Everyday Discrimination and Diurnal Cortisol During Adolescence PDF

HHS Public Access Author manuscript Horm Behav. Author manuscript; available in PMC 2017 April 01. Author Manuscript Published in final edited form as: Horm Behav. 2016 April ; 80: 76–81. doi:10...

HHS Public Access Author manuscript Horm Behav. Author manuscript; available in PMC 2017 April 01. Author Manuscript Published in final edited form as: Horm Behav. 2016 April ; 80: 76–81. doi:10.1016/j.yhbeh.2016.01.009. Everyday Discrimination and Diurnal Cortisol during Adolescence Virginia W. Huynh, Ph.D.a,*, Shu-Sha Angie Guan, Ph. D.a, David M. Almeida, Ph.D.b, Heather McCreath, Ph.D.c, and Andrew J. Fuligni, Ph.D.d,e,f aDepartment of Child and Adolescent Development, California State University, Northridge bDepartment of Human Development and Family Studies, Pennsylvania State University Author Manuscript cDavid Geffen School of Medicine, University of California, Los Angeles dDepartment of Psychology, University of California, Los Angeles eDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles fSemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles Abstract Purpose—To examine the associations of the frequency and type of everyday discrimination with diurnal cortisol and whether those associations depend upon adolescents’ ethnicity and gender. Author Manuscript Methods—Adolescents (N=292, Mage=16. 39 years, SD=.74; 58% female) reported the frequency of perceived everyday discrimination and whether they attributed that discrimination to race, gender, age, or height and weight. Five saliva samples were collected per day across 3 days and assayed for cortisol. Results—Higher frequency of everyday discrimination was associated with greater total daily cortisol output (area under the curve; AUC), lower wake and bedtime levels of cortisol, and less of a decline in cortisol across the day. These associations generally did not depend upon ethnicity or gender and attributions for the discrimination were not as consequential as the actual frequency of any type of unfair treatment. Conclusion—Everyday discrimination, regardless of its type, may contribute to heightened HPA activity among adolescents of different ethnic backgrounds and genders. Author Manuscript Keywords everyday discrimination; adolescents; HPA activity; cortisol; ethnicity * Corresponding Author. 18111 Nordhoff Street, Northridge, CA 91330-8263, Office: (818) 677-2510, Fax: (818) 677-2082, [email protected]. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  Huynh et al. Page 2 As a period marked by sensitivity to social evaluation, adolescence represents a time when Author Manuscript differential treatment according to social categories such as race and gender is of particular concern. Recent research on discrimination and health—long dominated by studies of adults —has documented how perceptions of unfair treatment have negative implications for psychological and physical well-being during the teenage years (Huynh, 2012; Huynh & Fuligni, 2010). A candidate biological system for understanding these effects is the hypothalamic-pituitary-adrenal (HPA) axis. HPA activity, as measured by the stress hormone cortisol, is particularly reactive during the adolescent years and is sensitive to the social- evaluative stress that characterizes discrimination (Dickerson & Kemeny, 2004; Romeo, 2013). Dysregulated HPA activity predicts chronic psychological (e.g., depression; Pariante, 2003) and physical (e.g., cardiovascular disease; Kumari, Shipley, Stafford, & Kivimaki, 2011) problems later in adulthood, highlighting the value of examining the link between discrimination and cortisol during adolescence. Author Manuscript In this current study, we explore associations between discrimination and HPA regulation by including measures of total cortisol output and examining the pattern across a day to provide insight to when dysregulation may take place. A typical cortisol pattern across the day is characterized by high morning wake levels, increasing to a peak 30 minutes after wake (i.e., cortisol awakening response [CAR]), a subsequent steep decline across the day, and ending with bed time levels much lower than that of wake levels (J. C. Pruessner et al., 1997; Wüst, Federenko, Hellhammer, & Kirschbaum, 2000). We assess total daily cortisol output by measuring the area under the curve (AUC), which is the average total cortisol output during the day and may reflect past exposure to frequent or severe stress. AUC has been found to be associated with individual level stressors (e.g., lower SES, immigrant status; Gustafsson, Gustafsson, & Nelson, 2006) and daily stressors (e.g., spent more time than usual in school; McHale et al., 2012) among adolescents. An increased CAR suggests anticipation of Author Manuscript negative, stressful events (Kunz-Ebrecht, Kirschbaum, Marmot, & Steptoe, 2004; Schlotz, Hellhammer, Schulz, & Stone, 2004). A flatter decline has been found to be associated with worse psychological (e.g., more depressive symptoms and lower feelings of control; Cohen et al., 2006) and physical adjustment (e.g., risk for cardiovascular disease; Matthews et al., 2006). Although recent work has suggested that racial discrimination during adolescence is predictive of cortisol levels during adulthood (Adam et al, 2015), only three recent studies have examined the existence of a link between discrimination and dysregulated HPA activity during adolescence. Zeiders and colleagues (2012) observed that Mexican American adolescents who reported frequent racial discrimination showed higher levels of total cortisol daily output. Examining the dynamic change in cortisol across the day, Skinner and Author Manuscript colleagues (2011) reported that among a sample of White and Black young adults (ages 19– 22) greater perceived discrimination was associated with a flatter diurnal rhythm of cortisol, a deviation from the typical steep daily decline. An additional study noted that ethnically- diverse adolescents who experienced more discrimination showed exaggerated cortisol elevations in response to negative daily affect (Doane & Zeiders, 2014). Previous research has established a link between discrimination and HPA activity. What remains to be discovered is whether the type of discrimination matters. Studies with both Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 3 adult and adolescent populations primarily focus on gender or racial discrimination. Author Manuscript However, unfair treatment also can arise from factors such as age and physical stature. Numerous pejorative labels surround the adolescent period (e.g., untrustworthy, unmanageable, lazy) and teenagers may report feeling mistreated because of these stereotypes (Gross & Hardin, 2007; Zebrowitz & Montepare, 2003). Those who are overweight or of short stature can experience unfair treatment because of the social value placed on thinness and height (Andreyeva, Puhl, & Brownell, 2008). A recent meta-analysis (Schmitt et al., 2014) found concealable (e.g. mental illness) and controllable (e.g., weight) stigmas had stronger effect sizes on psychological well-being than unconcealable and uncontrollable stigmas (e.g., race and gender). Two of the previously-mentioned studies of discrimination and cortisol among adolescents examined only racial and ethnic discrimination, an unconcealable and uncontrollably stigma (Skinner et al., 2011; Zeiders et al., 2012) and one did not specify (Doane & Zeiders, 2014). This current study considers Author Manuscript other categories of unfair treatment and their implications for HPA activity. Further, research on discrimination in general have only examined attributions at a scale level (i.e., asked “What do you think is the main reason for these experiences?”). Examining whether unfair treatment is attributed to different reasons offers the opportunity to determine the differential effects on HPA activity. This current study is one of the first to examine whether attributions of discrimination are differentially associated with adolescent HPA activity. It is also unclear whether the impact of discrimination is unique to some ethnic and gender groups. A study of young adults suggested that the implications of discrimination for HPA activity was specific to ethnic minorities (Zeiders, Hoyt, & Adam, 2014), but previous studies of adolescents from a single ethnic group observed linkages in several ethnic groups (i.e., African American, European American, Latino) and both genders (Doane & Zeiders, 2014; Skinner et al., 2011; Zeiders et al., 2012). We aimed to contribute to the literature by Author Manuscript directly examining variations across multiple ethnic and gender groups within the same study. Materials and Methods Sample Participants were recruited though mailings and presentations made in 10th and 11th grade classrooms in four public high schools in the Los Angeles area. These schools were chosen because they were composed of a large population of students from either Asian, European or Latin American backgrounds. In the first two schools, there was a majority of Asian (43%, 57%) and Latino (50%, 40%) students. In the third and fourth schools, there was a majority of Latino (38%, 23%) and White (51%, 63%) students. All 10th and 11th graders Author Manuscript and their parents were invited to participate and notified via classroom presentations and family mailings. Of the 316 adolescents who provided assent and parental consent, 293 (Mage = 16.39 years, SD =.74; 58% female) provided adequate saliva samples and had complete data for key variables. Adolescents came from Latin American (42%), European (29%), Asian (23%), and other ethnic backgrounds (6%), and according the primary caregivers, the families had a range of household incomes (M = $71,374, median = $51,500, Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 4 SD = $78,322, range = $0–$825,000). Median income was 10% lower than that of the Los Author Manuscript Angeles area ($57,271) at the time of the study in 2012 (U.S. Census Bureau, 2013). Procedures All procedures were approved by the UCLA Institutional Review Board. During a home visit, adolescents completed a computer-assisted questionnaire and interviewers measured height and weight. Adolescents were provided with saliva collection kits that included labeled and color-coded Salivettes (Sarstedt, Nümbrecht, Germany), a kitchen timer to assist with the timing of morning samples, an electronic date/time stamper (Dymo, Berkeley, California), a stamping booklet to document saliva collection, and a morning checklist to report wake times. Saliva collection began on the following day, for three consecutive days. Participants were instructed about providing saliva samples and recording the collection time in the stamping Author Manuscript booklet with the time stamper. They were also instructed not to eat, drink or brush their teeth 30 minutes before collection. During the initial visit, participants reported their expected schedules for the week. Using this information, interviewers scheduled and sent text message reminders through a commercial, bulk text messaging service (Red Oxygen, San Francisco, CA). Upon completion of the protocol, interviewers picked up the completed kits and adolescents received $50 and two movie tickets. A total of 98% of participants (n = 308) provided at least one saliva sample and 96.2% (n = 304) provided all 5 saliva samples for at least one day. Measures Everyday discrimination—Participants responded to 10 items with the prompt, “In your day-to-day life, over the last 12 months, how often have any of the following things Author Manuscript happened to you (Williams et al., 2008) on a four-point scale (1 = never, 2 = once, 3 = 2 or 3 times, 4 = 4 or more times). Example questions include “You have been treated with less courtesy than other people” and “You have received poorer service than other people at restaurants or stores.” This expanded version of the original 9-item measure (Williams, Yu, Jackson, & Anderson, 1997), which included the item “you are followed around in stores”, has been validated with Latino populations (Krieger, Smith, Naishadham, Hartman, & Barbeau, 2005). Because we were interested in actual frequency in the past year, we used different anchors from the original measure (a 6-point scale ranging from never to almost everyday). We computed an average frequency of general everyday discrimination by taking the mean of the scores on the 10 items. The measure demonstrated good internal consistency (α =.84) in the current study. Author Manuscript In prior uses of this measure, respondents typically have been asked one question at the end about their primary attribution (e.g., ethnicity, gender) for all of the discriminatory experiences listed in the measure. We were interested in the variability of different types of attributions, so we revised the measure to ask an attribution for every single item. For each item rated “2” or above, participants indicated whether they attributed the discrimination to one of the following categories that we selected from attributions in the original scale to be the most common and salient to adolescents: gender, race, age, or height or weight. We Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 5 calculated the number of times across the 10 items participants attributed discrimination to Author Manuscript each particular category (e.g., a participant who attributed two discrimination experiences to gender received a score of 2 for gender discrimination). Attribution scores, therefore, could range from 0–10 and those who never reported a particular attribution and those who reported “never” for all of the 10 items were scored 0. Salivary cortisol—Adolescents provided five saliva samples at designated times for three consecutive days: wake (sample 1), 15 minutes after wake (sample 2), 30 minutes after wake (sample 3), before dinner (sample 4), and at bed time (sample 5). Participants recorded samples in their stamping booklet with the electronic date/time stamper. Saliva samples were frozen and stored at −20 degrees C until shipped on dry ice to be assayed by Biochemisches Labor, Unversitaet Trier, Germany. After thawing, salivettes were centrifuged at 3,000 rpm for 5 min, which resulted in a clear supernatant of low viscosity. Author Manuscript Salivary concentrations were measured using commercially available chemiluminescence- immunoassay with high sensitivity (IBL International, Hamburg, Germany). The intra and interassay coefficients for cortisol were below 8%. Samples with cortisol values over 60 (n = 14) were removed (Stawski, Cichy, Piazza, & Almeida, 2013) and raw cortisol values were log-transformed. Morning samples in which participants reported more than 30 minutes between sample 1 and sample 2 (n = 12) or more than 60 minutes between collecting sample 1 and sample 3 (n = 10) for a particular day were flagged. Analyses excluding these cases did not change the results, therefore these samples were not excluded from the final analyses. Adolescents provided three days of cortisol samples on different days of the week. Only weekday samples were included in the analyses. In addition to examining associations with Author Manuscript cortisol levels at wake and bedtime, we calculated the cortisol awakening response (CAR), the linear decline from wake, and total daily cortisol output (AUC). AUC with respect to ground was only calculated for days where participants had all five cortical samples across the day, and used the trapezoid formula (Jens C Pruessner, Kirschbaum, Meinlschmid, & Hellhammer, 2003). CAR was calculated by subtracting sample 3 (30 mins after wake) from sample 1 (wake), and dividing by the time between samples. To calculate decline from wake, sample 5 (bed) was subtracted from sample 1 (wake), and this was divided by the time between samples. CAR and decline represent the average hourly rate of change in cortisol. Our cortisol parameters were log transformed and then averaged across the three days. Wake time—Participants reported when they awoke in the morning of each study day. Wake times were converted to hours in military time (M = 6.95, SD = 1.37) and controlled Author Manuscript for in all models given that cortisol rhythms are significantly influenced by sleep-wake cycles (Emma K. Adam & Kumari, 2009). Body Mass Index (BMI)—Research staff assessed participants’ height and weight. BMI was calculated by dividing weight by the square of height (i.e., Kg/M2). Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 6 Results Author Manuscript Descriptive Statistics and Correlations As shown in the last row of Table 1, everyday discrimination was infrequent (42 participants reported “never” on all items) and adolescents were more likely to attribute discrimination to their age and race than to their gender or height/weight, ts(292) = 3.13–5.84, ps <.05 after bonferonni correction. Discrimination frequency was associated with lower waking levels of cortisol, less of a daily decline, and greater bedtime levels. Attributions to gender was associated with greater AUC and bedtime levels of cortisol. There were no gender or ethnic differences in the frequency of everyday discrimination (see Table 2). Females were more likely to attribute discrimination to gender than males, t(291) = 4.77, p <.001. Adolescents from Latin American and Asian backgrounds were more likely to attribute discrimination to race than those with European backgrounds, and adolescents Author Manuscript from European and Asian backgrounds were more likely to attribute discrimination to age than their Latin American peers, F(3, 289) = 4.91–7.63, ps <.01. Discrimination Frequency, Attributions, and Cortisol Multiple regressions estimated the association between frequency of discrimination and the parameters of diurnal cortisol after controlling for average wake time, ethnicity, gender, age, and BMI (ns = 255–286). As shown in the last row of Table 3, a higher frequency of discrimination was associated with greater AUC, lower waking cortisol, greater bedtime cortisol, and a flatter daily decline. Girls evidenced higher AUC, waking, and bedtime levels of cortisol. Attributions were added to the model in order to estimate whether they predicted levels of Author Manuscript cortisol above and beyond the average frequency. Out of a total of 20 estimates across all attributions and cortisol parameters, only one was statistically significant. Attributing discrimination to ethnicity (b = −.14, SE =.06, p =.03) was associated with lower bedtime cortisol. All of the originally significant associations between frequency of discrimination and cortisol shown in Table 3 remained significant with all attributions in the models (see Table 4), and the link between frequency and CAR became marginally positive after the inclusion of attributions. Ethnic and Gender Variability in the Associations We first examined the interactions of discrimination frequency with ethnicity and gender by adding appropriate interaction terms to the models tested in Table 3. Out of a total of 35 interactions (6 ethnicity and 1 gender interactions × 5 cortisol parameters), only three were Author Manuscript significant. Results indicate that the association between discrimination and wake and decline significantly differed between Latino and European youth (ps <.05). Simple slope analyses indicated that whereas more discrimination was associated with lower waking cortisol and flatter decline among teenagers from European backgrounds (b = −.20, SE =. 06, p =.002; b = −.02, SE =.01, p =.04, respectively), these associations were not present among Latino youth (b = −.04, SE =.05, p =.38; b =.00, SE =.01, p =.76). Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 7 The association between discrimination frequency and decline differed by gender (p =.009). Author Manuscript Simple slope analyses indicated that the association between discrimination frequency and a flatter decline was significant for males (b =.02, SE =.01, p =.02), but not females (b = −. 00, SE =.01, p =.78). In order to test ethnic and gender variability in the associations between attributions and the cortisol parameters, we focused on only attributions to race/ethnicity and gender because (a) they were most theoretically-meaningful for potential variations by ethnicity and gender and, (b) to avoid chance findings that could occur from testing all of the possible interactions with all attributions measures. Out of 30 possible interactions between race/ethnicity attributions and ethnicity, a total of 4 were significant. Ethnic attributions were associated with lower AUC for Latino adolescents (b = −1.35, SE =.49, p =.007), who differed from other ethnic teens (b = 7.54, SE = 2.32, p =.02) and Asian teens (b = 1.64, SE =.95, p =. 09). Ethnic attributions were also associated with steeper decline for Latino adolescents (b Author Manuscript =.01, SE =.00, p =.02), who differed from their other ethnic peers (b = −.05, SE =.01, p =. 01). Other ethnic minority youth also differed on decline from their peers from European backgrounds (b =.02, SE =.02, p =.17). None of the 5 interactions between attributions to gender and gender were significant. Discussion Everyday discrimination occurred relatively infrequently, but adolescents who perceived higher rates of such unfair treatment evidenced elevated levels of cortisol across the day. This is consistent with three recent studies (Doane & Zeiders, 2014; Skinner et al., 2011; Zeiders et al., 2012), providing converging evidence that the discrimination-health risk link during adulthood may begin as early as adolescence. The heightened cortisol output was due Author Manuscript largely to less of a decline in cortisol across the day as evidenced by lower wake and higher bedtime levels. A flatter decline has been linked to psychological stress (Emma K Adam, Hawkley, Kudielka, & Cacioppo, 2006) and maladjustment (e.g., depressive symptoms, lower feelings of control; Cohen et al., 2006). There are also health implications given associations between a flatter decline and cardiovascular risk and breast cancer mortality (Emma K. Adam & Kumari, 2009; Cohen et al., 2006; Matthews et al., 2006; Sephton, Sapolsky, Kraemer, & Spiegel, 2000). Our novel measurement of different types of discrimination is an improvement from prior studies that ask participants to identify the main reason for unfair treatment because we examined what attributions are more frequent and whether they differentially impact HPA activity. Our results indicate that attributions may not matter as much as the frequency of any Author Manuscript type of discrimination. Although attributions to age and race were most common, there was no clear pattern associated with the type of attributions and the cortisol parameters (e.g., age attributions associated with lower CAR; race attributions associated with lower bedtime levels). This finding is noteworthy because much of the scholarship on the implications of discrimination on health has focused on ethnic or racial discrimination. Our results suggest that that unfair treatment due to a variety of reasons—age, race, gender, height or weight— could be consequential for adolescent health. This generalized reaction to any type of unfair Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 8 treatment may be particular to adolescence because (a) cortisol responses are stronger in Author Manuscript response to social-evaluative threat in which individuals could be negatively judged by others (Dickerson & Kemeny, 2004), (b) social evaluation is particularly salient during adolescence (Somerville, 2013), and (c) HPA reactivity is heightened during this period (Romeo, 2013). Alternatively, there may be other unmeasured person characteristics (e.g., external attribution style, etc.) that may explain why there were no differences by attribution category. Future work should include person characteristics (e.g., negative affect, attribution tendencies), examine whether some attributions are more important than others among other populations, and include other categories (e.g., religion, sexual orientation, social class). For instance, there is some evidence that unfair treatment due to race is more salient than other social identities (e.g., social class, age, gender) among Asian American college students (Wang, Leu, & Shoda, 2011). Further, our study directly compared the implications of discrimination for HPA activity Author Manuscript among adolescents from multiple ethnic groups. We conducted a total of 65 gender and ethnic interactions, and the 7 significant results were inconsistent. At times Latino youth appear to be less affected by discrimination (e.g., general discrimination associated with lower wake cortisol and marginally flatter among White but not Latino youth), but more affected by ethnic attributions specifically (e.g., associated with lower AUC and a steeper decline). Given the risk of Type I error, more studies need to replicate these results before further interpretation. Our findings suggest that the association between discrimination and various cortisol parameters is generally the same across groups. Despite Latino and Asian American adolescents attributing more discrimination to race than their European American peers, and females attributing more discrimination to gender than males, the associations between discrimination and atypical cortisol patterns were not stronger for ethnic minority and female adolescents. Author Manuscript These results suggest that even ethnic discrimination can be consequential for members of the majority group if they feel that they are not being treated fairly because of their race. In contrast, a recent study of young adults (Mage = 22.8 years) reported that the association between discrimination and cortisol existed only for members of ethnic minority groups, not European Americans (Zeiders et al., 2014). One explanation for these divergent findings is that, compared to adolescents, ethnic minority young adults may differentially attribute unfair treatment to race (Wang et al., 2011), but also may be differentially impacted by discrimination. Zeider et al. (2014) did not measure racial discrimination specifically, but found that whereas 24% of ethnic minority young adults attributed discrimination to race, only 5% of ethnic majority young adults did (gender was the most common attribution by ethnic majority young adults). By sampling multiple ethnic groups and explicitly testing for Author Manuscript variation according to ethnicity, our results suggest that ethnic differences may be present, but not in an obvious way. Indeed, others have observed the association between discrimination and cortisol parameters regardless of the specific ethnic group included (Doane & Zeiders, 2014; Skinner et al., 2011; Zeiders et al., 2012). Further, studies on the associations of discrimination with psychological and academic outcomes also did not find moderation by ethnic group (Huynh & Fuligni, 2010). Taken together, these ideas indicate that any kind of unfair treatment can trigger the HPA axis and can be similarly consequential for teenagers from different backgrounds. Yet, because ethnic minority youth report more Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 9 frequent discrimination, they may be more impacted by it over time. Consistent with this, a Author Manuscript recent study found higher racial discrimination during adolescence is associated with stress biology in adulthood, and these effects were more pervasive for Black than White adults (Adam et al., 2015).Future research should examine whether clear patterns emerge, over time, regarding the effects of discrimination on cortisol parameters of different ethnic groups. Limitations Daily reports of discrimination and cortisol would provide stronger evidence for this association than our measure of discrimination that assesses frequency over 12 months. However, one daily diary study found that ethnic discrimination occurred less than 1% of days over a two-week period (Huynh & Fuligni, 2010). Given how infrequent everyday discrimination is, measuring daily discrimination may be resource intensive because it Author Manuscript would require at least one month of daily reports and the corresponding cortisol measures. Another limitation is how attributions are measured. Adolescents can attribute unfair treatment to categories other than ones we listed, and it is also likely that multiple social categories (e.g., being a woman and Latina) contribute collectively to the experiences of individuals (Cole, 2009). Nevertheless, our study is one of the first to examine how discrimination may be attributed to different categories and how these attributions may be associated to adolescent health. Because adolescents contend with multiple social identities, it was valuable to examine how attributions to discrimination were distributed rather than simply asking the main reason for these experiences. Finally, our results may be unique to our sample in Southern California, as our participants were drawn from areas with a high percentage of Latino youth and few African American youth. The frequency of discrimination and attributions to race or ethnicity may be higher in areas where youth are Author Manuscript the clear ethnic minority or when differential treatment by ethnicity and race is made more salient by social movements and historical events. It is also possible that with larger subsamples, we would have more power to detect differences in the effect of attributions by gender and ethnicity. Conclusions In conclusion, the current study adds to the small, emerging body of research on discrimination and HPA activity during adolescence by suggesting that perceiving unfair treatment due to one’s membership in several social categories (e.g., race, gender, age, weight) can elevate diurnal cortisol levels among adolescents from different ethnicities and genders. Continuing research should focus on potential psychological mediators of this dynamic, but our study joins other recent research to suggest that HPA dysregulation may be Author Manuscript a key pathway by which everyday discrimination can get under the skin and compromise adolescent health. Acknowledgments This research was supported by funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01-HD062547), the UCLA California Center for Population Research, which is supported by the National Institute of Child Health and Human Development (R24-HD041022), and the UCLA Older Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 10 Americans Independence Center, which is supported by the National Institute of Aging (P30-AG028748), and the by NIH Research Infrastructure in Minority Institutions from the National Institute of Minority Health and Health Author Manuscript Disparities, (P20 MD003938). The content does not necessarily represent the official views of the National Institute of Child Health and Human Development, the National Institute of Aging, or the National Institutes of Health. References Adam EK, Hawkley LC, Kudielka BM, Cacioppo JT. Day-to-day dynamics of experience–cortisol associations in a population-based sample of older adults. Proceedings of the National Academy of Sciences. 2006; 103(45):17058–17063. Adam EK, Kumari M. Assessing salivary cortisol in large-scale, epidemiological research. Psychoneuroendocrinology. 2009; 34(10):1423–1436. [PubMed: 19647372] Andreyeva T, Puhl RM, Brownell KD. Changes in perceived weight discrimination among Americans, 1995–1996 through 2004–2006. Obesity. 2008; 16(5):1129–1134. [PubMed: 18356847] Cohen S, Schwartz JE, Epel E, Kirschbaum C, Sidney S, Seeman T. Socioeconomic status, race, and diurnal cortisol decline in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Psychosomatic Medicine. 2006; 68(1):41–50. [PubMed: 16449410] Author Manuscript Cole ER. Intersectionality and research in psychology. American Psychologist. 2009; 64(3):170. [PubMed: 19348518] Dickerson SS, Kemeny ME. Acute stressors and cortisol responses: a theoretical integration and synthesis of laboratory research. Psychological Bulletin. 2004; 130(3):355. [PubMed: 15122924] Doane LD, Zeiders KH. Contextual Moderators of Momentary Cortisol and Negative Affect in Adolescents' Daily Lives. Journal of Adolescenct Health. 2014; 54(5):536–542. Gross EF, Hardin CD. Implicit and explicit stereotyping of adolescents. Social Justice Research. 2007; 20(2):140–160. Gustafsson PE, Gustafsson PA, Nelson N. Cortisol levels and psychosocial factors in preadolescent children. Stress and Health. 2006; 22(1):3–9. Huynh VW. Ethnic Microaggressions and the Depressive and Somatic Symptoms of Latino and Asian American Adolescents. Journal of Youth and Adolescence. 2012:1–16. Huynh VW, Fuligni AJ. Discrimination Hurts: The Academic, Psychological, and Physical Well Being of Adolescents. Journal of Research on Adolescence. 2010; 20(4):916–941. Author Manuscript Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Social Science & Medicine. 2005; 61(7):1576–1596. [PubMed: 16005789] Kumari M, Shipley M, Stafford M, Kivimaki M. Association of diurnal patterns in salivary cortisol with all-cause and cardiovascular mortality: findings from the Whitehall II study. The Journal of Clinical Endocrinology & Metabolism. 2011; 96(5):1478–1485. [PubMed: 21346074] Kunz-Ebrecht SR, Kirschbaum C, Marmot M, Steptoe A. Differences in cortisol awakening response on work days and weekends in women and men from the Whitehall II cohort. Psychoneuroendocrinology. 2004; 29(4):516–528. [PubMed: 14749096] Matthews K, Schwartz J, Cohen S, Seeman T. Diurnal cortisol decline is related to coronary calcification: CARDIA study. Psychosomatic Medicine. 2006; 68(5):657–661. [PubMed: 17012518] McHale SM, Blocklin MK, Walter KN, Davis KD, Almeida DM, Klein LC. The Role of Daily Activities in Youths' Stress Physiology. Journal of Adolescent Health. 2012; 51(6):623–628. Author Manuscript [PubMed: 23174474] Pariante CM. Depression, stress and the adrenal axis. Journal of neuroendocrinology. 2003; 15(8):811– 812. [PubMed: 12834443] Pruessner JC, Kirschbaum C, Meinlschmid G, Hellhammer DH. Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change. Psychoneuroendocrinology. 2003; 28(7):916–931. [PubMed: 12892658] Pruessner JC, Wolf OT, Hellhammer DH, Buske-Kirschbaum A, Von Auer K, Jobst S, Kirschbaum C. Free cortisol levels after awakening: a reliable biological marker for the assessment of adrenocortical activity. Life Sciences. 1997; 61(26):2539–2549. [PubMed: 9416776] Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 11 Romeo RD. The Teenage Brain The Stress Response and the Adolescent Brain. Current Directions in Psychological Science. 2013; 22(2):140–145. [PubMed: 25541572] Author Manuscript Schlotz W, Hellhammer J, Schulz P, Stone AA. Perceived work overload and chronic worrying predict weekend–weekday differences in the cortisol awakening response. Psychosomatic Medicine. 2004; 66(2):207–214. [PubMed: 15039505] Sephton SE, Sapolsky RM, Kraemer HC, Spiegel D. Diurnal cortisol rhythm as a predictor of breast cancer survival. Journal of the National Cancer Institute. 2000; 92(12):994–1000. [PubMed: 10861311] Skinner ML, Shirtcliff EA, Haggerty KP, Coe CL, Catalano RF. Allostasis model facilitates understanding race differences in the diurnal cortisol rhythm. Development and Psychopathology. 2011; 23(4):1167. [PubMed: 22018088] Somerville LH. The teenage brain sensitivity to social evaluation. Current Directions in Psychological Science. 2013; 22(2):121–127. [PubMed: 24761055] Stawski RS, Cichy KE, Piazza JR, Almeida DM. Associations among daily stressors and salivary cortisol: Findings from the National Study of Daily Experiences. Psychoneuroendocrinology. 2013; 38(11):2654–2665. [PubMed: 23856186] Author Manuscript U.S. Census Bureau. Household income 2012. American Community Survey Briefs; 2013. (ACSBR/ 12-02) Wang J, Leu J, Shoda Y. When the Seemingly Innocuous "Stings": Racial Microaggressions and Their Emotional Consequences. Personality and Social Psychology Bulletin. 2011; 37(12):1666–1678. [PubMed: 21885859] Williams DR, Gonzalez HM, Williams S, Mohammed SA, Moomal H, Stein DJ. Perceived discrimination, race and health in South Africa. Social Science & Medicine. 2008; 67(3):441–452. [PubMed: 18486292] Williams DR, Yu Y, Jackson JS, Anderson NB. Racial Differences in Physical and Mental Health: Socio-economic Status, Stress and Discrimination. Journal of Health Psychology. Special Issue: Health and socio-economic position. 1997; 2(3):335–351. Wüst S, Federenko I, Hellhammer DH, Kirschbaum C. Genetic factors, perceived chronic stress, and the free cortisol response to awakening. Psychoneuroendocrinology. 2000; 25(7):707–720. [PubMed: 10938450] Author Manuscript Zebrowitz, LA.; Montepare, JM. Too young, too old: Stigmatizing adolescents and elders. In: Heatherton, TF.; Kleck, RE.; Hebl, MR.; Hull, JG., editors. The Social Psychology of Stigma. Guilford Press; 2003. p. 334-373. Zeiders KH, Doane LD, Roosa MW. Perceived discrimination and diurnal cortisol: Examining relations among Mexican American adolescents. Hormones and Behavior. 2012; 61(4):541–548. [PubMed: 22342577] Zeiders KH, Hoyt LT, Adam EK. Associations Between Self-Reported Discrimination and Diurnal Cortisol Rhythms Among Young Adults: The Moderating Role of Racial-Ethnic Minority Status. Psychoneuroendocrinology. 2014; 50:280–288. [PubMed: 25262035] Author Manuscript Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 12 Implications and Contribution Author Manuscript Adolescents who perceive discrimination in their daily lives—whether because of their race, gender, age, or physical size—experience elevated levels of HPA activity. Such perceptions of unfair treatment have potential implications for health. Author Manuscript Author Manuscript Author Manuscript Horm Behav. Author manuscript; available in PMC 2017 April 01.  Huynh et al. Page 13 Highlights Author Manuscript Discrimination frequency was associated with multiple cortisol parameters Associations generally did not depend upon ethnicity or gender Attributions for discrimination were not as consequential as the actual frequency Author Manuscript Author Manuscript Author Manuscript Horm Behav. Author manuscript; available in PMC 2017 April 01.  Author Manuscript Author Manuscript Author Manuscript Author Manuscript Table 1 Descriptive Statistics and Bivariate Correlations 1 2 3 4 5 6 7 8 9 10 Huynh et al. Discrimination 1. Frequency 1 2. Age Att.36*** 1 3. Race Att.51*** −.05 1 4. Gender Att.32***.06.00 1 5. Height/Weight Att.28***.07.01.04 1 Cortisol Parameters 6. Total Output (AUC).12+ −.00.02.13* −.04 1 7. Wake −.18** −.09 −.04.03 −.11+.12+ 1 8. Bed.13*.05 −.06.15* −.01.52***.02 1 9. CAR.06 −.07.02.08 −.04.32*** −.53*** −.09 1 10. Decline −.14* −.02.03 −.11+ −.06 −.47***.47*** −.63*** −.41*** 1 M(SD) 1.74(.60) 1.24(1.60) 1.00(1.79) 0.61(1.23) 0.60(1.12) 27.47(8.81) 2.77(.54) 0.63(.93) 0.50(1.50) 0.15(.07) N 293 293 293 293 293 258 292 292 268 268 Note. * p <.05, Horm Behav. Author manuscript; available in PMC 2017 April 01. ** p <.01, *** p <.001. “Frequency” refers to the overall frequency of discrimination, regardless of attribution, on a scale from 1 to 4. “Att” refer to the number of discrimination experiences attributed to a particular reason, with a potential range of 0 to 10. All cortisol parameter parameters are log-transformed values. Page 14  Author Manuscript Author Manuscript Author Manuscript Author Manuscript Table 2 Gender and Ethnic Differences Gender Ethnic Background M (SD) M (SD) Huynh et al. Male Female American Asian European Other (n = 123) (n = 170) (n = 123) (n = 67) (n = 86) (n = 17) Discrimination Frequency 1.75(0.66) 1.73(0.56) 1.76(0.64) 1.79(0.59) 1.66(0.54) 1.74(0.67) Discrimination Attributions Age 1.25(1.66) 1.23(1.55).83(1.36) 1.57(1.75) 1.49(1.60) 1.65(1.97) Race 1.08(2.02).95(1.60) 1.36(2.28) 1.24(1.49).26(0.81) 1.29(1.36) Gender.22(0.64).89(1.46).62(1.11).64(1.29).63(1.41).29(0.85) Height/Weight.67(1.30).54(0.98).52(0.92).63(0.97).64(1.41).76(1.44) Note. “Discrimination Frequency” refers to the overall frequency of discrimination, regardless of attribution, on a scale from 1 to 4. “Attributions” refer to the number of discrimination experiences attributed to a particular reason, with a potential range of 0 to 10. Horm Behav. Author manuscript; available in PMC 2017 April 01. Page 15  Author Manuscript Author Manuscript Author Manuscript Author Manuscript Table 3 Associations between Frequency of Discrimination and Cortisol AUC Wake Bed CAR Decline Huynh et al. b SE b SE b SE b SE b SE Intercept 26.59*** 1.2 2.70*** 0.07 0.58*** 0.12 0.36+ 0.20 0.16*** 0.01 Wake Time −1.56** 0.55 −0.02 0.03 0.10+ 0.06 −0.08 0.09 0.00 0.00 Asian −1.68 1.62 −0.15 0.10 −0.08 0.16 −0.15 0.27 −0.01 0.01 Latino −1.56 1.38 0.02 0.08 −0.18 0.14 −0.06 0.24 0.00 0.01 Other −4.16+ 2.5 −0.26+ 0.15 −0.26 0.25 0.16 0.43 0.02 0.02 Female 3.52** 1.09 0.22** 0.06 0.27* 0.11 0.29 0.19 −0.01 0.01 Age −1.07+ 0.55 −0.04 0.03 −0.05 0.05 −0.11 0.09 0.00 0.00 Income 0.54 0.58 −0.01 0.04 0.03 0.06 0.04 0.10 0.00 0.01 BMI −0.06 0.57 −0.01 0.03 0.14* 0.06 −0.12 0.10 0.00 0.01 Discrimination 1.23* 0.53 −0.09** 0.03 0.11* 0.05 0.10 0.09 −0.01* 0.00 N 255 286 286 263 264 Note. + p <.10, * p <.05, ** p <.01, Horm Behav. Author manuscript; available in PMC 2017 April 01. *** p <.001. Cortisol values were log-transformed. CAR (cortisol awakening response) and decline were centered at waking, and represent the hourly rate of change in cortisol levels. Ethnicity and gender was dummy- coded with youth with European backgrounds and males as the baseline. Wake time, age, income and discrimination variables were centered at the sample mean. Page 16  Author Manuscript Author Manuscript Author Manuscript Author Manuscript Table 4 Associations between Attributions of Discrimination and Cortisol AUC Wake Bed CAR Decline Huynh et al. b SE b SE b SE b SE b SE Intercept 26.52*** 1.25 2.72*** 0.07 0.57*** 0.12 0.40+ 0.21 0.16*** 0.01 Wake Time −1.59** 0.56 −0.02 0.03 0.10+ 0.06 −0.06 0.10 0.01 0.00 Asian −1.20 1.67 −0.18+ 0.10 0.01 0.16 −0.11 0.28 −0.01 0.01 Latino −1.45 1.47 −0.01 0.08 −0.14 0.14 −0.10 0.25 0.00 0.01 Other −3.42 2.56 −0.28+ 0.15 −0.14 0.25 0.26 0.44 0.01 0.02 Female 3.38** 1.14 0.21** 0.07 0.22+ 0.11 0.25 0.20 −0.01 0.01 Age −1.04+ 0.55 −0.05 0.03 −0.04 0.05 −0.10 0.09 0.00 0.00 Income 0.58 0.58 −0.01 0.04 0.03 0.06 0.05 0.10 0.00 0.01 BMI 0.14 0.60 0.00 0.04 0.17** 0.06 −0.11 0.10 0.00 0.01 Discrimination 2.10** 0.80 −0.12* 0.05 0.22** 0.08 0.24+ 0.14 −0.02* 0.01 Attributions Age −0.59 0.62 0.01 0.04 −0.06 0.06 −0.19+ 0.11 0.01 0.01 Race −0.89 0.69 0.05 0.04 −0.16* 0.07 −0.09 0.12 0.01* 0.01 Gender 0.02 0.62 0.02 0.04 0.05 0.06 0.01 0.10 0.00 0.01 Height/Weight −0.80 0.57 −0.02 0.03 −0.10+ 0.06 −0.09 0.10 0.00 0.01 N 255 286 286 263 265 Horm Behav. Author manuscript; available in PMC 2017 April 01. Note. + p <.10, * p <.05, ** p <.01, *** p <.001. Cortisol values were log-transformed. CAR (cortisol awakening response) and decline were centered at waking, and represent the hourly rate of change in cortisol levels. Ethnicity and gender was dummy- coded with youth with European backgrounds and males as the baseline. Wake time, age and discrimination variables were centered at the sample mean. Page 17