youthmay be at increased risk of developing mental and physicalhealth problems across their life span.
The group differences in measures of negative valence af-fect were small, suggesting either that the true difference inthe population is zero or that the effect is too small to have suf-ficient power to detect it with a sample of 370. Previousresearch with adults has noted that affect may not be felt andexpressed in the same manner across races (Jang, Kwag, &Chiriboga, 2012), or that there may be differences in the idealaffect to be expressed among cultures (Tsai, 2007), and differ-ent associations with risk of psychopathology across races(Moazen-Zadeh & Assari, 2016). Thus, it could be that ra-cial/ethnic minority youth were underreporting their negativeaffect, as some studies have found that African Americanstend to underreport negative affect (Bardwell & Dimsdale,2001). However, taking the findings at face value could alsosuggest that this was a high-functioning, low-risk minoritysample that experienced low levels of negative affect. While re-search is often biased toward finding deficits or signs of dep-rivation among minorities (for review, see Garcıa Coll et al.,1996), we must remember that there is considerable diversitywithin and between minorities and that many minority youthare in good mental health despite facing race-based stressors.
Differences in affect did not explain cortisol differences
The third goal of the study was to assess whether affectivepatterns explain racial/ethnic differences in diurnal cortisol.We did not find any evidence of statistical mediation thatwould indicate this. This is consistent with another study ofadolescents, which found that higher levels of negative affectwere associated with flatter slopes, but negative affect did notexplain racial/ethnic differences in cortisol slopes (DeSantiset al., 2007). There are several possible explanations for thesefindings. We may need to consider refining our models of re-lations between affect and HPA activity for a number of rea-sons. For instance, transactions between affect and HPAphysiology occur on multiple time scales, from moments todays and years (Adam, 2012), thus incorporating informationon either affect or HPA physiology from other time points
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may allow us to better parse out momentary correlations be-tween affect and physiology. Another possibility is that af-fect–physiology associations are moderated by various pro-tective factors (e.g., coping strategies) that may differ byrace/ethnicity. This would explain why some ethnic minori-ties differ from the majority group in levels of affect (e.g.,Asian American youth compared to Whites), but this didnot translate into physiological differences in HPA function-ing, perhaps due to successful coping efforts. Furthermore,research indicates that the HPA system serves multiple bio-logical functions (e.g., metabolic and immune) beyond its in-volvement in emotional processes (Gunnar & Adam, 2012).Thus, it may be that affect–cortisol associations are difficultto capture without a large panel of covariates relevant todiet, physical activity, immune function, and so on. Further-more, the HPA axis is only one of the body’s stress-mediatingsystems and likely cannot fully explain racial/ethnic healthdisparities on its own. Considering multisystem indicatorsof allostatic load may capture stronger differences by race/ethnicity (Doan & Evans, 2017).
A second possibility is that racial/ethnic differences inphysiology and affect may be small, making the detectionof relations between them challenging. Deficit models ofminority development often assume that racial/ethnic minori-ties should consistently show worse outcomes on any givenmeasure (Causadias, Vitriol, & Atkin, 2018), but our findingsindicate that differences between minority and White youthare not always found, and even when differences exist, effectsizes tend to be small. As such, the current study adds to theimportant evidence base documenting both similarities anddifferences between minority and White youth. These find-ings suggest that, even in a society where there is structuralinequality, we find evidence of equifinality (i.e., different de-velopmental pathways can lead to similar outcomes on corti-sol and affect) and multifinality (i.e., those with similar devel-opmental pathways can have differing outcomes; Cicchetti &Rogosch, 1996). The heterogeneity within and between ra-cial/ethnic groups should be further explored to understandthe developmental processes underlying equifinality andmultifinality.
Finally, another possible explanation is that the racial/eth-nic groups we considered here may be heterogeneous andcontain multiple meaningful subgroups once we considerother cultural aspects (e.g., immigration status, acculturationstress, family norms and values, and identity processes).Thus, a single aspect of racial/ethnic identity may not fullyexplain biological differences observed (for a more in-depthdiscussion of the complex nature of culture–biology inter-play, see Causadias, Telzer, & Gonzalez, 2018). For example,an individual’s identification with his/her culture could act inconcert with experiences they have in everyday life to influ-ence their biology and affect (Causadias, Telzer, & Gonzalez,2018; Zeiders, Causadias, & White, 2017). We propose that,until we account for multiple aspects of cultural identity, theobserved racial/ethnic differences in affect and physiologymay remain difficult to explain.
The roles of age, sex, and maternal education
Our results indicate some noteworthy effects for age and sexin predicting both diurnal cortisol and daily affect. Age was asignificant predictor of diurnal cortisol slopes and cortisolarea under the curve, such that older participants exhibitedflatter slopes and lower area under the curve than youngerparticipants. These findings are somewhat consistent withprevious research in adolescents, which has indicated thatolder adolescents exhibit flatter cortisol slopes (due to lowermorning and higher evening cortisol output) and higher areaunder the curve cortisol than younger adolescents (Gunnar,Wewerka, Frenn, Long, & Griggs, 2009; Shirtcliff et al.,2012). In previous work, however, the age of the participantsranged from age 9 to 15, while the participants in the currentstudy ranged from age 11.9 to 18. Previous research has pro-posed that there is a possible “U-shaped” curve in the cortisoloutput of older children and adolescents, such that levels de-crease in the preteen years, increase in early adolescence, andthen decrease across adolescence (Shirtcliff et al., 2012). Al-though this study did not examine children in the preteenstage, participants in the current study ranged from earlythrough later adolescence so the results may have capturedadolescents at the peak of the curve and on the way down.One hypothesis is that this curve signifies a time of increasedenvironmental and neurobiological sensitivity early in ado-lescence (Shirtcliff et al., 2012). Future research should ex-amine the full extent of this developmental pattern from latechildhood through early adulthood.
Sex was a significant predictor of cortisol area under thecurve, such that girls had higher area under the curve valuesthan boys. This is consistent with previous evidence indicat-ing that girls tend to exhibit higher afternoon and evening cor-tisol levels than boys (Klimes-Dougan, Hastings, Granger,Usher, & Zahn-Waxler, 2001), although more research isneeded to corroborate these patterns. When examining affect,there were no significant differences by age or sex for the pos-itive affect indices. However, there were significant differ-ences for both of the negative affect indices, such that olderparticipants and girls reported higher levels of negative affect(both low arousal and high arousal). This result is consistentwith previous research, which has indicated that there may beincreases in negative affect as adolescents get older (Larson,Moneta, Richards, & Wilson, 2002) and that girls tend to ex-hibit higher levels of negative affect than boys (Silk, Stein-berg, & Morris, 2003). Furthermore, girls have a higher inci-dence of depression during adolescence (Avenevoli,Swendsen, He, Burstein, & Merikangas, 2015). These find-ings point to a need to consider the cumulative roles ofrace/ethnicity, age, and sex in predicting and mitigating futurepsychopathology and health problems.
When examining the role of socioeconomic status, asindexed by maternal education, we found that racial/ethnicdifferences persisted after accounting for maternal educationstatistically, consistent with prior studies with children, ado-lescents, and adults (Bush et al., 2011; Cohen et al., 2006; De-
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Santis et al., 2007; Hajat et al., 2010; Martin et al., 2012).This suggests that maternal education does not explain theseracial/ethnic differences, even though differences by maternaleducation mirrored the patterns observed in ethnic minorityyouth, for example, flatter cortisol slopes were noted forboth minority youth and youth whose mothers had lower edu-cational attainment. These patterns may be explained by sim-ilar processes, for example, a higher stress burden in both eth-nic minority youth and low-socioeconomic status youth,which encompasses different types of stressors for eachgroup, though this possibility will need to be confirmed em-pirically in future research.
Conclusions
This study has some noteworthy strengths. This is a large anddiverse sample for adolescent research, which allows morefine-grained comparisons between the various racial/ethnicgroups rather than a simple contrast between minorities ver-sus Whites, which is common in prior literature. In addition,the frequent and rigorous sampling of cortisol (4 samples perday on 4 separate days) provides greater reliability of mea-surement than sampling participants on 1 or 2 days. However,this study is not without its limitations. As this was a cross-sectional design, we do not know how trait or long-term pat-terns of affect might relate to cortisol. Similarly, the measuresof diurnal cortisol included here only capture momentary out-put, while a more chronic measure of output, such as hair cor-tisol (Meyer & Novak, 2012), may uncover a different set ofassociations. Moreover, analyses in this study modeled corti-sol diurnal slopes by the difference between morning andevening levels. Therefore, we may have lost variabilityamong individuals using this approach. Relatedly, a higherfrequency sampling schedule would have added precisionto our area under the curve estimates (Hoyt, Ehrlich, Cham,& Adam, 2016). The number of cortisol samples was chosento minimize participant burden, and a recent study suggests
that while adding more samples might have improved theaccuracy of area under the curve estimates, it likely wouldnot have increased accuracy of the cortisol awakeningresponse and diurnal slope estimates though it would haveimposed a much higher burden on participants (Hoyt,Ehrlich, et al., 2016). Finally, another limitation is that theAfrican American and mixed/other groups had fewer than50 participants in each group, potentially limiting our abilityto detect significant differences between groups if thesedifferences exist. Future studies should replicate our analyseswith larger samples.
In sum, this study replicates a number of prior findings re-garding racial/ethnic differences in cortisol and affect, butalso raises novel questions regarding these patterns giventhat affective differences did not explain differences in hor-monal output. Race/ethnicity is frequently treated as a simpledemographic variable that is associated with specific experi-ences such as discrimination, but prior studies have shownthat this factor does not fully account for the observed ra-cial/ethnic differences in cortisol patterns. We hypothesizethat conceptualizing race/ethnicity in a broader culturalframework that includes numerous cultural aspects such asnorms, attitudes, media exposure, family and social networks,connections with a home country, and so on, may shed morelight on racial/ethnic differences in affect and biology thanour study and previous empirical investigations. In particular,understanding the complex influences on the socioemotionaldevelopment of minority youth will require a comprehensivemeasurement of developmental competencies and challengesat multiple levels of analysis (Garcıa Coll et al., 1996). Fur-thermore, more research is needed that will assess cultural as-pects of development, affect, biology, and health outcomeswithin the same participants, in order to test key assumptionsabout the pathways to racial/ethnic health disparities. Whenwe can better understand these pathways, we will be betterequipped to design culturally sensitive interventions thatcan effectively combat existing health disparities.
References
Adam, E. K. (2006). Transactions among adolescent trait and state emotion anddiurnal and momentary cortisol activity in naturalistic settings. Psychoneu-roendocrinology, 31, 664–679. doi:10.1016/j.psyneuen.2006.01.010
Adam, E. K. (2012). Emotion-cortisol transactions occur over multiple timescales in development: Implications for research on emotion and the de-velopment of emotional disorders. Monographs of the Society for Re-search in Child Development, 77, 17–27. doi:10.1111/j.1540-5834.2012.00657.x
Adam, E. K., Doane, L. D., Zinbarg, R. E., Mineka, S., Craske, M. G., &Griffith, J. W. (2010). Prospective prediction of major depressive disor-der from cortisol awakening responses in adolescence. Psychoneuroen-docrinology, 35, 921–931. doi:10.1016/j.psyneuen.2009.12.007
Adam, E. K., Heissel, J. A., Zeiders, K. H., Richeson, J. A., Ross, E. C.,Ehrlich, K. B., . . . Eccles, J. S. (2015). Developmental histories of per-ceived racial discrimination and diurnal cortisol profiles in adulthood:A 20-year prospective study. Psychoneuroendocrinology, 62, 279–291.doi:10.1016/j.psyneuen.2015.08.018
Adam, E. K., Quinn, M. E., Tavernier, R., McQuillan, M. T., Dahlke, K. A.,& Gilbert, K. E. (2017). Diurnal cortisol slopes and mental and physicalhealth outcomes: A systematic review and meta-analysis. Psychoneuro-endocrinology, 83, 25–41. doi:10.1016/j.psyneuen.2017.05.018
Alegria, M., Vallas, M., & Pumariega, A. J. (2010). Racial and ethnicdisparities in pediatric mental health. Child and Adolescent Psychiat-ric Clinics of North America, 19, 759–774. doi:10.1016/j.chc.2010.07.001
Avenevoli, S., Swendsen, J., He, J. P., Burstein, M., & Merikangas, K. R.(2015). Major depression in the National Comorbidity Survey—Adoles-cent Supplement: Prevalence, correlates, and treatment. Journal of theAmerican Academy of Child & Adolescent Psychiatry, 54, 37–44.doi:10.1016/j.jaac.2014.10.010
Bardwell, W. A., & Dimsdale, J. E. (2001). The impact of ethnicity and re-sponse bias on the self-report of negative affect. Journal of Applied Bio-behavioral Research, 6, 27–38.
Bateup, H. S., Booth, A., Shirtcliff, E. A., & Granger, D. A. (2002). Testos-terone, cortisol, and women’s competition. Evolution and Human Behav-ior, 23, 181–192. doi:10.1016/S1090-5138(01)00100-3
Bennett, G. G., Merritt, M. M., & Wolin, K. Y. (2004). Ethnicity, education,and the cortisol response to awakening: A preliminary investigation. Eth-nicity & Health, 9, 337–347. doi:10.1080/1355785042000285366
Boggero, I. A., Hostinar, C. E., Haak, E. A., Murphy, M. L. M., & Seger-strom, S. C. (2017). Psychosocial functioning and the cortisol awakeningresponse: Meta-analysis, P-curve analysis, and evaluation of the eviden-
Racial/ethnic disparities in cortisol and affect 15
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tial value in existing studies. Biological Psychology, 129, 207–230.doi:10.1016/j.biopsycho.2017.08.058
Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life asit is lived. Annual Review of Psychology, 54, 579–616. doi:10.1146/an-nurev.psych.54.101601.145030
Braveman, P. A., Cubbin, C., Egerter, S., Williams, D. R., & Pamuk, E.(2010). Socioeconomic disparities in health in the United States: Whatthe patterns tell us. American Journal of Public Health, 100(Suppl. 1),S186–S196. doi:10.2105/AJPH.2009.166082
Bush, N. R., Obradovic, J., Adler, N., & Boyce, W. T. (2011). Kindergartenstressors and cumulative adrenocortical activation: The “first straws” ofallostatic load? Development and Psychopathology, 23, 1089–1106.doi:10.1017/S0954579411000514
Carnegie, R., Araya, R., Ben-Shlomo, Y., Glover, V., O’Connor, T. G.,O’Donnell, K. J., . . . Lewis, G. (2014). Cortisol awakening responseand subsequent depression: Prospective longitudinal study. British Jour-nal of Psychiatry, 204, 137–143. doi:10.1192/bjp.bp.113.126250
Carre, , J., Muir, C., Belanger, J., & Putnam, S. K. (2006). Pre-competitionhormonal and psychological levels of elite hockey players: Relationshipto the “home advantage.” Physiology & Behavior, 89, 392–398.doi:10.1016/j.physbeh.2006.07.011
Causadias, J. M. (2013). A roadmap for the integration of culture into devel-opmental psychopathology. Development and Psychopathology, 25(4,Pt. 2), 1375–1398.
Causadias, J. M., Telzer, E. H., & Gonzales, N. A. (2018). Introduction toculture and biology interplay. In J. M. Causadias, E. H. Telzer, & N.A. Gonzales (Eds.), Handbook of culture and biology. New York: Wiley.
Causadias, J. M., Telzer, E. H., & Lee, R. M. (2017). Culture and biology in-terplay: An introduction. Cultural Diversity & Ethnic Minority Psychol-ogy, 23, 1–4. doi:10.1037/cdp0000121
Causadias, J. M., Vitriol, J. A., & Atkin, A. L. (2018). Do we overemphasizethe role of culture in the behavior of racial/ethinc minorities? Evidence ofa cultural (mis)attribution bias in American Psychology. American Psy-chologist. Advance online publication. doi:10.1037/amp0000099
Centers for Disease Control and Prevention. (2017). National Diabetes Statis-tics Report, 2017. Atlanta, GA: Centers for Disease Control and Preven-tion, U.S. Dept of Health and Human Services.
Chida, Y., & Steptoe, A. (2009). Cortisol awakening response and psychoso-cial factors: A systematic review and meta-analysis. Biological Psychol-ogy, 80, 265–278. doi:10.1016/j.biopsycho.2008.10.004
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in de-velopmental psychopathology. Development and Psychopathology, 8,597–600.
Cohen, S., Schwartz, J. E., Epel, E., Kirschbaum, C., Sidney, S., & Seeman,T. (2006). Socioeconomic status, race, and diurnal cortisol decline in theCoronary Artery Risk Development in Young Adults (CARDIA) Study.Psychosomatic Medicine, 68, 41–50. doi:10.1097/01.psy.0000195967.51768.ea
Dahl, R. E., & Gunnar, M. R. (2009). Heightened stress responsiveness andemotional reactivity during pubertal maturation: Implications for psycho-pathology. Development and Psychopathology, 21, 1–6. doi:10.1017/S0954579409000017
DeSantis, A. S., Adam, E. K., Doane, L. D., Mineka, S., Zinbarg, R. E., &Craske, M. G. (2007). Racial/ethnic differences in cortisol diurnalrhythms in a community sample of adolescents. Journal of AdolescentHealth, 41, 3–13. doi:10.1016/j.jadohealth.2007.03.006
Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol re-sponses: A theoretical integration and synthesis of laboratory research. Psy-chological Bulletin, 130, 355–391. doi:10.1037/0033-2909.130.3.355
Dinos, S., Khoshaba, B., Ashby, D., White, P. D., Nazroo, J., Wessely, S., &Bhui, K. S. (2009). A systematic review of chronic fatigue, its syndromesand ethnicity: Prevalence, severity, co-morbidity and coping. Interna-tional Journal of Epidemiology, 38, 1554–1570. doi:10.1093/ije/dyp147
Doan, S. N., & Evans, G. W. (2017). Relations among culture, poverty, stress,and allostatic load. In J. M. Causadias, E. H. Telzer, & N. A. Gonzalez(Eds.), The handbook of culture and biology. New York: Wiley.
Doane, L. D., Mineka, S., Zinbarg, R. E., Craske, M., Griffith, J. W., &Adam, E. K. (2013). Are flatter diurnal cortisol rhythms associatedwith major depression and anxiety disorders in late adolescence? Therole of life stress and daily negative emotion. Development and Psycho-pathology, 25, 629–642. doi:10.1017/S0954579413000060
Finch, W. H., Bolin, J. E., & Kelley, K. (2014). Multilevel modeling using R.Boca Raton, FL: CRC Press.
Flinn, M. V. (2006). Evolution and ontogeny of stress response to social chal-lenges in the human child. Developmental Review, 26, 138–174.doi:10.1016/j.dr.2006.02.003
Forbes, E. E., & Dahl, R. E. (2005). Neural systems of positive affect: Rele-vance to understanding child and adolescent depression? Developmentand Psychopathology, 17, 827–850. doi:10.1017/S095457940505039x
Forbes, E. E., & Dahl, R. E. (2012). Research Review: Altered rewardfunction in adolescent depression: What, when and how? Journal ofChild Psychology and Psychiatry, 53, 3–15. doi:10.1111/j.1469-7610.2011.02477.x
Fries, E., Dettenborn, L., & Kirschbaum, C. (2009). The cortisol awakeningresponse (CAR): Facts and future directions. International Journal ofPsychophysiology, 72, 67–73. doi:10.1016/j.ijpsycho.2008.03.014
Garcıa Coll, C., Lamberty, G., Jenkins, R., McAdoo, H. P., Crnic, K., Wasik,B. H., & Garcia, H. V. (1996). An integrative model for the study of de-velopmental competencies in minority children. Child Development, 67,1891–1914. doi:10.1111/j.1467-8624.1996.tb01834.x
Gross, J. J., Richards, J. M., & John, O. P. (2006). Emotion regulation in ev-eryday life. In D. K. Snyder, J. Simpson, & J. N. Huges (Eds.), Emotionregulation in couples and families: Pathways to dysfunction and health(pp. 13–35). Washington, DC: American Psychological Association.doi:10.1037/11468-001
Gunnar, M. R., & Adam, E. K. (2012). The hypothalamic-pituitary-adreno-cortical system and emotion: Current wisdom and future directions.Monographs of the Society for Research in Child Development, 77,109–119. doi:10.1111/j.1540-5834.2011.00669.x
Gunnar, M. R., Doom, J. R., & Esposito, E. A. (2015). Psychoneuroendo-crinology of stress: Normative development and individual differences.In R. M. Lerner (Ed.), Handbook of child psychology and developmentalscience (7th ed., Vol. 3, pp. 106–151). Hoboken, NJ: Wiley.
Gunnar, M. R., Wewerka, S., Frenn, K., Long, J. D., & Griggs, C. (2009).Developmental changes in hypothalamic-pituitary-adrenal activity overthe transition to adolescence: Normative changes and associations withpuberty. Developmental Psychopathology, 21, 69–85. doi:10.1017/S0954579409000054
Hajat, A., Diez-Roux, A., Franklin, T. G., Seeman, T., Shrager, S., Ranjit, N.,. . . Kirschbaum, C. (2010). Socioeconomic and race/ethnic differences indaily salivary cortisol profiles: The Multi-Ethnic Study of Atherosclero-sis. Psychoneuroendocrinology, 35, 932–943. doi:10.1016/j.psy-neuen.2009.12.009
Hostinar, C. E., McQuillan, M. T., Mirous, H. J., Grant, K. E., & Adam, E. K.(2014). Cortisol responses to a group public speaking task for adoles-cents: Variations by age, gender, and race. Psychoneuroendocrinology,50, 155–166. doi:10.1016/j.psyneuen.2014.08.015
Hoyt, L. T., Ehrlich, K. B., Cham, H., & Adam, E. K. (2016). Balancing sci-entific accuracy and participant burden: Testing the impact of samplingintensity on diurnal cortisol indices. Stress, 19, 476–485. doi:10.1080/10253890.2016.1206884
Hoyt, L. T., Zeiders, K. H., Ehrlich, K. B., & Adam, E. K. (2016). Positiveupshots of cortisol in everyday life. Emotion, 16, 431–435.doi:10.1037/emo0000174
Jang, Y., Kwag, K. H., & Chiriboga, D. A. (2012). Not saying I am happydoes not mean I am not: Cultural influences on responses to positive af-fect items in the CES-D. Journals of Gerontology. Series B, Psycholog-ical Sciences and Social Sciences, 65, 684–690. doi:10.1093/geronb/gbq052
Keselman, H. J., Algina, J., Kowalchuk, R. K., & Wolfinger, R. D. (1999).The analysis of repeated measurements: A comparison of mixed-modelSatterthwaite F tests and a nonpooled adjusted degrees of freedom multi-variate test. Communications in Statistics—Theory and Methods, 28,2967–2999. doi:10.1080/03610929908832460
Klimes-Dougan, B., Hastings, P. D., Granger, D. A., Usher, B. A., & Zahn-Waxler, C. (2001). Adrenocortical activity in at-risk and normally devel-oping adolescents: Individual differences in salivary cortisol basal levels,diurnal variation, and responses to social challenges. Development andPsychopathology, 13, 695–719.
Korous, K. M., Causadias, J. M., & Casper, D. M. (2017). Racial discrimina-tion and cortisol output: A meta-analysis. Social Science & Medicine,193, 90–100. doi:10.1016/j.socscimed.2017.09.042
Kudielka, B. M., Hellhammer, D. H., & Wust, S. (2009). Why do we respondso differently? Reviewing determinants of human salivary cortisol re-sponses to challenge. Psychoneuroendocrinology, 34, 2–18.doi:10.1016/j.psyneuen.2008.10.004
L. K. Deer et al.16
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Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest:Tests in linear mixed effects models. R package version 2.0-33 [Compu-ter software]. Retrieved from https://www.R-project.org/
Larson, R. W., Moneta, G., Richards, M. H., & Wilson, S. (2002). Continu-ity, stability, and change in daily emotional experience across adoles-cence. Child Development, 73, 1151–1165.
Lenth, R. V. (2016). Least-squares means: The R package lsmeans. Journalof Statistical Software, 69, 1–33. doi:10.18637/jss.v069.i01
Levy, D. J., Heissel, J. A., Richeson, J. A., & Adam, E. K. (2016). Psycho-logical and biological responses to race-based social stress as pathwaysto disparities in educational outcomes. American Psychologist, 71,455–473. doi:10.1037/a0040322
Lewis, M., Takai-Kawakami, K., Kawakami, K., & Sullivan, M. W. (2009).Cultural differences in emotional responses to success and failure. Inter-national Journal of Behavioral Development, 34, 53–61. doi:10.1177/0165025409348559
Lewis, T. T., Cogburn, C. D., & Williams, D. R. (2015). Self-reported experi-ences of discrimination and health: Scientific advances, ongoing contro-versies, and emerging issues. Annual Review Clinical Psychology, 11,407–440. doi:10.1146/annurev-clinpsy-032814-112728
Martin, C. G., Bruce, J., & Fisher, P. A. (2012). Racial and ethnic differencesin diurnal cortisol rhythms in preadolescents: The role of parental psycho-social risk and monitoring. Hormones and Behavior, 61, 661–668.doi:10.1016/j.yhbeh.2012.02.025
Masten, A. S., & Cicchetti, D. (2010). Developmental cascades. Developmentand Psychopathology, 22, 491–495. doi:10.1017/S0954579410000222
Mays, V. M., Cochran, S. D., & Barnes, N. W. (2007). Race, race-based dis-crimination, and health outcomes among African Americans. Annual Re-view of Psychology, 58, 201–225. doi:10.1146/annurev.psych.57.102904.190212
McCallum, T. J., Sorocco, K. H., & Fritsch, T. (2006). Mental health anddiurnal salivary cortisol patterns among African American and EuropeanAmerican female dementia family caregivers. American Journal of Ger-iatric Psychiatry, 14, 684–693. doi:10.1097/01.JGP.0000225109.85406.89
McNair, D. M., Lorr, M., & Droppelman, L. F. (1971). Manual for the profileof mood states. San Diego, CA: Educational and Industrial Testing Ser-vice.
Merikangas, K. R., He, J. P., Burstein, M., Swanson, S. A., Avenevoli, S.,Cui, L. H., . . . Swendsen, J. (2010). Lifetime prevalence of mentaldisorders in U.S. adolescents: Results from the National ComorbiditySurvey Replication—Adolescent Supplement (NCS-A). Journal of theAmerican Academy of Child & Adolescent Psychiatry, 49, 980–989.doi:10.1016/j.jaac.2010.05.017
Meyer, J. S., & Novak, M. A. (2012). Minireview: Hair cortisol: A novel bio-marker of hypothalamic-pituitary-adrenocortical activity. Endocrinol-ogy, 153, 4120–4127. doi:10.1210/en.2012-1226
Miller, G. E., Chen, E., & Zhou, E. S. (2007). If it goes up, must it comedown? Chronic stress and the hypothalamic-pituitary-adrenocorticalaxis in humans. Psychological Bulletin, 133, 25–45. doi:10.1037/0033-2909.133.1.25
Moazen-Zadeh, E., & Assari, S. (2016). Depressive symptoms predict majordepressive disorder after 15 years among Whites but not Blacks. Fron-tiers in Public Health, 4. doi:10.3389/fpubh.2016.00013
Ogden, C. L., Carroll, M. D., Curtin, L. R., McDowell, M. A., Tabak, C. J., &Flegal, K. M. (2006). Prevalence of overweight and obesity in the UnitedStates, 1999–2004. Journal of the American Medical Association, 295,1549–1555. doi:10.1001/jama.295.13.1549
Pressman, S. D., & Cohen, S. (2005). Does positive affect influence health?Psychological Bulletin, 131, 925–971. doi:10.1037/0033-2909.131.6.925
Pruessner, J. C., Kirschbaum, C., Meinlschmid, G., & Hellhammer, D. H.(2003). Two formulas for computation of the area under the curve repre-sent measures of total hormone concentration versus time-dependentchange. Psychoneuroendocrinology, 28, 916–931. doi:10.1016/S0306-4530(02)00108-7
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models:Applications and data analysis methods. Thousand Oaks, CA: Sage.
R Core Team. (2017). R: A language and environment for statistical comput-ing [Computer software]. Vienna, Austria: R Foundation for StatisticalComputing. Retrieved from https://www.R-project.org/
Ross, K. M., Murphy, M. L. M., Adam, E. K., Chen, E., & Miller, G. E.(2014). How stable are diurnal cortiosl activity indices in healthy indi-viduals? Evidence from three multi-wave studies. Psychoneuroendocrin-ology, 39, 184–193. doi:10.1016/j.psyneuen.2013.09.016
Ruttle, P. L., Javaras, K. N., Klein, M. H., Armstrong, J. M., Burk, L. R., &Essex, M. J. (2013). Concurrent and longitudinal associationsbetween diurnal cortisol and body mass index across adolescence.Journal of Adolescent Health, 52, 731–737. doi:10.1016/j.jado-health.2012.11.013
Sapolsky, R. M., Romero, L. M., & Munck, A. U. (2000). How do glucocor-ticoids influence stress responses? Integrating permissive, suppressive,stimulatory, and preparative actions. Endocrine Reviews, 21, 55–89.doi:10.1210/edrv.21.1.0389
Shields, G. S., Moons, W. G., & Slavich, G. M. (2017). Inflammation, self-regulation, and health: An immunologic model of self-regulatory failure.Perspectives on Psychological Science, 12, 588–612. doi:10.1177/1745691616689091
Shields, G. S., & Slavich, G. M. (2017). Lifetime stress exposure and health:A review of contemporary assessment methods and biological mecha-nisms. Social and Personality Psychology Compass, 11, e12335.doi:10.1111/spc3.12335
Shirtcliff, E. A., Allison, A. L., Armstrong, J. M., Slattery, M. J., Kalin, N. H.,& Essex, M. J. (2012). Longitudinal stability and developmental proper-ties of salivary cortisol levels and circadian rhythms from childhood toadolesence. Developmental Psychobiology, 54, 493–502. doi:10/1002/dev.20607
Silk, J. S., Steinberg, L., & Morris, A. S. (2003). Adolescents’ emotion reg-ulation in daily life: Links to depressive symptoms and problem behavior.Child Development, 74, 1869–1880.
Smith, S. M., & Vale, W. W. (2006). The role of the hypothalamic-pituitary-adrenal axis in neuroendocrine responses to stress. Dialogues ClinicalNeuroscience, 8, 383–395.
Spencer, R. L., & Deak, T. (2016). A users guide to HPA axis research. Phys-iology & Behavior, 178, 43–65. doi:10.1016/j.physbeh.2016.11.014
Suglia, S. F., Staudenmayer, J., Cohen, S., Enlow, M. B., Rich-Edwards,J. W., & Wright, R. J. (2010). Cumulative stress and cortisol disruptionamong black and Hispanic pregnant women in an urban cohort. Psycho-logical Trauma—Theory Research Practice and Policy, 2, 326–334.doi:10.1037/a0018953
Susman, E. J. (2007). Toward a psychobiologic understanding of youthhealth disparities. Journal of Adolescent Health, 41, 1–2. doi:10.1016/j.jadohealth.2007.04.002
Tsai, J. L. (2007). Ideal affect: Cultural causes and behavioral consequences.Perspectives in Psychological Science, 2, 242–259. doi:10.1111/j.1745-6916.2007.00043.x
Van den Bergh, B. R. H., & Van Calster, B. (2009). Diurnal cortisol profilesand evening cortisol in post-pubertal adolescents scoring high on theChildren’s Depression Inventory. Psychoneuroendocrinology, 34, 791–794. doi:10.1016/j.psyneuen.2008.12.008
Zeiders, K. H., Causadias, J. M., & White, R. M. B. (2017). The healthcorrelates of culture: Examining the association between ethnic-racialidentity and diurnal cortisol slopes. Journal of Adolescent Health, 62,349–351. doi:10.1016/j.jadohealth.2017.09.020
Zeiders, K. H., Doane, L. D., & Roosa, M. W. (2012). Perceived discrimina-tion and diurnal cortisol: Examining relations among Mexican Americanadolescents. Hormones and Behavior, 61, 541–548. doi:10.1016/j.yh-beh.2012.01.018
Zeiders, K. H., Hoyt, L. T., & Adam, E. K. (2014). Associations betweenself-reported discrimination and diurnal cortisol rhythms among youngadults: The moderating role of racial-ethnic minority status. Psychoneu-roendocrinology, 50, 280–288. doi:10.1016/j.psyneuen.2014.08.023
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