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How Discrimination Gets Under the Skin: Biological Determinants of Discrimination Associated with Dysregulation of the Brain-Gut Microbiome System and Psychological Symptoms

  • Author Footnotes
    ‡ Co-first authors
    Tien S. Dong
    Correspondence
    Corresponding Authors: Tien Dong, MD, PhD, Microbiome Center, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA; 650 Charles E Young Dr. South, Center for Health Sciences 44-133, Los Angeles CA 90095.
    Footnotes
    ‡ Co-first authors
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases

    David Geffen School of Medicine

    UCLA Microbiome Center, David Geffen School of Medicine at UCLA

    G. Oppenheimer Center for Neurobiology of Stress and Resilience

    University of California, Los Angeles

    Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA
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  • Author Footnotes
    ‡ Co-first authors
    Gilbert C. Gee
    Footnotes
    ‡ Co-first authors
    Affiliations
    Department of Community Health Sciences Fielding School of Public Health and

    California Center for Population Research, UCLA, UCLA
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  • Hiram Beltran-Sanchez
    Affiliations
    Department of Community Health Sciences Fielding School of Public Health and

    California Center for Population Research, UCLA, UCLA
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  • May Wang
    Affiliations
    Department of Community Health Sciences Fielding School of Public Health and
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  • Vadim Osadchiy
    Affiliations
    Department of Urology, David Geffen School of Medicine, UCLA
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  • Lisa A. Kilpatrick
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases

    David Geffen School of Medicine

    G. Oppenheimer Center for Neurobiology of Stress and Resilience

    University of California, Los Angeles
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  • Zixi Chen
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases
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  • Vishvak Subramanyam
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases
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  • Yurui Zhang
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases
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  • Yinming Guo
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases
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  • Jennifer S. Labus
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases

    David Geffen School of Medicine

    UCLA Microbiome Center, David Geffen School of Medicine at UCLA

    G. Oppenheimer Center for Neurobiology of Stress and Resilience

    University of California, Los Angeles
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  • Bruce Naliboff
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases

    David Geffen School of Medicine

    UCLA Microbiome Center, David Geffen School of Medicine at UCLA

    G. Oppenheimer Center for Neurobiology of Stress and Resilience

    University of California, Los Angeles
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  • Steve Cole
    Affiliations
    David Geffen School of Medicine

    University of California, Los Angeles

    Department of Psychiatry & Biobehavioral Sciences and Medicine, UCLA
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  • Xiaobei Zhang
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases

    David Geffen School of Medicine

    G. Oppenheimer Center for Neurobiology of Stress and Resilience

    University of California, Los Angeles
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  • Emeran A. Mayer
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases

    David Geffen School of Medicine

    UCLA Microbiome Center, David Geffen School of Medicine at UCLA

    G. Oppenheimer Center for Neurobiology of Stress and Resilience

    University of California, Los Angeles
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  • Arpana Gupta
    Correspondence
    Corresponding Authors: Arpana Gupta, PhD, G. Oppenheimer Center of Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, 10833 Le Conte Avenue, Center for Health Sciences 42-210, Los Angeles CA 90095.
    Affiliations
    Vatche and Tamar Manoukian Division of Digestive Diseases

    David Geffen School of Medicine

    UCLA Microbiome Center, David Geffen School of Medicine at UCLA

    G. Oppenheimer Center for Neurobiology of Stress and Resilience

    University of California, Los Angeles
    Search for articles by this author
  • Author Footnotes
    ‡ Co-first authors
Open AccessPublished:October 28, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.10.011

      Abstract

      Background

      Discrimination is associated with negative health outcomes as mediated in part by chronic stress, but a full understanding of the biological pathways are lacking. Here, we investigate the effects of discrimination involved in dysregulating the brain-gut microbiome (BGM) system.

      Methods

      154 participants underwent brain MRI to measure functional connectivity, fecal samples were obtained for 16s rRNA profiling and fecal metabolites, and serum for inflammatory markers, along with questionnaires. The everyday Discrimination Scale was administered to measure chronic and routine experiences of unfair treatment. A sparse partial least squares-discriminant analysis was conducted to predict BGM alterations as a function of discrimination, controlling for sex, age, body mass index, and diet. Associations between discrimination-related BGM alterations and psychological variables were assessed using a tripartite analysis.

      Results

      Discrimination was associated with anxiety, depression, and visceral sensitivity. Discrimination was associated with alterations of brain networks related to emotion, cognition and self-perception, and structural and functional changes in the gut microbiome. BGM discrimination-related associations vary by race/ethnicity. Among Black and Hispanic individuals, discrimination leads to brain network changes consistent with psychological coping and increased systemic inflammation. For White individuals, discrimination is related to anxiety but not inflammation, while for Asian individuals, the patterns suggest possible somatization and behavioral (e.g., dietary) responses to discrimination.

      Conclusions

      Discrimination is attributed to changes in the BGM system more skewed towards inflammation, threat response, emotional arousal, and psychological symptoms. By integrating diverse lines of research, our results demonstrate evidence that may explain how discrimination contributes to health inequalities.

      INTRODUCTION

      Structural racism contributes to health inequities and partially manifests as every day, mundane experiences of discrimination.(
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      ) Therefore, the major goal of this work is to examine how discrimination affects biology beyond the well-studied HPA axis, by examining how discrimination alters the BGM system.
      We propose a model highlighting the influence of discrimination on the bidirectional signaling between the brain and gut microbiome as mediated by inflammation. A novel aspect of this model reflects the dysregulation in connections between the central and enteric nervous systems (Figure 1).
      Figure thumbnail gr1
      Figure 1Conceptual model linking the brain-gut-microbiome (BGM) system to discrimination and clinical outcomes
      Furthermore, discrimination has been predominantly studied in Black individuals as compared to White individuals, and few have studied how it may affect racial groups differently. It would seem logical that the effects of discrimination on health would be stronger among people of color compared to White individuals, but the literature is mixed in this regard.(
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      To study a holistic view of how discrimination can affect biology, we performed a detailed analysis of discrimination on the BGM system in a racially diverse population to test whether: 1) experiences of everyday discrimination will be associated with increased alterations in stress brain connectivity pathways (resting state functional MRI) and stress related gut microbiome (16S sequencing and fecal metabolites) as mediated by increased inflammatory processes (peripheral blood mononuclear cells (PBMCs)), 2) these associations will be related to increased adverse behavioral and psychological measures, and 3) these associations will vary across Black, Asian, Hispanic, and White individuals.

      METHODS AND MATERIALS

      Participants: The final sample comprised of 154 adults from the 165 enrolled in the study. Participants were recruited from Los Angeles community and clinics.
      Participant data included: fMRI for resting state connectivity, anthropometrics, blood samples for genetic expression of inflammation via PBMCs, stool samples (microbiome and metabolomics), and survey questionnaires, including a diet history (Supplemental Table S1). Participants self-reported race/ethnicity (Black, Asian American, Hispanic, or White). Discrimination was measured using the Everyday Discrimination Scale (EDS).
      Statistical analysis: Group differences on demographic characteristics, brain, microbial taxa, and differential abundance testing for metabolomics, and PBMCs, were performed individually (adjusting for sex, age, body mass index (BMI), and diet). For fMRI and metabolite data, sparse partial least square linear discriminant analysis (sPLS-DA) was done to analyze brain and metabolite data using the Mixomics package in R. Because the number of variables far exceeds the number of study participants, sparse multivariate nonparametric models exhibit the most robust statistical power and consistency.(
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      Partial least squares for discrimination in fMRI data.
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      ) For PBMC, Mann-Whitney U test performed between patients with low or high discrimination and p-values were adjusted for multiple hypothesis testing using Bonferroni correction. Data was analyzed for factors associated with discrimination as well as within group differences associated with discrimination. Factors that were associated with high level of discrimination were then compared across race.
      Integrated analyses involving associations between different data sets were performed using Spearman’s correlation controlling for multiple hypothesis testing and presented as circos plots. Further details are provided in Supplemental Methods.

      RESULTS

      Participant Differences Associated with Discrimination

      Of the 154 participants (80 with high discrimination and 74 with low discrimination), the high discrimination group had higher levels of anxiety (p=0.009), depression (p=0.009), perceived stress (p=0.001), visceral sensitivity (p<0.001), early life adversity (p=0.009), neuroticism (p=0.01), and worse scores for mental health (p=0.01) and physical health (p=0.02) than the low discrimination group(Table 1). There were no significant differences in gender, age, BMI, education, marital status, and diet. When examining across race within the high levels of discrimination group, Hispanics reported the highest levels of early life adversity, and significantly higher than Asians (p=0.04). Black individuals reported the highest levels of resilience and significantly higher than Asians (p=0.02). Black individuals reported the lowest levels of neuroticism, and significantly lower than Asians (p=0.02), Hispanics (p=0.001), and White individuals (p=0.01). Black individuals had the lowest levels of trait anxiety, depression, and perceived stress, and significantly lower than Hispanics (p=0.03, 0.03, 0.02 respectively). Black individuals also had the highest reported scores for mental health and significantly higher than Hispanics (p=0.003). There were no significant differences for extraversion, social economic status, and self-reported physical health in individuals who experienced high levels of discrimination across the races.
      Table 1Participant characteristics and clinical questionnaire scores
      ALL PARTICIPANTSBLACKHISPANICASIANWHITE
      Low EDS (n=74)High EDS (n=80)P-valueLow EDS (n=7)High EDS (n=13)P-valueLow EDS (n=29)High EDS (n=33)P-valueLow EDS (n=18)High EDS (n=13)P-valueLow EDS (n=19)High EDS (n=21)P-value
      Mean EDS (SD)2.1 (2.1)12.9 (5.7)<0.0012.9 (1.9)15.6 (6.9)<0.0012.1 (2.1)12.4 (5.2)<0.0011.8 (2.1)13.6 (7.6)<0.0012.2 (2.3)11.6 (3.9)<0.001
      Male (n=43)

      Female (n=111)
      44.20%

      49.50%
      55.80%

      50.50%
      0.5516.70%83.30%0.2637.50%62.50%0.3980.00%20.00%0.3750.00%50.00%0.79
      42.90%57.10%50.00%50.00%53.85%46.15%45.80%54.20%
      Age (yr) (SD)32.6 (10.3)30.5 (10.3)0.2132.6 (10.3)30.5 (10.3)0.2132.6 (10.3)30.5 (10.3)0.2122.3 (9.5)29.0 (9.8)0.6332.6 (10.3)30.5 (10.3)0.21
      BMI (SD)29.9 (5.9)29.9 (5.8)0.9829.9 (5.9)29.9 (5.8)0.9829.9 (5.9)29.9 (5.8)0.9824.7 (4.0)25.0 (5.1)0.8329.9 (5.9)29.9 (5.8)0.98
      Education
      Some High School0.40.60.880.00%0.00%0.370.00%100.00%0.450.00%0.00%0.260.00%0.00%0.8
      High School Graduate47.30%52.70%20.00%80.00%50.00%50.00%100.00%0.00%36.40%63.60%
      College Graduate47.90%52.10%50.00%50.00%44.10%55.90%55.56%44.44%51.70%48.30%
      Marital Status
      Never Married43.90%56.10%0.3925.00%75.00%0.4343.20%56.80%0.3865.22%34.78%0.2234.60%65.40%0.03
      Married51.30%48.70%50.00%50.00%44.40%55.60%40.00%60.00%87.50%12.50%
      Divorced56.30%43.80%33.30%66.70%71.40%28.60%0.00%100.00%60.00%40.00%
      Widowed100.00%0.00%100.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%
      Questionnaire Scores
      ETI Total Score (SD)3.6 (4.3)5.5 (4.4)0.0093.3 (4.1)3.9 (4.5)0.764.3 (4.9)6.9 (4.7)0.042.2 (2.7)3.3 (3.3)0.324.2 (4.7)5.6 (3.9)0.3
      CDRISC Score (SD)80.5 (12.0)76.9 (13.3)0.0988.0 (9.4)84.9 (10.2)0.5279.7 (11.2)76.5 (12.9)0.3276.2 (13.8)72.6 (13.1)0.4881.8 (11.2)75.2 (14.4)0.12
      IPIP Neuroticism (SD)20.1 (6.3)23.1 (7.6)0.0117.4 (3.0)15.9 (5.8)0.5321.5 (5.8)24.8 (7.7)0.0720.8 (7.5)24.8 (6.3)0.1318.8 (6.4)23.7 (7.1)0.03
      IPIP Extraversion (SD)36.1 (7.0)34.3 (7.1)0.1236.3 (6.4)38.8 (8.1)0.537.2 (7.6)33.0 (7.1)0.0331.8 (7.5)33.3 (5.9)0.5538.5 (4.4)34.2 (6.7)0.03
      SES Score (SD)6.3 (1.4)5.8 (1.5)0.066.5 (0.5)6.5 (1.3)0.996.1 (1.5)5.4 (1.4)0.086 (0.8)5.7 (1.1)0.596.7 (1.6)6.2 (2.2)0.5
      PHQ Score (SD)4.5 (3.8)5.9 (4.2)0.032.9 (2.2)4.8 (3.9)0.24.7 (3.1)6.8 (4.2)0.044.3 (4.0)5.2 (4.4)0.585.1 (4.9)5.6 (4.4)0.72
      STAI Trait Score (SD)30.9 (7.5)35.6 (10.7)0.00227.4 (3.8)27.2 (6.2)0.9429.6 (5.9)37.8 (11.6)0.00134 (9.2)36.5 (8.1)0.4331.6 (8.4)36.7 (11.2)0.12
      HAD Anxiety (SD)4.2 (3.6)5.9 (3.7)0.0053.1 (3.2)4.5 (4.2)0.464.6 (3.3)7.1 (3.8)0.0084.8 (4.2)4.2 (3.1)0.633.8 (3.6)6.0 (3.2)0.04
      HAD Depression (SD)1.8 (1.8)2.9 (2.9)0.0091.3 (1.5)1.5 (2.4)0.862.1 (1.4)4.0 (3.3)0.0072.5 (2.4)1.7 (1.7)0.311.1 (1.6)2.8 (2.4)0.01
      PSS Score (SD)10.8 (5.7)14.8 (6.4)0.00110.1 (3.9)10.5 (5.4)0.8910.8 (5.0)16.5 (7.4)0.00112.3 (7.3)14.3 (5.2)0.4110.3 (5.5)15.1 (4.8)0.005
      SF12 Physical Score (SD)54.2 (3.1)52.4 (5.6)0.0252.7 (1.6)53.0 (4.1)0.8854.1 (2.6)52.1 (5.7)0.153.5 (4.3)50.7 (7.9)0.2255.4 (2.9)53.6 (4.2)0.12
      SF12 Mental Score (SD)53.1 (6.3)49.7 (9.4)0.0154.9 (4.6)56.8 (4.5)0.3853.6 (6.2)46.4 (10.9)0.00352.4 (5.8)52.1 (6.4)0.8951.8 (7.3)48.9 (8.3)0.25
      VSI Score (SD)7.2 (10.1)15.4 (17.7)0.00061.6 (2.9)11.3 (16.8)0.158.4 (10.3)17.3 (18.9)0.038.9 (13.1)18.8 (17.6)0.086.1 (7.6)13.0 (16.8)0.11
      Abbreviations: EDS: Everyday Discrimination Scale, BMI: body mass index, SD: standard deviation, SAD: Standard American Diet, NSAD: Non-Standard American Diet, ETI, Early Traumatic Inventory, CDRISC: Connor Davidson Resilience Scale, IPIP: International Personality Item Pool, SES: Socioeconomic Status, PHQ: Physical Health Questionnaire, STAI: Stair Trait Anxiety Inventory, HAD: Hospital Anxiety and Depression Scale, PSS: Perceived Stress Scale, SF12: Short Form Healthy Survey VSI: Visceral Sensitivity Index. P-values<0.05 are bolded
      For Black, Hispanic, and Asian participants, race was the most common reason for discrimination. For White participants, gender and age were the most common reasons for discrimination (Supplemental Table S2). The average EDS score for patients who had high levels of discrimination was similar across the difference races (p=0.18).

      Discrimination is Associated with Altered Brain Connectivity

      In the aggregated sample, high discrimination as compared to low discrimination was associated with increased connectivity between the default mode network (DMN) (supramarginal gyrus, superior temporal sulcus, lateral aspect of the superior temporal gyrus, inferior temporal sulcus, middle temporal gyrus, precuneus, transverse temporal sulcus) and the sensorimotor network (SMN) (precentral gyrus, heschl gyrus, subcentral gyrus, paracentral lobule, superior frontal gyrus, posterior lateral sulcus, inferior and superior part of the precentral sulcus). High discrimination was also associated with increased connectivity between the central autonomic network (CAN) (medial orbital gyrus, gyrus rectus, frontomarginal gyrus, orbital sulcus) with the emotional regulation network (ERN) (several subregions of the anterior cingulate cortex, orbital part of the inferior frontal gyrus, parahippocampal gyrus), salience network (SAL) (anterior insula, anterior mid cingulate cortex) and occipital network (middle and superior occipital sulcus, superior occipital gyrus, occipital pole). High discrimination as compared to low was associated with decreased connectivity between regions of the central executive network (CEN) (intraparietal sulcus, subparietal sulcus, superior parietal lobule, middle frontal gyrus) to various regions of the central autonomic, sensorimotor, and occipital networks (OCC) (Loadings and variables of importance from the sPLS-DA are listed in Supplemental Table S3-S7).
      More specific discrimination-based differences were observed in brain connectivity when stratified by race/ethnicity (Figure 2). For Black participants, high levels of discrimination as compared to low were related to higher connectivity within the CEN (orange) and with the DMN (blue). For Hispanic participants, high discrimination as compared to low was associated with higher connectivity within regions of the DMN, between regions of the DMN with regions from the CEN, SAL (yellow), CAN (red), and OCC (purple). For Asian participants, high discrimination as compared to low was associated with higher connectivity within regions of the SMN (green) and within the OCC. In addition, there was higher connectivity between regions of the CEN with CAN and DMN. For White participants, high discrimination as compared to low was associated with higher connectivity involving various brain regions within the ERN (pink) and reward (RN, grey), which was unique to this group and within the SMN, DMN, SAL, CAN, CEN, and OCC, which was also seen in the other groups.
      Figure thumbnail gr2
      Figure 2Brain regions associated with discrimination by race/ethnicity. sPLS-DA plots, resting state pair-wise differences by levels of discrimination, and anatomical diagram of brain regions associated with discrimination across the different races/ethnicities: Black individuals (A-B), Hispanics (C-D), Asians (E-F), and White individuals (G-H).
      Similar patterns were observed when investigating differences across race in the high discrimination group only: Black individuals had greater connectivity in the CEN, DMN and OCC but decreased connectivity in the CAN compared to White individuals. Hispanic individuals compared to Asian individuals had greater connectivity in the DMN and OCC, but decreased connectivity in the SMN; and compared to White individuals had increased connectivity in the CEN and DMN. Asian individuals compared to White individuals had increased connectivity in the SMN (details in Supplemental Table S8).

      Discrimination is Associated with Gut Microbiome and Metabolite Changes

      Microbiome and metabolite differences related to discrimination were only apparent when stratified by race/ethnicity (Figure 3).
      Figure thumbnail gr3
      Figure 3Microbiome and fecal metabolites associated with discrimination by race/ethnicity. A) Differential abundance testing by DESEq2 of bacterial taxa associated with discrimination in Black individuals. B) Taxonomic plot of genera with a relative abundance >1% by discrimination in Black individuals. Similar analysis represented for Hispanics (D,E), and White individuals (G,H). C) Fecal metabolites by discrimination in Black individuals, F) Hispanics, and I) Asians. Asians had no microbiome differences by discrimination level and White individuals had no metabolites that were different by discrimination level.
      Black participants had 9 bacterial species that were different by discrimination. High levels of Prevotella, Coprococcus and Tyzzerella, and lower levels of species belonging to Bacteroides, Parabacteroides, and Ruminococcaceae were observed in the high discrimination group compared to low. For Black participants, high discrimination was also associated with a reduced level of hydroxy-N6,N6,N6,-trimethyllysine as compared to low.
      In Hispanic individuals, high discrimination as compared to low was associated with a higher level of Bacteroides stercosis, and lower levels of valerate, levulinate, 3-(4-hydroxyphenyl)lactate (HPLA), pregnanolone/allopregnanolone sulfate, and isovalerate.
      For Asian participants, 11 fecal metabolites were higher in participants with high discrimination as compared to low. These include 3-beta-hydroxy-5-cholestenoate, beta-sitosterol, campesterol, cholesterol, desmosterol, fucosterol, palmitoyl-sphinganine, palmitoyl-sphingosine.
      For White participants, a high level of discrimination as compared to low was associated with changes in 7 bacterial species (reduction in Prevotella copri, Bacteroides salyersiae, Blautia stercosis, Faecalibacterium prausnitzii, and unknown species of Prevotella and Ruminococcaceae, and an elevation in Catenibacterium mitsuokai).
      Bacteria and metabolite differences across race in individuals experiencing high levels of discrimination are summarized in Supplemental Table S9 and S10. In this analysis of individuals who experienced high levels of discrimination, Prevotella copri was highest in Black individuals and Hispanics and was the lowest in White individuals (p=0.04). Similarly, of the metabolites analyzed in individuals who experienced high levels of discrimination, only isovalerate, valerate, and fucosterol were statistically different between the races. Isovalerate and valerate were significantly lower in Hispanics as compared to White individuals (p=0.04 and 0.04, respectively), and fucosterol was significantly higher in Asians than in White individuals (p=0.03).

      Effects of Discrimination on Inflammatory Markers

      Of the a prior set of immune markers, which included 19 genes involved in inflammation and 32 genes involved in type I IFN responses, 4 markers were associated with high discrimination as compared to low discrimination (Figure 4).
      Figure thumbnail gr4
      Figure 4Expression levels of several inflammatory markers extrapolated from PBMC. *p-value<0.05. IFI: Interferon induced protein, IL: Interleukin, IRF: Interferon regulatory factor, PTGS: Prostaglandin-Endoperoxide Synthase
      In Black participants, high discrimination as compared to low was associated with elevated levels of prostaglandin-endoperoxidase synthase 1 (PTGS1). In Hispanic participants, high discrimination as compared to low was associated with elevated levels of interferon induced protein 35 (IFI35) and interleukin-1β (IL1β). In White participants, high discrimination as compared to low was associated with a reduction in interferon regulatory factor 8 (IRF8). There were no inflammatory markers that were different in Asian participants.
      Examining only individuals who experienced high levels of discrimination across race, Black individuals had the highest level of PTGS1 and was significantly higher than White individuals (p=0.03). Hispanics have the highest level of IL1β and was significantly higher than White individuals (p=0.001). There was no statistical difference in the expression level of IFI35 or IRF8 in individuals who experienced high levels discrimination across race.

      Association Networks Differ by Discrimination and by Race/Ethnicity

      The association networks within the BGM system with high discrimination level and by race/ethnicity are represented in the connectograms (Figure 5).
      Figure thumbnail gr5
      Figure 5Networks depicting high discrimination associated with brain gut microbiome immune factors. Networks relating brain, PBMC, microbiome, and clinical questionnaire data by race in those individuals experiencing high discrimination. Red lines are positive associations and blue lines are negative associations.
      In Black participants, hydroxy-N6,N6,N6,-trimethyllysine was negatively associated with the DMN and CEN, inflammatory markers IRF8 and IL1B were positively associated with resilience which was also positively associated with the DMN and CEN. There were positive connections between stress and anxiety with several bacterial species (Parabacteriodes and Bacteriodes).
      Among Hispanic participants, inflammatory marker IRF8 was associated with higher levels of anxiety and neuroticism, and with the lipid metabolite valerate. Higher levels of physical symptoms (Patient Health Questionnaire (PHQ) and Short Form Survey 12 Physical Component Score (SF12 PCS)) were positively associated with the DMN and with the anterior mid cingulate cortex (key region of the SAL), but high Social Economic Status (SES) was also positively associated with the DMN.
      In Asian participants, there are several positive associations between metabolites related to cholesterol (lipid pathway), microbial species (Prevotella, Ruminococcaceae) and anxiety (state and trait), neuroticism, depression, and physical symptoms. Asian individuals who experienced high discrimination also had several connections to the SMN and OCC.
      White participants had several associations between the gut microbiome (Coprococcus, Ruminococcus, Ruminococcaceae, Parabacteriodes, Alistipes, Bacteriodes) and widespread brain networks (including the ERN and RN), and with anxiety, depression, neuroticism, early life adversity, stress, visceral sensitivity, and physical symptoms. It was the only group that demonstrated negative associations between resilience and other variables.

      DISCUSSION

      We examined the association between everyday experiences of discrimination and alterations in the BGM system. Generally, discrimination was associated to anxiety, depression, and worse measures of physical and mental health. However, these associations varied across race/ethnicity, with Black individuals having no association between discrimination and mental health.
      A history of discrimination was associated with widespread connectivity differences in several networks, but with race/ethnic differences contributing to the greatest variance. The differences seen in the BGM system between the different racial/ethnic groups could be due to the varying types of discrimination experienced by the different groups. A key feature of our work is the inclusion of multiple forms of discrimination. While racial discrimination is important, our study allowed participants to report on discrimination based on other factors (e.g. gender, age, religion). This was critical for capturing the spectrum of experiences for our diverse sample. For minorities, skin color and race were the most common reason for discrimination. For White individuals, gender and age were the most common reasons. Discrimination based on race and skin color can occur as early as childhood, a period of time that is critical for the development of the BGM system(
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      Discrimination and Altered Brain Connectivity

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      Discrimination-Associated Gut Microbiome and Metabolite Changes

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      Unlike the patterns observed in Hispanic and Black individuals, the microbiome and metabolite panel of Asians and White individuals are less related to inflammation. In Asian individuals, high discrimination was associated with higher levels of metabolites that have been implicated in lipid metabolism. This profile may suggest dietary preference of foods high in fat in Asians who experience high levels of discrimination. In White individuals, discrimination was associated with the lowest levels of P. copri.

      Discrimination-Associated Inflammatory Changes

      In Black individuals, discrimination was associated with higher levels of PTGS1 and in Hispanics, discrimination was associated with higher levels of IL-1β. Both of these were higher in Black and Hispanic individuals who experienced high levels of discrimination, as compared to White individuals who experienced similar levels of discrimination. PTGS1 is also known as cyclo-oxygenase 1 (COX1) and is the enzyme that catalyzes the conversion of arachidonate to prostaglandins. High levels of prostaglandins are produced in response to injury or infection and are major drivers of inflammation.(
      • Ricciotti E.
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      Prostaglandins and inflammation.
      ) Similarly, IL-1β is a pro-inflammatory cytokine that has been implicated in pain, inflammation and autoimmune conditions.(
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      Role of interleukin-1beta during pain and inflammation.
      ) These findings suggest that discrimination may lead to a chronic state of inflammation specially in Black individuals and Hispanics.

      Discrimination-related Changes within the BGM System and Clinical Implications

      The BGM patterns highlight that Black participants who experience high levels of discrimination are associated with higher levels of inflammatory biomarkers as compared to Black participants with lower levels of discrimination. Despite the increase in inflammatory markers, the group as a whole have the lowest levels of anxiety and depression irrespective of discrimination. Black participants as a group have the highest resilience scores of any race. These findings suggest that the effect of discrimination on mental health in this group is likely being buffered by top-down processes related to resilience and cognitive flexibility.(
      • Spence N.D.
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      Racial Discrimination, Cultural Resilience, and Stress.
      ,
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      ,
      • Szanton S.L.
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      Structural Racial Discrimination and Structural Resilience: Measurement Precedes Change.
      )
      In Hispanic participants, high discrimination was associated with peripheral markers (inflammation-IRF8 and gut microbes) and several clinical behaviors (anxiety, physical health symptoms), but SES and DMN activity related to better coping strategies and cognitive control could be overriding these negative effects. Similarly, some studies have demonstrated that SES can be protective against discrimination.(
      • Surachman A.
      • Jenkins A.I.C.
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      Socioeconomic status trajectories across the life course, daily discrimination, and inflammation among Black and white adults.
      ,

      Assari S, Gibbons FX, Simons RL (2018): Perceived Discrimination among Black Youth: An 18-Year Longitudinal Study. Behav Sci (Basel). 8.

      ,
      • Assari S.
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      ,
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      • et al.
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      ,
      • Cuevas A.G.
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      Assessing the role of socioeconomic status and discrimination exposure for racial disparities in inflammation.
      ,
      • White S.F.
      • Nusslock R.
      • Miller G.E.
      Low Socioeconomic Status Is Associated with a Greater Neural Response to Both Rewards and Losses.
      )
      In Asian participants with high discrimination, there were positive associations between metabolites related to cholesterol and to several clinical measures (anxiety, depression, physical symptoms) and with SMN activity (social pain and visceral somatosensory processes).(
      • Li L.
      • Di X.
      • Zhang H.
      • Huang G.
      • Zhang L.
      • Liang Z.
      • et al.
      Characterization of whole-brain task-modulated functional connectivity in response to nociceptive pain: A multisensory comparison study.
      ) This suggests that Asians with high discrimination are possibly eating foods that are high in fat in order to deal with the associated feelings of anxiety, depression and somatosensory/visceral signals, which is consistent with studies demonstrating the emphasis on physical symptoms as a way to deal with painful emotional and stressful situations.
      In White participants with high discrimination there were several widespread associations within the BGM system, and it was the only group that included connections to the ERN and RN (a network associated with emotional stress). This pattern together with the negative association with resilience, highlights the decreased regulatory deficiency in reacting and coordinating efficiently to novel and stressful experiences of discrimination in White participants.

      Limitations

      While this is the first study to examine discrimination across different racial groups in relation to the BGM system, there are several limitations to the current study. Black individuals were underrepresented in the study. This low sample size could make the analysis for Black individuals underpowered to discern small effect sizes as well as raise the possibility of sampling bias. However, we do not present any data that conflicts with previously published works regarding Black individuals and discrimination but rather expand on its relationship to the BGM system. Future studies looking into the BGM system and discrimination should attempt to increase the representation of Black individuals. Lastly, while a major strength of this paper is the incorporation of multiple biological systems, we did not examine other systems that are likely involved in discrimination such as the HPA axis and the autonomic nervous system. But this body of work shows that discrimination has a holistic effect on the body and the mind, and therefore, discrimination’s effect on health is complex and multifactorial.

      Conclusions

      Unfair treatment is experienced by all people. Our findings provide a preliminary framework for understanding how unfair treatment is perceived and processed in the brain, and how these are in turn related to inflammation, gut microbiome, and psychological symptoms. Of course, much more work remains, but it provides an initial step towards understanding how social inequalities become a whole-body experience and gives some understanding of how expressions of “racism makes me sick to the stomach” might have an actual manifestation in the body.

      Acknowledgements

      Declarations
      Ethics Approval and Consent to Participate
      All procedures complied were approved by the Institutional Review Board (16-000187, 15-001591) at the University of California, Los Angeles’s Office of Protection for Research Subjects. All participants provided written informed consent. Participants or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
      Consent for Publication: Not applicable.
      Availability of Data and Material: The datasets generated during and/or analyzed during the current study are not publicly available due to an ongoing collaboration with multiple principal investigators involving participant identifiers at the G. Oppenheimer Center for Neurobiology of Stress and Resilience. But data is available from the corresponding author on reasonable request.
      Competing Interests: AG is a scientific consultant to Yamaha. EAM is a scientific advisory board member of Danone, Axial Biotherapeutics, Amare, Mahana Therapeutics, Pendulum, Bloom Biosciences, Seed, and APC Microbiome Ireland. All other authors report no biomedical financial interests or potential conflicts of interest.
      Funding: This research was supported by grants from the National Institutes of Health including R01 MD015904 (AG), K23 DK106528 (AG), R03 DK121025(AG), T32 DK07180 (TD), ULTR001881/DK041301 (UCLA CURE/CTSI Pilot and Feasibility Study (AG), P50 DK064539 (EAM), R01 DK048351 (EAM), P30 DK041301; and pilot funds provided for brain scanning by the Ahmanson-Lovelace Brain Mapping Center. These funders played no role in study design, or the collection, analysis, and interpretation of the data.
      Author contributions:
      Conceptualization: TSD, GCG, AG
      Methodology: TSD, AG
      Formal Analysis: TSD, AG, ZC, VS, YZ, YG, S.C.
      Resources/Data Curation: AG
      Writing—Original draft preparation: TSD, GCG, HBS, MW, VO, LAK, JSL, BN, XZ, SC, EAM, AG
      Visualization: TSD, YZ, AG
      Supervision: AG
      Funding acquisition: AG
      All authors read and approved the final manuscript.

      Acknowledgements

      We would like to acknowledge the assistance of the Neuroimaging Core, Bioinformatics and Statistics Core, Microbiome Core, and the Biorepository Core of the UCLA Microbiome Center for their assistance with various processing, storage, and analyses assistance of the current manuscript. We would also like to acknowledge Dr. Steve Cole for his assistance in processing the PBMC samples.

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