Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

Abstract

Anxiety disorders are increasingly prevalent, affect people’s ability to do things, and decrease quality of life. Due to lack of objective tests, they are underdiagnosed and sub-optimally treated, resulting in adverse life events and/or addictions. We endeavored to discover blood biomarkers for anxiety, using a four-step approach. First, we used a longitudinal within-subject design in individuals with psychiatric disorders to discover blood gene expression changes between self-reported low anxiety and high anxiety states. Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics approach using other evidence in the field. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with clinically severe anxiety. Fourth, we tested these candidate biomarkers for clinical utility, i.e. ability to predict anxiety severity state, and future clinical worsening (hospitalizations with anxiety as a contributory cause), in another independent cohort of psychiatric subjects. We showed increased accuracy of individual biomarkers with a personalized approach, by gender and diagnosis, particularly in women. The biomarkers with the best overall evidence were GAD1, NTRK3, ADRA2A, FZD10, GRK4, and SLC6A4. Finally, we identified which of our biomarkers are targets of existing drugs (such as a valproate, omega-3 fatty acids, fluoxetine, lithium, sertraline, benzodiazepines, and ketamine), and thus can be used to match patients to medications and measure response to treatment. We also used our biomarker gene expression signature to identify drugs that could be repurposed for treating anxiety, such as estradiol, pirenperone, loperamide, and disopyramide. Given the detrimental impact of untreated anxiety, the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety medications, there is a urgent need for more precise and personalized approaches like the one we developed.

Full text

IMMEDIATE COMMUNICATION OPEN
Towards precision medicine for anxiety disorders: objective
assessment, risk prediction, pharmacogenomics,
and repurposed drugs
K. Roseberry
1,8
, H. Le-Niculescu
1,2,8
,D.F.Levey
1,5
, R. Bhagar
1
, K. Soe
1,6
, J. Rogers
1
, S. Palkowitz
1,3
, N. Pina
1,3
, W. A. Anastasiadis
1,3
,
S. S. Gill
1
, S. M. Kurian
4
, A. Shekhar
1,7
and A. B. Niculescu
1,2,3
✉
© The Author(s) 2023
Anxiety disorders are increasingly prevalent, affect people’s ability to do things, and decrease quality of life. Due to lack of objective
tests, they are underdiagnosed and sub-optimally treated, resulting in adverse life events and/or addictions. We endeavored to
discover blood biomarkers for anxiety, using a four-step approach. First, we used a longitudinal within-subject design in individuals
with psychiatric disorders to discover blood gene expression changes between self-reported low anxiety and high anxiety states.
Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics approach using other evidence in
the field. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects
with clinically severe anxiety. Fourth, we tested these candidate biomarkers for clinical utility, i.e. ability to predict anxiety severity
state, and future clinical worsening (hospitalizations with anxiety as a contributory cause), in another independent cohort of
psychiatric subjects. We showed increased accuracy of individual biomarkers with a personalized approach, by gender and
diagnosis, particularly in women. The biomarkers with the best overall evidence were GAD1, NTRK3, ADRA2A, FZD10, GRK4, and
SLC6A4. Finally, we identified which of our biomarkers are targets of existing drugs (such as a valproate, omega-3 fatty acids,
fluoxetine, lithium, sertraline, benzodiazepines, and ketamine), and thus can be used to match patients to medications and measure
response to treatment. We also used our biomarker gene expression signature to identify drugs that could be repurposed for
treating anxiety, such as estradiol, pirenperone, loperamide, and disopyramide. Given the detrimental impact of untreated anxiety,
the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety
medications, there is a urgent need for more precise and personalized approaches like the one we developed.
Molecular Psychiatry (2023) 28:2894–2912; https://doi.org/10.1038/s41380-023-01998-0
INTRODUCTION
“Man is not worried by real problems so much as by his
imagined anxieties about real problems.”
—Epictetus
Anxiety is increased reactivity in anticipation of events that are
perceived as potentially deleterious, overwhelming, or challenging.
Psychiatric patients may have increased anxiety, as well as increased
reasons for anxiety, due to their adverse life trajectory. As such, they
may be a high-yield population in which to try to identify blood
biomarkers for anxiety that are generalizable and trans-diagnostic.
Such markers would eliminate subjectivity from assessments,
provide some indication of risk, and help guide treatments [1].
First, we used a powerful longitudinal within-subject design in
individuals with psychiatric disorders to discover blood gene
expression changes between self-reported low anxiety and high
anxiety states, as measured by a visual analog scale we developed,
the Simplified Anxiety Scale (SAS-4), similar to our previously
published Simplified Mood Scale (SMS-7) [2]. Second, we prioritized
the list of candidate biomarkers with a Bayesian-like Convergent
Functional Genomics approach, comprehensively integrating pre-
vious human and animal model evidence in the field, from us and
others. Third, we validated our top biomarkers from discovery and
prioritization in an independent cohort of psychiatric subjects with
clinically severe anxiety. We prioritized a list of 95 candidate
biomarkers that had the most evidence from the first three steps.
Fourth, we tested if these candidate biomarkers are able to predict
anxiety severity state, and future clinical worsening (hospitalizations
with anxiety as the primary cause), in another independent cohort
of psychiatric subjects. We tested the biomarkers in all subjects in
the test cohort, as well as in a more personalized fashion by gender
and psychiatric diagnosis, showing increased accuracy with the
personalized approach, particularly in women. Fifth, we analyzed
Received: 13 September 2022 Revised: 29 January 2023 Accepted: 10 February 2023
Published online: 7 March 2023
1
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
2
Stark Neuroscience Research Institute, Indiana University School of Medicine,
Indianapolis, IN, USA.
3
Indianapolis VA Medical Center, Indianapolis, IN, USA.
4
Scripps Health and Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
5
Present
address: Yale School of Medicine, New Haven, CT, USA.
6
Present address: Cincinnati Children’s Hospital, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
7
Present
address: Office of the Dean, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
8
These authors contributed equally: K. Roseberry, H. Le-Niculescu.
✉email: anicules@iupui.edu
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the biological pathways and networks the biomarkers are involved
in, as well as which of our top biomarkers have evidence for
involvement in other psychiatric and related disorders. Sixth, we
identified which of our biomarkers are targets of existing drugs and
thus can be used for pharmacogenomic matching of patient to
treatment, and measuring of response to treatment. We also used
the top biomarkers gene expression signature to identify existing
medications used for other indications, as well as natural
compounds, that could be repurposed for treating anxiety.
Current medication treatments for anxiety (e.g., SSRIs, SNRIs,
benzodiazepines, antihistamines, etc.) do not work well in
everybody (e.g., low response/remission rates, trial-and-error
prescription, problematic side effects, etc.) [3]. Matching the right
individuals to the right medications using their biomarker profile is
a key actionable outcome of our work.
MATERIALS AND METHODS
Cohorts
Our study utilized 3 independent cohorts: discovery (major psychiatric
disorders with changes in state anxiety), validation (major psychiatric disorders
with clinically severe anxiety), and testing (an independent major psychiatric
disorders cohort for predicting state anxiety, and for predicting trait anxiety
(future hospitalization with anxiety as the primary reason) (Fig. 1A).
The psychiatric subjects are part of a larger longitudinal cohort of adults
that we are continuously collecting [4–6]. Subjects were recruited from the
patient population at the Indianapolis VA Medical Center. All subjects
understood and signed informed consent forms detailing the research
goals, procedure, caveats, and safeguards, per IRB-approved protocol.
Subjects completed diagnostic assessments and extensive structured
neuropsychological testing at each testing visit, 3–6 months apart or
whenever a new psychiatric hospitalization occurred. At each testing visit,
they received a series of rating scales, including a self-report visual analog
scale (1–100) for quantitatively assessing state anxiety at that particular
moment in time (Simplified Anxiety Scale- SAS-4). This 4-item scale looks at
anxiety overall, as well as fear, anger, and uncertainty. Each of the items are
a VAS of 0 to 100, related to that moment in time. As such, it generates
temporal, quantitative, and targeted data.
At each testing visit we collected whole blood (10 ml) in two RNA-
stabilizing PAXgene tubes, labeled with an anonymized ID number, and
stored at −80 °C in a locked freezer until the time of future processing.
Whole-blood RNA was extracted for microarray gene expression studies
from the PAXgene tubes, as detailed below.
For this study, our within-subject discovery cohort consisted of
58 subjects (41 males, 17 females) with multiple testing visits, who each
had at least one diametric change in anxiety state from low anxiety state
(SAS-4 score of ≤40/100) to a high anxiety state (SAS-4 score of ≥60/100),
or vice versa, from one testing visit to another (Figs. 1A, B and S1). There
were 2 subjects with 5 visits each, 3 subjects with 4 visits each, 21 subjects
with 3 visits each, and 32 subjects with 2 visits each resulting in a total of
149 blood samples for subsequent gene expression microarray studies
(Fig. 1A, B, and Table S1).
Our independent validation cohort, in which the top candidate
biomarker findings were validated for being even more changed in
expression, consisted of 40 subjects (32 male and 8 female) with clinically
severe anxiety (SAS-4 scores ≥60, and concordant high anxiety STAI State
scores ≥55) (Table S1).
For testing the biomarkers, we used an independent test cohort.
For state predictions, we predicted high anxiety state (SAS-4 ≥60) (161
male and 36 female subjects) and clinically severe anxiety (STAI ≥55) (159
male and 36 female subjects) (Fig. 1and Table S1).
For trait predictions of future hospitalizations with anxiety as a
contributory reason (Fig. 1and Table S1), we used a subset of the
independent test cohort for which we had longitudinal follow-up with
electronic medical records. The subjects’subsequent number of hospita-
lizations with anxiety was tabulated from electronic medical records.
Medications. The subjects in our study were all diagnosed with various
psychiatric disorders (Table S1) and had various medical co-morbidities. Their
medications were listed in their electronic medical records and documented
by us at the time of each testing visit. Medications can have a strong influence
on gene expression. However, there was no consistent pattern of any particular
type of medication. Our subjects were on a wide variety of different
medications, psychiatric and non-psychiatric. Furthermore, the independent
validation and testing cohort’s gene expression data was Z-scored by gender
and by diagnosis before being combined, to normalize for any such effects.
Some subjects may be non-compliant with their treatment and may have
changes in medications or drugs of abuse not reflected in their medical
records. Our goal is to find biomarkers that track anxiety, regardless if the
reason for it is internal biology or it is driven by external medications or drugs.
In fact, one would expect some of these biomarkers to be targets of
medications, as we show in this paper. Furthermore, the prioritization step that
occurs after the discovery step is based on a field-wide convergence with
literature that includes genetic data and animal model data, that are unrelated
to medication effects. Overall, the discovery, validation, and replication by
testing in independent cohorts of the biomarkers, with our design, occurs
despite the subjects having different genders, diagnoses, being on various
different medications, and other variables.
Blood gene expression experiments
RNA extraction. Whole blood (2.5 ml) was collected into each PaxGene
tube by routine venipuncture. PaxGene tubes contain proprietary reagents
for the stabilization of RNA. Total RNA was extracted and processed as
previously described [4–6].
Microarrays. Microarray work was carried out using previously described
methodology [4–7].
Of note, all genomic data was normalized (RMA for technical variability,
then z-scoring for biological variability), by gender and psychiatric
diagnosis, before being combined and analyzed.
See Supplementary Information for rest of “Materials and Methods”.
RESULTS
In Step 1 Discovery, we identified candidate blood gene expression
biomarkers that: 1. change in expression in blood between self-
reported low anxiety and high anxiety states, 2. track the anxiety state
across visits in a subject, and 3. track the anxiety state in multiple
subjects. We used a visual analog measure for anxiety state (SAS-4). At
a phenotypic level, the SAS-4 quantitates anxiety state at a particular
moment in time, and normalizes anxiety measurements in each
subject, comparing them to the lowest and highest anxiety that the
subject ever experienced (Fig. S1). It has a moderate to strong
correlation (R=0.67, p< 0.0001) with a current clinical scale for
anxiety state (the STAI State, Fig. S2).
We used a powerful within-subject and then across-subject design
in a longitudinally followed cohort of subjects (n=58 subjects, with
149 visits) who displayed at least a 50% change in the anxiety
measure (from below 40/100 to above 60/100) between at least two
consecutive testing visits, to identify differentially expressed genes
that track anxiety state. Using our 33% of maximum raw score
threshold (internal score of 2 pt) [5,6], we identified 10,573 unique
probesets (corresponding to 7195 unique genes) from Affymetrix
Absent/Present (AP) analyses and Differential Expression (DE) analyses
(Fig. 1D). These were carried forward to the prioritization step. This
represents approximately a fivefold enrichment of the 54,625
probesets on the Affymetrix array.
In Step 2 Prioritization, we used a Convergent Functional
Genomics (CFG) approach to prioritize the candidate biomarkers
identified in the discovery step (33% cutoff, internal score of
≥2 pt.) by using prior published literature evidence (genetic, gene
expression, and proteomic), from human and animal model
studies, for involvement in anxiety disorders (Fig. 1E and Table S2).
There were 284 probesets (corresponding to 238 unique genes)
that had a total score (combined discovery score and prioritization
CFG score) of 6 and above. These were carried forward to the
validation step. This represents approximately a tenfold enrich-
ment of the probesets on the Affymetrix array.
In Step 3 Validation, we validated the prioritized candidate
biomarkers for change in clinically severe anxiety, in a demo-
graphically matched cohort of (n=40 clinically severe anxiety) by
assessing which markers were stepwise changed in expression
from low anxiety in discovery cohort, to high anxiety in discovery
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cohort, to clinically severe anxiety in validation cohort (Fig. 1). Of
the 284 probesets after the prioritization step, 224 probesets were
not stepwise changed, and 57 were stepwise changed. Of these,
four probesets (corresponding to four unique genes) were
nominally significant.
Adding the scores from the first three steps into an overall
convergent functional evidence (CFE) score (Fig. 1), we ended up
with a list of 95 top candidate biomarkers (n =82 genes,
95 probesets), that had a CFE3 score ≥8, equal or better to 33% of
the maximum possible score of 24 after the first three steps, which
B.
C. D.
E.
EFNA5
P=2.02E-04
TGS1
P=2.38E-03
A.
Fig. 1 Steps 1–3: Discovery, prioritization, and validation of biomarkers for anxiety. A Cohorts used in study, depicting flow of discovery,
prioritization, validation of biomarkers from each step and independent testing cohorts. BDiscovery cohort longitudinal within-subject
analysis. Phchp### is study ID for each subject. V# denotes visit number. Red are high anxiety visits and blue are low anxiety visits.
CConvergent Functional Genomics evidence. DIn the validation step biomarkers are assessed for stepwise change from the validation group
with severe Anxiety, to the discovery groups of subjects with high Anxiety, low Anxiety, to the validation group with severe Anxiety, using
ANOVA. N =number of testing visits. The histograms depict a top increased and a top decreased biomarker in validation. EScoring at each of
the steps. Discovery probesets are identified based on their score for tracking anxiety with a maximum of 6 points (33% (2 pt), 50% (4 pt) and
80% (6 pt)). Prioritization with CFG for prior evidence of involvement in anxiety disorders. In the prioritization step probesets are converted to
their associated genes using Affymetrix annotation and GeneCards. Genes are prioritized and scored using CFG for anxiety evidence, with a
maximum of 12 points. Genes scoring at least 6 points out of a maximum possible of 18 total internal and external scores points are carried to
the validation step. Validation in an independent cohort of psychiatric patients with clinically severe anxiety (STAI State ≥55 and SAS-4 > =60).
Four biomarkers were nominally significant, and 57 biomarkers were stepwise changed. We selected for further testing in independent
cohorts the top candidate biomarkers, with a total score after the first 3 steps (CFE3) of 8 and above (n=95 biomarkers).
K. Roseberry et al.
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we decided to use as an empirical cutoff. This represents
approximately an over 500-fold enrichment of the probesets on
the Affymetrix array. These 95 top candidate biomarkers were carried
forward into analyses for understanding biological underpinnings.
They were also tested in Step 4 for clinical utility/predictive ability in
additional independent cohorts (Fig. 2and Table 1).
Biological understanding
Biological pathways. We carried out biological pathway analyses
using the list of top candidate biomarkers for anxiety (n=82
genes, 95 probesets), which suggests that Hippo signaling
pathway and CREB signaling pathway are involved (Table 2).
Depression, alcohol consumption, and attention deficit disorder/
conduct disorder/oppositional defiant disorder were top diseases
identified by the pathway analyses using DAVID, pointing out the
issue of co-morbidity, and Ingenuity identified neurological
disorders, organismal injury, and cancer as the top medical co-
morbidities.
Networks and interactions. We carried out a STRING analysis
(Fig. S4) of the top candidate biomarkers that revealed groups of
interacting proteins. In particular, HTR2A is at the overlap of a
network containing GAD1, GABBR1, and SLC6A4 (the serotonin
transporter), and one centered on PIK3R1 that also contains
CCKBR and IGFR1. A third network includes DLGAP1, DYNLL2, and
PTPRD. These networks may have biological significance and
could be targeted therapeutically. The first network may have to
do with reactivity, and contains genes that are targeted by the
Fig. 2 Best Single Biomarkers Predictors for Anxiety, State and Trait. From top candidate biomarkers after Steps 1–3 (Discovery,
Prioritization, Validation-Bold) (n=95). Bar graph shows best predictive biomarkers in each group. All markers with * are nominally significant
p< 0.05. Table underneath the figures displays the actual number of biomarkers for each group whose ROC AUC pvalues (A–C) and Cox Odds
Ratio pvalues (D) are at least nominally significant. Some gender and diagnosis groups are missing from the graph as they did not have any
significant biomarkers or that the cohort was too small with limited data for the z-scoring by gender-dx. Cross-sectional is based on levels at
one visit. Longitudinal is based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit,
and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white), and at the same level as the best
biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. Biomarkers perform better than chance.
Biomarkers performed better when personalized by gender and diagnosis. * nominally significant. ** survived Bonferroni correction for the
number of candidate biomarkers tested.
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Table 1. Top Anxiety Biomarkers: Convergent Functional Evidence (CFE).
Symbol/
Gene Name
Probesets Step 1
Discovery
(Direction
of Change
in High
Anxiety)
Method/
Score/%
Step 2
Prioritization
Convergent
Functional
Genomics
(CFG)
Evidence For
Involvement
in Anxiety
Score
Step 3
Validation
ANOVA p-
value/
Score
Step 4
Significant
Predictions of
High Anxiety
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Predictions of
High STAI
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Prediction of
First Year
Hosp. for
Anxiety ROC
AUC/p-value
3 pts ALL 2pts
Gender 1pts
Gender/Dx
Step 4
Significant
Predictions of
Future Hosp
for Anxiety
OR/p-value 3
pts ALL 2pts
Gender 1pts
Gender /Dx
Other Psychiatric
and Related
Disorders
Evidence
Drugs that
Modulate the
Biomarker in
Opposite
Direction to High
Anxiety
CFE
Polyevidence
Score for
Involvement
in Anxiety
(Based on
Steps 1–4)
GAD1
Glutamate
Decarboxylase 1
205278_at (I)
DE/4
62.3%
11 6.79E-01/0
Not
Stepwise
ALL
C: (42/486)
0.58/3.94E-02
Gender
Females
C: (17/88)
0.65/2.75E-02
Gender-dx
F-PSYCHOSIS
C: (5/28)
0.77/2.94E-02
F-SZ
C: (4/8)
0.94/2.17E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.69/4.73E-02
M-SZA
C: (3/32)
0.79/4.96E-02
ALL
C: (70/318)
1.3/5.52E-03
Gender
Males
C: (59/273)
1.34/2.80E-03
Gender-dx
M-BP
C: (13/96)
1.69/2.12E-02
M-PSYCHOSIS
C: (37/121)
1.33/9.36E-03
M-SZA
C: (23/58)
1.58/9.14E-03
Alcohol
Withdrawal
Autism
BP
Depression
Intellectual
Disability
MDD
Mood
Disorders NOS
Phencyclidine
PTSD
Suicide
SZ
Carbamazepine
Lithium
Omega-3
fatty acids
Valproate
22
NTRK3
Neurotrophic
Receptor
Tyrosine Kinase 3
215311_at (I)
DE/4
53.2%
4 3.26E-01/2
Stepwise
Gender
Males
L: (4/246)
0.77/2.98E-02
Gender-dx
M-PSYCHOSIS
L: (2/102)
0.9/2.83E-02
M-SZA
L: (2/46)
0.85/4.75E-02
ALL
C: (42/486)
0.59/3.04E-02
L: (21/291)
0.61/4.83E-02
Gender
Females
C: (17/88)
0.68/1.20E-02
L: (10/52)
0.7/2.85E-02
Gender-dx
F-BP
C: (4/31)
0.89/6.66E-03
M-MDD
L: (4/31)
0.82/1.96E-02
Gender-dx
F-PSYCHOSIS
C: (2/13)
0.91/3.78E-02
Gender-dx
F-SZA
C: (2/6)
1/3.20E-02
ALL
C: (70/318)
1.23/9.38E-03
Gender
Females
C: (11/45)
1.84/1.60E-02
Aging
Alcohol
Alzheimer’s
Disease
Bipolar II
BP
Longevity
MDD
Mood
Disorders NOS
Pain
PTSD
Risky Behavior
SAD
Social Isolation
Stress
Subsyndromal
symptomatic
depression
Suicide
SZ
Clozapine 19
ADRA2A
Adrenoceptor
Alpha 2A
209869_at (I)
AP/4
50%
4 9.07E-01/0
Not
Stepwise
Gender-dx
M-MDD
C: (2/57)
0.88/3.43E-02
Gender
Females
C: (17/88)
0.63/4.91E-02
F-SZ
C: (4/8)
0.88/4.07E-02
L: (3/5)
1/4.16E-02
ALL
C: (18/236)
0.65/1.80E-02
Gender
Males
C: (15/199)
0.67/1.44E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.81/3.22E-03
M-PTSD
C: (3/10)
0.86/4.37E-02
M-SZ
C: (4/42)
0.91/3.47E-03
ALL
C: (70/318)
1.41/4.65E-04
Gender
Males
C: (59/273)
1.44/5.74E-04
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.52/3.60E-03
M-PTSD
C: (5/12)
2.59/2.08E-02
M-SZ
C: (14/63)
2.14/1.61E-04
Alcohol
Bipolar
COVID-19
Depression
MDD
Pain
PTSD
Suicide
SZ
Carbamazepine
Clozapine
Norfluoxetine
Valproate
17
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Table 1. continued
Symbol/
Gene Name
Probesets Step 1
Discovery
(Direction
of Change
in High
Anxiety)
Method/
Score/%
Step 2
Prioritization
Convergent
Functional
Genomics
(CFG)
Evidence For
Involvement
in Anxiety
Score
Step 3
Validation
ANOVA p-
value/
Score
Step 4
Significant
Predictions of
High Anxiety
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Predictions of
High STAI
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Prediction of
First Year
Hosp. for
Anxiety ROC
AUC/p-value
3 pts ALL 2pts
Gender 1pts
Gender/Dx
Step 4
Significant
Predictions of
Future Hosp
for Anxiety
OR/p-value 3
pts ALL 2pts
Gender 1pts
Gender /Dx
Other Psychiatric
and Related
Disorders
Evidence
Drugs that
Modulate the
Biomarker in
Opposite
Direction to High
Anxiety
CFE
Polyevidence
Score for
Involvement
in Anxiety
(Based on
Steps 1–4)
FZD10
Frizzled Class
Receptor 10
219764_at (I)
DE/4
53.2%
2 3.57E-01/2
Stepwise
ALL
C: (42/486)
0.6/1.39E-02
L: (21/291)
0.64/1.43E-02
Gender
Females
C: (17/88)
0.69/7.42E-03
L: (10/52)
0.68/3.88E-02
F-BP
C: (4/31)
0.85/1.26E-02
F-SZA
C: (1/20)
1/4.97E-02
F-SZ
L: (3/5)
1/4.16E-02
ALL
C: (18/236)
0.67/7.66E-03
Gender
Males
C: (15/199)
0.68/1.22E-02
L: (4/113)
0.75/4.52E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.75/1.64E-02
L: (1/39)
1/4.57E-02
M-SZ
C: (4/42)
0.87/8.21E-03
L: (1/22)
1/4.90E-02
ALL
C: (70/318)
1.34/3.63E-03
Gender
Males
C: (59/273)
1.34/6.61E-03
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.55/2.70E-03
M-SZ
C: (14/63)
1.92/5.16E-03
Alcohol
Alzheimer’s
BP
Circadian
abnormalities
MDD
PTSD
Aripiprazole
Fluoxetine
Gamma
Frequency
17
GRK4
G Protein-
Coupled
Receptor
Kinase 4
210600_s_at (I)
DE/6
81.8%
0 3.40E-01/2
Stepwise
Gender-dx
M-MDD
C: (2/57)
0.87/3.77E-02
Gender
Females
C: (17/88)
0.64/3.26E-02
Gender-dx
F-BP
C: (4/31)
0.86/1.08E-02
M-MDD
L: (4/31)
0.85/1.26E-02
ALL
C: (18/236)
0.62/4.27E-02
Gender
Males
C: (15/199)
0.64/4.06E-02
ALL
C: (70/318)
1.32/4.46E-03
Gender
Males
C: (59/273)
1.32/8.99E-03
Gender-dx
F-MDD
C: (2/15)
1.87/4.51E-02
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.34/2.11E-02
Autism
Bipolar
Cannabis
Depression
PTSD
Stress
Clozapine
Gamma
Frequency
17
ATP1B2
ATPase Na+/K+
Transporting
Subunit Beta 2
204311_at (I)
DE/2
39%
4 1.65E-01/2
Stepwise
Gender-dx
M-MDD
C: (2/57)
0.9/2.81E-02
Gender
Females
C: (17/88)
0.64/3.75E-02
Gender-dx
F-BP
C: (4/31)
0.84/1.46E-02
F-PSYCHOSIS
C: (5/28)
0.77/3.37E-02
Gender
Males
C: (15/199)
0.63/4.18E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.79/5.55E-03
M-PTSD
C: (3/10)
0.86/4.37E-02
M-SZ
C: (4/42)
0.86/9.22E-03
ALL
C: (70/318)
1.59/1.91E-05
Gender
Females
C: (11/45)
1.68/2.97E-02
Males
C: (59/273)
1.56/1.33E-04
Gender-dx
F-MDD
C: (2/15)
3.16/3.10E-02
M-BP
C: (13/96)
1.61/1.79E-02
M-PSYCHOSIS
C: (37/121)
1.98/4.84E-05
M-SZ
C: (14/63)
2.39/7.75E-04
M-SZA
C: (23/58)
1.68/1.24E-02
Alcohol
Alzheimer’s
MDD
PTSD
Stress
Substance Abuse
Suicide
SZ
Acetyldigitoxin
Clozapine
Deslanoside
Digitoxin
Digoxin
Haloperidol
Lithium
Valproate
16
K. Roseberry et al.
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Molecular Psychiatry (2023) 28:2894 – 2912
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Table 1. continued
Symbol/
Gene Name
Probesets Step 1
Discovery
(Direction
of Change
in High
Anxiety)
Method/
Score/%
Step 2
Prioritization
Convergent
Functional
Genomics
(CFG)
Evidence For
Involvement
in Anxiety
Score
Step 3
Validation
ANOVA p-
value/
Score
Step 4
Significant
Predictions of
High Anxiety
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Predictions of
High STAI
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Prediction of
First Year
Hosp. for
Anxiety ROC
AUC/p-value
3 pts ALL 2pts
Gender 1pts
Gender/Dx
Step 4
Significant
Predictions of
Future Hosp
for Anxiety
OR/p-value 3
pts ALL 2pts
Gender 1pts
Gender /Dx
Other Psychiatric
and Related
Disorders
Evidence
Drugs that
Modulate the
Biomarker in
Opposite
Direction to High
Anxiety
CFE
Polyevidence
Score for
Involvement
in Anxiety
(Based on
Steps 1–4)
CLIC6
Chloride
Intracellular
Channel 6
242913_at (I)
AP/6
84.2%
2 5.06E-01/0
Not
Stepwise
Gender
Females
C: (17/88)
0.67/1.68E-02
Gender-dx
F-BP
C: (4/31)
0.8/2.97E-02
F-PSYCHOSIS
C: (5/28)
0.85/8.20E-03
F-SZ
C: (4/8)
0.97/1.47E-02
F-SZA
C: (1/20)
1/4.97E-02
ALL
C: (18/236)
0.62/4.11E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.71/3.17E-02
Gender-dx
M-SZ
C: (4/42)
0.8/2.44E-02
ALL
C: (70/318)
1.32/6.89E-03
Gender
Females
C: (11/45)
1.59/4.32E-02
Males
C: (59/273)
1.27/2.51E-02
Gender-dx
M-SZ
C: (14/63)
1.68/1.04E-02
Aging
Alcohol
Cocaine
MDD
Phencyclidine
Restraint Stress
Suicide
Clozapine
Omega-3
fatty acids
Gamma
Frequency
16
EFNA5
Ephrin A5
1559360_at (I)
DE/6
80.5%
(I)
AP/4
51.3%
2 2.02E-04/4
Nominal
Gender
Females
C: (17/88)
0.63/4.85E-02
Gender-dx
M-SZ
C: (6/90)
0.71/4.03E-02
ALL
L: (5/133)
0.74/3.34E-02
Gender
Males
L: (4/113)
0.75/4.67E-02
Gender-dx
F-SZA
C: (2/6)
1/3.20E-02
M-SZA
C: (3/32)
0.79/4.96E-02
Gender-dx
M-BP
C: (13/96)
1.41/2.92E-02
Alcohol
Alzheimer’s
BP
Cannabis
Chronic Fatigue
Syndrome
Depression
Insomnia
Intellect
Longevity
MDD
Morphine
Pain
Stress
Suicide
SZ
Omega-3
fatty acids
Gamma
Frequency
18
GPX7
Glutathione
Peroxidase 7
213170_at (D)
DE/4
64.3%
2 4.64E-01/2
Stepwise
ALL
C: (19/495)
0.63/2.63E-02
Gender-dx
F-BP
C: (4/31)
0.86/1.08E-02
F-SZA
C: (1/20)
1/4.97E-02
M-MDD
L: (4/31)
0.86/1.08E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.71/3.17E-02
M-SZ
C: (4/42)
0.82/1.99E-02
ALL
C: (70/318)
1.42/6.50E-03
Gender
Males
C: (59/273)
1.47/7.12E-03
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.5/2.40E-02
M-SZ
C: (14/63)
1.9/4.95E-02
Aging
MDD
Neuropathic pain
SZA
Mianserin
S-adenosyl
methionine (SAM)
16
K. Roseberry et al.
2900
Molecular Psychiatry (2023) 28:2894 – 2912
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Table 1. continued
Symbol/
Gene Name
Probesets Step 1
Discovery
(Direction
of Change
in High
Anxiety)
Method/
Score/%
Step 2
Prioritization
Convergent
Functional
Genomics
(CFG)
Evidence For
Involvement
in Anxiety
Score
Step 3
Validation
ANOVA p-
value/
Score
Step 4
Significant
Predictions of
High Anxiety
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Predictions of
High STAI
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Prediction of
First Year
Hosp. for
Anxiety ROC
AUC/p-value
3 pts ALL 2pts
Gender 1pts
Gender/Dx
Step 4
Significant
Predictions of
Future Hosp
for Anxiety
OR/p-value 3
pts ALL 2pts
Gender 1pts
Gender /Dx
Other Psychiatric
and Related
Disorders
Evidence
Drugs that
Modulate the
Biomarker in
Opposite
Direction to High
Anxiety
CFE
Polyevidence
Score for
Involvement
in Anxiety
(Based on
Steps 1–4)
NTRK3
Neurotrophic
Receptor
Tyrosine Kinase 3
215025_at (I)
DE/2
35.1%
4 6.58E-01/2
Stepwise
Gender-dx
M-BP
L: (2/93)
0.9/2.83E-02
ALL
C: (42/486)
0.61/9.05E-03
L: (21/291)
0.64/1.43E-02
Gender
Females
C: (17/88)
0.72/2.51E-03
Males
L: (11/239)
0.67/2.55E-02
Gender-dx
F-BP
C: (4/31)
0.86/1.08E-02
F-PSYCHOSIS
C: (5/28)
0.77/2.93E-02
F-SZ
C: (4/8)
0.94/1.92E-02
M-BP
L: (2/92)
0.93/1.84E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.7/4.55E-02
Gender-dx
M-SZ
C: (4/42)
0.82/1.99E-02
L: (1/22)
1/4.90E-02
ALL
C: (70/318)
1.36/5.70E-03
Gender
Males
C: (59/273)
1.4/4.76E-03
Gender-dx
M-BP
C: (13/96)
1.95/1.24E-02
M-PSYCHOSIS
C: (37/121)
1.4/1.87E-02
M-SZ
C: (14/63)
1.93/3.78E-03
Aging
Alcohol
Alzheimer’s
Disease
Bipolar I
BP
Depression
Longevity
MDD
Mood
Disorders NOS
Pain
PTSD
Risky Behavior
SAD
Social Isolation
Stress
Subsyndromal
symptomatic
depression
Suicide
SZ
Clozapine 16
SLC6A2
Solute Carrier
Family 6
Member 2
217214_s_at (I)
DE/2
40.3%
4 3.40E-01/2
Stepwise
Gender-dx
F-PSYCHOSIS
L: (4/17)
0.81/3.50E-02
F-SZA
L: (4/12)
0.81/4.47E-02
ALL
C: (42/486)
0.63/2.37E-03
L: (21/291)
0.62/3.78E-02
Gender
Females
C: (17/88)
0.76/4.78E-04
Gender-dx
F-MDD
C: (7/21)
0.77/2.62E-02
F-PSYCHOSIS
C: (5/28)
0.8/1.92E-02
F-SZ
C: (4/8)
0.94/2.17E-02
M-SZ
C: (6/90)
0.71/4.63E-02
Gender-dx
M-PSYCHOSIS
C: (7/74)
0.72/2.92E-02
M-SZA
C: (3/32)
0.87/1.78E-02
M-PSYCHOSIS
L: (1/39)
1/4.57E-02
M-SZ
L: (1/22)
1/4.90E-02
ALL
C: (70/318)
1.33/4.74E-03
Gender
Males
C: (59/273)
1.34/6.73E-03
Gender-dx
M-BP
C: (13/96)
1.61/4.50E-02
M-PSYCHOSIS
C: (37/121)
1.31/2.54E-02
Alcohol
Depression
MDD
Postpartum
Depression
PTSD
Suicide
Fluoxetine
Norfluoxetine
16
K. Roseberry et al.
2901
Molecular Psychiatry (2023) 28:2894 – 2912
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Table 1. continued
Symbol/
Gene Name
Probesets Step 1
Discovery
(Direction
of Change
in High
Anxiety)
Method/
Score/%
Step 2
Prioritization
Convergent
Functional
Genomics
(CFG)
Evidence For
Involvement
in Anxiety
Score
Step 3
Validation
ANOVA p-
value/
Score
Step 4
Significant
Predictions of
High Anxiety
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Predictions of
High STAI
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Prediction of
First Year
Hosp. for
Anxiety ROC
AUC/p-value
3 pts ALL 2pts
Gender 1pts
Gender/Dx
Step 4
Significant
Predictions of
Future Hosp
for Anxiety
OR/p-value 3
pts ALL 2pts
Gender 1pts
Gender /Dx
Other Psychiatric
and Related
Disorders
Evidence
Drugs that
Modulate the
Biomarker in
Opposite
Direction to High
Anxiety
CFE
Polyevidence
Score for
Involvement
in Anxiety
(Based on
Steps 1–4)
SLC6A4
Solute Carrier
Family 6
Member 4
242009_at (I)
DE/2
35.1%
10 8.51E-01/0
Not
Stepwise
Gender-dx
F-PSYCHOSIS
C: (2/13)
0.91/3.76E-02
F-SZA
C: (2/6)
1/3.20E-02
M-PTSD
C: (3/10)
0.86/4.37E-02
M-SZ
C: (4/42)
0.76/4.74E-02
ALL
C: (70/318)
1.21/1.15E-02
Gender
Males
C: (59/273)
1.19/2.77E-02
Gender-dx
M-PTSD
C: (5/12)
2.54/3.24E-02
Aging
Alcohol
Anxiety
BPD
Depression
Early Life Stress
MDD
MSK
Neuropathic pain
Pain
Postpartum
Depression
PTSD
Stress
Suicide
Agomelatine
Benzodiazepines
Citalopram
Clozapine
Omega-3
fatty acids
Oxycodone
Sertraline
Vortioxetine
16
TMEM138
Transmembrane
Protein 138
223113_at (D)
DE/4
63%
4 8.63E-01/0
Not
Stepwise
Gender
Males
L: (11/239)
0.66/4.12E-02
Gender-dx
F-BP
C: (4/31)
0.86/1.08E-02
ALL
L: (5/129)
0.72/4.61E-02
Gender-dx
M-PTSD
C: (3/10)
0.86/4.37E-02
ALL
C: (70/318)
1.3/1.46E-02
Gender
Males
C: (59/273)
1.34/1.20E-02
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.5/6.57E-03
M-PTSD
C: (5/12)
3.71/4.13E-02
M-SZA
C: (23/58)
1.51/2.83E-02
Alcohol
Brain arousal
Depression
Antidepressants 16
ANKRD28
Ankyrin Repeat
Domain 28
229307_at (I)
DE/6
80.5%
0 6.20E-01/2
Stepwise
ALL
C: (42/486)
0.62/5.30E-03
Gender
Males
C: (25/398)
0.72/1.31E-04
Gender-dx
M-MDD
C: (7/57)
0.89/3.97E-04
Gender-dx
M-PSYCHOSIS
C: (12/165)
0.72/5.72E-03
Gender-dx
M-SZ
C: (6/90)
0.84/3.13E-03
Gender-dx
F-PSYCHOSIS
C: (2/13)
0.91/3.76E-02
Gender-dx
F-SZA
C: (2/6)
1/3.20E-02
ALL
C: (70/318)
1.22/2.96E-02
Gender-dx
F-MDD
C: (2/15)
3.09/3.59E-02
Gender
Males
C: (59/273)
1.21/4.06E-02
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.42/7.30E-03
Gender-dx
M-SZA
C: (23/58)
1.61/1.24E-03
Alcohol
ASD
BP
Childhood Trauma
Depression
MDD
Mood instability
PTSD
Stress
Suicide
N/A 15
K. Roseberry et al.
2902
Molecular Psychiatry (2023) 28:2894 – 2912
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Table 1. continued
Symbol/
Gene Name
Probesets Step 1
Discovery
(Direction
of Change
in High
Anxiety)
Method/
Score/%
Step 2
Prioritization
Convergent
Functional
Genomics
(CFG)
Evidence For
Involvement
in Anxiety
Score
Step 3
Validation
ANOVA p-
value/
Score
Step 4
Significant
Predictions of
High Anxiety
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Predictions of
High STAI
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Prediction of
First Year
Hosp. for
Anxiety ROC
AUC/p-value
3 pts ALL 2pts
Gender 1pts
Gender/Dx
Step 4
Significant
Predictions of
Future Hosp
for Anxiety
OR/p-value 3
pts ALL 2pts
Gender 1pts
Gender /Dx
Other Psychiatric
and Related
Disorders
Evidence
Drugs that
Modulate the
Biomarker in
Opposite
Direction to High
Anxiety
CFE
Polyevidence
Score for
Involvement
in Anxiety
(Based on
Steps 1–4)
CCKBR
Cholecystokinin
B Receptor
210381_s_at (I)
DE/2
48%
4 4.37E-01/2
Stepwise
Gender-dx
F-BP
C: (4/31)
0.8/2.97E-02
Gender-dx
M-BP
C: (5/139)
0.74/3.70E-02
Gender-dx
F-BP
L: (3/18)
0.82/4.29E-02
ALL
C: (18/236)
0.63/3.01E-02
Gender-dx
F-PSYCHOSIS
C: (2/13)
0.91/3.78E-02
Gender-dx
F-SZA
C: (2/6)
1/3.20E-02
Gender
Males
C: (15/199)
0.63/4.48E-02
Gender-dx
M-PTSD
C: (3/10)
0.86/4.37E-02
Gender-dx
M-SZ
C: (4/42)
0.76/4.74E-02
ALL
C: (70/318)
1.33/4.28E-03
Gender
Males
C: (59/273)
1.32/9.50E-03
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.3/3.14E-02
Alcohol
BP
Chronic Stress
MDD
Phencyclidine
Suicide
SZ
Clozapine 15
DYNLL2
Dynein Light
Chain LC8-
Type 2
229106_at (D)
DE/2
39.3%
4 1.96E-01/2
Stepwise
Gender
Males
L: (4/246)
0.78/2.79E-02
Gender-dx
M-BP
L: (2/93)
0.85/4.50E-02
Gender-dx
F-SZA
C: (1/20)
1/4.97E-02
ALL
L: (5/129)
0.74/3.36E-02
Gender-dx
M-SZA
C: (23/58)
1.49/4.09E-02
Aging
Alcohol
Alzheimer’s
Disease
Methamphetamine
Stress
SZ
Benzodiazepines
Valproate
15
Hs.550187 240253_at (I)
DE/6
88.3%
0 2.74E-01/2
Stepwise
Gender
Females
C: (17/88)
0.72/2.59E-02
Gender-dx
F-BP
C: (4/31)
.84/1.46E-02
Gender
Females
C: (3/37)
0.8/4.23E-02
Gender-dx
F-SZA
C: (2/6)
1/3.20E-02
M-PSYCHOSIS
C: (7/74)
0.71/3.44E-02
M-SZA
C: (3/32)
0.9/1.29E-02
ALL
C: (70/318)
1.19/3.48E-02
Gender-dx
M-PSYCHOSIS
C: (37/121)
1.23/2.54E-02
M-SZ
C: (14/63)
1.27/3.90E-02
Suicide N/A 15
K. Roseberry et al.
2903
Molecular Psychiatry (2023) 28:2894 – 2912
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Table 1. continued
Symbol/
Gene Name
Probesets Step 1
Discovery
(Direction
of Change
in High
Anxiety)
Method/
Score/%
Step 2
Prioritization
Convergent
Functional
Genomics
(CFG)
Evidence For
Involvement
in Anxiety
Score
Step 3
Validation
ANOVA p-
value/
Score
Step 4
Significant
Predictions of
High Anxiety
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Predictions of
High STAI
State ROC
AUC/ p-value
3 pts ALL 2pts
Gender 1pts
Gender /Dx
Step 4
Significant
Prediction of
First Year
Hosp. for
Anxiety ROC
AUC/p-value
3 pts ALL 2pts
Gender 1pts
Gender/Dx
Step 4
Significant
Predictions of
Future Hosp
for Anxiety
OR/p-value 3
pts ALL 2pts
Gender 1pts
Gender /Dx
Other Psychiatric
and Related
Disorders
Evidence
Drugs that
Modulate the
Biomarker in
Opposite
Direction to High
Anxiety
CFE
Polyevidence
Score for
Involvement
in Anxiety
(Based on
Steps 1–4)
NRG1
Neuregulin 1
208232_x_at (I)
DE/4
65.8%
4 6.66E-01/2
Stepwise
Gender-dx
M-MDD
C: (2/57)
0.93/2.08E-02
M-BP
L: (2/93)
0.85/4.50E-02
Gender-dx
F-BP
C: (4/31)
0.81/2.59E-02
Gender-dx
M-PSYCHOSIS
C: (12/165)
0.73/3.69E-03
M-SZ
C: (6/90)
0.7/4.79E-02
M-SZA
C: (6/75)
0.76/1.66E-02
ALL
C: (70/318)
1.2/3.99E-02
Gender
Males
C: (59/273)
1.27/1.37E-02
Gender-dx
M-BP
C: (13/96)
1.66/1.80E-02
M-PSYCHOSIS
C: (37/121)
1.25/4.26E-02
M-SZA
C: (23/58)
1.32/4.07E-02
Aging
Alcohol
Alzheimer’s
BP
Chronic Stress
Cocaine
COVID-19
Depression
Longevity
MDD
Memory
Methamphetamine
Psychosis
PTSD
Suicide
SZ
Ketamine
Antipsychotics
Valproate
Lithium
15
TFRC
Transferrin
Receptor
207332_s_at (D)
DE/4
56%
2.00 7.11E-01/2
Stepwise
Gender
Females
L: (4/52)
0.82/1.66E-02
Gender-dx
F-PSYCHOSIS
C: (4/28)
0.77/4.39E-02
L: (4/17)
0.85/2.08E-02
F-SZA
L: (4/12)
0.84/3.09E-02
Gender-dx
F-BP
C: (4/31)
0.88/7.85E-03
Gender-dx
M-PTSD
C: (3/10)
0.9/2.64E-02
ALL
C: (70/318)
1.29/2.21E-02
Gender
Males
C: (59/273)
1.35/1.46E-02
Gender-dx
M-SZA
C: (23/58)
1.54/3.73E-02
Aging
Alcohol
Alzheimer’s
BP
Cocaine
Depression
Longevity
MDD (Recurrent)
Neuropathic Pain
SZ
Valproate 15
After Step 4 Testing in independent cohorts for state and trait predictive ability. For Step 4 Predictions, C: -cross-sectional (using levels from one visit), L: -longitudinal (using levels and slopes from multiple
visits). In ALL, by Gender, and personalized by Gender and Diagnosis (Gender/Dx). M-Males, F-Females. MDD-depression, BP-bipolar, SZ-schizophrenia, SZA-schizoaffective, PSYCHOSIS- schizophrenia and
schizoaffective combined, PTSD-post-traumatic stress disorder.
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Table 2. Biology of Anxiety Biomarkers. Top CFE3 ≥8(n=95 probesets, 82 genes).
A. KEGG Pathways Ingenuity Pathways
Term Count % PValue Top Canonical Pathways PValue Overlap
Top candidate biomarkers
(n=95 probesets, 82 genes)
Hippo Signaling Pathway 6 7.4 1.60E-03 CREB Signaling in Neurons 4.56E-06 1.8 % 11/606
Neuroactive Ligand-receptor Interaction 8 9.9 3.40E-03 Cardiac Hypertrophy Signaling (Enhanced) 1.12E-05 1.8 % 10/542
Proteoglycans in Cancer 6 7.4 5.20E-03 Relaxin Signaling 1.23E-05 3.9 % 6/155
Rap1 Signaling Pathway 6 7.4 5.70E-03 Molecular Mechanisms of Cancer 1.57E-05 2.0 % 9/446
cAMP Signaling Pathway 6 7.4 7.10E-03 G-Protein Coupled Receptor Signaling 1.81E-05 1.6 % 11/702
B. David Ingenuity Pathways Disease
Top candidate
biomarkers
(n=95 probesets,
82 genes)
#Term Count % PValue Diseases and Disorders PValue # Molecules
1 Depression 11 13.6 5.90E-09 Neurological Disease 2.43E-04–7.96E-
12
65
2 Several Psychiatric disorders 14 17.3 6.10E-09 Organismal Injury and
Abnormalities
2.46E-04–7.96E-
12
77
3 Alcohol consumption 10 12.3 3.10E-08 Cancer 2.45E-04–2.78E-
10
77
4 Attention deficit disorder Conduct disorder
Oppositional defiant disorder
7 8.6 6.60E-07 Hematological Disease 2.24E-04–2.78E-
10
45
5 Tourette Syndrome 5 6.2 2.30E-06 Immunological Disease 1.64E-04–2.78E-
10
49
6 Panic Disorder 6 7.4 5.20E-06
7 Bulimia 9 11.1 6.20E-06
8 Schizophrenia 14 17.3 8.40E-06
9 Tobacco Use Disorder 34 42 1.20E-05
10 Bipolar Disorder 8 9.9 1.30E-05
C.
Co-morbidity Percentile Match
Depression 83.33
Alcoholism 72.22
Stress 55.56
Schizophrenia 50.00
Bipolar 44.44
Aging 38.89
Dementia 38.89
PTSD 38.89
Suicide 38.89
Pain 27.78
Phencyclidine 27.78
Cocaine 16.67
Cannabis 11.11
Mood 11.11
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current standard treatments for anxiety, namely serotonin-
reuptake inhibitors and benzodiazepines. The second network
may have to do with activity, and contains genes that are involved
in neurotrophic functions. The third network may have to do with
connectivity, and contains genes that are involved in synaptic
structure and function.
Testing for clinical utility
In Step 4 Testing, we examined in independent cohorts from the
ones used for discovery or validation whether the 95 top
candidate biomarkers can assess high anxiety state
(n=197 subjects with 495 visits), clinical anxiety state
(n=195 subjects with 486 visits), as well as predict of future
psychiatric hospitalizations due to anxiety (n=130 subjects with
318 visits) (Fig. 2, and Table 1), using electronic medical records
follow-up data of our study subjects (up to 14.74 years from initial
visit at the time of the analyses) (Fig. 1, Table S1). The gene
expression data in the test cohorts was normalized (Z-scored)
across genders and various psychiatric diagnoses, before those
different demographic groups were combined. We used as
predictors biomarker levels information cross-sectionally, as well
as expanded longitudinal information about biomarker levels at
multiple visits. We tested the biomarkers in all subjects in the
independent test cohort, as well as in a more personalized fashion
by gender and psychiatric diagnosis.
For high anxiety state assessment across all subjects in the
independent test cohort, the best biomarker was ERCC6L2,
decreased in expression in high anxiety, with an AUC of 68 %
(p=0.004) cross-sectionally, and an AUC of 69% (p=0.03)
longitudinally (Fig. 2A). It also has an AUC of 72% (p=0.003)
cross-sectionally in males, and an AUC of 76% (p=0.02) cross-
sectionally in male bipolars. ERCC6L2 also has an AUC of 100%
(p=0.0007) longitudinally for clinical anxiety in males with
depression. ERCC6L2 (ERCC Excision Repair 6 Like 2) is a novel
gene for anxiety disorders, with no prior evidence of involvement
in the literature. ERCC6L2 is a member of the Snf2 family of
helicase-like proteins. The encoded protein may play a role in
mitochondrial function and DNA repair. Reactivity and repair may
be key functions of anxiety [8].
For assessment of clinical anxiety state in the independent test
cohort, SLC6A2, increased in expression in high anxiety in our
work, had an AUC of 63% (p=0.02) across all subjects, and 76%
(p=0.0005) in females, surviving Bonferroni correction for all 95
biomarkers tested. It also had a Cox regression Odds Ratio (OR) of
9.02 (p=0.0004) for predicting all future hospitalizations for
anxiety in males with schizophrenia, being Bonferroni significant.
SLC6A2 (Solute Carrier Family 6 Member 2) is the norepinephrine
transporter. Medications that block this transporter by itself, or in
conjunction with blocking SLC6A4 (Solute Carrier Family 6
Member 4, the serotonin transporter), another one of our findings,
also increased in expression in high anxiety, have been shown to
be useful clinically in anxiety disorders [9].
SLC6A4 is an example of a previously well-known gene
reproduced in this study, albeit with weaker evidence. For all
future hospitalizations with anxiety in the independent test cohort
SLC6A4, increased in expression in high anxiety, had an OR of 1.21
(p=0.01) across all subjects. It had an OR of 2.54 (p=0.03) in
PTSD. The product of this gene is the serotonin transporter, which
is the target of serotonin reuptake inhibitors used to treat stress
disorders, anxiety, as well as depression, conditions that are highly
related and co-morbid.
We also tested a panel of the 95 candidate biomarkers, which
showed synergistic benefits (better than any individual biomarker)
for predicting trait. The panel (BioM-95) was the best predictor of
future hospitalizations with anxiety in all patients (Cox regression
OR of 2.42, p=0.02), and an even better predictor in males (OR
2.69, p=0.015), and in males with psychosis (OR of 3.36,
Table 2. continued
C.
Co-morbidity Percentile Match
ASD 5.56
Fear 5.56
Memory 5.56
Methamphetamine 5.56
Morphine 5.56
Neurological 5.56
Schizoaffective 5.56
A Pathway Analyses, B Diseases, C Psychiatric co-morbidities. CGenomic co-morbidity for Anxiety for Top Biomarkers from Table 1(n=19). See also table S3.
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p=0.016). Interestingly, this was also better than a standard
clinical measure, STAI Trait, performed (OR 1.4, p=0.043).
Convergent Functional Evidence (CFE)
For the top candidate biomarkers (n=95), we computed into a
CFE score all the evidence from discovery (up to 6 points), CFG
prioritization (up to 12 points), validation (up to 6 points), and
testing (state high anxiety, state clinical anxiety, trait first-year
hospitalization with anxiety, trait all future hospitalizations with
anxiety- up to 3 points each if it significantly predicts in all
subjects, 2 points if in gender, 1 points if in gender/diagnosis). The
total score can be up to 36 points: 24 from our own new data, and
12 from literature data used for CFG. We weigh our new data more
than the literature data, as it is functionally related to anxiety in
three independent cohorts (discovery, validation, testing). The
goal is to highlight, based on the totality of our data and of the
evidence in the field to date, biomarkers that have all around
evidence: track anxiety, have convergent evidence for involve-
ment in anxiety disorders, and predict anxiety state and future
clinical events (Table 1).
The top blood biomarkers (n=19 probesets, in 18 genes) with
the strongest overall CFE for tracking and predicting anxiety
disorders, after all four steps (Table 1) were, in order of CFE4 score:
GAD1 (Glutamate Decarboxylase 1), NTRK3 (Neurotrophic Recep-
tor Tyrosine Kinase 3), ADRA2A (Adrenoceptor Alpha 2A), FZD10
(Frizzled Class Receptor 10), GRK4 (G Protein-Coupled Receptor
Kinase 4), ATP1B2 (ATPase Na+/K +Transporting Subunit Beta 2),
CLIC6 (Chloride Intracellular Channel 6), EFNA5 (Ephrin A5), GPX7
(Glutathione Peroxidase 7), again NTRK3 (Neurotrophic Receptor
Tyrosine Kinase 3), SLC6A2 (Solute Carrier Family 6 Member 2),
SLC6A4 (Solute Carrier Family 6 Member 4), TMEM138 (Trans-
membrane Protein 138), ANKRD28 (Ankyrin Repeat Domain 28),
CCKBR (Cholecystokinin B Receptor), DYNLL2 (Dynein Light Chain
LC8-Type 2), Hs.550187, NRG1 (Neuregulin 1), and TFRC (Transfer-
rin Receptor).
GAD1 (Glutamate Decarboxylase 1), the overall top biomarker for
anxiety in this study, synthesizes gamma-aminobutyric acid (GABA)
from glutamate. Abnormalities in the GABA neurotransmitter system
have been noted in subjects with mood and anxiety disorders. GAD1
has previous genetic evidence in anxiety and panic disorders [10]. It is
increased in expression in blood in high anxiety in our work. The gene
had been previously described to be hypomethylated in panic
disorders patients, which is consistent with higher expression of the
gene [11,12]. GAD1 in our studies modestly predicts clinically severe
anxiety state in all patients in the independent testing cohort (AUC
58%, p=0.04), with results being somewhat better in women (AUC
65%, p=0.03). It also predicts future hospitalizations with anxiety in
all (OR 1.3, p=0.005).
Therapeutics
Pharmacogenomics. Only one of the top biomarkers, DYNLL2,
has evidence for being modulated by benzodiazepines in the
opposite direction to that in high anxiety; the others do not, which
is interesting and clinically useful, as it brings to the fore other,
non-addictive, choices.
Overall, based on number of biomarkers modulated in
expression in opposite direction to anxiety, valproate (33%) had
the best evidence for broad efficacy in anxiety disorders (Table 3A),
followed by omega- 3 fatty acids (28%). Another alternative
treatment that was a top match was EEG gamma band frequency
(17%), which is increased by meditation and other mindfulness
practices. Lithium (11%) and fluoxetine (11%) were next, and
benzodiazepines (6%) were a lower match than that. Omega-3
fatty acids and meditation may be a widely deployable preventive
treatment, with minimal side-effects, including in women who are
or may become pregnant.
A number of individual top biomarkers are known to be modulated
by medications in current clinical use for treating affective disorders
and suicidality, such as lithium (GAD1, ATP1B2, NRG1), the
nutraceuticalomega-3fattyacids(GAD1,CLIC6,EFNA5,SLC6A4),and
antidepressants (ADRA2A, FZD10, GPX7, SLC6A2, SLC6A4, TMEM138)
(Tables 1and S4). This is of potential utility in pharmacogenomics
approaches matching anxious and suicidal patients to the right
medications, and monitoring response to treatment.
New drug discovery/repurposing. Bioinformatic analyses using the
gene expression signature of the panel of top biomarkers for high
anxiety (Table 3B) identified new potential therapeutics for
anxiety, such as the female sex hormone estradiol, the 5-HT2A
receptor antagonist pirenperone, the peripheral opioid receptor
agonist loperamide, and the antiarrhythmic disopyramide. Inter-
estingly, ESR1 (estrogen receptor 1) was one of the top genetic
findings in a recent independent GWAS study [13].
DISCUSSION
We describe a novel and comprehensive effort to discover and
validate blood biomarkers of relevance to anxiety, including
testing them in independent cohorts to evaluate predictive ability
and clinical utility. These biomarkers also open a window into
understanding the biology of anxiety disorders, as well as indicate
new and more precise therapeutic approaches.
Current clinical practice and the need for biomarkers
Assessing a person’s internal subjective feelings and thoughts, along
with more objective external ratings of actions and behaviors, are used
in clinical practice to assess anxiety and diagnose clinical anxiety
disorders, such as panic attacks and generalized anxiety disorder. Such
an approach is insufficient, and lagging those used in other medical
specialties. Moreover, there is a delay with a range of 9 to 23 years
between illness onset and diagnosis [14]. Blood biomarkers related to
anxiety would provide a critical objective measurement to inform
clinical assessments and treatment decisions.
Advantages of biomarkers
Blood biomarkers offer real-world clinical practice advantages. As
the brain cannot be readily biopsied in live individuals, and CSF is
less easily accessible than blood, we have endeavored over the
years to identify blood biomarkers for neuropsychiatric disorders.
A whole-blood approach facilities field deployment of sample
collection. The assessment of gene expression changes focuses on
our approach on immune cells. The ability to identify peripheral
gene expression changes that reflect brain activities is likely due to
the fact that the brain and immune system have developmental
commonalities, marked by shared reactivity and ensuing gene
expression patterns. There is also a bi-directional interaction
between the brain and immune system. Not all changes in
expression in peripheral cells are reflective of or germane to brain
activity. By carefully tracking a phenotype with our within-subject
design in the discovery step, and then using convergent
functional genomics prioritization, we are able to extract the
peripheral changes that do track and are relevant to the brain
activity studied, in this case anxiety state, and its disorders.
Subsequent validation and testing in independent cohorts
narrow the list to the best markers. In the end, we do not expect
to recapitulate in the blood all that happens in the brain. We just
want to have good accessible peripheral biomarkers—“liquid
biopsies”, as they are called in cancer.
Comprehensiveness
In this current work, we carried out extensive blood gene
expression studies in male and female subjects with major
psychiatric disorders, an enriched population in terms of co-
morbidity with anxiety disorders and variability of anxiety. In fact,
besides their primary clinical diagnosis, overall, over 20% of the
subjects in our study had a co-morbid clinical anxiety disorders
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Table 3. Therapeutics.
A
Treatment Percentile Match
Valproate 33.33
Clozapine 33.33
Omega-3 fatty acids 27.78
Gamma frequency 16.67
Fluoxetine 11.11
Lithium 11.11
Carbamazepine 11.11
Agomelatine 5.56
Benzodiazepines 5.56
Haloperidol 5.56
Imipramine 5.56
Ketamine 5.56
Mianserin 5.56
S-adenosyl methionine (SAM) 5.56
Sertraline 5.56
Vortioxetine 5.56
B
Connectivity Map (CMAP) analyses
rank cmap name score Roles
1 thalidomide −1 Originally introduced as a non-barbiturate hypnotic but withdrawn from the market due to teratogenic effects. It has been reintroduced
and used for a number of immunological and inflammatory disorders.
2 ethoxyquin −0.984 Ethoxyquin is a genotoxic quinoline.
3 estradiol −0.979 Female sex hormone
4 tetracaine −0.96 Local anesthetic
5 pirenperone −0.957 5-HT2A receptor antagonist described as an antipsychotic and tranquilizer which was never marketed.
6 atropine −0.953 Muscarinic antagonist
7 15(S)-15-methylprostaglandin E2 −0.933 Labor induction
8 loperamide −0.919 Peripheral opioid receptor agonist used to treat diarrhea
9 tropicamide −0.918 Muscarinic antagonist
10 esculetin −0.917 Plant toxin
11 isocorydine −0.916 Alkaloid
12 disopyramide −0.913 Antiarrhythmic
ABest existing treatments for Anxiety By matching to Top Biomarkers from Table 1(n=19). See also table S4. B. Drug repurposing for Anxiety using Connectivity Map [28] (CMAP) For Top Biomarkers from
Table 1(n=19). Direction of expression in high anxiety. 2 out of 4 Decreased and 10 out of 15 Increased probesets were present in HG-U133A, the array used for CMAP (from genes ADRA2A, ATP1B2, CCKBR,
FZD10, GAD1, GPX7, GRK4, NRG1, NTRK3, SLC6A2, TFRC). Drugs that have opposite gene expression effects to the gene expression signature.
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diagnosis, the highest percentage (37.3%) being those with major
depressive disorder (MDD) as their primary diagnosis (Table S1B).
The potential molecular-level co-morbidity between other psy-
chiatric disorders and anxiety disorders is underlined by the fact
that medications for other disorders (antidepressants, mood
stabilizers, even antipsychotics) are also used to treat PTSD and
anxiety disorders. Our primary goal was to discover and validate
biomarkers for anxiety, that are transdiagnostic. Secondarily, we
aimed to understand their universality vs. their specificity by
gender and psychiatric diagnosis.
Our studies were arranged in a step-wise fashion. First, we
endeavored to discover blood gene expression biomarkers for
anxiety using a longitudinal design, looking at differential
expression of genes in the blood of male and female subjects
with major psychiatric disorders (bipolar disorder, major depressive
disorder, schizophrenia/schizoaffective, and post-traumatic stress
disorder (PTSD)), high-risk populations prone to anxiety, which
constitute an enriched pool in which to look for biomarkers. We
compared low anxiety states to high anxiety states using a
powerful within-subject design [4–6,15], to generate a list of
differentially expressed genes. Second, we used a comprehensive
Convergent Functional Genomics (CFG) approach with the whole
body of knowledge in the field to prioritize from the list of
differentially expressed genes/biomarkers of relevance to anxiety.
CFG integrates multiple independent lines of evidence- genetic,
gene expression, and protein data, from brain and periphery, from
human and animal model studies, as a Bayesian strategy for
identifying and prioritizing findings, reducing the false-positives
and false-negatives inherent in each individual approach. Third, we
examined if the expression levels of the top biomarkers identified
by us as tracking anxiety state are changed even more strongly in
blood samples from an independent cohort of subjects with
clinically severe anxiety, to validate these biomarkers. Fourth, the
biomarkers thus discovered, prioritized, and validated were tested
in corresponding independent cohorts of psychiatric subjects.
Fifth, we used the biomarkers to match to existing psychiatric
medications, as well as to identify and potentially repurpose new
drugs for anxiety disorders treatment using bioinformatics
analyses. The series of studies was a systematic and comprehensive
approach to move the field forward towards precision medicine.
Power
We used a systematic discovery, prioritization, validation, and
testing approach, as we have done over the years for other
disorders [2,7,16–18]. For discovery, we used a hard to
accomplish but powerful within-subject design, with an Nof
58 subjects with 149 visits. A within-subject design factors out
genetic variability, as well as some medications, lifestyle, and
demographic effects on gene expression, permitting identification
of relevant signal with Ns as small as 1
15
. Another benefitofa
within-subject design may be accuracy/consistency of self-report
of psychiatric symptoms (“phene expression”), as it is the same
person reporting different states. This is similar in rationale to the
signal detection benefits it provides in gene expression.
Based on our work of over two decades in genetics and gene
expression, along with the results of others in the field, we
estimate that using a quantitative phenotype is up to 1 order of
magnitude more powerful than using a categorical diagnosis. The
within-subject longitudinal design, by factoring out all genetic and
some environmental variability, is up to 3 orders of magnitude
more powerful than an inter-subject case-control cross-sectional
design. Moreover, gene expression, by integrating the effects of
many SNPs and environment, is up to 3 orders of magnitude more
powerful than a genetic study. Combined, our approach may be
up to 6 orders of magnitude more powerful than a GWAS study,
even prior to the CFG literature-based prioritization step, which
encompasses all the independent work in the field prior to our
studies, which may add up to 1 order of magnitude as well. In
addition, the Validation and the Testing steps add additional 1
order of magnitude power each. As such, our approach may be up
to 10 orders of magnitude more powered to detect signal than
most current genetic study designs as used in GWAS.
Reproducibility
We reproduced and expanded our earlier findings in an animal
model of GABBR1, CCKBR, and DYNLL2 [8] as top genes involved
in anxiety.
Additionally, there is reproducibility with findings generated by
other independent large-scale studies that came out after our
analyses were completed, and were thus not included in our CFG
approach. A number of their top findings were present in our
candidate gene expression biomarkers for anxiety list that had
survived our initial whole-genome, unbiased, within-subject
Discovery step, before any CFG literature prioritization: two out
of their ten top genes for lifetime anxiety disorder in a UK Biobank
study [19], 6 out of 11 top genes for anxiety in a Million Veteran’s
Program study [13] and 13 out of 42 top blood biomarkers in a
study of intergenerational trauma [20] (see Supplementary
Information- Reproducibility file). This independent reproducibility
of findings between our studies and these other genetic and gene
expression studies, which are done in independent cohorts from
ours, with independent methodologies, is reassuring, and provides
strong convergent evidence for the validity and relevance of our
approach and of their approaches. Our work also provides
functional evidence for some of their top genetic hits.
Pathophysiology
A number of top candidate biomarkers identified by us have
biological roles that are related to signaling, in particular
decreased cAMP and CREB signaling (Table 2). This reproduces
one of the main conclusions we had in animal model studies of
anxiety over a decade ago [8]. Decreased cAMP signaling has also
been reported in ADHD, autism spectrum disorders and fragile X
syndrome. This provides a molecular underpinning for the
epidemiological data between anxiety and disrupted attention
and memory, and for the clinical co-morbidity between these
disorders. It also suggests that drugs that increase cAMP activity,
such as stimulants [21], as well as lithium, glutamate antagonists
such as magnesium, and PDE-4 inhibitors such as rolipram [22],
may be helpful in anxiety disorders. The results to date in clinical
trials of these agents have been mixed, which points to the issue
of heterogeneity in the population and the need to use a
personalized, biomarker-driven approach. Another top pathway is
the Hippo signaling pathway, which has been implicated in stress-
related psychiatric disorders [23].
The majority of top blood biomarkers we have identified have
prior evidence in human or animal model brain data from anxiety
disorders studies, which indicates their relevance to the patho-
physiology of anxiety disorders (Table S2). The co-directionality of
blood changes in our work and brain changes reported in the
literature needs to be interpreted with caution, as it may depend
on brain region.
The top candidate biomarkers also had prior evidence of
involvement in other psychiatric and related disorders (Table S3),
providing a molecular basis for co-morbidity and the possible
precursor effects of some these disorders on anxiety, and
conversely, the precursor role of anxiety in some of them. In
particular, a majority of them have an overlap with depression
(83%), as well as alcoholism (72%), stress (56%), schizophrenia
(50%) and bipolar disorders (44%), consistent with anxiety being a
common and often under-treated and under-appreciated factor in
most mental health disorders.
Phenomenology
The anxiety SAS-4 consists of four items (Supplementary Fig. S1). It
correlates well with a standard scale in the field, the STAI State,
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with a Pearson correlation coefficient R=0.67 (Supplementary
Fig. S2A). Our clustering analysis revealed the structure of anxiety
symptoms (Supplementary Fig. S3). Fear and Uncertainty were the
most closely related. Anger is more distant and less related to
other items on the scale. Anxiety reflects and underlies, in essence,
if an individual is perceiving that they may be facing an adverse
event, and are unhappy with their current state. Germane to that,
we show that SAS-4 shows an inverse correlation with items of a
visual analog scale for Life Satisfaction (Overall Satisfaction
R=−0.58, Happiness R=−0.59, Hope R=−0.54, Meaning
R=−0.59) (Fig. S2B).
Diagnostics
For the biomarkers identified by us, combining all the available
evidence from this current work into a CFE score, brings to
the fore biomarkers that have clinical utility for objective
assessment and risk prediction for anxiety disorders (Table 1).
These biomarkers should be tested individually as well as tested
as polygenic panels of biomarkers in future clinical studies and
practical clinical applications in the field. They may permit to
distinguish, upon an initial clinical presentation of anxiety,
whether the person is in fact severely anxious and at chronic risk
(Fig. 3). The integration of phenomic data, such as repeated
measures of SAS-4 (perhaps via a phone app in a daily fashion),
can further substantiate and elucidate the diagnosis of anxiety
disorders, distinguishing between an intermittent type such as
panic attacks, and continuous type such as Generalized Anxiety
Disorder.
In general, our predictive results with biomarkers were stronger
in females than in males, by an order of 10–20% points on AUCs.
While some of it may be biological, in terms of immune system
reactivity and brain–blood interplay being perhaps higher in
women, it is also possible that men are not as accurate as women
in terms of self-reporting anxiety symptoms (affecting our results
on state predictions), and do not seek help as much (affecting our
results on future hospitalizations predictions). If so, this under-
reporting makes the use of objective biomarker tests in men even
more necessary.
In regards to how our biomarker discoveries might be applied in
clinical laboratory settings, we suggest that panels of top
biomarkers, such as BioM-19, be used (Fig. 3). In practice, every
new patient tested would be normalized against the database of
similar patients already tested, and compared to them for ranking
and risk prediction purposes, regardless if a platform like
microarrays, RNA sequencing, or a more targeted one like PCR is
used in the end clinically. As databases get larger, normative
population levels can and should be established, similar to any
other laboratory measures. Moreover, longitudinal monitoring of
Fig. 3 Example of a potential report for physicians. Using the panel of the top biomarkers for anxiety from Table 1(n=19). This subject
(Phchp328) was previously described by us in a suicidality biomarker study, as high risk for suicide, and died by suicide a year after completing
our study. No information was provided to the patient’s clinicians by us at that time due to anonymity and privacy rules in research studies.
The raw expression values of the 19 biomarkers for the high and low anxiety groups were Z-scored by gender and diagnosis. We calculated as
thresholds the average expression value for a biomarker in the high anxiety group SAS-4 ≥60, and in the low anxiety group SAS-4 ≤40. The
first average should be higher than the second average in increased biomarkers, and the reverse is true for decreased biomarkers. 15 out of 19
biomarkers were thus concordant. We also calculated as thresholds the average expression value for a biomarker in the first-year
hospitalizations group, and in the not hospitalized in first-year group. We did the same thing for all future hospitalizations, and no future
hospitalizations. The first average should be higher than the second average in increased biomarkers, and the reverse is true for decreased
biomarkers. 18 out of 19 biomarkers were thus concordant for first year, and for all future. The Z-scored expression value of each increased in
expression biomarker was compared to the average value for the biomarker in the high anxiety group SAS-4 ≥60, and the average value of
the low anxiety group SAS-4 ≤40, resulting in scores of 1 if above high anxiety, 0 if below low anxiety, and 0.5 if it was in between. The reverse
was done for decreased in expression biomarkers. The digitized biomarkers were then added into a polygenic risk score and normalized for
the number of biomarkers in the panel, resulting in a percentile score. We did the same thing for first-year hospitalizations, and all future
hospitalizations, generating a combined score for chronic anxiety risk. The digitized biomarkers were also used for matching with existing
psychiatric medications and alternative treatments (nutraceuticals and others). We used our large datasets and literature databases to match
biomarkers to medications that had effects on gene expression opposite to their expression in high anxiety. Each medication matched to a
biomarker got the biomarker score of 1, 0.5, or 0. The scores for the medications were added, normalized for the number of biomarkers that
were 1 or 0.5 in that patient, resulting in a percentile match.
K. Roseberry et al.
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changes in biomarkers within an individual, measuring most
recent slope of change, maximum levels attained, and maximum
slope of change attained in the past, may be even more
informative than simple cross-sectional comparisons of levels
within an individual with normative populational levels, as we
have shown in our studies. For future point of care approaches,
research and development should focus on top individual
biomarkers, including at a protein level. One might look at the
best universal biomarkers (that are predictive in all), for reliability,
or at the best-personalized biomarkers (that are predictive by
gender, and even diagnosis), for higher accuracy.
Treatment
Biomarkers may also be useful for matching patients to
medications and measuring response to treatment (pharmaco-
genomics) (Fig. 3,Tables3andS4),aswellasnewdrug
discovery and repurposing (Table 3). From the pharmacoge-
nomics analyses, valproate was the top hit. Relatively recent
randomized controlled clinical trial data is supportive of the use
of valproate in anxiety disorders [24]. Other interesting novel
candidates were omega-3 fatty acids, and lithium. From the drug
repurposing analyses, estradiol was the top hit. Very recent
randomized controlled clinical trial data is supportive of the use
of estradiol in anxiety disorders [25]. Other interesting novel
candidates were loperamide and disopyramide. All these drugs
are relatively safe if used appropriately, and have been used in
clinical practice for other indications for decades, which
facilitates the direct translation to clinical practice of our
findings.
CONCLUSIONS
Overall, this work is a major step forward toward better under-
standing, diagnosing, and treating anxiety disorders. We hope
that our trait biomarkers for future risk may be useful in preventive
approaches, before the full-blown disorder manifests itself
(or re-occurs). Prevention could be accomplished with social,
psychological, or biological interventions (i.e., early targeted
use of medications or nutraceuticals). Given the fact that 1 in
3 people will have a clinical anxiety disorder episode in their
lifetime [26], that the prevalence seems to be increasing in younger
people [27], that anxiety disorders can severely affect quality of life,
sometimes leading to addictions such as alcoholism, and even
suicides, and that not all patients respond to current treatments, the
need for and importance of efforts such as ours cannot be
overstated.
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ACKNOWLEDGEMENTS
We would like to acknowledge our gratitude for the work and results of the many other
groups, cited in our paper, who have conducted and published studies (clinical, genetic,
and biological) in anxiety disorders. Combining their work with ours makes a
convergent approach possible. We would like to thank Mariah Hill for help with
building literature and drug databases. We also would particularly like to thank the
subjects in these studies and their families. Without their contribution, such work to
advance the understanding of anxiety disorders would not be possible. This work was
supported by NIH grants (R01MH117431) and a VA Merit Award (2I01CX000139) to ABN.
K. Roseberry et al.
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Content courtesy of Springer Nature, terms of use apply. Rights reserved

AUTHOR CONTRIBUTIONS
ABN designed the study and wrote the manuscript. KR, HLN, DFL, RB, KS, and JR
analyzed the data. SP, NP, and WAA organized, conducted, and scored testing in
psychiatric subjects. SG assisted with sample report generation. AS assisted with data
interpretation. SMK conducted microarray experiments. All authors discussed the
results and commented on the manuscript.
COMPETING INTERESTS
ABN is listed as an inventor on a patent application filed by Indiana University. ABN
and AS are co-founders, SMK is a consultant, and SG is a part-time employee of
MindX Sciences.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41380-023-01998-0.
Correspondence and requests for materials should be addressed to A. B. Niculescu.
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© The Author(s) 2023
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