Archival Report

BiologicalPsychiatry

How Discrimination Gets Under the Skin:
Biological Determinants of Discrimination
Associated With Dysregulation of the Brain-Gut
Microbiome System and PsychologicalSymptoms
Tien S. Dong, Gilbert C. Gee, Hiram Beltran-Sanchez, May Wang, Vadim Osadchiy,Lisa A. Kilpatrick, Zixi Chen, Vishvak Subramanyam, Yurui Zhang, Yinming Guo,
Jennifer S. Labus, Bruce Naliboff, Steve Cole, Xiaobei Zhang, Emeran A. Mayer, andArpana Gupta

ABSTRACTBACKGROUND: Discrimination is associated with negative health outcomes as mediated in part by chronic stress,
but a full understanding of the biological pathways is lacking. Here we investigate the effects of discrimination
involved in dysregulating the brain-gut microbiome (BGM) system.
METHODS: A total of 154 participants underwent brain magnetic resonance imaging to measure functional connectivity. Fecal samples were obtained for 16S ribosomal RNA proﬁling and fecal metabolites and serum for inﬂammatory markers, along with questionnaires. The Everyday Discrimination Scale was administered to measurechronic 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 assessedusing 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 andfunctional changes in the gut microbiome. BGM discrimination-related associations varied by race/ethnicity.
Among Black and Hispanic individuals, discrimination led to brain network changes consistent with psychologicalcoping and increased systemic inﬂammation. For White individuals, discrimination was 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 toward 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.
https://doi.org/10.1016/j.biopsych.2022.10.011

Structural racism contributes to health inequities and partiallymanifests as everyday, mundane experiences of discrimination(1–3). Self-reported discrimination is a risk factor for mental
and physical disorders. However, the understanding of how
discrimination gets embedded in biology and affects mental/
physical health remains unclear.
Previously, the mechanistic understanding of the link between discrimination and disease was mainly centered on the
hypothalamic-pituitary-adrenal (HPA) axis (4–6). However,recent studies have highlighted that the gut microbiome isextremely sensitive to stressors in inﬂuencing long-term healthand inﬂammation (7–9). Stress can impact the bottom-up

communication between the microbiome and the brain
through several pathways, including changes in HPA activity,
speciﬁc microbial metabolites, and vagus nerve and immuneinﬂammatory mechanisms (7,10–14).
Similarly, changes in brain activity can also inﬂuence thebody and the gut. Neuroimaging studies suggest that
discrimination rewires the brain, causing dysregulation ofstress networks (15–21). These top-down signals from thebrain inﬂuence gastrointestinal processes, including motility(22), intestinal permeability (23), and microbial gene expression(24–31). Dysbiosis in the brain-gut microbiome (BGM) systemhampers host homeostasis due to its role in immune

SEE COMMENTARY ON PAGE 186

ISSN: 0006-3223ª 2022 Society of Biological Psychiatry. This is an open access article under the203
CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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modulation and activation (32,33). Therefore, the major goal ofthis 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).
Furthermore, discrimination has been predominantly studied in Black individuals as compared with 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 than among
White individuals, but the literature is mixed in this regard (34).
Some studies show that discrimination is associated withincreased C-reactive protein levels among Black individuals(35), whereas other studies show no differences (36,37). One
study showed that among Asian immigrants, discrimination
was related to increased obesity, which is not as commonly
seen in other races (38), while other studies have shown thatamong Latinx individuals, discrimination is associated withoutcomes such as depression and substance use (39,40). This
raises an intriguing question as to why discrimination may havevarying effects across different races/ethnicities. Obesity,
substance abuse, and depression have all been linked to the
BGM system. Therefore, these differences may be related todifferences in how dysregulation of the BGM system occurs
within each race/ethnicity.
To study a holistic view of how discrimination can affectbiology, 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 associatedwith increased alterations in stress brain connectivity pathways (resting-state functional magnetic resonance imaging[fMRI]) 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 Asian, Black, Hispanic, and White
individuals.

METHODS AND MATERIALS
Ethics Approval and Consent to Participate
All procedures were approved by the Institutional Review
Board (16-000187, 15-001591) at the University of California,
Los Angeles, Ofﬁce of Protection for Research Subjects. All
participants provided written informed consent.

Participants
The ﬁnal sample comprised 154 adults from the 165 who were
initially enrolled in the study. Participants were recruited fromthe 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 andmetabolomics), and survey questionnaires, including a diethistory (Table S1 in Supplement 1). Participants self-reported
race/ethnicity (Asian American, Black, Hispanic, or White).
Discrimination was measured using the Everyday Discrimination Scale.

Statistical Analysis
Group differences in demographic characteristics; brain, microbial taxa, and differential abundance testing for metabolomics; and PBMCs were determined individually (adjustingfor sex, age, body mass index, and diet). For fMRI andmetabolite 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

Figure 1. Conceptual model linking the brain-gutmicrobiome system to discrimination and clinicaloutcomes. AAA, aromatic amino acid; EDS,
Everyday Discrimination Scale; IL-1b, interleukin 1b;
NFK-B, nuclear factor kappa light chain enhancer ofactivated B cells; PHQ, Physical Health Questionnaire; PSS, Perceived Stress Scale; SCFA, shortchain fatty acids.

Brain
Stress Networks(Cogni ve, Social Pain,
Aﬀec ve, Threat Appraisal)

Ethnicity/Race

Discrimina on(Chronic: EDS)

Inﬂamma on• Inﬂammatory cytokines• NFK-B• IL-1B• Prostaglandins• Interferon

Clinical (Mental/Physical)
Outcomes• Food Addic on• Psychological Symptoms(Depression/Anxiety)

• PHQ
• PSS
• Obesity/Diabetes/Hypertension

Gut Microbiome↓ SCFA, AAA Fermenta on(Indoles), Valerates↑ Glutamate, Kynurenine,
Tryptophan

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variables far exceeds the number of study participants, sparse
multivariate nonparametric models exhibit the most robust
statistical power and consistency (41). sPLS-DA operates using a supervised framework to ﬁnd linear combinations of alimited set of variables that predicts predeﬁned groups (42).
sPLS-DA is a well-established method for both data reduction
and classiﬁcation analysis for both fMRI and metabolite data
(42,43). Microbiome data were analyzed using QIIME2 and
DESEq2 in R. PBMC data were analyzed using a priori–
speciﬁed domains of immune function that have repeatedly
been found to vary in response to psychosocial risk factors(44). For PBMC, Mann-Whitney U test was performed between participants with low or high discrimination, and pvalues were adjusted for multiple hypothesis testing using
Bonferroni correction. Data were analyzed for factors associated with discrimination as well as within-group differences
associated with discrimination. Factors that were associatedwith high levels of discrimination were then compared acrossraces.
Integrated analyses involving associations between
different datasets were performed using Spearman’s correlation controlling for multiple hypothesis testing and presented
as circos plots. Further details are provided in Supplemental
Methods in Supplement 1.

RESULTS
Participant Differences Associated With
Discrimination
Of the 154 participants (80 with high discrimination and 74 withlow discrimination), the high discrimination group had higher
levels of anxiety (p = .009), depression (p = .009), perceived
stress (p = .001), visceral sensitivity (p , .001), early-life
adversity (p = .009), neuroticism (p = .01), and worse scoresfor mental health (p = .01) and physical health (p = .02) than the
low discrimination group (Table 1). There were no signiﬁcantdifferences in sex, age, body mass index, education, marital
status, and diet. When examining across race/ethnicity within
the high levels of discrimination group, Hispanic individuals
reported the highest levels of early-life adversity, levels
signiﬁcantly higher than in Asian individuals (p = .04). Black
individuals reported the highest levels of resilience, levels
signiﬁcantly higher than in Asian individuals (p = .02). Black
individuals reported the lowest levels of neuroticism, levels
signiﬁcantly lower than in Asian (p = .02), Hispanic (p = .001),
and White (p = .01) individuals. Black individuals had thelowest levels of trait anxiety, depression, and perceived stressand signiﬁcantly lower than in Hispanic individuals (p = .03, .03,
and .02, respectively). Black individuals also had the highest
reported scores for mental health, scores signiﬁcantly higher
than in Hispanic individuals (p = .003). There were no signiﬁcant differences in extraversion, socioeconomic status, andself-reported physical health in individuals who experienced
high levels of discrimination across the races.
For Asian, Black, and Hispanic participants, race was themost common reason for discrimination. For White participants, gender and age were the most common reasons fordiscrimination (Table S2 in Supplement 1). The average
Everyday Discrimination Scale score for participants who hadhigh levels of discrimination was similar across the different
races (p = .18).

Discrimination Is Associated With Altered Brain
Connectivity
In the aggregated sample, high discrimination as comparedwith low discrimination was associated with increased connectivity between the default mode network (DMN) (supramarginal gyrus, superior temporal sulcus, lateral aspect of thesuperior temporal gyrus, inferior temporal sulcus, middle
temporal gyrus, precuneus, and transverse temporal sulcus)
and the sensorimotor network (SMN) (precentral gyrus,
Heschl’s gyrus, subcentral gyrus, paracentral lobule, superior
frontal gyrus, posterior lateral sulcus, and 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, and orbital sulcus) and the emotion
regulation network (ERN) (several subregions of the anterior
cingulate cortex, orbital part of the inferior frontal gyrus, andparahippocampal gyrus), salience network (SAL) (anterior
insula, anterior midcingulate cortex), and occipital network(OCC) (middle and superior occipital sulcus, superior occipitalgyrus, and occipital pole). High discrimination as comparedwith low was associated with decreased connectivity between
regions of the central executive network (CEN) (intraparietal
sulcus, subparietal sulcus, superior parietal lobule, and middle
frontal gyrus) to various regions of the CAN, SMN, and OCC.
(Loadings and variables of importance from the sPLS-DA are
listed in Tables S3–S7 in Supplement 2.)
More speciﬁc discrimination-based differences wereobserved in brain connectivity when stratiﬁed by race/
ethnicity (Figure 2). For Black participants, high levels of
discrimination as compared with low were related to higher
connectivity within the CEN (orange) and with the DMN(blue). For Hispanic participants, high discrimination ascompared with low was associated with higher connectivity
within regions of the DMN and between regions of the DMN
with regions from the CEN, SAL (yellow), CAN (red), andOCC (purple). For Asian participants, high discrimination,
as compared with 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 with low was
associated with higher connectivity involving various brain
regions within the ERN (pink) and reward network (RN,gray), which was unique to this group and within the SMN,
DMN, SAL, CAN, CEN, and OCC, which was also seen in
the other groups.
Similar patterns were observed when investigating differences across races in the high discrimination group only: Black
individuals had greater connectivity in the CEN, DMN, andOCC but decreased connectivity in the CAN compared withWhite individuals. Hispanic individuals, compared with Asian
individuals, had greater connectivity in the DMN and OCC, but
decreased connectivity in the SMN, and compared with White
individuals, they had greater connectivity in the CEN and DMN.
Asian individuals, compared with White individuals, had

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Table 1. Participant Characteristics and Clinical Questionnaires
All Participants
Characteristics

Low EDS, High EDS,n = 74n = 80

EDS, Mean (SD)

2.1 (2.1)

Asian Participants
Low EDS, High EDS,n = 18n = 13p12.9 (5.7),.001a.55

Female, n = 111

49.50%

50.50%

Male, n = 4344.20%

55.80%

Age, Years, Mean(SD)

32.6 (10.3)

30.5 (10.3)

BMI, Mean (SD)29.9 (5.9)

Some highschool1.8 (2.1)

Black Participants
p13.6 (7.6),.001a.37

53.85%

46.15%

80.00%

20.00%.21

22.3 (9.5)

29.0 (9.8)

29.9 (5.8).98

24.7 (4.0)

40.00%

60.00%.88

High school
graduate

47.30%

College graduate

Low EDS, High EDS,n=7
n = 132.9 (1.9)

White Participants
p15.6 (6.9),.001a.26

42.90%

57.10%

16.70%

83.30%.63

32.6 (10.3)

30.5 (10.3)

25.0 (5.1).83

29.9 (5.9)

0.00%

0.00%

.26

52.70%

100.00%47.90%

52.10%

Never married

43.90%

56.10%

Married51.30%

48.70%

Divorced

56.30%

Widowed

100.00%Low EDS, High EDS,n = 19n = 212.2 (2.3)

Hispanic Participants
Low EDS, High EDS,n = 29n = 33p11.6 (3.9),.001a.79

45.80%

54.20%

50.00%

50.00%.21

32.6 (10.3)

30.5 (10.3)

29.9 (5.8).98

29.9 (5.9)

0.00%

0.00%

.37

0.00%

20.00%

55.56%

44.44%

65.22%

34.78%

40.00%

60.00%

43.80%

0.00%

0.00%

0.00%

2.1 (2.1)

p12.4 (5.2),.001a–.39

50.00%

50.00%

37.50%

62.50%.21

32.6 (10.3)

30.5 (10.3).21

29.9 (5.8).98

29.9 (5.9)

29.9 (5.8)

.98

0.00%

0.00%

.80

0.00%

100.00%.4580.00%

36.40%

63.60%

50.00%

50.00%

50.00%

50.00%

51.70%

48.30%

44.10%

55.90%

25.00%

75.00%

34.60%

65.40%

43.20%

56.80%

50.00%

50.00%

87.50%

12.50%

44.40%

55.60%

100.00%33.30%

66.70%

60.00%

40.00%

71.40%

28.60%

0.00%

100.00%

0.00%

0.00%

0.00%

0.00%

0.00%

3.3 (4.1)

3.9 (4.5).76

4.2 (4.7)

5.6 (3.9).30

4.3 (4.9)

6.9 (4.7)

Education

Marital Status
.39

.22

.43

.03a

.38

Questionnaire Scores, Mean (SD)
3.6 (4.3)

5.5 (4.4).009a

2.2 (2.7)

3.3 (3.3)

CDRISC

80.5 (12.0)

76.9 (13.3).09

76.2 (13.8)

72.6 (13.1).48

88.0 (9.4)

84.9 (10.2).52

81.8 (11.2)

75.2 (14.4).1279.7 (11.2)

76.5 (12.9).32

IPIP Neuroticism

20.1 (6.3)

23.1 (7.6).01a20.8 (7.5)

24.8 (6.3).13

17.4 (3.0)

15.9 (5.8).53

18.8 (6.4)

23.7 (7.1).03a21.5 (5.8)

24.8 (7.7).07

IPIP Extraversion

36.1 (7.0)

34.3 (7.1).12

31.8 (7.5)

33.3 (5.9).55

36.3 (6.4)

38.8 (8.1).50

38.5 (4.4)

34.2 (6.7).03a37.2 (7.6)

33.0 (7.1)

.03a

SES

6.3 (1.4)

5.8 (1.5).06

6 (0.8)5.7 (1.1).59

6.5 (0.5)

6.5 (1.3).99

6.7 (1.6)

6.2 (2.2).50

6.1 (1.5)

5.4 (1.4)

.08

PHQ

4.5 (3.8)

5.9 (4.2).03a

4.3 (4.0)

5.2 (4.4).58

2.9 (2.2)

4.8 (3.9).20

5.1 (4.9)

5.6 (4.4).72

4.7 (3.1)

6.8 (4.2).04a

STAI Trait

30.9 (7.5)

35.6 (10.7).002a

34 (9.2)

36.5 (8.1).43

27.4 (3.8)

27.2 (6.2).94

31.6 (8.4)

36.7 (11.2)

.12

a

.32

.04a

29.6 (5.9)

37.8 (11.6)

.001a

a

HAD Anxiety

4.2 (3.6)

5.9 (3.7).005

4.8 (4.2)

4.2 (3.1).63

3.1 (3.2)

4.5 (4.2).46

3.8 (3.6)

6.0 (3.2).04

4.6 (3.3)

7.1 (3.8).008a

HAD Depression

1.8 (1.8)

2.9 (2.9).009a

2.5 (2.4)

1.7 (1.7).31

1.3 (1.5)

1.5 (2.4).86

1.1 (1.6)

2.8 (2.4).01a

2.1 (1.4)

4.0 (3.3)

.007a

PSS

10.8 (5.7)

14.8 (6.4).001a12.3 (7.3)

14.3 (5.2).41

10.1 (3.9)

10.5 (5.4).89

10.3 (5.5)

15.1 (4.8).005a

10.8 (5.0)

16.5 (7.4)

.001a

a

SF12 Physical54.2 (3.1)

52.4 (5.6).02

53.5 (4.3)

50.7 (7.9).2252.7 (1.6)

53.0 (4.1).88

55.4 (2.9)

53.6 (4.2).12

54.1 (2.6)

52.1 (5.7).10

SF12 Mental

53.1 (6.3)

49.7 (9.4).01a52.4 (5.8)

52.1 (6.4).89

54.9 (4.6)

56.8 (4.5).38

51.8 (7.3)

48.9 (8.3).25

53.6 (6.2)

46.4 (10.9)

.003a

VSI

7.2 (10.1)

15.4 (17.7).0006a8.9 (13.1)

18.8 (17.6).08

1.6 (2.9)

11.3 (16.8).15

6.1 (7.6)

13.0 (16.8).11

8.4 (10.3)

17.3 (18.9).03a

BMI, body mass index; CDRISC, Connor Davidson Resilience Scale; EDS, Everyday Discrimination Scale; ETI, Early Traumatic Inventory; HAD, Hospital Anxiety and Depression Scale;
IPIP, International Personality Item Pool; PHQ, Physical Health Questionnaire; PSS, Perceived Stress Scale; SES, socioeconomic status; SF12, Short Form Healthy Survey; STAI, State-Trait
Anxiety Inventory; VSI, Visceral Sensitivity Index.
ap values , .05.

Biological Impact of Discrimination

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Figure 2. Brain regions associated with discrimination by race/ethnicity. Sparse partial least squarelinear discriminant analysis (sPLS-DA) plots, restingstate pairwise differences by levels of discrimination,
and anatomical diagram of brain regions associatedwith discrimination across the different races/ethnicities: Black (A, B), Hispanic (C, D), Asian (E, F),
and White (G, H). AngG, angular gyrus; AoCS,
anterior occipital sulcus; ATrCos, anterior transverse
collateral sulcus; CcS, calcarine sulcus; CgSMarp,
marginal branch of the cingulate sulcus; Cun,cuneus; EDS, Everyday Discrimination Scale;
FMarG/S, fronto-marginal gyrus and sulcus; FuG,
fusiform gyrus; HG, Heschl’s gyrus; InfCirInS, inferior
segment of circular sulcus of the insula; InfFGOpp,
opercular part of the inferior frontal gyrus; InfFS,
inferior frontal sulcus; InfOcG/S, interior occipitalgyrus/sulcus; InfPrCS, inferior part of the precentral
sulcus; IntPS/TrPS, intraparietal sulcus (interparietal
sulcus) and transverse parietal sulci; JS, sulcus
intermedius primus (of Jensen); LinG, lingual gyrus;
LORs, lateral orbital sulcus; MACgG/S, middleanterior part of the cingulate gyrus and sulcus;
MFG, middle frontal gyrus; MoCG, middle occipitalgyrus; MOcS/LuS, middle occipital sulcus andlunatus sulcus; MTG, middle temporal gyrus; OcPo,
occipital pole; OrG, orbital gyri; PaCL/S, paracentral
lobule and sulcus; PerCaS, pericallosal sulcus;
PrCun, precuneus; RG, gyrus rectus; SbCG/S, subcallosal gyrus/sulcus; SbPs, subparietal sulcus;
SuMarG, supramarginal gyrus; SupFG, superior
frontal gyrus; SupFS, superior frontal sulcus; SupPL,
superior parietal lobule; SupTG, superior temporalgyrus; SupTGLp, lateral aspect of the superior temporal gyrus; Thal, thalamus; TPo, temporo-parietooccipital; TrFPoG/S, transverse frontopolar gyri andsulci.

greater connectivity in the SMN (details in Table S8 in
Supplement 2).

Discrimination Is Associated With Gut Microbiomeand Metabolite Changes
Microbiome and metabolite differences related to discrimination were only apparent when stratiﬁed by race/ethnicity(Figure 3).
Black participants had 9 bacterial species that weredifferent by discrimination. High levels of Prevotella, Coprococcus, and Tyzzerella and lower levels of species belonging
to Bacteroides, Parabacteroides, and Ruminococcaceae wereobserved in the high discrimination group compared withthose found in the low discrimination group. For Black participants, high discrimination was also associated with a reduced
level of hydroxy-N6,N6,N6,-trimethyllysine as compared withlow discrimination.

In Hispanic individuals, high discrimination as comparedwith low was associated with a higher level of Bacteroides
stercosis and lower levels of valerate, levulinate, 3-(4hydroxyphenyl)lactate, pregnanolone/allopregnanolone sulfate, and isovalerate.
For Asian participants, 11 fecal metabolites were higher
in participants with high discrimination than in partipants
with low discrimination. These include 3-beta-hydroxy-5cholestenoate, beta-sitosterol, campesterol, cholesterol, desmosterol, fucosterol, palmitoyl-sphinganine, and palmitoylsphingosine.
For White participants, a high level of discrimination ascompared with 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 elevationin Catenibacterium mitsuokai).

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Figure 3. Microbiome 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 Hispanic (D, E) and White (G, H) individuals. Fecal metabolites by discrimination in Black (C), Hispanic (F), and Asian (I) individuals.
Asian individuals had no microbiome differences by discrimination level, and White individuals had no metabolites that were different by discrimination level.
EDS, Everyday Discrimination Scale.

Bacteria and metabolite differences across races in individuals experiencing high levels of discrimination are summarized in Tables S9 and S10 in Supplement 1. In this analysis
of individuals who experienced high levels of discrimination,
P copri was the highest in Black and Hispanic individuals andwas the lowest in White individuals (p = .04). Similarly, of the208

metabolites analyzed in individuals who experienced highlevels of discrimination, only isovalerate, valerate, and fucosterol were statistically different between the races. Isovalerate
and valerate were signiﬁcantly lower in Hispanic than in White
individuals (p = .04 and .04, respectively), and fucosterol was
signiﬁcantly higher in Asian than in White individuals (p = .03).

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Effects of Discrimination on Inﬂammatory Markers
Of the a priori set of immune markers, which included 19 genes
involved in inﬂammation and 32 genes involved in type I
interferon responses, 4 markers were associated with highdiscrimination as compared with low discrimination (Figure 4).
In Black participants, high discrimination as compared withlow was associated with elevated levels of prostaglandinendoperoxidase synthase 1 (PTGS1). In Hispanic participants,high discrimination as compared with low was associated withelevated levels of interferon-induced protein 35 (IFI35) andinterleukin 1b (IL1B). In White participants, high discriminationas compared with low was associated with a reduction ininterferon 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 races, Black individuals had the highest
level of PTGS1, which was signiﬁcantly higher than in White
individuals (p = .03). Hispanic individuals had the highest levelof IL1B, which was signiﬁcantly higher than in White individuals(p = .001). There was no statistical difference in the expression
level of IFI35 or IRF8 in individuals who experienced high levelsof discrimination across races.

Association Networks Differ by Discrimination andby Race/Ethnicity
The association networks within the BGM system with highdiscrimination levels and by race/ethnicity are represented in
the connectograms (Figure 5).
In Black participants, hydroxy-N6,N6,N6,-trimethyllysine
was negatively associated with the DMN and CEN, and inﬂammatory markers IRF8 and IL-1b were positively associatedwith resilience, which was also positively associated with the
DMN and CEN. There were positive connections between
stress and anxiety with several bacterial species (Parabacteroides and Bacteroides).
Among Hispanic participants, inﬂammatory marker IRF8
was associated with higher levels of anxiety and neuroticism
and with the lipid metabolite valerate. Higher levels of physicalsymptoms (Patient Health Questionnaire and Short Form
Survey 12 Physical Component Score) were positively

associated with the DMN and with the anterior midcingulate
cortex (key region of the SAL), but high socioeconomic status
was also positively associated with the DMN.
In Asian participants, there were several positive associations between metabolites related to cholesterol (lipid
pathway), microbial species (Prevotella, Ruminococcaceae),
and anxiety (state and trait), neuroticism, depression, andphysical symptoms. Asian individuals who experienced highdiscrimination also had several connections to the SMN and
OCC.
White participants had several associations between the gut
microbiome (Coprococcus, Ruminococcus, Ruminococcaceae, Parabacteroides, Alistipes, Bacteroides) and widespreadbrain networks (including the ERN and RN) and with anxiety,depression, neuroticism, early-life adversity, stress, visceralsensitivity, and physical symptoms. It was the only group that
demonstrated negative associations between resilience andother variables.

DISCUSSION
We examined the association between everyday experiencesof discrimination and alterations in the BGM system. Generally,
discrimination was associated with anxiety, depression, andworse measures of physical and mental health. However, theseassociations varied across race/ethnicity, with Black individuals having no association between discrimination andmental health.
A history of discrimination was associated with widespreadconnectivity 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, ourstudy allowed participants to report on discrimination based onother 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 andage were the most common reasons. Discrimination based on

Figure 4. Expression levels of several inﬂammatory markers extrapolated from peripheral blood mononuclear cells for Black (A), Hispanic (B), and White (C)participants. *p value , .05. EDS, Everyday Discrimination Scale.

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Figure 5. Networks depicting high discrimination associated with brain-gut microbiome immune factors. Networks relating brain, peripheral bloodmononuclear cells, microbiome, and clinical questionnaire data by race in those individuals experiencing high discrimination. Red lines are positive associations, and blue lines are negative associations.

race and skin color can occur as early as in childhood, a periodof time that is critical for the development of the BGM system(45), while discrimination based on gender and age is morecommon in young adulthood (46). One of the most common
reasons suggested to explain the cause of negative physicalhealth outcomes associated with discrimination is an increase
in allostatic load and the involvement of multiple biologicalsystems (47). Discrimination based on race and skin color,which can occur in early childhood, could lead to a longer
period of stress and allostatic load than other forms of
discrimination (46,48). These differences may alter the BGM
system in a way that may enhance vulnerability to various
behaviors and psychological symptoms.

Discrimination and Altered Brain Connectivity
Discrimination was linked with heightened self-reﬂectivenessand pain-related processing, as indicated by increased connectivity in the DMN and SMN (49–54). While self-generated
thoughts can be a source of creative insight and introspection, they can lead to distress and negatively impact performance of speciﬁc tasks (55). These results suggest thatalterations within the DMN and SMN may reﬂect difﬁculties
with cognitive and affective appraisal of pain in relation todiscrimination. Discrimination was also associated withheightened emotion regulation (ERN), autonomic (CAN), alertness (SAL), and attention toward salient stimuli (OCC). Experiences of discrimination are typically stressful (56), leading toanxious emotions and heightened cognitive load (57). However, the discrimination-related patterns in the brain showedstrong differences when examined by racial/ethnic groups.
In Black participants, discrimination was associated withhigher connectivity within the DMN and CEN. The DMN isimportant in self-recollection of past experiences (58–61) andmay be key in navigating stressful experiences (62–65), while
the CEN contributes to emotion regulation and inhibitory
control in stressed-evoked situations (16,66–68). Attributing
negative treatment externally can reduce negative emotions
and self-blame (19). Our data showed that Black individuals are

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more likely to attribute discrimination to race relative to othergroups (69,70), possibly using this as a coping mechanism toregulate distress accompanying discrimination.
In Hispanic participants, discrimination was associated withgreater connectivity within the DMN, CEN, SAL, CAN, andOCC. Psychosocial stress is associated with cognitive
impairment (71), and the SAL might be involved in regulation ofheightened vigilance associated with discrimination. Hispanic
individuals who experienced more discrimination were more
likely to have early-life trauma, higher levels of anxiety,depression, stress, and visceral sensitivity than those who
experienced less discrimination, similar to the hyperactivity of
SAL regions observed in patients with anxiety, depression, andposttraumatic stress disorder (72–74). The CAN can be triggered by challenges to unpleasant social/environmental situations (75) and may indicate altered regulatory functions via
viscerosensory mechanisms. These responses are not only
critical for adapting to internal or external challenges, but also
initiate signals that trigger emotion, affect decision making,and promote social behavior (76). Alterations in CAN highlighta risk factor for both mental and physical health problems. The
alterations in the OCC suggest enhanced attention towardthreatening stimuli experienced during discrimination (21).
In Asian individuals, discrimination was associated withhigher connectivity within the SMN. The SMN is engaged ininteroceptive, autonomic, sensory, motor, and reward processing (77–80). Increased SMN connectivity indicates disrupted sensory functions associated with somatization,
similarly observed in stressed individuals and patients withmajor depressive disorder (81,82). The anterior insula, together
with the SMN, has been implicated in social pain and, as aresult, in physical pain due to distress associated with exclusion (83–85).
White participants who experienced high levels of discrimination displayed a chaotic enhanced resting-state connectivity in numerous large-scale networks including the ERN and
RN, which may underscore the hypersensitivity and inability tocope with discrimination in comparison with other races/ethnicities. Disruptions in the communication between large-scale

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networks may reﬂect difﬁculty with reacting and coordinating
efﬁciently to experiences of discrimination (86). Accordingly,
some research shows that race-related stress can have a morenegative effect on mental health for White than for Black individuals (87,88).

Discrimination-Associated Gut Microbiome andMetabolite Changes
P copri was the only bacterium species that was signiﬁcantly
different across races. P copri was the highest in Black andHispanic individuals who experienced discrimination ascompared with White individuals who experienced discrimination. P copri produces a superoxide reductase and phosphoadenosine phosphosulfate reductase (89). These enzymes
let P copri utilize reactive oxygen species, allowing it tothrive in inﬂammatory environments as well as increaseinﬂammation (89). P copri is considered highly inﬂammatory
and has been found in rheumatoid arthritis and hepatic ﬁbrosis(90,91).
When examining metabolites, Black individuals with highdiscrimination had lower levels of hydroxy-N6,N6,N6trimethyllysine, and Hispanic individuals had lower levels of
branched-chain fatty acids as compared with Black and Hispanic individuals with low discrimination, respectively. Hydroxy-N6,N6,N6-trimethyllysine is a by-product of carnitine
biosynthesis. Carnitine has anti-inﬂammatory and cardioprotective properties (92) and has been associated with
reductions in interleukin 6 and tumor necrosis factor alpha (93).
Hispanic individuals had the lowest levels of branched-chain
fatty acids and had signiﬁcantly lower levels than White individuals who also experienced similar levels of discrimination.
Branched-chain fatty acids can have anti-inﬂammatory andanticancer properties and are important to colonic motility andhealth (94,95).
Unlike the patterns observed in Black and Hispanic individuals, the microbiome and metabolite panel of Asian andWhite 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 for foods
high in fat in Asian individuals who experience high levels of
discrimination. In White individuals, discrimination was associated with the lowest levels of P copri.

chronic state of inﬂammation, specially in Black and Hispanic
individuals.

Discrimination-Related Changes Within the BGM
System and Clinical Implications
The BGM patterns highlight that high levels of discrimination inBlack participants are associated with higher levels of inﬂammatory biomarkers as compared with Black participants withlower levels of discrimination. Despite the increase in inﬂammatory markers, the group as a whole showed the lowest
levels of anxiety and depression, irrespective of discrimination.
Black participants as a group had the highest resilience scoresof 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 andcognitive ﬂexibility (98–101).
In Hispanic participants, high discrimination was associatedwith peripheral markers (inﬂammation-IRF8 and gut microbes)
and several clinical behaviors (anxiety, physical health symptoms), but socioeconomic status and DMN activity related tobetter coping strategies and cognitive control could be overriding these negative effects. Similarly, some studies havedemonstrated that socioeconomic status can be protective
against discrimination (35,102–106).
In Asian participants with high discrimination, there werepositive associations between metabolites related to cholesterol and to several clinical measures (anxiety, depression,
physical symptoms) and with SMN activity (social pain andvisceral somatosensory processes) (50). This suggests that
Asian individuals with high discrimination are possibly eating
foods that are high in fat to deal with the associated feelings ofanxiety, depression, and somatosensory/visceral signals,
which is consistent with studies demonstrating the emphasison physical symptoms as a way to deal with painful emotionaland stressful situations.
In White participants with high discrimination, there wereseveral widespread associations within the BGM system, andit 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 andcoordinating efﬁciently to novel and stressful experiences of
discrimination in White participants.

Limitations
Discrimination-Associated Inﬂammatory Changes
In Black individuals, discrimination was associated with higher
levels of PTGS1, and in Hispanic individuals, discrimination
was associated with higher levels of IL1B. Both of these werehigher in Black and Hispanic individuals who experienced highlevels of discrimination, as compared with White individuals
who experienced similar levels of discrimination. PTGS1 is also
known as cyclo-oxygenase 1 (COX1) and is the enzyme thatcatalyzes the conversion of arachidonate to prostaglandins.
High levels of prostaglandins are produced in response to
injury or infection and are major drivers of inﬂammation (96).
Similarly, IL-1b is a proinﬂammatory cytokine that has been
implicated in pain, inﬂammation, and autoimmune conditions(97). These ﬁndings suggest that discrimination may lead to a

While this is the ﬁrst study to examine discrimination acrossdifferent racial groups in relation to the BGM system, there areseveral limitations to the current study. Black individuals were
underrepresented in the study. This low sample size could
make the analysis for Black individuals underpowered todiscern small effect sizes as well as raise the possibility ofsampling bias. However, we do not present any data thatconﬂict with previously published works regarding Black individuals and discrimination, but rather expand on their relation
to the BGM system. Future studies looking into the BGM
system and discrimination should attempt to increase therepresentation of Black individuals. Finally, while a major
strength of this article is the incorporation of multiple biologicalsystems, we did not examine other systems that are likely

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involved in discrimination such as the HPA axis and theautonomic 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 andmultifactorial.

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, andpsychological symptoms. Of course, much more work remains, but it provides an initial step toward understanding how
social inequalities become a whole-body experience and gives
some understanding of how expressions of “racism makes mesick to the stomach” might have an actual manifestation in the
body.

System, Los Angeles, California (TSD); Department of Community Health
Sciences Fielding School of Public Health, Los Angeles, California (GCG,
HB-S, MW); California Center for Population Research, University of California, Los Angeles, Los Angeles, California (GCG, HB-S); Department ofUrology, David Geffen School of Medicine, University of California, Los
Angeles, Los Angeles, California (VO); and Department of Psychiatry &Biobehavioral Sciences and Medicine, University of California, Los Angeles,
Los Angeles, California (SC).
TSD and GCG contributed equally to this work as joint ﬁrst authors.
Address correspondence to Tien S. Dong, M.D., Ph.D., at TSDong@
mednet.ucla.edu, or Arpana Gupta, Ph.D., at AGupta@mednet.ucla.edu.
Received Jun 3, 2022; revised Oct 11, 2022; accepted Oct 12, 2022.
Supplementary material cited in this article is available online at https://doi.org/10.1016/j.biopsych.2022.10.011.

REFERENCES
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ACKNOWLEDGMENTS AND DISCLOSURESThis research was supported by the National Institutes of Health (Grant Nos.
R01 MD015904 [to AG], K23 DK106528 [to AG], R03 DK121025 [to AG], T32
DK07180 [to TD], ULTR001881/DK041301 [University of California, Los
Angeles CURE/Digestive Diseases Research Core Center/Clinical andTranslational Science Institute Pilot and Feasibility Study] [to AG], P50
DK064539 [to EAM], R01 DK048351 [to EAM], and 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.
Funding was acquired by AG. TSD, GCG, and AG were involved inconceptualization of the study; TSD and AG developed the methodology;
and TSD, AG, ZC, VS, YZ, YG, and SC performed the formal analysis. AG
was responsible for resources/data curation. TSD, GCG, HB-S, MW, VO,
LAK, JSL, BN, XZ, SC, EAM, and AG contributed to writing and original draft
preparation. TSD, YZ, and AG contributed to visualization. The entire study
was supervised by AG. All authors read and approved the ﬁnal manuscript.
We acknowledge the assistance of the Neuroimaging Core, Bioinformatics and Statistics Core, Microbiome Core, and the Biorepository Core of
the University of California, Los Angeles Microbiome Center for theirassistance with various processing, storage, and analyses assistance of thecurrent manuscript. We acknowledge Dr. Steve Cole for his assistance inprocessing the PBMC samples.
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. However, data are available from the corresponding author on reasonable request.
Participants or the public were not involved in the design, conduct,
reporting, or dissemination plans of this research.
AG is a scientiﬁc consultant to Yamaha. EAM is a scientiﬁc advisoryboard 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.

ARTICLE INFORMATION
From the Vatche and Tamar Manoukian Division of Digestive Diseases,
University of California, Los Angeles, Los Angeles, California (TSD, LAK, ZC,
VS, YZ, YG, JSL, BN, XZ, EAM, AG); David Geffen School of Medicine,
University of California, Los Angeles, Los Angeles, California (TSD, LAK,
JSL, BN, SC, XZ, EAM, AG); UCLA Microbiome Center, David Geffen Schoolof Medicine, University of California, Los Angeles, Los Angeles, California(TSD, JSL, BN, EAM, AG); G. Oppenheimer Center for Neurobiology ofStress and Resilience, University of California, Los Angeles, Los Angeles,
California (TSD, LAK, JSL, BN, XZ, EAM, AG); Division of Gastroenterology,
Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare

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Biological Psychiatry August 1, 2023; 94:203–214 www.sobp.org/journal